Search Results (419)

Lighting in hospital patient rooms supports appropriate visual conditions, patient well-being, and sustainability-oriented healthcare retrofit decisions. Although previous studies have examined daylighting or artificial-lighting systems in healthcare buildings, limited research has systematically compared practical retrofit strategies within a consistent framework while considering both illuminance quantity and spatial uniformity. This study evaluates two existing single-bed patient rooms at Fırat University Hospital in Elazığ, Türkiye. The rooms had comparable dimensions but differed in façade orientation and window width. Three-dimensional models were developed in DIALux Evo 13, and simulations were conducted for the summer and winter solstices under clear- and overcast-sky conditions at 09:00, 12:00, and 18:00, together with an artificial-lighting-only scenario at 00:00. The scenarios included the existing lighting condition, increased wall reflectance, two LED luminaire replacement systems, and combined daylight and artificial-lighting conditions. Lighting performance was assessed using average illuminance, lighting uniformity, and daylight factor. Independent field measurements at 12 points showed close agreement with the simulations, with a mean absolute percentage error of 2.86%, an RMSE of 5.68 lx, and R² = 0.9996. The south-facing room generally received more daylight, but higher illuminance was often accompanied by lower uniformity. Under artificial-lighting-only conditions, increasing wall reflectance raised visual-task-area illuminance by 4.2–4.9%, whereas the LED systems produced increases of 48.6–53.7% in the north-facing room and 93.8–101.2% in the south-facing room. Overall, wall-reflectance modification provided a moderate improvement, whereas LED replacement produced greater improvements in illuminance and spatial uniformity. The findings provide preliminary, context-specific guidance for sustainability-oriented hospital patient-room retrofit projects. Full article

Plane-parallel atmospheric radiative transfer models neglect the Earth’s curvature, resulting in substantial optical path errors at large zenith angles and limiting the accuracy of satellite remote sensing retrievals. Fully spherical Monte Carlo models can accurately represent curvature and multiple-scattering effects, but their high computational cost limits their application in rapid or operational calculations. Pseudo-spherical approximations offer greater computational efficiency but generally retain plane-parallel assumptions for multiple scattering, which may reduce their accuracy in aerosol- and cloud-laden atmospheres. To address these limitations, this study develops a physics-guided, data-driven framework for efficient spherical radiance estimation. Reference spherical radiances were generated using a Monte Carlo radiative transfer model for representative clear-sky, aerosol-laden, and cloudy atmospheric scenarios. An extreme gradient boosting (XGBoost) model was then trained to map plane-parallel radiances to their spherical counterparts at wavelengths of 450, 550, and 650 nm. The predictors included wavelength, solar zenith angle (SZA), viewing zenith angle, azimuth angle, asymmetry factor, surface albedo, optical depth, single scattering albedo, the central height of a single aerosol or cloud layer and plane-parallel radiance. On the independent test set, the XGBoost model achieved a mean absolute percentage error (MAPE) of 5.71%. For the common clear-sky subset used to compare all three methods, the corresponding MAPEs of the plane-parallel and pseudo-spherical models were 29.61% and 19.65%, respectively. These results indicate that the proposed model can substantially reduce curvature-related radiance errors while retaining high computational efficiency across the atmospheric scenarios considered in this study. Full article

This paper presents a generic stochastic simulation framework for evaluating the operational feasibility of solar-powered unmanned aerial vehicles (UAVs) executing an invariant trajectory in high-variability climates. Unlike conventional approaches relying on idealised irradiance conditions, the proposed framework combines a modified ASHRAE radiation model corrected for local bias and variability with a semi-Markov process modelling stochastic transitions between cloud and sunlight states using parametrised state duration times. The environmental model is further extended with diurnal temperature variation and standard atmosphere effects. UAV motion is represented using a rigid body flight dynamics model combined with a cascaded trajectory tracking controller and an energy subsystem incorporating a lithium-ion battery model. Warsaw (Dfb climate) is used as a representative Central European test case characterised by frequent radiation deficits and highly variable atmospheric conditions. The simulations quantify the influence of environmental uncertainty and selected battery capacities on mission success probability across different solar-to-wing area ratios, with the mission entry at 70% initial battery state of charge and no additional manoeuvre losses or external atmospheric perturbations. The evaluations were conducted for a fixed mission start at solar noon on 15 July and were supplemented by an optimised mission scheduling analysis to establish upper flight-time limits. The results demonstrate the strong sensitivity of solar-assisted UAV operations to stochastic cloud conditions and support the design and mission planning for low-altitude long-endurance aircraft. Full article

