Search Results (1,671)

Although the role of the mammalian hippocampus in representing locomotor speed has been widely investigated, how the avian hippocampus represents continuous flight speed under free-flight conditions in the outdoor environment remains unclear. In this study, we used homing pigeons as a model system and synchronously recorded hippocampal formation (HF) local field potentials (LFPs), global positioning system (GPS) trajectories, and inertial measurement unit (IMU) data during natural homing flights. We aimed to determine whether and how the avian HF encodes flight speed. Flight-speed-related neural features were extracted from both frequency-domain and time-domain signals, including the 50–70 Hz power spectral density (PSD) ratio and theta-demodulated amplitude (DAmp). We then constructed models for discrete flight-speed state decoding and continuous flight-speed prediction. The results showed that the 50–70 Hz PSD ratio in the HF was significantly negatively correlated with flight speed, whereas DAmp was significantly positively correlated with flight speed. Both features exhibited consistent speed-related trends across different spatial release sites. Support vector machine (SVM)-based classification showed that PSD, DAmp, and their combined features could effectively decode four flight-speed states, including non-flight, low-speed, medium-speed, and high-speed states, with the combined features achieving the best performance. Further Gaussian process regression (GPR) analysis demonstrated that the combined features predicted continuous flight speed more accurately than either single feature. These findings provide evidence that the avian hippocampal formation encodes continuous flight speed during natural navigation through the complementary integration of frequency-domain and time-domain features, extending the known role of the avian hippocampal formation from static spatial mapping to dynamic self-motion representation. Full article

The full operation of BDS-3 enables users to obtain high-performance positioning services, benefiting from the surge in the number of Global Navigation Satellite System (GNSS) observations with multi-constellation multi-frequency signals. This overabundance is beneficial to improve in-flight navigation accuracy for small unmanned aerial vehicles (UAVs). However, it brings about two-fold challenges for conventional airborne GNSS/SINS tightly coupled (TC) systems. On one hand, limited airborne computing resources suffer from the “curse of dimensionality” caused by extremely high-dimensional GNSS observations (i.e., GNSS pseudo-ranges, pseudo-range rates, and time-differenced carrier phases from multi-system and multi-frequency, such as GPS L1/L2 and BDS B1/B2/B3, totaling up to over 100 observables per epoch), leading to increased calculation burden and potential latency. On the other hand, possible outliers can degrade the obtained navigation accuracy. To enhance overall performance, this paper proposes a computationally efficient Kalman filtering framework for tight integration between airborne GNSS and SINS via a high-dimensional robust information filter. The strategy of kinematic and static information filtering is utilized to handle the matrix inversion complexity caused by high-rate and high-dimensional Kalman measurement updates, and the technique of robust adaptive factor is used to resist the adverse effects of GNSS outliers and modeling errors. Both land vehicular and UAV flight tests indicate that the proposed algorithm outperforms its traditional TC counterparts, demonstrating an over 90% improvement in overall computational efficiency without any loss in accuracy, compared with conventional batch or sequential tightly coupled Kalman filtering. Full article

With the rapid development of the global shipping industry, the efficiency and accuracy of ship lifesaving systems remain significant challenges. To address the key issues of slow response and low intelligence in traditional maritime lifesaving systems, this study designs and implements an intelligent lifesaving system that integrates motion object detection with remote positioning and communication. A GPS positioning module is integrated for precise location acquisition, and GPRS technology is utilized to remotely transmit alarm information and location data to a rescue center. This constitutes a comprehensive technical solution comprising data acquisition, intelligent decision-making, wireless communication, and auxiliary rescue modules. Experimental results in a controlled wave pool environment demonstrate the system’s high efficacy, achieving a detection time of 15 s with a 96% accuracy and a 23.35% improvement in the rescue success rate under extreme conditions. The novelty of the proposed system lies in the integrated architectural approach and adaptive workflow. It intelligently synthesizes data from multiple sensors through a rule-based and model-driven decision pipeline. These findings suggest that the proposed integration enhances maritime rescue efficiency and reliability, highlighting its potential practical value for safe operations. Full article

