Search Results (688)

Environmental monitoring of freshwater ecosystems faces significant challenges in detecting illicit wastewater discharges, which often remain unrecognized due to their intermittent nature and limited spatial footprint. This study presents a novel integrated strategy combining satellite remote sensing, Unmanned Aerial Vehicle (UAV)-based proximal sensing, and in situ measurements to enhance pollution detection in vulnerable aquatic environments. The methodology was applied to Lake Avernus (Italy), a volcanic lake historically affected by eutrophication and toxic cyanobacterial blooms. Landsat 8–9 thermal analysis revealed no detectable anomalies, reflecting the limitations of its coarse spatial resolution. Sentinel-2 multispectral imagery was then analyzed through spectral indices, band ratios, and reflectance signatures, revealing localized variations in surface reflectance and spatial heterogeneity in water optical properties. These satellite-derived anomalies guided targeted high-resolution UAV surveys. UAV-based thermal imaging revealed an elevated-temperature zone along the adjacent shoreline. In situ field screening flagged a candidate chemical anomaly at this location. The hierarchical framework demonstrates that satellite screening effectively identifies areas of concern, while UAV thermal imaging enables high-resolution localization of features invisible to satellite sensors, and in situ measurements provide essential ground-truth validation. This replicable, low-cost methodology offers a powerful tool for early warning, surveillance, and sustainable management of sensitive freshwater ecosystems. Full article

While Thermal Infrared (TIR) sensors are standard for monitoring volcanic activity, their efficacy is severely compromised by meteorological clouds and dense volcanic ash. To overcome these optical limitations, we present the first ground-based application of a passive microwave radiometer for continuous volcano monitoring. Operating in the 10–12 GHz band, our Total Power Microwave Receiver is stationed 12 km from Mount Etna’s active craters to measure thermal emissions from eruptive hotspots. Unlike traditional TIR imaging, this low-cost, automated system exploits the atmospheric transparency of microwave wavelengths, enabling uninterrupted observation regardless of weather or solar illumination. We detail the system’s design and report its successful detection of volcanic phenomena during the 2023–2025 eruptive cycles, including the transit of a high-temperature ash cloud that triggered a significant radiometric peak. Our findings demonstrate that fixed-point microwave radiometry provides a reliable thermal signature of eruptive activity, offering a pioneering and highly accessible tool for the next generation of global volcanic early warning systems. Full article

Glass-ceramics are inorganic, non-metallic materials obtained by controlled crystallization of glasses through different processing routes; they contain at least one functional crystalline phase together with a residual glass, and the crystallized fraction may range from trace levels to nearly complete crystallization. Transparent glass-ceramics (TGCs) constitute the optically transparent subset of this class and combine a controlled crystalline microstructure with a residual amorphous matrix. Their transparency distinguishes them from conventional opaque glass-ceramics and is achieved by minimizing light scattering through careful control of crystallite size, volume fraction, spatial distribution, and refractive-index mismatch between the crystalline and glassy phases. Unlike conventional sintered ceramics, TGCs retain many of the processing advantages of glass while incorporating crystalline phases that can enhance mechanical, thermal, optical, or functional properties. Depending on their composition and microstructure, TGCs may exhibit improved hardness, fracture toughness, thermal stability, chemical durability, luminescence, nonlinear optical response, or ion-exchange strengthening capability. These features make TGCs attractive for applications requiring both optical clarity and advanced performance, including protective cover glass, transparent armour, precision optical substrates, laser and photonic components, optical sensors, and multifunctional host materials for rare-earth ions and nanoparticles. Full article

