Search Results (310)

Effective energy management in Low Earth Orbit satellites is critical, as inefficient energy management can significantly affect mission objectives. The dynamic and harsh space environment further complicates the development of effective energy management strategies. To address these challenges, we propose a Deep Reinforcement Learning approach using Deep-Q Network to develop an adaptive energy management framework for Low Earth Orbit satellites. Compared to traditional techniques, the proposed solution autonomously learns from environmental interaction, offering robustness to uncertainty and online adaptability. It adjusts to changing conditions without manual retraining, making it well-suited for handling modeling uncertainties and non-stationary dynamics typical of space operations. Training is conducted using a realistic satellite electric power system model with accurate component parameters and single-orbit power profiles derived from real space missions. Numerical simulations validate the controller performance across diverse scenarios, including multi-orbit settings, demonstrating superior adaptability and efficiency compared to conventional Maximum Power Point Tracking methods. Full article

Low Earth orbit (LEO) signal of opportunity (SOP) positioning relies on the accumulation of epochs obtained through prolonged observation periods. The contribution of an LEO satellite single epoch to positioning accuracy is influenced by multi-level characteristics that are challenging for traditional models. To address this limitation, we propose an Agent-Weighted Recursive Least Squares (RLS) Positioning Framework (AWR-PF). This framework employs an agent to comprehensively analyze individual epoch characteristics, assess their credibility, and convert them into adaptive weights for RLS iterations. We developed a novel Markov Decision Process (MDP) model to assist the agent in addressing the epoch weighting problem and trained the agent utilizing the Double Deep Q-Network (DDQN) algorithm on 107 h of Iridium signal data. Experimental validation on a separate 28 h Iridium signal test set through 97 positioning trials demonstrated that AWR-PF achieves superior average positioning accuracy compared to both standard RLS and randomly weighted RLS throughout nearly the entire iterative process. In a single positioning trial, AWR-PF improves positioning accuracy by up to 45.15% over standard RLS. To the best of our knowledge, this work represents the first instance where an AI algorithm is used as the core decision-maker in LEO SOP positioning, establishing a groundbreaking paradigm for future research. Full article

The implantable capsule grouting pile is a novel pile foundation technology in which a capsule is affixed to the side of the implanted pile to facilitate grouting and achieve extrusion-based reinforcement. This technique is designed to improve the bearing capacity of implanted piles in coastal areas with deep, soft soil. This study conducted model tests involving multiple grouting positions across different foundation types to refine the construction process and validate the enhancement of bearing capacity. Systematic measurements and quantitative analyses were performed to evaluate the earth pressure distribution around the pile, the resistance characteristics of the pile end, the evolution of side friction resistance, and the overall bearing performance. Special attention was given to variations in the lateral friction resistance adjustment coefficient under different working conditions. Furthermore, an actual case analysis was conducted based on typical soft soil geological conditions. The results indicated that the post-grouting process formed a dense soil ring through the expansion and extrusion of the capsule, resulting in increased soil strength around the pile due to increased lateral earth pressure. Compared to conventional piles, the grouted piles exhibited a synergistic improvement characterized by reduced pile end resistance, enhanced side friction resistance, and improved overall bearing capacity. The ultimate bearing capacity of model piles at different grouting depths across different foundation types increased by 6.8–22.3% compared with that of ordinary piles. In silty clay and clayey silt foundations, the adjustment coefficient η_s of lateral friction resistance of post-grouting piles ranged from 1.097 to 1.318 and increased with grouting depth. The findings contribute to the development of green pile foundation technology in coastal areas. Full article

This paper presents a novel framework for optimizing multimodal large language model (MLLM) inference through task offloading and resource allocation in UAV-assisted satellite edge computing (SEC) networks. MLLMs leverage transformer architectures to integrate heterogeneous data modalities for IoT applications, particularly real-time monitoring in remote areas. However, cloud computing dependency introduces latency, bandwidth, and privacy challenges, while IoT device limitations require efficient distributed computing solutions. SEC, utilizing low-earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs), extends mobile edge computing to provide ubiquitous computational resources for remote IoTDs. We formulate the joint optimization of MLLM task offloading and resource allocation as a mixed-integer nonlinear programming (MINLP) problem, minimizing latency and energy consumption while optimizing offloading decisions, power allocation, and UAV trajectories. To address the dynamic SEC environment characterized by satellite mobility, we propose an action-decoupled soft actor–critic (AD-SAC) algorithm with discrete–continuous hybrid action spaces. The simulation results demonstrate that our approach significantly outperforms conventional deep reinforcement learning methods in convergence and system cost reduction compared to baseline algorithms. Full article

