Search Results (426)

This paper presents the design, implementation, and experimental validation of a Condition Monitoring (CM) circuit for SiC-based Power Electronics Converters (PECs). The paper leverages in situ drain–source resistance (R_ds,on) measurements, interfaced with cloud connectivity for data processing and lifetime assessment, addressing key limitations in current state-of-the-art (SOTA) methods. Traditional approaches rely on expensive data acquisition systems under controlled laboratory conditions, making them unsuitable for real-world applications due to component variability, time delay, and noise sensitivity. Furthermore, these methods lack cloud interfacing for real-time data analysis and fail to provide comprehensive reliability metrics such as Remaining Useful Life (RUL). Additionally, the proposed CM method benefits from noise mitigation during switching transitions by utilizing delay circuits to ensure stable and accurate data capture. Moreover, collected data are transmitted to the cloud for long-term health assessment and damage evaluation. In this paper, experimental validation follows a structured design involving signal acquisition, filtering, cloud transmission, and temperature and thermal degradation tracking. Experimental testing has been conducted at different temperatures and operating conditions, considering coolant temperature variations (40 °C to 80 °C), and an output power of 7 kW. Results have demonstrated a clear correlation between temperature rise and R_ds,on variations, validating the ability of the proposed method to predict device degradation. Finally, by leveraging cloud computing, this work provides a practical solution for real-world Wide Band Gap (WBG)-based PEC reliability and lifetime assessment. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

The Copernicus European Regional Reanalysis (CERRA) was released in August 2022, providing a continental atmospheric reanalysis, and, in addition, the New European Wind Atlas (NEWA) is a recently released hindcast product that can be used to create a high temporal and spatial resolution wind resource atlas of Europe. In order to demonstrate the suitability of the NEWA and CERRA wind datasets for offshore wind energy applications, the accuracy of these datasets was assessed for the Mediterranean Sea, a basin with a high potential for the development of offshore wind projects. Long-term in situ measurements from 13 offshore locations along the basin were used in order to assess the performance of the CERRA and NEWA wind speed datasets in the hourly and seasonal time scales by using a variety of different evaluation tools. The results revealed that the CERRA dataset outperforms NEWA and is a reliable source for offshore wind energy assessment studies in the examined areas, although special attention should be paid to extreme value analysis of the wind speed. Full article

Snow plays a crucial role in global climate regulation, hydrological processes, and environmental change, making the accurate acquisition of snow depth data highly significant. In this study, we used Sentinel-1 radar data and employed a simulated annealing algorithm to select the optimal influencing factors from radar backscatter characteristics and spatiotemporal geographical parameters within the study area. Snow depth retrieval was subsequently performed using both random forest (RF) and Support Vector Machine (SVM) models. The retrieval results were validated against in situ measurements and compared with the long-term daily snow depth dataset of China for the period 2017–2019. The results indicate that the RF model achieves better agreement with the measured data than existing snow depth products. Specifically, in the Xinjiang region, the RF model demonstrates superior performance, with an R² of 0.92, a root mean square error (RMSE) of 2.61 cm, and a mean absolute error (MAE) of 1.42 cm. In contrast, the SVM regression model shows weaker agreement with the observations, with an R² lower than that of the existing snow depth product (0.51) in Xinjiang, and it performs poorly in other regions as well. Overall, the SVM model exhibits deficiencies in both predictive accuracy and spatial stability. This study provides a valuable reference for snow depth retrieval research based on active microwave remote sensing techniques. Full article

There is a lack of an effective approach to measure vacuum conditions inside sealed vacuum electronic devices (VEDs) and other small-space vacuum instruments. In this study, the application performance of an innovative low-pressure gas sensor based on the emission enhancements of multi-walled carbon nanotube (MWCNT) field emitters was investigated, and the in situ vacuum performance of X-ray tubes was studied for the advantages of miniature dimension and having low power consumption, extremely low outgassing, and low thermal disturbance compared to conventional ionization gauges. The MWCNT emitters with high crystallinity presented good pressure sensing performance for nitrogen, hydrogen, and an air mixture in the range of 10⁻⁷ to 10⁻³ Pa. The miniature MWCNT sensor is able to work and remain stable with high-temperature baking, important for VED applications. The sensor monitored the in situ pressures of the sealed X-ray tubes successfully with high-power operations and a long-term storage of over two years. The investigation showed that the vacuum of the sealed X-ray tube is typical at a low 10⁻⁴ Pa level, and pre-sealing degassing treatments are able to make the X-ray tube work under high vacuum levels with less outgassing and keep a stable high vacuum for a long period of time. Full article

The Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), and European Space Agency-Climate Change Initiative (ESA–CCI) soil moisture (SM) products are widely used in agricultural drought monitoring, water resource management, and climate analysis applications. However, the performance of these SM products varies significantly across regions and environmental conditions, due to in sensor characteristics, retrieval algorithms, and the lack of localized calibration. This study proposes a multi-source long short-term memory (MLSTM) for improving ASCAT, ESA–CCI, and SMAP SM products by combining in-situ SM measurements and four key auxiliary variables: precipitation (PRE), land surface temperature (LST), fractional vegetation cover (FVC), and evapotranspiration (ET). First, the in-situ measured data from four in-situ observation networks were corrected using the LSTM method to match the grid sizes of ASCAT (0.1°), ESA–CCI (0.25°), and SMAP (0.1°) SM products. The RPE, LST, FVC, and ET were used as inputs to the LSTM to obtain loss data against in-situ SM measurements. Second, the ASCAT, ESA–CCI, and SMAP SM datasets were used as inputs to the LSTM to generate loss data, which were subsequently corrected using LSTM-derived loss data based on in-situ SM measurements. When the mean squared error (MSE) loss values were minimized, the improvement for ASCAT, ESA–CCI, and SMAP products was considered the best. Finally, the improved ASCAT, ESA–CCI, and SMAP were produced and evaluated by the correlation coefficient (R), root mean square error (RMSE), and standard deviation (SD). The results showed that the RMSE values of the improved ASCAT, ESA–CCI, and SMAP products against the corrected in-situ SM data in the OZNET network were lower, i.e., 0.014 cm³/cm³, 0.019 cm³/cm³, and 0.034 cm³/cm³, respectively. Compared with the ESA–CCI and SMAP products, the ASCAT product was greatly improved, e.g., in the SNOTEL network, the Root Mean-Square Deviation (RMSD) values of 0.1049 cm³/cm³ (ASCAT) and 0.0662 cm³/cm³ (improved ASCAT). Overall, the MLSTM-based algorithm has the potential to improve the global satellite SM product. Full article

Since the mid-1980s, the Mediterranean Sea’s surface and deeper layers have warmed at unprecedented rates, with recent projections identifying it as one of the regions most impacted by rising global temperatures. Metrics that characterize phytoplankton abundance, phenology and size structure are widely utilized as ecological indicators that enable a quantitative assessment of the status of marine ecosystems in response to environmental change. Here, using an extensive, updated in situ pigment dataset collated from numerous past research campaigns across the Mediterranean Sea, we re-parameterized an abundance-based phytoplankton size class model that infers Chl-a concentration in three phytoplankton size classes: pico- (<2 μm), nano- (2–20 μm) and micro-phytoplankton (>20 μm). Following recent advancements made within this category of size class models, we also incorporated information of sea surface temperature (SST) into the model parameterization. By tying model parameters to SST, the performance of the re-parameterized model was improved based on comparisons with concurrent, independent in situ measurements. Similarly, the application of the model to remotely sensed ocean color observations revealed strong agreement between satellite-derived estimates of phytoplankton size structure and in situ observations, with a performance comparable to the current regional operational datasets on size structure. The proposed conceptual regional model, parameterized with the most extended in situ pigment dataset available to date for the area, serves as a suitable foundation for long-term (1997–present) analyses on phytoplankton size structure and ecological indicators (i.e., phenology), ultimately linking higher trophic level responses to a changing Mediterranean Sea. Full article

