Search Results (1,515)

In confined waters such as cylindrical aquaculture cages and ponds, underwater robots for cleaning, harvesting and other tasks that use single-beam mechanical scanning sonar for positioning and navigation are susceptible to multipath interference in complex water environments. Meanwhile, under extreme sea conditions, the severe attitude swaying of the robot and the slow-scanning characteristic of the sonar superimpose on each other, causing range stretching and helical deformation of the acoustic point cloud. To address these problems, this paper analyzes the deformation mechanism of single-beam sonar and proposes a spatiotemporal joint deformation compensation and localization-mapping method. First, an attitude-derived probabilistic confidence model is introduced as a lightweight robustness safeguard to characterize the geometric reliability of sonar echoes and reduce the contribution of low-confidence measurements during subsequent registration. Second, a beam-level spatiotemporal joint de-deformation algorithm is designed: the slant range in polar coordinates is flattened to eliminate nonlinear swaying deformation, and a beam-level displacement back-estimation based on the beam time offset and feedback velocity is employed to remove helical misalignment, thereby enhancing the underlying correction capability for dynamic deformation processes. Finally, a lightweight SLAM architecture that integrates keyframe-based dynamic sub-maps is constructed, where a confidence-weighted ICP is used to estimate the planar position with the heading provided by the compass and provide velocity-based closed-loop feedback, effectively mitigating the problem of global matching divergence caused by underlying dynamic deformations. Real-data-driven semi-physical disturbance tests based on measured pool data show that, under the injected ±45° roll disturbance and translational drift, the proposed method reduces the maximum point-to-reference error MaxAE from 1.059 m to 0.098 m. The reported mean internal registration residual decreases from 0.275 m for the traditional navigation odometry SLAM to 0.158 m for the proposed method, corresponding to a numerical reduction of approximately 42.5%. Under the evaluated conditions, the proposed method effectively mitigates point-cloud deformation and registration instability caused by robot swaying and slow-scanning sonar, while confidence weighting is retained as an auxiliary robustness mechanism for handling low-confidence correspondences. Full article

Anomaly detection in vehicular networks focuses on cybersecurity, leaving physical traffic-flow anomalies at urban intersections underserved by IoT sensing. This paper benchmarks anomaly detection on five days of hourly vehicle counts from four consecutive signalized intersections in Kuwait City, with a vehicle-to-infrastructure (V2I) latency feasibility analysis. Anomalies are synthetically injected because verified incident labels are unavailable; scores reflect detectability under the injection protocol rather than validated incident detection. Across ten injection seeds, CUSUM is the most accurate (mean F1 0.945, perfect precision on every seed), Isolation Forest attains the highest recall (0.955), and the LSTM-AE reaches F1 0.347; on misaligned anomaly classes, the margin narrows, and the LSTM-AE matches CUSUM on gradual drift. A corridor rule localizes detected corridor anomalies (9/9, conditional on detection). Hourly aggregation alone imposes an expected 1800 s detection delay, over 130 times the 13.5 s V2I budget at 80 km/h. A sub-second Tier-1 edge detector, evaluated in traffic-calibrated simulation, detects surges within budget (median 6.8 to 9.1 s, robust to signal-cycle platooning), whereas flow-cutoff detection requires roughly 21 s and overnight hours remain a blind spot. Results support a two-tier edge-cloud design and provide, to our knowledge, the first such benchmark on real Gulf-region corridor count data. Full article

The general circulation models are used for climate predictions and weather forecasting, resolving governing prognostic equations at resolutions at which the convection is typically unresolved. As convection is responsible for important feedback in the climate system and is associated with severe weather events, the climate and weather models must estimate the convection as a function of the resolved mean state of the atmosphere. This procedure is called convective parameterization, and the specific approach in which this task is achieved depends on the specific parameterization scheme employed by the general circulation model. However, almost all of the modern parameterization schemes are based on the same theoretical framework. Although state-of-the-art parameterization schemes still struggle to model convective transport and fractions of convective clouds accurately, limited attempts to change the fundamental theoretical bases have been made in recent years. The aim of this article is to discuss the theoretical bases under which the plume model is introduced in the mass-flux convective parameterization schemes and how these bases have changed in recent years as an attempt to change some of the current problems of the mass-flux parameterizations. This review examines the evolution of the plume model underlying mass–flux convection parameterizations. The discussion focuses on five key topics: (i) the interpretation of convective plumes as representations of cloud ensembles, (ii) entrainment and detrainment assumptions, (iii) prognostic formulations and convective memory, (iv) unified formulations for shallow and deep convection, and (v) challenges associated with convection in the gray zone. The review argues that many recent developments can be interpreted as relaxations of assumptions originally associated with the steady-state plume framework, and discusses the implications of these relaxations for the physical interpretation of mass–flux parameterizations. Full article

