update Mastersthesis.pdf and main.tex to enhance figure captions and...

update Mastersthesis.pdf and main.tex to enhance figure captions and descriptions for clarity; add new Zone.Identifier file for image

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.

Thesis_Docs/media/urls_request_count_log_scale.png:Zone.Identifier

+[ZoneTransfer]
+ZoneId=3