Search Results (1,022)

Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology, with its advantages in large-scale and high-precision deformation monitoring, has become an essential tool for monitoring surface subsidence in coal mining areas. To address the issue of missing deformation values resulting from interferometric decoherence when using InSAR technology for surface subsidence monitoring in mining areas, this study proposes a combined approach integrating SBAS-InSAR with KTree Adaptive Inverse Distance Weighting (KTree-AIDW). The method constructs a dynamic neighborhood search mechanism through the KTree algorithm, considering the spatial heterogeneity between the interpolation points and adjacent sample points, and optimizes the weight distribution of heterogeneous sample points. The study is based on Sentinel-1 data with a 12-day revisit cycle, focusing on the 2021 grouting working face of the Liangbei Mine in Yuzhou, Henan Province, China. The results show the following: (1) Along both the strike and dip lines, the correlation coefficient between the SBAS-InSAR + KTree-AIDW results and leveling result is 0.95, with an overall root mean square error (RMSE) of 22.08 mm and a relative root mean square error (RRMSE) of 9.48%. The Mean Absolute Error (MAE) of characteristic points in the decoherence region is 19.05 mm, indicating a significantly improved accuracy in the decoherence region compared to traditional methods. (2) The cumulative maximum subsidence in the study area reached 233 mm, with an average maximum subsidence rate of 171 mm/yr. The maximum positive/negative inclines were 2.4 mm/m and −2.9 mm/m; the maximum positive/negative curvatures were ±0.18 mm/m². The surface structures are within the threshold values specified for Class I damage. The proposed method effectively addresses the decoherence issue that leads to missing deformation data in mining areas, providing a novel technical approach to accurate surface subsidence monitoring under grouting and backfilling conditions. Full article

Object detection in synthetic aperture radar (SAR) imagery poses significant challenges due to low resolution, small objects, arbitrary orientations, and complex backgrounds. Standard object detectors often fail to capture sufficient semantic and geometric cues for such tiny targets. To address this issue, a new Convolutional Neural Network (CNN) framework called Deformable Vectorized Detection Network (DVDNet) has been proposed, specifically designed for detecting small, oriented, and densely packed objects in SAR images. The DVDNet consists of Grouped-Deformable Convolution for adaptive receptive field adjustment to diverse object scales, a Local Binary Pattern (LBP) Enhancement Module that enriches texture representations and enhances the visibility of small or camouflaged objects, and a Vector Decomposition Module that enables accurate regression of oriented bounding boxes via learnable geometric vectors. The DVDNet is embedded in a two-stage detection architecture and is particularly effective in preserving fine-grained features critical for mall object localization. The performance of DVDNet is validated on two SAR small target detection datasets, HRSID and SSDD, and it is experimentally demonstrated that it achieves 90.9% mAP on HRSID and 87.2% mAP on SSDD. The generalizability of DVDNet was also verified on the self-built SAR ship dataset and the remote sensing optical dataset HRSC2016. All these experiments show that DVDNet outperforms the standard detector. Notably, our framework shows substantial gains in precision and recall for small object subsets, validating the importance of combining deformable sampling, texture enhancement, and vector-based box representation for high-fidelity small object detection in complex SAR scenes. Full article

This paper presents a physics-guided machine learning (PGML) approach to model the I–V characteristics of GaN current aperture vertical field effect transistors (CAVET). By adopting the method of transfer learning and the shortcut structure, a physically guided neural network model is established. The shallow neural network with $tanh$ as the basis function is combined with a hypernetwork that dynamically generates its weight parameters. The influence of transconductance is added to the loss function. This model can synchronously predict the output and transfer characteristics of the device. Under the condition of small samples, the prediction error is controlled within 5%, and the $R^2$ value reaches above 0.99. The proposed PGML approach outperforms conventional approaches, ensuring physically meaningful and robust predictions for device optimization and circuit-level simulations. Full article

