diff --git a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
index 79b0e132faa8833cdb9f626c04848894178e7704..c12da8acdc0dbc7ede0e4a51e1fc5fbcfca49ad6 100644
--- a/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
+++ b/applications/lofar2/model/pfb_os/multirate_mixer.ipynb
@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "02689e50",
    "metadata": {},
    "outputs": [],
@@ -33,19 +33,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "id": "65235f50",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The autoreload extension is already loaded. To reload it, use:\n",
-      "  %reload_ext autoreload\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Auto reload module when it is changed\n",
     "%load_ext autoreload\n",
@@ -61,13 +52,13 @@
     "# Import rtdsp\n",
     "from rtdsp.firfilter import filterbank_frequency_response\n",
     "from rtdsp.fourier import dtft\n",
-    "from rtdsp.multirate import down, up, maximal_downsample_bpf\n",
+    "from rtdsp.multirate import down, up, maximal_downsample_bpf, non_maximal_downsample_bpf\n",
     "from rtdsp.plotting import plot_power_spectrum, plot_magnitude_spectrum"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 3,
    "id": "c49515de",
    "metadata": {},
    "outputs": [],
@@ -80,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 4,
    "id": "c5c90a6b",
    "metadata": {},
    "outputs": [],
@@ -92,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 5,
    "id": "6d3a14bc",
    "metadata": {},
    "outputs": [],
@@ -113,7 +104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "id": "9aa3a1ae",
    "metadata": {},
    "outputs": [],
@@ -134,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 7,
    "id": "0a69b385",
    "metadata": {},
    "outputs": [
@@ -165,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 8,
    "id": "0b979a1f",
    "metadata": {},
    "outputs": [
@@ -229,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 9,
    "id": "d76e42f5",
    "metadata": {},
    "outputs": [],
@@ -245,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 10,
    "id": "48d4a3b3",
    "metadata": {},
    "outputs": [],
@@ -264,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 11,
    "id": "0ae28649",
    "metadata": {},
    "outputs": [
@@ -274,7 +265,7 @@
      "text": [
       "powCarriers = 0.500000\n",
       "powNoise = 0.000000\n",
-      "SNR = 100.959873 dB\n"
+      "SNR = 100.959870 dB\n"
      ]
     }
    ],
@@ -287,7 +278,8 @@
     "mu = 0.0\n",
     "sigma = ampl * np.sqrt(0.5) / 10**(SNR_dB / 20)\n",
     "noise = rng.normal(mu, sigma, Nsamples)\n",
-    "xData += noise\n",
+    "if SNR_dB < 100:\n",
+    "    xData += noise\n",
     "\n",
     "# Check SNR, each extra carrie adds 3 dB\n",
     "powCarriers = np.sum(xData**2) / Nsamples\n",
@@ -298,6 +290,37 @@
     "print('SNR = %f dB' % snr_db)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9a46816c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f06c9276610>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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CBwUvTEvCl5dlAIDgVak+Hz1JJZWz5vemTZv8i5EoMRynpt2om9mi5sdx9w3XIi0pPqqSSj5p9xnwf3V9Bhanm3xySvAORcf8BkQLqpl3T0404t6bF8z8LnrGIQazR/vEEBwvvvgiHr51MQwG4Fy/NSoqCIuDgnlBJv7/aDG4xEHBaUnxePHFF7FsfjJuX5oOjvOVi4gGVM4SZIBfyddGyRbQZxYrBqxOJCcYhbpbL774YtTFox0VjMZFiJvJer1r+Xxcm5oIm8ONxgvsbwHN3qIGfFw8Kprf0bAFVNs5hGm3F1+4Jhm3LPaV+EmMj8ODK31hA9GSVHJ01kLkxRdfxIMrF8EYZ4iapJKxqWlh+1OsM4RvKkqM+I9OXcSv95xB3Uw5pBdffJHKc2IGFyV8/PHHuDY1EV/5wnwAiIosv6beUXAccNuSeViSYRKu84HzjRdGo8Jd3zDzt35opW9L9+OPPwYAPDyzxdvQwz4X43aXULeKHwcA3L50Hq5LT4LD5UXrZfZX8vzfevXNC4Sisx9//LHAxcme6NjG4sfBzwUAMMYZ8OAtPiUfDePovGrD2JQL85J8W9SAj4vVNy9EotG3BXR5lP2FIS9LfTGN/qKzD6/0cXMyCuY3ACGWjp8LH3/8MTKSE/D1L/q4aYgCndF8cQyemSzq5demCNcfFu0oRIP395OOQXxYf1FIquJ1BmnEDC5KuO+++wAANy30VT7uj4Ig5/5xn7C9aVFgtebMBSkwGIBpjxfDUZCRNXscPBc38lxYo4eLa1IScE1qonDdYDDghgWpAW1YBv+O/N8e8PHB/3tg3BkVXhV+/orH4fu3z8MSDUkM/WO+d1x+bQriZ7ao77vvPiQnGoUF1pUomBsWgYvAAtY3zsz3aBiDw+URsltvmhnHbDkVDd+URZjfgVxcPz8ZCUZfXbGrNvZDOAZmvpkl6b55wHNBGjGDixK6u32ZWItnCByIAiEwYPVNjMXzTAHXE4xxWJCaNNMm+sbBc3HdDBdXo2gM/DuL4R9HNAiyuePo7u4WuJn2eAOqhbMIp9uvHGfzcV16NM0L3zvy7wyI50b0jONqkHEAwHXz/LKWdSOen7tJ8XFIT44HEJ1c+Od3IBdxcQZhjkfVODICdQZpxAwuSuCPGloSVZNnxsrPmKvkl2RExzhcHi+GZoLi+XH4uYieVfzAOK9UgnERPePg3/E60TeVnJyMxPg4LJjx3LG+kueVY2J8HK5JSQj43XUCF+wbv1eCzG9+bvDfGetcAKJxzJobi2dkrcPlhdXOdgFUMRf8tqjARUYUGSrjwbkARIYj49+U18uJFiOBOoM0YgYXJcyfPx+ASDky/tEB/ncMNnmixVgZtDnBcUCC0YBrU3wKXeBiZgxjUy7mA85DKRVApBwZ5wKY66oH/HxcFyXeX7FnaPZB1UuiZAwA5igVYO7cYH0cHMeF9P6aEoyCQcz63LgigQvWxwAEX1DxiJaF4cjUNNxeDgYDsHiez0jkuSCNmMFFCe3t7QDEW4rsr4AHbL6JsXiWe9h3LTrGwSuMxfNMQjYZz0V6cjySZs76Yn07Ltj2Dw/+Gutboz7lOHccPB/RsnUiKPh5c5UKrzBHJqfhdLNtxAczVPxcRMf8HptyYdrtC8IOJqeixYi/GsTgijYugOBGPA//liLb4+AdDQtSk4TyOzwXpBEzuCjhoYceAuBfrVy1OZjP8AvnHhZWwIx76oIpeJ4Lg8EQNauugXArxyhZAVvtbjhcPuUoFsjC3IgSLsKt4q9JSRAO7GXdiA/mwea5uC7KuLg2NRFJ8cY5v48W76+fi7lyih/D0IST+Qy/YB5sHkuiZGs0nM4gjZjBRQk7d+4EACyalwSDAXB5OIxMsZvhZ3O4MDntW6EHjRuKNkEmUo48F0AUCWQJW4oDVrYz/PgxzE9JEEpCAH4+osUbEU6pGAyGKPLUzfVG8FxEy5ZisK04MaJlYRhsHDwXC1ITER9nAMf5QiRYhcPlERJewoahMM4F74FbEoQL0ogZXJTAHw0gzvBj+cPjBe28pHikJsXP+f3iKFEqV4JkWoqPabguSgRyuCxFnotpN9sZfoKCn7UVx/MRLVsn4bZ3AXF2HLvjcLo9QkkX8Tj8XPjnN8tGvD+ZJAQXM9dZX1AFM355LnwZfuyPI1impRjRozPmerCZPtonhrkQHw3AZ/hdtbH74c1Oi52NaHEP87ERYg9XABdRIATEmZbBDK6keCOuncnwY3kcobbieD6iZQV8JUzGKBAd23G8pyTRGCd8O4CfC35srGf4BfNGiOHP8GPX+AVE4wghp/hxsBynycf8ijMtxYgWr2mwUJrY0T5Rhr//+78X/t+vWNgVAuEyFMXXRxnP8Au2FSfmIhq2FPlMy/g4g1A6YTaiYRwDQeJUAD8fn4ctRfF1lschJJPMyrTkuTAlGDE/CjL8JG8pMjwGjuMiyqloWIxEXIjMXJ+c9jB9SD1vOIq9pmIuSCJmcFFCQUGB8P+Lo0A5XhEJ5GDISI6O4OBg4xBzEQ1K3p9pmSRkWs5GNNS48QuyQIHM88GPYXhyWsg8Yw3hyhDwiIYYLn6xF4oLILBwKKsIlxUnvs6yrBVnWi6aF15OsVzfLRIXqUnxmDcTnsLyNxXMcBRzQRIxg4sSvvWtbwn/L2QqMvzRXY2wijcYDP7VI8Nbo1eDbDkEcBEFWw7hMhR5+Ffy7I4jlJLn+bg2NRGJM2nYrG63Wx1u2Gc8usEKAgPRUTQ0lJdOPDeiYWvUX5w5VAyXbwxDE064Gc3w4+XnNbOSSQK4iAKd4f+mgnMBRMcWb7CC32IuSCJmcFHC6dOnhf+Phgy/YFWoZ4N1N/eE040Jpy/+RKzkQ3HBanBwpO1dIDpW8qGUPM+HwWBgPrCWf6+M5EDlKEY0bGOF8kYEzo0o8JpG8KoEZPhNsKnkQ23FBXCRwX7Q/JUInl+AfZ3hdPszLcXJPWIuSCJmcFHC4sWL/f+fzn6WYrDsvtlgXTnyf9/ZmZZiLngX/rTbizFGM/wGbJEFWTRtjc4eh5gP1jMVI2Uo+n7nHwOzRnyIcQTlglFv47Tbi6GJ4Gda8gjI8GNU3kqaF/OiYEEVIYYLEOk+RschPrZrvujYLjEXJBEzuCjBaPSvhnmvEcunpgfL7psN1lfyV0NsxYm5EB//wapikSLIWD/b0i3OtJy1/RMwNxhfAUcKDAb8c8bu8sDqYDPDL9Q5qWIu/FujbMop3mMlPrYrGFjfxgqVaRnAhZClyOYYgMAsxVBgPZxGnLwgTiYRc0ESMYOLEvr6+oT/5z86Vo//8Hg5wRgMt43lrwzOphAIVSxUzAXAfszNlQhxKgD7Hq7BCSe8M5mWC1MDxyHmg/VxRMpQBHxGfEZyQkB71hAq8D+YnGJ1DPx8FR/bFQzMjyPEwjAYF+IwCZbAcZyk0AfWT5MINb9n6wxSiBlclPCNb3xD+P+M5ASmz/AbnnDC4+UQZwAWpoVZOTJeNDRUyriYC4D9mmKR4lQAv4AYmmAzw29A2KKem2kp5oN1T12weknBwLKSD6ccA7lgdwxAaC/dbLAe3xjqCDUxF+IMPxYXhuN2F5xhzrTkwXq2pWDEzxrDbJ1BCjGDixLMZrPw/77jP9gVAvw7LUxLQrwx9CfB8hiA0FWoxVwAovgIRrdOIpUhAIBrUhKRYPQZMixm+PkF2dwxiPlg/ZvylxkJr+RZjtMUZ1rO/qbEXPBjYDXD70qI+T0b0bMwDC+nWI6Z5cdwTUpC0DMtebDORSgP12wuSIGoweV0OpGVlYVly5YhOTkZq1atwuHDh8Pes3btWhgMhpA/YtfeJ598ErJdfX09yaGoxk9/+tOAf7O8AlayimcxODjUOGZzIcR4MGioiLcQwrnqfcHB7MaqhNuKE/MRLUHz4bgQ/57F+c3Hz6Sb4pGcGKgcxVwsTE1CfJwBXkYz/ELVdZsNwWvK4PwGQi+o5ugMhj2OUhaFgH9eDM7sorAGqTqDFIgaXGvXrsU777yDH/3oR3jvvfdgNBrx5JNPora2NuQ9P/vZz1BUVBTwU1hYiJSUFHzpS1/C9ddfP+ee9evXz7lnxYoVJIeiGlu2bAn4t1DjhkFLP1L1Zh78isvp9mLczl6GX6hxzOaC5QNuI51pKQbbAjn09o+YD/aNeIkGF8OB2uFKvoi5EGf4sTiOUFtxs8FyjKbL48XwZHAlP0dnMOz9FbiIsEhfmJaIOIMvTniYQSM+lAd7NhekEF6iy0BDQwNKS0uxceNG/OpXvwIA/PjHP8aXv/xlvPrqq6irqwt637333ot777034FptbS2mpqbwox/9KOg9DzzwAJ599llSr04Fsw+/vG4eu+7hSAfC8uCP/xibcuGK1YH5YTKF9ECo2Kc5XDCcqjwQIqYgGK5jeBsr3MkFwQ4Tn5r2wOZ0I92UMKe9XnB7vMIZhJHmBsunSYTLtJw9Nxanm2AZd/juWa7J60mG1IUhy17TqzPHdgXLtJwrp9hdGApchCkjBADxxjgsTEvCVZsTV6yOiFvzWiPUgor5w6vLyspgNBqxbt064ZrJZMLLL7+MEydO4NKlS5L7KikpgcFgwA9/+MOQbWw2G9xu9rI3eMw+/JLlFbDUVby4DWvj8IbJtJzNBcsCWUoBWh4s100K902J+UhONCLdNHP8B2OKZWhiGl4OMMYZsCAtvMHF9JZimLpuc+QUy+OQuY3FYoaf/9iuuZmWoblgT05JOQ2DB6u6L1wyCfOHVzc3N2PlypVIT08PuH7PPfcAAFpaWiT143K5sGPHDqxevRo33HBD0DYvvfQS0tPTYTKZsGbNGjQ2Nobt0+l0wmq1Cj8TExOS3kUNnnnmmYB/s+welrpyFLdhTjlOhs60nM0FLwCGJ51wMRYcLDU2AmB9azR0mZFQfLAmkMVnWhrDlCEA2DZUwqXvh+aCrXEEHPgcQcmzfIZfuN2EaNIZchbprI7DaneHzLSczQUpEDO4+vv7sXTp0jnX+WsWi0VSPxUVFRgeHg66nZiYmIhnnnkG7733Hvbu3Yu33noLZ86cwQMPPIDm5uaQfW7YsAEZGRnCz0MPPSS8c05ODpxOp2DRZmdnY2BgADt27EBraytqampQUVGBzs5OFBQUwGazBbQdGxtDUVER2tvbceTIEVRVVeHs2bP4wx/+gOHhYaHt8SP7AQCdl6+iu7sb+/fvx/Hjx9HU1ISysjJYLJaAfj0eDzZv3oy+vj7s3r0bjY2NqK+vx759+9DT04Pc3Nw57z04OIjS0lK0traiuroaFRUV6OjowLZt2+a8t9VqRWFhITo6OtBlGQIATI30Y/v27RgaGgpo63A4kJeXh56eHkyPXwUANJ3rwq5du2CxWLBp0yZ4vV5kZ2fD6/Vi06ZNsFgs2LVrFxobG1FXVwez2Yyenh7k5eXB4XAE9D80NITt27fjzJkzqKqqwqFDh9DR0YHCwkJYrdaAtjabDdu2bUNHRwcqKipQXV2N1tZWFO4sBwAkG9yIN8YhOzsbTqcTubm52Lt3L/bt24f6+no0Njai+tAB4fiPK2NTAf1bLBaUlZWhqakJx48fx/79+9Hd3Y28vDzY7faAtsPDwygpKcHZs2dRVVWFI0eOoL29HUVFRRgbG5vz3gUFBejs7ERFRQVqamrQ2tqKHTt2YGBgQGh7qPak79vsOofe3l6Ul5ejvr4ep06dwp49e3D58mVs2bIFbrcbp0/WCFz09/ejrKwMzc3NqK2txYEDB9DV1YWtW7dicnIy4F1GRkZQUlKCtrY2VFZW4ujRo2hra0NxcTFGR0cD2k5MTCA/Px+dnZ04ePAgjh07hpaWFuzcuTPgvbOzs+FyuZCTk4OLFy/i8rANADDQex579+7FxYsXkZOTA5fLhd/85jcBc42bGgcA1DSexsGDB9HZ2Yn8/HxMTEwE9D86Oori4mK0tbXh6NGjqKysRFtbG0pKSjAyMhLQdnJyElu3bkVXVxcOHDiA2tpaNDc3o6ysDP39/QFt3W43tmzZgsuXL2PPnj04deqUb64d8f194bBGlBHnPj0BwBegPjoe+M2GkhElJSUBMiI7Oxt2ux15eXlEZcSJljYAwLnm+jkyIicnJ0BGdJ39FADwSUNLgIw4dOgQqqqqcObMmYgywmw2o66uDo2NjcRkRK9lAFPTvkzLjuaTEWVEEudT7uajxwQZUVpaisHBwYC2vIzo6ekJkBG7d+9GX18fNm/eDI/HQ0xGVJ/yHRnjGLs6R0YcPXo0QEb0d58DAPRcGZkz15xOJ3JyciLKCPE9JGXEmb/4dqxO1RyOKCOu9nYCAPYfrQ2QEXv37kVDQwMaGhrmyIjZc23nzp1oaWnBsWPHiMmInoERAMC8pDhUHq4IkBH79++XJCPKy8vR29sryY6oq6sDOEK46aabuCeeeGLO9a6uLg4A9+6770rq5wc/+AGXkJDADQ0NSWrf2dnJJScnc48//njINg6HgxsfHxd+qqurOQBcU1OTpGcowaeffhrw796hSS4zy8zd+usDnNfrpfZcJbjzzQouM8vMdVyxRmz7h4p2LjPLzP3rrlYN3kw6Dn92hcvMMnPf+dOxOb+bzQXHcdzqDUe5zCwz19Q7osXrScbPChu5zCwzV3C8J2Lbur8McZlZZm7Nxirq7yUHEw4Xl5ll5jKzzJzN4Zrz+9l8/POOFi4zy8xlV3Zq9YqSsK2uh8vMMnPrCk9FbOv2eLmbXtvPZWaZuf4xuwZvJx3f/dMxLjPLzFWc7Z/zu9lcfNx0icvMMnM/fP+EVq8nCZ0DVi4zy8x9+Y2Dktr/8P0TXGaWmdv16SXKbyYPGw6c4zKzzNwbe8/O+d1sLixjU1xmlpm76bX9nMfDls74xluHucwsM3fm8ljEtn86ep7LzDJzv9rRosGbSccnHVe5zCwz9/i71XN+F0xnqEVTUxNHzMOVnJwMp3PuloDD4RB+HwkTExPYu3cvHn/8cSxYsEDSc1esWIHvfe97qKqqgscTvIp7UlIS0tPThZ+0tDRJfavB5ORkwL95l6XD5YXVzk5cgcPlETIOJW0pMrrlEG5bdDYXvnZsHtQrNfUdCKzizDGU4cd/G2lJ8UgLkmk5mw9Wt+PkbJsYAzL82BpHuK240Fywtb3L18yTwgXA7jFF4bJ3Z3OxKC0JhpkMv6FJdsbhEh3bJSfWlLUtxXAFpoPpDBIgZnAtXboU/f39c67z15YtWxaxjz179oTNTgyF5cuXY3p6mtofSQmsVmvAv/kMP4CtD4+P7zAlxAnBy+HA6qGq4Q4Zns2Frx2j45CYMSpuw2f4sYJwGYrAXD5YzbbklbXUzCoWMxUDMy3njmM2F4sZjQuUE2cqbsec8Rtmfs/mgs/wA4ABhgzHwTCZlsHAKhfhZG0wnUECxAyuu+66C+fPn5/zoidPnhR+HwnFxcVIS0vDd7/7XVnP7u7uhslk0sRzJRW33nrrnGtLGBTIoQ7vDAXBq8KQAADCBwYH44JFgysg01LCyjElMR7zGMzwi+QZms0HswJZhofL1449D5c403JhkEzL2Vzw353N6cYkQ0a8lOOuxGD1UPRw44g2nRHpTEseSxitQRnq7F0gOBckQMzgevbZZ+HxeJCbmytcczqdyM/Px6pVq7B8ua+oy9TUFNrb2zE0NBRw/+DgII4cOYK//du/RUpKStBnDA4Ozrl2+vRplJeX47HHHkNcHDsnFVVXV8+5xqJikSvI+HasZfgNhFnFB+OCFwIsnW05PDkNt5eDweDbTpACFreAwmUoAnP5YD1LUYrxC7C5Ncq/y6K04JmWs7kQbwOzOI5wB7qLwWrJlHBGfLTojKthdhOCgR+D1eGGfTp42I8eEDLCg8zvYFyQALHCp6tWrcJzzz2H1157DVevXsWKFSuwbds2XLhwAR988IHQrqGhAWvWrMEbb7yBN998U7j+0Ucfwe12h91OfP7555GcnIzVq1dj8eLFaGtrQ25uLlJSUvD222+TGgoRPP/883OuLZqJ8eBd/CxgaGIagP/dImFBqq9ysJcDRianJRtqtDE08zcNNo6gXKSxyIXvXRakJoY901KMRfOS0Hl1AoMT7AjkcFwAc/ng2w1NOMFxnCRPqxbg+ZA6N9ic3/K44NtOON0YtDlx0yI2dg2EcUhciLDIhX3ag8kZg0OynGJwHIMydUa6KR5J8XFwun2xX8uvDe5Q0RrhvqlgXJAAUZdQYWEhXnnlFRQVFWH9+vVwuVwwm8148MEHI95bXFyMxYsX46//+q9DtnnqqacwNDSEd955Bz//+c/x0Ucf4emnn0ZjYyNuv/12kkNRjfz8/DnXrpmJ4bIydCzO+JRv8lwjsWp8XJwBGcm+cbB0vA//LsHGEZSLVPbGMDblexc5Ffz58Y5PMTQOe/hxzOaDH4PbywkKSW94vZzwbfCxl5HAj5fNbyr4GILNDb4ti+O4JlXa3LiGxTHYfbI2Ps4QNJkknM5gaRxydYbBYGDymxJ0RpBvKhgXJEDMwwX4Kstv3LgRGzduDNnm4YcfDppRdeLEiYj9r1+/HuvXr1f1jloh2NEAvEAeY1I5Sj9SZX5KIkanXGyNY0YIBBtHMC4ykme4mBGCLGB85l3mJ0vnImNmvGMMCbJISn42H6YEo7ACHpuaDqqMtIbN6QZ/1m6GRD748TI1LyIYv0HlVDK735R0LnzjtTnccHu8kj3GNCGeF8G8uMF1BnuGisCFHJ2RnIgBq5OtuTEVWt4yf7RPDIEIdjRAuiDI2FHycgUZIBrHFBvjmHZ7Bc9IsHEE4yIjmUHlqIALFsfBG46hxhENfPAew+QEI5LijZLuYdLzO8VzEdyIDccFS15T/m8qdW6IM66tDjaC//lvO13RvGBD1gJ+Q1yRnGJE94k92FJ1BgnEDC5KCHYO5HzGlAqgbPKwtgLmJ47BAMwLcvhxUC5mVmc2hxseLxs1rAQuZK0c2VPywko+xDcVjg9WxsErBlme32SGPdjJwT1cwblgz/vr92BL28aKN8YJx/uwYqxE8mAH48LviWfom5oK/00FQwZj3l+xBzuYARzuHGc1iBlclHDw4ME511hTKgBEcSrSJ898xmLR+DGkmxKCZmIF40JsYLIyDiWCjMVvKpLhGHRuMGasKPE2CluKDBkqkeLQws0NVrhwefwebCXb7azMjUiyNlp0hlVJGApjC0OxB9uUMNeDHYwLEogZXJQQrO4Yr1RY+egA/5aDksnDikAej+CNCMZFgjFOiBViZfUoN0gbEK2AGVnFcxwXUbEE44Nd5ShfwTtcXjhcbAT/RzIcg8opRrkAQm/HBcN8xuIbI3l+w3LBiKwFRN5fBVyw9k3J0RkkEDO4KOHKlStzrjEdVCtr5cjWlkMkQRaMC4C9+IhIhmMwsKZUHC4vpt2++mxy+JjPWIxHpK24YJiXFC94WJnxmkYwfsPJKVaUoxD7ZIoP6sEOBWGBy4i8jeT5DT4v/FuKrBzfpShoPoWtheFYhDjTUDpDLWIGFyUEmxz86szu8jCxAg4IHIxiD1ekYNRQgoo1Y0XNNhY7SsWf+p6SGDzYPBgfrI1DiefXYDAIwdqsfFORxhGMC9a2FP0LEenGL8DegipSyEC4eeHxcphgoPI/x3HqguYZ+aYiyVpaxm3M4KKEpUuXzrk2Lyke/AKNhRWwzeEGJzP1HWBwBRxhFR+MC197tpS8IoOLsRVwpNR3IDgfrJVMUbKKBxgcRwQPdjAuWCuZEqnMSCiwVjIlkgc7GBd8yRSAjW8qwIOtIO6XFS4ilUMKpTPUImZwUUJzc/Oca+KioSx8eLxAlZP6DrC3WhkPU08FCM4FwN4KWEkCAz8GVlbAUozGYHywVjJFySpe3J6FbypS6jsQnAvWQh+ULEQAdj3xcrgQt2dhgSv2YKeG8GAHA2ulRvw6I7isDcWFWsQMLkp48skng15nqRq1ksBgcXsWxgBEHkcoLjKEJAb9DRUgfCG+UDAlxCFxZgXMAh9Stn+C8cGqcpQTwwWwtZK3Of0e7FDb7eG4YKVkitwaXDxYzaYO5TUNrTPYkbdiWSvnCC7WEsYieU1DcaEWMYOLEoqLi4NeZ8k7pHTlyFpmXCRvRCguWErjD0h9lxk3xJKxMh5hCwsIzgdLSgVQlvoO+MfNgpKPlPoOBOeCtZIpSk7DAAK321lApOSekHKKoZIpir2NDMlaILLxG4oLtYgZXJQQ6mgAv7te/w9PsSDjV46MrIAjnUEYkguG3NyRireGA0vGipTYp+DHyTC2AlaQ+g6wFcMlpXhrMC4CioYywEek7Z9QyGBI1gKRQwZCyakMhowVJee9AuyVTImUhRw72ifKEOpoAJb24xULMkZXwHKOkgHY2v7hBZm4tIBUMLUClrD9E4wPZuOGZC5GWIpFk+KNCDU3WDq+S+nCkKV4WZfHK8RYhjLiQ8oppjzY4csphIJYrrGh+5TpDLWIGVyUsHbt2qDXWZo8SrN/WCsaGin1PRQXLG2NKk19BxhdAYcx4oPxwY+BhZIp4tR3uXwwNb8lGCoh5RSDixGl21gsebCB0PF0kbhgwVCJtC0aCgaDga1wmgje31BcqEXM4KKEnTt3Br2ewWDQvFxBJr6HpXGEEgIhuWBoDEqNX4CtcUgp3hqMj7REdkqmiFPfFSt5JriIPL9DzQ0WjRX5WYp+Wat3yRR+DPPCFG+NKKdY4iLK5VQkIz4UF2oRM7go4f777w96naWDn5UclsyDlfR3KcVbQ3LBkHJUuooH2IpFk2I4BuODpZIpSlPfAba2RqWEDISWUyx5fxV6G2e4cHs5ISFFLyidFwBbJ3soOYGBBys6A4js/Q3FhVrEDC5K6OrqCnqdqaB5hanvADvGivjU91DGSmQu2FkBK9lSZErJS/BGhOaDDe+v0tR3QFxqRH8upCj5UFz4z7bUv2TKmIKq/8DsoqH6ylvB8xtG1oacFwxtxY2r8MSzsk3tcHkiFm8NxYVaxAwuSkhNTQ16nSW3qpKz+3iwouTFqe+hireG4oIXfkysgAVDJV72vUytgCV46iLNDb2/KTXeRhZX8eE82BG50PmbEnuw5cYNAex9U+FkbUg5xcjiFvB/D9Hsiee5MIbxYIfiQi1iBhclzJs3L+h1VgwV8TsoE2RsZMZJSX0PxYW4aKjeClJpxqjvHna+KSmeushzQ18ulKa+++5hp2SKFA92SC4YUY5iD3aoYPNwYMVYkWLEh+aCDVkrfgclYSjzGVkYiku+hPJgh+JCLWIGFyV0dnYGvc5SZpyaGC5WCtlJEWShuGCpaKjS1HfxPXorFSmp70BoPuYz4v0dV1iDC2CrZIoUD3ZILhjZ/pFSvDUcWDFWpMzvyFwwoDNULdIZkbUSjMZQXKhFzOCihAcffDDodX7y2Jz6roA5LvI5a+HAytaolOOJQnEB+Meht3IksY3FChdAeG9EKD6YE8gKuGCpZIqU+R2aCzYWhmpkFCCORdOXC6sKLvi5xELRUKuKWFNW5JQUozGczlADIgaX0+lEVlYWli1bhuTkZKxatQqHDx+OeN8nn3wCg8EQ9Ke+vp7Yc/RApBRfjgNsDv0+PKWnvvNgZctBStZMuBRfVlbySus+AQyt4meenx4m9R2IXDJF75W8Gs8vwE4clxTDMVJZCP3nhfI4U0CcFa43F5FDBkJxMS+JjZIpLo8XNgke7FBgxRMvpeYhrbIQ8iN0g2Dt2rUoKyvDK6+8gltuuQUFBQV48sknUVVVJSm9cv369bj77rsDrq1YsYL4c7REqKMB+BXwhNONsSmXIgVLAmpS3wF2BHKkoqdA+GMaWIlFkzKOUJhdNFTJ1gsJSC3eGumoJb25UJO9C/g47Buz6zo3Aou3yp8brNThUuNtBBgahwQjPhQXfMmU0SkXxuwuLE43UXnHSLBK9GCHAivxy1I8XMwe7dPQ0IDS0lJs2LABGzduxLp161BZWYnMzEy8+uqrkvp44IEH8MILLwT8LFy4kPhztES4owFYqDckzpqRm/oOsLPloOb4EoCd+Ag1WyesrICljiHSUUt6r4CVHlzNg4VtaqkebClHkOlZMkVNbCPA3jZ1OCUfXk7pvzDkuQhXvDUcBJ3BiKwNZzQye7RPWVkZjEYj1q1bJ1wzmUx4+eWXceLECVy6dElSPzabDW536JovpJ6jFX72s5+F/B0LWw7EVo66bzlEXjmG44KFrVG1qe+sFA2VWi0/FB/sfFMqt7EYWMlL9WCH5IKRkilqsncBdrappWTvRovOiOZ5AUgz4sNxoQaqDa7m5masXLkS6enpAdfvueceAEBLS0vEPl566SWkp6fDZDJhzZo1aGxspPIcLbF169aQv2NBsagptOm7j40VsN9QCT0O1rlQm/oOsFE0VKoRH4oPVrZ31S5GWBiH1OKtobhgpWSKlKSYcGAn8zXyOFiXU1YJsjYceC5sOpdMGZfgbQzHhRqojuHq7+/H0qVL51znr1kslpD3JiYm4plnnsGTTz6JhQsXoq2tDb///e/xwAMPoK6uDl/96leJPMfpdMLpdAr/npiYiDwwlXjiiSdC/o6FyaP01Hce/H0uD4epaQ9Sk4iEA8qGlMrH4bhgYctBbeo74DfUWNhyiKQcQ/HBwire93wy3l89vSpCAkOEMYTigi+ZctXmxNiUC1+4hvgrSoLUcYQCC14Vr5cTvulw35QUOaXrgkql51fModXuwjWp+sYvh3M2hONCDVR7uOx2O5KSkuZcN5lMwu9DYfXq1SgrK8NPfvITfPe738W//Mu/oL6+HgaDAa+99hqx52zYsAEZGRnCz0MPPQTAZ8Tl5OTA6XQKe7bZ2dkYGBjAjh070NraipqaGlRUVKCzsxMFBQWw2WwBbcfGxlBUVIT29nYcOXIEVVVVOHv2LDZt2oTh4eGAtna7HXl5eYhzOQAAre1daGpqQllZGSwWS0Bbj8eDzZs3o6+vD7t370ZjYyPq6+uxb98+9PT0IDc3d857Dw4OorS0FK2traiurkZFRQU6Ojqwbdu2Oe99ZdRndBpcdhw6dAhVVVU4c+YMtm/fjqGhoYC2DocDeXl56OnpgdlsRl1dHT473QyjwbdK+eOWPHi9XmRnZ8Pr9WLTpk2wWCzYtWsXGhsbUVdXB7PZjJ6eHuTl5cHhcAT0PzQ0hO3bt+PMmTOoqqrCoUOH0NHRgcLCQlit1oC2NpsN27ZtQ0dHByoqKtA3NAYA+LS+FoODgwFtnU4ncnNzcejQIezbtw/19fVobGzE7t270dfXh82bN2OeiU/hn0Z2djYsFgvKysrQ1NSE48ePY//+/eju7kZeXh7sdntA/8PDwygpKcHZs2dRVVWFI0eOoL29HUVFRRgbG5vz3gUFBejs7ERFRQVqamrQ2tqKHTt2oPvyFQA+Qca/d05ODnp7e1FeXo76+nqcOnUKe/bsweXLl7Flyxa43e6A/pPjvDPf1F9QW1uLAwcOoKurC1u3bsXk5GRA25GREZSUlKCtrQ2VlZU4evQo2traUFxcjNHR0YC2ExMTyM/PR2dnJw4ePIhjx46hpaUFO3fuxMDAQEDb0QmH8E3t3bsXDQ0NaGhowN69e3Hx4kXk5OTA5XLhd7/7XcBc27lzJ1paWtDe2gTAVzT0g635mJiYCOx/dBTFxcVoa2vD0aNHUVlZiba2NpSUlGBkZCSg7eTkJLZu3Yquri4cOHAAtbW1aG5uRllZGfr7+wPaut1ubNmyBZcvX8aePXswMjOOphPH0NvbK1tG8FyMT7nCyoiSkpKQMqK7uxv79+/H8ePHFcmI8xcuAwAyTPFhZQQ/l4LJiHlJPuP/7PluRTKisbERu3btgsViwaZNmxTJCN6Ij/c6FcmIc6d939SQdQqlpaUhZURPT09IGeHxeALukSsjCoq3Cx7sphPHQsqIEydOhJQRifCF24zNfFNKZER/fz/KysrQ3NysSEa0d/vCdpKNnCIZkbP5P5E68021dfWGlRGz5xovI44dO4aDBw+is7MT+fnKZARvfJviPCFlRFVVVVgZcerUKdTX16O8vFyyjKirqwM4lbjjjju4Rx55ZM71zz77jAPAbdmyRXaf3//+97nExETO7XYTeY7D4eDGx8eFn+rqag4A19TUJPvdpOLkyZMhf7fhwDkuM8vM/d/yz6g9PxLe/rPvHd4sP6u4j2+8dZjLzDJzZ/vGCL6ZPNw98w5nLod+h3BcVHdc5TKzzNzj71bTeD1JIPEO67d/ymVmmbn3a7oIvpmyd8itDv8OofiYdnu4zCwzl5ll5kYmnDReMSJIvMP2k71cZpaZeym/gfDbSUdpg7R3CDc3nt18nMvMMnPm0xbSrycZz22uU/UOvUOTXGaWmbvt138m/GbScXHY9w63/vpA2HbhuPjDoQ4uM8vM/dvuVtKvJxnvzLzDv+5S/g6rNxzlMrPM3Ke9IwTfTNk7NIV5h3BcKEVTUxOn2sO1dOlS9Pf3z7nOX1u2bJnsPpcvX47p6WlMTk4SeU5SUhLS09OFn7S0NNnvRBIsbTko3Y/33atvwDknMfU9HNjY3lW3hQWwUVJhXEICQziIi4bqxYfa1HeAjW9KTUVwHiwcxK12G2t2yRQ9QFLWsjC/lXIhvlfP5B6rigQltVBtcN111104f/48rFZrwPWTJ08Kv5eL7u5umEymAMOIxnNoIphxyENvQwVQd3A1D70nj9TU9/Bc6B/grNZoBNjIxpKq5MPxoXe2Jf/cSMVbw4GFkilSi7eGnRsMLQyVLkZYKJki1WiUwoW+Rry6jFFA/7poAcVbFeoMNVBtcD377LPweDzIzc0VrjmdTuTn52PVqlVYvnw5AGBqagrt7e0YGhoS2g0ODs7p7/Tp0ygvL8djjz2GuDj/60l9DisQB/zPBguTh8RqRe8VMP/cSKnv4bgQr4Cdbn1WwGpT33338t9U6NIqtCE181XK3NDLWFFzcDUPpuZ3hG8qLBdMLAzVySlxyRS95VQkozFadIZSDzbg/x6Z8GCbQid6heNCDVQbXKtWrcJzzz2H1157Da+++ipyc3PxyCOP4MKFC0JwLOArXHr77bcHFBR7/vnn8e1vfxu//e1v8f777+OXv/wlVq9ejZSUFLz99tuKnsMK/vznP4f8nb8AnP5bDkq3TQD9M/zEK8dwqe/huJiXFA/+Vr2EgNr6NgAbGX5jEqvlh58beitHddm7QGBmHKdTyZRxwTMUPns4HBd6Z/g5XB44ZzzY6vjQV95K9dJJmRdMeOJVcKF3NrW4eGu8MbT5E44LNSCSy19YWIjXX38dRUVFGB0dxZ133gmz2RzxAMinnnoKxcXFeOedd2C1WrFo0SI8/fTTeOONN4Ie7aP0OXrgJz/5Scjf6S3IxM9WM3n03nKQKsjCccGvgMemXBifcmHxPO2PzVB7dh+g/wpYTvFWlucGCeN3dtHQNB1KpkhJfQfCc6H3NjXPhTHOoOpvqLexItVLF5YLBrapxwl6f/X+ptRwoQZEDq82mUzYuHEj+vv74XA40NDQgMcffzygzcMPPwyO4/Dmm28K19avX4+TJ09ieHgYLpcLFosFRUVFQY0tqc9hBTk5OSF/51/FT+u3AlZZ+BTQf8tBqsEVjgtAfMAt2+MIB70NFTnFW8PPDX1j6khwYUqIQ6JR36KhwjgiKBYpckp3D3aysuPHeOjt/fV7fsPL2nBc8PPbqmPR0DGJW6PhoLfOkOrBjqQzlIKIwRXDXIQ7/JKfPHzRUK3h8ngxoeLUdx56K3m1hyXzyND5nDK1FZwB/VfA/BhMCXERi7dKmRt6x9uo8XAZDAbB0NF9mzrC/A7Lhc7bu1KNxkjQ+5uSasSH40J8rx7B/xzHfS6yFKXGNjJ7eHUMwRHu8MvkBKOwAtZDCJBIfQf8horuylHhYck8dFcsBDNGbU59VsByUt/DHtIreBv1MRylCuRI0Hslb5XowZZysDvr8zsS9J7fUg2VcFzoXTJlQiRX1Hi49E60kmrEM3t4dQzB8dxzz4X8nXgFrIdXhUTqO8DQVlyEyROOC4CdzDh1gsx3L8cBNoce35R0ozEcH3qnjUsN/I8EPVfyAanvEb6psFzovL1LImYI0N+DPSbRiI8kp/QsmSJUZ5fgwQ4HVmStmnmhBjGDixJqamrC/l7PlTwpV70Qi6bX5JEoyCJxoWdmHInirYBvBcyXxtDFiJdhNIbjg5k6XCq9KnrGosnxYIflQueioWMEMkYB/ReG4xLnhlQ5pYexQsrzq38WsjRZG4kLpYgZXJRwyy23hP19ho5bDkLsk9ptE7334yWmvkfiQs8qzuLiraoVi47p73ICasPxoXcsGonsXUBsOOqwoBKlvkfyYIfjQlwyRY+4IRKeX/H9un1TEr2/EeWUjlu8pLjQu2SKlEPEgchcKEXM4KIEm80W9vd6GiskUt8Bv8E2Na1P0VCpqe+RuMjQ1VDxjUFt6jug8wpYxlZcOD6YiRtSuY2l59aonPkdjgtx0VA9jXhS27u6xw1FUPKSdYYuYSgzhgohncGXTNEaUndFInGhFDGDixLE50AGg55bDqRWK/NM+hYNlbo1GomL+ToaKmKPiprUd4CNFbAUQyUcH6ysgNUvRvRTjnI82NLnhg7jIORt1NNQERdvjfRNRYPOUMuFKSEOifH6lUwhpTOUImZwUcLNN98c9vd6FoAjcVgyMOvYDF0Ui7RxSOVCD0OFFBeAvopFzjjC8aHnCpjjOFgdvmBzYlsnjM/vSHPDH3Cu4zhUx5rqPwYpHuz/CjrDYDDoasRbCekMpYgZXJRQW1sb9vfzZs5xsjm0P/vO6iATGAz4x2HVYxxCtmX4cUTmYqakgo5jmEeCiyR+HDoIMv6bCnM+GY9wfJgS4hA/E3ek9Tgmpz1C6nukbyoS9P2mfM9MjxDbCESeG+ksyCmVXPBjmHC6NfeaWkXxdJE82P/VdAbL31QkLpQiZnBRQqS00nQdBTL/TLWCTNyH1srR6+WE4q2RFEtELpJ5AaC9oeLnQv3xL/5x6PhNSRDIkUqm8H1oPQ6e/wSjAaYEdaJRXy6kGyrS5ZSOc0Olkufv93LQ3GtqlSFro4ILEjojWc+FIRmdoRQxg4sSCgoKwv5e8AzpWPh0HgElr5eHa3JadJRMBCEQmQvf/bxnQEuQWsUDonHo6OGS8k2xOjd4/ueZ1MfT6csFPw6CXOjp/VUpp5Li45BgNAT0qRXozIvo5cLXBz83tB2Hw+URMsLnqdQZShEzuCgh0tEA8xhYrZCcPFqPgx9DgtGApPjwn3FkLnx/h2mPV/N6Q2S50E85+scR2XCUyodeHi6SXOjp4SLLhZ5ySn3ckF5bvHLmdyQuBA+XM9p1hj7flJj7SPF0saN9ogyRjgbQMzaCn7BkvCp6KUfp3ohIXKQl+rMto1vJ679NLWUckfjgY9G09g7RWohoHjdEkgudvimHy4NpD++NiGYlL934jcxFdBjxkaCX7uPHkJYUuT5d7GifKMOPfvSjsL+PFuUYCXrFFcgxVCJxERdnQFqivqsuEoJML6XCcZwsgRx5bugjkIXtnyRyXLg8nFAWQCvI+aZY5YJ/nsEAYW6qgd7jICGnWNAZJGJN9d4VIcGFUsQMLko4cOBA2N9/Hlz1vj7YF2SRuBD3w/I4IkGvMTjdXrg8XMA7hEPkucH+9k8kiL2m2nvqpC9GpHKh1xjSEuMRp+K8Vx76eU2l7ybI0Rlae02J6owk9mWtFJ2hBDGDixK++tWvhv09T7o4FV0LBHojolfJy/FGROIC0E/JW4m66vVZOfJjkOqNkDo3otnbGOg1ZVexSOeC3TFIQTSMQyoX+nhNo19nyPHCS9EZShAzuCihv78/7O/FpE9o+OHJ9UZEQjS4hyNxIe6H5XFEgt5KRao3IhIfesd4RL+Sl+5ViSyn9J4X6o1fcT8sG1yRuEjVyWvqERUhJpulyK6slaIzlCBmcFFCpEDuxPg4IbtOyw9P7I1IJRgboXVmnByBLCXFX2/lSFKQTU174PZotwKWWy8pEh96G/Ek4lQA/eoNyVEskbjQq14gPeOX3aD5SFzEiarVa8mH2CEQzWEocnYT1JaFCYWYwUUJS5YsidiGF8haGlyCME4iExuhl0D2Vz6OLJBZ5QIgW1BQrJz4orBaQK5yjMSHXkVDBc8QgWragD6Kxe3xYmrGGyHlm4rIhc6eIVJc6DW/5RQ+lSSndOCD/5uJz0FUA70XIlIWVFK4UIKYwUUJLS0tEdvoIZD9BezIKhWtCwrKWTnK4UJrTx3JwqcJxjgkJxh9/WpYHNFfMFSawRWJD722HOSUU5ACf0Fd7RdUAJAmYRyRufD1YXd54NLQayqnYKgU6LVNLadgqCw5peE3RTLO1NePvrKWlM5QgpjBRQnf+ta3IrbRI66AfDAq+6sVeVxoNw6XxwuHi1ytIXE/2npN5QnkSHzov70bvR4u/lnJCUYkGCOL90hciI02bb0q//WC5qXIKT08XKR1hn6lhMhyoQTEDC6n04msrCwsW7YMycnJWLVqFQ4fPhz2nlOnTuEXv/gF7rjjDqSmpuKLX/wi/u7v/g7nz5+f0/aTTz6BwWAI+lNfX09qGMRQUlISsU26DnEFJLewfP34xqD1wbByJo8ULvRUjoA0b4QU6DkOqQI5Eh/REOAsBXrEDcn1DEXiQuw11VZOkTZ+9a4XGHkc8uSUDmEohBciDpdXU6+pP5mEjM5QAjKSBcDatWtRVlaGV155BbfccgsKCgrw5JNPoqqqCvfff3/Qe/793/8dx48fx3PPPYc777wTV65cQXZ2Nr72ta+hvr4eX/7yl+fcs379etx9990B11asWEFqGMQg5WgAfZQj6WDUwINhIx2ZQAokjy8R96OHUpHqjZACPcch9ZuSepyMfllMZJW8llsnco1GqXLK7vIwbcRHgh6yluM4IZZSipJnXWeQSiYR6wibw41rUxOJ9BsJJI8fUwoiUr6hoQGlpaXYsGEDNm7ciHXr1qGyshKZmZl49dVXQ973T//0T+jt7cUf//hH/PSnP8Wvf/1rHDt2DG63G2+//XbQex544AG88MILAT8LFy4kMQyikHI0AF9DSp/VCpnJY0qIQ/xM8D2r45DChR4xHqS5EPelz/aPNENF6hEmE043vBrWqCMdN6TvgooMF76+2N+mjgQ9vKaT0x7wny+Jo33E/bAqa6Ug3hiHlEQ9vKZkdYYSEDG4ysrKYDQasW7dOuGayWTCyy+/jBMnTuDSpUtB71u9ejUSEwOt21tuuQV33HEHzp07F/J5NpsNbrf2xxvIwUsvvRSxTTQI5EjwHQyrp7ESeRysckFawQP6xEfIFciR+ODHwHHA5LQ2fHi9fm9ENMc3kubC15d+cUOkvCp6Go3xcQaYEiKrWjlySluvKbkjr3iwrvukcKEERAyu5uZmrFy5Eunp6QHX77nnHgDyIv45jsPAwEBIr9VLL72E9PR0mEwmrFmzBo2NjRH7dDqdsFqtws/ExITk91GKjz76KGIbPbYcSAej+vrSXrHIMVbkcBHNsRG+vvTccpA2jkh8JMXHIcHIe021GcfktBt8CCLp+MZo5gKIjrjASNDDayoeg5S6TvLkVPRy4etLx5JIhHSGEhAxuPr7+7F06dI51/lrFotFcl/FxcXo6+vD888/H3A9MTERzzzzDN577z3s3bsXb731Fs6cOYMHHngAzc3NYfvcsGEDMjIyhJ+HHnpIeO+cnBw4nU7BhZidnY2BgQHs2LEDra2tqKmpQUVFBTo7O1FQUACbzRbQdmxsDEVFRWhvb8eRI0dQVVWFs2fPwmazYXh4OKCt3W5HXl4euru7sX//fgz1+zx/bZ3dsFgsAW09Hg82b96Mvr4+7N69G42Njaivr8e+ffvQ09OD3NzcOe89ODiI0tJStLa2orq6GhUVFejo6MC2bduE9+Y/ukSDB4WFhejo6MChQ4dQVVWFM2fOYPv27RgaGgro1+FwIC8vDz09PTCbzairq0NjYyN27doFi8UCp23Ux92O3fB6vdi0aRMsFgt27dqFxsZG1NXVwWw2o6enB3l5eXA4HAH9Dw0NYfv27Thz5gyqqqpw6NAhdHR0oLCwEFarNaCtzWZDfsE2oRjfZ82n0NraitLSUgwODga0dTqdyM3NxYoVK7Bv3z7U19ejsbERu3fvRl9fHzZv3gyPx4Ps7GxBQY1NTaOsrAxNTU04fvw49u/fj+7ubuTl5cFutwf0Pzw8jJKSEpw9exZVVVU4cuQI2tvbUVRUhLGxsTnvXVBQgM7OTlRUVKCmpgZtnT0AAFOcd8575+TkoLe3F+Xl5aivr8epU6ewZ88eXL58GVu2bIHb7Q64p7+/H2VlZbBbRwAAp8+dR1dXF7Zu3YrJycmAtiMjIygpKUFbWxsqKytx9OhRtLW1obi4GKOjowFtJyYmkJ+fj87OThw8eBDHjh1DS0sLdu7ciYGBgYBvKiXBgJycHFy8eBF79+5FQ0MDGhoasHfvXly8eBE5OTlwuVy4fPlywFzbuXMnWlpacOzYMRw8eBB/+ctfkABfLamc/EKh7ejoKIqLi9HW1oajR4+isrISbW1tKCkpwcjISMB7T05OYuvWrejq6sKBAwdQW1uL5uZmlJWVob+/P6Ct2+3G5rwCAIDRwKG1uQn19fUoLy9Hb2+vYhkhfFOTjqAyoqSkJKKMOH78OJqamlBWViZJRrT9xfdNwWWXJCOMRuMcGcG3tVqtKCwshMHlAAB8eqZNsYzYtGkTvF7fdy5FRljt0wB8hqMSGbFt2zZ0dHSgoqIC1dXVuPiXDgA+r+m72f85R0b09PRElBH8PRaLRZKMyP+wFABgcDskyYhvfOMbc2REa2srduzYIcw1vkbd+JRyGdHc3Iza2locOHBAkow419ULAHBMjCqWEXxbl8uFnJwcJBm8M9/UuaAyYvZcmy0jOjs7kZ+fj4mJiYC2oWREcXGJqHZjQkQZ8aUvfWmOjNiyZQsuX76MPXv24NSpU7JlRF1dHcARwE033cQ98cQTc653dXVxALh3331XUj/nzp3j0tPTuXvvvZdzu90R23d2dnLJycnc448/Hradw+HgxsfHhZ/q6moOANfU1CTpvZRg//79EdvsbLzEZWaZuf/+wUlq7zEbP912isvMMnMf1l8g1uf3c05wmVlmbk/zZWJ9hsO4fZrLzDJzmVlmzj4d+TuRwkXf6BSXmWXmVvzrfs7r9ZJ4zYj44Fg3l5ll5n5eTO47/OOR81xmlpnLKjtNrM9IePK9Gi4zy8xVnhuQ1F4KHw/9rpLLzDJzDT3Dal9PEs71j3OZWWbua785RKzPUz3DXGaWmXvg3yuJ9RkJb5k/4zKzzNxv97dJai+Fi3/5+DSXmWXm/uPwebWvJxlf/c0hLjPLzLX3W4n1ecu/HuAys8zc5dEpYn2GQ+W5AS4zy8x9+481ktpL4WJvSx+XmWXmns+pU/t6kvHz4iYuM8vMba3tJtbnjz84yWVmmbkdpy4S6zMc7NNuQWeM26cjtpfChVw0NTVxRDxcycnJcDqdc647HA7h95Fw5coVfPvb30ZGRoYQExYJK1aswPe+9z1UVVXB4/GEbJeUlIT09HThJy0tLWLfajF7ezUY9EnxJRvD5etL2y0HwUtnjIMpIfJ3IocLLQ+GJR2nAkTH9o80PrTd4qW5baLLNrXEbGEWueA4jng2tbgvrcZhlRn7JE9nsBkvKxVaj0N8pF2ahCPtpHChBEQMrqVLlwY97JG/tmzZsrD3j4+P44knnsDY2BgOHjwYsb0Yy5cvx/T0NCYnJ+W9NGWkpqZGbBMNylEKtI4rkCuMpXChx8GwdIxfPWIj5I2DxblBeyHCaVSjTu78lsSFxuf3Od1euDy+v1c0Z/DS4ELPuMDo1hm+56QlSjvSTgoXSkDE4Lrrrrtw/vx5WK3WgOsnT54Ufh8KDocD3/nOd3D+/HmYzWZ86UtfkvXs7u5umEwmTbxWctDV1RWxjZ4ZZXS8Kmx6I6RwocfBsHK9EVKgtVLxeSPI86F1NhbNEh1uLyecKEAbco9hkcOFzamtZ8hg8C2ESEE/rylJLtjPfJUCrYt+05BRSkDE4Hr22Wfh8XiQm5srXHM6ncjPz8eqVauwfPlyAMDU1BTa29sxNDQEAPB4PHj++edx4sQJ7Ny5E/fee2/IZwwODs65dvr0aZSXl+Oxxx5DXBxbpxTdd999EduwnhorFVqvuuSOQQoXgPbHZvAKLJq3sRwuL9xeed4IaXND23HQyN4Ve01ZVSzyuNDYG5EkzRshFfp5TUlyoYfXlNx5rzxY92BL1RlyQcRKWbVqFZ577jm89tprePXVV5Gbm4tHHnkEFy5cwO9+9zuhXUNDA26//XYhkv+f//mfUV5ejieeeAIjIyP48MMPA37EeP755/Htb38bv/3tb/H+++/jl7/8JVavXo2UlJSQRVL1xMcffxyxDU/+1LQHbg2OOFDijZAC1uNtpHAh7o/VFbAU6CXI5HgjpM0NtgWyFIi9ptp56uSNQw4XWnsbSSp4QM9xkJwXvr+Jll5TqjpDI68pLZ0hF8T+goWFhXj99ddRVFSE0dFR3HnnnTCbzXjwwQdD3sPX59q3bx/27ds35/cvvPCC8P9PPfUUiouL8c4778BqtWLRokV4+umn8cYbb0T90T6Arz7M/BS6RxwEeiOiV8nL9UZIPaaB9XFIgdZeOmEMMrwRLB61REOpAD4+bA43s+Ngkwvynl9ff3qNQ5qslcJFaqIRcQbfUWo2hwvJiZGThtTA7fFiatqXkBbNOoP08WNKQWwfzmQyYePGjejv74fD4UBDQwMef/zxgDYPP/wwOI7Dm2++CcB3IDXHcSF/xFi/fj1OnjyJ4eFhuFwuWCwWFBUVMWlsAdKOBkgwxgkViK12+h8e/9HFGXwTlxS03nKw2skfXyLuj1WBLAW8QLG7PJocDKtkDCwetUSDC19/+sQFSvUOyTnah9Vgc6kQPFwayFpA/jikcGEwaOs1FXNOp/Cp1lyQ1RlywVbg0+cI//AP/yCpXbrw4dFX8uIz76RUPpYKvhifdtl98pSKdC50Uo7J5AO1AQjFYWlCiXKUwofm8XQUkkl8/Wk3DpfHC7vL542Q+k1J4SIjWZ+FCOktRa2TlPzzm7Cc0pAPfgzJCUYkGMmZC1oHzVtlylqpXMhFzOCihLy8PEnttFw90ji7z9cf22UhpHMxY/zaNRICdvKKRXwwrDZGvPwxSOHD742IXi4AcdyQdsoRgOAFiQQ5XDhcXkxrUKOO90DR8nBpXfuJmpz6HOgMrbyNcndFpHIhFzGDixL+23/7b5LaabmNRSNI29efPh4uqUJAOhfaueqn3V6hwGo0KxYlHi4pfOiVGUePCy3mt+8ZKYlGxEv0RkjhQmy8aTkO0nJKLw+X1HHIlVPa6ozonRe+59DRGXIRM7goQcqh2oDWypHWasXXn1YHw8rdcpDOhXZKXixopHojpELL4qdKvikpfHweMkbF/bFq/ErhQuw11cYT/1/TwyVVTmkZ+kDb+HW6tfGayh2HVC7kImZwUcL1118vqZ2Wqy7acSocB0xOs6dYpHKhx8pRjjdCKvTxcEkXyFL4YD2LSSr0CRkgy4WvT7a/KSnQ0vj1ejlMOGnJKT12RcjOizST1l5TebpPKhdyETO4KCHc2Y5i6OPhIivIkuLjkGA0zDyDPYHMJhd0BJmvT7a9KlL44McwMa2V15SWV0VLbyMdLnx9aqnkaRu/9McwOe0Gn2gv1RPPppyiE9tojDMImfKajENmkWmpXMhFzOCihKtXr0pq5z82g03lKAUGg0GX7Tip45DKheBt1KAYHy3j19endopFCJqXmIkFSOODHwPH+YwumvB6OeEZxLdOktn2DMmVU1qWIohmbyP/jASjAUnx0tSsbJ0RxVz4+tRjYShtbkjlQi5iBhclfOUrX5HUTsvMOLqTR0slL28c0rnQcvuHHhfaxnjIH4cUPkwJRiTObLXSHseEyBsRzcU2lXiG5MopTcbhpJUxyi+o3PBQ9pqKFbzUEjyydYaGC6po1xly5ZRULuQiZnBRwsGDByW1Yz3GQyq0GocnIDZC2jikc6GHl44GF3ooeenjkD836I6D5zvRGAdTAtnK3Xp4I+TEaLIop2h7uAAIMoQWlBi/bHNBT2fQ9pr6jrSTJ6ekciEXMYOLEtauXSupnZaFT2mdUSbuk/Y4xIJSqjCTzEWyX8HTPhiWVgKDuE9WlaN0PrQxgIU4FYIFaHnokVEmZ37LlVOaGo4ytqmlwJRgRGI87zXVxoinywV7weZyoFUBV6fbC5fHJ8+ljkMqF3IRM7go4f3335fUTlMPl52+e5j2aoWfnInx0r0R0rnwCQCXh/7BsHS9jdpvOcgRyHLnBu3tdn+hzSjnQkHBUKlcpAvzm+44OI6jKqe0MoCVbMXJnxfRviuiTQFX/nsyGIDURGl8SOVCLmIGFyVIPzA5+lN8fX1qMw4lKy6pXPAHw/qew944pIL1LQfWDhOnlRUn7tPmcNP3mjrlK0f5XNCdFw6XF+6Z+Cq62+20DS75sla2ztAkuSf64355LtKS4hEXJy2ejvnDq2MIhPQDkzVUjgoEslRopxzlK3ipXGh5MCxdJa/NytEXGyFfIEueG0naGvE0ufB4OeGcQ1qgyoVGhgrPdZwBQtkAktAuLpDOoe7A56Pwqa9PbRdUcrZ3Y4dXRxmee+45Se10iY2Iag+XfENFKhe+frVW8tFbFsLu8gjZXnLGIZUPrbepeQOPJAK9plp5VehxodUY0pLiJWf3yYH2C0Pyckps/GoVa0pne5fdBZUcnSEHMYOLEqqrqyW14z8Cu8sDl4de3FCgN4JG0Dy7gkwqF+J+WRyHVGg9BrneCOlzg93tH6kIrFHH3mJEPhfseYbkQDuvqfxxyNUZWnpNaSRasSxr5egMOYgZXJRw2223SWoXeMQBvQ8v0BsRvUreqsAbIZULQDuPI03Fot3K0T8GOd4IqXxoXRaCmpLXzFMnX7HI54K9MciB1lzI2U2QykVKohHGOPone7g8XsGgi2adoUTWytEZchAzuChhbGxMUrsEYxySE/gjDugpFv6jNsYZhINoSUL7rTjpAkAqF+J+WRyHVPB9Olxeql5TpZ4hqXx8HoLmff1qXN5ChmKRy4V2hgot41ebrFGackoca6qFzgACHQOkwHKMphydIQcxg4sS7Ha75LZaHP8hVio0YiO08gwpOUpGHhfabmPRUCy8MAZof1PKPENS+dCqTg+tuk88tDDip91eoZSJnG9KMhcae01p1EQT98ui8atEZ9A0gPkxpCQakWAkbypoXmePEhdyEDO4KOGmm26S3FaLVde4gho9cqBVzSQlqxV5XGhTb4jmkRnxxjghpoomH0rrJUnlI10jrwpNLgDROCjWTRIbQnK8EdK58Mkop9sLp5te3BDNuk/ifqkvqBTUdpMlp5LoHwmn3fauNmUhaOkMOYgZXJRw/PhxyW212DqhmYkFsF0WgjUunG4Ppt3yvRFyoIViUZr1KpUPlmu7yYEW4+DHkCqK75ECqVxoFWuqlZJnMYGBNTlF3/jVekFFR2fIARGDy+l0IisrC8uWLUNycjJWrVqFw4cPE79XzXO0xjPPPCO5rZbKkXacysS0G16KB8MqEWRKuKC56qIdGwFoo1iUBv5L5YPloFo50GZBpWx7VyoXxjiD4DWNZjnFcjb1f1WdMU3Za0qbCzkgYnCtXbsW77zzDn70ox/hvffeg9FoxJNPPona2lqi96p5jtbYtm2b5LbaKEdtMrE4zmd00YISb4QyLuiPQa43Qg60WD0qFchS+dAu2Dz6vSpKA//lzQ36njrtthTpjcHr5QQZKGcccrhI/xzoDO1iTeXPDTlcyIFqg6uhoQGlpaXYsGEDNm7ciHXr1qGyshKZmZl49dVXid2r5jl6QM7RAFqsuvyBg3SUiinBiEQjfzCsFpOH/PEl4n5Z9AzJgZbjkLstKvc4mQmnWyhpQgO0FYsWCSVKM0blzQ0tPXW0jV96Y5iYdoOvR0rjaB9xv6wZKnJgjBNnW2qxSKejM+RAtcFVVlYGo9GIdevWCddMJhNefvllnDhxApcuXSJyr5rn6AE5RwMI21hRHAAp7pvqClhB8L88LqJfqYj7ZnEcco+9AnxGFw14vJzQN+2tEy0yyuQajcrmRvQuRrSUtYnGOJgSpJfgUaIztJC1tBbpgNa7O3R0hhyoNriam5uxcuVKpKenB1y/5557AAAtLS1E7lXzHKfTCavVKvxMTExEGpZqvPDCC5LbztPEytfCq8LmOORwoaW3ka7BpaVXRd43JZWPpHgjEuN5rykdgSw25KJ7S1GZ0ShLTmliOGrj4Zqc9lDzmiqd3/K4YFPWygWr45DDhRyoNrj6+/uxdOnSOdf5axaLhci9ap6zYcMGZGRkCD8PPfSQ0GdOTg6cTqdg0WZnZ2NgYAA7duxAa2srampqUFFRgc7OThQUFMBmswW0HRsbQ1FREdrb23HkyBFUVVXh7NmzeOONNzA8PBzQ1m63Iy8vD93d3di/fz+OHz+OpqYmdLWfBQA0f9YutPV4PNi8eTP6+vqwe/duNDY2or6+Hvv27UNPTw9yc3PnvPfg4CBKS0vR2tqK6upqVFRUoKOjA9u2bcPIhK+uyDxTPLKzs2G1WlFYWIiOjg4cOnQIVVVVOHPmDLZv346hoaGAfh0OB/Ly8tDT0wOz2Yy6ujo0NjZi165dsFgs2LRpE7xeL+zWUQDA9rI9sFgs2LVrFxobG1FXVwez2Yyenh7k5eXB4XAE9D80NITt27fjzJkzqKqqwqFDh9DR0YHCwkJYrVah7R//lI3JaV9w5dXLF1BRUYHq6mq0traitLQUg4ODAf06nU7k5uaioKAA+/btQ319PRobG7F792709fVh8+bN8Hg8gRzNjGF0wo7jx49j//796O7uRl5eHux2e0Db4eFhlJSU4OzZs6iqqsKRI0fQ3t6OoqIijI2NBbS12WwoKChAZ2cn6hpbfB+my4EdO3ZgYGBgznvn5OSgt7cX5eXlqK+vx6lTp7Bnzx5cvnwZW7ZsgdvtDrinv78fZWVlaG5uRm1tLQYtFwEAJ5qaMTk5GdB2ZGQEJSUlaGtrQ2VlJY4ePYq2tjYUFxdjdHQ0oO3ExATy8/PR2dmJgwcP4tixY2hpacHOnTsxYpsM+KZcLhdycnJw8eJF7N27Fw0NDWhoaMDevXtx8eJF5OTkwOVy4Ve/+lXAXNu5cydaWlpw7NgxHDx4EJ2dncjPz8fExATivT5h+X5+EUZHR1FcXIy2tjYcPXoUlZWVaGtrQ0lJCUZGRgLee3JyElu3bkVXVxcOHDiA2tpaNDc3o6ysDP39/f62OR8AAIzgMHilH3v27MGpU6dQX1+P8vJy9Pb2qpYRcR4nAGBofCJARpSUlEiWEWVlZbBYLAFtxTKiobkVAGC3jsiSEb/73e8EGTH7vWfLCLt1BABQXdegWEZkZ2fD6/Vi06ZNQWXE6IycSjJ4FckIfq5t27YNHR0dc2SE0zYGHu9mbxZkRE9Pj2QZYbFYUFZWhqampqAyoqD4IwCAxzkpS0bs3LlTkBEVFRWoqalBa2trUBnBlykbGLUqlhEHDhxAV1cXtm7dGlRGtHzWAQAYtFxULCNmv/dsGWFwOQAAR2vqAmTE7LkWTkaI286WEUePHhW8mR5HYNtwMuLDDz8MaOt2u7FlyxZcvnxZsYyoq6sDOJW46aabuCeeeGLO9a6uLg4A9+677xK5V81zHA4HNz4+LvxUV1dzALimpqawY1ODc+fOSW77cdMlLjPLzL2QV0/tfV4uOMVlZpm5kpO91J7xw/dPcJlZZm5P82Uq/Y9NTnOZWWYuM8vMOVxuyffJ4aJ/zM5lZpm5m1/bz3m9XiWvGRF5x7q5zCwz94uST6n0z3Ecl13ZyWVmmblXd56m9own/qOGy8wyc1XtA7Luk8PHmo1VXGaWmTvZPSz39SShzTLOZWaZua//v8NU+uc4jmu8MMJlZpm5+//9KLVn/L99n3GZWWbu/zvQJus+OVy8tquVy8wyc+8e7pD7epJx1/+t4DKzzNz5K1Zqz1j5bwe4zCwzd3F4kkr/R89d4TKzzNx3/nRM1n1yuNh3uo/LzDJzf7elTu7rScY/ftjIZWaZuYLjPdSesXbrSS4zy8x9dOoilf6nnG5BZ9gcLsn3yeFCKpqamjjVHq7k5GQ4nc451x0Oh/B7EveqeU5SUhLS09OFn7S0tJBtSeHy5cuS22rhqqdd3BGgX4yPH0NSfByS4qXHRsjjwvf3cVM8GFZpwVA50KKooNKMMiV8UPum7HSTScR90yx8alWYwKCMCzrj4DhO8Ta1HNDeblcSZwqwpzO0iTXVRmfEGSCUNZECOVzIgWqDa+nSpejv759znb+2bNkyIveqeY4eMBqlk/t5KAvh65tuKQKlY5DDhRYHw37egublGivy5saMcnTSmRtaKpUJpxscRytuSNk45HBB+3gfu8sfV0WTD9olFZQWmWZNZ2hj/NKVU/zfJy1J3pF2criQA9UG11133YXz58/DarUGXD958qTwexL3qnmOHli0aJHktp+HFF9f33RXjkpLW8jhQouDYZWWU5AD2gfDcpw4u0/eOFiaG7whp4VS8Xg5TE3T8ZoqNbiY4mKmX2OcASkyvBFyQXscSkt0yOEiltwjDUqNRjlcyIFqg+vZZ5+Fx+NBbm6ucM3pdCI/Px+rVq3C8uXLAQBTU1Nob2/H0NCQ7HvltmUBra2tkttqcTAs7eNLAPqrLqVKRQ4X4v7pe+qi18M1JcryknvQsBw+tFLyNLnQxmuqzKuiiAtq3kZl3gi50M5rSpMLdr2mcvB50RlSofovuWrVKjz33HN47bXXcPXqVaxYsQLbtm3DhQsX8MEHHwjtGhoasGbNGrzxxht48803Zd0rty0LePzxxyW35T8Gh8uLabdXSIUnBTXeCDmgv3JU5o2Qw4W/f7sGXhUNYiM08EYky6g1BMidG3SPWtJCqRgMBswzxWNsygWbw4UlGSbiz1A6DllcJGnljaDHhbh/WrFoSj1DSnQG7zVNTSL/N/PHN9LTGbQ9dUp3E+TqDKkgotkLCwvxyiuvoKioCOvXr4fL5YLZbMaDDz5I9F41z9Ea27dvl9w28IgD8opF7I2gGxtB11OnVKnI4ULcP/VxUDpIHNBiDH6lItcbIYcPVo14uaDtNVW6dcISF1rEmfr6Z9OrIoeL5AS6XlPf+YZeABptKTIWoylXZ0gFkb+kyWTCxo0bsXHjxpBtHn744aCuTyn3KmmrN+QcDRBvjENKohFT0x7YHG4sSEsi+i5qvBFyoFUAJM3jSwAtVl30V/K88et00/GaqvFGKDtqKXq5AHjj2q6JASwHLB17pUXMkK9/rWJN6Rx5BdD3moo5TqPgPePxedEZUkFWCscgQO7RADQ/PDXeCDnQTjnSO75E3D99xUJvJZ9mous1VRozBLB1nIz2XhW63gi5Sl4JF7S3qWnGmQLsxmgq1Rk0xsGPISXRiHgjPTPh86