analysis/platform.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(0, '..')\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from diaphanous import (\n",
    "    REPORTS_PER_PLATFORM,\n",
    "    ingest_reports_per_platform,\n",
    "    long_ncmec_reports,\n",
    "    wide_ncmec_reports,\n",
    "    compare_all_platform_reports,\n",
    "    show,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "PKGS = [\n",
    "    \"iccCounts\", \"dplyr\", \"ggplot2\", \"patchwork\",\n",
    "    \"purrr\", \"scales\", \"svglite\", \"this.path\", \"tidyr\"\n",
    "]\n",
    "\n",
    "path = Path(\"..\") / \"rlib\"\n",
    "path.mkdir(exist_ok=True)\n",
    "RLIB = str(path)\n",
    "\n",
    "%load_ext rpy2.ipython\n",
    "\n",
    "import rpy2.robjects.packages as r_packages\n",
    "from rpy2.robjects.vectors import StrVector\n",
    "\n",
    "not_installed = [p for p in PKGS if not r_packages.isinstalled(p, lib_loc=RLIB)]\n",
    "if not_installed:\n",
    "    show(f\"\"\"\n",
    "        <strong style='file-size: 1.2em'><em>\n",
    "        Installing R package(s) {\", \".join(not_installed)}. This may take a while...\n",
    "        </em></strong>\n",
    "    \"\"\")\n",
    "\n",
    "    utils = r_packages.importr(\"utils\")\n",
    "    utils.chooseCRANmirror(ind=1)\n",
    "    utils.install_packages(StrVector(not_installed), lib=RLIB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h1>CSAM Reports per Platform</h1>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h2>Ingesting Platform Disclosures</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping metadata\n",
      "➖ Alphabet (no CSAM data)\n",
      "✅ Amazon\n",
      "➖ Apple (no CSAM data)\n",
      "➖ Automattic (no CSAM data)\n",
      "➖ Aylo (no CSAM data)\n",
      "✅ Discord\n",
      "✅ Facebook\n",
      "✅ GitHub\n",
      "✅ Google\n",
      "✅ Instagram\n",
      "✅ LinkedIn\n",
      "➖ Meta (no CSAM data)\n",
      "✅ Microsoft\n",
      "➖ Omegle (no CSAM data)\n",
      "✅ Pinterest\n",
      "✅ Pornhub\n",
      "➖ Quora (no CSAM data)\n",
      "✅ Reddit\n",
      "✅ Snap\n",
      "➖ Telegram (no transparency disclosures)\n",
      "✅ TikTok\n",
      "➖ Tumblr (no CSAM data)\n",
      "✅ Twitch\n",
      "✅ Twitter\n",
      "➖ WhatsApp (no CSAM data)\n",
      "➖ Wikimedia (no transparency disclosures)\n",
      "➖ Wordpress (no CSAM data)\n",
      "✅ X\n",
      "✅ YouTube\n",
      "✅ NCMEC\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h2>An Overview of Platform Disclosures</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "#T_eb846 .col6 {\n",
       "  text-align: start;\n",
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       "#T_eb846  {\n",
       "  font-variant-numeric: tabular-nums;\n",
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       "  margin-bottom: 2.0em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_eb846\">\n",
       "  <caption>Platforms and Their Disclosures</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_eb846_level0_col0\" class=\"col_heading level0 col0\" >social_media</th>\n",
       "      <th id=\"T_eb846_level0_col1\" class=\"col_heading level0 col1\" >data</th>\n",
       "      <th id=\"T_eb846_level0_col2\" class=\"col_heading level0 col2\" >history</th>\n",
       "      <th id=\"T_eb846_level0_col3\" class=\"col_heading level0 col3\" >terms</th>\n",
       "      <th id=\"T_eb846_level0_col4\" class=\"col_heading level0 col4\" >quantities</th>\n",
       "      <th id=\"T_eb846_level0_col5\" class=\"col_heading level0 col5\" >granularity</th>\n",
       "      <th id=\"T_eb846_level0_col6\" class=\"col_heading level0 col6\" >frequency</th>\n",
       "      <th id=\"T_eb846_level0_col7\" class=\"col_heading level0 col7\" >coverage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >platform</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row0\" class=\"row_heading level0 row0\" >Alphabet</th>\n",
       "      <td id=\"T_eb846_row0_col0\" class=\"data row0 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col4\" class=\"data row0 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col5\" class=\"data row0 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col6\" class=\"data row0 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row0_col7\" class=\"data row0 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row1\" class=\"row_heading level0 row1\" >Amazon</th>\n",
       "      <td id=\"T_eb846_row1_col0\" class=\"data row1 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row1_col2\" class=\"data row1 col2\" >linked list of pages</td>\n",
       "      <td id=\"T_eb846_row1_col3\" class=\"data row1 col3\" >CSAM</td>\n",
       "      <td id=\"T_eb846_row1_col4\" class=\"data row1 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row1_col5\" class=\"data row1 col5\" >Y</td>\n",
       "      <td id=\"T_eb846_row1_col6\" class=\"data row1 col6\" >Y</td>\n",
       "      <td id=\"T_eb846_row1_col7\" class=\"data row1 col7\" >2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row2\" class=\"row_heading level0 row2\" >Apple</th>\n",
       "      <td id=\"T_eb846_row2_col0\" class=\"data row2 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row2_col1\" class=\"data row2 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col4\" class=\"data row2 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col5\" class=\"data row2 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col6\" class=\"data row2 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row2_col7\" class=\"data row2 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row3\" class=\"row_heading level0 row3\" >Automattic</th>\n",
       "      <td id=\"T_eb846_row3_col0\" class=\"data row3 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row3_col1\" class=\"data row3 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col4\" class=\"data row3 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col5\" class=\"data row3 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col6\" class=\"data row3 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row3_col7\" class=\"data row3 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row4\" class=\"row_heading level0 row4\" >Aylo</th>\n",
       "      <td id=\"T_eb846_row4_col0\" class=\"data row4 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col4\" class=\"data row4 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col5\" class=\"data row4 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col6\" class=\"data row4 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row4_col7\" class=\"data row4 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row5\" class=\"row_heading level0 row5\" >Discord</th>\n",
       "      <td id=\"T_eb846_row5_col0\" class=\"data row5 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row5_col1\" class=\"data row5 col1\" >csv</td>\n",
       "      <td id=\"T_eb846_row5_col2\" class=\"data row5 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row5_col3\" class=\"data row5 col3\" >child safety; CSAM</td>\n",
       "      <td id=\"T_eb846_row5_col4\" class=\"data row5 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row5_col5\" class=\"data row5 col5\" >Q</td>\n",
       "      <td id=\"T_eb846_row5_col6\" class=\"data row5 col6\" >Q</td>\n",
       "      <td id=\"T_eb846_row5_col7\" class=\"data row5 col7\" >2020 H2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row6\" class=\"row_heading level0 row6\" >Facebook</th>\n",
       "      <td id=\"T_eb846_row6_col0\" class=\"data row6 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row6_col1\" class=\"data row6 col1\" >csv</td>\n",
       "      <td id=\"T_eb846_row6_col2\" class=\"data row6 col2\" >data</td>\n",
       "      <td id=\"T_eb846_row6_col3\" class=\"data row6 col3\" >child nudity & sexual exploitation; child sexual exploitation</td>\n",
       "      <td id=\"T_eb846_row6_col4\" class=\"data row6 col4\" >rounded</td>\n",
       "      <td id=\"T_eb846_row6_col5\" class=\"data row6 col5\" >Q</td>\n",
       "      <td id=\"T_eb846_row6_col6\" class=\"data row6 col6\" >Q</td>\n",
       "      <td id=\"T_eb846_row6_col7\" class=\"data row6 col7\" >2018 Q3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row7\" class=\"row_heading level0 row7\" >GitHub</th>\n",
       "      <td id=\"T_eb846_row7_col0\" class=\"data row7 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row7_col1\" class=\"data row7 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row7_col2\" class=\"data row7 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row7_col3\" class=\"data row7 col3\" >CSEAI</td>\n",
       "      <td id=\"T_eb846_row7_col4\" class=\"data row7 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row7_col5\" class=\"data row7 col5\" >Y</td>\n",
       "      <td id=\"T_eb846_row7_col6\" class=\"data row7 col6\" >Y</td>\n",
       "      <td id=\"T_eb846_row7_col7\" class=\"data row7 col7\" >2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row8\" class=\"row_heading level0 row8\" >Google</th>\n",
       "      <td id=\"T_eb846_row8_col0\" class=\"data row8 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row8_col1\" class=\"data row8 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row8_col2\" class=\"data row8 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row8_col3\" class=\"data row8 col3\" >CSAM</td>\n",
       "      <td id=\"T_eb846_row8_col4\" class=\"data row8 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row8_col5\" class=\"data row8 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row8_col6\" class=\"data row8 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row8_col7\" class=\"data row8 col7\" >2020 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row9\" class=\"row_heading level0 row9\" >Instagram</th>\n",
       "      <td id=\"T_eb846_row9_col0\" class=\"data row9 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row9_col1\" class=\"data row9 col1\" >csv</td>\n",
       "      <td id=\"T_eb846_row9_col2\" class=\"data row9 col2\" >data</td>\n",
       "      <td id=\"T_eb846_row9_col3\" class=\"data row9 col3\" >child nudity & sexual exploitation; sexual exploitation</td>\n",
       "      <td id=\"T_eb846_row9_col4\" class=\"data row9 col4\" >rounded</td>\n",
       "      <td id=\"T_eb846_row9_col5\" class=\"data row9 col5\" >Q</td>\n",
       "      <td id=\"T_eb846_row9_col6\" class=\"data row9 col6\" >Q</td>\n",
       "      <td id=\"T_eb846_row9_col7\" class=\"data row9 col7\" >2019 Q2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row10\" class=\"row_heading level0 row10\" >LinkedIn</th>\n",
       "      <td id=\"T_eb846_row10_col0\" class=\"data row10 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row10_col1\" class=\"data row10 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row10_col2\" class=\"data row10 col2\" >same page (tabs)</td>\n",
       "      <td id=\"T_eb846_row10_col3\" class=\"data row10 col3\" >child exploitation</td>\n",
       "      <td id=\"T_eb846_row10_col4\" class=\"data row10 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row10_col5\" class=\"data row10 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row10_col6\" class=\"data row10 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row10_col7\" class=\"data row10 col7\" >2019 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row11\" class=\"row_heading level0 row11\" >Meta</th>\n",
       "      <td id=\"T_eb846_row11_col0\" class=\"data row11 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row11_col1\" class=\"data row11 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col2\" class=\"data row11 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col3\" class=\"data row11 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col4\" class=\"data row11 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col5\" class=\"data row11 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col6\" class=\"data row11 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row11_col7\" class=\"data row11 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row12\" class=\"row_heading level0 row12\" >Microsoft</th>\n",
       "      <td id=\"T_eb846_row12_col0\" class=\"data row12 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row12_col1\" class=\"data row12 col1\" >xls</td>\n",
       "      <td id=\"T_eb846_row12_col2\" class=\"data row12 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row12_col3\" class=\"data row12 col3\" >CSAM</td>\n",
       "      <td id=\"T_eb846_row12_col4\" class=\"data row12 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row12_col5\" class=\"data row12 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row12_col6\" class=\"data row12 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row12_col7\" class=\"data row12 col7\" >2020 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row13\" class=\"row_heading level0 row13\" >Omegle</th>\n",
       "      <td id=\"T_eb846_row13_col0\" class=\"data row13 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row13_col1\" class=\"data row13 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col2\" class=\"data row13 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col3\" class=\"data row13 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col4\" class=\"data row13 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col5\" class=\"data row13 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col6\" class=\"data row13 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row13_col7\" class=\"data row13 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row14\" class=\"row_heading level0 row14\" >Pinterest</th>\n",
       "      <td id=\"T_eb846_row14_col0\" class=\"data row14 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row14_col1\" class=\"data row14 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row14_col2\" class=\"data row14 col2\" >same page (tabs)</td>\n",
       "      <td id=\"T_eb846_row14_col3\" class=\"data row14 col3\" >child safety; child sexual exploitation; CSAM</td>\n",
       "      <td id=\"T_eb846_row14_col4\" class=\"data row14 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row14_col5\" class=\"data row14 col5\" >Q</td>\n",
       "      <td id=\"T_eb846_row14_col6\" class=\"data row14 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row14_col7\" class=\"data row14 col7\" >2020 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row15\" class=\"row_heading level0 row15\" >Pornhub</th>\n",
       "      <td id=\"T_eb846_row15_col0\" class=\"data row15 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row15_col1\" class=\"data row15 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row15_col2\" class=\"data row15 col2\" >page archive</td>\n",
       "      <td id=\"T_eb846_row15_col3\" class=\"data row15 col3\" >C; S; A; M</td>\n",
       "      <td id=\"T_eb846_row15_col4\" class=\"data row15 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row15_col5\" class=\"data row15 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row15_col6\" class=\"data row15 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row15_col7\" class=\"data row15 col7\" >2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row16\" class=\"row_heading level0 row16\" >Quora</th>\n",
       "      <td id=\"T_eb846_row16_col0\" class=\"data row16 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row16_col1\" class=\"data row16 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col2\" class=\"data row16 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col3\" class=\"data row16 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col4\" class=\"data row16 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col5\" class=\"data row16 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col6\" class=\"data row16 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row16_col7\" class=\"data row16 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row17\" class=\"row_heading level0 row17\" >Reddit</th>\n",
       "      <td id=\"T_eb846_row17_col0\" class=\"data row17 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row17_col1\" class=\"data row17 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row17_col2\" class=\"data row17 col2\" >page archive</td>\n",
       "      <td id=\"T_eb846_row17_col3\" class=\"data row17 col3\" >minor sexualization; child sexual exploitation; CSAM</td>\n",
       "      <td id=\"T_eb846_row17_col4\" class=\"data row17 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row17_col5\" class=\"data row17 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row17_col6\" class=\"data row17 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row17_col7\" class=\"data row17 col7\" >2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row18\" class=\"row_heading level0 row18\" >Snap</th>\n",
       "      <td id=\"T_eb846_row18_col0\" class=\"data row18 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row18_col2\" class=\"data row18 col2\" >page archive</td>\n",
       "      <td id=\"T_eb846_row18_col3\" class=\"data row18 col3\" >child sexual exploitation and abuse imagery</td>\n",
       "      <td id=\"T_eb846_row18_col4\" class=\"data row18 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row18_col5\" class=\"data row18 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row18_col6\" class=\"data row18 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row18_col7\" class=\"data row18 col7\" >2019 H2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row19\" class=\"row_heading level0 row19\" >Telegram</th>\n",
       "      <td id=\"T_eb846_row19_col0\" class=\"data row19 col0\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col1\" class=\"data row19 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col2\" class=\"data row19 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col4\" class=\"data row19 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col5\" class=\"data row19 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col6\" class=\"data row19 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row19_col7\" class=\"data row19 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row20\" class=\"row_heading level0 row20\" >TikTok</th>\n",
       "      <td id=\"T_eb846_row20_col0\" class=\"data row20 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row20_col1\" class=\"data row20 col1\" >Excel, csv</td>\n",
       "      <td id=\"T_eb846_row20_col2\" class=\"data row20 col2\" >page archive</td>\n",
       "      <td id=\"T_eb846_row20_col3\" class=\"data row20 col3\" >sexual exploitation of minors; CSAM; youth exploitation and abuse</td>\n",
       "      <td id=\"T_eb846_row20_col4\" class=\"data row20 col4\" >fractions</td>\n",
       "      <td id=\"T_eb846_row20_col5\" class=\"data row20 col5\" >Q</td>\n",
       "      <td id=\"T_eb846_row20_col6\" class=\"data row20 col6\" >Q</td>\n",
       "      <td id=\"T_eb846_row20_col7\" class=\"data row20 col7\" >2022 Q1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row21\" class=\"row_heading level0 row21\" >Tumblr</th>\n",
       "      <td id=\"T_eb846_row21_col0\" class=\"data row21 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row21_col1\" class=\"data row21 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col2\" class=\"data row21 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col3\" class=\"data row21 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col4\" class=\"data row21 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col5\" class=\"data row21 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col6\" class=\"data row21 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row21_col7\" class=\"data row21 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row22\" class=\"row_heading level0 row22\" >Twitch</th>\n",
       "      <td id=\"T_eb846_row22_col0\" class=\"data row22 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row22_col1\" class=\"data row22 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row22_col2\" class=\"data row22 col2\" >page</td>\n",
       "      <td id=\"T_eb846_row22_col3\" class=\"data row22 col3\" >youth safety; child sexual exploitation and abuse</td>\n",
       "      <td id=\"T_eb846_row22_col4\" class=\"data row22 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row22_col5\" class=\"data row22 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row22_col6\" class=\"data row22 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row22_col7\" class=\"data row22 col7\" >2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row23\" class=\"row_heading level0 row23\" >Twitter</th>\n",
       "      <td id=\"T_eb846_row23_col0\" class=\"data row23 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row23_col1\" class=\"data row23 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row23_col2\" class=\"data row23 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row23_col3\" class=\"data row23 col3\" >child sexual exploitation</td>\n",
       "      <td id=\"T_eb846_row23_col4\" class=\"data row23 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row23_col5\" class=\"data row23 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row23_col6\" class=\"data row23 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row23_col7\" class=\"data row23 col7\" >2018 H2 - 2022 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row24\" class=\"row_heading level0 row24\" >WhatsApp</th>\n",
       "      <td id=\"T_eb846_row24_col0\" class=\"data row24 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row24_col1\" class=\"data row24 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col2\" class=\"data row24 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col3\" class=\"data row24 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col4\" class=\"data row24 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col5\" class=\"data row24 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col6\" class=\"data row24 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row24_col7\" class=\"data row24 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row25\" class=\"row_heading level0 row25\" >Wikimedia</th>\n",
       "      <td id=\"T_eb846_row25_col0\" class=\"data row25 col0\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col1\" class=\"data row25 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col2\" class=\"data row25 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col3\" class=\"data row25 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col4\" class=\"data row25 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col5\" class=\"data row25 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col6\" class=\"data row25 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row25_col7\" class=\"data row25 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row26\" class=\"row_heading level0 row26\" >Wordpress</th>\n",
       "      <td id=\"T_eb846_row26_col0\" class=\"data row26 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row26_col1\" class=\"data row26 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col2\" class=\"data row26 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col3\" class=\"data row26 col3\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col4\" class=\"data row26 col4\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col5\" class=\"data row26 col5\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col6\" class=\"data row26 col6\" >⋯</td>\n",
       "      <td id=\"T_eb846_row26_col7\" class=\"data row26 col7\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row27\" class=\"row_heading level0 row27\" >X</th>\n",
       "      <td id=\"T_eb846_row27_col0\" class=\"data row27 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row27_col1\" class=\"data row27 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row27_col2\" class=\"data row27 col2\" >⋯</td>\n",
       "      <td id=\"T_eb846_row27_col3\" class=\"data row27 col3\" >child sexual exploitation; physical child abuse</td>\n",
       "      <td id=\"T_eb846_row27_col4\" class=\"data row27 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row27_col5\" class=\"data row27 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row27_col6\" class=\"data row27 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row27_col7\" class=\"data row27 col7\" >2024 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row28\" class=\"row_heading level0 row28\" >YouTube</th>\n",
       "      <td id=\"T_eb846_row28_col0\" class=\"data row28 col0\" >True</td>\n",
       "      <td id=\"T_eb846_row28_col1\" class=\"data row28 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row28_col2\" class=\"data row28 col2\" >same page (dropdown)</td>\n",
       "      <td id=\"T_eb846_row28_col3\" class=\"data row28 col3\" >CSAM</td>\n",
       "      <td id=\"T_eb846_row28_col4\" class=\"data row28 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row28_col5\" class=\"data row28 col5\" >H</td>\n",
       "      <td id=\"T_eb846_row28_col6\" class=\"data row28 col6\" >H</td>\n",
       "      <td id=\"T_eb846_row28_col7\" class=\"data row28 col7\" >2020 H1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb846_level0_row29\" class=\"row_heading level0 row29\" >NCMEC</th>\n",
       "      <td id=\"T_eb846_row29_col0\" class=\"data row29 col0\" >False</td>\n",
       "      <td id=\"T_eb846_row29_col1\" class=\"data row29 col1\" >⋯</td>\n",
       "      <td id=\"T_eb846_row29_col2\" class=\"data row29 col2\" >page archive</td>\n",
       "      <td id=\"T_eb846_row29_col3\" class=\"data row29 col3\" >CSAM</td>\n",
       "      <td id=\"T_eb846_row29_col4\" class=\"data row29 col4\" >counts</td>\n",
       "      <td id=\"T_eb846_row29_col5\" class=\"data row29 col5\" >Y</td>\n",
       "      <td id=\"T_eb846_row29_col6\" class=\"data row29 col6\" >Y</td>\n",
       "      <td id=\"T_eb846_row29_col7\" class=\"data row29 col7\" >2019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10ac40f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('<h1>CSAM Reports per Platform</h1>')\n",
    "show('<h2>Ingesting Platform Disclosures</h2>')\n",
    "\n",
    "def logger(format: str, *args, **kwargs) -> None:\n",
    "    print(format.format(*args, **kwargs))\n",
    "\n",
    "platform_data = ingest_reports_per_platform(REPORTS_PER_PLATFORM, logger=logger)\n",
    "\n",
    "show('<h2>An Overview of Platform Disclosures</h2>')\n",
    "show(platform_data.features, caption='Platforms and Their Disclosures')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Ranking Service Providers by CyberTipline Report Counts</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_e005e caption {\n",
       "  caption-side: top;\n",
       "  font-size: 1.1em;\n",
       "  margin-bottom: 2ex;\n",
       "  vertical-align: center;\n",
       "  font-style: italic;\n",
       "}\n",
       "#T_e005e .level0 {\n",
       "  text-align: start;\n",
       "}\n",
       "#T_e005e .col0 {\n",
       "  text-align: start;\n",
       "}\n",
       "#T_e005e  {\n",
       "  font-variant-numeric: tabular-nums;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 2.0em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_e005e\">\n",
       "  <caption> Social Media by CSAM Reports Filed in 2019</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e005e_level0_col0\" class=\"col_heading level0 col0\" >platform</th>\n",
       "      <th id=\"T_e005e_level0_col1\" class=\"col_heading level0 col1\" >reports</th>\n",
       "      <th id=\"T_e005e_level0_col2\" class=\"col_heading level0 col2\" >reports_pct</th>\n",
       "      <th id=\"T_e005e_level0_col3\" class=\"col_heading level0 col3\" >cumpct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_e005e_row0_col0\" class=\"data row0 col0\" >Total</td>\n",
       "      <td id=\"T_e005e_row0_col1\" class=\"data row0 col1\" >16,987,361</td>\n",
       "      <td id=\"T_e005e_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_e005e_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row1\" class=\"row_heading level0 row1\" >2019</th>\n",
       "      <td id=\"T_e005e_row1_col0\" class=\"data row1 col0\" >ESP Total</td>\n",
       "      <td id=\"T_e005e_row1_col1\" class=\"data row1 col1\" >16,836,694</td>\n",
       "      <td id=\"T_e005e_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_e005e_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row2\" class=\"row_heading level0 row2\" >2019</th>\n",
       "      <td id=\"T_e005e_row2_col0\" class=\"data row2 col0\" >Meta</td>\n",
       "      <td id=\"T_e005e_row2_col1\" class=\"data row2 col1\" >15,884,511</td>\n",
       "      <td id=\"T_e005e_row2_col2\" class=\"data row2 col2\" >93.50782031</td>\n",
       "      <td id=\"T_e005e_row2_col3\" class=\"data row2 col3\" >93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row3\" class=\"row_heading level0 row3\" >2019</th>\n",
       "      <td id=\"T_e005e_row3_col0\" class=\"data row3 col0\" >Alphabet</td>\n",
       "      <td id=\"T_e005e_row3_col1\" class=\"data row3 col1\" >449,283</td>\n",
       "      <td id=\"T_e005e_row3_col2\" class=\"data row3 col2\" >2.64480751</td>\n",
       "      <td id=\"T_e005e_row3_col3\" class=\"data row3 col3\" >96.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row4\" class=\"row_heading level0 row4\" >2019</th>\n",
       "      <td id=\"T_e005e_row4_col0\" class=\"data row4 col0\" >Microsoft</td>\n",
       "      <td id=\"T_e005e_row4_col1\" class=\"data row4 col1\" >123,929</td>\n",
       "      <td id=\"T_e005e_row4_col2\" class=\"data row4 col2\" >0.72953651</td>\n",
       "      <td id=\"T_e005e_row4_col3\" class=\"data row4 col3\" >96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row5\" class=\"row_heading level0 row5\" >2019</th>\n",
       "      <td id=\"T_e005e_row5_col0\" class=\"data row5 col0\" >Snap</td>\n",
       "      <td id=\"T_e005e_row5_col1\" class=\"data row5 col1\" >82,030</td>\n",
       "      <td id=\"T_e005e_row5_col2\" class=\"data row5 col2\" >0.48288843</td>\n",
       "      <td id=\"T_e005e_row5_col3\" class=\"data row5 col3\" >97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row6\" class=\"row_heading level0 row6\" >2019</th>\n",
       "      <td id=\"T_e005e_row6_col0\" class=\"data row6 col0\" >X</td>\n",
       "      <td id=\"T_e005e_row6_col1\" class=\"data row6 col1\" >45,726</td>\n",
