Search Results (47,822)

This study investigates the non-monotonic embedment response of a PRC pipe pile retaining system in interbedded silty sand and silty clay using field monitoring and three-dimensional PLAXIS 3D analyses with the HSsmall model. Retaining structure deformation was jointly influenced by excavation depth H, relative embedment ratio λ, and three-dimensional corner restraint; the mean pile head displacement in the deep excavation zone was 1.59 times that in the shallow zone. During same-stratum embedment, increasing the retaining pile length L progressively reduced deformation and bending response. On the north side, increasing L from 18 to 23 m reduced the pile head, excavation base, and pile toe displacements by 29.54%, 32.92%, and 71.99%, respectively, while the maximum negative bending moment decreased by 14.34%. On the south side, increasing L from 20 to 23 m produced corresponding reductions of 25.86%, 28.43%, 59.18%, and 17.12%. After the pile toe entered the underlying silty clay layer, the calculated pile head and excavation base displacements and maximum negative bending moment increased again for L = 24–25 m, indicating a project-specific non-monotonic response associated with the change in pile toe stratigraphic condition. Retaining pile length affected ground settlement and basal heave magnitudes more strongly than the settlement trough location. For adjacent pipelines, large displacement and axial force responses occurred near the trough minimum, while appreciable bending remained within the high-gradient trough flank region; increasing burial depth generally reduced displacement and axial force, whereas bending moment varied non-monotonically. These findings provide case-specific guidance for embedment selection and buried utility protection in layered ground. The L = 24–25 m cross-stratum cases are numerical parametric predictions rather than independently field-validated configurations. Full article

Unmanned Aerial Vehicles (UAVs) have emerged as a flexible, cost-effective solution for connecting Internet of Things (IoT) devices where traditional infrastructure falls short. However, managing their limited energy alongside the diverse demands of densely deployed devices makes resource allocation a genuinely hard problem. This paper presents a Deep Reinforcement Learning (DRL) framework that jointly optimizes user scheduling, IoT device transmit power, bandwidth, and UAV movement in a 6G-enabled UAV-relay uplink network, using a deterministic large-scale air-to-ground path-loss channel model. The UAV acts as an aerial decode-and-forward relay between IoT devices and a Base Station (BS), with a Deep Q-Network (DQN) making decisions based on queue backlogs, channel conditions, UAV position, and remaining battery. The reward function balances Energy Efficiency (EE), queue stability, fairness, and battery longevity. We benchmark the DQN against six baselines; Round Robin (RR), Random Allocation (RA), the Single-to-Noise Ratio (Max-SNR), Proportional Fair (PF), a Lyapunov heuristic, and a GreedyEE scheme; across a range of device counts, traffic loads, battery budgets, and flight altitudes. Simulations consistently show that the DQN outperforms all baselines, including a RA baseline with equal access to UAV mobility; in EE, throughput, delay, and fairness, confirming that the gain stems from the learned joint control policy rather than from UAV mobility being available. Full article

Objective: This study aimed to investigate the video recording of an accidental fall from height caused by pushing and to calculate push force, as well as linear and angular velocity values, to be used in homicidal cases. Methods: In the study, the video recorded during the event of a person who was unintentionally pushed from a height was examined. A method was developed to calculate the linear and angular velocity of the person being pushed and the pushing force using the video frames. Results: Based on the investigation, the victim was pushed from behind with a force of approximately 315 N in 11/30 s. The simplified biomechanical model suggests that when the feet remain in contact with the ground at the onset of the fall, a rotational motion (angular velocity) is expected to accompany the horizontal displacement. This rotational component is observed to influence the fall trajectory and body orientation prior to surface contact. Additionally, the concurrent presence of linear and angular velocity while the feet remain stable on the ground highlights the contribution of an external force component, which may assist in assessing scenarios involving external intervention. Conclusions: The calculated kinematics and force magnitude provide physical parameters that may support or contribute to a forensic reconstruction when evaluated alongside the broader investigative evidence. The method proposed in this study is expected to shed light on the detailed evaluation of suspicion of homicide in fall from height events. Full article

