## 5 Performance Evaluation
### Discussion  
In our study, deployment feasibility is a core consideration, particularly in addressing the inherent challenges of intermittent connectivity in low Earth orbit (LEO) satellite networks. To this end, we explicitly introduce the visibility time window \(T^{\text{vis}}_{k,n}(t)\) into the MDP state space. This parameter represents the dynamic communication window between an IoTD \(k\) and satellite \(n\) at time \(t\). This design captures the time-varying availability characteristics of LEO satellites due to their orbital motion. By incorporating \(T^{\text{vis}}_{k,n}(t)\) into the MDP state space, our framework can adaptively respond to the dynamic changes in satellite connectivity. For instance, during task offloading and resource allocation, the algorithm makes optimized decisions based on the real-time visibility window of the satellite, choosing to offload tasks to the satellite only when the communication window is sufficient to support computation and data transmission. This mechanism effectively mitigates the impact of intermittent satellite connectivity on service reliability, thereby enhancing the system's robustness.  
While our proposed framework for MLLM inference in UAV-assisted SEC networks achieves significant performance improvements in terms of latency and energy efficiency, certain simplifying assumptions in the system model warrant further exploration to enhance its applicability to real-world scenarios. Below, we discuss key limitations and potential avenues for future research. In this work, we assume that IoTD locations remain static within each time slot to simplify the optimization problem and focus on the joint task offloading and resource allocation challenge. This assumption facilitates the development and evaluation of our AD-SAC algorithm by reducing the complexity of the dynamic environment. However, in practical IoT deployments, such as disaster response or maritime monitoring, IoTDs may exhibit mobility, leading to variations in network topology, channel conditions, and UAV coverage requirements. While our model accounts for inter-slot variations due to device mobility (as noted in Section 3.2), explicitly incorporating intra-slot IoTD mobility could improve the framework's robustness. Future work could extend our approach by integrating mobility models for IoTDs, such as random waypoint or trajectory-based patterns, and developing adaptive algorithms that dynamically adjust offloading decisions and UAV trajectories in response to these changes.  
Another limitation of our current model is the absence of explicit energy constraints or depletion models for UAVs and LEO satellites. We opted not to include these factors to maintain focus on optimizing MLLM task offloading and resource allocation, rather than energy management of the computing nodes. In our formulation, energy consumption is minimized from a system-wide perspective (e.g., transmission and inference energy), but  
Figure 8: The ablation studies of AD-SAC, AD-SAC-C, and AD-SAC-D. (**a**) Latency. (**b**) Energy consumption.  
the battery life and recharge cycles of UAVs and satellites are not directly modeled. In real-world deployments, especially in remote areas where recharging infrastructure is scarce, energy constraints are critical for ensuring the sustained operation of these mobile nodes. For instance, UAVs may need to return to base for recharging, and satellites may rely on solar energy with limited storage capacity. Future research could enhance our framework by incorporating energy-aware mechanisms, such as battery depletion models, recharge scheduling, or energy harvesting strategies (e.g., solar panels for UAVs and satellites). This would enable a more holistic optimization that balances computational performance with operational longevity.