Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario.

Satellite-ground communication is a critical component in the global communication system, significantly contributing to environmental monitoring, radio and television broadcasting, aerospace operations, and other domains. However, the technology encounters challenges in data transmission efficiency, due to the drastic alterations in the communication channel caused by the rapid movement of satellites. In comparison to traditional transmission methods, semantic communication (SemCom) technology enhances transmission efficiency by comprehending and leveraging the intrinsic meaning of information, making it ideal for image transmission in satellite communications.

Satellite Selection Strategy and Method for Signals of Opportunity Navigation and Positioning with LEO Communication Satellites.

Experts and scholars from various nations have proposed studying low Earth orbit (LEO) satellite signals as the space-based signals of opportunity (SOPs) for navigation and positioning. This method serves as a robust alternative in environments where global navigation satellite systems (GNSS) are unavailable or compromised, providing users with high-precision, anti-interference, secure, and dependable backup navigation solutions. The rapid evolution of LEO communication constellations has spurred the development of SOPs positioning technology using LEO satellites.

Electrochemical Sensor for Hydrogen Leakage Detection at Room Temperature.

The use of hydrogen as fuel presents many safety challenges due to its flammability and explosive nature, combined with its lack of color, taste, and odor. The purpose of this paper is to present an electrochemical sensor that can achieve rapid and accurate detection of hydrogen leakage. This paper presents both the component elements of the sensor, like sensing material, sensing element, and signal conditioning, as well as the electronic protection and signaling module of the critical concentrations of H.

Review of Short-Wavelength Infrared Flip-Chip Bump Bonding Process Technology.

Short-wave infrared (SWIR) imaging has a wide range of applications in civil and military fields. Over the past two decades, significant efforts have been devoted to developing high-resolution, high-sensitivity, and cost-effective SWIR sensors covering the spectral range from 0.9 μm to 3 μm.

Lightweight Deep Learning Model, ConvNeXt-U: An Improved U-Net Network for Extracting Cropland in Complex Landscapes from Gaofen-2 Images.

Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification.

MDAPT: Multi-Modal Depth Adversarial Prompt Tuning to Enhance the Adversarial Robustness of Visual Language Models.

Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models.

Validity Identification and Rectification of Water Surface Fast Fourier Transform-Based Space-Time Image Velocimetry (FFT-STIV) Results.

Fast Fourier Transform-based Space-Time Image Velocimetry (FFT-STIV) has gained considerable attention due to its accuracy and efficiency. However, issues such as false detection of MOT and blind areas lead to significant errors in complex environments. This paper analyzes the causes of FFT-STIV gross errors and then proposes a method for validity identification and rectification of FFT-STIV results.

Reducing Cross-Sensor Domain Gaps in Tactile Sensing via Few-Sample-Driven Style-to-Content Unsupervised Domain Adaptation.

Transferring knowledge learned from standard GelSight sensors to other visuotactile sensors is appealing for reducing data collection and annotation. However, such cross-sensor transfer is challenging due to the differences between sensors in internal light sources, imaging effects, and elastomer properties. By understanding the data collected from each type of visuotactile sensors as domains, we propose a few-sample-driven style-to-content unsupervised domain adaptation method to reduce cross-sensor domain gaps.

Monitoring Excavation-Induced Deformation of a Secant Pile Wall Using Distributed Fiber Optic Sensors.

This paper investigates the use of the BOTDA (Brillouin Optical Time-Domain Analysis) technology to monitor a large-scale bored pile wall in the field. Distributed fiber optic sensors (DFOSs) were deployed to measure internal temperature and strain changes during cement grouting, hardening, and excavation-induced deformation of a secant pile wall. The study details the geological conditions and DFOS installation process.

A PFM-Based Calibration Method for Low-Power High-Linearity Digital Pixel.

The nonlinearity problem of digital pixels restricts the reduction in power consumption at the pixel-level circuit. The main cause of nonlinearity is discussed in this article and low power consumption is attained by reducing the static current in capacitive transimpedance amplifiers (CTIAs) and comparators. Linearity was successfully improved through the use of an off-chip calibration method.

Detection Method for Bolt Loosening Based on Summation Coefficient of Absolute Spectrum Ratio.

A bolt loosening detection method based on the summation coefficient of the absolute spectrum ratio technique is proposed to address the prevalent issue of bolt loosening in mechanical connections. This proposed method involves initially collecting vibration and rotation speed signals of the motor bolt connection structure, acquiring the baseline spectrum curve of a healthy structure and the spectrum curves of non-healthy structures under different degrees of bolt looseness through chirp Fourier transform (CFT). Subsequently, the spectrum ratio curves between healthy and non-healthy structures are calculated for different degrees of bolt loosening, and then the Summation Coefficient of Absolute Spectrum Ratio (SCASR) is defined to indicate the looseness.

Fiber Bragg Grating Thermometry and Post-Treatment Ablation Size Analysis of Radiofrequency Thermal Ablation on Ex Vivo Liver, Kidney and Lung.

