## 4 Result Analysis
### Experimental Results  
The authors predicted the traffic change trend for the next day through experiments on the Abilene dataset and compared the performance of the four models. The evaluation metrics are shown in Table 1, and the prediction curve for Link 1 is shown in Figure 7. Since ARIMA failed to learn the hidden patterns among nonlinear traffic, the model could only predict the general trend of traffic changes, and there were significant differences in the predicted traffic volume. The prediction result of the BiGRU model with an attention mechanism barely met the authors' expectations, but was not precise enough, possibly due to its simple model structure. Both the LSTM with the teacher-forcing mechanism and the DST-LEO satellite-traffic prediction model achieved the best results in terms of both the predicted trends and peaks, which greatly met the authors' expectations. However, the DST-LEO satellite-traffic prediction model outperformed the others in terms of detail processing, with a MAPE of 2.1315 and an MAE of 0.0018. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**Abilene**} \\ \hline
**Models** & **MSE** & **RMSE** & **MAE** & **MAPE** \\ \hline ARIMA [25] & 0.0352 & 0.1875 & 0.1583 & 144.0793 \\ \hline BiGRU-Attention [26] & 0.0002 & 0.0157 & 0.0096 & 7.0319 \\ \hline LSTM-Teacher Forcing [27] & 3.92 \(\times\) 10\({}^{-5}\) & 0.0063 & 0.0045 & 4.2500 \\ \hline DLS-Traffic Prediction & 7.67 \(\times\) 10\({}^{-6}\) & 0.0028 & 0.0018 & 2.1315 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation metrics for the short-term prediction of the Abilene dataset. In the table, the DST-LEO satellite-traffic prediction model is simplified as DLS-Traffic Prediction. The authors predicted the traffic change trend for the next week through experiments on the Abilene dataset and compared the performance of the four models. The evaluation metrics are shown in Table 2, and the prediction curve for Link 7 is shown in Figure 8. Link 7 was selected here because the traffic volume changed significantly during the prediction period. The recurrent neural network models performed well on other links. By observing this link, we could see how the models performed when the prediction target changed significantly, which is consistent with the characteristic of large-scale changes in the service volume of low Earth orbit constellations within the same time period. As the number of prediction time steps increased, ARIMA not only failed to show good prediction results in terms of amplitude, but also failed to even predict the trend as well as in the short-term prediction. Therefore, this model is not suitable for this task. The BiGRU with an attention mechanism also did not perform well as the number of prediction time steps increased. It failed to predict the significant change in traffic amplitude on the fifth day, and the traffic prediction results significantly deviated from the true values in other time periods. Although the LSTM with the teacher-forcing mechanism did not have the support of an attention mechanism, since this mechanism directly uses elements from the true target sequence as the input for the next time step with a certain probability during the training process, it can timely correct the large deviations caused by the prediction results of the previous moment during training. Thus, it showed superior performance compared with the previous two models. The DST-LEO satellite-traffic prediction model can extract features of the terrestrial network from different levels due to its spatio-temporal attention mechanism. However, unlike the dataset constructed by the internal Low Earth Orbit constellation business simulation system, this dataset does not contain two types of traffic. As a result, the data separation and fusion techniques may not have fully realized their  
Figure 7: Short-term prediction curve diagram of Link 1 in the Abilene dataset.