## 6 Conclusions  
This paper proposed a network traffic prediction model, the DST-LEO satellite-traffic prediction model, which is suitable for low Earth orbit (LEO) constellations. By deploying this model in the low Earth orbit (LEO) constellation, accurate network traffic prediction  
Figure 9: The prediction curves of the DLS traffic prediction model for the long-term and short-term traffic in the internal LEO constellation service simulation system.  
for the LEO constellation can be achieved. This can optimize resource allocation, enhance the performance of the LEO constellation network, reduce unnecessary costs in operation management, and enable the system to adapt to the development of future businesses. The model mainly relies on the graph convolutional network (GCN) and long short-term memory (LSTM) to learn the relationship between the graph topology of the LEO constellation and the traffic time-series. The data separation and fusion module introduced by the authors can split the collected traffic and link status data and send them to the GCN and LSTM layers for training, respectively. Finally, the data are fused to a specific dimension to deeply explore the information contained in different data and discover the impact of link on-off caused by satellite position changes on traffic volume, which can more effectively improve the model's performance. The spatio-temporal attention mechanism introduced by the authors is different from the ordinary attention mechanism. The spatio-temporal attention mechanism enables the model to learn the importance of traffic information at each moment and process the LEO satellite traffic data involving space-time. By capturing the spatio-temporal dependencies in the data, it extracts important features, and all modules work together to complete a very accurate prediction task. In the comparison with other models, it was found that introducing an adaptive mechanism or a teacher-forcing mechanism may potentially improve the performance of the model. This represents a possible direction for future model development. However, the authors have not studied low-cost deployment. If the model is to be deployed on satellites, it is necessary to research more lightweight models or perform distributed computing on different satellites. This is the focus of future research, and is an important challenge for ensuring the quality of user services.  
Conceptualization, K.L., Y.Z. and S.L.; Methodology, K.L.; Software, K.L.; Validation, K.L.; Formal analysis, K.L., Y.Z. and S.L.; Investigation, K.L. and S.L.; Data curation, K.L.; Writing--original draft, K.L.; Writing--review and editing, K.L., Y.Z. and S.L.; Visualization, K.L.; Supervision, Y.Z. and S.L.; Project administration, K.L. and S.L.; Funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2022 YFB2902505).  
The dataset provided in this article is not easily accessible, as the data is part of an ongoing research project. Requests for access to the dataset should be sent directly to [EMAIL_ADDRESS].  
[PERSON] would like to thank [PERSON] and [PERSON] for their support and guidance during their research over the past two years and also appreciates the valuable feedback from the editors and reviewers on this article. The authors wish everyone all the best.  
The authors declare no conflicts of interest.