       "      <td id=\"T_e005e_row6_col2\" class=\"data row6 col2\" >0.26917660</td>\n",
       "      <td id=\"T_e005e_row6_col3\" class=\"data row6 col3\" >97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row7\" class=\"row_heading level0 row7\" >2019</th>\n",
       "      <td id=\"T_e005e_row7_col0\" class=\"data row7 col0\" >Discord</td>\n",
       "      <td id=\"T_e005e_row7_col1\" class=\"data row7 col1\" >19,480</td>\n",
       "      <td id=\"T_e005e_row7_col2\" class=\"data row7 col2\" >0.11467349</td>\n",
       "      <td id=\"T_e005e_row7_col3\" class=\"data row7 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row8\" class=\"row_heading level0 row8\" >2019</th>\n",
       "      <td id=\"T_e005e_row8_col0\" class=\"data row8 col0\" >Automattic</td>\n",
       "      <td id=\"T_e005e_row8_col1\" class=\"data row8 col1\" >10,443</td>\n",
       "      <td id=\"T_e005e_row8_col2\" class=\"data row8 col2\" >0.06147512</td>\n",
       "      <td id=\"T_e005e_row8_col3\" class=\"data row8 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row9\" class=\"row_heading level0 row9\" >2019</th>\n",
       "      <td id=\"T_e005e_row9_col0\" class=\"data row9 col0\" >Pinterest</td>\n",
       "      <td id=\"T_e005e_row9_col1\" class=\"data row9 col1\" >7,360</td>\n",
       "      <td id=\"T_e005e_row9_col2\" class=\"data row9 col2\" >0.04332633</td>\n",
       "      <td id=\"T_e005e_row9_col3\" class=\"data row9 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row10\" class=\"row_heading level0 row10\" >2019</th>\n",
       "      <td id=\"T_e005e_row10_col0\" class=\"data row10 col0\" >Omegle</td>\n",
       "      <td id=\"T_e005e_row10_col1\" class=\"data row10 col1\" >3,470</td>\n",
       "      <td id=\"T_e005e_row10_col2\" class=\"data row10 col2\" >0.02042695</td>\n",
       "      <td id=\"T_e005e_row10_col3\" class=\"data row10 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row11\" class=\"row_heading level0 row11\" >2019</th>\n",
       "      <td id=\"T_e005e_row11_col0\" class=\"data row11 col0\" >Reddit</td>\n",
       "      <td id=\"T_e005e_row11_col1\" class=\"data row11 col1\" >724</td>\n",
       "      <td id=\"T_e005e_row11_col2\" class=\"data row11 col2\" >0.00426199</td>\n",
       "      <td id=\"T_e005e_row11_col3\" class=\"data row11 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row12\" class=\"row_heading level0 row12\" >2019</th>\n",
       "      <td id=\"T_e005e_row12_col0\" class=\"data row12 col0\" >TikTok</td>\n",
       "      <td id=\"T_e005e_row12_col1\" class=\"data row12 col1\" >596</td>\n",
       "      <td id=\"T_e005e_row12_col2\" class=\"data row12 col2\" >0.00350849</td>\n",
       "      <td id=\"T_e005e_row12_col3\" class=\"data row12 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row13\" class=\"row_heading level0 row13\" >2019</th>\n",
       "      <td id=\"T_e005e_row13_col0\" class=\"data row13 col0\" >Amazon</td>\n",
       "      <td id=\"T_e005e_row13_col1\" class=\"data row13 col1\" >549</td>\n",
       "      <td id=\"T_e005e_row13_col2\" class=\"data row13 col2\" >0.00323181</td>\n",
       "      <td id=\"T_e005e_row13_col3\" class=\"data row13 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row14\" class=\"row_heading level0 row14\" >2019</th>\n",
       "      <td id=\"T_e005e_row14_col0\" class=\"data row14 col0\" >Apple</td>\n",
       "      <td id=\"T_e005e_row14_col1\" class=\"data row14 col1\" >205</td>\n",
       "      <td id=\"T_e005e_row14_col2\" class=\"data row14 col2\" >0.00120678</td>\n",
       "      <td id=\"T_e005e_row14_col3\" class=\"data row14 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row15\" class=\"row_heading level0 row15\" >2019</th>\n",
       "      <td id=\"T_e005e_row15_col0\" class=\"data row15 col0\" >Wikimedia</td>\n",
       "      <td id=\"T_e005e_row15_col1\" class=\"data row15 col1\" >13</td>\n",
       "      <td id=\"T_e005e_row15_col2\" class=\"data row15 col2\" >0.00007653</td>\n",
       "      <td id=\"T_e005e_row15_col3\" class=\"data row15 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row16\" class=\"row_heading level0 row16\" >2019</th>\n",
       "      <td id=\"T_e005e_row16_col0\" class=\"data row16 col0\" >Quora</td>\n",
       "      <td id=\"T_e005e_row16_col1\" class=\"data row16 col1\" >1</td>\n",
       "      <td id=\"T_e005e_row16_col2\" class=\"data row16 col2\" >0.00000589</td>\n",
       "      <td id=\"T_e005e_row16_col3\" class=\"data row16 col3\" >97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row17\" class=\"row_heading level0 row17\" >2019</th>\n",
       "      <td id=\"T_e005e_row17_col0\" class=\"data row17 col0\" >Aylo</td>\n",
       "      <td id=\"T_e005e_row17_col1\" class=\"data row17 col1\" >⋯</td>\n",
       "      <td id=\"T_e005e_row17_col2\" class=\"data row17 col2\" >⋯</td>\n",
       "      <td id=\"T_e005e_row17_col3\" class=\"data row17 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row18\" class=\"row_heading level0 row18\" >2019</th>\n",
       "      <td id=\"T_e005e_row18_col0\" class=\"data row18 col0\" >Telegram</td>\n",
       "      <td id=\"T_e005e_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_e005e_row18_col2\" class=\"data row18 col2\" >⋯</td>\n",
       "      <td id=\"T_e005e_row18_col3\" class=\"data row18 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e005e_level0_row19\" class=\"row_heading level0 row19\" >2019</th>\n",
       "      <td id=\"T_e005e_row19_col0\" class=\"data row19 col0\" >Survey Total</td>\n",
       "      <td id=\"T_e005e_row19_col1\" class=\"data row19 col1\" >16,628,320</td>\n",
       "      <td id=\"T_e005e_row19_col2\" class=\"data row19 col2\" >97.88642274</td>\n",
       "      <td id=\"T_e005e_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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       "<style type=\"text/css\">\n",
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       "<table id=\"T_b7caf\">\n",
       "  <caption> Social Media by CSAM Reports Filed in 2020</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b7caf_level0_col0\" class=\"col_heading level0 col0\" >platform</th>\n",
       "      <th id=\"T_b7caf_level0_col1\" class=\"col_heading level0 col1\" >reports</th>\n",
       "      <th id=\"T_b7caf_level0_col2\" class=\"col_heading level0 col2\" >reports_pct</th>\n",
       "      <th id=\"T_b7caf_level0_col3\" class=\"col_heading level0 col3\" >cumpct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
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       "  <tbody>\n",
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       "      <th id=\"T_b7caf_level0_row0\" class=\"row_heading level0 row0\" >2020</th>\n",
       "      <td id=\"T_b7caf_row0_col0\" class=\"data row0 col0\" >Total</td>\n",
       "      <td id=\"T_b7caf_row0_col1\" class=\"data row0 col1\" >21,751,085</td>\n",
       "      <td id=\"T_b7caf_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_b7caf_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_b7caf_row1_col0\" class=\"data row1 col0\" >ESP Total</td>\n",
       "      <td id=\"T_b7caf_row1_col1\" class=\"data row1 col1\" >21,447,786</td>\n",
       "      <td id=\"T_b7caf_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_b7caf_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row2\" class=\"row_heading level0 row2\" >2020</th>\n",
       "      <td id=\"T_b7caf_row2_col0\" class=\"data row2 col0\" >Meta</td>\n",
       "      <td id=\"T_b7caf_row2_col1\" class=\"data row2 col1\" >20,307,216</td>\n",
       "      <td id=\"T_b7caf_row2_col2\" class=\"data row2 col2\" >93.36185298</td>\n",
       "      <td id=\"T_b7caf_row2_col3\" class=\"data row2 col3\" >93.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row3\" class=\"row_heading level0 row3\" >2020</th>\n",
       "      <td id=\"T_b7caf_row3_col0\" class=\"data row3 col0\" >Alphabet</td>\n",
       "      <td id=\"T_b7caf_row3_col1\" class=\"data row3 col1\" >546,704</td>\n",
       "      <td id=\"T_b7caf_row3_col2\" class=\"data row3 col2\" >2.51345623</td>\n",
       "      <td id=\"T_b7caf_row3_col3\" class=\"data row3 col3\" >95.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row4\" class=\"row_heading level0 row4\" >2020</th>\n",
       "      <td id=\"T_b7caf_row4_col0\" class=\"data row4 col0\" >Snap</td>\n",
       "      <td id=\"T_b7caf_row4_col1\" class=\"data row4 col1\" >144,095</td>\n",
       "      <td id=\"T_b7caf_row4_col2\" class=\"data row4 col2\" >0.66247270</td>\n",
       "      <td id=\"T_b7caf_row4_col3\" class=\"data row4 col3\" >96.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row5\" class=\"row_heading level0 row5\" >2020</th>\n",
       "      <td id=\"T_b7caf_row5_col0\" class=\"data row5 col0\" >Microsoft</td>\n",
       "      <td id=\"T_b7caf_row5_col1\" class=\"data row5 col1\" >96,838</td>\n",
       "      <td id=\"T_b7caf_row5_col2\" class=\"data row5 col2\" >0.44520997</td>\n",
       "      <td id=\"T_b7caf_row5_col3\" class=\"data row5 col3\" >97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row6\" class=\"row_heading level0 row6\" >2020</th>\n",
       "      <td id=\"T_b7caf_row6_col0\" class=\"data row6 col0\" >X</td>\n",
       "      <td id=\"T_b7caf_row6_col1\" class=\"data row6 col1\" >65,062</td>\n",
       "      <td id=\"T_b7caf_row6_col2\" class=\"data row6 col2\" >0.29912071</td>\n",
       "      <td id=\"T_b7caf_row6_col3\" class=\"data row6 col3\" >97.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row7\" class=\"row_heading level0 row7\" >2020</th>\n",
       "      <td id=\"T_b7caf_row7_col0\" class=\"data row7 col0\" >TikTok</td>\n",
       "      <td id=\"T_b7caf_row7_col1\" class=\"data row7 col1\" >22,692</td>\n",
       "      <td id=\"T_b7caf_row7_col2\" class=\"data row7 col2\" >0.10432583</td>\n",
       "      <td id=\"T_b7caf_row7_col3\" class=\"data row7 col3\" >97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row8\" class=\"row_heading level0 row8\" >2020</th>\n",
       "      <td id=\"T_b7caf_row8_col0\" class=\"data row8 col0\" >Omegle</td>\n",
       "      <td id=\"T_b7caf_row8_col1\" class=\"data row8 col1\" >20,265</td>\n",
       "      <td id=\"T_b7caf_row8_col2\" class=\"data row8 col2\" >0.09316777</td>\n",
       "      <td id=\"T_b7caf_row8_col3\" class=\"data row8 col3\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row9\" class=\"row_heading level0 row9\" >2020</th>\n",
       "      <td id=\"T_b7caf_row9_col0\" class=\"data row9 col0\" >Discord</td>\n",
       "      <td id=\"T_b7caf_row9_col1\" class=\"data row9 col1\" >15,324</td>\n",
       "      <td id=\"T_b7caf_row9_col2\" class=\"data row9 col2\" >0.07045166</td>\n",
       "      <td id=\"T_b7caf_row9_col3\" class=\"data row9 col3\" >97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row10\" class=\"row_heading level0 row10\" >2020</th>\n",
       "      <td id=\"T_b7caf_row10_col0\" class=\"data row10 col0\" >Aylo</td>\n",
       "      <td id=\"T_b7caf_row10_col1\" class=\"data row10 col1\" >13,229</td>\n",
       "      <td id=\"T_b7caf_row10_col2\" class=\"data row10 col2\" >0.06081995</td>\n",
       "      <td id=\"T_b7caf_row10_col3\" class=\"data row10 col3\" >97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row11\" class=\"row_heading level0 row11\" >2020</th>\n",
       "      <td id=\"T_b7caf_row11_col0\" class=\"data row11 col0\" >Automattic</td>\n",
       "      <td id=\"T_b7caf_row11_col1\" class=\"data row11 col1\" >9,130</td>\n",
       "      <td id=\"T_b7caf_row11_col2\" class=\"data row11 col2\" >0.04197492</td>\n",
       "      <td id=\"T_b7caf_row11_col3\" class=\"data row11 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row12\" class=\"row_heading level0 row12\" >2020</th>\n",
       "      <td id=\"T_b7caf_row12_col0\" class=\"data row12 col0\" >Pinterest</td>\n",
       "      <td id=\"T_b7caf_row12_col1\" class=\"data row12 col1\" >3,432</td>\n",
       "      <td id=\"T_b7caf_row12_col2\" class=\"data row12 col2\" >0.01577852</td>\n",
       "      <td id=\"T_b7caf_row12_col3\" class=\"data row12 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row13\" class=\"row_heading level0 row13\" >2020</th>\n",
       "      <td id=\"T_b7caf_row13_col0\" class=\"data row13 col0\" >Amazon</td>\n",
       "      <td id=\"T_b7caf_row13_col1\" class=\"data row13 col1\" >2,235</td>\n",
       "      <td id=\"T_b7caf_row13_col2\" class=\"data row13 col2\" >0.01027535</td>\n",
       "      <td id=\"T_b7caf_row13_col3\" class=\"data row13 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row14\" class=\"row_heading level0 row14\" >2020</th>\n",
       "      <td id=\"T_b7caf_row14_col0\" class=\"data row14 col0\" >Reddit</td>\n",
       "      <td id=\"T_b7caf_row14_col1\" class=\"data row14 col1\" >2,233</td>\n",
       "      <td id=\"T_b7caf_row14_col2\" class=\"data row14 col2\" >0.01026615</td>\n",
       "      <td id=\"T_b7caf_row14_col3\" class=\"data row14 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row15\" class=\"row_heading level0 row15\" >2020</th>\n",
       "      <td id=\"T_b7caf_row15_col0\" class=\"data row15 col0\" >Apple</td>\n",
       "      <td id=\"T_b7caf_row15_col1\" class=\"data row15 col1\" >265</td>\n",
       "      <td id=\"T_b7caf_row15_col2\" class=\"data row15 col2\" >0.00121833</td>\n",
       "      <td id=\"T_b7caf_row15_col3\" class=\"data row15 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row16\" class=\"row_heading level0 row16\" >2020</th>\n",
       "      <td id=\"T_b7caf_row16_col0\" class=\"data row16 col0\" >Wikimedia</td>\n",
       "      <td id=\"T_b7caf_row16_col1\" class=\"data row16 col1\" >11</td>\n",
       "      <td id=\"T_b7caf_row16_col2\" class=\"data row16 col2\" >0.00005057</td>\n",
       "      <td id=\"T_b7caf_row16_col3\" class=\"data row16 col3\" >97.7</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row17\" class=\"row_heading level0 row17\" >2020</th>\n",
       "      <td id=\"T_b7caf_row17_col0\" class=\"data row17 col0\" >Quora</td>\n",
       "      <td id=\"T_b7caf_row17_col1\" class=\"data row17 col1\" >2</td>\n",
       "      <td id=\"T_b7caf_row17_col2\" class=\"data row17 col2\" >0.00000919</td>\n",
       "      <td id=\"T_b7caf_row17_col3\" class=\"data row17 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row18\" class=\"row_heading level0 row18\" >2020</th>\n",
       "      <td id=\"T_b7caf_row18_col0\" class=\"data row18 col0\" >Telegram</td>\n",
       "      <td id=\"T_b7caf_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_b7caf_row18_col2\" class=\"data row18 col2\" >⋯</td>\n",
       "      <td id=\"T_b7caf_row18_col3\" class=\"data row18 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_b7caf_level0_row19\" class=\"row_heading level0 row19\" >2020</th>\n",
       "      <td id=\"T_b7caf_row19_col0\" class=\"data row19 col0\" >Survey Total</td>\n",
       "      <td id=\"T_b7caf_row19_col1\" class=\"data row19 col1\" >21,248,733</td>\n",
       "      <td id=\"T_b7caf_row19_col2\" class=\"data row19 col2\" >97.69045084</td>\n",
       "      <td id=\"T_b7caf_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
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       "<table id=\"T_8b7e5\">\n",
       "  <caption> Social Media by CSAM Reports Filed in 2021</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_8b7e5_level0_col0\" class=\"col_heading level0 col0\" >platform</th>\n",
       "      <th id=\"T_8b7e5_level0_col1\" class=\"col_heading level0 col1\" >reports</th>\n",
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       "      <th id=\"T_8b7e5_level0_col3\" class=\"col_heading level0 col3\" >cumpct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th id=\"T_8b7e5_level0_row0\" class=\"row_heading level0 row0\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row0_col0\" class=\"data row0 col0\" >Total</td>\n",
       "      <td id=\"T_8b7e5_row0_col1\" class=\"data row0 col1\" >29,397,681</td>\n",
       "      <td id=\"T_8b7e5_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_8b7e5_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row1\" class=\"row_heading level0 row1\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row1_col0\" class=\"data row1 col0\" >ESP Total</td>\n",
       "      <td id=\"T_8b7e5_row1_col1\" class=\"data row1 col1\" >29,157,083</td>\n",
       "      <td id=\"T_8b7e5_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_8b7e5_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row2_col0\" class=\"data row2 col0\" >Meta</td>\n",
       "      <td id=\"T_8b7e5_row2_col1\" class=\"data row2 col1\" >26,885,302</td>\n",
       "      <td id=\"T_8b7e5_row2_col2\" class=\"data row2 col2\" >91.4538191</td>\n",
       "      <td id=\"T_8b7e5_row2_col3\" class=\"data row2 col3\" >91.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row3\" class=\"row_heading level0 row3\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row3_col0\" class=\"data row3 col0\" >Alphabet</td>\n",
       "      <td id=\"T_8b7e5_row3_col1\" class=\"data row3 col1\" >875,783</td>\n",
       "      <td id=\"T_8b7e5_row3_col2\" class=\"data row3 col2\" >2.9790887</td>\n",
       "      <td id=\"T_8b7e5_row3_col3\" class=\"data row3 col3\" >94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row4\" class=\"row_heading level0 row4\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row4_col0\" class=\"data row4 col0\" >Snap</td>\n",
       "      <td id=\"T_8b7e5_row4_col1\" class=\"data row4 col1\" >512,522</td>\n",
       "      <td id=\"T_8b7e5_row4_col2\" class=\"data row4 col2\" >1.7434096</td>\n",
       "      <td id=\"T_8b7e5_row4_col3\" class=\"data row4 col3\" >96.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row5\" class=\"row_heading level0 row5\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row5_col0\" class=\"data row5 col0\" >TikTok</td>\n",
       "      <td id=\"T_8b7e5_row5_col1\" class=\"data row5 col1\" >154,618</td>\n",
       "      <td id=\"T_8b7e5_row5_col2\" class=\"data row5 col2\" >0.5259531</td>\n",
       "      <td id=\"T_8b7e5_row5_col3\" class=\"data row5 col3\" >96.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row6\" class=\"row_heading level0 row6\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row6_col0\" class=\"data row6 col0\" >X</td>\n",
       "      <td id=\"T_8b7e5_row6_col1\" class=\"data row6 col1\" >86,666</td>\n",
       "      <td id=\"T_8b7e5_row6_col2\" class=\"data row6 col2\" >0.2948056</td>\n",
       "      <td id=\"T_8b7e5_row6_col3\" class=\"data row6 col3\" >97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row7\" class=\"row_heading level0 row7\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row7_col0\" class=\"data row7 col0\" >Microsoft</td>\n",
       "      <td id=\"T_8b7e5_row7_col1\" class=\"data row7 col1\" >78,887</td>\n",
       "      <td id=\"T_8b7e5_row7_col2\" class=\"data row7 col2\" >0.2683443</td>\n",
       "      <td id=\"T_8b7e5_row7_col3\" class=\"data row7 col3\" >97.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row8\" class=\"row_heading level0 row8\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row8_col0\" class=\"data row8 col0\" >Omegle</td>\n",
       "      <td id=\"T_8b7e5_row8_col1\" class=\"data row8 col1\" >46,924</td>\n",
       "      <td id=\"T_8b7e5_row8_col2\" class=\"data row8 col2\" >0.1596180</td>\n",
       "      <td id=\"T_8b7e5_row8_col3\" class=\"data row8 col3\" >97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row9\" class=\"row_heading level0 row9\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row9_col0\" class=\"data row9 col0\" >Amazon</td>\n",
       "      <td id=\"T_8b7e5_row9_col1\" class=\"data row9 col1\" >33,833</td>\n",
       "      <td id=\"T_8b7e5_row9_col2\" class=\"data row9 col2\" >0.1150873</td>\n",
       "      <td id=\"T_8b7e5_row9_col3\" class=\"data row9 col3\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row10\" class=\"row_heading level0 row10\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row10_col0\" class=\"data row10 col0\" >Discord</td>\n",
       "      <td id=\"T_8b7e5_row10_col1\" class=\"data row10 col1\" >29,606</td>\n",
       "      <td id=\"T_8b7e5_row10_col2\" class=\"data row10 col2\" >0.1007086</td>\n",
       "      <td id=\"T_8b7e5_row10_col3\" class=\"data row10 col3\" >97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row11\" class=\"row_heading level0 row11\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row11_col0\" class=\"data row11 col0\" >Reddit</td>\n",
       "      <td id=\"T_8b7e5_row11_col1\" class=\"data row11 col1\" >10,059</td>\n",
       "      <td id=\"T_8b7e5_row11_col2\" class=\"data row11 col2\" >0.0342170</td>\n",
       "      <td id=\"T_8b7e5_row11_col3\" class=\"data row11 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row12\" class=\"row_heading level0 row12\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row12_col0\" class=\"data row12 col0\" >Aylo</td>\n",
       "      <td id=\"T_8b7e5_row12_col1\" class=\"data row12 col1\" >9,103</td>\n",
       "      <td id=\"T_8b7e5_row12_col2\" class=\"data row12 col2\" >0.0309650</td>\n",
       "      <td id=\"T_8b7e5_row12_col3\" class=\"data row12 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row13\" class=\"row_heading level0 row13\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row13_col0\" class=\"data row13 col0\" >Automattic</td>\n",
       "      <td id=\"T_8b7e5_row13_col1\" class=\"data row13 col1\" >4,821</td>\n",
       "      <td id=\"T_8b7e5_row13_col2\" class=\"data row13 col2\" >0.0163993</td>\n",
       "      <td id=\"T_8b7e5_row13_col3\" class=\"data row13 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row14\" class=\"row_heading level0 row14\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row14_col0\" class=\"data row14 col0\" >Pinterest</td>\n",
       "      <td id=\"T_8b7e5_row14_col1\" class=\"data row14 col1\" >2,283</td>\n",
       "      <td id=\"T_8b7e5_row14_col2\" class=\"data row14 col2\" >0.0077659</td>\n",
       "      <td id=\"T_8b7e5_row14_col3\" class=\"data row14 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row15\" class=\"row_heading level0 row15\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row15_col0\" class=\"data row15 col0\" >Apple</td>\n",
       "      <td id=\"T_8b7e5_row15_col1\" class=\"data row15 col1\" >160</td>\n",
       "      <td id=\"T_8b7e5_row15_col2\" class=\"data row15 col2\" >0.0005443</td>\n",
       "      <td id=\"T_8b7e5_row15_col3\" class=\"data row15 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row16\" class=\"row_heading level0 row16\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row16_col0\" class=\"data row16 col0\" >Quora</td>\n",
       "      <td id=\"T_8b7e5_row16_col1\" class=\"data row16 col1\" >25</td>\n",
       "      <td id=\"T_8b7e5_row16_col2\" class=\"data row16 col2\" >0.0000850</td>\n",
       "      <td id=\"T_8b7e5_row16_col3\" class=\"data row16 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row17\" class=\"row_heading level0 row17\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row17_col0\" class=\"data row17 col0\" >Wikimedia</td>\n",
       "      <td id=\"T_8b7e5_row17_col1\" class=\"data row17 col1\" >8</td>\n",
       "      <td id=\"T_8b7e5_row17_col2\" class=\"data row17 col2\" >0.0000272</td>\n",
       "      <td id=\"T_8b7e5_row17_col3\" class=\"data row17 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row18\" class=\"row_heading level0 row18\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row18_col0\" class=\"data row18 col0\" >Telegram</td>\n",
       "      <td id=\"T_8b7e5_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_8b7e5_row18_col2\" class=\"data row18 col2\" >⋯</td>\n",
       "      <td id=\"T_8b7e5_row18_col3\" class=\"data row18 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_8b7e5_level0_row19\" class=\"row_heading level0 row19\" >2021</th>\n",
       "      <td id=\"T_8b7e5_row19_col0\" class=\"data row19 col0\" >Survey Total</td>\n",
       "      <td id=\"T_8b7e5_row19_col1\" class=\"data row19 col1\" >28,730,600</td>\n",
       "      <td id=\"T_8b7e5_row19_col2\" class=\"data row19 col2\" >97.7308380</td>\n",
       "      <td id=\"T_8b7e5_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10c142dd0>"
      ]
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       "<style type=\"text/css\">\n",
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       "</style>\n",
       "<table id=\"T_a7fe9\">\n",
       "  <caption> Social Media by CSAM Reports Filed in 2022</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_a7fe9_level0_col0\" class=\"col_heading level0 col0\" >platform</th>\n",
       "      <th id=\"T_a7fe9_level0_col1\" class=\"col_heading level0 col1\" >reports</th>\n",
       "      <th id=\"T_a7fe9_level0_col2\" class=\"col_heading level0 col2\" >reports_pct</th>\n",
       "      <th id=\"T_a7fe9_level0_col3\" class=\"col_heading level0 col3\" >cumpct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row0\" class=\"row_heading level0 row0\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row0_col0\" class=\"data row0 col0\" >Total</td>\n",
       "      <td id=\"T_a7fe9_row0_col1\" class=\"data row0 col1\" >32,059,029</td>\n",
       "      <td id=\"T_a7fe9_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_a7fe9_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row1\" class=\"row_heading level0 row1\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row1_col0\" class=\"data row1 col0\" >ESP Total</td>\n",
       "      <td id=\"T_a7fe9_row1_col1\" class=\"data row1 col1\" >31,802,525</td>\n",
       "      <td id=\"T_a7fe9_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_a7fe9_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row2\" class=\"row_heading level0 row2\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row2_col0\" class=\"data row2 col0\" >Meta</td>\n",
       "      <td id=\"T_a7fe9_row2_col1\" class=\"data row2 col1\" >27,190,665</td>\n",
       "      <td id=\"T_a7fe9_row2_col2\" class=\"data row2 col2\" >84.8143748</td>\n",
       "      <td id=\"T_a7fe9_row2_col3\" class=\"data row2 col3\" >84.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row3_col0\" class=\"data row3 col0\" >Alphabet</td>\n",
       "      <td id=\"T_a7fe9_row3_col1\" class=\"data row3 col1\" >2,174,548</td>\n",
       "      <td id=\"T_a7fe9_row3_col2\" class=\"data row3 col2\" >6.7829503</td>\n",
       "      <td id=\"T_a7fe9_row3_col3\" class=\"data row3 col3\" >91.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row4\" class=\"row_heading level0 row4\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row4_col0\" class=\"data row4 col0\" >Omegle</td>\n",
       "      <td id=\"T_a7fe9_row4_col1\" class=\"data row4 col1\" >608,601</td>\n",
       "      <td id=\"T_a7fe9_row4_col2\" class=\"data row4 col2\" >1.8983763</td>\n",
       "      <td id=\"T_a7fe9_row4_col3\" class=\"data row4 col3\" >93.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row5\" class=\"row_heading level0 row5\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row5_col0\" class=\"data row5 col0\" >Snap</td>\n",
       "      <td id=\"T_a7fe9_row5_col1\" class=\"data row5 col1\" >551,086</td>\n",
       "      <td id=\"T_a7fe9_row5_col2\" class=\"data row5 col2\" >1.7189728</td>\n",
       "      <td id=\"T_a7fe9_row5_col3\" class=\"data row5 col3\" >95.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row6\" class=\"row_heading level0 row6\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row6_col0\" class=\"data row6 col0\" >TikTok</td>\n",
       "      <td id=\"T_a7fe9_row6_col1\" class=\"data row6 col1\" >288,125</td>\n",
       "      <td id=\"T_a7fe9_row6_col2\" class=\"data row6 col2\" >0.8987328</td>\n",
       "      <td id=\"T_a7fe9_row6_col3\" class=\"data row6 col3\" >96.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row7\" class=\"row_heading level0 row7\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row7_col0\" class=\"data row7 col0\" >Discord</td>\n",
       "      <td id=\"T_a7fe9_row7_col1\" class=\"data row7 col1\" >169,800</td>\n",
       "      <td id=\"T_a7fe9_row7_col2\" class=\"data row7 col2\" >0.5296480</td>\n",
       "      <td id=\"T_a7fe9_row7_col3\" class=\"data row7 col3\" >96.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row8\" class=\"row_heading level0 row8\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row8_col0\" class=\"data row8 col0\" >Microsoft</td>\n",
       "      <td id=\"T_a7fe9_row8_col1\" class=\"data row8 col1\" >108,804</td>\n",
       "      <td id=\"T_a7fe9_row8_col2\" class=\"data row8 col2\" >0.3393864</td>\n",
       "      <td id=\"T_a7fe9_row8_col3\" class=\"data row8 col3\" >97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row9\" class=\"row_heading level0 row9\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row9_col0\" class=\"data row9 col0\" >X</td>\n",
       "      <td id=\"T_a7fe9_row9_col1\" class=\"data row9 col1\" >98,050</td>\n",
       "      <td id=\"T_a7fe9_row9_col2\" class=\"data row9 col2\" >0.3058421</td>\n",
       "      <td id=\"T_a7fe9_row9_col3\" class=\"data row9 col3\" >97.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row10\" class=\"row_heading level0 row10\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row10_col0\" class=\"data row10 col0\" >Amazon</td>\n",
       "      <td id=\"T_a7fe9_row10_col1\" class=\"data row10 col1\" >70,157</td>\n",
       "      <td id=\"T_a7fe9_row10_col2\" class=\"data row10 col2\" >0.2188369</td>\n",
       "      <td id=\"T_a7fe9_row10_col3\" class=\"data row10 col3\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row11\" class=\"row_heading level0 row11\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row11_col0\" class=\"data row11 col0\" >Reddit</td>\n",
       "      <td id=\"T_a7fe9_row11_col1\" class=\"data row11 col1\" >52,592</td>\n",
       "      <td id=\"T_a7fe9_row11_col2\" class=\"data row11 col2\" >0.1640474</td>\n",
       "      <td id=\"T_a7fe9_row11_col3\" class=\"data row11 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row12\" class=\"row_heading level0 row12\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row12_col0\" class=\"data row12 col0\" >Pinterest</td>\n",
       "      <td id=\"T_a7fe9_row12_col1\" class=\"data row12 col1\" >34,310</td>\n",
       "      <td id=\"T_a7fe9_row12_col2\" class=\"data row12 col2\" >0.1070213</td>\n",