Authenticity is a core principle in conservation guidelines and a key goal of heritage preservation, especially in Serbia, where many aging objects face ongoing deterioration. The subject of this study is the church within the Blagoveštenje Monastery complex in the Ovčar-Kablar gorge, built using stone from a local quarry at the beginning of the 17th century. The inclination of the structure, observed as progressively increasing over the centuries, raises important concerns regarding its stability. This research focuses on identifying the underlying causes of this phenomenon in order to support its long-term preservation. The methods used the study are long-term in situ observations including analysis, geodetic research, 3D laser imaging, geophysical, geological, archaeological research, evaluation of current condition, determination of structural failures and their cause and monitoring the structural behavior of elements. All methods were carried out in accordance with the definition of rehabilitation measures and the protection of masonry buildings. The main contribution of this study is identifying that the church’s inclination and deviation result from the northern foundation resting on weaker soil and a deeper rock mass compared to the southern side. The research approach and findings presented in this paper can serve as a guide for future endeavors aimed at identifying the causes of deformations and the restoration and structural rehabilitation of masonry buildings as cultural heritage. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

The southern Baltic Sea is characterized by highly variable weather conditions, particularly in autumn and winter, when storms, strong westerly winds, and temporary sea ice formation disrupt maritime operations. This study presents a climatographic overview and evaluates key hydrometeorological factors that influence the safe and efficient use of liquefied natural gas (LNG) as bunkering fuel in the region. The analysis draws on long-term meteorological and hydrological datasets (1971–2020), including satellite observations and in situ measurements. It identifies operational constraints, such as wind speed, wave height, visibility, and ice cover, and assesses their impact on LNG logistics and terminal functionality. Thresholds for safe operations are evaluated in accordance with IMO and ISO safety standards. An ice severity forecast for 2011–2030 was developed using the ECHAM5 global climate model under the A1B emission scenario, indicating potential seasonal risks to LNG operations. While baseline safety criteria are generally met, environmental variability in the region may still cause temporary disruptions. Findings underscore the need for resilient port infrastructure, including anti-icing systems, heated transfer equipment, and real-time environmental monitoring, to ensure operational continuity. Integrating weather forecasting into LNG logistics supports uninterrupted deliveries and contributes to EU goals for energy diversification and emissions reduction. The study concludes that strategic investments in LNG infrastructure—tailored to regional climatic conditions—can enhance energy security in the southern Baltic, provided environmental risks are systematically accounted for in operational planning. Full article

Prolonged conflicts in Iraq over the past four decades have profoundly disrupted environmental systems, not only through immediate post-conflict emissions—such as residues from munitions and explosives—but also via long-term infrastructural collapse, population displacement, and unsustainable resource practices. Despite growing concern over air quality in conflict-affected regions, comprehensive assessments integrating long-term data and localized measurements remain scarce. This study addresses this gap by analyzing the environmental consequences of sustained instability in Mosul, focusing on air pollution trends using both remote sensing data (1983–2023) and in situ monitoring of key pollutants—including PM_2.5, PM₁₀, TVOCs, NO₂, SO₂, and formaldehyde—at six urban sites during 2022–2023. The results indicate marked seasonal variations, with winter peaks in combustion-related pollutants (NO₂, SO₂) and elevated particulate concentrations in summer driven by sandstorm activity. Annual average concentrations of all six pollutants increased by 14–51%, frequently exceeding WHO air quality guidelines. These patterns coincide with worsening meteorological conditions, including higher temperatures, reduced rainfall, and more frequent storms, suggesting synergistic effects between climate stress and pollution. The findings highlight severe public health risks and emphasize the urgent need for integrated urban recovery strategies that promote sustainable infrastructure, environmental restoration, and resilience to climate change. Full article

