SPATZ/STAHR_antennas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# Handle all includes\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import os\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from spatz.utils.preprocess import preprocess_file\n",
    "from spatz.simulation import Simulation, UniformTimeSteps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Time             R      LONG      DECL  PROP_TANK@STAHR  Norm_Indep_Var  \\\n",
      "1 -0.100  6.360124e+06  0.367914  1.182477        41.963251            0.00   \n",
      "2 -0.099  6.360124e+06  0.367914  1.182477        41.961249            0.01   \n",
      "3 -0.099  6.360124e+06  0.367914  1.182477        41.961249            0.01   \n",
      "4 -0.098  6.360124e+06  0.367914  1.182477        41.959248            0.02   \n",
      "5 -0.098  6.360124e+06  0.367914  1.182477        41.959248            0.02   \n",
      "\n",
      "   mission_time  flight_time   julian_date      radius  ...  inertia_xy  \\\n",
      "1        -0.100        0.000  2.460599e+06  6360.12389  ...         0.0   \n",
      "2        -0.099        0.001  2.460599e+06  6360.12389  ...         0.0   \n",
      "3        -0.099        0.001  2.460599e+06  6360.12389  ...         0.0   \n",
      "4        -0.098        0.002  2.460599e+06  6360.12389  ...         0.0   \n",
      "5        -0.098        0.002  2.460599e+06  6360.12389  ...         0.0   \n",
      "\n",
      "   inertia_xz  inertia_yz  aero_coef_CD@Drogue_Chute  \\\n",
      "1         0.0         0.0                        0.0   \n",
      "2         0.0         0.0                        0.0   \n",
      "3         0.0         0.0                        0.0   \n",
      "4         0.0         0.0                        0.0   \n",
      "5         0.0         0.0                        0.0   \n",
      "\n",
      "   aero_area_ref@Drogue_Chute  aero_coef_CD@Reefed_Main_Chute  \\\n",
      "1                         0.0                             0.0   \n",
      "2                         0.0                             0.0   \n",
      "3                         0.0                             0.0   \n",
      "4                         0.0                             0.0   \n",
      "5                         0.0                             0.0   \n",
      "\n",
      "   aero_area_ref@Reefed_Main_Chute  aero_coef_CD@Main_Chute  \\\n",
      "1                              0.0                      0.0   \n",
      "2                              0.0                      0.0   \n",
      "3                              0.0                      0.0   \n",
      "4                              0.0                      0.0   \n",
      "5                              0.0                      0.0   \n",
      "\n",
      "   aero_area_ref@Main_Chute  Phase  \n",
      "1                       0.0  onpad  \n",
      "2                       0.0  onpad  \n",
      "3                       0.0  onpad  \n",
      "4                       0.0  onpad  \n",
      "5                       0.0  onpad  \n",
      "\n",
      "[5 rows x 275 columns]\n"
     ]
    }
   ],
   "source": [
    "# Do preprocessing\n",
    "PATH = \"data/simulations\"\n",
    "TMP = f\"{PATH}/temp\"\n",
    "FILE = \"13.5\"\n",
    "\n",
    "if os.path.isdir(TMP):\n",
    "    shutil.rmtree(TMP)\n",
    "os.mkdir(TMP)\n",
    "\n",
    "df = preprocess_file(f\"{PATH}/{FILE}.txt\")\n",
    "df.to_csv(f\"{TMP}/{FILE}.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create simulation objects\n",
    "timesteps = UniformTimeSteps(0.1, mu=0, sigma=0, delay_only=True)\n",
    "simulation = Simulation(timesteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spatz.sensors.antenna.pathloss import AntennaPathloss\n",
    "\n",
    "alt = simulation.add_observer(['altitude'])\n",
    "fspl = simulation.add_sensor(AntennaPathloss,frequency=2.45e9,rx_antenna_offset=np.array([-1500,-1500,0]))\n",
    "\n",
    "simulation.load(f\"{TMP}/{FILE}.csv\")\n",
    "logger = simulation.get_logger()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████▉| 521.40000000005/521.469806781021 [00:17<00:00, 29.06it/s]  \n"
     ]
    }
   ],
   "source": [
    "# Run simulation\n",
    "for step, t, dt in simulation.run(verbose=True):\n",
    "    gain = fspl()\n",
    "    alt()\n",
    "\n",
    "df = logger.get_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>antenna/pathloss/distance</th>\n",
       "      <th>antenna/pathloss/out</th>\n",
       "      <th>general/altitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>2121.343699</td>\n",
       "      <td>106.763326</td>\n",
       "      <td>319.189854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>2121.41328</td>\n",
       "      <td>106.763611</td>\n",