       "      <td id=\"T_a7fe9_row12_col3\" class=\"data row12 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row13\" class=\"row_heading level0 row13\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row13_col0\" class=\"data row13 col0\" >Automattic</td>\n",
       "      <td id=\"T_a7fe9_row13_col1\" class=\"data row13 col1\" >5,035</td>\n",
       "      <td id=\"T_a7fe9_row13_col2\" class=\"data row13 col2\" >0.0157054</td>\n",
       "      <td id=\"T_a7fe9_row13_col3\" class=\"data row13 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row14\" class=\"row_heading level0 row14\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row14_col0\" class=\"data row14 col0\" >Quora</td>\n",
       "      <td id=\"T_a7fe9_row14_col1\" class=\"data row14 col1\" >2,242</td>\n",
       "      <td id=\"T_a7fe9_row14_col2\" class=\"data row14 col2\" >0.0069933</td>\n",
       "      <td id=\"T_a7fe9_row14_col3\" class=\"data row14 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row15\" class=\"row_heading level0 row15\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row15_col0\" class=\"data row15 col0\" >Aylo</td>\n",
       "      <td id=\"T_a7fe9_row15_col1\" class=\"data row15 col1\" >2,095</td>\n",
       "      <td id=\"T_a7fe9_row15_col2\" class=\"data row15 col2\" >0.0065348</td>\n",
       "      <td id=\"T_a7fe9_row15_col3\" class=\"data row15 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row16\" class=\"row_heading level0 row16\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row16_col0\" class=\"data row16 col0\" >Apple</td>\n",
       "      <td id=\"T_a7fe9_row16_col1\" class=\"data row16 col1\" >234</td>\n",
       "      <td id=\"T_a7fe9_row16_col2\" class=\"data row16 col2\" >0.0007299</td>\n",
       "      <td id=\"T_a7fe9_row16_col3\" class=\"data row16 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row17\" class=\"row_heading level0 row17\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row17_col0\" class=\"data row17 col0\" >Wikimedia</td>\n",
       "      <td id=\"T_a7fe9_row17_col1\" class=\"data row17 col1\" >29</td>\n",
       "      <td id=\"T_a7fe9_row17_col2\" class=\"data row17 col2\" >0.0000905</td>\n",
       "      <td id=\"T_a7fe9_row17_col3\" class=\"data row17 col3\" >97.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row18\" class=\"row_heading level0 row18\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row18_col0\" class=\"data row18 col0\" >Telegram</td>\n",
       "      <td id=\"T_a7fe9_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_a7fe9_row18_col2\" class=\"data row18 col2\" >⋯</td>\n",
       "      <td id=\"T_a7fe9_row18_col3\" class=\"data row18 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a7fe9_level0_row19\" class=\"row_heading level0 row19\" >2022</th>\n",
       "      <td id=\"T_a7fe9_row19_col0\" class=\"data row19 col0\" >Survey Total</td>\n",
       "      <td id=\"T_a7fe9_row19_col1\" class=\"data row19 col1\" >31,356,373</td>\n",
       "      <td id=\"T_a7fe9_row19_col2\" class=\"data row19 col2\" >97.8082430</td>\n",
       "      <td id=\"T_a7fe9_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10cabcd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c856c caption {\n",
       "  caption-side: top;\n",
       "  font-size: 1.1em;\n",
       "  margin-bottom: 2ex;\n",
       "  vertical-align: center;\n",
       "  font-style: italic;\n",
       "}\n",
       "#T_c856c .level0 {\n",
       "  text-align: start;\n",
       "}\n",
       "#T_c856c .col0 {\n",
       "  text-align: start;\n",
       "}\n",
       "#T_c856c  {\n",
       "  font-variant-numeric: tabular-nums;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 2.0em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c856c\">\n",
       "  <caption> Social Media by CSAM Reports Filed in 2023</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c856c_level0_col0\" class=\"col_heading level0 col0\" >platform</th>\n",
       "      <th id=\"T_c856c_level0_col1\" class=\"col_heading level0 col1\" >reports</th>\n",
       "      <th id=\"T_c856c_level0_col2\" class=\"col_heading level0 col2\" >reports_pct</th>\n",
       "      <th id=\"T_c856c_level0_col3\" class=\"col_heading level0 col3\" >cumpct</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row0\" class=\"row_heading level0 row0\" >2023</th>\n",
       "      <td id=\"T_c856c_row0_col0\" class=\"data row0 col0\" >Total</td>\n",
       "      <td id=\"T_c856c_row0_col1\" class=\"data row0 col1\" >36,210,368</td>\n",
       "      <td id=\"T_c856c_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_c856c_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row1\" class=\"row_heading level0 row1\" >2023</th>\n",
       "      <td id=\"T_c856c_row1_col0\" class=\"data row1 col0\" >ESP Total</td>\n",
       "      <td id=\"T_c856c_row1_col1\" class=\"data row1 col1\" >35,944,826</td>\n",
       "      <td id=\"T_c856c_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_c856c_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row2\" class=\"row_heading level0 row2\" >2023</th>\n",
       "      <td id=\"T_c856c_row2_col0\" class=\"data row2 col0\" >Meta</td>\n",
       "      <td id=\"T_c856c_row2_col1\" class=\"data row2 col1\" >30,658,047</td>\n",
       "      <td id=\"T_c856c_row2_col2\" class=\"data row2 col2\" >84.6664883</td>\n",
       "      <td id=\"T_c856c_row2_col3\" class=\"data row2 col3\" >84.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row3\" class=\"row_heading level0 row3\" >2023</th>\n",
       "      <td id=\"T_c856c_row3_col0\" class=\"data row3 col0\" >Alphabet</td>\n",
       "      <td id=\"T_c856c_row3_col1\" class=\"data row3 col1\" >1,470,958</td>\n",
       "      <td id=\"T_c856c_row3_col2\" class=\"data row3 col2\" >4.0622564</td>\n",
       "      <td id=\"T_c856c_row3_col3\" class=\"data row3 col3\" >88.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_c856c_row4_col0\" class=\"data row4 col0\" >X</td>\n",
       "      <td id=\"T_c856c_row4_col1\" class=\"data row4 col1\" >870,503</td>\n",
       "      <td id=\"T_c856c_row4_col2\" class=\"data row4 col2\" >2.4040159</td>\n",
       "      <td id=\"T_c856c_row4_col3\" class=\"data row4 col3\" >91.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row5\" class=\"row_heading level0 row5\" >2023</th>\n",
       "      <td id=\"T_c856c_row5_col0\" class=\"data row5 col0\" >Snap</td>\n",
       "      <td id=\"T_c856c_row5_col1\" class=\"data row5 col1\" >713,055</td>\n",
       "      <td id=\"T_c856c_row5_col2\" class=\"data row5 col2\" >1.9692012</td>\n",
       "      <td id=\"T_c856c_row5_col3\" class=\"data row5 col3\" >93.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row6\" class=\"row_heading level0 row6\" >2023</th>\n",
       "      <td id=\"T_c856c_row6_col0\" class=\"data row6 col0\" >TikTok</td>\n",
       "      <td id=\"T_c856c_row6_col1\" class=\"data row6 col1\" >590,376</td>\n",
       "      <td id=\"T_c856c_row6_col2\" class=\"data row6 col2\" >1.6304060</td>\n",
       "      <td id=\"T_c856c_row6_col3\" class=\"data row6 col3\" >94.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row7\" class=\"row_heading level0 row7\" >2023</th>\n",
       "      <td id=\"T_c856c_row7_col0\" class=\"data row7 col0\" >Discord</td>\n",
       "      <td id=\"T_c856c_row7_col1\" class=\"data row7 col1\" >339,412</td>\n",
       "      <td id=\"T_c856c_row7_col2\" class=\"data row7 col2\" >0.9373337</td>\n",
       "      <td id=\"T_c856c_row7_col3\" class=\"data row7 col3\" >95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row8\" class=\"row_heading level0 row8\" >2023</th>\n",
       "      <td id=\"T_c856c_row8_col0\" class=\"data row8 col0\" >Reddit</td>\n",
       "      <td id=\"T_c856c_row8_col1\" class=\"data row8 col1\" >290,141</td>\n",
       "      <td id=\"T_c856c_row8_col2\" class=\"data row8 col2\" >0.8012650</td>\n",
       "      <td id=\"T_c856c_row8_col3\" class=\"data row8 col3\" >96.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row9\" class=\"row_heading level0 row9\" >2023</th>\n",
       "      <td id=\"T_c856c_row9_col0\" class=\"data row9 col0\" >Omegle</td>\n",
       "      <td id=\"T_c856c_row9_col1\" class=\"data row9 col1\" >188,102</td>\n",
       "      <td id=\"T_c856c_row9_col2\" class=\"data row9 col2\" >0.5194700</td>\n",
       "      <td id=\"T_c856c_row9_col3\" class=\"data row9 col3\" >97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row10\" class=\"row_heading level0 row10\" >2023</th>\n",
       "      <td id=\"T_c856c_row10_col0\" class=\"data row10 col0\" >Microsoft</td>\n",
       "      <td id=\"T_c856c_row10_col1\" class=\"data row10 col1\" >141,237</td>\n",
       "      <td id=\"T_c856c_row10_col2\" class=\"data row10 col2\" >0.3900457</td>\n",
       "      <td id=\"T_c856c_row10_col3\" class=\"data row10 col3\" >97.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row11\" class=\"row_heading level0 row11\" >2023</th>\n",
       "      <td id=\"T_c856c_row11_col0\" class=\"data row11 col0\" >Pinterest</td>\n",
       "      <td id=\"T_c856c_row11_col1\" class=\"data row11 col1\" >52,356</td>\n",
       "      <td id=\"T_c856c_row11_col2\" class=\"data row11 col2\" >0.1445884</td>\n",
       "      <td id=\"T_c856c_row11_col3\" class=\"data row11 col3\" >97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row12\" class=\"row_heading level0 row12\" >2023</th>\n",
       "      <td id=\"T_c856c_row12_col0\" class=\"data row12 col0\" >Amazon</td>\n",
       "      <td id=\"T_c856c_row12_col1\" class=\"data row12 col1\" >32,359</td>\n",
       "      <td id=\"T_c856c_row12_col2\" class=\"data row12 col2\" >0.0893639</td>\n",
       "      <td id=\"T_c856c_row12_col3\" class=\"data row12 col3\" >97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row13\" class=\"row_heading level0 row13\" >2023</th>\n",
       "      <td id=\"T_c856c_row13_col0\" class=\"data row13 col0\" >Automattic</td>\n",
       "      <td id=\"T_c856c_row13_col1\" class=\"data row13 col1\" >19,591</td>\n",
       "      <td id=\"T_c856c_row13_col2\" class=\"data row13 col2\" >0.0541033</td>\n",
       "      <td id=\"T_c856c_row13_col3\" class=\"data row13 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row14\" class=\"row_heading level0 row14\" >2023</th>\n",
       "      <td id=\"T_c856c_row14_col0\" class=\"data row14 col0\" >Quora</td>\n",
       "      <td id=\"T_c856c_row14_col1\" class=\"data row14 col1\" >6,135</td>\n",
       "      <td id=\"T_c856c_row14_col2\" class=\"data row14 col2\" >0.0169427</td>\n",
       "      <td id=\"T_c856c_row14_col3\" class=\"data row14 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row15\" class=\"row_heading level0 row15\" >2023</th>\n",
       "      <td id=\"T_c856c_row15_col0\" class=\"data row15 col0\" >Aylo</td>\n",
       "      <td id=\"T_c856c_row15_col1\" class=\"data row15 col1\" >2,596</td>\n",
       "      <td id=\"T_c856c_row15_col2\" class=\"data row15 col2\" >0.0071692</td>\n",
       "      <td id=\"T_c856c_row15_col3\" class=\"data row15 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row16\" class=\"row_heading level0 row16\" >2023</th>\n",
       "      <td id=\"T_c856c_row16_col0\" class=\"data row16 col0\" >Apple</td>\n",
       "      <td id=\"T_c856c_row16_col1\" class=\"data row16 col1\" >267</td>\n",
       "      <td id=\"T_c856c_row16_col2\" class=\"data row16 col2\" >0.0007374</td>\n",
       "      <td id=\"T_c856c_row16_col3\" class=\"data row16 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row17\" class=\"row_heading level0 row17\" >2023</th>\n",
       "      <td id=\"T_c856c_row17_col0\" class=\"data row17 col0\" >Wikimedia</td>\n",
       "      <td id=\"T_c856c_row17_col1\" class=\"data row17 col1\" >34</td>\n",
       "      <td id=\"T_c856c_row17_col2\" class=\"data row17 col2\" >0.0000939</td>\n",
       "      <td id=\"T_c856c_row17_col3\" class=\"data row17 col3\" >97.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row18\" class=\"row_heading level0 row18\" >2023</th>\n",
       "      <td id=\"T_c856c_row18_col0\" class=\"data row18 col0\" >Telegram</td>\n",
       "      <td id=\"T_c856c_row18_col1\" class=\"data row18 col1\" >⋯</td>\n",
       "      <td id=\"T_c856c_row18_col2\" class=\"data row18 col2\" >⋯</td>\n",
       "      <td id=\"T_c856c_row18_col3\" class=\"data row18 col3\" >⋯</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c856c_level0_row19\" class=\"row_heading level0 row19\" >2023</th>\n",
       "      <td id=\"T_c856c_row19_col0\" class=\"data row19 col0\" >Survey Total</td>\n",
       "      <td id=\"T_c856c_row19_col1\" class=\"data row19 col1\" >35,375,169</td>\n",
       "      <td id=\"T_c856c_row19_col2\" class=\"data row19 col2\" >97.6934810</td>\n",
       "      <td id=\"T_c856c_row19_col3\" class=\"data row19 col3\" >⋯</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10ac29990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('<h2>Ranking Service Providers by CyberTipline Report Counts</h2>')\n",
    "\n",
    "ncmec = long_ncmec_reports(platform_data)\n",
    "totals = ncmec[ncmec['platform'] == 'Total']['reports']\n",
    "\n",
    "year = ncmec.index.min()\n",
    "stop = ncmec.index.max()\n",
    "\n",
    "while year <= stop:\n",
    "    yearly = (\n",
    "        ncmec.loc[year]\n",
    "        .sort_values('reports', ascending=False)\n",
    "    )\n",
    "\n",
    "    survey_total = yearly.loc[~yearly['platform'].isin(['ESP Total', 'Total']), 'reports'].sum()\n",
    "    survey_frame = pd.DataFrame({\n",
    "        'period': [year],\n",
    "        'platform': ['Survey Total'],\n",
    "        'reports': [survey_total],\n",
    "    }).set_index('period')\n",
    "\n",
    "    yearly = (\n",
    "        pd.concat([yearly, survey_frame])\n",
    "        .assign(reports_pct=lambda df: df['reports'] / totals[year] * 100)\n",
    "    )\n",
    "\n",
    "    survey_pct = yearly.loc[yearly['platform'] == 'Survey Total', 'reports_pct']\n",
    "    yearly.loc[yearly['platform'].isin(['Survey Total', 'ESP Total', 'Total']), 'reports_pct'] = np.nan\n",
    "    yearly['cumpct'] = yearly['reports_pct'].cumsum()\n",
    "    yearly.loc[yearly['platform'] == 'Survey Total', 'reports_pct'] = survey_pct\n",
    "\n",
    "    show(\n",
    "        yearly,\n",
    "        caption=f' Social Media by CSAM Reports Filed in {year}',\n",
    "    )\n",
    "    year += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Meta, Google, and the Rest</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6b4df .level0 {\n",
       "  text-align: start;\n",
       "}\n",
       "#T_6b4df  {\n",
       "  font-variant-numeric: tabular-nums;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 2.0em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6b4df\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >platform</th>\n",
       "      <th id=\"T_6b4df_level0_col0\" class=\"col_heading level0 col0\" >Meta</th>\n",
       "      <th id=\"T_6b4df_level0_col1\" class=\"col_heading level0 col1\" >Google</th>\n",
       "      <th id=\"T_6b4df_level0_col2\" class=\"col_heading level0 col2\" >Others</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6b4df_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_6b4df_row0_col0\" class=\"data row0 col0\" >95.5</td>\n",
       "      <td id=\"T_6b4df_row0_col1\" class=\"data row0 col1\" >2.70</td>\n",
       "      <td id=\"T_6b4df_row0_col2\" class=\"data row0 col2\" >1.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b4df_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_6b4df_row1_col0\" class=\"data row1 col0\" >95.6</td>\n",
       "      <td id=\"T_6b4df_row1_col1\" class=\"data row1 col1\" >2.57</td>\n",
       "      <td id=\"T_6b4df_row1_col2\" class=\"data row1 col2\" >1.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b4df_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_6b4df_row2_col0\" class=\"data row2 col0\" >93.6</td>\n",
       "      <td id=\"T_6b4df_row2_col1\" class=\"data row2 col1\" >3.05</td>\n",
       "      <td id=\"T_6b4df_row2_col2\" class=\"data row2 col2\" >3.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b4df_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_6b4df_row3_col0\" class=\"data row3 col0\" >86.7</td>\n",
       "      <td id=\"T_6b4df_row3_col1\" class=\"data row3 col1\" >6.93</td>\n",
       "      <td id=\"T_6b4df_row3_col2\" class=\"data row3 col2\" >6.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6b4df_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_6b4df_row4_col0\" class=\"data row4 col0\" >86.7</td>\n",
       "      <td id=\"T_6b4df_row4_col1\" class=\"data row4 col1\" >4.16</td>\n",
       "      <td id=\"T_6b4df_row4_col2\" class=\"data row4 col2\" >9.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10c91c310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\"<h3>Meta, Google, and the Rest</h3>\")\n",
    "\n",
    "data = (\n",
    "    # Select rows with actual platform data\n",
    "    ncmec[~ncmec['platform'].isin(['Total', 'ESP Total'])]\n",
    "    # Compute yearly report percentage\n",
    "    .assign(reports_pct=lambda df: df['reports'] / df.groupby(df.index)['reports'].sum() * 100)\n",
    "    # Sort in descending order\n",
    "    .sort_values('reports_pct', ascending=False)\n",
    "    # Drop the reports column\n",
    "    .drop(columns=['reports'])\n",
    "    # Select rows with more than one percent\n",
    "    .query(\"reports_pct > 2.5\")\n",
    ")\n",
    "\n",
    "missing = (100 - data.groupby(data.index)['reports_pct'].sum()).to_frame()\n",
    "missing['platform'] = 'Others'\n",
    "\n",
    "data = pd.concat([data, missing]).pivot(columns='platform', values='reports_pct').fillna(0.0)\n",
    "#data = data[['Meta', 'Alphabet', 'X', 'Snap', 'Omegle', 'TikTok', 'Others']]\n",
    "data = data[['Meta', 'Alphabet', 'Others']]\n",
    "data = data.rename(columns={'Alphabet': 'Google'})\n",
    "\n",
    "show(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c786a caption {\n",
       "  caption-side: top;\n",
       "  font-size: 1.1em;\n",
       "  margin-bottom: 2ex;\n",
       "  vertical-align: center;\n",
       "  font-style: italic;\n",
       "}\n",
       "#T_c786a  {\n",
       "  font-variant-numeric: tabular-nums;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 2.0em;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c786a\">\n",
       "  <caption>surveyed social media's reports out of total</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c786a_level0_col0\" class=\"col_heading level0 col0\" >reports</th>\n",
       "      <th id=\"T_c786a_level0_col1\" class=\"col_heading level0 col1\" >totals</th>\n",
       "      <th id=\"T_c786a_level0_col2\" class=\"col_heading level0 col2\" >percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c786a_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_c786a_row0_col0\" class=\"data row0 col0\" >16,044,540</td>\n",
       "      <td id=\"T_c786a_row0_col1\" class=\"data row0 col1\" >16,987,361</td>\n",
       "      <td id=\"T_c786a_row0_col2\" class=\"data row0 col2\" >94.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c786a_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_c786a_row1_col0\" class=\"data row1 col0\" >20,769,347</td>\n",
       "      <td id=\"T_c786a_row1_col1\" class=\"data row1 col1\" >21,751,085</td>\n",
       "      <td id=\"T_c786a_row1_col2\" class=\"data row1 col2\" >95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c786a_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_c786a_row2_col0\" class=\"data row2 col0\" >27,987,999</td>\n",
       "      <td id=\"T_c786a_row2_col1\" class=\"data row2 col1\" >29,397,681</td>\n",
       "      <td id=\"T_c786a_row2_col2\" class=\"data row2 col2\" >95.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c786a_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_c786a_row3_col0\" class=\"data row3 col0\" >29,471,491</td>\n",
       "      <td id=\"T_c786a_row3_col1\" class=\"data row3 col1\" >32,059,029</td>\n",
       "      <td id=\"T_c786a_row3_col2\" class=\"data row3 col2\" >91.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c786a_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_c786a_row4_col0\" class=\"data row4 col0\" >34,123,654</td>\n",
       "      <td id=\"T_c786a_row4_col1\" class=\"data row4 col1\" >36,210,368</td>\n",
       "      <td id=\"T_c786a_row4_col2\" class=\"data row4 col2\" >94.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10cb40490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "platforms_again = ingest_reports_per_platform(REPORTS_PER_PLATFORM)\n",
    "\n",
    "# Use YouTube's disclosures together with NCMEC's disclosures\n",
    "youtube = (\n",
    "    platforms_again.disclosures[\"YouTube\"]\n",
    "    .drop(columns=\"pieces\")\n",
    "    .pipe(lambda df: df.groupby(df.index.year))\n",
    "    .sum()\n",
    "    .assign(platform=\"YouTube\")\n",
    "    .reset_index()\n",
    ")\n",
    "ncmec = (\n",
    "    platforms_again.disclosures[\"NCMEC\"]\n",
    "    .fillna(0)\n",
    "    .reset_index()\n",
    ")\n",
    "# Ensure NCMEC table has int for period, too\n",
    "ncmec[\"period\"] = ncmec[\"period\"].dt.year\n",
    "social_media_data = pd.concat([youtube, ncmec])\n",
    "\n",
    "# Select social media only\n",
    "social_media = (\n",
    "    platforms_again.features[\"social_media\"]\n",
    "    .astype(bool)\n",
    "    .fillna(False)\n",
    "    .reset_index()\n",
    "    .pipe(lambda df: df[df[\"social_media\"]])\n",
    "    .drop(columns=\"social_media\")\n",
    "    .merge(social_media_data, how=\"left\", on=\"platform\")\n",
    "    .groupby(\"period\")\n",
    "    .sum(numeric_only=True)\n",
    ")\n",
    "\n",
    "# Align index of totals, too\n",
    "subtotals = totals.to_frame().rename(columns={\"reports\": \"totals\"})\n",
    "subtotals.index = subtotals.index.year\n",
    "\n",
    "social_media = pd.concat([social_media, subtotals], axis=1)\n",
    "social_media[\"percent\"] = social_media[\"reports\"] / social_media[\"totals\"] * 100\n",
    "\n",
    "show(social_media, caption=\"surveyed social media's reports out of total\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 450x420 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "COLORS = [\"#2355a6\", \"#ffa628\", \"#be2423\", \"#be2423\"]\n",
    "\n",
    "def plot(data, ax, colors):\n",
    "    data.plot.bar(\n",
    "        ax=ax,\n",
    "        stacked=True,\n",
    "        zorder=3,\n",
    "        width=0.9,\n",
    "        color = colors,\n",
    "        figsize=[4.5, 4.2],\n",
    "    )\n",
    "    ax.set_xlim(-0.5, 4.5)\n",
    "    ax.set_xlabel('Year', fontstyle='italic')\n",
    "    ax.set_ylabel('Fraction of Reports (%)', fontstyle='italic')\n",
    "    ax.tick_params(axis='x', labelrotation=0)\n",
    "    ax.set_ylim(top=100)\n",
    "\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    ax.legend(handles[::-1], labels[::-1])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plot(data, ax, COLORS)\n",
    "\n",
    "fig.savefig('../figure/platforms-share.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "\n",
       "    Out of 22 surveyed social media platforms owned by\n",
       "    16 firms, 14 brands make transparency\n",
       "    disclosures. Furthermore, only 17 platforms disclose usable\n",
       "    piece or report counts.\n",
       "    "
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       "  <caption>Automattic</caption>\n",
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       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
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       "      <th id=\"T_cba07_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_cba07_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
       "      <td id=\"T_cba07_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_cba07_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_cba07_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_cba07_row0_col4\" class=\"data row0 col4\" >10,443</td>\n",
       "      <td id=\"T_cba07_row0_col5\" class=\"data row0 col5\" >0.0620</td>\n",
       "      <td id=\"T_cba07_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_cba07_row0_col7\" class=\"data row0 col7\" >0.0615</td>\n",
       "      <td id=\"T_cba07_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_cba07_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cba07_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_cba07_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_cba07_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_cba07_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_cba07_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_cba07_row1_col4\" class=\"data row1 col4\" >9,130</td>\n",
       "      <td id=\"T_cba07_row1_col5\" class=\"data row1 col5\" >0.0426</td>\n",
       "      <td id=\"T_cba07_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_cba07_row1_col7\" class=\"data row1 col7\" >0.0420</td>\n",
       "      <td id=\"T_cba07_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
       "      <td id=\"T_cba07_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_cba07_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_cba07_row2_col0\" class=\"data row2 col0\" >⋯</td>\n",
       "      <td id=\"T_cba07_row2_col1\" class=\"data row2 col1\" >⋯</td>\n",
       "      <td id=\"T_cba07_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_cba07_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_cba07_row2_col4\" class=\"data row2 col4\" >4,821</td>\n",
       "      <td id=\"T_cba07_row2_col5\" class=\"data row2 col5\" >0.0165</td>\n",
       "      <td id=\"T_cba07_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_cba07_row2_col7\" class=\"data row2 col7\" >0.0164</td>\n",
       "      <td id=\"T_cba07_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_cba07_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_cba07_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_cba07_row3_col0\" class=\"data row3 col0\" >⋯</td>\n",
       "      <td id=\"T_cba07_row3_col1\" class=\"data row3 col1\" >⋯</td>\n",
       "      <td id=\"T_cba07_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_cba07_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_cba07_row3_col4\" class=\"data row3 col4\" >5,035</td>\n",
       "      <td id=\"T_cba07_row3_col5\" class=\"data row3 col5\" >0.0158</td>\n",
       "      <td id=\"T_cba07_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_cba07_row3_col7\" class=\"data row3 col7\" >0.0157</td>\n",
       "      <td id=\"T_cba07_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_cba07_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_cba07_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_cba07_row4_col0\" class=\"data row4 col0\" >⋯</td>\n",
       "      <td id=\"T_cba07_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_cba07_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_cba07_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_cba07_row4_col4\" class=\"data row4 col4\" >19,591</td>\n",
       "      <td id=\"T_cba07_row4_col5\" class=\"data row4 col5\" >0.0545</td>\n",
       "      <td id=\"T_cba07_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_cba07_row4_col7\" class=\"data row4 col7\" >0.0541</td>\n",
       "      <td id=\"T_cba07_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_cba07_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "  </tbody>\n",
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       "#T_638b2_row3_col0, #T_638b2_row3_col1, #T_638b2_row3_col2, #T_638b2_row3_col3, #T_638b2_row3_col4, #T_638b2_row3_col5, #T_638b2_row3_col6, #T_638b2_row3_col7, #T_638b2_row3_col8, #T_638b2_row3_col9 {\n",
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       "#T_638b2_row4_col0, #T_638b2_row4_col1, #T_638b2_row4_col2, #T_638b2_row4_col3, #T_638b2_row4_col4, #T_638b2_row4_col5, #T_638b2_row4_col6, #T_638b2_row4_col7, #T_638b2_row4_col8, #T_638b2_row4_col9 {\n",
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       "  <caption>Aylo</caption>\n",
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       "      <th id=\"T_638b2_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
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       "      <th id=\"T_638b2_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_638b2_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col4\" class=\"data row0 col4\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col5\" class=\"data row0 col5\" >⋯</td>\n",
       "      <td id=\"T_638b2_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_638b2_row0_col7\" class=\"data row0 col7\" >⋯</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_638b2_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_638b2_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_638b2_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_638b2_row1_col2\" class=\"data row1 col2\" >4,171</td>\n",
       "      <td id=\"T_638b2_row1_col3\" class=\"data row1 col3\" >104.115</td>\n",
       "      <td id=\"T_638b2_row1_col4\" class=\"data row1 col4\" >13,229</td>\n",
       "      <td id=\"T_638b2_row1_col5\" class=\"data row1 col5\" >0.06168</td>\n",
       "      <td id=\"T_638b2_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_638b2_row1_col7\" class=\"data row1 col7\" >0.06082</td>\n",
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       "      <th id=\"T_638b2_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_638b2_row2_col0\" class=\"data row2 col0\" >20,401</td>\n",
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       "      <td id=\"T_638b2_row2_col5\" class=\"data row2 col5\" >0.03122</td>\n",
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       "      <td id=\"T_638b2_row3_col3\" class=\"data row3 col3\" >4.840</td>\n",
       "      <td id=\"T_638b2_row3_col4\" class=\"data row3 col4\" >2,095</td>\n",
       "      <td id=\"T_638b2_row3_col5\" class=\"data row3 col5\" >0.00659</td>\n",
       "      <td id=\"T_638b2_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_638b2_row3_col7\" class=\"data row3 col7\" >0.00653</td>\n",
       "      <td id=\"T_638b2_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_638b2_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "      <td id=\"T_638b2_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "      <th id=\"T_268ff_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
