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Search Results (289)

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19 pages, 18533 KiB  
Article
Modeling of Marine Assembly Logistics for an Offshore Floating Photovoltaic Plant Subject to Weather Dependencies
by Lu-Jan Huang, Simone Mancini and Minne de Jong
J. Mar. Sci. Eng. 2025, 13(8), 1493; https://doi.org/10.3390/jmse13081493 - 2 Aug 2025
Viewed by 96
Abstract
Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to [...] Read more.
Floating solar technology has gained significant attention as part of the global expansion of renewable energy due to its potential for installation in underutilized water bodies. Several countries, including the Netherlands, have initiated efforts to extend this technology from inland freshwater applications to open offshore environments, particularly within offshore wind farm areas. This development is motivated by the synergistic benefits of increasing site energy density and leveraging the existing offshore grid infrastructure. The deployment of offshore floating photovoltaic (OFPV) systems involves assembling multiple modular units in a marine environment, introducing operational risks that may give rise to safety concerns. To mitigate these risks, weather windows must be considered prior to the task execution to ensure continuity between weather-sensitive activities, which can also lead to additional time delays and increased costs. Consequently, optimizing marine logistics becomes crucial to achieving the cost reductions necessary for making OFPV technology economically viable. This study employs a simulation-based approach to estimate the installation duration of a 5 MWp OFPV plant at a Dutch offshore wind farm site, started in different months and under three distinct risk management scenarios. Based on 20 years of hindcast wave data, the results reveal the impacts of campaign start months and risk management policies on installation duration. Across all the scenarios, the installation duration during the autumn and winter period is 160% longer than the one in the spring and summer period. The average installation durations, based on results from 12 campaign start months, are 70, 80, and 130 days for the three risk management policies analyzed. The result variation highlights the additional time required to mitigate operational risks arising from potential discontinuity between highly interdependent tasks (e.g., offshore platform assembly and mooring). Additionally, it is found that the weather-induced delays are mainly associated with the campaigns of pre-laying anchors and platform and mooring line installation compared with the other campaigns. In conclusion, this study presents a logistics modeling methodology for OFPV systems, demonstrated through a representative case study based on a state-of-the-art truss-type design. The primary contribution lies in providing a framework to quantify the performance of OFPV installation strategies at an early design stage. The findings of this case study further highlight that marine installation logistics are highly sensitive to local marine conditions and the chosen installation strategy, and should be integrated early in the OFPV design process to help reduce the levelized cost of electricity. Full article
(This article belongs to the Special Issue Design, Modeling, and Development of Marine Renewable Energy Devices)
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37 pages, 7429 KiB  
Article
Study on the Influence of Window Size on the Thermal Comfort of Traditional One-Seal Dwellings (Yikeyin) in Kunming Under Natural Wind
by Yaoning Yang, Junfeng Yin, Jixiang Cai, Xinping Wang and Juncheng Zeng
Buildings 2025, 15(15), 2714; https://doi.org/10.3390/buildings15152714 - 1 Aug 2025
Viewed by 153
Abstract
Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio [...] Read more.
Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design. Full article
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19 pages, 4152 KiB  
Article
Optimization of Greenhouse Structure Parameters Based on Temperature and Velocity Distribution Characteristics by CFD—A Case Study in South China
by Xinyu Wei, Yizhi Ou, Ziwei Li, Jiaming Guo, Enli Lü, Fengxi Yang, Yanhua Liu and Bin Li
Agriculture 2025, 15(15), 1660; https://doi.org/10.3390/agriculture15151660 - 1 Aug 2025
Viewed by 190
Abstract
Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse [...] Read more.
