Search Results (212)

An accurate prediction of aircraft wheel and brake (W&B) temperatures is increasingly important for ensuring landing gear safety, supporting turnaround decision-making, and allowing for more effective condition monitoring. Although the thermal behavior of brake assemblies has been studied through component-level testing, analytical formulations, and numerical simulation, current understandings remain fragmented and limited in operational relevance. This paper discusses research across landing gear sensing, thermal modeling, and data-driven prediction to evaluate the state of knowledge supporting a non-intrusive, temperature-centric monitoring framework. Methods surveyed include optical, electromagnetic, acoustic, and infrared sensing techniques as well as traditional machine-learning methods, sequence-based models, and emerging hybrid physics–data approaches. The review synthesizes findings on conduction, convection, and radiation pathways; phase-dependent cooling behavior during landing roll, taxi, and wheel-well retraction; and the capabilities and limitations of existing numerical and empirical models. This study highlights four core gaps: the scarcity of real-flight thermal datasets, insufficient multi-physics integration, limited use of infrared thermography for spatial temperature mapping, and the absence of advanced predictive models for transient brake temperature evolution. Opportunities arise from emissivity-aware infrared thermography, multi-modal dataset development, and machine learning models capable of capturing transient thermal dynamics, while notable challenges relate to measurement uncertainty, environmental sensitivity, model generalization, and deployment constraints. Overall, this review establishes a coherent foundation for thermography-enabled temperature prediction framework for aircraft wheels and brakes. 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

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

In this work, different implementation strategies for a Time-Domain (TD) focusing procedure applied to airborne Synthetic Aperture Radar (SAR) raw data are presented, with the key objective of quantitatively assessing their computing time. In particular, two methodological approaches are proposed: a pixel-wise strategy, which processes each image pixel independently, and a matrix-wise strategy, which handles data blocks collectively. Both strategies are further extended to parallel execution frameworks to exploit multi-threading and multi-node capabilities. The presented analysis is conducted within the context of the airborne SAR infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council (CNR) in Naples, Italy. This infrastructure integrates an airborne SAR sensor and a high-performance Information Technology (IT) platform well-tailored to the parallel processing of huge amounts of data. Experimental results indicate an advantage of the pixel-wise strategy over the matrix-wise counterpart in terms of computing time. Furthermore, the adoption of parallel processing techniques yields substantial speedups, highlighting its relevance for time-critical SAR applications. These findings are particularly relevant in operational scenarios that demand a rapid data turnaround, such as near-real-time airborne monitoring in emergency response contexts. Full article

Optical fiber sensors (OFSs) have emerged as essential tools in the monitoring of physical, chemical, and bio-medical parameters in harsh situations due to their high sensitivity, electromagnetic interference (EMI) immunity, and long-term stability. However, the current literature contains scattered information in most reviews regarding individual sensing technologies or domains. This study provides a structured exploratory review in a novel inter-family analysis of both intrinsic and extrinsic configurations by analyzing more than 23,000 publications between 2019 and 2025 in five key domains: industry, medicine and biomedicine, environmental chemistry, civil/structural engineering, and aerospace. The analysis aims to critically discuss how functional principles/parameters and methods of interrogation affect the applicability of different OFS categories. The results reveal leading trends in the use of techniques like the use of fiber Bragg gratings (FBG) and distributed sensing in high-accuracy conditions or the rising role of extrinsic sensors in selective chemical situations and point out new approaches in areas like Artificial Intelligence (AI)- or Internet of Things (IoT)-integrated sensors. Further, this synthesis not only connects pieces of knowledge but also defines the technological barriers in terms of calibration cost and standardization: this provides strategic insight regarding future research and the scalability of industry deployment. Full article

Coal mining faces critical challenges due to variable geological conditions that affect intelligent mining and safe production. Ground-penetrating radar (GPR), a high-resolution and non-destructive sensing technology, is essential for precise geological detection. However, underground electromagnetic interference, multiple reflections, and complex media significantly degrade the signal-to-noise ratio (SNR), causing reflection signals to be obscured and geological interfaces to become blurred, thereby hindering accurate subsurface interpretation. Traditional denoising methods struggle to extract weak reflection signals under such complex noise conditions. To address these challenges, this study proposes a physics-guided conditional diffusion model that integrates physical constraints with deep learning to achieve intelligent denoising and weak-signal recovery for high-noise GPR data. Specifically, a dual-path GMM probabilistically models both feature signals and complex noise, while incorporating the wave equation ensures physical consistency with electromagnetic propagation. Experiments using a hybrid dataset combining field-measured noisy data and simulated features—evaluated using SSIM, PSNR, MAE, peak alignment, and structural continuity—demonstrate that the proposed method outperforms existing techniques in both noise suppression and signal reconstruction. Field tests in underground coal mines further confirm its practical applicability. Full article