Distinguishing crop from weed species at the seedling stage is a fine-grained morphological discrimination problem. We study it on the public Plant Seedlings benchmark, which contains twelve species (three crops and nine weeds) imaged at the seedling stage. Automated classifiers report near-ceiling accuracy on this benchmark. Yet its images are acquired in controlled trays containing soil, gravel, rulers, barcodes, and printed labels, so a model may identify a species from its acquisition context rather than its morphology. We audit two architectures, EfficientNet-B7 and ViT-B/16, trained on the V2 dataset (5539 images) and probed with plant-only, background-only, and background-swapped inputs. Near-ceiling models (96.1% and 96.4% over three seeds) recover the correct species for up to 47.7% of samples from the background alone (chance 8.3%) and lose about 60 points under background swapping. Context reliance is therefore a property of the benchmark, not any single architecture. Tracing this to its source, an independent learned representation of the plant-free background alone identifies the species at 72.4%. The reliance is correctable end to end: a consistency-regularisation scheme retains 92.1% full-image accuracy for the transformer with no segmentation at inference, at an architecture-dependent cost. Reported accuracy thus partly measures acquisition context, not morphology; morphological grounding should be measured and reported alongside accuracy. Full article

Underground petrochemical pipelines face severe risks from unreported third-party damage during excavation. Existing monitoring systems based on supervised deep learning or distributed acoustic sensing often require expensive hardware and large volumes of labeled defect data, and they exhibit high false-alarm rates in noisy urban environments. To overcome these limitations, this study proposes a cost-effective, field-feasible early warning framework utilizing attached acceleration sensors. High-frequency transient vibrations from physical impacts are demodulated into low-frequency rhythms using a Hilbert transform-based envelope algorithm, and these rhythms are then converted into 2D spectrograms via the Short-Time Fourier Transform. A 2D Convolutional Neural Network-Variational Autoencoder (2D CNN-VAE) is employed for unsupervised anomaly detection, trained specifically on normal background data. Furthermore, a hierarchical alarm classification logic is implemented to evaluate the reconstruction error, incorporating impulse noise rejection and temporal continuity checks. In a field demonstration, the proposed framework effectively suppressed false alarms caused by severe continuous noise, such as ground compacting, while reliably detecting sustained asphalt-breaking threats. This methodology demonstrates practical feasibility for isolating genuine excavation activities from transient environmental noise, offering a promising predictive maintenance approach that reduces alarm fatigue without requiring labeled defect data. Full article

With the rapid growth of the New Space era, the increasing number and diversity of Low Earth Orbit (LEO) satellites demand generic and reusable antenna control software capable of stable and accurate tracking across heterogeneous orbital and antenna environments. Previous works have primarily focused on two-axis antenna systems, relying on step-based tracking or Two-Line Element (TLE)-based orbit prediction. However, such approaches suffer from manual initialization requirements and gimbal lock at high elevation angles, limiting continuous tracking performance. Although three-axis antenna structures have been proposed to address these issues, their lack of software-level generality and experimental validation restricts practical reuse across antenna platforms. This paper presents a generic three-axis parabola antenna control software that integrates TLE-based orbit prediction, a gimbal lock avoidance algorithm, and an antenna specification reflection function. By decoupling antenna-specific parameters from the core tracking logic, the proposed system enables flexible adaptation to diverse antenna configurations. Experimental validation using a real three-axis antenna and simulations demonstrates high tracking accuracy, achieving an average gain error within 0.466 dBm for GEO-KOMPSAT-2A and stable signal strength above 55 dBm for the LEO satellite ARIRANG-5. Furthermore, analysis of 11,469 gimbal-lock-prone satellites confirms robust avoidance performance across diverse orbital conditions. Full article