This study presents the design, fabrication, and experimental validation of the Hermes reusable model rocket platform integrating additive manufacturing, onboard sensing, and telemetry capabilities for low-cost aerospace experimentation. The rocket was manufactured using modular Polyethylene Terephthalate Glycol (PETG) components produced through fused filament fabrication to achieve a lightweight and structurally robust configuration suitable for repeated flight operations. A custom flight computer based on a Raspberry Pi Zero 2W was developed to acquire in-flight data from an inertial measurement unit, barometric pressure sensor, and Global Positioning System module, while an onboard camera enabled post-flight trajectory assessment. Aerodynamic performance and stability were evaluated using OpenRocket simulations, and propulsion was provided by a cluster of Klima D9-5 solid rocket motors. Four experimental flights were conducted to evaluate the integrated system architecture, assess telemetry and sensor performance, and compare experimental flight data with simulation predictions. The recorded measurements successfully captured the primary flight phases, including launch, ascent, apogee, descent, and recovery. The experimental results showed qualitative agreement with the simulated flight profiles; however, deviations in apogee altitude, acceleration, and flight duration were observed due to aerodynamic drag, environmental disturbances, motor-performance variability, and implementation-related limitations. The flight campaigns additionally identified practical challenges associated with wireless telemetry reliability, GPS signal acquisition, electronic protection, and parachute deployment, leading to iterative system improvements. From a sensing perspective, the flight campaigns demonstrate the operation and limitations of a low-cost embedded acquisition architecture under dynamic conditions, including the effects of sampling rate, sensor calibration, synchronization, wireless-link interruption, and local data preservation on the quality of the recorded flight measurements. The presented platform demonstrates the feasibility of combining low-cost additive manufacturing techniques with commercially available embedded electronics for reusable aerospace testing and educational applications. The proposed system further provides a flexible experimental framework for flight-data acquisition, simulation validation, and iterative development in academic and amateur rocketry research. Full article

Directed inter-UAV Time-Differenced Carrier Phase (TDCP) couples an inter-node distance increment with a relative clock-bias increment, while phase differencing produces temporally correlated noise and near-planar formations retain a weak relative-height mode. We formulate a fixed-lag factor graph that combines reciprocal TDCP, Time-of-Arrival (TOA), Global Positioning System (GPS), clock dynamics, and calibrated navigation-frontend motion and altitude outputs. An invertible sum-and-difference transformation exposes geometry and clock-bias-increment-rate channels while full covariance propagation preserves the reciprocal-pair likelihood. Local information analysis shows that the reverse observation removes the single-direction geometry–clock-increment rank deficiency, and phase-level modeling yields the first-order moving-average covariance retained by block whitening. A causal motion-consistency test (MCT) compares the whitened geometry channel with an independent motion prediction before the current pair enters optimization; a flagged pair is assigned negligible information and its detector–estimator phase arc is reset. Implementation checks verify the exact four-bias-state residual, covariance-normalized reciprocal innovations, and operation under sparse single-direction ambiguity changes. Across ten paired runs at $p_s=5\%$, MCT detected all 770 injected events with a pair-level false-alarm rate of 0.169% and reduced the mean position RMSE by 96.6% relative to DCS. Full article

Space-based navigation systems rely on atomic clocks aboard satellites to provide precise time and positioning information through synchronized radio-frequency signals. At the core are Atomic Frequency References (AFRs), which stabilize a local oscillator using atomic transitions to achieve exceptional accuracy and stability. To maintain 1 m positioning precision, timing uncertainties below 3 ns are required, achievable only with high-quality atomic clocks. This paper surveys the principles, architectures, and performance of AFRs in satellite navigation, including the types deployed in major Global Navigation Satellite System (GNSS) constellations such as GPS, Galileo, GLONASS, and BeiDou, and discusses current practices and emerging trends in satellite timing technologies, including the nascent Low-Earth-Orbit Positioning, Navigation, and Timing (LEO-PNT) paradigm. Full article

On 14 February 2025, the outer cladding of the Chornobyl New Safe Confinement (NSC) was damaged by an explosion caused by a one-way attack unmanned aerial vehicle (UAV), creating a hole of about 15 m in diameter and requiring about 300 penetrations to be made in the cladding during firefighting. This created an urgent need to assess radiation dose rates above damaged areas to support repair planning and worker radiation protection. This study presents a UAV-based survey of the equivalent gamma dose rate distribution above the damaged northern side of the NSC outer cladding. The survey used a bespoke system, integrating a multirotor UAV, an AccuRad Personal Radiation Detector (PRD), onboard data acquisition and transmission modules, and ground-based and server-side analytical components. Measurements were performed under real post-incident field conditions, including restricted flight zones, wind-induced turbulence, proximity to large metallic structures, and electronic warfare interference. The dataset was filtered for Global Positioning System (GPS) reliability, transformed into a metric coordinate system, and processed for spatial interpolation and mapping. The resulting distribution showed a spatially non-uniform radiation field: the main damage zone had relatively low equivalent gamma dose rates, whereas the highest values, up to 1092 μSv/h, were recorded over areas of the NSC closest to the Shelter Object. The study demonstrates UAV-based radiation mapping of a damaged large-scale confinement structure and provides data supporting Chornobyl Nuclear Power Plant repair planning. Full article

