Search Results (217)

Accurate drone positioning is essential for applications such as photogrammetric surveying and environmental monitoring. Among differential GNSS techniques, Real-Time Kinematic (RTK) and Post-Processed Kinematic (PPK) are widely adopted to achieve centimeter-level positioning accuracy. However, their performance is typically assessed indirectly through the quality of derived products rather than by comparison with independent ground-truth measurements under dynamic flight conditions. This study presents a direct comparison of RTK and PPK positioning for drone applications using a robotic Total Station (TS) as an independent reference. The experimental platform is based on the Versatile External Synchronization and Telemetry Architecture (VESTA), which integrates differential GNSS receivers, an inertial measurement unit, and Coordinated Universal Time (UTC)-synchronized data logging. A synchronization procedure based on the GNSS Pulse-Per-Second (PPS) signal and the controlled motion of a TS prism was developed to accurately align GNSS and TS measurements. Flight experiments show that RTK and PPK achieve comparable accuracy when the reported positioning quality is optimal. However, for the tested short-baseline datasets and the RTKLIB-EX configuration, the PPK solution remained in float or degraded states for longer following GNSS degradation, resulting in larger transient errors than the real-time u-blox solution. These findings highlight that the choice between RTK and PPK should consider not only nominal positioning accuracy but also reconvergence behavior under dynamic operating conditions. Full article

Tunnel lining defects pose significant threats to structural integrity and operational safety. Traditional image processing and machine learning methods often suffer from limited accuracy and poor generalization under complex backgrounds. To address these limitations, this study proposes a lightweight intelligent recognition method based on You Only Look Once version 11 nano (YOLOv11n) for Ground Penetrating Radar (GPR) images of tunnel linings. The backbone is replaced with Mobile Network Version 3 (MobileNetV3) to reduce parameters and Floating Point Operations (FLOPs), while depthwise separable convolution and a streamlined Compressed 2-Stage Fused-Lite (C2f-Lite) structure are integrated into the Neck to further decrease computational overhead. Channel mapping layers are employed to ensure smooth feature transfer, and selective use of Squeeze-and-Excitation (SE) attention and Hard-Swish (H-swish) activation balances detection accuracy with efficiency. Evaluated on a low-power mobile workstation acting as an edge-precursor proxy platform, experimental results demonstrate that the improved YOLOv11n_MobileNetV3 model achieves high accuracy with a mean Average Precision (mAP) at 0.5 of 94.4% and mAP@0.5:0.95 of 62.4%, low computational cost of 4.7 Giga Floating Point Operations (GFLOPs), and fast inference speed of 45 Frames Per Second (FPS). Comparative analysis further confirms its superior balance of detection performance and efficiency over YOLO version 5 (YOLOv5) and YOLO version 8 (YOLOv8) baselines. The proposed approach provides a highly optimized, edge-oriented engineering solution for real-time tunnel lining defect inspection, establishing strong structural and theoretical feasibility for future deployment in embedded systems. Full article

Precise identification and monitoring of small agricultural water bodies are essential for sustainable water resources management in arid and semi-arid regions, where even limited water losses can significantly affect agricultural productivity and local water security. However, the accurate detection of small ponds remains a major challenge in remote sensing, to address this challenge, this study proposes an integrated three-step framework that combines high-resolution remote sensing imagery, machine and deep learning techniques, and hydrological analysis to identify agricultural ponds and quantify their evaporation losses in Bastam, Iran. In the first step, a dedicated annotated dataset comprising 1061 RGB satellite images, each with a spatial size of 256 × 256 pixels and a ground resolution of 0.5 m, was developed for model training and evaluation. Using this dataset, three deep learning models BiSeNet, UNet3+, and SegNet and four traditional supervised classifiers Maximum Likelihood, Neural Network, Mahalanobis Distance, and Minimum Distance were implemented and compared for pond detection. The results demonstrated that deep learning models consistently outperformed conventional classifiers in delineating small agricultural ponds. Among all evaluated methods, BiSeNet achieved the highest segmentation performance, with an IoU of 82.08%, an F1-score of 90.15%, a precision of 91.86%, and a recall of 88.50%. Among the conventional classifiers, Maximum Likelihood combined with a 5 × 5 spatial kernel produced the best performance, achieving an IoU of 76.90%, an F1-score of 86.93%, a precision of 90.87%, and a recall of 83.32%, whereas simpler classifiers such as Minimum Distance showed only marginal improvements after kernelization. In the final step, the detected ponds were used to estimate evaporation losses through the Meyer method. The hydrological analysis revealed a clear periodic pattern in evaporation and a cumulative water loss of 388,636.7 m³ over a nine-month period, highlighting the considerable impact of evaporation on the efficiency of small agricultural water storage systems in dry environments. Based on these findings, practical mitigation strategies, including evaporation-reducing chemical surface films and floating covers, are discussed as potential options for reducing water loss. Overall, the proposed framework demonstrates the clear advantage of deep learning for the accurate identification of small agricultural ponds and provides an integrated methodological basis for monitoring water bodies and evaluating associated evaporation losses. The study offers a practical and transferable approach for supporting agricultural water management and improving water-use efficiency in arid and semi-arid regions. Full article

