## 6 Simulation Results and Analysis  
This paper evaluates the proposed NRA-PPJC method through numerical simulations. A LEO satellite network consisting of 293 nodes is simulated, comprising both polar and inclined orbits. The inclined orbit has an altitude of 550 km and an inclination angle of 50\({}^{\circ}\), with a total of 19 orbital planes, each containing 11 satellites. The polar orbit has an altitude of 560 km and an inclination angle of 86.5\({}^{\circ}\), with a total of 6 orbital planes, each containing 14 satellites. The arrival processes of periodic services, high-bandwidth services, and computing-storage services are simulated in the network. Then, dataset construction and preprocessing of node resource awareness data are also conducted. Figure 3 presents a comparison of the robustness evaluation of the awareness-prediction joint compensation method. Figure 3a demonstrates the influence of awareness latency on compensation weights across different awareness latency threshold benchmarks. The prediction error of node resources is fixed at 5%, which is based on the average value of the relative prediction error of node resources in the whole network. It can be seen from the figure that as the awareness latency threshold is set lower, the compensation weight converges to 1 within a shorter awareness latency. This implies that when the awareness latency threshold is small, a smaller awareness latency results in a larger proportion of awareness latency data in the compensation data, and as the awareness latency increases, the proportion of prediction results will also rise. Figure 3b illustrates the variation in compensation weights in relation to prediction accuracy across different awareness latencies. Since the prediction accuracy of various resource types falls within 10%, the compensation weight will decrease as the prediction error rises, meaning that the proportion of prediction results in the compensation process will decrease accordingly. Moreover, for a fixed awareness latency threshold, the compensation weight increases rapidly as the latency increases, thereby increasing the proportion of prediction results in the compensation process. In summary, the proposed method allows for the adjustment of compensation weights based on prediction accuracy, and it can further undergo dynamic optimization in response to latency, enhancing the robustness of the method in scenarios characterized by high dynamics and varying prediction accuracies. Figure 4 shows comparisons of the compensation data, prediction results, and the awareness data, representing the actual status of onboard resources. Figure 4a shows the comparison for bandwidth resources. It can be seen from the figure that although the bandwidth resources are heavily utilized, the usage of high-bandwidth services is continuous. Therefore, the trend of change is relatively smooth over time. The prediction results based on the LSTM algorithm are close to the actual resource status, with a small error. After the compensation based on the prediction results, the compensation data can fit the actual results. The maximum relative error between prediction results and actual onboard state data is 4.14%, and the error of the compensation data is 2.72%, demonstrating the accuracy and reliability of the proposed algorithm. Figure 4b shows the comparison for computing resources. It can be seen from the figure that although the resource utilization is relatively low, the speed of resource occupation and release is rapid due to the characteristics of the services. This leads to significant fluctuations  
Figure 4: Comparison of the compensation model for the three types of resources. (**a**) Comparison for bandwidth resources; (**b**) Comparison for computing resources; (**c**) Comparison for storage resources; (**d**) Comparison of average relative error. Figure 3: Comparison of the robustness of the awareness–prediction joint compensation method. (**a**) Influence of awareness latency on \(\gamma\); (**b**) Influence of prediction error on \(\gamma\).