Greenhouses are applied to mitigate the deleterious effects of inclement weather, which facilitates the optimal growth and development of the crops. South China has a climate characterized by high temperature and high humidity, and the temperature and relative humidity inside a Venlo greenhouse are higher than those in the atmosphere. In this paper, the numerical model of the flow distribution of a Venlo greenhouse in South China was established using the CFD method, which mainly applied the DO model, the k-e turbulence model, and the porous medium model. The porous resistance characteristics of tomatoes were obtained through experimental research. The inertial resistances of tomato plants in the x, y, and z directions were 80,000,000, 18,000,000, and 120,000,000, respectively; the viscous resistances of tomato plants in the x, y, and z directions were 0.43, 0.60, and 0.63, respectively. The porosity of tomato plants was 0.996. The average difference between the temperature of the established numerical model and the experimental temperature was less than 0.11 °C, and the average relative error was 2.72%. This research also studied the effects of five management and structure parameters on the velocity and temperature distribution in a greenhouse. The optimal inlet velocity is 1.32 m/s, with the COF of velocity and temperature being 9.23% and 1.18%, respectively. The optimal skylight opening is 1.76 m, with the COF of velocity and temperature being 10.68% and 0.88%, respectively. The optimal side window opening is 0.67 m, with the COF of velocity and temperature being 9.25% and 2.10%, respectively. The optimal side window height is 1.18 m, with the COF of velocity and temperature being 9.50% and 1.33%, respectively. The optimal planting interval is 1.40 m, with the COF of velocity and temperature being 15.29% and 0.20%, respectively. The results provide a reference for the design and management of Venlo greenhouses in South China. Full article
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29 pages, 16630 KiB  
Article
Impact of Radar Data Assimilation on the Simulation of Typhoon Morakot
by Lingkun Ran and Cangrui Wu
Atmosphere 2025, 16(8), 910; https://doi.org/10.3390/atmos16080910 - 28 Jul 2025
Viewed by 213
Abstract
The high spatial resolution of radar data enables the detailed resolution of typhoon vortices and their embedded structures; the assimilation of radar data in the initialization of numerical weather prediction exerts an important influence on the forecasting of typhoon track, intensity, and structures [...] Read more.
The high spatial resolution of radar data enables the detailed resolution of typhoon vortices and their embedded structures; the assimilation of radar data in the initialization of numerical weather prediction exerts an important influence on the forecasting of typhoon track, intensity, and structures up to at least 12 h. For the case of typhoon Morakot (2009), Taiwan radar data was assimilated to adjust the dynamic and thermodynamic structures of the vortex in the model initialization by the three-dimensional variation data assimilation system in the Advanced Region Prediction System (ARPS). The radial wind was directly assimilated to tune the original unbalanced velocity fields through a 3-dimensional variation method, and complex cloud analysis was conducted by using the reflectivity data. The influence of radar data assimilation on typhoon prediction was examined at the stages of Morakot landing on Taiwan Island and subsequently going inland. The results showed that the assimilation made some improvement in the prediction of vortex intensity, track, and structures in the initialization and subsequent forecast. For example, besides deepening the central sea level pressure and enhancing the maximum surface wind speed, the radar data assimilation corrected the typhoon center movement to the best track and adjusted the size and inner-core structure of the vortex to be close to the observations. It was also shown that the specific humidity adjustment in the cloud analysis procedure during the assimilation time window played an important role, producing more hydrometeors and tuning the unbalanced moisture and temperature fields. The neighborhood-based ETS revealed that the assimilation with the specific humidity adjustment was propitious in improving forecast skill, specifically for smaller-scale reflectivity at the stage of Morakot landing, and for larger-scale reflectivity at the stage of Morakot going inland. The calculation of the intensity-scale skill score of the hourly precipitation forecast showed the assimilation with the specific humidity adjustment performed skillful forecasting for the spatial forecast-error scales of 30–160 km. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
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33 pages, 7605 KiB  
Article
Dynamic Heat Transfer Modelling and Thermal Performance Evaluation for Cadmium Telluride-Based Vacuum Photovoltaic Glazing
by Changyu Qiu, Hongxing Yang and Kaijun Dong
Buildings 2025, 15(15), 2612; https://doi.org/10.3390/buildings15152612 - 23 Jul 2025
Viewed by 260
Abstract
Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, [...] Read more.
Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, the cadmium telluride-based vacuum PV glazing has been developed to enhance the thermal performance of BIPV applications. To fully understand the complex thermal behaviour under real-world operational scenarios, this study introduces a one-dimensional transient heat transfer model that can efficiently capture the time-dependent thermal dynamics of this novel glazing system. Based on the numerical solutions using the explicit finite difference method (FDM), the temperature profile of the vacuum PV glazing can be obtained dynamically. Consequently, the heat gain of the semi-transparent vacuum PV glazing can be calculated under time-varying outdoor and indoor conditions. The validated heat transfer model was applied under four different scenarios, viz. summer daytime, summer nighttime, winter daytime, and winter nighttime, to provide a detailed analysis of the dynamic thermal behaviour, including the temperature variation and the energy flow. The dynamic thermal characteristics of the vacuum PV glazing calculated by the transient heat transfer model demonstrate its excellent thermal insulation and solar control capabilities. Moreover, the thermal performance of vacuum PV glazing was compared with a standard double-pane window under various weather conditions of a typical summer day and a typical winter day. The results indicate that the vacuum PV glazing can effectively minimise both heat gain and heat loss. The fluctuation of the inner surface temperature can be controlled within a limited range away from the set point of the indoor room temperature. Therefore, the vacuum PV glazing contributes to stabilising the temperature of the indoor environment despite the fluctuating solar radiation and periodic outdoor temperature. It is suggested that the vacuum PV glazing has the potential to enhance the climate adaptability of BIPV windows under different climate backgrounds. Full article
(This article belongs to the Collection Renewable Energy in Buildings)
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31 pages, 2250 KiB  
Article
Spatial and Temporal Correlations of COVID-19 Mortality in Europe with Atmospheric Cloudiness and Solar Radiation
by Adrian Iftime, Secil Omer, Victor-Andrei Burcea, Octavian Călinescu and Ramona-Madalina Babeș
ISPRS Int. J. Geo-Inf. 2025, 14(8), 283; https://doi.org/10.3390/ijgi14080283 - 22 Jul 2025
Viewed by 261
Abstract
Previous studies reported the links between the COVID-19 incidence and weather factors, but few investigated their impact and timing on mortality, at a continental scale. We systematically investigated the temporal relationship of COVID-19 mortality in the European countries in the 1st year of [...] Read more.
Previous studies reported the links between the COVID-19 incidence and weather factors, but few investigated their impact and timing on mortality, at a continental scale. We systematically investigated the temporal relationship of COVID-19 mortality in the European countries in the 1st year of pandemic (March–December 2020) with (i) solar insolation (W/m2) at the ground level and (ii) objective sky cloudiness (as decimal cloud fraction), both derived from satellite measurements. We checked the correlations of these factors within a sliding window of two months for the whole period. Linear-mixed effect modeling revealed that overall, for the European countries (adjusted for latitude), COVID-19 mortality was substantially negatively correlated with solar insolation in the previous month (std. beta −0.69). Separately, mortality was significantly correlated with the cloudiness in both the previous month (std. beta +0.14) and the respective month (std. beta +0.32). This time gap of ∼1 month between the COVID-19 mortality and correlated weather factors was previously unreported. The long-term monitoring of these factors might be important for epidemiological policy decisions especially in the initial period of potential future pandemics when effective medical treatment might not yet be available. Full article
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17 pages, 2951 KiB  
Article
Long-Term Rainfall–Runoff Relationships During Fallow Seasons in a Humid Region
by Rui Peng, Gary Feng, Ying Ouyang, Guihong Bi and John Brooks
Climate 2025, 13(7), 149; https://doi.org/10.3390/cli13070149 - 16 Jul 2025
Viewed by 653
Abstract
The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various [...] Read more.
