## 4 A Multi-Reporting Nodes Selection Algorithm Based on Lightweight Q-Learning for Various Services  
The dynamic selection method for multiple reporting nodes can determine the optimal reporting solution for any service path. However, differences in path hops, distances of ISLs, and available statuses of SGLs among various services further enhance the dynamism and complexity of multi-reporting nodes selection in concurrent multi-service scenarios. Therefore, it is necessary to develop a dynamic solving algorithm to select the optimal reporting solution for various service scenarios. The difficulty of this problem lies in not only adaptively adjusting to the unique characteristics of different service paths, but also reducing the computational complexity of solving the reporting solution within each service path. Traditional mathematical solving algorithms, such as dynamic programming and linear programming methods, rely on precise environmental models. For multi-service scenarios, such methods necessitate continuous re-modeling and are incapable of facilitating dynamic strategy updates. Machine learning and other artificial intelligence algorithms require substantial data support and lack proactive exploration mechanisms. Q-learning is a model-free reinforcement learning algorithm based on finite Markov decision processes that learns directly from interactions with the environment of uncertain or unknown settings [22]. It is particularly suitable for solving dynamic and complex problems and supports the timeliness of the decision-making process. In response to the above problems, the Q-learning algorithm is introduced to dynamically solve the dynamic selection problem of multiple reporting nodes in multiple service scenarios. However, during the dynamic partitioning of virtual boundaries, the Q-learning algorithm may encounter the problem of non-adjacent virtual boundary combinations, meaning that the virtual boundaries are discontinuous and the segments fail to achieve comprehensive coverage of nodes along the path. The Q-learning-based dynamic optimization algorithm traverses the effects of all potential actions A on state W and generates the corresponding Q-value table. As the number of virtual segments grows, the scope of search and calculation exhibits an exponential expansion, posing a significant challenge to the limited computational capabilities of satellite. A Q-learning algorithm based on virtual boundary preplanning is proposed, which can optimize the Q-learning search process and reduce the number of iterations. The algorithm flow is shown in Figure 4. Specifically, considering that the flexible selection method for multiple reporting nodes requires awareness of all nodes, and the APINT model needs to start telemetry from the starting points of the virtual boundaries of the segments separately, the virtual boundaries of the sub segments must be adjacent, with no nodes present between them. Meanwhile, the APINT model can expand the awareness range, and to enhance the average telemetry duration of segments, the lengths of these segments are relatively similar. Thus, the equal division method for virtual boundary initialization division is used. The number of satellites within the path is \(N_{\text{P2}}\), and the satellite number of each segment is \(N_{\text{V}}\)= \(\{N_{\text{V1}},N_{\text{V2}},\ldots,N_{\text{VY}}\}\), with \(N_{\text{Vi}}\simeq\frac{N_{\text{P2}}}{Y}\). Then, starting from the end of the first segment and the starting point of the second segment, the boundary nodes float back and forth within a certain range \(\Delta=\frac{N_{\text{Vi}}}{Y+x}\), where x is the range adjustment factor, which can dynamically adjust the virtual boundary. The process is repeated, and each segment boundary can float based on \(\Delta\).