Search Results (1,537)

In order to address the difficulty in acquiring degraded images for star sensors under hypersonic conditions, this paper proposes a star sensor imaging simulation model which can effectively analyze the influence of thermochemical non-equilibrium flow on the star sensor. Firstly, the two-temperature model is adopted to describe the relaxation phenomenon of hypersonic non-equilibrium flow, and the chemical reaction is simulated by Arrhenius law. Then, the effects of optical transmission and thermal radiation on star sensor imaging are quantitatively analyzed. Based on this degradation model, the degraded star images under two typical working conditions are simulated. The simulation results show that the radiation of the solid media has the most significant influence on the imaging of the star sensor. The detectable limit magnitudes of the star sensor obtained under the two working conditions are 3.28 and 4.55, respectively. The research results can provide important theory and technical support for the system design and algorithm testing of star sensors on near-space hypersonic platforms. Full article

This study introduces an all-fiber optic sensing network based on fiber Bragg grating (FBG) technology for structural health monitoring (SHM) of launch vehicle payload fairings under extreme thermo-mechanical conditions. A wavelength–space dual-multiplexing architecture enables full-field strain and temperature monitoring with minimal sensor deployment. Structural deformations are reconstructed from local measurements using the inverse finite element method (iFEM), achieving sub-millimeter accuracy. High-temperature experiments verified that FBG sensors maintain a strain accuracy of 0.8 με at 500 °C, significantly outperforming conventional sensors. Under 15 MPa mechanical loading and 420 °C thermal shock, the fairing structure exhibited no damage propagation. The sensing system captured real-time strain distributions and deformation profiles, confirming its suitability for aerospace SHM. The combined use of iFEM and FBG enables high-fidelity large-scale deformation reconstruction, offering a reliable solution for reusable aerospace structures operating in harsh environments. Full article

The evolution of next-generation urban infrastructure necessitates the deployment of intelligent pavement systems capable of real-time data acquisition, adaptive response, and predictive analytics. This article presents the design, implementation, and performance evaluation of the smart pavement system incorporating multimodal embedded sensors for traffic density analysis, structural health monitoring, and environmental surveillance. SPS integrates piezoelectric transducers, micro-electro-mechanical system accelerometers, inductive loop coils, fiber Bragg grating (FBG) sensors, and capacitive moisture and temperature sensors within the asphalt and sub-base layers, forming a distributed sensor network that interfaces with an edge-AI-enabled data acquisition and control module. Each sensor node performs localized pre-processing using low-power microcontrollers and transmits spatiotemporal data to a centralized IoT gateway over an adaptive mesh topology via long-range wide-area network or 5G-Vehicle-to-Everything protocols. Data fusion algorithms employing Kalman filters, sensor drift compensation models, and deep convolutional recurrent neural networks enable accurate classification of vehicular loads, traffic, and anomaly detection. Additionally, the system supports real-time air pollutant detection (e.g., NO₂, CO, and PM2.5) using embedded electrochemical and optical gas sensors linked to mobile roadside units. Field deployments on a 1.2 km highway testbed demonstrate the system’s capability to achieve 95.7% classification accuracy for vehicle type recognition, ±1.5 mm resolution in rut depth measurement, and ±0.2 °C thermal sensitivity across dynamic weather conditions. Predictive analytics driven by long short-term memory networks yield a 21.4% improvement in maintenance planning accuracy, significantly reducing unplanned downtimes and repair costs. The architecture also supports vehicle-to-infrastructure feedback loops for adaptive traffic signal control and incident response. The proposed SPS architecture demonstrates a scalable and resilient framework for cyber-physical infrastructure, paving the way for smart cities that are responsive, efficient, and sustainable. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

Reliable measurement of multiphase flow is fundamental to production evaluation in complex oil and gas wells. However, conventional sensors often suffer from low integration, limited measurement capability, and potential environmental impact. To address these challenges, a photoelectric composite three-phase flow sensor is developed, integrating multiple electrode rings for water holdup and liquid-phase velocity measurement, with dual optical-fiber probes for gas holdup and gas-phase velocity detection. A slip model is further applied to quantify the dependence of slip velocity on liquid holdup based on measured phase rates. Experimental results demonstrate high sensitivity to bubble-flow structures, accurate extraction of gas holdup and phase velocities, and stable full-range water holdup calibration from 0% to 100% at 5 V and 15 V with effective temperature and salinity compensation. And compared with existing technologies, the sensor designed in this paper has the advantages of high integration, a simple structure, multiple measurement parameters, and higher water-holding capacity resolution in low-saturation areas, providing more advanced technical means for conventional profile three-phase flow logging. Full article

