Search Results (897)

This study presents the design and implementation of a full-stack IoT ecosystem based on ESP32 microcontrollers and web-based visualization dashboards to support scientific reasoning in first-year engineering students. The proposed architecture integrates a four-layer model—perception, network, service, and application—enabling students to deploy real-time environmental monitoring systems for agriculture and beekeeping. Through a sixteen-week Project-Based Learning (PBL) intervention with 91 participants, we evaluated how this technological stack influences technical proficiency. Results indicate that the transition from local code execution to cloud-based telemetry increased perceived learning confidence from $\mu =3.9$ (Challenge phase) to $\mu =4.6$ (Reflection phase) on a 5-point scale. Furthermore, 96% of students identified the visualization dashboards as essential Human–Computer Interfaces (HCI) for debugging, effectively bridging the gap between raw sensor data and evidence-based argumentation. These findings demonstrate that integrating open-source IoT architectures provides a scalable mechanism to cultivate data literacy in early engineering education. Full article

To address the low efficiency and stress-inducing nature of traditional manual weighing for dairy cows, this study proposes a semantically guided 3D reconstruction and body weight estimation method for dairy cows. First, a dual-viewpoint Kinect V2 camera synchronous acquisition system captures top-view and side-view point cloud data from 150 calves and 150 lactating cows. Subsequently, the CSS-PointNet++ network model was designed. Building upon PointNet++, it incorporates Convolutional Block Attention Module (CBAM) and Attention-Weighted Hybrid Pooling Module (AHPM) to achieve precise semantic segmentation of the torso and limbs in the side-view point cloud. Based on this, point cloud registration algorithms were applied to align the dual-view point clouds. Missing parts were mirrored and completed using semantic information to achieve 3D reconstruction. Finally, a body weight estimation model was established based on volume and surface area through surface reconstruction. Experiments demonstrate that CSS-PointNet++ achieves an Overall Accuracy (OA) of 98.35% and a mean Intersection over Union (mIoU) of 95.61% in semantic segmentation tasks, representing improvements of 2.2% and 4.65% over PointNet++, respectively. In the weight estimation phase, the BP neural network (BPNN) delivers optimal performance: For the calf group, the Mean Absolute Error (MAE) was 1.8409 kg, Root Mean Square Error (RMSE) was 2.4895 kg, Mean Relative Error (MRE) was 1.49%, and Coefficient of Determination (R²) was 0.9204; for the lactating cows group, MAE was 12.5784 kg, RMSE was 14.4537 kg, MRE was 1.75%, and R² was 0.8628. This method enables 3D reconstruction and body weight estimation of cows during walking, providing an efficient and precise body weight monitoring solution for precision farming. Full article

Reality-capture has made point clouds a primary spatial data source, yet processing and integration limits hinder their potential. Prior reviews focus on isolated phases; by contrast, Smart Point Clouds (SPCs)—augmenting points with semantics, relations, and query interfaces to enable reasoning—received limited attention. This systematic review synthesizes the state-of-the-art SPC terminology and methods to propose a modular pipeline. Following PRISMA, we searched Scopus, Web of Science, and Google Scholar up to June 2025. We included English-language studies in geomatics and engineering presenting novel SPC methods. Fifty-eight publications met eligibility criteria: Direct (n = 22), Indirect (n = 22), and New Use (n = 14). We formalize an operative SPC definition—queryable, ontology-linked, provenance-aware—and map contributions across traditional point cloud processing stages (from acquisition to modeling). Evidence shows practical value in cultural heritage, urban planning, and AEC/FM via semantic queries, rule checks, and auditable updates. Comparative qualitative analysis reveals cross-study trends: higher and more uniform density stabilizes features but increases computation, and hybrid neuro-symbolic classification improves long-tail consistency; however, methodological heterogeneity precluded quantitative synthesis. We distill a configurable eight-module pipeline and identify open challenges in data at scale, domain transfer, temporal (4D) updates, surface exports, query usability, and sensor fusion. Finally, we recommend lightweight reporting standards to improve discoverability and reuse. Full article