The hydrological processes of agricultural fields during the fallow season in east-central Mississippi remain poorly understood, due to the region’s unique rainfall patterns. This study utilized long-term rainfall records from 1924 to 2023 to evaluate runoff characteristics and the runoff response to various rainfall events during fallow seasons in Mississippi by applying the DRAINMOD model. The analysis revealed that the average rainfall during the fallow season was 760 mm over the past 100 years, accounting for 65% of the annual total. In dry, normal, and wet fallow seasons, the average rainfall was 528, 751, and 1010 mm, respectively, corresponding to runoff of 227, 388, and 602 mm. Runoff frequency increased with wetter weather conditions, rising from 16 events in dry seasons to 23 in normal seasons and 30 in wet seasons. Over the past century, runoff dynamics were predominantly regulated by high-intensity rainfall events during the fallow season. Very heavy rainfall events (mean frequency = 11 events) generated 215 mm of runoff and accounted for 53% of the total runoff, while extreme rainfall events (mean frequency = 2 events) contributed 135 mm of runoff, making up 34% of the total runoff. Water table depth played a critical role in shaping spring runoff dynamics. As the water table decreased from 46 mm in March to 80 mm in May, the soil pore space increased from 5 mm in March to 14 mm in May. This increased soil infiltration and water storage capacity, leading to a steady decline in runoff. The study found that the mean daily runoff frequency dropped from 13.5% in March to 7.6% in May, while monthly runoff decreased from 74 to 38 mm. Increased extreme rainfall (R95p) in April contributed over 45% of the total runoff and resulted in the highest daily mean runoff of 20 mm, compared to 18 mm in March and 16 mm in May. The results from this century-long historical weather data could be used to enhance field-scale water resource management, predict potential runoff risks, and optimize planting windows in the humid east-central Mississippi. Full article
(This article belongs to the Section Weather, Events and Impacts)
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18 pages, 3618 KiB  
Article
Quality Assessment of Dual-Polarization C-Band SAR Data Influenced by Precipitation Based on Normalized Polarimetric Radar Vegetation Index
by Jisung Geba Chang, Simon Kraatz, Yisok Oh, Feng Gao and Martha Anderson
Remote Sens. 2025, 17(14), 2343; https://doi.org/10.3390/rs17142343 - 8 Jul 2025
Viewed by 513
Abstract
Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar [...] Read more.
Advanced Synthetic Aperture Radar (SAR) has become an essential modality in remote sensing, offering all-weather capabilities and sensitivity to vegetation biophysical parameters and surface conditions, while effectively complementing optical sensor data. This study evaluates the impact of precipitation on the Normalized Polarimetric Radar Vegetation Index (NPRVI) using dual-polarization Sentinel-1 C-band SAR data from agricultural fields at the Beltsville Agricultural Research Center (BARC). Field-measured precipitation and Global Precipitation Measurement (GPM) precipitation datasets were temporally aligned with Sentinel-1 acquisition times to assess the sensitivity of radar signals to precipitation events. NPRVI exhibited a strong sensitivity to precipitation, particularly within the 1 to 7 h prior to the satellite overpass, even for small amounts of precipitation. A quality assessment (QA) framework was developed to flag and correct precipitation-affected radar observations through interpolation. The adjusted NPRVI values, based on the QA framework using precipitation within a 6 h window, showed strong agreement between field- and GPM-derived data, with an RMSE of 0.09 and a relative RMSE of 19.8%, demonstrating that GPM data can serve as a viable alternative for quality adjustment despite its coarse spatial resolution. The adjusted NPRVI for both soybean and corn fields significantly improved the temporal consistency of the time series and closely followed NDVI trends, while also capturing crop-specific seasonal variations, especially during periods of NDVI saturation or limited variability. These findings underscore the value of the proposed radar-based QA framework in enhancing the interpretability of vegetation dynamics. NPRVI, when adjusted for precipitation effects, can serve as a reliable and complementary tool to optical vegetation indices in agricultural and environmental monitoring. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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20 pages, 6432 KiB  
Article
A Hybrid Framework for Photovoltaic Power Forecasting Using Shifted Windows Transformer-Based Spatiotemporal Feature Extraction
by Ping Tang, Ying Su, Weisheng Zhao, Qian Wang, Lianglin Zou and Jifeng Song
Energies 2025, 18(12), 3193; https://doi.org/10.3390/en18123193 - 18 Jun 2025
Viewed by 361
Abstract
Accurate photovoltaic (PV) power forecasting is essential to mitigating the security and stability challenges associated with PV integration into power grids. Ground-based sky images can quickly reveal cloud changes, and the spatiotemporal feature information extracted from these images can improve PV power forecasting. [...] Read more.
