## 5 Simulation Results and Analysis  
In this section, the proposed DRNSM-APINT method will be evaluated through simulation methods. A network of 1584 satellites has been simulated, with the structure similar to the first shell of Starlink, at an average orbital altitude of 550 km. There are 72 orbital planes, each containing 22 satellites. The network comprises 300 traffic services, with an average path length of 20 hops. The telemetry packet is placed at the head of the service packet and the telemetry header has a fixed size of 32 bytes. The maximum cumulative hop for telemetry data is 8 hops. Each node can insert 4 to 8 bytes of metadata, with an insertion time of 0.01 ms, and the propagation time for the active telemetry packet is uniformly set to 0.08 ms.  
To the best of our knowledge, limited studies have been conducted on satellite network measurement and selection of reporting nodes. Therefore, this paper adopts the latest methods in terrestrial network awareness research for application in satellite network scenarios, and compares them with the methods presented in this paper. 1. Flexible and active network telemetry scheme (FAINT): telemetry packets can be actively gathered and measured from nodes along the path, as specified, through the utilization of programmable technology [23]. 2. INT method for segmented routing (SRINT): hop-by-hop awareness of each node along the path utilizing the INT method [20]. 3. Energy-efficient data aggregation method (EEDA): the nodes are selected where there is relatively high remaining of bandwidth on the SGLs for reporting [24]. Figure 5 shows a comparison of the awareness time among the FAINT, SRINT, EEDA, and APINT model at different length of segments. From the figure, it can be seen that due to the FAINT method initiating active telemetry from the reporting node and requiring round-trip collection, the awareness time exhibits a notable increase as the number of hops grows, and is significantly higher than that of the other methods. The SRINT method employs INT to reduce the awareness time, but the awareness time remains relatively higher under the mechanism of target node reporting. The EEDA method selects nodes with abundant resources for reporting, thereby sacrificing a portion of the awareness time. The awareness time of the APINT method is notably shorter than that of the other comparative methods, and the increase in awareness time remains steady as the hop increases. When the hop length is 9, the awareness time of the APINT model is reduced by 34.5% compared to FAINT, 13.7% compared to SRINT, and 8.4% compared to the EEDA method, indicating a significant improvement in awareness timeliness. Figure 6 illustrates the comparison of awareness evaluation function values (EFV) and the awareness overhead under varying numbers of reporting nodes based on the DRNSM-APINT method. As can be seen from Figure 5(a), when the path length is less than 10, the EFV corresponding to a single reporting node scenario is smaller. The DRNSM-APINT will  
Figure 4: Q-learning algorithm based on virtual boundary preplanning. prioritize selecting the optimal single reporting node for reporting. Figure 6b illustrates that within the path length interval of 11 to 16, the EFV is minimal in the scenario with two reporting nodes, followed by the scenario with three reporting nodes. And the EFV under single reporting node scenario is the highest. The DRNSM-APINT will prioritize selecting two reporting nodes. From Figure 6c, it can be observed that when the length of path further increases from 18 to 30, the DRNSM-APINT will give priority to deploying three reporting nodes. The corresponding EFV is smaller compared to scenarios where there are two or four reporting nodes. To sum up, the DRNSM-APINT can flexibly and dynamically adjust the number of reporting nodes based on varying service path lengths, and determine the optimal location of the reporting nodes in corresponding scenarios. Figure 5: Comparison of awareness time across different methods.