Search Results (1,204)

Recent advances in satellite missions, particularly the Landsat, Sentinel, and Gaofen series, have led to the rapid accumulation of high-quality remote sensing data with frequent revisits. As these data have become more widely available, Satellite Image Time Series (SITS) have become an important tool for monitoring Earth surface dynamics. SITS now supports a wide range of applications, including precision agriculture, Land Use/Cover Change (LULCC) monitoring, environmental management, and disaster response. This growth has also promoted the development of advanced SITS classification datasets. However, existing reviews have mainly focused on SITS classification algorithms or specific applications, while systematic comparisons of public SITS benchmark datasets remain limited. This lack of synthesis makes it difficult for researchers to navigate fragmented resources and select datasets that match specific scientific or operational tasks. To address this gap, this paper provides a comprehensive review and analysis of 29 publicly available medium-to-high-resolution SITS classification benchmark datasets released between 2017 and 2025. These datasets are intended for training, testing, and validating land-cover classification algorithms, rather than for direct use as operational map products. We conduct a detailed statistical and comparative analysis of these datasets, focusing on their key characteristics across spectral, temporal, and spatial dimensions, as well as their labeling systems. In addition, this review summarizes the SITS classification algorithms that have been developed and benchmarked using these datasets. Finally, we identify the main challenges in constructing and applying SITS classification datasets and discuss future research directions, particularly in data reconstruction, multimodal fusion, change analysis, and advanced model architectures. This survey provides the research community with a systematic overview of SITS classification benchmark datasets and aims to support continued progress in this rapidly developing field. Full article

Terahertz (THz) fingerprint detection is central to identifying characteristic absorption fingerprints of biomolecules derived from their intrinsic rotational and vibrational modes. The development of guided-mode resonance (GMR) technology together with pixelated design offers a new approach to enhance the recognition capability of such fingerprint spectra. Here, a novel secondary grating metasurface based on cycloolefin polymer (COP) is proposed, which adopts an ultra-minimalist dual-pixel complementary architecture to excite high-quality (Q)-factor GMR. Its spectral resolution does not exceed 50 GHz, enabling precise capture of target molecular characteristic information and meeting the requirements of broadband fingerprint sensing. More importantly, the design regulates the dual-pixel grating units through parameter gradient optimization and incorporates a dual regulation mode of static pixel-targeted coverage and dynamic angle fine tuning. By adjusting geometric parameters and incident angles, broadband coverage from 1.15 THz to 2.20 THz is achieved, which can accurately match the multi-fingerprint detection requirements of glutamic acid (Glu) and glutamine (Gln). This metasurface sensor, integrating the advantages of pixelation and high-Q-factor GMR characteristics, provides an effective strategy for enhanced broadband THz fingerprint sensing and shows broad application potential in the field of biochemical trace detection. Full article

Structural Health Monitoring (SHM) increasingly relies on data-driven approaches to detect structural changes under environmental and operational variability, yet the limited availability and imbalance of baseline data remain critical challenges. This study proposes a novel framework for vibration-based SHM that combines Convolutional Neural Networks and Variational Autoencoders to model structural response in the frequency domain through Cross-Spectral Matrices. The methodology includes a tailored data representation based on Cholesky factorisation, a CNN-VAE architecture with structural constraints to ensure data consistency, and an Enhanced Loss Function designed to improve sensitivity to modal characteristics. The trained model is used both as a generative tool to produce realistic synthetic data and as a feature extractor through latent variable distributions. Validation on an experimental truss structure subject to thermal variability shows that the model accurately reproduces the statistical distribution of natural frequencies and spectral features, while generating plausible synthetic responses. The proposed approach enables baseline enhancement through data balancing and supports effective damage detection using both modal features and latent space indicators. These results demonstrate that the framework can improve the robustness of vibration-based SHM systems and can be integrated with existing frequency domain monitoring techniques, offering a practical data-driven solution for real-world applications. Full article

