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Cooperative transmission is a promising solution for reliable communication in underwater acoustic sensor networks. To fully exploit the benefits of cooperative system, efficient management of relay resources is required. However, the challenging working conditions, including imprecise channel information, limited observation feedback and dynamic underwater environment, call for an adaptive relay selection strategy for improving system capacity performance. In this paper, we present an innovative Partial Expert-based Reinforcement Adversarial Learning (PER-AL) strategy that unifies expert assisted learning and self-adjusting learning in a integral adversarial multi-armed bandit framework. The user constantly interacts with the environment and optimizes the relay decision by analyzing all gathered observation rewards, rather than relying on prior channel information. By introducing a novel expert learning mechanism into the initial learning stage, expert generator and expert learner are proposed to control the performance of experts and provide optimal expert advice to reinforce the initial relay learning for efficiency. With the deepening learning, the relay quality is then evaluated by analyzing the historical observable rewards directly through a Exp3 policy without engaging complexity too much. The proposed algorithm could automatically adapt to the underwater conditions and detect the optimal relay within a short time, significantly improving system capacity without jeopardizing the adversarial regret guarantee. Finally, extensive simulation results demonstrated the superior of PER-AL algorithm compared with recent existing CSI-based and CSI-free solution benchmarks. Particularly, PER-AL algorithm has a 28.3% improvement of the total network capacity and achieves a 44.5% decrease of implementation time. [ABSTRACT FROM AUTHOR]

The non-contact electromagnetic detumbling technologies for space debris and defunct satellites attract a lot of interest from many researchers, in which service satellites use one or two electromagnetic devices onboard to generate a controllable torque to reduce the rotation of the target so that the robotic arm can capture the target without hazardous collisions. However, there are many challenges in modeling the interactions between electromagnetic devices and the target. In this study, the electromagnetic devices and the target are simplified as magnetic dipoles and a slowly rotating conducting shell with typical shape, respectively. The interactions of one and two magnetic dipoles with a slowly rotating conducting spherical shell are derived. Then, the interaction between a slowly rotating conducting non-spherical geometry (a cube or a cylinder) and two magnetic dipoles is calculated numerically. The numerical results show that non-spherical geometries can be regarded as spherical shells in terms of calculating the electromagnetic interactions when the magnetic dipoles are far away from the geometries. [ABSTRACT FROM AUTHOR]

In this study, adversarial graph bandit theory is used to rapidly select the optimal attack node in underwater acoustic sensor networks (UASNs) with unknown topology. To ensure the flexibility and elusiveness of underwater attack, we propose a bandit-based hybrid attack mode that combines active jamming and passive eavesdropping. We also present a virtual expert-guided online learning algorithm to select the optimal node without priori topology information and complex calculation. The virtual expert mechanism is proposed to guide the algorithm learning. The expert establishes a virtual topology configuration, which addresses the blind exploration and energy consumption of attackers to a large extent. With the acoustic broadcast characteristic, we also put forward an expert self-updating method to follow the changes of real networks. This method enables the algorithm to commendably adapt to the dynamic environments. Simulation results verify the strong adaptability and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]