Search Results (74)

A Digital Twin is a virtual environment that simulates, predicts, and optimizes the performance of its physical counterpart. Digital Twin models hold great potential in wireless networking testing and development. This paper aims to envision our concept of simulating the operation of different sensors in vehicle test-track conditions. Vehicle parameters are embedded into the edge computing entity, which uses them to generate a test configuration for the Digital Twin. This configuration is then applied in simulated sensor-output prediction, ultimately producing event data for the vehicle entity. The sensor suite—comprising radar, cameras, GPS and LiDAR—is modeled to provide the multi-modal input required for generating simulated perception data in the Digital Twin. To ensure realistic perception behavior, the physical vehicle is represented within a digital environment that reproduces the actual test track. This allows LiDAR occlusions to be attributed to genuine environmental structures (e.g., trees, buildings, other vehicles) rather than simulation artifacts. Within the Digital Twin, the objective is to evaluate how sensor signals—such as radar waves and LiDAR light pulses—propagate through the environment and how real-world obstacles may weaken or distort them. Historical datasets are used to calibrate and validate the Digital Twin, ensuring that the simulated sensor behavior aligns with real-world observations; the data collected during previous test runs can be used for visualization and analysis. Weather conditions are modeled to evaluate how rain, fog and snow impact sensor performance within the Digital Twin environment, to learn about the effects and predict sensor operation in different weather conditions. In this article, we examine the Digital Twin of our test track as a development environment for designing, deploying and testing ITS-enhanced road-weather services and warnings. These services integrate real-world road-weather observations, forecast data, roadside sensors and on-board vehicle measurements to support safe driving and optimize vehicle trajectories for both passenger and autonomous vehicles. This research is expected to benefit stakeholders involved in automotive testing, simulation and road-weather service development. Full article

Free-space optical (FSO) communication is a promising alternative to radio-frequency (RF) and optical fiber systems due to its high data rates and large bandwidth. However, its performance is highly susceptible to atmospheric conditions such as fog, rain, snow, and haze. This paper analyzes fog-induced signal attenuation in the Morogoro and Mbeya regions of Tanzania using the Kim and Kruse attenuation models. To improve link performance, a quadrature amplitude modulation (QAM) multiple-input multiple-output (MIMO) FSO link was designed and analyzed using OptiSystem 22.0. In Mbeya, light fog conditions with 0.5 km visibility resulted in an attenuation of 32 dB/km, a bit error rate (BER) of 4.5 × 10⁻²³, and a quality factor of 9.79 over a 2.62 km link. In Morogoro, dense fog with 0.05 km visibility led to an attenuation of 339 dB/km, a BER of 1.12 × 10⁻¹⁵, and a maximum link range of 0.305 km. Experimental measurements were further conducted under clear, moderate, and dense fog conditions to systematically evaluate the FSO link performance. The results demonstrated that MIMO techniques significantly enhanced link performance by mitigating fog effects. Moreover, a dedicated application was developed to analyze transmission errors and evaluate system performance metrics. Additionally, a mathematical model of the FSO link was developed to describe and forecast the performance of the MIMO FSO system in atmospheric conditions impacted by fog. Full article

Accurate nitrogen dioxide (NO₂) forecasting is crucial for proactive emission control and issuing public health warnings. This study provides the first evaluation of the China Meteorological Administration’s (CMA) operational CUACE/Haze-Fog V3.0 numerical prediction system, assessing its daily NO₂ forecast accuracy against independent satellite measurements and in situ observations. We compare model forecasts with TROPOspheric Monitoring Instrument (TROPOMI) satellite column data and observations from 1677 Chinese ground monitoring stations, focusing on four key regions: the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, and Urumqi. An optimal spatial resolution of 0.15° × 0.15° was determined for TROPOMI data processing. The results indicate a strong seasonal dependency in model performance. The model systematically underestimates NO₂ concentrations in winter but performs significantly better in summer. This systematic bias is confirmed by a Normalized Mean Bias (NMB) consistently below −20% in northern regions during the winter. In the Beijing–Tianjin–Hebei region, the Root Mean Square Error (RMSE) reached 3.57 × 10¹⁵ molec/cm² (vs. TROPOMI) and 1.09 × 10¹⁵ molec/cm³ (vs. ground stations) in winter, decreasing to 0.95 and 0.91, respectively, in summer. Critically, this winter bias pertains to pollution magnitude rather than temporal correlation; the model captures pollution trends but underestimates peak severity. Our study reveals a ‘vertical decoupling’ in the operational forecasting system. While the model utilizes surface data assimilation to correct surface pollutants, this study demonstrates that these corrections fail to propagate vertically to the total NO₂ column during winter stable boundary layer conditions. This finding has broader implications for chemical transport models (CTMs): relying solely on surface data assimilation is insufficient for constraining column burdens in regions with complex vertical stratification. We propose that future operational systems integrate satellite-based vertical constraints to resolve the systematic winter bias identified here. Full article