As crucial weather-scale systems widely affecting the global marine-atmospheric boundary layer, cyclones exert a regulatory effect on aerosols in the marine boundary layer through interrelated physical and chemical processes, including dynamic uplift, strong wind forcing, precipitation scavenging, and cloud microphysical interactions. Following an overview of aerosol properties in the marine boundary layer and synoptic cyclone characteristics, this paper reviews the full-process regulation mechanisms and mutual feedback effects of tropical and extratropical cyclones on aerosol generation, long-range transport, and removal, integrating the latest advances in observational, numerical, and theoretical studies. Cyclone-driven aerosol generation has two key pathways: mechanical fragmentation of sea surfaces in cyclones’ strong wind cores, emitting sea salt aerosols of varying particle sizes, and cyclone-induced disturbances triggering photochemical and heterogeneous reactions that accelerate secondary aerosol formation. Cyclone movement, with strong advection and updrafts, enables cross-ocean long-distance transport and upper troposphere injection of aerosols in the marine boundary layer, altering their global distribution. Wet deposition (rainout and washout) is the dominant removal mechanism, eliminating aerosols and mediating the cyclone–aerosol–cloud feedback loop, where aerosols as cloud condensation nuclei or ice nuclei regulate cyclone intensity, precipitation, and cloud cover. Current challenges (e.g., emission quantification uncertainties, incomplete microphysical understanding, model limitations) and prospects (e.g., enhanced long-term observations, improved model parameterization) are discussed. This review provides a scientific basis for aerosol-climate effect studies under extreme weather and references for related fields. Full article

Accurately forecasting waypoint-level tropical cyclone (TC) intensity, defined as the local wind speed at specific maritime route waypoints under TC influence, is crucial for navigation safety and voyage planning. Conventional studies mainly focus on the central intensity of TC systems and underutilize the complementary value of multimodal meteorological data with inconsistent sampling intervals. To address these challenges, this study proposes a multimodal-augmented conditional diffusion model (MADiff) for waypoint-level TC intensity prediction. To exploit the potential of multimodal inputs, we first design a temporal-adaptive dynamic convolution module (TDConv) to capture multi-timescale features, mitigating multimodal sampling discrepancies without rigid temporal alignment. Second, we develop a discriminative cross-fusion module (DisCF) to aggregate multi-timescale features across diverse modalities, quantifying multimodal heterogeneity and integrating valuable modality-specific features while suppressing noise interference. Fused features are fed into a diffusion model with physics-informed regularization to generate final intensity forecasts. Extensive experiments on four Western North Pacific datasets show that MADiff achieves average MAE and RMSE values of 2.08 kt and 2.37 kt, respectively, for 12 h intensity forecasting. Compared with the state-of-the-art baseline (TC-Clouds-DP), MADiff yields substantial performance improvements, reducing MAE by 16.3% and RMSE by 10.6% on average. This study provides an effective framework for fine-grained TC intensity forecasting, offering valuable insights for extreme marine weather early warning and intelligent navigation decision-making. Full article

The transition to a new industrial paradigm beyond Industry 5.0 demands hyperconnectivity and massive processing, creating a paradox where high computational demand can threaten corporate sustainability goals (ESG). This study aims to structure and prioritize a Multilayer Framework of multicloud technologies to balance disruptive advances, lean optimization, and decarbonization. A mixed, sequential, and exploratory-normative approach was used. Initially, the literature was triangulated with expert panels and suppliers to map 21 technological functionalities, structuring them into four layers of a bidirectional value flow. Then, a hybrid multi-criteria modeling (AHP-TOPSIS) was applied to rank these technologies against five market constraints. Calibration with AHP revealed that Cyber Resilience, approximately 38%, and Process Optimization, approximately 27%, lead executive priorities, surpassing environmental impact or cost efficiency. As a result, the TOPSIS ranking highlighted Human–Machine Symbiosis (BCI/neuroergonomic readiness), Zero Trust architecture, Federated Learning, and GenAI KPI Analytics as the leading functionalities in their respective layers, with Hyper-BPM emerging as a closely associated optimization engine at the governance layer. Finally, the proposed roadmap was assessed using an anonymized industrial Proof of Concept (PoC) in a brownfield advanced manufacturing facility, providing evidence of its operational feasibility for integrating lean optimization with legacy systems and ESG-oriented monitoring. It is concluded that industrial sustainability does not rely solely on green technologies, but on decentralized orchestration along the Edge-Cloud continuum. Environmental gains are therefore more likely to emerge when cyber governance and lean-oriented operational management jointly support decentralized multicloud orchestration. Full article