Improving HVAC energy efficiency while maintaining collective thermal comfort remains challenging in multi-occupant shared indoor environments, where occupants differ in thermal sensation, activity level, clothing condition, and spatial distribution. This study develops and field-validates an integrated edge-cloud IoT framework that connects non-invasive occupant-state sensing, INT8 edge thermal-sensation inference, and group-comfort-oriented HVAC setpoint optimization for classroom-based shared spaces. The proposed system integrates localized temperature–humidity sensing, vision-derived occupancy, posture, and clothing estimation, cloud-based thermal sensation model training, and edge-deployed real-time control on a HUB 8735 ULTRA device. A 4-day model-training data collection campaign with structured questionnaires was first conducted to obtain occupants’ Thermal Sensation Votes (TSVs) as ground-truth labels. The trained model was compressed from Float32 to INT8 through post-training quantization and deployed on the edge device for real-time inference. Predicted individual TSV values were then transformed into a PPD-inspired TSV-derived dissatisfaction index and used to determine the HVAC setpoint through rolling-horizon group comfort optimization. A separate eight-school-day single-blind daily-block A/B field validation was conducted, with four validation days assigned to the proposed smart control strategy and four days assigned to a fixed 25 °C baseline. The validation dataset included 2194 valid TSV questionnaire responses, which were aggregated into 116 valid 30 min classroom sessions for statistical comparison. The proposed control achieved a session-level mean TSV of −0.13, compared with −0.66 under the baseline, with Welch’s t(100) = 11.2, p < 0.001 and Cohen’s d = 2.11. Daily HVAC energy use decreased from 2.61 to 2.32 kWh/day, corresponding to a cumulative reduction of 1.16 kWh, or 11.1%, over the validation period. These results support the short-term feasibility of the proposed classroom-level human-centric HVAC control framework. However, because the validation was limited to a short-term classroom setting without full weather/load normalization, longer multi-season and multi-room studies are required to further evaluate generalizability and long-term energy performance. Full article

Low-visibility weather poses a significant threat to aviation safety and operational efficiency. Lhasa Airport is situated in a high-altitude river valley region prone to dust events. Clarifying the characteristics and dominant factors of low-visibility weather is crucial for enhancing aviation meteorological support capabilities. This study systematically analyzed the weather types, spatiotemporal distribution characteristics, and key meteorological influencing factors of low-visibility events using ground-based automated meteorological observation data, monthly summary records, and wind lidar data from Lhasa Airport from January 2020 to July 2024. The results indicate that dust weather (blowing sand and floating dust) is the primary cause of low visibility, accounting for over 90% of low-visibility days. Low-visibility events exhibit significant monthly variations, with the longest cumulative duration occurring in January and February, while being extremely rare from July to November. Under low-visibility conditions, visibility levels are predominantly concentrated in the 3–4 km range (72.5%), and most events last less than one hour. Factor analysis reveals that north, easterly, and west winds are commonly associated with dust-induced low visibility. Wind speed demonstrates a significant negative correlation with visibility and serves as the primary driving factor. Moderate relative humidity is most conducive to maintaining visibility between 3 and 5 km. Case studies further confirm that sustained strong low-level winds (≥10 m/s), high surface wind speeds (≥7.5 m/s), and intense upward motion are the key dynamic conditions for triggering and maintaining dust-related low-visibility processes. This study presents, for the first time, quantitative thresholds for key meteorological parameters and their season-dependent characteristics for dust-induced low-visibility events at Lhasa Airport, and proposes actionable forecasting indicators. These findings offer clear practical implications for aviation meteorological services at high-altitude valley airports. Full article

