## 5 Performance Evaluation
### Numerical Results
To investigate the impact of the number of IoTDs on the efficiency of the proposed algorithm, we conducted a series of comparative analyses involving five distinct algorithms, including AD-SAC, PPO, D3 QN, DDPG, Hybrid-PPO, and Random. As illustrated in Figure 5, the increase in the number of IoTDs leads to higher service latency and energy consumption across all evaluated algorithms. This performance can be attributed to the diminishing resource availability of nearby UAVs, as the number of IoTDs increases, which  
Figure 4: Convergence performance under different critic learning rates. Figure 3: Comparison of convergence performance under different methods. (**a**) Weighted sum cost. (**b**) Latency. (**c**) Energy consumption. necessitates offloading MLLM tasks to more distant LEO satellites, ultimately resulting in increased service latency and transmission energy consumption. Additionally, as the number of IoTDs rises, the AD-SAC algorithm consistently achieves the lowest service latency and energy consumption, showcasing a strong adaptability to varying environmental conditions. This is because our proposed AD-SAC algorithm stems from its integration of a hybrid action space to approximate optimal decisions on offloading and resource allocation, thereby optimizing convergence efficiency and reducing latency and energy consumption. Figure 6 demonstrates the impact of maximum latency constraints \(T^{max}\) on the performance of five distinct task offloading algorithms. It is intuitive that Figure 6 reveals a direct correlation between increasing \(T^{max}\) and heightened service latency across all assessed algorithms. This trend can be attributed to the algorithms' propensity to offload a greater number of tasks to LEO satellite infrastructure when latency constraints are more loose. While this scheme results in higher latency, it simultaneously enhances service reliability. Notably, the proposed AD-SAC algorithm consistently achieves the lowest service latency as \(T^{max}\) increases. This outstanding performance is attributed to AD-SAC's preference for offloading tasks to nearby UAVs and LEO satellites, effectively reducing communication latency. Figure 6 further emphasizes that the AD-SAC algorithm also shows the lowest energy consumption. This efficiency primarily arises from its inclination to offload tasks to proximate servers, unlike other algorithms that often rely on more distant LEO satellites, resulting in higher communication energy costs. Additionally, as the maximum service delay threshold expands, a marked reduction in energy consumption is observed across all algorithms. This decline is driven by the increased task offloading to LEO satellites, which utilize their advanced computational efficiency to lower overall energy demands. We evaluated the impact of varying MMMU accuracy requirements on system performance by conducting experiments with accuracy thresholds ranging from 0.3 to 0.7. Figure 7 illustrates the relationship between accuracy requirements and system metrics. The experimental results demonstrate an increase in both latency and energy consumption as accuracy requirements become more stringent. Specifically, when the accuracy requirement increases from 0.3 to 0.7, the average system latency rises from 1.41 s to 2.19 s, representing a 55.3% increase. Similarly, energy consumption exhibits a substantial growth from 377 per time slot to 782 per time slot, marking a 107.4% increase. The most significant performance degradation occurs in the transition from accuracy requirement 0.5 to 0.6, where we observe a sharp increase in latency and energy consumption. This indicates a critical threshold where the SEC system shifts from predominantly UAV-based processing to satellite-based processing. It can be observed that our proposed AD-SAC algorithm with a hybrid action space demonstrates significant advantages compared to D3 QN, PPO, DDPG, Hybrid-PPO,  
Figure 5: Comparison of latency and energy consumption vs. number of IoTDs across different methods. (**a**) Latency. (**b**) Energy consumption.