The Fengyun-4 microwave satellite provides high-temporal-frequency observations at the 183 GHz band, providing unprecedented data for all-weather, three-dimensional measurements of atmospheric parameters. It is of importance to establish a simulated brightness temperature (BT) dataset for this band prior to launch, which can support the relevant quantitative applications significantly. Compared with clear-sky conditions, the accuracy of BT simulations under cloudy ones is considerably lower, primarily due to the influence of the adopted ice particle models. Up until now, few studies have systematically investigated ice particle model selection for different cloud types at the 183 GHz frequency band. In this paper, multi-sensor observations from Cloud Profiling Radar (CPR), Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), and Visible Infrared Imaging Radiometer Suite (VIIRS) were used as realistic atmospheric profiles. Using the high-precision radiative transfer model Atmospheric Radiative Transfer Simulator (ARTS), BT simulations at 183 GHz were performed to explore the optimal ice particle models for seven classical cloud types. The main conclusions are given as follows: (1) The sensitivity of simulated cloud radiances to ice particle habits differs with respect to different cloud phases. For altocumulus (Ac), stratocumulus (Sc), and cumulus (Cu) clouds, the different choices of ice particle model have little impacts on the simulated brightness temperatures (<1 K), with RMSEs below 3 K across multiple models, indicating that various models can be applied directly for such simulations. (2) For some mixed-phase clouds, including altostratus (As), nimbostratus (Ns), and deep convective (Dc) clouds, the Small Block Aggregate (SBA) and Small Plate Aggregate (SPA) models demonstrate good performance for As clouds, with RMSEs below 2.5 K, while the SBA, SPA, and Large Column Aggregate (LCA) models exhibit similarly good performance for Ns clouds, also achieving RMSEs below 2.5 K. For Dc clouds, although the SBA model yields RMSEs of approximately 10 K, it still provides a substantial improvement over the spherical model, whereas for cirrus (Ci) clouds, any non-spherical ice particle models are applicable, with RMSEs below 2 K. (3) Within the 183 GHz frequency band, channels with the higher weighting-function peaks are less sensitive to variable adoptions of ice particle models. These results offer valuable references for accurate radiative transfer simulations on 183 GHz frequency. Full article

Supercooled liquid water (SLW) in mixed-phase clouds significantly influences precipitation efficiency and aviation safety. However, a comprehensive understanding of its vertical structure has been hampered by a lack of sustained, vertically resolved observations over the North China Plain. This study presents the first systematic analysis of SLW vertical distribution and microphysics in this region, utilizing a year-long dataset (2022) from synergistic ground-based instruments in Beijing. Our retrieval approach integrates Ka-band cloud radar, microwave radiometer, ceilometer, and radiosonde data, combining fuzzy-logic phase classification with a liquid water content inversion constrained by column liquid water path. Key findings reveal a distinct bimodal seasonality: SLW primarily occurs at mid-to-upper levels (4–7.5 km) during spring and summer, driven by convective lofting, while winter SLW is confined to lower altitudes (1–2 km) under stable atmospheric conditions. The temperature-dependent occurrence probability of SLW clouds has an annual maximum at −12 °C. The diurnal variation in SLW in summer shows peaks in the afternoon and at night, corresponding to convective cloud activity. Spring, autumn, and winter do not exhibit strong diurnal variations. Retrieved microphysical properties, including liquid water content and droplet effective radius, are consistent with in situ aircraft measurements, validating our methodology. This analysis provides a critical observational benchmark and offers actionable insights for improving cloud microphysics parameterizations in models and optimizing weather modification strategies, such as seeding altitude and timing, in this water-stressed region. Full article

Sporadic-E (Es) layers can strongly perturb HF/VHF propagation and create intermittent interference, motivating higher-revisit monitoring at the frequencies most affected. EsTRACE (Es-layer TRAnsient Cloud Explorer) is a PlanarSat mission concept that transmits sequential beacons in the 28/50 MHz amateur bands using FT4 (weak-signal digital) and CW (continuous wave) waveforms and leverages distributed amateur receiver networks for near-real-time SNR mapping. This paper documents the early-phase spacecraft design from the Bid/proposal phase (Bid), through the Conceptual Design Review (CoDR), to the Preliminary Design Review (PDR), using a power-first sizing loop that couples link-budget closure to duty cycle and solar-array area under a free-tumbling, batteryless constraint. The analysis supports conceptual feasibility of the architecture under stated antenna and ground-segment assumptions; on-orbit demonstration and measured RF/antenna characterization are identified as required future validation steps. Full article

