Search Results (428)

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

To better understand the characteristics and causes of acid rain pollution in Huangshan City, China, in the context of reduced atmospheric pollutant emissions, this study systematically analyzes precipitation monitoring data from Huangshan City for the period 2013–2025. The analytical methods included volume-weighted mean, neutralization factor, and linear regression analysis. The results indicate that, with 2017 as a turning point, acid rain in Huangshan City transitioned from high-level fluctuations to a stabilization phase at medium-to-low levels. However, the annual mean pH remained below 5.6, indicating that the acid rain problem persists. Regarding pollutant emission reductions, sulfur dioxide (SO₂) control has achieved significant results, but nitrogen oxide (NO_x) pollution remains prominent due to factors such as a sharp increase in vehicle ownership. Analysis of the chemical composition of precipitation shows that the SO₄²⁻/NO₃⁻ ratio decreased from 4.09 to 0.92, and the acid rain type has shifted from sulfate-dominated to mixed sulfate-nitrate-dominated. In precipitation, highly specific ion pairings are observed: Ca²⁺ with SO₄²⁻ (r = 0.989) and NH₄⁺ with NO₃⁻ (r = 0.839). These two ion pairs together account for 81.4% of the total cations, forming two independent neutralization mechanisms—below-cloud and in-cloud—which explains the relative stability of precipitation pH despite a decline in total ion concentration. Furthermore, interannual variability in precipitation amount, particularly extreme wet events, is a key external factor driving fluctuations in acid rain frequency under stable emission conditions. The dominant driver of acid rain frequency variability has shifted from emission-dominated to precipitation-dominated. Full article

Remote sensing imagery is often degraded by cloud cover, causing severe information loss and hindering downstream Earth observation tasks. Although recent deep learning methods, including CNN- and Transformer-based models, have achieved promising progress in cloud removal, they mainly operate in the spatial domain and largely overlook the frequency-domain discrepancies introduced by clouds of different types and densities. This limitation restricts their ability to generalize across diverse cloud corruption scenarios. To address this issue, we propose a Frequency Interaction Cloud Removal Network (FI-CRNet), which introduces a novel Frequency-Aware Modulation (FAM) mechanism for high-fidelity cloud-free image reconstruction. The FAM module consists of two components. First, the Frequency Decomposition (FD) module explicitly separates input features into low-frequency cloud-affected components and high-frequency detail-rich components through spectral analysis, while aligning them with decoder features via cross-attention. Second, the Cross-Frequency Interaction (CFI) module adaptively integrates these components through a dual-gate weighting mechanism, including spatial and channel gates, to suppress cloud interference while enhancing structural and textural details. By jointly modeling frequency-domain cues and spatial features, FI-CRNet enables robust and adaptive reconstruction under diverse cloud conditions. Extensive experiments show that our method outperforms state-of-the-art techniques across diverse cloud scenarios. Full article

We present the first results of polar mesospheric cloud (PMC) detection using ultraviolet observations from TROPOMI (TROPOspheric Monitoring Instrument). An improved retrieval algorithm, developed on the basis of the SBUV-type approach and adapted to TROPOMI UV1 (270–300 nm) measurements, combines spatial binning, iterative Rayleigh background modeling, and adaptive thresholding to extract PMC signals from the background atmosphere. The robustness of the TROPOMI retrievals is evaluated through multi-scale comparisons with PMC data from the Cloud Imaging and Particle Size experiment (CIPS) and the Ozone Mapping and Profiler Suite Nadir Profiler (OMPS-NP). Compared with CIPS, the two datasets show broadly consistent hemispheric-scale horizontal structures and a westward wave-like phase progression consistent with possible quasi-5-day planetary-wave modulation, despite local-time differences. Compared with OMPS-NP, residual albedo under matched spatiotemporal conditions shows strong agreement for bright PMCs, whereas differences in spatial resolution lead to discrepancies in the detection of faint clouds. Seasonal-scale comparisons of PMC occurrence frequency also show consistent variability among the datasets. These results demonstrate that TROPOMI can resolve PMC structures smaller than 250 km that are difficult to detect with current low-resolution instruments. TROPOMI therefore provides a bridge between long-term coarse-resolution records and high-resolution observations, offering valuable data for studies of mesospheric dynamics and climate change. Full article

