Search Results (1,445)

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished. Full article

Rapid and real-time monitoring of blood glucose concentration is critical for the diagnosis and management of diabetes, while conventional invasive detection methods suffer from inconvenience and discomfort, making non-invasive detection a research hotspot. In this study, a dual complementary split-ring resonator (DS-CSRR) operating at 3.3 GHz was designed and fabricated for non-invasive glucose concentration detection, aiming to address the problems of low sensitivity and large size of existing microwave glucose sensors. The sensor was fabricated on a low-cost FR4 dielectric substrate with dimensions of 20 × 30 × 0.8 mm³, and two U-shaped slots were incorporated into the traditional DS-CSRR structure to realize cross-polarization excitation. This design not only enhances the interaction between the electric field and glucose solution but also optimizes the quality factor (Q) and electric field distribution of the resonator without changing the overall size. Compared with the traditional DS-CSRR, the Q factor of the modified structure is increased to 130 under no-load conditions. The transmission coefficient Signal Port 2 to Port 1 (S21) of the sensor loaded with glucose solutions of different concentrations was measured using a vector network analyzer (VNA). The experimental results show a good linear frequency shift with the increase in glucose concentration, with a measured sensitivity of 1.95 kHz/(mg·dL⁻¹). In addition, the sensor is characterized by miniaturization, low cost and easy fabrication due to the adoption of standard PCB fabrication processes. This study successfully demonstrates a non-invasive microwave sensor with high sensitivity for glucose concentration detection, which has promising application potential in personal continuous glucose monitoring, and also provides a useful design strategy for the development of miniaturized high-sensitivity microwave biosensors. Full article

Flexible piezoelectric materials demonstrate broad application potential in wearable health monitoring, human–machine interaction, and biosensing. However, the piezoelectric response of pure PVDF-TrFE is limited and insufficient to meet the requirements for highly sensitive sensing. In this study, ZnO/PVDF-TrFE composite films with varying ZnO doping contents (3–11 wt%) were fabricated and systematically characterized in terms of their structural, thermal, and electrical properties. The results indicate that ZnO significantly promotes the formation of the polar β-phase in PVDF-TrFE, with the maximum β-phase content (F_β = 24.76%) and optimal piezoelectric performance achieved at 9 wt% ZnO doping. Devices based on this optimal composition exhibited stable ultrasonic transmission and reception capabilities under high-frequency pulse excitation, enabling sensitive detection of minor static pressure variations (e.g., contact pressure) through changes in ultrasonic echo signals, thereby realizing wearable conformity monitoring. Moreover, a sensor designed with a three-channel flexible substrate successfully captured human wrist pulse signals with high accuracy, demonstrating the practical utility and reliability of the device in flexible bio-electronic sensing applications. Full article

Surface plasmon resonance (SPR) sensors based on nanostructured metasurfaces offer enhanced sensitivity through engineered electromagnetic responses. In this study, we present an analytical and numerical investigation of the plasmonic behavior of gold nanopillar (Au-NP) and nanohole (Au-NH) arrays under both p- and s-polarized illumination, employing the Effective Medium Theory (EMT) in combination with the Transfer Matrix Method (TMM). The study combines Effective Medium Theory (EMT) and the Transfer Matrix Method (TMM) to describe the macroscopic optical response of multilayer plasmonic systems. For p-polarization, the nanostructure geometry strongly modulates the real and imaginary parts of the effective permittivity, with nanoholes supporting stronger SPR coupling and reduced optical losses compared to nanopillars. Under s-polarization, the effective permittivity remains largely invariant, primarily driven by the filling fraction. The analysis reveals that polarization-dependent behavior arises from boundary-condition-mediated coupling mechanisms governing surface plasmon excitation, aligning with classical plasmonic theory. Benchmarking against analytical dispersion relations and published experimental data for Au/BK7 systems shows close agreement within ±2°, confirming the physical consistency of the EMT–TMM framework. These results provide a systematic description of how polarization and filling fraction jointly modulate SPR coupling. The results offer a foundation for the rational design of plasmonic coatings and SPR-supporting metasurfaces by elucidating macroscopic coupling trends; however, no quantitative sensor performance metrics, such as refractive index sensitivity or figure of merit, are evaluated in this work. Full article

