## 1 Introduction
(2) The instability of the SGL results in a limited number of optional reporting nodes and poor dynamic selection of reporting nodes. The SGLs of nodes have short duration and poor stability leading to a limited number of reporting nodes that can be selected on the path. At this point, the awareness data must either wait on the satellite or switch to another reporting node, thereby increasing the reporting time. (3) The high complexity of dynamic awareness caused by the dual heterogeneity of network topology and services. The dynamism of the topology reduces the timeliness of awareness and increases the difficulty of overall network awareness. The dynamism of services enhances the complexity of adjusting awareness methods in different time-varying scenarios. The fixed measurement and reporting strategy exhibits limited flexibility when it comes to optimizing the measurement process or the selection of reporting nodes in dual dynamic scenarios, making it impossible to be adjusted adaptively based on actual awareness requirements. In response to the challenges of network awareness in LEO satellite networks, a dynamic reporting nodes selection method for network awareness based on active-passive integrated network telemetry (DRNSM-APINT) is proposed, which can flexibly adjust the -telemetry mechanism and dynamically select the optimal number and position of reporting nodes for different services. Specifically, the method proposes an active-passive integrated network telemetry (APINT) model. Unlike the existing active and passive telemetry systems, which are both initiated by a single node, in this method, passive telemetry and active telemetry are initiated by the source and target nodes, respectively, converging toward the center of the path to shorten the awareness path. The optimal location for the reporting node is selected based on constraints related to the transmission time of ISLs and the status of SGLs, thereby reducing the time of measurement and reporting, and enhancing the time-liness of awareness in single-node reporting scenarios. Furthermore, considering that the single-node reporting mechanism in LEO satellite network cannot meet the awareness time-liness requirements of multi-hop and long-distance paths, an optimized selection method for multiple reporting nodes is proposed. The concept of virtual boundaries is introduced to divide the awareness path. Additionally, within each segment, the APINT model is utilized to select the optimal reporting nodes. Then, by comparing the effective-ness of selecting multiple reporting nodes across various virtual boundary partitioning scenarios, the most suitable number of reporting nodes for the current awareness path is determined, thereby enabling optimized and flexible selection of multiple reporting nodes. However, due to variations in path hops, distance of ISLs and SGLs, and the characteristics of these links among different services, the optimal number of reporting nodes for each service path varies in multi-service scenarios. Consequently, it is necessary to dynamically devise an optimal reporting scheme for each service, which further complicates the solution process. Considering the varying selection requirements for reporting nodes across diverse service scenarios, based on the model-free characteristic and the strong adaptability to dynamic scenarios of Q-learning, a lightweight Q-learning algorithm based on a pre-planned virtual boundary is proposed. The boundary is divided using the equal division method, and the selection of virtual boundaries for each service path is pre planned through dynamic boundary floating. The method can exclude discontinuous boundary combinations, decrease the number of search iterations required in Q-learning, and simplify the complexity of the algorithm's solution process.