Synthetic aperture radar (SAR), as a high-resolution microwave remote sensing imaging technology, plays an indispensable role in both military and civilian applications. However, in complex electromagnetic countermeasure environments, radio frequency interference (RFI) severely degrades SAR imaging quality. SAR anti-interference, as a countermeasure method, has significantly practical values. Although deep learning-based anti-interference techniques have demonstrated notable advantages, two key issues remain unresolved: 1. Strong coupling between interference suppression and SAR imaging—most existing methods rely on raw echo data, leading to a complex processing pipeline and error accumulation. 2. Scarcity of labeled data—the lack of high-quality labeled data severely restricts model deployment. To address these challenges, this work constructs a standardized dataset and conducts comprehensive validation experiments based on this dataset. The main contributions are as follows: Firstly, this work establishes the mathematical model for block-type interference, laying a theoretical foundation for the subsequent construction of RFI-polluted data. Secondly, this work constructs a block-type interference dataset, which includes the labeled data constructed by our laboratory and open-source data from the Sentinel-1 satellites, providing reliable data support for deep learning. Thirdly, this work proposes an image subspace-based interference suppression method, which eliminates the dependence on raw echo data and significantly simplifies the processing pipeline. Finally, this work makes a fair comparison of the current works, summarizes the existing problems, and looks forward to possible future research directions. Full article

Displacement self-sensing active magnetic bearings (AMBs) have garnered significant attention from both academia and industry for their potential to reduce cost, enable system integration, and enhance reliability. While numerous self-sensing methodologies have been researched, the field lacks a unified framework for discussing their theoretical foundation and practical applicability. This paper analyzes and summarizes various displacement self-sensing methods, deriving the underlying principles and essence of these techniques, and clarifying the intrinsic interconnections of different schemes. The process of self-sensing is constructed through two steps: online inductance estimation and electromagnetic inductance modeling. A novel framework is then proposed, categorizing online inductance estimation, with dedicated discussion on modeling and handling critical nonlinearity like magnetic saturation and the eddy current effect. Furthermore, this review conducts a systematic comparative analysis, evaluating prevalent schemes against key performance metrics such as robustness, stability, signal-to-noise ratio (SNR), and system complexity. Finally, persistent challenges and future research trends are discussed. This review provides a valuable reference for both researchers and engineers when selecting and implementing self-sensing technologies for AMB systems. Full article

The Copernicus Programme is a joint European initiative developed by the European Commission (EC) and the European Space Agency (ESA) to provide accurate, up-to-date, and comprehensive Earth observation data for environmental monitoring, climate change analysis, disaster management, and security. The Copernicus program comprises a series of dedicated satellite missions, i.e., the Sentinels spanning a wide range of the electromagnetic spectrum with different sensing techniques. Sentinel-1 is the radar imaging component of Copernicus. It is a two-satellite constellation placed in the same orbit and spaced 180° apart. The all-weather, day-and-night images of Earth’s surface are systematically provided by Sentinel-1 to the Copernicus service component and to scientific users. The Sentinel-1 SAR data are suitable for interferometric and radiometric applications, whose performance depends on the thermal noise level in the data. The paper provides a comprehensive overview of the activities spanning 10 years, focused on properly measuring, characterizing, and removing the thermal noise from S-1 data. Full article

Three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrates have demonstrated remarkable abilities of ultrasensitive and reproducible molecular detection. The combination of both electromagnetic and chemical enhancement processes, light trapping, and multiple scattering effects of 3D structures are what enhance their performance. The principles of underlying enhancements are summarized systematically, and the main types of 3D substrates—vertically aligned nanowires, dendritic and fractal nanostructures, porous frameworks and aerogels, core–shell and hollow nanospheres, and hierarchical hybrid structures—are categorized in this review. Advances in fabrication techniques, such as template-assisted growth, electrochemical and galvanic deposition, dealloying and freeze-drying, self-assembly, and hybrid integration, are critically evaluated in terms of structural tunability and scalability. Novel developments in the field of biosensing are also highlighted, including non-enzymatic glucose sensing, tumor biomarker sensing, and drug delivery. The remaining limitations, such as low reproducibility, mechanical stability, and substrate standardization, are also noted, and future directions, such as stimuli-responsive designs, multifunctional hybrid platforms, and data-driven optimization strategies of SERS technologies, are also included. Full article