With the continuous development and innovation of thermal management technology for lithium-ion batteries, the advantages of direct immersion liquid cooling technology have become increasingly prominent. However, at present, there is relatively little research on immersion liquid cooling systems, and current research is still mainly focused on small-capacity battery systems. Therefore, taking a large-capacity battery pack as the research object, a new type of single-phase immersion liquid cooling system was designed. The battery pack has a charge and discharge rate of 1C, consists of 52 cells, and has a total capacity of 52.249 kWh. It was compared with traditional liquid cooling and static immersion liquid cooling. Then, the effects of the aperture of the flow distributor, the inlet flow rate of the cooling liquid, and the type of cooling liquid on the cooling performance of the dynamic immersion battery pack were discussed. The holes on the flow distribution plate are primarily designed to facilitate a relatively uniform distribution of incoming liquid flow. Our research found that compared with traditional liquid cooling and static immersion liquid cooling, the overall cooling performance of the dynamic immersion cooling system was significantly improved, with the maximum temperature T_max decreasing by 7.8 °C and 6.6 °C, the maximum temperature difference ΔT_max of the entire pack decreasing by 5.5 °C and 5.8 °C, and the maximum temperature difference U-DΔT_max between the top and bottom surfaces of the battery pack decreasing by 10.1 °C and 8.96 °C. An appropriate aperture had a positive impact on the cooling effect of the battery pack, with the best effect at a aperture of 4 mm. T_max and ΔT_max gradually decreased with an increase in the flow rate of the cooling liquid, with T_max decreasing from 42.3 °C to 31 °C and ΔT_max decreasing from 14.8 °C to 7.9 °C, but the rate of the temperature decrease gradually decreased. Deionized water in the cooling liquid had the best cooling effect, while ethyl silicone oil had the worst cooling effect. The novel single-phase immersion cooling system developed in this study serves as a valuable reference for the design of immersion liquid cooling systems in large-capacity battery packs, contributing to enhanced temperature uniformity and improved system safety. Full article

This study presents an integrated geomechanical modeling framework for predicting multi-scale fracture networks and their activity in the Longmaxi Formation shale reservoir, northern Luzhou region, southeastern Sichuan Basin—an area shaped by complex, multi-phase tectonic deformation that poses significant challenges for resource prospecting. The workflow begins with quantitative characterization of key mechanical parameters, including uniaxial compressive strength, Young’s modulus, Poisson’s ratio, and tensile strength, obtained from core experiments and log-based inversion. These parameters form the foundation for multi-phase finite element simulations that reconstruct paleo- and present-day stress fields associated with the Indosinian (NW–SE compression), Yanshanian (NWW–SEE compression), and Himalayan (near W–E compression) deformation phases. Optimized Mohr–Coulomb and tensile failure criteria, coupled with a multi-phase stress superposition algorithm, enable quantitative prediction of fracture density, aperture, and orientation through successive tectonic cycles. The results reveal that the Longmaxi Formation’s high brittleness and lithological heterogeneity interact with evolving stress regimes to produce fracture systems that are strongly anisotropic and phase-dependent: initial NE–SW-oriented domains established during the Indosinian phase were intensified during Yanshanian reactivation, while Himalayan uplift induced regional stress attenuation with limited new fracture formation. The cumulative stress effects yield fracture networks concentrated along NE–SW fold axes, fault zones, and intersection zones. By integrating geomechanical predictions with seismic attributes and borehole observations, the study constructs a discrete fracture network that captures both large-scale tectonic fractures and small-scale features beyond seismic resolution. Fracture activity is further assessed using friction coefficient analysis, delineating zones of high activity along fold–fault intersections and stress concentration areas. This principle-driven approach demonstrates how mechanical characterization, stress field evolution, and fracture mechanics can be combined into a unified predictive tool, offering a transferable methodology for structurally complex, multi-deformation reservoirs. Beyond its relevance to shale gas development, the framework exemplifies how advanced geomechanical modeling can enhance resource prospecting efficiency and accuracy in diverse geological settings. Full article

The precise mapping of rock joint traces is fundamental to the design and safety assessment of foundations, retaining structures, and underground cavities in building and civil engineering. Existing deep learning approaches either impose prohibitive computational demands for on-site deployment or disrupt the topological continuity of subpixel lineaments that govern rock mass behavior. This study presents BATNet-Lite, a lightweight encoder–decoder architecture optimized for joint segmentation on resource-constrained devices. The encoder introduces a Boundary-Aware Token-Mixing (BATM) block that separates feature maps into patch tokens and directionally pooled stripe tokens, and a bidirectional attention mechanism subsequently transfers global context to local descriptors while refining stripe features, thereby capturing long-range connectivity with negligible overhead. A complementary Multi-Scale Line Enhancement (MLE) module combines depth-wise dilated and deformable convolutions to yield scale-invariant responses to joints of varying apertures. In the decoder, a Skeletal-Contrastive Decoder (SCD) employs dual heads to predict segmentation and skeleton maps simultaneously, while an InfoNCE-based contrastive loss enforces their topological consistency without requiring explicit skeleton labels. Training leverages a composite focal Tversky and edge IoU loss under a curriculum-thinning schedule, improving edge adherence and continuity. Ablation experiments confirm that BATM, MLE, and SCD each contribute substantial gains in boundary accuracy and connectivity preservation. By delivering topology-preserving joint maps with small parameters, BATNet-Lite facilitates rapid geological data acquisition for tunnel face mapping, slope inspection, and subsurface digital twin development, thereby supporting safer and more efficient building and underground engineering practice. Full article

