## 5 Verification  
To verify the performance of ESCB, a lot of simulations are finished. The basic scenario of the simulation is constructed by STK and Network X packets. A walker delta constellation can be described as T/P/F, T is the total number of satellites, P is the number of orbital planes and F is the phase factor. In order to more effectively verify this scheme, a constellation with \(1152/48/24\) is introduced as an orbit topology. In addition to the orbit scenario, the simulation settings are also very important, as shown in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Starlink** \\ \hline Simulation time & 2400 min \\ Traffic generate period & 1 min \\ Packet transmit step & 10 ms \\
1 & 28–30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation network parameters. In order to evaluate the performance of ESCB routing scheme, some key performance comparisons are performed with the virtual-topology-based Dijkstra shortest path routing (VT-SPR) algorithm [23; 24]. At the same time, it also made a comprehensive performance comparison with Load-Balancing Routing Algorithm (LBRA), hybrid routing algorithm (HRA), Joint Depth-First-Search and Dijkstra algorithm (JDDA) and Q-learning-based dynamic distributed Routing scheme (QRLSN). When the update period of SIP signaling is set to 30 s, it can be known that the update time of the node status is less than 5 s. The routing path selection of each data packet can be guaranteed within 10 milliseconds. Distributed routing operations can ensure that the node status is updated in time. These parameters have been optimized a series of times during simulation and debugging. Finally, the most stable and globally optimal result is selected as the adjustment parameter. As shown in Figure 9, the congestion parameters of two different traffics will have a certain impact on the packet loss rate. In order to take into account the two traffic models at the same time, the global optimal parameters are selected as seat adjustment factors. Similarly, \(\delta\), \(\varepsilon\) and \(\gamma\) are introduced to adjust past, present and forecast energy factors. They are defined as memory factors for residual energy. As shown in Figure 10, it is the optimization curve of the memory factor. They will affect the remaining energy of the system. Higher and more stable residual energy is the optimization goal of them. Figure 10: Memory adjustment factor optimizing curve. Figure 9: Traffic adjustment factor optimizing curve. In the optimization objective function, \(\eta\), \(\kappa\) and \(\
u\) are used to balance different optimization objectives. Different businesses require different QoS requirements. Congestion, energy balance and delay can be controlled separately to match different business needs. For common applications, these parameters can be evenly distributed. The final parameter selection is shown in Table 3.  
To more realistically verify the routing scheme, two different traffic services are contributed separately. The delivery loss ratio (DLR) for access services (AS) is shown in Figure 11. The maximum DLR of ESCB is below 0.5%. The traffic density 1 is set as 28, 29 and 30. The results indicate that the routing scheme maintains effective data transmission and keeps high delivery success ratio with the traffic density increases. Its DLR performance is much better than VT-SP. The delivery loss ratio (DLR) for bearer services (BS) is shown in Figure 12. The maximum DLR of ESCB is below 0.5%. The traffic density \(l\) is defined as 28, 29 and 30. The results indicate that the routing scheme maintains effective data transmission and keeps high delivery success ratio as the traffic density increases. The comparing results also show the superiority of the specific scheme. Figure 11: Delivery loss ratio for access services.