Abstract Ambient noise cross‐correlation has emerged as a key technique in low‐frequency seismology, enabling coda‐attenuation measurements at frequencies difficult to access with traditional earthquake‐based approaches, especially in regions with insufficient seismicity. This work establishes the first comprehensive coda‐wave attenuation tomography of continental China across the 3–30 s period band, utilizing Empirical Green's Functions retrieved from ambient noise cross‐correlations, with attenuation measured from the Rayleigh‐wave coda. By applying a bound‐constrained Gauss‐Newton inversion scheme, we derive a high‐resolution frequency‐dependent Qc model that reveals pronounced lateral variations in crustal attenuation. Our tomographic results demonstrate a systematic relationship between attenuation structure and major tectonic blocks: prominent high‐attenuation anomalies correlate with sedimentary basins and active tectonic zones, whereas stable cratonic regions exhibit consistently elevated Qc values. These spatial patterns show a strong correspondence with active fault systems and modern seismicity distributions, thereby providing new constraints on regional tectonic processes and vital insights for seismic hazard assessment. Collectively, the Qc model presented in this study establishes a robust reference for investigating crustal heterogeneity throughout continental China, effectively complementing existing seismic velocity models to advance the integrated interpretation of the region's complex tectonic architecture.

Ferrotitanium slag (FTS) is a solid waste with a chemical composition of CaO-Al2O3-TiO2 system, where the TiO2-containing component is particularly sensitive to atmospheric fluctuations, leading to changes in its valence state. Given the challenges in controlling atmospheric fluctuations in the actual operating environments of refractory castables, studying the impact of atmospheric conditions on the performance and structure of these castables is crucial for optimizing the resource utilization of FTS. This study compared the performance of FTS-based castables, including a control group without FTS and heat-treated samples exposed to air and carbon-buried conditions. The results revealed that, compared to the control group, the relative cold compression strength values of the air-treated and carbon-buried groups are 21% and 67%, respectively, while the relative cold modulus of rupture values are 22% and 53%. The decline in performance of the air-treated samples could be primarily attributed to the branching evolution of FTS aggregates. This branching process is accompanied by color changes, which are influenced by the oxidation of TiO2-containing components. The oxidation reaction accelerates the outward migration of the components in FTS, by facilitating the structural transformation of Ti-doped hibonite crystals and increasing the high-temperature liquid phase, which in turn influences the branching evolution. Consequently, FTS-based castables must account for performance changes induced by atmospheric fluctuations in service environments, with aggregate color serving as a key visual indicator for analyzing these changes. This is of great significance for evaluating the differences in design performance and actual application performance.

Extracting vectorized building instances from remote sensing imagery provides an intuitive representation of building distribution and details, which is essential for urban planning. The prevailing approach for building detection involves segmenting building masks and subsequently vectorizing them to obtain building polygons. However, significant variations in building scales and spatial distributions in underdeveloped and rural areas pose substantial challenges to existing detection algorithms. Additionally, the diversity of architectural styles and the scarcity of finely annotated datasets exacerbate these issues. To address these challenges, we propose a robust building detection framework, HRMamba, which employs a high-resolution network optimized with Mamba for efficient feature extraction. Our framework also utilizes attraction fields for line segmentation and integrates building vertices to construct building contours and adjacency lines, thereby generating vector polygons of building instances. On the CrowdAI dataset, our method achieves an Average Precision (AP) of 68.3% and an AP50 of 92.5%. Furthermore, we developed a building polygon dataset for the Tianjin and Jinan regions in China. Experimental results demonstrate that our approach effectively handles complex scenarios, achieving an AP of 63.2% with geometrically accurate building instance polygons.

With the increasing number of embedded devices, the demand for these devices is continually growing. Even in today's fast-paced technological era, many vulnerabilities still exist in embedded devices. Firmware vendors often release code packages in binary form due to reasons such as copyright and security concerns, and they typically remove symbol information upon release, complicating reverse analysis. Symbol information is fundamental to analyzing binary code in firmware, which is crucial for the accuracy, precision, and recall of vulnerability detection in binary code. The absence of symbol information, coupled with optimizations during compilation and the mixed arrangement of data and instructions, further complicates vulnerability detection in binary code. To address these issues, we propose a method for stripped binary code vulnerability detection based on sensitive function identification, which extracts attributes from the stripped binary code in the target firmware and uses the Euclidean distance metric algorithm to identify and label sensitive functions in the binary code, detecting five types of potential vulnerabilities. Experimental results show that vulnerability detection based on sensitive function identification is feasible and significantly improves accuracy, precision, and recall.