Accurate photovoltaic (PV) power forecasting is essential to mitigating the security and stability challenges associated with PV integration into power grids. Ground-based sky images can quickly reveal cloud changes, and the spatiotemporal feature information extracted from these images can improve PV power forecasting. Therefore, this paper proposes a hybrid framework based on shifted windows Transformer (Swin Transformer), convolutional neural network, and long short-term memory network to comprehensively extract spatiotemporal feature information, including global spatial, local spatial, and temporal features, from ground-based sky images and PV power data. The mean absolute error and root mean squared error are reduced by 13.06% and 4.49% compared with ResNet-18. The experimental results indicate that the proposed framework demonstrates competitive predictive performance and generalization capability across different time horizons and weather conditions compared with benchmark frameworks. Full article
(This article belongs to the Section B: Energy and Environment)
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10 pages, 1104 KiB  
Article
Comparative Analysis of Extreme Flood Characteristics in the Huai River Basin: Insights from the 2020 Catastrophic Event
by Youbing Hu, Shijin Xu, Kai Wang, Shuxian Liang, Cui Su, Zhigang Feng and Mengjie Zhao
Water 2025, 17(12), 1815; https://doi.org/10.3390/w17121815 - 17 Jun 2025
Viewed by 380
Abstract
Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with [...] Read more.
Catastrophic floods in monsoon-driven river systems pose significant challenges to flood resilience. In July 2020, China’s Huai River Basin (HRB) encountered an unprecedented basin-wide flood event characterized by anomalous southward displacement of the rain belt. This event established a new historical record with the three typical hydrological stations (Wangjiaba, Runheji, and Lutaizi sections) along the mainstem of the Huai River exceeded their guaranteed water levels within 11 h and synchronously reached peak flood levels within a 9-h window, whereas the inter-station lag times during the 2003 and 2007 floods ranged from 24 to 48 h, causing a critical emergency in the flood defense. By integrating operational hydrological data, meteorological reports, and empirical rainfall-runoff model schemes for the Meiyu periods of 2003, 2007, and 2020, this research systematically dissects the 2020 flood’s spatial composition patterns. Comparative analyses across spatiotemporal rainfall distribution, intensity metrics, and flood peak response dynamics reveal distinct characteristics of southward-shifted torrential rain and flood variability. The findings provide critical technical guidance for defending against extreme weather events and unprecedented hydrological disasters, directly supporting revisions to flood control planning in the Huai River Ecological and Economic Zone. Full article
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16 pages, 1926 KiB  
Article
Design and Implementation of an LSTM Model with Embeddings on MCUs for Prediction of Meteorological Variables
by Jhan Piero Paulo Merma Yucra, David Juan Cerezo Quina, German Alberto Echaiz Espinoza, Manuel Alejandro Valderrama Solis, Daniel Domingo Yanyachi Aco Cardenas and Andrés Ortiz Salazar
Sensors 2025, 25(12), 3601; https://doi.org/10.3390/s25123601 - 7 Jun 2025
Viewed by 964
Abstract
The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we [...] Read more.
The use of recurrent neural networks has proven effective in time series prediction tasks such as weather. However, their use in resource-limited systems such as MCUs presents difficulties in terms of both size and stability with longer prediction windows. In this context, we propose a variant of the LSTM model, which we call SE-LSTM (Single Embedding LSTM), which uses embedding techniques to vectorially represent seasonality and latent patterns through variables such as temperature and humidity. The proposal is systematically compared in two parts: The first compares it against other reference architectures such as CNN-LSTM, TCN, LMU, and TPA-LSTM. The second stage, which includes implementation, compares it against the CNN-LSTM, LSTM, and TCN networks. Metrics such as the MAE and MSE are used along with the network weight, a crucial aspect for MCUs such as an ESP32 or Raspberry Pi Pico. An analysis of the memory usage, energy consumption, and generalization across different regions is also included. The results show that the use of embedding optimizes the network space without sacrificing the performance, which is crucial for edge computing. This research is part of a larger project, which focuses on improving agricultural monitoring systems. Full article
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25 pages, 4263 KiB  
Article
An Autofocus Method for Long Synthetic Time and Large Swath Synthetic Aperture Radar Imaging Under Multiple Non-Ideal Factors
by Kaiwen Zhu, Zhen Wang, Zehua Dong, Han Li and Linghao Li
Remote Sens. 2025, 17(11), 1946; https://doi.org/10.3390/rs17111946 - 4 Jun 2025
Viewed by 466
Abstract
Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including [...] Read more.
