Search Results (143)

Satellite pointing mechanisms for earth observation require ultra-low speed scanning (approximately ${70}^{\prime \prime }/\mathrm{s}$) and precise variable-speed compensation. However, traditional Field-Oriented Control (FOC) suffers from significant velocity bias and instability under these conditions. To address these issues, this paper proposes a position-loop-based speed control scheme integrated with a variable structure control strategy. By substituting the speed command with a position loop, the proposed method effectively suppresses steady-state velocity bias, while the variable structure strategy mitigates fluctuations during variable-speed motion. Experimental results indicate that, compared to traditional FOC, the proposed method reduces velocity bias error by over $30\%$ during uniform tracking and decreases the amplitude of velocity fluctuations by more than $40\%$ in variable-speed scenarios. This strategy significantly enhances the control precision of satellite pointing mechanisms and improves on-orbit imaging compensation accuracy. Full article

Multi-satellite sequential detection is crucial for maritime target identification and tracking. However, inherent satellite revisit patterns and maritime target motion often result in fragmented track segments, necessitating effective multi-satellite track association to ensure continuity. Existing methods predominantly rely on track information and statistical signal parameters, rendering them susceptible to localization errors and ineffective in scenarios characterized by dense targets and overlapping radar parameters. To overcome these limitations, this paper proposes an attention-guided track-pulse-sequence target association network (AG-TPS-TAN). First, the asymmetric dual-branch network operates by incorporating both track data and electromagnetic signal data, processing the latter in the form of raw pulse sequences instead of the conventional statistical parameters. Second, within the track branch, we enhance the feature representation by incorporating a novel track-point-aware attention mechanism which can autonomously identify and weight critical points indicative of motion continuity, such as interruption boundaries and maneuvering points. Third, we introduce a dual-feature fusion module optimized with a combined loss function, which pulls feature representations of the same target closer together while pushing apart those from different targets, thereby enhancing both feature consistency and discriminability. Experiments were conducted on a public AIS trajectory dataset, constructing a dataset containing both motion trajectories and electromagnetic signals. Evaluations under varying target numbers showed that the proposed AG-TPS-TAN achieved average association accuracies of 93.91% for 5 targets and 63.83% for 50 targets. Against this, the track-only method TSADCNN scored 76.08% and 25.64%, and the signal-statistics-based method scored 77.12% and 29.56%, for 5 and 50 targets, respectively, thus exhibiting a clear advantage for the proposed approach. Full article

Satellite Clock Bias (SCB) is a major source of error in Precise Point Positioning (PPP). The real-time service products from the International GNSS Service (IGS) are susceptible to network interruptions. Such disruptions can compromise product availability and, consequently, degrade positioning accuracy. We introduce the CNN-LSTM-Attention model to address this challenge. The model enhances a Long Short-Term Memory (LSTM) network by integrating Convolutional Neural Networks (CNNs) and an Attention mechanism. The proposed model can efficiently extract data features and balance the weight allocation in the Attention mechanism, thereby improving both the accuracy and stability of predictions. Across various forecasting horizons (1, 2, 4, and 6 h), the CNN-LSTM-Attention model demonstrates prediction accuracy improvements of (76.95%, 66.84%, 65.92%, 84.33%, and 43.87%), (72.59%, 65.61%, 74.60%, 82.98%, and 51.13%), (70.45%, 68.52%, 81.63%, 88.44%, and 60.49%), and (70.26%, 70.51%, 84.28%, 93.66%, and 66.76%), respectively, across the five benchmark models: Linear Polynomial (LP), Quadratic Polynomial (QP), Autoregressive Integrated Moving Average (ARIMA), Backpropagation Neural Network (BP), and LSTM models. Furthermore, in dynamic PPP experiments utilizing IGS tracking stations, the model predictions achieve positioning accuracy comparable to that of post-processed products. This proves that the proposed model demonstrates superior accuracy and stability for predicting SCB, while also satisfying the demands of positioning applications. Full article