In recent years, there has been growing interest in applying vision–language models (VLMs) to quantitative remote sensing. This study evaluates whether three commercial VLMs (GPT-4o, GPT-5.5, and Claude Sonnet 4.6) can detect and classify the severity of harmful algal blooms (HABs) from Sentinel-2 satellite imagery of western Lake Erie and compares them against classical machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost)) trained on both a three-band red, green, blue (RGB) composite representation of the imagery and a 10-band multi-spectral reflectance representation. Forty bloom events identified from the National Oceanic and Atmospheric Administration (NOAA) Harmful Algal Bloom Operational Forecast System (HAB-OFS) severity assessments were assembled into the evaluation dataset, spanning seven bloom seasons (2019–2025). For binary bloom detection, the VLMs did not match the classical RGB classifiers; their F1 scores (0.69–0.75) fell below the best RGB classifier (Random Forest, 0.76) and below a trivial always-present baseline (F1 = 0.77), and they carried false positive rates of 73–93% on bloom-absent images, against 27–40% for the RGB classifiers. The VLMs reached high recall by labeling most scenes as bloom-positive, which makes them operationally unreliable in this configuration. For severity classification, the VLMs assigned 60–70% of their predictions to the “moderate” category regardless of actual conditions and identified at most one of the two severe blooms, whereas the classical classifiers tracked the ground-truth distribution and delivered two to nearly three times the exact-match accuracy (0.44–0.59 vs. 0.20–0.225). The strongest method across all metrics was the multi-spectral SVM (F1 = 0.833, false positive rate 27%, accuracy 0.795). Switching the same SVM from RGB to multi-spectral features raised accuracy from 0.675 to 0.795, a 12-percentage-point gain that measures the spectral information carried by red-edge and shortwave infrared bands that are accessible through multi-spectral sensors but unavailable to standard VLM vision encoders. Feature-importance analysis showed that the multi-spectral classifiers ranked chlorophyll-specific indices, the Normalized Difference Chlorophyll Index (NDCI) and the Floating Algae Index (FAI), among their top predictors, the same signatures used in established operational algorithms, while the RGB classifiers relied on red-channel variability and green-dominant pixel fractions because RGB inputs cannot compute those indices. Two compounded limitations therefore constrain off-the-shelf VLMs for aquatic remote sensing: the limited spectral information available through standard RGB channels and a mismatch between the land-dominated training distributions of these models and aquatic optical conditions. Domain-specific classifiers operating on multi-spectral data remain the more suitable tools for continued development of HAB monitoring and water-quality retrieval. Full article

Radon (²²²Rn) is a chemically inert (noble) gas, naturally occurring α-emitter radionuclide, and the direct progeny of ²²⁶Radium; it is produced in the uranium (²³⁸U) decay chain. Short-term measurements of the concentration of radon can be performed to identify locations and objects with potentially increased concentrations. The goal of this study is to present a comparison of models for the seasonal prediction of ²²²Rn active concentration variations from the results of ²²²Rn short-term measurements acquired by active instruments. Several predictive models are compared in this study, with estimation and validation datasets from ²²²Rn concentration measurements from two significant micro-locations: the St. Barbara mine and a ground-floor room of the University of Zagreb Faculty of Mining, Geology and Petroleum engineering (UNIZG-FMGPE) building, in Zagreb, Croatia. MATLAB version R2025b System identification and a Signal multiresolution analyzer were used for the estimation of predictive models and validation for mid-term prediction. This research provides one method for estimating the concentration variations from a smaller number of observations from 8 days of measurements. It shows that the best models for the estimation and prediction of radon concentration time series are the auto-regressive non-linear ARX model (NLarx), with a one-step-ahead prediction fit of up to around 90% for a minimum measurement duration of 8 days, 192 samples, and a 1 h floating mean for the estimation and ARMAX estimation from the reconstructed signal as a simple polynomial approximation of the original measurement signal, with a one-step-ahead prediction fit of almost 100%. The ARMAX model with a one-step-ahead predicted output gives excellent estimation of the MODWT and EMD reconstructed signal, which has approximately the same mean as the original signal and, thus, can be used for indirect prediction of the Rn mean. The NLarx model showed good results in the validation of Rn concentration variations; thus, this model is selected as the preferred model to predict Rn concentration variations from short-term measurements. Full article