The use of extremely high frequency (EHF) bands for satellite and terrestrial communications has increased because of the large available bandwidth and the potential for high data rates. At the same time, signal propagation in this range is strongly affected by atmospheric absorption and phase dispersion, both of which vary with altitude and weather conditions. Accurate Earth-to-space link-budget estimation therefore requires a propagation model that accounts not only for local attenuation but also for the refracted path through the atmosphere. In this work, Earth-to-space cumulative attenuation is analyzed by combining the real and imaginary parts of atmospheric refractivity along the propagation path. The real part determines the ray geometry, while the imaginary part determines the accumulated loss. The model is evaluated for clear-sky, fog, and rain conditions and for different transmission elevation angles. The results show the strong dependence of cumulative attenuation on frequency, weather conditions, and path geometry across EHF satellite communication bands. Full article

FY-4B/GIIRS temperature-profile products provide an important source of atmospheric sounding information for the radiosonde-sparse Tibetan Plateau, but their error structure and correctability over complex terrain remain insufficiently understood. In this study, the FY-4B/GIIRS temperature profiles over the central-eastern Tibetan Plateau were systematically evaluated using collocated satellite and radiosonde observations from 2023 to 2025. In addition to conventional validation metrics, an error-structure diagnosis was conducted to distinguish the relative contributions of systematic and random components of the satellite–radiosonde differences across 12 cloud–season–layer scenarios. The results show that product quality varies markedly with cloud condition and season. Clear-sky samples are more frequent during the cold season, whereas cloudy samples increase during the warm season, and the proportion of high-quality retrievals decreases substantially under cloudy conditions. Retrieval errors also exhibit clear vertical dependence, with the highest accuracy in the middle layer and larger errors in the lower and upper layers. The error-structure diagnosis further reveals pronounced scenario dependence: random errors dominate most clear-sky conditions, whereas systematic biases are particularly evident in the clear-sky lower layer in winter, the clear-sky upper layer in summer, and most cloudy scenarios. A scenario-based LightGBM framework effectively reduces these systematic differences across the 12 cloud–season–layer scenarios. Test-set $R^2$ ranges from 0.43 to 0.82, while mean Bias decreases from −1.66 to 0.01 K and mean RMSE from 3.35 to 1.78 K, corresponding to an average reduction of 45.56%. In contrast, the residual-error magnitude is less predictable, with STD-model $R^2$ values of 0.09–0.37. These findings indicate that error-structure diagnosis can help identify correctable retrieval-error components and that scenario-based correction provides an effective approach for improving the reliability of FY-4B/GIIRS temperature profiles over complex plateau terrain. Full article

Photovoltaic–thermal (PVT) collectors co-generate electricity and heat—natural front ends for thermochemical energy storage (TCES)—provided the heat transfer fluid stays above the reactor’s desorption temperature. Using an eight-node model of a 0.6834 m² collector at 8.64° N whose thermal core is partially validated against measured data from the same tropical–coastal site (rooftop PV module temperature, RMSE 3.8 °C; prototype absorber-to-water heat transfer, RMSE 1.3 °C), flow-regulated to the ≈95 °C SrCl₂/NH₃ desorption threshold, we quantify the energy and exergy delivered for charging under tropical–monsoon intermittency. The 95 °C setpoint operation, the ≈5.3 h charging window, and all reported exergy yields are simulated: the built prototype delivered hot water peaking at 79 °C and did not reach the 95 °C setpoint. On a measured clear-sky day (clearness index K_t = 0.52), the collector yields 1.38 kWh of energy but only 0.43 kWh of exergy (first-law efficiency ≈ 38%; gross exergy efficiency ≈ 13%); across a 30-seed synthetic-intermittency ensemble, the exergy yield is 0.678 kWh at ≈14% gross exergy efficiency (≈52% combined first-law efficiency). In both cases, the thermal stream dominates the energy output while the electrical stream dominates the exergy output—on the sunlit day, the exergy is about 80% electrical—because 95 °C heat carries a Carnot factor (exergetic quality factor, 1 − T_a/T₇, at the instantaneous ambient dead state) of only ≈0.18 and an integrated Bejan/Kotas thermal-exergy quality of only ≈0.09. The controller holds the outlet within 1.4 K of the setpoint for ≈5.3 h, whereas no fixed flow in the 0.5–5.0 L min⁻¹ range ever reaches it: feedback control is a structural enabler, not an optimisation. On overcast days, the threshold is never reached and charging heat collapses to zero, leaving a PV-only generator. Exergy delivery is nonetheless nearly controller-independent: the accumulated exergy delivery deficit after a 50% irradiance drop is 937 kJ, a controller-independent value changing only 1.3% across a systematic 4 × 4 gain sweep (K_p 0.33–2.7×, K_d 0.25–5× of nominal), and predictive control improves it by ≤1%. For PVT–TCES at this scale, the decisive lever is deployability, not control sophistication. Full article

