Search Results (3,311)

An integrated ESP32-based measurement system called PV-Scope is presented for real-time photovoltaic (PV) module efficiency characterization and small off-grid system testing under field conditions. The system includes pyranometer-calibrated irradiance sensors using a solar simulator, maximum power point tracking, and comprehensive environmental monitoring to enable accurate performance assessment of PV modules across diverse technologies, manufacturers and installation conditions. Unlike standard test condition (STC) measurements at cell temperatures of 25 °C, this system captures the interactions between efficiency and environmental variables that significantly impact real-world efficiency. In particular, measurement of temperature-dependent efficiency under local conditions and validation of temperature-dependent models for extending the results to other environmental conditions are enabled with cell temperature monitoring in addition to ambient temperature, humidity, and wind speed. PV-Scope is designed for integrated sensing versatility, portable outdoor testing, and order-of-magnitude cost savings compared to commercial equipment to meet measurement needs across research, education, and practical PV innovation, including bifacial module testing, assessment of cooling techniques, tandem and multi-junction testing, and agrivoltaics. Full article

Vegetation indices (VIs) are a widely adopted and straightforward tool for non-contact estimation of chlorophyll and carotenoid content in plant leaves. However, VI-based method accuracy depends critically on instrument configuration and calibration procedures. This study aimed to evaluate the sensitivity of VI-based pigment assessment to variations in spectral channel parameters (central wavelength and bandwidth) as well as to changes in calibration details defined by the specific VI formula. Pigment content was measured in leaves of Lactuca sativa L. and Cucumis sativus L. at contrasting developmental stages using VI-based reflection spectroscopy across the 450–950 nm spectral range with various protocols and spectrophotometry as the reference method. VI values were calculated with varying central wavelength and widths of spectral bands, and across different VI formulas. Comparative analysis of the obtained measurements revealed that even minor shifts in central wavelengths of less than 20 nm or the use of an alternative index formula could lead to relative errors of 42–77% in the estimation of chlorophylls and carotenoids content, while changes in bandwidth had a much smaller impact, resulting in only 2–5% relative errors. Even with identical parameters of spectral channels, the choice of an appropriate VI and its regression model could introduce significant errors, ranging from 36% to 86%. These findings highlight the critical role of instrument specifications and calibration models in the VIs-based method accuracy and stability, as measurement errors can lead to suboptimal agronomic decisions. Moreover, our study underscores that comparing results from different sensors or platforms can be unreliable unless the channel parameters and calibration details are clearly specified. Therefore, standardization and transparency in VIs assignment is vital to ensure reproducibility and cross-compatibility in non-destructive pigment monitoring by using various devices. Full article

LuTan-1(LT-1) is China’s first L-band differential interferometric synthetic aperture radar system, comprising two multi-polarization SAR satellites, LT-1A and LT-1B. The satellite uses differential deformation measurement and interferometric altimetry technology to realize surface deformation monitoring and topographic mapping in designated areas. It has the characteristics of all-weather, all-time, and multi-polarization and can be applied to military and civilian fields. In order to further improve the accuracy of image geometric positioning, this paper analyzes the error sources of geometric positioning for the differential deformation measurement mode (strip 1) of the satellite service. The in-orbit data of three years since the launch (2022–2024) are selected to analyze the positioning accuracy and stability of the uncontrolled plane based on the corner reflector and active calibrator deployed in the calibration field. The experimental results show that the positioning accuracy of the satellite strip 1 image without a control plane meets the requirements of the in-orbit index and remains relatively stable. The geometric precision correction positioning accuracy after error source compensation is better than 3.0 m, providing a favorable support for the subsequent application. Full article

