diff --git a/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf b/Thesis_Docs/Nikkhah_Nasab-Aida-Mastersthesis.pdf
index 94a51000a5488b770965bd5a97fd156fd0ee6de9..ced7e5f818c9e00e89aba35eb06fd9c9b4c5ce8d 100644
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diff --git a/Thesis_Docs/main.tex b/Thesis_Docs/main.tex
index 425629fec6f0060257668fcce303a6f61286231d..6fffb083676e0c2b6411b3f7c81340b3f4b1dc3f 100644
--- a/Thesis_Docs/main.tex
+++ b/Thesis_Docs/main.tex
@@ -409,11 +409,11 @@ Understanding the distribution and frequency of URL requests is for identifying
 \begin{figure}
     \centering
     \includegraphics[width=\textwidth]{../Thesis_Docs/media/urls_request_count_log_scale.png}
-    \caption{Request counts of URLs (log scale). The X-axis represents URL hostnames index on a logarithmic scale that is sorted in descending order, and the Y-axis shows the number of visits on a logarithmic scale.}
+    \caption{Request counts of URLs (log scale). The X-axis represents URL hostnames index that is sorted in descending order, and the Y-axis shows the number of visits on a logarithmic scale.}
     \label{fig:requestcount}
 \end{figure}
 
-Figure \ref{fig:requestcount} illustrates the request counts for different URL hostnames, with both the x-axis (URL index) and y-axis (request count) set to a logarithmic scale. The x-axis represents the index of URLs in descending order of request count, meaning URLs with the highest traffic appear on the left. Due to the log scale, bars for lower indices (high-traffic URLs) appear wider, while those for higher indices (low-traffic URLs) are compressed. The y-axis, also in log scale, shows the distribution of request counts, highlighting a steep drop-off where a few URLs receive significantly higher traffic, while most receive fewer requests. This pattern indicates a power-law distribution, common in network and web traffic analysis.
+Figure \ref{fig:requestcount} illustrates the request counts for different URL hostnames. The x-axis represents the index of URLs in descending order of request count, meaning URLs with the highest traffic appear on the left. Due to the log scale, bars for lower indices (high-traffic URLs) appear wider, while those for higher indices (low-traffic URLs) are compressed. The y-axis, also in log scale, shows the distribution of request counts, highlighting a steep drop-off where a few URLs receive significantly higher traffic, while most receive fewer requests. This pattern indicates a power-law distribution, common in network and web traffic analysis.
 
 \section{24-Hour URL Visit Analysis}
 Understanding the temporal patterns of URL visits is essential for identifying peak usage times, detecting anomalies, and optimizing network resources. This section analyzes the distribution of URL visits over a 24-hour period, providing insights into user activity patterns throughout the day.
diff --git a/Thesis_Docs/media/urls_request_count_log_scale.png b/Thesis_Docs/media/urls_request_count_log_scale.png
index 3d99ca2bf86fdd2d81f580ee5f7864c7ec3c7918..6ad4f2531ac52a7789b731a16c19f0db0ad33573 100644
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diff --git a/Thesis_Docs/media/urls_request_count_log_scale.png:Zone.Identifier b/Thesis_Docs/media/urls_request_count_log_scale.png:Zone.Identifier
new file mode 100644
index 0000000000000000000000000000000000000000..a45e1ac4c0c673681b8767bd60e9ab3cdb7dd8d7
--- /dev/null
+++ b/Thesis_Docs/media/urls_request_count_log_scale.png:Zone.Identifier
@@ -0,0 +1,2 @@
+[ZoneTransfer]
+ZoneId=3