Pipelines play a vital role in material transportation within industrial settings. This review synthesizes detection technologies for early-stage small gas leaks from pipelines in the industrial sector, with a focus on acoustic-based methods, optical gas imaging (OGI), and multimodal fusion approaches. It encompasses detection principles, inherent challenges, mitigation strategies, and the state of the art (SOTA). Small leaks refer to low flow leakage originating from defects with apertures at millimeter or submillimeter scales, posing significant detection difficulties. Acoustic detection leverages the acoustic wave signals generated by gas leaks for non-contact monitoring, offering advantages such as rapid response and broad coverage. However, its susceptibility to environmental noise interference often triggers false alarms. This limitation can be mitigated through time-frequency analysis, multi-sensor fusion, and deep-learning algorithms—effectively enhancing leak signals, suppressing background noise, and thereby improving the system’s detection robustness and accuracy. OGI utilizes infrared imaging technology to visualize leakage gas and is applicable to the detection of various polar gases. Its primary limitations include low image resolution, low contrast, and interference from complex backgrounds. Mitigation techniques involve background subtraction, optical flow estimation, fully convolutional neural networks (FCNNs), and vision transformers (ViTs), which enhance image contrast and extract multi-scale features to boost detection precision. Multimodal fusion technology integrates data from diverse sensors, such as acoustic and optical devices. Key challenges lie in achieving spatiotemporal synchronization across multiple sensors and effectively fusing heterogeneous data streams. Current methodologies primarily utilize decision-level fusion and feature-level fusion techniques. Decision-level fusion offers high flexibility and ease of implementation but lacks inter-feature interaction; it is less effective than feature-level fusion when correlations exist between heterogeneous features. Feature-level fusion amalgamates data from different modalities during the feature extraction phase, generating a unified cross-modal representation that effectively resolves inter-modal heterogeneity. In conclusion, we posit that multimodal fusion holds significant potential for further enhancing detection accuracy beyond the capabilities of existing single-modality technologies and is poised to become a major focus of future research in this domain. Full article

This study focuses on investigating the damage characteristics and mechanisms of Slickwo clean fracturing fluid to the reservoir by using the deep coal seam in the Yan’an gas field as the research subject. During the experiment, fracturing fluids with varying A content were employed to displace coal and rock cores. The impact of these fluids on the permeability and pore structure of coal and rock was analyzed using a combination of nuclear magnetic resonance and high-pressure mercury injection technology. The findings indicate that the permeability damage rates of cores Y-1 and Y-2 post-displacement are 48.4% and 53.6% correspondingly, with the damage worsening as the agent A content increases. NMR data reveals that the fracturing fluid exhibits the highest retention in small pores, followed by medium-sized pores, and the least in large pores. The rise in agent A content enhanced the retention degree in individual pore throats and overall, increasing from 62.24% to 68.74%. The escalation in agent A content results in higher macromolecular residues, causing seepage channel blockages and enhancing the adsorption properties between fracturing fluid and coal rock. This phenomenon leads to inadequate backflow, primarily in smaller apertures. Simultaneously, the interaction between the gel breaker and clay minerals triggers particle migration, blockage, and expansion, consequently diminishing the permeability of coal and rock and inducing specific damages. Full article

Curved prisms can serve as core components of dispersive spectroscopy and converge light paths, making them widely used in spectral imaging technology. Their positional stability, surface shape errors, and temperature stability in optical systems directly affect the performance of spectral imaging systems. On the basis of the analysis of design indicators and optimization of the support structure for curved prisms, a multipoint flexible adhesive support structure (MPPASS) of large rectangular curved prisms for space-based application is proposed. The novelty of the MPPASS lies in its ability to achieve micro-stress and high stability support for large-aperture rectangular optical elements through the bonding of peripheral small points and the introduction of flexible bonding rings. The design principles of the adhesive support structure were deeply studied, and on this basis, the engineering design, finite element analysis, adhesive testing, and mechanical testing of large curved prisms were completed. The designed curved prism assembly has a maximum deformation displacement of 0.0085 mm and a maximum tilt angle of 0.65” under gravity loading, a first-order frequency of 1003.5 Hz, and a maximum acceleration amplification factor of 3.12 in the X, Y, and Z directions. The root mean square (RMS) variation value of the mirror shape errors for the curved prism assembly was 5.26 nm under a uniform temperature load of 20 ± 1 °C, and the RMS value of the mirror shape errors was 0.019 λ after mechanical testing. The installation surface flatness of 0.02 mm did not significantly affect its mirror shape errors. The experimental results verified the rationality of the design, temperature stability, and mechanical stability of the MPPASS. Full article

