Search Results (820)

Accurate monitoring of crop phenology is essential for precision agriculture and yield forecasting. However, satellite-derived time series often suffer from inherent noise, such as residual atmospheric effects and mixed pixels, as well as a frequent lack of ground-truth data in agriculture. In response, this study proposes an algorithm to define the type of spectral signatures for the principal phenological stages of crops, using them as the foundation for training supervised machine learning classification models. The algorithm was developed using Fuzzy C-Means (FCM) clustering to identify the spectral signature reference groups in winter wheat across the Burgos region (Spain) during the 2020 and 2021 growing seasons. To enhance cluster independence and biological coherence, a multi-step filtering process was implemented, including spectral purity (membership degree, SAM, and SAMder) and temporal coherence filters. The filtered and labeled dataset (80% original Burgos dataset) was used to train supervised classification models (KNN and XGBoost). The models’ reliability was verified through three wheat tests (remaining 20%), labeled using other clustering techniques, and an independent barley dataset from diverse geographic locations (Valladolid and Soria). The filtering process significantly improved cluster stability by removing outliers and transition spectral signatures. The supervised models demonstrated exceptional performance; the KNN model slightly outperformed XGB, achieving a mean Accuracy of 0.977, a Kappa of 0.967, and an F1-score of 0.977 in the wheat external test. Furthermore, the model showed, when applied to barley, that its phenological spectral signatures are equivalent in shape to those of wheat, with an Accuracy of 0.965 and an F1-score of 0.974. In addition, it was verified that the type spectral signatures remain the same regardless of the location. This study presents a robust classification tool capable of labeling four key phenological stages (tillering, stem elongation, ripening, and senescence) without ground truth. By effectively removing inherent satellite noise, the proposed methodology produces organized, cleaned datasets. This structured foundation is critical for future research integrating spectral signatures with harvester data to develop high-precision yield prediction models. Full article

Soil organic carbon (SOC) is essential for ecosystem stability and long-term carbon storage in alpine grasslands, yet the relative importance and interactions of hydrothermal and biotic controls remain poorly understood at regional scales. In this study, we quantified surface SOC (0–20 cm) across the Yellow River Source Region (YRSR) on the northeastern Tibetan Plateau, a climate-sensitive alpine headwater system characterized by strong hydrothermal gradients and freeze–thaw dynamics. Field-based SOC measurements were integrated with multi-source remote sensing and reanalysis data that describe thermal conditions, moisture processes, vegetation productivity, soil properties, topography, and human influence. A two-step screening approach was applied using Boruta and variance inflation factor filtering, followed by modeling with random forest. The model outputs were interpreted using Shapley Additive Explanations (SHAP). SOC displayed significant spatial heterogeneity across the region. Vegetation productivity, moisture availability, and thermal conditions were identified as the dominant nonlinear drivers of SOC variation. Moisture availability emerged as a central regulator of SOC, affecting it both directly and indirectly through vegetation productivity and thermal conditions. These findings underscore the importance of hydrothermal stability in sustaining soil carbon stocks and provide a quantitative basis for adaptive grassland management under a warming climate. Full article

This article proposes a calibration method combining line chromatic confocal and 3D point cloud processing to solve surface damage and low efficiency in traditional roughness sample calibration. Line chromatic confocal sensors scan roughness samples to obtain dense point clouds. We propose a back projection mechanism, the adaptive density-based spatial clustering of applications with noise statistical outlier removal (BPM-ADBSCAN-SOR) algorithm that utilizes the ADBSCAN and SOR algorithms to address outlier noise and near-field noise in low-resolution point clouds, respectively, and then employs bounding boxes to crop the original high-resolution point cloud, thereby achieving multi-scale noise removal and point cloud clustering. We propose a Steady-State Confidence-Weighted Robust Gaussian Filtering (SSCW-RGF) algorithm, which calculates the range of the steady-state region, designs a steady-state region credibility weighting function to apply a weighted correction to the baseline fitting results, and then incorporates M-estimation theory to develop a robust Gaussian filtering algorithm weighted by steady-state region credibility, thereby mitigating the impact of outliers on Gaussian baseline fitting. Experiments verify the system accuracy: repeatability standard deviation is 0.0355 μm, relative repeatability error 0.3984%. Compared with sample block nominal values, the maximum absolute error is −0.745 μm, meeting specification tolerance. Compared with the contact profilometer, the maximum absolute error is 0.050 μm, the maximum relative error is +4.5%, and the calibration efficiency is improved by 90%. It provides a new approach for surface roughness calibration Full article

