Search Results (1,028)

The fractal characteristics of coal pore–fracture networks and their evolution under compression are essential for predicting rock mass failure and fluid transport. This study combines micro-CT scanning with fractal theory and seepage mechanics to investigate the structural evolution of coal under uniaxial compression and its impact on fluid transport. CT scans were performed at four characteristic stages (initial, elastic, plastic, and failure) to reconstruct three-dimensional fracture networks. Quantitative analysis reveals that fracture porosity increases sequentially from 0.44% to 5.01%, with the failure stage reaching 11.4 times the initial value. Fracture length and aperture distributions follow power-law scaling, and their fractal dimensions exhibit distinct evolution patterns: length dimension increases from 2.43 to a peak of 2.56 in the plastic stage and then drops to 2.47 at failure, while aperture dimension decreases from 2.29 to a trough of 2.12 before rebounding to 2.26. These patterns reflect a dynamic adjustment of network complexity, transitioning from primary fractures to micro-fracture dominance and finally to main fracture coalescence. Based on the Knudsen number, three diffusion regimes of Fick, transition and Knudsen are identified. A fractal permeability model is developed by idealizing the pore space as tortuous capillaries, showing that permeability scales with the fourth power of the maximum pore diameter and is positively influenced by the fractal dimension and the number of large pores. Furthermore, a coupled seepage–stress model is derived, incorporating pressure transmission, shear transmission, and crack opening coefficients. The damage variable is expressed as a function of stress level and fractal dimension. These findings provide theoretical support for predicting gas transport and failure behavior in coal under coupled hydro-mechanical conditions. Full article

High-viscosity sodium alginate solutions (4.5% by mass, apparent viscosity 1 × 10⁴–2 × 10⁴ cP) are widely used in the preparation of hydrogels, wet spinning, and biomedical materials. Residual bubbles can cause internal voids in hydrogels, mechanical heterogeneity, fiber breakage during spinning, and reduced strength, and can severely affect the cell compatibility and clinical safety of biomaterials. Due to the difficulty of bubble migration, coalescence, and rupture in high-viscosity systems, traditional vacuum-standing degassing takes up to 24 h and is extremely inefficient, severely limiting the quality of subsequent processing. To address this issue, this study proposes a novel vacuum-assisted centrifugal recirculating degassing method for highly viscous sodium alginate solutions and aims to establish a kinetic framework for describing its overall degassing behavior. Using the number density of bubbles larger than 0.5 mm in diameter as an evaluation metric, we conducted vacuum-standing control experiments and univariate experiments with different screen mesh apertures (5, 1.5, 0.3, and 0.07 mm). We experimentally verified a continuous kinetic model of bubble number decay based on vacuum bubble expansion, centrifugally enhanced migration, and removal probability during the cycle. The results indicate that the bubble removal effect of 40 min of vacuum–centrifugal cyclic degassing is equivalent to that of 4 h of vacuum static settling, representing a 450% increase in degassing efficiency. There is an optimal range for a screen aperture, with the best degassing effect observed at 0.3 mm, achieving a bubble removal rate of 83.69%. The established kinetic model exhibits good fitting accuracy (RMSE = 0.17, MAPE = 5.9%) and can accurately predict degassing efficiency under different process conditions. This study provides a quantifiable, modelable, and optimizable process scheme for rapid degassing of high-viscosity sodium alginate solutions, and offers a theoretical reference for the development of degassing technologies for high-viscosity polysaccharide fluids. Full article