The continuous annealing process is widely used in the production of advanced high-strength steels. However, to tightly regulate the mechanical properties of the steel, precise control of processing parameters is needed. Although some techniques are available to monitor the mechanical properties of the steel on entry and exit to the furnace, monitoring the evolving microstructure of the steel through installation of sensors in the annealing line is extremely challenging due to the high temperature, high speed of the steel strip and limited space in the furnace. This study presents the development and validation of a multifrequency electromagnetic sensor system for real-time monitoring of microstructural transformations in steel during thermal cycling, intended for deployment in a continuous annealing line. Experiments were conducted on austenitic stainless steel to study the signal response to an increase in resistivity without a change in magnetic permeability. Pure nickel was tested to investigate the response to a change in magnetic permeability and the ferromagnetic-to-paramagnetic transition at its Curie temperature. A ferritic stainless steel was also tested to assess the performance of the system for high-temperature ferromagnetic materials and a higher-temperature ferromagnetic-to-paramagnetic transition. The tests indicate a strong response to material resistivity and permeability changes, with complementary information from different frequencies. Test results are supplemented by a finite element modelling study into the effect of a change in frequency and permeability on sensor response, with a discussion on the implications of experimental and modelling results for future applications. The results show that the developed system has the potential to characterise thermally induced changes in steels, establishing proof of concept for non-destructive, high-temperature electromagnetic sensing in steel processing and setting the foundation for further industrial deployment in phase and recrystallisation monitoring. Full article

This paper presents the design, simulation, and fabrication of a horn antenna integrated with a dielectric lens for focusing RF energy at 10 GHz. The antenna system combines established electromagnetic principles with 3D printing techniques to produce a cost-effective alternative to commercial focusing antennas. The design methodology employs the lensmaker’s formula and Snell’s law to determine lens curvature for achieving a specified focal length of 100 mm. COMSOL Multiphysics simulations indicate that adding a PTFE lens increases power density concentration compared to a standard horn antenna, with a simulated focal point at approximately 100 mm. Surface roughness analysis based on the Rayleigh criterion supports 3D printing suitability for this application. Experimental validation includes radiation pattern measurements of the antenna without the lens and power density measurements versus distance with the lens, both showing good agreement with simulation results. The measured focal length was $95\pm 5$ mm, closely matching simulation predictions. This work presents an approach for implementing focused RF delivery solutions for medical treatments, wireless power transfer, and precision sensing at significantly lower costs than commercial alternatives. Full article

Internet of Things (IoT)-based solar insecticidal lamps (SIL-IoTs) offer an eco-friendly alternative by merging solar energy harvesting with intelligent sensing, advancing sustainable smart agriculture. However, SIL-IoTs encounter practical challenges, e.g., hardware aging, electromagnetic interference, and abnormal data patterns. Therefore, developing an effective fault detection and diagnosis (FDD) system is essential. In this survey, we systematically identify and address the core challenges of implementing FDD of SIL-IoTs. Firstly, the fuzzy boundaries of sample features lead to complex feature interactions that increase the difficulty of accurate FDD. Secondly, the category imbalance in the fault samples limits the generalizability of the FDD models. Thirdly, models trained on single scenarios struggle to adapt to diverse and dynamic field conditions. To overcome these challenges, we propose a multi-level solution by discussing and merging existing FDD methods: (1) a data augmentation strategy can be adopted to improve model performance on small-sample datasets; (2) federated learning (FL) can be employed to enhance adaptability to heterogeneous environments, while transfer learning (TL) addresses data scarcity; and (3) deep learning techniques can be used to reduce dependence on labeled data; these methods provide a robust framework for intelligent and adaptive FDD of SIL-IoTs, supporting long-term reliability of IoT devices in smart agriculture. Full article

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Measuring pH is a critical parameter in environmental monitoring, biomedical diagnostics, food safety, and industrial processes. Optical fiber sensors have proven highly effective for pH detection due to their exceptional sensitivity, rapid response, and resistance to electromagnetic interference, making them well suited for real-time monitoring. This review offers a comprehensive analysis of recent advances in optical fiber-based pH sensors, covering key techniques such as fluorescence-based, absorbance-based, evanescent wave, and interferometric methods. Innovations in Fiber Bragg Grating and Surface Plasmon Resonance technologies are also examined. The discussion extends to the impact of pH-sensitive coatings—ranging from nanomaterials and polymeric films to graphene-based compounds—on enhancing sensor performance. Recent advancements have also enabled automation in data analysis and improvements in remote sensing capabilities. The review further compares the economic viability of optical fiber sensors with traditional electrochemical methods, while acknowledging persistent issues such as temperature cross-sensitivity, long-term stability, and fabrication costs. Overall, recent developments have broadened the functionality and application scope of these sensors by improving efficiency, accuracy, and scalability. Future research directions are outlined, including advanced optical interrogation techniques, such as Addressed Fiber Bragg Structures (AFBSs), microwave photonic integration, and optimized material selection. These approaches aim to enhance performance, reduce costs, and enable the broader adoption of optical fiber pH sensors. Full article