Unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) networks face severe physical-layer security challenges in the presence of eavesdroppers. To address this issue, this paper proposes a secure graph neural network (Sec-GNN) based multi-dimensional anti-eavesdropping optimization method. The approach models the node-spatial relationships among the UAV, legitimate users, and eavesdroppers as a graph structure. By leveraging the message-passing mechanism of graph neural networks—sequently performing message generation, message aggregation, and node update—it dynamically integrates network topology information and node interaction features. This enables end-to-end joint optimization of the UAV’s three-dimensional position, beamforming vectors, and multi-user power allocation strategies. The method does not rely on explicit channel state information and directly generates near-optimal resource allocation schemes based on node location information and observable signal features. Experimental results demonstrate the superiority of Sec-GNN across various scenarios, and ablation studies confirm that partial optimization leads to significant performance degradation, thereby verifying the necessity of multi-dimensional joint design. The proposed framework provides an efficient and scalable solution for secure resource management in dynamic space-air-ground integrated networks.

This article addresses the challenging problem of constrained optimal control for human–machine systems subject to external disturbances and the bounded rationality of the human operator. To this end, a novel game-theoretic framework is proposed. Unlike monolithic game formulations, the framework uniquely disaggregates the control problem by transforming it into a multifaceted game via logarithmic barrier functions (BFs): it models human–machine cooperation as a positive-sum game oriented toward shared objectives, and disturbance rejection as a zero-sum game tailored for robustness enhancement. To capture the nonideal human decision-making, we integrate the level-$k$reasoning framework to model the operator’s bounded cognitive dynamics. The corresponding coupled Hamilton–Jacobi–Isaacs (HJI) equations for this human–machine game are derived, and critically, a rigorous proof of global asymptotic stability (GAS) for the transformed system is provided, establishing a solid theoretical foundation. For online implementation without requiring prior knowledge of the system dynamics, we develop a resource-efficient learning architecture based on the adaptive dynamic programming (ADP) and a novel dynamic event-triggered mechanism (DETM). A key feature of this architecture is a fuzzy logic-based module for dynamic authority allocation, which adaptively adjusts control sharing in real time. Rigorous analysis demonstrates that all signals in the closed-loop system are uniformly ultimately bounded and that Zeno behavior is precluded. Simulation results are presented to validate the effectiveness and superiority of the proposed control strategy.

Intelligent Reflecting Surfaces (IRS) enhance spectral efficiency by adjusting reflection phase shifts, while Non-Orthogonal Multiple Access (NOMA) increases system capacity. Consequently, IRS-assisted NOMA communications have garnered significant research interest. However, the passive nature of the IRS, lacking authentication and security protocols, makes these systems vulnerable to external eavesdropping due to the openness of electromagnetic signal propagation and reflection. NOMA’s inherent multi-user signal superposition also introduces internal eavesdropping risks during user pairing. This paper investigates secure transmissions in IRS-assisted NOMA systems with heterogeneous resource configuration in wireless networks to mitigate both external and internal eavesdropping. To maximize the sum secrecy rate of legitimate users, we propose a combinatorial optimization graph neural network (CO-GNN) approach to jointly optimize beamforming at the base station, power allocation of NOMA users, and phase shifts of IRS for dynamic heterogeneous resource allocation, thereby enabling the design of dual-link or multi-link secure transmissions in the presence of eavesdroppers on the same or heterogeneous links. The CO-GNN algorithm simplifies the complex mathematical problem-solving process, eliminates the need for channel estimation, and enhances scalability. Simulation results demonstrate that the proposed algorithm significantly enhances the secure transmission performance of the system.

Relay and friendly interference collaboration are effective technologies within the physical layer security (PLS) domain for countering eavesdroppers in wireless transmissions. However, the dynamic nature of wireless channels means that the quality of legitimate channels may not consistently exceed that of eavesdropping channels, posing challenges in ensuring the security performance of wireless links. In this paper, we present a multi-destination node selection framework aimed at enhancing the security of wireless communication systems. The strategic selection of relays, in conjunction with the deployment of friendly interference, is designed to optimize the reliability and secrecy performance of wireless link transmissions. Specifically, we propose a collaboration-based node selection (CNS) strategy that leverages selection combining (SC) and maximal ratio combining (MRC) to enhance system reliability and security. Through theoretical analysis, we derive closed-form expressions for secrecy capacity, outage probability (OP), intercept probability (IP), and asymptotic outage probability. The simulation results validate our analytical conclusions and demonstrate the effectiveness of the CNS scheme. Importantly, the introduction of interference nodes strengthens system security and reliability, while increasing relays or destinations primarily optimizes system robustness.

In this article, for the nonzero-sum (NZS) differential games problem of uncertain nonlinear systems with state constraints, an adaptive robust stabilization scheme based on the control barrier function (CBF) is presented under the influence of random disturbances and control input matrix uncertainty. To deal with the impact of uncertainty on the system, the nominal system of the original system is adopted and the cost functions associated with each player are appropriately chosen to convert the robust regulation problem of multiplayer differential games into an optimal regulation problem. Furthermore, the purpose of combining the cost function relevant to each player with the CBF is to make the system states evolve in the safe area. Different from the classical actor–critic dual neural network (NN), each player only needs a critic NN to approach the corresponding cost function without the restriction of the initial stabilizing control. Combined with the Lyapunov stability theory, under the combined influence of random disturbances and state constraints, the state and critic NN weights of the closed-loop system are guaranteed to be uniformly ultimately bounded (UUB). Finally, two simulation examples are used to verify the validity of the presented scheme.

Variable switching frequency is an unsolved problem in controlled-type soft-switching inverter. This paper presents two controlled-type soft-switching current modulations capable to achieve fixed switching frequency, one is by the combination of hysteresis control and triangular carrier control, the other is by variable hysteresis bandwidth. Meanwhile, a prototype of a single-phase full-bridge inverter and its digital control method have been designed and tested under the two fixed switching frequency modulations. Experimental results are given to demonstrate the validity and feasibility.

The efficiency and life expectancy of PV grid-tie micro-inverter is of critical effect to its performance. The Actively Clamped Resonant DC-link Inverter has remarkable features of simple structure and high efficiency with only one auxiliary switch. The short-circuit control is the traditional clamp circuit control strategy. But the short-circuit process increases the DC-link losses and the control complexity. A no short-circuit control strategy is presented to overcome the above problems. The calculation results show that no short-circuit control strategy reduces the loss of inverter. Film capacitor is chosen to be the clamp capacitor.

Understanding aspect-oriented systems, without appropriate tool support, is a difficult and a recognized problem in the research community. Surprisingly, little has been done to help developers understand the impact of the static crosscutting constructs of AspectJ on base programs. Questions of interest to developers such as: which statements in a base program are affected by a given inter-type declaration, or how has the behavior of the affected statements been modified, are still outstanding. This paper presents analysis techniques for inferring the impact of the static crosscutting constructs of AspectJ on base programs, and tools for visualizing the results of the analysis; thus improving the comprehension of AspectJ systems and guarding against unintended modifications. Our analyses are implemented as extensions to the AspectBench compiler, and integrated in the Eclipse IDE as a plugin. We present experiments on several open source systems to investigate the effectiveness and suitability of our analysis techniques and tools.

Over the past decade, a number of architecture description languages (ADLs) have been proposed to facilitate modeling and analysis of software architecture. While each claims to have various benefits, to date, there have been few studies to assess the relative merits of these approaches. In this paper, we describe our experience using two ADLs to model a system initially described in UML, and compare their effectiveness in identifying system design flaws. We also describe the techniques we used for extracting architectural models from a UML system description.