A dynamic filtering system that can choose in real time between two different noise filters depending on the dynamics of the measured environment is presented. Unlike other adaptive filters approaches, this system does not require prior knowledge of the environment beyond noise characteristics. We implemented this system into a Brillouin optical time-domain analysis (BOTDA) sensing scheme using a closed-circuit control system for dynamic tracking of the Brillouin Frequency Shift (BFS) along the sensing fiber using a Proportional-Integral-Derivative (PID) controller. Through experiments and numerical simulations, we compare this method to the filtering capabilities of P and PI controllers chosen as optimal in a previous work for closed-circuit BOTDA (CC-BOTDA). Results show that the adaptive noise filter provides a dynamic response comparable to the other controllers, while increasing noise suppression by a factor between 30% and beyond 100%, showing how an adaptive system can improve suppression with only knowledge of the measurement noise. Full article

Accurate estimation of above-ground biomass (AGB) is vital for carbon accounting, biodiversity conservation, and sustainable forest management, especially in tropical regions under strong anthropogenic pressure. This study estimated and mapped AGB in the Atacora Mountain Chain, Togo, using a multi-source remote sensing approach within Google Earth Engine (GEE). Field data from 421 plots of the 2021 National Forest Inventory were combined with Sentinel-1 Synthetic Aperture Radar, Sentinel-2 multispectral imagery, bioclimatic variables from WorldClim, and topographic data. A Random Forest regression model evaluated the predictive capacity of different variable combinations. The best model, integrating SAR, optical, and climatic variables (S1S2allBio), achieved R² = 0.90, MAE = 13.42 Mg/ha, and RMSE = 22.54 Mg/ha, outperforming models without climate data. Dense forests stored the highest biomass (124.2 Mg/ha), while tree/shrub savannas had the lowest (25.38 Mg/ha). Spatially, ~60% of the area had biomass ≤ 50 Mg/ha. Precipitation correlated positively with AGB (r = 0.55), whereas temperature showed negative correlations. This work demonstrates the effectiveness of integrating multi-sensor satellite data with climatic predictors for accurate biomass mapping in complex tropical landscapes. The approach supports national forest monitoring, REDD+ programs, and ecosystem restoration, contributing to SDGs 13, 15, and 12 and offering a scalable method for other tropical regions. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

This study presents a robust deep learning-based approach for temperature sensing using Fiber Specklegram Sensors (FSS), leveraging an extended experimental framework to evaluate model generalization. A convolutional neural network (CNN), specifically a customized MobileNet architecture (MNet-reg), was trained on multiple experimental datasets to assess the impact of increasing data availability on sensing accuracy. Generalization is evaluated as cross-dataset performance under unseen experimental realizations, rather than under controlled intra-dataset splits. The experimental setup utilized a multi-mode optical fiber (MMF) (core diameter 62.5 µm) subjected to controlled thermal cycles via a PID-regulated heating system. The curated dataset comprises 24,528 specklegram images captured over a temperature range of 25.00 °C to 200.00 °C with increments of ~0.20 °C. The experimental results demonstrate that models trained with an increasing number of datasets (from 1 to 13) significantly improve accuracy, reducing Mean Absolute Error (MAE) from 13.39 to 0.69 °C, and achieving a Root Mean Square Error (RMSE) of 0.90 °C with an R² score of 0.99. Our systematic analysis establishes that scaling experimental data diversity—through training on multiple independent realizations—is the foundational strategy to overcome domain shift and enable robust cross-dataset generalization. Full article