In cloud-based distributed systems, trace anomaly detection plays a vital role in maintaining system reliability by identifying early signs of performance degradation or faults. However, existing methods often fail to capture the complex temporal and structural dependencies inherent in trace data. To address this, we propose a novel dual-branch Transformer-based framework that integrates both temporal modeling and causal reasoning. The first branch encodes the original trace data to capture direct service-level dynamics, while the second employs phase-space reconstruction to reveal nonlinear temporal interactions by embedding time-delayed representations. To better capture how anomalies propagate across services, we introduce a causal propagation module that leverages directed service call graphs to enforce the time order and directionality during feature aggregation, ensuring anomaly signals propagate along realistic causal paths. Additionally, we propose a hybrid loss function combining the reconstruction error with symmetric Kullback–Leibler divergence between attention maps from the two branches, enabling the model to distinguish normal and anomalous patterns more effectively. Extensive experiments conducted on multiple real-world trace datasets demonstrate that our method consistently outperforms state-of-the-art baselines in terms of precision, recall, and F1 score. The proposed framework proves robust across diverse scenarios, offering improved detection accuracy, and robustness to noisy or complex service dependencies. Full article

Persistent cloud cover during the growing season and mosaic cropping patterns introduce temporal gaps and mixed pixels, undermining the reliability of large-scale crop identification and acreage statistics. To address these issues, we develop a high spatiotemporal-resolution remote-sensing approach tailored to heterogeneous farmlands. First, an improved Spatiotemporal Adaptive Reflectance Fusion Model (STARFM) is used to fuse Landsat, Sentinel-2, and MODIS observations, reconstructing a continuous Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial and 8-day temporal resolution. Second, at the field scale, we derive phenological descriptors from the reconstructed series—key phenophase timing, amplitude, temporal trend, and growth rate—and use a Random Forest (RF) classifier for detailed crop discrimination. We further integrate SHapley Additive exPlanations (SHAP) to quantify each feature’s class-discriminative contribution and signed effect, thereby guiding feature-set optimization and threshold refinement. Finally, we generate a 2024 crop distribution map and conduct comparative evaluations. Relative to baselines without fusion or without phenological variables, the fused series mitigates single-sensor limitations under frequent cloud/rain and irregular acquisitions, enhances NDVI continuity and robustness, and reveals inter-crop temporal phase shifts that, when jointly exploited, reduce early-season confusion and improve identification accuracy. Independent validation yields an overall accuracy (OA) of 90.78% and a Cohen’s kappa(κ) coefficient of 0.882. Coupling dense NDVI reconstruction with phenology-aware constraints and SHAP-based interpretability demonstrably improves the accuracy and reliability of cropping-structure extraction in complex agricultural regions and provides a reusable pathway for regional-scale precision agricultural monitoring. Full article

The integration of edge and cloud computing is critical for resource-intensive applications which require low-latency communication, high reliability, and efficient resource utilisation. The service placement problem in these environments poses significant challenges owing to dynamic network conditions, heterogeneous resource availability, and the necessity for real-time decision-making. Because determining an optimal service placement in such networks is an NP-complete problem, the existing solutions rely on fast but suboptimal heuristics or computationally intensive metaheuristics. Neither approach meets the real-time demands of online scenarios, owing to its inefficiency or high computational overhead. In this study, we propose a lightweight learning-based approach for the online placement of services with multi-version components in edge-to-cloud computing. The proposed approach utilises a Shallow Neural Network (SNN) with both weight and power coefficients optimised using a Genetic Algorithm (GA). The use of an SNN ensures low computational overhead during the training phase and almost instant inference when deployed, making it well suited for real-time and online service placement in edge-to-cloud environments where rapid decision-making is crucial. The proposed method (SNN-GA) is specifically evaluated in AR/VR-based remote repair and maintenance scenarios, developed in collaboration with our industrial partner, and demonstrated robust performance and scalability across a wide range of problem sizes. The experimental results show that SNN-GA reduces the service response time by up to 27% compared to metaheuristics and 55% compared to heuristics at larger scales. It also achieves over 95% platform reliability, outperforming heuristics (which remain below 85%) and metaheuristics (which decrease to 90% at larger scales). Full article