Surface-enhanced Raman spectroscopy (SERS) enables ultrasensitive molecular detection but produces high-dimensional and substrate-dependent spectral data that are difficult to analyze using conventional methods. The integration of machine learning (ML) provides new opportunities for extracting chemical information from complex SERS datasets and for optimizing nanostructured substrates that determine signal enhancement. This review summarizes recent advances in ML-assisted SERS across the analytical workflow. Data characteristics and preprocessing strategies are first outlined, followed by an overview of supervised, unsupervised, and deep learning approaches for spectral classification and quantitative analysis. Applications in biomarker discovery and spectral fingerprint recognition are discussed, with emphasis on model interpretability. In addition, ML-driven strategies for substrate optimization, including surrogate modeling and inverse design, are highlighted as emerging directions for improving enhancement efficiency. Current challenges, such as data scarcity, limited generalization, and real-time deployment constraints, are also examined. The convergence of ML and SERS is gradually shifting Raman-based analysis toward more predictive and integrated sensing frameworks. Full article

Condition monitoring in engineering systems requires analytical frameworks that connect physically meaningful signal representations with statistically consistent decision mechanisms. Although spectral analysis, time–frequency methods, and machine learning have each advanced significantly, they are often treated as separate methodological domains. This work presents a unified structural framework that integrates classical spectral techniques, time–frequency representations, and supervised learning within a coherent monitoring architecture. Rather than providing a systematic survey, the study adopts a conceptual perspective to explicitly describe the analytical linkage between signal transformation, feature construction, and statistical inference. The discussion begins with Fourier-based descriptors and power spectral density formulations, and extends to short-time Fourier transform and continuous wavelet transform frameworks, highlighting their resolution characteristics for non-stationary signals. These representations are then connected to feature-space construction and learning-based decision models through an explicit mapping between physical signal properties and statistical inference mechanisms. An illustrative synthetic analysis is included to demonstrate how representation fidelity influences feature-space structure and downstream classification behaviour under transient conditions. These results are intended to provide conceptual insight rather than generalizable performance claims. Applications across multiple engineering domains are discussed to highlight the generality of the proposed framework. Finally, key research challenges, including dynamic operating regimes, data imbalance, interpretability, and computational constraints, are outlined. The proposed framework emphasises the complementary roles of transform-based representation and learning-based inference, providing a structured foundation for scalable and interpretable condition monitoring systems. Full article

Field measurements of ship motions at berth are often sparse, heterogeneous, and collected across multiple vessels and locations, limiting the applicability of conventional response-modelling approaches. This study presents a statistical framework for analysing sea-state-conditioned motion responses using long-term monitoring data with incomplete overlap between degrees of freedom (DoF). Each DoF is analysed independently and conditioned on significant wave height ($H_s$) and peak wave period ($T_p$), with directional values retained across the full angular range (0–360°) and examined separately. A two-stage quality-control procedure combining plausibility checks and robust regression removes inconsistent response–sea-state pairs while preserving dominant behaviour. Motion response envelopes are derived by binning observations in sea-state space and computing median and upper-percentile statistics. To quantify sampling uncertainty, bootstrap resampling provides 95% confidence intervals for envelopes and derived metrics, ensuring robust comparative conclusions. Results show systematic growth in motion variability with increasing $H_s$, with surge exhibiting the strongest translational sensitivity and roll the largest amplification. Synthetic sea surfaces generated using a spectral random-phase approach reproduce prescribed sea-state characteristics, supporting physical interpretation. The study contributes a data-driven framework for heterogeneous berth datasets, robust quality control, uncertainty-aware response envelopes, and statistically consistent synthetic seas, aligning field-based monitoring with practical port operability assessment. Full article

