Search Results (2,296)

Physical-layer security offers low probability of interception (LPI) in wireless communication systems. While prior methods such as the directional beamforming and secrecy coding schemes require knowledge of the eavesdropper (Eve)’s channel, passive eavesdropping limits their practicality. Artificial additive noise and artificial fast fading (AFF) schemes address the issue by degrading detection ability of a potential Eve without knowing its channel information. In particular, AFF achieves LPI by effectively shortening the coherence time of Eve’s channel using a random precoder while keeping the legitimate receiver (Bob)’s channel deterministic. In this paper, we propose a novel AFF design for spatial multiplexing multi-input multi-output (MIMO) amplify-and-forward (AF) relay systems. First, we formulate an optimization problem to achieve minimum mean squared error (MMSE) of Bob’s signals while guaranteeing LPI conditions from Eve, which is generally non-convex. To tackle the non-convexity of the problem, we apply a convex set approximation technique and thereby derive a simple closed-form design. Finally, we evaluated the performance of both Bob and Eve via computer simulations to demonstrate the effectiveness of our proposed design. Full article

The rapid rise of inverter-based resources (IBRs) such as solar, wind, and battery energy storage is transforming power grids and creating new challenges for protection. Unlike synchronous generators, many IBRs are interfaced through grid-following (GFL) inverters that operate as controlled current sources and rely on an external voltage reference, resulting in fault responses that are current-limited and controller-shaped. These characteristics reduce fault current magnitude and can undermine conventional protection schemes. In contrast, emerging grid-forming (GFM) inverters behave as voltage sources that establish local voltage and frequency, offering improved disturbance support but still transitioning to current-limited operation under severe faults. This review summarizes GFL versus GFM operating principles and deployments, compares their behavior under balanced and unbalanced faults, and evaluates protection impacts using a protection-relevant taxonomy supported by illustrative electromagnetic transient (EMT) case studies. Key challenges, including underreach/overreach of impedance-based elements, reduced overcurrent sensitivity, and directional misoperation, are identified. Mitigation options are discussed, spanning adaptive/supervised relaying, communication-assisted and differential protection, and inverter-side fault current shaping and GFM integration. The implications of IEEE 1547-2018 and IEEE 2800-2022 are reviewed to clarify ride-through and support requirements that constrain protection design in high-IBR systems. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

Under the dual imperatives of carbon neutrality and marine energy transition, hydrogen has emerged as an emerging energy storage carrier, offering a new pathway for offshore wind power consumption. This study addresses the critical challenges of offshore wind power intermittency and hydrogen transport efficiency bottlenecks by proposing an innovative solution. A coordinated planning method for far-offshore wind–hydrogen systems considering nearshore relay points is developed, establishing a multi-stage optimization framework of “offshore hydrogen production—relay point storage and transportation—hierarchical vessel delivery”. By optimizing hydrogen transport routes through coordinated allocation of electrolyzers, storage tanks, and vessel transportation, and designing a hierarchical transportation model that differentiates between ocean-going and nearshore vessels, the simulation results of a coastal area in China demonstrate that, compared with traditional methods, the proposed approach reduces investment costs and operation costs by nearly 10% while decreasing the monthly wind curtailment rate by 10.53%. Full article

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures. Full article

Unmanned aerial vehicle (UAV)-assisted satellite downlink transmission is a promising solution for improving coverage and throughput under challenging propagation conditions. However, the achievable performance gains are fundamentally constrained by the coupling between access transmission and the satellite–UAV backhaul, especially when decode-and-forward (DF) relaying and hybrid multiple access are employed. In this paper, we investigate the problem of end-to-end downlink sum-rate maximization in a UAV-assisted satellite network with hybrid non-orthogonal multiple access (NOMA)/orthogonal multiple access (OMA) transmission. We propose a performance-driven end-to-end optimization framework, in which UAV placement is optimized as an outer-layer control variable through an iterative procedure. For each candidate UAV position, a greedy transmission mode selection mechanism and a KKT-based satellite-to-UAV backhaul bandwidth allocation scheme are jointly executed in the inner layer to evaluate the resulting end-to-end downlink performance, whose feedback is then used to update the UAV position until convergence. Simulation results show that the proposed framework consistently outperforms benchmark schemes without requiring additional spectrum or transmit power. Under low satellite elevation angles, the proposed design improves system sum rate and spectral efficiency by approximately 25–35% compared with satellite-only NOMA transmission. In addition, the average user rate is increased by up to 37% under moderate network sizes, while maintaining stable relative gains as the number of users increases, confirming the effectiveness and scalability of the proposed approach. Full article

