Search Results (1,635)

As distribution networks become increasingly intelligent, primary–secondary integrated distribution switches are replacing the traditional electromagnetic type. However, the high degree of integration intensifies inherent electromagnetic compatibility (EMC) challenges. This paper presents a field-circuit hybrid full-wave model to investigate switch characteristics during lightning strikes. A 3D full-wave model of the switch and a distributed parameter circuit model of the connecting lines are coupled via a network parameter matrix. This approach comprehensively accounts for the impacts of transmission lines and structural components on electromagnetic disturbances. Simulation and experimental results reveal that lightning strikes induce high-frequency damped oscillatory waves, primarily caused by traveling wave reflections along overhead lines. The characteristic frequency of disturbance is inversely proportional to the transmission line length. Additionally, internal components significantly influence this frequency; specifically, a larger voltage dividing capacitance in the voltage transformer results in a lower frequency. Model validation was performed using a 20 m transmission line setup. A 75 kV standard lightning impulse was injected into Phase B. At a distance of 500 mm from the voltage transformer, the measured radiated electric field amplitude was 14.12 kV/m (deviation < 5%), and the characteristic frequency was 1.11 MHz (deviation < 20%). These findings offer vital guidance for the lightning protection and EMC design of primary–secondary integrated distribution switches. Full article

Lightning rods, while essential for protecting weather radars from direct lightning strikes, act as persistent non-meteorological scatterers that can interfere with signal transmission and reception and thereby degrade detection accuracy and product quality. Existing studies have mainly focused on X-band and C-band systems, and robust, measurement-based quantitative assessments for S-band dual-polarization radars remain scarce. In this study, a controllable tilting lightning rod, a high-precision Far-field Antenna Measurement System (FAMS), and an S-band dual-polarization weather radar (SAD radar) are jointly employed to systematically quantify lightning-rod impacts on antenna electromagnetic parameters under different rod elevation angles and azimuth configurations. Typical precipitation events were analyzed to evaluate the influence of the lightning rods on dual-polarization parameters. The results show that the lightning rod substantially elevates sidelobe levels, with a maximum enhancement of 4.55 dB, while producing only limited changes in the antenna main-beam azimuth and beamwidth. Differential reflectivity () is the most sensitive polarimetric parameter, exhibiting a persistent positive bias of about 0.24–0.25 dB in snowfall and mixed-phase precipitation, while no persistent azimuthal anomaly is evident during freezing rain; the co-polar correlation coefficient () is only marginally affected. Collectively, these results provide quantitative, far-field evidence of lightning-rod interference in S-band dual-polarization radars and provide practical guidance for more reasonable lightning-rod placement and configuration, as well as useful references for -oriented polarimetric quality-control and correction strategies. Full article

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

The removal of protein-bound uremic toxins (PBUTs) from the blood of kidney failure patients with conventional dialysis is limited. However, as their harmful effects and association with morbidity and mortality in dialysis patients are increasingly recognized, PBUTs have become important therapeutic targets. In this review, PBUT removal with current state-of-the-art dialysis technologies and future perspectives are discussed. Strategies to enhance PBUT clearance include methods that interfere with PBUT–albumin binding, such as chemical displacers, high ionic strength, pH changes, or electromagnetic fields, thereby increasing the free fraction available for dialysis. While these methods have shown promise in vitro, and some also in vivo, long-term safety data are lacking. PBUT removal can also be increased by adsorption, either directly via hemoperfusion, or indirectly, e.g., via sorbents incorporated in a mixed-matrix membrane or dissolved in the dialysate. In the kidney, PBUTs are secreted in the proximal tubules; hence, a cell-based bioartificial kidney (BAK) that secretes PBUTs is proposed as an add-on to current dialysis. Yet both PBUT adsorption strategies and, in particular, BAKs face considerable challenges in upscaling and mass production at acceptable costs. In conclusion, many novel technologies are under development, all requiring further (pre)clinical testing and upscaling before these strategies can be applied in the clinic. Full article