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       "      <td id=\"T_268ff_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_268ff_row0_col4\" class=\"data row0 col4\" >19,480</td>\n",
       "      <td id=\"T_268ff_row0_col5\" class=\"data row0 col5\" >0.1157</td>\n",
       "      <td id=\"T_268ff_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_268ff_row0_col7\" class=\"data row0 col7\" >0.1147</td>\n",
       "      <td id=\"T_268ff_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_268ff_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_268ff_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_268ff_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_268ff_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_268ff_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_268ff_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_268ff_row1_col4\" class=\"data row1 col4\" >15,324</td>\n",
       "      <td id=\"T_268ff_row1_col5\" class=\"data row1 col5\" >0.0714</td>\n",
       "      <td id=\"T_268ff_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_268ff_row1_col7\" class=\"data row1 col7\" >0.0705</td>\n",
       "      <td id=\"T_268ff_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
       "      <td id=\"T_268ff_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_268ff_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_268ff_row2_col0\" class=\"data row2 col0\" >⋯</td>\n",
       "      <td id=\"T_268ff_row2_col1\" class=\"data row2 col1\" >⋯</td>\n",
       "      <td id=\"T_268ff_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_268ff_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_268ff_row2_col4\" class=\"data row2 col4\" >29,606</td>\n",
       "      <td id=\"T_268ff_row2_col5\" class=\"data row2 col5\" >0.1015</td>\n",
       "      <td id=\"T_268ff_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_268ff_row2_col7\" class=\"data row2 col7\" >0.1007</td>\n",
       "      <td id=\"T_268ff_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_268ff_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_268ff_row3_col0\" class=\"data row3 col0\" >⋯</td>\n",
       "      <td id=\"T_268ff_row3_col1\" class=\"data row3 col1\" >⋯</td>\n",
       "      <td id=\"T_268ff_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_268ff_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_268ff_row3_col4\" class=\"data row3 col4\" >169,800</td>\n",
       "      <td id=\"T_268ff_row3_col5\" class=\"data row3 col5\" >0.5339</td>\n",
       "      <td id=\"T_268ff_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_268ff_row3_col7\" class=\"data row3 col7\" >0.5296</td>\n",
       "      <td id=\"T_268ff_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_268ff_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_268ff_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_268ff_row4_col0\" class=\"data row4 col0\" >⋯</td>\n",
       "      <td id=\"T_268ff_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_268ff_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_268ff_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_268ff_row4_col4\" class=\"data row4 col4\" >339,412</td>\n",
       "      <td id=\"T_268ff_row4_col5\" class=\"data row4 col5\" >0.9443</td>\n",
       "      <td id=\"T_268ff_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_268ff_row4_col7\" class=\"data row4 col7\" >0.9373</td>\n",
       "      <td id=\"T_268ff_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_268ff_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "<table id=\"T_12b49\">\n",
       "  <caption>Google</caption>\n",
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_12b49_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
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       "      <td id=\"T_12b49_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
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       "      <td id=\"T_12b49_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_12b49_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_12b49_row0_col4\" class=\"data row0 col4\" >449,283</td>\n",
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       "      <td id=\"T_12b49_row0_col7\" class=\"data row0 col7\" >2.645</td>\n",
       "      <td id=\"T_12b49_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_12b49_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_12b49_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_12b49_row1_col0\" class=\"data row1 col0\" >4,437,853</td>\n",
       "      <td id=\"T_12b49_row1_col1\" class=\"data row1 col1\" >8.100</td>\n",
       "      <td id=\"T_12b49_row1_col2\" class=\"data row1 col2\" >547,875</td>\n",
       "      <td id=\"T_12b49_row1_col3\" class=\"data row1 col3\" >-0.2140</td>\n",
       "      <td id=\"T_12b49_row1_col4\" class=\"data row1 col4\" >546,704</td>\n",
       "      <td id=\"T_12b49_row1_col5\" class=\"data row1 col5\" >2.549</td>\n",
       "      <td id=\"T_12b49_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_12b49_row1_col7\" class=\"data row1 col7\" >2.513</td>\n",
       "      <td id=\"T_12b49_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
       "      <td id=\"T_12b49_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_12b49_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
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       "      <td id=\"T_12b49_row2_col1\" class=\"data row2 col1\" >7.694</td>\n",
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       "      <td id=\"T_12b49_row2_col4\" class=\"data row2 col4\" >875,783</td>\n",
       "      <td id=\"T_12b49_row2_col5\" class=\"data row2 col5\" >3.004</td>\n",
       "      <td id=\"T_12b49_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_12b49_row2_col7\" class=\"data row2 col7\" >2.979</td>\n",
       "      <td id=\"T_12b49_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_12b49_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
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       "      <th id=\"T_12b49_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_12b49_row3_col0\" class=\"data row3 col0\" >13,402,885</td>\n",
       "      <td id=\"T_12b49_row3_col1\" class=\"data row3 col1\" >6.164</td>\n",
       "      <td id=\"T_12b49_row3_col2\" class=\"data row3 col2\" >2,174,319</td>\n",
       "      <td id=\"T_12b49_row3_col3\" class=\"data row3 col3\" >0.0105</td>\n",
       "      <td id=\"T_12b49_row3_col4\" class=\"data row3 col4\" >2,174,548</td>\n",
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       "      <td id=\"T_12b49_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_12b49_row3_col7\" class=\"data row3 col7\" >6.783</td>\n",
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       "      <td id=\"T_12b49_row4_col1\" class=\"data row4 col1\" >5.404</td>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <th id=\"T_2f90e_level0_col4\" class=\"col_heading level0 col4\" >NCMEC</th>\n",
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       "      <td id=\"T_2f90e_row0_col1\" class=\"data row0 col1\" >2.354</td>\n",
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       "      <td id=\"T_2f90e_row0_col4\" class=\"data row0 col4\" >15,884,511</td>\n",
       "      <td id=\"T_2f90e_row0_col5\" class=\"data row0 col5\" >94.345</td>\n",
       "      <td id=\"T_2f90e_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_2f90e_row0_col7\" class=\"data row0 col7\" >93.508</td>\n",
       "      <td id=\"T_2f90e_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_2f90e_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_2f90e_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_2f90e_row1_col0\" class=\"data row1 col0\" >38,890,800</td>\n",
       "      <td id=\"T_2f90e_row1_col1\" class=\"data row1 col1\" >1.915</td>\n",
       "      <td id=\"T_2f90e_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row1_col4\" class=\"data row1 col4\" >20,307,216</td>\n",
       "      <td id=\"T_2f90e_row1_col5\" class=\"data row1 col5\" >94.682</td>\n",
       "      <td id=\"T_2f90e_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_2f90e_row1_col7\" class=\"data row1 col7\" >93.362</td>\n",
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       "      <td id=\"T_2f90e_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row2_col4\" class=\"data row2 col4\" >26,885,302</td>\n",
       "      <td id=\"T_2f90e_row2_col5\" class=\"data row2 col5\" >92.208</td>\n",
       "      <td id=\"T_2f90e_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_2f90e_row2_col7\" class=\"data row2 col7\" >91.454</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_2f90e_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_2f90e_row3_col0\" class=\"data row3 col0\" >105,800,000</td>\n",
       "      <td id=\"T_2f90e_row3_col1\" class=\"data row3 col1\" >3.891</td>\n",
       "      <td id=\"T_2f90e_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row3_col4\" class=\"data row3 col4\" >27,190,665</td>\n",
       "      <td id=\"T_2f90e_row3_col5\" class=\"data row3 col5\" >85.498</td>\n",
       "      <td id=\"T_2f90e_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_2f90e_row3_col7\" class=\"data row3 col7\" >84.814</td>\n",
       "      <td id=\"T_2f90e_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_2f90e_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_2f90e_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_2f90e_row4_col0\" class=\"data row4 col0\" >63,300,000</td>\n",
       "      <td id=\"T_2f90e_row4_col1\" class=\"data row4 col1\" >2.065</td>\n",
       "      <td id=\"T_2f90e_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_2f90e_row4_col4\" class=\"data row4 col4\" >30,658,047</td>\n",
       "      <td id=\"T_2f90e_row4_col5\" class=\"data row4 col5\" >85.292</td>\n",
       "      <td id=\"T_2f90e_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_2f90e_row4_col7\" class=\"data row4 col7\" >84.666</td>\n",
       "      <td id=\"T_2f90e_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_2f90e_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "<table id=\"T_e626a\">\n",
       "  <caption>Microsoft</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_e626a_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
       "      <th id=\"T_e626a_level0_col1\" class=\"col_heading level0 col1\" >π</th>\n",
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       "      <th id=\"T_e626a_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_e626a_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
       "      <td id=\"T_e626a_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_e626a_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_e626a_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_e626a_row0_col4\" class=\"data row0 col4\" >123,929</td>\n",
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       "      <td id=\"T_e626a_row0_col7\" class=\"data row0 col7\" >0.730</td>\n",
       "      <td id=\"T_e626a_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_e626a_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_e626a_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_e626a_row1_col0\" class=\"data row1 col0\" >1,256,449.000</td>\n",
       "      <td id=\"T_e626a_row1_col1\" class=\"data row1 col1\" >13.029</td>\n",
       "      <td id=\"T_e626a_row1_col2\" class=\"data row1 col2\" >96,435.000</td>\n",
       "      <td id=\"T_e626a_row1_col3\" class=\"data row1 col3\" >0.4170</td>\n",
       "      <td id=\"T_e626a_row1_col4\" class=\"data row1 col4\" >96,838</td>\n",
       "      <td id=\"T_e626a_row1_col5\" class=\"data row1 col5\" >0.452</td>\n",
       "      <td id=\"T_e626a_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_e626a_row1_col7\" class=\"data row1 col7\" >0.445</td>\n",
       "      <td id=\"T_e626a_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
       "      <td id=\"T_e626a_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e626a_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_e626a_row2_col0\" class=\"data row2 col0\" >564,157.000</td>\n",
       "      <td id=\"T_e626a_row2_col1\" class=\"data row2 col1\" >7.148</td>\n",
       "      <td id=\"T_e626a_row2_col2\" class=\"data row2 col2\" >78,930.000</td>\n",
       "      <td id=\"T_e626a_row2_col3\" class=\"data row2 col3\" >-0.0545</td>\n",
       "      <td id=\"T_e626a_row2_col4\" class=\"data row2 col4\" >78,887</td>\n",
       "      <td id=\"T_e626a_row2_col5\" class=\"data row2 col5\" >0.271</td>\n",
       "      <td id=\"T_e626a_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_e626a_row2_col7\" class=\"data row2 col7\" >0.268</td>\n",
       "      <td id=\"T_e626a_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_e626a_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e626a_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_e626a_row3_col0\" class=\"data row3 col0\" >450,447.000</td>\n",
       "      <td id=\"T_e626a_row3_col1\" class=\"data row3 col1\" >4.186</td>\n",
       "      <td id=\"T_e626a_row3_col2\" class=\"data row3 col2\" >107,605.000</td>\n",
       "      <td id=\"T_e626a_row3_col3\" class=\"data row3 col3\" >1.1081</td>\n",
       "      <td id=\"T_e626a_row3_col4\" class=\"data row3 col4\" >108,804</td>\n",
       "      <td id=\"T_e626a_row3_col5\" class=\"data row3 col5\" >0.342</td>\n",
       "      <td id=\"T_e626a_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_e626a_row3_col7\" class=\"data row3 col7\" >0.339</td>\n",
       "      <td id=\"T_e626a_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_e626a_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_e626a_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_e626a_row4_col0\" class=\"data row4 col0\" >402,630.000</td>\n",
       "      <td id=\"T_e626a_row4_col1\" class=\"data row4 col1\" >2.861</td>\n",
       "      <td id=\"T_e626a_row4_col2\" class=\"data row4 col2\" >140,720.000</td>\n",
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       "      <td id=\"T_e626a_row4_col4\" class=\"data row4 col4\" >141,237</td>\n",
       "      <td id=\"T_e626a_row4_col5\" class=\"data row4 col5\" >0.393</td>\n",
       "      <td id=\"T_e626a_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_e626a_row4_col7\" class=\"data row4 col7\" >0.390</td>\n",
       "      <td id=\"T_e626a_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_e626a_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "  <caption>Omegle</caption>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d9195_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
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       "      <td id=\"T_d9195_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
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       "      <td id=\"T_d9195_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_d9195_row0_col4\" class=\"data row0 col4\" >3,470</td>\n",
       "      <td id=\"T_d9195_row0_col5\" class=\"data row0 col5\" >0.0206</td>\n",
       "      <td id=\"T_d9195_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_d9195_row0_col7\" class=\"data row0 col7\" >0.0204</td>\n",
       "      <td id=\"T_d9195_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_d9195_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_d9195_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_d9195_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_d9195_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_d9195_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_d9195_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_d9195_row1_col4\" class=\"data row1 col4\" >20,265</td>\n",
       "      <td id=\"T_d9195_row1_col5\" class=\"data row1 col5\" >0.0945</td>\n",
       "      <td id=\"T_d9195_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_d9195_row1_col7\" class=\"data row1 col7\" >0.0932</td>\n",
       "      <td id=\"T_d9195_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
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       "      <th id=\"T_d9195_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
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       "      <td id=\"T_d9195_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_d9195_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_d9195_row2_col4\" class=\"data row2 col4\" >46,924</td>\n",
       "      <td id=\"T_d9195_row2_col5\" class=\"data row2 col5\" >0.1609</td>\n",
       "      <td id=\"T_d9195_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_d9195_row2_col7\" class=\"data row2 col7\" >0.1596</td>\n",
       "      <td id=\"T_d9195_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_d9195_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
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       "      <td id=\"T_d9195_row3_col1\" class=\"data row3 col1\" >⋯</td>\n",
       "      <td id=\"T_d9195_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_d9195_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_d9195_row3_col4\" class=\"data row3 col4\" >608,601</td>\n",
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       "      <td id=\"T_d9195_row3_col7\" class=\"data row3 col7\" >1.8984</td>\n",
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       "      <td id=\"T_d9195_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "      <th id=\"T_d9195_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
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       "      <td id=\"T_d9195_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_d9195_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_d9195_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_d9195_row4_col4\" class=\"data row4 col4\" >188,102</td>\n",
       "      <td id=\"T_d9195_row4_col5\" class=\"data row4 col5\" >0.5233</td>\n",
       "      <td id=\"T_d9195_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_d9195_row4_col7\" class=\"data row4 col7\" >0.5195</td>\n",
       "      <td id=\"T_d9195_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_d9195_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
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       "  background-color: #fdd4c2;\n",
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       "#T_b410f_row3_col0, #T_b410f_row3_col1, #T_b410f_row3_col2, #T_b410f_row3_col3, #T_b410f_row3_col4, #T_b410f_row3_col5, #T_b410f_row3_col6, #T_b410f_row3_col7, #T_b410f_row3_col8, #T_b410f_row3_col9, #T_b410f_row4_col0, #T_b410f_row4_col1, #T_b410f_row4_col2, #T_b410f_row4_col3, #T_b410f_row4_col4, #T_b410f_row4_col5, #T_b410f_row4_col6, #T_b410f_row4_col7, #T_b410f_row4_col8, #T_b410f_row4_col9 {\n",
       "  background-color: #fee3d7;\n",
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       "  <caption>Pinterest</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_b410f_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
       "      <th id=\"T_b410f_level0_col1\" class=\"col_heading level0 col1\" >π</th>\n",
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       "      <th id=\"T_b410f_level0_col4\" class=\"col_heading level0 col4\" >NCMEC</th>\n",
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       "      <th id=\"T_b410f_level0_col6\" class=\"col_heading level0 col6\" >esp</th>\n",
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       "      <th id=\"T_b410f_level0_col9\" class=\"col_heading level0 col9\" >esp/total%</th>\n",
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       "  <tbody>\n",
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       "      <th id=\"T_b410f_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_b410f_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
       "      <td id=\"T_b410f_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_b410f_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_b410f_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_b410f_row0_col4\" class=\"data row0 col4\" >7,360</td>\n",
       "      <td id=\"T_b410f_row0_col5\" class=\"data row0 col5\" >0.04371</td>\n",
       "      <td id=\"T_b410f_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_b410f_row0_col7\" class=\"data row0 col7\" >0.04333</td>\n",
       "      <td id=\"T_b410f_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_b410f_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_b410f_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_b410f_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_b410f_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_b410f_row1_col2\" class=\"data row1 col2\" >3,432</td>\n",
       "      <td id=\"T_b410f_row1_col3\" class=\"data row1 col3\" >≡</td>\n",
       "      <td id=\"T_b410f_row1_col4\" class=\"data row1 col4\" >3,432</td>\n",
       "      <td id=\"T_b410f_row1_col5\" class=\"data row1 col5\" >0.01600</td>\n",
       "      <td id=\"T_b410f_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_b410f_row1_col7\" class=\"data row1 col7\" >0.01578</td>\n",
       "      <td id=\"T_b410f_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
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       "      <th id=\"T_b410f_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_b410f_row2_col0\" class=\"data row2 col0\" >1,608</td>\n",
       "      <td id=\"T_b410f_row2_col1\" class=\"data row2 col1\" >0.599</td>\n",
       "      <td id=\"T_b410f_row2_col2\" class=\"data row2 col2\" >2,684</td>\n",
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       "      <td id=\"T_b410f_row2_col4\" class=\"data row2 col4\" >2,283</td>\n",
       "      <td id=\"T_b410f_row2_col5\" class=\"data row2 col5\" >0.00783</td>\n",
       "      <td id=\"T_b410f_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_b410f_row2_col7\" class=\"data row2 col7\" >0.00777</td>\n",
       "      <td id=\"T_b410f_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_b410f_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
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       "      <th id=\"T_b410f_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_b410f_row3_col0\" class=\"data row3 col0\" >37,136</td>\n",
       "      <td id=\"T_b410f_row3_col1\" class=\"data row3 col1\" >1.127</td>\n",
       "      <td id=\"T_b410f_row3_col2\" class=\"data row3 col2\" >32,964</td>\n",
       "      <td id=\"T_b410f_row3_col3\" class=\"data row3 col3\" >4.002</td>\n",
       "      <td id=\"T_b410f_row3_col4\" class=\"data row3 col4\" >34,310</td>\n",
       "      <td id=\"T_b410f_row3_col5\" class=\"data row3 col5\" >0.10788</td>\n",
       "      <td id=\"T_b410f_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_b410f_row3_col7\" class=\"data row3 col7\" >0.10702</td>\n",
       "      <td id=\"T_b410f_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_b410f_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_b410f_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_b410f_row4_col0\" class=\"data row4 col0\" >57,774</td>\n",
       "      <td id=\"T_b410f_row4_col1\" class=\"data row4 col1\" >1.145</td>\n",
       "      <td id=\"T_b410f_row4_col2\" class=\"data row4 col2\" >50,437</td>\n",
       "      <td id=\"T_b410f_row4_col3\" class=\"data row4 col3\" >3.734</td>\n",
       "      <td id=\"T_b410f_row4_col4\" class=\"data row4 col4\" >52,356</td>\n",
       "      <td id=\"T_b410f_row4_col5\" class=\"data row4 col5\" >0.14566</td>\n",
       "      <td id=\"T_b410f_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_b410f_row4_col7\" class=\"data row4 col7\" >0.14459</td>\n",
       "      <td id=\"T_b410f_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_b410f_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
       "    </tr>\n",
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       "  <caption>Quora</caption>\n",
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       "      <th id=\"T_ff192_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
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       "      <td id=\"T_ff192_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
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       "      <td id=\"T_ff192_row0_col5\" class=\"data row0 col5\" >0.00000594</td>\n",
       "      <td id=\"T_ff192_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_ff192_row0_col7\" class=\"data row0 col7\" >0.00000589</td>\n",
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       "      <td id=\"T_ff192_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
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       "      <th id=\"T_ff192_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_ff192_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_ff192_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_ff192_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_ff192_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_ff192_row1_col4\" class=\"data row1 col4\" >2</td>\n",
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       "      <td id=\"T_ff192_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_ff192_row1_col7\" class=\"data row1 col7\" >0.00000919</td>\n",
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       "      <td id=\"T_ff192_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
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       "      <td id=\"T_ff192_row2_col7\" class=\"data row2 col7\" >0.00008504</td>\n",
       "      <td id=\"T_ff192_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_ff192_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
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       "      <td id=\"T_ff192_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_ff192_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
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       "      <td id=\"T_ff192_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_ff192_row4_col4\" class=\"data row4 col4\" >6,135</td>\n",
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       "      <th class=\"blank level0\" >&nbsp;</th>\n",
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       "      <th id=\"T_d975d_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_d975d_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_d975d_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
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       "      <td id=\"T_d975d_row1_col4\" class=\"data row1 col4\" >2,233</td>\n",
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       "      <td id=\"T_d975d_row2_col3\" class=\"data row2 col3\" >≡</td>\n",
       "      <td id=\"T_d975d_row2_col4\" class=\"data row2 col4\" >10,059</td>\n",
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       "      <td id=\"T_d975d_row3_col3\" class=\"data row3 col3\" >≡</td>\n",
       "      <td id=\"T_d975d_row3_col4\" class=\"data row3 col4\" >52,592</td>\n",
       "      <td id=\"T_d975d_row3_col5\" class=\"data row3 col5\" >0.16537</td>\n",
       "      <td id=\"T_d975d_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_d975d_row3_col7\" class=\"data row3 col7\" >0.16405</td>\n",
       "      <td id=\"T_d975d_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_d975d_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "      <th id=\"T_d975d_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_d975d_row4_col0\" class=\"data row4 col0\" >⋯</td>\n",
       "      <td id=\"T_d975d_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_d975d_row4_col2\" class=\"data row4 col2\" >290,121</td>\n",
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       "      <td id=\"T_d975d_row4_col4\" class=\"data row4 col4\" >290,141</td>\n",
       "      <td id=\"T_d975d_row4_col5\" class=\"data row4 col5\" >0.80718</td>\n",
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       "      <td id=\"T_bbd4d_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_bbd4d_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_bbd4d_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
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       "      <th id=\"T_bbd4d_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_bbd4d_row2_col0\" class=\"data row2 col0\" >⋯</td>\n",
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       "  <caption>TikTok</caption>\n",
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       "      <th id=\"T_d54d4_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_d54d4_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
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       "      <td id=\"T_d54d4_row0_col4\" class=\"data row0 col4\" >596</td>\n",
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       "      <td id=\"T_d54d4_row0_col7\" class=\"data row0 col7\" >0.00351</td>\n",
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       "      <td id=\"T_d54d4_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d54d4_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_d54d4_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row1_col4\" class=\"data row1 col4\" >22,692</td>\n",
       "      <td id=\"T_d54d4_row1_col5\" class=\"data row1 col5\" >0.10580</td>\n",
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       "      <td id=\"T_d54d4_row1_col7\" class=\"data row1 col7\" >0.10433</td>\n",
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       "      <td id=\"T_d54d4_row2_col1\" class=\"data row2 col1\" >⋯</td>\n",
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       "      <td id=\"T_d54d4_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row3_col4\" class=\"data row3 col4\" >288,125</td>\n",
       "      <td id=\"T_d54d4_row3_col5\" class=\"data row3 col5\" >0.90598</td>\n",
       "      <td id=\"T_d54d4_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_d54d4_row3_col7\" class=\"data row3 col7\" >0.89873</td>\n",
       "      <td id=\"T_d54d4_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_d54d4_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
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       "      <td id=\"T_d54d4_row4_col0\" class=\"data row4 col0\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_d54d4_row4_col4\" class=\"data row4 col4\" >590,376</td>\n",
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       "  <caption>Twitch (Also Included with Amazon)</caption>\n",