Existing vehicle-mounted satellite terminals primarily rely on mechanical or purely analog electronically steered antennas. They lack protocol-level relay capability and usually provide only short-range hotspot connectivity. These limitations make it difficult for such systems to deliver stable, high-throughput satellite access for personal mobile devices in dynamic vehicular environments, especially in remote regions without terrestrial networks. This paper proposes an intelligent vehicle repeater for satellite networks (IVRSN) that builds a dedicated satellite–vehicle–device relay architecture. It enables reliable broadband connectivity for conventional mobile terminals without requiring specialized satellite hardware. The IVRSN consists of three key technical components. Firstly, a dual-mode relay coverage mechanism is designed to support energy-efficient in-vehicle access and extended out-of-vehicle coverage. Secondly, a DoA-assisted, attitude-compensated hybrid beamforming scheme is developed. It combines subspace-based direction estimation with inertial sensor measurements to maintain high-precision satellite pointing under vehicle dynamics. Finally, a bidirectional protocol conversion module is introduced to ensure compatibility between ground wireless protocols and satellite link-layer formats with integrity-checked data forwarding. Compared to existing solutions, the proposed IVRSN provides higher stability and broader device compatibility, making it a feasible solution for high-speed, high-quality communications in remote or disaster regions. Full article

Navigation errors due to drifting in inertial systems using low-cost sensors are some of the main challenges for land vehicle navigation in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose an autonomous navigation strategy with a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU), referred to as the wheeled inertial navigation system (INS), to effectively suppress drifted navigation errors. The position, velocity, and attitude (PVA) of the vehicle are predicted through the inertial mechanization algorithm, while gyro outputs are utilized to derive the vehicle’s forward velocity, which is treated as an observation with non-holonomic constraints (NHCs) to estimate the inertial navigation error states. To establish a theoretical foundation for wheeled INS error characteristics, a comprehensive system observability analysis is conducted from an analytical point of view. The wheel rotation significantly improves the observability of gyro errors perpendicular to the rotation axis, which effectively suppresses azimuth errors, horizontal velocity, and position errors. This leads to the superior navigation performance of a wheeled INS over the traditional odometer (OD)/NHC/INS. Moreover, a hybrid extended particle filter (EPF), which fuses the extended Kalman filter (EKF) and PF, is proposed to update the vehicle’s navigation states. It has the advantages of (1) dealing with the system’s non-linearity and non-Gaussian noises, and (2) simultaneously achieving both a high level of accuracy in its estimation and tolerable computational complexity. Kinematic field test results indicate that the proposed wheeled INS is able to provide an accurate navigation solution in GNSS-denied environments. When a total distance of over 26 km is traveled, the maximum position drift rate is only 0.47% and the root mean square (RMS) of the heading error is 1.13°. Full article

This paper presents an improved knowledge-based genetic algorithm for relay satellite task scheduling. It aims to address the issue of ensuring high-priority tasks under complex constraints and resource competition. The algorithm enhances the high-priority task completion ratio by incorporating multiple knowledge sorting strategies and a dynamic crossover mechanism during population initialization. Simulation results show that the proposed algorithm improves the high-priority task completion ratio by an average of 11.78 percentage points compared to the second-best algorithm. In environments with high load, resource constraints, and capacity shrinkage, the proposed algorithm outperforms in scheduling efficiency, robustness, and adaptability. It demonstrates effectiveness in multi-task and multi-resource competitive environments. IKBGA provides a highly targeted and scalable optimization solution for relay satellite task scheduling, with strong application potential. Full article