In this study, we report a reproducible in situ photochemical method for the simultaneous synthesis of metallic and hybrid metal/metal oxide nanoparticles (NPs) within a UV–curable polymer matrix. A series of epoxy diacrylate-based formulations (BEA) was prepared, consisting of Epoxy diacrylate, Di(Ethylene glycol)ethyl ether acrylate (DEGEEA), and Phenylbis (2,4,6-trimethylbenzoyl) phosphine oxide (BAPO), which served as a Type I photoinitiator. These formulations were designed to enable the simultaneous photopolymerization and photoreduction of metal precursors at various Ag⁺/Co²⁺ ratios, resulting in nanocomposites containing in situ-formed Ag NPs, cobalt oxide NPs, and hybrid Ag–Co₃O₄ nanostructures. The photochemical, magnetic, and dielectric properties of the resulting nanocomposites were evaluated in comparison with those of the pure polymer using UV–Vis and Fourier Transform Infrared Spectroscopy (FT-IR), Photo-Differential Scanning Calorimetry (Photo-DSC), Thermogravimetric Analysis (TGA), Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD), Impedance Analysis, and Vibrating Sample Magnetometry (VSM). Photo-DSC studies revealed that the highest conversion values were obtained for the BEA-Ag₁Co₁, BEA-Co, and BEA-Ag₁Co₂ samples, demonstrating that the presence of Co₃O₄ NPs enhances polymerization efficiency because of cobalt species participating in redox-assisted radical generation under UV irradiation, increasing the number of initiating radicals and leading to faster curing and higher final conversion. On the other hand, the Ag NPs, due to the SPR band formation at around 400 nm, compete with photoinitiator absorbance and result in a gradual decrease in conversion values. Crystal structures of the NPs were confirmed by XRD analyses. The dielectric and magnetic characteristics of the nanocomposites suggest potential applicability in energy-storage systems, electromagnetic interference mitigation, radar-absorbing materials, and related multifunctional electronic applications. Full article

Achieving high reliability remains the critical challenge for pulsed power supplies (PPS), whose core components are susceptible to severe degradation and catastrophic failure due to long-term operation under electrical, thermal and magnetic stresses, particularly those associated with high voltage and high current. This reliability challenge fundamentally limits the widespread deployment of PPSs in defense and industrial applications. This article provides a comprehensive and systematic review of the reliability challenges and recent technological progress concerning PPSs, focusing on three hierarchical levels: component, system integration, and extreme operating environments. The review investigates the underlying failure mechanisms, degradation characteristics, and structural optimization of key components, such as energy storage capacitors and power switches. Furthermore, it elaborates on advanced system-level techniques, including novel thermal management topologies, jitter control methods for multi-module synchronization, and electromagnetic interference (EMI) source suppression and coupling path optimization. The primary conclusion is that achieving long-term, high-frequency operation depends on multi-physics field modeling and robust, integrated design approaches at all three levels. In summary, this review outlines important research directions for future advancements and offers technical guidance to help speed up the development of next-generation PPS systems characterized by high power density, frequent repetition, and outstanding reliability. Full article

In this study, a Metamaterial Incident Photon Reconstruction Theory (MIPRT) is developed to describe the modulation process of metamaterials on incident photons. The theory includes the Invariant Incident Photon Hypothesis and Resonant Phase Deconstruction and Quantification; it reveals the modulation characteristics of metamaterials on incident photons, not by first absorption and then re-emission but by inducing coherent destructive interference, which brings about redistribution of the spatial probability of photon occurrence. This theory is validated in a single-layer metamaterial, and a unique relationship between the resonant phase and amplitude is derived and confirmed by simulation. The proposed MIPRT brings a comprehensive understanding of the electromagnetic (EM) response characteristics of metamaterials and provides a new idea for metamaterial theory from another perspective. Full article

Variable switching frequency modulation (VSFM) is an easy-to-implement and low-cost method to reduce electromagnetic interference (EMI) of power electronics, yet changes in loss and harmonic behavior make it hard to decide the parameters of the filter and the switching frequency (SF) variation range. In this article, a new VSFM method characterized by evenly distributed SF is proposed, and it is easy to implement. In order to handle the induced variation in loss and current total harmonic distortion (THD) behavior, current dynamics of a full-bridge grid-tied inverter under constant SF modulation (CSFM) are analyzed through multidimensional Fourier decomposition (MFD), and then the results are extended to VSFM. Based on these dynamics, loss of MOSFETs and THD of grid-connected current are estimated through the trapezoidal integral rule, and the analytical expressions of these indexes can be derived. After this, parameters needed for VSFM can be determined while meeting the minimum MOSFET loss and fixed current THD constraint. The performance of EMI, loss, and current harmonic is revealed through simulations and experiments and compared with the CSFM and classical VSFM methods. Full article