The sustainable development of geotechnical infrastructure necessitates using durable, efficient, and environmentally resilient reinforcement materials. This study investigates the pullout performance of geostraps to assess their potential as a sustainable alternative to conventional geosynthetics. This study focuses on the pullout performance of geostraps, flexible, polymeric reinforcement materials. There has not been a thorough study of their pullout resistance, which directly affects the stability and durability of reinforced soil structures. Pullout tests were conducted on sandy soil in a controlled environment. The experimental findings from the pullout test were then validated in a numerical model. The model was used to determine the pullout resistance of different grades of geostraps for comparative analysis. This helped to identify the possible application areas based on the pullout capacity of various grades. The results obtained for the geostraps were then compared with those in the established literature on geogrids. Initially, the pullout resistance of the M65 geostrap was up to 20% higher than that of a biaxial geogrid. This makes it a suitable option for reinforced earth applications. However, the maximum pullout resistance of geogrids was up to 8% higher than that of geostraps when subjected to a surcharge of 17 kN m⁻² in poorly graded sand. This study highlights the potential of geostraps as reinforcement materials, particularly in challenging environments where conventional geosynthetics may underperform. Future research may explore their behaviour with different soil types and other controlled environmental factors to establish their broader applicability and design charts. Full article

This study investigates the deformation characteristics and base stability of a circular diaphragm wall support system (external diameter: 90 m, wall thickness: 1.5 m) with pit bottom reinforcement for the South Anchorage deep foundation pit of the Zhangjinggao Yangtze River Bridge, which uses layered and partitioned top-down excavation combined with lining construction. Through field monitoring (deep horizontal displacement of the diaphragm wall, vertical displacement at the wall top, and earth pressure) and numerical simulations (PLAXIS Strength Reduction Method), we systematically analyzed the deformation evolution and failure mechanisms during construction. The results indicate the following: (1) Under the synergistic effect of the circular diaphragm wall, lining, and pit bottom reinforcement, the maximum horizontal displacement at the wall top was less than 30 mm and the vertical displacement was 0.04%H, both significantly below code-specified thresholds, verifying the effectiveness of the support system and pit bottom reinforcement. (2) Earth pressure exhibited a “decrease-then-increase” trend during the excavation proceeds. High-pressure jet grouting pile reinforcement at the pit base significantly enhanced basal constraints, leading to earth pressure below the Rankine active limit during intermediate stages and converging toward theoretical values as deformation progressed. (3) Without reinforcement, hydraulic uplift failure manifested as sand layer suspension and soil shear. After reinforcement, failure modes shifted to basal uplift and wall-external soil sliding, demonstrating that high-pressure jet grouting pile reinforcement had positive contribution basal heave stability by improving soil shear strength. (4) Improved stability verification methods for anti-heave and anti-hydraulic-uplift were proposed, incorporating soil shear strength contributions to overcome the underestimation of reinforcement effects in traditional pressure equilibrium and Terzaghi bearing capacity models. This study provides theoretical and practical references for similar deep foundation pit projects and offers systematic solutions for the safety design and deformation characteristics of circular diaphragm walls with pit bottom reinforcement. Full article

Geospatial navigation in GPS-denied environments presents significant challenges, particularly for autonomous vehicles operating in complex, unmapped regions. We explore the Earth’s geomagnetic field, a globally distributed and naturally occurring resource, as a reliable alternative for navigation. Since vehicles can only observe the geomagnetic field along their traversed paths, they must rely on incomplete information to infer the navigation strategy; therefore, we formulate the navigation problem as a partially observed Markov decision process (POMDP). To address this POMDP, we employ proximal policy optimization with long short-term memory (PPO-LSTM), a deep reinforcement learning framework that captures temporal dependencies and mitigates the effects of noise. Using real-world geomagnetic data from the international geomagnetic reference field (IGRF) model, we validate our approach through experiments under noisy conditions. The results demonstrate that PPO-LSTM outperforms baseline algorithms, achieving smoother trajectories and higher heading accuracy. This framework effectively handles the uncertainty and partial observability inherent in geomagnetic navigation, enabling robust policies that adapt to complex gradients and offering a robust solution for geospatial navigation. Full article