Macrobenthos are crucial for maintaining the ecological balance of aquatic ecosystems. Given the high macrobenthic biodiversity of Beibu Gulf and its rapid economic development, this study evaluates the current state of the macrobenthic community and the impact of anthropogenic activities. The present study identified 82 macrobenthic species from the intertidal zones of northern Beibu Gulf, China, primarily mollusks (43.9%) and annelids (31.7%). The density and biomass of intertidal macrobenthos ranged from 113.45 f 86.59 to 582.40 f 179.07 ind./m2 and 76.39 f 111.46 to 786.12 f 609.17 g/m2, respectively. The Shannon-Weiner-Index, Margalef richness index, and Pielou evenness index ranged from 1.01 to 1.99, 1.38 to 2.52, and 0.80 to 1.38, respectively, suggested moderate anthropogenic influence. The dominant species were Pillucina vietnamica (Zorina, 1978), Cryptonetna producta (Kuroda & T. Habe, 1951), Praxillella cf. Affinis (M. Sars in G.O. Sars, 1872), Mictyris brevidactylus (Stimpson, 1858), and Cerithidea cingulata (Gmelin, 1791). One-way ANOSIM and MDS plots revealed that the intertidal macrobenthos formed three clusters: macrobenthos in the river mouth formed a single cluster, macrobenthos in areas with intensive human activity (factories, aquaculture, rural villages) formed another cluster, and macrobenthos in regions with less anthropogenic impact (near to urban city with sufficient sewage management system and nuclear power plant) formed a third cluster. Correlation analyses revealed that macrobenthos density, biomass, and biodiversity were mainly negatively associated with chlorophyll-a, total organic content, and sulfide levels. These findings provide valuable information on the current status of macrobenthic biodiversity in the northern Beibu Gulf, China. This information can serve as a guide for coastal planning and environmental management in the northern Beibu Gulf, China.

Three-dimensional reconstruction is a key technology employed to represent the virtual reality of the real world, where reconstruction based on drone data offers significant value to the field of measurement. Due to the existing challenges in aerial scenes reconstruction, namely the difficulty in achieving high-quaility reconstruction along with dynamic objects, in this study, we presents a solution for dynamic aerial scenes reconstruction, named DA-NeRF. It features the two-stage training strategy and Dynamic Aerial Optimization Module(DAOM). DAOM leverages generative network and autoencoder to generate appearance embedding and feature vector of the scene, capturing dynamic information of the scene as comprehensively as possible to enhance the accuracy of reconstruction. The proposed training strategy allows simply training a model to mainly reconstruct the static scene and providing the color vector at first. And then it utilize the dynamic object information from the scene and the weights of static scene to reconstruct the complete aerial scene with a high degree of fidelity. Finally, this work performs experimental validations on the UAVid datasets, with results indicating that the proposed method outperforms current techniques.

The spatial distribution of seismic landslides is influenced by a wide range of factors. Understanding the relative importance of these factor is crucial for accurately predicting seismic landslide risks. However, most recently studies on factor importance have focused on individual earthquake events, making it difficult to identify overarching patterns and differences across multiple cases. This study focuses on the eastern margin of the Qinghai-Xizang Plateau and analyzes six earthquake-induced landslide events. Using the decisiveness (DC), we quantitatively assessed the absolute importance of 14 influencing factors: seismic intensity, seismic fault distance, geologic age, non-seismic fault distance, elevation, slope, aspect, geomorphology type, average annual precipitation, river distance, soil type, vegetation type, land use type, and distance to roads. These factors are categorized into six groups: seismic, geo-tectonic, topographic-geomorphic, meteorological-hydrological, soil-vegetation, and human-activity. The results show that: overall, seismic, geo-tectonic, topographic-geomorphic, and meteorological-hydrological factors have a greater influence on seismic landslide occurence, whereas soil-vegetation and human-activity factors are less significant. Moreover, the importance of individual factors varies with earthquake magnitude: for larger-scale earthquakes seismic and geo-tectonic factors dominates; for smaller-scale earthquakes, topographic and hydrological conditions are more influential. By ensuring consistency in the landslide and influencing factor datasets and employing an absolute importance assessment approach across multiple cases, this study provides a systematic analysis of the key drivers of seismic landslides. The findings offer valuable insights for seismic landslide risk assessment and mitigation strategies.

