Search Results (211)

Goaf gas drainage and residual-coal spontaneous-combustion prevention are often designed independently, even though both are controlled by the same leakage-flow, oxygen-transport and heat-release fields in a longwall goaf. This decoupled design may reduce methane accumulation while unintentionally enlarging the oxidation zone. Taking the No. 1217 fully mechanized working face of Zhongxing Coal Mine, Shanxi Province, China, as an engineering prototype, this study develops an integrated laboratory-field numerical framework to quantify the drainage-induced evolution of the three zones of spontaneous combustion. Programmed temperature-rise experiments on the No. 2 coal seam were used to determine the oxygen-consumption rate, heat-release intensity and apparent activation energy under oxygen concentrations of 3–21%, yielding a critical oxygen concentration of 5.9%. Bundle-tube monitoring and distributed optical-fiber temperature sensing delineated the in situ three-zone boundaries, and a three-dimensional CFD model coupling porous-media seepage, species transport and Arrhenius-type heat generation was validated against the field data, with most relative errors below 5%. Parametric simulations for buried-pipe depths of 20, 30 and 50 m and negative pressures of 15 and 20 kPa reveal a pronounced asymmetric response: drainage compresses and advances the return-side oxidation zone toward the working face, but drives the inlet-side oxidation zone deeper into the goaf by enhancing oxygen-bearing leakage. Within the investigated parameter space, a buried depth of 30 m and a negative pressure of 20 kPa provide the best compromise, reducing the return-side oxidation-zone width from 32 to 21 m and the upper-corner methane concentration from 6.80% to 0.58%. The results demonstrate that drainage design should be constrained simultaneously by methane dilution and oxidation-zone control, and provide a quantitative basis for coordinating gas extraction with fire prevention in gas-rich, oxidation-prone longwall panels. Full article

With the continuous evolution toward sixth-generation (6G) wireless communication systems, emerging scenarios such as terahertz transmission, integrated sensing and communication (ISAC), and ultra-massive multiple-input multiple-output (MIMO) have significantly increased the complexity, nonlinearity, and uncertainty of wireless propagation environments. The conventional model-driven paradigm, established upon Shannon information theory and precise mathematical modeling, is increasingly constrained by model-mismatch issues in real-world deployments. This paper systematically reviews recent advances in deep learning-enabled physical-layer signal processing. We examine intelligent channel estimation, signal detection, and end-to-end communication systems based on autoencoder architectures. We then analyze key technical challenges—including interpretability, data dependence, computational complexity, privacy and security in distributed learning, and system-level performance-overhead trade-offs—along with state-of-the-art solution strategies such as deep unfolding, transfer learning, model compression, federated learning, and lightweight design. Future evolutionary directions toward AI-native 6G networks, integrated sensing-communication-computing architectures, and intelligent reconfigurable wireless environments are discussed. Furthermore, emerging generative AI techniques, including diffusion models, are identified as a promising direction for addressing data scarcity and enhancing system adaptability. The study demonstrates that hybrid intelligence—integrating model-based prior knowledge with data-driven learning—will become the dominant design philosophy for next-generation intelligent physical-layer systems. Full article

In the reconstruction problem of compressed sensing, to address the challenge of adapting common sparse constraints to diverse data, we propose a data-driven inverse-weighted regularization for adaptive data matching to enhance the ability of sparse constraints. Specifically, we formulate a weighted regularization term based on the data transform domain, positing that higher values in the sparsity-promoting transform domain correspond to a greater probability of effective signals. Therefore, when solving sparse optimization problems, we inversely weight this portion based on the inverse relationship with the coefficient magnitude, thereby reducing its impact and mitigating damage to effective signals. However, recognizing that noise and other irrelevant signals are sparse and approximately uniformly distributed in the transform domain, we can increase the weight of this portion to boost the sparsity constraint in the transform domain, thereby enhancing noise suppression. Consequently, we presented the corresponding solution algorithm and convergence proof for inverse-weighted sparse regularization, along with an application example in the context of the Radon transform. Experimental data tests indicate that inverse-weighted sparse regularization enhances the capability of sparse constraints, protects effective signals, suppresses noise, and improves the recovery accuracy of compressive sensing algorithms, as demonstrated in natural image enhancement and seismic multiple suppression. Full article