IzpCJmcFHCyy+/LKs9ze04qyDI6CoVPnialnJUOg75XGiTxSQ32FwOaB8Mq8YzJIcP2gc/0z7UnQdNxRLgjZA5DllcJPvGMO32wkGhRp0W2bvi/mkb8fzfSypY0hlKDnxWAto6w6ZQ1srlQipiBhclFBcXy2pPc7WiRdFTX/+0A7WVjUM+F/SK8TlcHkwLsRFaxQ3RMOKVe+nk8OEvGkq38CltLtKpcuGbb2lJ8UJcj1TI4SIw1pSCnFJYMFQutIsLpC2naHq4NNYZlBIYlM5vuVxIRczgooRHHnlEVnua21iaxanM9D/hdFM5GFbpOORzQd8zBNCNjQC0GYcSz5AcPrTactBqG4vq9i5lLgK9pjTHQXsholVyj7xxsKQztM4YnfbQ8ZoqHYdcLqQiZnBRwrlz52S11yaGSxuPCgChDAVJKB2HfC7oeerEtYbkeiPkgq4RP7P9I3PbBJDHx+clhov/O7G2vcuWnNJGydMMGfB4xSV45I2DLS600RlpifHgw4pZGodcLqQiZnBRwvz582W1p1lvSCtBlhRvFLLh6MaiyRuHfC5oruK14UL8DJpBtUrGIYcPvv9JUWkTktBu64RN5ah8btAIfdAqgYGerBUvNOV+U3K50CLul/a8iIszIC2RPXkrlwupiBlclGAyyUvTpbnq0ioY1fcM9hSLXC4+D6t43zPY9KrI4UPM9QThcXi8HCZnjtrRrhQBW8pR/tyg7zX9PBi/ifFxSIqXV4KHLTmljYfL9wyK43AqS4qRy4VUxAwuSujp6ZHV/vPjVWFPycvngl4xPn0EGVvjkMOHT3H5xBRpj4TYgIvmwqdqtkWVyyl25rdc8P1PTXvg9niJ9q0mJpBFLmjHNgJa6Qx5c0MuF1IRM7goYfXq1bLas2ioKAEtJe/2eIWDf+WOQy4XdL10enBBc/tH/jhYmRu8AZcUH0e8MOxsaFEWQksuqIQ+aHCQ+Oz+SceaqjF+lc+L6PU2ip9BehwcxymWt3K5kIqYwUUJu3btktU+5h4Oj8DYCHnjkM+FXzmSPhhWq6NkAG3q9CgZh1w+0ikJZK0C5n3PYNODzZac0sarIvaakh6HGkNFKReseU3lgtY3ZXeJj7SjqzOkImZwUYL842Siv/Cp+Bm0lKMpQb43QjYXM0XyPF4OdsKpylq66rWJp6N7tA8AzKOU4efPtPx8cKFkfiuXU2THIfZGKMl8lQv+GaQ9dWoKhrLChbhPTeYGZS7iDEBqorx4utjRPlEGlo728Rd/i97MODWeIblciA+GJV2QT0sPF83tH/6bUmI4yuWDVtFQrbLixM+wOVzkvaZ25Ua88uNkyHIxNS32RkTvdruaItOKuaBQFFjN0V1yQU1niIqeyj3SLna0T5ThH//xH2W1p1v7SfugeVqrFSVjkMsFfzCs77nsjEMuaCkVjvPXGlKykpc/N+iMQ6tjfXzP8P2dvByEzEhSUBP7JJcL2tu7xjgDkhPkeSOUgFZMnZZy6vNQLNv3DDrjUBNnKpcLqYgZXJSQm5srqz3NY1g+DzFcao5gkcuF7zl0Vl1aKnlaSmVy2gO+JJYWfPCr7Gg2fk0JcYif8ZqyNA75coru9u48U7xsb4QS0DoySo0HWy4X/BgmnNEda0p7QaWVzpCCmMFFCd/97ndltedXwNNuL5xucivgQG9E9Cp5NbFPcrkAtFDy0RuozfcXH2eAKUG+CJHLB3WBrMG2SaDXlJ3MOFa40KrQJg8WPdjyuaDnNdWSD1oeLi25kIqYwUUJDQ0NstqniT4KksJMrTdCLmgreSWTRy4X4uewtOUgF/S8dP4xKPFGyOWD1na7llz4nkNLsWg3N6iPQQPjV/wcloxfuVzQ8po63R5Mu331yaK5WLaaRboSnSEFMYOLEpYvXy6rvTHOIGRSkPzw1Hoj5IL25FEikOVyAWixdaJdoDZpr6naMcjlg5YRr2XQvO85tBJKlBuOyrmIduN3ZhzE63ApN37lckHLayruK03DhSFLslaJzpCCmMFFCW63/I+HxupRrTdCLqitgBUeCAso44J+7Sf6giwtiY7XVO12g1w+6AtkrbexyI1D7I1QoljY4UJr45fuNpYSrwprOiM10Z+tTRMsbikq4UIKYgYXJQwODsq+h695QsPDpUVtG4BebRg141DGBflxaF1ryBhnwLwkeitgpYaKXD78XFBSjhrNDZrK0WCAwLUcKOVi2uOFg2CNOi1roomfQy8pRis5RX4cnzudoREXUhAzuCjhy1/+sux7aAhkvkaP1qt48tk/ysehjAvyWaNOtxfTHt4bEb18qMkYBeTzQb+2m9ZcEPQ2znCRlhiPOAXeCLlcpCXGg3eUk5wbVhXKUQmoHRdlV+6pUySnZkIsSM5vvbZ3rYRr1GmtM6SAiMHldDqRlZWFZcuWITk5GatWrcLhw4cj3nfq1Cn84he/wB133IHU1FR88YtfxN/93d/h/Pnzc9p+8sknMBgMQX/q6+tJDIMopIx/NmgoFqvWwagzY5gUFTIkATVbDmq4ILsV5+PCYPApLi1AQ7GoqaYNyOeD9ukFWikWGuNQOwa5XMTFGYRvlyWvqVywmNzDipzSMs7U9xzfGFweDk43ucPE1YxDCRdSQOTrXrt2LcrKyvDKK6/glltuQUFBAZ588klUVVXh/vvvD3nfv//7v+P48eN47rnncOedd+LKlSvIzs7G1772NdTX1we1MtevX4+777474NqKFStIDIMoXnzxRdn3CNlYUb1aER0M63AjI4XMpFVTLV8NFzSUo1JvhBLQUCxqY5/k8kG/8KlWWyc0DRVlY1A2N+Jhc7o/FwYXSVnr8XJCeQbt5RRJr6m2XKTOeE05zrcoNREqfKvGw6WECylQ7eFqaGhAaWkpNmzYgI0bN2LdunWorKxEZmYmXn311bD3/tM//RN6e3vxxz/+ET/96U/x61//GseOHYPb7cbbb78d9J4HHngAL7zwQsDPwoUL1Q6DON5//33Z99DMONFqtSI+GJbkloMaJa+GC7KxEdoKMvGz6Hi4lI1DLh/8tzs17YHbQ3IFHP1lIdQav8rmBs1xRG/c0ISoLyXjUKczyG/vasVFXJxBSPBhxYhXwoUUqDa4ysrKYDQasW7dOuGayWTCyy+/jBMnTuDSpUsh7129ejUSExMDrt1yyy244447cO7cuZD32Ww2alkEpKDk8Eu67mEtlTxb21jKuKAxBm0FmfhZdIxfZeOQfXi16NudIJTG7/Z4MSV4I7TdOmFFqQDsyCn9Cp+SDxlIio9DYrx81aqEC7peU+10Bg0DWI2cYvbw6ubmZqxcuRLp6ekB1++55x4AQEtLi6z+OI7DwMBASK/VSy+9hPT0dJhMJqxZswaNjY2K3ps2lBx+STPGQ4sq8zxolFRQIwSUcEFnK+7z5eFSOg65fCQY44QacqTGITbctPdwUYjRVGg0sjc3tA2at7s8cBHymqodgzIu2IsLVAKa35QWh7pLheq/aH9/P5YuXTrnOn/NYrHI6q+4uBh9fX34zW9+E3A9MTERzzzzDJ588kksXLgQbW1t+P3vf48HHngAdXV1+OpXvxqyT6fTCafTKfx7YmJC1jspwfPPPy/7ns9DAKTvWWTH4fJ4YXcp90Yo4YLOyvHz5W1U+k0pmxsJcLicxDx1/BhMCXFIMGqTrE0j81WtclTKhfjZJKBXTTTAtxV4TWpimNbSoPacVNZ0hlaxjQD5cYiPtNNKZ0hBgKTxer1wOBySfvj0TbvdjqSkpDkdm0wm4fdS0d7ejv/xP/4H7r333jlBa6tXr0ZZWRl+8pOf4Lvf/S7+5V/+BfX19TAYDHjttdfC9rthwwZkZGQIPw899BAAn7GYk5MDp9MpWLTZ2dkYGBjAjh070NraipqaGlRUVKCzsxMFBQWw2WwBbcfGxlBUVIT29nYcOXIEVVVVOHv2LDZs2IDh4eGAtna7HXl5eeju7sb+/ftx/PhxNDU1oaysDBaLBfU1VQCAtr/0wOPxYPPmzejr68Pu3bvR2NiI+vp67Nu3Dz09PcjNzZ3z3oODgygtLUVrayuqq6tRUVGB/uExAEAC3AFtrVYrCgsL0dHRgUOHDqGqqgpnzpzB9u3bMTQ0FNDW4XAgLy8PPT09MJvNqKurQ2NjI3bt2gWLxYJNmzbB6/UiOzsbXq8XY4NXAACVtSfQ2NiIuro6mM1m9PT0IC8vDw6HI6D/oaEhbN++HWfOnEFVVRUOHTqEjo4OFBYWwmq14j+yt/jJdNmxbds2dHR0oKKiAtXV1WhtbUVpaSkGBwcD+nU6ncjNzcVHH32Effv2ob6+Ho2Njdi9ezf6+vqwefNmeDyegHssFgvKysrQc963pW2zu9DV1YW8vDzY7faAtsPDwygpKcHZs2dRVVWFI0eOoL29HUVFRRgbGwtoa7PZcPTYCQDAxOgQampq0Nraih07dmBgYGDOe+fk5KC3txfl5eWor6/HqVOnsGfPHly+fBlbtmyB2x3IZ39/P8rKytDc3Iza2locOHAAXV1dOP9ZCwCgoblVaDsyMoKSkhK0tbWhsrISR48eRVtbG4qLizE6OhrQ78TEBPLz89HZ2YmDBw/i2LFjuDpmBQC4HYFzweVyIScnBxcvXsTevXvR0NCAhoYG7N27FxcvXkROTg5cLhd+/etfB8y1nTt3oqWlBceOHcPBgwfR2dmJ/Px8TExMCP177DYAwO59f0ZbWxuOHj2KyspKtLW1oaSkBCMjIwHvMjk5ia1bt6KrqwsHDhxAbW0tmpubUVZWhv7+frxf8CEAwOiZhtvtxpYtW3D58mXs2bMHp06dQn19PcrLy9Hb20tMRowM9AEABscmcPbsWZSUlMiWEeK2Ho8HxxuafLLsYo8iGfHHP/4RHR0d2LZt25z3DiUjBi5fAAAcralTLCM2bdoEi8WCXbt2obGxEaMTDgCAdWhAkYyYPdciyYgEYxziDT7PVm5BEXp6emTLiKamJhw/fhz79+9Hd3c3Pt53AAAwNT6iSEbs378fBQUF6OzsREVFhSQZ4ZgYAwD0DY4olhFbt27F5OSk0LalrQMA0H6mRbGMaGlpwc6dO+e8dygZMTk2DAAwVxyBy+WaM9ekyIjs7GyMjo6iuLgYn7Z+JmTJ9134i2wZsWvXroC2JGREXV0dwIlQVVXFAZD0c+7cOY7jOO6OO+7gHnnkEW42PvvsMw4At2XLljm/C4b+/n7upptu4pYvX8719fVJuofjOO773/8+l5iYyLnd7pBtHA4HNz4+LvxUV1dzALimpibJz5GL06dPy77nSNsVLjPLzH3nT8eIvcdzm+u4zCwzt7/VQqzPSPjHDxu5zCwzt62uh0h/vUOTXGaWmbvt139WdL8SLqacbi4zy8xlZpm5CYdL0XNn4w+HOrjMLDP3b7tbifQnBYUnLnCZWWbuZ4WNxPpcveEol5ll5j7tHVF0vxI+vpddy2VmmblDn11R9MzZONE1xGVmmblHfl9FpD8p6BywcZlZZu6v3jhIrM9f7WjhMrPM3KaqTkX3K+Hi7T+f4zKzzNyb5WcVPXM2vF4vd+O/+ObawLidSJ9ScPdbh7nMLDN35vIYkf52fXqJy8wycy/k1Su6XwkXNeevcplZZu7xd6sVPTMYfvj+CS4zy8zt/vQysT4j4X9t/5TLzDJz79d0Eemvf8zOZWaZuZte2895vV7Z9yvhIhKampq4AN/nbbfdhvz8fEgBv2W4dOlS9PX1zfl9f38/AGDZsmUR+xofH8cTTzyBsbExHDt2TNI9PJYvX47p6WlMTk7OiSPjkZSUFOCFS0tLk9y/UoyOjsq+h26Mh4bbWISL8akdgxIu+INh3V4OVocLqQqqeM+G2oKhSpBOYRtLKFKpsBK1srlBNo1fFy74kyScbni9HJHSIGpjuNRxQUZOTU57wJfs0zr04arNSUzeqi2nwIrO0CeGi57OUHKknRIupCDgL7pkyRKsXbtWVgd33XUXqqqqYLVaAwyekydPCr8PB4fDge985zs4f/48jhw5gi996Uuynt/d3Q2TyaSJESUHDodD9j00asNoHYzqexbZ/Xi1BpcSLviDYUenXLA53FiaoejRAfg8BM17veLYCO34IJ1QomcmFscBk9NuInNSbVKMMjlFmgtfP/FxBiE5QguQzuAV4tAUFplWpTOInk6iX9wvqTI8amMClXAhBaq/7meffRYejwe5ubnCNafTifz8fKxatSrg1O2pqSm0t7djaGgIAODxePD888/jxIkT2LlzJ+69996Qzwl2ttHp06dRXl6Oxx57DHFxbJ1SdMMNN8i+h2aqsi6rFWKTR53RqIQL8fNIKxY9ykKQGsPktBv86RtKg2pZmBt6BAYnxcchwWiYeT7puaFsfivhgnRCiXgMSrwRSkH+m9KeC/5ZEzNeUxLQ08NFbpE+MwaFxq9SnREJqv+iq1atwnPPPYfXXnsNV69exYoVK7Bt2zZcuHABH3zwQUDbhoYGrFmzBm+88QbefPNN/PM//zPKy8vxne98ByMjI/jwww8D2r/wwgvC/z///PNITk7G6tWrsXjxYrS1tSE3NxcpKSkhi6Tqifr6etx6662y7uE/Ov5gWLUVd0l4I5SAdIqvWgGghAvx80gbjlqW6KClVBKMBqHArVwomxv+7TgS0EOp+LymCRiZnCZuOCo14tVxEb0LEYC819SqcmGohAuaXtOYzpCvMyKByF+0sLAQr7/+OoqKijA6Ooo777wTZrMZDz74YNj7+Bpd+/btw759++b8XmxwPfXUUyguLsY777wDq9WKRYsW4emnn8Ybb7zB5NE+Tz/9tOx70kRxQjaHW7XBRcIboQSseSOUcAFQGIcz+stCiL2NSr0RSvgg7m3UYSHCP89ncLGhWNRxQdgboQMXAHk5pSUXvNfU5eFgc6g3uBwuD6Zn6pJFcxiKWiNeqc6IBCL7cCaTCRs3bkR/fz8cDgcaGhrw+OOPz2n38MMPg+M4vPnmmwB8B1JzHBfyR4z169fj5MmTGB4ehsvlgsViQVFREZPGFgBs27ZN9j3GgCMO1AtkEt4IJSC/FadOICvhwvc8WuPQXpDxXlO1IFEvSQkf9GI8tPWq0NvGUjYONVywshWnFKx5VZRwwXtNxc9XA3EfaQQShaRC8DYS85qq201QqjMiga3Ap88RlB4NQFKYkfBGKAH5GA91Sp4FLsT9aKlY0mYOhhU/Xw1IjIGFo5Z086rwGbwElHygN0K7uSFeiMxeGCuBfsYvae+vtkde8SBpOPJ9pCXFw0ggi1YqWPM2Mnu0TwzBofRoALIGl/ZbWL7n0dvGUgKlXJCM8eA4ThfFEhdnQFoiOYEsJGEoDEYF2DhORg9vo+955BdUBgMEjuVCDRcuDwenW/2xOPp7uNiI0WRDZ+jFxedDZ0RCzOCihB//+MeK7iOp5NUc+KwGQr0hwspRqSBTzgU5QeZ0e+HycAH9agW+XhZJgcxzrARK+CB9uK3aY1iUgiwXfm+E0ppeSrgQe01JeOr0yBgVP4/0cVFK69Op1RlkuNBfZ5DxmqqTU0q5iISYwUUJe/bsUXQfyboqepSE8D3PN1knpz1wEzgYVm1xR+VckCvGx/dhMACpCr0RSkHnm1IukJXw8XkofOp7Hkku1CtHJVzEiWJNSRQ/5fvQ2vglHReoVt6q1xkEuNBZZ7g8HBwuAjpD5fxWykUkxAwuSli1apWi+0i6VvXO/gEglKVQA7XjUM4FOQ8XPwY13gilYG3LQQkf5D1cem+dkPMMac0FQNoTr3cMl/oxuD1eTE17AvqVC/U6g41vSglSE42IE2JNyXnqtNYZkRAzuCiht7dX0X0kVyt6CbIEY5xQMZpsLJqycSjngvz2j9aueoCWQFY+DiV88PPC7vLARcBrqtvWCWPGr1o5xco4lIDkGMQLS6XjYIsLbeeFwSDymhL11GmrMyIhZnBRQkKCMqLJKkd9BJnvmeTjCpSOQzkX5LZ/9OWCvEBWs/2jhI80sddU5ThcHi/sLt4bEb1KnoTxq3ZuRLOSpxEva0qIQ4JRmVplQWfotSvieyY7uk8pF5EQM7goYeHChYruY00gKwWNcSj1RrDBhf4GFxmvqfpxKOEjwRiH5JlCwGr5EBtsaVG9pagPF75nklTyesUN+Z7ncHlVe01JxDYq5YKs1/TzpjOUfVNKuYiEmMFFCWfPnlV0n3/ykMw40XO1ot4bwQdRKhXISrlgbStOKVgbh3I+yHgc+W8yOcGo2BuhFDTiAtUYKmq5iObFiNjYVjsOEmP4r8wFQC5Ok+PER9opk1NKuYiEmMFFCX/zN3+j6D4+pZhE9o++k4dMVpmNgDdCORe+55E4GJYFQcbKN6V6bqg0uPTyqABiLtgwfhVzQTRkQL9Y05REn9eUlJzSlQuC35Qei3Re3qr9pianPeDFtdI5rpSLSIgZXJRQWlqq6D5SSgUAxmcmYEZyouq+5CKD0Dj4MaQlxSv2RijmYkaQeTlgYlqdscKPY77CGj1qQIoLQDwO5d+UUj4yCC1GeMU0P0VPLtQbvyS+KfVcqPumPF5OONdSXz7IyClduSCpM1K01xnphL4pfgyJojAEuVDKRSTEDC5KUHo0AD95xqbUT56xqemAPrUEqXGQGINSLkwJRiHbclztOOz6c6F2DICfTz34EMYx87dUijG7+jEoBf/MCadbddzQOANcjKn2DLnA17mMySllXKQT1Rn6zw213xTPRXqy8iPtYkf7RBmUHg0wX1AqBL0ROqwc+WeqHQcJ5ajmmAbek6NeIOu3cuS5GFNpqDhcHiG7L0PFN6V2bhDjQgfPr7gKOam5oWZ+K+WC558UF6mJ2sfTAeSUPAlZq3hepPjHoLZKu56eeFKyll+I6MFFJMQMLkr46U9/qui++TMKWe0KmOM44cPVxeASJo86JU9i8ijlQvxctcYKC1uKagUZ7+qPMwDzkpTHeCjlIyOF0Ap4hks95oUxziDEx5Dzqig3HBXLKVLeCMFQ0d749T2X9/6q9JpOqZ/fanXGtNurukq7rjpDWKST8WDrwUUkxAwuSvjwww8V3ScOVlSzlz017YF7JnJQTbyNUmQQ8nCRWDkq5QIQb2PpPw6lIOVtHBd5G9VUy1fKB/8dE+NCB+MX8CtI9eNQH/ukmIuZMZCKt9FjCwsg/02p8WAr5SI10Yj4mfmoZhxOt9+DrYfOIC2n9NIZ4RAzuCjh0UcfVXRfvDFOyKxQs3rk702M91d91xLEVsAEtn+UcgGIPFyEtk70Mbh8fzun2wvHjEBVAlLeCKV8+L0R+m85qAGJlTzHccL9asahlgu1Hmz+fr25UD2/CRjxSrkwGAxEPPG8oWIw6JPBSy6eTl+dEQ4xg4sSPvvsM8X3khACgiBTETioBsIqnlCwuRqBrIYLUh4uEts/SpGaaIRxZgWs7puaSRlX6Y1Qyoc/3obM9o9eXhUSimVq2gOXhwvoTwmUciGONVVTMkVvDxepbepxAoYjCTml5psSJ2Fofd4r/1yAhPGrLxfhEDO4KOHaa69VfC+JbCwSGUxqQCwYlcA41HDBG45qVvIeLyeUAdCDD4PBIPI4Kh+H2IhXA6V8EAvU5jNGdYobIqFYBA+2itR3QDkXvNHt5SCUdVACPT2/AEklr6+cIvlN6ba9S2qrXWedEQ4xg4sSEhOVC3MS2RokMpjUQLzloGYFTMJVr4YLEoJMHOei+0pezQqY0DellA/SWYr6xXCpX4wIHtMUdR5spVyQKpmiZ8YoII7hIhQ0r2JuqNIZKerHofu8IFQyRW8uwiFmcFHCxYsXFd9LIuBcb0GWIVoBqykaSkLJq+GChHLkx5CaaERivD5TjoSxQirYXCkf5ILNdV6MJKsPOB8npBxVzQ0CAed6ZoyKn6tmDF4vJxQdVSNv1XFBbn7r5fklVTKFxDa1Gi7CIWZwUcI3v/lNxfcSVY46CTJyK2D1sU/quCChVPRNfRc/W42SJ1VLTCkfpFbAJKrlqwGJgHNS81vV3CAQqG0lZMQrBQkPts3hJlK8VQ0XZBbpZEIGlEJcMkVveauGi3BQbXA5nU5kZWVh2bJlSE5OxqpVq3D48GFJ937yyScwGAxBf+rr64k+S2vs3r1b8b0kVl3CylGnyeN7tnpjhYRiIcIFEaNRTy4IxHARUo5K+RCvgJUajk63B1PT6ou3qgGJ+EZ/vI06o1HN3CASN8RIxqiaoqH8nFLrwVYlp/gwFJ1lrVr4Y2bVeH/V6z41XISD6tzPtWvXoqysDK+88gpuueUWFBQU4Mknn0RVVRXuv/9+SX2sX78ed999d8C1FStWUHmWVvjHf/xHxfeSKBqqd+o7/+wrVofiyUOqeKsaLkhkxrEgyEgc/0HKcFTKhzHOgHmmeNgcbozZXViQliS7j4DUdxXFW9WApKGiFxcAoVg0O5nMV6WYXTQ0OVF+AoJfRqkzfklwQSKeTs9FOomEMRLxy2q4CAdVHq6GhgaUlpZiw4YN2LhxI9atW4fKykpkZmbi1VdfldzPAw88gBdeeCHgZ+HChVSepRX+8z//U/G9JFKV9TxKhodaY2WSUPFWVVx8Dlbx4mezsAJWw4fakil6p74DZGLRSMU+qeKC92CrWBj6lbw+ckpcNFSpnCKV3UdkXhAwVPTUGWrnt9iDrZfOCAdVBldZWRmMRiPWrVsnXDOZTHj55Zdx4sQJXLp0SXJfNpsNbnfo4GqSz9ICP//5zxXfSybFV/9tLLXj4D0qiUZ1xVvVcMELADVFQ/Wu+wSI6iYxYDiq4kNlVhmpbVE1IBLDRcgboUpOqVSOpIq3qoHBYCAmp9TObzVcsOTBVgO1XJAq3qqGi3BQZXA1Nzdj5cqVSE9PD7h+zz33AABaWlok9fPSSy8hPT0dJpMJa9asQWNjI9FnOZ1OWK1W4WdiYkLSe6nB5s2bFd/LC1ESAc4sKBalK3l/1oy61Hc1XKQlxasuGjpOKN5GDYTYCAJbo2rHoWpuqFTyLHh+SRQNJWX8quFCbVFgcfFWPb2/agPOSXl+SegMEuVrWNAZSj3x/BjSTeo82Gq4CAdVBld/fz+WLl065zp/zWKxhL0/MTERzzzzDN577z3s3bsXb731Fs6cOYMHHngAzc3NxJ61YcMGZGRkCD8PPfSQ0GdOTg6cTqdwOnh2djYGBgawY8cOtLa2oqamBhUVFejs7ERBQQFsNltA27GxMRQVFaG9vR1HjhxBVVUVzp49i7i4OAwPDwe0tdvtyMvLQ3d3N/bv34/jx4+jqakJZWVlsFgsQtsDe8t87zdiRV9fH3bv3o3GxkbU19dj37596OnpQW5u7pz3HhwcRGlpKVpbW3F1zGdUjg9asG3btjnvbbVaUVhYiI6ODhw6dAhVVVU4c+YMtm/fjqGhoYC2DocDeXl56OnpgdlsRl1dHRobG7Fr1y5YLBZs2rQJXq8X2dnZ8Hq92LRpEywWC65evgAAaPvLBZjNZvT09CAvLw8OhyOg/6GhIWzfvh1nzpxBVVUVDh06hI6ODuzcux8A4LXbhLY2mw3btm1DR0cHKioqUF1djdbWVpSWlmJwcDCgX6fTidzcXHzjG9/Avn37UF9fj8bGRuzevRt9fX3YvHkzPB5PwD0WiwVlZWVoamrC8ePHceDAAcxL9E2TgdHAv+Hw8DBKSkpw9uxZVFVV4ciRI2hvb0dRURHGxsaEtvXNrQCAv7SdRmdnJyoqKlBTU4PW1lbs2LEDAwMDc947JycHvb29KC8vR319PU6dOoU9e/bg8uXL2LJlC9xud8A9/f39KCsrQ3NzM2pra3HgwAF0dXVh69atmJycRM2RgwCAzl4LRkZGUFJSgra2NlRWVuLo0aNoa2tDcXExRkdHA/qdmJhAfn4+2jvOC8LscncHdu7cOee9XS4XcnJycPHiRezduxcNDQ1oaGjA3r17cfHiReTk5MDlcsFqtQbMtZ07d6KlpQXHjh3DwYMH0dnZifz8fExMTAT0Pzo6irGrvnnecPozVFZWoq2tDSUlJRgZGQloOzk5ia1bt6KrqwsHDhxAbW0tmpubcaSmzjcvZvrJzs6G2+3Gli1bcPnyZezZswenTp1CfX09ysvL0dvbS1xGtDT43sHLARevXFUkIz77S4/v73C8WrGMqK6uxoIFC9DR0aFIRrScPA4AaP6sQ5GM6LhwGQAQHwc0nzqpSEYUFhbCarUGtJUrI5zWEQDAkZo6RTKiocVXlfzKxW7Y7XZFMiI7OxuPPfYYCgoKFMmIC+fbAABDtinFMqK3fwgAcPTP5YpkRGdnJw4ePIhjx46hpaVFkYzoPncWAHCsvlGRjLg04OMyJZ7D0aNHFcmIsrIy3HfffQFtSciIuro6gJuBx+Ph7Ha7pB+v18txHMfddNNN3BNPPMHNRldXFweAe/fdd+f8LhI6Ozu55ORk7vHHHw+4ruZZDoeDGx8fF36qq6s5AFxTU5Ps95OKjz/+WPG9V8btXGaWmbvxX8ycx+NV1Mftr/+Zy8wyc71Dk4rfQy2yKzu5zCwz9793tii6f3+rhcvMMnPPbj6u6j3UcMFxHLdmYxWXmWXm6ruGFN3/k/wGLjPLzG0/2avqPdSgqXeEy8wyc/e9fVTR/aOTTi4zy8xlZpm5abdH1buo4eNfd7VymVlm7p1DHYruf7+mi8vMMnP/s+RTxe9AArf++gCXmWXmLg4rm5/f+o8aLjPLzH3ScVXVe6jhwnzaNz+f21yn6P7P+sa5zCwz9/X/d1jxO5DASzPzs7RB2fz8zb7PuMwsM7fhwDlV76GGi5EJ9fPzzjcruMwsM9c5YFX8HmqRW+2bn/9ru7L5efizK1xmlpn77p+OqXoPtTojGJqamjjBw1VTU4Pk5GRJPx0dHQCA5ORkOJ1OzIbD4RB+LxcrVqzA9773PVRVVcHj8cfMqHlWUlIS0tPThZ+0tDTZ7yUXX/ziFxXfq7ZoKAup7wDB7R+VW1hquADUJzHoXfUfUB/DxXORmmhEglFdNRk1fJDaptaTC0D9aRIkUt8BMlwoDzbXN36Lh9rtOFLbu2q4UFsyxUOoeKtakJK1akMG1OqMUBCiym677Tbk5+dLuonfxlu6dCn6+vrm/L6/vx8AsGzZMkUvtXz5ckxPT2NyclKI2aL1LFqYnlYeK8MXDXW4vBifciHdJG8i80olTsfUd0B9bRhSAlkNFwAJY0W/g6t58DFctpmioXKNJpLFW9XwobZkCguxjYCoZIra4H8d54bqAGdGuFCr5McJ1TxUwwVfNNSqsGSKzeEiUrxVLdQbv/pzEQ6CNl6yZAnWrl0r6+a77roLVVVVsFqtAcHsJ0+eFH6vBN3d3TCZTAGeKFrPooWRkRFV92ckJ8DhcmLc7sJymffygixdx9R3QBRUqzrYXN3kUcuF2oDzcbt+B1fzSBdl7FgVCGRSXADq+GBlBawWagLOSaW+Ayq5UBk0z4LnFyAwDkJZyKp1RkqCz+BSIG/5safoePwYoL5kipURnREKqv6yzz77LDweD3Jzc4VrTqcT+fn5WLVqFZYv95sKU1NTaG9vx9DQkHBtcHBwTp+nT59GeXk5HnvsMcTF+V9PzrNYwB133KHqfjVbDiykvgPqtxxIrYDVcqFmJc8xkPoOAPHGOCFNWomxIqwcCYxBDR+srIDVQs12O6nUd0AlFypLpuh93isP1R5sUTa1GpDSGUpKprDk+QVUeLAJGfFquQgFVQbXqlWr8Nxzz+G1117Dq6++itzcXDzyyCO4cOECfve73wW0bWhowO233y5E8QPA888/j29/+9v47W9/i/fffx+//OUvsXr1aqSkpODtt99W/CwWcPToUVX3Z6gwVlhIfQdI1OEiM3nUcqEmVZmV1Hfx89UoeRJjUMOH2hUwazFcSsZBsnirGi7UlkxhJoZLpQebVPFWYnJKxSJdb52htmQKKW+jWi5CQXWAT2FhIV5//XUUFRVhdHQUd955J8xmMx588MGI9z711FMoLi7GO++8A6vVikWLFuHpp5/GG2+8EfRoHzXP0hovvPCCqvvVrOTHGfNw8StgU4K8YzOE4q0qhYBaLtRsOfCCLNEYh2SZ4yeNjOQEXIJdUVAtyeKtavhQu/1DcmtUDTJUrORJjkENFwaDAfOTEzA8OY0x+zSWZJhk3U9q+0ct1BRw5TjOX79KpeGoq5xixPPLB//zCWNy45dJxZqq5SIUVG/WmkwmbNy4Ef39/XA4HGhoaMDjjz8+p93DDz8MjuPw5ptvCtfWr1+PkydPYnh4GC6XCxaLBUVFRUGNLTnPYgF5eXmq7leTjUVy+0cNxCtgZeMgYzgS40LJypEPmFdZvJUE/EkMKrymBLZ/1PAh3nJQswLWe26o8f6S3P5ROzfUGCuscKFmcWt3eTDt8fr6UTkOUnJKbw+2GvAJY4AyeUsqe1ctF6GgX3Tc5xy/+MUvVN2vZuuEFQ8XvwIGlAkBUitH1VyoMFRY4QJQpxxJCmQ1fKgpmcJK6jugckFFcPtH/dxQszBkw8PFy1o1nl8SHmxSckrNNrXeBhegLn6ZlJxSy0UoxAwuShDHqimBfwWsxhuh/+RRNQ5CWw6quVBjqDDEhbptanJbDmr4ULMCZiX1HVBXMoXk9g8pOaXI+0uw1Iga8GPgS6bIAT+X0pPVe7B11Rl2/zj0hppkK1Z0RijEDC5K+P73v6/qflVbDowEQPreQVnAOcnUd7VcqMliYiX1XfwOqrZ3CYxDPR/KVsDi4q16pr4D6rapSXobVXOhIuCc1PaPWswumSIHJAP/VesMFck9pAL/SUCp7vN6uYDzd9VALRehEDO4KOHw4cOq7leTGcdKAKT4HeQqFpKp72q5EK+A3QpXwHpvYfnegY0VMCk+5Cp5UqtfElA6BoCsB5sYF2oWhjrzoaZkCsnirap1BgkPNgMLQ//ckOvBdhPzYKvlIhRiBhclfPnLX1Z1v7Afr2PsEwkojUUjmfqulgvx5LU65MUNsZL6DqiL8SC5AlbNh0JP3ThLnl8CWcgkDBXVckrhwnDa7fV7sFmYGwrDBkh6G9VzoT4WjYlF+szfUqm3MSXRiKR4dfF0arkIhZjBRQniAq9KQGIfmwVBptYbQUIAqOUi3hgnHJEk1zvEyvElgPItB9LFW1XPDYXGClOeXxVFQ0nGPpHiQp0HmwE+FBYN9XvpGOBCza4Ioa04EhC2qWXKWpJGo1ouQiFmcFGCy6WsThAPdTVV2NnGUrxyJFi8VS0XvvdQJsxYSbcGlCtHu4ts8Va1fCiNRWOJCzUlU0jGPqnnQqEHe8awSTclCH8HPaH0myIZ26iaC1HIgNySKf5sav11hlLvL0kPNgmdEQwxg4sSMjMzVd3PT2CHS94KWJz6zoJima9wP56kh0stF4DyIGdWqv4D4gBnZWMgVbxV/dxQuQJmYF6oKZlC0oOtlgulJ2KwxAWgRsmTM37VcjG7aKhUcBzHVlkIhYtb1nRGMMQMLkrgD9VWCqUrYJZS3wFRvI3C7R8SY1DLBaC8FhcrgcGA8qKhfqORTPFWtXwoVY4seX4B5dXmSRorenPBwvYuQOCbYoALpSVTxMVbmZBTCuOXxxnTGcEQM7go4amnnlJ1v8FgUCQExKnvCUb96VVqqJDc/lHLBaBiBcxQ3JDSoqHCEUuExqCWD6VZTKTHoRZKxuEVebBJZIyq5ULpNjVLdZ8A9VuKJL4pEnJKSckUvm2C0YCURH2PHwNUxP0SXIiQ4CIY9NfIn1MUFhaq7kO8Jy8V4wQDaklAaUYZyRUwCS4Uj4OhuCFTghFJ8fJXwKQD/9XyoVQ5spS9CygzVkimvgMkuPDJGbklU1iTU35DRenCUP04iOgMBXND7PnV+/gxQIXxSzDwnwQXwRAzuCiBxNEASpQ8S1tYgPKMMpIBkCS4UDKOgNR3RraxlAgz0sHmxI4wifJtLCUB5yRT3wH1XAQUDZVRMoUlzy+gvtQIiXEQ0RkKvEMsJZMA6oPmScja2NE+UQYSRwMoCThn5eBqHsIK2CFvBUwyAJIIFyoMFRLFW0lB0ZYDwdR3QD0fSkumsJT6DihTjqSNRrVcBBQNleEdYsnzC6hI7iEob0nKKTnzm2TgPwkoLZlCcksxdrRPlOHFF19U3YewApY1edgSZKpXwATGQYQLBVsO46KYIbXFW0lBSVYZ6YwytXyoD9Rmy9uoyPgltBVHRk4pWRgy5olXIGun3V5MEvRgk5RTSrYUWdEZ4oQxvQxHElwEQ8zgooRdu3ap7kPNCpgVQaa0aCjJrVESXKQr8jayxQWgzFgZJxxsrpaPDAUrYHHxVvY8XPI92BnJZDymJOWUHGOFtdAHJVyQ9mAT4UJB5itrCQwBCWM66T4SXARDzOCihG9+85uq+1CTpchK6jugrGgoyVUXCS6U1OFiLWYIEAVq67gCVsvHPAUr4KlpUfFWRvhQ8k2RLlBJZG4oyET2e7DZkFNKSqaIi7eS8GDrrTNY8fwCymJmSYYMkOAiGGIGFyVcuHBBdR9KXPWsbSkC8uOfxKnvJAxHklwoSmBgRKkAarOYyHxTavkQr4CljoNvx0rqO6DUUCE7v0nMjQxFcUNsySklJVNIj0EvOcXSwdU85CYxBBZvZUNnBEPM4KIEk8mkug8lh3iyFgAJyM8qI536ToQLQTm6wHFSV8AMergUVGknncJPhg95Wyespb4DKrOQCSlHklwoGQcrc0NJ0VDSHmzSckoqWDN+AfklUxwur1C8lQQfJLgIhpjBRQnXXHON6j7UFLFjafLIjSvgV/ypiUYkxqv/RIlwMTMGj5fDhFPiCpixjFFAaQwXWcVCgg+529RjDK7iFW2bEN7+ITk3pI7D6+VEZV9Y4kNewDnpY7tIcqEk9IGVeDpAfAyZPJ1ByoNNgotgiBlclNDW1qa6D37yjExK90aMMBYbAQDXyBwH347UGEhwIV4BSx4Hk1z43kXqGDiOE9pewxAfcscxOumauY8dpXKNqGTKtFtayZTRKZ4LMuPQg4txO1vHj/Hg5e2w1G+KQS7kjgEA8flNAnLHIdYZJDzYJLgIBmIGl9PpRFZWFpYtW4bk5GSsWrUKhw8fjnjf2rVrYTAYQv709fUJbT/55JOQ7err60kNhQgeffRR1X1cl+5za161OSQHcg6MOwLuZQHXzfO9y4DVKan9gJUfQxKR55PgAvD/TaWO48q4c+Y+MuMgAf5dBmwOSe2tDjfsM5mAixniQxiHVdo4rsy0W8zQvJifkoDEmeO3rkrk4wrh+U2Ci8XCvJDHxbWpiUSKt5LCdXLHwSAX/LsMTzrhklj30C9v2Zkbgu6Lcp0xG8QMrrVr1+Kdd97Bj370I7z33nswGo148sknUVtbG/a+n/3sZygqKgr4KSwsREpKCr70pS/h+uuvn3PP+vXr59yzYsUKUkMhgo8++kh1H4vmJcFgAFweTvCWhIPN4RLqwrCl5H2T54pOgowEF4DfcJQ6DkEIzGNPkA1YnZJi0fgxZCQnwJRARjmS4GPxPHnKkUUuDAaDYMTKHQcp45cEF9fNU2j8zmNHRgEiI35cn3GQ4OLalEQkGA3gOGDQFtlYcbg8GJ3ZUmRLZ/je5YpULvjFLaH5TUpnzAaRYi4NDQ0oLS3Fxo0b8atf/QoA8OMf/xhf/vKX8eqrr6Kuri7kvffeey/uvffegGu1tbWYmprCj370o6D3PPDAA3j22WdJvDo1kDgaIMEYhwWpSRiacGLA6sDCtPATgve8zDPFIyWRjcrmAHBdhkzlaOM9Q2QmD6ljGvhxXJWpHJdksKPkeUU97fZibMqFa1LDbyMIYyC4+iXBx5IMv+EoBX4u2FEqgO/vennULmkc026vsMVCig/SXHAcF3FL5yqD8wLw/02len957wupcZDgIi7OgMXzTOgbs2PA6sCy+clh2/NGWVJ8HFPbu9fJ5EJYUDHERTAQ8XCVlZXBaDRi3bp1wjWTyYSXX34ZJ06cwKVLl2T1V1JSAoPBgB/+8Ich29hsNrjd0iuXaw1SRwPwCkKKsUJDOZLAEpmuen6FSUqQEeNCxqrL7fFiaIKs4UgCSfFGXDtjZEnx1AneRoLKkQQf/DclfQXM3rYJ4P+7ShkHv+2YYDQIHKoFCS74v6nd5ZF0mgTvjWBNTvm5kBgyQFjekpJTcrbbr4iMX1aydwGRzpA4v0nrPqaP9mlubsbKlSuRnp4ecP2ee+4BALS0tEjuy+VyYceOHVi9ejVuuOGGoG1eeuklpKenw2QyYc2aNWhsbAzbp9PphNVqFX4mJiYkv49S/P3f/z2RfoRtLAlCgFmlMiMARqdckiqDXyG8H0+MCxlbo4MTTng5ID7OgAWElCMp8FsgUsbh34oj5xkiwYfSrTjm5oaMrVFhO3EeOeVIggtTglHwjshR8izF0wHyuOA4TiSnyIyDuJySYKwIOoOhrXbAP4bJaQ9sjsgZl6zqjNkgYnD19/dj6dKlc67z1ywWi+S+KioqMDw8HHQ7MTExEc888wzee+897N27F2+99RbOnDmDBx54AM3NzSH73LBhAzIyMoSfhx56SHjvnJwcOJ1OwaLNzs7GwMAAduzYgdbWVtTU1KCiogKdnZ0oKCiAzWYLaDs2NoaioiK0t7fjyJEjqKqqwtmzZ/HLX/4Sw8PDAW3tdjvy8vLQ3d2N/fv34/jx42hqakJZWRksFktAW4/Hg82bNyMt3megNLd3o76+Hvv27UNPTw9yc3PnvHdX/7Dv7+SZQnV1NSoqKtDR0YFt27bNeW+r1YrCwkJ0dHTg0KFDqKqqwpkzZ7B9+3YMDQ0FtHU4HMjLy0NPTw/MZjPq6urQ2NiIXbt2wWKxYNOmTfB6vcjOzobX68WmTZtgsViwa9cuNDY24rPmU0iI88UL/SmvEA6HI6D/oaEhbN++HWfOnEFVVZUwjtP1NbBarQFtbTYbtm3bho6ODlRUVKC6uhqtra0oLS3F4OBgQFun04nc3Fy8++672LdvH+rr69HY2Ijdu3ejr68PmzdvhsfjCbjHYrGgrKwMTU1NOH78OPbv34/u7m7k5eXhGpNvugxYfe8/PDyMkpISnD17FlVVVThy5Aja29tRVFSEv/QNAfAZN//5n5tgs9lQUFCAzs5OVFRUoKamBq2trdixYwcGBgbmvHdOTg56e3tRXl6O+vp6nDp1Cnv27MHly5exZcsWuN3ugHv6+/tRVlaG5uZm1NbW4sCBA+jq6sLWrVsxOTkZ0HZBinHmm+pBZWUljh49ira2NhQXF2N0dDSg7aXhCeGbOnjwII4dO4aWlhbs3Llzznu7XC7k5OTg4sWL2Lt3LxoaGtDQ0IC9e/fi4sWLyMnJgcvlws9+9rOAubZz5060tLTg2LFjOHjwIDo7O5Gfn4+JiYmA/kdHR1FcXIy2tjZ0nj4FwJfFVPhhCUZGRgLaTk5OYuvWrejq6sL+/fthGZ0CALTUfYL+/v6Atm63G1u2bMHly5exZ88enDp1CvX19SgvL0dvby81GVFSUoJ5M/P7ysw3FU5GnLvQD8DnjRDLiL6+PuzevRuNjY0RZcTg4CBKS0vR2tqK6upq/Nu//RsRGbF4nm9R0dJxIaKMuDJuBwCYOIcgI+rq6mA2m9HT04O8vLyIMuLQoUPo6OhAYWEhERnR09OD86cbAACXhm0RZUThRx8LmaVNx6sEGWG32wPahpMRY2NjAW1zc3OJyIjUOJ+X0TIW+C7BZMRn3b6ktAWp8QFtR0ZGUFJSgra2togyYmJiAvn5+ejs7CQmI/Lf3yIcCfdZd19EGcEbjqlxbkFGHD16FJWVlWhra0NJSXgZceDAAdTW1qK5uRllZWX405/+RFxG1NXVAdwseDwezm63S/rxer0cx3HcTTfdxD3xxBOzu+K6uro4ANy7774753eh8IMf/IBLSEjghoaGJLXv7OzkkpOTuccffzxkG4fDwY2Pjws/1dXVHACuqalJ8nvJRXd3N5F+3jtynsvMMnNZZacjtv0/e85wmVlm7ncHzxF5Nkk8+LtKLjPLzDX0DEdse8f/OchlZpm5rqs2Is8mxUVDzzCXmWXmHvj3yoht/3ymn8vMMnNPbaol8mySyCo7zWVmmbn/OHw+YtufbjvFZWaZuaITF4g9nwQfXq+Xu+VfD3CZWWbu0shk2LZjU9NcZpaZy8wyc/Zpt+pnk8Se5stcZpaZez6nLmLbD451c5lZZu7nH5KTW6Tmxn//4CSXmWXmdpy6GLHtk+/VcJlZZu7ouStEnk0KV60OLjPLzN3wL2bO5faEbXuuf5zLzDJzX/3NIWLPJ8XF5k/+wmVmmblfljZHbPuW+TMuM8vMvWX+jMizSeLRP3zCZWaZudrOwYhtv/qbQ1xmlpk71z9O5NmkuBCjqamJm+PhqqmpQXJysqSfjo4OAEBycjKczrlbXg6HQ/i9FExMTGDv3r14/PHHsWDBAkn3rFixAt/73vdQVVUFjyf4dlVSUhLS09OFn7S0NEl9q8HZs2eJ9CNka8hw1bO2bQKIt0bDj2PC6RYKi5IaBykuloi2FLkIGX4sZsXxkLM1SiMukAQfcjL8aGRaksISUdZoJNDYFiUmp2RkKrK6vbsgNRHxcTMZfhPh+eDlGMlMS310Bntxpjykxmk63R6hDhcpeUuKi9mYk8p22223IT8/X9LN/Jbh0qVLA+pl8ejv97nAly1bJqm/PXv2hM1ODIXly5djenoak5OTc+LI9MKiRYuI9CNrP57hySM1U5Ef57ykeKQmkcm0JMXFonnSM/zEwaisQU69IRpxgSTnxuVRe8T4Rv8Y2MpQBALnNxchw490nApAjgv+O4+k5KfdXgxNzChHxuSUL8MvCZZxB66MO7A0I7SjgEYGMnGdIcX4ZTTuF/DHaUYaB58tmhgfR+wkCVJczMYcjbZkyRKsXbtWVid33XUXqqqqYLVaAwyekydPCr+XguLiYqSlpeG73/2urOd3d3fDZDJp4rmSivh4MsYCP6GvSqipcpWCN4IUlkj0RvBjIJkVR4oLU4IR16QkYHTKhQGbI6zBxeoqHpCe+RqQaUmwnAKxuSHRcGSbC3+Gn83pRroptMKgoeRJcSG1KDDvOUowGnAtQ5XNeVyXYYJl3BFxHPzvScpa0vNCStFQvuwCiwtD/zikze8l6eSSSUhxMRtEguafffZZeDwe5ObmCtecTify8/OxatUqLF++XLg+NTWF9vZ2DA0NBfQxODiII0eO4G//9m+RkpIS9DmDg4Nzrp0+fRrl5eV47LHHEBfHzklFckthhALvIh2ZnIbTHTrDz+PlBKOMRcXiX3VF8EZQWMWT4gKQ7nEkXfmYJKR6uIYmpuHlAGOcAQtT2eND6jhYLZcCzMrwi/hNkZ/fWnPh34ozIS6OnTIEPKRmKtLItCTNhTg8Ixg4jmM2SxGQ7jVlXWeIQcSMW7VqFZ577jm89tpruHr1KlasWIFt27bhwoUL+OCDDwLaNjQ0YM2aNXjjjTfw5ptvCtc/+ugjuN3usNuJzz//PJKTk7F69WosXrwYbW1tyM3NRUpKCt5++20SQyEGviSGWsxPSUBifBym3V5ctTqx/NrgxujwhBMeL4c4A7AwjcGVo8S6KjTi0EhxAfjeq/2KTbJiYVHJ83/boYlpTLu9IQ8IF1fSJqkcSfEhNVaF5dhGwDeOcbsLV6wO3HLdvKBtApQjg3NDarwNywsRQLqSH6Awv0lxkZoUj3lJ8bA53bgy7sCKxcF3fsbtLjhnMi1JnVxAEouFkz2khgywx8VsEHMJFRYW4pVXXkFRURHWr18Pl8sFs9mMBx98UNL9xcXFWLx4Mf76r/86ZJunnnoKQ0NDeOedd/Dzn/8cH330EZ5++mk0Njbi9ttvJzUUIigvLyfSj8FgkLR1wq9+F81LQryRHU8fD6EadYTKwVcpuOpJcQFID3Lmx0Fya5QU+OM/gPDBwbS24kjxsURiXOAAw1wA0rbjbE7/mZYszg1+y3lowgl3mDP8WDx9QQzJXlNhK46coUJSTkk5FYP/3q5JYS+ZBBCF00QKQ7GxrTPEILZRaTKZsHHjRmzcuDFsu4cffjhohteJEyciPmP9+vVYv3694nfUEuKq+2pxXXoSLo5MhV11Mb+KF2UphgsOprFaIcqFhBXwpNMNG+FMS5IQH/9xZdyB60Mc/0FrK44UH1LjhljeUgSkxaLxHpV0UzySE8kpR1JcLEhNgjHOAI+Xw9DEdEiDink5JTHWlE/UWExwK460zvjL1Ymo1hlCDJfNt3tjDOFlZ11niMGeK+Rzgs2bNxPrS0rcEOuTh3dZO91ejNtDVw6mMQ6yXEQ+4JYfQ1pSPNIIZVqShhTFQiu7jxQfszP8QoHlLEVA3/lNigvjTIYfEH4xwnJWHCBta9Tl8WJ4kuw5igAlnRHFXCxMS0ScwRefPBzGEy/MDUa5ECNmcFECycMvxZZ+KLCcoQj4M/yA8B4JGgfb0uAi3NYo63EqgLTtOFpbcaT44LngM/yCQZxpyerckFIyZYDwQck8SM4NKdtxNLL7SMK/FRdaRg3anOA48pmWVHRGmHGw7vmNN8ZhYRq/MNRW9zF9eHUMc0Hy8EtJK2DGV/FA5FWXNyDTktw46HChfewTSUhaAVMSyKT4SE40It3k8yCG8jiKMy0XpLE5N6TFaNL5psjOjcheU9bnBv9eNqcbkyGMeH8yCdlMS811RhQtDEPJKS7gTEs2dYYYMYOLEp5++mlifUmJG2J9SxGInKk4NOmEeybTchFB5UiUi5kxDE864QoRHMwbY6yuHAFpWaO0vikafISaG/z1RWlJIWNA9IaUbEtaCyqSXETajqOlHElCHAYQio+BKOBCzoKK1WQSQJypGHwcVrsbDpdPDpOUUyS5ECNmcFFCXV0dsb6kFIC7SmnLgSQireT5MSxMI5tpSZKLBam+DD+O820tBEM0CDIp2Za0vBFE50ZG+HFEExeDM8HBwUDL20iSi+sicDHhdGNqeibTkmE+InnqaGVa0pgXUrIUWV4Y8lmgocbBh3bMJ5xpSZILMWIGFyXceOONxPoSr4BDBQdHh4cr/EqeRrYJQJYLPsMPCLMCFs5RZHMVD0SOt5madsPm8G2pkFYsZOdG+HH4DRV2uVgw433zchDizWaDlvFLlIsIRUP56/NM8UhJZDOZBIj8TfF1oUhmKAJ0dMZVmxPeEEZ8NOiMSF5TWoVbSXIhRszgogT+4G4S4CeEw+WF1T43rsDh8giZfyxPnkjBwbQEAEkugMiZiiyfo8hDHBsRzIjnBRmNTEuSfEgVyCyv4gMy/DT+pohyESH0IRq22gHxNxXe+GWZi0VpSYgzAG4vh6HJueNwiY/tYpgPqSEDpD3YpHUGj5jBRQljY2PE+jIlGIVDOYNlx/ECIDnBH0TMIiJtY/kzFMl6I0hyAUReAV+lcAQLafBG49S0J+jxHzxHNCpQk+Qj8vYPPw52uQD87xdsHB4vJ2xfk/6myHIhzcPF8rwAIi8MaW3vkuRCnOEXLFNxaMKXaRkfZ8CCMGfC6g3+WwmVbXmVkgebtM7gETO4KIF05Xtx4dDZEAfUkjq8kwYkr1YIu4eJcxHmXEivl4sKxZKSGI95fIZfED5opoyT5EP6liK7XADhD3cfmnD6z7QknGlJlgvfu9kcbkxNzzXio2ELC/CHAkTyxJNejFCTU2F0Bulju0gjoteU0jdF6+SamMFFCZWVlUT7C5epGDWCTDjDL3iG3xVKdZ9IcxGuhtXw5DTcXg4Gg++YJZYRbuvkCkVDhSQfUgUyy9u7gIiLYPN7RjnSyLQkycU8UwJSZ6rgB1PyA5Q82KQR6ZuicY4iQEFnhPmmoiGZBPCPYdzugmPmaCsxeNlFWveR5oJHzOCihHCHcCsBvwIOlq0RDRmKgC/DLz7Ol+EXLDiYVvFW0lyE28biry1MS0ICg2daihHOOzQgrOLJf1Mk+eDHECrDLxqK0ALiLcW584LmGIjPjTCZitHg+QXCb2NNON2YnMm0JD0O4jojTIZfNGQoAr6jrEwJPjkaTE5dtUWHzuDBtkaIYnzwwQdE+wu3WokWD1dchOBgWuPQkotoUfCAtHHQyO4jycfCMBl+4kxL1udGuJIpNA0V4nMjTKbilSiIbQQCFyKzM/x4uTUvKR6phJNJaHERzTrDYDCETYyhldlOmgseMYOLEkgfDRCuwnm0TB4gdECqw+XB2JQv05L0aoU0F4JyDGM0sr5yBPwr4KDKkd82oeA1JcmHMc4gFMmdLZD5f6cmGjHPlEDsmTQgbGOF+6YY5wIIvx1HayuONBbNS4JhJsNveHI64Hc0t+KI6wyBiyDeRsbPURQj1MJQfGzXdYS3qWNH+0QZSB8NEHYFHCWCDAidxs+Py5QQh/RksitH0lzwAmBy2gObI/Ag7qgUZEHjbehl95HnI3h9t6haiISpUUcrTgWgNzdmf1MeL4fBKChDAAAJAWf4BTfiachaajojrBEfPZ742VwMipNJUsmOI3a0T5ThBz/4AdH+pG0pRs/kmb3qEruGSWdakuYiNSke85KCZ/hFl5IPLsh8Z1rSUyy05sbscURLzBAgOsMvSIYfzXGQ5yK4oTI04YuxizMAC9PYLUPAQzCAZ3tNKWUoAjrpDMIZ4TTg9/4G1xk0Mi1Jc8EjZnBRQkVFBdH+MhemIM7gCw6+PDolXL9qc+DyqB0AcMPCVKLPpIGbFvneseXSaMD1lktjvt9TGANpLgD/OJovjgVcF8axiH0ubp55x3NXbAFK/jOLFS4Ph9REoxBzRxKk+bhpURoAoGU2FzP/vjEKuJhnShCyWsXjcHu8aL3s+/eNUTA3BC4ujQUU1OXnyQ0LUoke20ULNy30j0MM/t83z4yTJEhzsfzaZCQYDRi3u9A9OCFcH52cxoWhSQDRMTd4nRBSZ1AYAw2dAcQMLmq48847ifaXbkrANzKvBQBUtV8Vrn/SPggA+MoXMojX6KGBh1cuBgCcujAqVMcHgKMzY1pz22LizyTNBQA8fKvvPStFXFwamcL5gQkY4wx4YMUi4s8kjZsXpeH6+cmYdntR95dh4frR9gEAwAO3LKKiHEnz8fCtvr91VcdVIVOR4zj/N3Ur+W+KBh5e6RvHUdE31dQ7CqvDjfkpCbhr+XzizyTNxaobr0VyghH94w6c67cJ1ytnvqmHo4SLNbfN5cLh8qC2cwiA/5sjCdJcpCTGY9WNCwAEyqnq84PwcsBtS+ZhaUYy0WfSAP/NNF8aw7AoMaaS4vymoTOAmMFFDYODg8T7fOR234clFgK8cnzktuuIP48GvrggBbcsToPHy6HmvO9vNDY1jaZe3+qFxuShwcWjM1zUnB/EtNtXU4wXAN/IvAYZKWwHaQO+DKBHg3xT/Dj47400SPPx9cxrkG6Kx+iUS1gFd16dwOVROxLj43DfigVEn0cLPBeVQbhYc+ti4jW4APJcmBKMuG/FQgB+I8vr5VA5szB8lNI3RRoPrVyMOANwrt8Ky5hvB6G+exh2lwdL0k340tJ04s+kojNum/tN8XM9WrhYkmHCHcvSwXHAJx2+v9GE0436bt8i8REKi3QaXAAxg4saPJ65RdrU4tGZD6uuaxhT02443R4cm1lxRcvkAfyKnBcC1ecH4fFyuPW6eVh+bQrx59Hg4svLMrBoXhImpz1o6BkBABw551MwUcWFIJAHwHG+Kvmtl8cB0PMMkeYjwRiHh2be9ei5qwH/XX3zAqYPShbj/lsWIcFoQM/QJLpmtoB45UhDqQCU5NTM939khoMzfeMYmnAiLSked99wLfHn0cC1qYn42hevAeDnQLwQoXGiB00uGnpGYHW44PJ48UkH/01FxyId8Os+noNj5wfh8nC4cWGqsI1NEjS4AGIGFzV84QtfIN7nisVpWH6tbwuotnMIJ7tHMDXtwXXpSbhjGfkVFy08OjPR+S0gXjnS8qjQ4CIuzoBHeCXfPoAJpxsnu32GVzQJsm/etADJCUYMWJ34zGIVtqu/snw+tUr5NPiYLZB578qjlAwVGkhLisc3b5rZAjp3Fb3Dk/jLVd8W9YMr6WxR0+CCN9RPXx7DoM0pGCwPrlyIxPjoUTnCwvCcbzHCyyla3xQNLjIXpOLmRalwezkcOz+ExgujsDncuDY1kcoWNS08crtPpvI7CrQXIjS4AFQaXBMTE3jjjTfwrW99C9deey0MBgMKCgpk9eF0OpGVlYVly5YhOTkZq1atwuHDh1W31RunTp0i3qfBYBCMlcr2q/4V1210Vly08LUvzkdGcgLGplw4dWFEWHHREmQ0uABEW7znrqK2cxDTHi8yF6QIwejRAFOCEfffwm8BXfVvN1A0VGjw8dDKRYgzAO1XbDjbN+7foo4igwvwK5Cj7QPC/L77hmuQkUxni5oGF0syTPjy9fwW0FXB+I2mhQjgXxge7xpGy6Ux9I3ZkRQfh9U3L6TyPFpy6tEZY8X3TfGxdIuobFHTwp3XZ2BhWiJsTjcaekaEhWG06QxVBtfQ0BB+85vf4Ny5c/jKV76iqI+1a9finXfewY9+9CO89957MBqNePLJJ1FbW6uqrd74zne+Q6Vfv0C+GnXxWzzijXFC0OnvKzqEoOCvzrjwSYMWF/evWIhEYxwujkwh71gPgOgzfgG/0Prz2StCUDCtlSNAh49rUhPx9Uzf9/Nm+WdCUPAXriG/RU0T/N/91IVR7GmxAPArfhqgJ6d877y94SLO9llhMNAJNKeJldf5k0p+u/8cAOC+FQuRPHNeJGnQ1hmfdAwK27w0vykaiIszCJ7T/zhyHsOT05iXFI9vUNqipsWFKoNr6dKl6O/vR29vLzZu3Cj7/oaGBpSWlmLDhg3YuHEj1q1bh8rKSmRmZuLVV19V3JYFfPjhh1T6XXXTtUhJNGLQ5sSlkegKChaDFwKNomB5WisuWlykJsXjmzf7/vb8OKJNkAF+L9C5fivsLvpb1LT44JU8zwVNo5EWMhekYsVMUsnpmbR3WlvtAD0ueCP+05lyEHctnx8VWdRiiJNKtPimaHHBJ5WMTE6jZ2gS8XEGPLCSjpeOJmZz8eDKRdS2qGlxoeptk5KSsGTJEsX3l5WVwWg0Yt26dcI1k8mEl19+GSdOnMClS5cUtWUBtI4GSIo34oFb/JMlmoKCxXhoZaBLm6Ygo8UFEOjSTk004p4boyMoWIzr0k34q+szhH8/ctt1VL10tPiYnawQTckLYoi/qRsWpFCpTceDFhd/dX1gmZpoiqUTY7ZcikY5JU4qAYB7brwW6YwfdRUMfFIJj2jkQtcIxubmZqxcuRLp6YGr6XvuuQcA0NLSoqgtC6B1NAAQ6EWJVkE2P8W/BUQzKBigy4V40tNccdGGeBy0vylafNyyOA1fuMZXV+ialATctZzOFjVtiLmgbfxSO8IkzoBHbvPP6WgLe+DBJ5UAwO1L07FsPr26VXR1hvibik6dIU4qob1F/bk82qe/vx9Lly6dc52/ZrFYFLWdDafTCavVKvxMTEyEbEsKL774IrW+H77NFyBsMERfULAYfz3jgbjnhmupBQUDdLlYfm0Kbr1uHgB/cGo04q9n3t2UEIfVlLeoafFhMBiEcay5jd4WNW18PfMazJ+p4/bXlL10NOcGPx+un5+M25fOo/YcmjAl+HcUopmLh1YuQvzMfIhmOcUbjl/74jVYQHGLmhYXgsHl9XrhcDgk/YiPbFADu92OpKS5fzSTyST8Xknb2diwYQMyMjKEn4ceegiAz4jLycmB0+kULNrs7GwMDAxgx44daG1tRU1NDSoqKtDZ2YmCggLYbLaAtmNjYygqKkJ7ezuOHDmCqqoqnD17Fv/6r/+K4eHhgLZ2ux15eXno7u7G/v37cfz4cTQ1NaGsrAwWiyWgrcfjwebNm9HX14fdu3ejsbER9fX12LdvHyaH+vG3i4fxzrN/hT3FW4V7BgcHUVpaitbWVlRXV6OiogIdHR3Ytm3bnPe2Wq0oLCxER0cHDh06hKqqKpw5cwbbt2/H0NBQQFuHw4G8vDz09PTAbDajrq4OjY2N2LVrFywWCzZt2gSv14vs7Gx4vV5s2rQJFosFu3btQmNjI+rq6mA2m9HT04O8vDw4HA5kZ2fjx/fegPvTR/Crh6/H9u3bcebMGVRVVeHQoUPo6OhAYWEhrFZrwLvYbDZs27YNHR0dqKioQHV1NVpbW1FaWorBwcGAtk6nE7m5ucjNzcW+fftQX1+PxsZG7N69G319fdi8eTM8Hk/APRaLBWVlZWhqasLx48exf/9+dHd3Iy8vD3a7PaDt8PAwSkpK8D++noa/W5mA9JFzaG9vR1FREcbGxua8d0FBATo7O1FRUYGamhq0trZix44dGBgYmPPeOTk56O3tRXl5Oerr63Hq1Cns2bMHly9fxpYtW+B2uwPu6e/vR1lZGZqbm1FbW4sDBw6gq6sLW7duxeTkZEDbkZERlJSUoK2tDZWVlTh69CiM1j4884Up/OGpW7E1d4vQduL/b+9+Y5o69ziAf2G6ghJYwM1CEGECkpU1MrM1eDdx5t6oMTC3gcA0mxNjNKPEm20xJHMYTH0xl7gsxAGZHtwG21KmQ9lk0w2dc1ga/rxgQNo4ioAVw0BIhdZSnvtC26wU7hR7ep55fp+kL3x60n6TrzznyTmn59hsEAQBZrMZDQ0NuHjxItrb26HX631yO51OVFRU4OrVq6irq0NzczOam5tRV1eHq1evoqKiAk6nE3v27PH6W9Pr9Whvb8fFixfR0NAAs9kMQRBgs9m8Pn9kZATV1dXo7OzETz/9hJ9//hmdnZ2oqanB8PAwysrK8N9/J2NV+AiKMuJw7NgxXLlyBd9//z1+/fVXtLW1oba2Flar1etzJycnUV5ejv7+fnz77bcwGo24fPkyTp06hd7eXtHmiJqamhnniCrhGErWRiMvEZi63j2nOaKnpweVlZU+uafPETqdTrQ5InK8D5viJlHynyU4cuTInOYI9+cPDQ2JOkf09PTMOkfs25iCf4WPYFfGsjnPER0dHWhsbMS5c+dmnSNqampEmyOqhUoc3JiALQm3cbPPNKc5orOzE9XV1RgZGfHaVow5YvrfmnuOWDrZh00JQdi9MnzOc4R721u3bs06RwiC4Pc54rfffgPYXY2NjQzAPb26urrYdEajkQFggiD4vDcblUrF1q5d6zP++++/MwCsvLx8TttOZ7fb2ejoqOd14cIFBoC1tLTcc9b7ZTKZRPtscn+oC75QH/ygLvhBXfBDjC5aWlqY52rrlJQUCIKAezHTqb25iI6OxsDAgM+41WoFAMTExMxp2+kUCoXX0bGwMP/fmXa6P/74A0lJSaJ/D/l71AVfqA9+UBf8oC74IVYXngWXUqnEtm3b/P4F/8+KFSvQ2NiIsbExr4vhDQaD5/25bMuD0FD+HwoqF9QFX6gPflAX/KAu+CFWFwG7aH58fBzd3d0YGhryjGVnZ8PlcqGystIz5nA4IAgCNBoNlixZMqdtefDYY49JHYHcRV3whfrgB3XBD+qCH2J18cA3cHJfFOr+leDp06fR398PANBqtYiIuHN/n+bmZrz44osoKSnB/v37AQAajQY5OTkoLi7GjRs3kJiYiOPHj8NiseDo0aNe33M/2/Kgu7sbarVa6hgE1AVvqA9+UBf8oC74IVYXD7zg+vDDD9Hb2+v594kTJ3DixAkAwNatWz0Lrtl89tln2LdvHz7//HOMjIxArVajvr4eq1evfqBtpeb+JSSRHnXBF+qDH9QFP6gLfojVxQOfUrRYLGCMzfiKj4/3bLdmzRowxjxHt9xCQkJw6NAhWK1W2O12NDc3Y926dTN+1/1sKzW9Xi91BHIXdcEX6oMf1AU/qAt+iNVFEGN+uqnWP0hraytWrlyJlpYWPPPMM1LHIYQQQshDrLW1Vdo7zT/MxHxMA7k/1AVfqA9+UBf8oC74IVYXdIRLpCNcDodjxjvjk8CjLvhCffCDuuAHdcEPMbqgI1wiqqqqkjoCuYu64Av1wQ/qgh/UBT/E6uKBf6X4T+R+7mJXV5do3xEbG4vW1lbRPp/cO+qCL9QHP6gLflAX/BCji66uLnkuuCwWC4A7t60ghBBCCBGbLK/hGhoawg8//ID4+HhRbuFvs9mQkZGBCxcuBOS5jWR21AVfqA9+UBf8oC74IVYXExMT8lxwiW1sbAwREREYHR31eu4jCTzqgi/UBz+oC35QF/wQswu6aJ4QQgghRGS04CKEEEIIERktuESgUChQUlJC91ThAHXBF+qDH9QFP6gLfojZBV3DRQghhBAiMjrCRQghhBAiMlpwEUIIIYSIjBZchBBCCCEiowUXIYQQQojIaMFFCCGEECIyWnD5kcPhwN69exETE4PQ0FBoNBqcPXtW6liyZDQaUVhYCJVKhYULFyIuLg6bN2+GyWSSOprs6XQ6BAUFITU1VeoostXa2oqsrCxERkZiwYIFSE1Nxccffyx1LFkym83Iy8tDbGwsFixYgJSUFJSWlmJ8fFzqaA8lm82GkpISrF+/HpGRkQgKCkJVVdWM2/p7n063hfCj/Px81NbWYs+ePUhKSkJVVRWMRiMaGxvx/PPPSx1PVrKzs3Hp0iXk5ORArVbj+vXrKCsrg81mw+XLl2lnL5H+/n4sX74cQUFBiI+PR0dHh9SRZOfHH39EZmYm0tLSkJubi7CwMFy5cgVTU1P44IMPpI4nK319fVCr1YiIiMCuXbsQGRmJpqYmVFVVISsrC3V1dVJHfOhYLBYkJCQgLi4OTz75JM6fPw9BELBt2zafbf2+T2fELwwGAwPADh065BmbmJhgy5YtY+np6RImk6dLly4xh8PhNWYymZhCoWBbtmyRKBXJzc1la9euZRkZGUylUkkdR3ZGR0fZ4sWL2csvv8xcLpfUcWRPp9MxAKyjo8Nr/PXXX2cA2PDwsETJHl52u51ZrVbGGGNGo5EBYIIg+Gwnxj6dTin6SW1tLR555BHs3LnTMxYSEoKCggI0NTWhr69PwnTys2rVKjz66KNeY0lJSVCpVOjq6pIolbz98ssvqK2txUcffSR1FNmqqanB4OAgdDodgoODcevWLUxNTUkdS7bGxsYAAIsXL/Yaj46ORnBwsM8cRh6cQqGAUqn82+3E2KfTgstP2trakJyc7PN08eeeew4A0N7eLkEq8leMMQwODmLRokVSR5Edl8sFrVaLHTt24Omnn5Y6jmydO3cO4eHhGBgYwPLlyxEWFobw8HDs3r0bdrtd6niys2bNGgBAQUEB2tvb0dfXh6+//hqffPIJioqKsHDhQmkDypgY+3RacPmJ1WpFdHS0z7h77Nq1a4GORKaprq7GwMAAcnNzpY4iO+Xl5ejt7cWBAwekjiJrZrMZk5OTeOmll7Bu3Tp888032L59O8rLy/Hmm29KHU921q9fjwMHDuDs2bNIS0tDXFwc8vLyoNVqcfjwYanjyZoY+/R5D5yKAAAmJiZmfNhlSEiI530ine7ubrz11ltIT0/HG2+8IXUcWfnzzz/x/vvvY9++fXj88celjiNrNpsN4+Pj2LVrl+dXia+88gpu376NiooKlJaWIikpSeKU8hIfH4/Vq1fj1VdfRVRUFL777jscPHgQSqUShYWFUseTLTH26bTg8pPQ0FA4HA6fcfdh+tDQ0EBHInddv34dGzduREREhOe8PAmc9957D5GRkdBqtVJHkT33PJSfn+81/tprr6GiogJNTU204Aqgr776Cjt37oTJZEJsbCyAOwvgqakp7N27F/n5+YiKipI4pTyJsU+nU4p+Eh0dDavV6jPuHouJiQl0JAJgdHQUGzZswM2bN9HQ0EA9BJjZbEZlZSWKiopw7do1WCwWWCwW2O12OJ1OWCwWDA8PSx1TNtz//6dfpP3EE08AAEZGRgKeSc6OHDmCtLQ0z2LLLSsrC+Pj42hra5MoGRFjn04LLj9ZsWIFTCaT51cnbgaDwfM+CSy73Y7MzEyYTCbU19fjqaeekjqS7AwMDGBqagpFRUVISEjwvAwGA0wmExISElBaWip1TNlYuXIlgDu9/JX7ehQ65RtYg4ODcLlcPuNOpxMAMDk5GehI5C4x9um04PKT7OxsuFwuVFZWesYcDgcEQYBGo8GSJUskTCc/LpcLubm5aGpqgl6vR3p6utSRZCk1NRUnT570ealUKsTFxeHkyZMoKCiQOqZsbN68GQBw9OhRr/FPP/0U8+bN8/xqjgRGcnIy2trafJ6A8eWXXyI4OBhqtVqiZESMfTpdw+UnGo0GOTk5KC4uxo0bN5CYmIjjx4/DYrH4TG5EfG+//TZOnTqFzMxMDA8P44svvvB6f+vWrRIlk5dFixZh06ZNPuPue3HN9B4RT1paGrZv345jx45hcnISGRkZOH/+PPR6PYqLi+mUe4C9++67OHPmDF544QUUFhYiKioK9fX1OHPmDHbs2EF9iKSsrAw3b970HNk9ffo0+vv7AQBarRYRERHi7NPndLtUMqOJiQn2zjvvMKVSyRQKBXv22WdZQ0OD1LFkKSMjgwGY9UWkRXeal87t27fZ/v372dKlS9n8+fNZYmIiO3z4sNSxZMtgMLANGzYwpVLJ5s+fz5KTk5lOp2NOp1PqaA+tpUuXzrpv6Onp8Wzn7306PUuREEIIIURkdA0XIYQQQojIaMFFCCGEECKy/wFHtdaDzvV4UQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(n_sub, xData)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "06e69ebc",