Ballistic missiles exhibit high velocities and rapid maneuverability after atmospheric reentry, posing substantial challenges for anti-ballistic missile (ABM) interception. This paper presents an integrated interception framework that combines an input estimation method with an improved particle swarm optimization-based guidance law (IPSOG). The input estimation approach processes noisy radar measurements to estimate target states in the presence of unknown system inputs and measurement noise. Its performance is evaluated through simulations and compared with the extended Kalman filter (EKF), demonstrating improved estimation accuracy and robustness under highly maneuvering conditions. An improved particle swarm optimization algorithm is employed to design the interceptor guidance law. Compared with conventional proportional navigation guidance (PNG), the proposed guidance method provides enhanced adaptability to target maneuvers. Numerical simulations are conducted to evaluate interception performance against maneuvering ballistic missile targets. Results show reductions in miss distance and interception time while maintaining lower average lateral acceleration and a larger effective interception region. These results indicate that the proposed framework improves both target state estimation and interceptor guidance performance for highly maneuvering ballistic missile targets. Full article

Naphthalene and methylnaphthalene (Nap and MN) are the most abundant polycyclic aromatic hydrocarbons (PAHs) and are important precursors of secondary organic aerosol (SOA) in the atmosphere. 1.2-Phthalic acid (1,2-PhA) and 4-methylphthalic acid (4-MPhA) are usually treated as tracers of SOA from Nap and MN. However, the two tracers also have primary sources, and directly using the tracers to estimate SOA would lead to an overestimation. In this study, we conducted a one-year synchronous observation of the two-ring PAH SOA (SOA_2-rings) tracers at nine sites in the Pearl River Delta (PRD) region. We measured and filtered the suitable emission characteristics of SOA_2-rings tracers for biomass burning, coal combustion, industrial processes and vehicle exhaust sources. Then, we developed a method to distinguish 1,2-PhA and 4-MPhA from primary emissions and secondary formation. The average proportions of 1,2-PhA_pri and 4-MPhA_pri in 1,2-PhA and 4-MPhA were 26.7% and 29.2%, respectively. The direct application of measured 1,2-PhA for estimating SOA_2-rings would lead to an overestimation exceeding 30% in the PRD. Furthermore, we estimated SOA_2-rings using the separated 1,2-PhA_sec and 4-MPhA_sec by the tracer-based method. The average contribution of MN to SOA was around three times that of Nap. In addition, when combined with monocyclic aromatic SOA (SOA_1-ring) and biogenic SOA, the contributions of SOA_1-ring (21%) and SOA_2-rings (25%) to total SOA were comparable. SOA_2-rings was even the largest contributor to total SOA (~44%) in winter. This study revealed that whether to separate the SOA_2-rings tracers for primary emissions and secondary formation is essential in SOA estimation and highlighted that two-ring PAHs make a significant contribution to SOA in the PRD. Full article