Stratospheric solar-powered unmanned aerial vehicles (UAVs), commonly operated as High-Altitude Pseudo-Satellites (HAPS), promise satellite-like persistence for Earth observation, communications and remote sensing, but their feasibility is governed by a tight coupling between solar energy availability and onboard energy demand. This study presents an energy-balance simulation framework that predicts the diurnal charge–discharge behaviour and endurance of solar-powered UAVs. The framework couples a physics-based environmental irradiance model—astronomical solar position, an air-mass and pressure-scaled broadband atmospheric transmission and an eccentricity-corrected extraterrestrial irradiance—with a wing-geometry photovoltaic collection model that reduces the airfoil camber, planform, dihedral and cell layout of a real wing to three scalar coefficients, replacing the flat-plate assumption common in solar-UAV sizing. The closed-form collection coefficient captures the full dependence of collected power on sun position and aircraft heading and admits an exact orbit-averaging result for circular loiter. The model is implemented as a reproducible, modular tool with single-day, annual and global analysis modes. It is validated against a ground-based photovoltaic charging campaign conducted on the as-built Aurora solar UAV demonstrator (5.6 m span, 8 kg) over three clear-sky days spanning a 90-day seasonal range: predicted and measured wing-collected power agree with a Pearson correlation of 0.998, a coefficient of determination of 0.993, an RMS error of 6.0% and a daily-energy agreement within 3.5%. A structured residual identifies an unmodelled photovoltaic temperature effect bounded at the 6% level. The framework provides HAPS designers and operators with a transparent, validated tool for feasibility screening, component selection and mission planning across latitude and season. Full article

On-orbit absolute radiometric calibration tracks sensor radiometric degradation and ensures accuracy for quantitative applications. Low-cost commercial satellites lack expensive on-board calibration systems, and traditional alternative methods cannot achieve automated, reliable calibration for large sensor fleets with low resource consumption. Pseudo-invariant calibration sites require no ground instruments, but commercial satellites’ heavy imaging schedules hinder frequent PICS observations. The Jilin-1 constellation is a large commercial constellation composed of satellites carrying multispectral imagers with resolution better than 1 m, and it has no on-board calibration system. Thus, an automated calibration method applicable to numerous imagers is needed, one that requires widely available clear-sky ground targets. Using MCD43A2 and MCD12Q1 products, we generate calibration region vectors to transfer the MODIS radiometric reference to Jilin-1. A look-up table (LUT) is constructed for the input parameters of the MODTRAN model. Calibration pixels and model input parameters are then extracted from Jilin-1 imagery using the region vectors, the LUT is interpolated to obtain the at-aperture radiance for each pixel and band, and on-orbit absolute radiometric calibration coefficients are calculated. The proposed method requires no ground-based synchronous experiments, achieves a high level of automation, and does not consume commercial imaging resources. The site calibration validation based on RadCalNet for the JL1GF02F PMS1 sensor shows that the maximum relative difference in the method across all bands is less than 4%. Full article

Clouds frequently degrade optical satellite imagery, limiting the reliability of remote sensing models. However, in the literature, cloud-free analyses are often performed by excluding cloudy images from machine learning datasets and methods. This restricts their usefulness in time-critical scenarios such as disaster response, where waiting for cloud-free imagery is impractical. Cloud removal can mitigate this issue, but methods remain imperfect and may introduce visual artifacts. Therefore, it is desirable to develop cloud-robust methods by combining optical imagery with radar data, a modality unaffected by clouds. While datasets for machine learning combine optical and radar data, most researchers exclude cloudy images from training and evaluation. We identify this exclusion as a limitation that reduces applicability to cloudy scenarios and address it by introducing CloudyBigEarthNet (CBEN), a dataset of paired optical and radar images containing cloud occlusions for land-use and land-cover classification. Using average precision (AP), we show that state-of-the-art methods trained on clear-sky optical and radar data suffer performance drops of between 23.8 and 33.4 AP points when tested on cloudy imagery. We adapt these methods using cloudy images during training and improve AP on cloudy test cases by 17.2 to 28.7 AP points. Code and dataset have been published. Full article