3D concrete printing (3DP) enables automated construction with reduced material waste and enhanced geometric flexibility. However, its structural performance remains sensitive to anisotropy, mix design, and printing parameters, thereby complicating quality control. Self-sensing cementitious materials provide a promising approach by enabling intrinsic strain monitoring during fabrication and service. In this study, a hybrid multi-material printing strategy was developed using a conductive cement-based mix incorporating graphite (G), milled carbon microfibers (MCMF), and chopped carbon microfibers (CCMF), alongside a plain cement-based matrix. Based on percolation analysis, an optimal composition of 2 wt.% G, 0.25 wt.% MCMF, and 0.0625 wt.% CCMF was selected. Reinforced beam specimens were fabricated with the conductive material embedded in either the tensile (bottom) or compressive (top) region, combined with two internal architectures: diagonal infill and solid-base configuration. Four configurations were defined: Pattern 1 (bottom/diagonal), Pattern 2 (bottom/solid-base), Pattern 3 (top/diagonal), and Pattern 4 (top/solid-base). Cyclic three-point bending tests with spatially distributed electrical measurements were conducted to evaluate the electromechanical response in the elastic range. Specimens with the conductive layer located in the tensile region (Patterns 1 and 2) consistently exhibited higher gauge factors than those in the compressive region (Patterns 3 and 4). Pattern 2 exhibited the best sensing performance, with an average gauge factor of 556 and SNR of 31. Across all configurations, SNR decreased with increasing electrode spacing, with reductions of up to 31.0%, demonstrating the effect of current path length on sensing performance. Full article

Environmental sound classification is an important component of smart city sensing systems, supporting applications such as urban noise analysis, public safety monitoring, and real-time situational awareness. However, high-accuracy models are often difficult to deploy on low-power edge devices because of memory, computational, and latency constraints. This study aims to address this deployment gap by developing a lightweight compression pipeline for a hybrid convolutional and Kolmogorov–Arnold Network-based model. The proposed pipeline consists of three stages. First, structured channel pruning is applied to remove redundant convolutional filters while preserving hardware-efficient dense operations. Second, selective quantization-aware training is applied to the most computation-dominant layers, namely the third convolutional layer and the fully connected layer. Third, knowledge distillation is used to recover accuracy by training the compressed model under the guidance of the baseline model. Experiments were conducted on ESC-10, ESC-50, FSC22, and UrbanSound8K. The proposed pipeline reduced the average parameter count from 511,033 to 50,774 and reduced the model size while maintaining competitive accuracy across all benchmarks. The final model preserved the baseline accuracy of 96.75% on ESC-10, while accuracy decreased only from 88.25% to 86.50% on ESC-50, from 87.92% to 86.38% on FSC22, and from 85.13% to 84.52% on UrbanSound8K. These results show that the proposed compression pipeline provides an effective accuracy–efficiency trade-off for real-time audio classification on resource-constrained devices. Therefore, the resulting compressed model supports the scalable deployment of distributed acoustic sensing systems for real-time smart city monitoring and decision-making. Full article

Compressed sensing (CS) applied in the ambiguity domain offers an effective approach for recovering time–frequency distributions (TFDs) of non-stationary signals from sparse representations. Existing methods predominantly rely on the Wigner–Ville distribution (WVD) as the initial representation due to its simplicity and high auto-term concentration. However, the WVD performs poorly for signals with higher-order frequency-modulated (FM) components and is highly sensitive to interference and noise, which then propagate into the reconstruction. This paper introduces the systematic use of the L-Wigner distribution (LWD) as the initial representation for CS-based reconstruction, providing front-end adaptability to signal characteristics. By generating a controllable family of TFDs ranging from the spectrogram to cross-term-free polynomial WVDs, the LWD enables effective suppression of interference and noise while simultaneously enhancing auto-term localization for nonlinear FM components. The proposed LWD-based reconstruction framework is evaluated against the conventional WVD-based method using several state-of-the-art reconstruction algorithms, whose parameters are jointly optimized through a multi-objective meta-heuristic framework to ensure a fair comparison. Experiments on noisy synthetic signals and real-world gravitational-wave data demonstrate consistent performance gains. On synthetic signals, the proposed approach reduces the average reconstruction error index by up to 36.6%, improves the ${\ell }_1$-reconstruction error by up to 75.8%, and achieves complete elimination of cross-term energy. In addition, robustness analysis under additive white Gaussian noise shows up to a 75% improvement in ${\ell }_1$ performance. For real gravitational-wave data, the method reduces the mean reconstruction index by up to 5.8% while maintaining auto-term preservation and eliminating cross-term artifacts. These results establish the LWD-based initialization as an effective and general strategy for TFD reconstruction in complex signal environments. Full article