Ensuring food security requires innovative, sustainable pest management solutions. The Solar Insecticidal Lamp Internet of Things (SIL-IoT) represents such an advancement, yet its reliability in harsh, variable outdoor environments is compromised by frequent component and sensor faults, threatening effective pest control and data integrity. This paper presents a comprehensive survey on fault detection (FD) for SIL-IoT systems, systematically analyzing their unique challenges, including electromagnetic interference, resource constraints, data scarcity, and network instability. To address these challenges, we investigate countermeasures, including blind source separation for signal decomposition under interference, lightweight model techniques for edge deployment, and transfer/self-supervised learning for low-cost fault modeling across diverse agricultural scenarios. A dedicated case study, utilizing sensor fault data of SIL-IoT, demonstrates the efficacy of these approaches: an empirical mode decomposition-enhanced model achieved 97.89% accuracy, while a depthwise separable-based convolutional neural network variant reduced computational cost by 88.7% with comparable performance. This survey not only synthesizes the state of the art but also provides a structured framework and actionable insights for developing robust, efficient, and scalable FD solutions, thereby enhancing the operational reliability and sustainability of SIL-IoT systems. Full article

Frequency-hopping (FH) communication is widely employed in modern wireless communication systems due to its strong resistance to interference. Accurate detection of FH signals is therefore essential for effective spectrum monitoring and reliable communication in complex electromagnetic environments. However, real-world electromagnetic environments are highly complex and dynamic, with substantial noise and multiple interfering signals coexisting. These conditions pose significant challenges to frequency-hopping signal detection, particularly in terms of low signal-to-noise ratios and co-channel interference. To address these challenges, this paper proposes a frequency-hopping signal detection method based on time–frequency cancellation and the Hough transform. The received signals are first preprocessed using time–frequency cancellation and singular value decomposition to suppress noise and fixed-frequency interference. Subsequently, the time–frequency characteristics of the preprocessed signals are extracted, and the time–frequency cancellation ratio is computed to perform an initial determination of the presence of frequency-hopping signals. To further reduce false detections caused by multiple interference sources, the Hough transform is applied to analyze the time–frequency spectrum in greater detail. By jointly exploiting the geometric and statistical characteristics of the signals, accurate detection of frequency-hopping signals is achieved. Experimental results demonstrate that the proposed method enables precise detection of frequency-hopping signals under challenging electromagnetic conditions. Full article

In high-speed railways, the reliability of jointless track circuits largely hinges on the operational integrity of compensation capacitors. These capacitors are periodically installed along the track to mitigate rail inductive impedance and stabilize signal transmission. The induced voltage response, referred to as the compensation-capacitor signal, serves as a critical diagnostic indicator of circuit health. Yet it is often distorted by electromagnetic interference and structural resonance, posing significant challenges for robust feature extraction. To address this challenge, we propose a Disturbance-Robust Feature Distillation (DRFD) framework that performs multi-perspective modeling and validation of robust features. The framework formulates a unified multi-objective optimization model that jointly considers statistical significance, environmental stability, and structural separability. These objectives are harmonized through an adaptive Bayesian weighting mechanism, enabling automatic identification of disturbance-resistant and discriminative features under complex operating conditions. Experimental evaluations on real-world datasets collected at a 100 kHz sampling rate from roadbed, tunnel, and bridge environments demonstrate that the DRFD framework achieves 96.2% accuracy and 95.4% F1-score, outperforming the best-performing baseline by 4.2–7.8% in accuracy and 6.5% in F1-score. Moreover, the framework achieves the lowest cross-condition relative variance (RV < 0.015), confirming its high robustness against electromagnetic and structural disturbances. The extracted core features—Root Mean Square (RMS), Peak Factor (PF), and Center Frequency (CF)—faithfully capture the intrinsic electromagnetic behaviors of compensation capacitors, thus linking statistical robustness with physical interpretability for enhanced reliability assessment of railway signal systems. Full article

The widespread use of low-cost, highly maneuverable unmanned aerial vehicles (UAVs), such as racing drones, has raised numerous security concerns. These UAVs commonly employ LoRa (Long Range) communication protocols, which feature long-range transmission and strong anti-interference capabilities. However, traditional countermeasure techniques targeting LoRa-based links often suffer from delayed response, poor adaptability, and high power consumption. To address these challenges, this study first leverages neural networks to achieve efficient detection and reverse extraction of key parameters from LoRa signals in complex electromagnetic environments. Subsequently, a continuous preamble injection jamming method is designed based on the extracted target signal parameters. By protocol-level injection, this method disrupts the synchronization and demodulation processes of UAV communication links, significantly enhancing jamming efficiency while reducing energy consumption. Experimental results demonstrate that, compared with conventional approaches, the proposed continuous preamble injection jamming method achieves improved signal detection accuracy, jamming energy efficiency, and effective range. To the best of our knowledge, this protocol-aware scheme, which integrates neural network-based signal perception and denoising, offers a promising and cost-effective technical pathway for UAV countermeasures. Full article

