## 5 Simulation and Performance Analysis
### Dataset and Feature Selection Process  
In order to acquire packet loss data in a satellite network and subsequently evaluate the performance of loss differentiation algorithms, we employed NS2 to simulate the occurrence of packet loss in a satellite network based on the Iridium constellation. Specifically, channel error-induced packet loss was observed at the last hop of the wireless link, while congestion-induced packet loss occurred at the bottleneck link. The network topology is illustrated in Figure 2.  
The network parameter configuration is as follows: The satellite-to-ground link has a bandwidth of 10 Mbps, while the inter-satellite link has a bandwidth of 25 Mbps. Background traffic consists of TCP flows generated by ON/OFF sources following a Pareto distribution with shape parameters \(\alpha=1.2\), and the means were, respectively, set to \(\mu_{1}=500\) ms and \(\mu_{2}=1300\) ms. The flow rate is set at 10 Mbps, packet size at 1000 bytes, and cache size at 50 packets. To induce congestion packet loss, initially there are two TCP flows present in the system, followed by the addition of \(N\) TCP flows every 10 s. Four cases are considered for \(N\): \(N=2\), 4, 6, and 8. Additionally, three different bit error rates (1%, 3%, and 5%) are employed along with a tail-dropping queue management strategy to obtain the dataset on packet loss.  
These data were then utilized in the Relief algorithm to derive the weight results for each feature, as presented in Table 5.  
The importance ranking of each feature is derived from the aforementioned table. Subsequently, based on their respective feature weights, they are sequentially incorporated into the classification model in descending order. The resulting classification accuracy is then examined. Notably, when the feature reaches approximately the 14 th position, all algorithms achieve their highest accuracy levels, as depicted in Figure 3. Consequently, following the Relief algorithm-based feature selection, only the initial 14 features were retained for further analysis in this study.  
The Relief algorithm is capable of ranking features, but it fails to capture the interdependence between them. In order to assess the relevance of two features in terms of their informativeness given each other, we opted for employing mutual information. Consequently, we utilized this method to investigate the correlation among 14 features selected by the Relief algorithm, and the corresponding results are presented in Table 6 below.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Confusion Matrix**} & \multicolumn{2}{c}{**True Class**} \\ \cline{2-4}  & \multicolumn{2}{c}{**Positive**} & **Negative** \\ \hline \multirow{2}{*}{Predicting classes} & Positive & True Positive (TP) & False Positive (FP) \\ \cline{2-4}  & Negative & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.  
Figure 2: Simulation topology of satellite network.  
In the table, the bold numbers 0.96 and 1 indicate a significant correlation coefficients between \(F_{8}\) and \(F_{5}\) with \(F_{7}\). To further streamline the features, these two features (\(F_{8}\) and \(F_{5}\)) were eliminated in subsequent model inputs, leaving only the remaining 12 features intact.