## 4 Result Analysis  
### Datasets  
This subsection introduces the datasets used for model training in the paper. First, to verify the model's performance, the authors selected the dynamic traffic part of the Abilene dataset from the SNDlib of the terrestrial network. The Abilene data are an important dataset for network research. The Abilene network consists of 12 main nodes, which are the key hubs in the network and undertake functions such as data reception, processing, and forwarding. Data were collected every 5 min daily for six months. The network structure of Abilene is shown in Figure 5. Since the data volumes between four paths (ATLAng and ATLAM5, ATLAng and IPLSng, IPLSng and CHINng, WASHng and NYCMng) were relatively small, only the data sizes of the other 11 paths were processed for model training. The adjacency matrix required by the GCN was set according to the connection relationships in the figure. If there was a path between two nodes, it was regarded as 1 (connected); otherwise, it was regarded as 0 (disconnected). The reasons for selecting this dataset are as follows. Firstly, the Abilene dataset is from a real-world terrestrial network, with data collected from the backbone network in the United States. Secondly, there are obvious topological relationships on the ground. For example, if the network traffic passing through the DNVRng node increases, the traffic of adjacent nodes will also increase accordingly. The model can learn the network topology through the GCN. The topology between satellites at a certain moment is similar, and the GCN can detect the traffic changes between adjacent topological structures.  
Next, after using the ground network traffic dataset experiment to demonstrate the superior performance and shortcomings of the model, further practice was carried out using the low Earth orbit (LEO) constellation verification environment simulated by the  
Figure 5: Schematic diagram of the network structure of the Abilene dataset.  
LEO constellation service simulation system developed internally by the research unit where the authors are located to optimize the performance of the model.  
This verification environment simulated sixty LEO satellites, one protocol gateway station, and multiple terminals. Each LEO satellite was equipped with an on-board routing and switching node that ran an inter-satellite routing protocol to achieve data forwarding within and between satellites. The specific details are shown in Figure 6. Satellite terminals simulated the network layer and various communication-related functions of real-user terminal devices. Two end-user servers simulated multiple services such as video, voice, text, and pictures, and achieved communication through the simulated satellite system. To better match the real-world changes in system service traffic, the inter-satellite links not only switched on and off according to changes in position, but also simulated the communication impacts caused by external factors such as faults, atmospheric conditions, and electromagnetic interference. The authors designed the external interference factors from two aspects. On the one hand, the authors created a natural-factor pattern that followed a Poisson distribution. In this case, the traffic volume on the link was reduced to 80% of the original to simulate packet loss during service transmission. On the other hand, in the simulation state of the normal system operation, a certain inter-satellite link carrying services was randomly selected to generate a terminal failure, thereby simulating the sudden failure of load-bearing equipment in the operating system. Relevant researchers collected the traffic data of 104 links at a time interval of 5 s. The traffic of a certain satellite node at a certain moment was collected via IP and then summed up, and the on-off status of the inter-satellite links at that moment was recorded. These data were used for subsequent model training.