Timely wildfire monitoring depends critically on optical and thermal infrared sensor observations from spaceborne instruments. The TS-SatFire benchmark (2025) consolidates multispectral VIIRS image stacks from Suomi-NPP and NOAA-20 for three tasks: active fire (AF) detection, burned area (BA) mapping, and fire progression (FP) prediction. We make two contributions. First, a systematic label-quality audit reveals that many fires lack ground-truth annotations; 18 training fires and 2 test fires were excluded for AF, and the two unannotated test fires cannot be scored by any model. We further document the benchmark’s scoring procedure, which differs from ours in ways that make the two sets of figures incomparable, and the BA label encoding in the released GeoTIFFs; the BA task is only audited. Second, we propose SpaSE-UNet3D, a spatial squeeze-and-excitation 3D U-Net whose spatial-only $(1,3,3)$ convolutions avoid temporal mixing on short observation windows, while SE channel attention reweights the VIIRS spectral bands dynamically. With micro-averaging over all test pixels, it reaches F1 = $0.8549\pm 0.0005$ on AF and $0.3845\pm 0.0221$ on FP at TS = 2, matching or exceeding the strongest published baselines on their respective terms. A single-day AF input reaches $0.8520\pm 0.0008$, within 0.003 of the two-day figure, indicating that one acquisition carries most of the detectable signal, whereas published baselines use up to six days; on FP, we use one third of their temporal context. An ablation shows the spatial-only design matches the accuracy of a full $(3,3,3)$ network with $2.72\times$ fewer parameters. Code and results are publicly available. Full article

The objective of this study was to identify chitosan formulations suitable for sustainable functional integration into composite materials. To this end, the influence of solvent type (acetic acid and lactic acid) and chitosan molecular weight on film formation, rheological behavior, thermal response, thermo-optical properties, chemical structure, and piezoelectric performance was systematically investigated. Optimized casting and drying procedures produced transparent, homogeneous, and mechanically stable films suitable for comprehensive characterization. Rheological and thermo-optical analyses demonstrated that solvent selection strongly influenced polymer network formation and molecular mobility. Films prepared using acetic acid exhibited denser and stiffer polymer networks with improved dimensional stability, whereas lactic acid produced more flexible and elastic films. Thermogravimetric analysis revealed only minor differences in the intrinsic thermal stability of the investigated films, while FTIR confirmed that the solvent systems did not alter the chemical structure of chitosan. Electrical measurements carried out whilst the system was subjected to periodic mechanical excitation revealed weak but equally periodic electrical signals, which demonstrate a sensor functionality. These results demonstrate that chitosan films possess tunable structural, thermal, and potential mechanoelectrical sensor properties and highlight their potential as sustainable, functionally integrated components in composite material systems. Full article

Visual geo-localization is a predominant approach for unmanned aerial vehicles (UAVs) operating in Global Navigation Satellite System (GNSS)-denied environments, typically achieved by matching UAV-captured visible optical images with satellite base maps. However, under low-light conditions, visible cameras struggle to capture distinct features. While infrared sensors can capture clear features in such scenarios, the significant modality gap between thermal infrared images and optical satellite base maps makes accurate matching highly challenging. In this paper, we propose a novel cross-modal super-resolution matching and geo-localization method constrained by geographic consistency. First, a geographic consistency normalization module is introduced to narrow the modality gap between satellite optical images and thermal infrared images, thereby enhancing cross-modal matchability. Subsequently, a thermal infrared super-resolution enhancement module is employed to improve the spatial resolution and detail representation of the images, effectively increasing feature discriminability in low-texture regions. Finally, an end-to-end dense matching module is utilized to strengthen the stability of cross-modal correspondence estimation, ultimately improving geo-localization accuracy in low-light environments. Extensive experiments conducted on both a self-constructed network dataset and a real-world flight dataset demonstrate that the proposed method outperforms current competitive approaches. The proposed framework is not a simple combination of existing enhancement and matching modules, but a task-driven design that jointly addresses cross-modal discrepancy, low-resolution thermal imagery, and robust correspondence estimation. Experiments on self-constructed and public datasets demonstrate its robustness and superiority, achieving average geo-localization errors of 1.31 m and 8.04 m, respectively. Full article