Against the backdrop of intensifying global climate change, reducing carbon emissions has become a shared global objective. Blue carbon, as a significant carbon sink type, still lacks a mature assessment framework. Monitoring carbon fluxes in marine salt marsh wetlands is a core technology for accurately evaluating blue carbon potential. In response, this study independently developed a spatiotemporally coupled carbon flux monitoring system for marine salt marsh wetlands. The system consists of real-time monitoring equipment, a cloud-based intelligent storage and visualization analysis platform, and a terminal assessment system. It enables the real-time monitoring of carbon fluxes across multiple spatial scales and integrates time-series patterns to assess carbon sequestration potential from multiple dimensions. To address the bottleneck of sensor accuracy, a multi-algorithm fusion technology was innovatively developed, significantly enhancing the accuracy of monitoring data. A modular integrated design was employed to construct a land–air integrated monitoring architecture, which is adaptable to the complex environments of salt marsh wetlands. This facilitates long-term automated monitoring while reducing the need for manual intervention. The terminal assessment system processes spatial-scale data using the DeNitrification-DeComposition model (DNDC 9.5) and integrates time-series carbon flux patterns, enabling precise quantification of marine carbon sink potential through spatiotemporal comprehensive analysis. The system first completed performance verification during the experimental phase, acquiring a total of 5760 sets of valid monitoring data, with a data qualification rate of 99.72%. The proposed multi-algorithm fusion method kept monitoring data fluctuations within 0.5%, and the relative error of the spatiotemporal integrated prediction was as low as 0.31%, thereby ensuring the stability and accuracy of long-term in situ monitoring. Based on this, a one-year field validation was conducted in a 100-hectare coastal salt marsh wetland in Dafeng, Yancheng. Using a spatiotemporal coupling assessment, the annual total carbon sequestration of this area was estimated at 1498.4 tons of carbon, with an assessment error of only 5.1%, achieving precise quantification of the blue carbon sink in the salt marsh wetland. This study provides reliable technical support for evaluating the carbon sequestration capacity of coastal salt marsh wetlands, contributing to the implementation of carbon emission reduction strategies. It also offers a scientific basis for global carbon cycle research and carbon sink management decision-making. Full article

Airfield lighting control (ALC) is critical for ensuring safe, efficient, and compliant airport operations, especially under low-visibility conditions. However, current centralized control architectures cannot adequately meet the real-time responsiveness, scalability, and reliability requirements of Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Level IV. To overcome these limitations, this paper proposes a novel cloud–edge–end collaborative architecture for a mobile ALC scenario, in which we formulate a joint task computing and energy consumption optimization problem to maximize long-term system utility under latency, computation, and communication constraints. In this way, the mobile airfield lighting (MAL) system can also quickly adapt its optimal formation pattern based on the airport environment, lighting conditions, and the type of aircraft taking off or landing via efficient computation, thereby achieving the best navigational assistance effect. For solving such an optimization problem, a framework that combines K-medoids with the Improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) is proposed to integrate the efficiency of clustering for rough allocation and the high-precision dynamic optimization capability of the improved TD3. The training depends on edge nodes and the cloud to achieve online performance. Finally, the extensive simulation proved that our novel algorithm is efficient. Full article

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas. Full article

In evaluating energy consumption in building complexes, the influence of urban microclimate variations—primarily driven by the urban heat island (UHI) effect—is often overlooked, leading to modeling inaccuracies. This study develops a numerical simulation framework integrating Weather Research and Forecasting (WRF) and EnergyPlus to assess the energy consumption of university dormitories while accounting for regional microclimate conditions. This is because university dormitories serve as a key indicator for measuring the type of high-density residential buildings in China. The model incorporates dynamic microclimate variables, including ambient temperature, relative humidity, wind speed, solar radiation, and cloud cover, to simulate dormitory energy consumption profiles. Simulation results are validated against measured data, yielding an annual energy consumption error of −1.03%. Quantitative analysis indicates that ignoring the microclimate effect and directly using data from nearby meteorological stations or TMY data has a limited impact on the annual total energy consumption but has a significant impact on seasonal results. To improve the simulation accuracy of building complexes, more attention should be paid to temperature and relative humidity. Moreover, for areas with low occupant density and a high shape coefficient, energy consumption simulation should also consider the local microclimate factors. Full article

The CALIPSO V4.10 5 km cloud-layer product contains a small yet influential fraction of low-confidence and “unknown” cloud-type labels, which constrains its effectiveness in climatological analyses and limits its utility for downstream Earth system applications. To improve the practical usability and completeness of these observations, this study develops a multilayer perceptron (MLP)-based refinement framework using global summer daytime CALIPSO data from 2006–2021. High-confidence cloud samples (76% of the dataset), defined as cases with high Feature Type QA and high Ice/Water Phase QA, were used as the reliable supervision subset to train the MLP model using 11 geolocation-, optical-, and microphysics-related variables, including cloud optical depth, cloud thickness, depolarization ratio, and color ratio. The trained model was subsequently applied to a separately defined low-confidence cloud subset (~5% of the dataset), consisting of cases with high Feature Type QA but low Ice/Water Phase QA, of which over 60% were originally labeled as “unknown”, to generate probabilistic assignments of three cloud types: ice clouds, water clouds, and oriented ice crystals. Evaluation using withheld high-confidence samples indicates a strong level of agreement with operational CALIPSO classifications (~94.99%). Moreover, the refined low-confidence results exhibit physically coherent vertical structural characteristics consistent with established cloud thermodynamic regimes. It is emphasized that the proposed framework does not establish an independent physical truth beyond CALIOP’s measurement capability; instead, it provides a physically consistent and statistically robust approach to improving the completeness and practical usability of CALIPSO cloud-type products for large-scale scientific and modeling applications. Full article

