## 5 Simulation and Performance Analysis
### Classifier Performance Analysis
To validate the performance enhancement of our proposed integrated detection method LDM-HEL compared to existing methods such as LDM-satellite [11], LLD [12], Spike method, Zigzag method, and ZBS method [4; 5; 6]; we plotted average performance results for four detection indicators (accuracy, precision, recall, and F1-score) across five different detection methods as shown in Figure 4.  
Firstly, as depicted in Figure 4, the proposed LDM-HEL, the LDM-Satellite, and the LLD method have shown significant performance enhancements compared to conventional classifiers. Moreover, the accuracy, precision, recall, and F1-score metrics of the proposed heterogeneous ensemble method surpass those of the other five classifiers, indicating its superior comprehensive detection performance and low false negative rate in effectively identifying bit errors and packet losses. Analyzing the causes of the performance difference, it was found that the Spike schemes only consider one packet loss feature and need to set a threshold to determine the  
Figure 4: Performance comparison between proposed LDM-HEL and the other five classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Individual** & \multicolumn{6}{c}{**Q Statistics**} \\ \cline{2-7}
**Classifier** & **DT** & **LR** & **kNN** & **BP** & **Mean** & **System** \\ \hline LSSVM & 0.764 & 0.985 & 0.886 & 0.997 & 0.908 & \\ DT & & 0.699 & 0.954 & 0.735 & 0.788 & \\ LR & & & 0.850 & 0.996 & 0.882 & 0.874 \\ kNN & & & & 0.873 & 0.891 & \\ BP & & & & & 0.900 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Q statistics among individual classifiers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Individual** & \multicolumn{6}{c}{**DF Values**} \\ \cline{2-5}
**Classifier** & **DT** & **LR** & **kNN** & **BP** & **Mean** & **System** \\ \hline LSSVM & 0.068 & 0.161 & 0.077 & 0.172 & 0.119 & \\ DT & & 0.069 & 0.081 & 0.071 & 0.072 & \\ LR & & & 0.078 & 0.191 & 0.125 & 0.105 \\ kNN & & & & 0.080 & 0.079 & \\ BP & & & & & 0.128 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: DF values among individual classifiers. packet loss type. The setting of the threshold is related to the characteristics of the network and the experience of the manager, which affects the packet loss classification performance. The ZigZag and ZBS schemes consider multi-features, and the packet loss differentiation ability is improved, but its threshold also needs to be set reasonably. The LDM-satellite algorithm has better performance, but it uses a homogeneous integrated model, and its classification accuracy is not optimal. RTT is employed in the LLD algorithm. The change of RTT in the low-orbit satellite network not only reflects the network congestion, but also may be caused by the change of the distance between nodes, so it is easy to cause packet loss and misjudgment. Due to the heterogeneous individual classification model and the integration strategy based on stochastic gradient descent, the accuracy rate, accuracy rate, recall rate, and F1-score indexes of the proposed algorithm are higher than those of the other five classifiers, which indicates that the proposed heterogeneous integration detection method has good comprehensive detection performance and low false negatives, and can effectively detect packet loss caused by channel error. Furthermore, employing the heterogeneous integration architecture proposed in this paper and adjusting the approach for generating final integration results by utilizing the voting method, ACC weighting method based on accuracy assessment, as well as the SGD-based weighting method for comparison purposes (as depicted in Figure 5), reveals that the SGD-based weighting significantly enhances accuracy, precision, recall, and F1-score measurements. This enhancement is attributed to the dynamic adaptive weighted integration method introduced in the integration process, which dynamically adjusts the voting weight of each base classifier, thereby improving the generalization ability of the multi-classifier integrated detection model.