Sea ice is highly dynamic. Differences in the sea ice drift velocity and direction can cause deformations such as ridges and rubble fields or open up leads. These and other deformations have a major impact on the interaction between the atmosphere, sea ice and the ocean, and strongly influence ship navigability in polar waters. Spaceborne Synthetic Aperture Radar (SAR) data is well suited to observing the sea ice and retrieving sea ice drift vector fields at a small scale (<1 km), revealing deformation zones. This paper introduces a software processor designed to retrieve high-resolution sea ice drift vector fields from pairs of subsequent SAR acquisitions using phase correlation embedded in a multiscale Gaussian image pyramid. We assess the accuracy of the algorithm by using drift buoys and landfast ice boundaries manually outlined from large series of TerraSAR-X acquisitions taken during winter and spring sea ice break up. In particular, we provide a first analysis of the localization accuracy in deformation zones. Overall, our experiments show that deformation zones are well detected, but can be misplaced by up to 1.1 km. An additional interferometric analysis narrows down the location of the landfast ice boundary. Full article

In this work, we show the probability density functions (PDFs) and cumulative density functions (CDFs) of the nonnormalized field components and the associated powers received inside coupled reverberation chambers (CRCs), considering two canonical cases of single electrically small coupling apertures (ESCAs). These two cases involve one-dimensional (1D) and two-dimensional (2D) single electrically small CAs, respectively. We achieve normalized statistics from the nonnormalized ones for both field components and associated powers. We show that the comparison of the mean square values (MSVs) of the nonnormalized PDFs of the field components to the mean values (MVs) of the related nonnormalized PDFs of the powers is a proper method to corroborate the accuracy of the same achieved theoretical distributions, when they are achieved in an independent way. The achieved theoretical results are also validated by measurements. Moreover, for the sake of completeness and rigor of published results, we show two useful cases of the results from the measurements using two electrically large CAs. Full article

Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article

Synthetic aperture radar images can be used for ship target detection. However, due to the unclear ship outline in SAR images, noise and land background factors affect the difficulty and accuracy of ship (especially small target ship) detection. Therefore, based on the YOLOv5s model, this paper improves its backbone network and feature fusion network algorithm to improve the accuracy of ship detection target recognition. First, the LSKModule is used to improve the backbone network of YOLOv5s. By adaptively aggregating the features extracted by large-size convolution kernels to fully obtain context information, at the same time, key features are enhanced and noise interference is suppressed. Secondly, multiple Depthwise Separable Convolution layers are added to the SPPF (Spatial Pyramid Pooling-Fast) structure. Although a small number of parameters and calculations are introduced, features of different receptive fields can be extracted. Third, the feature fusion network of YOLOv5s is improved based on BIFPN, and the shallow feature map is used to optimize the small target detection performance. Finally, the CoordConv module is added before the detect head of YOLOv5, and two coordinate channels are added during the convolution operation to further improve the accuracy of target detection. The map50 of this method for the SSDD dataset and HRSID dataset reached 97.6% and 91.7%, respectively, and was compared with a variety of advanced target detection models. The results show that the detection accuracy of this method is higher than other similar target detection algorithms. Full article

Ion beam figuring (IBF) is an ultra-high-precision surface finishing technology characterized by a distinct trade-off between the spot size of the removal function and its corresponding figuring capabilities. A larger spot size for the removal function leads to higher processing efficiency but lower figuring ability. Conversely, a smaller spot size results in higher figuring ability but lower efficiency. Adjusting the spot size of the removal function using tools with an aperture is a possible approach. However, existing variable-aperture tools have certain limitations in IBF processing. To leverage the advantages of both large and small spot sizes for the removal function during IBF processing, an in situ dynamic beam variable-aperture device has been designed. This device optimizes the parameters of diaphragm sheets and employs FOC for dynamic aperture adjustment. Simulations show that 12 numbers of 0.1 mm-thick sheets minimize removal function distortion, with the thermal strain-induced area variation being <5%. FOC enables rapid (≤0.45 s full range) and precise aperture control. Experiments confirm adjustable spot sizes (FWHM 0.7–17.2 mm) with Gaussian distribution (correlation >96.7%), operational parameter stability (relative change rate ≤5%), and high repeatable positioning precision (relative change rate ≤3.2% in repeated adjustments). The design enhances IBF efficiency, flexibility, and accuracy by enabling in situ spot size optimization, overcoming conventional limitations. Full article

The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods. Full article