The theory of orbital forcing as formulated by Milankovitch involves the mediation by the advance (retreat) of ice sheets and the resulting variations in terrestrial albedo. This approach poses a major problem: that of the period of glacial cycles, which varies over time, as happened during the Mid-Pleistocene Transition (MPT). Here, we show that various hypotheses are called into question because of the finding of a second transition, the Early Quaternary Transition (EQT), resulting from the million-year period eccentricity parameter. We propose to complement the orbital forcing theory to explain both the MPT and the EQT by invoking the mediation of western boundary currents (WBCs) and the resulting variations in heat transfer from the low to the high latitudes. From observational and theoretical considerations, it appears that very long-period Rossby waves winding around subtropical gyres, the so-called “gyral” Rossby waves (GRWs), are resonantly forced in subharmonic modes from variations in solar irradiance resulting from the solar and orbital cycles. Two mutually reinforcing positive feedbacks of the climate response to orbital forcing have been evidenced: namely the change in the albedo resulting from the cyclic growth and retreat of ice sheets in accordance with the standard Milankovitch theory, and the modulation of the velocity of the WBCs of subtropical gyres. Due to the inherited resonance properties of GRWs, the response of the climate system to orbital forcing is sensitive to small changes in the forcing periods. For both the MPT and the EQT, the transition occurred when the forcing period merged with one of the natural periods of the climate system. The MPT occurred 1.25 Ma ago, when the dominant period shifted from 41 ka to 98 ka, with both periods corresponding to changes in the Earth’s obliquity and eccentricity. The EQT occurred 2.38 Ma ago, when the dominant period shifted from 408 ka to 786 ka, with both periods corresponding to changes in the Earth’s eccentricity. Through this paradigm shift, the objective of this self-consistent approach is essentially to spark new debates around a problem that has been pending since the discovery of glacial–interglacial cycles, where many hypotheses have been put forward without, however, fully answering all our questions. Full article

Given the problems of uneven distribution, strong time variability of ground service demands, and low utilization rate of on-board resources in Low-Earth-Orbit (LEO) satellite communication systems, how to efficiently utilize limited beam resources to flexibly and dynamically serve ground users has become a research hotspot. This paper studies the dynamic resource allocation and interference suppression strategies for beam hopping satellite communication systems. Specifically, in the full-frequency-reuse scenario, we adopt spatial isolation techniques to avoid co-channel interference between beams and construct a multi-objective optimization problem by introducing weight coefficients, aiming to maximize user satisfaction and minimize transmission delay simultaneously. We model this optimization problem as a Markov decision process and apply a value decomposition network (VDN) algorithm based on cooperative multi-agent reinforcement learning (MARL-VDN) to reduce computational complexity. In this algorithm framework, each beam acts as an agent, making independent decisions on hopping patterns and power allocation strategies, while achieving multi-agent cooperative optimization through sharing global states and joint reward mechanisms. Simulation results show that the applied algorithm can effectively enhance user satisfaction, reduce delay, and maintain high resource utilization in dynamic service demand scenarios. Additionally, the offline-trained MARL-VDN model can be deployed on LEO satellites in a distributed mode to achieve real-time on-board resource allocation on demand. Full article