Accurate forecasting of sediment accumulation under extreme hydrodynamic forcing is essential for coastal engineering design and harbor management. This study evaluates the performance of Dynamic Mode Decomposition (DMD), optimized DMD (optDMD), and optimized DMD with stability constraints (optDMDs) for reconstructing and forecasting sediment accumulation height fields at the Dilderesi River mouth under a 50-year return period flood scenario. Sediment height fields generated using Delft3D are represented through reduced-order modal decompositions and the truncation rank is determined based on reconstruction-error analysis. Although all formulations reproduce the training data with negligible error, their predictive behavior differs during temporal extrapolation. Standard DMD exhibits rapid error growth at longer lead times. The optDMD formulation improves short- and intermediate-horizon performance but shows gradual degradation at extended lead times. Optimized DMD with stability constraints provides the most consistent long-horizon forecasts, maintaining high Nash–Sutcliffe efficiency and low RMSE across the full 9 h prediction interval. Examination of the continuous-time eigenvalue distributions and modal dynamics indicates that spectral characteristics of the reduced-order representation govern forecast robustness. The results demonstrate that enforcing spectral stability within reduced-order frameworks substantially enhances morphodynamic forecasting reliability under extreme flood conditions. The proposed approach provides a computationally efficient and physically consistent tool for sediment dynamics prediction in coastal engineering applications. Full article

Electro-optic frequency combs (EOFCs), generated through the microwave-driven modulation of continuous-wave lasers, have emerged as a highly reconfigurable and system-compatible class of optical frequency combs with growing importance in microwave photonics, coherent communications, spectroscopy, and precision metrology. In contrast to mode-locked lasers and Kerr microresonator combs, EOFCs offer electrically programmable repetition rates, deterministic phase coherence, and intrinsic compatibility with radiofrequency electronic systems, making them particularly attractive for integrated and application-oriented implementations. As EOFCs evolve toward broader bandwidths, lower power consumption, and full on-chip integration, their achievable performance is increasingly constrained by the interplay between electro-optic physical mechanisms, modulator architectures, and material platform properties. This review establishes a unified analytical framework that systematically connects EOFC generation mechanisms, device configurations, key performance metrics, and platform-level limitations. We first summarize the fundamental electro-optic effects underpinning EOFC generation and analytically examine representative modulator architectures, including phase modulators, Mach–Zehnder modulators, and microresonator-based schemes, to clarify their respective comb-generation characteristics. Key performance determinants, such as modulation depth, bandwidth, electro-optic efficiency, and optical loss, are then discussed to elucidate their coupled influence on comb-line count, spectral flatness, output power, and phase noise. Subsequently, the performance of EOFCs implemented on major integrated platforms, including Silicon on Insulator (SOI), Indium Phosphide on Insulator (InPOI), Lithium Niobate on Insulator (LNOI), and Lithium Tantalate on Insulator (LTOI), is comparatively reviewed to highlight the material-dependent advantages and constraints. Finally, emerging directions based on heterogeneous integration and ferroelectric materials with ultrahigh electro-optic coefficients are discussed as promising pathways to overcome the current performance bottlenecks. This review provides clear physical insights and engineering guidance for the future development of high-performance, integrated EOFC systems. Full article

Rapid and non-destructive nitrogen diagnosis in fruit orchards is critical for precision fertilization management and crop yield optimization. This study develops and evaluates a practical hyperspectral preprocessing pipeline for leaf nitrogen estimation in Korla fragrant pear (Pyrus sinkiangensis Yü), a commercially important cultivar in southern Xinjiang, China. Hyperspectral reflectance data and corresponding nitrogen measurements were collected from mature leaves of slender-spindle-trained trees. Four preprocessing strategies, comprising multiplicative scatter correction (MSC), wavelet threshold denoising, and their sequential combinations, were systematically compared to assess their effects on spectral information retention and model performance. The successive projections algorithm (SPA) was applied for characteristic wavelength selection, and four regression models, including linear regression (LR), partial least squares regression (PLSR), random forest (RF), and XGBoost, were constructed and evaluated. Results demonstrated that combined preprocessing strategies outperformed single-method approaches, and that preprocessing order significantly influenced predictive accuracy. Nonlinear models consistently outperformed linear models, confirming a pronounced nonlinear relationship between hyperspectral features and leaf nitrogen content. The MSC, followed by wavelet threshold denoising, combined with SPA and XGBoost, achieved the best predictive performance, with R² = 0.754, RMSE = 0.179 mg/g, and RPD = 2.017 on the test set. These findings provide a methodological reference for hyperspectral nitrogen monitoring and preprocessing workflow design under controlled conditions, with potential for further validation in field applications. Full article