@@ -308,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 13,
    "id": "adc33e70",
    "metadata": {},
    "outputs": [],
@@ -340,36 +363,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 14,
    "id": "9d3fb1c8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Mixer local oscillator (LO) for channel k\n",
-    "kLo = np.fix(np.round(subbands[0])) \n",
+    "kLo = int(np.round(subbands[0])) \n",
     "w_k = 2 * np.pi * kLo / Ndft\n",
     "LO = np.exp(-1j * w_k * n_s)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "id": "50334d52",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f48da4b6b80>"
+       "<matplotlib.legend.Legend at 0x7f06c37e6160>"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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5Y2NjrnPnzty8efM4Hx+fOtfBcRXTHtjb21f5vahuioh79+5xkyZN4oyNjbkmTZpwEydO5O7du8ctWrSoxlNwHDlyhJs1axZnbW3NGRkZcU2bNuX69OnDbdu2TWMKhurqyMjI4JYtW8aZm5tzBgYG3IABAzgfHx9ux44dHADO19dXObYy10WLFlVZT1Xfh9p+L7U1BUelAwcOcIMGDeKMjIyUj1/VPGjV1VNTz5494z7//HOuU6dOXKNGjThTU1POzs6O27lzp3JMfXz/avuzWDnNRlVe9LN49OhRbty4cVzz5s05PT09rnXr1tzo0aO53377Te31V9spOF42fU9NXxuk4RBxHMfVSzdICCGMWLBgATw9PREdHY3u3bsLXQ4hhFSJzkkjhLyx0tLSNJYFBATgyJEj6Nq1KzVohBCmvRZNmkwmw7fffgsrKysYGhrCzs5O+em2mggPD8eUKVNgamoKIyMj2Nra4n//+x+PFRNCWDBp0iQMGzYMK1euxNdff413330XY8eOhY6ODvbs2SN0eYQQ8kKvxeHOuXPnwtvbG2vXrkXnzp3h5uaGO3fuwN/f/6UnD1+6dAmTJ09Gv379MHv2bBgbGyM+Ph4KhQK//vprPT0DQogQdu3aBU9PT8THx6OwsBDNmjXD8OHDsX79erWrQRBCCIuYb9JCQkJgZ2eH7du3Kz8VI5VKYWtrCwsLixfOd1RQUIAuXbpg2LBh8Pb2fqVPchFCCCGE1CfmuxZvb2+IxWKsWLFCuczAwADLli3D7du38eTJk2rX9fLyQnp6On755Rfo6OiguLhYbYJRQgghhBBWMd+kRUREoEuXLhqXOKmcCftFM5hfvnwZJiYmSElJQdeuXWFsbAwTExN8+umnVU4USgghhBDCCuYns01LS4OlpaXG8splqamp1a4bFxeH8vJyvP/++1i2bBm2bt2Ka9euYc+ePcjLy8Phw4erXVcmk6ld3DcrKws3btyAjY0NDA0NX+EZEUIIIYS8mEQiYb9Jk0gk0NfX11heeUkYiURS7bpFRUUoKSnBJ598ovw057Rp01BaWgonJyf8+OOP6Ny5c5Xrbt26VXl5FUIIIYSQ+sZ8k2ZoaKi2R6tS5eHKF+3Vqrxv7ty5asvnzZsHJycn3L59u9ombf369Vi3bp3y66CgIIwfPx6HDh3ibW6lzMxMtGjRgpdtk9qjPNhBWbCDsmAHZcEWbefx4MED9ps0S0vLKi/iWzlJpZWVVbXrWllZ4f79+2jZsqXacgsLCwBAbm5utevq6+ur7cGrvJZa9+7d0b9//5o/gVrw9vbG+PHjedk2qT3Kgx2UBTsoC3ZQFmzhIw/mPzjQt29fPHz4EAUFBWrLg4ODlfdXZ8CAAQCg0eRVnsfG2l8gHTt2FLoEooLyYAdlwQ7Kgh2UBVv4yIP5Jm3GjBmQy+VwdnZWLpPJZHB1dYWdnR3atm0LACgpKUFMTAyysrKU42bNmgUA2L9/v9o2XVxcoKuri9GjR/P/BGqBPnHKFsqDHZQFOygLdlAWbOEjD+YPd9rZ2WHmzJlYv349MjIyYGNjA3d3dyQmJqo1XyEhIRgzZgw2btyITZs2AQD69euHpUuX4sCBAygvL4e9vT2uXbuGY8eOYf369S88VCqEvLw8oUsgKigPdlAW7KAs2EFZsIWPPJhv0gDg4MGD+OGHH+Dh4YHc3Fz07t0bZ86cwahRo1667r59+9CuXTu4urrC19cX7du3x++//461a9fyX3gt0cWe2UJ5sIOyYAdlwQ7Kgi185PFaNGkGBgbYvn07tm/fXu2Y0aNHo6orXOnp6WHjxo3YuHEjnyWqkcvlKCsrq/V6t27dYm7vXkOkq6sLsViMq1evolOnTkKXQwDKgiGUBTsoC7bwkQfz1+5kRXh4OAYMGICwsLBqP93JcRyePXtW512eHMdBJBK9QpVEW8RiMZo2bQoLCwvKhAESiYQmkWYEZcEOyoIt2s4jPDz89diT9rqobNAsLCxgZGRU61/u2dnZMDMz46k6UhMcx6G8vBwFBQVITk6GQqGo8ooXpH7t378fq1atEroMAsqCJZQFW/jIg/ak1dDL9qTJ5XI8fPgQFhYW1Gi9IbKyspCVlYXOnTtDLBYLXQ4hhJAGJDw8nP0pOF4XleegGRkZ1XkbGRkZ2iqHaIFMJgPHcXU6v5Bol4ODg9AlkH9QFuygLNjCRx7UpGnZq5y/ZGpqqsVKyKtq2rSp0CWQfzx/aTciHMqCHZQFW/jIg5o0huTn5wtdAlFRWFgodAnkHxcvXhS6BPIPyoIdlAVb+MiDmjSGvMqhUqJ99KkpdvTu3VvoEurMxwfo0wcwNKz418dH6IpezeucxZuGsmALH3nQpzsZUlZWRo3BK3Bzc8OSJUuQkJCADh06vPL2ysvLX70oohWZmZlCl1AtBadAfE487mfeR1RGFJLzkyEtl0JaLsXT4EG4/dvXgEgBcDq4d4/D9OkiHD8OTJsmdOV1w3IWDQ1lwRY+8qAmjRDCPLlcLnQJauQKOW4m38Rf9/+CT4wPnhU9q3rgoQ0AKho0AOA4ESCSY96aOCxutAtvd3obYzqOganh63M+KmtZNGSUBVv4yIOaNIbo6ekJXQJRoatLLw9WtGnTRugSAABl8jJ43PXAT9d/QmJeonK5ga4BerToAVsLW1g3t4aRnhH0xfpYt6Unyp8/q4QTQ5beHk5hTnAKc4IIIoxsPxKzeszC9B7T0cq4Vf0+qVpiJQtCWbCGjzzotxBDSkpKGsThzuLiYjRu3FjoMl5KIpEIXQL5x507d9CtWzfBHp/jOBy9fxTfX/0e8bnxAIBmBs3wQbcPMKvHLLzV6S00EjfSWM+lG3DvHqA6G6VIxKGDTSmm2H0Ov8d+iM6MxvWk67iedB2rz6+GfQd7ZcNm0diivp5ijQmdBfkXZcEWPvKgDw4whMUpH/z9/SESieDr66txn5eXF0QiEW7fvl3t+m5ubhCJRAgICMDKlSthYWGh9tfG+fPnMXLkSDRu3BhNmjTBu+++i/v376tt4+7du1i8eDE6deoEAwMDtGrVCkuXLkV2drb2nmgVmjRpwuv2Sc1NnjxZsMfOLsnGjGMzMPf4XMTnxsPcyBw73t6BlHUpcH3fFRM7T6yyQQOAjRsrGrTKmXlEoopDnr9taYpdE3bh/sr7SFqbhN/e+Q12re3AgcO1xGtYeW4lLH+zxDse7+Dg3wdRVFpUj8/4xYTMgqijLNjCRx7UpDEkJydH6BI0jB49Gm3btoWnp6fGfZ6enrC2tsbQoUNfup2VK1ciOjoa//nPf/B///d/AAAPDw+8++67MDY2xrZt2/DDDz8gOjoaI0aMQGJionJdPz8/PH78GEuWLMGePXswZ84cHDlyBJMmTQKfF8ygKVHYcejQIUEe91L8JfRy7AWfBz7Q09HDJvtNSPg8AV8O+xJGei//NPa0acDx40Dv3oCBQcW/Pj7A1Kn/jmnXtB3WDV2HoOVBSPg8Ab+O+xUDrQZCwSng99gPi04sQssdLTHfZz7Ox51HuULYD7QIlQXRRFmwhY886LJQNfSyy0JJpVIkJCSgY8eOMDAwUC7nOA4lZSX1WeoLGenV/pqi3333HXbu3In09HTl3r7MzExYWVlhw4YN2LRpU7XrVn7icsSIEbh27Zry8kpFRUVo27YtZs6cCWdnZ+X49PR0dO3aFbNmzVIur+qitUeOHMHcuXNx/fp1jBw5Uu2xtPXpzuoyJQ3D7qDdWHtxLQCgu3l3eE7zRD/LfvX2+PE58fC65wWPux6Iy4lTLrdobIG5tnPxYe8P0d+y/ytNoE0IYRddYL0elJSVwHirsdBlKBWtL0LjRrU7H2zhwoXYunUrvL29sWzZMgDA0aNHUV5ejgULFtRoGx999JHa9S/9/PyQl5eHuXPnIisrS7lcLBbDzs4O/v7+ymWqDZpUKkVRURGGDBkCoOKHuLJJ0za+D6eSmnNwcKi3C0lzHIfvrnyH/976LwBgRf8V+H3C7zXac6ZN1qbW+MH+B3w/6nvcSb0Dj789cOT+EWQUZ2B38G7sDt6NbubdML/XfEzvPh3dW3Svl7rqMws++PgAmzcDDx8CXbpUHJJ+3aZDqXwO0dHl6NFD97V8DsCbkYUqPl4b1KSRl+rWrRsGDRoET09PZZPm6emJIUOGwMbGpkbb6Nixo9rXcXEVewbGjh1b5XgTExPl/3NycrB582YcOXJE4/qmfB6SbNasGQoKCnjbPqm5RYsW1cvjlCvKseL0CrhGugIAtozdgv8b8X+C7q0SiUQY3HowBrcejJ3jd+JS/CV43PXAydiTiMmKwQ/+P+AH/x/Q1awrPuj2ASZ1noShbYZCT8zPp8XrK4vnScokyJPmoaSsROMmLZeiXFEOBaeAnJNDrpBr/KvgFPj7mjX2/98kiEQcOE6knLdu9W/XYDcuBToiHeiIdCDWESv/ryPSgVgkVrtPBBF0RDoQiUQQQaT8VxvLXubSGWN8vqTNP89BV/kcdrs+xTvvsXPu4vNEUH9ul84YY82S1hpZ/M81henn8TwzIzOYG5kD4Oe1QU0az4z0jFC0vmY/cFnZWTA3M+e9nrpYuHAhPv/8czx9+hQymQxBQUG1upjs84crFQoFgIrz0lq10pxyQHX6i1mzZiEwMBBff/01+vbtC2NjYygUCkyYMEG5HT5Qg8aO48ePY/Hixbw+BsdxWHl2JVwjXaEj0oHze85Y1n8Zr49ZW3piPbzb5V282+VdFMgKcDz6OLwfeOPy48uIzY7FtlvbsO3WNpjom+Ctjm9hVPtRGNFuBPq26gtdnVd7u6/c6/HggSG6d3/1vR7linJkl2Qjozij6luJ+tda+fCEYyQABTjVeesgx55fm2FP4ZhX3359cIwEYKXxHD7/vywgqf4Ox78yx0gAlhrPY83/Zb5Wz2PDyA34eezPAPh5n6ImjWcikajGhxfFzcUwaMTmuU9z5szBunXrcPjwYUgkEujp6WH27Nl13p61tTUAwMLCAuPGjat2XG5uLq5cuYLNmzfjP//5j3J55Z44PhkZGSEvL4/3xyEvN3z4cN4fY3PAZvwZ/id0RDo4OuMoZvSYwftjvgoTfRMs6bcES/otQYGsAGcfnsXZuLO4GH8RWSVZ8I3xhW9MxaeyjfSM0LdVX/Rt2Rd9W/VFV/OusDG1gaWxZY323vj4ANOnV346VRf37lV8rXrlBI7jUCArqHHTlV2SDQ61OyVaR6QDIz0jjZuBrgF0dXSVe73EOmKNf3VEOvDO6Q6FxuflxNDJ6YG3Or2t3BOn4BTKW+VeuOfv4ziu4l9w4DhO+W9dlym4mv3BmZPdFZqf+RMD2d2YnRS5qlPfc1/wPJobNK+XurTBUPffHRB8vE9Rk8YQmUzG7Anq5ubmmDhxIg4dOgSpVIoJEybA3Lzue/3Gjx8PExMTbNmyBWPGjNGYyDczMxMtWrRQnsf2/It8165ddX7smiotLeX9MUjNPH78GJ07d+Zt+06hTtgcsBkAsHfSXuYbtOeZ6Jtgbq+5mNtrLhScAuFp4bj8+DJuJt/ErSe3kCfNQ+CTQAQ+CVRbz1DXEFZNrNDKuBVaGrdEc4PmMG5kjCaNmkCsI1Y2KX9+tQoQtVTZ6wFApMCHn8djQ9oU5EnzkCPJQam8dq8ZEUQwNzKHRWML5a2FUQu1r1VvJvomr3Touc9PVc1bB/Tq0QiXPrxU5+3Wpz6eVT+H3j0NEPnN63MebR+vFzyPb9mb6aAm+HifoiaNITo6bM+IsnDhQsyYUfHL66effnqlbZmYmMDR0REffvgh+vfvjzlz5qBFixZITk7G2bNnMXz4cDg4OMDExASjRo3Cr7/+irKyMrRu3RqXLl1CQkKCNp7SC7GeR0PC5yTPFx5dwMpzKwEAP4z6AZ8M/IS3x6oPOiIdDLQaiIFWAwFUXFs0JisGkc8iEfksEn+n/41HOY+QlJcESbkE8bnxygl6q/Xke+WlrZQ4HZQ8a4OYrBi1xU0aNam2yXr+ZmZoBrGOGPVl40bVPYL//rtxY72V8MrehOcAvDnPQxUf71PUpDFE9dOPLJo8eTKaN28OhUKBKVOmvPL25s2bBysrK/z3v//F9u3bIZPJ0Lp1a4wcORJLlixRjvPy8sLq1auxd+9ecByHd955B+fPn4eVldUr1/Ai1KSxo1mzZrxs90n+E8z3mQ8Fp8CSvkuwefRmXh5HSDoiHfRo0QM9WvTAvF7zlMvL5GVIyk9CWmEa0ovT8azoGfKl+SgqLUJhaSEUnEJ5+NCrbQ6yEi3VGjWRSIH21jK4LvJHM4NmMDU0RQujFjDUY/eqKZXz1v34IxAbC3TtWtEUqM5bxzrV5/DggQLdu+u8ds8BeDOyeB4f71M0T1oN1XWetNrIycmBqSmb5xQAQHl5OaysrDB58mTs379f6HJ49+zZM+Tm5tI8aQz466+/MGvWLK1us0xeBns3e9x+ehsDLAfg1tJb0NfV1+pjvCnUz0n799/nJ+Yl9YuP1wWpO23nER4eTlccYAnrlyE6ceIEMjMzsXDhQqFLqRevw/VFGwp7e3utb3P9lfW4/fQ2muo3xV8z/6IG7QVUr5ygr89VeeUEUv/4eF2QuuMjD2rSGJKbmyt0CVUKDg7Gn3/+iXXr1qFfv34N5o2BpuBgx7Fjx7S6vTMPz+C3278BAFzfd0Wn5p20uv030bRpQGQksGPHXkRGUoPGAm2/Lsir4SMPatIYYmFhIXQJVXJ0dMSnn34KCwsLHDx4UOhy6o2ZmZnQJZB/aHMW7zxpHlacXgEAWDN4DaZ2p26jNl7nqw28aSgLtvCRBzVpDHl+Nn1WuLm5oby8HKGhobC1tRW6nHpDl4ViR20mTn6ZLy9+ibSiNHQx64L/jvuv1rbbUGgzC/JqKAu28JEHNWkMeZV5x4j2NW/++kyo+Kb76KOPtLKdS/GXcCDyAEQQ4cCUA0x/EpFV2sqCvDrKgi185EFNGkNozw1b6GoD7HBzc3vlbRSVFikPc64avArD2/F/FYM3kTayINpBWbCFjzyoSWOI6kXFifCMjY2FLoH8Y8KECa+8jR+u/oCk/CR0aNYBW97aooWqGiZtZEG0g7JgCx95UJPGEIlEInQJRIVUKhW6BPKPv//++5XWj86Mxp6QPQAAp/ecYNyIGvC6etUsiPZQFmzhIw9q0hiiq0sXgGAJ61eAaEhe5ZPPHMdh7YW1kHNyfNDtA7xj/Y4WK2t4WP0UekNEWbCFjzyoSWPIq1w4mGgf5cGOV2mYTz88Db/HfmgkboQdb+/QYlUNE/3xwg7Kgi185EFNGkNKS0uFLoGoKCsrE7oE8o+UlJQ6rScrl2HdxXUAgHVD1sHa1FqbZTVIdc2CaB9lwRY+8qAmjSF0GSK2GBrS9AysGDhwYJ3W2x28G/G58bA0tsR3I7/TclUNU12zINpHWbCFjzyoSWPImzjlw7Vr1yASiXDt2jWhS6m1wsJCoUsg/zhz5kyt18mV5GLLjYpPcW59ayua6LN9bdzXRV2yIPygLNjCRx7UpDGkRYsWQpdAVJiamgpdAvnH8uXLa73O9sDtyJflo5dFL3zY50MeqmqY6pIF4QdlwRY+8qAmjSGZmZlCl6B1o0aNgkQiwahRo4QupdZycnKELoH8Y9++fbUa/6zoGXYH7wYA/Dz2Z+iI6K1OW2qbBeEPZcEWPvJg/p1LJpPh22+/hZWVFQwNDWFnZwc/P79ab+eXX36BSCRi+tqTb+LHqXV0dGBgYAAdHeZ/1DTQBdbZUdsLF2+5sQUlZSWwa22HyV0m81RVw0QX9WYHZcGWBnmB9cWLF2Pnzp2YP38+du/eDbFYjEmTJuHmzZs13sbTp0+xZcsW5k/MZ/EC6/7+/hCJRPD19dW4z8vLCyKRCLdv3652/arOSRs9ejRsbW1x9+5d2Nvbw8jICDY2NvD29gYABAQEwM7ODoaGhujatSsuX76sts2kpCSsXLkSXbt2haGhIczMzDBz5kwkJiZqPH7lYxgaGqJNmzb4+eef4erqCpFIVOV4VXSZLnbU5sLFiXmJ2Bda8Rftlre20FQqWkYX9WYHZcEWPvJgevbUkJAQHDlyBNu3b8dXX30FAFi4cCFsbW3xzTffIDAwsEbb+eqrrzBkyBDI5XJkZWXxWXK1ikuLq71PrCOGga4BmjVr9tKxOiIdtYtC12ZsXYwePRpt27aFp6cnpk6dqnafp6cnrK2tMXTo0FpvNzc3F++99x7mzJmDmTNnwtHREXPmzIGnpyfWrl2LTz75BPPmzcP27dsxY8YMPHnyBE2aVJz4fefOHQQGBmLOnDlo06YNEhMT4ejoiNGjRyM6OhpGRkYAKj4OPWbMGIhEIqxfvx6NGzeGi4sL9PX1a1RjkyZNUFBQUOvnRrRv+vTpNR67OWAzyhRlGNdpHMZ2HMtjVQ1TbbIg/KIs2MJHHkw3ad7e3hCLxVixYoVymYGBAZYtW4bvvvsOT548Qdu2bV+4jevXr8Pb2xsRERFYvXo13yVXy3hr9ZehmdR5Es7OO4uioiKYmprCYocFSspKqhxr394e1xZfU37dYXcHZJVU3XgOtBqIOx/deaW6RSIRFixYgJ07dyI/Px9NmzYFUHH+3KVLl7Bhw4Y6bTc1NRVeXl6YO3cuAODtt99Gt27dMG/ePAQGBsLOzg4A0L17d4wfPx7Hjx/H4sWLAQDvvvsuZsyYoba9yZMnY+jQoTh+/Dg+/LDiJPFt27YhNzcX4eHh6Nu3LwBgyZIl6Ny5c41qLCmpOgNS/27duqWReVUe5z6Gx98eAICfx/zMd1kNUk2zIPyjLNjCRx5MH+6MiIhAly5dNC48PnjwYABAZGTkC9eXy+VYvXo1li9fjl69etXqsWUyGQoKCpS3oqKiWq1fFwYGBrw/Rl0sXLgQMplMeTgSAI4ePYry8nIsWLCgTts0NjbGnDlzlF937doVzZo1Q/fu3ZUNGgDl/x8/fqxcpjp/WVlZGbKzs2FjY4NmzZohPDxced+FCxcwdOhQZYMGVHxic/78+TWqsaZ73Aj/rK1rNgntr7d+hZyTY7z1eNi1sXv5CqTWapoF4R9lwRY+8mC6SUtLS4OlpaXG8splqampL1x/3759SEpKwk8//VTrx966dSuaNm2qvNnb2ytrcnJygkwmUx5/dnBwQFZWFoqLiyGRSFBYWIj8/HxIpVJkZWVBLpfj8fLHKFpfhMfLHyPv6zwkfZyEzLWZSF2VCtcJrpBIJMjLy0N5eTmiFkUpxxZ8W4CEjxKQvS4bT1c+xbH3j6G4uBg5OTkoLS1FyLwQ5djC/yvE4+WPkfNlDp58+gTnZ55HUVER8vLyIJPJkJmZCYVCoTz3LSMjA2VlZcjJyUFJSUmVdWdkZKBbt27o27cvDh06hKysLEilUhw8eBCDBg2ClZUVsrOzUVZWprZdhUKBzMxM5VUUJBIJiouLkZubC47jlBlmZGSA4zhkZGSgadOmaNWqFYqLi5V1VzauOTk5yu0nJSVhw4YNaN26NfT19WFubo4WLVogLy8P6enpyrqTkpLQpk0b5WFuqVSK/Px8tGnTBkDFvHTV1S2TyVBUVASZTIaoqCj4+voiJSUFjo6OkMvlatmnpqbC29sbYWFhuHXrFs6ePYvHjx/DxcUFEolEbWx2dja8vLwQFRUFf39/XL58GTExMfDw8EBeXp7a2MLCQri5uSEuLg4XL17E9evXcffuXfz1119IT09XGyuTyeDk5ISkpCScOnUKQUFBuHPnDk6cOIGnT59i3759KC8vV1snLS1NuZf55s2bOHfuHOLj43HgwAEUFxerjc3JyYGXlxeio6Nx9epVXLlyBdHR0fD09ERubq7a2KKiIri6uiIuLg4XLlzAjRs3EBkZiWPHjmnUXVZWBicnJyQnJ+PkyZMICQlBSEgITp48ieTkZDg5OaGsrAxeXl7KddLT03Hs2DFERkbixo0buHDhAuLi4rDzz51wjXQFAGwYuQEODg7Izc2Fp6cnoqOjceXKFVy9ehXR0dHw8vJCTk6OWi3FxcU4cOAA4uPjce7cOdy8eRMRERHw9vZGWlqa2tjy8nLs27cPT58+xYkTJ3Dnzh0EBQXh1KlTSEpKqvI9Ij09HX/99Rfu3r2L69ev4+LFi4iLi4ObmxsKCwvVxubl5cHDwwMxMTG4fPky/P39ERUVBS8vL2RnZ6uNlUgkcHFxwePHj3H27FncunULYWFh8Pb2RmpqqtpYuVwOR0dHpKSkwNfXF6GhoQgKCsLp06eRkJAAZ2dnjbozMzNx5MgR3L17FwEBAQgICEBsbCzc3d016i4oKMDBgwcRGxuLS5cuwd/fH/fu3cPhw4eRlZWlNlYqlcLFxQUJCQk4c+YMAgMDERoaCh8fH6SmpmLv3r1QKBRwcHCAQqHA3r17kZqaCh8fH4SGhiIwMBBnzpxBQkICXFxcIJVKNd6TDx8+jHv37sHf3x+XLl1CbGwsDh48iIKCAo3Xmru7O2JjY3Hx4kUEBATg7t27OHLkCDIzMzVea87OzkhISMDp06cRFBSE0NBQQd4jsrKy6D3in/eI519rVb1HuLq6oqioSG2sNt8jVN+ntPEeERgYCHAM69SpEzdx4kSN5fHx8RwA7vfff6923aysLM7U1JTbsWOHcpm9vT3Xs2fPGj22VCrl8vPzlbeAgAAOABcWFlbleIlEwkVHR3MSiaRG269KXl5endfl2549ezgdHR3uyZMn3KNHjzgAnIODw0vX8/f35wBw/v7+ymXV5dC+fXvu3Xff1VgOgPvss8+UXy9btozT0dHh1q1bxx07doy7dOkS5+fnx5mZmXGLFi1SjmvUqBG3cOFCje3t3r2bA8AlJCS8sPaMjIxXzpRox9mzZ186Zu35tRw2gRvlOqoeKmq4apIFqR+UBVu0nUdYWBjH9DlphoaGkMlkGsulUqny/up8//33MDU1rfN5aPr6+mqHu4yNqz+nTFtYPrw2Z84crFu3DocPH4ZEIoGenh5mz54tSC3e3t5YtGgRfvvtN+UyqVSqccWG9u3b49GjRxrrV7WsKo0aNXqlOon2dO3a9YX3ZxRnwCnMCUDFXjTCn5dlQeoPZcEWPvJg+nCnpaUl0tLSNJZXLrOysqpyvbi4ODg7O2PNmjVITU1FYmIiEhMTIZVKUVZWhsTERCYnKq2P897qytzcHBMnTsShQ4fg6emJCRMmwNzcXJBaxGIxOI5TW7Znzx7I5XK1ZePHj8ft27fVzl3MycmBp6dnjR6HPjjAjoCAgBfevytoFyTlEgyyGoS3O71dT1U1TC/LgtQfyoItfOTB9J60vn37wt/fHwUFBWofHggODlbeX5WUlBQoFAqsWbMGa9as0bi/Y8eO+Pzzz7Fr1y4+yq6z5s2bC13CCy1cuFD5yZW6nOenLe+99x48PDzQtGlT9OjRA7dv38bly5c1Jp/95ptvcOjQIbz99ttYvXq1cgqOdu3aIScn56XzZ5mYmLyR11N9Hb1or22BrAB77+wFULEXjeZF45dQe9CJJsqCLXzkwfSetBkzZkAul8PZ2Vm5TCaTwdXVFXZ2dsrpN0pKShATE6OcA83W1ha+vr4at549e6Jdu3bw9fXFsmXLBHlOL8L65KmTJ09G8+bN0bRpU0yZMkWwOnbv3o2FCxfC09MTX375JdLS0nD58mWNQ9Jt27aFv78/unfvji1btmDXrl1YtGgRli5dCuDln6alBo0drq6u1d73Z9ifKJAVoLt5d0zuSlcX4NuLsiD1i7JgCx95iLjnjxsxZtasWfD19cUXX3wBGxsbuLu7IyQkBFeuXFFeD/LatWsYM2YMNm7ciE2bNlW7rdGjRyMrKwtRUVG1riM8PBwDBgxAWFgY+vfvr3G/VCpFQkICOnbsyOxUGq+qvLwcVlZWmDx5Mvbv3y90OXW2du1aODk5oaioCGKxuNpxDSHT112ZvAzW/7PGk4IncJnsgmX92fvjixBC6iI8PJztPWkAcPDgQaxduxYeHh5Ys2YNysrKcObMmdfygt0vw+JloVSdOHECmZmZWLhwodCl1JhEIlH7Ojs7Gx4eHhgxYsQLG7TKsYQN1V1u5Vj0MTwpeIKWjVtifu+azX9HXg1diogdlAVb+MiD+T1prKiPPWnl5eXQ1WXvNMHg4GDcvXsXP/30E8zNzdUmjGVd3759MXr0aHTv3h3p6enYv38/UlNT1fbEVqeoqAhPnjyhPWkMyMnJgampqdoyjuMwwHkAIp5F4KcxP+H7Ud8LVF3DUlUWRBiUBVu0ncdrsSetIcnPzxe6hCo5Ojri008/hYWFBQ4ePCh0ObUyadIknDt3Dl988QW2bduGdu3a4fz58zXaE1tYWFgPFZKauHDhgsYy/0R/RDyLgKGuIT4d+KkAVTVMVWVBhEFZsIWPPNjbbdOAVV4YnDVubm5wc3MTuow62bJlC7Zs2VKndQ0NDenDA4yo6pPcOwJ3AACW9lsKMyMzjfsJP6r7VD2pf5QFW/jIg/akMaSsrEzoEoiK8vJyoUsg/3j27Jna1zFZMTj/6DxEEGHtkLXCFNVAPZ8FEQ5lwRY+8qAmTcvoFL83B2XJjuezcAipOEF3StcpsDG1EaKkBoteF+ygLNjCRx7UpGlJ5Qn/r7L3RU9PT1vlEC162adACf8sLS2V/8+X5sMt0g0AsMZOc7Jqwi/VLIiwKAu28JEHNWlaIhaLIRaLUVBQUOdt0GWI2MFxHHJzc6Gvr0/NMwMiIiKU/3eNdEVxWTF6tuiJMR3GCFhVw6SaBREWZcEWPvKgDw5oiUgkgoWFBdLS0qCvr4/GjRvX+vI0+vr6yovHE2FwHIeysjLk5+ejrKwMrVq1ErokgopP6QKAXCHHnpA9ACr2otEloOpfZRZEeJQFW/jIg5o0LWratCkkEgmysrKQmZlZ6/ULCwvRpEkTHiojtaWvr4/w8HD07NlT6FIIAE9PT6xatQrnH53H49zHaG7QHPN70eS1QqjMggiPsmALH3nQZLY19LLJbFXJ5XL6pOZrTCwW0yFORr3j8Q78Hvvh62Ff49e3fxW6HEII4U14eDjtSeND5flpteXg4EB/FTGE8mCHg4MDxs0ZB7/HftAR6WDloJVCl9Rg0euCHZQFW/jIg/ak1VBt9qTVVVFREYyNjXnZNqk9yoMdRUVF+OHmD9gVvAuTu0zGqbmnhC6pwaLXBTsoC7ZoOw+6LBRjjh07JnQJRAXlwY5DRw/B7W83AKBLQAmMXhfsoCzYwkce1KQxZMSIEUKXQFRQHuzIb5OPPGkeOjTrgPE244Uup0Gj1wU7KAu28JEHNWkMiY+PF7oEooLyYIfrPVcAwMcDPoaOiN62hESvC3ZQFmzhIw/64ABDGjduLHQJRAXlITwfH+D/vpcgLi4CMIuDhU07gHYeCIpeF+ygLNjCRx7UpDGE5khjC+UhLB8fYPp0ACJ9gNMBMmyxbIEOmhkC06YJXV3DRa8LdlAWbOEjDzpuwJC4uDihSyAqKA9hbd4MiERcRYMGAJwORCLgxx+Frauho9cFOygLtvCRBzVpDBk1apTQJRAVlIewHj4EOE79sk8cB8TGClQQAUCvC5ZQFmzhIw9q0hhCH6dmC+UhrC5dOECkUFsmEgFduwpUEAFArwuWUBZs4SMPmsy2hupjMltCyL+2OMdgw8fdAJEc4MQQiSr2pPn4AFOnCl0dIYTwiyazZYyDg4PQJRAVlIewYlpsAWZNg2n7FOjqlqN3b2rQWECvC3ZQFmzhIw/ak1ZD9bEnraysjC7szRDKQzjZJdlovbM1ZHIZgpcHo59FP8qCEfS6YAdlwRZt50F70hhz4MABoUsgKigP4bj/7Q6ZXIZ+rfphkNUgyoIhlAU7KAu28JEHNWkMmThxotAlEBWUhzAUnAL7QvcBAD4Z+AlEIhFlwRDKgh2UBVv4yIOaNIZEREQIXQJRQXkIwz/BH3E5cWjSqAnm9ZoHgLJgCWXBDsqCLXzkQU0aQywtLYUugaigPISxL6xiL9qHvT+EcSNjAJQFSygLdlAWbOEjD2rSCCHMSCtMw4mYEwCAjwd+LGwxhBAiMGrSGJKWliZ0CUQF5VH/9kfsR7miHMPaDkPvlr2VyykLdlAW7KAs2MJHHtSkMaRfv35Cl0BUUB71S66QwznMGQDw6cBP1e6jLNhBWbCDsmALH3lQk8aQ8+fPC10CUUF51K/zj87jScETmBqaYkaPGer3URbMoCzYQVmwhY88aDLbGqLJbBseyqN+vef1Hs7GncWXQ7/Ejnd2qN1HWbCDsmAHZcEWmsz2Defk5CR0CUQF5VF/EvMScS7uHADg4wGaHxigLNhBWbCDsmALH3nQnrQaogusE8KfDVc2YMvNLRjXaRz8PvQTuhxCCBEc7UljDF0sly2UR/0olZdif8R+AMAnAz6pcgxlwQ7Kgh2UBVv4yIOaNIbMnDlT6BKICsqjfpyMOYn04nS0Mm6FKV2nVDmGsmAHZcEOyoItfORBTRpDrl+/LnQJRAXlUT8cQx0BAMv7LYeeuOqTbikLdlAW7KAs2MJHHq9NkyaTyfDtt9/CysoKhoaGsLOzg5/fi89duXPnDlatWoWePXuicePGaNeuHWbNmoWHDx/WU9W107lzZ6FLICooD/7FZMXAP9EfOiIdfDTgo2rHURbsoCzYQVmwhY88XpsmbfHixdi5cyfmz5+P3bt3QywWY9KkSbh582a162zbtg3Hjx/HW2+9hd27d2PFihW4fv06+vfvj6ioqHqsvmYKCwuFLoGooDz4Vzl57bud30W7pu2qHUdZsIOyYAdlwRY+8tDV+hZ5EBISgiNHjmD79u346quvAAALFy6Era0tvvnmGwQGBla53rp16+Dl5YVGjRopl82ePRu9evXCf//7Xxw6dKhe6q+p4uJioUsgKigPfknKJHCLdAMAfDKw6g8MVKIs2EFZsIOyYAsfebwWe9K8vb0hFouxYsUK5TIDAwMsW7YMt2/fxpMnT6pcb9iwYWoNGlCxO7Jnz5548OABrzXXhbW1tdAlEBWUB7/+uv8XcqW5aN+0PcZbj3/hWMqCHZQFOygLtvCRx2vRpEVERKBLly4wMTFRWz548GAAQGRkZI23xXEc0tPTYW5u/sJxMpkMBQUFyltRUVGt666tFx26JfWP8uDX3jt7AVRMXivWEb9wLGXBDsqCHZQFW/jI47Vo0tLS0mBpaamxvHJZampqjbfl6emJlJQUzJ49+4Xjtm7diqZNmypv9vb2ylqcnJwgk8mUc6I4ODggPT0df/31F+7evYvr16/j4sWLiIuLg5ubGwoLC9XG5uXlwcPDAzExMbh8+TL8/f2V58hlZ2erjZVIJHBxccHjx49x9uxZ3Lp1C2FhYfD29kZqaqraWLlcDkdHR6SkpMDX1xehoaEICgrC6dOnkZCQAGdnZ426MzMzceTIEdy9excBAQG4ePEiYmNj4e7urlF3QUEBDh48iNjYWFy6dAn+/v64d+8eDh8+jKysLLWxUqkULi4uSEhIwJkzZxAYGIjQ0FD4+PggNTUVe/fuhUKhgIODAxQKBfbu3YvU1FT4+PggNDQUgYGBOHPmDBISEuDi4gKpVKq2/aysLBw+fBj37t2Dv78/Ll26hNjYWBw8eBAFBQVqYwsLC+Hu7o7Y2FhcvHgRAQEBuHv3Lo4cOYLMzEy1sTKZDM7OzkhISICxsTGCgoIQGhoKX19fpKSkwNHREXK5XG2d1NRUeHt7IywsDLdu3cLZs2fx+PFjuLi4QCKRqI3Nzs6Gl5cXoqKi4O/vj8uXLyMmJgYeHh7Iy8vTqNvNzQ1xcXG4ePEirl+/jrt37+Kvv/5Cenq6Rt1OTk5ISkrCqVOnEBQUhDt37uDEiRN4+vQp9u3bh/LycrV10tLS4O3tjYiICNy8eRPnzp1DfHw8Dhw4gOLiYrWxOTk58PLyQnR0NK5evYorV64gOjoanp6eyM3NVRtbVFQEV1dXxMXF4cKFC7hx4wYiIyNx7NgxZd0hKSG4k3oHjcSNoBelh+TkZJw8eRIhISEICQnByZMnkZycDCcnJ5SVlSE3N1fttXbs2DFERkbixo0buHDhAuLi4uDq6oqioiK1WnJzc+Hp6Yno6GhcuXIFV69eRXR0NLy8vJCTk6M2tri4GAcOHEB8fDzOnTuHmzdvIiIiAt7e3khLS1MbW15ejn379uHp06c4ceIE7ty5g6CgIJw6dQpJSUm8vUd4eXkJ/h5hampK7xH/vEecPn1a0PeIiRMnvrHvEZVjy8rK4OTk9NL3iOdfa0K8R6i+T2njPSIwMBDgXgOdOnXiJk6cqLE8Pj6eA8D9/vvvNdrOgwcPOBMTE27o0KFceXn5C8dKpVIuPz9feQsICOAAcGFhYXV5CjWyZ88e3rZNao/y4M9C34UcNoH70OfDGo2nLNhBWbCDsmCLtvMICwvjXovLQtna2qJly5a4cuWK2vLo6Gj07NkT+/btw8cfa17vT9WzZ88wfPhwlJWVISgoCFZWVrWqgS4LRYh2ZBZnos3vbVAqL0Xw8mAMbj1Y6JIIIYQ5r81loSwtLZGWlqaxvHLZyxqu/Px8TJw4EXl5ebhw4UKtG7T6Qpf4YAvlwY/9EftRKi/FQKuBNW7QKAt2UBbsoCzY0mAvC9W3b188fPgQBQUFasuDg4OV91dHKpVi8uTJePjwIc6cOYMePXrwWeormT9/vtAlEBWUh/aVK8qVVxhYNWhVjdejLNhBWbCDsmALH3m8Fk3ajBkzIJfL4ezsrFwmk8ng6uoKOzs7tG3bFgBQUlKCmJgYZGVlAQDkcjlmz56N27dv49ixYxg6dKgg9dfUuXPnhC6BqKA8tO/MwzNIzk+GmaEZZtu++MM7qigLdlAW7KAs2MJHHq/FZLZ2dnaYOXMm1q9fj4yMDNjY2MDd3R2JiYnYv3+/clxISAjGjBmDjRs3YtOmTfjyyy9x6tQpTJ48GTk5ORqT1y5YsKC+n8oL9evXT+gSiArKQ/scQioOB3zU/yMY6BrUeD3Kgh2UBTsoC7bwkcdr0aQBwMGDB/HDDz/Aw8MDubm56N27N86cOYNRo0ZVu07l/GmnT5/G6dOnNe5nrUlLS0tj+nBsQ0N5aNeDzAe4knAFOiKdl15h4HmUBTsoC3ZQFmzhI4/XpkkzMDDA9u3bsX379mrHjB49GqofVr127Vo9VKY9IpFI6BKICspDu/648wcAYHKXyWjfrH2t1qUs2EFZsIOyYAsfebwW56Q1FK1atRK6BKKC8tCeQlkh3P92BwCsGlzzDwxUoizYQVmwg7JgCx95UJPGkNpc3orwj/LQHo+7HigsLURXs654q+NbtV6fsmAHZcEOyoItfORBTRpDJkyYIHQJRAXloR0cxyk/MPDZoM/qdEiAsmAHZcEOyoItfORBTRpDvLy8hC6BqKA8tMM/0R8Psh7AuJExFvVdVKdtUBbsoCzYQVmwhY88XovLQrGALgtFSN1MOzoNvjG+WDlwJfa+u1focggh5LXw2lwWqqGgS3ywhfJ4dY9yHuFEzAkAwGeDP6vzdigLdlAW7KAs2MJHHrQnrYbqY09acXExGjduzMu2Se1RHq9u1blV2HtnLybaTMS5+XWfjZuyYAdlwQ7Kgi3azoP2pDHm6NGjQpdAVFAeryZHkgPXSFcAwJdDv3ylbVEW7KAs2EFZsIWPPKhJY4i9vb3QJRAVlMer2Re6DyVlJejTsg/Gdhz7StuiLNhBWbCDsmALH3lQk8aQ2NhYoUsgKiiPupOVy7AnZA+Air1orzoTN2XBDsqCHZQFW/jIg5o0hpiYmAhdAlFBedTd4ajDeFb0DFZNrDDbdvYrb4+yYAdlwQ7Kgi185EFNGkPoBFC2UB51o+AU2BG4AwCwZvAaNBI3euVtUhbsoCzYQVmwhY88qEljSHx8vNAlEBWUR92cjj2N+5n3YaJvgo8HfqyVbVIW7KAs2EFZsIWPPKhJY8jw4cOFLoGooDxqj+M4/HLjFwAVl4BqZtBMK9ulLNhBWbCDsmALH3lQk8aQ48ePC10CUUF51N7lx5dxJ/UODHUNsXbIWq1tl7JgB2XBDsqCLXzkQZPZ1hBdFoqQlxvtNhoBSQFYM3gNdk/cLXQ5hBDy2qLJbBlDl/hgC+VRO7eSbyEgKQB6Onr4evjXWt02ZcEOyoIdlAVb6LJQAqqPPWnl5eXQ1dXlZduk9iiP2pnkOQnnH53H8n7L8eeUP7W6bcqCHZQFOygLtmg7D9qTxhgXFxehSyAqKI+aC3oahPOPzkMsEuPbEd9qffuUBTsoC3ZQFmzhIw9q0hjy3nvvCV0CUUF51NyGqxsAAIv6LIKNqY3Wt09ZsIOyYAdlwRY+8qAmjSGhoaFCl0BUUB41czXhKq4mXIWejh7+Y/8fXh6DsmAHZcEOyoItfORRZZN2/vx5vP322zAzM4Ouri7EYrHGjWhf69athS6BqKA8Xo7jOOVetI8HfIz2zdrz8jiUBTsoC3ZQFmzhIw+NJu348eN47733kJ6ejjlz5kChUGDu3LmYM2cODA0N0bt3b/znP/z8tdzQyeVyoUsgKiiPlzsbdxZBT4NgqGuIDaM28PY4lAU7KAt2UBZs4SMPjSZt69atGDx4MCIiIrB582YAwNKlS+Hp6YmoqCikpaWhY8eOWi+EABkZGUKXQFRQHi+m4BT4/ur3AIDVg1ejlXEr3h6LsmAHZcEOyoItfOSh0aRFR0djzpw5EIvFyo+SlpWVAQA6dOiAlStXYtu2bVovhAB9+vQRugSigvJ4MfdId/yd/jdM9E3wzfBveH0syoIdlAU7KAu28JGHRpNmZGSERo0aAQCaNWsGfX19pKWlKe9v2bIlEhIStF4IAS5cuCB0CUQF5VG9Qlkhvrv6HQDgh1E/wMzIjNfHoyzYQVmwg7JgCx95aExmO3z4cPTv3x979uwBAAwdOhRGRka4ePEiysvLMX78eKSnpyMmJkbrxbCsPiazlclk0NfX52XbpPYoj+qtv7we/731X9iY2uD+yvtoJG7E6+NRFuygLNhBWbBF23lUOZnt1KlTcfLkSchkMgDAhg0bcO3aNTRr1gwtWrTAjRs38H//939aK4L8688/tTtLO3k1lEfVHuc+xs6gnQCAne/s5L1BAygLllAW7KAs2MJHHjW6LNSNGzfg4+MDsViMd999F2PGjNF6IayjC6wTUmHa0WnwjfHF253exsUFFyESiYQuiRBC3jg1vizUyJEj8fvvv2PHjh0NskGrL3SxXLZQHprOx52Hb4wvxCIxfh//e701aJQFOygLdlAWbOEjD40mTSwWw8vLq9oVjh49SpPZ8mTmzJlCl0BUUB7qCmWF+PjMxwCAz+0+R0+LnvX22JQFOygLdlAWbOEjD40m7WVHP+VyOR3e4ElAQIDQJRAVlIe69VfW40nBE3Rq3gk/jvmxXh+bsmAHZcEOyoItfORR5eHO6pqwgoICXLx4Eebm5lovhADdunUTugSigvL4142kG9h7Zy8A4M/Jf6Jxo8b1+viUBTsoC3ZQFmzhIw8dANi8ebPympwikQgLFiyo8nqdzZs3h4eHB+bMmaP1QgiQl5cndAlEBeVRQVImwfLTywEAy/otw9iOY+u9BsqCHZQFOygLtvCRhy4ADB48GCtXrgTHcfjjjz/w9ttvo0uXLmoDRSIRGjdujAEDBmDatGlaL4QAEolE6BKICsqjwhcXv8DD7IewNLbEjnd2CFIDZcEOyoIdlAVb+MhDFwAmTpyIiRMnAgCKi4vxySefwM7OTusPRl6sU6dOQpdAVFAewNGoo3AKc4IIIrh94IZmBs0EqYOyYAdlwQ7Kgi185KFxTpqrqytzDZpMJsO3334LKysrGBoaws7ODn5+fryvW99u3boldAkEgI8P0KcP0LNnR/TpU/F1Q/Qo5xE+Ov0RAOC7kd/hHet3BKuFXhvsoCzYQVmwhY88qp3M9unTp4iIiEB+fj4UCoXG/QsXLtR6MdWZO3cuvL29sXbtWnTu3Blubm64c+cO/P39MWLECN7WVVUfk9kWFhaiSZMmvGybqFNwCkjKJJCWS6Ej0oFYRww9HT2cO2WAGTNEEIkAjoPy3+PHgYZ0lF9aLsWw/cMQ8SwCI9uNxNVFV6GroytYPfTaYAdlwQ7Kgi3aziM8PFyzSZNKpVi0aBGOHz8OhUIBkUiknJZD9VOfcrlca4W8SEhICOzs7LB9+3Z89dVXyhptbW1hYWGBwMBAXtZ9Xn00aQ4ODli1ahUv236TlcpLkV6UjmdFz/Cs6BnSitI0/p8jyUFJWYnyJi2XVr0xx0ggvRfUdjKLFGhlnYm9p26hs2lndDHrAn3dN/d6eQpOgXnH5+Ho/aMwNzJH5MeRaG3SWtCaXufXho8PsHkz8PAh0KULsHHj693wv85ZvGkoC7ZoO48qm7R169Zhz549+PnnnzF06FCMHj0a7u7usLS0xK5du5CamoqDBw/C1tZWa4W8yDfffIOdO3ciJycHJiYmyuVbt27Fd999h+TkZLRt21br6z6Pzybtm2+APXsAqRQwMABWrwZ+/VWrD1EvtPk8SuWlyCrJQkZxBjKLM5FRnFFlE/as6BmyJdnaexI/lwDlhprLdSXA90YAAD0dPfRq2Qv9W/XHkDZDYNfaDo0bNUa+LB950jzkS/PV/j+1+1TYWlS8Xq4nXccP/j9AWi5V7smTlkshk8ugI9LBr+N+xYd9PgQABD4JxKITi9CkURM0M2imcRvXaRyGtR1W5dN4lcbgq0tf4bfbv0FPRw8XFlwQ5NOcbwofH2D6dGjsmfU6KkPbIXeUf1ykF6cjvSgd6cXpKCotwsweM/HxwIqJgxPzEjF0/1AoOAXEIjHEOmLo6ugq/z+j+wz88tYvACr2gK48uxJNGjVBE/0mMNE3Uf6/mUEzdGzWUW0SYo7jajzv5ZvSbL4Jz+NNeA7Am/M8+BIeHg6N4xfe3t5YsmQJvv32W2RnV/zya926NcaOHYtx48Zh7Nix2Lt3LxwdHeulyIiICHTp0kWtyQIqPpEKAJGRkdU2Wq+ybn355htg+3YA4ACIIJVy2L5dhIDEAIz9+AJEEEEkEkEEEYa1HQYdkQ5EECEuJw45JTmACNCBDiACRBBV3C8Swa61HfTEehBBhEc5j5BVkgWR6N/7xRAr/z+kzRAY6BpAR6SDx7mPkV6cDh1UHAKsXEc5tvUQNG7UGDoiHSTmJSKlIAU6Ih24bOsMn/02Gs8joygdn21IRlJeEp4UPKloTsolygalpKwERaVFkJRLkCPJQUZxBjKKM5Avy6/V91FXRxetjFvB0tgSrYxbafzfzMgMjfUaw0jPCIa6htARVewpk3NyFJUVoUhWhNYmrTFsvx5SnlSx/SZZaGLQHAWyApQpyhCeFo7wtHC4RLi8tLb2zdorm7R8aT6uJ12vdqzqHr7i0mI8ynlU7dgmjZoom7Sw1DBM8JyA9k3bwyBuLm7t+BIiEQeOE+HePQ7Tp4tqdMh2d9Bu/Hb7NwCA6/uuzDRor+MegzxpHr7ZoAuRqDE4rqIRqmzUNv+oQOzMkdWu28uil/L/ujq6eFb0rNqxBbIC5f/zpflwjXStduyHvT/EwakHAVT8rBlvMUZTg6aafwToN8PwdsOxtN9SAKrNpvrP1J+HcjBtGmCkZwQDXYOafWNqgOM4KDgFFJwCemI95fKM4gyUyktRrihHuaIccoUcck4OuUIOA10DdDbrrBwb/DQYknKJ2rgbF1rgv2sGqTyPiuf17e4Q9LCPUatBhIrMjBsZY2r3qcrl5+POI0eSUzFGpcEVQQQDXQO1sVceX0FmSWaVz1FXRxczesxQfn096Xq1OYsgwsyeFbPZV5fFl78Hwu7tFADAtO7TINapuDJQSEoIkvOTq/tWY3KXycojAxFpEXic+1jz8f95nhNsJsBIr+KP1Xvp9xCXE1ftdsd1GgcT/YrfvdGZ0YjJUv/+BvlZYfvnQzSy+J9rClrbBVe73VHtR8HcqGKu1vicePyd/ne1Y4e1HYZWxq0AVPyxE54WXu1Yu9Z2yiMGTwueIiQlpNqxAywHoH2z9hrL+Xif0mjSMjIylE2MoWHFHoXi4mLl/dOnT8ePP/5Yb01aWloaLC0tNZZXLktNTeVlXZlMBplMpvy6qKioxjXXxp49lf8TqfzLIcR3EEJ6jublMXnhXoLKBq1CxfNw/7MJ3FsO1trDiCDCxwM+xvB2w9HKuBVuP7mN/4X8D3o6FQ1pWlEanhY8Vb5xH51xFG91egsA4BbphhWnV6BcUQ4Omqdi+s72ha5Ouyoft1xRjlxprvLr1YNXw7iRMW4m38Ttp7dRrihXG9/coLlyr4V1c2vl8oFWA3Fs5jEY6Bqo3RqJG4HjOLQxaaMcO6j1INxYcgNFpUXIk+ap3XIlubBr8+8HfBLzEpFVkoWskizAYz8ABTiuohHlOBFEIg4//ijCtGkVhzMrm39V7pHu+OLiFwCArW9txfze82sSSb1YsGCB0CW8VJ40D64RrghNC8WdlDsVv8AelQCc+veZ44DERwawbm6NlsYt0bJxxa2VcStYNLaAib4JerTooRzfsnFLRH4cCR2RjvLnWs7Jlc2HZZN/3+MM9QyxZewWFJYWolBWWPFvaSEKZAXIk+ahs+m/TUy+NB9yTo4cSY6y6VBVpihTNmkbNyn+qf3fnylAjo++TMZHj/pharep8Jnt8899HFruaFnxc/bPH5iVf+SJIMK4TuOUjSIAtNnZBiVlJcrGq0xRpnw9je04FlcWXlGO7ebQTe11qMqutR2Clgcpv55xbAaeFjxVH+QYCfXXRkXTvOvXJpDlLqpyux2bdVRrvL73/77aX/YWjS3Uxm4O2IwbyTeqHGvcyFitSdt6cysuPLpQ5VjVJm3zZgAihUYWv/3XCMifBQAo+a4EhjoVv78dQhzgcdejyu0CQNbXWcomzTnMGfvC9lU7NmltEto1rXiPdP/bXfkHXVWiV0bDpEVFk3Yk6gh+uv6T+oBqstj6iy7SFkyvdrsBiwMwqv0oAMD5R+ex+vzqaseem3cOEztXzFxxNeEqlp1aVu3YYzOPKfO4lXwLc45XPx+s+wfuWNhM87x8Pt6nNJq0li1bKvegGRkZoXnz5oiNjcXkyZMBVFx1QCqt5nweHkgkEujra57/Y2BgoLyfj3W3bt2KzZs3ayxPS0uDk5MTFi9ejD///BOrVq2Cg4MDZs6ciYCAAHTr1g15eXmQSCTo1KkTbt26henTp8Pd3V05dsGCBTh9+jQGDRoEqbQr/m1sKomAckPoQx+6erooLy+HWCyGZSPLiuumioBnxc9QilLI5XLo6v4zRlesHGuqYwpdPV0oOAXyZHkoRSkUCgV0dHSU/8oVFZf4MhQZQiwWQ87JIZPLUM7923BU1cxUqdyg2udh0cgCpVwpissrmn0dHR0o5Apl3TpiHdjr2WPGiBl49vgZYrgYeCVUff1YDhz04/QxYcgE+Pn5ocSwpKIxqcaxE8cw+KPB8PHxQapFKsoUZWr3N9JpBDEnhqmxKc6fO49nz96v4nkAOsWt8dOwX/Ao6hGmTZqGvy/+jQ1rNsDBwQGuC11x6NohJIoTcSP9BuKL4pErzUXus1xEpkci+EEwvp7wNbJvZmP5/OUojSxFt97dkJmSCalcCvM25rhz5w4mT56MQwcOKX9OFi1ahEdXH2H48OF4nPIYVoZWaNasGWJSY2Bvb49jx45h2KphcHBwwPzF8/G92fdo36c9Pv65OxTPfXCb40SIieHg4LAXHd/piHlH5mFUh1EwKzHDW13eQlBaEP549AcAYKzxWKzqu0r5V6GDgwPmzZuHCxcuoG/fvnj27Bk4joOlpSUiIiIwadIkeHp6KscuXrwYx44dw4gRIxAfH4/GjRujSZMmiIuLw6hRo3Ds2DHl2I8//hgHDhzAxIkTERERofwDKi0tDf369cP58+exdOlSfPXVV3BxcVG+1q5fv47OnTujsLAQxcXFsLa2xs2bNzFz5ky4ubkptz9//nycO3cO/fr1Q1paGkQiEVq1aoXIyEhMmDABXl5eyrFLlizB0aNHYW9vj9jYWJiYmKBx48aIj4/H8OHDcfz4ceXYD5d+iB/+/AHDhg6DQaoBWrdujVxpLtZdXaf2fRebxUOe0QPg/s1DJOLQpk0x9nbd++97xKSK94hPV30KBwcHdFzQER4eHhg0aBCePn0KsViMFi1aIPpuNMaPH4/Dhw8raxm4bCBcXFwwduxYPHjwAKOajYJBMwMkJCRg2LBh8PHxUY79dMSncHR0xJQpUxAUHITzb51HnjQPTzKfwLytOW6F3UKfwX3gd9MP07tPh4ODA2bPno2YmObQ/HUhBrK7AgAMdA3g4OCAhQsXwtvXu9q9RwCQI8mBg4MDli9fjkOHDiFHkgNJedXvxXKFHA4ODli5ciUcHR0hFomhK6o41Ksj0oEOdNBItxHKy8rRtFFTtZ/ZDk06gJNyMDIwAifnKo4qZHcDp/HaAMoyrGFrYItmzZpBIpFAR0cHunq6KC4qRreW3dS2O7DdQEiyJTA1M0VRURH09PQgEokgk8nQxrwNHB0dsWLFCjg6OqK/TX88S32GFi1aIL8gH4YGhlAoFCgvL4epiSlcXFwwf/587N+/H7279kZ8YjwsWlogJycHxsbGKCstA8dx0DfQh4eHByZPnozoaGOA08xClN0dfZr1ha6uLnyO+2DcmHE4duwYuvbuChtdG7SybIX0Z+lobtocxcXFyrovnL8Aezt7nDlzBh1tO6KTbidYWVkhNTUV5ubmKCgogIGBARRyBW5cu4EhtkMQEBAAqy5WamMtWlggNzcXjY0bo7S0FOEh4eA6coiIiIBZOzO1sS1btkRQNVlkJquPtWxlifT0irqLioqQHJeMkPQQpKWlQbe5Lqz1rNGyZUs8e/YMlpaWSEtLg7m5OfLz85H5JBM3nt1AcXExyg3KYaNvA3Mzc2RkZCjHtmjRArm5uSjMKMSVtCsQiUQoFhWji0EXNG/eHJmZmcqxLVu2RGZmJsoLynHu3DmN94gffvhB+T71ySefwMXFBe+99x5CQ0PRunVryOVyZGRkoE+fPrhw4cJL+4i7d+9qnpM2a9YsSCQSnD59GgCwePFinD9/Hjt37oRCocCXX36Jfv364eLFi9W+CLXJ1tYWLVu2xJUrV9SWR0dHo2fPnti3bx8+/vhjra/7/J60yMhI2Nvba/2cNEPDinO4nmdgALA4T2HlIYjnb+ZNG0MmBZ5vcOr6PBScQvmXdeUeg8r/mxqaKv/yy5HkILUwVXlf5V/tYh0xxCIx2jVthyb6FZ+2qdwjpauji8Z6jWGoZ6jxicU+fYB79yreLCqJREDv3kBkZM1qf5L/BMcfHMeRqCMITvl3t72uji7GW4/Hwj4LMaXrFK0eHlJV9XPg0Lu3CJGRwIYrG7Dl5pYq1x3TYQwOTz+MlsYteamtrmJiYgS9BE6ZvAwhKSG4knAFVxKu4PaT2yhTlMG+vT2uLb6mHPfx6Y/RtmlbDLIahIFWAxFwwazKc9J8fICpU6t/PNa86HURGl7x+qx8TSo4BWKyYsBxHDhwGv+a6JugU/N/55OKzoyGjkgHejp60NXRVbvp6+rDuJFxvTyPmr6+hfYmPAfgzXkeqrT9PhUeHg5wz7lx4wa3Zs0aTiqVchzHccnJyVyXLl04kUjEiUQizsbGhouJiXl+Nd6MGzeO6969u8byy5cvcwC4U6dO8bLu88LCwjgAXFhYWI3XqYmvv+a4ih9T9ds332j1YXj3pjyP48cr6haJ1P/18anb9uJz4rmtN7ZyfRz7cNgE5a3p1qbcilMruFvJtziFQlGvz6GktIQLSAzg/nP1P5zlDku1urAJ3P2M+8ptPcp+xKUWpGq1vrrw8/MT7LHnes/ljLcYa3yf2v3ejvvk9Ccvze/4cY7r04fjDAwq/q3rz5KQtP26EMqb8DzehOfAcW/O81Cl7fepsLAwTqNJq4pcLuciIyO5e/fucWVlZVot4mW++uorTiwWc/n5+WrLf/nlFw4Al5yczMu6z+OrSeO4igbHwIDjAAVnYPD6NTaV/n0e3Gv9PCp/qerplWv1l2p0RjS34coGrt3v7dR+2dv8z4b78dqPXEJugnYeiHt5YxCQGMC13dmWwyZw4s1i7rvL33Fbb2zlZvw1Q63pmOs9l8MmcD339uTWnFvDnYw5yeVJ8rRWZ01dvXqV1+0rFAruUfYjzinUiVt1dpXafe95vcdhEzizbWbczL9mcvvu7OPisuO03lyzjq/XRX17U5pmyoI92n6fCgsL46qdzJYVwcHBGDJkiNpcZzKZDLa2tjAzM0NQUMWJoiUlJUhOToa5uTnMzc1rtW5N1Mc8aVFRUfU2tQl5Ob7yUHAKBCQGwP1vd3hHe6O47N8P5ti3t8eiPoswtftUXi7BlFaYhp+v/wzHUEdw4GBjagOvaV4Y1HpQleMneU7ChUcX1M5NFIvEGGg1EO9Yv4PNozfXeAqHV8FHFs+KnuFqwlVceVxxCDMpP0l5X+LnicpPb4WnhUNHpIPeLXsrPxXckNH7FDsoC7ZoO48qp+C4cuUKLl26hPj4eOXsuTY2Nhg/fjzGjBmjtQevKTs7O8ycORPr169HRkYGbGxs4O7ujsTEROzfv185LiQkBGPGjMHGjRuxadOmWq3Lirt379ILjiF85aEj0sGYjmMwpuMYOExygO8DX7j/7Y6rCVcRkBSAgKQAfHzmY4zrNA7Tu0/Hu13eVX6MvKaen3/o82/yEGuxFXtC9ihP0l7Sdwn+N/F/Lzzn59z8c8guyYZ/or+ymYnLiUNwSjDknBw/jvlROdY90h1dzLqgn2U/rZ9v96pZVP4tWtlQ/hjwIzZe26g2Rk9HD0PaDMFbHd9CI3Ej5fL+lvz8Ufa6ovcpdlAWbOElj8rdak+fPuWGDh3K6ejoKM8/U73p6OhwI0aM4FJT6//8FIlEwn311Vdcq1atOH19fW7QoEHchQsX1Mb4+/tzALiNGzfWet2a4PNwZ6WsrCzetk1qr77zSMpL4rZc38L12NtD4/ynXn/04tZdWMd53/fm4nPiX3io7d9zPRQV5waK5BX/zprKYRO4oS5DuauP675bPikviXONcOWO3DuiXFYoK+R0f9TlsAmc3o963EDngdzKMys590h3LiYzhpMr5HV6rOPHOa53b47T11dwvXtXfF0TGUUZnF+8H7fj1g5ujvccrvVvrbnrideV9x+NOsphE7i++/pyX178kjv38BxXKCusU40NDb1PsYOyYIu281Ae7iwtLcXgwYMRFRWF5cuX48MPP4StrS2MjY1RVFSEqKgoHDx4EPv370efPn0QFBQEPT29l/V/bxS6LFTDI2QeMVkxOB59HL4xvghPC9eYCqWpflNYm1pXzLFl3BIGYgNI5RVXMTj9xc8oedoJape2ghxGbeJx9MpDvNv5Xa0fonyS/wSfX/gcN5NvVjn9wor+K+A02QkAICmTwO+xHzo264iOzTtWuyevutn6KyflrfzEb+WnCkNTQ/GD/w/4+9nfSCtK09jez2N+xoZRGwAAJWUlkJRJYGZkpq1vQYNB71PsoCzYwttlodzc3LB06VJ4enpi7ty51a7g5eWFBQsWwM3NrV4vsM6C+mjSCKlKVkmW8lBjWFoYojKiUCovrX6Fai5tVR/TunAch6T8JAQ/DUZwSjBCUkIQlhaGn8b8hK+GVZwXejf9Lvrs66Ncp7FeY+Wkrib6JljUZxHm9pr7z0f0OeVs/QAAkQIGVg/RdO1oZJZkYsfbO/DF0IoJeENTQzHoz3/PrbNubo0+rfqgb8u+GNFuBOza2ClnSyeEENYpz0nz8fHB8OHDX9igAcC8efPg6OiI48ePN7gmrT7QX0VsYSUPcyNzzLadjdm2swFUXNc0JisGyfnJyus9lspLYahrCANdA/zmKcXTRwZqzY1IBHTtyn+tIpEIHZp1QIdmHZT1lsnL1JpKWbkMAywH4HHuY+RKc1FcVozHuY+Vl6MZ3WE0gIrz6bjnZusHpwNpentIi9MBAAl5Ccq7erboiT8m/YE+rfqgl0Uv5fx4RLtYeV0QyoI1fOQh4jiOa9++PT766CN8//33L13h559/xp9//omkpKSXjn2T1MeeNIlEorwUFxHe65pHdYcJWZxAtVBWqLy4eEZxBgpLC9GvVT/0atmryj1pIhGHjl0L4eOfAIvGFmjRuIXGhMSEX6/r6+JNRFmwRdt5hIeHV5y0kpWVhdatW9dopdatWyMrq/rL8JC68/T0FLoEouJ1zWPatIrztnr3rjjE2bs3mw0aADTRbwIbUxsMbzccU7tPxcI+C9GrZcXFxTdurLzuaMXYimZThB1bTNCnVR9YNrGkBk0Ar+vr4k1EWbCFjzx0gOqvcVmVRo0a1eu1OxuSsWPHCl0CUfE65zFtWsWlVSSSin9ZbNBeRrXZbNRIwXSz2ZC8zq+LNw1lwRY+8lB+/Ku4uBg5OTkvvRUVFWm9CFLhwYMHQpdAVFAewqtsNn18zr+2zeabhl4X7KAs2MJHHspjBZ988gk++eSTl67AcVy9zDDeEDVr1kzoEogKyoMdlAU7KAt2UBZs4SMPXQDYuHHjy8aRemBgoN1Z2smroTzYQVmwg7JgB2XBFj7y0GjSgoODYWdnp/UHIi+XkJCAAQMGCF0G+QflwQ7Kgh2UBTsoC7bwkYfG1YKHDh2KLl264KeffkJCQkJV6xCeDBs2TOgSiArKgx2UBTsoC3ZQFmzhIw+NJu3QoUPo3LkzfvrpJ9jY2GD48OHYt28fcnJytP7gRJ2Pj4/QJRAVlAc7KAt2UBbsoCzYwkceIo7juKruyMrKwpEjR+Dl5YWgoCA0atQIEyZMwIIFCzBlyhQ0atRI68WwjC4LRQghhJD6opzMtirm5uZYtWoVAgMDERcXhw0bNiAmJgazZ89Gq1atsGLFCty8ebM+633jOTg4CF0CUUF5sIOyYAdlwQ7Kgi185FHtnjRVqampOHz4MDw8PHD37l00b94curq6yMrKQv/+/eHu7o4ePXpovTiW1MeeNLlcDrFYzMu2Se1RHuygLNhBWbCDsmCLtvN44Z60wsJCuLq6Yty4cWjfvj2+++47dOjQAd7e3nj27BlSU1Nx9OhRZGRkYMmSJVorqiFzdnYWugSigvJgB2XBDsqCHZQFW/jIQ+PCdydPnoSnpyfOnDkDqVSKQYMGYdeuXZgzZw7MzMzUxs6YMQO5ubn47LPPtF5YQzRlyhShSyAqKA92UBbsoCzYQVmwhY88NPakTZ06FcHBwfjiiy/w4MEDBAcH47PPPtNo0Cr16dMH8+fP13phDVFISIjQJRAVlAc7KAt2UBbsoCzYwkceGnvSrl69itGjR9d4A4MHD8bgwYO1WVOD1bZtW6FLICooD3ZQFuygLNhBWbCFjzw09qTVpkEj2lVeXi50CUQF5cEOyoIdlAU7KAu28JFHtR8cIPUvMzNT6BKICsqDHZQFOygLdlAWbOEjD2rSGGJrayt0CUQF5cEOyoIdlAU7KAu28JEHNWkM8fPzE7oEooLyYAdlwQ7Kgh2UBVv4yKNGk9mS+pnMViaTQV9fn5dtk9qjPNhBWbCDsmAHZcEWbefxwslsSf37888/hS6BqKA82EFZsIOyYAdlwRY+8qA9aTVEF1gnhBBCSH2hPWmMoYvlsoXyYAdlwQ7Kgh2UBVsEu8A6qZ89aZmZmWjRogUv2ya1R3mwg7JgB2XBDsqCLdrOg/akMebKlStCl0BUUB7soCzYQVmwg7JgCx95UJPGkB49eghdAlFBebCDsmAHZcEOyoItfORBTRpDcnNzhS6BqKA82EFZsIOyYAdlwRY+8qAmjSFSqVToEogKyoMdlAU7KAt2UBZs4SMPatIY0qFDB6FLICooD3ZQFuygLNhBWbCFjzyoSWNIUFCQ0CUQFZQHOygLdlAW7KAs2MJHHjQFRw3VxxQchYWFaNKkCS/bJrVHebCDsmAHZcEOyoIt2s6DpuBgjLu7u9AlEBWUBzsoC3ZQFuygLNjCRx60J62G6LJQhBBCCKkvtCeNMXSJD7ZQHuygLNhBWbCDsmALH3m8Fk2aTCbDt99+CysrKxgaGsLOzg5+fn4vXe/OnTtYtWoVevbsicaNG6Ndu3aYNWsWHj58WA9V197ChQuFLoGooDzYQVmwg7JgB2XBFj7yeC2atMWLF2Pnzp2YP38+du/eDbFYjEmTJuHmzZsvXG/btm04fvw43nrrLezevRsrVqzA9evX0b9/f0RFRdVT9TV34sQJoUsgKigPdlAW7KAs2EFZsIWPPJg/Jy0kJAR2dnbYvn07vvrqKwAVE8bZ2trCwsICgYGB1a4bGBiIgQMHolGjRsplcXFx6NWrF2bMmIFDhw7VuI76OCctNjYWXbt25WXbpPYoD3ZQFuygLNhBWbBF23m8FuekeXt7QywWY8WKFcplBgYGWLZsGW7fvo0nT55Uu+6wYcPUGjQA6Ny5M3r27IkHDx7wVnNdJSUlCV0CUUF5sIOyYAdlwQ7Kgi185MF8kxYREYEuXbrAxMREbfngwYMBAJGRkbXaHsdxSE9Ph7m5+QvHyWQyFBQUKG9FRUW1epy60NPT4/0xSM1RHuygLNhBWbCDsmALH3kw36SlpaXB0tJSY3nlstTU1Fptz9PTEykpKZg9e/YLx23duhVNmzZV3uzt7ZX1ODk5QSaTKT/J4eDggPT0dPz111+4e/curl+/josXLyIuLg5ubm4oLCxUG5uXlwcPDw/ExMTg8uXL8Pf3R1RUFMLCwpCdna02ViKRwMXFBY8fP8bZs2dx69YthIWFwdvbG6mpqWpj5XI5HB0dkZKSAl9fX4SGhiIoKAinT59GQkICnJ2dNerOzMzEkSNHcPfuXQQEBODixYuIjY2Fu7u7Rt0FBQU4ePAgYmNjcenSJfj7++PevXs4fPgwsrKy1MZKpVK4uLggISEBZ86cQWBgIEJDQ+Hj44PU1FTs3bsXCoUCDg4OUCgU2Lt3L1JTU+Hj44PQ0FAEBgbizJkzSEhIgIuLC6RSqdr2s7KycPjwYdy7dw/+/v64dOkSYmNjcfDgQRQUFKiNLSwshLu7O2JjY3Hx4kUEBATg7t27OHLkCDIzM9XGymQyODs7IyEhAQ8ePEBQUBBCQ0Ph6+uLlJQUODo6Qi6Xq62TmpoKb29vhIWF4datWzh79iweP34MFxcXSCQStbHZ2dnw8vJCVFQU/P39cfnyZcTExMDDwwN5eXkadbu5uSEuLg4XL17E9evXcffuXfz1119IT0/XqNvJyQlJSUk4deoUgoKCcOfOHZw4cQJPnz7Fvn37UF5errZOWloavL29ERERgZs3b+LcuXOIj4/HgQMHUFxcrDY2JycHXl5eiI6OxtWrV3HlyhVER0fD09MTubm5amOLiorg6uqKuLg4XLhwATdu3EBkZCSOHTumUXdZWRmcnJyQnJyMkydPIiQkBCEhITh58iSSk5Ph5OSEsrIyXL16Ve21duzYMURGRuLGjRu4cOEC4uLi4OrqiqKiIrXt5+bmwtPTE9HR0bhy5QquXr2K6OhoeHl5IScnR21scXExDhw4gPj4eJw7dw43b95EREQEvL29kZaWpja2vLwc+/btw9OnT3HixAncuXMHQUFBOHXqFJKSknh7j/Dy8hL8PeLRo0f0HvHPe8Tp06cFfY8wMjKi94