Radiofrequency ablation (RFA) is a minimally invasive procedure that utilizes localized heat to treat tumors by inducing localized tissue thermal damage. The present study aimed to evaluate the temperature evolution and spatial distribution, ablation size, and reproducibility of ablation zones in ex vivo liver, kidney, and lung using a commercial device, i.e.

Deep Learning-Based Pointer Meter Reading Recognition for Advancing Manufacturing Digital Transformation Research.

With the digital transformation of the manufacturing industry, data monitoring and collecting in the manufacturing process become essential. Pointer meter reading recognition (PMRR) is a key element in data monitoring throughout the manufacturing process. However, existing PMRR methods have low accuracy and insufficient robustness due to issues such as blur, uneven illumination, tilt, and complex backgrounds in meter images.

YOLOv8-LCNET: An Improved YOLOv8 Automatic Crater Detection Algorithm and Application in the Chang'e-6 Landing Area.

The Chang'e-6 (CE-6) landing area on the far side of the Moon is located in the southern part of the Apollo basin within the South Pole-Aitken (SPA) basin. The statistical analysis of impact craters in this region is crucial for ensuring a safe landing and supporting geological research. Aiming at existing impact crater identification problems such as complex background, low identification accuracy, and high computational costs, an efficient impact crater automatic detection model named YOLOv8-LCNET (YOLOv8-Lunar Crater Net) based on the YOLOv8 network is proposed.

Temperature Dependence on Microstructure, Crystallization Orientation, and Piezoelectric Properties of ZnO Films.

This study has investigated the effects of different annealing temperatures on the microstructure, chemical composition, phase structure, and piezoelectric properties of ZnO films. The analysis focuses on how annealing temperature influences the oxygen content and the preferred c-axis (002) orientation of the films. It was found that annealing significantly increases the grain size and optimizes the columnar crystal structure, though excessive high-temperature annealing leads to structural degradation.

Application of PN Code in Time Delay Measurement of Telephone Network.

Telephone time service is a wired time service that transmits time signals through a telephone network, with the advantages of simple receiving equipment and wide coverage. But the performance of time service is not high, usually several milliseconds. The time delay measurement of the telephone network is an important factor limiting the improvement in timing performance.

Robust Multi-Subtype Identification of Breast Cancer Pathological Images Based on a Dual-Branch Frequency Domain Fusion Network.

Breast cancer (BC) is one of the most lethal cancers worldwide, and its early diagnosis is critical for improving patient survival rates. However, the extraction of key information from complex medical images and the attainment of high-precision classification present a significant challenge. In the field of signal processing, texture-rich images typically exhibit periodic patterns and structures, which are manifested as significant energy concentrations at specific frequencies in the frequency domain.

A Framework of State Estimation on Laminar Grinding Based on the CT Image-Force Model.

It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image-force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs.

Multi-Task Federated Split Learning Across Multi-Modal Data with Privacy Preservation.

With the advancement of federated learning (FL), there is a growing demand for schemes that support multi-task learning on multi-modal data while ensuring robust privacy protection, especially in applications like intelligent connected vehicles. Traditional FL schemes often struggle with the complexities introduced by multi-modal data and diverse task requirements, such as increased communication overhead and computational burdens. In this paper, we propose a novel privacy-preserving scheme for multi-task federated split learning across multi-modal data (MTFSLaMM).

Research on the Application of Silver Nanowire-Based Non-Magnetic Transparent Heating Films in SERF Magnetometers.

We propose a non-magnetic transparent heating film based on silver nanowires (Ag-NWs) for application in spin-exchange relaxation-free (SERF) magnetic field measurement devices. To achieve ultra-high sensitivity in atomic magnetometers, the atoms within the alkali metal vapor cell must be maintained in a stable and uniform high-temperature environment. Ag-NWs, as a transparent conductive material with exceptional electrical conductivity, are well suited for this application.

DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN-Transformer Network.

Electroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders. However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods.

Scale-Consistent and Temporally Ensembled Unsupervised Domain Adaptation for Object Detection.

Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.

MonoSeg: An Infrared UAV Perspective Vehicle Instance Segmentation Model with Strong Adaptability and Integrity.

Despite rapid progress in UAV-based infrared vehicle detection, achieving reliable target recognition remains challenging due to dynamic viewpoint variations and platform instability. The inherent limitations of infrared imaging, particularly low contrast ratios and thermal crossover effects, significantly compromise detection accuracy. Moreover, the computational constraints of edge computing platforms pose a fundamental challenge in balancing real-time processing requirements with detection performance.

An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis.

Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information.

Characterization of Micro-Crack Orientation in a Thin Plate Using Quasi-Static Component Generated by Incident Ultrasonic Lamb Waves.

The directivity of the quasi-static component (QSC) is quantitatively investigated for evaluating the orientation of a micro-crack buried in a thin solid plate using the numerical simulation method. Based on the bilinear stress-strain constitutive model, a three-dimensional (3D) finite element model (FEM) is built for investigating the nonlinear interaction between primary Lamb waves and the micro-crack. When the primary Lamb waves at A0 mode impinge on the micro-crack, under the modulation of the contact acoustic nonlinearity (CAN), the micro-crack itself will induce QSC.