Geothermal energy and underground thermal storage (UTES) are vital to the low-carbon energy transition, yet their optimization is bottlenecked by multi-scale heterogeneity, coupled thermal–hydraulic–mechanical–chemical (THMC) processes, and the high computational cost of full-physics simulations. This review systematically evaluates machine learning (ML) as a foundational paradigm for overcoming these computational and scale-bridging challenges. We categorize current advances into three key functional roles. First, data-driven upscaling directly maps pore-scale features to macro-scale effective properties, replacing traditional empirical homogenization. Second, deep surrogate models mimic high-fidelity THMC simulations at a fraction of the computational cost, enabling real-time prediction and uncertainty quantification. Third, physics-informed digital twins integrate real-time sensor streams with cloud architectures for dynamic reservoir management. Furthermore, we address the generalization limits of purely data-driven approaches, highlighting physics-informed machine learning (PIML) and hybrid architectures that embed conservation laws as strict constraints. Finally, we outline future pathways toward multimodal data fusion and edge-cloud deployment, marking a shift from static offline modeling to dynamic, physics-safeguarded real-time reservoir optimization. Full article

Terrestrial laser scanning (TLS), also referred to as terrestrial LiDAR, has become an essential close-range remote sensing technique for high-precision engineering surveying, deformation monitoring, industrial inspection, and cultural heritage documentation. The geometric reliability of TLS point clouds strongly depends on the effective compensation of instrumental systematic errors through in situ self-calibration. However, conventional target-based self-calibration often suffers from strong coupling between calibration parameters and exterior orientation parameters, whereas recently developed coplanarity-constrained formulations generally require highly redundant target networks, limiting their field efficiency. To address this limitation, this study proposes a variance inflation factor (VIF)-driven minimal network design strategy for efficient in situ geometric self-calibration of TLS systems. Unlike the commonly used geometric dilution of precision, VIF provides a dimensionless statistical alternative that effectively resolves the dimensional inconsistency inherent in traditional GDOP when handling mixed angular and distance parameters. A differential evolution algorithm is employed to search for hybrid calibration networks that minimize parameter coupling while preserving the physical interpretability of the National Institute of Standards and Technology (NIST) 10-parameter instrumental error model. Five digital twin simulation experiments and a physical validation experiment using a Faro Focus 350 scanner were conducted to evaluate the proposed method. The results show that the optimized network substantially reduces the number of required targets while maintaining high calibration accuracy. The final configuration, which combines VIF-optimized target placement with a dual-station height-difference constraint, reduces the condition number of the normal equations to below 60 and yields a mean system VIF close to 10. The maximum parameter correlation coefficient among the key calibration parameters is constrained to approximately 0.75, indicating near-optimal parameter decoupling under the limited field-of-view geometry of the instrument. These findings demonstrate that the proposed VIF-driven network design provides a highly effective strategy for field-efficient TLS self-calibration and improves the geometric reliability of terrestrial LiDAR point clouds in high-precision remote sensing applications. Full article

The Greater Cape Floristic Region (GCFR) in South Africa is a fire-prone biodiversity hotspot where high species richness, structural complexity, and small plant sizes (0.0001–4 m²) pose substantial challenges for remote sensing-based biodiversity assessment. Spectral similarity among species and the mismatch between plant size and sensor pixel dimensions limit the capacity of current and forthcoming spaceborne systems to resolve individual species and accurately detect plot-level diversity changes. We therefore developed a physics-based simulation framework that couples fynbos trait measurements with radiative transfer modeling in the DIRSIG (Digital Imaging and Remote Sensing Image Generation) environment towards quantifying information loss across spectral and spatial scales and to define theoretical limits for biodiversity monitoring. We constructed a three-dimensional virtual scene of post-fire fynbos communities in Grootbos Private Nature Reserve, integrating high-resolution imagery, terrestrial laser scanning (TLS), and structure-from-motion (SfM)-derived point clouds. Field measurements of mean diameter and percent cover were used to scale vegetation models and constrain species abundance. We distributed plant instances using a blue noise sampling algorithm, guided by density maps derived from unmanned aerial system (UAS) imagery. Species-specific optical properties were parameterized using field-measured reflectance data and the PROSPECT radiative transfer model, while terrain structure was derived from SfM-based digital terrain models. The integrated scene was used to simulate multispectral (DJI Mavic 3 MSI), hyperspectral (AVIRIS-NG), and light detection and ranging (LiDAR) observations. Agreement between simulated outputs were evaluated against corresponding field-acquired datasets using spectral signatures and vegetation indices. This framework enables systematic assessment of sensor specification effects on spectral biodiversity metrics and provides a pathway for evaluating theoretical limits of species discrimination across airborne and satellite platforms. Full article