h/3iOef60J8R6h+j6ljfeIwMDA+j0nTaFQoLS0tEZj9fX1IRKJYG1tja5du+LcuXNq9z9+/BjW1tb4/fffsXbt2hptMyYmBnZ2dujZsydu3LgBsVhc7ViZTAaZTKb8OjIyEvb29ryek3b48GHMnTuXl22T2qM82EFZsIOyYAdlwRZt5xEeHg5drW2tBq5fv44xY8bUaOyDBw/QrVs3GBoaqjVLlSqvNm9oaFij7T179gzvvvsumjZtqjzP7UX09fWhr6+v/NrY2LhGj/Mq3n77bd4fg9Qc5cEOyoIdlAU7KAu28JFHvTZp3bp1g6ura43GVh7OtLS0REpKisb9aWlpAAArK6uXbis/Px8TJ05EXl4ebty4UaN1hHDkyBGsWrVK6DLIPygPdlAW7KAs2EFZsIWPPJifguPrr7/G77//jpycHLUPD2zZsgUbNmxAcnIy2rZtW+36UqkU77zzDsLCwnD58mUMHTq0TnXQZaEIIYQQUl9eiyk4ZsyYAblcDmdnZ+UymUwGV1dX2NnZqTVoJSUliImJQVZWFgBALpdj9uzZuH37No4dO1bnBq2+0CU+2EJ5sIOyYAdlwQ7Kgi185MH8njQAmDVrFnx9ffHFF1/AxsYG7u7uCAkJwZUrVzBq1CjluGvXrmHMmDHYuHEjNm3ahLVr12L37t2YPHkyZs2apbHdBQsW1LiG+tiTJpVKYWBgwMu2Se1RHuygLNhBWbCDsmCLtvN4LfakAcDBgwexdu1aeHh4YM2aNSgrK8OZM2fUGrSqVM6hdvr0aXz44YcaN9bU5goIhH+UBzsoC3ZQFuygLNjCRx6vxZ40FtTHnrSEhAR07NiRl22T2qM82EFZsIOyYAdlwRZt5/Ha7ElrKO7fvy90CUQF5cEOyoIdlAU7KAu28JEHNWkMMTU1FboEooLyYAdlwQ7Kgh2UBVv4yIOaNIY8fzF4IizKgx2UBTsoC3ZQFmzhIw9q0hiSnJwsdAlEBeXBDsqCHZQFOygLtvCRBzVpDBkyZIjQJRAVlAc7KAt2UBbsoCzYwkce1KQxxNfXV+gSiArKgx2UBTsoC3ZQFmzhIw+agqOG6mMKDoVCAR0d6ptZQXmwg7JgB2XBDsqCLdrOg6bgYMwff/whdAlEBeXBDsqCHZQFOygLtvCRB+1JqyHak9bwUB7soCzYQVmwg7JgC+1Je8M5OjoKXQJRQXmwg7JgB2XBDsqCLXzkQU0aQ6ZOnSp0CUQF5cEOyoIdlAU7KAu28JEHNWkMCQoKEroEooLyYAdlwQ7Kgh2UBVv4yIOaNIa0a9dO6BKICsqDHZQFOygLdlAWbOEjD2rSGFJaWip0CUQF5cEOyoIdlAU7KAu28JEHNWkMycnJEboEooLyYAdlwQ7Kgh2UBVv4yIOaNIb07NlT6BKICsqDHZQFOygLdlAWbOEjD2rSGHLlyhWhSyAqKA92UBbsoCzYQVmwhY88aDLbGqqPyWylUikMDAx42TapPcqDHZQFOygLdlAWbNF2HjSZLWNcXFyELoGooDzYQVmwg7JgB2XBFj7yoD1pNVQfe9IIIYQQQgDak8YcBwcHoUsgKigPdlAW7KAs2EFZsIWPPGhPWg3Vx560rKwsmJub87JtUnuUBzsoC3ZQFuygLNii7TxoTxpj/Pz8hC6BqKA82EFZsIOyYAdlwRY+8qAmjSG2trZCl0BUUB7soCzYQVmwg7JgCx95UJPGkKysLKFLICooD3ZQFuygLNhBWbCFjzyoSWNIWVmZ0CUQFZQHOygLdlAW7KAs2MJHHtSkMaR9+/ZCl0BUUB7soCzYQVmwg7JgCx95UJPGkODgYKFLICooD3ZQFuygLNhBWbCFjzxoCo4aqo8pOAoKCmBiYsLLtkntUR7soCzYQVmwg7Jgi7bzoCk4GHPw4EGhSyAqKA92UBbsoCzYQVmwhY88aE9aDdFloQghhBBSX2hPGmPoEh9soTzYQVmwg7JgB2XBFroslIDqY09aYWEhmjRpwsu2Se1RHuygLNhBWbCDsmCLtvOgPWmM8fHxEboEooLyYAdlwQ7Kgh2UBVv4yIOaNIYMGTJE6BKICsqDHZQFOygLdlAWbOEjD2rSGJKYmCh0CUQF5cEOyoIdlAU7KAu28JEH802aTCbDt99+CysrKxgaGsLOzq7OV5r/5ZdfIBKJmL0orYGBgdAlEBWUBzsoC3ZQFuygLNjCRx7MN2mLFy/Gzp07MX/+fOzevRtisRiTJk3CzZs3a7Wdp0+fYsuWLWjcuDFPlb665s2bC10CUUF5sIOyYAdlwQ7Kgi185MF0kxYSEoIjR45g69at2L59O1asWIGrV6+iffv2+Oabb2q1ra+++gpDhgzBwIEDear21UVHRwtdAlFBebCDsmAHZcEOyoItfOTBdJPm7e0NsViMFStWKJcZGBhg2bJluH37Np48eVKj7Vy/fh3e3t7YtWsXT5Vqx1tvvSV0CUQF5cEOyoIdlAU7KAu28JEH001aREQEunTponEtrMGDBwMAIiMjX7oNuVyO1atXY/ny5ejVq1eNH1smk6GgoEB5KyoqqlXtdXH06FHeH4PUHOXBDsqCHZQFOygLtvCRB9NNWlpaGiwtLTWWVy5LTU196Tb27duHpKQk/PTTT7V67K1bt6Jp06bKm729vbImJycnyGQy5ezCDg4OSE9Px19//YW7d+/i+vXruHjxIuLi4uDm5obCwkK1sXl5efDw8EBMTAwuX74Mf39/REVFwdTUFNnZ2WpjJRIJXFxc8PjxY5w9exa3bt1CWFgYvL29kZqaqjZWLpfD0dERKSkp8PX1RWhoKIKCgnD69GkkJCTA2dlZo+7MzEwcOXIEd+/eRUBAAC5evIjY2Fi4u7tr1F1QUICDBw8iNjYWly5dgr+/P+7du4fDhw8jKytLbaxUKoWLiwsSEhJw5swZBAYGIjQ0FD4+PkhNTcXevXuhUCjg4OAAhUKBvXv3IjU1FT4+PggNDUVgYCDOnDmDhIQEuLi4QCqVqm0/KysLhw8fxr179+Dv749Lly4hNjYWBw8eREFBgdrYwsJCuLu7IzY2FhcvXkRAQADu3r2LI0eOIDMzU22sTCaDs7MzEhIS0L59ewQFBSE0NBS+vr5ISUmBo6Mj5HK52jqpqanw9vZGWFgYbt26hbNnz+Lx48dwcXGBRCJRG5udnQ0vLy9ERUXB398fly9fRkxMDDw8PJCXl6dRt5ubG+Li4nDx4kVcv34dd+/exV9//YX09HSNup2cnJCUlIRTp04hKCgId+7cwYkTJ/D06VPs27cP5eXlauukpaXB29sbERERuHnzJs6dO4f4+HgcOHAAxcXFamNzcnLg5eWF6OhoXL16FVeuXEF0dDQ8PT2Rm5urNraoqAiurq6Ii4vDhQsXcOPGDURGRuLYsWMadZeVlcHJyQnJyck4efIkQkJCEBISgpMnTyI5ORlOTk4oKytTvi4rX2vHjh1DZGQkbty4gQsXLiAuLg6urq4oKipS235ubi48PT0RHR2NK1eu4OrVq4iOjoaXlxdycnLUxhYXF+PAgQOIj4/HuXPncPPmTURERMDb2xtpaWlqY8vLy7Fv3z48ffoUJ06cwJ07dxAUFIRTp04hKSmJt/cILy8vwd8jOnfuTO8R/7xHnD59WtD3iEWLFtF7xD/vEc+/1oR4j1B9n9LGe0RgYCDA1RO5XM5JJJIa3RQKBcdxHNepUydu4sSJGtuKj4/nAHC///77Cx8zKyuLMzU15Xbs2KFcZm9vz/Xs2fOl9UqlUi4/P195CwgI4ABwYWFhtXvitbBnzx7etk1qj/JgB2XBDsqCHZQFW7SdR1hYGKeLenL9+nWMGTOmRmMfPHiAbt26wdDQEDKZTON+qVQKADA0NHzhdr7//nuYmppi9erVta5XX18f+vr6yq+NjY1rvY3a+uijj3h/DFJzlAc7KAt2UBbsoCzYwkce9dakdevWDa6urjUaW3k409LSEikpKRr3V+5WtLKyqnYbcXFxcHZ2xq5du9QOi0qlUpSVlSExMREmJiYwNTWtzdPglbu7u9qHJIiwKA92UBbsoCzYQVmwhY886q1Ja9WqFRYvXlyrdfr27Qt/f38UFBSofXggODhYeX91UlJSoFAosGbNGqxZs0bj/o4dO+Lzzz9n6hOfb7/9ttAlEBWUBzsoC3ZQFuygLNjCRx5Mf3BgxowZkMvlcHZ2Vi6TyWRwdXWFnZ0d2rZtq1xeUlKCmJgYZGVlAQBsbW3h6+urcevZsyfatWsHX19fLFu2rN6f04tERUUJXQJRQXmwg7JgB2XBDsqCLXzkUW970urCzs4OM2fOxPr165GRkQEbGxu4u7sjMTER+/fvVxsbEhKCMWPGYOPGjdi0aRPMzc3xwQcfaGyzcs9ZVfcJrUWLFkKXQFRQHuygLNhBWbCDsmALH3kw3aQBwMGDB/HDDz/Aw8MDubm56N27N86cOYNRo0YJXZrW6eoyH0eDQnmwg7JgB2XBDsqCLXzkwfThTqDiCgPbt29HWloapFIpQkJCMH78eI1xo0ePBsdx2LRp0wu3d+3aNWZ3Edf0CgqkflAe7KAs2EFZsIOyYAsfeTDfpDUklVdSIGygPNhBWbCDsmAHZcEWPvKgJo0hp06dEroEooLyYAdlwQ7Kgh2UBVv4yEPEcRyn9a2+gcLDwzFgwACEhYWhf//+vDyGXC6HWCzmZduk9igPdlAW7KAs2EFZsEXbeYSHh9OeNJY4OjoKXQJRQXmwg7JgB2XBDsqCLXzkQXvSaqg+9qQRQgghhAC0J405Dg4OQpdAVFAe7KAs2EFZsIOyYAsfeVCTxpBp06YJXQJRQXmwg7JgB2XBDsqCLXzkQU0aQwIDA4UugaigPNhBWbCDsmAHZcEWPvKgJo0hHTt2FLoEooLyYAdlwQ7Kgh2UBVv4yIOaNIZIpVKhSyAqKA92UBbsoCzYQVmwhY88qEljSF5entAlEBWUBzsoC3ZQFuygLNjCRx7UpDGke/fuQpdAVFAe7KAs2EFZsIOyYAsfeVCTxpCrV68KXQJRQXmwg7JgB2XBDsqCLXzkQZPZ1lB9TGYrkUhgaGjIy7ZJ7VEe7KAs2EFZsIOyYIu286DJbBmzf/9+oUsgKigPdlAW7KAs2EFZsIWPPGhPWg3RZaEIIYQQUl9oTxpj6BIfbKE82EFZsIOyYAdlwRY+8qA9aTVUH3vSsrOzYWZmxsu2Se1RHuygLNhBWbCDsmCLtvOgPWmMuXjxotAlEBWUBzsoC3ZQFuygLNjCRx7UpDGkd+/eQpdAVFAe7KAs2EFZsIOyYAsfeVCTxpDMzEyhSyAqKA92UBbsoCzYQVmwhY88qEljiFwuF7oEooLyYAdlwQ7Kgh2UBVv4yIOaNIa0adNG6BKICsqDHZQFOygLdlAWbOEjD2rSGHLnzh2hSyAqKA92UBbsoCzYQVmwhY88aAqOGqqPKTjy8vLQrFkzXrZNao/yYAdlwQ7Kgh2UBVu0nQdNwcGYQ4cOCV0CUUF5sIOyYAdlwQ7Kgi185EF70mqILgtFCCGEkPpCe9IYQ5f4YAvlwQ7Kgh2UBTsoC7bQZaEEVB970goLC9GkSRNetk1qj/JgB2XBDsqCHZQFW7SdB+1JY8zx48eFLoGooDzYQVmwg7JgB2XBFj7yoCaNIcOHDxe6BKKC8mAHZcEOyoIdlAVb+MiDmjSGPH78WOgSiArKgx2UBTsoC3ZQFmzhIw9q0hhiaGgodAlEBeXBDsqCHZQFOygLtvCRBzVpDKFJCdlCebCDsmAHZcEOyoItfORBTRpDYmJihC6BqKA82EFZsIOyYAdlwRY+8qAmjSH29vZCl0BUUB7soCzYQVmwg7JgCx95MN+kyWQyfPvtt7CysoKhoSHs7Ozg5+dXq22Eh4djypQpMDU1hZGREWxtbfG///2Pp4rr7tixY0KXQFRQHuygLNhBWbCDsmALH3kwP5nt3Llz4e3tjbVr16Jz585wc3PDnTt34O/vjxEjRrx0/UuXLmHy5Mno168fZs+eDWNjY8THx0OhUODXX3+tcR10WShCCCGE1Jfw8HCAY1hwcDAHgNu+fbtymUQi4aytrbmhQ4e+dP38/HyuZcuW3NSpUzm5XP5KtYSFhXEAuLCwsFfazovs2bOHt22T2qM82EFZsIOyYAdlwRZt5xEWFsYxvSftm2++wc6dO5GTkwMTExPl8q1bt+K7775DcnIy2rZtW+36+/btw6efforo6Gh0794dxcXFMDQ0hI5O7Y/y1seeNJlMBn19fV62TWqP8mAHZcEOyoIdlAVbtJ0H85eFioiIQJcuXdQaNAAYPHgwACAyMvKF61++fBkmJiZISUlB165dYWxsDBMTE3z66aeQSqV8lV1nbm5uQpdAVFAe7KAs2EFZsIOyYAsfeehqfYtalJaWBktLS43llctSU1NfuH5cXBzKy8vx/vvvY9myZdi6dSuuXbuGPXv2IC8vD4cPH652XZlMBplMpvw6KysLAPDgwYO6PJUaadOmTcUxaMIEyoMdlAU7KAt2UBZs0XYeDx48qL8mTaFQoLS0tEZj9fX1IRKJIJFIqtx1aGBgAACQSCQv3E5RURFKSkrwySefKD/NOW3aNJSWlsLJyQk//vgjOnfuXOW6W7duxebNmzWWL1iwoEbPgRBCCCHkVdRbk3b9+nWMGTOmRmMfPHiAbt26wdDQUG1vVqXKQ5UvuwRD5f1z585VWz5v3jw4OTnh9u3b1TZp69evx7p165RfZ2Vl4caNG7CxseHl0g9FRUWwt7dHQEAAjI2Ntb59UjuUBzsoC3ZQFuygLNjCRx4SiaT+mrRu3brB1dW1RmMrD2daWloiJSVF4/60tDQAgJWV1Qu3Y2Vlhfv376Nly5Zqyy0sLAAAubm51a6rr6+vthfPxMQEnTp1qlH9dVFQUAAA6Nu3r8Y5eKT+UR7soCzYQVmwg7JgC1951FuT1qpVKyxevLhW6/Tt2xf+/v4oKChQe9LBwcHK+19kwIAB8PPzU35woFLluWwtWrSoVT2EEEIIIfWF6U93zpgxA3K5HM7OzsplMpkMrq6usLOzU5t+o6SkBDExMcoT/AFg1qxZAID9+/erbdfFxQW6uroYPXo0v0+AEEIIIaSOmP50p52dHWbOnIn169cjIyMDNjY2cHd3R2JiokbjFRISgjFjxmDjxo3YtGkTAKBfv35YunQpDhw4gPLyctjb2+PatWs4duwY1q9f/9LDpfVJX18fGzdupDlvGEF5sIOyYAdlwQ7Kgi185cH0ZLZAxYcEfvjhBxw6dAi5ubno3bs3fvrpJ4wfP15t3LVr1zSaNAAoKyvDli1b4OrqitTUVLRv3x6fffYZ1q5dW79PhBBCCCGkFphv0gghhBBCGiKmz0kjhBBCCGmoqEkjhBBCCGEQNWmEEEIIIQyiJo0BMpkM3377LaysrGBoaAg7Ozv4+fkJXVaDc+fOHaxatQo9e/ZE48aN0a5dO8yaNQsPHz4UurQG75dffoFIJIKtra3QpTRY4eHhmDJlCkxNTWFkZARbW1vl5fZI/YqLi8OcOXPQpk0bGBkZoVu3bvjxxx9RUlIidGlvrKKiImzcuBETJkyAqakpRCJRtRdU1+bvdPrgAAPmzp0Lb29vrF27Fp07d4abmxvu3LkDf39/jBgxQujyGowZM2bg1q1bmDlzJnr37o1nz57BwcEBRUVFCAoKogZBIE+fPkXXrl0hEonQoUMHREVFCV1Sg3Pp0iVMnjwZ/fr1w+zZs2FsbIz4+HgoFAr8+uuvQpfXoDx58gS9e/dG06ZN8cknn8DU1BS3b9+Gm5sbpkyZgpMnTwpd4hspMTERHTt2RLt27dCpUydcu3YNrq6uVU7Sr9Xf6RwRVHBwMAeA2759u3KZRCLhrK2tuaFDhwpYWcNz69YtTiaTqS17+PAhp6+vz82fP1+gqsjs2bO5sWPHcvb29lzPnj2FLqfByc/P51q2bMlNnTqVk8vlQpfT4P3yyy8cAC4qKkpt+cKFCzkAXE5OjkCVvdmkUimXlpbGcRzH3blzhwPAubq6aozT9u90OtwpMG9vb4jFYqxYsUK5zMDAAMuWLcPt27fx5MkTAatrWIYNG4ZGjRqpLevcuTN69uyJBw8eCFRVw3b9+nV4e3tj165dQpfSYHl5eSE9PR2//PILdHR0UFxcDIVCIXRZDVblNSKfvya1paUldHR0NN7DiHbo6+ujVatWLx2n7d/p1KQJLCIiAl26dNG4IOvgwYMBAJGRkQJURSpxHIf09HSYm5sLXUqDI5fLsXr1aixfvhy9evUSupwG6/LlyzAxMVFeA9nY2BgmJib49NNPIZVKhS6vwam8nOGyZcsQGRmJJ0+e4OjRo3B0dMSaNWvQuHFjYQts4LT9O52aNIGlpaXB0tJSY3nlssqLwRNheHp6IiUlBbNnzxa6lAZn3759SEpKwk8//SR0KQ1aXFwcysvL8f7772P8+PE4fvw4li5din379mHJkiVCl9fgTJgwAT/99BP8/PzQr18/tGvXDnPmzMHq1avx+++/C11eg6ft3+lMX7uzIZBIJFVe68vAwEB5PxFGTEwMPvvsMwwdOhSLFi0SupwGJTs7G//5z3/www8/oEWLFkKX06AVFRWhpKQEn3zyifLTnNOmTUNpaSmcnJzw448/onPnzgJX2bB06NABo0aNwvTp02FmZoazZ89iy5YtaNWqFVatWiV0eQ2atn+nU5MmMENDQ8hkMo3llYcRDA0N67skAuDZs2d499130bRpU+U5BqT+fP/99zA1NcXq1auFLqXBq3wPmjt3rtryefPmwcnJCbdv36YmrR4dOXIEK1aswMOHD9GmTRsAFU2zQqHAt99+i7lz58LMzEzgKhsubf9Op8OdArO0tERaWprG8splVlZW9V1Sg5efn4+JEyciLy8PFy5coAzqWVxcHJydnbFmzRqkpqYiMTERiYmJkEqlKCsrQ2JiInJycoQus8Go/Pl//kR1CwsLAEBubm6919SQ/fHHH+jXr5+yQas0ZcoUlJSUICIiQqDKCKD93+nUpAmsb9++ePjwofITO5WCg4OV95P6I5VKMXnyZDx8+BBnzpxBjx49hC6pwUlJSYFCocCaNWvQsWNH5S04OBgPHz5Ex44d8eOPPwpdZoMxYMAAABW5qKo8t4YOR9ev9PR0yOVyjeVlZWUAgPLy8vouiajQ9u90atIENmPGDMjlcjg7OyuXyWQyuLq6ws7ODm3bthWwuoZFLpdj9uzZuH37No4dO4ahQ4cKXVKDZGtrC19fX41bz5490a5dO/j6+mLZsmVCl9lgzJo1CwCwf/9+teUuLi7Q1dVVftqQ1I8uXbogIiJC40oohw8fho6ODnr37i1QZQTQ/u90OidNYHZ2dpg5cybWr1+PjIwM2NjYwN3dHYmJiRpvioRfX375JU6dOoXJkycjJycHhw4dUrt/wYIFAlXWsJibm+ODDz7QWF45V1pV9xH+9OvXD0uXLsWBAwdQXl4Oe3t7XLt2DceOHcP69evpdIB69vXXX+P8+fMYOXIkVq1aBTMzM5w5cwbnz5/H8uXLKQ8eOTg4IC8vT7kX+fTp03j69CkAYPXq1WjatKn2f6fXdfZdoj0SiYT76quvuFatWnH6+vrcoEGDuAsXLghdVoNjb2/PAaj2RoRFVxwQTmlpKbdp0yauffv2nJ6eHmdjY8P9/vvvQpfVYAUHB3MTJ07kWrVqxenp6XFdunThfvnlF66srEzo0t5o7du3r/b3Q0JCgnKcNn+n07U7CSGEEEIYROekEUIIIYQwiJo0QgghhBAGUZNGCCGEEMIgatIIIYQQQhhETRohhBBCCIOoSSOEEEIIYRA1aYQQQgghDKImjRBCCCGEQdSkEUIIIYQwiJo0QsgbZfHixejQoYPQZSht2rQJIpEIIpEIxsbGgtSQmJgIkUiEHTt2vHTs2rVrBa+XEFKBmjRCCPMqm4aX3a5duyZ0qdXy8PBQXmDZzc2tRs9HiGbzww8/hIeHB0aOHFnvj00IUacrdAGEEPIyHh4eal8fPHgQfn5+Gsu7d++OP//8EwqFoj7Lq5EFCxYo/z9q1CiN2pcvX47BgwdjxYoVymVC7MkaMGAABgwYgMuXLyM8PLzeH58Q8i9q0gghzFNtcAAgKCgIfn5+GstfF506dUKnTp3Uln3yySfo1KnTa/ucCCHaR4c7CSFvlOfPSVM9H2vv3r3o1KkTjIyM8M477+DJkyfgOA4//fQT2rRpA0NDQ7z//vvIycnR2O758+cxcuRING7cGE2aNMG7776L+/fv8/Y8ysrKsHnzZnTu3BkGBgYwMzPDiBEj4OfnpxwzevRojB49WmPdF52X9/vvv6N9+/YwNDSEvb09oqKieHoGhJBXRXvSCCENgqenJ0pLS7F69Wrk5OTg119/xaxZszB27Fhcu3YN3377LR49eoQ9e/bgq6++woEDB5Trenh4YNGiRRg/fjy2bduGkpISODo6YsSIEYiIiODl3LFNmzZh69atysOgBQUFCA0NRXh4ON5+++06bfPgwYMoLCzEZ599BqlUit27d2Ps2LG4d+8eWrZsqeVnQAh5VdSkEUIahJSUFMTFxaFp06YAALlcjq1bt0IikSA0NBS6uhVvh5mZmfD09ISjoyP09fVRVFSENWvWYPny5XB2dlZub9GiRejatSu2bNmitlxbzp49i0mTJml1248ePUJcXBxat24NAJgwYQLs7Oywbds27Ny5U2uPQwjRDjrcSQhpEGbOnKls0ADAzs4OQMX5bpUNWuXy0tJSpKSkAAD8/PyQl5eHuXPnIisrS3kTi8Wws7ODv78/L/U2a9YM9+/fR1xcnNa2+cEHHygbNAAYPHgw7OzscO7cOa09BiFEe6hJI4Q0CO3atVP7urJha9u2bZXLc3NzAUDZJI0dOxYtWrRQu126dAkZGRm81Pvjjz8iLy8PXbp0Qa9evfD111/j7t27r7TNzp07ayzr0qULEhMTX2m7hBB+0OFOQkiDIBaLa7Wc4zgAUE7n4eHhgVatWmmMU90Lp02jRo1CfHw8Tp48iUuXLsHFxQW///479u3bh+XLlwOomD+usk5Vcrmcl5oIIfWLmjRCCHkBa2trAICFhQXGjRtXr49tamqKJUuWYMmSJSgqKsKoUaOwadMmZZPWvHlzPH78WGO9pKSkKrdX1aHThw8fMnWFBkLIv+hwJyGEvMD48eNhYmKCLVu2oKysTOP+zMxMXh43Oztb7WtjY2PY2NhAJpMpl1lbWyMmJkathr///hu3bt2qcpsnTpxQnmsHACEhIQgODsbEiRO1XD0hRBtoTxohhLyAiYkJHB0d8eGHH6J///6YM2cOWrRogeTkZJw9exbDhw+Hg4OD1h+3R48eGD16NAYMGABTU1OEhobC29sbq1atUo5ZunQpdu7cifHjx2PZsmXIyMjAvn370LNnTxQUFGhs08bGBiNGjMCnn34KmUyGXbt2wczMDN98843W6yeEvDpq0ggh5CXmzZsHKysr/Pe//8X27dshk8nQunVrjBw5EkuWLOHlMdesWYNTp07h0qVLkMlkaN++PX7++Wd8/fXXyjHdu3fHwYMH8Z///Afr1q1Djx494OHhAS8vryqvY7pw4ULo6Ohg165dyMjIwODBg+Hg4ABLS0tengMh5NWIuKrOOiWEEKIVmzZtwubNm5GZmQmRSAQzMzOhS3qh4uJiSCQSrF69GqdPn0ZRUZHQJRHSYNE5aYQQUg9atGiB9u3bC13GS23YsAEtWrTAkSNHhC6FkAaP9qQRQgiPHj9+rPwEpq6ubpXX2mTJw4cPkZycDOD1qJeQN9n/A0pFIgjy+ZSxAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 700x400 with 1 Axes>"
       ]
@@ -409,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "id": "bd3cf3b3",
    "metadata": {},
    "outputs": [
@@ -422,7 +445,7 @@
        "       [ 9, 10, 11]])"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -434,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 17,
    "id": "25317d4e",
    "metadata": {},
    "outputs": [
@@ -480,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 18,
    "id": "6f50284f",
    "metadata": {},
    "outputs": [
@@ -506,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 19,
    "id": "3ae19d32",
    "metadata": {},
    "outputs": [
@@ -552,7 +575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 20,
    "id": "7a8f3e37",
    "metadata": {},
    "outputs": [
@@ -561,13 +584,14 @@
      "output_type": "stream",
      "text": [
       "Ndft  = 16\n",
-      "> Log downsample_bpf():\n",
-      ". len(x) = 160\n",
-      ". Ndown  = 16\n",
-      ". Nx     = 145\n",
-      ". Nxp    = 10\n",
-      ". len(y) = 10\n",
-      ". k      = 1.0\n",
+      "[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "> Log maximal_downsample_bpf():\n",
+      "  . len(x)  = 160\n",
+      "  . Nx      = 145\n",
+      "  . Nxp     = 10\n",
+      "  . len(yc) = 10\n",
+      "  . Ndown   = 16\n",
+      "  . k       = 1\n",
       "\n",
       "PASSED\n"
      ]
@@ -576,17 +600,17 @@
    "source": [
     "print('Ndft  =', Ndft)\n",
     "if Ndown == Ndft:\n",
-    "    yDownBpf = maximal_downsample_bpf(xData, Ndown, kLo, hPrototype)\n",
-    "    yDownBpfLo = yDownBpf  # = yDownBpf * LOdown, because LOdown = 1 when Ndown == Ndft\n",
+    "    yMaxDownBpf = maximal_downsample_bpf(xData, Ndown, kLo, hPrototype)\n",
+    "    yMaxDownBpfLo = yMaxDownBpf  # = yMaxDownBpf * LOdown, because LOdown = 1 when Ndown == Ndft\n",
     "\n",
-    "    if np.all(np.isclose(yDown, yDownBpfLo)):\n",
+    "    if np.all(np.isclose(yDown, yMaxDownBpfLo)):\n",
     "        print('PASSED')\n",
     "    else:\n",
     "        print('FAILED')\n",
     "        plt.plot(m_sub, yDown.real, 'g.-')\n",
     "        plt.plot(m_sub, yDown.imag, 'g.--')\n",
-    "        plt.plot(m_sub, yDownBpfLo.real, 'r-')\n",
-    "        plt.plot(m_sub, yDownBpfLo.imag, 'r--')"
+    "        plt.plot(m_sub, yMaxDownBpfLo.real, 'r-')\n",
+    "        plt.plot(m_sub, yMaxDownBpfLo.imag, 'r--')"
    ]
   },
   {
@@ -599,9 +623,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "6dfb2975",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
+      "> non_maximal_downsample_bpf():\n",
+      "  . len(x)   = 160\n",
+      "  . Nx       = 145\n",
+      "  . Nblocks  = 10\n",
+      "  . len(yc)  = 10\n",
+      "  . Ndown    = 16\n",
+      "  . Ndft     = 16\n",
+      "  . k        = 1\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f06c37505e0>]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 700x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Ros = 1\n",
+    "yDownBpf = non_maximal_downsample_bpf(xData, Ndown//Ros, kLo, Ndft, hPrototype)\n",
+    "yDownBpfLo = yDownBpf * LOdown\n",
+    "\n",
+    "#if np.all(np.isclose(yDown, yDownBpfLo)):\n",
+    "#    print('PASSED')\n",
+    "#else:\n",
+    "#    print('FAILED')\n",
+    "m_s_os = down(n_s, Ndown//Ros)  # = m_i * Tdown, time in seconds\n",
+    "m_sub_os = m_s_os / Ndft\n",
+    "\n",
+    "plt.plot(m_sub, yDown.real, 'g.-')\n",
+    "plt.plot(m_sub, yDown.imag, 'g.--')\n",
+    "plt.plot(m_sub_os, yDownBpfLo.real, 'r-')\n",
+    "plt.plot(m_sub_os, yDownBpfLo.imag, 'r--')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9db1a318",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5d4d0d6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-15.99965088374785"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "360 / np.angle(yDownBpfLo[8], deg=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abd757f8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db607857",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/applications/lofar2/model/rtdsp/multirate.py b/applications/lofar2/model/rtdsp/multirate.py
index d6e322ae8a83bb079683dff677330c9177621818..145e6e38cfe03f48d3ed5b3c4bea3c4a7c3bba1d 100644
--- a/applications/lofar2/model/rtdsp/multirate.py
+++ b/applications/lofar2/model/rtdsp/multirate.py
@@ -39,6 +39,8 @@ import numpy as np
 from scipy import signal
 from .utilities import c_rtol, c_atol, ceil_div
 