The AVIF (AV1 Image File Format) is a modern, royalty-free image format that leverages the AV1 video codec for superior compression efficiency, supporting both lossy and lossless modes. Its entropy encoding relies on a multi-symbol context-adaptive arithmetic coder (range coding with adaptive cumulative distribution functions (CDFs)), which is effective for general imagery but may not optimally exploit the repetitive structures common in Geographic Information System (GIS) maps/data. This paper proposes replacing AVIF’s entropy encoder with the Burrows–Wheeler Transform (BWT), a reversible preprocessing algorithm that rearranges data to create runs of similar symbols, enhancing subsequent compression. We detail the technical steps for modification, drawing from AV1’s open-source implementation, and explain why BWT is advantageous for GIS raster maps/data, which often feature large uniform areas, limited color palettes, and spatial redundancies. Empirical evidence from related studies on BWT-based image compression shows improvements in lossless scenarios, potentially considerably reducing file sizes over standard methods while preserving data integrity critical for geospatial analysis. This swap could improve storage, transmission, and processing efficiency in GIS applications, such as remote sensing and cartography. The discussion includes challenges like computational overhead and compatibility, with recommendations for implementations. The resulting BWT-AVIF hybrid produces a non-standard AV1 bit-stream that is not compliant with the AV1 or AVIF specifications and therefore requires custom decoders. It is presented here as a research prototype for GIS-specific compression rather than a compliant AVIF extension. Full article

With the increasing number and enhanced sensing capabilities of satellites, the volume of satellite imagery has substantially surpassed the available bandwidth of satellite-to-ground links. Recently, with the adoption of commercial on-board GPUs, Learned Image Compression (LIC) offers the potential to mitigate this bottleneck by virtue of its superior rate–distortion performance over traditional codecs. However, existing LIC solutions operate in isolation on single satellites and underutilize the overlapping observations, which limits further gains in compression performance. In this paper, we propose Distributed Latent Representation Clustering (DLRC), which represents the first attempt to integrate real-time multi-satellite observation redundancy elimination into LIC. DLRC first introduces a local latent representation clustering mechanism. It discretizes the latent representation of LIC into compact cluster signatures on each satellite with lightweight computational overhead. Subsequently, DLRC presents a global cluster signature synchronization strategy. By exchanging signatures with negligible communication overhead, it enables multiple satellites to identify globally redundant local observations on a per-signature basis. By coding and downlinking only the latent representation corresponding to globally unique signatures, DLRC achieves non-redundant downlink in a training-free paradigm while remaining compatible with existing LIC architectures. Through extensive experiments, we demonstrate that DLRC achieves efficient bits per pixel reduction compared to independent LIC solutions while maintaining comparable reconstruction quality. Full article

In-well hydraulic fracture monitoring based on joint low-frequency distributed acoustic sensing (LF-DAS)/distributed temperature sensing (DTS) enables the acquisition of optical fiber mechanical strain data, which reflect fracture propagation and rock deformation during hydraulic fracturing. This paper presents an analytical method to interpret the mechanical strain profile measured by in-well LF-DAS/DTS during the fracturing process based on strain transfer theory and the Sneddon solution for fracture propagation. The analytical method is validated by a numerical model that simulates the strain field induced by fracture propagation. The sensitivity of the fiber strain to key factors, such as fracture geometry parameters and gauge length, is analyzed. The results indicate that compressive strain in the formation adjacent to the propagating fracture remains observable from the mechanical strain profile under the low fiber lag parameter condition. The presented method is applied to analyze the mechanical strain profile measured from a fractured horizontal well. Considering the reactivation of the pre-existing fracture, the location of the fractures is identified, and the fractures’ geometric parameters are inverted. This study provides a quantitative evaluation method for fracture geometry characterization based on joint LF-DAS/DTS fracturing monitoring. Full article