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       "      <td id=\"T_61e0f_row0_col7\" class=\"data row0 col7\" >0.00992</td>\n",
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       "      <td id=\"T_61e0f_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
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       "  <caption>Wikimedia</caption>\n",
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       "      <td id=\"T_ebc31_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_ebc31_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_ebc31_row1_col4\" class=\"data row1 col4\" >11</td>\n",
       "      <td id=\"T_ebc31_row1_col5\" class=\"data row1 col5\" >0.0000513</td>\n",
       "      <td id=\"T_ebc31_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_ebc31_row1_col7\" class=\"data row1 col7\" >0.0000506</td>\n",
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       "  font-variant-numeric: tabular-nums;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 2.0em;\n",
       "}\n",
       "#T_ac11d .col5 {\n",
       "  color: #999;\n",
       "}\n",
       "#T_ac11d .col6 {\n",
       "  color: #999;\n",
       "}\n",
       "#T_ac11d .col8 {\n",
       "  color: #999;\n",
       "}\n",
       "#T_ac11d .col9 {\n",
       "  color: #999;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_ac11d\">\n",
       "  <caption>X</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_ac11d_level0_col0\" class=\"col_heading level0 col0\" >pieces</th>\n",
       "      <th id=\"T_ac11d_level0_col1\" class=\"col_heading level0 col1\" >π</th>\n",
       "      <th id=\"T_ac11d_level0_col2\" class=\"col_heading level0 col2\" >reports</th>\n",
       "      <th id=\"T_ac11d_level0_col3\" class=\"col_heading level0 col3\" >Δ%</th>\n",
       "      <th id=\"T_ac11d_level0_col4\" class=\"col_heading level0 col4\" >NCMEC</th>\n",
       "      <th id=\"T_ac11d_level0_col5\" class=\"col_heading level0 col5\" >esp%</th>\n",
       "      <th id=\"T_ac11d_level0_col6\" class=\"col_heading level0 col6\" >esp</th>\n",
       "      <th id=\"T_ac11d_level0_col7\" class=\"col_heading level0 col7\" >total%</th>\n",
       "      <th id=\"T_ac11d_level0_col8\" class=\"col_heading level0 col8\" >total</th>\n",
       "      <th id=\"T_ac11d_level0_col9\" class=\"col_heading level0 col9\" >esp/total%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >period</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_ac11d_level0_row0\" class=\"row_heading level0 row0\" >2019</th>\n",
       "      <td id=\"T_ac11d_row0_col0\" class=\"data row0 col0\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row0_col1\" class=\"data row0 col1\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row0_col2\" class=\"data row0 col2\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row0_col3\" class=\"data row0 col3\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row0_col4\" class=\"data row0 col4\" >45,726</td>\n",
       "      <td id=\"T_ac11d_row0_col5\" class=\"data row0 col5\" >0.272</td>\n",
       "      <td id=\"T_ac11d_row0_col6\" class=\"data row0 col6\" >16,836,694</td>\n",
       "      <td id=\"T_ac11d_row0_col7\" class=\"data row0 col7\" >0.269</td>\n",
       "      <td id=\"T_ac11d_row0_col8\" class=\"data row0 col8\" >16,987,361</td>\n",
       "      <td id=\"T_ac11d_row0_col9\" class=\"data row0 col9\" >99.113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac11d_level0_row1\" class=\"row_heading level0 row1\" >2020</th>\n",
       "      <td id=\"T_ac11d_row1_col0\" class=\"data row1 col0\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row1_col1\" class=\"data row1 col1\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row1_col2\" class=\"data row1 col2\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row1_col3\" class=\"data row1 col3\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row1_col4\" class=\"data row1 col4\" >65,062</td>\n",
       "      <td id=\"T_ac11d_row1_col5\" class=\"data row1 col5\" >0.303</td>\n",
       "      <td id=\"T_ac11d_row1_col6\" class=\"data row1 col6\" >21,447,786</td>\n",
       "      <td id=\"T_ac11d_row1_col7\" class=\"data row1 col7\" >0.299</td>\n",
       "      <td id=\"T_ac11d_row1_col8\" class=\"data row1 col8\" >21,751,085</td>\n",
       "      <td id=\"T_ac11d_row1_col9\" class=\"data row1 col9\" >98.606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac11d_level0_row2\" class=\"row_heading level0 row2\" >2021</th>\n",
       "      <td id=\"T_ac11d_row2_col0\" class=\"data row2 col0\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row2_col1\" class=\"data row2 col1\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row2_col2\" class=\"data row2 col2\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row2_col3\" class=\"data row2 col3\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row2_col4\" class=\"data row2 col4\" >86,666</td>\n",
       "      <td id=\"T_ac11d_row2_col5\" class=\"data row2 col5\" >0.297</td>\n",
       "      <td id=\"T_ac11d_row2_col6\" class=\"data row2 col6\" >29,157,083</td>\n",
       "      <td id=\"T_ac11d_row2_col7\" class=\"data row2 col7\" >0.295</td>\n",
       "      <td id=\"T_ac11d_row2_col8\" class=\"data row2 col8\" >29,397,681</td>\n",
       "      <td id=\"T_ac11d_row2_col9\" class=\"data row2 col9\" >99.182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac11d_level0_row3\" class=\"row_heading level0 row3\" >2022</th>\n",
       "      <td id=\"T_ac11d_row3_col0\" class=\"data row3 col0\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row3_col1\" class=\"data row3 col1\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row3_col2\" class=\"data row3 col2\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row3_col3\" class=\"data row3 col3\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row3_col4\" class=\"data row3 col4\" >98,050</td>\n",
       "      <td id=\"T_ac11d_row3_col5\" class=\"data row3 col5\" >0.308</td>\n",
       "      <td id=\"T_ac11d_row3_col6\" class=\"data row3 col6\" >31,802,525</td>\n",
       "      <td id=\"T_ac11d_row3_col7\" class=\"data row3 col7\" >0.306</td>\n",
       "      <td id=\"T_ac11d_row3_col8\" class=\"data row3 col8\" >32,059,029</td>\n",
       "      <td id=\"T_ac11d_row3_col9\" class=\"data row3 col9\" >99.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_ac11d_level0_row4\" class=\"row_heading level0 row4\" >2023</th>\n",
       "      <td id=\"T_ac11d_row4_col0\" class=\"data row4 col0\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row4_col1\" class=\"data row4 col1\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row4_col2\" class=\"data row4 col2\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row4_col3\" class=\"data row4 col3\" >⋯</td>\n",
       "      <td id=\"T_ac11d_row4_col4\" class=\"data row4 col4\" >870,503</td>\n",
       "      <td id=\"T_ac11d_row4_col5\" class=\"data row4 col5\" >2.422</td>\n",
       "      <td id=\"T_ac11d_row4_col6\" class=\"data row4 col6\" >35,944,826</td>\n",
       "      <td id=\"T_ac11d_row4_col7\" class=\"data row4 col7\" >2.404</td>\n",
       "      <td id=\"T_ac11d_row4_col8\" class=\"data row4 col8\" >36,210,368</td>\n",
       "      <td id=\"T_ac11d_row4_col9\" class=\"data row4 col9\" >99.267</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x10ef690d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show('<h2>Per Service Provider Disclosures: Provider vs NCMEC</h2>')\n",
    "\n",
    "# No piece counts are available for the rest of Microsoft in 2019.\n",
    "# So we drop LinkedIn's data, which is in the noise already.\n",
    "linkedin = platform_data.disclosures[\"LinkedIn\"]\n",
    "linkedin.loc[linkedin.index.year == 2019, \"pieces\"] = np.nan\n",
    "reddit = platform_data.disclosures[\"Reddit\"]\n",
    "reddit.loc[pd.Period(\"2023-01\", freq=\"6M\"), \"pieces\"] = np.nan\n",
    "github = platform_data.disclosures[\"GitHub\"]\n",
    "github.loc[github.index.year == 2023, \"reports\"] = np.nan\n",
    "\n",
    "comparisons = compare_all_platform_reports(platform_data)\n",
    "\n",
    "from diaphanous.platform.compare import compare_twitch\n",
    "twitch = compare_twitch(platform_data)\n",
    "comparisons['Twitch'] = twitch\n",
    "\n",
    "comparisons['Google'] = comparisons['Alphabet']\n",
    "del comparisons['Alphabet']\n",
    "\n",
    "brand_holders = len(platform_data.brands)\n",
    "brand_holder_brands = sum(len(brands) for brands in platform_data.brands.values())\n",
    "# Account for '@' and 'NCMEC':\n",
    "surveyed_brands = len(REPORTS_PER_PLATFORM) - 1 - 1 - brand_holders\n",
    "surveyed_firms = surveyed_brands - brand_holder_brands + brand_holders\n",
    "# Account for 'NCMEC':\n",
    "brands_with_disclosures = len(\n",
    "    set(platform_data.disclosures) - set(platform_data.brands)\n",
    ") - 1\n",
    "\n",
    "show(\n",
    "    f\"\"\"\n",
    "    Out of {surveyed_brands} surveyed social media platforms owned by\n",
    "    {surveyed_firms} firms, {brands_with_disclosures} brands make transparency\n",
    "    disclosures. Furthermore, only {len(comparisons)} platforms disclose usable\n",
    "    piece or report counts.\n",
    "    \"\"\"\n",
    ")\n",
    "\n",
    "provider_names = sorted(comparisons)\n",
    "for provider in provider_names:\n",
    "    if provider == 'Twitch':\n",
    "        caption = 'Twitch (Also Included with Amazon)'\n",
    "    else:\n",
    "        caption = provider\n",
    "    show(comparisons[provider], caption=caption, min_precision=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>All Percent Difference Values</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count     27.000000\n",
      "mean       4.377616\n",
      "std       20.280630\n",
      "min      -16.146567\n",
      "25%        0.000000\n",
      "50%        0.060081\n",
      "75%        3.248436\n",
      "max      104.114943\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h3>Percent Difference Values w/o Outliers</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    25.000000\n",
      "mean      1.209091\n",
      "std       1.757575\n",
      "min      -0.213964\n",
      "25%       0.000000\n",
      "50%       0.060081\n",
      "75%       3.109067\n",
      "max       4.839892\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<h3>Percent Difference Values w/o Outliers & Rounded</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "count    25.000000\n",
      "mean      1.204000\n",
      "std       1.749638\n",
      "min      -0.200000\n",
      "25%       0.000000\n",
      "50%       0.100000\n",
      "75%       3.100000\n",
      "max       4.800000\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "After rounding to one decimal, there are 9 zeroes and 16 positive values"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "difference_values = []\n",
    "for provider in provider_names:\n",
    "    if provider == \"Twitch\":\n",
    "        continue\n",
    "\n",
    "    deltas = comparisons[provider]\n",
    "    deltas = deltas[~deltas[\"Δ%\"].isnull()][\"Δ%\"]\n",
    "\n",
    "    difference_values.extend(deltas)\n",
    "\n",
    "show(\"<h3>All Percent Difference Values</h3>\")\n",
    "dv = pd.Series(difference_values)\n",
    "print(dv.describe())\n",
    "\n",
    "show(\"<h3>Percent Difference Values w/o Outliers</h3>\")\n",
    "dv2 = pd.Series([v for v in dv if abs(v) < 5.0])\n",
    "print(dv2.describe())\n",
    "\n",
    "show(\"<h3>Percent Difference Values w/o Outliers & Rounded</h3>\")\n",
    "dv3 = pd.Series([round(v, 1) for v in dv if abs(v) < 5.0])\n",
    "print(dv3.describe())\n",
    "show(\n",
    "    \"After rounding to one decimal, there are \"\n",
    "    f\"{sum([1 for v in dv3 if v == 0])} zeroes \"\n",
    "    f\"and {sum([1 for v in dv3 if v != 0])} positive values\"\n",
    ")\n",
    "dv3.hist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Comparable Report Counts</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<em style='font-size: 1.2em'>Building long data frame with comparable reports...</em>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<em style='font-size: 1.2em'>Erasing service provider identities from comparable reports...</em>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<h3>Distribution of Report Counts</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Possibly, not normal..."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\"<h2>Comparable Report Counts</h2>\")\n",
    "show(\"<em style='font-size: 1.2em'>Building long data frame with comparable reports...</em>\")\n",
    "\n",
    "comparable_reports = None\n",
    "for provider in provider_names:\n",
    "    if provider == \"Twitch\":\n",
    "        continue\n",
    "\n",
    "    # We are only interested in years with report counts for platform and NCMEC\n",
    "    data = comparisons[provider][[\"reports\", \"NCMEC\"]]\n",
    "    data = data[data[\"reports\"].notna() & data[\"NCMEC\"].notna()]\n",
    "    if len(data) == 0:\n",
    "        continue\n",
    "\n",
    "    # Reshape as long frame with year, observer, count, and topic columns\n",
    "    data = (data\n",
    "        .assign(year=data.index.year)\n",
    "        .reset_index(drop=True)\n",
    "        .rename(columns={\"reports\": provider})\n",
    "        .melt(id_vars=\"year\", value_vars=[\"year\", provider, \"NCMEC\"])\n",
    "        .rename(columns={\"variable\": \"observer\", \"value\": \"count\"})\n",
    "        .assign(topic=provider)\n",
    "        .assign(count=lambda df: df[\"count\"].astype(\"int64\"))\n",
    "    )\n",
    "\n",
    "    if comparable_reports is None:\n",
    "        comparable_reports = data\n",
    "    else:\n",
    "        comparable_reports = pd.concat([comparable_reports, data])\n",
    "\n",
    "# The index repeats the same counts over and over again for each provider\n",
    "comparable_reports = comparable_reports.reset_index(drop=True)\n",
    "comparable_reports.to_csv(\"../data/comparable-reports-by-provider.csv\", index=False)\n",
    "\n",
    "show(\"<em style='font-size: 1.2em'>Erasing service provider identities from comparable reports...</em>\")\n",
    "\n",
    "cp = comparable_reports\n",
    "cp.loc[cp[\"observer\"] != \"NCMEC\", \"observer\"] = \"ServiceProvider\"\n",
    "cp[\"id\"] = cp[\"topic\"].astype(str) + cp[\"year\"].astype(str)\n",
    "cp = cp[[\"id\", \"year\", \"observer\", \"count\"]]\n",
    "cp.to_csv(\"../data/comparable-reports.csv\", index=False)\n",
    "\n",
    "show(\"<h3>Distribution of Report Counts</h3>\")\n",
    "show(\"Possibly, not normal...\")\n",
    "\n",
    "counts1 = (\n",
    "    cp[cp[\"observer\"] == \"ServiceProvider\"]\n",
    "    [\"count\"]\n",
    "    .sort_values()\n",
    "    .reset_index(drop=True)\n",
    "    .hist()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Amazon\"\n",
      "[1] \"The mean of the percentage differences is 1.960%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Aylo\"\n",
      "[1] \"The mean of the percentage differences is 28.355%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Google\"\n",
      "[1] \"The mean of the percentage differences is 0.084%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Microsoft\"\n",
      "[1] \"The mean of the percentage differences is 0.459%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Pinterest\"\n",
      "[1] \"The mean of the percentage differences is -2.103%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Reddit\"\n",
      "[1] \"The mean of the percentage differences is 0.001%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"Processing Snap\"\n",
      "[1] \"The mean of the percentage differences is 1.585%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n",
      "[1] \"----------------------------------------------------------------------------\"\n",
      "[1] \"The mean of the percentage differences is 4.378%.\"\n",
      "Scale for x is already present.\n",
      "Adding another scale for x, which will replace the existing scale.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Attaching package: ‘dplyr’\n",
       "\n",
       "The following objects are masked from ‘package:stats’:\n",
       "\n",
       "    filter, lag\n",
       "\n",
       "The following objects are masked from ‘package:base’:\n",
       "\n",
       "    intersect, setdiff, setequal, union\n",
       "\n",
       "\n",
       "Attaching package: ‘purrr’\n",
       "\n",
       "The following object is masked from ‘package:scales’:\n",
       "\n",
       "    discard\n",
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R -i RLIB\n",
    "\n",
    ".libPaths(RLIB)\n",
    "\n",
    "library(dplyr)\n",
    "library(ggplot2)\n",
    "library(patchwork)\n",
    "library(scales)\n",
    "library(purrr)\n",
    "\n",
    "data_path <- function(csv_name) {\n",
    "  return(normalizePath(sprintf(\"../data/%s.csv\", csv_name)))\n",
    "}\n",
    "\n",
    "format_mean_zero <- number_format()\n",
    "format_mean_kilo <- number_format(scale=0.001, suffix=\"k\")\n",
    "format_mean_mega <- number_format(scale=0.000001, suffix=\"M\")\n",
    "format_mean <- function(x) {\n",
    "  dplyr::case_when(\n",
    "    is.na(x) ~ \"\",\n",
    "    x == 0 ~ format_mean_zero(x),\n",
    "    x < 1000000 ~ format_mean_kilo(x),\n",
    "    TRUE ~ format_mean_mega(x)\n",
    "  )\n",
    "}\n",
    "\n",
    "format_all_means <- function(xs) map(xs, format_mean)\n",
    "\n",
    "plot_percent_diff_over_mean <- function(data, title) {\n",
    "  data <- data |>\n",
    "    group_by(id) |>\n",
    "    summarize(\n",
    "      Mean = mean(count),\n",
    "      Diff = combn(count, diff, m=2),\n",
    "      Year = factor(mean(year)) # mean() serves as identity function to quiet R warning\n",
    "    ) |>\n",
    "    mutate(PctDiff = Diff / Mean * 100)\n",
    "\n",
    "  ymax <- max(abs(data$PctDiff))\n",
    "  ymean <- mean(data$PctDiff)\n",
    "  print(sprintf(\"The mean of the percentage differences is %.3f%%.\", ymean))\n",
    "  xmax <- max(data$Mean)\n",
    "  xmin <- min(data$Mean)\n",
    "  xrange <- xmax - xmin\n",
    "\n",
    "  # A custom color palette derived from the Plasma scale:\n",
    "  # > library(viridis)\n",
    "  # > plasma(7)\n",
    "  # Discard the first and last entries.\n",
    "  color_selection <- c(\n",
    "    \"2019\" = \"#5D01A6\",\n",
    "    \"2020\" = \"#9C179E\",\n",
    "    \"2021\" = \"#CC4678\",\n",
    "    \"2022\" = \"#ED7953\",\n",
    "    \"2023\" = \"#FDB32F\"\n",
    "  )\n",
    "\n",
    "  # Lines before points so that points are drawn above lines\n",
    "  graph <- ggplot(data = data, aes(x = Mean, y = (PctDiff), color = Year)) +\n",
    "    geom_hline(yintercept = 0) +\n",
    "    geom_hline(yintercept = ymean, linetype = \"dashed\") +\n",
    "    geom_point(size=2.5) +\n",
    "    xlim(xmin - 0.05 * xrange, xmax + 0.05 * xrange) +\n",
    "    ylim(ymax * (-1), ymax) +\n",
    "    xlab(ifelse(title == \"All Providers\", \"log(Mean)\", \"Mean\")) +\n",
    "    ylab(\"Δ% (Mean)\") +\n",
    "    labs(title = title)\n",
    "\n",
    "  if (title == \"Reddit\") {\n",
    "    # Reddit is the only provider with data for all five years.\n",
    "    # Hence we enable the legend for this graph.\n",
    "    graph <- graph + scale_color_manual(values = color_selection)\n",
    "  } else {\n",
    "    graph <- graph + scale_color_manual(guide = \"none\", values = color_selection)\n",
    "  }\n",
    "\n",
    "  if (title == \"All Providers\") {\n",
    "    graph <- graph + scale_x_continuous(labels = format_all_means, trans='log10')\n",
    "  } else {\n",
    "    graph <- graph + scale_x_continuous(labels = format_all_means)\n",
    "  }\n",
    "\n",
    "  graph <- graph + theme_light() #linedraw()\n",
    "  return(graph)\n",
    "}\n",
    "\n",
    "per_provider_data <- read.csv(data_path(\"comparable-reports-by-provider\"))\n",
    "providers <- unique(per_provider_data$topic)\n",
    "ba_plots <- vector(\"list\", length(providers) + 1)\n",
    "\n",
    "index <- 0\n",
    "for (provider in providers) {\n",
    "  index <- index + 1\n",
    "\n",
    "  print(\"----------------------------------------------------------------------------\")\n",
    "  print(sprintf(\"Processing %s\", provider))\n",
    "  provider.data <- per_provider_data |> filter(topic == provider)\n",
    "  provider.data$id <- provider.data$year\n",
    "  ba_plots[[index]] <- plot_percent_diff_over_mean(provider.data, provider)\n",
    "}\n",
    "\n",
    "print(\"----------------------------------------------------------------------------\")\n",
    "all_data <- read.csv(data_path(\"comparable-reports\"))\n",
    "ba_plots[[length(providers) + 1]] <- plot_percent_diff_over_mean(\n",
    "  all_data,\n",
    "  \"All Providers\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Bland-Altman Plots: Percentage Difference vs Mean<h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\"<h3>Bland-Altman Plots: Percentage Difference vs Mean<h3>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%R\n",
    "# Combine plots into 3x3 grid and display grid\n",
    "design <- \"ABC\n",
    "           DEF\n",
    "           GHH\"\n",
    "\n",
    "grid <- wrap_plots(ba_plots, ncol=3, guides='collect', design=design) &\n",
    "    theme(\n",
    "        # legend.position = \"bottom\",\n",
    "        legend.text = element_text(face = \"italic\"),\n",
    "        legend.title = element_text(face = \"italic\")\n",
    "    )\n",
    "print(grid)\n",
    "ggsave(\"../figure/comparable-reports.svg\", grid, width=8, height=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Provider Bias for Undercounting</h3>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    A positive percent difference means that the provider's count is smaller\n",
       "    than NCMEC's, i.e., an undercount. The mean percentage difference for all\n",
       "    providers but Pinterest is positive. Out of the underlying 30 pairs, 5 are\n",
       "    negative, 6 are zero because of equal counts, and 19 are positive.\n",
       "\n",
       "    The <a href=\"https://en.wikipedia.org/wiki/Sign_test\">sign test</a> yields a\n",
       "    p-value of 0.003305. We therefore reject the null hypothesis of no bias and\n",
       "    accept the alternative hypothesis of bias.\n",
       "\n",
       "    Technically, the bias could be providers favoring undercounts, NCMEC\n",
       "    favoring overcounts, or both. At the same time, the incentive to underreport\n",
       "    seems stronger for service providers. Discord's choice of reporting only\n",
       "    some reports and Twitter disclosing only unique pieces seemingly reflect\n",
       "    just that incentive.\n",
       "\n",
       "    The reputational risk is real, see The Daily Beast's \"Facebook Is a Hotbed\n",
       "    of 'Child Sexual Abuse Material' with 20.3 Million Reports, Far More Than\n",
       "    Pornhub.\"\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\"<h3>Provider Bias for Undercounting</h3>\")\n",
    "show(\"\"\"\n",
    "    A positive percent difference means that the provider's count is smaller\n",
    "    than NCMEC's, i.e., an undercount. The mean percentage difference for all\n",
    "    providers but Pinterest is positive. Out of the underlying 30 pairs, 5 are\n",
    "    negative, 6 are zero because of equal counts, and 19 are positive.\n",
    "\n",
    "    The <a href=\"https://en.wikipedia.org/wiki/Sign_test\">sign test</a> yields a\n",
    "    p-value of 0.003305. We therefore reject the null hypothesis of no bias and\n",
    "    accept the alternative hypothesis of bias.\n",
    "\n",
    "    Technically, the bias could be providers favoring undercounts, NCMEC\n",
    "    favoring overcounts, or both. At the same time, the incentive to underreport\n",
    "    seems stronger for service providers. Discord's choice of reporting only\n",
    "    some reports and Twitter disclosing only unique pieces seemingly reflect\n",
    "    just that incentive.\n",
    "\n",
    "    The reputational risk is real, see The Daily Beast's \"Facebook Is a Hotbed\n",
    "    of 'Child Sexual Abuse Material' with 20.3 Million Reports, Far More Than\n",
    "    Pornhub.\"\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "`summarise()` has grouped output by 'topic'. You can override using the\n",
      "`.groups` argument.\n",
      "# A tibble: 3 × 2\n",
      "   the_sign     n\n",
      "  <dbl[1d]> <int>\n",
      "1        -1     5\n",
      "2         0     6\n",
      "3         1    16\n",
      "\n",
      "\tExact binomial test\n",
      "\n",
      "data:  16 and 21\n",
      "number of successes = 16, number of trials = 21, p-value = 0.0133\n",
      "alternative hypothesis: true probability of success is greater than 0.5\n",
      "95 percent confidence interval:\n",
      " 0.5630237 1.0000000\n",
      "sample estimates:\n",
      "probability of success \n",
      "             0.7619048 \n",
      "\n"
     ]
    }
   ],
   "source": [
    "%%R\n",
    "\n",
    "data <- read.csv(\"../data/comparable-reports-by-provider.csv\") |>\n",
    "  group_by(topic, year) |>\n",
    "  summarize(the_mean = mean(count), the_difference = combn(count, diff, m=2)) |>\n",
    "  mutate(pct_diff = the_difference / the_mean * 100) |>\n",
    "  ungroup() |>\n",
    "  select(topic, year, pct_diff) |>\n",
    "  mutate(the_sign = sign(pct_diff)) |>\n",
    "  group_by(the_sign) |>\n",
    "  tally() |>\n",
    "  print()\n",
    "\n",
    "binom.test(x=16, n=21, p=0.5, alternative=\"greater\", conf.level=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In descending order of report counts for 2023:\n",
    "COLOR_BREWER = [\n",
    "    \"#1b9e77\",\n",
    "    \"#d95f02\",\n",
    "    \"#7570b3\",\n",
    "    \"#e7298a\",\n",
    "    \"#66a61e\",\n",
    "    \"#e6ab02\",\n",
    "    \"#a6761d\",\n",
    "    \"#666666\",\n",
    "]\n",
    "\n",
    "COLOR_GRIMM = [\n",
    "    \"#153dff\",\n",
    "    \"#b32322\",\n",
    "    \"#22a424\",\n",
    "    \"#8727c0\",\n",
    "    \"#824a01\",\n",
    "    \"#0f6b94\",\n",
    "    \"#a80590\",\n",
    "    \"#69534f\",\n",
    "    \"#545ba1\",\n",
    "    \"#a04309\",\n",
    "    \"#2d761d\",\n",
    "    \"#0e70c7\",\n",
    "    \"#b12145\",\n",
    "    \"#6d48b6\",\n",
    "]\n",
    "\n",
    "COLOR_OBSERVABLE = [\n",
    "  \"#4269d0\", # blue\n",
    "  \"#efb118\", # orange\n",
    "  \"#ff725c\", # red\n",
    "  \"#6cc5b0\", # cyan\n",
    "  \"#3ca951\", # green\n",
    "  \"#ff8ab7\", # pink\n",
    "  \"#a463f2\", # purple\n",
    "  \"#97bbf5\", # light blue\n",
    "  \"#9c6b4e\", # brown\n",
    "  \"#9498a0\", # gray\n",
    "]\n",
    "\n",
    "COLOR_GRIMM_OBSERVABLE = [\n",
    "    # \"#2b51c1\", # blue\n",
    "    # \"#e18101\", # orange\n",
    "    # \"#d6402c\", # red\n",
    "    # \"#0aa78d\", # cyan\n",
    "    # \"#e05f95\", # pink\n",
    "    # \"#069335\", # green\n",
    "    # \"#874bcb\", # purple\n",
    "    # \"#5889d7\", # light blue\n",
    "    # \"#8c5332\", # brown\n",
    "    # \"#6c717d\", # gray\n",
    "    \"#2b51c1\", # blue\n",
    "    \"#e18101\", # orange\n",
    "    \"#d6402c\", # red\n",
    "    \"#0aa78d\", # cyan\n",
    "    \"#874bcb\", # purple\n",
    "    \"#069335\", # green\n",
    "    \"#e05f95\", # pink\n",
    "    \"#5889d7\", # light blue\n",
    "    \"#8c5332\", # brown\n",