Huanghuai sheep, a newly developed meat-specialized breed in China, are valued for their rapid growth and high meat quality, but the optimal slaughter age and the molecular basis of these traits remain poorly understood. Gaining insight into these mechanisms is vital for improving production efficiency and guiding molecular breeding in this economically important breed. Although previous studies have described the phenotypic characteristics of Huanghuai sheep, the genetic regulatory networks controlling muscle growth and meat quality at different developmental stages remain unclear. No thorough analysis of growth traits and transcriptomic variations across key age points has been conducted. Therefore, in this study, we aimed to evaluate how growth stage influences muscle development, carcass characteristics, and meat quality in Huanghuai sheep by integrating phenotypic characterization with transcriptomic profiling to identify key genes and molecular pathways underlying these economically important traits throughout development. Sixty Huanghuai sheep were assigned to three groups (twenty per group) representing key developmental stages (3, 9, and 18 months of age). Carcass traits and meat quality were evaluated. RNA sequencing of the longissimus dorsi muscle was performed to identify differentially expressed genes (DEGs), followed by bioinformatics analysis and experimental validation. The results indicated that the 9-month-old sheep presented a favorable balance of dressing percentage and intramuscular unsaturated fatty acid content, while those aged 18 months old exhibited the highest dressing percentage (61.23%). Transcriptome analysis identified 1395 DEGs (p < 0.05 and |log2FC| > 1) and enrichment analysis revealed key pathways involved in thyroid hormone synthesis, skeletal muscle satellite cell proliferation, and skeletal muscle tissue growth. Several candidate genes for muscle development (e.g., ACTC1, SIX2, HK2) and meat quality (e.g., TLR2, CHI3L1, ACOT7) were identified and validated. Their expression patterns showed significant correlations between critical growth performance and fatty acid composition metrics. These findings provide novel insights into the molecular networks regulating economically important traits in Huanghuai sheep, offering valuable targets for future molecular breeding programs aimed at enhancing productivity and meat quality. Full article

In remote sensing monitoring of forest fires, smoke and clouds exhibit similar spectral characteristics in satellite imagery, which can easily lead to clouds being misjudged as smoke. This incorrect discrimination may result in missed detections or false alarms of fire points. The precise differentiation of smoke and clouds has become increasingly challenging, significantly limiting the ability to accurately identify fires in their early stages. Additionally, electromagnetic waves penetrating the smoke and clouds interact with the underlying surface, which interferes with the effective separation of smoke and clouds. In response to the aforementioned issues, this paper systematically studies the impact mechanism of different underlying surfaces on the spectral response of smoke and clouds. We constructed a dataset using sample collection and gradation methods. It contains smoke at varying concentrations and clouds of different thicknesses over three typical underlying surfaces: vegetation, soil, and water. Based on the analysis of spectral characteristics, analysis of variance (ANOVA) was applied to screen sensitive bands suitable for the separation of smoke and clouds. Furthermore, considering the distribution characteristics of smoke and cloud samples in spectral space, single-band threshold models, visible-band index (VBI) models, ratio index models, and Fisher smoke and cloud recognition index (FSCRI) models were developed for three typical underlying surfaces. The validation results demonstrate that the FSCRI models significantly outperform other models in terms of both robustness and accuracy. Their recognition accuracy rates for smoke and clouds in the underlying surfaces of vegetation, soil and water reached 95.5%, 93.5% and 99%, respectively. The proposed method effectively suppresses cloud interference to improve smoke and cloud separation. This capability enables more accurate early detection of forest fires and localization of their sources. Full article

We present experimental results related to the features and geophysical conditions for the occurrence of the long-delay echo (LDE) signals in the medium-wave (MW) frequency range observed on 20 January 2025, at the Gor’kovskaya observatory near St. Petersburg (60.27° N, 29.38° E). A total of 19 series of experiments on guiding MF in the magnetosphere were carried out, while LDE signals were only registered on January 20, 2025, in evening hours, when the most disturbed conditions were observed (Kp = 4+, ΣKp = 27−). It was found that the LDE signals, with delay times of 310–322 ms, were observed in the evening hours under disturbed magnetic conditions. In such a case, the MW propagates into the magnetosphere to the magnetically conjugate point, is reflected from the topside ionosphere, and returns. The frequency of sounding signal f_SS exceeded the critical frequency of the F2 layer at Gor’kovskaya observatory foF2_GRK but was less than the critical frequency at the magnetic conjugated point foF2_MCP, foF2_GRK < f_SS < foF2_MCP. The LDE signals were observed in the narrow frequency range from 2100 to 2400 kHz. The background geophysical conditions during the occurrence of LDE signals were analyzed using the CADI ionosonde data and Swarm satellite observations. The plausible generation mechanisms for MW guiding in the magnetosphere are discussed. Full article