Since its launch in 2015, the Sentinel-2 mission has become a cornerstone of moderate-resolution vegetation monitoring, enabling spatially explicit and temporally dense observations of terrestrial ecosystems. Its combination of 10–20 m spatial resolution, a revisit interval of less than five days, and a spectral configuration including red-edge and Short-Wave Infrared (SWIR) bands has transformed optical vegetation monitoring beyond coarse-resolution greenness products. This review synthesises the use of Sentinel-2 for vegetation monitoring, with emphasis on phenology and growth dynamics, biomass and carbon estimation, vegetation stress detection, and associated methodological developments. A systematic Scopus search identified 1700 publications, of which 1097 studies were retained following thematic and methodological screening. The results reveal rapid growth in Sentinel-2-based research after 2018, reflecting its transition into a widely adopted data source supported by cloud-based processing platforms and harmonised data products. Research output is concentrated in a limited number of journals and regions, with Europe and Asia dominating contributions, while other regions remain underrepresented. Phenology and growth monitoring, biomass and carbon assessment, and vegetation stress analysis emerged as the principal application domains. Across these themes, methodological development has shifted from vegetation indices towards machine learning, hybrid radiative-transfer modelling, and multi-sensor data fusion. The reviewed evidence indicates that no single methodological approach consistently outperforms others; rather, performance depends on the target variable, ecosystem characteristics, and the treatment of observational uncertainty. Sentinel-2 has transformed vegetation monitoring by enabling spatially explicit assessment of vegetation phenology, biomass, carbon dynamics, and stress across ecosystems. However, important challenges remain, including uncertainty propagation, limited sensitivity to early physiological stress, the absence of thermal observations, and uneven validation across ecosystem types. Future progress will depend on uncertainty-aware retrieval frameworks, physically informed hybrid models, multi-sensor integration, and expanded calibration and validation across underrepresented ecosystems. Full article

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

LiDAR sensors provide reliable physical distance measurements using laser signals, enabling accurate acquisition of 3D information for various optical systems. However, they are costly, require significant weight and space, and their reliability and accuracy degrade under adverse environmental and weather conditions. In contrast, thermal cameras operating in the infrared spectrum can capture stable visual information even in challenging scenarios such as nighttime, low-light, and rain. However, they cannot directly provide the physical 3D depth information that LiDAR offers. To design efficient optical systems, there is a growing need for techniques that transform thermal image data into LiDAR-like depth information. While deep learning models can theoretically learn direct mappings between thermal and LiDAR modalities, the scarcity of acquiring paired thermal–LiDAR datasets and the difficulty of acquiring them make this task challenging. In this paper, we propose a thermal image-to-LiDAR depth transformation framework. Our method leverages large-scale pretrained visual models for depth estimation to generate initial depth predictions from thermal inputs. Since pretrained RGB-based models face a modality gap when applied to thermal data, we introduce a two-stage depth refinement. Stage 1 corrects global scale inconsistencies, and Stage 2 refines local structural details. Experiments on the MS2 dataset demonstrate that the proposed framework consistently improves the initial DepthPro outputs across day, night, and rainy conditions. Both quantitative metrics and qualitative comparisons show that RGB-pretrained depth predictions can provide useful structural cues for thermal depth estimation when their global scale and local structural errors are explicitly refined. Full article

Wildfires are significant ecological and environmental disasters, impacting forests, ecosystems, climate stability and human life. The visible-spectrum imagery-based traditional wildfire monitoring system can fail to perform well in the presence of smoke, haze and low lighting. A number of machine learning and deep learning techniques have been proposed, but most of the studies do not provide an integrated Short-Wave Infrared (SWIR) optical-electronic payload framework along with an intelligent optimization technique. The objective of this research is to design an intelligent SWIR-based optical-electronic payload architecture for accurate detection and remote sensing of wildfire and Earth applications via deep learning and optimization techniques. The proposed framework is based on Sentinel-2 SWIR satellite data layers with wildfire and non-wildfire samples. To enhance the quality of the images and the representation of their spectral domain, the following preprocessing operations are carried out: resizing, image normalization, SWIR band extraction, and data augmentation. The following spectral feature extraction techniques are then used: burn area analysis, vegetation stress analysis, and thermal anomaly detection. The framework also incorporates SWIR optical payload design, electronic subsystem development and SWIR InGaAs sensor modeling. Finally, a Hybrid Convolutional Neural Network (CNN)–Residual Network 50 (ResNet50) model optimized by Grey Wolf Optimization (GWO) is used for wildfire classification and hyperparameter tuning. The proposed framework achieved an accuracy of 91.03%, precision of 91.27%, recall of 91.03%, and F1-score of 91.01%. The wildfire detection capability, classification robustness, and convergence performance were enhanced through the integration of SWIR spectral analysis, hybrid deep learning and GWO. The proposed framework offers an effective and trustworthy solution for intelligent wildfire monitoring and Earth remote sensing applications with enhanced spectral sensing and classification performance. Full article