With the increasing adoption of cooperative multi-UAV systems in applications such as cargo delivery and ground reconnaissance, the demand for scalable and efficient path planning methods has grown substantially. However, traditional heuristic algorithms are frequently trapped in local optima, require task-specific manual tuning, and exhibit limited generalization capabilities. Furthermore, their dependence on iterative optimization renders them unsuitable for large-scale real-time applications. To address these challenges, this paper introduces an end-to-end deep reinforcement learning framework that bypasses the reliance on handcrafted heuristic rules. The proposed method leverages an encoder–decoder architecture with multi-head attention (MHA), where the encoder generates embeddings for UAVs and task parameters, while the decoder dynamically selects actions based on contextual embeddings and enforces feasibility through a masking mechanism. The MHA module effectively models global spatial-task dependencies among nodes, enhancing solution quality. Additionally, we integrate a Multi-Start Greedy Rollout Baseline to evaluate diverse trajectories via parallelized greedy searches, thereby reducing policy gradient variance and improving training stability. Experiments demonstrated significant improvements in scalability, particularly in 100-node scenarios, where our method drastically reduced inference time compared to conventional methods, while maintaining a competitive path cost efficiency. A further validation on simulated mission environments and real-world geospatial data (sourced from Google Earth) underscored the robust generalization of the framework. This work advances large-scale UAV mission planning by offering a scalable, adaptive, and computationally efficient solution. Full article

The multi-agile Earth observation satellite scheduling problem is challenging because of its large solution space and substantial task volume. This study generates observation schemes for static tasks over an execution period. To balance solution quality and computational efficiency, a deep reinforcement learning (DRL)-based algorithmic framework is proposed. A Markov decision process (MDP) is formulated as the foundational model for the DRL architecture. To mitigate problem complexity, the action space is decomposed into two interdependent decision layers: task sequencing and resource allocation. Given the resource occupation constraints during action execution, a novel reward function is designed by integrating resource occupation utility into the immediate reward mechanism. Corresponding to these dual decision layers, a Two-Phase Hybrid Optimization (TPHO) framework is developed. The task sequencing subproblem is addressed through an encoder–decoder architecture based on sequence-to-sequence learning. To preserve resource diversity throughout the scheduling horizon, a maximum residual capacity (MRC) heuristic is introduced. A comprehensive experimental suite is constructed, incorporating multi-satellite scheduling scenarios with capacity and temporal constraints. The experimental results demonstrate that the TPHO framework with MRC rules achieves superior performance, yielding a total reward improvement exceeding 16% compared with the A-ALNS algorithm in the most complex scenario involving 1200 tasks, yet requiring less than 3% of the computational duration of A-ALNS. Full article

Composite reinforcement concrete square piles exhibit excellent bending resistance and deformation capacity, along with construction advantages such as ease of transportation. In recent years, they have been widely adopted in building pile foundation applications. However, their seismic behavior, particularly under multi-directional excitation, remains inadequately explored. This study employs large-scale shaking table tests to evaluate the seismic response of a single composite reinforcement square pile embedded in a soft clay foundation under different horizontal excitations (0° and 45°) and two distinct ground motions (Wenchuan Songpan and Chi-Chi) to assess directional anisotropy and resonance effects, with explicit consideration of soil–structure interaction (SSI). The key findings include the following: the dynamic earth pressure along the pile exhibits a distribution pattern of “large at the top, small at the middle and bottom”. And SSI reduced pile–soil compression by 20–30% under 45° excitation compared to 0°. The dynamic strain in outer longitudinal reinforcement in pile corners increased by 30–60% under 45° excitation compared to 0°. Under seismic excitation considering SSI, the bending moment along the pile exhibited an “upper-middle maximum” pattern, peaking at depths of 3–5 times the pile diameter. Axial forces peaked at the pile head and decreased with depth. While bending moment responses were consistent between 0° and 45° excitations, axial forces under 45° loading were marginally greater than those under 0°. The Chi-Chi motion induced a bending moment about four times greater than the Songpan motion, highlighting the resonance risks when the ground motion frequencies align with the pile–soil system’s fundamental frequency. Full article