High-resolution remote sensing understanding requires models to preserve small spatial evidence, account for acquisition-dependent appearance, and separate genuine geographic change from nuisance variation. We introduce GeoGATE, a geo-sensor-guided framework that combines typed acquisition conditioning, budget-constrained adaptive token acquisition, metadata-compatible evidence retrieval, and reliability-aware temporal reasoning. LoRA adaptation and NF4 quantization support efficient training and deployment. On the VRSBench test split, GeoGATE reaches 53.4 BLEU-1, 36.8 BLEU-2, 18.2 BLEU-4, 56.4 Acc@0.5, 82.3 VQA, 25.1 METEOR, and 42.6 ROUGE-L, outperforming the controlled GeoGATE (Base) configuration across captioning, question answering, and grounding. Component ablations associate adaptive slicing most strongly with localization, retrieval with language and VQA, and language model adaptation with all reported tasks. NF4 reduces measured video memory from 24.5 GiB to 7.2 GiB with only minor metric changes. These experiments support the single-image language and grounding components. Dedicated cross-sensor and bi-temporal benchmarks are not reported; the corresponding modules are therefore presented as architectural extensions rather than validated performance claims. Full article

Existing metasurface-based radar countermeasures commonly manipulate echoes from metasurface-covered regions, whereas protecting exposed scattering centers that cannot accommodate metasurfaces remains challenging. This paper presents a multi-region time-coding metasurface (MRTCM) architecture for coherent target cancellation and deceptive jamming. The proposed system uses multiple independently controlled metasurface regions distributed over available platform surfaces. Each region generates zeroth-order and higher-order harmonic components through periodic phase modulation, and these components coherently combine with echoes from uncovered areas. The zeroth-order component is invariant under cyclic shifts of the coding sequence, enabling stable cancellation during multi-pulse coherent processing without locking radar-pulse arrival to the start of the coding cycle. Higher-order harmonics can shift the apparent range of the metasurface response and support cancellation at range-separated cells, but require accurate timing and synchronization. Numerical simulations demonstrate more than 40 dB suppression in high-resolution range profiles and validate the cancellation behavior under moving-target detection processing. The MRTCM architecture therefore provides a theoretically grounded framework for electromagnetic protection when full metasurface coverage is impractical. Full article

The ubiquity of surveillance networks in public infrastructure presents a significant, yet underutilized, opportunity to assist vulnerable populations, particularly individuals who are blind or have low vision (BLV). However, transforming these passive video feeds into active guidance systems requires accurate camera calibration, a process that is traditionally labor-intensive and unscalable in large facilities. This paper introduces a novel, automated framework that leverages a mobile quadruped robot (Boston Dynamics Spot) as a dynamic calibration agent. We propose a single-pass approach with two planar calibration algorithms, which mines unstructured robotic trajectories to construct distinct geometric features on the ground plane. By synthesizing “virtual rectangles” from the robot’s odometry, our method first recovers camera focal length through vanishing point estimation by constructing virtual rectangles, and then solves for extrinsic 6-DoF pose using two algorithms: our proposed Virtual Rectangle (ViR) algorithm and a standard planar Perspective-n-Point (PnP) algorithm. Experimental validation using real-world data demonstrates the system’s robustness to sensor noise, maintaining focal length errors around $5\%$, rotational errors around $5^{\circ }$ and relative translation errors of approximately $10\%$, while synthetic simulations indicate focal length errors generally under 10%, rotational errors under $1.5^{\circ }$, and translation errors also around 10% despite heavy image and object disturbances. This work eliminates the need for manual calibration targets for dynamic camera calibration, effectively converting static security infrastructure into a metric sensing network capable of supporting high-fidelity social robotics applications. Full article