With the upgrading of geostationary meteorological satellites, their capabilities in Convective Initiation (CI) identification have been enhanced. To improve the applicability of the ARGI-based CI algorithm in central and eastern China, this study uses Fengyun-4A data, integrates radar and precipitation data to construct a True_CI dataset, and defines False_CI events (satellite-identified events without radar or precipitation signals) for comparative analysis. The results show that True_CI events tend to have longer durations, larger cloud cluster areas, and lower central cloud-top brightness temperature (BT) during development. They exhibit distinct features such as reduced differences between water vapor and infrared channels, increased cloud optical thickness, and ice-phase transformation 30 min before CI occurrence—features absent in most False_CI events. Based on these comparative findings, a new satellite-based CI definition is proposed with a set of reference thresholds, which should be adjusted for different latitudes and seasons. The evaluation of the Defined_CI events (defined using the CI definition) via True_CI events indicates that the CI definition on satellite cloud imagery proposed in this study is reliable, and suggests that further research on the pre-CI environmental conditions of weak convection is needed. Supported by hyperspectral data or numerical model products, such research will help clarify which cloud clusters are prone to developing into convective weather. Full article

Industry 5.0 involves the integration of advanced technologies, collaboration between humans and intelligent machines, resilience and sustainability, all of which are essential for the advancement of the food services industry. This analysis reviews the scientific literature on Industries 4.0 and 5.0 technologies, whether experimental or implemented, focused on producing large meals in food service. The review has been conducted through a systematic search, covering aspects from consumer ordering and the cooking process to distribution while considering management, quality control, and sustainability. A total of thirty-one articles, published between 2006 and 2025, were selected, with the majority focusing on Industry 5.0 (71%) and a significant proportion on testing phases (77.4%). In the context of Food Service Perspectives, the emphasis has been placed on customer service (32.3%), highlighting the use of Artificial Intelligence (AI)-powered robots for serving customers and AI for service personalization. Sustainability has also received attention (29%), focusing on AI and machine learning (ML) applications aimed at waste reduction. In management (22.6%), AI has been applied to optimize production schedules, enhance menu engineering, and improve overall management. Big Data (BD) and ML were utilized for sales analysis, while Blockchain technology was employed for traceability. Cooking innovations (9.7%) centered on automation, particularly the use of collaborative robots (cobots). For Quality Control (6.4%), AI, along with the Internet of Things (IoT) and Cloud Computing, has been used to monitor the physical aspects of food. The study underscores the importance of strategic investments in technology to optimize processes and resources, personalize services, and ensure food quality, thereby promoting balance and sustainability. Full article

The spatial characteristics of the dry beach in tailings ponds are critical indicators for the safety assessment of tailings dams. This study presents a method for dry beach extraction that combines deep learning-based semantic segmentation with 3D reconstruction, overcoming the limitations of 2D methods in spatial analysis. The workflow includes four steps: (1) High-resolution 3D point clouds are reconstructed from UAV images, and the projection matrix of each image is derived to link 2D pixels with 3D points. (2) AlexNet and GoogLeNet are employed to extract image features and automatically select images containing the dry beach boundary. (3) A DeepLabv3+ network is trained on manually labeled samples to perform semantic segmentation of the dry beach, with a lightweight incremental training strategy for enhanced adaptability. (4) Boundary pixels are detected and back-projected into 3D space to generate consistent point cloud boundaries. The method was validated on two-phase UAV datasets from a tailings pond in Yunnan Province, China. In phase I, the model achieved high segmentation performance, with a mean Accuracy and IoU of approximately 0.95 and a BF of 0.8267. When applied to phase II without retraining, the model maintained stable performance on dam boundaries, while slight performance degradation was observed on hillside and water boundaries. The 3D back-projection converted 2D boundary pixels into 3D coordinates, enabling the extraction of dry beach point clouds and supporting reliable dry beach length monitoring and deposition morphology analysis. Full article