Ginger (Zingiber officinale) fields in northern China are often covered by plastic mulch film and shade netting, so Sentinel-2 records management materials as well as the crop canopy. Because these materials and their deployment differ among production systems, models calibrated locally may transfer poorly. We defined three observation windows for the main study counties: spring film mulching, summer shade-net coverage, and autumn exposed-canopy greening. Within each window, spectral bands, vegetation indices, and gray-level co-occurrence matrix (GLCM) textures were extracted from single Sentinel-2 scenes across three northern Chinese counties with contrasting practices. A cross-regional Random Forest Gini ranking, weighted by the similar county model-sample totals, retained four variables per feature family per stage (36 of 120 variables across the three-stage stack). The spectral-index-texture scheme achieved within-county F1 scores of 95.00% in Changyi, 94.83% in Qingzhou, and 95.58% in Fengrun. Leave-one-county-out (LOCO) model fitting returned a mean F1 of 94.73% compared with 95.14% for the within-county splits. Because the fixed-feature protocol was selected using all three counties, this LOCO test evaluates county-held-out classifier fitting rather than a fully nested feature-selection pipeline. Independent field verification with Global Positioning System (GPS) points ranged from 85.80% to 89.95%, and village-level area estimates agreed with remote-sensing totals (R² = 0.869). The 36-variable protocol performed similarly to the full 120-variable input (95.14% vs. 95.24% mean F1), and the selected features were stable across the three tested weighting rules. In Funing County, where shade nets are absent, omitting the shading stage on the basis of local agronomic practice yielded 93.75% accuracy against 96 independent GPS points. The results support management timing as a practical guide for ginger mapping within the tested northern production systems; wider climatic validation and fully nested transfer tests are still needed. Full article

Precision agriculture (PA) has emerged as a transformative approach for improving agricultural productivity, resource-use efficiency, and environmental sustainability through the integration of digital technologies, including Global Positioning Systems (GPSs), Geographic Information Systems (GISs), remote sensing, the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), and autonomous systems. Although previous reviews have primarily emphasized technological innovation, adoption trends, or environmental outcomes, they have provided limited synthesis of the economic mechanisms linking technology adoption, resource allocation, production efficiency, investment performance, and long-term sustainability. A structured narrative–systematic review was conducted using peer-reviewed research retrieved from Scopus, Web of Science, and Google Scholar, covering studies published between 2004 and 2026. An integrated analytical framework combining technology adoption theory, resource economics, and production-efficiency models was employed to explain how digital technologies generate economic value while identifying methodological limitations, geographical bias, unresolved research questions, and future research priorities. The review demonstrates that GPS-guided machinery, variable-rate technologies, smart irrigation systems, AI-driven decision-support tools, and integrated digital platforms improve water- and nutrient-use efficiency, labor productivity, production efficiency, and farm profitability. However, economic performance remains highly context-dependent, varying according to farm size, crop type, climatic conditions, institutional support, digital infrastructure, resource scarcity, and policy environments. Methodological inconsistencies in return on investment (ROI), net present value (NPV), lifecycle costing, ecosystem-service valuation, and environmental externality assessment reduce comparability among studies and complicate evidence-based policymaking. The review further identifies a pronounced geographical concentration of evidence in North America, Europe, and Australia, with comparatively limited understanding of PA economics in China, India, Brazil, Sub-Saharan Africa, and Southeast Asia. Persistent challenges include high capital costs, unequal access among smallholder farmers, data governance concerns, interoperability limitations, uncertainty in long-term investment performance, and limited integration of agricultural insurance, climate-risk management, and digital finance. By integrating economic theory, methodological comparison, geographical analysis, sustainability valuation, and policy perspectives within a unified conceptual framework, this review highlights the need for standardized economic evaluation methodologies, broader geographical representation, and interdisciplinary research to support evidence-based policy and the sustainable digital transformation of global agriculture. Full article