This study examines why ice covers on the Churchill River in Labrador crack during winter and how weather, river flow, freezing conditions, and riverbed features contribute to these events. Using data from 2010 to 2025 and satellite imagery, the study shows that cracks most often occur in December to February when heavy snow, rapid flow changes, or long cold periods place stress on the ice. Cracking also frequently starts near sandbars where the ice is weaker. The results highlight that no single factor causes cracking. Instead, a combination of snow load, temperature, flow variability, and local river conditions determines when and where cracks form. There is also a disconnect from flow regulation since cracks also formed in 2012 before the construction of the dam began in 2015. A field survey was also carried out employing a combination of borehole jack (BHJ) testing and ground-penetrating radar (GPR) surveys to quantify spatial variations in ice strength and thickness across a portion of the lower Churchill River across two sandbars. In situ BHJ measurements were conducted at multiple sites to determine confined compressive ice strength under both floating and grounded conditions, revealing substantial local variability linked to differences in ice support and the presence of white versus black ice. Complementary GPR transects using 500 MHz and 1000 MHz systems provided high-resolution profiles of ice thickness and internal structure, enabling identification of transitions between grounded and floating ice. The integrated BHJ–GPR approach allowed direct comparison between point-scale strength measurements and spatially continuous thickness and grounding patterns, demonstrating that grounded ice and ice containing higher proportions of white ice exhibited more complex stress states and greater variability in mechanical response. Together, these measurements highlight the importance of combining geophysical surveying with in situ mechanical testing to better understand how environmental conditions control ice integrity and potentially influence ice-jam lodgement propensity along regulated subarctic rivers. Full article

Capacitive electrocardiography (cECG) enables non-contact heart rate monitoring through clothing, but motion artifacts remain a critical limitation for practical applications. We present a novel motion artifact removal method using non-contact floating electrodes as noise references combined with multi-reference Normalized Least Mean Squares (NLMS) adaptive filtering. The floating electrodes, positioned without skin contact, couple primarily to ambient 50 Hz mains interference, which becomes amplitude-modulated during motion due to changes in electrode–body capacitance. Six reference signals are derived from this noise electrode: band-pass-filtered signal and its derivative (capturing baseline-type artifacts), envelope and its derivative (capturing amplitude modulation patterns), and envelope asymmetry and its derivative (capturing non-linear electrode response during motion). The NLMS algorithm adaptively combines these references to estimate and remove motion artifacts while preserving QRS morphology through low-pass filtering of the correction signal. A hysteresis-based motion detector with minimum duration constraints enables selective application of artifact removal only during motion periods, leaving rest-period ECG unmodified. We present this as a proof-of-concept validation of a novel reference-electrode architecture for motion artifact suppression in non-contact ECG. The method was validated on 7 subjects across 24 recording sessions using two electrode configurations in two environments with different electromagnetic interference levels. Controlled axial rotation motion was induced at three frequencies using a custom apparatus with IMU-based gamification for protocol adherence. Performance was evaluated using R-peak detection F1 score against gel surface-contact electrodes ground truth and RMS reduction in motion regions. Results demonstrate consistent improvement in R-peak detection accuracy during motion periods with substantial artifact energy reduction. The proposed method is designed to address motion artifacts regardless of their physical source, though the present validation focused on subject-induced motion. Full article

