In this paper, we explore the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems to provide real-time SAR imaging and information transmission. Considering the high-mobility characteristics of satellite channels and limited processing capabilities of satellite payloads, we propose an integrated communication and remote sensing architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Both communication and SAR imaging functionalities are achieved with an integrated transceiver onboard the LEO satellite, utilizing the same waveform and radio frequency (RF) front-end. Based on such an architecture, we propose a transmission protocol compatible with the 5G NR standard using downlink pilots for joint channel estimation and SAR imaging. Furthermore, we design a unified signal processing framework for the integrated satellite receiver to simultaneously achieve high-performance channel sensing, low-complexity channel equalization and interference-free SAR imaging. Finally, the performance of the proposed integrated system is demonstrated through comprehensive analysis and extensive simulations in the sub-6 GHz band. Moreover, a software-defined radio (SDR) prototype is presented to validate its effectiveness for real-time SAR imaging and information transmission in satellite direct-connect user equipment (UE) scenarios within the millimeter-wave (mmWave) band.

To provide multiple-satellite coverage for global Internet of Things (IoT), a low Earth orbit (LEO) satellite IoT constellation usually contains multiple-layer orbits with different altitudes. However, the performance of multiple-layer LEO satellite IoT constellations under practical Rician fading satellite channels remains unknown due to complex theoretical modeling and intractable mathematical analysis. To address these challenges, this paper proposes a stochastic geometry-based modeling and analysis framework for multiple-layer LEO satellite IoT constellations, integrating Rician channel modeling and Cox point processes. Specifically, we introduce a novel channel approximation method to overcome the intractable expressions caused by the Rician fading. Building on this method, we derive exact closed-form expressions for key performance metrics, including connectivity probability, coverage probability, and transmission rate, especially in the case of IoT short-packet transmission. Extensive simulation results validate the accuracy and effectiveness of the proposed model and reveal significant design insights. The results not only provide new theoretical perspectives for modeling and analysis of LEO satellite IoT constellations but also offer practical guidance for system deployment and optimization.

In this paper, we investigate the integration of communication and synthetic aperture radar (SAR)-based remote sensing in low Earth orbit (LEO) satellite systems. To address the high-mobility characteristic of LEO satellites, we propose an integrated system architecture based on an orthogonal delay-Doppler division multiplexing (ODDM) signal waveform. Specifically, we provide a wireless frame compatible with the 5G NR standard for signal sharing and design a unified channel sensing scheme that utilizes shared ODDM signals for both channel estimation in communication and interference-free range reconstruction in SAR imaging. Finally, numerical simulation results confirm the effectiveness of the proposed scheme.

Structural health monitoring (SHM) faces a fundamental challenge in reconciling predictive performance with physical interpretability for infrastructure diagnostics. Conventional deep learning (DL) approaches neglect essential mechanisms governing crack width variation—including thermal gradients, hysteretic responses, and phase-shifted correlations—limiting their reliability in real-world applications. To bridge this gap, we propose a mechanism-guided optimization (MGO) framework that integrates domain knowledge into the Informer architecture through physics-informed enhancements: auto-correlation modeling for capturing temperature-crack hysteresis, static gated fusion for multi-feature integration, and adaptive elastic net regularization for feature selection. Validated on cable-stayed bridge monitoring data, our framework achieves significant mean absolute error reductions (MAE) (5 %–60 %) and root mean square error reductions (RMSE) (10 %–55 %) versus baseline Informer across all cracks and prediction horizons, with diebold-mariano (DM) tests confirming statistical superiority in most cases. Crucially, it demonstrates superior precision relative to six state-of-the-art benchmarks across all evaluation scenarios. The ordinary least squares (OLS)-enhanced variant further delivers volatility reduction, while sensor failure tests establish quantifiable robustness benchmarks through MAE progression from 0.013 mm to 0.391 mm. This work establishes an interpretable, physics-grounded paradigm that explicitly links environmental drivers to structural degradation.

Taint-style vulnerabilities, such as OS command injection and path traversal, are common and severe software weaknesses. There exists an inherent trade-off between analysis scalability and accuracy in detecting such vulnerabilities. On one hand, existing syntax-directed approaches often make compromises in the analysis accuracy on dynamic features like bracket syntax. On the other hand, existing abstract interpretation often faces the issue of state explosion in the abstract domain, thus leading to a scalability problem.In this paper, we present a novel approach, called FAST, to scale the vulnerability discovery of JavaScript packages via a novel abstract interpretation approach that relies on two new techniques, called bottom-up and top-down abstract interpretation. The former abstractly interprets functions based on scopes instead of call sequences to construct dynamic call edges. Then, the latter follows specific control-flow paths and prunes the program to skip statements unrelated to the sink. If an end-to-end data-flow path is found, FAST queries the satisfiability of constraints along the path and verifies the exploitability to reduce human efforts.We implement a prototype of FAST and evaluate it against real-world Node.js packages. We show that FAST is able to find 242 zero-day vulnerabilities in NPM with 21 CVE identifiers being assigned. Our evaluation also shows that FAST can scale to real-world applications such as NodeBB and popular frameworks such as total.js and strapi in finding legacy vulnerabilities that no prior works can.

The success of clustering algorithms in object segmentation depends on the quality of the evaluation criterion. However, sonar images are seriously affected by noise. Most of the existing evaluation criteria such as the Davies Bouldin (DB) criterion only considers their pixel value, and sonar image information extraction is not sufficient. As a result, they fail to achieve good underwater object segmentation results. To overcome this problem, this study proposes an improved DB criterion with pulse ‐coupled neural network (PCNN), which is called the DB‐PCNN. In the calculation process of DB‐PCNN, the role of internal activity items in PCNN is considered, which can make better use of pixel information in adjacent space on the sonar image. The experimental results show that DB‐PCNN can further improve the accuracy of underwater object segmentation and has certain adaptability to different optimisation frameworks.

This study presents a generative adversarial network (GAN) called conditional Wasserstein GAN‐gradient penalty (CWGAN‐GP)&DenseNet and ResNet, and a convolutional neural network (CNN) called improved CNN to complete underwater sonar image classification. Specifically, to solve the problem of insufficient underwater sonar image data, the CWGAN‐GP&DR is developed to expand underwater sonar image data set. Besides, to improve the analysis and utilisation of the feature map and reduce the misclassification rate of categories with similar probabilities, improved CNN is proposed to complete the final underwater sonar image classification. Finally, compared with other methods, the CWGAN‐GP&DR generate better underwater sonar images and effectively expand the underwater sonar image data set. Moreover, compared with the original data set and other expanded data set, the highest accuracy rate of 85.00% can be obtained on the CWGAN‐GP&DR expanded data set by CNN. Furthermore, CNN, CNN‐bais and improved CNN are used to perform classification experiments on each data set, and the accuracy of the improved CNN is the highest on all data sets and reached the highest accuracy of 87.71% on CWGAN‐GP&DR expanded data set. The experimental results demonstrate that the proposed method can effectively improve the performance of underwater sonar image classification.

The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background.