Integrating solar power into electricity grids requires accurate short-term forecasting of the global horizontal irradiance to accurately predict the expected solar power generation. This paper compares five tree-based machine learning models against a Persistence baseline for multi-resolution forecasting in arid climates. A 13-year dataset from Basra, Iraq, has been employed in this study for verification purposes, and the models are tested across various very-short- to short-term forecasting horizons of 5, 10, 15, 30, and 60 min. Unlike most existing studies that focus on single forecasting horizons or mixed climatic conditions, this work systematically benchmarks multi-resolution irradiance forecasting under distinct sky conditions in a hot arid environment using a strict anti-data-leakage framework. To avoid data leakage in these models, feature engineering has used only lagged inputs. The dataset has been split into three groups for training, validation, and testing (respectively 70, 15, and 15% of the entire available dataset). The models were then tested separately under clear, partly cloudy, and cloudy skies. Numerical studies prove that picking the best model depends heavily on the forecast horizon. For very-short-term predictions, the Persistence model was competitive (RMSE = 21.32 W/m²), while the Gradient Boosting model proved slightly more accurate (RMSE = 17.65 W/m²). For the 60 min horizon, the boosting models took a clear lead. The HistGradientBoosting model resulted in a 67% reduction in the RMSE compared to the Persistence baseline. Also, the top-performing model changed depending on the weather and the time scale. Gradient Boosting was the clear winner for short-term clear sky forecasts, while XGBoost handled the longer horizons. Partly cloudy skies showed a rotating mix of different boosting algorithms taking the lead. However, studies show that when skies were fully overcast, complex machine learning models fail to capture chaotic patterns, making the simple Persistence baseline a necessary reliability safeguard. The results reveal that no single model consistently dominates all forecasting horizons and weather conditions, highlighting the necessity of adaptive model selection for operational solar forecasting. These findings highlight the importance of horizon- and weather-adaptive model selection for operational solar forecasting. Rather than relying on a single universal algorithm, grid operators in arid regions can improve forecasting reliability by dynamically selecting models based on prevailing sky conditions and forecast horizons. Full article

The generation of high-quality maritime samples is gradually becoming a key and challenging issue, due to the data thirst for training maritime intelligent models. However, existing methods mainly focus on static sample generation, which cannot meet the requirements of algorithms for dynamic decision. In this paper, an innovative method for generating high-quality marine dynamic data is proposed based on diffusion models. Considering the sensitivity of the diffusion model to prompts, a text enhancement module is first designed to perform semantic enhancement on the input text from the perspective of an expert in maritime climatology. Meanwhile, a directional image editing module is proposed to extract masks of interest from the input image, resulting in separate sea surface and sky regions. Then the image, mask and the enhanced text are sent together into the diffusion model to generate a high-quality directionally edited image. Finally, a video generation diffusion model is designed to convert the edited image into a dynamic data sequence. The entire framework has a clear sense of hierarchy and stable generation effect. We performed quantitative and qualitative experiments to prove that our method has significant advantages in data quality and controllability against existing SOTA methods. Full article

Surface solar ultraviolet (UV) radiation represents an essential component of shortwave solar radiation, with important implications for atmospheric chemistry and climate studies. Reliable, high-quality records of surface solar UV radiation are essential for UV-related research and applications; however, ground-based UV observations remain sparse worldwide. This study presents a novel broadband parametric model, based on physical principles, for estimating solar UV irradiance ($0.280–0.400 \mu \mathrm{m}$) under clear-sky conditions. The model is computationally efficient and suitable for practical applications. The proposed approach is based on the SMARTS2 spectral radiative transfer model and employs an interdependent integration scheme to derive broadband UV irradiance from spectrally resolved shortwave radiation. The model performance is evaluated against high-quality measurements from the Baseline Surface Radiation Network (BSRN) and compared with an established parameterization. The proposed model demonstrates improved performance at both validation sites, reducing the mean nRMSE from 8.88% to 7.64% at Izaña and from 60.69% to 29.24% at Payerne, while also substantially decreasing the bias under more challenging atmospheric conditions, although the nRMSE at Payerne remains relatively high. These results highlight the potential of the proposed approach as an efficient and physically consistent tool for clear-sky UV irradiance estimation. Full article