Ship detection in synthetic aperture radar (SAR) imagery, an indispensable all-weather technology for marine engineering and coastal safety, remains challenging in complex nearshore scenes due to coupled speckle noise, sea–land clutter, large scale variation, and extreme class imbalance. Existing decoupled pipelines fail to jointly mitigate these degradations, leading to high false alarm rates and poor generalization. We propose DN-AnchorNet, an end-to-end unified framework integrating a detection-oriented structure-preserving enhancement branch, a scale-adaptive anchor mechanism, and an adaptive weighted Smooth L1 loss. The detection-guided enhancement branch operates without paired clean data to preserve critical ship structures. The scale-adaptive anchor design enhances matching for small, elongated, and arbitrarily oriented ships, while the tailored loss improves regression robustness through dynamic threshold adjustment and valid positive-sample regression masking under class imbalance. Extensive experiments under the adopted fixed nearshore stress-test protocol of RSDD-SAR and SSDD+ show that DN-AnchorNet achieves the best overall performance among the compared representative oriented object detectors in this evaluation setting, with AP₅₀ values of 0.699 and 0.610, and F1-scores of 0.757 and 0.689, respectively. A strict zero-shot cross-dataset evaluation on HRSID provides supplementary evidence of DN-AnchorNet’s transferability to unseen marine SAR conditions. These results suggest that joint optimization can achieve a favorable accuracy–false-detection balance under challenging nearshore SAR detection conditions. Full article

Passive reception of spaceborne synthetic aperture radar (SAR) signals is of great significance for acquiring target characteristics and identifying SAR operating states. With the rapidly growing demand for high-quality SAR signal reception, signal-receiving arrays are prone to beam performance deterioration and difficulty in beamforming under wide-angle scanning conditions. Traditional uniform arrays fail to meet practical engineering requirements and cannot balance multiple conflicting performance indicators. To address the above technical bottlenecks, this paper proposes a design method of a non-uniform planar receiving array based on the MOEA/D-HPSO algorithm. Taking maximum sidelobe level (MSL), array gain (G), and beamwidth (BW) as core performance indicators, a multi-objective optimization model of SAR signal-receiving array for wide-angle scanning is established. This method integrates the multi-objective decomposition strategy and hybrid genetic particle swarm optimization mechanism, decomposes complex multi-objective problems into several scalar subproblems, obtains uniformly distributed Pareto fronts, and effectively improves the diversity of solution sets. Simulation experimental results show that the proposed algorithm is superior to traditional mainstream algorithms such as NSGA-II and MOEA/D-DE in terms of convergence accuracy, solution set distribution, and various performance indicators. Typical array design examples verify that the proposed method can adapt to various engineering application scenarios and provide technical support for spaceborne SAR signal reception and spectrum management. Full article

To investigate the three-dimensional seepage response and safety implications of high concrete-face rockfill dams (CFRDs) under waterstop failure scenarios, this study establishes a refined three-dimensional finite element model for a high CFRD at the JD Hydropower Station using COMSOL (version 6.1) Multiphysics. A comparative analysis is conducted for six representative scenarios, including peripheral joint failure, single vertical joint failure, overall vertical joint failure, and combined failures. The seepage safety assessment is based on the phreatic surface, seepage discharge, hydraulic gradients in key zones, and left- and right-bank abutment bypass seepage. The results show that waterstop failure significantly changes the seepage field, phreatic surface, leakage discharge, and hydraulic gradients. Among the six scenarios, S5, representing overall vertical joint failure with an aperture of 0.5 mm for each of the 41 vertical joints, produces the most unfavorable leakage response, with the total seepage discharge reaching 3010.46 L/s and the water level behind the face slab reaching 3888.23 m. In contrast, peripheral joint failure mainly induces local hydraulic-gradient concentration in the special cushion zone. Under S1, the maximum hydraulic gradient in the special cushion zone reaches 2.72, exceeding the allowable value of 0.72. The results also reveal asymmetric bypass seepage around the dam abutments, with the right-bank foundation leakage being 90.4–137.7% higher than that on the left bank. These findings clarify the distinct seepage risk mechanisms of different waterstop failures and provide support for waterstop design, construction quality control, targeted monitoring, and operation-stage safety assessment of high CFRDs. Full article