Rock bolts are the primary ground support mechanism in underground mining. Yet verification of their installation is rarely captured in a spatially precise, retrievable form, leaving operators without an auditable as-built record for regulatory review or post-incident reconstruction. This paper presents an automated rock bolt detection process that closes this documentation gap using dense point clouds from an underground hard rock mine acquired by terrestrial laser scan. The method computes per-point ambient occlusion (AO) on closure plane-sealed chambers using a PCV implementation of the ShadeVIS principle, forms candidates from a multi-scale protrusion field, and segments them by prominence watershed before classifying each candidate with PCA-based geometric descriptors, without machine learning or training data. Installation perpendicularity is applied as a per-detection confidence cue, and detections are reported in confidence tiers that concentrate human review on the ambiguous minority. Validated against a database of 1447 bolts across 20 walls in two areas of an underground mine, the system achieved an overall recall of 83.7%, with human review completing the inventory to 100%. The physics-based design transfers across bolt types and mine geometries through parameter re-tuning rather than retraining, addressing the core limitation of deep learning methods, which require site-specific labelled datasets. Full article

Oxygen-related absorption features provide fundamental constraints for passive atmospheric remote sensing in the reflected-solar spectrum. Because molecular oxygen is well-mixed in the dry atmosphere, O₂ absorption links measured radiance to atmospheric mass, pressure, and effective photon path length, while O₂-O₂ (O₄) collision-induced absorption provides complementary sensitivity to lower-tropospheric photon paths. This review synthesizes the spectroscopic basis, radiative-transfer mechanisms, satellite implementations, retrieval algorithms, and atmospheric applications of O₂ and O₄ measurements from the ultraviolet to the shortwave infrared. Particular emphasis is placed on the O₂ B-band near 687 nm, the O₂ A-band near 760 nm, O₄ bands in the UV–visible range, and the O₂ band near 1.27 µm. These features support retrievals of cloud fraction, cloud pressure, optical centroid pressure, aerosol layer height, surface pressure, dry-air column abundance, and light-path corrections for greenhouse gas observations. We review major algorithmic approaches, including cloud-as-reflecting-boundary models, cloud-as-layer models, DOAS-based retrievals, optimal-estimation frameworks, photon path-length distribution methods, and machine learning or hybrid techniques. Key applications include cloud climatology, aerosol vertical characterization, air mass factor correction, XCO₂ and XCH₄ retrievals, carbon-cycle studies, and multi-mission data integration. Remaining challenges include spectroscopic uncertainty, aerosol and cloud scattering degeneracy, surface bidirectional reflectance, three-dimensional radiative-transfer effects, wavelength-dependent path mismatch, and inconsistent uncertainty characterization. Future progress will depend on improved spectroscopy, active–passive validation, multi-angle polarimetry, physically constrained machine learning, and harmonized multi-mission retrieval frameworks. Full article

Remote sensing of clouds and precipitation is undergoing a significant transition from descriptive observations toward process-oriented diagnoses. This transition has not progressed linearly, but has gradually taken place alongside the rapidly developing multi-source observation capabilities over recent decades. In terms of clouds and precipitation observational platforms, satellites provide continuous global-scale monitoring, airborne platforms complement high-resolution sampling of key processes, and ground-based observations offer long-term vertical structure evolution, which form an integrated space–air–ground observation system. In terms of clouds and precipitation retrieval algorithms, active–passive combination remote sensing significantly improves the ability to retrieve macro- and microphysical characteristics and structures, and machine learning methods further expand parameter estimation capabilities in complex scenarios. Nevertheless, key bottlenecks still persist in retrieval non-uniqueness, sensor trade-offs, cross-platform calibration, and validation over oceans, mountains, and polar regions. Based on the above background, this paper provides a systematic review of recent progress in clouds and precipitation physics remote sensing, focusing on the development of multi-platform collaborative observations, the evolution of microphysical parameter retrieval methods, and the improvements in remote sensing characterization of cloud and precipitation formation mechanisms. It further points out that future development will increasingly rely on improved uncertainty quantification, incorporation of physical constraints into retrieval frameworks, and the establishment of standardized multi-source datasets. Full article