Industrial aluminum-block heating processes exhibit nonlinear dynamics, substantial time delays, and stringent requirements for fault detection and diagnosis, especially in semiconductor manufacturing and other high-precision electronic processes, where slight temperature deviations can accelerate device degradation or even cause catastrophic failures. To address these challenges, this study presents a digital twin-based intelligent heating platform for aluminum blocks with a dual-artificial-intelligence framework (dual-AI) for control and diagnosis, which is applicable to multi-port aluminum-block heating systems. The system enables real-time observation and simulation of high-temperature operational conditions via virtual-real interaction. The platform precisely regulates a nonlinear temperature control system with a prolonged time delay by integrating a conventional proportional–integral–derivative (PID) controller with a Levenberg–Marquardt-optimized backpropagation (LM-optimized BP) neural network. Simultaneously, a relay is employed to sever the connection to the heater, thereby simulating an open-circuit fault. Throughout this procedure, sensor data are gathered simultaneously, facilitating the creation of a spatiotemporal time-series dataset under both normal and fault conditions. A one-dimensional convolutional neural network (1D-CNN) is trained to attain high-accuracy fault detection and localization. PID+LM-BP achieves a response time of about 200 s in simulation. In the 100 °C to 105 °C step experiment, it reaches a settling time of 6 min with a 3 °C overshoot. Fault detection uses a 0.38 °C threshold defined based on the absolute minute-to-minute change of the 1-min mean temperature. Full article

Multipartite Gaussian Einstein–Podolsky–Rosen (EPR) steering is a key resource for quantum networks, but in practice it is strongly degraded by channel loss and excess noise. This motivates the need to distill multipartite Gaussian EPR steering across all relevant mode partitions. Here we propose and analyze a measurement-based noiseless linear amplification (NLA) protocol that distills Gaussian EPR steering in a four-mode square cluster state transmitted through lossy and noisy channels. Starting from a CV cluster shared by a transmitted node A and three local nodes (B, C, and D), we reconstruct the covariance matrix of the Gaussian cluster state and evaluate Gaussian steering monotones for all $(1+1)$, $(1+2)$, and $(1+3)$ bipartitions before and after applying measurement-based NLA. We show that appropriate conditioning on the noisy mode or on selected relay nodes systematically restores and enhances directional steering, extends both one-way and two-way steerable regions, and preserves the monogamy constraints characteristic of Gaussian graph states. Taken together, these results show that measurement-based NLA provides a practical route to distributing robust multipartite steering in CV cluster architectures, thereby strengthening the foundations for continuous-variable quantum information processing. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability. Full article

Multi-hop relaying can solve the problems of limited single-hop wireless communication distance, poor signal quality, or the inability to communicate directly by “relaying” data transmission through multiple intermediate nodes. It serves as the cornerstone for building large-scale, highly reliable, and self-adapting wireless networks, especially for the Internet of Things (IoT) and future 6G. This paper focuses on a decode-and-forward (DF) multi-hop relay network that employs simultaneous wireless information and power transfer (SWIPT) technology, with relays operating in a passive state. We first investigate the optimization of the power splitting (PS) ratio at each relay, given the source node transmit power, to maximize end-to-end network throughput. Subsequently, we jointly optimized the PS ratios and the source transmit power to minimize the source transmit power while satisfying the system’s minimum quality of service (QoS) requirement. Although both problems are non-convex, they can be reformulated as convex optimization problems. Closed-form optimal solutions are then derived based on the Karush–Kuhn–Tucker (KKT) conditions and a recursive method, respectively. Moreover, we find that the closed-form optimal solutions for the PS ratios corresponding to the two problems are identical. Through simulations, we validate that the performance of the two proposed schemes based on the closed-form solutions is optimal, while also demonstrating their extremely fast algorithm execution speeds, thereby proving the deployment value of the two proposed schemes in practical communication scenarios. Full article