Rainfall-induced delayed instability of fractured rock slopes is strongly affected by fracture preferential flow, hydro-mechanical coupling, and spatial matrix heterogeneity. However, the coupled influence of stress-dependent fracture aperture evolution and heterogeneous matrix properties on delayed slope deformation remains insufficiently quantified. In this study, a two-dimensional discrete fracture network (DFN)–equivalent continuum coupled model was established using spectral random field theory and a representative Monte Carlo-generated fracture geometry. The spectral exponent β = 1.0–2.5 was adopted to characterize different degrees of matrix heterogeneity, and rainfall infiltration–stress coupling simulations were conducted under an extreme rainfall scenario followed by drainage. The results indicate that the wetting front advances irregularly in the heterogeneous matrix, while fracture preferential flow accelerates rainwater infiltration and promotes local pore-pressure accumulation near the phreatic surface. After rainfall cessation, water stored in fractures continues to recharge the deep matrix, leading to delayed pore-pressure increase and post-rainfall deformation. The simulated fracture aperture shows an initial closure followed by gradual dilation, which is controlled by the competition between saturation-induced stress redistribution and pore-pressure-driven effective stress reduction. Under a common strength reduction factor of FOS = 1.4, stronger matrix heterogeneity results in more pronounced plastic strain concentration and larger displacement amplitude along the potential slip zone. These findings suggest that fracture aperture evolution and matrix heterogeneity jointly influence delayed deformation and potential failure-zone development in rainfall-affected fractured rock slopes. The conclusions should be interpreted within the scope of a two-dimensional DFN–equivalent continuum numerical framework with prescribed rainfall conditions and representative fracture/random-field realizations. Full article

Deep learning-based automatic target recognition (ATR) for synthetic aperture radar (SAR) imagery requires large quantities of high-quality annotated data, yet real SAR samples are costly and difficult to obtain. Existing generative adversarial network (GAN)-based SAR generation methods often suffer from limited authenticity and insufficient diversity. To address these issues, we propose CA-DDPM, a conditionally embedded attention-aided denoising diffusion probabilistic model (DDPM) for high-quality multi-category SAR image generation. CA-DDPM employs a unified conditional embedding that fuses time-step and category information, injected into a U-Net backbone through a feature-wise linear modulation (FiLM)-based mechanism to achieve step-aware and class-aware denoising. Attention blocks are further incorporated to enhance the modeling of structural dependencies and fine scattering details. To evaluate generation quality, we develop a three-dimensional assessment framework that jointly examines authenticity, diversity, and utility in ATR. Authenticity is quantified using local and global similarity metrics under a unified Hungarian-matched statistical procedure, together with an SAR-adapted Fréchet inception distance (SAR-FID). Diversity is assessed through inter-category feature clustering, an SAR Inception Score (SAR-IS), and a newly proposed intra-category grayscale histogram-based metric. Utility is evaluated by hybrid training experiments across multiple ATR models. Experiments on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate that CA-DDPM produces more realistic and diverse SAR images than representative GAN- and DDPM-based baselines, and it effectively improves downstream ATR performance through data augmentation. Full article

Gases are prone to accumulating in mines. Untimely gas drainage can easily trigger gas outbursts, which may further lead to gas explosions, directly endangering personnel lives and mine safety. Therefore, gas control during gob-side entry driving (roadway excavation adjacent to the goaf) in high-gas mines is crucial to ensuring successful and safe mining and excavation. The 110505 track haulage gateway is a typical high-gas gob-side driving gateway. The measured maximum gas content of the lower No.5 coal seam is 6.0289 m³/t. At present, without a scientific basis for optimizing core parameters, such as the spacing and diameter of gas drainage boreholes, gas drainage is incomplete, and triangular gas pressure zones are likely to form between boreholes. As a result, the risk of gas accumulation is high. This not only exacerbates the danger of unpredicted gas outbursts but also seriously hinders the rapid excavation of the gateway and the progress of mining and further excavation. Based on a mechanical framework coupling coal seam and methane migration, and focusing on the relationships between factors such as borehole spacing, borehole aperture, methane drainage duration, and overall gas drainage efficiency, a model incorporating dual pore distribution and unified permeability characteristics was constructed. Numerical modeling was performed using the COMSOL Multiphysics platform to examine the influences of different borehole spacings and apertures on underground gas drainage in coal seams. The results indicate that reducing borehole spacing contributes to a more pronounced decline in gas pressure and a lower peak pressure between neighboring boreholes. When an interval spacing of 0.3 m was adopted for the drilling layout arrangement, the peak gaseous potential within the surrounding rock matrix dropped to 0.48 MPa following continuous drainage over a duration of 20 days, a reduction of 44%, and there was no obvious triangular zone of pressure. In contrast, borehole diameter had a minor effect on gas drainage efficiency, and the maximum gas pressure after 20 days was less than 0.52 MPa under different borehole diameters. This work establishes a theoretical foundation and offers practical guidance for high-efficiency gas drainage during gob-side entry driving, which is of vital importance for achieving safe and rapid excavation in high-gas mines. Full article