Missile-borne active phased array antennas have been widely used in missile guidance for their beam agility, multifunctionality, and strong anti-interference capabilities. However, due to space constraints on the platform and difficulty in heat dissipation, the thermal power consumption of the antenna array can easily lead to excessive temperature, causing two primary issues: first, temperature-induced drift in T/R components, resulting in amplitude and phase errors in the feed current; second, temperature-dependent ripple voltage in the array’s secondary power supply, which exacerbates feed errors. Both issues degrade the electromagnetic performance of the array antenna. To mitigate these effects, this paper investigates feed errors and compensation methods in high-temperature environments. First, a synchronous Buck circuit ripple coefficient model is developed, and an electromagnetic–temperature coupling model is established, incorporating temperature-dependent feed current characteristics, and the law of electromagnetic performance changes is analyzed. On this basis, an electromagnetic performance compensation method based on a genetic algorithm is proposed to optimize the quantization compensation amount of the amplitude and phase of each element under the effect of high temperature. Full article

Transition metal (TM)-doped zinc oxide (ZnO) and zirconium dioxide (ZrO₂) nanocrystals exhibit complex correlations between crystal structure, defect chemistry, vibrational properties, and magnetic behavior that are strongly governed by synthesis route and dopant incorporation mechanisms. This review critically summarizes recent progress on Fe-, Mn-, and Co-doped ZnO and ZrO₂ nanocrystals synthesized by wet chemical, hydrothermal, and microwave-assisted hydrothermal methods, with emphasis on synthesis-driven phase evolution and apparent solubility limits. ZnO and ZrO₂ are treated as complementary host lattices: ZnO is a semiconducting, piezoelectric oxide with narrow solubility limits for most 3d dopants, while ZrO₂ is a dielectric, polymorphic oxide in which transition metal doping may stabilize tetragonal or cubic phases. Structural and microstructural studies using X-ray diffraction, electron microscopy, Raman spectroscopy, and Mössbauer spectroscopy demonstrate that at low dopant concentrations, TM ions may be partially incorporated into the host lattice, giving rise to diluted or defect-mediated magnetic behavior. When solubility limits are exceeded, nanoscopic secondary oxide phases emerge, leading to superparamagnetic, ferrimagnetic, or spin-glass-like responses. Magnetic measurements, including DC magnetization and AC susceptibility, reveal a continuous evolution from paramagnetism in lightly doped samples to dynamic magnetic states characteristic of nanoscale magnetic entities. Vibrational spectroscopy highlights phonon confinement, surface optical phonons, and disorder-activated modes that sensitively reflect nanocrystal size, lattice strain, and defect populations, and often correlate with magnetic dynamics. Rather than classifying these materials as diluted magnetic semiconductors, this review adopts a synthesis-driven and correlation-based framework that links dopant incorporation, local structural disorder, vibrational fingerprints, and magnetic response. By emphasizing multi-technique characterization strategies required to distinguish intrinsic from extrinsic magnetic contributions, this review provides practical guidelines for interpreting magnetism in TM-doped oxide nanocrystals and outlines implications for applications in photocatalysis, sensing, biomedicine, and electromagnetic interference (EMI) shielding. Full article

With the continuous proliferation of radio frequency devices, electromagnetic environments in various regions are becoming increasingly complex. Effective monitoring of the electromagnetic environment and identification of interference sources have thus become critical tasks for maintaining order in the electromagnetic spectrum. In recent years, rapid advances in UAV technology have spurred exploration of UAV-based electromagnetic spectrum monitoring as a novel approach. However, the limited payload capacity and endurance of UAVs constrain their monitoring capabilities. To address these challenges, we propose HMUDRL, a distributed heterogeneous multi-agent deep reinforcement learning algorithm. By leveraging cooperative operation between cluster-head UAVs (CH) and cluster-monitoring UAVs (CM) within a heterogeneous UAV swarm, HMUDRL enables high-precision detection and wide-area localization of UHF radiation source. Furthermore, we integrate a minimum-gap localization algorithm that exploits the spatial distribution of multiple CM to accurately pinpoint anomalous radiation sources. Simulation results validate the effectiveness of HMUDRL: in the later stages of training, the success rate of localizing target radiation sources converges to 96.1%, representing an average improvement of 1.8% over baseline algorithms; localization accuracy, measured by root mean square error (RMSE), is enhanced by approximately 87.3% compared to baselines; and communication overhead is reduced by more than 80% relative to homogeneous architectures. These results demonstrate that HMUDRL effectively addresses the challenges of data transmission control and sensing-localization performance faced by UAVs in UHF spectrum monitoring. Full article