The sudden turn of tropical cyclones (TCs) can rapidly alter the affected disaster-prone regions and associated rainfall distributions, posing severe threats to coastal areas and creating major challenges for operational forecasting. However, most of these events occur over the open ocean, where the scarcity of in situ observations limits our understanding of how precipitation and cloud microphysical processes evolve during the sudden turning. In this study, we analyzed the precipitation evolution and associated microphysical characteristics during the sudden turn of Super Typhoon Vongfong (2014) using the latest GPM satellite observations. The main findings are as follows: (1) During the sudden-turning period, the precipitation coverage expanded significantly. Strong convective precipitation was distributed from the inner eyewall to the outer eyewall and spiral rainbands and weakened in intensity, whereas stratiform precipitation broadened in coverage and intensified. (2) The increase in stratiform precipitation was attributed primarily to increased cloud water content, which strengthened collision–coalescence processes, promoted the formation of larger and more numerous raindrops, and consequently increased precipitation efficiency and intensity. (3) The weakening of convective precipitation was related to the reduction in eyewall updrafts, which suppressed ice-phase processes and limited the development of deep convection. Full article

Stratocumulus clouds are cloud systems composed of stratiform clouds with embedded convective clouds, possessing strong catalytic potential and serving as key target cloud systems for weather modification operations. In this study, the parameterization of ice nucleation for silver iodide (AgI) particles was applied to the Thompson microphysics scheme in the WRF model. Numerical experiments were designed for a stratocumulus cloud that occurred over the Hulunbuir region, northeastern China, on 31 May 2021, to investigate how the structure and evolution of cloud macro- and microphysical properties and precipitation formation respond to glaciogenic seeding. The simulation results indicate that AgI nucleation increased ice concentrations at 4–5 km altitude, enhancing ice crystal formation through condensation–freezing and deposition nucleation and the growth of ice particles through auto-conversion and riming, leading to increased precipitation. The results also show that owing to the non-uniform distribution of supercooled water within this stratocumulus cloud system, the consumption of AgI and the enhanced ice nucleation release latent heat more strongly in regions with higher supercooled water content. This leads to more pronounced isolated updrafts, altering the structure of shear lines and subsequently influencing regional precipitation distribution after silver iodide seeding concludes. These findings reveal that seeding influences both the microphysical and dynamic structures within clouds and highlight the non-uniform seeding effects within cloud systems. This study contributes to a deeper understanding of the effects of artificial seeding on stratocumulus clouds in high-latitude regions and holds significant reference value for artificial weather modification efforts in mixed-phase stratiform clouds. Full article

For large curved parts with complex surfaces, which often exhibit both symmetry and asymmetry in their geometric features, the multi-view combined with the phase shift method and highlight regions treatment method has been proposed and applied to the online measurement system. The hardware components of the measuring system include a self-designed multi-vision platform and a multi-view three-dimensional measurement platform composed of rotating platform, robot and linear guide rail. The overall calibration of the system was conducted to guarantee the effectiveness of the measurement point cloud splicing of each viewing angle. And the system integrates the three-dimensional measurement technology of multi vision combined with the phase shift method and online measure system to realize full coverage and high-precision measurement of the impeller—addressing both its inherent symmetry (regular blade arrangement) and local asymmetry (irregular edge details)—and controls the relative error of the measured size and the actual size within 1%. In addition, the highlight regions treatment method has also been proposed. By adjusting the camera’s exposure time to change the light intensity of the captured images, images under different exposures and their valid pixels are obtained, thereby facilitating the synthesis of a composite image free of highlight phenomena. Experimental results demonstrate that the proposed method can achieve full-coverage measurement of the measured object and effective measurement of highlight regions. Full article