## 6 Simulation Results and Analysis
in resource trends, which in turn reduces the prediction accuracy. The compensation data fit well with the awareness data. However, the compensation results can reduce some of the prediction errors for the rapidly changing peak areas. The maxi-mum relative error between prediction results and actual onboard state data is 9.89%, and the error of the compensation data is 2.98%, effectively addressing the issue of poor prediction accuracy in dynamic environments. Figure 4c shows the comparison for storage resources. It can be seen from the figure that the utilization is relatively low. However, the frequency of resource occupation and release is higher than that of computing resources, resulting in many spikes, which also affects the prediction accuracy. The maximum relative error between prediction results and actual onboard state data is 4.19%. This indicates that although spikes can affect accuracy, the inherent characteristics of resource occupation and release still allow for better prediction performance than scenarios with rapid fluctuations in computing resources. The compensation data fit well with the awareness data. The maximum relative error be-tween compensation data and actual onboard state data is 2.12%, which effectively enhances the fitting accuracy and improves data reliability. Figure 4d demonstrates the average relative errors between the prediction results and the actual onboard data as well as the average relative errors of the compensation data. From the graph, it can be seen that the relative errors of the compensation data are consistently smaller than those of the prediction results, suggesting that the compensation model can significantly enhance the accuracy of the data. To further evaluate the effectiveness of the proposed method, actual awareness data are introduced for testing. Due to the limited availability of open-source satellite network data, a LEO satellite network consisting of 293 satellites was established based on existing systems using container technology. Programmable switches were employed to measure the transmission port rate (TPR) and reception port rate (RPR) per second of device ports in both non-service and high-bandwidth service scenarios, thereby showcasing the actual bandwidth capabilities of the nodes. Especially when there is no traffic passing through, due to the presence of awareness packets or other signals in the network, the node port rate remains small but is not zero. Figure 5 illustrates the comparison among the actual awareness data, prediction results, and compensation data. The background flow, acting as noise, exhibits a high degree of randomness and is characterized by frequent and significant changes. Regarding the issue of frequent changes, prediction algorithms may produce deteriorated prediction results at peak points, and their prediction performance may be reduced compared to simulated awareness data. This is exemplified in the 57 th time slot in Figure 5a, where the error between the predicted result and the actual awareness data is approximately 22.7%. However, after joint compensation, the error between the compensated data and the actual awareness data drops to only 6.8%. For problems involving significant changes, the prediction method will produce significant errors. For instance, in the 57 th time slice depicted in Figure 5b, the prediction method exhibits a relative error of 14.5%, whereas the compensated data demonstrates a relative prediction error of only 5.7%. The above analysis indicates that for actual awareness data, prediction algorithms cannot maintain the high prediction accuracy during simulation due to the volatility and randomness inherent in the data. Nevertheless, the joint compensation algorithm presented in this paper has the potential to enhance the accuracy and reliability of the data. From the Figure 5c,d, it can be seen that the actual awareness data are similar to the simulation results in this article in terms of overall trend.