The increasing frequency and severity of heatwaves present significant environmental and socio-economic challenges. While both rapid economic expansion and sluggish growth could intensify heatwave risks, current research predominantly examines the heatwaves' impacts on economic performance, overlooking how economic development influences heatwave risks. Coastal cities, particularly major ports, are pivotal in the global economy and are anticipated to experience the greatest increase in heatwave exposure (0.81 to 2.31 x 10(4) person-times year(-1) under SSP1-2.6 to SSP5-8.5) relative to other urban areas. Quantifying population exposure in global coastal cities by 2100, we investigated the diverse effects of future economic development on heatwave exposure. A robust correlation between economic development and heatwave exposure was identified across nearly all studied coastal cities (the majority showing correlation coefficients > 0.8). GDP variations significantly affect heatwave exposure on the global average level; yet, the magnitude and mode of this impact vary among cities (P <0.01, R-2 = 0.90 on average). We highlighted 14 typical cities requiring special attention and defined three risk types: Development Imbalance Risk, Economic Lag Risk, and Potential Risk Alert, leading to possible targeted strategies for coordinated socio-economic and heatwave mitigation efforts. The results contribute to integrating sustainable economic growth with climate resilience strategies.

The Non-Access Stratum (NAS) protocol, a critical signaling protocol in the radio access networks of LTE and 5G systems, plays a pivotal role in ensuring the security and stability of communication sessions between user equipment (UE) and the core network. Exploitation of vulnerabilities in the NAS protocol by attackers can lead to severe consequences. However, the low-latency requirements of the NAS protocol pose challenges for fuzzing, as prolonged message mutation and coverage statistics processing can increase the risk of communication session timeouts and interruptions. To address these challenges, this paper proposes a coverage-guided fuzzing method for the NAS protocol. By leveraging a timing selection algorithm, the method optimizes the timing of code coverage statistics collection, ensuring efficient guidance of the fuzzing process while maintaining uninterrupted communication sessions. This approach not only enhances code coverage but also improves testing efficiency. We design and implement the NASFuzzer framework and evaluate it on an open-source project. Experimental results demonstrate significant improvements, with code line coverage increasing by 24.8% and function coverage by 19.5%. Furthermore, NASFuzzer successfully identifies three previously unknown vulnerabilities in the open-source project.

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. This study analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, this method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Utilizing numerical simulation, a model for the advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space, and the velocity structure of the model is retrieved through this velocity analysis method. In the engineering example, the fracture property, water content, and weathering degree of the surrounding rock are predicted accurately.

Electronic certificates are applied in several aspects, such as governance, education, and engineering, etc. Electronic certificates could improve the environment of business. However, forged electronic certificates have also emerged as a result which could lead to disrupt social order, and even seriously undermine a fair and just business environment. Applying image forgery detection on electronic certificates could be achieved to distinguish whether the certificate is genuine or not. Although image processing had a good development, such as using edge detection method to image forgery detection. But edge detection method cannot balance operation time and accuracy. In this paper, two algorithms, normalized cross correlation (NCC) and structural similarity (SSIM) are adopted to identify the electronic certificates correctly with an official electronic certificates database. Two algorithms have different direction: for NCC, it measures the correlation of two images, for SSIM it evaluates a pair of images from three aspects: luminance, contrast and structure. Compare with edge detection method, both NCC and SSIM could give a good accuracy result and lower computational cost. Two algorithms reflect good availability on electronic certificate images forgery detection with an official electronic certificate database.

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in Tunnel Boring Machine (TBM) construction tunnels, traditional methods have significant technical limitations. This paper analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. Utilizing numerical simulation, a model for advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space. The seismic wave reflection method can effectively receive information from unfavorable geological formations in front of the tunnel face. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, the method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Field verification of advanced geological forecasting based on ellipsoid positioning velocity analysis for TBM tunnels is conducted. The results indicate that this method can be used to obtain the three-dimensional velocity of the predicted unfavorable geological formations in front of the tunnel and is effective for the application of geological forecasting in TBM construction tunnels, thus providing crucial references and assurances for construction.

