## 3 Network Resource Awareness and Resource Status Prediction
### Construction of Resource Awareness Dataset
#### 3.2.2 Preprocessing of Dataset  
Based on the in-band telemetry awareness method, the frequency of node status updates is related not only to the awareness delay on the service path but also to the intensity of the service. On the one hand, a large number of service requests pass through the nodes in certain areas of the network, leading to an increased frequency of awareness at each node. However, in certain areas of the network where the number of service requests is small, there are fewer services passing through the nodes, resulting in a decreased frequency of awareness at each node. On the other hand, at the same node, the frequency of resource awareness also varies across different time slots. The number of service requests passing through the node is higher in the current time slot than other slots, resulting in a larger interval between the awareness data. Therefore, the resource awareness data of the satellite node itself is nonlinear, and the data intervals between nodes are significantly different. To address the above problems, preprocessing with timestamp synchronization is necessary for the resource status datasets of each node. First, the minimum value \(\mathrm{T}_{\mathrm{d-min}}^{\mathrm{S}_{i}}\) of the transmission latency from multiple services is taken as the standard awareness time slice in the dataset, representing the minimum data interval. The dataset is resampled and divided based on the interval, and multiple sets of awareness data in the interval are weighted and fused. Considering the nonlinearity of awareness data in time sequence caused by the sparsity of services and awareness delay caused by the difference of the path length, taking the minimum delay as the acquisition interval can increase the sampling frequency and reduce the data groups in the sampling interval, which can effectively alleviate the problem of data information loss when multiple groups of data are fused in the weighting process. The weighted calculation formula for data fusion to ensure the correlation between sets of data and mitigate the influence of outliers on data weighting is as follows:  
\[\widetilde{x}_{\omega}(t)=\sum_{k=1}^{\mathrm{A}_{\mathrm{f}}}\omega_{k}x_{k} (t), \tag{8}\]  
where \(\mathrm{A}_{\mathrm{f}}\) is the awareness frequency within the interval \(\mathrm{T}_{\mathrm{d-min}}^{\mathrm{S}_{i}}\). \(x_{k}(t)\) is the awareness data within the corresponding interval, and \(\omega_{k}\) is the weight corresponding to each data. According to the differences in factors such as nodes and time periods, \(\omega_{k}\) can be calculated using an exponential amplification weighting model, which is as follows:  
\[\omega_{k}=\frac{x_{k}(t)^{h}}{\sum_{j=1}^{\mathrm{A}_{\mathrm{f}}}x_{j}(t)^{ h}}, \tag{9}\]  
where the weights satisfy the condition that \(\sum_{i=1}^{\mathrm{A}_{\mathrm{f}}}\omega_{i}=1\), and \(h\) represents the weighted amplification coefficient, ranging from 0 to 5. The specific value of \(h\) influences the data fusion effect. (1) When \(h=0\), the weights degrade to a simple average. (2) When \(h=1\), the weights degrade to linear weighting. (3) When \(h>1\), higher weights are assigned to high-value data, making them suitable for emphasizing outliers or key values. (4) When \(0<h<1\), the influence of high-value data is reduced, while the weights of low-value data are enhanced, making them suitable for smoothing noise or mitigating extreme values. The value of \(h\) can be dynamically adjusted according to the characteristics of the actual data in order to compensate for correlations between data and prevent excessive information loss. After temporal synchronization, the data are normalized and scaled within the range of \([0,1]\). The normalization formula is as follows:  
\[x_{\mathrm{data}}=\frac{x-x_{\mathrm{min}}}{x_{\mathrm{max}}-x_{\mathrm{min }}}, \tag{10}\]  
where \(x_{\mathrm{max}}\) and \(x_{\mathrm{min}}\) represent the maximum and minimum values in the fused data, respectively.