       "      <td>319.754649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.3</td>\n",
       "      <td>2121.528819</td>\n",
       "      <td>106.764084</td>\n",
       "      <td>320.689767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.4</td>\n",
       "      <td>2121.690116</td>\n",
       "      <td>106.764744</td>\n",
       "      <td>321.98961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.5</td>\n",
       "      <td>2121.897233</td>\n",
       "      <td>106.765592</td>\n",
       "      <td>323.649225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5209</th>\n",
       "      <td>521.0</td>\n",
       "      <td>10823.478921</td>\n",
       "      <td>120.918442</td>\n",
       "      <td>321.436986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>521.1</td>\n",
       "      <td>10823.478982</td>\n",
       "      <td>120.918442</td>\n",
       "      <td>320.918266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5211</th>\n",
       "      <td>521.2</td>\n",
       "      <td>10823.479069</td>\n",
       "      <td>120.918443</td>\n",
       "      <td>320.399547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5212</th>\n",
       "      <td>521.3</td>\n",
       "      <td>10823.47918</td>\n",
       "      <td>120.918443</td>\n",
       "      <td>319.880828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>521.4</td>\n",
       "      <td>10823.479316</td>\n",
       "      <td>120.918443</td>\n",
       "      <td>319.362108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       time antenna/pathloss/distance antenna/pathloss/out general/altitude\n",
       "0       0.1               2121.343699           106.763326       319.189854\n",
       "1       0.2                2121.41328           106.763611       319.754649\n",
       "2       0.3               2121.528819           106.764084       320.689767\n",
       "3       0.4               2121.690116           106.764744        321.98961\n",
       "4       0.5               2121.897233           106.765592       323.649225\n",
       "...     ...                       ...                  ...              ...\n",
       "5209  521.0              10823.478921           120.918442       321.436986\n",
       "5210  521.1              10823.478982           120.918442       320.918266\n",
       "5211  521.2              10823.479069           120.918443       320.399547\n",
       "5212  521.3               10823.47918           120.918443       319.880828\n",
       "5213  521.4              10823.479316           120.918443       319.362108\n",
       "\n",
       "[5214 rows x 4 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124.65577958452681\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/pathloss/out'][1:], label='FSPL')\n",
    "plt.ylabel(\"Frespace pathloss [dB]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.max(df['antenna/pathloss/out'][1:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16643.081265141995\n"
     ]
    },
    {
     "data": {
      "image/png": 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tLIWLXIZHg7xtHY7VyWQyfDyoNbo2DcAtfTlGrDiAC7lFtg6LiIhsjEUTWez45YpRpiZ1veCmcLFxNNJQuMjx72Ht0FzjjWsFJXh1+T7kF+ltHRYREdkQiyaymLFoCnfCqbm7ebspsOK1xxDs44Y/rxbijf8cQElZua3DIiIiG2HRRBYzrmcKD3buogkAND5uWP5aR3irXLEvIxf/b81hGAx8uC8RUW3EooksJhZNTj7SZNRco8aSl9rDVS7DhkOX8enmk7YOiYiIbIBFE1lEV6zH+esVi6Jrw0iTUZemAfhkcBsAwJe7zuHLnX/aOCIiIqppLJrIIiev3AQAhPi4wc9TaeNoatZz7evjvd7NAQCzfz2JH/dfsHFERERUk1g0kUWOX67Y1LK2TM393ZtPPoJ/dmsMAHhv7WFsOZZl44iIiKimsGgii9SmReD3817v5vhHh4rNL8f8kIbkP6/bOiQiIqoBLJrIIrVtEfi9yGQyfDywNWLCg1BaZsAb3x/A0Uv5tg6LiIgkxqKJzFZuEHAmuwBAxR1ltZmrixwLhkaiU5g/CkrKMPzbfTibU2DrsIiISEI2LZp27dqFZ555BiEhIZDJZFi/fv1927755puQyWSYN2+eyfHc3FwMGzYMarUavr6+GDlyJAoKTP94HT58GF27doWbmxtCQ0ORkJBQqf81a9agefPmcHNzQ+vWrbFp0yZrXKJTycwtQkmZASpXOUL9PWwdjs25KVzw9fAOaFVPjeuFpRj29V6cv15o67CIiEgiNi2aCgsLERERgcWLFz+w3bp167B3716EhIRUOjds2DAcO3YMWq0WGzduxK5duzBq1CjxvE6nQ0xMDBo2bIjU1FR89tlnmD59OpYtWya22bNnD4YOHYqRI0ciLS0NAwYMwIABA3D06FHrXawTOJ1dcedck0AvuMhlNo7GPni7KfDda4/h0SAvZOtK8OJXKbh4g8+pIyJyRjYtmnr37o1Zs2Zh4MCB921z6dIljBkzBitXroRCoTA5d+LECWzevBlff/01OnXqhC5dumDhwoVYtWoVLl++DABYuXIlSktL8e2336Jly5YYMmQI3nnnHcydO1fsZ/78+ejVqxcmTZqEFi1a4MMPP0S7du2waNEiaS7cQZ25XTQ540N6q6OOlwr/93onNA7wxKW8Wxj2dQqy8ottHRYREVmZq60DeBCDwYCXX34ZkyZNQsuWLSudT05Ohq+vLzp06CAei46OhlwuR0pKCgYOHIjk5GR069YNSuWdPYViY2Px6aef4saNG/Dz80NycjImTJhg0ndsbOwDpwtLSkpQUlIivtbpKhZI6/V66PXWfbCrsT9r92upk7cXgT8S4GHzWKzBmnn1c3PBilfb48Vv9uP89SK8+NVerBzZAQFeqmr37Yjs5TvrbJhX6TC30nCEvFoSm10XTZ9++ilcXV3xzjvv3PN8VlYWAgMDTY65urrC398fWVlZYpuwsDCTNkFBQeI5Pz8/ZGVlicfubmPs415mz56NGTNmVDqelJQEDw9p1vtotVpJ+jVX6p8uAGTIv3ASmzadsGks1mTNvI5oBCwodMG5a4UYtHAHxoSXw1PxsHc5L1t/Z50V8yod5lYa9pzXoiLzl1TYbdGUmpqK+fPn4+DBg5DJ7G/9zJQpU0xGp3Q6HUJDQxETEwO12rp3lun1emi1WvTs2bPSFGVNKSs34P/t2wZAwNA+TyLUz/EXgkuV125PFuHFb/bjys0S/OeiH757rT38PGrX7un28J11RsyrdJhbaThCXo0zReaw26Lp999/R05ODho0aCAeKy8vx8SJEzFv3jz89ddf0Gg0yMnJMXlfWVkZcnNzodFoAAAajQbZ2dkmbYyvH9bGeP5eVCoVVKrKUy8KhUKyL4aUfT/M+RsF0JcLcFe4oFGAGnInWghu7bw20fgg8Y1OGLJsL05k3cQry1Ox8vVOqFMLp+ps+Z11ZsyrdJhbadhzXi2Jy273aXr55Zdx+PBhpKeniz8hISGYNGkStmzZAgCIiopCXl4eUlNTxfdt374dBoMBnTp1Etvs2rXLZM5Sq9WiWbNm8PPzE9ts27bN5PO1Wi2ioqKkvkyHcWcRuJdTFUxSaRLojVWjOqOutwons25iyLK9yLnJxeFERI7MpkVTQUGBWBABQEZGBtLT05GZmYk6deqgVatWJj8KhQIajQbNmjUDALRo0QK9evXCG2+8gX379uGPP/7A6NGjMWTIEHF7ghdffBFKpRIjR47EsWPHsHr1asyfP99kam3s2LHYvHkz5syZg5MnT2L69Ok4cOAARo8eXeM5sVenbhdNTXnnnNmaBHpj9ajO0KjdcCanAEOW7UW2joUTEZGjsmnRdODAAURGRiIyMhIAMGHCBERGRiI+Pt7sPlauXInmzZujR48e6NOnD7p06WKyB5OPjw+SkpKQkZGB9u3bY+LEiYiPjzfZy+nxxx9HYmIili1bhoiICPz0009Yv349WrVqZb2LdXDGncAfDfKycSSOpXFdL6z+Z2fU83XHuauFeOHLZFzOu2XrsIiIqApsuqbpqaeegiAIZrf/66+/Kh3z9/dHYmLiA9/Xpk0b/P777w9s8/zzz+P55583O5ba5jRHmqqsYR1PrBrVGUO/2ou/rhfhhWXJ+OGNzqjvBIvpiYhqE7td00T2Q19uQMa1iseDcGPLqgn198Dqf0ahYR0PXMi9hRe+3CvmlIiIHAOLJnqozNwilBkq7pwLVrvZOhyHVc/XHatHRYk7hz+/dA+OXc63dVhERGQmFk30UOeuVoyIhAV48s65atL4uGH1P6MQHqzGtYJSDPlyL/Zl5No6LCIiMgOLJnqoc1crFoE3rutp40icQ11vFVb9szMea+SPmyVlePmbFGw7kf3wNxIRkU2xaKKHMq69aVyXd85Zi9pNge9HPoYezQNRUmbAqP+kYl3aRVuHRURED8CiiR7KOD33CEearMpN4YKlL7fHwMh6KDcIGL/6EJb/kWHrsIiI6D5YNNFDnbt2e3ougCNN1qZwkWPO8xF47YlGAIAZG47jk19PwmAwfysOIiKqGSya6IHyb+lxraAUABDGkSZJyOUyxPcLx8SejwIAlu78E2NXp6OkrNzGkRER0d1YNNEDGReBB6lV8FLZ7fOdHZ5MJsOYHk3x+fMRcJXLsOHQZbz89T7kFZXaOjQiIrqNRRM9kHE9E6fmasZz7evjuxGPwVvlin1/5WLQkj24kFtk67CIiAgsmughxPVMnJqrMU80CcCat6IQ7OOGc1cLMfDff+DwxTxbh0VEVOuxaKIH4nYDttFco8a6t59Ai9ubYL7w5V78euSKrcMiIqrVWDTRA92ZnuNIU03T+Ljhx392RrdH6+KWvhxvrTyIBdvOWPSQayIish4WTXRfBoNw10gTiyZb8HZT4NvhHcQtCeZqT2P0D2m4Vco764iIahqLJrqvS3m3UFJmgNJFjvp+HrYOp9ZydZHjg2da4pNBraFwkeGXw1fw/Jd7cDnvlq1DIyKqVVg00X0ZR5ka1PGACx/Ua3NDHmuAla93hr+nEkcv6fDsoj9wMPOGrcMiIqo1WDTRfZ2/fat7ozqcmrMXj4X54+e4J9Bc441rBSUY8uVe/LAvk+uciIhqAIsmuq/M6xUjTQ3rcGrOnoT6e+C/bz2O2JZBKC03YMraI5j802EU67nOiYhISiya6L7OX68YaWLRZH88Va5YMqw9JsU2g1wGrEm9iMFL9iDzOjfCJCKSCosmuq/M29NzDfxZNNkjuVyGuKeb4D8jO8HfU4ljl3V4ZtFu/HYyx9ahERE5JRZNdE+CINw10sQ1TfbsiSYB2DimCyJCfZF/S48R3+3HXO1plBu4zomIyJpYNNE9Xb1Zglv6cshlQD1fd1uHQw8R4uuOH//ZGS91bgBBABZsO4NhX+9FVn6xrUMjInIaLJronox3zoX4ukPpyq+JI1C5umDWgNb44oUIeChdsPdcLnrP34XtJ7NtHRoRkVNwNafR4cOHLe44PDwcrq5mdU92iIvAHdfAyPqIqO+LMT+k4dhlHUasOICRXcIwuVczqFxdbB0eEZHDMquqadu2LWQymdl7wcjlcpw+fRqNGzeuVnBkO8btBhr4cz2TI2pc1wtr334cn/x6Esv/+Avf7M7AvoxcLBwaiUZ8jiARUZWYPRSUkpKCunXrPrSdIAho1apVtYIi27uzsSVHmhyVytUFHzzTEo8/EoBJPx3CkUv56LPgd0zrF44hHUMhk3GXdyIiS5hVND355JNo0qQJfH19zeq0W7ducHfn4mFHxuk559EzPAi/ju2KcavSkZKRiylrj0B7PBufDGqNQLWbrcMjInIYZq3w/e2338wumABg06ZNCA4OrmpMZAfu7NHEqRxnEOzjjh/e6Iz3+7SA0kWO7SdzEDNvF345fMXWoREROQzeFkWV3CzWI7ewFEDFw3rJOcjlMrzRrTE2jOmCliFq5BXpEZd4EONWpSG/SG/r8IiI7J5FRdOZM2fw3//+FxkZGQCAX375Bd26dUPHjh3x0Ucf8aGhTsI4NRfgpYSXindAOptmGm+se/sJjOneBHIZsD79MmLncWsCIqKHMbtoWrduHcLDw/Hiiy+iRYsW+P777/Hcc8/B09MTQUFBmD59OhISEqSMlWoIH5/i/JSuckyMaYaf3nocYQGeyNIVY8SKA3jnhzRcLyixdXhERHbJ7KLpo48+wuTJk1FcXIwlS5bgzTffxOzZs/Hrr79i48aNWLx4MVasWCFhqFRTLt6oKJpCWTQ5vXYN/LDpna54o2sY5DLgf4cuI3ruTqxLu8iRYyKivzG7aDp16hRGjBgBmUyG4cOHo7S0FNHR0eL5mJgYnD9/XpIgqWZdunELAB+fUlu4K13wft9wrHv7CTTXeONGkR7jVx/Cq8v3iwU0ERFZUDQVFhbC29u74k1yOdzd3eHhcWckwt3dHSUlHNZ3BhdvF031/TjSVJtEhPpiw5gumBTbDEoXOXaevoqYL3bhq13noC832Do8IiKbM7tokslkJpvh/f01OY9LebdHmvw40lTbKFzkiHu6CTaN7YqOjfxQVFqOjzadQN8FvyP5z+u2Do+IyKbMvjVKEAQ8+uijYqFUUFCAyMhIyOVy8Tw5PkEQ7hppYtFUWzUJ9MLqUVH4KfUiPtl8EqezCzD0q73o3zYE/+rTAkHcFJOIaiGzi6bly5db/cN37dqFzz77DKmpqbhy5QrWrVuHAQMGAAD0ej2mTp2KTZs24dy5c/Dx8UF0dDQ++eQThISEiH3k5uZizJgx2LBhA+RyOQYPHoz58+fDy8tLbHP48GHExcVh//79qFu3LsaMGYPJkyebxLJmzRpMmzYNf/31F5o2bYpPP/0Uffr0sfo127v8W3oUlJQB4Jqm2k4ul+EfHUMR0zIInyedwsqUTPycfhnbTuRgXHRTDH+8ERQu3OqNiGoPs4um4cOHW/3DCwsLERERgREjRmDQoEEm54qKinDw4EFMmzYNERERuHHjBsaOHYtnn30WBw4cENsNGzYMV65cgVarhV6vx2uvvYZRo0YhMTERAKDT6RATE4Po6GgsXboUR44cwYgRI+Dr64tRo0YBAPbs2YOhQ4di9uzZ6NevHxITEzFgwAAcPHiw1j1HzzjKFOClgpvCxcbRkD3w9VBi1oDWeKFDA0z7+SjSL+Rh1i8nkLgvE1N6t0B0i0BO1RNRrWDTnQt79+6N3r173/Ocj48PtFqtybFFixbhscceQ2ZmJho0aIATJ05g8+bN2L9/Pzp06AAAWLhwIfr06YPPP/8cISEhWLlyJUpLS/Htt99CqVSiZcuWSE9Px9y5c8Wiaf78+ejVqxcmTZoEAPjwww+h1WqxaNEiLF269J7xlZSUmCx81+l0ACpGyPR66+6ubOzP2v3ey/lrNwEA9XzdauTzbKkm8+oMmgd5YPXrHfHftEv4XHsG564W4o3vD6BTmB+m9GqGliFqsS1zKw3mVTrMrTQcIa+WxGZW0eTn52f2f0nm5uaa/eGWys/Ph0wmE5+Dl5ycDF9fX7FgAoDo6GjI5XKkpKRg4MCBSE5ORrdu3aBUKsU2sbGx+PTTT3Hjxg34+fkhOTkZEyZMMPms2NhYrF+//r6xzJ49GzNmzKh0PCkpyeSuQmv6exEphR1XZABcIL91A5s2bZL88+xBTeTVmXgCeDcc0F6SY8cVGVIybmDgkmR0qCugX6gBvqo7bZlbaTCv0mFupWHPeS0qMn9rFbOKpnnz5om/X79+HbNmzUJsbCyioqIAVBQvW7ZswbRp0yyL1ALFxcV49913MXToUKjVFf9Fm5WVhcDAQJN2rq6u8Pf3R1ZWltgmLCzMpE1QUJB4zs/PD1lZWeKxu9sY+7iXKVOmmBRaOp0OoaGhiImJEeOzFr1eD61Wi549e0KhUFi17787uOkk8FcmOrRojD6xj0r6WbZWk3l1RoNQcaflHO0ZbDichf1XZTiS54rXohrilU71sG/3DubWyvidlQ5zKw1HyKtxpsgcZhVNd69nGjx4MGbOnInRo0eLx9555x0sWrQIW7duxfjx4y0I1Tx6vR7/+Mc/IAgClixZYvX+q0KlUkGlUlU6rlAoJPtiSNm30eX8iinHBgFedvsFt7aayKuzalRXgYUvtsfIrnn46Jfj2P/XDSzZlYGV+y6ga10ZnjTI4MHcWh2/s9JhbqVhz3m1JC6Lb33ZsmULevXqVel4r169sHXrVku7eyhjwXT+/HlotVqTURyNRoOcnByT9mVlZcjNzYVGoxHbZGebPojU+PphbYznaxPjbuD1eeccWaBtqC9+/GcUvny5PR4N8oKuuAy/XHDB03N/x9e/n0OxvtzWIRIRVZvFRVOdOnXw888/Vzr+888/o06dOlYJyshYMJ05cwZbt26t1H9UVBTy8vKQmpoqHtu+fTsMBgM6deokttm1a5fJQi+tVotmzZrBz89PbLNt2zaTvrVarTj9WJsYH5vBPZrIUjKZDLEtNfh1bDfMea41AtwE3CjSY9YvJ/DkZ7/hP8l/sXgiIodm8d1zM2bMwOuvv44dO3aIhUlKSgo2b96Mr776yqK+CgoKcPbsWfF1RkYG0tPT4e/vj+DgYDz33HM4ePAgNm7ciPLycnGNkb+/P5RKJVq0aIFevXrhjTfewNKlS6HX6zF69GgMGTJE3MvpxRdfxIwZMzBy5Ei8++67OHr0KObPn48vvvhC/NyxY8fiySefxJw5c9C3b1+sWrUKBw4cwLJlyyxNj0PTFeuhK769RxOLJqoiF7kMz0YEQ3YxDcWaCCzecQ6X8m5h2s/HsGD7WbzRNQwvdmoIL5VNb94lIrKYxSNNr776Kv744w+o1WqsXbsWa9euhVqtxu7du/Hqq69a1NeBAwcQGRmJyMhIAMCECRMQGRmJ+Ph4XLp0Cf/73/9w8eJFtG3bFsHBweLPnj17xD5WrlyJ5s2bo0ePHujTpw+6dOliUuz4+PggKSkJGRkZaN++PSZOnIj4+HhxuwEAePzxx5GYmIhly5YhIiICP/30E9avX1/r9mgyTs35eyrhoeQfNKoeFxnwfPt62P7/nsTM/i0R4uOGqzdL8PGmk3jik+34QnsaNwpLbR0mEZHZqvSXsVOnTli5cmW1P/ypp5564ONXzHk0i7+/v7iR5f20adMGv//++wPbPP/883j++ecf+nnOzFg0cSdwsiaVqwteiWqEIR0bYH36JSzd8SfOXSvE/G1n8NXv5/DiYw0woksYQvi9IyI7Z9ZIkyW34wHAzZs3qxQM2daV/IqiKcSXzxUj61O6yvGPDqHQTngS/x7WDi1D1CgqLcfXuzPQNeE3jE48iIOZN2wdJhHRfZlVNPn5+VW6S+1B6tWrh3PnzlU5KLKNK/nFAIBgH/4XP0nHRS5Dn9bB2DimC1a81hGdG/uj3CBg4+ErGPTvPRiw+A/879Bl6MsNtg6ViMiEWdNzgiDg66+/NnkI7oPY83bpdH9Zt4smjQ9Hmkh6MpkMTzULxFPNAnH8sg7L/8jAz+mXkX4hD+/8kAaN2g3DOjXAPzqGIkjN7yQR2Z5ZRVODBg0sujNOo9HY7SZWdH+Xb0/PBbNoohoWHqLGZ89HYHKv5khMycR/9p5Hlq4Yc7SnMW/bGfRoHoihnRqgW9O6cJHz4cBEZBtmFU1//fWXxGGQPcji9BzZWF1vFcZGN8WbTzXGL4ev4Id9mdj/1w0kHc9G0vFs1PN1xwsdQ/GPDqEcESWiGsf7yglAxRTsnTVN/GNEtqVydcGgdvUxqF19nMm+iR/2XcB/D17EpbxbmKs9jXlbT+OJJgEY0LYeYltpuOcTEdUI/j8NAQBuFOlRUlax8DZQXfmZekS20jTIG/HPhGNyr2bYfDQLiSmZ2PdXLn4/cw2/n7mG99cfQWxLDQZE1kPXJgFwdbF4+zkiIrOwaCIAd7YbCPBSQuXqYuNoiCpzU7hgQGQ9DIish8zrRViffgnr0i4h41ohfk6/jJ/TLyPAS4lerTTo3SoYncL8WUARkVWxaCIAvHOOHEuDOh54p0dTjOneBIcu5mN92iVsOHQZ1wpK8X97M/F/ezPh56FAz/Ag9GqlwRNNAvgfA0RUbSyaCAD3aCLHJJPJ0DbUF21DffF+3xbYffYaNh/JQtLxLNwo0uPHAxfx44GL8Fa54unmgXi6eV10a1oXdbw4BU1ElrO4aNq8eTO8vLzQpUsXAMDixYvx1VdfITw8HIsXL4afn5/VgyTpXeF2A+TgFC5yPN0sEE83C8RH5a2wLyMXvx7NwpZjWci5WYL/HbqM/x26DJkMiKjvi6ebBeKpZnXRup4P5NzGgIjMYPGE/6RJk8THqhw5cgQTJ05Enz59kJGRgQkTJlg9QKoZVzg9R07E1UWOx5sE4MMBrbB3Sg/89GYU3n7qEbQIVkMQgPQLefhi62n0X/wHHvt4K8b8kIaVKedx7mqBWc+8JKLayeKRpoyMDISHhwMA/vvf/6Jfv374+OOPcfDgQfTp08fqAVLNyOJ2A+Sk5HIZOjTyR4dG/pjcqzmy8oux83QOfjt5FbvPXsO1glJsOHQZGw5dBgAEqVXo3LgOOjeug05h/ggL8IRMxpEoIqpC0aRUKlFUVAQA2Lp1K1555RUAgL+/v8UP9iX7IY40qbmmiZybxscNL3RsgBc6NkBpmQEHM29g77nrSP7zOtIy85CtKxHvxgMAXw8F2ob6IjLUD5ENfBER6gsfdz7xgKg2srho6tKlCyZMmIAnnngC+/btw+rVqwEAp0+fRv369a0eIEmvYmPLijVNIb4caaLaQ+kqF0eVxkUDxfpypGXmIfncdez98zrSL+Yhr0iPHaeuYsepq+L7HqnriWYab9TzdYevhxJqdwV83RXwuevH10MBbzcFH/tC5EQsLpoWLVqEt99+Gz/99BOWLFmCevXqAQB+/fVX9OrVy+oBkvR0t8pQrK/Y2JIPRqXazE3hgqhH6iDqkTpAT6C0zIATV3RIv5CHtMwbSLuQh/PXi/Dn1UL8ebXQrD693VzFQsrbzRVeKgXUbq4Vv7u5wtut4ri3mwLeKlfxdy9jG6UrF6oT2QmLi6YGDRpg48aNlY5/8cUXVgmIal72zYqpObWbK9wU3MuGyEjpKkdEaMWU3PDHGwEArheU4PDFfJy7Vois/FvIv6VHXpEe+bcqfnS39Mi7pUdRaTkA4GZxGW4Wl+HijVtVikEmA7yUFQWWl8oFZbdc8NPVVHi5KeCudIGn0hUeKhd4KFzhqXKBh9IVHkoXeChd4KlyvdPmrmMqVznXaRFVgcVF08GDB6FQKNC6dWsAwM8//4zly5cjPDwc06dPh1KptHqQJK0cXQkAIJCjTEQPVcdLVbHn00PalZYZoCvWi0WVrliPgtsF1M1iPQpKKn43OV6ix83iMvF1abkBggDcLCnDzZKy2z3LkHHzerWuQS6DWFy5K12gcpVD5Xr7n4qK390Udx1zlUOlqPhd6SKHi4sMcpkMLjIZ5HIZXGSAi1wGmUwGF/md4/Lbx+W3j8tldx0ztpPJIJcDLsY28rv7xl3vu7tvmPTpcvuzjH3+/bNYIJK1WFw0/fOf/8R7772H1q1b49y5cxgyZAgGDhyINWvWoKioCPPmzZMgTJLS1YKKkaZAb274R2QtSlc5ArxUCKjGRprF+nKxuLpZrEdeYTF2/LEPzVtFoMQAFJWUobC0HLdKK/5ZVFKGotJyFJWWo7C0DEUl5SjSV/yzsPTONLxBAApKylAgFmLOTSZDpQJNLq8ormSoKMBkMqC0xAUfH90J+e3jstvtZbhdlMlkwO1/3v0+mezO+btfG9vced+dNhX/NPZzr/fe/uy7Pkt++7zsbzHI5QBw+/23r/f2EfH6ZWIuTAtImcy0XcX77vr99i/iux7W/m/nDAYDzl6Q48y2s3BxcbHoM++O0UijdsOgdrZbP21x0XT69Gm0bdsWALBmzRp069YNiYmJ+OOPPzBkyBAWTQ5IHGli0URkV9wULnBTuIiFl16vx42TAvpEhkChsPwOvnKDgFv6crHYKiwpQ0lZOUr0BpSUGSp+LzPcfn379zIDSvR3/V5WjrJyAQYBMAgCyg0CygUBgvF3w53jBkG487vxuCDAcPs95Qbced/t4wYB4nvv9HH7mLGdUNFf+e02DyMIQJkgABCA8ge1lEGnL7E4r/Qwcmy5eM4qPbVr4OtYRZMgCDAYKv5rZevWrejXrx8AIDQ0FNeuXbNudFQjcm5yeo6oNnCRy+ClcoWXyrmeoGUwFll3FVMGY3FmuHP87kLM+E9BAAQApaV67Pr9dzzRpQtcXFzFc8aiDbhdKBoECMCd94ptjH3d+SwBt/9m/q0dbr/fYMBdfRk/72/vFT/P2JdpuzvXX/HZxr1ZxVLydl+3f719ThBf3/Pc3/owbvh6v/Z/3w9WbA+gvNyA8+fPo0HDBpDL5ffsA3fHfY8Y79awjgdsyeL/5XTo0AGzZs1CdHQ0du7ciSVLlgCo2PQyKCjI6gGS9MSiiSNNROSA5HIZ5JBV62Gqer0ef3oC4cHqKo3i0b3p9Xps2pSBPn3CnSKvFj9GZd68eTh48CBGjx6N999/H02aNAEA/PTTT3j88cetHiBJL0dXsaapLosmIiKi+7K4MG/Tpg2OHDlS6fhnn30mLvIix3JVHGni9BwREdH9VHk0MzU1FSdOnAAAhIeHo127dlYLimrWnTVNHGkiIiK6H4uLppycHLzwwgvYuXMnfH19AQB5eXl4+umnsWrVKtStW9faMZKEikrv3HbMNU1ERET3Z/GapjFjxqCgoADHjh1Dbm4ucnNzcfToUeh0OrzzzjtSxEgSMm434K5wcbo7aoiIiKzJ4r+