The development of artificial intelligence makes it possible to rapidly segment landslides. However, there are still some challenges in landslide segmentation based on remote sensing images, such as low segmentation accuracy, caused by similar features, inhomogeneous features, and blurred boundaries. To address these issues, we propose a novel deep learning model called AST-UNet in this paper. This model is based on structure of SwinUNet, attaching a channel Attention and spatial intersection (CASI) module as a parallel branch of the encoder, and a spatial detail enhancement (SDE) module in the skip connection. Specifically, (1) the spatial intersection module expands the spatial attention range, alleviating noise in the image and enhances the continuity of landslides in segmentation results; (2) the channel attention module refines the spatial attention weights by feature modeling in the channel dimension, improving the model’s ability to differentiate targets that closely resemble landslides; and (3) the spatial detail enhancement module increases the accuracy for landslide boundaries by strengthening the attention of the decoder to detailed features. We use the landslide data from the area of Luding, Sichuan to conduct experiments. The comparative analyses with state-of-the-art (SOTA) models, including FCN, UNet, DeepLab V3+, TransFuse, TranUNet, and SwinUNet, prove the superiority of our AST-UNet for landslide segmentation. The generalization of our model is also verified in the experiments. The proposed AST-UNet obtains an F1-score of 90.14%, mIoU of 83.45%, foreground IoU of 70.81%, and Hausdorff distance of 3.73, respectively, on the experimental datasets.

CONTEXT: There is an urgent need for a comprehensive assessment of cropland fragmentation that can provide valuable insights for guiding policies related to cropland protection, restoration, and other relevant measures. Despite the growing number of studies focusing on cropland fragmentation, a nationwide, long-term, and fine-scale understanding of the spatiotemporal changes in cropland fragmentation across China remains lacking. OBJECTIVE: This study aimed to construct a novel comprehensive index that quantitatively assesses the level of farmland fragmentation, to fully elucidate the spatial and temporal dynamics of farmland fragmentation in China at the county level over the last two decades, and to identify the relationships between farmland fragmentation and key factors from anthropogenic and natural perspectives. METHODS: Utilizing the 30 m annual and continuous time series Landsat-derived annual China land cover dataset (CLCD) from 2000 to 2021, we integrated three landscape pattern metrics- patch density (PD), mean patch area (MPA), and edge density (ED)- to devise a new comprehensive cropland fragmentation index (CFI). We also developed a Beta regression method to identify the relationships between CFI and key factors from human and natural perspectives. RESULTS AND CONCLUSIONS: The results show that counties with high cropland fragmentation (CFI > 0.8) are predominantly located in the central part of China and the coastal areas, and the counties with low cropland fragmentation (CFI < 0.2) are mainly in the North China Plain, the Northeast Plain, and some areas in the northwest. Among counties experiencing an increase or decrease in fragmentation, the primary types of cropland conversions are between cropland and grassland, as well as between cropland and forests. A third major type of conversion involves the transformation of cropland into impervious surfaces. CFI had a significantly negative linear relationship with total area of cropland in each county while showing a positive relationship with the total cropland patches in each county. The variables CFI and slope, and CFI and nighttime light (NTL) data, exhibited a quadratic parabolic relationship. SIGNIFICANCE: The findings presented in this study provide crucial insights into the underlying causes and patterns of farmland fragmentation in China. These insights will serve as a valuable resource for policymakers and land managers, enabling them to devise effective strategies for sustainable land use planning and promoting rural development in the country.

The unprecedented built-up area expansion in China has resulted in a significant occupation of cropland. Yet, to date, we have not achieved continuous monitoring of the occupation of cropland by different types of built-up land, nor have their annual impacts on non-agriculturalization been revealed. Here, for the first time, we have generated a dataset spanning from 1990 to 2020, consisting of annual areas for urban land (UL), rural residential land (RRL), and other built-up land (OBL) and their occupation of cropland at the county level across China. Subsequently, the annual contributions of these three types of built-up land expansion to nonagriculturalization in China were unveiled. Our results show that the average annual conversion of cropland into UL, RRL, and OBL was 1520.60 km2, 2 , 1464.60 km2, 2 , and 987.44 km2, 2 , respectively, during the study period. Among these, only the conversion of cropland into OBL exhibited a significant increasing trend of 4.64 km2/a. 2 / a . Overall, different types of built-up land have distinct spatial impacts on the non-agriculturalization of cropland at the county level, which undergo significant changes over time. The non-agriculturalization of cropland in China is significantly influenced by the expansion of built-up areas, particularly in counties in the eastern regions. The northern regions are most affected by UL expansion and RRL expansion, followed by the southern regions. OBL expansion has significantly impacted the non-agriculturalization of counties in the southern regions, with its influence gradually extending from eastern to western China during the study period. Before 2015, the expansion of UL had a higher impact on non-agriculturalization compared to RRL and OBL. However, from 2015 to 2020, in 62.52% of counties, the expansion of RRL contributed more to non-agriculturalization than the expansion of RRL and OBL. It is suggested that China should pay more attention to protecting cropland from being occupied by RRL expansion. This study provides insights that can inform sustainable land use planning and facilitate the development of targeted cropland protection policies.