In the fields of atomic physics, quantum sensing, and precision measurement, 852 nm lasers are essential for the resonant excitation and manipulation of the cesium (Cs) D₂ transition ($6S_{1/2}\to 6P_{3/2}$). While significant global progress has been made in developing 852 nm laser based on distributed feedback (DFB) lasers and external cavity diode lasers (ECDL), the burgeoning demand for portable and integrated quantum instruments imposes stringent requirements on miniaturization and long-term, maintenance-free operation. To address the challenge of mode competition in Faraday lasers, this work demonstrates a frequency-stabilized semiconductor laser based on an atomic frequency-selective architecture. By utilizing a customized Faraday Anomalous Dispersion Optical Filter (FADOF) for frequency selection, the laser wavelength automatically corresponds to the Cs 852 nm D₂ transition, offering “Plug-and-play” operation. To further enhance integration, we propose and demonstrate a miniaturized Faraday laser architecture that resolves the instability caused by the mismatch between the FADOF transmission bandwidth and the free spectral range (FSR) of the external cavity. By employing a 7000 Gs magnetic field, the FADOF bandwidth is actively broadened to ∼15 GHz, while the cavity length is concurrently compressed to 30 mm to maximize FSR to effectively suppressing unstable mode competition. The resulting laser achieves a highly compact dimension of $102\times 109\times 96{\mathrm{mm}}^3$. Performance testing demonstrates a Lorentzian fitted linewidth of $16.4\mathrm{kHz}$ and a 1-s frequency stability of $3.05\times {10}^{-13}$ after modulation transfer spectroscopy (MTS)-based frequency locking. This robust and autonomous 852 nm laser source provides a critical technological foundation for the miniaturization of high-performance quantum sensors. Full article

Polarimetric high-resolution range profiles (HRRPs) contain rich amplitude and phase information scattered from targets, making them essential for radar remote sensing applications. However, current HRRP imaging methods still face challenges in achieving precise full-polarization measurements. In addition, they are either affected by off-grid errors thus introducing spurious scattering centers (SCs), fail to utilize polarimetric priors from the channels, or encounter high computational complexity. Some of these issues limit the quality of polarimetric HRRPs, while others result in excessive computational load, hindering their application on orbital remote sensing platforms. This paper proposes a fast gridless polarimetric HRRP imaging method. First, we introduce the novel virtual full polarization sparse stepped-frequency waveforms (VFP-SSFW) to improve channel isolation, in which each pulse is transmitted with either horizontal (H) or vertical (V) polarization, selected uniformly at random. Then, we propose a polarimetric atomic norm minimization (P-ANM)-based imaging framework formulated within distributed compressed sensing (DCS), which fully exploits the joint sparsity across polarization channels while inherently eliminating off-grid errors. Additionally, we develop a fast algorithm based on alternating direction method of multipliers (ADMM) to enable efficient implementation. The proposed method can circumvent transmission channel crosstalk and can efficiently yield high-quality polarimetric HRRPs with co-registered SCs. The validity of the proposed method is demonstrated through simulated, electromagnetic, and measured experimental results. Full article

Global freshwater resources face severe water quality degradation, with chlorophyll-a (Chl-a) concentration serving as a critical eutrophication indicator. While deep learning methods enable accurate Chl-a retrieval from remote sensing reflectance (Rrs) spectra, the scarcity of paired Rrs-Chl-a samples limits model generalization and causes overfitting, particularly in optically complex inland waters. To address this data bottleneck, we propose a physics-constrained latent diffusion model for synthesizing high-fidelity paired Rrs-Chl-a data to augment limited training sets for deep learning-based water quality retrieval. Our framework integrates three key innovations: (1) a lightweight variational autoencoder achieving 8.6:1 latent space compression, reducing computational overhead while preserving spectral features; (2) band-selective attention mechanisms targeting chlorophyll-sensitive wavelengths (440, 550, 680, and 700–750 nm) based on bio-optical principles; and (3) physics-guided conditional encoding that captures concentration-dependent spectral responses across oligotrophic to eutrophic regimes. Evaluated on the GLORIA dataset, our model demonstrates superior performance in spectral similarity (0.535), sample diversity (0.072), and distribution matching (Fréchet distance 0.0008) compared to conventional generative models. When applied to data augmentation, synthetic spectra improved downstream Chl-a retrieval from R²= 0.75 to 0.91, reducing RMSE by 39%. This physics-informed generative approach addresses data scarcity in aquatic remote sensing research, supporting global needs for enhanced understanding of inland and coastal water quality dynamics in data-limited regions. Full article