Infrared photodetectors are important for military, medical, and environmental applications. Dual-color quantum well infrared photodetectors (QWIPs) are attractive because they can provide multi-spectral information, but their performance is often limited by high dark current. In this study, we designed and fabricated two dual-color QWIPs. Sample A exhibits rectification-like dark-current behavior, whereas Sample B shows a nearly symmetric current–voltage characteristic together with an approximately two-order-of-magnitude reduction in dark current under the same operating condition. By combining secondary ion mass spectrometry (SIMS), scanning spreading resistance microscopy (SSRM), energy-band simulations, and optoelectronic characterization, we show that Sample B exhibits a larger disparity in effective carrier distribution between the two quantum-well groups than Sample A. The experimental results and simulations consistently indicate that this disparity, together with the higher barrier design, is associated with a redistribution of the internal potential and a stronger voltage drop across the lightly doped region, which is consistent with reduced thermally activated carrier transport. Although the lower carrier concentration in the lightly doped wells is accompanied by reduced blackbody responsivity, the stronger suppression of dark current leads to a higher peak blackbody detectivity under identical blackbody-illumination conditions. At 50 K and −1.5 V, the peak blackbody detectivity of Sample B is approximately four times that of Sample A. These results support the conclusion that combining barrier-height design with controlled inter-group carrier disparity is an effective strategy for tuning carrier transport and improving the peak blackbody detectivity trade-off in dual-color QWIPs within the conditions examined here. Full article

Broadband antireflective surface technology constitutes a crucial technique in optoelectronic devices, playing a key role in reducing optical losses. Ultrafast laser processing provides a flexible route for fabricating micro-nano structures on metallic surfaces because it enables efficient fabrication, high spatial resolution, and minimal chemical consumption. This study uses a variable-angle scanning strategy to texture the copper surface, produce a series of antireflection arrayed micro-nano structures, and study the spectral reflectance characteristics of the copper surface. The results exhibit that 90° orthogonal scanning favors the formation of an arrayed microcone structure, which shows lower reflectance than the non-orthogonal scanning strategies in the 200–1300 nm band, with a minimum reflectance of 0.94%. The 60° and 45° cross-scanning based on the non-orthogonal strategy favors the formation of microcavity structures, and shows low reflectance in the 1300–2500 nm band, with the maximum reflectance remaining below 5%. Laser-induced periodic surface structures (LIPSS) are observed on the structures fabricated by all strategies. This work demonstrates that the scanning angle itself can be used to switch the dominant surface morphology and thereby tailor the spectral antireflection response, and lies in establishing a clear processing–structure–spectral response relationship for copper surfaces, which provides a designable route for wavelength-selective optical absorption in photothermal conversion, infrared detection, and sensing applications. Full article

Sandstone-type uranium deposits represent one of the most significant uranium deposit types in China, predominantly hosted in Meso-Cenozoic sedimentary basins in the northern part of the country. Due to characteristics such as deep burial of orebodies, fine grain size of ores, and strong heterogeneity, traditional geological logging methods have limitations in rapidly and accurately identifying alteration minerals and mineralization indicator information. Core spectral technology (wavelength range approximately 400–2500 nm), particularly short-wave infrared spectroscopy (SWIR, 1300–2500 nm), enables rapid, non-destructive, and quantitative extraction of alteration mineral information from drill cores. This provides robust technical support for reconstructing metallogenic environments, delineating oxidation–reduction zones, and prospecting and prediction in sandstone-type uranium deposits. This review systematically examines the spectral absorption characteristics and geological significance of key alteration minerals (e.g., clay minerals, carbonate minerals, iron oxides, and hydrocarbon substances) in sandstone-type uranium deposits. It elaborates on the current application status of core spectral technology in sandstone-type uranium exploration within typical basins in northern China, such as the Ordos, Songliao, Erlian, and Qaidam Basins. These applications include alteration mineral mapping, oxidation–reduction zone delineation, and metallogenic fluid tracing. Due to the unique characteristics of host rock lithology, alteration mineral assemblages, and fluid properties in sandstone-type uranium deposits, the application of this technology also faces certain challenges, such as difficulties in spectral interpretation and insufficient accuracy in quantitative inversion. Integrating this technique with multiple methods, including petrography and X-ray diffraction (XRD), will facilitate more effective applications in both metallogenic research and prospecting practices for sandstone-type uranium deposits in northern China. Full article

