## 4 Awareness-Prediction Joint Compensation Methods
The actual awareness data received by the ground control center at the current moment for node \(\mathrm{S}_{i}\) are \(\mathrm{R}_{\mathrm{S}_{i}}^{\prime}(t-\tau)\), where \(\tau\) is the awareness latency of the node, and \(\tau=\mathrm{T}_{\mathrm{d}}^{\mathrm{S}_{i}}\). The dynamic compensation model based on the awareness data and the predicted results is as follows:  
\[\bar{\mathrm{R}}_{\mathrm{S}_{i}}^{k}(\mathrm{t})=\gamma\cdot\bar{\mathrm{R}} _{\mathrm{S}_{i}}^{\prime}(\mathrm{t})+(1-\gamma)\cdot\bar{\mathrm{R}}_{ \mathrm{S}_{i}}^{\prime}(t-\tau), \tag{17}\]  
where \(\gamma\) is the compensation weight, and \(\gamma~{}\in~{}[0,~{}1]\). Furthermore, the design of \(\gamma\) is based on the awareness latency and the accuracy of the prediction model, and its calculation formula is as follows:  
\[\gamma=\frac{1}{1+\mathrm{e}^{-\beta\cdot[(\tau-\tau_{0})+\mu_{k}\cdot(1- \delta_{k}(t))]}}, \tag{18}\]  
where \(\tau\) is the awareness latency, \(\tau_{0}\) is the awareness latency threshold, \(\beta\) is the latency coefficient, and \(\mu_{k}\) is the resource type weight. \(\delta_{k}(t)\) is the prediction error of the \(k\)-th resource. The impact analysis of each parameter is as follows:  
1. \(\gamma\) is the weight of the awareness latency data and the prediction results in the compensation process. When \(\gamma\) is large, the credibility of the current awareness data is high, and its weight is relatively large. When \(\gamma\) is small, the prediction results account for a larger proportion in the compensation process, thereby dynamically adjusting the ratio of awareness latency data and prediction results. 2. \(\beta\) controls the speed of the change in \(\gamma\) weight, representing the sensitivity to changes in awareness latency and prediction error during the compensation process. When \(\beta\) is large, if \((\tau~{}-~{}\tau_{0})+\mu_{k}\cdot\delta_{k}(t)~{}>~{}0\), then \(\gamma=1\). If \((\tau~{}-~{}\tau_{0})+\mu_{k}\cdot\delta_{k}(t)~{}<~{}0\), then \(\gamma~{}=~{}0\). If \((\tau~{}-~{}\tau_{0})+\mu_{k}\cdot\delta_{k}(t)=0\), then \(\gamma~{}\approx~{}0.5\), and at this point, the weights of awareness latency data and prediction results are the same, independent of the parameters. 3. \(\tau_{0}\) is the awareness latency threshold. When \(\tau_{0}\) increases, it enhances the tolerance of the compensation data to latency, meaning that at this time, there is a greater reliance on prediction data. When \(\tau_{0}\) is small, it is more sensitive to awareness latency and relies more on awareness latency data. Figure 2: Scenario introduction of awareness–prediction joint compensation. \(\mu_{k}\) is used to adjust the impact of prediction errors on weights, as there are differences in prediction accuracy when using LSTM models for different types of resources on the satellite. Therefore, if the current resource prediction results are unreliable, the value of \(\mu_{k}\) can be increased to ensure that the impact of awareness delays is more significant during the weight update process. When \(\mu_{k}\) is small, it reduces the impact of prediction errors on \(\gamma\), making the compensation process more reliant on adjusting weights based on latency differences. 5. \(\delta_{k}(t)\) is the prediction error for different resource types. When \(\delta_{k}(t)\) is large, the compensation process relies more on awareness latency data. When \(\delta_{k}(t)\) is small, its corrective effect on \(\gamma\) is stronger, and the reliability of the prediction results during the compensation process is higher. The above compensation model complies with the characteristics of LEO satellite networks. The awareness latency of the node is higher for satellites that are relatively far from the gateway station in the network, and the reliability of the awareness latency data decreases significantly with the increase in latency. At this point, compensation for the awareness latency data can be performed based on the predicted results of the node's re-sources.