In this paper, a high-gain wideband filtering antenna with metasurface structures is presented for Sub-6 GHz 5G applications. The proposed antenna consists of a 3 × 3 metasurface array, a driven patch, a short-circuited stepped impedance resonator (SIR) feedline, and two parasitic patches. The metasurface is used to manipulate the modal behavior of the radiator and to introduce an additional resonant mode for bandwidth enhancement. Meanwhile, two radiation nulls are generated by different mechanisms to realize filtering performance. The low-frequency radiation null at 2.81 GHz is introduced by the short-circuited SIR feedline, whereas the high-frequency radiation null at 5.76 GHz is produced by radiation cancelation among the driven patch, parasitic patches, and metasurface. The measured results show a 10 dB impedance bandwidth of 35.5% from 3.62 to 5.18 GHz and an average realized gain of 8.61 dBi. In addition, the proposed antenna achieves lower- and upper-band selectivity of 42.57 dB/GHz and 33.43 dB/GHz, respectively. The proposed antenna also achieves a compact radiation aperture of 0.60 × 0.60 λ₀² and effective out-of-band radiation suppression, making it a promising candidate for integrated 5G RF front-ends. Full article

A geometry-aware deep learning framework for the reduction of atmospheric noise in SAR (Synthetic Aperture Radar) interferograms has been proposed and validated in this study. Our model has obvious advantages over existing ones in the following three aspects: (1) our objective is to reconstruct the original SAR imagery using an autoencoder and then eliminate noise by subtracting the reconstructed data from the raw data. However, our network architecture is not symmetric, and we choose to employ HRNet-w32 to preserve the details of the input dataset. (2) A deep supervision module equipped with diverse feature-unleashing mechanisms (including geometric, multispectral, and sematic features) is also developed to enhance the model’s predictive capability and interpretability. (3) We emphasize the significance of fractal geometry and variogram inference in the loss function, given that atmospheric disturbances, specifically humidity, clouds, and fogs, often exhibit statistically fractal characteristics. Compared with existing methods and ablation studies, our framework achieves relatively robust APS suppression performance across multiple quantitative metrics, including the Mean Squared Error (MSE), Nash–Sutcliffe Efficiency (NSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), and Coefficient of Correlation (CoC), with improvements of at least 5.0% over the baselines. Full article

Compressed sensing (CS) and matrix completion algorithms (MCA) have each introduced sparse and low-rank priors into synthetic aperture radar (SAR) imaging. However, their combined use reveals a fundamental zero-sum trade-off: enhancing spatial continuity tends to obscure weak targets, while strengthening sparse recovery amplifies off-grid artifacts. This inherent conflict is further exacerbated by static regularization, which imposes a rigid global compromise and prevents genuine synergy between the two priors. To overcome this limitation, this paper proposes a Spatio-Temporal Adaptive Synergistic Constraint Imaging (STASCI) algorithm, which dynamically balances the two priors in a scene-aware manner. The core of STASCI is a unified regularization framework. The low-rank constraint models’ spatial continuity in the background to suppress off-grid artifacts. The sparse constraint, enhanced by a non-convex Geman-McClure function, is employed to detect weak targets and compensate for detail loss. A key innovation is a spatio-temporal dual-dimensional regularization mechanism that employs Sobel operators to probe local spatial gradients and dynamically adjusts the strength of each prior according to regional scene characteristics. This enables adaptive synergy rather than a fixed trade-off. The optimization is solved via the alternating direction method of multipliers (ADMM), with the low-rank subproblem accelerated by randomized singular value decomposition (RSVD). Final imaging is performed using the Range Migration Algorithm (RMA). Experiments on real measurements and public datasets demonstrate that STASCI breaks the conventional detail-background trade-off. It effectively suppresses off-grid artifacts while retaining weak targets, leading to significant improvements in imaging accuracy and robustness across complex scenarios. Full article