Latitudinal gradients are widely recognized as a key macro-environmental driver shaping microbial biogeographic patterns; however, the spatial organization of sediment archaeal communities in estuarine ecosystems and the mechanisms underlying their assembly remain insufficiently understood. This study is based on sediment samples collected from three representative estuarine regions spanning distinct latitudes along the Chinese coastline—the North China Sea (NCS), East China Sea (ECS), and South China Sea (SCS). Based on 16S rRNA high-throughput sequencing, combined with null-model inference and molecular ecological network (MEN) analyses, we characterized latitudinal patterns in archaeal community distributions, assembly processes, and cross-regional interaction architectures. The results showed that archaeal communities exhibited obvious spatial segregation across three regions, with both community richness and network complexity increasing significantly toward lower latitudes. Nitrate (NO₃⁻), ferric iron (Fe³⁺), and dissolved oxygen (DO) were identified as key environmental factors governing archaeal community structure. Notably, archaeal community assembly processes exhibited a clear latitudinal gradient: deterministic processes, particularly environmental filtering, were more obvious at lower latitudes, whereas the contributions of stochastic processes—including dispersal limitation and ecological drift—increased markedly at higher latitudes. A MEN analysis further revealed that archaeal networks at lower latitudes exhibited higher connectivity, modularity, and stability, suggesting that interspecific interactions may enhance ecosystem resistance to environmental disturbance under more stable environmental conditions. Overall, this study demonstrates that macro-environmental gradients jointly shape archaeal biogeographic patterns via multiple pathways, including modulation of environmental filtering, dispersal dynamics, and cross-regional interactions. These findings deepened our understanding of the stable mechanisms governing the diversity and biogeographical distribution of archaea in estuarine systems. Full article

The automatic reading recognition of digital instruments is crucial for achieving metering automation and intelligent inspection. However, in non-standardized industrial environments, the masking effect caused by the coupling of non-uniform low-light conditions and the reflective surfaces of instrument panels severely degrades the displayed information, significantly limiting the recognition performance. Conventional image processing methods, while aiming to restore the imaging quality of instrument panels through low-light enhancement, inevitably introduce overexposure and indiscriminately amplify background noise during this process. To address the two key challenges of illumination recovery and noise suppression in the process of restoring panel image quality under non-uniform low-light conditions, this paper proposes a coarse-to-fine cascaded perception framework (CFCP). First, a lightweight YOLOv10 detector is employed to coarsely localize the meter reading region under non-uniform illumination conditions. Second, an Adaptive Illumination Correction Module (AICM) is designed to decouple and correct the illumination component at the pixel level, effectively restoring details in dark areas. Then, an Illumination-invariant Feature Perception Module (IFPM) is embedded at the feature level to dynamically perceive illumination-invariant features and filter out noise interference. Finally, the refined detection results are fed into a lightweight sequence recognition network to obtain the final meter readings. Experiments on a self-built industrial digital instrument dataset show that the proposed method achieves 93.2% recognition accuracy, with 17.1 ms latency and only 7.9 M parameters. Full article

Deformation monitoring of roadway surrounding rock is the key link to ensure the safety production of the coal mine. The traditional monitoring method can only obtain the displacement information of discrete measuring points, and it is difficult to fully reflect the spatial distribution characteristics and evolution law of surrounding rock deformation. Based on the engineering background of the extra-thick coal seam roadway in the Yushupo Coal Mine, Shanxi Province, China, this study proposes a set of full-space deformation monitoring methods for roadway surrounding rock based on explosion-proof mobile 3D laser scanning technology. Firstly, a hierarchical denoising method based on improved statistical filtering is established. The quality of point cloud data is effectively improved by region clipping, a connectivity analysis guided by multi-dimensional geometric features and adaptive density threshold three-level processing strategy. Secondly, a hierarchical point cloud registration method combining physical anchor geometric constraints and deep learning patch guided matching is proposed to reduce the registration error to millimeter level. Finally, the deformation evaluation of surrounding rock is carried out by combining the overall deformation identification with the quantitative analysis of local section slices. The engineering application results show that the deformation of the roadway floor is the most significant during the monitoring period, the maximum deformation is 90.0 mm, and the average deformation is 46.9 mm. The maximum deformation of the roof is 35.0 mm, and the convergence of both sides is asymmetric. Compared with the total station, the results show that the maximum displacement error in each direction does not exceed 5 mm, and the standard deviation is within 1.3 mm, which meets the engineering accuracy requirements of coal mine roadway deformation monitoring. This study provides a complete technical scheme for panoramic and high-precision monitoring of surrounding rock deformation in coal mine roadway. Full article