To address the demand for early hydrogen monitoring in power equipment insulation systems, a fiber-optic Fabry–Perot (F-P) hydrogen sensor based on a Pd–Cu alloy microcantilever is proposed. The microcantilever serves as the force-sensitive structure, with a Pd–Cu alloy film deposited as the hydrogen-sensitive layer and an Au reflective layer introduced to enhance optical reflection and suppress thermal drift. Hydrogen absorption induces volume expansion of the Pd–Cu film, causing cantilever bending and a consequent variation in the F-P cavity length, which leads to a shift in the characteristic wavelength of the reflected spectrum and enables wavelength-demodulated hydrogen detection. Finite element analysis was conducted to investigate the stress distribution and displacement response, confirming the effective amplification effect of the microcantilever structure. Sensor fabrication, packaging, and hydrogen response experiments were subsequently carried out. The results show a good linear response in the hydrogen concentration range of 0–300 ppm, with a wavelength sensitivity of approximately 20.8 pm/ppm and a limit of detection of 3.24 ppm. In a 24 h stability test, the baseline fluctuation standard deviation was 22.46 pm, indicating good stability and repeatable sensing performance. Temperature variation produced a wavelength sensitivity of approximately 0.2734 nm/°C, and environmental condition tests further demonstrated stable operation under temperature, humidity, vibration, and electromagnetic disturbances. The proposed sensor exhibits immunity to electromagnetic interference, intrinsic safety, and compatibility with miniaturized integration, showing promising potential for low-concentration hydrogen monitoring in power equipment. Full article

Spacecraft electrostatic discharge (ESD) can generate transient electric-field disturbances, pulse currents, and electromagnetic coupling that threaten onboard electronics and mission reliability. Localizing discharge sources in confined spacecraft spaces remains difficult because conventional time-difference-of-arrival, radio-frequency, acoustic, and optical methods often require strict synchronization, large arrays, suitable propagation media, line-of-sight conditions, or explicit propagation models. This study develops a spacecraft-oriented, ground-validated electrostatic-induction sensor-array framework for three-dimensional localization of transient discharge events. Different from conventional received-signal-strength-based Apollonius localization, the proposed approach uses relative transient electrostatic-induction response features, including root-mean-square value, envelope energy, and integrated energy, and calibrates their feature ratios into distance-ratio constraints using a Power + offset mapping. These calibrated constraints are interpreted as Apollonius-sphere constraints, and the source coordinate is estimated by nonlinear residual minimization without relying on high-precision arrival-time picking. The method is evaluated on a controlled one-cubic-meter confined-space laboratory platform with known sensor geometry. Ten independent test positions show centimeter-level localization accuracy in the present calibration domain. The envelope-energy representation performs best, with a mean three-dimensional error of 7.01 cm, a median error of 6.94 cm, and a maximum error of 10.05 cm. These results demonstrate the feasibility of the sensing–calibration–inversion chain as a preliminary ground-based proof of concept. The reported accuracy should not be interpreted as direct on-orbit performance because the present experiment does not include vacuum, plasma, thermal gradients, spacecraft materials, metallic enclosures, or electronics integrated for spacecraft onboard operation; further environment-specific calibration and validation are required before practical spacecraft deployment. Full article