Synthetic aperture radar (SAR) is an all-weather and all-day imaging technique for Earth observation. Achieving efficient observation, high resolution, and wide swath coverage have remained critical development goals in SAR technology, which inherently require extended synthetic aperture time. However, various non-ideal factors, including atmospheric disturbances, orbital perturbations, and antenna vibrations. degrade imaging performance, causing defocusing and ghost targets. Furthermore, the long synthetic time and large imaging swath further enlarge the temporal and spatial variability of these factors and seriously degrade the imaging effect. These inherent challenges make autofocusing indispensable for SAR imaging with a long synthetic time and large swath. In this paper, a novel autofocus method specifically designed to address these non-ideal factors is proposed for SAR imaging with a long synthetic time and large swath. The innovation of the method mainly consists of two parts. The first is the autofocus for multiple non-ideal factors, which is accomplished by an improved phase gradient autofocus (PGA) equipped with amplitude error estimation and discrete windowing. PGA with amplitude error estimation can solve the problem of defocus, and discrete windowing can focus the energy of paired echoes. The second is an error fusion and interpolation method for a long synthetic time and large swath. This method fuses errors among sub-apertures in the long synthetic time and can fulfill autofocus for blocks where strong scatterers are not sufficient in the large swath. The proposed method can effectively achieve SAR focusing with a long synthetic time and large swath, considering spatial and temporal variant non-ideal factors. Point target simulations and distributed target simulations based on real scenarios are conducted to validate the proposed method. Full article
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31 pages, 3470 KiB  
Article
Reducing Cooling Energy Demand in Saudi Arabian Residential Buildings Using Passive Design Approaches
by Lucelia Rodrigues, Benjamin Abraham Cherian and Serik Tokbolat
Buildings 2025, 15(11), 1895; https://doi.org/10.3390/buildings15111895 - 30 May 2025
Viewed by 1030
Abstract
In Saudi Arabia’s hot and arid climate, residential buildings account for over half of national electricity consumption, with cooling demands alone responsible for more than 70% of this use. This paper explores the hypothesis that contemporary villa designs are inherently inefficient and that [...] Read more.
In Saudi Arabia’s hot and arid climate, residential buildings account for over half of national electricity consumption, with cooling demands alone responsible for more than 70% of this use. This paper explores the hypothesis that contemporary villa designs are inherently inefficient and that current building regulations fall short of enabling adequate thermal performance. This issue is expected to become increasingly significant in the near future as external temperatures continue to rise. The study aims to assess whether passive design strategies rooted in both engineering and architectural principles can offer substantial reductions in cooling energy demand under current and future climatic conditions. A typical detached villa was simulated using IES-VE to test a range of passive measures, including optimized window-to-wall ratios, enhanced glazing configurations, varied envelope constructions, solar shading devices, and wind-tower-based natural ventilation. Parametric simulations were conducted under current climate data and extended to future weather scenarios. Unlike many prior studies, this work integrates these strategies holistically and evaluates their combined impact, rather than in isolation while assessing the impact of future weather in the region. The findings revealed that individual measures such as insulated ceilings and reduced window-to-wall ratios significantly lowered cooling loads. When applied in combination, these strategies achieved a 68% reduction in cooling energy use compared to the base-case villa. While full passive performance year-round remains unfeasible in such extreme conditions, the study demonstrates a clear pathway toward energy-efficient housing in the Gulf region. Full article
(This article belongs to the Section Building Energy, Physics, Environment, and Systems)
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14 pages, 2601 KiB  
Article
Lightning Damage Detection Method Using Autoencoder: A Case Study on Wind Turbines with Different Blade Damage Patterns
by Takuto Matsui, Kazuki Matsuoka and Kazuo Yamamoto
Wind 2025, 5(2), 12; https://doi.org/10.3390/wind5020012 - 22 May 2025
Viewed by 554
Abstract
There have been numerous reported accidents of lightning strikes damaging wind turbine blades, which poses a serious problem. In certain accidents, the blades that were struck by lightning continued to rotate, resulting in breakage due to centrifugal force. Considering this background, wind turbines [...] Read more.