Chiral bound states in the continuum (BIC) metasurfaces have emerged as a promising platform for enhancing light–matter interactions, which have potential applications in advanced photonic and quantum information devices. However, simultaneously achieving near-perfect circular dichroism and highly efficient nonlinear conversion with highly symmetric structures in metasurfaces remains an open challenge. In this work, we design a $C_4$-symmetric chiral metasurface composed of eight elliptical silicon nanorods on a ${\mathrm{SiO}}_2$ substrate, where monocrystalline silicon is used as the nonlinear optical material. By combining simulations and nonlinear time-domain coupled-mode theory (TCMT), we discovered that both the optimal chirality and the nonlinear conversion efficiency can be attained simultaneously due to the critical coupling between the metasurface mode and the quasi-BIC mode. Meanwhile, a near-perfect circular dichroism (CD = 0.99) and a high nonlinear conversion efficiency of $7\times {10}^{-5}$ under a radiation intensity of $5{\mathrm{kW}/\mathrm{cm}}^2$ are numerically achieved due to the robustness of bound states in the continuum. This work offers a promising route toward high-performance chiral nonlinear photonic components, which is of great importance for the development of ultra-compact optical devices such as circular polarization detectors, chiral sensors, and nonlinear photonic chips for integrated optical and quantum information systems. Our research not only contributes to the fundamental understanding of chiral metasurfaces but also provides a practical approach for achieving high-efficiency nonlinear optical devices. Full article

Wildfires represent a significant global environmental challenge, necessitating advanced monitoring and assessment techniques. This study explores the integration of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 optical data within a Google Earth Engine (GEE) framework to enhance wildfire detection, burned area estimation, and severity assessment. By leveraging SAR’s capability to penetrate atmospheric obstructions and optical data’s spectral sensitivity to vegetation changes, the proposed methodology addresses limitations of single-sensor approaches. The results demonstrate strong correlations between SAR-based indices, such as the Radar Vegetation Index (RVI) and Dual-Polarized SAR Vegetation Index (DPSVI), and traditional optical indices, including the Normalized Burn Ratio (NBR) and differenced NBR (ΔNBR). Despite challenges related to terrain influence, sensor resolution differences, and computational demands, the integration of multi-sensor data in a cloud-based environment offers a scalable and efficient solution for wildfire monitoring. During the peak of the fire events, significant atmospheric obstruction was technically verified using Sentinel-2 metadata and the QA60 cloud mask band, which confirmed persistent cloud cover and thick smoke plumes over the study areas. This interference limited the reliability of purely optical monitoring, further justifying the integration of SAR data. Future research should focus on refining data fusion techniques, incorporating additional datasets such as thermal infrared imagery and meteorological variables, and enhancing automation through artificial intelligence (AI). This study underscores the potential of remote sensing advancements in improving fire management strategies and global wildfire mitigation efforts. Full article

Spatial light field metrics, such as cylindrical illuminance, provide essential information for qualitative lighting evaluation, yet they remain far less common in practice than horizontal illuminance. To address this gap, we present a multi-sensor prototype that simultaneously measures horizontal illuminance E_h and approximates mean cylindrical illuminance E_z from a set of vertical illuminances uniformly distributed around a cylindrical surface. The device uses a flexible PCB wrapped around a support barrel, along with an inertial and magnetic measurement unit for orientation tracking. The measurements enable direct calculation of the modelling factor defined in the technical standard EN 12 464 and the visualization of the directional light distribution using polar plots and an illuminance solid. Results show that the prototype approximates mean cylindrical illuminance with high accuracy while preserving directional information, allowing the illuminance solid to be decomposed into vector and symmetric components. Compared with conventional approximation methods, the proposed multi-sensor approach reduces spatial error and yields richer data for lighting analysis. These findings indicate that multi-sensor systems can bridge the gap between theoretical spatial metrics and practical photometry and support the improved modelling evaluation and integration of qualitative lighting parameters into routine workflows. Full article