Global navigation satellite systems (GNSSs) are essential for navigation in aviation, transportation, and autonomous systems, yet they remain vulnerable to intentional interference such as jamming and spoofing. Unlike prior studies that primarily focus on positioning error, this work emphasizes acquisition-phase behavior, analyzing the impact of interference on time-to-first-fix (TTFF) and post-attack reacquisition time. A controlled and repeatable laboratory testbed based on software-defined radio (SDR) was developed to emulate Global Positioning System (GPS) L1 and Galileo E1 signals under multiple interference scenarios, including narrowband jamming, static spoofing, and dynamic spoofing. Five commercial GNSS receivers were evaluated under identical conditions. The results show that jamming causes an immediate loss of positioning capability, reducing the empirical navigation-fix probability to near zero and significantly increasing reacquisition time, with recovery-phase empirical fix probabilities ranging from 0.062 to 0.991 depending on receiver class. In contrast, spoofing maintains high attack-phase empirical navigation-fix probabilities ranging from 0.730 to 0.907 while introducing persistent and undetected errors. Static position spoofing was found to produce position offsets that persisted into the recovery phase, delaying the return to the authentic navigation solution. For most receivers, however, correct positioning was restored within the observation window. Multi-constellation spoofing further increases attack effectiveness, raising fix continuity by more than 0.15 compared to single-constellation cases. Multi-band receivers demonstrate increased resilience by delaying spoof acceptance by more than 4 min in extended scenarios, rather than preventing it entirely. The proposed methodology enables reproducible evaluation of GNSS receiver robustness and demonstrates that navigation-fix continuity alone is not a reliable indicator of navigation integrity during spoofing attacks. Overall, the results demonstrate that navigation-fix continuity alone cannot be regarded as a reliable indicator of navigation integrity and highlight the importance of complementary integrity-monitoring mechanisms for GNSS-dependent systems. The reported observations were obtained under controlled laboratory conditions and should be interpreted within the context of the adopted experimental methodology rather than as a direct representation of operational performance in real-world environments. Full article

Freeze–thaw (F/T) processes play a critical role in the regulation of soil hydrothermal dynamics, land–atmosphere energy exchange, and ecosystem functioning. The spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for land surface F/T state detection; however, its monitoring capability remains limited by spatial resolution, revisit interval, observation coverage, and complex land surface conditions. In this study, Tianmu-1 (TM-1) multi-GNSS-R observations were used to detect daily land surface F/T states over the mid-to-high latitudes of the Northern Hemisphere. First, surface reflectivity observations from multi-GNSS, including the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo, and GLONASS, were fused using a weighted averaging method based on the number of specular reflection points. Then, TM-1 multi-GNSS-R reflectivity was used as the primary remote-sensing input, while vegetation water content (VWC), surface roughness, and snow cover information were introduced as auxiliary environmental variables. The Soil Moisture Active Passive (SMAP) F/T product was used to provide supervised reference labels for developing Bayesian-optimized extreme gradient boosting (XGBoost) models for F/T state classification. Evaluation against SMAP F/T reference labels showed that the multi-GNSS fusion model achieved an area under the curve (AUC) of 0.853 and an overall accuracy of 77.3% without incorporating snow cover information, outperforming the single-GNSS models. After incorporating snow cover information, the AUC increased to 0.959, and the overall accuracy reached 89.3%. Shapley additive explanations (SHAP) analysis further showed that snow cover made the largest contribution to the final model output, suggesting that its improvement effect may reflect both physical snow-related surface information and seasonal contextual information. An independent point-based comparison with in situ observations from the international soil moisture network (ISMN) showed that the TM-1 F/T classification accuracy reached 85.2% after incorporating snow cover information, which was comparable to that of the SMAP product. These results demonstrate that TM-1 multi-GNSS-R observations have promising potential for detecting land surface F/T states during the autumn–winter freezing development period, and that integrating multi-GNSS-R reflectivity with snow cover information can substantially improve classification performance and spatial consistency within the available observation period. Full article

Autonomous inspection of ballast water tanks requires three-dimensional (3D) LiDAR-based simultaneous localization and mapping (SLAM) in Global Positioning System (GPS)-denied, geometrically repetitive interiors, where sensing, mapping, and control modules share a limited onboard memory budget. Graph SLAM backends that rely on sparse factorization can incur fill-in, increasing peak memory and limiting deployment on edge computers. The proposed architecture couples a robust hierarchical bundle adjustment frontend with a factorization-free Kaczmarz backend. The frontend combines residual-adaptive weighting, damped and bounded pose updates, soft fallback, local-map compression, and memory-aware keyframe control. The backend stores the whitened Jacobian in compressed sparse row (CSR) format and performs row-wise projections without explicitly forming the normal equations, a Cholesky factor, or a transpose cache. Evaluation was conducted on Norwegian University of Science and Technology (NTNU) Ballast Water Tank missions 1–3, containing 851, 1202, and 1084 LiDAR frames. Following robust local bundle adjustment and verified similarity alignment, translational root-mean-square errors were 0.080, 0.110, and 0.127 m, corresponding to 0.137%, 0.143%, and 0.122% of the reference path lengths; archived baseline ratios ranged from 0.281% to 0.372%. These results support a numerical architecture that combines frontend stabilization, row-wise optimization, and memory-aware policies for resource-constrained marine inspection robots. Full article