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator enhanced with optical pulse compression to improve sensitivity. Building on this vibration acquisition stage, automatic leakage detection algorithms are implemented by searching for leak-induced activity in the frequency domain, which is well suited to revealing leakage-related features. After acquiring a baseline calibration to characterize normal-condition vibrations at each sensing position, leakage candidates are identified by comparing distribution-based metrics computed over multiple measurements against the corresponding baseline statistics. Two automatic leakage detection strategies are developed. First, low-complexity feature-based metrics are implemented, enabling continuous monitoring with minimal computational requirements. Second, an autoencoder-based anomaly detection technique is introduced, which also relies on location-specific normal-condition calibration but reduces the dependence on prior knowledge of the expected leakage vibration signatures. A real-world field trial on an urban network demonstrates reliable detection and localization using controlled leak events generated in the field, with measurements performed over a 17 km sensing fiber and an effective spatial resolution of 2.6 m. Benchmarking against a commercial punctual electro-acoustic leak detector yields consistent trends. Overall, the proposed system could complement existing technologies by enabling automated, continuous city-scale monitoring over already deployed dark fiber infrastructure. Full article

Distributed optical fiber vibration sensing (DVS) has shown strong potential in perimeter security, pipeline leakage monitoring, transportation safety, and structural health diagnostics owing to its high sensitivity, long-range coverage, and immunity to electromagnetic interference. However, severe cross-scene distribution mismatch is often encountered in real-world deployments: indoor, outdoor, and pipeline environments exhibit markedly different noise patterns and time–frequency characteristics, thereby degrading the generalization ability of models trained in a single scene. To address this challenge, we propose a Progressive Multi-Strategy Domain Alignment (PMSDA) framework for label-disjoint cross-scene vibration recognition. PMSDA uses a compact expansion–compression encoder together with complementary alignment mechanisms—maximum mean discrepancy (MMD), correlation alignment (CORAL), and adversarial domain discrimination—to learn a scene-robust latent space from a labeled indoor source and two unlabeled target domains (outdoor and pipeline) within a single alternating-training model. Because the fine-grained source and target label spaces are disjoint, PMSDA is formulated as a representation-transfer framework rather than a standard label-shared unsupervised domain adaptation method; target-domain recognition is therefore performed through domain-specific prototype clustering in the aligned latent space. On three representative scenes with nine event classes in total, PMSDA achieved 89.5% accuracy, 86.7% macro-F1, and 0.93 AUC for Indoor→Outdoor, and 85.8%, 84.7%, and 0.87, respectively, for Indoor→Pipeline, outperforming traditional feature+SVM/RF pipelines, CNN/ResNet baselines, and representation-transfer baselines adapted from DANN/CDAN/SHOT under the same evaluation protocol. These results indicate that PMSDA is a promising and effective framework for offline cross-scene DVS evaluation under disjoint target event sets. Full article

The integration of real-time monitoring capabilities into concrete materials offers significant potential for improving the safety and durability of building infrastructure. This study investigates the real-time electromechanical behavior of steel fiber-reinforced self-sensing concrete under compressive and splitting tensile loading. Eighteen cubes (150 × 150 × 150 mm) and eighteen cylinders (150 × 300 mm) containing 0.5%, 1.5%, and 3% steel fiber volume fractions were tested. Electrical resistance was continuously recorded at one-second intervals using an Arduino–ESP32-based system, enabling in situ tracking of damage evolution. The conductive steel fiber network functioned as an intrinsic sensing phase, where load-induced microstructural changes altered electrical pathways. Resistance variations consistently preceded visible cracking, with pronounced nonlinear increases observed at 65–80% of peak load, indicating micro-crack initiation. Distinct electromechanical stages were identified, including elastic stability, compaction-induced resistance reduction near yield, and rapid resistance growth during crack propagation. Higher fiber contents improved both mechanical performance and sensing sensitivity through enhanced crack-bridging and conductive network stability. Although curing age influenced baseline resistance, reliable real-time damage detection was achieved across all specimens. The findings demonstrate the feasibility of steel fiber-reinforced concrete as a cost-effective, distributed monitoring material for early damage detection in building structures. Full article