The Designation Rgo-Based Heterostructure Towards Highly Efficient Microwave Absorption Performance and Excellent Flame Retardancy

Due to the flexible deployment and high mobility, unmanned aerial vehicle (UAV)-aided continuous emergency communications have recently been regarded as a key solution to provide data transmission for disaster areas. In practice, due to the limited onboard energy and state deterioration, UAVs need energy supplement and maintenance. However, existing researches mainly focus on the deployment of UAVs and rarely study their operation and maintenance policy. To ensure the continuous and reliable execution of communication tasks, a dynamic operation and maintenance policy is proposed to assign tasks and determine maintenance activities for UAVs. First, a dynamic operation and maintenance policy composed of task assignment policy and maintenance policy is proposed. Next, the dynamic operation and maintenance joint optimization problem is formulated as a Markov decision process (MDP) to optimize the performance of the UAV swarm, including coverage, fairness, operation and maintenance cost. Then, a deep reinforcement learning approach is tailored to optimize the proposed MDP, where a state preprocessing method is proposed to eliminate repeated states, and an action mask method is utilized to satisfy operational constraints. Finally, the proposed approach is tested by its application in the operation and maintenance of a UAV swarm for the continuous emergency communication