Steels remain among the most widely used structural and engineering materials in modern infrastructure, energy systems, and industrial facilities. Their long-term reliability depends critically on the early detection of corrosion damage and microstructural degradation. This review surveys recent advances in two complementary families of non-destructive evaluation (NDE) methods: magnetic techniques, including magnetic Barkhausen noise (MBN), magnetic flux leakage (MFL), eddy current testing (ECT), and magnetic hysteresis analysis; and electrochemical methods including electrochemical impedance spectroscopy (EIS), linear polarization resistance (LPR), scanning vibrating electrode technique (SVET), and electrochemical noise (EN). Recent progress in sensor miniaturization, signal processing algorithms, and multi-technique integration is reviewed. Particular attention is given to the sensitivity of these methods to microstructural changes reported in the literature, including carbide dissolution, phase transformations, temper embrittlement, and sensitization in stainless steels, as well as to the conditions under which such sensitivity has been demonstrated. The potential synergy between magnetic and electrochemical monitoring is discussed as a possible pathway toward more robust, condition-based maintenance frameworks. Challenges related to field deployment, environmental interference, calibration, and data interpretation are identified, and future directions—including machine learning-assisted analysis and multi-physics sensor arrays—are outlined. Full article

Magnetoelectric (ME) materials that exhibit simultaneous coupling between electric polarization and magnetization have attracted significant attention due to their potential technological applications in the emerging generation of multifunctional devices. In this research, Ba_0.85Ca_0.15Ti_0.92Zr_0.08O₃-CoFe₂O₄:x (x = 0.1, 0.2, 0.3% mol) composites were synthesized using solid-state and sol–gel combustion chemical methods to elucidate their ME coupling at ultra-low concentrations of the magnetic phase. Rietveld refinement and Raman spectroscopy results confirm a shift in the morphotropic phase boundary (MPB), evidenced by an increase in the tetragonal phase relative to the orthorhombic structure. High stability of the P4mm and Amm2 symmetries is reached at 1300 °C without diffusion of Fe and Co into the octahedral site. At this temperature, the CoFe₂O₄ spinel structure remains stable without secondary phases. The orthorhombic phase fraction decreases from 55% to 37% as the magnetic phase fraction increases, driven by stress and constraint rather than ionic interactions alone. The Curie temperature decreases from 99 to 90 °C, attributed to the grain-size reduction effect rather than structural disorder. The dielectric permittivity (ε_r) reaches an absolute value of 5070 and progressively decreases with increasing magnetic saturation. An increase in compressive residual stress is observed, which ensures the mechanical stability of the electroceramics. Magnetoelectric (ME) coupling, evaluated through measurements of electric polarization as a function of the magnetic field, shows an increase from 3.8 to 4.9 μC/cm² under a magnetic field of 50 Oe. The composites with x = 0.2 and 0.3 mol% exhibit potential for applications in fast-switching magnetoelectric devices and magnetic field sensors. Full article

Accurate and continuous monitoring of flood dynamics is fundamental to understanding wetland hydrological processes and their ecological implications, yet it remains challenging due to the inherent trade-off between spatial and temporal resolution in remote sensing observations. This study advances flood monitoring methodology by developing and validating a spatiotemporal fusion framework specifically designed for multi-source Synthetic Aperture Radar (SAR) data—an approach that has remained underdeveloped despite its critical importance for all-weather wetland observation. We propose the Fusion SAR Operational Monitoring (FSOM) framework, which integrates three established components—the Flexible Spatiotemporal Data Fusion (FSDAF) model, the Sentinel-1 Dual-Polarized Water Index (SDWI), and automated thresholding classification—into a coherent processing chain that generates consistent high-resolution flood extent time series from multi-sensor SAR data (Sentinel-1 and GF-3). The FSOM was applied to the Chen Lake Wetland from 2020 to 2024, producing a monthly flood map dataset at 5 m spatial resolution. Quantitative validation demonstrated the superiority of the FSOM-derived products. Compared to water classifications using original Sentinel-1 data, the FSOM results achieved a significantly higher overall accuracy (exceeding 90%) and Kappa coefficient (>0.90) than the Sentinel-1 results, which had overall accuracy (exceeding 86%) and Kappa coefficient (>0.75). Critically, the producer’s accuracy for water bodies consistently surpassed 91%, indicating a substantial reduction in omission errors and markedly improved detection of small water bodies. These results confirm the effectiveness of the proposed FSOM framework in mitigating the spatiotemporal resolution trade-off, thereby providing a reliable high-fidelity data foundation to support precise wetland conservation and flood disaster emergency response. The framework thus offers a practical tool for scientists and water resource managers seeking to enhance monitoring capabilities in the world’s most dynamic and ecologically significant wetland ecosystems. Full article