Urban mobility plays a critical role in ensuring resilience during natural disasters such as floods, yet developing reliable traffic models remains challenging for medium-sized cities with limited monitoring infrastructure. This study developed a hybrid traffic modeling approach that integrates Google Traffic data with field measurements to address incomplete digital coverage in flood-prone areas of Loja, Ecuador. The methodology involved collecting 1501 field speed measurements and 235,690 Google Typical Traffic observations using exclusively open-source tools and freely available data sources. Adjustment factors ranging from 0.25 to 0.97 revealed systematic discrepancies between Google Traffic estimates and field observations, highlighting the need for local calibration. The resulting traffic network model encompassing 4966 nodes and 5425 edges accurately simulated flood impacts, with the most critical scenario (Thursday 17–19, 100% road impact) showing travel time increases of 1123% and congestion index deterioration from 1.79 to 21.69. Statistical validation confirmed significant increases in both travel times (p = 0.0231) and distances (p = 0.0207) under flood conditions across five representative routes. This research demonstrates that accurate traffic models can be developed through intelligent integration of heterogeneous data sources, providing a scalable solution for enhancing urban mobility analysis and emergency preparedness in resource-constrained cities facing climate-related transportation challenges. Full article

Efficient water management is essential for optimizing agricultural productivity in water-scarce regions such as the Lis Valley, Portugal. In situ measurements of soil moisture content (SMC) and electrical conductivity (EC), together with Sentinel-2-derived vegetation indices, were used to assess the crop water status and evapotranspiration dynamics of pumpkin (Cucurbita moschata ‘Butternut’) during the 2020 growing season. SMC and EC were measured at depths of 10, 20, 30, 50, and 70 cm using a TDR sensor, with strong correlations observed in the upper layers, indicating that EC can complement direct SMC measurements in characterizing near-surface moisture conditions. Sentinel-2 imagery was acquired to compute NDVI, SAVI, EVI, and GCI. In addition, NDVI values obtained from both a GreenSeeker^® sensor and Sentinel-2 imagery were compared, showing a similar temporal pattern during the season. By replacing the standard FAO-56 K_c values with those derived from each vegetation index, ET_a was recalculated to incorporate actual crop condition variability detected via satellite. ET_a estimates from RS-assisted vegetation indices agreed with those obtained using the FAO-56 method; independent ET_a measurements were not available for validation. Although such agreement is partly expected due to calibration, its confirmation for Cucurbita moschata under Mediterranean conditions—where published references are scarce—reinforces the method’s practical applicability for water management in data-limited settings. Water Productivity (WP) was estimated as 8.32 kg m⁻³, and Water Use Efficiency (WUE FAO-56) was calculated as 0.64 kg m⁻³, indicating high water use efficiency under Mediterranean smallholder irrigation conditions. These findings demonstrate that integrating high-resolution RS with continuous soil moisture monitoring can enhance precision irrigation strategies, increase crop yields, and conserve water resources in the Lis Valley. Full article

This study analyzed how external forcings, such as meteorological conditions and inflows, influence the average water surface temperature (WST) of the urban Lake Paranoá, Brasília-Brazil, using both in situ measurements and remote sensing estimates over a 40-year period. The temperature model calibrated for Lake Paranoá with no time lag (0-day delay) presented the following metrics: R² = 0.92, RMSE = 0.59 °C, demonstrating the feasibility of obtaining reliable thermal estimates from remote sensing even in urban water bodies. Simple and multiple regression analyses were applied to identify the main external drivers of WST across different temporal scales. A warming trend of 0.036 °C/yr in lake surface temperature was observed, higher than the concurrent increase in air temperature (0.026 °C/yr), suggesting enhanced thermal stratification that may impact water quality. The most influential variables on WST were air temperature, relative humidity, and wind speed, with varying degrees of influence depending on the time scale considered (daily, monthly, annual or seasonal). Remote sensing proved to be essential for overcoming the limitations of traditional monitoring, such as temporal gaps and limited spatial coverage, and allowed detailed mapping of thermal patterns throughout the lake. Integrating these data into hydrodynamic models enhances their diagnostic, predictive, and decision-support capabilities in the context of climate change. Full article