SmzdvxtatW9GiRQvxWHh4OBYvXoyYmBirBkfSu3tqjrvmEhER3Z/FI00Gg+Getw0qFApx/yZyHDk3uRs4ERGROSwumrp3746xY8fi8uXL4rFLly5h/Pjx6NGjh1WDI+nd2Q2cd84RERE9iMVF06JFi6DT6dCoUSM88sgjeOSRRxAWFgadToeFCxdKESNJyDg9xz2aiIiIHsziNU2hoaE4ePAgtm7dipMnTwIAWrRogejoaKsHR9ITp+e43QAREdEDVel2KZlMhp49e6Jnz57WjodqGDe2JCIiMo9ZRdOCBQvM7pDbDjiWO2uaONJERET0IGYVTV988YVZnclkMhZNDobTc0REROYxayF4RkaGWT/nzp2z6MN37dqFZ555BiEhIZDJZFi/fr3JeUEQEB8fj+DgYLi7uyM6OhpnzpwxaZObm4thw4ZBrVbD19cXI0eOREFBgUmbw4cPo2vXrnBzc0NoaCgSEhIqxbJmzRo0b94cbm5uaN26NTZt2mTRtTii0jIDbhTpAXB6joiI6GEsvnvOmgoLCxEREYHFixff83xCQgIWLFiApUuXIiUlBZ6enoiNjUVxcbHYZtiwYTh27Bi0Wi02btyIXbt2YdSoUeJ5nU6HmJgYNGzYEKmpqfjss88wffp0LFu2TGyzZ88eDB06FCNHjkRaWhoGDBiAAQMG4OjRo9JdvB24VlAxNadwkcHXvfLeW0RERHSHxQvBy8vLsWLFCmzbtg05OTmVNrTcvn272X317t0bvXv3vuc5QRAwb948TJ06Ff379wcAfP/99wgKCsL69esxZMgQnDhxAps3b8b+/fvRoUMHAMDChQvRp08ffP755wgJCcHKlStRWlqKb7/9FkqlEi1btkR6ejrmzp0rFlfz589Hr169MGnSJADAhx9+CK1Wi0WLFmHp0qWWpshhXC8oBQDU8VRBLudu4ERERA9icdE0duxYrFixAn379kWrVq0ke/RGRkYGsrKyTLYy8PHxQadOnZCcnIwhQ4YgOTkZvr6+YsEEANHR0ZDL5UhJScHAgQORnJyMbt26QalUim1iY2Px6aef4saNG/Dz80NycjImTJhg8vmxsbGVpgvvVlJSgpKSEvG1TqcDAOj1euj1+upevgljf9buNzu/EADg76mwet+OQKq8EnMrFeZVOsytNBwhr5bEZnHRtGrVKvz444/o06ePpW+1SFZWFgAgKCjI5HhQUJB4LisrC4GBgSbnXV1d4e/vb9ImLCysUh/Gc35+fsjKynrg59zL7NmzMWPGjErHk5KS4OHhYc4lWkyr1Vq1v305MgAuMBTl14o1XPdj7bzSHcytNJhX6TC30rDnvBYVFZnd1uKiSalUokmTJpa+zelMmTLFZHRKp9MhNDQUMTExUKvVVv0svV4PrVaLnj173vO5f1V1aXcG8OcZNG9UD336tLZav45CqrwScysV5lU6zK00HCGvxpkic1hcNE2cOBHz58/HokWLJJuaAwCNRgMAyM7ORnBwsHg8Ozsbbdu2Fdvk5OSYvK+srAy5ubni+zUaDbKzs03aGF8/rI3x/L2oVCqoVJVv01coFJJ9Mazdd96tcgBAXbWb3X6Za4KU/85qO+ZWGsyrdJhbadhzXi2Jy6yiadCgQSavt2/fjl9//RUtW7as9GFr1641+8MfJCwsDBqNBtu2bROLJJ1Oh5SUFLz11lsAgKioKOTl5SE1NRXt27cXYzMYDOjUqZPY5v3334derxdj1Wq1aNasGfz8/MQ227Ztw7hx48TP12q1iIqKssq12Ktrt3cDr+PFPZqIiIgexqyiycfHx+T1wIEDrfLhBQUFOHv2rPg6IyMD6enp8Pf3R4MGDTBu3DjMmjULTZs2RVhYGKZNm4aQkBAMGDAAQMUz73r16oU33ngDS5cuhV6vx+jRozFkyBCEhIQAAF588UXMmDEDI0eOxLvvvoujR49i/vz5Jht2jh07Fk8++STmzJmDvn37YtWqVThw4IDJtgTO6Fqh8e455UNaEhERkVlF0/LlyyX58AMHDuDpp58WXxvXCA0fPhwrVqzA5MmTUVhYiFGjRiEvLw9dunTB5s2b4eZ2ZyPGlStXYvTo0ejRowfkcjkGDx5s8tgXHx8fJCUlIS4uDu3bt0dAQADi4+NN9nJ6/PHHkZiYiKlTp+Jf//oXmjZtivXr16NVq1aSXLe9uH57n6YAjjQRERE9lMVrmrp37461a9fC19fX5LhOp8OAAQMs2qfpqaeegiAI9z0vk8kwc+ZMzJw5875t/P39kZiY+MDPadOmDX7//fcHtnn++efx/PPPPzhgJ2Pcp4lFExER0cNZvCP4jh07UFpaWul4cXHxQwsTsh+CIOB6oXFNE6fniIiIHsbskabDhw+Lvx8/ftxkD6Py8nJs3rwZ9erVs250JBldcRn05RWjfP5c00RERPRQZhdNbdu2hUwmg0wmQ/fu3Sudd3d3x8KFC60aHEnHuJ7JW+UKN4WLjaMhIiKyf2YXTRkZGRAEAY0bN8a+fftQt25d8ZxSqURgYCBcXPjH11FcN945x6k5IiIis5hdNDVs2BAAKj2glxyTcaSJezQRERGZx+K754yOHz+OzMzMSovCn3322WoHRdK7VsA9moiIiCxhcdF07tw5DBw4EEeOHIFMJhO3DDA+UqW8vNy6EZIkjNsNcKSJiIjIPBZvOTB27FiEhYUhJycHHh4eOHbsGHbt2oUOHTpgx44dEoRIUjBuNxDANU1ERERmsXikKTk5Gdu3b0dAQADkcjnkcjm6dOmC2bNn45133kFaWpoUcZKVXef0HBERkUUsHmkqLy+Ht7c3ACAgIACXL18GULFQ/NSpU9aNjiRzjQvBiYiILGLxSFOrVq1w6NAhhIWFoVOnTkhISIBSqcSyZcvQuHFjKWIkCXDLASIiIstYXDRNnToVhYWFAICZM2eiX79+6Nq1K+rUqYPVq1dbPUCSBh/WS0REZBmLi6bY2Fjx9yZNmuDkyZPIzc2Fn5+feAcd2beycgNuFOkBsGgiIiIyV5X3aQKACxcuAABCQ0OtEgzVjNyiiqk5uQzwdVfYOBoiIiLHYPFC8LKyMkybNg0+Pj5o1KgRGjVqBB8fH0ydOhV6vV6KGMnKjHfO+XuqIJdzdJCIiMgcFo80jRkzBmvXrkVCQgKioqIAVGxDMH36dFy/fh1LliyxepBkXdxugIiIyHIWF02JiYlYtWoVevfuLR5r06YNQkNDMXToUBZNDsC4saU/iyYiIiKzWTw9p1Kp0KhRo0rHw8LCoFTyj7AjyLu9CNzPk+uZiIiIzGVx0TR69Gh8+OGHKCkpEY+VlJTgo48+wujRo60aHEkj9/YeTX4eLHKJiIjMZfH0XFpaGrZt24b69esjIiICAHDo0CGUlpaiR48eGDRokNh27dq11ouUrCaviEUTERGRpSwumnx9fTF48GCTY9xywLHkitNzLJqIiIjMZXHRtHz5cinioBp0Z6SJa5qIiIjMZfGaJnJ84pomjjQRERGZjUVTLSTePcc1TURERGZj0VQLGUea/Fk0ERERmY1FUy1TrC/HLX05AMCX+zQRERGZrVpFU3FxsbXioBpy4/YicFe5DN6qaj2vmYiIqFaxuGgyGAz48MMPUa9ePXh5eeHcuXMAgGnTpuGbb76xeoBkXTcKK9Yz+XooIZPxYb1ERETmsrhomjVrFlasWIGEhASTx6a0atUKX3/9tVWDI+szjjT5c2qOiIjIIhYXTd9//z2WLVuGYcOGwcXFRTweERGBkydPWjU4sj5j0eTLReBEREQWsbhounTpEpo0aVLpuMFggF6vt0pQJJ0bhdzYkoiIqCosLprCw8Px+++/Vzr+008/ITIy0ipBkXRu3N6jyZ8bWxIREVnE4tun4uPjMXz4cFy6dAkGgwFr167FqVOn8P3332Pjxo1SxEhWZNyjidNzRERElrF4pKl///7YsGEDtm7dCk9PT8THx+PEiRPYsGEDevbsKUWMZEXG585xY0siIiLLVGmjnq5du0Kr1Vo7FqoBuUXGLQe4pomIiMgSFo807d+/HykpKZWOp6Sk4MCBA1YJyqi8vBzTpk1DWFgY3N3d8cgjj+DDDz+EIAhiG0EQEB8fj+DgYLi7uyM6Ohpnzpwx6Sc3NxfDhg2DWq2Gr68vRo4ciYKCApM2hw8fRteuXeHm5obQ0FAkJCRY9VrshTjSxDVNREREFrG4aIqLi8OFCxcqHb906RLi4uKsEpTRp59+iiVLlmDRokU4ceIEPv30UyQkJGDhwoVim4SEBCxYsABLly5FSkoKPD09ERsba7Jb+bBhw3Ds2DFotVps3LgRu3btwqhRo8TzOp0OMTExaNiwIVJTU/HZZ59h+vTpWLZsmVWvxx5wTRMREVHVWDw9d/z4cbRr167S8cjISBw/ftwqQRnt2bMH/fv3R9++fQEAjRo1wg8//IB9+/YBqBhlmjdvHqZOnYr+/fsDqNhHKigoCOvXr8eQIUNw4sQJbN68Gfv370eHDh0AAAsXLkSfPn3w+eefIyQkBCtXrkRpaSm+/fZbKJVKtGzZEunp6Zg7d65JceUM8nj3HBERUZVYXDSpVCpkZ2ejcePGJsevXLkCV1frPsvs8ccfx7Jly3D69Gk8+uijOHToEHbv3o25c+cCADIyMpCVlYXo6GjxPT4+PujUqROSk5MxZMgQJCcnw9fXVyyYACA6OhpyuRwpKSkYOHAgkpOT0a1bN5MdzmNjY/Hpp5/ixo0b8PPzqxRbSUkJSkpKxNc6nQ4AoNfrrb5flbG/6vZbWmZAQUkZAMBLIav1+2pZK69UGXMrDeZVOsytNBwhr5bEZnGVExMTgylTpuDnn3+Gj48PACAvLw//+te/rH733HvvvQedTofmzZvDxcUF5eXl+OijjzBs2DAAQFZWFgAgKCjI5H1BQUHiuaysLAQGBpqcd3V1hb+/v0mbsLCwSn0Yz92raJo9ezZmzJhR6XhSUhI8PDyqcrkPVd3F9/mlAOAKGQTs/k0LOR89B6D6eaX7Y26lwbxKh7mVhj3ntaioyOy2FhdNn3/+Obp164aGDRuKm1mmp6cjKCgI//nPfyzt7oF+/PFHrFy5EomJieKU2bhx4xASEoLhw4db9bMsNWXKFEyYMEF8rdPpEBoaipiYGKjVaqt+ll6vh1arRc+ePaFQVP2ut1NZN4HUZPh6KNGv79NWjNAxWSuvVBlzKw3mVTrMrTQcIa/GmSJzWFw01atXD4cPH8bKlStx6NAhuLu747XXXsPQoUOtnpBJkybhvffew5AhQwAArVu3xvnz5zF79mwMHz4cGo0GAJCdnY3g4GDxfdnZ2Wjbti0AQKPRICcnx6TfsrIy5Obmiu/XaDTIzs42aWN8bWzzdyqVCiqVqtJxhUIh2Rejun3fLK2469DfU2m3X15bkPLfWW3H3EqDeZUOcysNe86rJXFVaRGSp6dnjSyQLioqglxueoOfi4sLDAYDACAsLAwajQbbtm0TiySdToeUlBS89dZbAICoqCjk5eUhNTUV7du3BwBs374dBoMBnTp1Etu8//770Ov1YvK0Wi2aNWt2z6k5R2V8WK8f75wjIiKyWJVXbh8/fhyZmZkoLS01Of7ss89WOyijZ555Bh999BEaNGiAli1bIi0tDXPnzsWIESMAADKZDOPGjcOsWbPQtGlThIWFYdq0aQgJCcGAAQMAAC1atECvXr3wxhtvYOnSpdDr9Rg9ejSGDBmCkJAQAMCLL76IGTNmYOTIkXj33Xdx9OhRzJ8/H1988YXVrsUeiEUT75wjIiKymMVF07lz5zBw4EAcOXIEMplM3GhSJqtYVVxeXm614BYuXIhp06bh7bffRk5ODkJCQvDPf/4T8fHxYpvJkyejsLAQo0aNQl5eHrp06YLNmzfDzc1NbLNy5UqMHj0aPXr0gFwux+DBg7FgwQLxvI+PD5KSkhAXF4f27dsjICAA8fHxTrfdwI1C40iTfQ6REhER2TOLi6axY8ciLCwM27ZtQ1hYGPbt24fr169j4sSJ+Pzzz60anLe3N+bNm4d58+bdt41MJsPMmTMxc+bM+7bx9/dHYmLiAz+rTZs2+P3336saqkO4cXuPJo40ERERWc7ioik5ORnbt29HQEAA5HI55HI5unTpgtmzZ+Odd95BWlqaFHGSFdwZaWLRREREZCmLH6NSXl4Ob29vAEBAQAAuX74MAGjYsCFOnTpl3ejIqoxrmvxZNBEREVnM4pGmVq1a4dChQwgLC0OnTp2QkJAApVKJZcuWVdolnOxL7u3pOV+uaSIiIrKYxUXT1KlTUVhYCACYOXMm+vXrh65du6JOnTpYvXq11QMk68kzjjRxTRMREZHFLC6aYmNjxd+bNGmCkydPIjc3F35+fuIddGSfcm+vafLl9BwREZHFqvWE3QsXLgAAQkNDrRIMSUdfbsDN4oqH9XKkiYiIyHIWLwQvKyvDtGnT4OPjg0aNGqFRo0bw8fHB1KlT7fopxrVd3u31TDIZ4OPONU1ERESWsnikacyYMVi7di0SEhIQFRUFoGIbgunTp+P69etYsmSJ1YOk6jOuZ1K7KeAi5zQqERGRpSwumhITE7Fq1Sr07t1bPNamTRuEhoZi6NChLJrslHE9E6fmiIiIqsbi6TmVSoVGjRpVOh4WFgalkn+Q7dUNbjdARERULRYXTaNHj8aHH36IkpIS8VhJSQk++ugjjB492qrBkfVwY0siIqLqsXh6Li0tDdu2bUP9+vUREREBADh06BBKS0vRo0cPDBo0SGy7du1a60VK1WIsmrjdABERUdVYXDT5+vpi8ODBJse45YD9uyGuaeL0HBERUVVYXDQtX75cijhIYnfWNHGkiYiIqCosXtN069YtFBUVia/Pnz+PefPmISkpyaqBkXXd4N1zRERE1WJx0dS/f398//33AIC8vDw89thjmDNnDvr378/tBuyYcU2TH++eIyIiqhKLi6aDBw+ia9euAICffvoJGo0G58+fx/fff48FCxZYPUCyDuP0nB+n54iIiKrE4qKpqKgI3t7eAICkpCQMGjQIcrkcnTt3xvnz560eIFmHONLE6TkiIqIqsbhoatKkCdavX48LFy5gy5YtiImJAQDk5ORArVZbPUCqvnKDgPxbHGkiIiKqDouLpvj4ePy///f/0KhRIzz22GPi8+eSkpIQGRlp9QCp+vJv6SEIFb9zR3AiIqKqsXjLgeeeew5dunTBlStXxM0tAaBHjx4YOHCgVYMj6zA+d87bzRUKF4vrZCIiIkIVRpoAQKPRwNvbG1qtFrdu3QIAdOzYEc2bN7dqcGQdeUXcboCIiKi6LC6arl+/jh49euDRRx9Fnz59cOXKFQDAyJEjMXHiRKsHSNVnHGnixpZERERVZ3HRNH78eCgUCmRmZsLDw0M8/sILL2Dz5s1WDY6sI+/2dgP+XM9ERERUZRavaUpKSsKWLVtQv359k+NNmzbllgN2Klfc2JIjTURERFVl8UhTYWGhyQiTUW5uLlQqlVWCIuviHk1ERETVZ3HR1LVrV/ExKgAgk8lgMBiQkJCAp59+2qrBkXUYnzvHR6gQERFVncXTcwkJCejRowcOHDiA0tJSTJ48GceOHUNubi7++OMPKWKkahIfocKRJiIioiqzeKSpVatWOH36NLp06YL+/fujsLAQgwYNQlpaGh555BEpYqRqujPSxKKJiIioqiwaadLr9ejVqxeWLl2K999/X6qYyMpucCE4ERFRtVk00qRQKHD48GGpYiGJ3Jme45omIiKiqrJ4eu6ll17CN998I0UsJAGDQbizIzhHmoiIiKrM4oXgZWVl+Pbbb7F161a0b98enp6eJufnzp1rteCo+nTFehjEh/WyaCIiIqoqi4umo0ePol27dgCA06dPm5yTyWTWiYqsxjg156VyhdKVD+slIiKqKrOLpnPnziEsLAy//fablPGQld157hzXMxEREVWH2UMPTZs2xdWrV8XXL7zwArKzsyUJiqxHXM/EPZqIiIiqxeyiSRAEk9ebNm1CYWGh1QP6u0uXLuGll15CnTp14O7ujtatW+PAgQMmccXHxyM4OBju7u6Ijo7GmTNnTPrIzc3FsGHDoFar4evri5EjR6KgoMCkzeHDh9G1a1e4ubkhNDQUCQkJkl9bTbgz0sSiiYiIqDrsepHLjRs38MQTT0ChUODXX3/F8ePHMWfOHPj5+YltEhISsGDBAixduhQpKSnw9PREbGwsiouLxTbDhg3DsWPHoNVqsXHjRuzatQujRo0Sz+t0OsTExKBhw4ZITU3FZ599hunTp2PZsmU1er1SMBZNdTjSREREVC1mr2mSyWSVFnpLvfD7008/RWhoKJYvXy4eCwsLE38XBAHz5s3D1KlT0b9/fwDA999/j6CgIKxfvx5DhgzBiRMnsHnzZuzfvx8dOnQAACxcuBB9+vTB559/jpCQEKxcuRKlpaX49ttvoVQq0bJlS6Snp2Pu3LkmxdXdSkpKUFJSIr7W6XQAKjYA1ev1Vs2Dsb+q9JujuwUA8HN3tXpcjq46eaUHY26lwbxKh7mVhiPk1ZLYZMLf593uQy6Xo3fv3lCpVACADRs2oHv37pW2HFi7dq0FoT5YeHg4YmNjcfHiRezcuRP16tXD22+/jTfeeANAxeL0Rx55BGlpaWjbtq34vieffBJt27bF/Pnz8e2332LixIm4ceOGeL6srAxubm5Ys2YNBg4ciFdeeQU6nQ7r168X2/z222/o3r07cnNzTUa2jKZPn44ZM2ZUOp6YmAgPDw+r5aC6/u+sHPuvyvFMg3JE1zPrXzUREVGtUVRUhBdffBH5+flQq9UPbGv2SNPw4cNNXr/00ktVi84C586dw5IlSzBhwgT861//wv79+/HOO+9AqVRi+PDhyMrKAgAEBQWZvC8oKEg8l5WVhcDAQJPzrq6u8Pf3N2lz9wjW3X1mZWXds2iaMmUKJkyYIL7W6XQIDQ1FTEzMQ5NuKb1eD61Wi549e0KhsOwuuLXfHwSuXsPj7dugT7t6Vo3L0VUnr/RgzK00mFfpMLfScIS8GmeKzGF20XT3FFlNMRgM6NChAz7++GMAQGRkJI4ePYqlS5dWKuJqmkqlEkfd7qZQKCT7YlSl79zb+zQFqt3t9gtra1L+O6vtmFtpMK/SYW6lYc95tSQuu14IHhwcjPDwcJNjLVq0QGZmJgBAo9EAQKWtD7Kzs8VzGo0GOTk5JufLysqQm5tr0uZefdz9GY7qekHFuqs6XpULPCIiIjKfXRdNTzzxBE6dOmVy7PTp02jYsCGAikXhGo0G27ZtE8/rdDqkpKQgKioKABAVFYW8vDykpqaKbbZv3w6DwYBOnTqJbXbt2mWyGEyr1aJZs2b3nJpzFIIg4BrvniMiIrIKuy6axo8fj7179+Ljjz/G2bNnkZiYiGXLliEuLg5Axd1748aNw6xZs/C///0PR44cwSuvvIKQkBAMGDAAQMXIVK9evfDGG29g3759+OOPPzB69GgMGTIEISEhAIAXX3wRSqUSI0eOxLFjx7B69WrMnz/fZM2SIyooKUNpmQEAUMeLRRMREVF1WPzsuZrUsWNHrFu3DlOmTMHMmTMRFhaGefPmYdiwYWKbyZMno7CwEKNGjUJeXh66dOmCzZs3w83NTWyzcuVKjB49Gj169IBcLsfgwYOxYMEC8byPjw+SkpIQFxeH9u3bIyAgAPHx8ffdbsBRGPdo8lC6wENp1/+qiYiI7J7d/yXt168f+vXrd9/zMpkMM2fOxMyZM+/bxt/fH4mJiQ/8nDZt2uD333+vcpz26FoBH6FCRERkLXY9PUfVw0XgRERE1sOiyYldvz09F8CRJiIiompj0eTExOfOcRE4ERFRtbFocmLXbk/P+Xtyeo6IiKi6WDQ5seu3F4IHcKSJiIio2lg0ObHrhcaF4CyaiIiIqotFkxMzjjTV4fQcERFRtbFocmLXuRCciIjIalg0OSmDQbhz9xxHmoiIiKqNRZOTyr+lR7lBAMAdwYmIiKyBRZOTMk7Nqd1coXTlv2YiIqLq4l9TJ2V8hEoAH6FCRERkFSyanJRxpIlTc0RERNbBoslJXSvgHk1ERETWxKLJSV29WVE0BXq72TgSIiIi58CiyUnl6IxFE9c0ERERWQOLJid19fb0XF0WTURERFbBoslJ5dwsBgAEqlk0ERERWQOLJidlXNNU14trmoiIiKyBRZMTKjcIuHb7Yb0caSIiIrIOFk1O6EZRKcoNAmQyoA73aSIiIrIKFk1OyHjnXB1PJVxd+K+YiIjIGvgX1Qld5SNUiIiIrI5FkxPK0RnvnOMicCIiImth0eSExD2aONJERERkNSyanJBxTRM3tiQiIrIeFk1OyDjSxEeoEBERWQ+LJid0lSNNREREVseiyQlxpImIiMj6WDQ5IePdcxxpIiIish4WTU6msKQMhaXlALjlABERkTWxaHIyxgf1uitc4Kl0sXE0REREzoNFk5PJFje2VEEmk9k4GiIiIufBosnJZN0umoJ9ODVHRERkTSyanExWvrFocrdxJERERM7FoYqmTz75BDKZDOPGjROPFRcXIy4uDnXq1IGXlxcGDx6M7Oxsk/dlZmaib9++8PDwQGBgICZNmoSysjKTNjt27EC7du2gUqnQpEkTrFixogauyPqu3C6aNBxpIiIisiqHKZr279+PL7/8Em3atDE5Pn78eGzYsAFr1qzBzp07cfnyZQwaNEg8X15ejr59+6K0tBR79uzBd999hxUrViA+Pl5sk5GRgb59++Lpp59Geno6xo0bh9dffx1btmypseuzFuNIk4Z3zhEREVmVq60DMEdBQQGGDRuGr776CrNmzRKP5+fn45tvvkFiYiK6d+8OAFi+fDlatGiBvXv3onPnzkhKSsLx48exdetWBAUFoW3btvjwww/x7rvvYvr06VAqlVi6dCnCwsIwZ84cAECLFi2we/dufPHFF4iNjb1nTCUlJSgpKRFf63Q6AIBer4der7fq9Rv7M6ffy/lFAIC6ngqrx+FsLMkrWYa5lQbzKh3mVhqOkFdLYnOIoikuLg59+/ZFdHS0SdGUmpoKvV6P6Oho8Vjz5s3RoEEDJCcno3PnzkhOTkbr1q0RFBQktomNjcVbb72FY8eOITIyEsnJySZ9GNvcPQ34d7Nnz8aMGTMqHU9KSoKHh0c1rvb+tFrtQ9ucz3YBIMOfRw9A/5ckYTgdc/JKVcPcSoN5lQ5zKw17zmtRUZHZbe2+aFq1ahUOHjyI/fv3VzqXlZUFpVIJX19fk+NBQUHIysoS29xdMBnPG889qI1Op8OtW7fg7l55UfWUKVMwYcIE8bVOp0NoaChiYmKgVqstv9AH0Ov10Gq16NmzJxQKxX3blZUbMH7vVgDA4N49uCP4Q5ibV7IccysN5lU6zK00HCGvxpkic9h10XThwgWMHTsWWq0Wbm72tUZHpVJBpapclCgUCsm+GA/r+1rRLRgEwFUug8bXE3I592kyh5T/zmo75lYazKt0mFtp2HNeLYnLrheCp6amIicnB+3atYOrqytcXV2xc+dOLFiwAK6urggKCkJpaSny8vJM3pednQ2NRgMA0Gg0le6mM75+WBu1Wn3PUSZ7ZbxzLkjtxoKJiIjIyuy6aOrRoweOHDmC9PR08adDhw4YNmyY+LtCocC2bdvE95w6dQqZmZmIiooCAERFReHIkSPIyckR22i1WqjVaoSHh4tt7u7D2MbYh6O4s0eTfY3KEREROQO7np7z9vZGq1atTI55enqiTp064vGRI0diwoQJ8Pf3h1qtxpgxYxAVFYXOnTsDAGJiYhAeHo6XX34ZCQkJyMrKwtSpUxEXFydOr7355ptYtGgRJk+ejBEjRmD79u348ccf8csvv9TsBVcT92giIiKSjl0XTeb44osvIJfLMXjwYJSUlCA2Nhb//ve/xfMuLi7YuHEj3nrrLURFRcHT0xPDhw/HzJkzxTZhYWH45ZdfMH78eMyfPx/169fH119/fd/tBuxVVv4tANyjiYiISAoOVzTt2LHD5LWbmxsWL16MxYsX3/c9DRs2xKZNmx7Y71NPPYW0tDRrhGgzWbqKfaM40kRERGR9dr2miSxzJa9ipInPnSMiIrI+Fk1O5OKNiqKpvh+LJiIiImtj0eQkSsrKkX2zYiE4iyYiIiLrY9HkJC7nFUMQAA+lC/w9lbYOh4iIyOmwaHISF29UPDunvp87ZDJubElERGRtLJqcxJ31TNI8LJiIiKi2Y9HkJC7k3hlpIiIiIutj0eQkjCNNoRxpIiIikgSLJidx95omIiIisj4WTU6Ca5qIiIikxaLJCRTry5Fzs+IRKhxpIiIikgaLJidgnJrzVLrA10Nh42iIiIicE4smJ3DuaiEAIKyuJ/doIiIikgiLJieQca2iaGoc4GXjSIiIiJwXiyYnII40BXjaOBIiIiLnxaLJCYgjTXVZNBEREUmFRZMTOHetAACn54iIiKTEosnB5d/S41pBKQCgUQD3aCIiIpIKiyYHZ5yaC/RWwduN2w0QERFJhUWTgzuddRMA0CSQU3NERERSYtHk4I5f0QEAWgSrbRwJERGRc2PR5OBOsGgiIiKqESyaHJggCDh5e3quRbC3jaMhIiJybiyaHNilvFvIv6WHq1zGNU1EREQSY9HkwA5m5gEAwkPUULm62DYYIiIiJ8eiyYGl/pULAGjf0M/GkRARETk/Fk0O7MD5GwBYNBEREdUEFk0OKr9IL945x6KJiIhIeiyaHNTOM1dhEICmgV4I9nG3dThEREROj0WTg/rtZA4AoHvzQBtHQkREVDuwaHJAhSVlSDqWBQCIDg+ycTRERES1A4smB/TL4SsoLC1Hozoe6MD1TERERDWCRZOD0Zcb8O8dZwEAQx5rAJlMZuOIiIiIagcWTQ5m/tYz+Ot6Eep4KvFy54a2DoeIiKjWcLV1APRggiDg6s0SZNwEPthwHIn7LgIA4p8Jh6eK//qIiIhqit2PNM2ePRsdO3aEt7c3AgMDMWDAAJw6dcqkTXFxMeLi4lCnTh14eXlh8ODByM7ONmmTmZmJvn37wsPDA4GBgZg0aRLKyspM2uzYsQPt2rWDSqVCkyZNsGLFCqkv76F2nr6KxxN2Yt5RV7FgGhfdFP3b1rNxZERERLWL3RdNO3fuRFxcHPbu3QutVgu9Xo+YmBgUFhaKbcaPH48NGzZgzZo12LlzJy5fvoxBgwaJ58vLy9G3b1+UlpZiz549+O6777BixQrEx8eLbTIyMtC3b188/fTTSE9Px7hx4/D6669jy5YtNXq9f1ffzx0yGeCjFNC9WV2sfL0TxkU/atOYiIiIaiXBweTk5AgAhJ07dwqCIAh5eXmCQqEQ1qxZI7Y5ceKEAEBITk4WBEEQNm3aJMjlciErK0tss2TJEkGtVgslJSWCIAjC5MmThZYtW5p81gsvvCDExsaaFVd+fr4AQMjPz6/W9f1deblBKLxVLKxfv14oLS21at+1XWlpKfMqEeZWGsyrdJhbaThCXi35++1wi2Ly8/MBAP7+/gCA1NRU6PV6REdHi22aN2+OBg0aIDk5GZ07d0ZycjJat26NoKA7exrFxsbirbfewrFjxxAZGYnk5GSTPoxtxo0bd884SkpKUFJSIr7W6SoeaaLX66HX661yrSJDudg3WY8xn8yr9TG30mBepcPcSsMR8mpJbA5VNBkMBowbNw5PPPEEWrVqBQDIysqCUqmEr6+vSdugoCBkZWWJbe4umIznjece1Ean0+HWrVtwdzd9VMns2bMxY8aMSjEmJSXBw8Oj6hf5AFqtVpJ+azvmVTrMrTSYV+kwt9Kw57wWFRWZ3dahiqa4uDgcPXoUu3fvtnUomDJlCiZMmCC+1ul0CA0NRUxMDNRqtVU/S6/XQ6vVomfPnlAoFFbtuzZjXqXD3EqDeZUOcysNR8ircabIHA5TNI0ePRobN27Erl27UL9+ffG4RqNBaWkp8vLyTEabsrOzodFoxDb79u0z6c94d93dbf5+x112djbUanWlUSYAUKlUUKlUlY4rFArJvhhS9l2bMa/SYW6lwbxKh7mVhj3n1ZK47P7uOUEQMHr0aKxbtw7bt29HWFiYyfn27dtDoVBg27Zt4rFTp04hMzMTUVFRAICoqCgcOXIEOTk5YhutVgu1Wo3w8HCxzd19GNsY+yAiIqLaze5HmuLi4pCYmIiff/4Z3t7e4hokHx8fuLu7w8fHByNHjsSECRPg7+8PtVqNMWPGICoqCp07dwYAxMTEIDw8HC+//DISEhKQlZWFqVOnIi4uThwtevPNN7Fo0SJMnjwZI0aMwPbt2/Hjjz/il19+sdm1ExERkf2w+5GmJUuWID8/H0899RSCg4PFn9WrV4ttvvjiC/Tr1w+DBw9Gt27doNFosHbtWvG8i4sLNm7cCBcXF0RFReGll17CK6+8gpkzZ4ptwsLC8Msvv0Cr1SIiIgJz5szB119/jdjY2Bq9XiIiIrJPdj/SJAjCQ9u4ublh8eLFWLx48X3bNGzYEJs2bXpgP0899RTS0tIsjpGIiIicn92PNBERERHZAxZNRERERGZg0URERERkBhZNRERERGZg0URERERkBhZNRERERGaw+y0HHIVxawRLnmFjLr1ej6KiIuh0Orvdht4RMa/SYW6lwbxKh7mVhiPk1fh325wtjlg0WcnNmzcBAKGhoTaOhIiIiCx18+ZN+Pj4PLCNTDCntKKHMhgMuHz5Mry9vSGTyazat06nQ2hoKC5cuAC1Wm3Vvmsz5lU6zK00mFfpMLfScIS8CoKAmzdvIiQkBHL5g1ctcaTJSuRyOerXry/pZ6jVarv90jky5lU6zK00mFfpMLfSsPe8PmyEyYgLwYmIiIjMwKKJiIiIyAwsmhyASqXCBx98AJVKZetQnArzKh3mVhrMq3SYW2k4W165EJyIiIjIDBxpIiIiIjIDiyYiIiIiM7BoIiIiIjIDiyYiIiIiM7BosnOLFy9Go0aN4Obmhk6dOmHfvn22Dsnu7dq1C8888wxCQkIgk8mwfv16k/OCICA+Ph7BwcFwd3dHdHQ0zpw5Y9ImNzcXw4YNg1qthq+vL0aOHImCgoIavAr7M3v2bHTs2BHe3t4IDAzEgAEDcOrUKZM2xcXFiIuLQ506deDl5YXBgwcjOzvbpE1mZib69u0LDw8PBAYGYtKkSSgrK6vJS7ErS5YsQZs2bcTN/6KiovDrr7+K55lT6/jkk08gk8kwbtw48RhzWzXTp0+HTCYz+WnevLl43pnzyqLJjq1evRoTJkzABx98gIMHDyIiIgKxsbHIycmxdWh2rbCwEBEREVi8ePE9zyckJGDBggVYunQpUlJS4OnpidjYWBQXF4tthg0bhmPHjkGr1WLjxo3YtWsXRo0aVVOXYJd27tyJuLg47N27F1qtFnq9HjExMSgsLBTbjB8/Hhs2bMCaNWuwc+dOXL58GYMGDRLPl5eXo2/fvigtLcWePXvw3XffYcWKFYiPj7fFJdmF+vXr45NPPkFqaioOHDiA7t27o3///jh27BgA5tQa9u/fjy+//BJt2rQxOc7cVl3Lli1x5coV8Wf37t3iOafOq0B267HHHhPi4uLE1+Xl5UJISIgwe/ZsG0blWAAI69atE18bDAZBo9EIn332mXgsLy9PUKlUwg8//CAIgiAcP35cACDs379fbPPrr78KMplMuHTpUo3Fbu9ycnIEAMLOnTsFQajIo0KhENasWSO2OXHihABASE5OFgRBEDZt2iTI5XIhKytLbLNkyRJBrVYLJSUlNXsBdszPz0/4+uuvmVMruHnzptC0aVNBq9UKTz75pDB27FhBEPh9rY4PPvhAiIiIuOc5Z88rR5rsVGlpKVJTUxEdHS0ek8vliI6ORnJysg0jc2wZGRnIysoyyauPjw86deok5jU5ORm+vr7o0KGD2CY6OhpyuRwpKSk1HrO9ys/PBwD4+/sDAFJTU6HX601y27x5czRo0MAkt61bt0ZQUJDYJjY2FjqdThxZqc3Ky8uxatUqFBYWIioqijm1gri4OPTt29ckhwC/r9V15swZhISEoHHjxhg2bBgyMzMBOH9e+cBeO3Xt2jWUl5ebfKkAICgoCCdPnrRRVI4vKysLAO6ZV+O5rKwsBAYGmpx3dXWFv7+/2Ka2MxgMGDduHJ544gm0atUKQEXelEolfH19Tdr+Pbf3yr3xXG115MgRREVFobi4GF5eXli3bh3Cw8ORnp7OnFbDqlWrcPDgQezfv7/SOX5fq65Tp05YsWIFmjVrhitXrmDGjBno2rUrjh496vR5ZdFERBaLi4vD0aNHTdYxUNU1a9YM6enpyM/Px08//YThw4dj586dtg7LoV24cAFjx46FVquFm5ubrcNxKr179xZ/b9OmDTp16oSGDRvixx9/hLu7uw0jkx6n5+xUQEAAXFxcKt1xkJ2dDY1GY6OoHJ8xdw/Kq0ajqbTYvqysDLm5ucw9gNGjR2Pjxo347bffUL9+ffG4RqNBaWkp8vLyTNr/Pbf3yr3xXG2lVCrRpEkTtG/fHrNnz0ZERATmz5/PnFZDamoqcnJy0K5dO7i6usLV1RU7d+7EggUL4OrqiqCgIObWSnx9ffHoo4/i7NmzTv+dZdFkp5RKJdq3b49t27aJxwwGA7Zt24aoqCgbRubYwsLCoNFoTPKq0+mQkpIi5jUqKgp5eXlITU0V22zfvh0GgwGdOnWq8ZjthSAIGD16NNatW4ft27cjLCzM5Hz79u2hUChMcnvq1ClkZmaa5PbIkSMmRalWq4VarUZ4eHjNXIgDMBgMKCkpYU6roUePHjhy5AjS09PFnw4dOmDYsGHi78ytdRQUFODPP/9EcHCw839nbb0Sne5v1apVgkqlElasWCEcP35cGDVqlODr62tyxwFVdvPmTSEtLU1IS0sTAAhz584V0tLShPPnzwuCIAiffPKJ4OvrK/z888/C4cOHhf79+wthYWHCrVu3xD569eolREZGCikpKcLu3buFpk2bCkOHDrXVJdmFt956S/Dx8RF27NghXLlyRfwpKioS27z55ptCgwYNhO3btwsHDhwQoqKihKioKPF8WVmZ0KpVKyEmJkZIT08XNm/eLNStW1eYMmWKLS7JLrz33nvCzp07hYyMDOHw4cPCe++9J8hkMiEpKUkQBObUmu6+e04QmNuqmjhxorBjxw4hIyND+OOPP4To6GghICBAyMnJEQTBufPKosnOLVy4UGjQoIGgVCqFxx57TNi7d6+tQ7J7v/32mwCg0s/w4cMFQajYdmDatGlCUFCQoFKphB49eginTp0y6eP69evC0KFDBS8vL0GtVguvvfaacPPmTRtcjf24V04BCMuXLxfb3Lp1S3j77bcFPz8/wcPDQxg4cKBw5coVk37++usvoXfv3oK7u7sQEBAgTJw4UdDr9TV8NfZjxIgRQsOGDQWlUinUrVtX6NGjh1gwCQJzak1/L5qY26p54YUXhODgYEGpVAr16tUTXnjhBeHs2bPieWfOq0wQBME2Y1xEREREjoNrmoiIiIjMwKKJiIiIyAwsmoiIiIjMwKKJiIiIyAwsmoiIiIjMwKKJiIiIyAwsmoiIiIjMwKKJiIiIyAwsmojIab366qsYMGBAjX/uihUrIJPJIJPJMG7cOLPe8+qrr4rvWb9+vaTxEVHVuNo6ACKiqpDJZA88/8EHH2D+/Pmw1UMP1Go1Tp06BU9PT7Paz58/H5988gmCg4MljoyIqopFExE5pCtXroi/r169GvHx8Th16pR4zMvLC15eXrYIDUBFUafRaMxu7+PjAx8fHwkjIqLq4vQcETkkjUYj/vj4+IhFivHHy8ur0vTcU089hTFjxmDcuHHw8/NDUFAQvvrqKxQWFuK1116Dt7c3mjRpgl9//dXks44ePYrevXvDy8sLQUFBePnll3Ht2jWLY/73v/+Npk2bws3NDUFBQXjuueeqmwYiqkEsmoioVvnuu+8QEBCAffv2YcyYMXjrrbfw/PPP4/HHH8fBgwcRExODl19+GUVFRQCAvLw8dO/eHZGRkThw4AA2b96M7Oxs/OMf/7Docw8cOIB33nkHM2fOxKlTp7B582Z069ZNikskIolweo6IapWIiAhMnToVADBlyhR88sknCAgIwBtvvAEAiI+Px5IlS3D48GF07twZixYtQmRkJD7++GOxj2+//RahoaE4ffo0Hn30UbM+NzMzE56enujXrx+8vb3RsGFDREZGWv8CiUgyHGkiolqlTZs24u8uLi6oU6cOWrduLR4LCgoCAOTk5AAADh06hN9++01cI+Xl5YXmzZsDAP7880+zP7dnz55o2LAhGjdujJdffhkrV64UR7OIyDGwaCKiWkWhUJi8lslkJseMd+UZDAYAQEFBAZ555hmkp6eb/Jw5c8ai6TVvb28cPHgQP/zwA4KDgxEfH4+IiAjk5eVV/6KIqEZweo6I6AHatWuH//73v2jUqBFcXav3f5murq6Ijo5GdHQ0PvjgA/j6+mL79u0YNGiQlaIlIilxpImI6AHi4uKQm5uLoUOHYv/+/fjzzz+xZcsWvPbaaygvLze7n40bN2LBggVIT0/H+fPn8f3338NgMKBZs2YSRk9E1sSiiYjoAUJCQvDHH3+gvLwcMTExaN26NcaNGwdfX1/I5eb/X6ivry/Wrl2L7t27o0WLFli6dCl++OEHtGzZUsLoiciaZIKttsslInJSK1aswLhx46q0Xkkmk2HdunU2efwLET0YR5qIiCSQn58PLy8vvPvuu2a1f/PNN226gzkRPRxHmoiIrOzmzZvIzs4GUDEtFxAQ8ND35OTkQKfTAQCCg4PNfmYdEdUcFk1EREREZuD0HBEREZEZWDQRERERmYFFExEREZEZWDQRERERmYFFExEREZEZWDQRERERmYFFExEREZEZWDQRERERmeH/A3QymFJt3jQkAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(df['time'][1:], df['antenna/pathloss/distance'][1:], label='FSPL')\n",
    "plt.ylabel(\"Frespace pathloss [dB]\")\n",
    "plt.xlabel(\"Time [s]\")\n",
    "\n",
    "print(np.max(df['antenna/pathloss/distance'][1:]))"
   ]
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}