In the current context of digital transformation, Micro-, Small-, and Medium-Sized Enterprises (MSMEs) are increasingly exposed to cybersecurity risks. This exposure is intensified by the limited adoption of international standards for identifying impacts, low budgets, and shortages of trained personnel, which collectively result in the absence of structured control plans for mitigating cyber risks. (1) This study proposes a mechanism for selecting a cybersecurity risk analysis and management methodology suited to Colombian MSMEs by applying the multi-criteria Analytic Hierarchy Process (AHP) method. (2) The employed approach is qualitative and follows the AHP procedure to select the most suitable option that can be applied to cybersecurity. This selection process evaluated different criteria in five standards: ISO/IEC 27005:2022, NIST SP 800-30, OCTAVE-S, MAGERIT, and EBIOS-RM. (3) The AHP method enabled, in a practical manner, the selection of OCTAVE-S as the primary methodology, complemented with elements from other standards. Finally, the proposed methodology was implemented in a cloud-based web application called the Risk Analysis Module, integrated into the Keru IT security platform. It is concluded that the multi-criteria AHP method is effective and allows organizations to select the standards most appropriate to their needs, with potential applicability to other types of decisions. Full article

Transforming a conventional university campus into a smart campus by leveraging modern technologies aims to deliver university services efficiently, effectively, and at low cost. Modern technologies enhance campus life by providing services, such as smart classrooms and campus security, on demand. Seamless service delivery requires reliable and efficient access to the services that take into consideration the dynamic contextual attributes related to, e.g., end-device mobility, latency sensitivity, and resource constraints. University staff, students, and visitors frequently submit different types of service requests on the move, which requires a robust orchestration framework capable of managing these requests across edge-cloud environments. The orchestration framework needs to intelligently distribute the workload, taking into consideration the latency sensitivity requirements and contextual conditions, including resource constraints. Therefore, a fuzzy-logic orchestration framework for smart-campus environments in edge-cloud architecture is proposed. The framework incorporates key factors, including user speed, resource utilization, and request delay sensitivity, in the decision-making process to satisfy both service consumers and service providers. It prioritizes latency-sensitive requests while simultaneously enhancing resource utilization efficiency. Simulation-based experimental results demonstrate the effectiveness of the proposed framework compared with benchmark approaches in orchestrating incoming workloads under several user and contextual conditions. Additionally, the results show that the proposed framework improves the execution rate by 30% compared to benchmark models and achieves more than double resource utilization efficiency. Full article

As the most prevalent type of existing building in China, masonry structures are susceptible to cracking due to the low tensile strength of the masonry material. In the event of a sudden, strong earthquake, they are highly prone to brittle collapse, leaving occupants little time and space to escape. Based on this, combining the advantages of the elastoplastic mechanical theory and the nonlinear finite element (FE) method, this study adopts different modeling methods: integral modeling (IM), contact element discrete modeling (CEDM), spring element discrete modeling (SEDM), and co-node discrete modeling (CNDM). FE models of unreinforced masonry walls (UMWs) are established, respectively, and a monotonic pushover mechanical performance analysis is carried out. The accuracy of the adopted modeling methods is verified against existing test results for UMW specimens. Through parametric analysis of aspect ratios (0.5, 0.75, 1.0, and 1.25), axial compression ratios (0.1, 0.3, 0.5, 0.7, and 0.8), and mortar strengths (M5, M7.5, and M10), the characteristic mechanical performance factors of UMWs are determined. A novel strength index is proposed to discriminate between failure modes and elucidate the damage mechanism of UMWs. The results indicate that the ultimate load and its corresponding displacement change systematically with variations in aspect ratios, axial compression ratios, and mortar strengths. Furthermore, integrating stress cloud maps with the proposed strength index provides a quantitative basis for discriminating between flexural and shear failure modes in UMWs. All four modeling methods can, to varying degrees, capture the pushover behavior of UMWs, and quantifiable selection schemes are provided to balance analysis accuracy and computational cost. The analytical methods and findings presented in this work can be applied to performance assessment, seismic design, and engineering practice of UMWs. Full article