In this study we describe the refinement process from the first to the second phase of a teaching–learning sequence development and implementation. The TLS comprises several experimental activities that aim to support understanding of Control of Variables Strategy (CVS) reasoning in the context of floating/sinking and properties of magnets. The research was carried out during a science laboratory course in a department of early childhood education. The participants numbered 67 in the first phase of the survey and 45 pre-service early childhood teachers (referred to as student teachers) in the second phase. The analysis is theoretically grounded in Pickering’s model of scientific practice, as adapted in science education, which provides the analytical framework for identifying and categorizing refinement changes. The results showed that the refinements are differentiated from each other according to the factors that guide them. Specifically, the three refinement changes guided by the educational factor were local-guided, i.e., related to a specific activity dealing with the student teachers’ educational needs, and the other two, also driven by the scientific factor, were holistic-open refinements, i.e., related to a set of activities adjusting the TLS to the new scientific trends. These findings contribute to the literature on Teaching-Learning Sequence development by illustrating how theoretically grounded analysis can make refinement processes more explicit and analytically interpretable. Full article

Resource-constrained wearable systems often need to be able to execute signal processing and AI workloads. There are many trade-offs to consider for this type of application. This paper presents a lightweight convolution-aware soft processor for embedded signal-processing on resource-constrained wearable devices. This architecture represents a middle ground for signal-processing applications between dedicated accelerators and lightweight soft processors. The proposed architecture integrates a two-lane SIMD integer datapath with a split-stage IEEE-754 floating-point accumulation pipeline. The split-stage design enables overlap between multiplication, accumulation, and operand fetch, improving arithmetic utilization while maintaining low resource costs. The processor was implemented on the Artix-7-based Basys3 platform and evaluated using one-dimensional convolution workloads. The experimental results demonstrate a $6\times$ speedup over MicroBlaze-class soft processors while maintaining the same static power usage (0.073 W), and only requiring 44% higher dynamic power consumption. The architecture achieves this with significantly fewer FPGA resources than accelerator-based solutions such as DPU overlays. The proposed architecture provides a practical alternative for wearable and resource-constrained FPGA systems requiring deterministic convolution performance, demonstrating a balanced design point for embedded wearable platforms where software-defined flexibility and convolution acceleration are both required. Full article

Can artificial intelligence discover, from raw experience and without human supervision, concepts that humans have discovered? One challenge is that human concepts themselves are fluid: conceptual boundaries can shift, split, and merge as inquiry progresses (e.g., Pluto is no longer considered a planet). To make progress, we need a definition of “concept” that is not merely a dictionary label, but a structure that can be revised, compared, and aligned across agents. We propose an algorithmic information viewpoint that treats a concept as an information object defined only through its structural relation to an agent’s total experience. The core constraint is determination: a set of parts forms a reversible consistency relation if any missing part is recoverable from the others (up to the standard logarithmic slack in Kolmogorov complexity). This reversibility prevents “concepts” from floating free of experience and turns concept existence into a checkable structural claim. To judge whether a decomposition is natural, we define excess information, measuring the redundancy overhead introduced by splitting experience into multiple separately described parts. On top of these definitions, we formulate dialectics as an optimization dynamics: as new patches of information appear (or become contested), competing concepts bid to explain them via shorter conditional descriptions, driving systematic expansion, contraction, splitting, and merging. Finally, we formalize low-cost concept transmission and multi-agent alignment using small grounds that allow another agent to reconstruct the same concept under a shared protocol, making communication a concrete compute-bits trade-off. Full article