Presented here are additional explanations for five key points offered by Rojero et al. in their 2025 publication in this journal, regarding characteristics of hyper-aggressive phage 0524phi7-1. These are (i) that the “bypassing of the evolution of host resistance” has been seen in other phages, especially dual-receptor generalist phages; (ii) that the “clearing of semi-turbid plaques” could be due to a phenomenon known as lysis inhibition collapse, (iii) that the “formation of satellite plaques” is reminiscent of the morphology of plaques generated by phage T4 star mutants, (iv) that “multi-day plaque enlargement” has been seen in other phages such as phage T7 but may also be explained by other phenomena including endolysin release, (v) that suggestions of phage “swimming” could be explained by virion diffusion within empty volumes found within maturing bacterial lawns. In particular, phage plaques that display lysis inhibition can influence the surrounding bacterial lawn well beyond their visible region. This presumably occurs via a reaction–diffusion mechanism whose leading edge of virion diffusion fails to display lysis inhibition, but which leaves in its wake lysis-inhibited bacterial infections that may not lyse in a timely manner. Phage-infected bacteria thus may be found well beyond a plaque’s visible boundaries, along with diffusing endolysin. Full article

Myotonic dystrophy type 1 (DM1) results from the toxicity of RNA produced from the mutant allele of the DMPK gene. The mechanism by which the toxic RNA causes muscular dystrophy in DM1 is unknown. Dystrophy in DM1 is associated with defective muscle regeneration and repair. Here, we used the BaCl₂-induced damage model of muscle injury to study muscle regeneration in the HSALR mouse model of DM1. We have previously shown delayed muscle regeneration and deleterious effects on satellite cell numbers in another mouse model of RNA toxicity using similar experimental approaches. We found that HSALR mice show no apparent deleterious effects on satellite cell number or early markers of muscle regeneration. Further analysis at later time points after damage showed increased numbers of internal nuclei as compared to control mice undergoing the same protocol. Muscle fiber type analysis using immunostaining for type IIA and IIB fibers identified a switch to slower fibers (increased fraction of IIA and reduced fraction of IIB fibers) after regeneration in HSALR mice as compared to regenerated muscle from wildtype mice. Full article

The rapid expansion of satellite networks for advanced communication and space exploration has ensured that robust cybersecurity for inter-satellite links has become a critical challenge. Traditional security models rely on centralized trust authorities, and node-specific protections are no longer sufficient, particularly when system failures or attacks affect groups of satellites or agent clusters. To address this problem, we propose a blockchain-enabled decentralized zero-trust model based on post-quantum cryptography (BEDZTM-PQC) to improve the security of satellite communications via continuous authentication and anomaly detection. This model introduces a group-based security framework, where satellite teams operate under a zero-trust architecture (ZTA) enforced by blockchain smart contracts and threshold cryptographic mechanisms. Each group shares the responsibility for local anomaly detection and policy enforcement while maintaining decentralized coordination through hierarchical federated learning, allowing for collaborative model training without centralizing sensitive telemetry data. A post-quantum cryptography (PQC) algorithm is employed for future-proof communication and authentication protocols against quantum computing threats. Furthermore, the system enhances network reliability by incorporating redundant communication channels, consensus-based anomaly validation, and group trust scoring, thus eliminating single points of failure at both the node and team levels. The proposed BEDZTM-PQC is implemented in MATLAB, and its performance is evaluated using key metrics, including accuracy, latency, security robustness, trust management, anomaly detection accuracy, performance scalability, and security rate with respect to different numbers of input satellite users. Full article