Understanding the dynamics of the urban atmospheric boundary layer is critical for accurate meteorological and air quality modeling. Utilizing one year of continuous Doppler wind lidar observations, this study investigates the seasonal and diurnal variability of wind fields, low-level jets (LLJs), and mixing-layer height (MLH) at an urban site in Beijing. Results show that horizontal winds are strongest in winter and spring and weaker in summer, with northwesterly flow dominating in winter and more diverse patterns in summer, while the corrected vertical-velocity distributions show seasonally varying structures and are interpreted cautiously as frequency-distribution characteristics. A distinct diurnal phase reversal in wind speed is identified near 0.3 km. LLJs occur predominantly at night, with core heights descending from 1.2–1.6 km in winter to 0.6–0.8 km in summer, and are associated with enhanced vertical shear. MLH reaches its deepest development in spring, with clear-sky peaks exceeding 1.5 km, while summer growth is comparatively limited and is associated with stronger latent heat partitioning. These findings indicate that wind fields, LLJs, and MLH exhibit coherent seasonal and diurnal covariations, while their direct causal relationships require further process-oriented analysis. This study provides a year-long observational basis for evaluating urban ABL parameterizations. Full article

Land surface temperature (LST) is a key variable in the physics of land surface processes on both regional and global scales. This paper addresses the all-sky (clear-sky and cloudy-sky) LSTs retrieval from the data acquired by the Medium-Resolution Spectral Imager II on Fengyun 3D (FY-3D) satellite. First, an improved split-window algorithm to retrieve clear-sky LSTs is developed using numerical radiative transfer modeling experiments. Then, clear-sky LSTs are retrieved from MERSI-II/FY-3D data in January and July 2022 over an Asian area (70°E~130°E, 10°N~50°N), and cross-validated against MODIS/Aqua LST/emissivity (LST/E) Daily version 6 (MYD11C1 V6) product. Next, a hybrid method combining the eXtreme Gradient Boosting (XGBoost) model and the surface energy balance theory is developed to estimate cloudy-sky LSTs. After that, cloudy-sky LSTs are estimated from the MERSI-II data and validated with the China Meteorological Administration Land Data Assimilation System Version 2 (CLDAS V2) dataset. Against the MYD11C1 LSTs, the root mean square error (RMSE), bias and coefficient of determination (R²) of the retrieved clear-sky LSTs are 1.15 K, 0.01 ± 1.14 K, and 0.99, respectively. Against the CLDAS LSTs, the RMSE, bias and R² of the estimated hypothetical clear-sky LSTs are 4.05 K, 0.75 ± 3.98 K and 0.91, respectively, while they are 3.69 K, 0.36 ± 3.67 K, and 0.92 for the retrieved cloudy-sky LSTs, respectively, which indicates that the retrieval accuracy of cloudy-sky LSTs is improved after the cloud radiation effect correction. The all-sky LSTs retrieved in this study are accurate and consistent with the results in previous studies. Full article

Infrared small-UAV detection remains difficult because the target often appears as a weak thermal point rather than a clear object. This problem is clear in the SIDD dataset, where most test targets are smaller than 32 × 32 pixels. To address this case, this paper proposes S-Drone-YOLO, a compact YOLO-based detector that maintains a high-resolution P2 prediction path and leverages it carefully during classification. The model starts from a lightweight YOLOv5-style detector. It adds a stride-4 P2 path and replaces the C3 neck blocks with C2fAttn to improve feature reuse before prediction. Two components are then added to the Architecture II design. The Coordinate-Aware Residual C2f Block, CAR-C2f, strengthens the P2 branch using coordinate attention and residual scaling. The P2-Guided Quality-Aware Detection Head (P2-QADH) combines local P2 details with nearby P3 context. It produces a quality map that adjusts the classification logits. The regression branch, output tensor format, and training loss interface remain unchanged. On the SIDD infrared drone dataset, S-Drone-YOLO reaches 0.988 precision, 0.939 recall, 0.699 mAP50-95, and 0.962 F1-score. It uses 6.45 M parameters and 31.3 GFLOPs. Compared with the Architecture I model, recall increases by 0.8 percentage points and mAP50-95 increases by 0.4 percentage points. At the same time, the parameter count decreases by 20.3%, and GFLOPs decrease by 43.7%. Fine-tuning on five RGB UAV datasets and a second thermal dataset (ThermalUAV2UAV) yields F1 scores ranging from 0.941 to 0.999, with an mAP50-95 of 0.843 on the thermal dataset. The background analysis also shows stable F1-scores across sky, sea, city, and mountain scenes. These results suggest that controlled P2 guidance can improve infrared small-UAV detection while keeping the model size practical. Full article