There have been numerous reported accidents of lightning strikes damaging wind turbine blades, which poses a serious problem. In certain accidents, the blades that were struck by lightning continued to rotate, resulting in breakage due to centrifugal force. Considering this background, wind turbines situated in Japan have been mandated to be equipped with emergency stop devices. Consequently, upon detection of a lightning strike by the device installed on the wind turbine, the turbine is promptly stopped. In order to restart the wind turbine, it is necessary to verify its soundness by conducting a visual inspection. However, conducting prompt inspections can be difficult due to various factors, including inclement weather. Therefore, this process prolongs the downtime of wind turbines and reduces their availability. In this study, an approach was proposed: a SCADA data analysis method using an autoencoder to assess the soundness of wind turbines without visual inspection. The present method selected wind speed and rotational speed as effective features, employing a sliding window for pre-processing, based on previous studies. Besides, the assessment of a trained autoencoder was conducted through the utilization of the confusion matrix and the receiver operating characteristic curve. It was suggested that the availability of wind turbines could be improved by employing this proposed method to remotely and automatically verify the soundness after lightning detection. Full article
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21 pages, 2147 KiB  
Article
Runoff Prediction Method Based on Pangu-Weather
by Wentao Yang, Hui Qin, Yongsheng Jie, Yuhua Qu, Taiheng Zhang, Chenghong Li and Li Tan
Water 2025, 17(9), 1405; https://doi.org/10.3390/w17091405 - 7 May 2025
Cited by 1 | Viewed by 868
Abstract
Runoff prediction is a complex hydrological, nonlinear time-series problem. Many machine learning methods have been put forth in recent years to predict runoff. A sliding window method is typically used to preprocess the data in order to rebuild the time series of runoff [...] Read more.
Runoff prediction is a complex hydrological, nonlinear time-series problem. Many machine learning methods have been put forth in recent years to predict runoff. A sliding window method is typically used to preprocess the data in order to rebuild the time series of runoff data into a standard machine learning dataset. The size of the window is a variable parameter that is commonly referred to as the time step. With developments in computer and AI technology, data-driven models have demonstrated tremendous potential for runoff prediction. And AI technology has opened up a new avenue for weather prediction, with Pangu-Weather demonstrating considerable improvements in both accuracy and processing efficiency. This study creates two novel prediction models, LSTM-Pangu and GRU-Pangu, by combining Pangu with Long Short-Term Memory (LSTM) and the Gate Recurrent Unit (GRU). We concentrated on the Beipanjiang River Basin in China, using Guizhou Qianyuan Power Company Limited’s daily runoff data and meteorological satellite data from the Climate Data Store platform to forecast daily runoff. These models were used to anticipate runoff on various future days (known as the lead time). The results show that regardless of time step, both LSTM-Pangu and GRU-Pangu outperform the LSTM and GRU models. Furthermore, this advantage is more evident as the advance time increases. When the time step is 7 and the lead time is 5, the Nash–Sutcliffe Efficiency (NSE) of the LSTM-Pangu model improves by 8.1% compared to the LSTM model, while the NSE of the GRU-Pangu model improves by 11.7% compared to the GRU model. Furthermore, LSTM-Pangu and GRU-Pangu outperform LSTM and GRU models in terms of the predictive accuracy under high-flow conditions, highlighting their significant advantages in flood forecasting. This integration strategy displays great transferability and may be expanded to other typical data-driven models. Full article
(This article belongs to the Section Hydrology)
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