Despite the rapid expansion of unmanned aerial vehicle (UAV) applications across precision agriculture, logistics, infrastructure inspection, disaster response, and aerial surveying, objective and quantitative hardware evaluation criteria for UAV components remain insufficiently developed. This paper proposes quantitative key performance indicators (KPIs) for thirteen core hardware subsystems, including airframe and propulsion, battery and power supply, flight control, wireless communication, imaging (camera), Global Positioning System (GPS)/Global Navigation Satellite System (GNSS) positioning, thermal management, acoustic and vibration characteristics, AI-based autonomous flight, electromagnetic compatibility (EMC), cybersecurity, and reliability and environmental qualification, together with LiDAR payload evaluation criteria. International standardization activities by 3GPP (Release 15/17), IEEE (1936–1958 series), American society for photogrammetry and remote sensing (ASPRS), and national regulatory frameworks are synthesized to define measurable performance metrics and recommended test methods for each subsystem. An integrated KPI matrix maps application-domain-specific performance targets—encompassing surveying (real-time kinematic (RTK) horizontal accuracy ≤ 2 cm root-mean-square error (RMSE), ground sample distance (GSD) ≤ 2 cm/px), infrastructure inspection (LiDAR payload up to 8 kg, beyond visual line-of-sight (BVLOS) latency ≤ 140 ms), and logistics delivery (payload ≥ 2 kg, precision landing ≤ 50 cm)—demonstrating that no universal platform can simultaneously satisfy all domain requirements. A fuzzy-AHP weighting procedure and inter-subsystem coupling analysis are introduced to address size, weight, and power (SWaP) trade-off relationships that purely additive scoring models cannot capture. The proposed evaluation framework is intended to contribute practically to UAV standardization, certification, and quality management across the full design–procurement–operation lifecycle. Full article

Monitoring athlete readiness in youth rugby players is important for understanding short-term responses to match demands and supporting recovery management. Here, we aimed to investigate short-term readiness by integrating subjective measures, clinically relevant tests, and match load data. This single-team study considered 28 male rugby players (17.8 ± 0.2 years), monitored every four consecutive days (match day [MD], MD+1, MD+2, MD+3). Readiness was assessed using four subjective dimensions (fatigue upon waking, mood, sleep quality, and muscle soreness), which were normalized to individual best values and combined into a composite readiness index (4-dRI; 0–1). The Adductor Squeeze Test (AST), Sit-and-Reach Test (SRT), and Global Positioning System-derived metrics (MD only) were also assessed. The 4-dRI decreased by approximately 30% at 24 h post-match (p < 0.001), indicating a substantial reduction in perceived readiness. Athletes exposed to higher GPS-derived match loads reported lower readiness on MD+1. AST showed moderate associations with the 4-dRI (ρ = 0.40, p < 0.05), whereas SRT appeared less responsive to short-term changes in readiness. These findings indicate that both the 4-dRI and AST were responsive to short-term changes in readiness across the competition period. Overall, these integrated data may contribute to athlete-monitoring strategies in team sport settings. Full article

Ride-hailing global positioning system (GPS) mobile probe data provide flexible urban traffic observations, but their sparse and uneven coverage makes model evaluation difficult because observed targets, valid predictions, and historical input support do not always coincide. This study audits ultra-short-term road-segment speed forecasting under sparse mobile sensing using a mask-consistent support-chain framework. A three-day GPS dataset is aggregated into 5 min speed observations over 1970 road segments and used as a controlled sparse-sensing case study rather than a general-purpose long-term forecasting benchmark. The evaluation protocol distinguishes the full test grid, the set of directly observed target speeds, model-valid prediction support, strict complete-history support, and common-support subsets for coverage-limited baselines. The directly observed target set is used as the primary relaxed support because it retains all verifiable ground-truth targets, while strict and common-support subsets are reported as sensitivity checks. Under this support-conditioned evaluation, the adaptive graph convolutional recurrent network (AGCRN) is associated with lower mean absolute error (MAE) among full-coverage models, the historical mean (HIST_MEAN) baseline is associated with lower root mean squared error (RMSE), and congestion recall remains below 0.24 for all full-coverage deep models. These complementary results indicate conditional and metric-dependent strengths rather than universal model superiority. Because the dataset covers only three consecutive days, weekday/weekend variation, incident-specific fluctuations, seasonal effects, and spatial transferability cannot be fully examined and are treated as limitations. Overall, the findings show that evaluation support should be reported as a first-order experimental factor alongside model accuracy under sparse mobile probe sensing. Full article