Precise and continuous monitoring of thermal effects are critical for ensuring the structural safety of bridges and preventing potential failures. This study presents a methodology integrating unmanned aerial vehicle (UAV)-based thermal measurements with interferometric synthetic aperture radar (InSAR) satellite data to assess and monitor the thermomechanical response of bridges. A three-dimensional (3D) finite element model (FEM) of a prestressed concrete (PC) bridge was developed and validated using in situ displacement measurements. High-resolution, 3D temperature distributions of bridge elements were obtained daily and seasonally using UAV-based infrared thermography (UAV–IRT). Thermal maps were validated with point temperature measurements on the structure. Simultaneously, long-term wide-area deformation trends were investigated using satellite-based InSAR observations. The thermo-mechanical displacement behavior derived from UAV–IRT measurements was compared with historical InSAR-derived seasonal deformation patterns to develop an integrated multi-source structural monitoring framework. The behavior of the bridge in daily and seasonal temperature cycles was simulated and analyzed by integrating UAV–IRT thermal load data into FEM. Maximum stress levels occurring under the most adverse thermal loading conditions and over a one-year period were calculated, taking into account stress limits. The FEM revealed a maximum vertical displacement of 12.3 mm under extreme thermal loading, with tensile stresses in the deck mid-depth exceeding the 3.5 MPa limit, signaling a potential risk for thermally induced cracking. Integration of UAV–IRT thermal observations and historical InSAR deformation measurements revealed vertical temperature gradients of up to 24 °C during summer conditions and indicated that the observed structural response was predominantly governed by thermo-elastic deformation. UAV-satellite methodology offers a rapid, economical, and comprehensive solution for the structural health monitoring of bridges exposed to thermal effects. Full article

Synthetic aperture radar (SAR) is of great value in intelligent image interpretation. However, the acquisition of real SAR data is costly, and manual annotation heavily relies on expert experience. These factors severely restrict the development of SAR intelligent interpretation algorithms. Meanwhile, the high-frequency details of SAR images contain rich target information. Traditional generation methods cannot effectively capture these key features. To address the above issues, this paper proposes a dual-branch feature decoupling generative adversarial network (GAN) with wavelet constraint designed to achieve high-quality and parameter-controllable SAR image generation. The framework leverages discrete wavelet transform (DWT) to separate spatial structure from high-frequency details, which are independently modeled by a structure branch and a detail branch, respectively. A wavelet consistency loss function is introduced to constrain the distribution of generated and real images in high-frequency subbands, thereby enhancing the model’s capability to model scattering details. To fuse features from the two branches, a cross-attention fusion module is adopted to realize the adaptive compensation of structural features with texture details. Furthermore, to achieve joint control over the semantic attributes and azimuth of generated samples, the framework further integrates auxiliary classification and azimuth regression tasks. A multi-task learning mechanism is constructed to realize precise control over the target’s semantic category and azimuth. For the continuous variable of azimuth, an angle-aware hypernetwork transform module is introduced to perform dynamic convolution modulation on the structure branch at the feature map scale, which improves the model’s fine control capability over continuous azimuth variations. Experimental results on the MSTAR dataset demonstrate that the proposed model can significantly improve the semantic consistency and visual fidelity of the generated samples. The generated samples exhibit high statistical alignment with real data distributions, confirming the model’s effectiveness in characterizing the feature space of SAR imagery and enabling controllable SAR data simulation, thereby augmenting datasets for image interpretation tasks. Full article