Accurate estimation of Total Phosphorus, referred to as “Phosphorus, Total” (PPUT; µg/L) in the sourced monitoring data, is essential for understanding eutrophication dynamics and guiding water-quality management in inland lakes. However, lake-wide PPUT mapping at high resolution is challenging to achieve using conventional in-situ sampling, and nearshore gradients are often poorly resolved by medium- or low-resolution satellite sensors. This study exploits multi-generation PlanetScope imagery (Dove Classic, Dove-R, and SuperDove; 3–5 m, near-daily revisit) to develop a hybrid AI framework for PPUT retrieval in Lake Simcoe, Ontario, Canada. PlanetScope surface reflectance, short-term meteorological descriptors (3 to 7-day aggregates of air temperature, wind speed, precipitation, and sea-level pressure), and in-situ Secchi depth (SSD) were used to train five ensemble-learning models (HistGradientBoosting, CatBoost, RandomForest, ExtraTrees, and GradientBoosting) across eight feature-group regimes that progressively extend from bands-only, to combinations with spectral indices and day-of-year (DOY), and finally to SSD-inclusive full-feature configurations. The inclusion of SSD led to a strong and systematic performance gain, with mean R² increasing from about 0.67 (SSD-free) to 0.94 (SSD-aware), confirming that vertically integrated optical clarity is the dominant constraint on PPUT retrieval and cannot be reconstructed from surface reflectance alone. To enable scalable SSD-free monitoring, a knowledge-distillation strategy was implemented in which an SSD-aware teacher transfers its learned representation to a student using only satellite and meteorological inputs. The optimal student model, based on a compact subset of 40 predictors, achieved R² = 0.83, RMSE = 9.82 µg/L, and MAE = 5.41 µg/L, retaining approximately 88% of the teacher’s explanatory power. Application of the student model to PlanetScope scenes from 2020 to 2025 produces meter-scale PPUT maps; a 26 July 2024 case study shows that >97% of the lake surface remains below 10 µg/L, while rare (<1%) but coherent hotspots above 20 µg/L align with tributary mouths and narrow channels. The results demonstrate that combining commercial high-resolution imagery with physics-informed feature engineering and knowledge transfer enables scalable and operationally relevant monitoring of lake phosphorus dynamics. These high-resolution PPUT maps enable lake managers to identify nearshore nutrient hotspots, tributary plume structures. In doing so, the proposed framework supports targeted field sampling, early warning for eutrophication events, and more robust, lake-wide nutrient budgeting. Full article

Transparent conductive oxides (TCOs), such as indium tin oxide (ITO), are essential for flexible electronics; however, conventional vacuum-based deposition is costly and thermally aggressive for polymers. This study investigated the surface functionalization of PET substrates with ITO thin film-based forced hydrolysis as a low-cost, reproducible alternative. $\mathrm{S}\mathrm{n}{\mathrm{O}}_2$ nanoparticles were synthesized by forced hydrolysis at 180 °C for 3 h and 6 h, yielding crystalline nanoparticles with a cassiterite phase and an average crystallite size of 20.34 nm. The process showed high reproducibility, enabling consistent structural properties without complex equipment or high-temperature treatments. The $\mathrm{S}\mathrm{n}{\mathrm{O}}_2$ sample obtained at 3 h was incorporated into commercial ${\mathrm{I}\mathrm{n}}_2{\mathrm{O}}_3$ to form a mixed In–Sn–O oxide, which was subsequently deposited onto PET substrates by spin coating onto UV-activated PET. The resulting 1.1 µm ITO films demonstrated good adhesion (4B according to ASTM D3359), a low resistivity of 1.27 × 10⁻⁶ Ω·m, and an average optical transmittance of 80% in the visible range. Although their resistivity is higher than vacuum-processed films, this route provides a superior balance of mechanical robustness, featuring a hardness of (H) of 3.8 GPa and an elastic modulus (E) of 110 GPa. These results highlight forced hydrolysis as a reproducible route for producing ITO/PET thin films. The thickness was strategically optimized to act as a structural buffer, preventing crack propagation during bending. Forced hydrolysis-driven PET sheet functionalization is an effective route for producing durable ITO/PET electrodes that are suitable for flexible sensors and solar cells. Full article

This study investigates the impact of microresistor topology on the sensing characteristics of MoS₂-based chemoresistive cortisol sensors. It is done to address the critical need for robust, non-invasive cortisol monitoring in wearable applications, where mechanical stability under strain is paramount, and to explore underexplored topological effects on sensor performance. The research is conducted by fabricating MoS₂-based meander structures on flexible PDMS substrates, featuring various microresistor designs, including long-shoulder and short-shoulder topologies, both with and without integrated mechanical ribs. Sensor performance is evaluated in resistance change mode across a range of cortisol concentrations (2.5 to 500 ng/mL) and subjected to significant mechanical bending stress. Electrical parameters such as contact resistance and parasitic capacitance, as well as temperature dependence, are also analyzed. The results demonstrate that the incorporation of ribs significantly enhances the mechanical stability and preserves the reliable sensing function of the long-shoulder topology under bending stress, improving sensitivity from 0.9 kΩ/ng/mL (without ribs) to 130.6 kΩ/ng/mL (with ribs) after bending. While temperature influences baseline resistance and response magnitude consistent with MoS₂ semiconductor properties and aptamer binding kinetics, the short-shoulder design, even with ribs, showed less optimal performance. The primary advantage of the proposed device lies in its enhanced mechanical reliability under continuous strain, crucial for wearable electronics, alongside a simpler design compared to complex microfluidic or optical systems, thus enabling lower manufacturing costs and easier mass production. Full article