+c_firA = [1.0]  # FIR b = coefs, a = 1
+
 
 def down(x, D, phase=0):
     """Downsample x[n] by factor D, xD[m] = x[m D], m = n // D
@@ -60,16 +62,17 @@ def up(x, U):
 
 
 ###############################################################################
-# Polyphase filter
+# Polyphase filter structure for input block processing
 ###############################################################################
 
 class PolyPhaseFirFilterStructure:
     """Polyphase FIR filter structure (PFS) per block of data
 
-    Input:
-    . coefs: FIR coefficients b[0 : Nphases * Ntaps - 1].
-    . Nphases: number of polyphases, is number of rows (axis=0) in the
-               polyphase structure.
+    Purpose of PFS implementation is to avoid multiply by zero values in case
+    of upsampling and to avoid calculating samples that will be discarded in
+    case of downsampling. The output result of the PFS FIR is identical to
+    using the FIR filter. The spectral aliasing and spectral replication are
+    due to the sample rate change, not to the implementation structure.
 
     The PFS is is suitable for downsampling and for upsampling:
     - Upsampling uses the PFS as Transposed Direct-Form FIR filter, where the
@@ -84,66 +87,71 @@ class PolyPhaseFirFilterStructure:
       uses a delay line of z^(-1) called type 1 structure [CROCHIERE 3.3.3,
       VAIDYANATHAN 4.3].
 
+    Input:
+    . coefs: FIR coefficients b[0 : Nphases * Ntaps - 1].
+    . Nphases: number of polyphases, is number of rows (axis=0) in the
+               polyphase structure.
     Derived:
     . Ntaps: number of taps per polyphase, is number of columns (axis=1) in
       the polyphase structure.
     . polyCoefs: FIR coefficients storage with Nphases rows and Ntaps columns.
     . polyDelays: FIR taps storage with Nphases rows and Ntaps columns.
+
+    Storage of FIR coefficients and FIR data:
+      Stream of input samples x[n], n >= 0 for increasing time. Show index n
+      also as value so x[n] = n:
+
+                 oldest sample                             newest sample
+        x         v                                                  v
+        samples  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...]
+        blocks   [         0,          1,            2,              3]
+                                                                     ^
+                                                           newest block
+
+      The delayLine index corresponds to the FIR coefficient index k of
+      b[k] = impulse response h[k]. For Ncoefs = 12, with Nphases = 4 rows and
+      Ntaps = 3 columns:
+
+        polyCoefs = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11] = b[k]
+                                 row = polyphase
+        polyCoefs = [[0, 4, 8],    0
+                     [1, 5, 9],    1
+                     [2, 6,10],    2
+                     [3, 7,11]]    3
+             column:  0, 1, 2
+
+      Shift x[n] samples into delayLine from left, show sample index n in (n)
+      and delayLine index k in [k]. Shift in per sample or per block.
+
+        shift in -->(15,14,13,12,11,10, 9, 8, 7, 6, 5, 4) --> shift out
+        delayLine = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11]
+
+      For polyDelays shift in from left-top (n = 15 is newest input sample)
+      and shift-out from right-bottom (n = 4 is oldest sample).
+
+                       v              v
+        polyDelays = [[0, 4, 8],   ((15,11, 7),
+                      [1, 5, 9],    (14,10, 6),
+                      [2, 6,10],    (13, 9, 5),
+                      [3, 7,11]]    (12, 8, 4))
+                             v              v
+
+      Output sample y[n] = sum(polyCoefs * polyDelays).
+
+      For downsampling by Ndown = Nphases shift blocks of Ndown samples in
+      from left in polyDelays, put newest block[3] = x[12,13,14,15] in first
+      column [0] of polyDelays.
+
+      Store input samples in polyDelays structure, use mapping to access as
+      delayLine:
+        . map_to_delay_line()
+        . map_to_poly_delays()
     """
     def __init__(self, Nphases, coefs):
         self.coefs = coefs
         self.Ncoefs = len(coefs)
         self.Nphases = Nphases  # branches, rows
         self.Ntaps = ceil_div(self.Ncoefs, Nphases)  # taps, columns
-
-        # Stream of input samples x[n], n >= 0 for increasing time. Show index
-        # n also as value so x[n] = n:
-        #
-        #            oldest sample                             newest sample
-        #   x         v                                                  v
-        #   samples  [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, ...]
-        #   blocks   [         0,          1,            2,              3]
-        #                                                                ^
-        #                                                      newest block
-        #
-        # The delayLine index corresponds to the FIR coefficient index k of
-        # b[k] = impulse response h[k]. For Ncoefs = 12, with Nphases = 4 rows
-        # and Ntaps = 3 columns:
-        #
-        #   polyCoefs = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11] = b[k]
-        #                            row = polyphase
-        #   polyCoefs = [[0, 4, 8],    0
-        #                [1, 5, 9],    1
-        #                [2, 6,10],    2
-        #                [3, 7,11]]    3
-        #        column:  0, 1, 2
-        #
-        # Shift x[n] samples into delayLine from left, show sample index n in
-        # (n) and delayLine index k in [k]. Shift in per sample or per block.
-        #
-        #   shift in -->(15,14,13,12,11,10, 9, 8, 7, 6, 5, 4) --> shift out
-        #   delayLine = [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11]
-        #
-        # For polyDelays shift in from left-top (n = 15 is newest input sample)
-        # and shift-out from right-bottom (n = 4 is oldest sample).
-        #
-        #                  v              v
-        #   polyDelays = [[0, 4, 8],   ((15,11, 7),
-        #                 [1, 5, 9],    (14,10, 6),
-        #                 [2, 6,10],    (13, 9, 5),
-        #                 [3, 7,11]]    (12, 8, 4))
-        #                        v              v
-        #
-        # Output sample y[n] = sum(polyCoefs * polyDelays).
-        #
-        # For downsampling by Ndown = Nphases shift blocks of Ndown samples in
-        # from left in polyDelays, put newest block[3] = x[12,13,14,15] in
-        # first column [0] of polyDelays.
-        #
-        # Store input samples in polyDelays structure, use mapping to access as
-        # delayLine:
-        #   . map_to_delay_line()
-        #   . map_to_poly_delays()
         self.init_poly_coeffs()
         self.reset_poly_delays()
 
@@ -231,52 +239,134 @@ class PolyPhaseFirFilterStructure:
         return pfsData
 
 
-def poly_data_for_downsampling_whole_x(x, Ndown):
+###############################################################################
+# Up, down, resample low pass input signal
+###############################################################################
+
+def polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros):
     """Polyphase data structure for downsampling whole signal x.
 
     The polyphase structure has Ndown branches and branch size suitable to use
     lfilter() once per polyphase branch for whole signal x. Instead of using
     pfs.polyDelays per pfs.Ntaps.
-    . Prepend x with Ndown - 1 zeros to have first down sampled sample at m = 0
-      start at n = 0.
-    . Skip any remaining last samples from x, that are not enough yield a new
-      output FIR sum.
+    . Prepend x with Nzeros = Ndown - 1 zeros to have first down sampled sample
+      at m = 0 start at n = 0.
+    . Skip any remaining last samples from x, that are not enough to yield a
+      new output FIR sum.
 
     Input:
-    . x: input samples x[n] for n = 0 : Lx - 1
+    . x: all input samples x[n] for n = 0 : Lx - 1
     . Ndown: downsample rate and number of polyphase branches
+    . Nzeros: number of zero samples to prepend for x
     Return:
     . polyX: polyphase data structure with size (Ndown, Nxp) for Ndown branches
-             and Nxp samples from x per branch.
+        and Nxp samples from x per branch.
     . Nx: Total number of samples from x, including prepended Ndown - 1 zeros.
-    . Nxp: Total number of samples used from x per polyphase branch, is Ny the
-           number of samples that will be in downsampled output y[m] = x[m D]
-           for m = 0, 1, 2, ..., Nxp - 1.
+    . Nxp: Total number of samples used from x per polyphase branch, is the
+        number of samples Ny, that will be in downsampled output y[m] = x[m D],
+        for m = 0, 1, 2, ..., Nxp - 1.
     """
     Lx = len(x)
-    # Numer of samples per polyphase
-    Nxp = (Ndown - 1 + Lx) // Ndown
-    # Used number of samples from x
-    Nx = 1 + Ndown * (Nxp - 1)
+    Nxp = (Nzeros - 1 + Lx) // Ndown  # Number of samples per polyphase
+    Nx = 1 + Ndown * (Nxp - 1)  # Used number of samples from x
 
     # Load x into polyX with Ndown rows = polyphases
     # . prepend x with Ndown - 1 zeros
     # . skip any last remaining samples from x, that are not enough yield a new
     #   output FIR sum.
-    # . store data in time order per branch, so with oldest data left, to match
-    #   use with lfilter(). Note this differs from order of tap data in
-    #   pfs.polyDelays where newest data is left, to match use in
-    #   pfs.filter_block that uses pfs.polyDelays * pfs.polyCoefs to filter the
-    #   block.
     polyX = np.zeros(Ndown * Nxp)
     polyX[Ndown - 1] = x[0]
     polyX[Ndown:] = x[1 : Nx]
-    # . Newest sample in top branch, to match branch order in the
-    #   pfs.polyCoefs, therefore do flipud(polyX)
+    # . Store data in time order per branch, so with oldest data left, to match
+    #   use with lfilter(). Note this differs from order of tap data in
+    #   pfs.polyDelays where newest data is left, because the block data is
+    #   flipped by shift_in_data(), to match use in pfs.filter_block, so that
+    #   it can use pfs.polyDelays * pfs.polyCoefs to filter the block.
+    # . The newest sample x[-1] has to be in top branch of polyX and the oldest
+    #   sample x[0] in bottom branch, to match branch order of 0:Ndown-1 from
+    #   top to bottom in the pfs.polyCoefs, therefore do flipud(polyX). This
+    #   flipud() is similar as the flip() per block in shift_in_data().
+    #
+    #         x[0] is oldest                              x[-1] is newest
+    #           |                                            |
+    #   x[n] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15]
+    #          (0, 1, 2, 3, 4, 5, 6, 7, 8, 9,10,11,12,13,14,15), x[n] = n
+    #                                                              newest
+    #   poly                 poly        v                             |
+    #   Coefs = [[0, 4, 8],  Delays = ((15,11, 7),  polyX = ((3, 7,11,15),
+    #            [1, 5, 9],            (14,10, 6),           (2, 6,10,14),
+    #            [2, 6,10],            (13, 9, 5),           (1, 5, 9,13),
+    #            [3, 7,11]]            (12, 8, 4))           (0, 4, 8,12))
+    #                                          v              |
+    #                                                       oldest
     polyX = np.flipud(polyX.reshape(Nxp, Ndown).T)
     return polyX, Nx, Nxp
 
 
+def polyphase_frontend(x, Nphases, coefs, sampling):
+    """Calculate PFS FIR filter output of x for Nphases.
+
+    The polyY[Nphases] output of this PFS frontend can be:
+    . summed for dowsampling by factor D = Nphases
+    . used as is for upsampling by factor U = Nphases
+    . used with a range of Nphases LOs for a single channel downsampler
+      downconverter, or upsampler upconverter
+    . used with a IDFT for a analysis filterbank (PFB), or synthesis PFB.
+
+    Input:
+    . x: Input signal x[n]
+    . Nphases: number of polyphase branches in PFS
+    . coefs: prototype FIR filter coefficients for anti aliasing LPF. The
+        len(coefs) typically is multiple of Nphases. If shorter, then the
+        coefs are extended with zeros.
+    . sampling: 'up' or 'down'
+    Return:
+    . polyY[Nphases]: Output of the FIR filtered branches in the PFS.
+    . Nx = np.size(polyY), total number of samples from x
+    . Nxp = np.size(polyY, axis=1), total number of samples from x per
+        polyphase.
+    """
+    # Use polyphase FIR filter coefficients from pfs class.
+    pfs = PolyPhaseFirFilterStructure(Nphases, coefs)
+
+    # Define polyphases for whole data signal x with Nx samples, instead of
+    # using polyDelays per Ntaps from pfs class, to be able to use
+    # signal.lfilter() per polyphase branch for the whole data signal x.
+    if sampling == 'up':
+        Nx = len(x)
+        Nxp = Nx
+        Nup = Nphases
+        # Filter whole x per polyphase, so Nxp = Nx, because the Nup branches
+        # each use all x to yield Nup output samples per input sample from x.
+        # The commutator index order for upsampling is p = 0, 1, .., Nup - 1,
+        # so from top to bottom in the PFS.
+        polyY = np.zeros((Nup, Nx))
+        pCommutator = range(Nup)
+        for p in pCommutator:
+            polyY[p] = signal.lfilter(pfs.polyCoefs[p], c_firA, x)
+    else:
+        # 'down':
+        # . Prepend x with Ndown - 1 zeros, to have y[m] for m = 0 start at
+        #   n = 0 of x[n].
+        # . Size of polyX is (Ndown, Nxp), and length of y is Ny = Nxp is
+        #   length of each branch.
+        Ndown = Nphases
+        Nzeros = Ndown - 1
+        polyX, Nx, Nxp = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
+        print(polyX[:, 0])
+        # Filter Ndown parts of x per polyphase, because the FIR filter output
+        # y will sum. The commutator index order for downsampling is p =
+        # Ndown - 1,..., 1, 0, so from bottom to top in the PFS. However, the
+        # commutator index order is only relevant when the branches are
+        # calculated sequentially to reuse the same hardware, because for the
+        # output y the branches are summed anyway.
+        polyY = np.zeros((Ndown, Nxp))
+        pCommutator = np.flip(np.arange(Ndown))
+        for p in pCommutator:
+            polyY[p] = signal.lfilter(pfs.polyCoefs[p], c_firA, polyX[p])
+    return polyY, Nx, Nxp
+
+
 def upsample(x, Nup, coefs, verify=False, verbosity=1):  # interpolate
     """Upsample x by factor U = Nup and LPF.
 
@@ -312,52 +402,31 @@ def upsample(x, Nup, coefs, verify=False, verbosity=1):  # interpolate
       - when (Ncoefs - 1) % (2 * U) == 0, then the group delay is an integer
         number of ts periods.
     """
-    Nx = len(x)
-    a = [1.0]  # FIR b = coefs, a = 1
-
-    # Polyphase implementation to avoid multiply by zero values
-    #   coefs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-    #   polyCoefs = [[ 0,  4,  8],   # p = 0
-    #                [ 1,  5,  9],   # p = 1
-    #                [ 2,  6, 10],   # p = 2
-    #                [ 3,  7, 11]])  # p = 3
-    pfs = PolyPhaseFirFilterStructure(Nup, coefs)
-    polyCoefs = pfs.polyCoefs
-
-    # Poly phases for data
-    # . Use polyX for whole data signal x with Nx samples, instead of
-    #   pfs.polyDelays per pfs.Ntaps, to be able to use signal.lfilter() per
-    #   branch for whole data signal x.
-    polyY = np.zeros((Nup, Nx))
-
-    # Filter x per polyphase, because the Nup branches each use all x to yield
-    # Nup output samples per input sample from x. The commutator index order
-    # for upsampling is p = 0, 1, .., Nup - 1, so from top to bottom in pfs.
-    pCommutator = range(Nup)
-    for p in pCommutator:
-        polyY[p] = signal.lfilter(polyCoefs[p], a, x)
-
-    # Output Nup samples for every input sample
+    # Polyphase FIR filter input x
+    polyY, Nx, _ = polyphase_frontend(x, Nup, coefs, 'up')
+
+    # Output Nup samples y[m] for every input sample x[n]
     y = polyY.T.reshape(1, Nup * Nx)[0]
 
     if verify:
-        # Inefficient upsampling implementation with multiply by zero values
+        # Inefficient upsampling implementation with multiply by zero values.
+        # Verify that x --> U --> LPF --> y yields identical result y as with
+        # using the PFS: x --> PFS FIR --> y.
         xZeros = np.zeros((Nup, Nx))
         xZeros[0] = x
         xZeros = xZeros.T.reshape(1, Nup * Nx)[0]  # upsample
-        yVerify = signal.lfilter(coefs, a, xZeros)  # LPF
+        yVerify = signal.lfilter(coefs, c_firA, xZeros)  # LPF
         print('> Verify upsample():')
         if np.allclose(y, yVerify, rtol=c_rtol, atol=c_atol):
-            print('. PASSED: correct upsample result')
+            print('  . PASSED: correct upsample result')
         else:
-            print('. ERROR: wrong upsample result')
+            print('  . ERROR: wrong upsample result')
             return False
-
     if verbosity:
         print('> Log upsample():')
-        print('. Nup    =', str(Nup))
-        print('. Nx     =', str(Nx))
-        print('. len(y) =', str(len(y)))
+        print('  . Nup    =', str(Nup))
+        print('  . Nx     =', str(Nx))
+        print('  . len(y) =', str(len(y)))
         print('')
     return y
 
@@ -397,208 +466,37 @@ def downsample(x, Ndown, coefs, verify=False, verbosity=1):  # decimate
       - when (Ncoefs - 1) % (2 * D) == 0, then the group delay is an integer
         number of tsDown periods
     """
-    a = [1.0]  # FIR b = coefs, a = 1
-
-    # Polyphase implementation to avoid calculating values that are removed
-    pfs = PolyPhaseFirFilterStructure(Ndown, coefs)
-    polyCoefs = pfs.polyCoefs
-
-    # Poly phases for whole data signal x, prepended with Ndown - 1 zeros.
-    # Size of polyX is (Ndown, Nxp), and length of y is Ny = Nxp is length
-    # of each branch.
-    polyX, Nx, Nxp = poly_data_for_downsampling_whole_x(x, Ndown)
-
-    # Filter x per polyphase, commutator index order for downsampling is
-    # p = Ndown - 1, ..., 1, 0, so from bottom to top in pfs. However, the
-    # commutator index order is only relevant when the branches are
-    # calculated sequentially to reuse the same hardware, because for the
-    # output y the branches are summed anyway.
-    polyY = np.zeros((Ndown, Nxp))
-    pCommutator = np.flip(np.arange(Ndown))
-    for p in pCommutator:
-        polyY[p] = signal.lfilter(polyCoefs[p], a, polyX[p])
-
-    # Sum the branch outputs to get single downsampled output value
+    # Polyphase FIR filter input x
+    polyY, Nx, Nxp = polyphase_frontend(x, Ndown, coefs, 'down')
+
+    # FIR filter sum
     y = np.sum(polyY, axis=0)
 
     if verify:
         # Inefficient downsampling implementation with calculating values that
-        # are removed, so x --> LPF --> D --> y:
+        # are removed. Verify that x --> LPF --> D --> y yields identical
+        # result y as with using the PFS: x --> PFS FIR --> y.
         yVerify = np.zeros(Ndown * Nxp)
-        yVerify[0 : Nx] = signal.lfilter(coefs, a, x[0 : Nx])  # LPF
+        yVerify[0 : Nx] = signal.lfilter(coefs, c_firA, x[0 : Nx])  # LPF
         yVerify = yVerify.reshape(Nxp, Ndown).T   # polyphases
         yVerify = yVerify[0]   # downsample by D
         print('> Verify downsample():')
         if np.allclose(y, yVerify, rtol=c_rtol, atol=c_atol):
-            print('. PASSED: correct downsample result')
+            print('  . PASSED: correct downsample result')
         else:
-            print('. ERROR: wrong downsample result')
+            print('  . ERROR: wrong downsample result')
             return False
-
     if verbosity:
         print('> Log downsample():')
-        print('. len(x) =', str(len(x)))
-        print('. Ndown  =', str(Ndown))
-        print('. Nx     =', str(Nx))
-        print('. Nxp    =', str(Nxp))
-        print('. len(y) =', str(len(y)))  # = Nxp
-        print('')
-    return y
-
-
-def maximal_downsample_bpf(x, Ndown, k, coefs, verbosity=1):
-    """BPF x at bin k in range(Ndown) and downsample x by factor D = Ndown.
-
-    Implement maximal downsampling down converter for one bin (= critically
-    sampled) [HARRIS Fig 6.14].
-
-    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
-    frequency bins, is DFT size. The downsampling is maximal so Ndown = Ndft.
-    The polyphase structure has Nphases = Ndown branches, so the input x
-    data that shifts in remains in each branch. Therefore each branch can be
-    FIR filtered independently for the whole input x using polyX.
-
-    . see downsample()
-
-    Input:
-    . x: Input signal x[n]
-    . Ndown: downsample factor
-    . k: Index of BPF center frequency w_k = 2 pi k / Ndown
-    . coefs: prototype FIR filter coefficients for anti aliasing BPF
-    - verbosity: when > 0 print() status, else no print()
-    Return:
-    . y: Downsampled and down converted output signal y[m], m = n // D for bin
-         k. Complex baseband signal.
-    """
-    a = [1.0]  # FIR b = coefs, a = 1
-
-    # Polyphase implementation to avoid calculating values that are removed
-    #   coefs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-    #   polyCoefs = [[ 3,  7, 11],
-    #                [ 2,  6, 10],
-    #                [ 1,  5,  9],
-    #                [ 0,  4,  8]])
-    pfs = PolyPhaseFirFilterStructure(Ndown, coefs,)
-    polyCoefs = pfs.polyCoefs
-
-    # Poly phases for whole data signal x, prepended with Ndown - 1 zeros
-    polyX, Nx, Nxp = poly_data_for_downsampling_whole_x(x, Ndown)
-
-    # Filter x per polyphase [HARRIS Fig 6.12, 6.13]
-    polyY = np.zeros((Ndown, Nxp))
-    for p in range(Ndown):
-        polyY[p] = signal.lfilter(polyCoefs[p], a, polyX[p])
-
-    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
-    # [HARRIS Eq 6.8]
-    polyYC = np.zeros((Ndown, Nxp), dtype='cfloat')
-    for p in range(Ndown):
-        polyYC[p] = polyY[p] * np.exp(1j * 2 * np.pi * p * k / Ndown)
-
-    # Sum the branch outputs to get single downsampled and downconverted output
-    # value
-    y = np.sum(polyYC, axis=0)
-
-    if verbosity:
-        print('> Log downsample_bpf():')
-        print('. len(x) =', str(len(x)))
-        print('. Ndown  =', str(Ndown))
-        print('. Nx     =', str(Nx))
-        print('. Nxp    =', str(Nxp))
-        print('. len(y) =', str(len(y)))  # = Nxp
-        print('. k      =', str(k))
+        print('  . len(x) =', str(len(x)))
+        print('  . Ndown  =', str(Ndown))
+        print('  . Nx     =', str(Nx))
+        print('  . Nxp    =', str(Nxp))
+        print('  . len(y) =', str(len(y)))  # = Nxp
         print('')
     return y
 
 
-# def non_maximal_downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
-#    """BPF x at bin k in range(Ndown) and downsample x by factor D = Ndown [HARRIS Fig 6.14]
-#
-#    Implement nonmaximal downsampling down converter for one bin, extend [HARRIS Fig 6.14].
-#
-#    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of frequency bins, is DFT size. The polyphase
-#    FIR structure has Nphases = Ndft branches, to fit the requested number of bins. The polyphase FIR structure
-#    is maximally downsampled (= critically sampled) for Ndown = Ndft, but it can support any Ndown <= Ndft. The
-#    input data shifts in per Ndown samples, so it appears in different branches when Ndown < Ndft and a new block
-#    is shifted in. Therefore the input data cannot be FIR filtered per branch for the whole input x. Instead it
-#    needs to be FIR filtered per block of Ndown input samples from x, using pfs.polyDelays in pfs.filter_block().
-#
-#    . see downsample()
-#
-#    Input:
-#    . x: Input signal x[n]
-#    . Ndown: downsample factor
-#    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
-#    . Ndft: DFT size, number of polyphases in FIR structure
-#    . coefs: prototype FIR filter coefficients for anti aliasing BPF
-#    - verbosity: when > 0 print() status, else no print()
-#    Return:
-#    . y: Downsampled and down converted output signal y[m], m = n // D for bin
-#         k. Complex baseband signal.
-#    """
-#    a = [1.0]  # FIR b = coefs, a = 1
-#
-#    # Polyphase implementation to avoid calculating values that are removed
-#    #   coefs = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
-#    #   polyCoefs = [[ 3,  7, 11],
-#    #                [ 2,  6, 10],
-#    #                [ 1,  5,  9],
-#    #                [ 0,  4,  8]])
-#    pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
-#
-#    # Prepended x with Ndown - 1 zeros
-#    Lzeros = Ndown - 1
-#    Ldown = (Lzeros + len(x)) // Ndown  # number of downsampled samples
-#    Lin =  Ldown * Ndown - Lzeros  # number of input samples from x
-#
-#    # Prepare storage per block
-#    pfsOutput = np.zeros((Ldown, Ndft))
-#    binOutput = np.zeros((down)
-#
-#    xData = np.zeros(Ndown)
-#    xData[Ndown - 1] = x[0]
-#    for b in range(Ldown):
-#        # Filtered signal
-#        pfsOutput[b] = pfs.filter_block(xData)
-#        # Phase rotate per polyphase for bin k, due to delay line at branch inputs [HARRIS Eq 6.8]
-#        for p in range(Ndft):
-#             pCommutator = Ndft - 1 - p
-#             pfsOutput[p] = pfsOutput[p] * np.exp(1j * 2 * np.pi * pCommutator * k / Ndft)
-#        # Sum the branch outputs to get single downsampled and downconverted output value
-#        y = np.sum(pfsOutput, axis=0)
-#
-#
-#
-#
-#    # Filter x per polyphase, order in polyCoefs accounts for commutator [HARRIS Fig 6.12, 6.13]
-#    polyY = np.zeros((Ndown, Nxp))
-#    for p in range(Ndown):
-#        polyY[p] = signal.lfilter(polyCoefs[p], a, polyX[p])
-#
-#    # Phase rotate per polyphase, due to delay line at branch inputs [HARRIS Eq 6.8]
-#    # . polyY can use index p, because order in polyY accounts for commutator,
-#    # . phase rotator needs to use pCommutator to account for commutator, to fit
-#    #   order in polyY and polyCoefs
-#    polyYC = np.zeros((Ndown, Nxp), dtype='cfloat')
-#    for p in range(Ndown):
-#        pCommutator = Ndown - 1 - p
-#        polyYC[p] = polyY[p] * np.exp(1j * 2 * np.pi * pCommutator * k / Ndown)
-#
-#    # Sum the branch outputs to get single downsampled and downconverted output value
-#    y = np.sum(polyYC, axis=0)
-#
-#    if verbosity:
-#        print('> downsample_bpf():')
-#        print('. len(x) =', str(len(x)))
-#        print('. Ndown  =', str(Ndown))
-#        print('. Nx     =', str(Nx))
-#        print('. Nxp    =', str(Nxp))
-#        print('. len(y) =', str(len(y)))  # = Nxp
-#        print('. k      =', str(k))
-#        print('')
-#    return y
-
-
 def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate and decimate by Nup / Ndown
     """Resample x by factor U / D = Nup / Ndown
 
@@ -618,9 +516,9 @@ def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate an
     downsampled samples. Each phase uses a different set of coefficients from
     the LPF to filter Ndown delay phases of the input sequence x.
 
-    Resampling is upsampling with downsampling by phase selection
+    Resampling is upsampling with downsampling by phase selection:
     The resampling is done by first upsampling and then downsampling, because
-    then only one shareed LPF is needed. For upsampling an LPF is always
+    then only one shared LPF is needed. For upsampling an LPF is always
     needed, because it has to construct the inserted Nup - 1 zero values. For
     downsampling the LPF of the upsampling already has restricted the input
     band width (BW), provided that the LPF has BW < fNyquist / U and BW <
@@ -682,8 +580,7 @@ def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate an
         v[0] = x
         v = v.T.reshape(1, Nup * Nx)[0]  # upsample
         # . LPF
-        a = [1.0]  # FIR b = coefs, a = 1
-        w = signal.lfilter(coefs, a, v)
+        w = signal.lfilter(coefs, c_firA, v)
         # . Downsampling with calculating values that are removed
         yVerify = np.zeros(Ndown * Nyp)
         yVerify[0 : Ny] = w[0 : Ny]
@@ -692,18 +589,140 @@ def resample(x, Nup, Ndown, coefs, verify=False, verbosity=1):  # interpolate an
 
         print('> Verify resample():')
         if np.allclose(y, yVerify, rtol=c_rtol, atol=c_atol):
-            print('. PASSED: correct resample result')
+            print('  . PASSED: correct resample result')
         else:
-            print('. ERROR: wrong resample result')
+            print('  . ERROR: wrong resample result')
             return False
-
     if verbosity:
         print('> Log resample():')
-        print('. len(x) =', str(len(x)))
-        print('. Nx     =', str(Nx))
-        print('. len(v) =', str(len(v)))
-        print('. Ny     =', str(Ny))
-        print('. Nyp    =', str(Nyp))
-        print('. len(y) =', str(len(y)))
+        print('  . len(x) =', str(len(x)))
+        print('  . Nx     =', str(Nx))
+        print('  . len(v) =', str(len(v)))
+        print('  . Ny     =', str(Ny))
+        print('  . Nyp    =', str(Nyp))
+        print('  . len(y) =', str(len(y)))
         print('')
     return y
+
+
+###############################################################################
+# Single bandpass channel up and down sampling and up and down conversion
+###############################################################################
+
+def phasor_arr(k, Ndft, sign):
+    """Return array of phasors: exp(+-j 2pi k / Ndft) for k in 0 : Ndft - 1
+    """
+    if sign == 'positive':
+        return np.array([np.exp(2j * np.pi * p * k / Ndft) for p in range(Ndft)])
+    else:  # 'negative'
+        return np.array([np.exp(-2j * np.pi * p * k / Ndft) for p in range(Ndft)])
+
+
+def maximal_downsample_bpf(x, Ndown, k, coefs, verbosity=1):
+    """BPF x at bin k in range(Ndown) and downsample x by factor D = Ndown.
+
+    Implement maximal downsampling down converter for one bin (= critically
+    sampled) [HARRIS Fig 6.14].
+
+    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
+    frequency bins, is DFT size. The downsampling is maximal so Ndown = Ndft.
+    The polyphase structure has Nphases = Ndown branches, so the input x
+    data that shifts in remains in each branch. Therefore each branch can be
+    FIR filtered independently for the whole input x using polyX.
+
+    Input:
+    . x: Input signal x[n]
+    . Ndown: downsample factor
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndown
+    . coefs: prototype FIR filter coefficients for anti aliasing BPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Downsampled and down converted output signal yc[m], m = n // D for
+          bin k. Complex baseband signal.
+    """
+    # Polyphase FIR filter input x
+    polyY, Nx, Nxp = polyphase_frontend(x, Ndown, coefs, 'down')
+
+    # Phase rotate per polyphase for bin k, due to delay line at branch inputs
+    # [HARRIS Eq 6.8]
+    polyYC = np.zeros((Ndown, Nxp), dtype='cfloat')
+    phasors = phasor_arr(k, Ndown, 'positive')
+    for p in range(Ndown):
+        polyYC[p] = polyY[p] * phasors[p]  # row = row * scalar
+
+    # Sum the branch outputs to get single downsampled and downconverted output
+    # complex baseband value yc.
+    yc = np.sum(polyYC, axis=0)
+
+    if verbosity:
+        print('> Log maximal_downsample_bpf():')
+        print('  . len(x)  =', str(len(x)))
+        print('  . Nx      =', str(Nx))
+        print('  . Nxp     =', str(Nxp))
+        print('  . len(yc) =', str(len(yc)))  # = Nxp
+        print('  . Ndown   =', str(Ndown))
+        print('  . k       =', str(k))
+        print('')
+    return yc
+
+
+def non_maximal_downsample_bpf(x, Ndown, k, Ndft, coefs, verbosity=1):
+    """BPF x at bin k in range(Ndown) and downsample x by factor D = Ndown
+
+    Implement nonmaximal downsampling down converter for one bin, extend
+    [HARRIS Fig 6.14].
+
+    The BPF is centered at w_k = 2pi k / Ndft, where Ndft is number of
+    frequency bins, is DFT size. The polyphase FIR structure has Nphases = Ndft
+    branches, to fit the requested number of bins. The polyphase FIR structure
+    is maximally downsampled (= critically sampled) for Ndown = Ndft, but it
+    can support any Ndown <= Ndft. The input data shifts in per Ndown samples,
+    so it appears in different branches when Ndown < Ndft and a new block is
+    shifted in. Therefore the input data cannot be FIR filtered per branch for
+    the whole input x. Instead it needs to be FIR filtered per block of Ndown
+    input samples from x, using pfs.polyDelays in pfs.filter_block().
+
+    Input:
+    . x: Input signal x[n]
+    . Ndown: downsample factor
+    . k: Index of BPF center frequency w_k = 2 pi k / Ndft
+    . Ndft: DFT size, number of polyphases in PFS FIR filter
+    . coefs: prototype LPF FIR filter coefficients for anti aliasing BPF
+    - verbosity: when > 0 print() status, else no print()
+    Return:
+    . yc: Downsampled and down converted output signal yc[m], m = n // D for
+          bin k. Complex baseband signal.
+    """
+    # Prepend x with Ndown - 1 zeros, and represent x in Nblocks of Ndown
+    # samples
+    Nzeros = Ndown - 1
+    xBlocks, Nx, Nblocks = polyphase_data_for_downsampling_whole_x(x, Ndown, Nzeros)
+    print(xBlocks[:, 0])
+
+    # Prepare output
+    yc = np.zeros(Nblocks, dtype='cfloat')
+
+    # PFS with Ndft polyphases
+    pfs = PolyPhaseFirFilterStructure(Ndft, coefs)
+    phasors = phasor_arr(k, Ndft, 'positive')
+
+    for b in range(Nblocks):
+        # Filter block
+        inData = xBlocks[:, b]
+        pfsData = pfs.filter_block(inData)
+        # Phase rotate polyphases for bin k [HARRIS Eq 6.8]
+        pfsBinData = pfsData * phasors
+        # Sum the polyphases to get single downsampled and downconverted output value
+        yc[b] = np.sum(pfsBinData)
+
+    if verbosity:
+        print('> non_maximal_downsample_bpf():')
+        print('  . len(x)   =', str(len(x)))
+        print('  . Nx       =', str(Nx))
+        print('  . Nblocks  =', str(Nblocks))
+        print('  . len(yc)  =', str(len(yc)))  # = Nblocks
+        print('  . Ndown    =', str(Ndown))
+        print('  . Ndft     =', str(Ndft))
+        print('  . k        =', str(k))
+        print('')
+    return yc