Optical and Synthetic Aperture Radar (SAR) image registration is a fundamental task in remote sensing information fusion, yet it remains challenging due to significant differences in imaging mechanisms, radiation characteristics, and noise properties between the two modalities. Existing public datasets suffer from limited resolution, small scale, and insufficient scene diversity, and these limitations have hindered algorithm development. This paper constructs a large-scale, high-resolution optical–SAR registration dataset based on the HongTu-1 satellite 3-m SAR imagery and Google Earth optical imagery at zoom level 17, covering diverse scenes across China with a standardized pipeline including terrain correction, geometric alignment, standardized slicing, and quality filtering. Building upon this dataset, a hand-crafted keypoint-based cross-modal registration method is proposed, incorporating multi-level edge filtering and hybrid feature detection. Unlike conventional hand-crafted methods such as RIFT, SRIF, and LNIFT, which mainly refine keypoint detection, description, or matching within a SIFT-style pipeline, the core novelty of this work lies in SAR-specific preprocessing and multi-level hybrid filtering. These components are designed to suppress speckle while extracting more stable and discriminative shared edge responses for cross-modal registration. An improved Log-domain Total Variation (Log-TV) denoising model is introduced for SAR preprocessing. A hybrid edge filtering framework combining phase congruency analysis and Structured Random Forest (SRF) edge detection is constructed within a Gaussian scale space. A dual-branch feature detection scheme integrating blob and corner features is designed with a robust orientation assignment strategy. Feature description uses the Gradient Location–Orientation Histogram (GLOH) descriptor with Principal Component Analysis (PCA) reduction, while geometric estimation employs the Fast Sample Consensus (FSC) algorithm. Experiments on the self-constructed HT dataset and on the public OSdataset and SAR2Opt benchmarks show that the proposed method consistently achieves low RMSE and high success rates. It also maintains competitive efficiency among hand-crafted methods while retaining strong robustness to scale and rotation variations. Full article

Multimodal object detection based on synthetic aperture radar (SAR) and optical imagery is of great significance in remote sensing, particularly under adverse weather conditions, nighttime environments, and complex background scenarios. Although SAR imagery has unique advantages under all-weather conditions, its object detection performance still faces challenges in low-texture regions and cluttered scenes. Optical imagery provides rich spatial and texture information, but its applicability is limited in harsh environments. To overcome the limitations of unimodal SAR object detection, this paper proposes a novel multimodal object detection framework, termed StarRoute-DBNet, to improve detection accuracy and robustness through multimodal data fusion and efficient feature interaction. Specifically, a FocusGraph (Graph Convolution-Based Feature Relationship Modeling) module is first designed to adaptively model the spatial relationships between optical and SAR features via graph convolutional networks (GCNs), thereby capturing complex cross-modal spatial dependencies. This module enhances feature interaction across modalities, improves the localization accuracy of oriented targets, and shows clear advantages for small-object detection in complex backgrounds. Second, to alleviate the loss of critical information during downsampling, a PhaseRoute (Sparse Routing Polyphase Downsampling Module) is introduced, which combines multi-phase decomposition with a Top-2 sparse routing strategy to preserve informative spatial cues. By incorporating Gumbel noise into the routing process, the proposed module further improves routing flexibility, detection accuracy, and model robustness. In addition, a Multi-Scale Shuffle-Gated Fusion (MSSGF) module is proposed to address the multi-scale issue in multimodal feature fusion. This module integrates multi-scale convolutional branches, channel shuffles, and dual-attention mechanisms to enhance feature interaction across scales, while an adaptive weighted fusion strategy is employed to dynamically adjust the fusion weights of multimodal features. As a result, the proposed method significantly improves detection accuracy and robustness, especially in complex scenes. Extensive experiments conducted on the MVSDA dataset and the M4-SAR dataset demonstrate that the proposed StarRoute-DBNet consistently outperforms existing state-of-the-art methods under complex backgrounds and adverse conditions. In particular, it achieves clear advantages in oriented object detection and small-object detection, verifying its effectiveness and robustness for cross-modal remote sensing object detection. Full article