As human space missions become longer and more autonomous, robots are expected to assume broader responsibilities in inspection, maintenance, logistics, scientific support, and crew assistance. Among available robot forms, humanoid robotic astronauts are especially relevant because their anthropomorphic embodiment is compatible with human-centered habitats, tools, interfaces, and procedures. Their deployment in orbital and planetary environments, however, introduces challenges that differ from those of terrestrial humanoids, including floating-base dynamics, intermittent contact, whole-body coordination, constrained perception, and delayed supervision. This review contributes a mission-oriented and astronaut-centered synthesis of humanoid robotic astronauts, distinguishing itself from platform-by-platform or morphology-only surveys. It treats these systems as mission-compatible embodied agents whose feasibility depends on the coupling among mission context, morphology, contact behavior, perception, autonomy, and validation evidence. The primary goals are threefold: to classify representative platforms according to mission context, to synthesize the core technical foundations required for mission-compatible operation, and to identify cross-cutting deployment bottlenecks and benchmarking priorities for future development. Representative systems are organized into intravehicular assistance, extravehicular operations and on-orbit servicing, and surface exploration or transitional scenarios, showing how mission demands shape embodiment, mobility, manipulation, autonomy, and validation strategies. This review further summarizes recent progress in microgravity dynamics and contact mechanics, multimodal perception and scene understanding, whole-body motion planning and control, teleoperation and supervised autonomy, and evaluation and benchmarking methods. The analysis indicates that humanoid robotic astronauts are not simple extensions of terrestrial humanoids but astronaut-oriented embodied systems for mission-constrained environments. Three priorities are identified for future development: contact-rich whole-body intelligence under support transitions, delay-tolerant supervised autonomy with explicit authority handoff, and systematic benchmarking pipelines that connect simulation, ground analogs, short-duration microgravity tests, human-in-the-loop trials, and mission-context demonstrations. Full article

This article presents a low ripple, high voltage-conversion-ratio (VCR = 6), two-stage step-up converter intended for power-burst applications. The first boost stage raises the battery voltage to a maximum of 35 V, while the subsequent low dropout regulator (LDO) stage suppresses the ripple of the final output. Unlike conventional structures in which control circuits operate above a ground-referenced rail, the proposed shunt-current-reusing technique places most of the control circuits within a narrow floating dropout region (V_DROP) between the boost output (V_BST) and the LDO output (V_OUT), thereby achieving nearly 100% current efficiency through current recycling. Adaptive adjustment of V_DROP (0.5 V at light load and 0.65 V at heavy load) balances output ripple against the loss of the LDO stage. Consequently, the proposed converter achieves both high efficiency (>85%) and low ripple (<2 mV) over a load range from 200 μA to 100 mA, with a peak efficiency of 90.4% at a 20 mA load. Hysteretic control of the boost stage combined with the high bandwidth (BW = 1.2 MHz) of the LDO stage yields a fast transient response (<20 μs). The proposed techniques address the requirements of applications that demand high intermittent power bursts (>1 W) at high supply voltage (>20 V) while maintaining low quiescent current consumption under most load conditions (<10 mA), as exemplified by light detection and ranging (LiDAR), haptic sensors, and micro electromechanical system (MEMS) drivers. Full article

This research explores the impact of ownership structure on the financial performance of Initial Public Offerings (IPOs) in North African markets, a key emerging region that remains insufficiently examined in the academic literature. Drawing on agency theory, signalling theory, and liquidity theory, the study investigates how different shareholder configurations—particularly managerial shareholding, ownership concentration, institutional investor presence, and float—influence both initial underpricing and long-run market performance. Based on a sample of 228 IPO transactions conducted between 2005 and 2023 across six countries (Morocco, Egypt, Tunisia, Algeria, Libya, and Mauritania), the research adopts a quantitative methodology grounded in a hypothetico-deductive approach. The findings support the signalling theory premise that managerial retention constitutes a credible quality signal, showing a strong positive relationship between post-IPO managerial shareholding (MOWN) and long-run performance measured by the 36-month Buy-and-Hold Abnormal Return (BHAR). Ownership concentration (CONC) reduces underpricing while improving long-term performance, reflecting stronger governance discipline. Institutional investor presence (INST) exerts a significant direct effect on both performance dimensions. Conversely, firm size shows no direct significant effect, a result consistent with the institutional specificities of North African markets. These findings underscore the complex mechanisms behind IPO success in this context and offer practical and theoretical implications regarding governance practices and institutional frameworks. The study also outlines avenues for future research, including a deeper examination of regional governance dynamics. Full article