Aerosols exert significant impacts on Earth’s radiation balance through direct and indirect effects, with the latter representing the largest uncertainty in current climate models. To clarify aerosol–cloud interactions over China, this study synergized MODIS satellite retrievals (2015–2020) with in-situ MA60 aircraft observations across six representative regions. Satellite data provided aerosol optical depth (AOD), cloud optical depth (COD), cloud effective radius (CER), and cloud phase, while aircraft measurements delivered vertical profiles and microphysical properties of aerosols and cloud droplets. Results show that AOD exhibits a “high east, low west” pattern, with hotspots in the North China Plain and Sichuan Basin, and a significant decreasing trend over polluted regions. Cloud phase is spatially heterogeneous, with water clouds dominating the southeast and ice clouds prevailing in the northwest (>85% over the Tibetan Plateau). Water cloud COD shows a southeast-high–northwest-low distribution, with a clear inverse CER-COD correlation. Aircraft data reveal that polluted northern sites have high near-surface aerosol concentrations with small effective diameters (~0.3 μm), while cloud droplet number concentration and size spectra vary markedly across regions. These findings provide a robust observational foundation for improving aerosol–cloud interaction parameterizations in climate models. Full article

Electric Vehicle Charging Systems (EVCSs) have become a critical component of the global transition toward sustainable and intelligent transportation. However, their tight integration with heterogeneous cyber–physical, vehicular, and cloud-based infrastructures exposes them to an expanding attack surface, including data poisoning, malware injection, denial-of-service, and man-in-the-middle (MITM) attacks. Existing security solutions largely rely on isolated detection mechanisms and lack a unified semantic representation of EVCS assets, attack propagation paths, and mitigation dependencies, limiting their effectiveness in complex and evolving threat scenarios. To address these challenges, this paper proposes EVCS-SecOnt, an ontology-driven cybersecurity framework for modeling, reasoning, and mitigating security threats in EVCS infrastructures. The proposed ontology formalizes relationships across four core modules, namely Attack Surface, Attack Classification, Protection Mechanisms, and Risk and Mitigation, enabling holistic threat representation and TARA-based risk assessment. EVCS-SecOnt incorporates standard semantic namespaces (em:, seas:, uiote:, sch:, and time:) to ensure interoperability and is instantiated using the CICEVSE2024 dataset to support observation-level security reasoning. A unified SPARQL-based analytical workflow is employed to perform global ontology validation, attack–risk–severity correlation, mitigation prioritization, and observation-level inference using statistical feature vectors. Experimental results demonstrate that the ontology captures multiple attack classes, risk levels, severity categories, and mitigation strategies, enabling automated identification of critical attack scenarios and context-aware defense recommendations. The validation demonstrates logical consistency, semantic traceability, and query-based coverage of the ontology across attack classes, risk levels, severity categories, and mitigation strategies. EVCS-SecOnt enhances the interpretability, reusability, and explainability of EVCS cybersecurity management by bridging operational data with semantic intelligence. The proposed framework supports adaptive protection, risk-aware decision-making, and ontology-driven security analytics, providing a semantic foundation for next-generation e-mobility and smart charging infrastructures. Full article

Digital twins are digital representations of physical entities that enable real-time updates through data transmission between the physical and virtual domains. Based on a cloud–edge–device framework, this paper investigates methods for real-time data transmission, processing, and storage during the polishing process of a belt grinding robot. On this basis, a digital twin monitoring framework is constructed for blade-specific belt grinding robots. First, a virtual robot model was constructed using a joint modeling workflow in SolidWorks 2025 and 3ds Max 2025, incorporating a lightweight high-fidelity mesh processing algorithm based on the QEM method. Second, a data acquisition and transmission architecture was proposed for the belt grinding robot, enabling data reading, writing, and real-time monitoring during machining, as well as establishing a cloud–edge–device database. Finally, a cloud–edge–device digital twin monitoring framework for the blade belt grinding robot was developed, based on real-time monitoring of grinding process data. This work establishes a foundational data acquisition and visualization platform for the blade grinding robot, providing the necessary cyber-physical infrastructure, which future predictive models can develop and validate. Full article