Hydrogen, a promising clean energy carrier, needs safe detection due to its flammability. Conventional electrical hydrogen sensors have drawbacks like high operating temperatures, poor selectivity and ignition risks. We propose an optical sensor using long-period fiber gratings (LPGs) coated with Pt-SiO₂ nanomaterials. It works via catalytic reaction: H₂ reacts with O₂ on Pt nanoparticles, releasing heat that shifts LPG’s resonant wavelength. Structural characterization showed porous SiO₂ with uniform Pt, ensuring efficiency and stability. Experiments proved it sensitively responds to 0.5–2.5% H₂ (max wavelength shift 7.544 nm), with fast response/recovery, good repeatability/reversibility. Logistic fitting (R² = 0.999) confirmed strong correlation. This sensor, safe, sensitive and stable, has great potential for real-time H₂ monitoring in critical environments. Full article

The transition toward Industry 4.0 requires advanced sensing platforms capable of delivering real-time, high-fidelity data under extreme industrial conditions. Thin-film sensors, leveraging both photonic and functional approaches, are emerging as key enablers of this transformation. By exploiting optical phenomena such as Fabry–Pérot interference, guided-mode resonance, plasmonics, and photonic crystal effects, thin-film photonic devices provide highly sensitive, electromagnetic interference-immune, and remotely interrogated solutions for monitoring temperature, strain, and chemical environments. Complementarily, functional thin films including oxide-based chemiresistors, nanoparticle coatings, and flexible electronic skins extend sensing capabilities to diverse industrial contexts, from hazardous gas detection to structural health monitoring. This review surveys the fundamental optical principles, material platforms, and deposition strategies that underpin thin-film sensors, emphasizing advances in nanostructured oxides, 2D materials, hybrid perovskites, and additive manufacturing methods. Application-focused sections highlight their deployment in temperature and stress monitoring, chemical leakage detection, and industrial safety. Integration into Internet of Things (IoT) networks, cyber-physical systems, and photonic integrated circuits is examined, alongside challenges related to durability, reproducibility, and packaging. Future directions point to AI-driven signal processing, flexible and printable architectures, and autonomous self-calibration. Together, these developments position thin-film sensors as foundational technologies for intelligent, resilient, and adaptive manufacturing in Industry 4.0. Full article

The simultaneous measurement of strain and temperature using Fiber Bragg Grating (FBG) sensors presents a significant challenge due to the intrinsic cross-sensitivity of the Bragg wavelength. While recent studies have increasingly employed “black-box” machine learning algorithms to address this ambiguity, such approaches often overlook the physical limitations of the sensor’s spectral response. This paper challenges the assumption that advanced algorithms alone can compensate for data that is physically ambiguous. We propose a “Sensor-Algorithm Co-Design” methodology, demonstrating that robust discrimination is achievable only when the sensor architecture exhibits a unique, orthogonal physical signature. Using a rigorous Transfer Matrix Method (TMM) and 4 × 4 polarization analysis, we evaluate three distinct architectures. Quantitative analysis reveals that a standard Quadratically Chirped FBG (QC-FBG) functions as an “ill-conditioned baseline” failing to distinguish measurands due to feature space collapse ($K_{cond}>4600$). Conversely, we validate two robust co-designs: (1) An Amplitude-Modulated Superstructure FBG (S-FBG) paired with an Artificial Neural Network (ANN), utilizing thermally induced duty-cycle variations to achieve high accuracy ($~3.4 °\mathrm{C}$ error) under noise; and (2) A Polarization-Diverse Inverse-Gaussian FBG (IG-FBG) paired with a 4 × 4 K-matrix, exploiting strain-induced birefringence ($K_{cond}\approx 64$). Furthermore, we address the data scarcity issue in AI-driven sensing by introducing a Physics-Informed Neural Network (PINN) strategy. By embedding TMM physics directly into the loss function, the PINN improves data efficiency by 2.2× compared to standard models, effectively bridging the gap between physical modeling and data-driven inference, addressing the critical data scarcity bottleneck identified in recent optical sensing roadmaps. Full article