High-precision detection of hazardous gases with low refractive indices ranging from 1.000 to 1.100, specifically including methane, carbon monoxide, and sulfur dioxide, is critical for industrial safety, yet conventional sensors often suffer from limited sensitivity and severe thermal cross-sensitivity. This work presents a Magneto-Optical Differential Photonic Crystals Sensor (MO-DPCS) utilizing indium antimonide (InSb) to address these constraints. Employing the Multi-Objective Dragonfly Algorithm (MODA), the system was inversely optimized to maximize magneto-optical polarization splitting while rigorously maintaining an ultra-high transmission efficiency. Crucially, an angular interrogation architecture operating under oblique incidence was established to maximize the magneto-optical non-reciprocity, where the detection was realized by fixing the terahertz source frequency and monitoring the precise angular displacements of the steep spectral edges. A differential detection technique was employed to utilize the non-reciprocal phase changes wherein Transverse Electric (TE) and Transverse Magnetic (TM) modes display contrasting kinematic characteristics in the presence of an external magnetic field. The findings indicate that with an adjusted magnetic field of 0.033 T, the MO-DPCS attains an exceptional differential sensitivity of 30.8°/RIU, much above the 0.8°/RIU seen in the unmagnetized condition. The differential approach efficiently eliminates common-mode thermal noise, minimizing temperature-induced drift to below 0.35° across a 1 K range. The suggested MO-DPCS offers a robust, self-referencing solution for stable and high-sensitivity gas sensing applications with a detection limit of 4.18 × 10⁻⁴ RIU. Full article

Background: Wearable sensors and global positioning systems (GPS) can enable objective monitoring of training loads in outdoor endurance sports. In biathlons, comparing training characteristics across developmental stages can help identify structural gaps and support evidence-informed progression within long-term athlete development (LTAD). This study aimed to quantitatively compare the annual training characteristics of Korean male biathlon national team (NT) and university (UNV) athletes. Methods: Annual physical training data (2022–2024) from NT (n = 6) and UNV (n = 6) athletes were collected using Catapult Vector S7 GPS devices and Polar H10 heart rate monitors. Training volume, intensity distribution (zones 1–3 based on %HRmax), modality (skiing vs. running), and periodization were compared using Mann–Whitney U tests with rank-biserial correlation (r_rb). Results: NT athletes accumulated a higher annual training time and distance than UNV athletes (812 vs. 606 h; 6359 vs. 4130 km; p = 0.002, r_rb = 1.000 for both). The NT athletes spent a lower proportion of time on low-intensity training and a higher proportion on mid and high intensities than UNV athletes (p ≤ 0.015). During high-intensity training, NT athletes maintained a higher proportion of ski-specific training, whereas UNV athletes relied more on running (skiing: 78.5% vs. 46.4%; running: 21.5% vs. 53.6%; both p < 0.001, r_rb = 1.000). The UNV group also showed a more concentrated structure during competition periods than NT athletes (COMP: 28.3% vs. 14.6%; p < 0.05). The absolute annual strength training time did not differ, but UNV athletes showed a higher strength ratio (23.3% vs. 16.8%; p < 0.001, r_rb = 1.000). Conclusion: UNV athletes exhibited a lower total volume, more low-intensity-skewed distribution, and reduced ski-specific exposure during high-intensity training compared with NT athletes. These observed structural gaps can provide empirical benchmarks that may help coaches plan stage-appropriate progression, and they illustrate the practical value of GPS- and wearable-based monitoring for identifying training divergences across developmental stages. Full article