A notable decline in the frequency of sea fog inflows and an increase in low-cloud ceiling height were observed following the construction of the Saemangeum Seawall west of the Gunsan Airport, an area traditionally prone to frequent sea fog events. To the mechanisms underlying these changes, a numerical experiment was conducted using the Weather Research and Forecasting model. An 11-m-high seawall was used as a physical barrier, and an elevated sea surface temperature (SST) was established within the enclosed area to simulate realistic post-construction conditions. The model successfully reconstructed sea fog occurrences, and the cloud–water mixing ratio effectively captured the spatial distribution of sea fog. Deviations from the control experiment showed a consistent pattern of reduced cloud–water mixing ratios near the surface and enhanced concentrations at high levels. Decreased buoyancy frequency in the surface layer enhanced atmospheric instability, inducing upward motion and intensified condensation activity. Increases in the turbulence kinetic energy within the planetary boundary layer (TKE within the PBL), vertical wind shear, and temperature further corroborated the reduction in sea fog and enhanced stratus formation. These findings indicate that the increased SST and seawall significantly influence the modification of the sea fog structure and its inflow dynamics. Full article

The volatile nature of cryptocurrency markets demands real-time analytical capabilities that traditional centralized computing architectures struggle to provide. This paper presents a novel hybrid cloud–edge computing framework for cryptocurrency market forecasting, leveraging distributed systems to enable low-latency prediction models. Our approach integrates machine learning algorithms across a distributed network: edge nodes perform real-time data preprocessing and feature extraction, while the cloud infrastructure handles deep learning model training and global pattern recognition. The proposed architecture uses a three-tier system comprising edge nodes for immediate data capture, fog layers for intermediate processing and local inference, and cloud servers for comprehensive model training on historical blockchain data. A federated learning mechanism allows edge nodes to contribute to a global prediction model while preserving data locality and reducing network latency. The experimental results show a 40% reduction in prediction latency compared to cloud-only solutions while maintaining comparable accuracy in forecasting Bitcoin and Ethereum price movements. The system processes over 10,000 transactions per second and delivers real-time insights with sub-second response times. Integration with blockchain ensures data integrity and provides transparent audit trails for all predictions. Full article

The accurate prediction of atmospheric low-visibility events due to fog, haze or atmospheric pollution is an extremely important problem, with major consequences for transportation systems, and with alternative applications in agriculture, forest ecology and ecosystems management. In this paper, we provide a comprehensive literature review and analysis of AI-based methods applied to fog and low-visibility events forecasting. We also discuss the main general issues which arise when dealing with AI-based techniques in this kind of problem, open research questions, novel AI approaches and data sources which can be exploited. Finally, the most important new AI-based methodologies which can improve atmospheric visibility forecasting are also revised, including computational experiments on the application of ordinal classification approaches to a problem of low-visibility events prediction in two Spanish airports from METAR data. Full article

The objective of this study is to analyze microphysical parameters affecting visibility parameterizations of a freezing fog case that occurred on 19 February 2022, during the Cold Fog Amongst Complex Terrain (CFACT) project conducted in a high-elevation alpine valley in Utah, USA. Observations are collected using visibility, droplet spectra, ice crystal spectra, and aerosol spectral instruments, as well as in-situ meteorological instruments. Particle phase is determined from relative humidity with respect to water (RH_w) as well as ground cloud imaging probe (GCIP), ceilometer (CL61) depolarization ratio, and icing accumulation on the platforms. Results showed that freezing droplet density can affect visibility (Vis) up to 100 m during Vis less than 1 km. In addition, increasing volume can lead to up to a 2 μm increase in droplet radius due to a change in the chemical composition of aerosols from Sodium Chloride (NaCl) to Ammonium Nitrate (NH₄NO₃). Overall, comparisons suggested that Vis parameterizations are highly variable, and freezing fog conditions resulted in lower Vis values compared to warm fog microphysical parameterizations. Furthermore, riming of freezing fog conditions can lead to more than 50% uncertainty in Vis. It is concluded that changing aerosol composition and freezing fog droplet density and riming can play a major role in Vis simulations. Full article