    "    \"#6c717d\", # gray\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h2>Report Counts Per Provider Per Year</h2>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Year', ylabel='Reports'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GAQM9l/ED/W4HxMWhMRh6uPVC9C0SGIUQQvQJLpuNyo0bKF2xgtLlK6jZucPvfpVOh+Jw4LZaMR84gPnAgcM+lyE6us1QqQsPR6VSdfVbEqLPkMAohBCiV1IUhbq9ez0BccVyytetw221+p0TMnIU0TNnEj1rJuHjJwBgLS7GUlCA5VAhlkLPl7XwEJZDhdQXFOC2WrGVlmIrLaVqy5ZWX1t6KYXwJ4FRCCFEr2GvqKB01UpKl3uHmYuL/e43xMR4AuLMWUTNmI4hMqrFcwQmJRGYlNTq8yuKgqOqqkWgtBQe8lweKsRWUiK9lEI0I4FRCCFEj3Hb7VRs2kSZtxexevt2aLJoRW0wEDl5sicgzppJ8NBhvyuEqVQq9OHh6MPDCR09utVzXDYb1qIiX4CUXkohJDAKIYToRoqiYD5wgFLvQpXytWtx1df7nRMyYgRR3l7EiIkTuz1UaQwGApOTCUxObvV+6aUU7VGfn8/Pxx/HrC+/InTkyJ5uTodJYBRCCNGl7JWVlK1a5ZuLaD10yO9+fWQk0TNnET1zJlEzZhAQE9NDLT060kvZN1hLS9n36j8p+eUXrEVFaIODCUxOJv6cc0mcNw+N0djTTexTJDAKIYToVG6Hg8rMTMpWLKdk+XKqt271H2bW64mYNMk3FzE4LQ2Vun+VBT6qXsrKyhaBUnopO4c5N5dVF12ILiSE4XfeRXBaGmq9ntrdu8n96EMCYmOJmzOnp5vZp0hgFEII8bsoioI5J5uy5SsoXbGCsjWrcZnNfucEDxvmmYc4cyaRkyYd8707KpUKfUQE+ogI6aXsAtseeRiVRsPMzz5HazL5jgcmJRF38sm+4u6WwkK2PfooZatXoVKpiZ49m9GPPIIhqnExVfZ773Hg//4PS9EhTAkJDP3zTSScd57v/rr9+9ly/31Ub92KKTGJUQ8/zNr5VzDxn/8k7uRTWm1fzZ7d7HzmGSo2bEBjNBI9cxajHngAfUREF31Hfj8JjEIIIdrNXl1N+erVvrmIlvx8v/v14RFEzZzhHWaeiTEuroda2nf97l7KwgJspaXHXC+lvbKS0hUrGH7nXX5hsSmVSoXidrP++uvRBpqY9v77KE4X2xY9wsZbb2X6++8DcOiH79n+xOOMeuBBombMoOTnn9my8F4C4uKImjYNxeVi/Y03YBwwkJlLPsVpNrPj6afabJ+jpoY1l11G0oUXMeqBB3FZrex87jk23noL0959r9O/H51FAqMQQogjcjudVGVtoXT5CkqXL6cqawu43b77VTodERMm+OYihowc2e+GmXub39tLWV9YgKWwsN/1UppzckBRCByU6nf8+0kTcdtsAKRcdhlR02dQu2c3J/7yK8aBAwHIWPw8v512KlVZWYSlp3Pg//6PxHnzSLnsMgCCrr6aysxMDrzxf0RNm0bpihXU5+Yy7b33CYiOBiBtwZ2snX/FYduX/c7bhI4cxfC77vIdG/vMMyydNZO6gwcJSk097GN7kgRGIYQQrTLn5vrK3ZStWoWzrs7v/qDBQ4ieNdMzzDx5ymF7c0TP+X29lJ5A2V96KWcu+RTcbjbfuQC33U7d/n0EDBjgC4sAwUOHogsJoW7/PsLS06nbv5+kP/7R73nCJ4wn+z//AaDu4AGMAwb4wiJAWHp6m+2o2bmLsrVr+F/6mBb31efmSGAUQgjRuzlqaylfs9rTi7hiOfW5uX7368LCiJ4xg6iZs4ieMcPvD63om35PL6WloPF2b+qlDExOBpUK84GD/se9xdzVhoDf/Rq/h7PeTOyJJzLi7nta3GfoxRUCJDAKIcQxSnG5qMrK8pW7qcrMRHG5fPertFrCx40nepZnNXPoqFGoNJoebLHoCR3upWwSKLuzl1IfHk70jJlkv/MOKVdccdie76DBQ7Ae8sz3bPjwU7t3L46aGoKGDPGeM5jKjRtJnHe+73GVGzc13p86CMuhQ9jKynwLZaq3ZrXZvtBRozj0/fcYExJQa/tODOs7LRVCCPG71RcUULp8OWUrVlC2aiWOmhq/+wNTU33lbiKnTEEbFNRDLRV9RYd7KQsK/W63t5fSGhJ82DaNfvRRVl10ISvOO5dht95KcNpwVGo1VVlZmA/sJ2z0aKJmzCB4WBqb71zAqAcexO1yse2Rh4mYPIWwMZ5h5cHXXsvGW28lZOQoomZMp2TpzxT98D1T/vM2ANEzZ2JKSiLz7rsYce9CnOY6dr3w14bvTKttS7nscnI/+ojNt9/O4OuuQxcaijknh8JvvmbsU0/32g9lKkVpUhzrGFRTU0NoaCjV1dWEhIT0dHOEEKJTOevqKF+71rea2XzQf5hOFxJC1PQZ3p1VZmJKSOihlopjWau9lM0Cpa201O8x9W43Fx3MJi8vj4RWfm6tJSXs++c/KPn1VyxFRaj1eoKHDGHAaaeRcullaIzGzi2rk7UVU1IiI+5dyPrrrmXym/8mZvbsVnd6qcs+yK7nnqN8zRpcdjum+HiiZ81m5AMP9NrV6BIYJTAKIfoRxeWievt2T0BcvoLKzZtQnE7f/SqNhrCMDN9q5tAxY/rUsJg4djXvpczfsYPpDz182MDYUyo2bmDVRRdxwtKfDzuM3xfJbwkhhOjjLIWFvnmIZatW4aiq8rvflJTkCYizZhI5dRq64MMP5QnRWzWfS6maPAUeeriHW+Wp1ag1BRKYkoI5J4ftjz9O+IQJ/SosggRGIYToc5z19ZSvW+spebN8BXX79/ndrw0KImr6dM/OKjNm9Ls/XEL0Ji6zmV3PPYelsNBTsH7GdEbed39PN6vTSWAUQoheTnG7qdmxw9eLWLFxI4rD0XiCWk1Y+ljPYpXZswhLHyvDzEJ0k4Tz5pFw3ryebkaXk98oQgjRC1mLi30LVcpWrMReWeF3vzE+nuhZszyrmadNQx8a2kMtFUIcCyQwCiFEL+CyWChfv963s0rtnj1+92sCA4maOs2zN/OsmQQmp/Ta1ZRC9CSX28VFP15MtDGKf87+h+94rb2WU785nfNSz+OujAU92MK+SQKjEEL0AEVRqN21y9eLWLF+PW67vfEElYqw9HRPQJw5i/CMDNQ6Xc81WIg+QqPWsHjas5z57dl8cfALzkk9B4BFGx4jTB/GrWNu7uEW9k0SGIUQoptYS0u9PYgrKFu5AltZmd/9AQMG+MrdRE2fjj48vIdaKkTflhqSyt0Zd/HohseZFjeNLWVZfJPzDZ+dugS9Rt/TzeuTJDAKIUQXcdlsVGzYQOnyZZSuWEHtrl1+92uMRiKnTvXtrBI4aJAMMwvRSeanXcEP+T9y56q72V21m5tH38SI8BE93aw+q18ExoMHD3LVVVdRXFyMRqNhzZo1BAYG9nSzhBDHGEVRqN27h7LlnnmI5evW4bbZ/M4JHT3aU+5m5kzCx41DYzD0UGuF6N9UKhWPT3qUU74+lbSwNG4YdX1PN6lP6xeB8U9/+hNPPPEEs2bNoqKiAoP8AhZCdBNbeRllK1f55iLaSkr87jfExhIzyxMQo6ZNxxAZ2UMtFeLY88n+/2LUGMmvy6eovoiEoN6zI0xf0+cD4/bt29HpdMyaNQuAiIiIHm6REKI/c9lsVG7aSOlyz1zEmh3b/e5XBwQQOXmyby5i0NChMswsRA/YWLqJf+96i7dO/DevbPsHC9fczzsn/Uf+P3ZQjwfGZcuWsXjxYjZu3MihQ4f47LPPOPfcc/3OeeWVV1i8eDFFRUWMHTuWl156icmTJwOwd+9egoKCOOussygoKOCCCy7g/vv7X4V1IUTPUBSFuv37vXszL6di3TpcFovfOSEjRhI9aybRM2cSPmGiDDML0cMsTgv3rL6XS4ZdzLS4qSQGJXD6N2fy/t73uXTYpT3dvD6pxwOj2Wxm7NixXHXVVcyb17JS+kcffcSCBQt49dVXmTJlCi+++CJz585l9+7dxMTE4HQ6Wb58OZmZmcTExHDqqacyadIkTj755FZfz2azYWsyp6impgYAh8OBo+nOCUKIY5a9spLy1aspX7mS8lUrsRYV+d2vj4omasZ0ImfMJHLaNAxRUb773IBbfpcI0eWcTudh71uc+TwKCvdk3A1AQlACC8ffyzObnuW4gcfJ0HQHqBRFUXq6EQ1UKlWLHsYpU6YwadIkXn75ZQDcbjeJiYnccsstLFy4kNWrV7No0SK+//57ABYvXgzA3Xff3eprLFq0iEcffbTF8ffffx+TydTJ70gI0Sc4nZCTjWr3HlR7dkN+PqomvxoVrRYGDUIZloaSlgYDBoAMawnRo+rr67nkkksYPHgwer2em266iZtuuom1xWu5fOl83p/zLhNjJvo95k8/X4nT7ZKh6Q7o1YHRbrdjMpn473//6xci58+fT1VVFV988QVOp5NJkybx888/ExoayjnnnMP111/PmWee2eprtNbDmJiYSFlZGSEhIV359oQQvYSiKNRnZ1O2aiXlK1Z4hpnr6/3OCRo2jKgZM4icMcMzzBwQ0EOtFUK0pqCggNTUVPLy8khIkB7DrtbjQ9JtKSsrw+VyERsb63c8NjaWXd56ZlqtlqeeeorZs2ejKAqnnHLKYcMigMFgaHUVtU6nQye7KAjRb9mrqihbvZoy72pmS0GB3/36iAjPQpVZM4maPoOAZr93hBC9i1bbqyNMv9MvvtunnXYap512Wk83QwjRSyiKgr2igrp9+yhbvYrS5Suo2poFbrfvHLVOT8SkiUR5i2aHDB+OSq3uwVYLIdoj15zb0004pvTqwBgVFYVGo6G4uNjveHFxMXFxcT3UKiFEb6AoCvbycsw52ZizszHn5FCfk4M5JwdzdjbOuroWjwkaMtSzq8qsmURMmoxW5i0L0ec43U7+b+cbvLDmr4c9p77CzvK/Z7P/twrMZXYCQrXEpAUx46ZkEsaHdmNr+49eHRj1ej0TJkxg6dKlvjmMbrebpUuXcvPNsnm4EP2doijYSksbw6A3GDbcdppbhkIflQrjgAGEjx/v2VllxgyMAwZ0X+OFEJ1ud+Vu7lmzkG0V23A3GTFo7tNbd+B2uDnj6TTCEo2Yy+3krK7CUiUVDDqqxwNjXV0d+/bt890+ePAgmZmZREREkJSUxIIFC5g/fz4TJ05k8uTJvPjii5jNZq688soebLUQorMoioK1uNivd9Cck0N9rud288UoflQqjPHxBCYnE5icQmByMqbkZAJTUjAlJko9RCH6CbvLzqvb/8U/tv8Th9tBiD6EPw+5geu4rsW51hon+RuqueTtsSRNDgMgND6AgemNC1ufGf4bpz0+jH2/lXNwRSXBsQZOvHcQQ0/0lMhyuxS+e3gPOWuqMJfZCRlgYNwlA5l0RePimq8X7sJW6yR2RBAb3yvEZXcz8swYTn5gCBp9/5ve0uOBccOGDZxwwgm+2wsWLAA8K6HfeustLrroIkpLS3n44YcpKioiIyOD7777rsVCGCFE76W43ViLiny9g+ac7MaAmJOD22o9/IPVakzxCX5h0BMQkzEmJEgoFKKf21q+jYVr7mNXlWex65yEOTw++VHs5fZWz9ebNOhNGvYsLWNgRgjaw4S3Fa/kcMJdgzjx7sFseLeAr+7axY0/T8EYpkNxKwTHGjj3xZEYw7UUbK7hu4f3EBStZ8RpMb7nyFldhdag5pK3x1JdYOXb+3djDNNx3B2pnf+N6GG9qqxOT6ipqSE0NJTq6mopqyPE7+ALhdkHG4NhtjcY5ubiblLOqjmVRoMxIcEXBANTUjzhMDkFU3w8ar2+G9+JEKI3sLlsvLT1ZV7b8TouxUWEIZxHJj7CGcmno1KpyM/PJzExsdWyOru+L+W7h/fgtLqJHRlE0qRQRpwRQ0xaEODpYZx+YxKzb/MEO3u9ixfGr+DC18cwaFbrWwz/8NhezGV2zvv7KMDTw7jvl3Ju+nUqOqMGgM0fFvLLcwe4Y8MMVOr+Veexx3sYhRB9h+JyYTl0yH+RScP1vFzc9tY/8QOotFpMCQlNwmDjMLIxPh61lLUSQnhtKt3MwjUL2V9zAIAzks/gkYkPERkQeVSPHz43miHHR5K3oYrCLbUcWFbBmjfyOO3xNNLneRbNNoRH8PRKGoI0mJv0Wm58r4CsJUXUHLLhtLlwORRihwf5vU7M8CBfWAQYmBGCvd5FzSEbofH9q3arBEYhhB+304mlsLDFIhNzTjb1eXkobWx7p9LpMCUm+oJg095C48CBqKVumhCiDRanhRe2/JV/73oLBYWogCgem/wocxNPafdzaQ1qUmdEkDojghl/TubbB3ez4uVsX2BUa5v1AKpA8a6j2fFNCb88d4AT7x3EwIwQDIFa1r6RR2FWze99i32W/PYW4hjkdjiwFBT4gmDT3sL6/HyUNvZoVev0mJKSmg0de64bBwxApdEc9rFCCHE4a4vXsnDN/eTWeeorzht0Hvel34fBEkjZPjP1lQ4s3q/6SgeHcoqP8Iz+ogab2PtT2VGdm7+pmvhxIYy/JN53rDLP0uK8kl11OKwudAGe33uFmTXoTRpCBvS/udUSGIXop9x2O/UFBY0LTJr0FloKCtoOhQYDgUlJLRaZBKakEhAbK6FQCNFubpeCtdqBpcrpF/6qysws27OaA4V5DK8/h0nWcAa6k1BqtbxWk3XY57O6zK0et1Q6+Oz2HaSfH0dMWiD6QA1F2+pY+0YeQ0+KOqq2RiQb2f5FMQeWVxCWEMC2L4sp2lpLaIL/MLPbofC/B/Yw/cYkqgusrHg5m/GXDux38xdBAqMQfZrLZsOSn99ykUlODpbCAhSX67CPVQcE+IKgqcl8wsDkZALi4mTXEyHEYSluBWuN09fbZ6ly+IXA5qGwvtKBtcYJh1lmqyOJNJJ8tz11E7wfalVgDNViDNdhCtdh9H5ZVTXwRCvPFahhYHow69/KpyrPgtupEBxnYOwfBjDt+qSWD2hFxh8HUryzji8W7ACVipFnxDDu4oEcWF7hd17ytDDCk428d1kmLrvCiDNimHlLylG9Rl8jq6RllbTo5Vw2G/W5uU12MmnSU1hY6LfdXXMak8kTCJMah40bAmJAbCwqVf/7FCyEaB9FUbDVuhrDX5NLS1XzMOgJgtZqh2++X3sFhGoxhGko1xVRoMrBaqpDF6rixOGzGJE0pDEYhnkuDSFa1JqWv6vaWiXdHRrqMJ7/yuhuf+2eID2MQvQCLqvVGwqzW/QWWg4dgjY+12kCA/16B5sOIxuioyUUCnEMURQFu7mV8Ffl9OvtszTtFaxyoBx+MKJNhmCNX8AzNgl6DT2BpnCt77gxVMevRb/w4LqHKbZ45iBekXY5d429k0BdYCd+J0Rnk8AoRDdxWSyYc3O9cwn95xVai4rafKw2MIjAlJaLTAKTU9BHRkooFKIfUhQFR7271YDXIvxVOqn39ga6HR0bONQHavyHfcO0LcJg432e+zW6o5+6Ummr5OG1T/J59hcApASn8MzUp5gUM6lD7RXdSwKjEJ3IaTb7ho+bl6SxFbe9ok8bHNy4wKTJ0HFgcgr6iAgJhUK0obykmJ++/IIBiYmMnzaDoF44xchhcR1+nl+Vf4+g1XvbZe9Y+NOZ1I09fWFNe/uaXIY16fkL1x12R5TO8F3udzy8fhHl1nLUKjVXDb+SO9JvJ0Dbd2sVnvnM8J5uQreSwChEOznr6vyHjXMbr9tKS9t8rC4srNVFJoHJyejCwyUUCtEB2zZt4K2/v0h9XR0An/z7DZ589f8ICQvrstd02tzNwt/h5v81LgxxWjs26U9rUGOKaNLrF9Za+GsMhcYwra/MS08rs5SxaMOj/C/3OwCGhg7hmalPkxGV0eHnNFeXUrB7LdvW/tihx3/xwpVYzVVc9NBnALy98ARiB41l7nUvdrhNh/P4GWr+8OCnDJ92bqc/d3eTwChEKxy1ta2Wo6nPycFW1nYdL114eKuFqwOTk9F34R8wIY41bpeLbz75iP/992MAElMHAaDRav3C4tKvviQ2fiAj0jPQtFI83ml3Nwa8Zj1/rYbCKgeO+o6FP41OhSlCR0BY64HPr+fPe7vpTiJ9haIofJX9FY9tfIJKWyUalYYbRl3PTaP/jEFz9DUKnQ4bRfs3U7B7LQW711G4ey2VRZ7dX6ytDL0/fkbbvaSzL3mYude/2Oa88Kb+fmUq1SU5h70//aT5nLPg30f1XH2dBEZxzLJXV/uvOm5SksZeUdHmY/UREf7Dxg3Xk5LRh4Z20zsQ4thltVh4bfEz7MraAsDsuadx/p+uQo2GyqJaSveasVQ6qCiq4rN3/oNbcaHXBhJnGkOkMgZdbZyv989u7tiKD7VO5Rn2DdNhDG9Z9qX5kLApXIfOpO73IwlF9UU8vO4Rlhb8DMCI8OE8O/UZRkWMavNxiqJQWXSAgl1rfAGx+EAmLmfLLUcjE4YTNHA0/PiJ3/E73in0Xd++/CN+e/cR/vyvXb5jemMQeqP/9n5tufrFdb7yZHk7V/Hfpy7gz6/twmD0THnQGoxH/Vx9nQRG0ecpioLLbMZRW4ujtgZnbS2O2lqctbU4a+t81x21tTiqq6jPy8ecm4OjsrLN5zVERbUsXJ3s6S3UBQd307sTomcpbgWXU8HtUHA53LgcCm5nw6XnmP993uvNH+M91vTxjY9t8rjmz293N7uv4fndFGosqLQ6BtbMpfiNEbz06toW4c+lNWOKGkN92B7smMmtWUMua9DYQwi0DyfQMRId4ag0tBrwmg7z+vUIhuvQB2r6ffhrD0VR+O+B//LkxqepddSiU+u4efRNXD/qOnTqlnvFW+uqKNizzhsQ11G4Zx31NS1HcEwhUcSnTSF++BTi06YwcOgkAoLCyM/Ph0X+gTEoIs53PcAUCiqV3zFoOSTd3N513/DZ4ks57c+vMOaES33HjcERAASGxhAQFAbAhm/+yZpP/0J1WR5hsanM+uMDpJ94+WG/R7+++wibv3udSx7/jtjU9MOe1xtJYBQ9SlEUXPX1TUKdf+BrvKzzXNbU4KxrGghrcdTVtVmLsC2GmBhfEPTrLUxKQht09J9ChTgSRVGahCNvYHJ6g1TT8NQQwpqGJEfTY/73e0JV0xB25Of3C29N728l0HW03EpXUFBA5UaleIZoQ9RzMOlq0diisNC4c5FKDQGhDcEuBGP4BQSEqalVZVNQk0lu8RYc1FATs45Z143gpLNnYAjSgAoJgB1UUFfAA+seZPmhFQCkR6bzzNSnSQsbBoDL6aAke6uv57Bg9xrK83e3eB6NVk/c4HHEp01mYNoUEtKmEhaX2m3/Llt/fZ9vX76R8+55j2GTz2zz3F2rPuP7125n7rV/JXXcHPau+5ov/3oVIZEJpIw9we9cRVH4/tVb2bv+G+Y/t4yIgUO68m10CQmMosMURcFlsTQGu5oaz2Vda4HP29vXPPDV1bW5G0l7qLRatMHB6LxfWu+XrsmlLjgEY3y8d/g4CW2g1P3qrxS3QvGOOqy1zjbC15F6wpodb9qr1kqvV0MgbBHovOf0CyrPPDyNVo1ap/Jc13mvNxzTNjnWcL/3mEan8l33XKrQ6NUtjzV5jFqnwoWNX1a+i0qt4qwzrkOj03jPU6H2Pq9GpyYgREtAiPYwW7ONBE7HbrOxdeN61i9fxswzjicgxPOncNXSn1jz2y9Mmjmb8dOmEygjCUfkVtx8sPcDnt28GLPTjF6t5/b027ggcg7FWzfy4+7XKNizjkP7NuK0tdyLOXzAYE/vYdpk4odNIXZwBlpdz+zDvP7rV/jl7Qf54yNfkjzmuCOev/rTvzD2pPlMPPPPAESet4D8XWtZ/elf/AKj2+Xk8+cvp2j/ZuY/t5yQqPjDPWWvJoHxGKUoCm6r1X+4tqZJ715dy8DnqGkIeY3HOy3saTTeUBfivQxqEfb8728ZBtUBAdI7IKgusLL1syKyPi2iptDW081pU0OgUmvVzUKSfyDzBSJt0+utBbaWQUujPVIg8w9cvmMNbdL7t0ml6f5euEP5ebz+/HMU5eej1mjQJ/yRhJTUDj+f3mBgwvSZTJg+0+/4+uW/sW/Hdvbt2M7Hb77OyIxxTJp1HOkTJ6E39EyI6c1yanO4b+0DbMxfTVSFjVmWSCba46j69lH+UXFDi/MNgaHED5vsDYhTGJg2mcDQ6B5oeUs7VyzBXF3ClYtXMHDY0dWFLMvbyfhTr/U7ljhyOuu++LvfsR9fX4BGZ+Cqv6zGFHp0e1n3RhIY+yBFUXDbbP69dw29e00CX4tevpqGIVxv2HM6j/xiR0Ot9vTehYSgDWoa5IKaBbuQVoOeNjgYjdEoYU90mMPqYs+PZWR9WkTOmirffrX6QA0hAw2tB6EjBCa/cNZWIGv+OF2zcKZv5Zi3l01+5o9sw8oVvPfPl7FZrYRGRHDNgrt/V1hsy+U338rGlStYv/w38rOz2bphPVs3rMcQEMC4adO57MabUR/je6y7XS6Kc7ex5LcX2bD5C2LL6rmk2o5aASgkn60AqNQaYlPTPT2HaVOJT5tCZPywXrtHfdzgcRTt30Tmj28yYOjETv2/mTpuDtt/+5D9m773mxPZ10hg7AEum62V3ruaxjl5rQU+XyCsw1FXi+JwdE5j1Gp0Qd7ePG/g8x/Obezpa/3+YDQmk/zhE91OURSKttWS9WkxO74uxlbb2NudPC2M9HlxDDs5qtfUoxPt43Q4+PSd//Drt18DMGz0GK66/c4ura0YERXNyeecx8nnnEdhXi7rly9jw4pllJeUUFVe7hcWC/NyGZCQ2O9/99VVFHnmHe5ZS8GuteTvWYfTagZgcJPzQqITiR/mHVoePpUBg8ejCzD1TKM7IHzAIE6+5nneXngCKrWG0258+YiPiUocQd6OlYydM993LG/HKqKTRvqdN2zK2QybfBafLb4UlVrD6OP+2Ont7w7tDoxLliwhISGBKVOmdEV7ej2XzdZy0UXD3Lymq3PrDhcI63A7WpYI6BCVCm2QtxcvxNt7F9T6cK02pMntJr2AmsDAfv8LT/Qv9RV2tn1ZwtYlRZTuNfuOhww0MOa8OMacF0dYQt/dPUJ4vPHXv7Bl3RoA5p53Pmf+8RI0mu4L/wMTkzjnkss4++JLObB7N01/TVZVVPDkgtuIiI5h0qzZTJo1mwEJid3Wtq7isFko2r+pycKUta3WIHRoVVRGmhg2+kTmTJlPwvCpBEcO7IEWd67I+GFc/vTPvLPwBNQa7RELeU87/y6WPHMRcYPHkZoxh73rvmLXqk+57MmWBcWHTz+Pc+58my/+cgVqjZaRMy/oonfRddodGB988EGeeuop3+3zzz+fFStWcMEFF/Diiy+i07VcOt8XHHz3XYxO5+GHdb2Bz23vxLAXGOjfe9dKoGsr8GkDA3tt974QncntVDiwvIKsT4vY90u5bwGJRq8i7ZRo0s+PI3lK2GEWOoi+6MQzz2L/rh1c9uebSZ84ucfaoVKpGDzcfwu4guyD6A0GykuK+W7JJ3y35BMSUlKZNOs4Js2cRVhkZA+19ugpikJF4V7yd62hsKHm4cEtuF3NpiqpVITED2Z/iJ09QRbKIg2MGX4ST057ioGBA3qm8V0oKiGNy55ayjv3nYBareHka/5y2HOHTzuXude9yJpP/8L3r91OWGwqZ9/xJinpx7d6/siZF6Aobr74yxWoVGpGzJjXRe+ia6gU5SjLnXuZTCa2b99OamoqP/30E/PmzeNvf/sbr7zyCmeccQaPPvpoV7W1S9TU1BAaGspHqSmY2hG+tIFBjXP0QprNzWs+bNv8fgl7QhyV8gP1bP2siK2fF2MubfywNmBMMGPmxTHy9GgCQvvmh1Thz+12U1SQz8DEJN8xq8VCgLF3Fka2Wa1s3bCe9SuWsX3zJtzeBYAqlYobFj7AmAkTe7iF/upryincs84bENdRsGcd1rqWtWgDw2J9NQ9jh4znW2cW/9z/bxxuByH6EB6c8ADzUs/rFSNT+fn5JCYmkpeXR0JCQk83p99rdw9jaGgoNptn5eGSJUuYP38+V155JUOGDOHKK6/sc4GxQdzcuYRFRbU+rNs88AUGourGoREhjiW2Oie7visla0kRBZtrfMeN4TpGnx3DmHlxxKRJjcz+xFxby39e+ht7d2zjnmcW+4Z3e2tYBDAEBDBx5iwmzpxFXW0Nm1evZv3y38jZv48hw0f4zsvasA6H3c6YCd230trlsFN8cItfzcOKwn0tztPqA4gbPN437zA+bQqh0UmoVCq2lm/j9jX3savKs0vKnIQ5PD75UWKMMd3yHkTv0+7AeNxxx/HPf/6TW2+9lU8++YQPP/wQgKSkJAoKCjq9gd0l49nnCAkJ6elmCHFMUhSFvA3VZC0pYvf3pTgsnkLsKjUMmh1B+vkDGHJcBBq99Mr3N7n79/H6X56jvKQEnV5PUX5+n5sPGBQcwqxT5jLrlLmYa2sxNqnv+u0nH5O7fx8BRiNjJ09l0qzZpI1J77T5mIqiUFWcTcHutb6h5UP7N+FytCwpFRE/zFfzMCFtKjGp6Wi0/j30NpeNl7a+zGs7XseluIgwhPPIxEc4I/n0XtGr2NnCbn6Fd689jTPHDurppvi8t2Yn9y1ZQe7ia498cjdqd2B85plnOPXUU3n55ZeZMGECJ554IgD79+8nPDy80xsohOi/aopsbPu8iK2fFVOZ01jUNyLVSPq8OEadHUtwrNS/648URWHlTz/w8Ruv43Q6iYqN5dq77iUxtff84e6IpsW+3S4XI9LHUldTTUVpKWt/+4W1v/1CSFgY46fPZPLs40gZMrRdz281V1O4Z72v97Bwz1rMVSUtzjMGR/hqHTasXm7Y2u5wNpVuZuGaheyvOQDAGcln8MjEh4gM6P1zMpu78Z2lfLDW0zuqVasJDzQwamAk508cyqVTRqD2znfe/dSfCDPKIrmj0e7AmJKSwq5duzh48CAJCQm+MgMfffQRkyYdXbFLIcSxy2l3s+/ncrKWHOLgykoU766OepOG4adHkz4vjvhxIf2yN0N42G02Pnz9Vdb8+gsA6RMnc8Utt2IK7F9TDdQaDedcejlnXXwpB/fsZv3yZWxctYKaqip+/fZrqivKufauew/7eLfLSUnOdgp3ryV/t2e/5bK8ndBs6YFaoyV2UIZfzcOIgUOO+v+QxWnhhS1/5d+73kJBISogiscmP8rcxFN+1/vvaXNGJvHKZSficiuU1FpYuiOH+/67gi837+eD689Aq1ETG9Jzu33ZnS702r4zva3dgXHgwIG+RS9Nvfjii9TW1nZaw4QQ/UvxzjqyPi1i+5fFWKsbV2ImTgwl/fw40k6JRh/Yd355io777bv/sebXX1Cp1Zxz8WXMOefcfl0QW61WM3j4CAYPH8EfrryanVmZrF+2jIkzZ/nOKTlUyOuLnyE1JYZgbQUVOZs4tHcDDlt9i+cLi01hYNoU4odNJmH4VOIGj0Or71gv2dritSxccz+5dbkAnJd6Hg9OuJ8wQ1iHnq830Ws1vkA4MCyIjMRoJqbEcc5LX/D+2l1cMX2k35C03enigU9X8mXmfqrqbUQHG7lq5mgWzJ0AQFW9jUVfrOKbrIPUWOwMig7lkbOnceqYFAC+2Lyfp79Zy4GyamJDArnuuDHcctI4X3vGPPw2l08bwf7Sar7NOsCZYwfzz8tP4r01O3n6m3WU11k5cUQiUwf3ztXn7Q6MRUVFOFopGm21Wrnlllv46KOPOqVhQoi+z1LlYMfXJWR9WkTxjjrf8eBYPaPPjWPMebFEpPSd4r6ic5x4xplk793N7FNPJ230mJ5uTrfSaLWMHj+RYSNHcGjfRlYtWUzB7nVkbc2h3DGQgtxcUBT0bgdGVxQhxloSh433DC17t9QLCo/93e2oc9SxePPzvLv3PQDiTHE8MflxTog//nc/d292XFoCo+Mj+SpzP1dM9y+w/a9fs/jf1oP8+6q5JEQEUVBZR0Gl5/eW263wh398RZ3NwWtXnExqdAi7DlWi8Q5tZ+aWcOWb37Pw9EnMGz+UtQcPcddHy4gIDODSqY2LoF5emsndp01k4WmeEdkN2UXc8t4vPHL2VM5IH8RPO3N4+pv13fTdaJ+jDowTJ05k4kTPdjkbN25k9uzZBDaZ2GuxWPj000+7pJFCiL7D7VLIXlVJ1qdF7P2pDJfDM3ym1qkYdlIU6efHkTI9HLVGhpyPFS6Xi2Xf/49ZJ89Fq9Oh0WrbHIrtbxS3m7L83RTsXuObe1iSvRXF3bg7kQY9odpynKahmB0B2DXR2DXRmLVaIsImMv7MazutvuOKQyu4f+2DFJg9C1UvGnIR9427l2B98BEe2T8Miw1ne2F5i+N5lbUMig5l2uABqFQqkiIaF8L+ujuPjTklrHvwEobEhgGQEhXqu/+VnzM5Li2Be7xBcEhsGLuLKnnpp81+gXHWsHi/Xscnv17LnJFJ3HbyeN/j1h0o4qeduZ36njvDUQfGK664gnXr1qEoCueccw5ut5vU1FTGjh1LRkYGBw4cIDk5uSvbKoToxSpzLZ6aiZ8VU1vUuEIzZngg6fPiGHlWLKZwqZl4rKmurOCNv/6FfTu2U1ZcxB+uvKanm9TlzNWlnmC4yzPvsHDvemzm6hbnBUcOZOCwKSQM9/QcDhgyAb0xiPKSYtavWM765cs4lJfL7m1ZmIIa53eWl5YQHhGJup0rrWvsNTy96Rk+3v8JAAmBCTw15QlmDJjx+95wH6MArX1cvWTqCM576QsmPvYeJ41M4tTRKZw4wlMXdGt+GQPDAn1hsbndRZWcnu4/VW/KoDj++csWXG43Gu+Ui3FJ/mWJdhdXtlihPSk1rm8HxltvvRWA5cuXs3LlSsrKysjMzCQzM5NffvkFt9vNq6++2mUNFUL0PvZ6F7t/8NRMzFvf+AcxIFTLyDNjSD8/jriRx0avhWhp747tvPHCYmqqqggwGhk8fOSRH9THOB02ivZvbqx5uGsNVcUHW5ynNRgZOGSiX83DkKjWi01HxsRy6rwLOHXeBeRnZ1NSWOCr4agoCi89vghrvYWJM2YyadZxJA0efMQFLj/n/8xD6x6myFIMwBVpl3PX2DsJ1PXcoo+esruokuTIlmX0MhKj2fLoFfy0I4dfd+fzpze/5/i0RN6+5lQCdO2ewdcqk6Hvfmhu13fA6XQybtw4XC4XGRkZZGRkdFGzhBC9laIoFGbWkPVpETu/LcVu9g6rqSB1Rjjp58cx9MQotIb+u4hBtE1RFH768gu+eO9t3G43AxKTuO6ue4mNj+/ppv0uiqJQeWi/Nxx6AmLRgc24nS3n9UcljvDVPIxPm0pMymjUmvaHjoSUFBJSUny3qyrKMdfUYq6r5edvvuLnb74iZsBAJs6cxaRZs4kd6P89rrRV8sSGJ/k8+wsAkoOTeXbq00yK6XtVTSx2J1n5pWzKKWFTTjFrd7UM5kfy2+58dhSW8+cTxrZ6f4hRz7wJQ5k3YSjnZAzm/H98RaXZyqj4SAqrzOwrrmq1lzEtLpy1B4r8jq09UMSQmDBf72Jr0mLD2Zhd7HdsQ7PbvUW7fnq1Wi3Lly/H5XId+WQhRL9SV2pn+5fFZC0povxA48rNsMQAxsyLY8y5sYQMkHpmxzqL2czbr7zElnVrAJg8+zguvu5GDAF972fDUltJ4Z51jb2He9ZiqWk5980UEuXtNfQsTBk4dBIBQWFd0qbwyCiefv1Ndm7JZP3yZWxZv5aSQ4V8+8lHfPvJR5z+h4s486KLAfgu9zseXr+Icms5apWaq4Zfye3pt2HU9t4ddBo4XW52HqpgU06xJyDmlrCjsByXu7GkkGK3tPEMnrI1xTVmv7I6f/1hE3NHp3DxlLQW57+8NJO4UBPpCdGoVSo+37yP2BAToUYDM4fGM33IQK74v//x5LyZpEaHsre4EpUK5oxM5qaTxnHic5/w3P/WM2/8UNYdLOL137by/EWz22zj9cenM/eFT3npp82cnp7K0p25/LSj9w1HQwdWSf/xj3/kyy+/5Pbbb++C5gghehOXw83+3yrIWlLE/mXlKN7PitoANcPnRpN+fhyJE0NRqWUBi/Coqa5i99YtaLVaLrjyGmadMrdP1NR0OR2UZG9t0nu4lvL83S3O02j1xA0eR/zwKd6C2FMIi0vt1veo1ekYM3ESYyZOwmqxsGX9WtYvW8aurEwGDx9BmaWMRRseZdm2nwmtNDFs+BCenv00GVEZ3dbG9lAUhYNl1WzM9gTDTTnFZOWVYXE4W5wbE2xkfHIs45NjSDLCxa8f/nl/2pFL2v1voVWrCTMZGB0fyTMXzOKSKcN9hbubCg7Q8befNnOgpAqNWs245Bg+vvFM37nvXHMqD362kqvf+oF6u4NBUaE8cs40wDOc/e+r5vL0N2tZ/N0GYkNM3H/GZL8FL62ZlBrH3y45nme+Wc9T36zjuLQE7jp1Aou/29CO72D3UClKswqgR3DnnXfy9ttvc//993Pdddf5rZTui2pqaggNDaW6ulq2BhTCq3Svma2fFrHty2LqyxuH2+IzQkg/P47hp0VjCOqcOT2i/9m6YT3BYWHt3sWkuyiKQk1pnl84PLR/E05byx6r8AGDfeVs4tOmEDtoLFpd79x9qKaqkp/LfuOJzKeotFUyfFscyXsjPQFzwkQmzpzN6PET0On1PdrOQ1VmNuUWszmnhI05JWzOLaGqvuVWhiEBejKSon0BcXxyDPFhQb5wnp+fT2JiInl5eSQktD4fVHSedgfGE044gaysLCorKzEYDEyYMIFx48Yxbtw4MjIyGD9+fFe1tUtIYBTCw1rrZOc3npqJh7Iai/AHRukYfU4sY+bFETW4b39AFJ3PYbfz37feZPy06aSNSe/p5rTKVl/Lob0bPOFwj2dhSl1lUYvzAgLDvFvpTfZtqxcYGt0DLW6/ovoiHl73CEsLfgZgRPhwLredx57fNnMoP893ntFkImPqNCbPOo6ho0Z3ecH0qnobmbklbPQOLW/OLaGwytziPINWw5iEKG8wjGV8UgxDYsJa7QlsIIGxe7U7MDbIzc0lMzOTLVu2sGXLFjIzM8nOzsbpbNmF3JtJYBTHMsWtkLOuiq1Litj9QxlOm2efPrVWxeDjI0ifF8egWRFodLKARbRUXlLM688/R+6B/YSEhfPYK6/6VvP2FLfLRVneTr+ah6W521Hcbr/zVGoNsaljffMO49OmEBk/DFUf23FGURSWHFjCExufotZRi06t4+bRN3H9qOvQqXUoikJBTjbrlv/GhhXLqSr3zMEMDg3lqdfeRNPO0jxtsdidbM0vY2NOMZtzS9iUU8K+kqoW56lVKobHhTM+xRMMx6fEMnJARLu3ydu/cS9DJg6TwNhNOjymlJSURFJSEmeffbbvWF1dXRuPEEL0FtUFVrZ+XsTWT4upLrD6jkcNMTFmXhyjz44lMKpnh61E77Zt0wbe+vuL1NfVERgczBU339rtYdHldFCWv4viA5kUH8ikaH8mhXvXY7e0/FsUEp3o3WfZExAHDB6PLqBv7zJUUFfAA+seZPmhFQCkR6bzzNSnSQsb5jtHpVKRkJJKQkoq5156Bft27mD98t8ICQv3hUW3y8XfH1/EsFGjmThzNjEDjrw1XcOilM3eOYcbc0rYWViBs1kwB0iJCmF8UgzjkmOYkBxLemIUQYaO/36xltSzc/EGsl5d2+Hn6IiS3/L55eQlnFdyA/qw1n/WD769g813/sa80hsB2PbYGgq+3M/cDZd2Z1O7RIcC43vvvcfXX3+NzWZjzJgxXHLJJaSlpREU1L82jheiP3FYXez9qZysTw+RvbrKU70WMARpGHGGp2bigDHBfWKBgug5bpeLbz7+kP8t8RR/Thk6jGsW3E1EdNcO3VrN1ZQczKKoIRwe2EJpzjZcTnuLc3UBgQwcOsmzMMXbexgc0Tv35+0It+Lmg70f8OzmxZidZvRqPXeMvZ2rhl+JVn34P+tqtZpho0YzbNRov+P7du5gz7at7Nm2la8/+oCUIUOZNOs4xk+fQWh4uG9RyqaGOYc5JWzJKz3sopRxybFM8M45HJcUQ2RQ56zKtlVY2f3CRva+sgWn2YFbaRlOG6y9+gey39kJgEqrxpQQROK8oYxeNBVNQPfNv05bMJ6hNzWW8Fl79Q84qmzMXHJWt7Whs7T7u/bMM8/wxBNPcO655xIWFsb333/PU089xYMPPsgjjzzSFW0UQnSQoigUbasj69MidnxTgq2m8Rd88tQw0s+PY9icKHTGzhuWEv2X3Wbj1WefYlfWFgCOO+10zr/iSrS6zitG3LAgpSEYFh/MovhAJpVFB1o9X28MJm5QBrGDxhKbOpaBaZOJThzZ7l1Q+oqc2hzuW/sAa4s9vWsToifwzNSnGBQy6AiPPLykwUO44ubb2LBiGTuztpC9by/Z+/byyVtvYAsbyAZjCjnulr2xwQE6MpI8vYbjkmKYkOK/KKWz2Ktt7Pn7Zvb8bTOOGs8HhPAJMaTdPBguO/zj4uYmM/n1k1Ecbio2lbDu6h9ABWOfntmp7WuLLkgP/aQvrd2B8dVXX+Wjjz7ijDPO8B1buXIlF110EYmJiVx11VWd2kAhRPvVV9jZ/lUJWUuKKN3TOME8ZKCBMefGMWZeLGEJvb8Wm+hddHo9IWFh6A0GLr3hz0yaddzvej6X00FZ3s7GcHhgC0UHMrHWVbZ6fmh0ErFNwmHcoAzCYlP63LzDjnC5Xby9522ez3wBq8uKUWPkrow7uXzYZWjUHQ/HnkUpZWyyhbApdjJbB8ViKD5IirWIKEc1hsoC6lQD0ZuCSE+IJiM2mHGD4pk0OP6Ii1J+L6fZwd5XMtn1l43YKz2rqEPHRDFm0TQGnplKQUFBm4/X6DUY4zwL9UyJweS8l0jR0lzG4pm/vXPxBg68sQ1rkZmgoeGMun8yiec3ruwv/N9BNt+5DEt+LZFTBpByecsSOQff3sG2R1djK7MSd3IyUTMG+t3fdEh622NrfL2eH+n/BsAJP55PzHF9Y/5luwNjWVkZo0f7d2fPmDGDV155hQcffFACoxA9xO1UOLDCUzNx36/luB2eMWeNXkXayZ6aiclTw6RmomgXRVFwOhzo9HpUKhUXX3cjc+ddwICExHY9j9VcTfHBLd7hZE84LM3Z3uqQslqjJSpppKfn0BsMYweNxRgc0Vlvq0/ZX72fhWvuZ1PZJgCmxk7l6SlPkhSc1K7naViUsinXWwz7cItSgpLRDskgKlJPgq2M/557LqPio9BrNbz36itseu1tmDodZs1myMhRnb7S2mlxsv+1LHYu3oCtxFPqKDgtnNEPTyXx/KEd+h1Wta2MsjWHMCV5FrfufHY92e/vYsLLJxI8JIzSFQWs+dP3GKKNxMxOoD6vlpUXfsOQG9MZfPUYKjYWs+Xe5X7PWb6uiPXX/cSYJ6aTcPZgDv2Qw7bH1hy2DWkLxlOzqwJHrZ3Jr58MgD6i7xS0b3dgnDhxIl9++SW33HKL3/GMjAwOHGh9yEAI0XUqDtaT9WkR2z4vpq608Y9v3Ohg0s+PY+Tp0QSE9t39S0XPsVosvPvPl3HY7Vx/z32o1WoMAQFthsXmQ8pFBzwhsbX9lQEMphBiB2UQN2ist/cwg+ikkb221mF3crqdvLHzDV7M+jt2t50gbSD3jr+XPw65CLWq7ZDmdLnZVVTh20ZvU04JOw6zKCU5MsRX53B8cixjD7MoRVEUDu7ZjcVsZuXSH1m59EfCIiJ92xImpPy+AuYuu4sDb25j5zPrsRR6RkaCBocy6oEpJF2chlrTvmBa+O1BloT/A7fTjdvmQqVWMf7F43HZnOx4dj3HfzePqKmeua1Bg0IpXVnI/te3EjM7gX3/yiJoUCjjnvPs1BKSFk71tjJ2Pb/R9/x7XtpM3NxkRtw1EYDgYeGUrT5E0Q/ZrbZHF6RHY9Titrl8PZ99SbsD42OPPcbpp5+Ow+Hguuuu8y10+eSTT0hpst+lEKLr2Oqc7Pq+lK1LisjfVOM7bgzXMersGNLnxRGT1k8mzogecSgvj9eef5bignzUGg15Bw6QPGSI3zkuh52y/F0dGlKO84bDsNgUWWjVit2Vu7lnzUK2VWwDYNaAWTw15QkGBg5sca6iKGSX1XgXpXhK2mzJK6Xe3nJRSnSTnVLGJ3lC4tEuSlGpVNy/+K/s9a603rx6FVUV5fz05ef89OXnjBo/gZvuf6jd79XtcJH97k62P7WO+hxPDVhTYhAjH5hC6uUjUOs6NuQec3wCE146EafZwZ6/b0alVZM4byjV28tx1Tv57bTP/NthdxGW4Vm8VbOrksjJcX73N4TLBjW7Kkk4Z3Czc+IOGxj7unYHxtmzZ/PJJ59wzTXXcP/995OUlITVaqW0tJQPP/ywK9oohMDzRyF/YzVZS4rY9X0pjnpPT4FKDYNmRZB+fhxDjo9Eo+//87lE11q/Yhnvv/oPbFYroRERXLPgHmLjosjZ+ptvOLnoQCZluTtkSLmT2V12Xt3+L/6x/Z843A5C9CE8OOEB5qWe5wvWxTVm3zZ6m709iJWt7JTSsCjFEww9ITEh/PctSlFrNKSNHkPa6DFcdM31bN+0kfXLf2Prxg0kpjYuvHE6HKxc+iPjp00nODSs1edyu9zkfriH7U+uoW5fNQABAwIZuXASg64ahcbw+1Yza006god4Xnvy6yfz/YT3OPDvbYSOigJg1hdnYxzo/8FaY+ifi6U6Q4f+NU477TSys7P5+eef2b59O8HBwZx00kkMGtTxVVpCiNbVFtvY+lkRWz8rpjKnceuyiBQj6efHMersWIJjZfhO/H5Oh4Ml//k3v333LQAx0UEMiank2+dOp6o4u9XHGAJDfYEwLtXTaxiVNEKGlDtga/k2Fq65j11VuwCYkzCHe8Y8SH6pmxd/3OSbd1hQ1bLOpF6rZkx8lN82ekNjwrt0UYpOpyNjylQypkzFYjbjcrt89+3I3MxH//can7z5fwxPH8ukWbMZO3kqAUYjilsh/7N9bHt0DTW7KgAwRBkZcc9EBl+fjtbY+WVvVGoVI+6dROY9yzh9+3zUBg31ubXEzG59wUnI8HAKvvafRlG+tqjFOeXri9o8pzm1XoPi6tB+KT2uQ/8qNTU1bN26laCgIK6++mpCQ0M7u11CHNOcdjf7fi4n69MiDq6ooKHcmN6kYfhpngUs8eNCZChP/C4uh53SvJ2+wtfLVu2hos4z3zXIsQd17i4O5jaeHxqT7DffMG5QBqExyfJz+DvZXDZe2voy/9r2Jra6CPTWaaTpT2TTfh0TPvm8xfkqFQyPi/DbRm9UfGS7d0rpTMZA/zl5Go2G5CFDydm3lx2Zm9mRuRmd/p8MHTiSoLWBGNZpUStqdGEGhi8Yz9CbMzwlaLpQ4gVD2XLfCva/vpXhd4xn893LUNwK0TMGYq+xU7aqEF2wntQrRjL4ujHsfnEzmQuXM+jK0VRuKubgOzv8nm/ozRn8fNwn7HphI/FnDaLox1wO/ZDTZhsCk4Mp+jGHmt2VGCID0IXqOzzk3t3aHRg///xz5s+fT22tZ56BVqvlvPPOY/HixSQltW/FlhDCX/GuOrKWFLHjq2IsVY3zjxImhpI+L47hc6PRB/aNXy6id7HWVVF8cIvfkHJp7nbcTofvHJUqDLVhMmHObSQlRhM7aH7jYpTUsRiDw3vwHfQvLrebXYcq+Xz7ev6zaSnllUZc9deD4vn/XUZjOaykyGAmeIPhuOQYxiZGExzQu3diGjV+AqPGT6DkUCHrly9jzfc/U15dwo7sTIiFKTEzmHDNTIbdNv6wu6Z0NrVWzdAb09n1l42cuedKDNFGdj63gQ0Hq9GFGQgfF8OIeycBEJgUwoyPTmfz3cvY+8oWIifFMubx6ay/9iff80VNGcDEV09i22Nr2PboGmJPTGTkfZPZ8dThd6AZdPVoSpYV8OO0D3DWOfpUWZ127yU9aNAgjj/+eJ5//nmCg4NZvXo1L7zwAitWrGD58uWMGNGyTlFvJntJi55mqXKw4xtPzcTiHY1DTUExesacG8uY8+KISO3bW5iJ7qMoCtUlOZ5geHALxb5VytktzwVUpniSBw/xDSlHJI5iwOB0GVLuRIqikFNe45136FmxnJlXiuVwi1KabKM3LimGqOC+WzO1ZFk+2xatpmRFAXWhdZSmlMAINQv++jSGSM/7WvKfN1GrNUyaNZv45KNfBJWfn09iYqLsJd1N2h0YTSYT27ZtazFf8eabb2bPnj388MMPndrAriaBUfQEt0shZ3UlWZ8WsefHMlzemolqnYqhJ0aSfn4cqTMiUGtkqE8cnsthpzR3h6d8zcFMbzjcgtVc1er5YbEp3t7CdEIGDOe3X7dwcO9+7nrqGb8FC+L3Ka4x++YbbsopYXNuCRVma4vzVBobmsASRsaHcMOkM5k5JIXE8P6xPWfZ2kNsW7Sa4qV5AKgNGoZcP4YRd08kILZx+NpqsXDv1fNx2D2LpwYkJjHJW6YnMia2zdeQwNi9OlSHcffu3S0C4x133MGYMWM6rWFC9EdVeRZfzcSaQ42rGqPTAhl7fhwjz4rFFC41E0VLltrKJoWvPZeleTv8hpQbqLU6opNGNS5GGZRBTEq6b0g5Z98+Xv/Ls1SUlqLT6yktKpLA2EHVFhuZuaVszi1hY7anpE1+ZeuLUkYMDAdjPvucy9AGFRMfoefJqY9zQvzx3d/wLlKxuYRtj67m0LfZAKh1alKvGsXIhZMxxbcs9aXVavnTrbezfvkytm3cwKG8XL784D2+/OA9BqUN58Qzz2b8tOnd/C66x58vOJfr7llIxuSpPd2Uo9LuwDh37lxuuukmPvjgA6ZMmeI7fuDAAQYObFkfSohjncPiYvcPZWQtOUTuumrf8YBQLSPPiCH9/DhiR3b+/quib/IbUm4SDqtLWp9MHxAY1rLwdeIINLrWCy+v+PEHPnnzdZxOJ9FxcVxz5z0SFo+S1eFkW0GZX0mbPcUta042LEoZ561zOCE5lir1Lh7Z+DAF5gICgIuGXMR94+4lWB/c/W+kC1RtK2P7Y2vI/3w/ACqNipTLRzDq/ikEphx+9E6r0zFu6nTGTZ1OvbmOzDVrWL9iGXu2beXA7l2kT5rsO9dht+NyuQgwHt0QfUVZKd989CE7MjdRV1tLaFg46ZOncPofLiQoWEYU26vdgfHpp5+mvr6e6dOnM2XKFDIyMnC5XHz//fe89tprXdFGIfocRVEo3FJL1pIidn5bgt3sLTehgtTp4aSfH8fQk6LQGqRm4rGstSHlogOZ2MzVrZ7vG1JuKGEzOIPQ6KSj+rBht9n44LVXWfvbLwCkT5rMFTffiilQCry3xuV2s7uo0lMI2zu0vL2wHIer5U4pSZHBjE/yzjlstiilxl7D05ue4eP9nwCQEJjAU1OeYMaAGd36frpK7Z5Ktj2+htyP93gnxULyH9MY9eAUgoe2b5GUKTCI6SfNYfpJc6iqqGDjyhWMmzbNd//mNat5/1//IH3SZCbNnE1IVPRhn6usuIjF999LzICBXHn7nUTFxHIoL5dP3/kPOzZv4u6nniUwuH+E9e7S7sBYW1vLnj17yMzMZMuWLb7LQ4cOceGFFzJ69GjS09NJT0/nhhtu6Io2C9Fr1ZXa2f5lMVmfFlG+v953PDQhgPR5cYw+N5bQgX1n71DReSy1Fb5A2LBauSx3B25Xy4UPrQ0px6aOJSAorMOvv+rnn1j72y+o1GrOueQyTj7nPOnV9mpYlNIw53BjTjFZeaWYW1mUEhVkbLKNXgzjk2IPuyjl5/yfeWjdwxRZigG4fNjl3J1xJ4G6vrctXHN1B6vZ/uRact7dheL2zMFOmDeE0Q9NJXRU5O9+/rCICE4662y/Y7u3ZWG32diwYjkbViwnIjbuMI+GD19/DY1Wyy0PLUJv8CzgioiOJiF1EI/cfANffvAeF193Aw/eeC0zTjqZ4sJCtqxbQ2BQMBdefS2pw9J475+vsHtbFpExsVz+51v8djrat3MHX7z3DrkH9hMYHEzG5Kmcc+nlGAI8v9+rKyt495+vsGfbVkLCwjj74kv54v13OfGMszjxzLNbbXNFWSmf/uff7NySiUqtZsjwkfzhqquPOJezu7R70cvhlJaW+oXIrKwssrKyOuOpu5QsehG/l8vhZv+yCrKWFLH/t3IUb2eiNkBN2txo0ufFkTQpFFUXFtAVvYeiKFQVZ3uHkzN9q5SrS3NbPT8gKNwXDBvK1xxuSPn3cLtcvP3KS0w/8SSGjT6255uX1NR7wqF3xfKmnNYXpQQZvDulNGyjlxJzVItSKm2VPLHhST7P/gKA5OBknp36NJNiJnXJ++lO9fm17HhmPQfe3I7i9PS2Djw9ldGPTCV8XEyXvraiKOTs28v6FcvYuHIFaRnjueqW21osejHX1nLPVVdw1sWXcuq8C1o8z3uvvsLmNatZ/O93eOjP12GzWDn7kstIGzOGpV9/yfrlyxg0bDjTTzyJ+JQUPn/3bYoLC3jory+hUqkoLTrEU3fdwVl/vITREyZSW13Dx2+8RnxKClfcdCsAf3/sEepqarj4uhvRaDUseetNsvft5ZxLLvMFxqZzGF1OJ0/edTupw9I48YyzUGs0fPffT8g9sJ8H/vIiWl3Pz23vUOHulStX8ssvv6DT6bjpppsICgoiOjqak08+mZNPPrmz2yhEr1S2z0zWkiK2fVlMfXnjwoOBY4NJP38Aw0+LJiC483csEL2H02HzDSn75hse3NLGkHJqi8LXIdGJXdLT53I6+fV/3zDrlFPRGwyoNRr+dOvtnf46vV2NxU5mXkmTVcvFrS5K0WnUjEmI8itpMzQ2DI26fdNGvsv9jofXL6LcWo5apeaq4Vdye/ptGLV9tzQOgKXIzM7FG9j/2lbcNs+n4tg5SYxZNK3FnstdRaVSkTJ0GClDhzHviivJOXgQuK3FeSWHDqEoCnGHWTkdF59Ifd2P1NV4/p+OGj+eWafMBeD0Cy5i+fffkTxkCOOne6YNnHLuPBbffy81VVWEhofz/WdLmDRrti/4xQwYyB+uuoa/PvIgF197A+WlJezK2sK9zzzv65W89MabWXTLjYd9bxtWrUBxK1x2482+3weX33QLd86/lD3btzEyY1zHvmmdqN1/zV577TXuuOMOTjrpJL777jv+8Ic/EBQUxDvvvMOoUaMYP358V7RTiF7BWutk5zclbP2siMIttb7jgVE6Rp0dS/q8OKKG9P3hJtFSe4eUY5JHNw4nD8ogNiX9dw0pt0d1ZQVvvPA8+3buoCg/n0tvvKlbXren2Rwuz6KUnGJfOZs9xZU0H0dTqSAtNpxxybFM8A4tjxoYheF37LhRZi1n0fpF/C/3OwCGhAzm2WnPkBGV8TveUc+zlVvY9fxG9v5jCy6L52c9euZARj86nZhZ8T3WLo1Ggz7gCNN7jnL8ND45xXc9JCzMcywp2XesYS/s2upqQsPDyc/OpjAnm/XLlzW+lKKguN2UlRRTUliIWqMhsUk1mZgBAzAFHX6+cEF2NqVFh1hw+cV+x50OB2XFbW832F3aHRife+453nrrLf7whz8Q3GTCqMPhYNGiRXz55Zed2kAhepriVshdV0XWp0Xs/qEMp9UzDKPSwJDjPTUTB82KQKOTBSz9gaIoVBUd9FulXHJwy1EPKccNyiAqYXinDykfrT3bt/HmX5+npqqKAKORkeP6/4f48joLf/1hE28s34bF0TLAJ0YE+4LhuKRYMpI6b6cURVH4KvsrHtv4BJW2SjQqDdePvI6bx9yEQdN3i5/bq2zsfnETe17KxFnrqZEYMTmOMYumEnvS0S206inRA+JQqVQU5efBlJYla4oK8jAFBREU4tnWWKNp/KDQ8L7U2qbHPJeKd49Wm9XKzJPncvzpZ7Z47oioKEoKC9vdZpvVStKgwfzptgUt7gvuJdPl2h0YCwoKmDx5covjEydO5MEHH+yURgnRG1QXWtn6WRFbPyumOr9xflPkYBPp58cx+uxYAqN69/Zcom1Oh43SnO3+8w0PbsFWX9Pq+eFxgxpXKXt7DkOiEnrFH09FUfjxi8/48v13cbvdDExK5tq77iF2YM/1AnW1WqudV37O5JWfM6m1eqaFRAYF+HZImZASw7ikGKKDu2anpOL6Yh5a9zBLC34GYET4cJ6d+gyjIkZ1yet1B0etnb0vZ7Lrr5twVHlqxYaNjWbMoqkMOD21V/ysH0lQcAjD08ey7PvvOPHMs32LXgCqKytZv3wZU447ocPvJSl1EIfy84gZMKDV+2Pj43G7XOQfPEDSYM+QdMmhQ9TXtZwK0SAxdRAbV60gODQUo6l37uzV7sA4evRoNm/eTHJyst/x4OBgqqqqOqtdQvQIp83Nnp/KyFpSRPbqSt+QhiFIw4jTPTUTB6T3j50YjjX1NeUtCl+X5e1sdUhZo9UTnTzab65hTGo6AYGhPdDyI6s31/H2y38na/06AKYcdwIXX3eD3x/K/sTqcPJ/y7bx1x83Ul7n+TCXnhDFw2dP5aQRXd/7pSgKSw4s4YmNT1HrqEWn1nHT6D9zw6jr0al7fnFCRzjrHex7NYtdz2/EVmYBIGREBKMfmUrCuUP63KK9i665jucfWMjLTzzKWRdfSmRMDIfy8vjsnbcIi4jk7Isv7fBzn3zuPBbffw8f/d9rTD9pDoaAAA7l5bErawsXXXMdcfEJDE8fy3v/+gcXX3sDGo2WJW//G51e39hd2czk2cfx05ef8+qzT3HWRRcTFhlFRWkJmWvXcPK55xEeGdXh9naWdgfGhx9+mNtuu41RozyfoBr+Y/7www8kJSV1buuE6AaKolC8vY6sT4vY/nUJtprGAJE0JYz08+NIOzkKnbHj85tE9zNXl7Lx21cp3LOB4oOZ1JTmtXqeMTiiReHrqIThaLR95w+/pb6e/Tt3otVqufDqa5kx55R++aHG6XLz3pqdPPe/DRRUeXprhsaG8cAZUzg7YzDqbgg1heZC7l/7AMsPrQAgPTKdZ6Y+TVrYsC5/7a7gsjnZ/3/b2PnseqxFnlJgQUNCGf3QVBIvHIZa0zen2sQMGMi9zz7PNx99wBsvLMZcV0dIWBhjJ03h9Asv+l01GBNSUrjjsSf58v13eeGh+wGIio1jwvTG2przb7mNd//xMi88/AAhYWGcc+nlHMrLRXeYqSp6g4E7HnuSz999m9eefxarxUJYRARpY9IJMPaOHscOldX5y1/+wqJFi7BYLFxzzTXU19fz0Ucf8dxzz3HbbS1XLPVmUlbn2FVfYWf7VyVkfVpE6W6z73jIAAOjz4sl/bw4whL79srGY5HLYWf916+w7IPHWqxW7s1Dyr/XrqwtGE2BfrXi+gu3W+GzTft46pu17C/1/JsmhAdx7+mTuHjycLTdEGrcipsP937IM5ufw+w0o1fruT39Nq4ecRVadd+rhuB2uDj49k52PLWW+jxP+DYlBzPqwSmkXDoCtbb3B8W+tJd0ZXkZD1x/Dbc+/CjD08f2dHM6pEM/5XfeeSfnn38+77zzDpmZmeh0Ol5//XWuuOKKzm6fEJ3K7VQ4uNJTM3HvL+W4HZ7PSxq9imFzokg/P47kqeGoNX0/QByL9q7/lh9eX0BFwR4A4gaPY+yc+cQNGterh5Tby2G38/Gbr5M+cTJjJnpq+/XVP0JtURSFH3fk8PhXa9maXwZ4CmffOXcCV84cRYCue4JaTm0O9619gLXFawEYHzWeZ6c9zaCQvrelotvlJuf93Wx/ci3mA57wbYwPYuR9k0j90yg0ehlJ6Qy7t2Zhs1oZmJRMdWUln7/7HyJjYhg6su/Ob23X/7acnBx2795Neno6KSkpPPTQQ373FxYWyn7SotdwWFyU76+ndK+Zsn1mSvfWU7S91q9mYtyoINLPj2PkGTEEhPadIUjhryxvFz/+353s2/A/AALDYjjhiicZO+dPqDX96w9gWXERrz//HHkHD5C5dg2P/+O1o95bty9Zta+Qx79aw+r9hwAICdBzy5xx3HB8eqetcD4Sl9vF23ve5vnMF7C6rBg1Ru7KuJPLh12GRt23fq4Ut0Lef/ey7bE11O7x7H9tiDEy8t5JDL52DJqAvtNLqigKVquTsvL6I5/cQ1wuF1+8/y5lxUUEGI0MShvOn267A42273yfmzvqln/wwQdcccUVno2/AwL417/+xeWXX05ubi7vv/8+n332GRs3bsTpbDmBXIiu5LS5KT9YT9leM2V7zZTu81yvyre2WofLGKZl1NmxjJkXR+xw2Ue3L7PWVbHsg8dY/9XLuF1O1FodU865jVl/fBCDqf9NMdm6YT1vvfQiFrOZoJAQrrxtQb8Li1vySnn8qzX8tMNTxihAp+G649K5fc54IoK6b1vN/dX7WbjmfjaVbQJgauxUnp7yJEnBfWuuvqIoFHx5gG2PrqZ6WzkA+ogAht81gaE3jkUb2PMflO12F7W1VmrqbJ7LGhu1dVZqam3U1tqoqbVSW2ujtq7xutPpxuGwdMrrP/LE96QkR3Dl5Z23E8/IjHGdXmz74yWZ/LB0D9U1Vu6+/XgmT+zen8WjDoyPP/44t9xyC1dffTX3338/N954I3v27OGZZ55h8ODBnHTSSSxcuLAr2yqOcS6Hm4psC2X7zJTtbew5rMyx4C2P1YIxXEf0UBNRQwKJGhpI9FATA9JD0Op7//wccXhul4vMH97gl7cfpL7GM1Q5dPJZnHzN80TGD+3h1nU+t8vFVx99wPef/heA1GFpXL3gLiKionu4ZZ1nb3ElT369ls837wdAq1ZzxfQR3H3qJAaEdV8xfKfbyRs73+DFrL9jd9sJ0gZy7/h7+eOQi1Cr+s7vDUVRKPo+h62PrqZyYwkAuhA9abePZ9itGehCumYFvcvlprauWdCrtVFT57leU+MNgg3hsNaGzdaxjiZdG/MsX/7XSn5bvp+TTxzGdVf512L8v7fW8v1Puzlu1mBuvn4Gd91+fLfMg/098guq+OSzLO6+/XiGDokmKFDPn29fwhmnjuCMU0d2SxuOOjDu37+f2267jeTkZF555RWSkpJYuXIlWVlZjBgxoivbKI4xbpdCVa7F11PoGU42U5Ft8c05bM4QoiV6iImooYHecGgiemgggZFSJ7G/yc76lR9eu4Pig1sAiEocwSnXvsDgCXN7uGVdw+Fw8I+nHmf31iwAjj/tDOZd8adesbdsZ8irqOW5/63n/bW7cLkVVCr4w8Rh3Hf6ZFKju3fO6e7K3dyzZiHbKrYBMGvALJ6a8gQDA/vWVKviX/PY+shqyld7hvO1gTqG3pxB2h3jMUQcfS+t261grrd7Q19D0PMGP79jniBYW2vDXG/vUJs1GhXBQQEEBxsICTYQEuy5HhwcQEiwwXs8gOCgxuulpUX89/3DP2dkpImVaw4y/7KJGPSeuGO3u1ix6iBRkY0fQoKDOh6eFUXB7VbQdHHgLC7xLEyaNKFrthI9GkcdGB0OB0bv0EdCQgIBAQE8//zzEhZFhyluheoCq7ensN43nFy+34zL3now1Js0RDXtMfSGxKAYfb9Y6SoOr6o4m5/euIedKz29bAGBYRx32aNMOP2GPlUCp710Oh0xAwZycM9uLr3xJibNnN3TTeoUpbX1vPDDJt5YvhW70zNEcNqYFB48cyqj4iO7tS12l51/7fgXr2z7Jw63gxB9CA+Mv5/zB83rU79XSlcVsm3Rakp+zQdAE6BhyI1jGX7XBAxRRqw2JyWldb6g19AD2BD+PEO+3uveIOh2t7uQCioVBAUavIHPP+iFhgT4BcNgbzA0GXWd/r0elBJJUXEt69bnMmuGZ4HS2g05REUFEhPdOB2p+ZC0w+HioyWZrFh1kOoaK5ERgZx39mhOOn4o23cUseipH7j/7pP44JPN5OZV8dC9cxg2NJp3PtjIyjXZWCx2BqVG8qdLJzFksKd+Yp3Zxhv/WceWrYVYrU4iI0zMO3sMJxznqWqQk1fJv99Zz569pRgMGqZMSmb+pRMxBuj4eEkmn3zm+cB44eXvADByeCylZWbeencDb727AYBP3u3ahcftmn35/vvvc+qppzJ8+HA0Gg3h4eFd1a5uV1Zeh05vJMCg7VO/IPoCRVGoLbJ5guHeet8ClPL9ZhyW1seStQFqIgd5egkbegujhgQSMtAg/z7HGLvVzMpPnmH1kudxOWyo1GomnHY9x136KKbQni9m2xUURcFus2Hw7pV7wZVXc+IZZxEb3/d3bam22Hh5aSb//GULdTbPArSZQ+N5+KypTB4U1+3t2Vq+jYVr7mNX1S4A5iScxGOTHiXWFNvtbWkPh8PlC3cFm4rY+c52ineVY9ersE8woRsRjiopkDX2Kmof+5baOisOx2Hm7hyB0ahrDHdBjUGvoecvOCiAkJDGXsHAQD0ade8Y4j3xuCH8smyfLzD+8ts+Tpg9mO07iw/7mJdeXcGefaVceflkUpLDKSmpo6bO6nfOex9t4vKLJxAbE0xgoJ53PtjImvU53Hz9DKKiAvni6+088dxPvPSX8wgOMvDhfzPJL6jigbtPIjg4gKLiGux2FwBWq4Mnn/2JYUOjeeax06musfLq/63mjf+s4+brZ3DWGaOIjg7iH6+t4rWX/wCAVqvm7vu/Ys4JwzjphO6ZhnPUgXHWrFk88sgj3HnnnYSHh2O1Wvnb3/7G9OnTGT16NMOGDUPbh1f/LFj4FTqdEa1WTXCQgaAgg+8Tke+69ysoqOE/ied6UKC+y7uj+wJFUTCX2inbV0/pPrN3EYonINrqXK0+RqNTETHIRLR3GNkzzzCQ0PgAKW1zjFMUhW2/vs/Sfy+ktrwAgJT0Ezjl+heJTRnTw63rOlaLhXf/8RLmujpuefAR1BoNOp2uz4dFi93J68uy+OsPm6is92w5Ny4phofPmsrxw7u/FqbNZeOlrS/z2o7XcSkuwg3hPDzxIc5KPrPb2+Jyuamrs3kXfTQZ6m3oBWyYE1jTuDDEam02788EjG9S4NleD/tariLW6dSEBAc0GfJtDHohQY09fr77gwzodH1rRXhTs2YM4v2PN1Fa5hnS3bWnlNtvnn3YwFh4qIbVa3N4aOEc0kd7piLExrQs8n3R+WMZO8Zzv9Xq4Iele7jp+umMG+v5f3rD1dP487ZCfv51L+ecOZqyMjOpyREMHuT5kNu0h3PF6oPYHS5uvn4GAQGe0ZKr50/mmb/8wmV/HE9YqJFAk2d6VXhY4yI3tVqF0aj1O9aVjjrh/fbbbwDs3buXjRs3smnTJjZt2sTbb79NVVUVer2eYcOGkZWV1WWN7UoNE16dTjeVVRYqq9q3+irQpG8RJBtDpp6g4GaBM8iAoQ/3ZtZXOijd45lfWLbPuwBlrxlrdeuTl1UaiEhp6ClsDIbhSUbU2r75PRBdp2D3Or5/7XYKdq0BICw2lZOveZ60aef22f8zR6MwL5fXFz9LcWEBao2Gg3v3Mnj48J5u1u/icLl4Z9VOFn+3gUPVngL5aXHhPHDmFM4aO6hH/j03l21m4er72FfjWWBzetLpPDLpYaICfv9QuKIo1Nc7fEGvYW5fTStDvg1zAs1mO+3fQgNUbgW9TcFgVwgJCyB2eBQRA4Jb9gA2XO/jf3c6IjQkgPEZCfy6bD+KojA+I56Q4MPP48zOqUCtVjFyeNu93YNSG0c3ikvqcLncDB8W4zum1aoZMiiKgkJPrcu5c9J4/m+/ciC7grFjBjJ5QiJp3vPzC6pJSQr3hUWAtGExKIpC4aEawkJ7RyWEdncJDh06lKFDh/LHP/7Rd+zgwYNs2LCBzZs3d2rjutMb/7wQvcFEXcMnvDqb53rDV63/7Ybr9fWeIRVzvR1zvZ3iktqjfk2dTu0XLP1CZrOezYYw2t1d/dYap2cIeY93nqF3AUrTWoZNqdQQlmj0G0aOHhpIRIoRjaxMFkdQW3GIn9+6n6yl/wFAFxDIrIseYMq5t6PVd19JlZ6wfvlvvPfqP7DbbIRFRHL1grv7dFh0uxX+u3EPT32zjuyyGgASI4K57/TJXDR5WI8MWVqcFv665UXe3PVvFBSiAqJ4bNIi5ia1vmBKURRsNmerQa9x/p9/yZe6OhsuVwfSHxAUpPcM77YS9EKCA9CZnZR+vI/Kr7LRWd3oHApJFwxl9BNTCRkR8Xu+Nf3aCbOH8Mbbnn3Wr5k/uc1z9UdZuDzA0L74NG5sPP948Xw2ZxaQta2QR5/+kVNPTuOKSya263l6UqeMIaemppKamsof/vCHzni6dktJSSEkJAS1Wk14eDi//PJLu59DpVJhDNBhDNARHXX0tfkahhKaB0n/0GlvvO497nK5cTjcVFZaqKw8+t5MlQpMJn2rw+PNQ2fTXk3DEX64bXVOT5Frb8mahtXJtcWHX/EWmhBA1JCGeYaensPIQSZ0AX13+EL0DKfdytrPX2TFx09ht3iGjtJPms+J858kOLJvrVBtL6fDwZK3/81v//sWgLQx6Vx1+wKCQ8N6tmEdpCgK/9uazRNfr2VHoafuX0ywkbtOncj86aMw9NDw5tritSxc9SCHKsoIsEdzXOTJnBv7B9y7NXyycYsv/DUPhg5H69NpjiQgQNs41NtkeLfxmP/wb1tTm8y5Nex4ej0H/7MDxekmEIg/axCjHp5K+Nj+U1qpq2SMHYjT6UKFirHpbf8+SUoMR1EUduwq8g1JH0lsTBBarZpde0p8+cHpdLP/QDlnnNq4MDg0JIDjZw/m+NmDGbF0D+98sJErLplIQnwovy7fj9Xq8PUy7t5TgkqlYuCAw9eT1WrVHVqU1FF9d9JhM6tWrSIoqPuLMGs0akJDjYS2o8u4oUq9X8g8il5Ni8WBooDZbMdstlNU3J7eTI0nRAbqMWi0aJ0aVFYVSo2CvcyNq8KNxqlG69SgdarROtVoXGpUqAiOMzTWMhwSSPQwE5GDAtEHSjAUv4+iKOxe/Tk/vXE3lUUHAIhPm8Lc6/9GfFrbPQH9xTv/eJn1yz1Tfk47/w+cceEf++zuNMv3FPD4V2tYd7AIgFCjgdvmjOP649MJNHT+SnaXy01VtZWqagtVVZ6vpqGvptZGdW09eeUlWM0uopyX0zCQWAK8xsajeh2tVu0X9FoEwWADIUGNZWCCgwxH3VPVFsshMzueXc+B/9uG27tAIm5uMqMfmUrkxO5fINRXadRqXnz2HN/1tsREB3HczMH84/VVXHX5ZJKTwikrM1NdY2X61JRWHxMQoOOUk9J454ONBAUafItebHYnJx7vWQX94X8zGZwaQUJ8GE6nm42Z+cTHe8pGzZo+iI+XbOHlf63kwnljqam18ebb65g9c1Cbw9HRUUHs2FXM9KmpvrmpXanfBMa+RKVSYTTqMBp1fhNfj8TpdFNn9gbL2rZ7NWtqbdRUWzHX23ErCg6Hi4rKeioqW9lKKdT71bydQGCgnuBgAwVBBoI1BoJLDQRZDAQfOHyvZkO9KyHaUpy9lR9eu4PsLT8DEBw5kBP/9Axjjr8EVS9ZYdkdTjn3PPZu38bF19/ImAl9Z3iqqc05JTz21Rp+2ZUHgEmv5Ybj07l1zjjCTO3/I2azOb1zyes9gbCq3jMaU2Whqtp76Q2HRzf3T4cGT2BVq1W+aT9Nh3yDgwMIDTG0WvKlu6tnWEvr2fX8Rvb9cwsuqycoxhyXwOhHpxE9vX/3uHcVk+noa/Jee+VU3v94E//31lpq62xERQYy7+y2F9pdetF4FEXhpVdXYLU6GJQayYP3zCEo0FPjUadV895Hmyktq0Ov1zAiLZbbb5oFgMGg5YF75/Dvd9az8OFv/crqtOWiCzJ47c013HLnpzgc7i4vq6NSlI5Mte08y5YtY/HixWzcuJFDhw7x2Wefce655/qd88orr7B48WKKiooYO3YsL730EpMnN/Y+pKamEhERgVqt5vbbb+fSSy896tevqakhNDSU6upqQkL63lZiLoebylxL484n3pqGFTn1KC5QUHCrFZxaF06tG6fWjToEDHFadBFqVMHgNig41C7qbQ5fGLVYW5+jeDT0ek2bw+OtrTQPNOlRq4+didjHsvqacn5792E2/u9fKG43Gp2BaeffxYwL7kVv7P9bNbrdbnL37yNl6DDfMYfDga4PFuLeXVTBE1+t5astnt5hnUbNn2aM4q5TJxAb4r87i6Io1JntVHmDYEPoa3rZEAgtlqP//aNWqwgNDSA8zERYaAChIQEYTGrW16xiU81qnHoL4aGB3DHpRo4fPB2Tsff+rrFXWtn1wib2vpyJ0+z5HkROHcCYR6cRe0JiD7eu98nPzycxMZG8vDwSEhJ6ujn9Xo93BZnNZsaOHctVV13FvHnzWtz/0UcfsWDBAl599VWmTJnCiy++yNy5c9m9ezcxMZ4VRitWrCA+Pp5Dhw4xZ84cxowZQ3p6ene/lS7ldilU5Vn8ViSX7aun/GD94Xc/Cdb4Fp1EDTERNSyQ6CGBmCKPXCDV4XRRV2f36730Gy5vZSi9zuyZ7G23uyivqKe84ug3hlepVAQFtrXS3NDqSvPOGPYR3cPldLDx21f57b1FWOsqARgx4wJOuupZwuNSe7h13aOutob//P1FdmZtYcFjTzIozbOopa+FxZzyGp75dj0frduN4lYIQMWZI1KYN2YIejcsW7qvRRisqrbgdB59HUC9XkN4mJGwMCPhYSbP9VBjk2Oey+Bgg98w4y8Fv/Dg2ocoUhVDKFw+7HLuzriTQF33bS/YXo4aG3v+nsnuv23CUe2ZNx4+PoYxi6YRNzf5mFrVLHqvHu9hbEqlUrXoYZwyZQqTJk3i5ZdfBjyfzhMTE7nlllta3bv67rvvZtSoUfzpT39q9TVsNhs2m813u6amhsTERMrKynpFD6PiVqg5ZPOEwX31lO2zUL6/nooDFpy21n/Z6oxqIoeYiBxs8gTDIUYih5i6ffcTRVGwWBzUeoOmZ/jce71h4Y+5yfU6O3VmW8t6Yu1g0Gs8tTCDvGEzsMl172VQYJPrQZ4dBXprD0N/dXDzjyx9827K8nYCEJ0yhjlX/4XkMcf1cMu6T87+ffz7xb9QWVaGTqfj0htvZty06T3drMOy2Zze4V8r1dUWqqqtFJTUsHpXIXlF1ehcKvSKCoPSvukDQYF6QkMDPKEv1EhYWABhoUbCvMfCQo2Ehgag0buxuW1YnVasLs+XxXvZ9FjT27uqdvFd/vcAJAcl8cSkJ5gU3XuH+Z1mBwf+tY09f92EvcLzdylkVAQjH5rMgDNTJSgeQUFBAampqdLD2E16dWC02+2YTCb++9//+oXI+fPnU1VVxRdffIHZbMbtdhMcHExdXR3HHXccr776KpMmTWr1NRYtWsSjjz7a4vj777+PyWRq5RFdQ1HAWa3CVqzxfqmxFWmwlWhQ7K3/klBpFfQxLgLi3OhjXRhiXRhi3ejC3Kj68JQvl0vBZlew2z2XNpv30u7GZmvtuOfY7/nJ1elAr1Oh06nQ61To9Sq/2zrvbd8xvQp9k8dI4Dw6tupCCle/RW3uegA0ASHETbyEiOFzUKmPjd5hRVHI37ubnevXorjdmIKDyTjuRILDu78MiqIo2B0KFouCxapgsbo9lxZ3422LgtXqxtGez3EqBZ3BjTbAidrgRB1gR2WwQYANxWDBZbDgMphxGMw4NTYcigM7DhyKAwf2lrdxotCx/+AqVBynm82p+rnoVb1zL3nF7sb1Yx3Oz6qg2tMRoBqoRXthOOppJlTy++Wo1NfXc8kllzB48GD0ej033XQTN910U083q9/q8SHptpSVleFyuYiN9d+iKTY2ll27PNs4FRcXc9555wHgcrm49tprDxsWAe677z4WLFjgu93Qw3jKKad0SQ+joijUlzs8PYb7PXMNy/d7vmy1rZdrUGtVRKR6egmjhpiIHOypayi7nzRSFIV6i8NXtqiuSQkj/17Mxp7M2jo7Npvnr6DDAQ6HAh38o2QwaAk06TCZ9JhMOgJNeoxGz6XJezzQpMNkbHbbpMdk1PXpnROOhq2+hlUfP822r1/C7XSg1miZcMafmXHhAwQEhfV087qNzWrl4zdeY8fa1QCMmTiJS2+8CaOpc4ZHFUXB4XZgttdTWlVLWWUt5VVmKivNVFXbqKm2UVfrwFzjpL7WjdWsoLiO/neIW+PEoXFgcWuxqcCmUnAG1KBE70QJz8NhqMNhqMOprwfVUfxfUoB2DihoVBoCNAGeL20AARoDARqj/6XWcxmoDeTMpDNIj+ydU5LcdhfZb+9k17MbcRZ6ipibUkIYcf9EEi8ahlrbhz/594CCAs8OUL/++qv0MHaDXh0Yj8agQYPYsmXLUZ9vMBgwGAwtjut0ut89j8hS6WjcEm9fvW8nFEtV27ufeIaRA337JocnGdHo5BfHkej1esJC2/eH1+FwUV9vx1zvwFxv91w326m3eC7N9Q7v/fYmlw7vfY2B02ZzYrM5qWhHDc2mdDoNgSY9gYHeQGn0XA9sEkA9IbPhnMbjgSY9er2mVw5XKW43W356i5//cz/mqhIABk84lVOufYGoxL5bhLq9HG4HVqeVlSt+ZMOK5ajUaqacN4dhJ4xnszkLa7WlcYi16ZCr04LNZcPi9N5vs2OpUbDWgaNehbNOg7tej9uiR20xorYa0doC0doDUXE0Pw+ec5y6ehwGMw5DrSfweYNfw5ddb8Zck0Rt4QQUZyDgRGMsw5S0Cl34ATRqNUaNEZM2gABNGEZNHAZtAMZmAc6oNfqFPaMmAIMmwHu84TzPcU8YbPY4bQA6dd+a39kat9NN9ns72fHkOszZniLmxoQgRj0whdQrRqDu5x8gu0pf3o64L+rV3+2oqCg0Gg3Fxf57PhYXFxMX13M1qKy1zsZQ6C1wXbbXjLnsMCv7VBCe5N39xFvLMGqoiYhUE1rZ/aRb6XSadtfNbMrpdGOxNAmcDWGz3o7Z3BhCG8KmL4B6z2vYGcjhcHlqx1V3LHBqNGpPkGzo1fSGzUBfb2azwGnUNTlHT0BA55cJyd2+gu//dTtF+zcBEBE/jFOufYGhk07v1NfpKIfb4TffzdIQ1JwWrC4bFpcFm7PpPDnvce+l1WXB6r3fdoTHuxTv6IECI1IHUJRQzXfurbDUc0zjCEBnC0ZnC/J9aZtc19li0dkC0TgbS9LovF+Ho6jcOA1m3AEWlAArapMdtcmBNtCFPtBNQBAEBKsJDNZgMgRg0BgwasMJ0AzwhTODysD63Q7+u7KSmmrPz+rAcCM3nzyc8yYMJki3AIPGgE595IVzAtwuN3kf72Hb42up21cFQECciRH3TmLw1aPRBPTqP8FC+OnVP616vZ4JEyawdOlS3xxGt9vN0qVLufnmm7v89e1mF2X7vTufeLfEK9tXT22R7bCPCY337n4yzBsMG3Y/MconyP5Aq1V7a7N1rECqy+3GanE268FsEja9PZ3Ne0F9AdTsQFEUXC43NTVWamqsHWqHSqXy67FsCJMmo65JT6e+9XNMnhqiDStTq0tyWfrvhWxf9iEABlMIsy95mEln3oxG1zVzyCxOC+XW8sYvWzllFs9l4/EKKmwV1DvMWF02nErHF1cdFbcKvTWI1H1DKYt2o3EGorcGY9WHEpMfTPz+YLTWQFTWAFTuo/99oNGBKVhNUIiO4BAdIaEGwkKNRIQHEhUeSExEKNERIUSGBqHpYMFvRVH4OusgT361hl1FnhXscSEm7j5tEpdPG4FeK7+/2kNRFAo+38/WR9dQs8Oz240hysjwuyYw5IZ0tKa+32sqjj09Hhjr6urYt2+f7/bBgwfJzMwkIiKCpKQkFixYwPz585k4cSKTJ0/mxRdfxGw2c+WVV3ZqO4p31ZJbZKFsrzcY7q2nuuDwf4yDY/Xe7fAah5KjBsvuJ6JtGrWnZzAwsGNBqmGXoMYezMYh88aezsbA6T+87jnP5XKjKIpvx6COMhq1aBUrzvoi1K5U1EG3ETMgnphRE8l2hVH6497GXk5v4GwIpCajHm2T+VpOt5MqWxXl1nLKfKGvjHJrRYtgWG6toN559CWbmlOh8gyDapsNj2qMzYZNPUOpesWE2hKAymJEsepwm/U4zBocZjXWOrDWKdTXuDCbnb4psSEFR25HUJC+SZkY/7IxTUvHGI1d25v36648HvtqDZtyPFMIwkwG7jh5PNceNwaTXoJNeyiKwqH/ZbN10WqqMksB0IUZGH7HeIbenIEuuHcuwhHiaPT4Kulff/2VE044ocXx+fPn89ZbbwHw8ssv+wp3Z2Rk8Pe//50pU6Z0yus3FO5+ZMg3BGhazocLjNJ5h5ADiR5i8oXEgJAez9pCtJtnlayLerP/kHljD2ZjCD1cKLXbO7a3bgtaJ26dHafWgl1Tj0tnxaW14tLZvJdWnM1uu7Q233k6nYaogCgiAyKJCogkssVXBBGGCAJ1QZ55cd5AqFd7/mh3RRFpFDcalZ3omFAGDoxqGQbDjb7rPb3waUN2EY99uYZlezzpNlCv5cYTM7j5xAzCTC3neYvDUxSF4p/z2PrIairWebZF1AbpGHbrONJuH48+TL6fXUEKd3evHg+MPa0hMD494XsSh8d4gmHDvslDAzGFyydsIQDsLju7d/zKb288ROm+XbhUJpSQAbgnzqQ6Mpoasw1zvR1rvROb1YXKoUfjMKBxBqBxBKBxGjyXrs7546nTqf17MJsOoQc2zNXUUWe2+YJgZxWRDvPuLBIaGsDBnZtY9+t3qF31JCQN4Lq77yFmQO/dvm1HYTlPfL2Wb7MOAqDXqrlq5mgWnDKBmJDuKy3WX5SuKGDrotWULvMEb41Ry9A/j2X4nRMwRHVsrrQ4OhIYu5d0k3nduHQKoaGtbKgsRD/lVtzU2GtaDPeWW8sps5b55gGWW8upqypi6IYchu6vRQUoGhXbR+nYPrwal/Z/nicM8X41EagNJDIgwtfrF2GIJFIfSQiRBClhGJUQDK4gdE4jKrsei8XZbB5ny17QxoVDbqqrrVRXd2weJ/y+YeF6cx1vv/R3sjasQwtMPf4E/njtDehbqcLQG2SXVfPUN+v4ZMMeFAXUKhWXTB3OPadNJCmi5zct6GvK1xexbdFqin7MBUCt1zD4ujGMuGcixrjeu6uMEB0lgdFLVvyJ/sDqtDYJf/4LQHwh0BsMK6wVR1wIonYpjNhTzbStFeidnsGIgsFRlMxMJyQygTObDAH7DQsbIokIiMCo7fweloaFQ42lkBoXDjWfx2mxOgk06bpkWNhht5O9by9anY4Lr7qWGXNO7pW/R4qqzSz+bgP/WbkDp9vTq3rOuME8cMYUhsWF93Dr+p7KzFK2Pbqawm88PbQqrZpBV45k5MLJmBKDe7h1QnQdCYxC9GIut4tKe1Wz8NfQI1jhXRjSGArNTnO7XyNEH0KkoXHen6cnMALT/jyqv/oIW4lnlWfM4HGcdsPfSBo5s7PfZrs0XTgUHdVz7QgNj+Dau+5Fp9OSNHhIzzXkMCrNVv7202b+9WsWFu+2LSeNSOKhs6aQkRTTw63re6p3lLPtsTXkf+pZpKlSq0i+bDij7p9C0CAZnRL9nwRGIbqRoiiYnWa/4d7mvYFlTa5X2irbvUWaXq0j0rsYxBcCDf4LQhp6A8MN4Rg0/kOopbk7+OH1BRzY9AMAQeFxnDj/KdJPugKV+titG2q32fj4zdcZnp7BxBme0Dx4eO8rRl5ns/PqL1n8felmaiyeVfBTBsXx0FlTmTk0vodb1/fU7q1k+xNryflwt2cVvAqSLhzGqAenEpImPbTi2CGBUYjfyeF2UOEX/ipa9AaWNZknaHMdvo5na1SoCDeEeXr+vMO9TXsDG4aAo4xRRAZEEKQN6tDQqKW2kmXvL2L91/9AcbvQaPVMOe8OZl54PwbTsT3UVlZcxGuLnyU/+yCbVq9iZEYGpsCgnm6WH5vDxb9XbuMv32+ktNZTEH5UfCQPnzWVU0Yl98rh8t9DURTyynPIyt1EVu5msnI3sadoF/HhCYxLmURG8kTGpU4iJWpQh967ObuG7U+tJfudnSguz4e2hHMHM+rhqYSN7sGubSF6iARGIZpRFMWzGKSVeYBNewQbQmC1vbrdr2HSmlrt+WvtWLghHK266/6rul1ONn33Or+++zCWGs/wc9q0c5lz9WIiBgzustftK7LWr+M/L72Ipb6eoJAQrrr9zl4VFp0uNx+u280z364jv7IOgEHRodx/xmTmjR+KWt0/gmKdtZZt+Vt84XBr7mYqzRUtzsspO0hO2UE+3/AxAOGBEZ7wmDKJcSkTGRk/Bp328PUQ6wvq2PHMOg6+uR23wzPnc8DpKYx+ZBoR42QoXxy7JDCKY4LL7aLcVk5xfUmTFcAN8wA91yuaHHO421F7D9CoNEQYIlrt+WtxLCASk7Z3lC85uOVnfnjtDkqytwIQnTyKU679K4PGzenhlvU8l8vFVx++zw+fLQEgdVga19x5N+GRvaN3SVEUvszcz5Nfr2NPsWd3loFhgdxz6iQunTYcXQd3fekN3G43B0v3kZW7mS05G8nK28z+4j00rwKn0+gZGT+G9KRxpCeNY9jAkeSVZbM5ZwObs9ezLW8LleYKftnxA7/s8EyxMGgNjEocy7iUSYxPmcTYpPGEmMKwFpvZuXgD+/61FbfNU2s09qRERj8yjaipA7r9eyBEbyN1GL11GKurqwkJkdISfZHVaaXIUkxxfRFF9UUU1RdTbCmm2HtZVF9MiaWkcX/foxSkC/LO94s6TG9gY49gmCEMtarvzO+rPHSAH9+4m92rPwPAGBzBcZc9yoTTrketkc+RLqeTl594lN3bPEH6+NPOYN4Vf0Kr6/m6rIqi8PPOPB7/ag2ZeZ7dRCICA7jjlPFcM2sMRn3f+/errq/y9Rx6eg8zqbPVtjhvYHiiLxymJ41n+MCR6LWHL2Nkd9rYWbCNTdnrycz2hMiq+soW5yWQSOz2SOJzB5BYGM/gMcNJXzSdmOOktl9vJnUYu1ff+80ijhmKolBhq/ALfg1B8FB9ke/40Q4Jq1VqogKiiA6IIqJpEGzRG+gJgs0Xg/QHtvpaVn7yNGs+fQGX045KrWHiGTdy3KWLMAZH9HTzeg2NVkvioMFk79vLpTfcxMSZs3q6SQCsPXCIx75cw8p9hQAEGXTcdGIGN52YQYixb2w753Q52Ve8my05m3zzD3PKDrQ4L0BnZHTiWF84TE8aR1Rw+4aE9VoDY5MnMDZ5Ahzn+Z2SXbqfzdkb2Lh3Deu2rqJYKSKfPPJH5bFxVCYAMSFxjMufyLiVk8hImciwuBFo5YOUOMZJD6P0MPYIu8tOiaXELwQW1Rd5ewqLKbYUUVxfgt19dHsdGzVGYk2xxJpiGWCM812PM8b6rkcHRHfpXMDeTHG7yfrlXX5+6z7qKg4BkJoxh1Ou+ysxyaN6uHW9g6IoWC0WjCbPdAGXy0V5SQkxA3p+OHJbQRmPf7WW77dlA2DQarh6lmd3lqjg3r2bSFltSZPew81sy9uC1WFpcV5y1CBfOBybPJ4hsWldEtIcdXb2vrKF3S9sxF5po85kpnJaLXWnOdij3sOugm043f71SU36QMYmj/ctpElPHIfJIMW5e5r0MHavY/Ovp+gyiqJQ66ilyBsAiy3eIeJmvYMVtpaT1Q8nwhDBAJM3BHoDYJwpljhTHLHGOOJMsQTrgvvdKtDOkr9rDd//63YK96wDIHzAYE6+5i8Mm3KWfM+8LPX1vPuPl6mqKOeO/2fvLMOjOLswfK9v3F2BkIQIwQlQihYqUErdoaXu7i11L1CXr0op9VJ3vEBwQowkQNxd1mW+H7tsEhI8nrmvi4vdmXdn3s3aM+c95zlPPYtcoUAmk/W4WDxYVc/zv27j+125CALIpBKuSB7GA2eNIdSr91WuG80G9pVmtqpc3k1pXVG7ca4qNxLDR9gjh6NIDBuBp0vXWtSYdWYOvL+XrFd2YKiyCVb3WG8mPnE2oedHIbEXB+mMOtKL9rA7fzu783eQWrCTZkMTW3I3siV3IwAyqYyYoDhHIc3IyLH4uQd06fxFRHoaMcIoRhiPG7PVTJW+yib+tBWOaGCbyKC2Ap2lffSgI5RShV0EBjqigYGthGGgcwB+Tn79cmm4O2isLmHNpw+TtnYFAEonVyZf+hjj5t2JXCH+TQ9RWlTI/155iYrSEmRyOXc88RRD43o26lpa38zLf2zn8y1ZWKy2r+jzR0XxyDnjiQrw7NG5HUIQBMrrS9uIw6zSdIzmtrZREomEIQHRJNnF4fDwkQzyi0LaTZ6eFoOZgx9nkPnidvRlNmN71yEexD+WTPil0UhlR5+HxWphf3m2o5Bmd952yhtK240L9Q5nROQYRtktfQb7D+225zhQOdkI438v34CxuZ7pT3/ThbPrf4iCURSMAGhMmpY8QUd0sG3eYJW+CqtgPa7jeSg97KLPFgFsLQIPbfdSeYkRri7AZNCRsmoJm755AZNBCxIJI2YuZNrVz+HqHdjT0+tVbNuwnpXvv4PRYMDTx4fr732AQdExPTafmmYdS//exYcb09CbbEVas+IjeHTOeJLC/HpsXmCLvGWW7HWIw70FO6lqqmw3ztPZi+ERLeIwITQJV3X3R0OtJgv5K7LIeG4b2kJbAY1zuBvxj44j8sphSE+hLWRZXYlDQO7J30FOeVa7Cm53Jw9GRI5hpN3SJz50OCqF+pSek0hbjiYY/3v5Bg78bbtYlsoVuPiHMeSMy0m8/AHMOg0goHT1PO5zfTbTmWlPfUX4pHM78RmcHN9dEUvc+bcRd8Ft3XpecUm6n2MVrNTqaynX2UXgYVXEh6KDzabm4zqeXCLHz8nPIfwOCUHHkrFzIAFO/l3SQ1jk6AiCQNam71n98QPUV+QDEDpsIrNvXEbw0DE9O7lehslk4vtPP2bDX38AEDs8iWvuvAc3j55p8dakN/LO2lTeWr2bJr3N0mnCkCAen5vMxKjgbp9Pa1PsVHvlck5ZFhZrW6eBQ0uzh8Th8PBRhPn0rEm41WKl8KscMp5NofmArSBOHeRC3MNjGXxNPDLVqf/sBXmFEOQVwtkj5gHQpGtkb+Fu+zL2dtKKdtOoa2BD1mo2ZK0GbBZA8aGJNlPxyDGMjBjb5cvwA52QsWcw6f73sZiMFG/9k61v3o1UpiDx8vt7bE5WswmpvOfdFk4GMcLYhyOMBovBLgIrWhWLVLRYy9ijgsfrKegqd3GIvkD7UnHgYXmDPiofZNK+6+/WXyk/mMrfH9xFQdp6ANx9Q5lxzUvET7lUjOJ2wPK33yBl7RoAzr7oEs6+8GKkPeBbqDeZ+WhjOkv+3klNsx6AxFBfnpibzMy48G577Rym2K0qlzuyn/Fz82d4xGiHtU1cyHCclL3j4lCwChT/sJ/0p1No3GfLkVb5OTHsgTEMuWE4cqfui4+YLCb2lWawJ38Hu+wisra5ut24wf5RjkKaUZFjCfXufx15ugpBEDiYmUpUwsgjRhgPX3b++8G5mLVNuIdFt9n35z2z8RqcgEypJvePT5HKFcTMuY4RCx4DbBE9TUWh4zguAeFc+MU+AAo3/ULq589TX7APZ58ghsy6guFXPOiwJ/tspjPj71hGyfa/Kd+9jviL7mLEgseO+jhBEEhd/hz7/1yOrr4Slbs3EZPnM/621/jzntlU7N3Y5rku+Ffb+X/gDhAjjL0QQRCoN9bbI4AVlOvKHbdb+wvWGdp/oXeEBAm+al9HfmDryuHWeYOuit7TvULk+NA0VLHu88fZ/deHCFYrcqWaCRfcz8QLH0CpFqs4j8Ts+ReQm5HOpdfdSPyo0d1+frPFysqt+3jp9+2U1Nui+1H+njw6ZxzzRkR1aXcWq9XKwcrclsrl4zTFTooYTYBHUK8TNIeEYsZzW2nIsHUqUnqpiLlnNENvTULh2v12QwqZgsSwESSGjeCqydc5Ira787c7PCHzqvZzsNL274ftXwHg4+rnKKIZGTmWmOA4FLK+GY06VQRBwNBYS3N5Ps0VhTSX5dNcUWC7X15ou605MaEkV6ox2LtZHc6Bv78g7sLbOefN9VRlbuW/V27AP2ECwaNnMOftjXx9YQST7n+fkLFnILEHTSrSNvHfS9cz7rZXCUiYRFPZQbYstS0Tj7j6UcexU5c/z6jrnmbcza8gkcmP+biCjT+S+f1bnP7YZ3hGxKGrK6fugM0TdtqTX/LzjeOJPudaos++5oT/rqeCKBi7GZPVRJWuqpWPYNsqYlvuYOVx9xtWy9RtRWC7vMFA/Jx8UUgH5pdOf8ViNrHj17dZv/IpDBrbslvc6Zcw45oX8fSP6OHZ9T6sVisH92URZS9mCQgO4ck33kEm796vQKtV4Mfd+3nu160cqLK9biGerjx49lguHx+L/BgFGCdDvaaOtKLdrVrqHdsUOyliNDFBw45qit3TCFaBou9zyXhuG42ZNhGgcFcSfcdIou8cidKj98xdIpEQ7htJuG8k88ZcBECdppY9jkKaHWSU7KWmuYp/0//g33RbqoRa4URi+EhG2QXk8PCRPZIP2lUYmxvaisDyfJrLC2z/Kgowadu/T9twnEVFgiBQtmstJTv+Zdh5N6NvaB/t9Rqc4BB57qFRZP30HmW71hE8egZqT1v+sNLVA6dWeeCpy58n8dJ7iZp1JQBuwYMYsfAJdv7v0TaCcdD0ixl65tWO+5tfvemoj9NUFuHkHUDwqOlI5QpcA8Lwix0LgMrdG4lUhsLJrc1cugNRMHYSgiDQbGpuFw08fMm4Rl+DwPFlAXirvOxiMNCxJHx4RbG70r3XXfGLdC37d/zB3/+7l5pi25JI4JCRzL5hGeEJvcNcurfR3NjIp28sJWtvKrc/tpjY4UkA3SoWBUHgn8wCnvllK2nFth8rH1c1984ew7WnxaNWdM5czBYzueX72vgeHssUOyl8NInhI07YFLunsFqsFH+/n4znWwlFDyXRt48k+vYRKL36RmGJl4s30+JmMS1uFgB6k56M4lRHIc3ugh006RrZfmAz2w9sBmzNB6KDhrXqjT2WQM+e9wk9EiadhuaKAjTlBTS1ixQWYGw69iqZk08groGRuAZE4Bpo++cWGIlLYAT1RgkMGnzExxan/MEXc/ywWkwIViuDp19C0tWPsvXNu9uN9Rqc0Oa+s3cg+vr2RV2tqT2YRmXGFvaufNmxTbBasBj1mPVa5Gqbp6tvzKgTelzk6eeT9f1bfH9VHCFjziBk/GzCJpzT4124RMF4HFisFqr11Y6q4SPlDWrNxxceV0gVbQpGAh3Lwy2RQX8nf9FORqQN1cXZ/PO/e9m/43cAnD38mL7gOZJmXtMj+Xd9gfzcHD587RVqq6tQKJU0Nx0jYtEFbN5fyjO/pLDlgM0w3V2t5LYZI7h5WhJu6lNbLj1RU+wke/VyV5lidyVWi5Xi7+wRRXuOosLDHlG8fSRKz779falWqBk9aDyjB40HWlIHDhXS7C7YQUltEftKM9hXmsFXWz4DIMgzxFFIMypyLEMCorstz9xiNNBc2UoElhW0ihgWoK+vOuYx1J5+uASE4xYY6RCErgERuAZF4uIfhlx15BzZxuLiox47cMQUku98HalcibNv0FEFV7tCFImkXZrG4Zh1zYxY8Bjhp81rt0+mbLlwOSQcj/dxLv6hnPdpKmW71lC6cw1b37iLjG+WceaSv3u0YKZvfWN0IdsqttNc02yrIm5VQWwrHKk+7j7E7kr3dvYxbfMGA/FWefWpvsMiPYu+uZ6NXz3Dtp/fxGoxI5UrGDf3diZf9jhql56p6u3NHMzeR8buXeRmpHMwJxurxYJ/UDDX3/cAIRGR3TaP1KIqnv0lhX8ybcnyaoWM609P5O4zRuPteuJRsDam2AW23MPSuvY/mD1hit2VWC1Wir7NJeO5rTRl2yJSCk8VMXeMZOhtI/q8UDwSUqmUqMAYogJjuCjZtnRZ2VjeppAmuzSTsvoSyvaU8PueHwFwU7szPHyUo5AmPjTppAuTrGYTmsriNiLw0L+m8nx0NWXHPIbS1bNFCB6KFAZFOCKGCqeuy52Xq51xDxnSKceSyhUIlra2ct5DR9BQlHPC5ziex8lVToRNOIewCecQO+9GfrxmBHV56fgMHYlMrkSwHp8m6UxEwWjn+nU3IHU+soiTSWT4tSocaRGBga2EoT/OcucjHkNE5ESwWizs+edj1i5/DG2D7Up96Lg5nHHdq/iERPfw7HoHJpOJgv25DIkd5kjNWPf7b+zY1FJFODJ5AlfefBtOLt1TBLS/op7nftvKql37AZBLpVw1YRj3nzWGYM/j+3FsbYqdao8eZpWkY7K0bZUpkUiICohp02+5O02xuxKrxUrRNzlkPL+trVC80y4Ue1GOYnfh7x7IrOFzmDV8DgAaQzNphbsdhTSphbto0jeyKWcdm3LWASCXyhkWkujoSjMicgw+rr6A7TtGV1NGU3l+q2XjlkihtqoYwXp071252sUmBB0iMBLXwJaI4Yl4HfZmXAMiKNu9Fv+EZKQKFSo3L5KufJjVj12Ai38YEafPRyKRUncwjbq8DEZd++QRj3Wsx+3/63MEqwXf2LHIVc4c/PdLZConXPzDAXAJjKBi739ETrsQmUKF2sO3W/4GomC0E+keSYhPSLvK4UORQl+1r2gnI9JtFKRv4K/376Li4B4AfEJjmXX9EqLGnNmzE+thzCYT+ftzyc1IJyc9jYM52ZiMRh5f9iZBoWEAJI4di0QiITohkeiEBPwCuyfHq7iuiZd+387KrfuwWAUkErhw9FAePmccg/08j/pYnVFHRvFee1GKbYm5I1NsLxdvEluJw54yxe5KrBYrhV/nkPn8NppybEJR6aUi+s5RDL01aUAKxSPhonIleehkkofa8pfNFjM55VmOjjS787dT1VRJWtFu0op2s3zjBwD4CkpCdQKBNQ0ENRjwMlg4Uia8TKm2LxOH28XgoUhhOK5BkajcfQZEHv2Ym15g+3sPkfP7Jzj7BnPhF/sIGXsGM579ntQVL5D+9RKkMgUe4dEMPWvhUY91rMcpXT1I+/I1tr/7EILVgtegeGY88x1qDx8ARi54nC3LbueHqxKwmgzdZqsj+jD2YR9Gkf5HfWUB/370AFn/fQuA2sWT069YzJhzbkHWR81eO4PstL38tep7Dmbvw2ho6yDg7unJ1bfdSdyIkT0yt+omHa/9vZOPNqZhNNuiMWcmRPLY3PEkhLS/8hcEgcKa/FYt9fqOKXZXYjUfEopbacqtB0DprbZFFG9NQuEuCsXDsVnP1NhzCFtXGdvuN5UXUIeRElcFJa5ySlwV1HTgR+lskTBY6ka0WwjDA+OJjxyNV0gUrgHhOHkFIOmlEeuTbQ0ocnKIEUYRkV6AUa9h87cvseWHVzEb9UikUkadeQNTr3wa525abugNWCwWig4eICc9nZjE4URERQG2yOK+vakAuLp7EB0fz9D4BKITEgkMCe0REdWgM/DW6j28uzaVZoPNHH9SVDBPnJvM+MEtUc1mfRPpRXvaFKf0NVPsrsRqtlLwZTaZL2yjeX89YBeKd49i6C1JKNy630exN2FsrnfkDDqsZ1otG5t1R+/S5SmVEqwIYJqnLXcQnwBK1LDfVEd2QwGZFVloMZJOI+nNjfywPwtV/i8khI1wLGMnRYzG3UnMlx7oiIJRRKQHEQSB9PVfsvrjB2mqKQEgInEqs29cRsCg4T08u67HarFQnJ9Hdno6ORlpHMjKRK+zVfnOOu98h2AcMiyOixddT3R8AkFh3dcBpSN0RjMfbkhjyd87qdPaop0jwvx44txkpkSHkFe1nx+2rT9uU+xDlcu90RS7K7GarRSs3Efmi9to3m/zpFT6qIm9exRRNw8coWjSNTt8Bw+vMm4uL8DYXH/MYzj5BLWtMm5VYOLiH3rUylqj2UBWSbqjkGZP/g7qtXXszNvKzrytQEuu7KFK7BERYwj26pkLNZGeQ1ySFpekRXqI0pzt/PXB3RRn2TzWPAMimbnoVWInzh8QX8S11VU8d+9d6DSaNtudXFwYGhfPuMlTGDVxUg/Nrj0mi4UVW7J4+Y8dlDXY5jzUX8nFo51xkpext3AX6UWpRzXFPiQOe7spdlfiEIovbHP0elb6qIm9ZzRRNw3vd0LRYtS38x9siRgWYOjARPpw1J5+HVcZB0Xi6h/WxsLlVBEEgfyqA45Cmt352ymsyW83zt890CYe7Z1pooOGdXuev7gk3b2IglEUjCLdTFNtGWs/e5TUfz8FQKF24bSLHyZ5/j3IO/GLvzdgtVopLSy0FalkpOHh5c2l198I2H6YHly0ELPZRNSwOGISEomOTyQkIqJX+UparQLf78zluV83U1J3EJW0FC+nCrydqqnTlLQbf8gUO+mQrU0fMsXuSqwmC/kr95H14naHUFT5OhFzzyibUOyBFn6dgdVsormyqKXKuFWnkubyfHQ15cc8htLNq6WoJMhWUHLInNo1IAKFU8+2+axpqmJ3wQ5bIU3BDvaVpGO2mtuMcVa6kBQxyuEJOTxsJM6qrp23KBi7F1EwioJRpJswmwxs/XEZ/339HEZ73tHwGVczfcHzuPkE9/DsOo+yoiKy0/faRWI6mlZm2R5eXjz/wceOCGp1RTlevn7IepFAPERVYwWfbvyTr1JW06Q7gFJajlRiajcuwnewPXI4ss+aYnclVpOF/C/2kfnidjQH7ULRz4nYe0Yz5MbEXi8UrRYL2urSVrmDbQtMtNUlx7aecXI9bMnYZj1zKGKodO1b+YE6o470oj2O3th7C3a1i6zLpDJig+NbdaUZg597QKfOQxSM3YsoGEXBKNLFCIJAdspP/PvR/dSVHQAgOHocZ974OiGx43t4dqeGIAjUVlfh49cSQXvhgXsoOtjSjk6lVjMkdpitSCU+kcihQ3vdkrstjyvDYWuz7eB2apvbR4Zc1W4MDxvpsLbp66bYXYnVZCF/RZZNKOY1AqDytwnFqBuHI3fpHVX/gtWKrra8fZVxeaHNn7CyCMFiPuoxHNYzbZaNIx1RQ5W7d697z3cmFquF/eXZjo40u/O2U95Q2m5cqHe4QzyOjBx7yp6homDsXkTBKApGkS6kMj+dvz+4m7zU1QC4egcx45oXSZx6Ra+1qjgagiBQVV5GTnoaOen2CGJzE69+ugKV2rac/stXK8nLySY6PoGh8YlERkV1a9/mYyEIAmX1Ja1sbTo2xRYEsAj+RAUlcvG4GUwYOq7fmGJ3JRajTShmvbgdTX4roXjvGKJuSOx2oSgIAoaGars5dWGrZeNDkcICrCbDUY8hlStwObRk3Gq52M0eKVR7BfRrQXgylNWV2MSjPRcypzyrXfGXh7MnSRGjGWXvix0XkohKcfxpOaJg7F5EwSgKRpEuQNtYw/ovFrPz9/cQrFZkChXJ8+/htIsfRtmFrbC6in17U9mydg25GenU19a02adQKrnn6ecdFc29Da1RS2Zx2jFNsWUyV5oNARiswZiFUC4eN42HzplMoEfP5o/1FSxGC/mfZ5H54ja0BbblSXWAM7H3jmbIDYnInbtWKFotFsp2rqa+cF+7AhOzXnPUx0qkUpz9QjtYNrbddvIO6lV5tX2RJl0jqYW7HIU0aUW70Zv0bcYoZEoSwoY7lrFHRIw5agT/eARjasFOFrx7AZNipvL2NZ925lMacIiCURSMIp2I1WJm5+/vsW7FYvTNNq+92EkXMPPal/EKHNTDszs+aioryMlIZ1jSSDy9vQFY+9uvfPvJhwDI5XIGRcc4fBAHDY1Goex9eWh7C3fz0s9Pklmyt50ptlwqJzpoGIMCEsitdGV9rhST1QOJRMLFY2N4+OyxRPr2rbyynsJitJC/PJPMF7ejLbQLxUC7ULy+64UigLG5gY0vXEPx1j87HiCR4OwT1EYEtiwbR+DiF3JU6xmRzsdkMbGvNMNRSLM7fzu1ze0rxgf7RzkKaUZFjiXUu8W8/ngE4+LvHsBZ6cyq7V/z8/1r8XcP7NLn1Z8RBaMoGEU6iYO7/+Hv/91DVUEGAP6Ricy+YRmRSdN6eGZHp7a6yr68nEZuRjo1lbbo2xU338qkGWcAUFFawrb162wCMToGpap3W8L8vPM7nv7hYYxm21Jja1PspPBR+LoP4a01GXzyXwYmi61g4Zzhg3hs7niGBfn05NT7DBaDmbzPMsl6eUcboTjs/jEMvi4ReQcdRbqCxuL9rHniIhoKs5EqVIRPmotrYCRugRGOZWMX/zBkyt79nh3oCIJAUU2Bo5BmT/4O8qr2txvn6+bniECGOEUwY+ysIwpGrUHD9GfH8OUdv/Lu30sYGjSM66ffBsD2A1tY9MElvHvt57z+54vkVe5neMRoXr78LTKL03j112eobCzn9GEzePKClx0m+v9lr+N/a95gf3kOUqmUpPBRPHjuk4T5RALwzj9LeO/fZe3m8sxFrzFvzEUYzQaW/PYcf6T+gsbQTHxoIvfPWUxCWFKbeX1w/UqW/f4CBytziQmK5+mLX2WQ35BO+mufHKJgFAWjyClSW7qffz68j5ytPwPg5O7DtKueYeTs65D24mrZooMH+d9rL1FdUdFmu1QmI2JIFDPmnNurfBCPB7PFzLI/XmD5xv8BMC1uFg+e+yRBniFIJBLqtQbe/Hc3765LRWu0FTJMiQnlibnJjI7s3ArO/orFYCbv00yyXt6OtshW7a8OcrEJxUUJ3SYUAUq2/8OG5xZgbK7H2TeYaU99jW/M6G47v0jXUttcQ2rBTnYXbGd33g4ySvZitrQ4FVgMVjJfLzmiYFy1/Wu+SfmcL2//lfVZ//LyL0/x6/0bkEgkDmE2PHwU957zKGqFE/d/cQv+HoEoZUruPOshtEYNdy+/gYVTbuTaqbcA8E/a77Ze9YHD0Bo1vP33a5TWFfPtnX8ilUrRGjRojS0pEL/t/pG3/36NL277maGBMbz482L+TfudxRe8TLBXCJ+sf491mf/w2wMb8XD2dMwrMWwkd5/9MF4u3jyz6hGsVgvLb1nV9X/0o9B7f81ERHo5Bm0jG796jq0/LcNqNiGVyRkz5xZOv2wxTm69p3K2oa7OET0MiYhkyplnA+Dt50dNZSVSqZTwwUOITkhkaHwCQ2KHoXbqey3pGrX1PPDl7WzOWQ/ADdPv4JYz7rF9iRtNvL9uL8v+2U2DzhZ1HB3hb+vOEhPWk9PuM1gMZg5+kkHWyzvQFbcSig+MYfC13SsUBUEg8/s32fnBIwhWK35x45m6eCXOPkHHfrBIn8Hb1Ydp8bOYFj8LAL1JT0ZxqqOQZlv21qM+ftX2rzhn5HwAJkVPpVl/HzsOpjB2yATHmNtm38fIyLEAzB97Ca//+RK/P7CRUJ8IAM5IPJttB7Y4BOMZiWe3OcfTF73KlKdHcKAyl6GBMTirXBz+k6kFu3jrr1d49uIlDA2MQWvU8k3KCp656DUmx9pWnhZf8BIpuRv5YftXXDPlJsdxbz/zfsYMTgZg0dRbuPWThRhM+hMqCupsRMEoInKCCFYrqas/Y82nj6Cpt0XnhoyazRnXL8EvfFgPzw6aGurJsXsg5qSnU1FS7NgXFRfvEIwubm7c9dSzhEYOwsnZuaem2ynkVe7njs8WUVCdh1qh5tmLlzBr+ByMZgvL/0vjlT93UNGoBWBYkDePzhnPOcMHiZWtx4FFbxeKr7QIRacQV1tE8dp4ZOru/RmxGPVsWXo7B/75AoCoM68m+Y7XxSXnAYBaoWb0oPGMHmSzIyssKiTipYgOx+ZVHSC9KJWlV9tWG+QyObOHz2XV9q/aCMbowJbvbB9XP9QKJ4dYBPB29SWtKNVxv6A6j7f/fo20ot3Ua+qwCraUlvL6EoYGxjjGldWVcNfy61lw+o3MTpoLQHFNAWaLiZGRYxzjFDIFCWEjyKtsu/zeel6HjP9rm2sI8go5nj9VlyAKRhGRE6AocxN/vX8XZft3AuAdPJRZ1y8hauzZPSY+TEajo+jEarXy5B23tmm3J5FICI0cRHRCArGJSW0eOzQuvlvn2hVs3LeGB1feTrOhiQCPIG6Y+RL5dd7c8vlqNuQUU1xnEzkRPu48fM44LhozFJlojXNMLHozBz+2C8WSVkLxgTEMvqb7hSKAtrqUtU9eSvW+HUikMsbe/BKx590sCv8BilRy5M/xqu1fYbaamfncWMc2QRBQypU8rHvGsa21yb5EIkEha1v8JJFIEIQWY/bbP72WYM8QFl/wEv5uAVgFK+cvPQNTq6VyrVHLHZ8tIiliFLfOuvekntvh8wIc4rSnEAWjiMhx0FBVxOpPHiJj/ZcAqJzdmXzZ44ybezsyRfdWCGuamsjNyiAnPZ3cjHQMeh1Pv/0+AFKplKFx8dRUVhKdkMDQ+ASGDovHxc2tW+fYHTTpDbz22zJ+2PYOICCVRbK7bC7XL89oMy7A3Zn7zxzD1RPjUMpFa5RjYdGbOfBROvte2YGu1Hbh4RTaSiiqeuZnoyprG2ufvBRdTTlKNy+mPr6CoFG9u6BMpGcwW8z8svMH7jvncSZEn95m313Lr+OP1J8Y5HfiNmD1mjryqw6w+IIXHVHOXXnb2owRBIFHvroTq2Dl+UuWtbmYCfWJQCFTsjt/B8FetpxLk8VEelEqV5626ITn092IglFE5CiY9Fq2/PAqm757CbNBBxIJI2ddy9SrnsXVq/uKJHIy0tm7fRs56WmUFOS3M8Ctq6nGy8cXgOvve7BXtto7FRp1RtJKqtlTWElqURV7Ckupqf8SF3kmAM2mJGo1ZwAy/N2cGBHuT1KYHyPC/ZgWG4azUrRMORZmnZmDH6WT9coO9GU2oegc5sqwB8YyaGFcjwlFgP1/r2DL0tuwmox4RsYx7elvcA8e3GPzEendbNi3mkZdA/PHXoKbU9ti1pkJZ7Fq+9fcc/ajJ3xcdycPPJ29+H7rSvzcAiirL+H1P15sM+adf5aQkvsf71+3Aq2xpQDGVe2Os9KZi5OvZOnvz+Hh7EmQZzCfrH8PvUnH+WMvOfkn3E2IglFEpAMEQSBz4zes/vhBGqoKAQiPn8ysG5cRNGRkl55bp9WyPyuT2OFJKBQ2obM7ZTPr//jdMSYgJJTohARi4hMZGh+Pm4enY19fF4sNOgOpRVW2f4VV7Cmq4kBVPYc0skzShK/qB1zk5QhICfK5iGnxFzIi3J8RYf4EeYpG2yeCWWfm4IdpNqFYbsvzdA5zZdiDYxm0oGeFotViZucHj5L5/ZsAhE2cw+SHPkLh3P8i5iKdxw/bvyJ56GntxCLAzMSz+WT9e+SUZ53wcaVSKS9f/hYv/ryY85eeQaTfYB469ymuff9ix5ideVvRGjVc9c78No89ZKtz11kPIQhWHv36LjQGDfGhiby3aAXuzp4nPJ/uRrTVEW11RA6jbP8u/vrgLooy/gPA3S+MmYteIe60i7okV0qv03FgXxY56bZK5sKDB7Bardz99HOOHMN9e1PZtWUT0fG2SmYPr95ThX0q1Gv19oihTSDuKariYFVDh2NDPF0Z6t9EadUH6I31uDl5sOTK9xgf1besf3oLZp2ZA/9LY9+rrYRiuBtxD40l8uo4ZMqevfAwNNWx/tmrKdtpa6s5/MqHGHH1Y32ypaZI1yC2BuxexAijiIgdTX0la5c/yu6/PwZBQK5yYtKFDzLh/PtQqDu/inh/ZgarViyn4MB+rJa2nUj8AgPRaVsKV2KHJxE7POnwQ/Qpapvt4rCokj32CGJ+dWOHY8O83RgR5udYVk4K82Nzzm88/f0zmCxGogJjeOPqD9tUM4ocH2atyS4Ud6KvsAvFCDfiHhpH5FXDelwoAtQXZLHmiYtpKjmAXO3MpPs/IHLK+T09LRGRAY0oGEUGPBaTkW2/vMnGL5/BoLUJmISplzN94Qt4+J26R5/RYCAvJ5uc9DSi4uIZljQCALlSSV5ONgA+/v5Exyc6ClW8ff1O+bw9SXWTjj1FlY7o4Z6iKopqmzocG+HjzohwP4dATArzw8e1xQfycDPu6fGzee6Spbio+l5P7p7ErDGx/4M0spe0CEWXSHeGPTSWyCt7h1AEKNryGxtfuBaTtgmXgHCmP/U13lF9+2JJRKQ/IApGkQGLIAjkbv+Nf/53L7WluQAERY1m9o3LCIs7+WVOk9FoE4h2L8T8nGzMZltXkYn1dQ7BGDZoMFfdejvR8Qn4+PfdLiOVjVpb1NC+rJxaVOWwsjmcQb7u9lxDW+RweKgfXi5HNqJt1NbzwMrb2Jy7AYAbZ9zJzTPvRiouSx43Zo2J/e/vZd+SnRgqdYBNKMY9bBOKUkXvEIqCIJD25Svs/uQpEAQChk9m6hMrUHv27YsnEZH+gigYRQYkVYVZ/PO/eziw6y8AXDwDmL7weZJmLDjhHClBEBy5jVpNMw9ffy0mo7HNGA9vb6LjE4kfOcqxTSaTMWHajFN8Jt1LeYPGHjG0CcS9xVWU1ms6HBvl72lbUg7zI8kuDj2dj99c+WBFLncuv85uxu3Esxe/xqzhczrrqfR7zBoT+9+zC8Uqu1Ac7GHLUbwittcIRQCzXsumV28if913AMTMvZ5xt76KVC5Wt4uI9BZEwSgyoNA11bHhy6fY/svbCFYLUrmC8fPuYvKlj6JyPr6iJ7PJRMGBA+RkpJGTnoZKreamBx8BwNnFFR9/f7TNGqITEoiOTyA6IRG/wKA+ZS4sCAKl9RpHzuGhauVD3VJaI5HAUH8vR65hUphNHLo7nbw/ZWsz7iDPEF5f8CGxwX3fZLw7MDUb2f/eXrKX7morFB8eS+TlvUsoAjRXFLF28cXU7k9FIpMz/vYlxMy5rqenJSIichiiYBQZMBRlbuabZ85D21gNQHTyPM5Y9Arewcc2cC08sJ+svankpKdxYF8WRoPBsU+pUmE2mZDbLXDuefp5XNzc+oxAFASB4rpmRyFKaqGtKKWqSddurFQiITrAk6RWy8oJIb64qTvHvFwQBD5Z/y6v//kSgiAwatA4XrvyPXxcfTvl+P0ZU7OR/e/ahWK17bVzHeJB3MPjiLgsptcJRYCKtE2se+py9PVVqDx8mbp4JYHDT+vpaYmIiHSAKBhFBgSV+el89eQc9Jp6fMPjmH3DUgaPPKPDsVaLhdKiQkIjBzm2/bRyBVmpexz3XdzcbNHD+ESiExKRyVs+Sq692J5JEAQKa5vs+Yb2vMPiKmqa9e3GyqQSYgK9HcvKh8Shi6prlgn1Jj1PfvcAv+/5EYALx13Ow/OeRiHv3k46fQ1Tk5H976ayb+kujDW219E1yoO4h8YRcXksUnnvzPfM+fUjtr51D1azCa8hiUx/+ltcA8J7eloiIiJHQBSMIv2e+soCVj5xJnpNPaHDJnLls3+3scmxWiwUF+STm5FOdnoa+7My0Wu1PP/Bx3h6ewMwfMw4lCqVo5I5KCy81xdeCIJAfnWjw9/wUJeUOq2h3Vi5VEpskJejICUpzI/4EJ9u65BSXl/GXcuvI7MkDblUzoPnPsUlE67qlnP3VUxNRnLfSSV76S6MtYeEomdLRLGXCkWr2cS2d+4n++cPAIg4/Xwm3f8+CifRcF1EpLP4YlslD68qoPCFsccefJyIglGkX6NpqOKLx2bTVFOKX0Q8ly7+2SEW9+1NZd3vv5GblYFO07Zww8nZmcrSEodgnHLW2Uw56+xun//xYrUK5FU3OCKGNnFYTYOuvThUyKQMC/JuU60cF+yDWtEzXwepBTu5a/kN1DRX4ensxWtXvsfYIRN6ZC59AVOjwSYUl+12CEW3oZ7EPTKe8Euie61QBNDXV7Hu6Sup2LsRgJHXLCbx8gf6TPqGSN9jW34Ts9/IYGasJ9/eEHtCj33hzyJ+S6vjv/uHd9HsTowjicDEp3dx85QgbpkS5Nh2/ghfZg3r3AYPomAU6bcYtE18ufgcakty8PAL57Rr3kUib/H3a2qoZ+8OW+N4tZMTQ4bFER2fSExCAqGRg5D20hZ7VqvAgap6R7VyalE1e4uqaNQb241VyqXEB/u2McEeFuSDqpfks/24/RueWfUIJouRoYGxvL7gQ0K9xWXJjjA1Gsh9O5XsZbsw1tkuBNyGehL3qF0oynqvUASoPZjGmscvQlNRiNzJlckPf0z4RLHqXaRrWZ5SyY2TA/l8ayVlDUaCPAZGiouTUoqTsnO/E8TWgPbWgPmlFYQF+CGVile6/QGzycBXi+eQl7oauVsYrsOuJCstg7mXXcFZF1wEQGN9PSlr1xCdkEDY4CG9sgezxWolt6K+zbJyWnE1zQZTu7EquYyEEB9GhPs7qpWHBXmjlPe+52W2mFny+3Os+O8jAGbEn8lzlyzFWSUuSx6OscEmFHNebyUUo72If3QcYRf3fqEIULDhR/57+TrMei1uwYOZ9vQ3eEXG9fS0RPowtRoz7/6ZyWMXJh2xNWCzwULM4p2suyeR5/8sJj7ImfvOCAE6jtb9mlbLFR/n0LA0mS+2VXLLlwfbHO+dywZzxTh/iuoMPPBDPutzGpBKJMyI9eCVCyLxd7OJ0UORyRtPD+TFP4up05q5dKwfr5wfyZtry3h7fRlWQeCm04O43z4fgLfWlfHFtkryawx4Ocs5M96Lp+eG46qSsXF/A3Pebtv/+qHZIfy3v5H/DrRtinBo/oc/vz/S63jp72Iyy7S4qGRMHOzGF9fGHPffXIww2hm+eDlSlROeTiq8XNR4u6jxcrbdbn3f9r/9vottv7taKS6p9CKsFgs/vbaA3L2b0arHoLWEIKRlIJFK0TS2tKJz9/Rk1vze027MbLGSU1HnWFZOLawirbgKjdHcbqyTQt5GHI4I9yMm0AtFLxS9h9OgreeBlbeyJde2LHnTzLu4acZdvT4ntLsxNhjIfXMP2W/sxlRvF4oxXsQ/Op6wi4b2CaEoWK2kfv4cqZ+/AEDQqOlMeWw5KnfvHp6ZSF9EEAR2FDTz8eYKfthTg6654wYBh1i1p4ah/k4M9XfiktG+PPRjPvfODD6u3+vzR/iSWaZj9b56frp5GADuajlWq8BlH2XjqpTx221xWKwC936XzzWf5fLbbS3WX3k1ev7Jquf7G2PJqzZw9ac55NfoifJz4vfb4tia18StXx1kWrQ7YyLcAJBK4KX5kUT4qMivMXDvd3k88UshSy4cxPhIN148L4Ln/yxmx8O2zkcuKhk3Tg7itFf3snCCPwuS/Y/4fP7KqOOKT7K5b2YI718RhdFs5e+s+mP+HVojCsZWCALUaQ3UaQ0crGo47sfJpBK8nG0C0ttFjafzYQLTRY23i8o+pmWci1IhCs1ORhAEfn7rNlJSMtCoZoBECoLAiPHJzL3sCoJCT73VX2dgsljILq9r0x0lrbganam9OHRWyhkeajO/Tgq1icPoAC/kfUAwHM7Bilzu+GwRhTX5qBVOPHfJUs5I7L25oT2Bsd5Azpu7yXlzj0Mousd6E//oOEIv7BtCEcCkbeK/l6+n8L+fARh2/m2MufF5pDLxZ0fkxGg2WPh2ZzUfba4graTFCzYmQMmOozzu85RKLhlts+SaGetJo87CfwcamRzlccxzOimluKqkyKUSAtxblrHXZNeTWaZl72MjCfWyNSJ4/4ohjH9pLzsLmxkdbmtZahXg7UuH4KaWERvozOSh7uRW6vnu+likUglD/Z1YtqaUDbmNDsHYOgcxwlvNY2eFcfd3eSy5cBBKuRR3JxkSaDMfVCCTgKtK1nb7Ybz6bwkXjPTlkbNafgMTQ05sRUf85No58OIirHIVdVo9tRo9dRqD7X+tnrpD9+23azV66rW2/VqjGYtVoLpZR3Vze9+6o6GQSdtFMo8UxWx9v7sqV/siG798ho1rt6KTDwEgOiGReZdfxaDo6B6bk9FsYV95rUMc7imsJKO0Br3J0m6sq0pBYqhvm2rloQGeyPpB9G191r889OUdaAzNBHuF8vrVHxITLC5LHsJYbyDnjd3kvLkbU4MtH9V9mDfxj44n9IKoPiMUAZpK81jzxMXU52cgVSiZcNebRM0Wq95FTozMMi0fbarg6x3VNBls35dqhYTzR/iwaFIAAdJ6wp/s+LG5lTp2FmocS65ymYTzR/rweUrVcQnGI5FToSPEU+UQiwCxgc54OMnIqdA5BGO4lwo3dcuKj7+rAplE0ibtzd9NQXVzS3rR2uwGlqwuIbdSR5PegtkqoDcJaI0WnE+x13taifaoEcjjQRSMdpRyGe7uzvi7Ox97cCv0JjN1GkOL0NQa7AKz5X6tRu/Ydui+wWzBZLFS0ajtsHvG0VArZC3RyjZRzKMvo/eWQofOxmQyYdDryN64kvVfPImrxAXXoEQuveluYocndWsU12CykFVW0yIOi6rIKK3GaLa2G+umVjDcHjEcEebPiHA/hvh59rs8WkEQ+HjdO7zx18sIgsDoQeN57cr38Hb16emp9QqMdXqbUHxrT4tQjPOx5SheMBRJH3s/lO1ex/qnr8TQVIuTdwBTn/wK/7jxPT0tkT6CwWzlp9RaPtpUQUpeS27eED81104M4PKxfni72KRLcfGRVwKXp1RitgrEPLnTsU0QQCWX8orOjFQi4fACDpOl80o6FLK2n1uJpINt2CKRAAW1ei75cB+LJgbwxNlheDnL2ZLXxG1fHcRoETgxZdIeteLULzhFwXiKqBVygjzlBHkef2hXEAR0JnObSGZ9B5HN1pFMmwA1YLZa0ZsslDVoKGvouIfvkXBRyjuMZHYUxWy9r7fmxVktFrZtXM+vX3+Jn48rjXveAGDapfcw9cqnuvz8epOZzNIae7WyTSBmltZgsrQXhx5OKpLCfO0m2DZxOMjXo9+Jw8PRGXU8+f0D/LHnJwAuSr6Sh+Y+KZpxYxOK2a/vJvetPZgabULRI97HFlE8P6rPCUVBENj343tsf/cBBKsFn5jRTHvqK1x8Q479YJEBz8FqPZ9uqWDF1ipqNLbUHJkUzknwZtGkAKYMdT/ui3+zReCrHdU8Ny+C6TFto4mXf5zNd7tqCPdW0mSwoDFYcFHZfuPSStr+pipkUiyH1QVHBzhRUm+guM7giDLuK9fSoLMQE+DEybKnSINVgOfmRTh+F1btqWkzRtnBfAAUcikW69HFbkKwM+tzG7ly/MlHGUXB2ANIJBKclQqclQpCvdyO+3GCINBsMLVEMpv1bSKbHUUybUvqBqyCgMZoRmNsprju6InCh+OmVrQVlHax6XmUYiBPZ1WXLaMKgsDe7dv4eeUKyoqLAGioLMRXkDL27OuYcsWTnX5OndFMRmm1QxzuKaxkX1kdZmt7cejprLL7G7YsK0f6Hv+XXX9BNOPuGEOtnpzXd5P7diuhmOBD/GPjCT2v7wlFAIvRwNY37yL3j88AGDzzMibc/RZy1cn/gIr0f8wWgT8z6/h4cwWr97VEC0M8lSyc4M9V4/1Pygbnz8w66rVmrhrvh4dTW5lz7nBvPt9ayQ83DsNZIeXp34q48fRAdhY0s3J7VZuxEd4qCmoM7C3REOKhxFUtY1q0B3FBzly/Yj8vnBeJxSpwz3d5nDbEjVH25eiTYbCvGpNF4P2N5ZwZ78XWvCY+3lzZZky4t4pmg5V1OQ0kBjvjpJTirJQR7qVi88EmLhhpRCWX4OPaPm3twdkhnPtOFoN8VFwwyhezReDvrDrunnH8F3SiYOxDSCQS3NRK3NRKIk5gNc9qFWjUG+1C8ngimTbB2aAzIAjQpDfRpDdRWNN07JO1wsNJZSv2OXy5vHV+5mFRTXe16qhRt5yMdH764nPycrIBUDupUevSUev3ETfpfM686a1TFmZao4m04mp7vqFNIGaX13Z4BeftonaYXyfZI4fh3n2nj3RXsSd/B3d/fqPDjHvJVe8zZnByT0+rRzHU6Mh5fTc5b6dibrILxURfEh4bT8i8IX1SKALoastZ+9TlVGWkIJFKGXXds8RfdOeA/wyIHJmyBiPLUyr5LKWSknrbZ0EigRkxHiyaFMCsYV7IZSf//vk8pZKp0R7txCLAvOE+vL6mjNIGAx9cGcXjPxfyWUolU4a689DsUO78Js8x9twkb37eW8uctzNp0FkctjpfLorhgR/yOfutjDa2OqdCYogLz8+LYNmaUp76rYiJQ9xYfE4YN6484BgzfpAb107055rludRqzDw0O4SHzwzj0bNCuevbPEY8txuDWaBhafvv2slRHny2MJqX/y5m6epS3NQyJg45sTa2og+j3YexoaEB917cA7gnsFitNGiNbYTloRzNI4nMOo2+QwPp40UqkeDprGqTm3loCV1ZmkP1hl8AkCmURI8ZS9muj5E2FBCdOJHLn/4NuUJ1jDO0pdlgJK24JXK4t6iK7PI6rB18LPzcnBwRwyR79DDUy1X8YTyMVdu/5tlVj4pm3HYMNTqyl9kiimZ7grvncF/iHxtPyLl9VygC1OTsYs3iS9BWlaBw8WDKo58RMm5WT09LpBditQqsz23ko80V/J5ey6HMHR8XOVeN92fhBH8G+apP6JjFxcWEhYUd0YdRpHMRBaMoGDsdk8VCvdbQLopZpzG0jXIetozekd+gRLAiSGxL23KrmTnVmylW+5HuMhi9rEUcyqWSNtFLT4fgbJuP6apSklNR54ge5lbW0dEnIMDd2SYOw/0ceYfBni6iODwKZouZ1357li82fQzAjISzeO7iJQPWjNtQrSN72S5y30ltEYpJfjahOHdwnxaKAAfXfMPmV2/CYtTjHhbN9Ge+xSN0aE9PS6SXUasx8cW2Kj7ZUsmBKr1j+4RBbiyaFMC5Sd6oTrKdpSgYuxdxSVqk01HIZPi5OePndmJ1XQaTxZZzqdVTXFbBzr9+pa6shOC5V1OntS2p1zYOQqk14Hkgi2azFr3MDbNEjtkqUNWko6rpxKyNAII9XdpVKwd6DEyRc7I0aOu5/4tbSNn/HwA3z7ybG2fcOSDNuA3VOrKX2oWixi4UR/iR8Nh4gucO7vMXHVaLhd0fP0n6168BEDJuNqc/8ilK15O3KhHpXwiCwPaCZj7aVMGqPTUYzLarcjeVjEvH+nLtxADigk617lekuxEFo0ivQaWQ4S4TSFnzO2t/+wWT0ba0fXaImqFxowEwGXR88fhsivL/w9UrkIWvbkLtHXbckcxGvZFIH3dHd5QRYf4nbKUk0pYDFTnc8dkiimoKBrQZt75KS/aSXex/b69DKHqN9Cf+sfEEzxnU54UigLG5gQ3PL6Rk218AJFxyLyOvfbLX9l0X6V6a9Ba+3VXNx5sqSCttsYsbHuLMokkBXDjKF1eV+F7pq4iCUaRXYDQYWPf7b/z14/foNDZrg8Exscy74iqGxtnaLVktZr5/8VKKMv5D5eLB5c/8iVfgIABClK6EeJ18hZrIybEu8x8e/urOAW3Gra+0CcXc91KxaG1pFV6j7ELxnP4hFAEainNZ8/hFNBblIFOqmXjvuwyecUlPT0ukF5BRquWjzRV8c5jB9gUjfbl2oj+jw8Vc7/6AKBhFepzqinJee+xhGurqAAgOD+fcy68kcfRYx5eMIAj8+sYN5G77BblSzaVP/EzAoOE9Oe0BjSAIfLTubd786xUEQWDM4GReveLdAWXGra/Usm/JTva/t7dFKI72J+Gx8QSd3X+EIkDJtr9Z/9wCTJoGnP1CmP7U1/hEj+rpaYn0IHqTlZ9Sa/hoUwVb81us2qLsBtuXtTLYFukfiK+mSI/j7eePm4cncoWCOZdezthJk9stca3+5CFS//0UiVTGBQ99TXjC5B6arYjOqGPxd/fzZ6qtR/DFyVfx4LlPopANjJaV+goN+5bsYv/7LULRe0wA8Y+NJ+isyH4lFAVBIOObZez66HEEqxW/+GSmLV6Jk3dgT09NpIc4UKXnky0VfLGtilq7wbZcKuGcRC8WTQrg9KiB5zk7UBAFo0i3IggCWal7WPPrL1x/3wOo1GqkUik3PvAQ7l7eKBTtRceWH15jy/evADDnjg+IHj/3pM6dub2KP77IxdNXjZefE55+arz81Hj62v55eKuRnoL310CgvL6UO5dfR1ZJOnKpnIfmPcXFyQPDjFtXrmHfkp0ceD8Ni84uFMcGkPB4MoGzI/rdj6TZoGPL0ts4+O+XAAw9awHjb1+GTHli1lUifR+zReCPDJvB9prsFoPt0FYG24EnYbAt0rcQBaNIt5GXk82PX3xObkY6AOv/+J1Z888HwMc/oMPHpK5ezr8f3Q/AjGteZMQZ15z0+SuKmynIbqCg1Rdea6RSCR4+KpuQ9HWyCclWotLLzwlXT2W/b+d3JFqbcXu5ePPale8NCDNuXbmGfa/t5MAHrYTiuEASHh9P4Kz+JxQBNNUlrF18KTXZO5FIZYy95WVi593UL5+ryJEprW8x2C5taDHYnhnrybUT/U/ZYFukbyEKRpEup7SokJ9XrmDv9m0AyBUKppx5FhNnzDzq43K3/cYvyxYBkDz/HiZccP8pzSNpUiA+Ac7UVemor9ZTV6WnvlpPfZWe+ho9VotAXZVtex71HR5DJpfg4dNWRB4uLF09lP3uh3XV9q95ZtUjmC0mooOG8frVHxLiHdbT0+pSdGUa9r22wyYU9bZEfp/xgcQ/nkzgGeH97jU+RFXmNtY+eQm62gpUbt5MeWIFQSOn9vS0RLoJq1VgXW4DH22q4I+MOofBtq+rnCvHnZzBtkj/QBSMIl2G1WJhxXtvs3XdWgRBQCKVkjx1GudcdCnefn5HfWxR5ma+e/FiBKuFxOlXMfPal0/5B9rb3wlv/45721otAk31BoeIbCMq7dsaavVYzAK1FTpqK47s9yhXSNuISC9f2/K3TWDatju7KvqE4DBbzLz62zOs3PQJADMTzuLZfm7GrSvTkPXqDg7+r5VQTA4i4fHxBMzsv0IRYP9fn7Nl2e1YTUY8I+OZ/vQ3uAUP6ulpiXQDtRoTK7ZV8cnmSg5WtxhsTxrixrUTA5g7/OQNtkX6B6JgFOkypDIZRr0eQRAYmTyBOZdeTlDosaNSlfnpfPXkHMwGHVFjzmbunR8i6WIDaKnMFjn08DnylbPFYqWx1tBGRB4uLJvqDZhNVqrLtFSXaY94LKVK1lZEtsqrPLTNyaVni0gGmhm3rrSZrFd3cuB/aVjt1iA+E+xCcUb/FopWi5kd7z9M1g9vAxA2aS6TH/wQhbNbD89MpCsRBIFt+c18vLmtwba7WsalY2wG28NEg20RO2JrQLE1YKeh02pZ/ctPTJg23ZGTWF1RTnNjI5FDo4/rGPWVBXx63ySaakoJHTaRK5/9G4W673xhmU1WGmpt4tG2vG0TlK1FZXPD8fXaVjnJ2onIQ8Ly0G2VU9dc8+0vz+bO5ddRVFOAk9KZ5y5ZysyEs7rkXD2NtqSZfa/u4MCH6Q6h6DsxiPjHkwmYHtavhSKAobGW9c9eTdmuNQAkXfUwSVc92uUXaSI9R5Pewtc7bdHE9FYG20mhLjaD7ZE+uPQBg22xNWD30m8ijFqtlmHDhnHRRRfx6quv9vR0BhQmo5GNf//Jnz98R3NjIzWVlSy4/U4AfAMC8Q04PgsOTUMVXzw2m6aaUvwi4rl08c99SiyCbTnaJ8AZn4Ajz9tktLQTkYcLS22TCYPOQnlhM+WFzUc8lpOr3LHk3briu7XQVJ7gF/+6zH946Ms70Bo1BHuF8caCD4kOGnZCx+gLaIubyHplBwc/zmgRipOCSXh8PP7T+r9QBKjLz2TtExfTVHoQudqZ0x74kIjTz+vpaYl0EWklGj7eXME3O6tpNtiSE9UKCReO9OXaSQGMCnMZEO97kZOj3wjG5557juTk/l+x2ZuwWixs3bCO377+itrqKgD8g4NJHDP2hI9l0Dbx5eJzqC3JwcMvnMuf/gMnN+/OnnKvQKGU4Rfsgl/wkfMADXpzS0FOa2FZrXNs02nM6JrN6JqbKM1vOuKxXNwVR7QS8vJzwsNHhUIpQxAEPlz7Nm/9bTPjHjt4Aq9e+S5eLv3rddAWtRKKRptQ9JscQvxj4/GfGjpgfjALN/3CxhcXYdY14xoYwbSnv8F7cGJPT0ukk9GbrPy4p4aPNlewrZXB9lB/NYsmBnDpWD+8nPuNFBDpQvrFuyQ3N5d9+/Yxd+5c0tPTe3o6A4K0nTv48fPPKCsuAsDT24dzLr6U5GnTkZ1gX1mzycC3z11AWe4OnN19ufzZv3D3HdjLCyq1nIBQVwJCj9zuUKcxtYtUHp5XadRb0DSa0DSaKDl4ZFHp6qlAI62i0tzIEMXFxEYN5ryhc6jNA8FPh4ePClkfT3jXFjWR+fJ28j7JbBGKp4eQ8Hgy/lMGzvtNEATSVr7M7k+eAiAw6XSmPLECtYdvD89MpDM5UKXj482VfLGtijpti8H2HLvB9mTRYFvkBOlxwbhhwwZeeeUVdu7cSVlZGatWreK8885rM+btt9/mlVdeoby8nKSkJN58803GjRvn2H/ffffxyiuvsHnz5pOex6RXUnFz88BZKcVJKcVZIcVZJcNZYb+vlOKskLXcVkpxUshwUbbar5ThpJDiorLtc1ZKkfVTz7783BzKiotwdnVl9vwLmXLmWShVJ27oa7VY+GnJQvL2/ItC7cJlT/2Gb2hMF8y4/+HkosDJRUFQRMeFCYIgoGs2H7Hq+1C00mS00lxvAjzxYyQAzTWwYmvLxZdECu5eqnZL3n3B+FxT2EjWyzvI+yQDq8m2DOc/JdQWURxAQhHApNOw6ZUbKdjwAwAx825k3M0vI5UPjC49/R2zReD3jDo+2lTBupwWv9kwLyULJgRw9Xg/AtxFg22Rk6PHBaNGoyEpKYlrr72W888/v93+r7/+mnvuuYf33nuP8ePHs2zZMmbPnk12djb+/v789NNPREdHEx0dfUqCsaDGiKT5yFWtJ4tKLrELUFlbMapsdV/ZIkxtAtQuPDsUo7b/D4lWtVzaLUbShQcPABA+eAgAM+fOQyqVMu2cOTi7HDkKdjQEQeCvD+4kc8PXSOUKLn7sB4KjT3w5W6RjJBIJzm4KnN0UBA/qWFTuytvG/Z/cg65ewEcaxiUjbsZdCGyXV2kxCzTUGGioMfQZ4/PmA/XsW7KTvE8zW4TiVLtQPH1gCUWA5opC1j5xMbUH9iKVKxh/+1Kiz7m2p6cl0gmU1BscBttlDSbAZrB9RqwniyYFcMYwz34bvBDpPnpVlbREImkXYRw/fjxjx47lrbfeAsBqtRIWFsbtt9/OQw89xMMPP8yKFSuQyWQ0NzdjMpm49957eeKJJzo8h8FgwGAwOO43NjYSFhbG37vykahc0Rqt6ExWx/86oxXNodsmK1qjBZ3RirbVGK3RNu7Qba3JSnf+VZ0UEoegPCRIW4Sp7bZDZLaKmDop2orVQ9tai9Xmmkr+/eFr9qRsJnLoUO566rlOW8bY9PXzbPzyKZBImHfv5ww77aJOOa7I8fHjjm944ZcnbGbcgcNYcsX7BHmFtBtntQpoGozUVxtsArJGT32VwSEmG6ptQtJqPfabXiaX4OFti1R6+Knx9FXh6WP/3y4sXdxPzaOydls5Oa/vofSng2Cfkt+UEIY9Mhbf04JP+rh9mcq0TWx87moMDdWoPHyZ/NgK/BMm9PS0RE4Bq1Vg/f4mPtlSxV9Z9Q6DbT9XOVeM9eWq8b5EePfvNo4lJSUMGjRIrJLuJnq1YDQajTg7O/Pdd9+1EZELFiygvr6en376qc3jP/30U9LT049aJf3kk0/y1FNPtdu+cuVKnJ07pyJXEMBkBYNVgtEiwWiVYLDY/h263XYbjm1G+7ZDj239v8HScjyjtfuuFiWCFZlgQikVcFbJUclAJRNQyQSUUgGlTEB16P/Wt1v9r5IJKGWgtN/WHlxP7fZPUQg6wsdfQUBi/7Rs6Y1YrBb+zF9FSvl6AOJ9RnB+1JUoZSf/4yJYwagDfbMEfTMYmiUYNLbbeo3Edl8LCMd+30plAioXULkKqF0F1K1uq1xA7SogV9kiKI7zWwSsO7WYf25EyG65IJSOdEI+3wPpsIHbmcKQ9g+6DZ+B1YLMLxKXc+5F6ibmK/ZVmowSNpQ7sbbUiUp9yyJhrIeR6cFaxvgZ6OPpxseNVqvl8ssvZ8iQISiVSm699VZuvfXWnp5Wv6XHl6SPRnV1NRaLhYCAtn2GAwIC2Ldv30kd8+GHH+aee+5x3D8UYZw1a1af8mG0WgV05rbRTc1hkU5HxPOwCGn7bVZH1FRjsNCkNaA3C1gltreHIJFilqgwA1rD0ed1/JwPQ+wpCDUg34AjSurcOtrZKirq0sG2Q8v7R4uiHtouLslAvbaOh766ne3lWwC4acZdXDf1tm5JfrcZnxsdkcn6agMNjtaMtohlU50Rq0WCrhF0jUeek1IlxcNXjYeXEnmDAWNaFZIyIyq9DLWnmsFzIoi/cyTucf2rwvtEsJiM7HzvQfav+xiA8NPPJ/nut5H3MasqEbvBdoGGT7ZU8fPeOowWW5zHTS3l0tE+LEz2Iyag4y5W/ZmSkhIA1q1bJ0YYu4FeLRhPlIULFx5zjEqlQtVBcYZCoUCh6FuJ3yoVeHZyh7ZtG9bz6RtLAYiMiWPWRZcTNDi6lRi1OG5r7MvzOpMVjcG2r7UYPVycao0WGps11NXVY5KoMMmcELBdCput0Ki30GhvxdYVqOS2pfsoPzXDQ10YHuJMUqgLcUHOA6LlVW55Nnd+toji2kKclM48f8kyZiSc2W3nVyhAHazCP/jI3UPMJisNNfqjtmhsbjBiNFipKtFSVWLPO/Z0tf2zs6tM4JenUvH2dyJ+vD+nz43AcwD1v9XXV7Hu6Suo2PsfSCSMuuZJEi67T6yK7WM06s18vaOaTzZXktGqc9QIu8H2BX3EYLurkMv7lYTp9fTqv7avry8ymYyKioo22ysqKggMPD4zaJGjYzGbqSwrIyjM1rJvzGmTSd+5g7GTTydh9JhO/YEpy93J8ofPxKhrJnbSBZz/wJeYkbZEOI0WtKYOxKjRiu4w8akxWFpFUC1to6n24xy6fwiDWcBgNrO9oJntBS1+ZHKphNhAJ4eAHB7iQkKIM+7qXv3xOCHWZvzNw1/didaoIcQ7jNev/ojooNienlY75AopPoHO+AR2HAVrzK4ja9lOcr7fj1YixaiSIwS5oh4ZgMXHiYY6I/VVerTNNuPzsoJmygqaWfN9HqNOD2Lq/EjCh3p087PqXmr3p7LmiYvRVBahcHZj8sMfEzbhnJ6elsgJkFai4aNNFXy7q8Vg20kh5YJRPlw7MYDR4SdXaCgicir06l9EpVLJ6NGjWb16tSOH0Wq1snr1am677baenVwfx2q1snPzf/z61UqMBgNPvfUeSpUKqVTKtXff2+nnqynJZeXiszHqmolMms78+1cgk8uRASq5FK8uWiUTBAG9SXCI0QadmX3lOvYUa9hbrGFviZY6rZn0Ui3ppVpWbq92PHawr7pFRIa6kBTigp9b34pCC4LA/9a+xVt/vQLA2CETefWKd/qUGbcgCFRvLiV7yS5KfrUVsiiBwDEBxN4zmpDzhiA9LEJ8yPi85GATG38p4EB6HTvWlrJjbSlDEryYel4kickBvdIG6FTIX/8Dm165AbNei1vIEKY//Q2eEf2vS09/RGe08mNqDR9tqmhzQRvtr+Za0WBbpBfQ4+++5uZm9u/f77ifl5fHnj178Pb2Jjw8nHvuuYcFCxYwZswYxo0bx7Jly9BoNFxzzTU9OOu+iyAIZO7exU8rP6c4Px8ANw8PyouLCB8S1SXnbKopZeXjs9E2VBE4ZBQXP/YDckX3VO9JJBKclDZrIx8gzEtFQrALF46yJf0LgkBxvZG9xRpS7QJyb4mGknojB6v1HKzW82NqreN4QR4Khoe4OCKRw0OdCfdS9cqlPq1Ry+Jv7+Ovvb8CcOmEBdw/9wkUsr4heq0WKyU/HmDf0l3Ubiu3bZRAyNzBxNw9Ct9JwUf8u7c2Ph91ehCFuQ2s+zGfXevLOJBex4H0OnwCnZgyL5LkWaGo+/gPsWC1smf5s+xd8SIAwaNncvpjn6Fy8+rhmYkciyMZbM8dbjPYPm2IaLAt0jvo8SrpdevWMW3atHbbFyxYwKeffgrAW2+95TDuHjFiBG+88Qbjx4/vlPM3Njbi4eFBQ0NDnyp6ORkOZu/jxy8+Z39mBgBqZ2fOmDefaWfPQe3UNQnT+uZ6PntwCpX5aXgHR7Hg5Y24egUc+4E9THWzib0lrURksYYD1foO7ZI8nWUkBttEZJI9N3Kov1OPFtmU1ZVw5/Lr2FeagVwq55HznuHC8Vf02HxOBFOzkbzPMsl5YzeavEYApCoZg64eRvQdo3CPOXkRVF+tZ+OvBWz6vQhtk82vTu0sZ+KZYZx+bgTefbBwwKRtYuOLiyjabLswiLvwDkZf/yxSWd8Wwf0Zk8XK7+k2g+31uY2O7WFeShZOCOAq0WD7uCguLiYsLEy01ekmelww9jQDRTBWlpXy5O23ACBXKJh61jnMmn8+rm5d95xNBh1fPD6booz/cPUKZOGrm/AKHNRl5+tqmvQWMkq1dhGpIbVEw75yHSZL+4+Qk0JKfLCzQ0AOD7EV16gVXV9csytvG3d/fiN1mhq8XHxYctV7jB7UORdYXYmuTEPuO6kc+GAvxjpbOb7SR83Qm5OIumk4av/Oy1sw6i1sW13Cuh/zqSzWADbj8eGTApg2P5JBw/pGZK6x9CBrn7iY+vxMpAolE+9+myGz+saFwUCkuM7AZymVLE+ppLyxxWB71jBPFk0MYKZosH1CiIKxexEFYz8WjDqtFqdW3pIfLnkFJydnzrroYrx9/br03FaLmW+evYDcbb+gcvFgwUvrCRg0vEvP2RMYzFb2lescInJvsYb0Ui2aVsU2h5BLJcQEOJEU6mxfznYhsZOLa77bupLnf3ocs8VEbHA8y67+H8FevfuLtCGzhuxluyhYme3o8ewa5UHMXaOIvHIYcueuW0K3WgWydlSxdlU+OXtqHNsjYz2ZOj+SpEkByGS9s4K+dOca1j97FcamOpx8Apn25Ff4DRt37AeKdCtWq8Ca7AY+3lzBHxl1HPK393NVcHWyHwsnBBDezw22uwpRMHYvomDsh4KxqaGeP77/jpS1q3lsyRt4+9nEodVqRSrt+h8/QRD45fVFpP7zKXKlmiue+YvwhMldft7egsUqcLBaT+pheZG1GnOH4wf5qkgKaSmsGR7qjL/biS1HmSwmXv31Gb7c/CkAsxLP4emLX8NZ2Ts99wRBoHJ9MdlLd1H2R75ju+/EIGLuHk3wnEFIu1moleQ1sm5VPjvWlmIx274WvfzVnD43gglnhuHs2jtyPwVBIGvVO+x47yEEqwXf2DFMe/IrnH0HZheb3kp1s4kVW6v4ZEsF+TUtBraTo9y5ZqI/cxO9UQ4AO6+uRBSM3YsoGO2CcdMHTzJ47DR8Y8f0WWNbnVbL6l9+YvUvP2HQ6wGYf9UCzpg3v1vnsfqTh9j83ctIpDIuevR7YpLP7dbz90YEQaCk3tgSibTnRRbXGzscH+iuaFVYY1vWjvDuuLimXlPHvV/czPYDtl7qt826j+un394rE+WtJgtF3+8ne+ku6nZX2jZKIPS8KFshS3JQz04QaKwz8N+vhfz3WyHNDbbXR+UkI3lWKKefG4FfcCebn54AFqOBlNfvYP9fnwMweOblTLznLWTKgeMx2ZsRBIGUvCY+2lTBT6m1DoNtD7WMy8b6ce2kgAFpsN1ViIKxexEFo10wvjtFjZNcgkQmxzsqCf/4CfjHJ+Mfn9zrr9xNRiMb/vqTP3/4Fk1TEwDhQ6I474qriB2e1K1z2fLDa/z70f0AzL3rI0acIVazH42aZhOpJRr2FmvtQlLD/qqOi2s8nGStKrRty9oIhdzz+SJKaotwVrrw/KXLmB4/u/ufyDEwNRk5+HE6OW/uQVtoe4/KnOQMWhhH9O0jcYvy7NkJdoDJaGHH2lLWrcqnzG5zIpFAYnIAU+dHMiTBq1tFubamjHVPXkZV1jYkUimjb3ieuAt654XBQOOQwfbHmyvILNM5to8MazHYdlYOXIPtrkIUjN2LKBjtgvG3Ry5Gc2An2urSdmNcAyPwj5+AX9x4/BMm4BkZj1TWOz78VouFZ++9k/LiYgACgkM49/IrGDF+Qrf/kKSuXs7PSxYCMH3hC0y66MFuPX9/odlgId2+jJ1qz4vMOkJxjQQDCkkBXupqFp0+k5lxg4nvpuKa40Fb0kzuW3s48GEapkPROn8nht4ygqgbE1H59P5oiyAIZO+uYe2qPLJ2tPh0hka5M21+JCMnByHv4r939b4drH3yUrTVpShdPTn90c8IGXtGl55T5NikFmv4eHMF3+6sduQtOymkXGg32B4lGmx3KaJg7F5Ewdgqh9HNzQ1NZRGV6VuozEyhKn0LdXnpCNa2BQwKZ3f8ho3DPyEZv7hk/IaNReF85HZnnY0gCG3E4M9ffkHK2jWcc8mlJE+djqwHxGzutt/4+pnzEKwWkuffzcxFr4qRj07EaLaSVa5zWP38k7GfgjoFAu2XImVSiA1wtkUh7cvaiSHOeDh1n81K/d4qspftpuCrbASz7fPjFuNFzN2jiLw8Flkf7aJTXtjMuh/z2b66BJNdIHj4qJg8N4JJZ4fhcoK5p8fDwdVfsfm1W7AY9XiExzL96W9wD+0az1SRY6MzWlm1p4aPNlewo5XBdkyAE4smBnDJWF88u/GzNpARBWP3IgrGYxS9GDWNVO/bbheRW6nO2oZJ29RmjEQqxWtwoi0KGZ+Mf/wEXAPCumS+2elp/LRiOfOvXsjQuHgADHo9EokEZQc9sruDoszNrHjsDMwGHYnTr2Le3Z8g6YbimoGI1qjliW/u5e+03xAECbNH3sLYoQvJKNOzt1hL6jGKa4aHuNiXtW1L2p3p9SYIAhWrC8leuovyfwod2/1ODyH27lEEnTUIST+xDGluMLLp90I2/lJIo90CSKGSMm5GCFPnRxIQeuqRJavFwq6PniDjG1tv99DxZzL5kU9RuvSP4ry+xv5KHR9vruCL7VXUa23V/AqZhLnDvVk0MYBJQ9zEi+RuRhSM3YsoGE+wStpqsVCfl05lxhYqM7ZSmbEFTUVhu3HOfiH2PMgJ+MePx2vI8FMy0i08sJ+fVq4gK3UPALHDk7jjiadO+nidRWVBBp89cDr65jqixpzNxY+vQibvHdWk/Y3SumLuWn69zYxbpuCRec9w4fjL24w5VFyzt1VeZOoximtaF9YMD3Eh0ufEOtdYjBaKvslh39JdNKTZlmwlUgmhFwwl5u6R+Izpv33fTUYLuzeUs3ZVHiUHWy4k48f5MfW8SKJH+JyUiDA217Ph+YWUbPsbgMTL7mPEwsW9JhVmoGCyWPktrY6PNlewoZXBdriXkoUTbQbbJ+poINJ5HEsw6kor2bfscyo3bMdY14jaz5vAWROJuf1KlF7ihdeJIgrGTrDV0VSXUJWeYheRKdTuT0WwWtqMkatd8B021lZIE5eMX9x4lK4exzx2RWkJv3y1kl2bNwEgk8s57YxZnHXBxbh7ep7UfDuL+soCPr3vNJpqSgiNncAVz/2NUt1zFaT9mZ15W7nn85scZtxLr3qfUYOO33OvVmMitVVhTWrxsYtrDgnI4aEuRPs7IT+s77KxwcDBD9PJeWsPuhLb0pzcRcGga+OJuX0kLpED5wtZEAT2p9WydlU+GVsrHX/X4EFuTD0vktFTg1AcZ9FDQ1EOax6/iMbiXGQqJybd9x6Dpl3UhbMXOZziOgOfbrEZbFc0tRhszx7myaJJAcyIFQ22ewMH96QzZGRih4JRU1jGxgvuxHVQKLH3LsQ5NJCm3HwyX/wfVqOZyT+8jtKza76jrEYTUmX/C5yIgrELfBhNOg3V2TuoTN9CVWYKlRlbMWka2g6SSPCKjLctYSdMwD8uGdegyDbRiN+++Yo/vvsGq9WKRCJh7OTTmXPJZfgG9HzERtNQxaf3T6a2JAff8DgWvrwBJzfvnp5Wv+RwM+7Xr/6QIK+QUz5us8HWueaQgEw9SnGNWiEhPsgmIGOdZbivK0SyPANpvW05Vh3kQvStSQy5PhGl18C2eKks0bD+p3y2/l2C0WC7cHTzVHLanHBOOyccN88jp44Ub/2TDc8txKRtxNkvhOlPf4PP0JHdNfUBjdUqsDq7no82VfJXZovBtr+bgquT/VmQ7C8abPcwgiDQuC+PitVbKP83hdI9GVxWsLZDwZhyzSM05uQzY/UnyNQtr5u+qpbVUxcQOv8Mkp69g58Hz2Lse4sJmjXJMeb3pPkkPH4z4RfOAqBxXx7pz7xD7a4sZE4qgs88jfhHb0LuYiva233/K5gam/EcHkPe5z8jUyqYueFzilb9y8FPVtGcV4zcSY3vhBEkPH4TKt++0UnqcMTM3C5A4eRC0IgpBI2YAoBgtVJfkGWLQKanUJWZQlPpQery0qnLSyfn1w8BcPIOsOdBTsA/IRlvH2+sVisJo8dw7mVXEhoZ2YPPqgWDtokvF59DbUkOHn7hXPHMn6JY7AJMFhOv/PI0X235DOh8M25XlYzxg9wYP6ilYMtotrKvQsfeVj2000o1NBus7CzUsLNQYx+pQHpZEiFaI0lhriRPDMA70o0gtZyBvkDnH+LCRbfEc85V0Wz+s4gNPxdQX63njxX7+efrg4yZHszU8yIJjmz5uwuCQMY3S9n54eMgCPjHT2Dqkytx6gN91/s6VU0mVmyr5JPNlRTUtjXYvnZiAHMSvUSD7R7EYjBSs20vFau3Uv7vFnSllcd8jLG+kcoNOxl278I2YhFA7edN6LzplP62nuHP3H7MY5m1OrYsfBjvkXGc/uObGGrqSX14KWlPvsXIV+53jKvavAe5qwsTlr/o2CaYzMTeswDXwWEYaurJeO59dt//KsmfPHcCf4HegygYuwGJVIrXoHi8BsUTM+c6AHS15VRmpFCZkUJVRgo1ubvR1VZQsPFHCjb+CIBM5cSssDjC/Y1QnoXB2x2Ve88KM4vJyLfPXUBZ7g6c3H24/Jk/cfcVk407mzpNLfd9cUuLGffs+7l+2m1dnlSvlEsdhTFgEzKlf+az9p297DnQSJGvC4V+zhQHu9Eol1HkqqKozsSvvxU7jhHpo3IsZY/oguKavoKzm4KZFw1m2vxI9vxXztpV+RTmNJDyVzEpfxUTM9KHafMHEZXgQsrSW8lb8zUAQ8++hvG3L0WmGHh/s+5CEAS2HGzio802g+1DUXUPJxmXj/Xj2okBRIsG2z2Gsa6RinXbKP93C1Ubd2Ju1jr2ydQqfCeNJHDmBCwxYTAysd3jNfklIAi4RoV3eHzXIeGYGpow1tQfcy4lP6/FajAx8rX7kTvb3hOJT97G1uufYNgD16H2s0UL5U5qRrxwd5ul6PCLz3TcdgkPIvGJW9hw3m2YNTpHdLIvIQrGHsLJO5CIyecROmEOm1b/w8avV2KtLcRXaWBEVBA1WdsxNNXSsH8naft3Oh7nER5ry4NMsBXUuIUM6bbKPMFq5cclC8jb8y8KtQuXPfkbvmGx3XLugURO2T7uXN5ixv3Cpa8zLX5Wt87BYjBT8FU22Ut20ZhViwIYJ5Nw4Vg/Yu4ahecIP0objG0Mx1OLNRTVGcmvMZBfY+DnvbWO4wW4KRyFNYc62JxocU1fRSaXMnpqMKOmBJGXWc/aVXns3VJB9u4asnfX4KKowkdXi69UTfKtzxNz7o0D4u/SEzToWgy2s8pbDLZHhbuwaGIA54sG2z1G88EiylenUP7vFmp3ZkIrOzuVnzeBM8YTMCMZ34kjkTvZ0l6Ki4uPdDgbx8i4O548w6b9hbgPG+wQiwDeo+PBakWTV+QQjG4xke2OV5+WQ/brn9OYdRBjY7PjOelKK3EbGnHMc/c2RMHYQ1itVnZu/o9fv1pJVXk5AD7BsUy59HLGnDYZiURCY1GOfRnbVkzTWJxLQ+E+Ggr3kfvHpwCoPf1shuLxE/BPmIDP0JHIlJ2fZyMIAn+9fyeZG75GKldw8WM/EBJz/EUXIsfHmoy/ePirO9EZtYR6h/PGgo+ICozptvMbavUc+GAvue+koi+3XdXL3ZQMuS6B6NtG4BzWsowa4qkixFPFWQkt+Ti1GpOjd/Zee15kbpWeiiYT/2TV809WvWOsh1pGor2wJin0yMU1/QWJRMLgeC8Gx3tRU67lz09S2LGhHo3JD438Jqqcb8K3fghBdQY8vAd2Hmhns6dIw0ebK/huVzVau3+ms1LKhaN8uXaiPyPDRIPt7sZqtlC3K4Pyf1MoX52CJq+t+HMfNpjAGckEzJiAZ+LQE7Jqc4kIAYmEpv1FBHXQ+Kr5QCFKHw8U7q62aqbDhKVg7tia7GjIndt+Zs1aHSkLH8Fv8mhGLX0IpY8HutJKUhY8gtV04sfvDYiCsQc4mL2Pr/73HsX5+QC4e3py1gUXM2nmGcgVLVcoHuExeITHMPSshQDoG6odS9iV6VuoztmFvr6Kos2/UrT5VwCkCiU+0aPatDZUe/qd8pw3fvUs2399GyQS5t3zGYNHil0mOhNBEPhg9Ru8/c9rAIwbMpFXr3gXT5fuSY5uPthAzpu7OfhJBhat7cvMKdSV6NtHMvjaeJQex3cR4u2iYGq0B1OjWxwANAYLGWVaex9t2/9ZZVoa9Bb+O9DEfwda7GjUCglxQS0iMinEhbggZ5yU/SuHrHb3t1g33EmiSYbG90qqFXOpqzbx91cHWP3dQUZPCWbq/EhChwycSvPORmu08MPuGj7eXNEq9xZiA5y4VjTY7hFMTRoqN+ygYnUKFeu2Yapv+exLFHJ8k5PsIjEZ55CTz99Vernjd9oo8r/4hSGLzm9X9FL80xoirzrXNtbbA31ly2pIc14JFl1LLqtbVDhF3/+DWatzRBlrd2aAVIrLoCP7LTcfKMJY10jcA4twCvYHbBHHvoz4aekBJBIpxfn5qJ2dOWPefKadPQe107HzGdQevoRPnEP4xDkAWIwGavbvsUcgt1CVkYK+vooqu6jMsD/OPSQKv4Rkhy+kR1j0CV2t7fzjfdavWAzA7BuWkTDl0hN+ziJHRmvU8vg39/BP2u8AXD7pGu4753Hkp+DbebzUbC8ne8kuilftR7CXhXom+RFz9yjCLxqKVHHqy3MuKhnjIt0YF9m2uCa7Qufwi0xtVVyzq1DDrlY/8DIpRPs7tYpEOpMY4tInf+ytFjM73nuIrFXvADDotHM57cHnkCqdSdtSybpV+RzMrGPb6hK2rS4harg30+ZHEj/OH6lo43Jc5Fbq+HiTzWC7QddisD0vyZtrJwYwcbBosN2daIvLKV+dQsXqFKq37kVoFV1TeLoRMG08gTOS8Zs8GoVb59myJT55G/9ddBdbFjzMMIetTgEZL/wPl0GhxNx+JQB+E0aQ9/nPeI+KQ7BYyXzpQySKlu+WkHnT2bdsObvve4WYO6/CWNtA2lNvEzZ/hmM5uiOcgv2RKhUcXP4TkZfPoSknn5w3V3ba8+sJRFudLrDVOZzSwgIKDx4geep0x7Yta1eTOGYsrm6dd05BEGgqPdhmGbuhIKvdOJWbN35x42wV2QkT8I0ZjVzVsWDN/O87vn/xEhAEJl/6GFOverrT5itiM+O+87PryC7LRC5T8Nh5z3H+uK4V5IJVoPS3PLKX7qTqv5be6YGzI4i9exT+08J65AfVahXIq9E7/CJtEUkN1c0dL99EeKsYHnooEmmLSgZ69N5CEX1DDeufvYry3esASLr6UZKufLjdxVtBdj1rV+WzZ2M5VruI9wtxZsq8SMafEYKqE9oqCoKA2SpgsYLF2nLbKgiYLQIWwXbfbBWwttpvse+3ttpvaXUci9X2WHPr+4c91iIIWA97rNkqtDp3y5ys1sPOI7Q97uHzrtGY2d6qXV+4t4prJvhz1Xh//Nz6ny9eb0SwWqnfm23PR0yhKTuvzX7XwaEEzJxA4PRkvEbFIZWf/EXpsYy7tcXlZL/+OZXrd2CoqQdBIGj2aYxc8oAjD1JfUcPuB16ldmcGan8fEp64mZ13vnBStjrj3m/bTKP457Xse/Vj9JW1eCQMZejNl7Dt+sVM+fVdPOKGnPTz7ilEwdiFgrGmsoJfv/6KbRvWIZPLeeqtd/Hy8e3UcxwLQ2MtVVnbHJY+1dk7sBh0bcZIZHJ8ho5o6UyTkIyTdyB5qWv48omzsZiNjDrrBs6+9V3xyrwTaW3G7e3qy9Kr3mdk5NguO59ZZ6bgiyyyl+6iKbceAKlCSvhlMcTcOQrPxO59bx4PgiBQ1mBqU1izt1hDYV3HnWv83RRtCmsSQ5xxVsqOLGws2MVRW/FicQiYlv02AdNKYAntRVEbYWM5JJ4ENHXV5G/8Bb22GeRKAkbOwCkg0i6e2gosm3gCodmE04EGnPObkZlseXcWuYS6UFcqQ13Qq2RthJdDYAm0EX0dPd/+/K0vlcDsOC8WTfJnRoynGJntBsw6PdWbdlP+7xYq1mzFUF3XslMqxWdMvEMkug7uPFeNE20NuG/pcg58/D0Tlr+I98hhnTaPgYIoGLtAMDbW1/Pn99+y8Z+/sNiTZ0dNmMj5V1+Dt9+p5xOeClazidr9qXZLH9tStq6mvN04J98gahvK0QlGgkdN58KnfkWmEK/QO4vvtn7B8z8+jtlqJjY4gdev/l+nmHF3hKFax/739pL7biqGKtvFgsJDSdQNwxl6axJOwX0v4b9WYyattKWwZm+JltxKncNsuT8htwoMaTAQV2vA3S4crUC+u4JMLzU1nbw0L5GAXCpBJgWZRIJcKkFqv2/bfuhfy36ZVIK09X4JyGUSpBLbfbmUVo+z7bdttx1HKpW0faz9nI55SFvNw35smaRl36F/SpmE06LcCfMSDba7Gn1lDRVrbN6IVZt2YzW0XMTJXZ3xnzKWgBnjCZgyrsva8J1ML+nCb//C1KRh8MLzTig1S0QUjJ0qGPU6Hf/+/COrf/kJg14P2Ho+z7v8KiKiojpjup2OIAhoKgrbLGPX5aW3qxpTuHjgN2ysw87HN3YsCiexDeCJYrKYePmXp/h6y3IAzhw+l6cuehUnZed7cjXl1pH9xm7yl2dh0dkuXJwj3Ii5YySDFsaj6Gc9cLVGW+ea1BKNbVm7WENWuRazVbAJG7vIaCNs7LcPFzbtRFIH4sQhdhzHPlzYCNTv301t1lYkWHH3CyZi0jmonVzaHkvSVoy1FV4Su/ACKVCX00DRf2XUtioU8hnsRuy0YMJH+qKQHz6/lnm1fr4y+3OWy7Cfv+X5iKsIIh1h67Jy0BZFXL2V+r3ZbfY7hQQQODOZwBkT8BmX2C2t8U5GMIqcPKJg7ETBWF9by+LbbsJkNBI+JIrzrriK2OFJnTTT7qGpppRP752IvqIYH49ggkOGUZO9C7Ne02acRCrDa0him2VsFz/xA3s06jS13LfiZrYf3IJEIuG2Wfdz3bRbO/0HunpLKfuW7KLk5wNg/3R7jfIn9p7RhJ4fhVTsWtHlmHQaNr18vcOEP3beTYy9+SWk8s75ES3a38D6HwvYub4Ui9n2InsHODFlXgTJs0JxchFXA0ROnUNdVsr/tRWtHN5lxWtELAEzbCLRLSay2y82RMHYvYiC8RQEo8ViITttL3EjWnq9rv39Vzy9vRkxfkKfu1LXN9fz2YNTqMxPwytoCAtf+Q9XrwCsFjN1B9NaOtOkb0FT1d4w1cU/zNYbO95Wke01OAFpN1T69gVyyvZxx2eLKK2zmXG/eNkbTI3rPGsiq8VKyU8HyF66i5qtLSkGwWcPIuaeUfhNDulz78e+SnN5AWueuJi6g2lI5QrG37GM6LOv6ZJzNdTq2fhLIZt+L0TTaAJA5SRjwplhTJkXgU9A57SRFBk4GGobqFy3jfLVKVRu2IFF05LzLlOr8DttFAEzkgmYPh61X892HhMFY/ciCsaTEIyCILBnawo/f/kFFSXF3PPM80QNi+vimXYtJoOOlY+fSWHGRly9Aln4yn94BQ0+4nhNZbE9B9KWC1l3YC9CK2d+ALmTK36xY/FPSMYvfgJ+w8ahdBl4vnKr0//kka/vQmfUEuYTwetXf9hpZtxmjYm85ZnkvLGb5gMNAEiVMiKvjCXmzlG4DxN7fHcn5akbWff0FRgaqlF7+jPtyS/xT5jQ5ec16i1sX1PCuh/zqSiyrQZIpDB8QgDTzh/EoGGe4gWDyBFpPlhE+b9bKP83hdpdh3VZ8fcmcPp4AmZMwG/SyHa9mXsSUTB2L6JgPEHBmJ22l5+++Jz8/bkAuLi6cdkNNzFq4qSunmqXYbWY+fa5C8nZ+jMqFw8WvLSegEHDT+gYJl0z1Vnbqcy0mYpXZW7DpG1sM0YileI5KB7/uJbWhi4B4f32h8xqtfLBmjd4558lAIyPmsSrV7yLh7PnKR9bX6Eh95297H9/L8ZaW76s0ltN1I3Dibp5OE6BYn5pd7Pv5w/Y9vZ9CBYz3kNHMO3Jr3ENOLKxb1dgtQrs21XN2h/yyN5d49geEePB1PMiGXFaIDIxJWHAYzVbqN2ZQYW9FZ8mv6TN/lPpstKdiIKxexEF43EKxsID+/nxi8/ZtzcVAJVazfQ55zJz7jycXPruj7MgCPz6+nXs+ecTZAoVVzz7FxEJp5/yca0WC/UFmXbxuJXK9C00l+e3G+fkE+RYwvaPT8Y7KqnT8rx6Eq1Bw2Pf3MO/6X8AnWfG3ZhVS/bru8j/Yh9Wg82U2HWIB9F3jmTQVXHIxdy1bsdiMrLt7XvJ+fUjACKnXcSke99Fru7Z5eDS/CbW/ZjPjjWlmO3V1Z6+ak4/N4KJZ4bhLPoSDihMjRoqN+6g/N8tVK7bjqmhoy4rEwiYMf6Uuqx0J6Jg7F5EwXgcgtFisbD4tpuorapCJpcz+YzZnHnBRbh7enbvZLuA1Z8+zOZvX0IilXLRI98TM2Fel51LW1PW0towYws1uXsQLG1NmeVqZ3xjxjhyIf3ixqNy6572eJ1FSW0Rdy6/jpyyLOQyBY/Pf575Yy856eMJgkDVxhKyl+yi9PcWE1yf8YHE3jOa4HMHI5X1zghAf0dXV8m6p6+gMm0TSCSMuvYpEi69t1dFzZvqDfz3WyH//VpIU73N+kSplpF8RihTzovAL7jvXvCKHJ1DXVbK/91Czda9CGaLY5/Syx3/qeO6pMtKdyEKxu5FFIxHEIz1NTW4eXoik9lc6FPWrWXf3lTmXHIZvgF94+rrWKSsWsI/H94HwNw7P2TErGu79fxmvZbq7J2OtoaVmVsxNtW1G+cZGWcTj/ZIpFvw4F71g9yaHQdTuHfFTdRpak/ZjNtqtlL8w372Ld1J3U57daIEQs4dQuw9o/CdENyJMxc5UWpy97B28SVoKotQOLtz+iOfEJp8Vk9P64iYjBZ2ritj3ap8SvNt0SWJBOLH+zNtfiRRid699nMlcnw4uqz8m0L56q7tstIbEAVj9yIKxsMEY3NTI3//+APr//idSxbdwMQZM3t6il3C3jWf89NrCwCYvvAFJl30YA/PyPZl11CUbfODzEyhKj2FxpL97capvfwdy9h+8cn4RI1Apuz5ROxvUj7nxZ8WY7aaGRaSwOtXf0ig54mLOlOTkbxPM8h+YzfaAtsPu0wtI3JBHDF3jMRtaN+KuPZH8tZ+x6ZXb8Ri0OEeEsW0p7/BMyK2p6d1XAiCQM6eGtb9mE/GtirH9tAh7kydH8mo04OQK8SIdV/BrNVRtWk3FatTOu6yMjbBZn3TyV1WegOiYOxeRMFoF4yVFRXs2Lief35ahV6rBWDMpMlce/e9PTzDzid3++98/fQ8BKuF8efdxRnXvdZrIwu6ukqqMlOoTE+hMjOFmpxdWE1t28JJFSp8Y0bbBeR4/OOTUXt0X5s7k8XESz8/yTcpnwNwZtK5PHXhKydsxq0rbSb37VT2/y8NU70BAJWfE0NvTmLIjYmo/USLlJ5GsFrZ/enTpK18GYDgMTM5/dHP+lzaxCEqippZ92M+21aXYDLY8hzdvVVMnhPOpLPDce3FvbkHMvqKGsrX2LwRe6rLSm9AFIzdiygY7YLxrqsuw2AXiiERkcy74kriR47utULqZCnO2sLnj87EbNCROO1K5t3zaa+tgOsIi1FPTc7uls40mVsxNFS3G+ceFt2mmMY9LLpLXsva5hru++JmdhxMQSKRcPvs+1k09cTMuOvTq8letovCL7Ox2osT3IZ6EnP3KCKuGIa8k1u/iZwcRk0jG1+8luItvwMQd9GdjL7uWaSyvr2sB6BpNLLpjyI2/lJAQ43tYkWhlDJ2RghTz4skMLzvtY/sTwiCQGPWQcpXb6Hi3xTq03La7HcODXAYaHdXl5XegCgYuxdRMNoF46JzzyY4NJS5l17O6EmTkfYhEXW8VBZk8NkDp6NvriNqzFlc/PiPyPp4RbIgCDSW7LdVY9t9IRsKs9uNU7n72KKPccl4hMfgGhCOa2AESlfPkz53TlmW3Yy7GBeVKy9c+vpxm3ELgkDl2iL2Ld1F+V8Fju1+pwUTc/dogs8ZhETavy5W+jKNJQdY88TFNBRkIVWomHj3WwyZdUVPT6vTMZus7N5YxtpV+RTvb7HFihvjx9T5kcSM9Ol3F9G9FYvBSM3WvfZWfCnoyqra7Ld1WZlA4IzkHumy0hsQBWP3IgpGu2D87btvmXXuPOSKvi2gjkRDZSGf3DeJppoSQmKTufK5f1Cq+15V3PGgb6ihKmurbRk7Yws12TuxGPUdjlU4u+MaEI5LYDiu/jYR6RIQjqt/GK6BEag8fDv8Iv43/Q8e/fpuhxn3Gws+YkhA9DHnZjVZKPw2l+ylu6hPtf0ASKQSQudHEXP3KHzGBZ7akxfpdEp3rmb9s1djbKrDySeQaU99jV/syRUy9RUEQeBAeh3rVuWTllLhaC0fFOnK1PMiGTMtGIWy70dWexuOLiv/bqFy485e3WWlNyAKxu5FFIyd2Eu6t6JtqObTByZTU5yNb3gcC1/egJPbwPmysZiM1O7fQ2VGCtVZ22kqy0NTWYS+vuqYj5WrnXHxD8c1IAyXgHCc/cP5tXkfXx/4B4DkqNN45Yp3jmnGbWo0cOCjDHLe3I2uuBkAmbOcwdfEE337SFwHe5zy8xTpXARBIPP7t9j5wcMIViu+sWOZ9uSXOPsOrOr0qlING34uYMtfxRj1du9PDyWnzQnntHPCcffq+YKzvoogCPYuK7Z8xA67rMxIJmB6cq/rstIbEAVj9yIKxn4uGI26Zj5/ZAalOdtx9wvjmlc34e4rfrAATDoNmsoimisK0FQU0lxeSHNloe12RQG6mvI2441S+DPSjVz7D+SoCh3TKky4+YfZo5PhtuhkgD1KGRAOOjf2v5fOwQ/TMTXaEtPVAc4MvTWJITcMR+Wt7vbnLXJsLEY9W5bdwYG/VwAwZNaVTLjrDWTKgft6aZtNbPmziA2/FFBXaYvYy+QSxkwLZtr8QQQPcuvhGfYNDnVZKf93CxX/bkFTUNpmv/uwwQTOnEDgjAl4JET1qRzz7kYUjN2LKBj7sWC0mIx89dRcDu7+Byd3Hxa+vBHfsL5h/dEbsBj1aCqLaa4oJK8glef3fEaRoRaZAGfXKYgpqGjXP7sdghQM7kj0XqhUAfiNiydk+ijcQyJxCYzAxS8UmUKsRO1NaGvKWLv4Uqr3bUcilTLmxhcZdv6JFTL1ZywWK6mbKli3Kp/8ffWO7TEjfZh6XiTDxvghFfNv22Bq1FC5YTvlq1M67rIyYQSB05MJmJGMc4h/D860byEKxu5FFIz9VDAKViurXrmSjA1foVC7cNXzqwmJGdfT0+qTtDbj9nH1Y+lV7zMicgxWswltdSnN9ohkc3kh1bszqUnfh0FbjqBqAKnl6AeXSHDyDsQ1MKJtlPJQPqV/WI+3mBtIVO3bztrFl6KrKUPp5sWUx5YTPHpGT0+r15KXVcfaVfmkbipHsF87+Ye6MPW8SMbNCEGpHrh5jpqiMnuv5hRqtnXQZWXaOAJnTMB/8mjkruJn/GQQBWP3IgrGfigYBUHgr/fuYPuvbyOVK7h08S8MGTWrp6fVJ/l6y+e89LPNjDsuJJFlV/+vnRm3xWCm8OscspfuoiGjBgCJTELoBYOJWBSGwk9Lc0XLUrdNYNruH6kYpzVqT39cAsJsld0BEbbbdoHpEhCO0qV/vG97mgP/rGTzkluxmgx4hMcy/ZlvcQ8Z0tPT6hPUVujY8HMBm/8sQq+1tft0dlMw6ewwTp8bgYdP/1/KF6xW6lKz7SJxC005+W32uw4Jc1jfeI8ahqQf2DH1NKJg7F5EwdgPBePGr55l3edPgETC/PtWkDD1sp6eUp/DZDby4i9P8m2KLYetIzNuY52eA/9LI+ftVPRlGgDkrgoGL0og+rYRuEQc/f0kCAL6+ip7DmURzeUFLTmU5TZxadY1H3OuSjcvXP3DcAmMaCsq7beVbl7icupRsFos7PrwcTK+XQZA6ISzmfzQx6IQPwn0WjNb/ylm3Y/51JTbKnxlcgkjTw9i2vxIwqL6V3GXo8vKv1uoWLutTZcViUyK95gEW9HKjAm4DgrpwZn2T7pSMN5x1h9c9/gohk/sH62AOwPREbifsfOP921iEZh9wzJRLJ4Etc013LviJnbmbUUikXDH7Ae4duotDtGlyW8k+83d5H2cgVljAsAp2IWht41gyHWJKD2Pr5JRIpHg5OWPk5d/hzYtgiBgbKpzLHlrKopa3bZFKY1NdRib6qhtqqP2wN4OzyN3csU1IMJR6W27bbcSCghH7ek/YAWloamODc8toHTHvwAkXn4/IxcuFgsNThK1s5wp8yKZPCeCtJQK1v2Yz4H0OnasKWXHmlKGJHgxbf4gEsb7I5X1zfecvqLGZqC9eitVCvNGFgAAGYZJREFUmzvushI4Ixn/qWNReooXHT1NXlYdy+5LYdhoP256ekxPT6dPIwrGfkTWpu/5451bATjt0kcZd+7tPTyjvkd2aSZ3Lr/OYcb94mVvMGWYrZ947c4K9i3ZRfH3uQhWW2DeI8GHmLtHE35JNLJO9qWTSCSo3L1RuXvjM3REh2NM2qa2IrLcvtxdabutr6/ErGumPj+D+vyMDo8hU6pb5U2GtxWVAWE4eQf1i24mh9NQmM2axy+isWQ/MpUTk+57n0HTLuzpafULpDIJSZMCSZoUSGFuA+tW5bNrQxkH0us4kF6Hb5AzU+ZFkDwrFFUv72Tk6LJiN9DusMvKzAkETk8eUF1W+gpb/irm9LkRpPxdTEONfkCkR3QVvfuTKnLc5KeuZdXLVyBYrYw683qmXvl0T0+pz/FP2u88+vXd6E06wn0ieWPBRwzyi6L0t4PsW7qLqg0ljrEBM8OJvXsUATPDezQ6p3B2w2tQPF6D4jvcb9Zr7dZBrSOTLVZC2poyLEY9jUU5NBbldHgMqVyBs1+oPW/SHqV0FOlE4OwbjLSPdQwqTvmDDc9fg0nbiItfKNOe/uaIolzk1Agf6sHVDyRx7rUxbPilgM2/F1FdpuX797L4/fNcJpwZxunnRuDtf2K917sSi8FITUoq5atT2ndZkUjwSooZ8F1WegPHyqgz6Mzs3lDGfW9MpKnOyNZ/SjjjksE8s2gDk84OY8aFgx1jiw808vJtm3j8o9PxC27f1KI0r4nv388kP6sehUrGiEmBzL8httdf8HQmA+eZ9mPK9u/i62fOw2I2EjvxfM665R3xC+wEsFqtvLd6Ge/9uwyACUMn8+IFb1C7qow/ln5OU7YtL0kilxJ+STQxd43CK8mvB2d8/MjVzniEx+ARHtPhfovJiKaq2B6dLHBEJg9FKTWVxVjNJprL8mguy+vwGBKpFGef4FY5lOEOH0rXAFult0zZOwyHBUEg/avX2PXxYhAE/BMmMnXxSpy8RCuTrsbTV82518Qw+7IhbPu3hPU/FlBZomHN93msW5XPiNMCmTo/kshYzx6Zn6Gmnop126hYndJxl5XJowiYLnZZ6WwsZis6jRltswlds8n2v8bccrvZjE7Tctu234S2yUR9fcNRj71rQxkBoa4EhLoyZnowP7yfxRmXDCZ5Vihb/ylpIxi3/lPMkASvDsWiQW/mnce2M2iYJ/e+PpHmeiNfvp7Gt+9kcuW9wzv9b9JbEQVjH6e2dD9fLj4bo66JiMSpzL9/Rb9cPuwqtAYNj359N6sz/gTg8jELOTvnLNYnfo+h0vaDoXBXMuT6RIbemoRzaP8yJ5YplLgHD8Y9eHCH+60WM9rqsjZ5k5qKgpYoZWUR1kOis6qYyrRNHR7HySfQVtXtqO62V3oHhOPiH47CqevbVJr1Wja/dgt5a78BIPqcaxl32xLRB7ObUanlTJ4TwaSzw8ncXsW6VfnkpNawa0MZuzaUETnMk2nzIxk+MQCZrOtySQVBoPlAkS2K+O8WandnddhlJXDGBHwnjhC7rBwBQRAw6i3HFHe6ZjNajU0Utt5n0B3Deuxo5z6GDW7K38WMmW5ztRg2xhfdEhP702oZf0YIv3+eS0F2PRExnljMVnasLeW86zr2Kd65tgyz0cqV9w1HpbbJpgtvjuODp3Zy7rUxA6bbkSgY+zBNtWV88dhsNPWVBA4ZySVP/Ih8AHeiOFGKawu587PryC3fh0Kq4GrDAkKu8yFLtw0A5zBXou8YyeBr4lG4D4wvhMORyuS4BoThGhDW4X7BakVXW05zZZEjStlaVGoqCzHrtehqytHVlFOVta3D46g8fO3niWjVLadVpbfrqVXXaiqLWbP4Ympz9yCRyRl366vEnnvDKR1T5NSQSiUkjPcnYbw/JQcbWfdjPjvWlpKfVc8nWXvw9nfi9HMjmHBmKE4unZPyYDVbqN2R7shHbNdlJW6IQyQOpC4rFou1Rew1nViUT6cxY7WcutmKykmGs6sCJxcFTq5ynFwV9vtynN1s251dbfsOjatvquL9pI6PV1HcTEF2A9c9PgoAmUzKqClBbPmrmKvvTyJunB8pfxcTEeNJ+tZKzCYrIycHdXis8qJmgge7OcQiwOB4LwQrVBZrRMEo0rvRN9ez8omzqK/IwytoCJc99TsqZ7Ei73jZfmAL9664iXptHe5md+Z/fw6BpR5YMOM5wo/Ye0YTdkEUUoUYrT0aEqkUZ99gW3/luPHt9guCgKGhuo33ZIt9kM1KyKRtxNBQjaGhmpqc3R2eR+HigWtguMN78nCjc5W7zxHTMCrTt7D2ycvQ11ei8vBl6hNfEJg0uVP/DiKnRshgd664ZzhzF8bw32+FbPy1gNpKHT9+uI8/vsgleVYoU+ZF4ht04gbXji4r/26hcv2ONl1WpEoFPslJjn7NfbXLiiAIGA0WRyTvaOLu0NJv63GnEuU7hFQmaSfoDt13OiT8XBUdCkEnV/lJRZOtxUdekk75qxirReDxK9Y6tgkIyBVSdLfEMWF2KCte3cv8G4aR8ncxo6YEDWij+eNBFIx9EJNBx9dPz6Myby+uXoFc8cxfuHqJXlHHy1ebPuOlX57EIlgIrAjgop/n4d7sRtBZkcTcPQr/KaFiDmgnIZFIUHv6ofb0wzdmdIdjjM31LZHJyiLH7UNWQobGGkyaBuoOpFF3IK3DY8jVLvbIZFibXt662gp2/u9RrGYTXoMTmf70N7gGRnTlUxY5Bdy9VZx91VBmXjyYHWtLWbcqn/LCZtb/VMCGXwpITA5g2vxIBscf3VtUU2jvsrK673RZOTzKd6S8vjYisNX9noryHbqtVMt6zfemxWJl2+oSzrs+lthRvm32ffj0LnauK2PimWEo1TL++62QrJ3V3PlK+wveQwSGubLtnxIMerMjyngwow6J1NbZaKAgCsY+htVi5oeXL6cwYyMqZ3cue/oPvII6zj8TaUttWRXPf/QofzfZ8hXjs2KZu/4soi5JIObOUXjE+/TwDAcmSldPvKM88Y7qeG3JpGtuE51sLSo1lYXoaisw6zU0FGTRUJDV4TEiJp/HpAc+QOHk2pVPRaSTUKpkTDwzjAmzQ9m3q5p1P+aTtaOavZsr2Lu5gvChHkydH8nIyYHI5LbIlFmnZ//731D2538ddlkJnDGBgBnJXdZlpU2U73iXc/tBlK83krG1Cm2TiQmz26czJE0KIOWvYk47J5zxM0P55ZNs/IKdGTTM64jHGzMtmN9X5LLi1b2cdeVQmhuMfPduJmOnhwyY5WgQBWOfQhAEfnvzRnJSfkKmUHHJ4p8JHHyEBA4RAEyNBrZ8s4avtn/OVuetmJQmEOCMndO4esINRL8/AqeggXOF2BdROLniFRmHV2Rch/stRj3NdhGpab30XVGIobGWIWdcTsIl9wyYfLT+hEQiYdhoP4aN9qOsoIn1PxawfU0JhbkNLH85lZ8+2ufIc8x+YiklP9uWHyUyKd5jEwmcPv6EuqxYLFb0jsje0ZdzD4/y6TQmLGYxytcb2PJXETEjfTvMfR1xWiCrv8ujJK+R5Nmh/P31AZJnHb1LjFIt45Znx/L9+5m8dufmNrY6AwmxNWAfag24+tOH2fztS0ikUi565HtiJszr6Sn1SkzNRop+2c9vf3zPP5a/yA8tdOwLaPZnUeTNXHDzVShcxepYEZG+RlO9gc1/FLHxl0Ia6wwAyOUQWLWH8MZUxjx8NR6TxmKSqo+7aOPQuM6K8h0SdwM5ytcdnGprwAPptbz18DaeWj5tQEUKTxZRMPYRwZiyain/fHgvAHPu+B8jZy/q4Rn1LsxaE2V/5JPx/U7+LP2VHcN20+huS26XCBLGyMdy5YzrmDp9tngFLiLSDzAZLexaX8bqr3MoLzF06rHFKF/f4GQFo8loobnByBevpeHmpWTBgyO6bpL9CHFJug+wd80Kh1icvuB5USzasejNlP1VQNG3OWzftpmtMdvJjM7GEmaLErjhxnnxF3HF3OsI9urcxvQiIiI9i0IpY8xkPzSvPU1hkZmKqGmUmnwRhLZRvkPiTozyiRxi1/oyVi5LI2SwO1feN3CMt08VUTD2cnK3/84vy64FYPx5dzHxogd7eEY9i8VooeKfAgq/zaXgtxzSgtPZkbSb0vnljjEx3nFcNeM6ZifNQaUQfSlFRPor6c+8S3NOPkG+Xlz64RwEZ1ekUokY5RM5KuPPCGX8GWIQ4UQRBWMvpjhrC9+9cBFWi5nEaVdyxqJXB+SXoNVkoWJtMUXf5lD80wFqzFXsGr6X3ZfuRets78YiVTA7aS6XTVpIYtiInp2wiIhIl1Py6zoKvvwdJBJGLXkQtd+Rq1xFREROHVEw9lIqCzL48sk5mA06osacxdy7PhpQVZ5Ws5WqDcUUfptL8Y/7MdToKAwtZsdpu8kZsh+r1JZ66+8eyMUTruKCcZfh4+p7jKOKiIj0BzQFpaQ+sgyAobdcit9po3p2QiIiAwBRMPZCGioLWfn4meib6wiJTeaCh79BJu+c1li9GavFSvWmUoq+y6Xoh1wMlTqMCiNpw7LYdW4qlZ5VjrFjB0/g0okLmBY3C7lMfBuLiAwUrEYTO+94HnOzFu8xCcTceXVPT0lEZEAw4H9pDxWJNzY29vBMbGgbavjiidnUlhfjExrD2XevRG+0oDf2jvl1NoJVoHZHOUWrDlDy8wH05VoA6jzq2Ts9k/SYDHQSPQBqq5ozR8zjwnGXMyRgKABajbbH5i4iItL9ZL78EaWpmSg8XBn6zK00azU9PSWRHqKpyeaEMcDNXrqNAW+rc/DgQYYMGdLT0xARERERERE5Cfbs2UNSktjEoqsZ8BFGb29vAAoLC/Hw8Ojh2Yh0NY2NjQ7frt7suynSOYiv98BCfL0HFvX19URERBAeHt7TUxkQDHjBKLUXknh4eIhfMAMId3d38fUeQIiv98BCfL0HFrIu6A0u0p6BU3YrIiIiIiIiIiJyUoiCUURERERERERE5KgMeMGoUqlYvHgxKpXYeHwgIL7eAwvx9R5YiK/3wEJ8vbuXAV8lLSIiIiIiIiIicnQGfIRRRERERERERETk6IiCUURERERERERE5KiIglFEREREREREROSoiIJRRERERERERETkqPR5wfjCCy8wduxY3Nzc8Pf357zzziM7O7vNGL1ez6233oqPjw+urq5ccMEFVFRU/L+9u41p6nzDAH7xIrRVq9aBxOAbYahTMgSjEqOEMME4jcEsGupbsqBjYrB+gGhKxCYmxqAhGsRE95ZsEMVplC3gZhQRI5joSrcyTMdQohHEbaIwHQK9/x+MdRXWP+rhQOH6Jf3AOc95znPnKnC3pyd1G5ORkYGYmBgEBgYiKiqq13MVFxcjKioKOp0OU6ZMQW5ubn+VRf9BibxtNhtSUlIwadIkaLVazJw5EwcPHuxxrkuXLiE6OhqBgYEIDw/HV1991d/l0SvUyrupqQlGoxERERHw9fWFyWRSozx6hVp5nz59GkuWLEFQUBD0ej1iY2Pxww8/qFIjvaRW3leuXMHChQsxfvx4aLVazJgxA3l5earUOJR4fcNYUVGB9PR0VFdX4/z58+js7ERiYiL+/vvlF9Jv374d3333HU6ePImKigrcu3cPq1at6jHXxx9/jDVr1vR6nrKyMqxduxZpaWmw2+0oKChAXl4e8vPz+6026kmJvG/cuIHg4GB88803qK2thdlsxs6dO92yvHXrFj788EPEx8ejpqYGJpMJqamp/KeiMrXy7ujoQFBQELKzs/mdtANIrbwvX76MJUuWoLS0FDdu3EB8fDxWrFgBq9Wqar3DnVp5jxw5Elu3bsXly5dRV1eH7OxsZGdn4+jRo6rW6/VkiGlpaREAUlFRISIira2tMmLECDl58qRrTF1dnQCQqqqqHsfn5OTI+++/32N7SkqKfPTRR27bDh06JKGhoeJ0OpUtgvrsbfN+YcuWLRIfH+/6OSsrS2bNmuU2Zs2aNZKUlKRwBfQ6+ivvf4uLi5Nt27Ypum56M2rk/cJ7770nFotFmYXTG1Ez7+TkZFm3bp0yCx8mvP4dxlc9evQIAGAwGAA8f/XR2dmJDz74wDVmxowZmDx5Mqqqqvo8b0dHBzQajds2rVaLu3fvorGxUYGV05tQKu9Hjx655gCAqqoqtzkAICkp6bWeM6S8/sqbBie18nY6nWhra+NzYoCplbfVasXVq1cRFxen0MqHhyHVMDqdTphMJixcuBCzZ88GADQ3NyMgIABjx451GzthwgQ0Nzf3ee6kpCScPn0aFy5cgNPphMPhwIEDBwA8//wTqU+pvK9evYoTJ05g8+bNrm3Nzc2YMGFCjzkeP36Mp0+fKlsI9Ul/5k2Dj5p579+/H+3t7Vi9erVi66fXo0beoaGhCAwMxNy5c5Geno7U1FTF6xjK/Ad6AUpKT0+H3W7HlStXFJ9706ZN+P3337F8+XJ0dnZCr9dj27Zt2L17N3x9h1Tf7TWUyNtut2PlypXIyclBYmKigqsjpTHv4UWtvIuKimCxWHD27FkEBwe/8bno7aiRd2VlJdrb21FdXY0dO3YgPDwcKSkpb7PsYWXIdDpbt27F999/j/LycoSGhrq2h4SE4NmzZ2htbXUbf//+fYSEhPR5fh8fH+zbtw/t7e1obGxEc3Mz5s2bBwAICwtTpAbqOyXy/vXXX5GQkIDNmzcjOzvbbV9ISEiPO+nv378PvV4PrVarbDH0f/V33jS4qJX38ePHkZqaiuLi4h4fQSH1qJX3tGnTEBkZiU2bNmH79u3YvXu30qUMbQP9Icq35XQ6JT09XSZOnCgOh6PH/hcfmv32229d227evPnaN730Zv369RIbG/vGa6fXp1TedrtdgoODJTMzs9fzZGVlyezZs922paSk8KYXlamV97/xppeBo2beRUVFotFo5MyZM8oWQX02EL/fL1gsFpkyZcpbrX+48fqG8dNPP5UxY8bIpUuXpKmpyfV48uSJa0xaWppMnjxZLl68KNevX5fY2Ngejd5vv/0mVqtVPvnkE4mIiBCr1SpWq1U6OjpEROTBgwdy5MgRqaurE6vVKhkZGaLRaOTatWuq1jvcKZH3L7/8IkFBQbJu3Tq3OVpaWlxjGhoaRKfTSWZmptTV1cnhw4fFz89Pzp07p2q9w51aeYuI63c+JiZGjEajWK1Wqa2tVa1WUi/vwsJC8ff3l8OHD7uNaW1tVbXe4U6tvPPz86WkpEQcDoc4HA757LPPZPTo0WI2m1Wt19t5fcMIoNfHl19+6Rrz9OlT2bJli4wbN050Op0kJydLU1OT2zxxcXG9znPr1i0Red4wLliwQEaOHCk6nU4SEhKkurpaxUpJRJm8c3Jyep3j1Veb5eXlEhUVJQEBARIWFuZ2DlKHmnn3ZQz1L7Xy/q+/9xs3blSvWFIt70OHDsmsWbNEp9OJXq+XOXPmSEFBgXR3d6tYrffzERF5m0vaRERERDS0DZmbXoiIiIiof7BhJCIiIiKP2DASERERkUdsGImIiIjIIzaMREREROQRG0YiIiIi8ogNIxERERF5xIaRiIiIiDxiw0hEREREHrFhJCKv0NXVhbCwMGRkZPTYl5aWhnfffRd//PHHAKyMiGjoY8NIRF7B398fO3fuxBdffIG//vrLtX3v3r04deoUysrK8M477yh6zq6uLkXnIyLyVmwYichrbNy4EQaDAfn5+QCAwsJC7NmzByUlJQgICIDRaMS4ceNgMBiwdu1aPHz40HWs1WrFihUrEBISAq1Wi+joaFRUVLj23759Gz4+PiguLsaiRYsQGBiIkpIS1WskIhqM2DASkdcICAhAVlYW8vPzUVpaitTUVHz99dcICgpCTEwMwsPDUV1djfPnz6O+vh6ZmZmuY+/duwej0Yjy8nLU1NQgJiYGq1evdu232WwAgNzcXOzatQu1tbVISEhQvUYiosHIR0RkoBdBRNRX//zzD6ZNm4aWlhYcOHAAJpMJiYmJiI2NhcVicY07deoUMjMz0dDQ0Os8DocD06dPx8OHDzF27FhYLBbk5ubCbrdj6tSpKlVDROQd2DASkdcxGo24c+cOKisr0djYiKlTp0Kr1cLX9+VFk+7ubkyaNAkOhwMAUFRUhGPHjqG+vh6tra3o7u6Gn58f2traAACrVq2CRqNBUVHRgNRERDSY+Q/0AoiIXtfPP/+MpUuXAnh+KdlgMODatWs9xmm1WgCAxWJBQUEBLBYL5s6dizFjxiAvL891GRoAampqsGPHDnUKICLyMmwYicirPHnyBDdv3oTZbAYAjBgxAm1tbZg4cSJ0Ol2vx3z++ecwm81IS0sDALS3t6OsrAzLli0DADx+/Bi3b9/GnDlz1CmCiMjL8KYXIvIqNpsN3d3diI6OBgDMnz8fer0eGzZsgM1mQ319Pc6dOweTyeQ6Zvz48fjxxx9RX1+PyspKJCcn4+7du64G0Wazwc/PD5GRkQNREhHRoMeGkYi8yk8//YRRo0YhIiICAGAwGFBaWoo///wTixcvRnR0NMxmM8LCwlzHFBQUoKGhAZGRkcjKyoLJZEJXVxeioqIAPG8Yp0+fDo1GMxAlERENerzphYiIiIg84juMREREROQRG0YiIiIi8ogNIxERERF5xIaRiIiIiDxiw0hEREREHrFhJCIiIiKP2DASERERkUdsGImIiIjIIzaMREREROQRG0YiIiIi8ogNIxERERF59D9dIb6KJmJtuQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show(\"<h2>Report Counts Per Provider Per Year</h2>\")\n",
    "\n",
    "import matplotlib as mp\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Currently unused:\n",
    "def mega_tick(y: int | float, _pos: object) -> str:\n",
    "    if y == 0:\n",
    "        return \"0\"\n",
    "    if y % 1_000_000 == 0:\n",
    "        return format(int(y) // 1_000_000, \",\") + \"M\"\n",
    "    return format(int(y) / 1_000_000, \",\") + \"M\"\n",
    "\n",
    "ncmec = wide_ncmec_reports(platform_data).rename(columns={\"Alphabet\": \"Google\"})\n",
    "\n",
    "OFFSETS = {\n",
    "    \"Meta\": 0,\n",
    "    \"Google\": 4,\n",
    "    \"X\": 7.5,\n",
    "    \"Snap\": 3.5,\n",
    "    \"TikTok\": -2,\n",
    "    \"Discord\": 3,\n",
    "    \"Reddit\": -2.5,\n",
    "    \"Omegle\": 0,\n",
    "    \"Microsoft\": -2.5,\n",
    "    \"Pinterest\": 0,\n",
    "    \"Amazon\": 0,\n",
    "    \"Automattic\": 0,\n",
    "    \"Quora\": 0,\n",
    "    \"Aylo\": 0,\n",
    "}\n",
    "\n",
    "ALL_PLATFORMS = list(OFFSETS.keys())\n",
    "\n",
    "def plot(ax, platforms, colors, dash_omegle=False):\n",
    "    settings = { p: (OFFSETS[p], c) for p, c in zip(platforms, colors)}\n",
    "\n",
    "    ncmec[platforms].plot(ax=ax)\n",
    "\n",
    "    ax.set_title(None)\n",
    "    ax.legend().remove()\n",
    "    ax.set_xlabel(\"Year\", fontstyle=\"italic\")\n",
    "    # ax.get_yaxis().set_major_formatter(mp.ticker.FuncFormatter(mega_tick))\n",
    "    ax.set_ylabel(\"Reports\", fontstyle=\"italic\")\n",
    "    ax.grid(axis=\"y\")\n",
    "    ax.set_ylim(1800, 36_000_000)\n",
    "    ax.set_yscale(\"symlog\")\n",
    "\n",
    "    positions = [ncmec.loc[\"2023\", p] for p in platforms]\n",
    "    alt = ax.secondary_yaxis(location=\"right\")\n",
    "    alt.set_ylim(1800, 36_000_000)\n",
    "    alt.set_yscale(\"symlog\")\n",
    "    alt.set_yticks(positions, labels=platforms)\n",
    "    alt.tick_params(axis=\"y\", length=0)\n",
    "\n",
    "    for platform, line, label in zip(platforms, ax.get_lines(), alt.get_yticklabels()):\n",
    "        raw_offset, color = settings[platform]\n",
    "\n",
    "        line.set_color(color)\n",
    "        if platform == \"Omegle\" and dash_omegle:\n",
    "            line.set_linestyle(\"dashed\")\n",
    "        label.set_color(color)\n",
    "\n",
    "        if raw_offset != 0:\n",
    "            offset = mp.transforms.ScaledTranslation(\n",
    "                0, raw_offset/72, ax.figure.dpi_scale_trans\n",
    "            )\n",
    "            label.set_transform(label.get_transform() + offset)\n",
    "\n",
    "    return ax\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plot(ax, ALL_PLATFORMS, COLOR_GRIMM, dash_omegle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(\n",
    "    nrows=1, ncols=2,\n",
    "    figsize=[7, 4.8],\n",
    "    gridspec_kw=dict(wspace=0.27),\n",
    ")\n",
    "\n",
    "P1 = ['Meta', 'Google', 'X', 'Snap', 'TikTok', 'Reddit', 'Amazon', 'Quora']\n",
    "P2 = ['Meta', 'Google', 'Discord', 'Omegle', 'Microsoft', 'Pinterest', 'Automattic', 'Aylo']\n",
    "\n",
    "plot(ax1, P1, COLOR_GRIMM_OBSERVABLE)\n",
    "plot(ax2, P2, COLOR_GRIMM_OBSERVABLE)\n",
    "ax2.set_ylabel(None)\n",
    "ax2.tick_params(labelleft=False)\n",
    "fig.savefig(\"../figure/platforms.svg\", bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}