LiDAR serves as the primary sensor for acquiring environmental information in intelligent driving systems. However, under adverse weather conditions, point cloud signals obtained by LiDAR suffer from intensity attenuation and noise interference, leading to a decline in segmentation accuracy. To address these issues, this paper designs a lightweight semantic segmentation system based on the Gated Visual State Space Model (VMamba), named RainMamba. Specifically, the system utilizes spherical projection to transform point clouds into 2D sequences and constructs a physical perception feature embedding module guided by the Beer–Lambert law to explicitly model and suppress spatial noise at the source. Subsequently, an uncertainty-weighted cross-modal correction module is employed to incorporate RGB images for dynamically calibrating the degraded point cloud data. Finally, a VMamba backbone is adopted to establish global dependencies with linear complexity. Experimental results on the SemanticKITTI dataset demonstrate that the system achieves an inference speed of 83 FPS, with a relative mIoU improvement of approximately 7.2% compared to the real-time baseline PolarNet. Furthermore, zero-shot evaluations on the real-world SemanticSTF dataset validate the system’s robust Sim-to-Real generalization capability. Notably, RainMamba delivers highly competitive accuracy comparable to the state-of-the-art heavy-weight model PTv3 while requiring a significantly lower parameter footprint, thereby demonstrating its immense potential for practical edge-computing deployment. Full article

Monocular 3D reconstruction remains a persistent challenge for autonomous driving systems in Degraded Visual Environments (DVEs) with extreme glare and low illumination, such as highway tunnels, due to the lack of reliable texture cues. This paper proposes a physics-aware deep learning framework that overcomes these limitations by fusing polarization sensing with conventional intensity imaging. Unlike traditional end-to-end data-driven fusion strategies, we propose a Modality-Aligned Parameter Injectionstrategy. By remapping the weight space of the input layer, this strategy achieves a smooth transfer of the pre-trained Vision Transformer (i.e., MiDaS) to multi-modal inputs. Its core advantage lies in the seamless integration of four-channel polarization geometric information while fully preserving the pre-trained semantic representation capabilities of the backbone network, thereby avoiding the overfitting risk associated with training from scratch on small-sample data. Furthermore, we design a Reliability-Aware Gating mechanism that dynamically re-weights appearance and geometric cues based on intensity saturation and the physical validity of polarization signals as measured by the Degree of Linear Polarization (DoLP). We validate the proposed method on our self-constructed POLAR-GLV benchmark, a real-world dataset collected specifically for high dynamic range tunnel scenarios. Extensive experiments demonstrate that our method consistently outperforms intensity-only baselines, reducing geometric reconstruction error by 24.2% in high-glare tunnel exit zones and 10.0% at tunnel entrances. Crucially, compared to multi-stream fusion architectures, these performance gains come with negligible additional computational cost, making the framework highly suitable for resource-constrained onboard inference environments. Full article

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. To address this limitation, this study investigates the feasibility of using geostationary satellite observations to enhance the spatial completeness of Sentinel-2 NDVI at its standard revisit intervals through cloud gap-filling applications. Geostationary Ocean Color Imager II (GOCI-II) data (250 m) was used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI (10 m) served as the reference dataset. To enable cross-sensor integration, a data-driven transformation framework was developed to convert GOCI-II NDVI into MSI-like NDVI while preserving dominant spatial variation patterns rather than pursuing strict pixel-level super-resolution. The transformed NDVI was assessed through spatial comparisons and statistical metrics, including correlation coefficient, mean absolute error, root mean square error (RMSE), normalized RMSE, and structural similarity index measure. Results show that geostationary-derived NDVI captures broad spatial organization and field-scale variability observed in MSI NDVI. Building on this cross-scale consistency, cloud gap-filling experiments demonstrate that temporally adjacent transformed NDVI scenes maintain consistent variation patterns, supporting their complementary use for compensating cloud-induced gaps. Although reduced contrast and magnitude-dependent biases remain, primarily due to the large spatial resolution difference and sub-pixel heterogeneity, an intermediate-resolution (80 m) sensitivity analysis indicates improved stability when the resolution gap is reduced. Overall, these findings highlight the practical potential of integrating geostationary and polar-orbiting observations to improve NDVI spatial continuity in cloud-prone regions. Full article