Temperature forecasting is critical for public safety, environmental risk management, and energy conservation. However, reliable forecasting becomes challenging in regions where governmental institutions lack adequate measurement infrastructure. To address this limitation, the present study aims to improve temperature forecasting by collecting temperature, pressure, and humidity data through IoT sensor networks. The study further seeks to identify the most effective method for the real-time processing of large-scale datasets generated by sensor measurements and to ensure data reliability. The collected data were pre-processed using Discrete Wavelet Transform (DWT) to extract essential features and reduce noise. Subsequently, three wavelet-processed deep-learning models were employed: Wavelet-processed Artificial Neural Networks (W-ANN), Wavelet-processed Long Short-Term Memory Networks (W-LSTM), and Wavelet-processed Bidirectional Long Short-Term Memory Networks (W-BiLSTM). Among these, the W-BiLSTM model yielded the highest performance, achieving a test accuracy of 97% and a Mean Absolute Percentage Error (MAPE) of 2%. It significantly outperformed the W-LSTM and W-ANN models in predictive accuracy. Forecasts were validated using data obtained from the Turkish State Meteorological Service (TSMS), yielding a 94% concordance, thereby confirming the robustness of the proposed approach. The findings demonstrate that the W-BiLSTM-based model enables reliable temperature forecasting, even in regions with insufficient governmental measurement infrastructure. Accordingly, this approach holds considerable potential for supporting data-driven decision-making in environmental risk management and energy conservation. Full article

This paper proposes a fault self-healing recovery strategy for passive low-voltage power station areas (LVPSAs). Firstly, being aware of the typical structure and communication conditions of the LVPSAs, a fog computing load forecasting method is proposed based on a dynamic aggregation of incremental learning models. This forecasting method embeds two weighted ultra-short-term load forecasting techniques of complementary characteristics and mines real-time load to learn incrementally, and thanks to this mechanism, the method can efficiently make predictions of low-voltage loads with trivial computational burden and data storage. Secondly, the low-voltage power restoration problem is overall formulated as a three-stage mixed integer program. Specifically, the master problem is essentially a mixed integer linear program, which is mainly intended for determining the reconfiguration of binary switch states, while the slave problem, aiming at minimizing load curtailment constrained by power flow balance along with inevitable load forecast errors, is cast as mixed integer type-1 Wasserstein distributionally robust optimization. The column-and-constraint generation technique is employed to expedite the model-resolving process after the slave problem with integer variables eliminated is equated with the Karush–Kuhn–Tucker conditions. Comparative case studies are conducted to demonstrate the performance of the proposed method. Full article

This study introduces a novel one-dimensional Fractional Time–Space Stochastic Advection–Diffusion Equation that revolutionizes the modeling of moisture transport within atmospheric boundary layers adjacent to oceanic surfaces. By synthesizing fractional calculus, advective transport mechanisms, and pink noise stochasticity, the proposed model captures the intricate interplay between temporal memory effects, non-local turbulent diffusion, and the correlated-fluctuations characteristic of complex ocean–atmosphere interactions. The framework employs the Caputo fractional derivative to represent temporal persistence and the fractional Laplacian to model non-local turbulent diffusion, and incorporates a stochastic term with a $1/f$ power spectral density to simulate environmental variability. An efficient numerical solution methodology is derived utilizing complementary Fourier and Laplace transforms, which elegantly converts spatial fractional operators into algebraic expressions and yields closed-form solutions via Mittag–Leffler functions. This method’s application to a benchmark coastal domain demonstrates that stronger advection significantly increases the spatial extent of conditions exceeding fog formation thresholds, revealing advection’s critical role in moisture transport dynamics. Numerical simulations demonstrate the model’s capacity to reproduce both anomalous diffusion phenomena and realistic stochastic variability, while convergence analysis confirms the numerical scheme’s robustness against varying noise intensities. This integrated fractional stochastic framework substantially advances atmospheric moisture modeling capabilities, with direct applications to meteorological forecasting, coastal climate assessment, and environmental engineering. Full article

Sea fog significantly impacts marine activities, ecosystems, and radiation balance. We analyzed the decadal variation characteristics of sea fog frequency (SFF) over the northwestern Pacific and investigated the roles of the Pacific decadal oscillation (PDO) and sea surface temperature (SST) warming in driving these changes. The results show that SFF experienced a significant and sudden decadal increase around 1978 (up by 12.9%) and a prominent decadal decrease around 1999 (down by 7.8%). The sudden increase in SFF around 1978 was closely related to the PDO. A positive PDO phase induced unusual anticyclonic circulation and southerly winds over the northwestern Pacific, enhancing low-level atmospheric stability and moisture supply, thus facilitating sea fog formation. Nevertheless, the decrease in SFF around 1999 was related to SST warming in the north Pacific. The rise in sea temperatures weakened the SST front south of the foggy region, reducing the cooling and condensation of warm air necessary for sea fog formation. This study enhances the understanding of the decadal variability mechanism of SFF over the northwestern Pacific regulated by large-scale circulation systems and provides a reference for future sea fog forecasting work. Full article