Honey quality and authenticity assessment require rapid and reliable analytical tools capable of supporting both laboratory and on-site applications. Near-infrared (NIR) spectroscopy represents a non-destructive and cost-effective approach; however, its performance depends on instrument characteristics and chemometric strategies. This study compared one benchtop and two portable NIR-based systems for predicting key physicochemical parameters (moisture, electrical conductivity, glucose, fructose, reducing sugars, pH, hydroxymethylfurfural, and diastatic activity) and for discriminating botanical origin in 80 Italian honey samples. Spectral data were processed using multiple pre-processing techniques and algorithms (PLS, k-NN, Random Forest, SVM), with and without wavelength selection (siPLS and CARS-PLS), under cross-validation schemes. The benchtop system achieved the highest regression performance (R² up to 0.91 for glucose and electrical conductivity) and the most reliable botanical classification (balanced accuracy = 0.90). Portable systems showed moderate predictive ability for bulk compositional parameters (R² up to 0.86 for glucose) but limited classification performance. Wavelength selection resulted in only marginal improvements. Hydroxymethylfurfural and diastatic activity were poorly predicted (R² up to 0.49), likely due to their low concentrations. Summarising, the main outcomes suggested that tested portable NIR settings are also suitable for rapid quantitative screening of chemical traits, whereas the benchtop system provide higher precision for botanical qualitative authentication. Full article

Cardiac auscultation remains a widely used non-invasive method for assessing cardiac function; however, conventional acoustic stethoscopes are limited by subjective interpretation and lack of digital signal-handling capabilities. This study presents the design and fabrication of a chitosan-based diaphragm digital stethoscope using a biopolymer-derived acoustic interface. Chitosan was extracted from mealworm larvae shells through sequential chemical processing and subsequently processed into a glycerol-plasticized film via solution casting to obtain a flexible diaphragm. The mechanical properties of the diaphragm were evaluated to assess its suitability for acoustic applications. The diaphragm was mechanically coupled to a piezoelectric sensor and integrated into a custom 3D-printed chest piece connected to a microcontroller-based acquisition system. Heart sound signals were acquired from four conventional auscultation sites (aortic, pulmonic, tricuspid, and mitral regions). The recorded signals were processed using band-pass filtering, envelope extraction, and time–frequency analysis to visualize waveform morphology and frequency content. The signals obtained exhibited temporal and spectral features consistent with reported phonocardiography characteristics, including identifiable S1 and S2 components. These results demonstrate the feasibility of using chitosan-based diaphragm materials for heart sound acquisition in a digital stethoscope configuration, providing a low-complexity platform for further development of biopolymer-based acoustic sensing devices. Full article

Gem-quality garnets exhibit significant potential for U-Pb geochronological applications due to their advantageous characteristics, including high closure temperatures (750–850 °C), optical transparency, chemical homogeneity, and low inclusion content. This study focuses on the gem-quality yellow-green grossular garnet variety (commonly termed Mali garnet), a unique gemstone exclusively occurring in contact metamorphic deposits of Western Africa’s Republic of Mali. Despite its mineralogical significance, fundamental aspects, including precise age determination and chromophore mechanisms of Mali garnet, remain poorly constrained. Here, we conducted standard gemological characterization, spectroscopic analyses (UV–Vis, FTIR, and Raman), electron probe microanalysis (EPMA), micro-X-ray fluorescence (μ-XRF) elemental mapping, and in situ trace element and laser ablation U-Pb geochronological analysis on Mali garnets. The spectral data and chemical composition studies reveal that the coloration of Malian garnets is primarily attributed to the presence of iron and chromium. Our U-Pb geochronological results yield a crystallization age of 197 ± 3 Ma for the Mali garnet samples. The robustness of garnet U-Pb systems in preserving crystallization ages through multiple thermal events supports their application to Precambrian polymetamorphic terranes, where zircon systems are frequently reset. Full article