The article presents a method for detecting low-intensity DDoS attacks, focused on identifying difficult-to-detect “low-and-slow” scenarios that remain undetectable by traditional defence systems. The key feature of the developed method is the statistical criteria’s (χ² and T statistics, energy ratio, reconstruction errors) integration with a combined neural network architecture, including convolutional and transformer blocks coupled with an autoencoder and a calibrated regressor. The developed neural network architecture combines mathematical validity and high sensitivity to weak anomalies with the ability to generate interpretable artefacts that are suitable for subsequent forensic analysis. The developed method implements a multi-layered process, according to which the first level statistically evaluates the flow intensity and interpacket intervals, and the second level processes features using a neural network module, generating an integral blend-score S metric. ROC-AUC and PR-AUC metrics, learning curve analysis, and the estimate of the calibration error (ECE) were used for validation. Experimental results demonstrated the superiority of the proposed method over existing approaches, as the achieved values of ROC-AUC and PR-AUC were 0.80 and 0.866, respectively, with an ECE level of 0.04, indicating a high accuracy of attack detection. The study’s contribution lies in a method combining statistical and neural network analysis development, as well as in ensuring the evidentiary value of the results through the generation of structured incident reports (PCAP slices, time windows, cryptographic hashes). The obtained results expand the toolkit for cyber-attack analysis and open up prospects for the methods’ practical application in monitoring systems and law enforcement agencies. Full article

This paper describes a laboratory set-up designed to support hands-on learning of heat transfer principles in aerospace engineering education. Developed within the framework of experiential and project-based learning, the set-up enables students to experimentally characterize the convective coefficient of a cooling fan and the thermo-optical properties of aluminum plates with different surface coatings, specifically their absorptivity and emissivity. A custom-built, LED-based radiation source (the ESAT Sun simulator) and a calibrated temperature acquisition system are used to emulate and monitor radiative heating under controlled conditions. Simplified physical models are developed for both the ESAT Sun simulator and the plates that capture the dominant thermal dynamics via first-order energy balances. The laboratory workflow includes real-time data acquisition, curve fitting, and thermal model inversion to estimate the convective and thermo-optical coefficients. The results demonstrate good agreement between the model predictions and observed temperatures, which supports the suitability of the set-up for education. The proposed activities can strengthen the student’s understanding of convective and radiative heat transport in aerospace applications while also fostering skills in data analysis, physical and numerical reasoning, and system-level thinking. Opportunities exist to expand the material library, refine the physical modeling, and evaluate the long-term pedagogical impact of the educational set-up described here. Full article

The Black Sea is a semi-enclosed basin subject to intense anthropogenic pressures and transboundary pollution, making reliable and comparable monitoring data essential for large-scale environmental assessments. However, national practices differ considerably, hindering data integration and coordinated reporting under international frameworks. This study, conducted within the Horizon 2020 project “Advancing Black Sea Research and Innovation to Co-develop Blue Growth within Resilient Ecosystems” (BRIDGE-BS), evaluated pollutant surveillance methodologies with a focus on heavy metals and priority organic contaminants (polycyclic aromatic hydrocarbons, polychlorinated biphenyls, organochlorine pesticides). Standard Operating Procedures (SOPs) were collected from institutions across Black Sea countries and systematically compared for water, sediment, and biota matrices. The analysis revealed shared reliance on internationally recognized techniques but also heterogeneity in sediment fraction selection, digestion and extraction conditions, instrumental approaches, and quality assurance/quality control (QA/QC) documentation. To complement this assessment, an intercalibration (IC) exercise was organized through the QUASIMEME proficiency testing scheme, accompanied by a follow-up structured questionnaire sent to participant institutions. While individual results remain confidential, collective feedback highlighted common challenges in calibration, blank correction, certified reference materials (CRMs) availability, digestion variability, instrument maintenance, and the reporting of uncertainty and detection limits. Together, these findings confirm that harmonization in the Black Sea requires not only improved comparability of laboratory methods but also the future alignment of assessment methodologies, including indicators and thresholds, to support coherent, basin-wide environmental evaluations under regional conventions and EU directives. Full article