Wave energy is a promising renewable resource supporting the decarbonization of energy systems. However, its significant temporal variability necessitates long-term datasets for accurate resource assessment. A common approach to obtaining such data is through climate reanalysis datasets. Nevertheless, reanalysis data may not accurately capture the local characteristics of wave energy at specific sites. This study proposes a supervised machine-learning (ML) approach to estimate long-term wave energy at locations with only short-term in situ measurements. The method involves training ML models using concurrent short-term buoy data and ERA5 reanalysis data, enabling the extension of wave energy estimates over longer periods using only reanalysis inputs. As a case study, hourly mean significant wave height and energy period data from 2000 to 2023 were analyzed, collected by a deep-water buoy off the coast of Gran Canaria (Canary Islands, Spain). Among the ML techniques evaluated, Multiple Linear Regression (MLR) and Support Vector Regression yielded the most favorable error metrics. MLR was selected due to its lower computational complexity, greater interpretability, and ease of implementation, aligning with the principle of parsimony, particularly in contexts where model transparency is essential. The MLR model achieved a mean absolute error (MAE) of 2.56 kW/m and a root mean square error (RMSE) of 4.49 kW/m, significantly outperforming the direct use of ERA5 data, which resulted in an MAE of 4.38 kW/m and an RMSE of 7.1 kW/m. These findings underscore the effectiveness of the proposed approach in enhancing long-term wave energy estimations using limited in situ data. Full article

Achieving both indoor environmental quality (IEQ) and energy efficiency in school buildings remains a challenge, particularly in older structures where renovation strategies often lack site-specific validation. This study evaluates the impact of energy retrofits on a 1970s primary school in France by integrating in situ measurements with a validated numerical model for forecasting energy demand and IEQ. Temperature, humidity, and CO₂ levels were recorded before and after renovations, which included insulation upgrades and an air handling unit replacement. Results indicate significant improvements in winter thermal comfort (PPD < 20%) with a reduced heating water temperature (65 °C to 55 °C) and stable indoor air quality (CO₂ < 800 ppm), without the need for window ventilation. Night-flushing ventilation proved effective in mitigating overheating by shifting peak temperatures outside school hours, contributing to enhanced thermal regulation. Long-term energy consumption analysis (2019–2022) revealed substantial reductions in gas and electricity use, 15% and 29% of energy saving for electricity and gas, supporting the effectiveness of the applied renovation strategies. However, summer overheating (up to 30 °C) persisted, particularly in south-facing upper floors with extensive glazing, underscoring the need for additional optimization in solar gain management and heating control. By providing empirical validation of renovation outcomes, this study bridges the gap between theoretical predictions and real-world effectiveness, offering a data-driven framework for enhancing IEQ and energy performance in aging school infrastructure. Full article

This work presents a data-driven modeling framework for predicting image-derived crystal size metrics in seeded cooling crystallization using Long Short-Term Memory (LSTM) neural networks. The model leverages in situ microscopy data to predict square-weighted D10, D50, D90, and particle counts based solely on seed loading and temperature profiles, without requiring real-time supersaturation measurements. To enhance predictive power, engineered process descriptors—including temperature derivatives and integrals—were incorporated as dynamic features. Experimental validation was performed using creatine monohydrate crystallization from aqueous solution, with LSTM models trained on a diverse dataset encompassing variable seed loadings and cooling profiles. The feature-engineered LSTM model consistently outperformed its non-engineered counterpart, particularly under nonlinear cooling conditions where crystallization dynamics were the most complex. This approach offers a practical alternative to mechanistic models and spectroscopic process analytical technology (PAT) tools by enabling accurate prediction of chord length distribution (CLD) metrics from routinely collected data. The framework is easily transferable to other crystallization systems and provides a low-complexity, high-accuracy tool for accelerating lab-scale crystallization development. Full article

Understanding the complex phenomena of interactions between the elements of a combined piled raft foundation (CPRF) is essential for the proper design of such foundations. To evaluate the effects of mutual influence among the CPRF’s elements, a series of long-term measurements of selected physical quantities related to the performance of the foundation were conducted on a building with a frame structure, stiffening walls, and monolithic technology, consisting of seven aboveground stories and one underground story. The analysis distinguishes the real deformations resulting from temperature changes and from stress strains resulting from load changes. The two types of deformations were subjected to further interpretation of only changes in the stress and strain over time. Changes in stress values in the subsoil, as well as strain measurements in the vertical direction of concrete columns, were recorded to assess the load distribution between the CPRF’s components. The numerical analysis results obtained for a fragment of the monitored foundation were compared with actual measurement results to verify the numerical model of interaction between the structure and the soil. Field monitoring and FEA methods were used to compare the long-term deformation analysis, and they helped to minimize the monitoring time. This comparison also served to supplement and simultaneously expand the dataset of test results on a real-world scale. Full article