When the channel wave passes through the abnormal body, the dispersion curve appears interval, dislocation, and discontinuity, resulting in the non-standard and large error of the “velocity-frequency” pickup result of the transmitted channel wave dispersion curve, which directly leads to the inaccurate tomography. Therefore, aiming at this problem, the imaging method of dispersion curve variability function is proposed: (1) count the breakpoints of dispersion curve according to three types; (2) set the weighting factor for the breakpoint according to the spectrum curve; (3) the variability function is constructed for the dispersion curve, and the corresponding variation value is obtained; and (4) the variability value is back projected into the imaging grid space to obtain the abnormal body information in the detection area. The example verification results show that the imaging results of this method are accurate and stable, and non-convergence is caused by cyclic iteration, which provides a new imaging mode for the detection of complex structures in coal seams.

Numerical simulation of three-dimensional (3D) seismic wavefields forms the basis of the research on the migration methods of 3D seismic data based on wave equations. Because the simulation precision of wavefield extrapolation determines the imaging accuracy to a certain extent, it is very important to study how to enhance the forward modeling precision of 3D seismic wavefields. Thus, we build on an optimized 3D staggered-grid finite-difference (SFD) method with high simulation precision based on two-dimensional (2D) seismic modeling. Since it generates the corresponding difference coefficients by utilizing the least square (LS) method to minimize the objective function constructed by the time-space domain dispersion relation of the 3D acoustic wave equation, our optimized time-space domain LS-based 3D SFD method can effectively enhance the modeling precision of the 3D seismic wavefields in theory compared with the 3D SFD methods based on the Taylor-series expansion (TE), especially for the large wavenumber range. Examining the numerical dispersion, algorithm stability and computational cost, we compare our optimized time-space domain LS-based 3D SFD method with three conventional TE-based and LS-based 3D SFD methods to illustrate and demonstrate its effectiveness and feasibility. The numerical examples from different 3D models suggest that our optimized time-space domain LS-based 3D SFD method can generate less numerical dispersion and higher simulation accuracy for 3D seismic wavefields than three other conventional 3D SFD methods, but its stability condition is stricter and its computational cost is slightly higher.

The China–Pakistan Economic Corridor is the pilot area of the Belt and Road, where glaciers and lakes are widely distributed. Recent years, global warming has accelerated the expansion of glacier lakes, which increased the risk of natural disasters such as glacier lake outburst. It is important to monitor the glacier lakes in this region. In this paper, we propose a method combining the object-oriented image analysis with boundary recognition (OOBR) to extract lakes in several study areas of China–Pakistan Economic Corridor (CPEC). This method recognized the lake boundary with the symmetrical characteristic according to the principle of seed growth of watershed algorithm, which can correct the boundary extracted by the object-oriented method. The overall accuracy of the proposed method is up to 98.5% with Landsat series images. The experiments also show that the overall accuracy of our method is always higher than that of the object-oriented method with different segmentation scales mentioned in this paper. The proposed method improved the overall accuracy on the basis of the results obtained by the object-oriented method, and the results with the proposed method are more robust to the seeds than that with the boundary correction method of the watershed algorithm. Therefore, the proposed method can obtain a high extraction accuracy while reducing the complexity of the object-oriented extraction.

在贵阳轨道交通3号线盾构隧道施工中,采用普通刀具易出现滚刀多边形磨损、偏磨、刀刃崩裂等异常磨损和刀圈脱落现象.为改进滚刀刀圈的耐磨、耐冲击性能,提高刀具的综合寿命,提出采用球状碳化钨刀具替换普通刀具,并介绍了激光熔覆焊工艺以及宏观磨损检测工艺.经过施工现场的实际应用,发现球状碳化钨刀具具有更好的耐磨性,新型球状碳化钨刀具平均磨损量为0.00255 mm/延米,相邻普通刀具平均磨损量为0.01655 mm/延米.在第338～736环掘进中,新型球状碳化钨刀具最大磨损量为0.015 mm/延米,刀具无崩刃现象,滚刀轴承、密封等均无异常,有效解决了普通刀具易磨损、磨损不均的问题,为喀斯特地质条件下盾构刀具的选型提供参考.