Understanding the composition of phytoplankton assemblages and monitoring changes in their diversity is a key factor in the comprehension of global biogeochemical cycles, climate regulation and marine ecosystem health, especially in the context of increasing global warming. Regional empirical algorithms for phytoplankton satellite estimates of size classes (PSCs) and functional types (PFTs) in the Mediterranean Sea have been developed and implemented in the EU Copernicus Marine Service since 2019. Here, we present an update of the PSC and PFT algorithms operational in the Copernicus catalogue since the end of 2024. Results show an overall improvement in the model performance, in line with Copernicus Marine Service requirements focused on the continuous enhancement of the accuracy of distributed biogeochemical variables. Finally, the new algorithms were applied to a time series of over 25 years of satellite data (1998–2024), enabling the identification of key changes in phytoplankton composition at both monthly and basin scales. These insights were made possible by an algorithm re-calibration based on updated and more comprehensive regional pigment ratios. Full article

Shipboard ballast water management systems (BWMS) commonly employ chlorine or other oxidants to treat ballast. Oxidant-based BWMS inject these biocides to meet a concentration threshold or target value that is lethal to most aquatic organisms. Resulting concentrations of total residual oxidant (TRO) may span two orders of magnitude between initial doses (e.g., ~10 mg L⁻¹) and discharged ballast, which must meet discharge limits (e.g., <0.1 mg L⁻¹). Here, we evaluated three TRO instruments (two colorimetric-based and one based on amperometry) that have been integrated into BWMS for use in shipboard applications. Our study quantified accuracy and precision using test waters along a range of temperatures and salinities, using a pipe loop to mimic in-line shipboard operations, where the instruments continuously sample and analyze circulating water. Linear regression analysis compared the instruments to a standard reference method along a range of concentrations relevant to oxidant-based BWMS. In general, measurements from the TRO sensors showed strong linear relationships to the reference method, but slopes of these relationships were significantly <1 in all but one instance. Precision—measured as the coefficient of variation—ranged from 2 to 4%. These initial tests occurred on units shipped directly from the manufacturer, immediately following calibration and quality checks, and in a controlled laboratory environment. Thus, in this context, our evaluations represent a “best-case” outcome. We recommend that laboratory studies (as described here) be paired with endurance trials and in-service monitoring to include tests in a shipboard environment. These trials should evaluate TRO instruments that are integrated with BWMS and functioning under normal ship operations, measuring both high (treated ballast) and low (neutralized discharge) concentrations of TRO. Shipboard trials in concert with frequent calibration checks will reduce the risks of under- or overestimating TRO concentrations, as both outcomes may harm the environment. Full article

In response to the imperative to mitigate methane—one of the most potent greenhouse gases—this study proposes and tests an integrated workflow for designing methane drainage boreholes targeting post-mining areas in an active underground coal mine (Pniówek, Poland). The workflow combines the following: (1) forecasting methane emissions from goafs and active longwalls for 2024–2040; (2) 3D geological characterization (structural and lithofacies models); (3) selection and sealing of goaf zones; and (4) optimization of well placement, drilling, and performance evaluation of drainage boreholes, including an assessment of energy use from the recovered gas. Applying the method delineated priority capture zones and estimated recoverable rates under multiple scenarios. Preliminary field data from a borehole near seam 362/1 indicate stable methane inflow to the drainage system and a concomitant reduction in methane load within the ventilation network. The integrated design improves targeting efficiency and provides a quantitative basis for scheduling, risk management, and sizing of surface-to-underground infrastructure. The results suggest that systematic drainage of post-mining voids can enhance safety, limit fugitive emissions, and create opportunities for on-site power generation. The approach is transferable to other active mines with legacy workings, provided site-specific calibration and monitoring are implemented. Full article

This work explores the relationship between landscape metrics and surface water in the Galicia-North Portugal Euroregion, employing 6,220,767 linear regression models through Python scripts to predict surface water quality. The Iberian Biological Monitoring Working Party (IBMWP) index, based on benthic macroinvertebrate communities from 40 sites across Portugal (PT) and Galicia (GL), served as the biological indicator. The models were initially selected based on linear regression assumptions (17 tests), and validated against real-world data, evaluating statistical performance through indicators such as R-squared, mean absolute percentage error (MAPE), and percentage bias (PBIAS). Results indicated that GL had a higher macroinvertebrate abundance, whereas Portugal showed greater diversity and family richness. Statistical analysis revealed that landscape significantly influenced water quality, with land use composition and configuration driving differences in ecological conditions between regions. The best-performing models demonstrated a high R-squared value of 0.7, a MAPE of 27% for calibration (PT) and 10% for the validation (GL), indicating a strong predictive relationship. The models provide valuable insights into the complex interactions between landscape patterns and water quality, highlighting how variations in landscape structure can directly affect ecological integrity. These findings reinforce the need for strategic land management to preserve water quality and emphasizing the importance of transboundary governance across the Euroregion to foster sustainable development. Full article