Autonomous driving sensor fusion housings (SFHs) integrate LiDAR, radar, camera, and computing modules within a shared mechanical and thermal enclosure. This review examines how coefficient of thermal expansion (CTE) mismatch among housing polymers, aluminum heat spreaders, substrates, and solder joints can contribute to interfacial delamination, solder joint fatigue, optical misalignment, and Thermomechanical Coupling Interference (TMCI). Using a structured narrative review of 99 publications and authoritative standards from primarily 2009 to 2026, the article organizes the evidence into a 4 × 4 taxonomy linking four failure mechanisms with experimental, computational, AI/ML, and qualification-oriented approaches. The review explicitly distinguishes direct literature evidence, transferred package-level evidence, model-based extrapolation, and author-derived conceptual estimates. Accordingly, TMCI temperature increments, sensor spacing values, optical drift estimates, and lifetime projections are discussed only as case-specific screening-level hypotheses unless directly validated in the cited literature. Five research gaps are identified: standardized multi-sensor TMCI validation, aging-corrected material and solder fatigue databases, long-term qualification of thermally conductive nanocomposites, SFH-specific validation of physics-informed digital twins, and integrated multi-failure testing. The contribution of this article is therefore primarily structural and agenda setting: it clarifies what is supported by direct evidence, what is transferred from adjacent domains, and what remains to be validated before robust SFH-level reliability guidance can be established. Full article

The accurate detection and quantification of electromagnetic radiation in the infrared (IR) and terahertz (THz) regions are critical for modern applications, yet they remain challenging due to the “THz gap” and the limitations of current room-temperature technologies. This paper proposes a novel uncooled IR–THz power sensor based on a hybrid graphene oxide (GO) and polydimethylsiloxane (PDMS) bilayer integrated into a surface plasmon resonance (SPR) architecture in the Kretschmann configuration. The device exploits the broadband optical absorption of GO to efficiently convert incident radiation into heat, while the high thermo-optic coefficient of the PDMS layer translates these thermal variations into measurable refractive index shifts. Finite Element Method (FEM) modeling was employed to optimize the sensor design, predicting a linear angular shift of 0.093 deg/mW. Experimental results confirm the theoretical expectations, demonstrating a high sensitivity of 0.083 deg/mW and an exceptionally low limit of detection and resolution on the order of 15 nW. By eliminating the need for cryogenic cooling or vacuum packaging, this platform offers a compact, low-cost, and high-performance solution for next-generation IR–THz metrology. Full article

The safe and accurate measurement of liquid hydrogen (LH₂) tank fill levels is a critical enabling technology for the adoption of hydrogen as a sustainable aviation fuel. Although LH₂ fill level measurement techniques have been applied in industrial, automotive, and space applications, no system has yet been validated at the scale, robustness, and precision required for modern aircraft Fuel Quantity Indication Systems (FQIS). Differentialpressure sensors are commonly employed in industrial cryogenic systems and hydrogen refueling stations; however, their accuracy is strongly influenced by dynamic effects such as filling transients and liquid sloshing, rendering them unsuitable for aviation-grade FQIS requirements which call for high accuracy and reliability. While simulations and analytical studies propose alternative LH₂ level sensing concepts, experimental validation and direct comparative assessments of different sensor architectures remain scarce. Furthermore, although several manufacturers offer LH₂ fill-level sensors, the stated measurement accuracies have not been independently verified, highlighting the need for systematic experimental investigation under representative operating conditions. A complete evaluation of an ${\mathrm{LH}}_2$ FQIS requires testing under anticipated flight conditions, including accelerations, varying attitudes, vibrations, dynamic sloshing, and long-term cycling. As a preliminary investigation, this work experimentally evaluates five liquid level sensing concepts based on measurements of dielectric constant, thermal capacity, and optical absorption properties using liquid nitrogen (${\mathrm{LN}}_2$) as a representative surrogate for ${\mathrm{LH}}_2$ under quasi-static conditions. The results demonstrate that optical absorption-based sensors in the near-infrared spectrum are unsuitable for LH₂ and LN₂ liquid level measurement. In contrast, capacitive probes and resistive thermal devices (RTDs) exhibit robust and repeatable performance under cryogenic conditions, demonstrating measurement resolutions of better than $5.1\mathrm{mm}$. These findings provide experimentally grounded guidance for the development of future LH₂-compatible FQIS architectures for aviation applications. Full article