The synergistic effects of glucose (Glu) concentration and clay mineral type (kaolinite [Kao], montmorillonite [Mon]) on abiotic humification via the polyphenol-Maillard reaction remain poorly understood. To address these scientific challenges, a series of controlled, sterile batch experiments was conducted. Specifically, a glucose concentration gradient (0, 0.03, 0.06, 0.12, and 0.24 mol/L) was established; Kao and Mon were separately introduced as mineral catalysts; and the Maillard reaction was facilitated in the presence of catechol and glycine under strictly abiotic conditions to preclude any potential biological interference. Comprehensive analyses were performed on the reaction products—namely, the supernatant and the dark-brown residue generated during the reaction process. These analyses included: the E₄/E₆ ratio and total organic carbon (TOC) content of the supernatant; the carbon-based ratio of humic-like acid to fulvic-like acid (C_HLA/C_FLA); and the structural characteristics of humic-like acid (HLA) isolated from the dark-brown residue. Results showed dynamic E₄/E₆ ratio and TOC changes in the supernatant were accurately described by the Logistic function. Kao favored soluble organic C accumulation and enhanced retention of early-stage, low-molecular-weight intermediates in the dark-brown residue, while Mon promoted humic-like substances (HLS) polymerization and aromatic condensation. FTIR spectroscopy analysis identified optimal Glu thresholds for maximal HLS formation—0.03 mol/L for Kao and 0.06 mol/L for Mon—indicating non-linear, rather than monotonic, dependence on Glu dosage. Comparative pre- and post-reaction Fourier-transform infrared (FTIR) spectroscopy further demonstrated that Mon, owing to Mg–OH octahedral sites arising from isomorphic substitution, formed more stable Cat chelates than Kao. These chelates effectively stabilized surface-bound hydroxyl-associated water molecules and modulated the electron cloud distribution around Si–O bonds. Collectively, this study clarified the dual regulatory role of Glu concentration and clay mineral identity in abiotic humification pathways, advanced mechanistic understanding of clay mineral-mediated polyphenol-Maillard reactions, and established a scientific foundation for optimizing humification efficiency in both engineered and natural systems. Full article

Context-aware mobile applications operating in environments with intermittent or unreliable connectivity must support offline-first behavior while preserving consistent decision-making and timely synchronization. Traditional cloud-centric architectures often fail to provide adequate availability, responsiveness, and reliable context reasoning under such conditions. This paper presents CAMS-F Edge DTN, an edge-centric runtime designed to support offline-first context-aware applications operating under intermittent connectivity. The proposed approach extends the CAMS domain-specific language (DSL) with declarative policies for semantic reconciliation, opportunistic synchronization, and context-aware conflict resolution. The runtime integrates Conflict-Free Replicated Data Types (CRDTs), opportunistic communication channels such as Bluetooth and Wi-Fi Direct, and MQTT-SN messaging to enable robust data exchange across mobile, vehicular, and edge nodes. CAMS F-Edge DTN supports offline-first execution by allowing applications to evaluate contextual rules locally and reconcile distributed state asynchronously when connectivity becomes available. The approach is evaluated through controlled experiments and case studies in rural logistics and healthcare distribution scenarios. The experimental results show that the proposed architecture maintains 96–99% operational availability under intermittent connectivity and up to 100% availability during fully offline operation, while achieving low-latency local reasoning (<10 ms median latency) and deterministic state convergence through CRDT-based synchronization mechanisms. Full article

The leg structure of swine is closely related to their robustness and longevity. Animals with sound legs generally have longer productive lifespans and higher reproductive efficiency, whereas leg defects can markedly impair performance and shorten service life. To address the high subjectivity, low efficiency, and poor consistency of traditional leg-structure evaluation by humans, this study developed an automatic scoring system for swine leg structure based on 3D point clouds. The hardware components of the system include the acquisition channel, a multi-view time-of-flight (ToF) depth camera array, an industrial computer, and a star-type synchronization hub. The core algorithm modules include point cloud preprocessing, leg segmentation, geometric feature extraction, and structure-based scoring. Body orientation was corrected using principal component analysis (PCA). An adaptive limb region segmentation method was proposed that combines iterative cropping with geometric verification. Two point cloud tasks were performed: key structural points were extracted via multi-scale curvature analysis, and angular and symmetry parameters of the fore- and hindlimbs were computed in the sagittal and coronal planes. Following a “classify first, then score” strategy, a nine-level linear scoring model was constructed. Field validation showed that the classification accuracy exceeded 90%, the scores were significantly negatively correlated with the degree of structural deviation, and multi-frame resampling yielded good repeatability. The processing time per animal ranged from 1.6 s to 3.0 s, which met the requirements for real-time applications. These results demonstrated that the proposed method could automatically identify and quantitatively evaluate swine leg structure, providing efficient and reliable technical support for objective selection and smart pig farming. Full article