## 4 Awareness-Prediction Joint Compensation Methods  
LSTM is a traditional resource state prediction approach [23; 26]. However, the innovation of this study lies in applying the prediction model to the specific challenges faced by satellite network resource awareness. The issues of varying time intervals in data awareness among nodes and differing prediction step sizes are addressed by dynamically adjusting the prediction step size based on node-aware latency. The prediction accuracy of network resource status based on LSTM varies with the change in the prediction step size. When the awareness latency is high, the error in the resource status prediction results increases. Additionally, the randomness of services and the instability of links in satellite networks lead to significant fluctuations in resource changes, which result in poor data continuity and stability, thereby reducing the prediction accuracy. Specifically, in scenarios characterized by significant resource fluctuations, such as those exhibiting spikes in resource curves, the retention of early growth trends in resources, attributed to the gating mechanism and the cumulative impact of errors, can cause prediction results to diverge substantially from actual values, thereby generating significant errors. Consequently, relying on these predictions can undermine the reliability and precision of the decision-making. The awareness data also have poor timeliness due to the awareness latency caused by the dynamic topology of LEO satellite networks. Specifically, after the satellite node completes the writing operation of the metadata at time \(t_{e}\), the ground control center can only receive the resource status data after a certain awareness latency \(T_{d}^{S_{i}}\), with the current time being \(t_{l}=~{}t_{e}+T_{d}^{S_{i}}\). At the current moment, the onboard resources may have already changed, meaning that the awareness data received at the ground control center do not represent the real-time resource status of the node, but rather awareness latency data. The actual resource status on the satellite may fall into one of the following situations. (1) With no new services arriving, the resources occupied by previous services on the satellite remain unreleased. The state of onboard resources unchanged, and the awareness latency data are equivalent to the quantity of onboard resources. (2) With no new services or fewer services arriving, the resources occupied by previous services are significantly released, resulting in an increase in available onboard resources. The quantity of awareness latency data is smaller than the quantity of onboard resources. (3) With the arrival of new services, the resources occupied by previous services have yet to be released, and the quantity of onboard resources is smaller than the awareness latency data. (4) With the arrival of new services, the resources occupied by previous services are released and the onboard resources may not change, or they may be greater or less than that of the awareness latency data. As shown in Figure 2, both the awareness latency data received on the ground and the resource status prediction results exhibit poor accuracy and timeliness. To address the above problem, a dynamic compensation model based on awareness data and prediction results is proposed. The model dynamically compensates for awareness latency data based on the prediction results and dynamically adjusts the compensation weights for each node according to the awareness latency and prediction accuracy, ensuring that the authenticity and accuracy of the compensated data are closer to the actual onboard resource status. Specifically, the LSTM-based prediction algorithm uses the awareness latency \(\mathrm{T}_{\mathrm{d}}^{\mathrm{S}_{i}}\) as the prediction step length, so the predicted result of node \(\mathrm{S}_{i}\) for the \(k\)-th type of resource status at time \(t\) is \(\mathrm{R}_{\mathrm{S}_{i}}(t)\).