diff --git a/PyMEOS_Examples/AIS.ipynb b/PyMEOS_Examples/AIS.ipynb
index 619bdbd..536d76d 100644
--- a/PyMEOS_Examples/AIS.ipynb
+++ b/PyMEOS_Examples/AIS.ipynb
@@ -3,7 +3,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "# AIS Example\n",
@@ -26,23 +29,33 @@
     "\n",
     "Note that due to its size, the data file used is not provided. It can be downloaded using the following URL:\n",
     "\n",
-    "[https://web.ais.dk/aisdata/aisdk-2023-08-01.zip](https://web.ais.dk/aisdata/aisdk-2023-08-01.zip)\n",
     "\n",
-    "Store the file in the [data](./data) directory. There is no need to decompress it, since we will use `pandas` to read the file and it supports reading compressed files."
+    "\n",
+    "http://aisdata.ais.dk/?prefix=2023/\n",
+    "\n",
+    "On that page, download the file aisdk-2023-08.zip \n",
+    "After extracting it, use the file aisdk-2023-08-01.csv for your analysis.\n",
+    "\n",
+    "Store the file in the [data](./data) directory. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-02-03T18:53:15.803897500Z",
      "start_time": "2024-02-03T18:53:14.766157300Z"
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-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
    },
    "outputs": [],
    "source": [
+    "\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "from pymeos import *\n",
@@ -57,7 +70,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "## Reading from File ([MEOS Example](https://libmeos.org/tutorialprograms/meos_read_ais/))\n",
@@ -68,7 +84,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "First, let's read the CSV file using pandas. We will read only the columns we're interested in, and remove the observations that are either erroneous or not in Denmark."
@@ -76,21 +95,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {
     "ExecuteTime": {
      "end_time": "2024-02-03T18:53:35.800662100Z",
      "start_time": "2024-02-03T18:53:17.511652900Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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    },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 27.5 s, sys: 954 ms, total: 28.5 s\n",
-      "Wall time: 28.5 s\n"
+      "CPU times: user 14 s, sys: 2.28 s, total: 16.2 s\n",
+      "Wall time: 16.6 s\n"
      ]
     },
     {
@@ -175,7 +197,7 @@
        "5 2023-08-01  219014072  54.918928   9.597852  0.0"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,7 +205,7 @@
    "source": [
     "%%time\n",
     "ais = pd.read_csv(\n",
-    "    \"./data/aisdk-2023-08-01.zip\",\n",
+    "    \"./data/aisdk-2023-08-01.csv\",\n",
     "    usecols=[\"# Timestamp\", \"MMSI\", \"Latitude\", \"Longitude\", \"SOG\"],\n",
     ")\n",
     "ais.columns = [\"t\", \"mmsi\", \"lat\", \"lon\", \"sog\"]\n",
@@ -201,7 +223,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
    },
    "source": [
     "Now, we will create the PyMEOS object representing the position and the SOG."
@@ -209,33 +234,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {
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      "start_time": "2024-02-03T18:53:35.791660600Z"
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+    "collapsed": false,
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    "outputs": [
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@@ -265,7 +279,10 @@
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@@ -364,7 +381,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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    },
    "source": [
     "## Assembling Trips ([MEOS Example](https://libmeos.org/tutorialprograms/meos_assemble_ais/))\n",
@@ -380,7 +400,10 @@
      "end_time": "2024-02-03T19:00:18.427946600Z",
      "start_time": "2024-02-03T18:59:47.786832800Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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    "outputs": [
     {
@@ -500,7 +523,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "Here we can see that PyMEOS has been able to reduce the number of points stored (and thus memory used) without losing any information."
@@ -514,7 +540,10 @@
      "end_time": "2024-02-03T19:01:16.569003700Z",
      "start_time": "2024-02-03T19:01:16.409027Z"
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-    "collapsed": false
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+    "jupyter": {
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     {
@@ -663,7 +692,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "We can visualize the trajectories and the SOG evolutions by plotting them. We will plot only 100 of the trajectories."
@@ -677,7 +709,10 @@
      "end_time": "2024-02-03T19:01:27.858702300Z",
      "start_time": "2024-02-03T19:01:17.861083800Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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    "outputs": [
     {
@@ -703,7 +738,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "## Storing in MobilityDB ([MEOS Example](https://libmeos.org/tutorialprograms/meos_store_ais/))\n",
@@ -715,7 +753,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "First, set up the connection parameters. Change any of the following values according to your configuration. If you are using the docker image, you don't need to change anything (except maybe the port)."
@@ -729,7 +770,10 @@
      "end_time": "2024-02-03T19:01:31.835316300Z",
      "start_time": "2024-02-03T19:01:31.716322500Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
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    "outputs": [],
    "source": [
@@ -750,7 +794,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "Now, we will create the table where we will write our objects."
@@ -764,7 +811,10 @@
      "end_time": "2024-02-03T19:01:33.640159400Z",
      "start_time": "2024-02-03T19:01:33.607158500Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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@@ -779,7 +829,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "Let's insert now the rows of the DataFrame into the MobilityDB table. First, we'll create a SQL INSERT query with all the values, and then we will execute it in MobilityDB."
@@ -793,7 +846,10 @@
      "end_time": "2024-02-03T19:01:42.605529200Z",
      "start_time": "2024-02-03T19:01:35.004396600Z"
     },
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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@@ -811,7 +867,10 @@
      "end_time": "2024-02-03T19:02:31.561016100Z",
      "start_time": "2024-02-03T19:01:42.608530900Z"
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-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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@@ -832,7 +891,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "Let's check how many rows we just added"
@@ -846,7 +908,10 @@
      "end_time": "2024-02-03T19:02:31.570017700Z",
      "start_time": "2024-02-03T19:02:31.555016400Z"
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-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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@@ -868,7 +933,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
    },
    "source": [
     "Now, we will read one of the records that we just wrote"
@@ -882,7 +950,10 @@
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      "start_time": "2024-02-03T19:02:31.567018600Z"
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-    "collapsed": false
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@@ -897,7 +968,10 @@
     "ExecuteTime": {
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-    "collapsed": false
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@@ -922,7 +996,10 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "jupyter": {
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+    }
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    "source": [
     "Finally, let's close the connection and wrap everything up"
@@ -935,7 +1012,10 @@
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@@ -947,7 +1027,7 @@
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@@ -961,9 +1041,9 @@
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