As one of the payloads on board the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) spacecraft, the ultraviolet imager (UVI) aims to capture N2 Lyman–Birge–Hopfield (LBH) aurora continuously on a high-eccentricity orbit. The UVI instrument includes an intensified charge-coupled device (ICCD) for far ultraviolet (FUV) wavelength. It comprises a sealed image intensifier, a relay lens system, a CCD, and a mechanical housing. ICCD’s performance characteristics are evaluated before integrating with the optical system of the UVI, including the quantum efficiency, radiant gain, background characteristics, excess noise factor, image quality, and signal-to-noise ratio (SNR). The testing procedure and results are presented and discussed. The results demonstrate that the comprehensive performance of the detector is good, and provide critical technical support for quantitative applications. Full article

In flexible AC/DC microgrids, a variety of non-traditional power equipment are in operation, such as AC/DC interlinking converters. Moreover, due to the influence of interlinking converters, short-circuit fault characteristics in microgrids are much different from those in traditional distribution networks, which makes existing protection methods have poor adaptability. This paper introduces an active power variable-based differential protection method, which is suitable for various non-traditional power equipment. While in normal operation or during an external fault state, internal active power losses are rather minimum, resulting in nearly zero power difference between equipment terminals. However, during an internal fault state, the active power difference between terminals becomes extremely large, which can be adopted as protection criteria. The selectivity and rapidity are verified by simulation cases, and the aforementioned method is applicable to various non-traditional equipment, such as single-phase AC/DC converters, three-phase AC/DC converters, etc. Full article

Low-level wind shear (LLWS) is a critical aviation hazard that can cause flight disruptions and pose significant safety risks. Despite its operational importance, forecasting LLWS remains a challenging task. To improve LLWS prediction, probabilistic forecasting approaches based on ensemble prediction systems are increasingly used. In this study, LLWS forecasts were generated using a high-resolution, limited-area ensemble model, which allows for the representation of forecast uncertainty and variability in atmospheric conditions. Forecasts for Incheon International Airport were generated twice daily over the period from December 2018 to February 2020. To enhance forecast skill, statistical post-processing techniques, specifically Ensemble Model Output Statistics (EMOS), were applied and calibrated using Aircraft Meteorological Data Relay (AMDAR) observations. Prior to calibration, rank histograms were examined to assess the reliability and distributional consistency of the ensemble forecasts. Forecast performance was evaluated using commonly applied probabilistic verification metrics, including the mean absolute error (MAE), the continuous ranked probability score (CRPS), and probability integral transform (PIT). The results indicate that ensemble forecasts adjusted through statistical post-processing generally provide more reliable and accurate predictions than the unprocessed raw ensemble outputs. Full article

In this study, we consider multi-pair user frequency division duplexing massive MIMO relay systems and design a two-stage combining and beamforming (TSCB) scheme based on statistical channel state information (S-CSI). By leveraging S-CSI to co-design the pre-combining matrix and the pre-beamforming matrix, the scheme reduces the equivalent channel matrix dimensions, thereby cutting the pilot overhead. In the first stage, the two matrices are constructed through a selection of beams from a discrete Fourier transform codebook and mathematically cast as a multivariate optimization problem. An alternative optimization algorithm is proposed by splitting it into three sub-problems. The first two are 0–1 integer programming problems solved by iterative beam selection, while the third is a convex problem that is solved using a convex optimization algorithm. In the second stage, the reduced-dimension equivalent matrices are then estimated with low overhead, and a digital precoding matrix is then designed using zero-forcing algorithms. Simulations confirm the TSCB scheme’s superior ESE performance over that of existing methods. Full article