Commercial operations across trading, import/export, logistics, and technology distribution are being reshaped by the convergence of artificial intelligence (AI), machine learning, multi-agent robotics, and Extended Reality (XR). Small and Medium-sized Enterprises (SMEs) feel this shift acutely: they face the same pressures as their larger competitors; labor shortages, high-SKU inventories that resist tidy categorization, narrow margins, and customer expectations set by Amazon-grade fulfilment, but rarely command the capital or the structured warehouse environments that make industrial automation straightforward. Existing frameworks for AI-driven automation and digital twins have been developed primarily for large-scale industrial settings and do not account for the capital, infrastructure, and organizational constraints specific to SMEs, leaving a gap in SME-scoped integration models. This research addresses that gap by asking how AI-driven robotics and immersive technologies can be integrated to optimize commercial workflows in SMEs operating in dynamic logistics and trading environments. The proposed framework is grounded in Sociotechnical Systems Theory, which treats technology and organizational workflows as jointly designed and mutually adapting, and follows a Design Science orientation in which the architecture itself is constructed as an evaluable artifact rather than a purely descriptive model. Methodologically, the study conducts a narrative synthesis of literature on embodied AI, computer vision, digital twins, VR training, and AR-assisted operations, combined with workflow analysis to identify where SMEs lose the most time and money. These are translated into a four-layer system architecture (perception, cognition, execution, integration) deployed through a four-phase implementation model: needs assessment, digital-twin and VR pre-training, controlled hardware pilot, and AR-supported scaling. The contribution of the study is twofold: conceptually, it brings together several technologies that are often discussed separately in the literature, while focusing specifically on the needs and constraints of SMEs while practically, it proposes a phased roadmap that can help SMEs adopt these technologies gradually, reducing both financial and operational risks. The approach also emphasizes human–robot collaboration rather than replacing human workers. The study does not include experimental validation, it presents a conceptual architecture and implementation roadmap consistent with a Design Science artifact-construction stage that can serve as a basis for empirical testing in real commercial environments. Full article

High-resolution measurements of near-surface optical turbulence over the Tibetan Plateau are essential for optical propagation studies, astronomical site characterization, and adaptive optics applications. In this study, a multi-level tethered UAV system was deployed in Ngari Prefecture, China, to obtain synchronous in situ observations of atmospheric optical turbulence from 10 to 190 m above ground. Using measurements at 10–90 m as inputs, we developed a physics-informed reconstruction framework to estimate $C_n^2$ profiles at 110–190 m, thereby approximately doubling the vertical range covered relative to the directly used observations. The framework decomposes the turbulence structure into an equilibrium component describing the large-scale vertical profile and a residual component representing departures from equilibrium. The equilibrium structure is modeled using a dynamically fitted power-law relationship, while a residual-learning module captures short-term variability associated with evolving thermal and dynamical processes. On the independent test period from the same field campaign, the reconstructed profiles achieve an average RMSE below 0.6 in $\lg \left(C_n^2\right)$ and an average $R^2$ above 0.75, outperforming empirical extrapolation and representative data-driven baselines. The proposed framework provides reliable estimates of upper-layer optical turbulence under daytime and transition-period conditions over the Ngari Plateau. Full article

This paper investigates the seismic performance of a nonlinear passive control device, namely a Bi-Stable Mass Damper–Inerter (BSMDI), designed to mitigate structural displacements. The system combines a mass damper connected to the primary structure through a bistable (snap-through) nonlinear element with a grounded inerter, enabling the exploitation of both nonlinear energy transfer mechanisms and enhanced inertial effects. The main objective of the study is to assess the effectiveness of the proposed BSMDI in reducing the maximum displacement response of structures subjected to seismic excitation. The novelty of the work lies in the synergistic integration of bistable nonlinear dynamics and inerter-based inertial amplification, together with a systematic parametric investigation aimed at identifying effective configurations in terms of both bistable parameters and inertance. The study is carried out on a two-degree-of-freedom system, in which the primary structure to be protected is represented by an equivalent single-degree-of-freedom model. This system is coupled to a mass damper through a bistable element, which is in turn connected to a grounded inerter device. A comprehensive parametric study is performed by varying the dimensionless stiffness and cubic coefficients of the bistable element, as well as the inertance ratio, while keeping the damper mass ratio small. The system performance is assessed using a displacement-based index defined as the ratio between the peak response of the controlled structure and that of the uncontrolled configuration. Performance maps and corresponding optimal curves are derived for three different seismic inputs. The present results suggest that the inerter plays a crucial role in achieving effective vibration mitigation, being significantly more effective than the damper mass alone. Overall, the proposed device appears to provide an efficient solution for seismic displacement mitigation. Full article

