## 4 Result Analysis
### Experimental Results
potential, leading to minor discrepancies between the predicted traffic volume and trends and the actual values at certain moments. Ablation experiments: The authors verified the impact of each module on the target task by removing the complex structure of the model (replacing LSTM with GRU), the bidirectional model design, GCN, the data separation and fusion module, and the spatio-temporal attention mechanism, respectively, and then verified the effectiveness of different modules. The DST-LEO satellite-traffic prediction model was trained on the Abielene dataset after removing the corresponding modules to predict the traffic changes for the next week. The corresponding indicators were as follows. As can be seen from Table 3, different modules played roles in different aspects. It can be seen that GCN and the spatio-temporal attention mechanism had a relatively large impact on the model performance. For networks with topological structures, these two modules can effectively process graph-structured data, deeply mine node feature information, and enhance the model's ability to handle dynamically changing data. The model complexity, bidirectional design, and data separation and fusion enhanced the model's fitting ability. All modules worked together to ultimately achieve an accurate traffic prediction task. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**Abielene**} \\ \hline
**Models** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline ARIMA [25] & 0.0292 & 0.1709 & 0.1404 & 154.6146 \\ \hline BiGRU-Attention [26] & 0.0002 & 0.0141 & 0.0072 & 10.0634 \\ \hline LSTM-Teacher Forcing [27] & 5.95 \(\times\) 10\({}^{-5}\) & 0.0077 & 0.0052 & 6.2994 \\ \hline DLS-Traffic Prediction & 2.98 \(\times\) 10\({}^{-5}\) & 0.0054 & 0.0039 & 4.2922 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation metrics for the long-term prediction of the Abielene dataset. Figure 8: Long-term prediction curve diagram of Link 7 in the Abielene dataset. After the authors verified the superior performance of the model through the Abilene dataset, the next step was to use the simulated Low Earth Orbit (LEO) constellation network traffic data collected from the internal LEO constellation service simulation system for testing, predicting the traffic at different time intervals. The internal LEO constellation service simulation system closely replicates the real-world situation. Unlike terrestrial networks, it specifies the communication rules between adjacent satellites. Two satellites can communicate via direct inter-satellite links or through relay satellites, and its traffic has complex spatio-temporal characteristics. On the one hand, the traffic shows periodic, trend-based, and random changes over time. For example, during specific time periods, frequent user activities in certain regions lead to periodic peaks in the satellite traffic of the corresponding areas. Meanwhile, with the development of services and the growth in the user population, the traffic may exhibit an upward or downward trend. On the other hand, there is a spatial correlation among the traffic of different satellites. The traffic of adjacent satellites may influence each other because the geographical areas they cover overlap or they are on the same service chain. The spatio-temporal attention mechanism introduced by the authors for the model could not only capture the time-varying patterns of satellite nodes, but also the on-off positional relationships of the dynamic topology. Secondly, the bidirectional design of BiLSTM enabled the model to obtain more comprehensive context information and better handle long-distance dependencies. The data separation and fusion module designed by the authors could effectively capture when a satellite will disconnect the inter-satellite link with surrounding satellites by separately learning the traffic and link relationships, which helps the traffic prediction capture the trends of sharp increases and decreases.