Corrosion in steel pipelines can cause critical failures in industrial systems, while conventional inspection methods such as radiography and ultrasonic testing are costly and require specialized personnel. This study presents a mobile computer vision system for automated corrosion detection inside steel pipes using deep learning-based visual analysis. The proposed system consists of a Raspberry Pi 4-based mobile robot equipped with a high-resolution camera for internal inspection. Acquired images were processed using color-space transformations (RGB–HSV), filtering, and segmentation. Convolutional neural networks and semantic segmentation models, including YOLOv8-seg (Instance segmentation) and DeepLabV3 (Semantic segmentation), were trained on a custom corrosion image dataset to identify corroded regions. Real-time visualization was implemented via Flask-based video streaming. Experimental results demonstrated high detection accuracy for uniform corrosion, achieving a mean Intersection over Union (mIoU) above 0.98 and a precision of 0.99 with the YOLOv8-seg model. These results indicate that the proposed system enables reliable and automated corrosion inspection, with the potential to reduce inspection costs and improve operational efficiency. Future work will focus on enhancing real-time performance through hardware optimization. Full article

This study investigates the dynamic behavior of the suction-side lubrication gap between bushing and gear in external gear pumps (EGPs), with emphasis on how surface texturing and bushing micromotion influence the effective stiffness and damping of the oil film. A three-dimensional CFD model of a lubrication gap between bushing and gear is developed to resolve the coupled sliding–squeezing hydrodynamics arising under realistic suction-side operating conditions. Steady-state simulations are used to determine the nonlinear static force–gap height relationship and extract the hydrodynamic stiffness, while transient simulations with harmonic perturbations are post-processed to evaluate the damping coefficient through acceleration-based filtering. The results show that both stiffness and damping increase sharply as the gap height decreases due to the strong confinement of the lubricant in the small-clearance region. Increasing the textured area slightly enlarges the effective gap height and reduces the hydrodynamic load capacity, leading to lower stiffness and damping values; this behavior highlights that the choice of an appropriate texturing configuration is a critical design parameter. Overall, the study provides a comprehensive dynamic characterization of textured bushing–gear lubrication films in EGP and offers quantitative data for developing lumped parameter models of EGP with textured bushings. Full article

Pipeline leakage can lead to catastrophic consequences, and traditional sensor-based detection methods often struggle to identify changes caused by slow or minor leaks. This paper proposes a real-time machine vision-based method for detecting liquid leakage in pipelines, suitable for complex industrial scenarios. By extracting droplet foreground regions and constructing a detection model based on the contour and motion features of droplets, the proposed method effectively filters out interference from lighting variations, equipment vibrations, and personnel movement in industrial environments, while accurately identifying the vertical motion characteristics of dripping liquids. An experimental platform was established to validate the effectiveness of the proposed approach. The results demonstrate that the proposed method achieves a detection rate of 98.04%, a false alarm rate of 5.26%, and a processing speed of 90.71 fps. Comparative experiments show that this method significantly outperforms traditional approaches, such as the dense optical flow method, which yields a higher false alarm rate and a processing speed of only 2.2 fps under the same test conditions. These findings confirm that our approach offers a more accurate and efficient solution for real-time pipeline liquid leakage detection. Full article

In seismic imaging, the trace mixing process involves merging neighboring traces in seismic data to enhance the signal-to-noise ratio and improve the continuity and spatial coherence of seismic data. In regions with complex subsurface structures, current trace mix filters are often ineffective as they introduce artifacts that reduce interpretability and obscure the signatures of important structures, such as faults and folds. We introduce the selective trace mix as a novel, data-dependent filter. This filter enhances amplitude consistency, spatial coherence, and the definition of reflections, while it preserves complex structures and maintains their clarity. Selective trace mix uses sequential steps of evaluation, referencing, exclusion, weighting, and normalization of all samples within the filter operator. As a result, selective trace mix is a temporally and spatially variable, data-dependent filter. The filter’s effectiveness is validated using both synthetic and real field seismic data. Synthetic data is a portion of the Marmousi seismic model, while real data include land and marine seismic datasets imaging complex subsurface fault/fold structures. When compared to three of the commonly used conventional filters, the selective trace mix yields far better results in terms of horizon integrity and fault clarity. Full article