The discrete-serpentine heterogeneous multi-arm space robot (DSHMASR) has more advantages than single discrete space robots or single serpentine space robots in complex tasks of on-orbit servicing. However, the mechanical structure complexity of the DSHMASR poses challenges for modeling and motion planning. In this paper, a coupled kinematic model and a coordinated trajectory planning method for the DSHMASR were proposed to address these issues. Firstly, an uncontrolled satellite and the DSHMASR were modeled based on the momentum conservation law. The generalized Jacobian matrix J_g of the space robotic system was derived. Secondly, the manipulation capability of the DSHMASR was analyzed based on the null-space of J_g. Furthermore, the cooperative capturing-monitoring trajectory planning method for DSHMASR was presented through the manipulability optimization. The expected trajectory of each arm’s tip can be obtained by pose deviations and velocity deviations between the tip and the target point. Additionally, the optimized joint velocities of each arm were calculated by combining differential kinematics and manipulability optimization. Therefore, the manipulability of DSHMASR in the direction of the capture operation was enhanced simultaneously as it approached the target satellite. Finally, the proposed algorithm was demonstrated by establishing the Adams–Simulink co-simulation model. Comparisons with traditional approaches further confirm the outperformance of the proposed method in terms of manipulation capability. Full article

This study establishes quantitative relationships between neighborhood layouts, as evaluated by key neighborhood morphological parameters and pedestrian wind environments across China’s five major climate zones. We analyzed 3204 residential neighborhoods using satellite imaging and simulated 281 scenarios by CFD simulations, identifying six typical neighborhood layouts and quantifying their performance in terms of climate specific wind comfort criteria. This work takes an approach that takes into account mechanical wind effects and region-specific criteria for evaluating pedestrian-level wind environment performance, going beyond previous studies that utilize universal evaluation standards. The most influential parameter is building enclosure ratio with sensitivity indices of 0.844 for winter wind proofing. Closed perimeter layout confers 15–20% better winter wind proofing in cold climates and semi-open design enhances summer ventilation by 12–18% in hot climates according to our cross-climate analysis. Quantitative optimization adopting regression technique (R² = 0.727–0.810) points to an optimal enclosure ratio of 0.25–0.28 or 0.52–0.61 with aspect ratio of 1.75–2.75. The results can provide evidence-based design guidelines for high-rise residential neighborhood planning and pedestrian wind environment, aiming to improve urban livability and support climate adaptation strategies across a broad range of climate zones. Full article

As the world is technologically advancing, the integration of FSO communication in non-terrestrial platforms is transforming the landscape of global connectivity. By enabling high-data-rate inter-satellite links, secure UAV–ground channels, and efficient HAPS backhaul, FSO technology is paving the way for sustainable 6G non-terrestrial networks. However, the stringent requirement for precise line-of-sight (LoS) alignment between the optical transmitter and receivers poses a hindrance in practical deployment. As non-terrestrial missions require continuous movement across the mission area, the platform is subject to vibrations, dynamic motion, and environmental disturbances. This makes maintaining the LoS between the transceivers difficult. While fine-pointing mechanisms such as fast steering mirrors and adaptive optics are effective for microradian angular corrections, they rely heavily on an initial coarse alignment to maintain the LoS. Coarse pointing modules or gimbals serve as the primary mechanical interface for steering and stabilizing the optical beam over wide angular ranges. This survey presents a comprehensive analysis of coarse pointing and gimbal modules that are being used in FSO communication systems for non-terrestrial platforms. The paper classifies gimbal architectures based on actuation type, degrees of freedom, and stabilization strategies. Key design trade-offs are examined, including angular precision, mechanical inertia, bandwidth, and power consumption, which directly impact system responsiveness and tracking accuracy. This paper also highlights emerging trends such as AI-driven pointing prediction and lightweight gimbal design for SWap-constrained platforms. The final part of the paper discusses open challenges and research directions in developing scalable and resilient coarse pointing systems for aerial FSO networks. Full article