In the context of global agricultural modernization, the early and accurate detection of sugarcane leaf diseases is critical for ensuring stable sugar production. However, existing deep learning models still face significant challenges in complex field environments, such as blurred lesion edges, scale variation, and limited generalization capability. To address these issues, this study constructs an efficient recognition model for sugarcane disease detection, named CaneFocus-Net, specifically designed for precise identification of sugarcane leaf diseases. Based on a single-stage detection architecture, the model introduces a lightweight cross-stage feature fusion module (CP) to optimize feature transfer efficiency. It also designs a module combining a channel-spatial adaptive calibration mechanism with multi-scale pooling aggregation to enhance the backbone network’s ability to extract multi-scale lesion features. Furthermore, by expanding the high-resolution shallow feature layer to enhance sensitivity toward small-sized targets and adopting a phased adaptive nonlinear optimization strategy, detection and localization accuracy along with convergence efficiency have been further improved. Test results on public datasets demonstrate that this method significantly enhances recognition performance for fuzzy lesions and multi-scale targets while maintaining high inference speed. Compared to the baseline model, precision, recall, and mean average precision (mAP50 and mAP50-95) improved by 1.9%, 4.6%, 1.5%, and 1.4%, respectively, demonstrating strong generalization capabilities and practical application potential. This provides reliable technical support for intelligent monitoring of sugarcane diseases in the field. Full article

The Huanjing-2A/2B (HJ-2A/2B) satellites are China’s next-generation environmental monitoring satellites, equipped with four visible light wide-swath charge-coupled device (CCD) sensors. These sensors enable the acquisition of 16-m multispectral imagery (16m-MSI) with a swath width of 800 km through field-of-view stitching. However, traditional vicarious calibration techniques are limited by their calibration frequency, making them insufficient for continuous monitoring requirements. To address this challenge, the present study proposes a spectral-angle difference correction-based cross-calibration approach, using the Landsat 8/9 Operational Land Imager (OLI) as the reference sensor to calibrate the HJ-2A/2B CCD sensors. This method improves both radiometric accuracy and temporal frequency. The study utilizes cloud-free image pairs of HJ-2A/2B CCD and Landsat 8/9 OLI, acquired simultaneously at the Dunhuang and Golmud calibration sites between 2021 and 2024, in combination with atmospheric parameters from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) dataset and historical ground-measured spectral reflectance data for cross-calibration. The methodology includes spatial matching and resampling of the image pairs, along with the identification of radiometrically stable homogeneous regions. To account for sensor viewing geometry differences, an observation-angle linear correction model is introduced. Spectral band adjustment factors (SBAFs) are also applied to correct for discrepancies in spectral response functions (SRFs) across sensors. Experimental results demonstrate that the cross-calibration coefficients differ by less than 10% compared to vicarious calibration results from the China Centre for Resources Satellite Data and Application (CRESDA). Additionally, using Sentinel-2 MSI as the reference sensor, the cross-calibration coefficients were independently validated through cross-validation. The results indicate that the radiometrically corrected HJ-2A/2B 16m-MSI CCD data, based on these coefficients, exhibit improved radiometric consistency with Sentinel-2 MSI observations. Further analysis shows that the cross-calibration method significantly enhances radiometric consistency across the HJ-2A/2B 16m-MSI CCD sensors, with radiometric response differences between CCD1 and CCD4 maintained below 3%. Error analysis quantifies the impact of atmospheric parameters and surface reflectance on calibration accuracy, with total uncertainty calculated. The proposed spectral-angle correction-based cross-calibration method not only improves calibration accuracy but also offers reliable technical support for long-term radiometric performance monitoring of the HJ-2A/2B 16m-MSI CCD sensors. Full article