In this paper, we utilize the Advanced Research version of the Weather Research and Forecasting model (ARWv4) to explore how the fog is affected by the basin’s topography during a radiation fog event in the Sichuan Basin in December 2016 by setting up three sets of terrain tests. The simulation results demonstrate that the fog area in the expanded basin terrain emerges 40 min earlier than in the original topography control test (CTL), with the fog area extent marginally reduced. Conversely, the fog area in the reduced basin terrain emerges one hour earlier than in the CTL, with the fog area extent increased by 133.5%. Basin topography is an essential factor influencing the humidity, temperature, and dynamical fields. The expansion of basin topography was shown to be unfavorable for water vapor convergence. Moreover, the area exhibiting relative humidity levels exceeding 95% at the peak of the fog intensity was smaller than that observed in CTL. The impact of radiative cooling was diminished, and the thickness and intensity of the inversion layer were reduced compared to CTL. In addition, the wind speed in the marginal area exceeded 5 m s⁻¹, and the fog formation was observed only in the central portion of the basin, where wind speeds ranged from 0 to 3 m s⁻¹. In contrast, the change in the topography of the narrowed basin resulted in the opposite phenomenon overall. This work emphasizes the importance of basin topography in forming and developing the fog in the Sichuan Basin. Full article

This study is aimed to improve the intelligence level, efficiency, and accuracy of ship safety and security systems by contributing to the development of marine weather forecasting. The accurate and prompt recognition of weather fax charts is very important for navigation safety. This study employed many artificial intelligent (AI) methods including a vectorization approach and target recognition algorithm to automatically detect the severe weather information from Japanese and US weather charts. This enabled the expansion of an existing auto-response marine forecasting system’s applications toward north Pacific and Atlantic Oceans, thus enhancing decision-making capabilities and response measures for sailing ships at actual sea. The OpenCV image processing method and YOLOv5s/YOLO8vn algorithm were utilized to make template matches and locate warning symbols and weather reports from surface weather charts. After these improvements, the average accuracy of the model significantly increased from 0.920 to 0.928, and the detection rate of a single image reached a maximum of 1.2 ms. Additionally, OCR technology was applied to retract texts from weather reports and highlighted the marine areas where dense fog and great wind conditions are likely to occur. Finally, the field tests confirmed that this auto and intelligent system could assist the navigator within 2–3 min and thus greatly enhance the navigation safety in specific areas in the sailing routes with minor text-based communication costs. Full article

The Arctic Seas are attractive for shipping, fisheries, and other marine activities due to the abundant resources of the Arctic. The shrinking ice cover allows for the opening of activities in increasingly larger areas of the Arctic. This paper evaluates the possibility of executing all-year complex marine activities, here termed “marine operations”, in the Norwegian Sea and the ice-free part of the Barents Sea. The approach used during the preparation of this review paper is to identify constraints to marine operations so users can be aware of the limitations of performing such operations. The weather conditions in the Norwegian Sea and the Barents Sea are well known, and these seas are considered representative of ice-free or partly ice-free Arctic Seas with considerable marine activities. Similar conditions could be expected for other Arctic Seas during periods without ice cover. Marine operations require safe and stable working conditions for several days. The characteristics of marine operations are discussed, and the particulars of the Norwegian Sea and the Barents Sea physical environments are highlighted. Emphasis is on the wind and wave conditions in unpredictable polar low-pressure situations. Furthermore, situations with fog are discussed. The large uncertainties in forecasting the initiation and the tracks of polar lows represent the main concern for executing marine operations all year. Improvements in forecasting the occurrence and the path of polar lows would extend the weather window when marine operations could be carried out. Discussions of the potential for similar conditions in the wider Arctic Seas during ice-free periods are presented. Full article

Smart agriculture stands as a promising domain for IoT-enabled technologies, with the potential to elevate crop quality, quantity, and operational efficiency. However, implementing a smart agriculture system encounters challenges such as the high latency and bandwidth consumption linked to cloud computing, Internet disconnections in rural locales, and the imperative of cost efficiency for farmers. Addressing these hurdles, this paper advocates a fog-based smart agriculture infrastructure integrating edge computing and LoRa communication. We tackle farmers’ prime concern of animal intrusion by presenting a solution leveraging low-cost PIR sensors, cameras, and computer vision to detect intrusions and predict animal locations using an innovative algorithm. Our system detects intrusions pre-emptively, identifies intruders, forecasts their movements, and promptly alerts farmers. Additionally, we compare our proposed strategy with other approaches and measure their power consumptions, demonstrating significant energy savings afforded by our strategy. Experimental results highlight the effectiveness, energy efficiency, and cost-effectiveness of our system compared to state-of-the-art systems. Full article