Pile–anchor structures offer an effective way to reinforce slopes in earthquake-prone regions. Static and quasi-static analysis on pile–anchor structures has been widely conducted, but their dynamic behaviors have not been well addressed. This study explores the dynamic behavior of a bedding rock slope strengthened by pile–anchor structures in a seismic-prone region of China. We propose a method for the automatic application of viscoelastic boundaries and input of seismic waves in ABAQUS (version 2021) using MATLAB R2023a programming. A series of numerical simulations for the pile–anchor-reinforced slope under seismic motions with different acceleration amplitudes and excitation directions are performed. We find that the PGA amplification factors at the slope surface are larger than those in the middle of the slope, which is because the bedding planes near the slope surface cause reflections of seismic waves. The maximum axial force of the anchors of the upper and lower rows is greater than that of the middle rows. For example, under an acceleration amplitude of 0.1 g, the maximum axial forces of the anchors with numbers ranging from 1 to 6 are 466, 462, 461, 460, 461, and 463 kN, respectively. The distribution of the peak values of the earth pressure presents a significant change around the sliding surface. The maximum bending moment of the pile increases from 0.55 × 10³ to 0.90 × 10³ kN·m as the acceleration amplitudes of the seismic waves increase from 0.2 to 0.3 g, indicating that the pile can bear the load caused by the movement of the slope. Full article

Rainfall-induced landslide mitigation remains a critical research focus in geotechnical engineering, particularly for safeguarding buildings and infrastructure in unstable terrain. This study investigates the stabilizing performance of slopes reinforced with negative Poisson’s ratio (NPR) anchor cables under rainfall conditions through physical model tests. A scaled geological model of a heavily weathered rock slope is constructed using similarity-based materials, building a comprehensive experimental setup that integrates an artificial rainfall simulation system, a model-scale NPR anchor cable reinforcement system, and a multi-parameter data monitoring system. Real-time measurements of NPR anchor cable axial forces and slope internal stresses were obtained during simulated rainfall events. The experimental results reveal distinct response times and force distributions between upper and lower NPR anchor cables in reaction to rainfall-induced slope deformation, reflecting the temporal and spatial evolution of the slope’s internal sliding surface—including its generation, expansion, and full penetration. Monitoring data on volumetric water content, earth pressure, and pore water pressure within the slope further elucidate the evolution of effective stress in the rock–soil mass under saturation. Comparative analysis of NPR cable forces and effective stress trends demonstrates that NPR anchor cables provide adaptive stress compensation, dynamically counteracting internal stress redistribution in the slope. In addition, the structural characteristics of NPR anchor cables can effectively absorb the energy released by landslides, mitigating large deformations that could endanger adjacent buildings. These findings highlight the potential of NPR anchor cables as an innovative reinforcement strategy for rainfall-triggered landslide prevention, offering practical solutions for slope stabilization near buildings and enhancing the resilience of building-related infrastructure. Full article

The Gobi Wall is a 321 km-long structure made of earth, stone, and wood, located in the Gobi highland desert of Mongolia. It is the least understood section of the medieval wall system that extends from China into Mongolia. This study aims to determine its builders, purpose, and chronology. Additionally, we seek to better understand the ecological implications of constructing such an extensive system of walls, trenches, garrisons, and fortresses in the remote and harsh environment of the Gobi Desert. Our field expedition combined remote sensing, pedestrian surveys, and targeted excavations at key sites. The results indicate that the garrison walls and main long wall were primarily constructed using rammed earth, with wood and stone reinforcements. Excavations of garrisons uncovered evidence of long-term occupation, including artifacts spanning from 2nd c. BCE to 19th c. CE. According to our findings, the main construction and usage phase of the wall and its associated structures occurred throughout the Xi Xia dynasty (1038–1227 CE), a period characterized by advanced frontier defense systems and significant geopolitical shifts. This study challenges the perception of such structures as being purely defensive, revealing the Gobi Wall’s multifunctional role as an imperial tool for demarcating boundaries, managing populations and resources, and consolidating territorial control. Furthermore, our spatial and ecological analysis demonstrates that the distribution of local resources, such as water and wood, was critical in determining the route of the wall and the placement of associated garrisons and forts. Other geographic factors, including the location of mountain passes and the spread of sand dunes, were strategically utilized to enhance the effectiveness of the wall system. The results of this study reshape our understanding of medieval Inner Asian imperial infrastructure and its lasting impact on geopolitical landscapes. By integrating historical and archeological evidence with geographical analysis of the locations of garrisons and fortifications, we underscore the Xi Xia kingdom’s strategic emphasis on regulating trade, securing transportation routes, and monitoring frontier movement. Full article