As a core technology in 3D measurement, laser stripe center extraction is widely applied in industrial inspection, robot navigation, and biomedicine. However, traditional methods struggle to balance denoising effectiveness and positioning accuracy when handling complex noise and non-uniform width stripes. To address this bottleneck, this paper proposes a denoising-adaptive weighted average width stripe center extraction algorithm based on an improved Hessian Matrix, integrating deep learning with traditional image processing for high-precision extraction. A U-Net++ denoising network with a spatial attention module is designed to focus on stripe regions, supplemented by a distance-aware mechanism that dynamically adjusts denoising intensity based on pixel-stripe distance. For center extraction, an improved Hessian Matrix algorithm is proposed, incorporating a curvature-adaptive FIR filter and adaptive weighted average width calculation to adapt to stripe morphology changes. Experimental results show the algorithm outperforms comparative methods, achieving 35.26 dB (PSNR), 0.962 (SSIM), and 6.14 (RMSE) in denoising. Under 200 μs, 500 μs, 1000 μs, and 1500 μs exposure conditions, the absolute radius errors are reduced to 0.2052 mm, 0.1743 mm, 0.0268 mm, and 0.0281 mm, respectively, verifying its reliability and stability in practical applications. Full article

All-sky meteor radars are widely employed to observe the near-space atmospheric wind field, a crucial parameter of the near-space environment. Owing to the spatiotemporal uncertainty in meteor count distribution, meteor radars may encounter measurement errors and data gaps when retrieving atmospheric wind fields. Using Kalman filter assimilation technology in combination with the HWM14, this study utilizes atmospheric wind field observation data from the Qujing meteor radar (25.6°N, 103.7°E) to study atmospheric horizontal wind fields within the altitude range of 80–100 km. The assimilation results indicate that the accuracy of the HWM14′s atmospheric wind field is significantly improved after Kalman filter-based assimilation. The discrepancy between the assimilated wind field analysis values and the meteor radar wind field values is notably reduced: the average maximum error of zonal wind speed is 12.0 m/s at 90 km altitude, representing a 55.0% improvement compared to the pre-assimilation state; the average maximum error of the meridional wind speed is 14.2 m/s at 100 km altitude, a 53.4% improvement. Furthermore, the standard deviation of the deviation between the assimilated wind field analysis values and the meteor radar wind field values is also substantially decreased. The assimilated atmospheric wind field information holds great significance for further investigating atmospheric disturbance variations and dynamic processes in the near-space region. Full article

The Mw 6.4 Petrinja earthquake on 29 December 2020 ruptured the Petrinja-Pokupsko fault system in central Croatia, producing widespread coseismic deformation and subsequent postseismic processes. This study examines ground displacements in the Petrinja area from 2019 to 2022 using Sentinel-1 SAR data processed with SBAS time series analysis. Interferometric phase residuals were filtered using temporal coherence masking and RMS cut-off criteria to ensure high-quality displacement estimates. Line-of-sight (LOS) velocity fields were derived separately for ascending and descending tracks, combined into horizontal and vertical components, and rotated into a fault-parallel direction. Fault-parallel velocities were also extracted with pixel-wise coseismic offsets removed to isolate postseismic transients. Pre-event displacements are generally small and often within measurement uncertainties. However, because the 2019–2022 observation window includes the mainshock and concentrated early postseismic motion, robust estimation of long-term interseismic rates (millimeters per year) is not possible from this dataset. Such rates from independent regional GNSS measurements are therefore included solely for tectonic context and visual illustration. A clear surface displacement jump exceeding 20 cm was detected, with opposite signs in ascending and descending geometries, reflecting predominant right-lateral strike-slip motion. Following the removal of the coseismic jump, weighted profile analysis identifies residual transients of up to ±1.5 cm/yr near the fault, consistent with dominant shallow afterslip. Possible contributions from viscoelastic relaxation are noted, as such processes produce broader, longer-timescale deformation patterns that cannot be excluded without extended observations or forward modeling. These geodetic observations quantify the immediate postseismic deformation and provide constraints on near-fault slip patterns following the mainshock. Full article