Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.

This study examines whether state capital refocusing promotes innovation-driven development by reshaping the scale and technological direction of innovation in state-owned enterprises (SOEs). Using 499 firm-year observations for 42 Beijing municipal SOEs from 2013 to 2024, we distinguish innovation output from innovation allocation across invention-oriented, green, and internationally oriented patents. Two-way fixed-effects estimates show that a one-standard-deviation increase in core business focus is associated with increases of 0.610, 0.468, 0.383, and 0.090 log points in overall, invention-oriented, green, and internationally oriented innovation output, respectively. Refocusing also raises the shares of invention-oriented and green innovation, while leaving the international share unchanged. The main patterns remain broadly robust to alternative measures, additional controls, alternative specifications, and a continuous-treatment difference-in-differences design based on the 2020–2022 SOE Reform Three-Year Action Plan. Mechanism analyses provide evidence consistent with industrial-chain control as an important transmission channel, while heterogeneity analyses show that functional missions, industrial-chain positions, and strategic-sector status condition the effects. The findings indicate that state capital refocusing influences not only how much SOEs innovate, but also where innovation resources are directed, providing firm-level evidence on how public-capital reallocation can support mission-oriented innovation.