This work presents a microwave attitude sensing method and device based on localized perturbation of a split-ring resonator (SRR). The sensor comprises a planar SRR, parallel microstrip feed lines and a metallic disk that can move along a circular trajectory. When the sensor’s orientation is modified in a plane perpendicular to the ground, the metallic disk moves within the constrained structure under the influence of gravity and changes its position relative to the SRR, modulating the local near field and the microstrip coupling state. Consequently, variations in angle are observed across multiple S-parameter channels. The mechanism is validated through simulation and experimental measurements. The measured S-parameters are used to construct a circular residual mixture-of-experts Gaussian process regression (MoE-GPR) model, which is developed for 360° angle reconstruction. In leave-one-angle-out (LOAO) validation on data sampled at 2.5° intervals, the proposed reconstruction method achieves a mean absolute error (MAE) of 0.700°. When trained on data sampled at 10° intervals and tested on a dataset sampled at 2.5° intervals, the proposed method achieves an MAE of 1.125°, demonstrating its generalization across different angular sampling conditions. As no active electronics are required at the moving sensing element, the proposed configuration has potential for integration with RF sensing and communication platforms, as well as for inclination sensing referenced to gravity, orientation detection and structural health monitoring. Full article

Magnetic resonance imaging (MRI) is universally recognized as the gold standard for evaluating anterior cruciate ligament (ACL) injuries. However, differentiating partial tears from complete ruptures using the standard sagittal plane remains a diagnostic challenge. The oblique coronal orientation of the ACL often leads to volume averaging artifacts and inaccurate interpretations. While axial imaging offers a favorable alternative, distal tears near the tibial attachment are frequently missed. This review synthesizes current concepts in the anatomical and radiological evaluation of the ACL and introduces the “Namaste sign,” a novel and proposed preliminary imaging marker observed on coronal MRI to help assess ACL integrity. Grounded in the distinct native double-bundle architecture of the ACL, the Namaste sign utilizes the coronal plane to evaluate the transverse relationship and physiological tension of the anteromedial and posterolateral bundles. In a normal knee, these bundles converge symmetrically from their distinct tibial footprints and maintain tight contact with the lateral femoral condyle, mimicking hands pressed together in a traditional “Namaste” gesture. This article details the anatomical basis of this sign, provides a systematic method for its identification, and illustrates how morphological variations such as “broken hands,” “absent hands,” “absent proximal hug,” or “separation of hands” may correspond to specific ACL lesions. As a preliminary conceptual marker, the Namaste sign is intended to serve as an educational adjunct to combined three-plane evaluation rather than a standalone diagnostic criterion. Notably, this sign is not applicable to postoperative ACL reconstructions. A preliminary pilot study of 50 cases utilizing thin-slice MRI (≤3 mm) demonstrated nearly 100% visibility of the sign, 98% diagnostic accuracy, and almost perfect inter-observer agreement between two radiologists with more than 10 years of experience (Cohen’s kappa = 0.92). However, future diagnostic studies with rigorous arthroscopic reference standards are required to establish its clinical performance and reliability in larger cohorts. Full article

The Moon is an important focal point for expanding space exploration, acting as both a testing ground and connecting network for interplanetary missions. With long-term Moon-based habitation on the horizon, exercise and recreational facilities for space crews are becoming important avenues for maintaining physiological and psychological well-being. This review focuses on the efficacy of virtual reality exergaming interventions for space-related training performance on the Moon and potential long-term celestial habitation. Databases from five different search engines were screened using terms related to exercise and the Moon. The inclusion criteria included populations related to space or terrestrial simulations using virtual reality or exergaming types of training interventions, under different gravitational forces with physiological or psychological exercise outcomes. A total of seven full-length articles were selected for final review. Findings from the review indicate that virtual reality-based exergames can be a lightweight, portable and enjoyable exercise tool for space-related ventures. Although exergames were found to significantly improve psychological parameters such as motivation, perceived exertion, mood, anxiety level, adherence and enjoyment compared to non-virtual reality tools, physiological improvements were not significant. Virtual reality exergames carry multiple potential applications for Moon-based training, operations and habitation owing to their potential to improve physiological and psychological exercise measures. Full article