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This paper presents the development and validation of a cost-effective 3D-printed conductive phantom for EEG sensing system validation that achieves 85% cost reduction (£48.10 vs. £300–£500) and 48-hour fabrication time while providing consistent electrical properties suitable for standardized electrode testing. The phantom was fabricated using conductive PLA filament in a two-component design with a conductive upper section and a non-conductive base for structural support. Comprehensive validation employed three complementary approaches: DC resistance measurements (821–1502 Ω), complex impedance spectroscopy at 100 Hz across anatomical regions (3.01–6.4 kΩ with capacitive behavior), and 8-channel EEG system testing (5–11 kΩ impedance range). The electrical characterization revealed spatial heterogeneity and consistent electrical properties suitable for comparative electrode evaluation and EEG sensing system validation applications. To establish context, we analyzed six existing phantom technologies including commercial injection-molded phantoms, saline solutions, hydrogels, silicone models, textile-based alternatives, and multi-material implementations. This analysis identifies critical accessibility barriers in current technologies, particularly cost constraints (£5000–20,000 tooling) and extended production timelines that limit widespread adoption. The validated 3D-printed phantom addresses these limitations while providing appropriate electrical properties for standardized EEG electrode testing. The demonstrated compatibility with clinical EEG acquisition systems establishes the phantom’s suitability for electrode performance evaluation and multi-channel system validation as a standardized testing platform, ultimately contributing to democratized access to EEG sensing system validation capabilities for broader research communities.

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.

In this paper, we delve into the innovative application of large language models (LLMs) and their extension, large vision-language models (LVLMs), in the field of remote sensing (RS) image analysis. We particularly emphasize their multi-tasking potential with a focus on image captioning and visual question answering (VQA). In particular, we introduce an improved version of the Large Language and Vision Assistant Model (LLaVA), specifically adapted for RS imagery through a low-rank adaptation approach. To evaluate the model performance, we create the RS-instructions dataset, a comprehensive benchmark dataset that integrates four diverse single-task datasets related to captioning and VQA. The experimental results confirm the model’s effectiveness, marking a step forward toward the development of efficient multi-task models for RS image analysis.

Spatio-temporal fusion (STF) aims at fusing (temporally dense) coarse resolution images and (temporally sparse) fine resolution images to generate image series with adequate temporal and spatial resolution. In the last decade, STF has drawn a lot of attention and many STF methods have been developed. However, to date the STF domain still lacks benchmark datasets, which is a pressing issue that needs to be addressed in order to foster the development of this field. In this review, we provide (for the first time in the literature) a robust benchmark STF dataset that includes three important characteristics: (1) diversity of regions, (2) long timespan, and (3) challenging scenarios. We also provide a survey of highly representative STF techniques, along with a detailed quantitative and qualitative comparison of their performance with our newly presented benchmark dataset. The proposed dataset is public and available online.

In soybean, there is a lack of research aiming to compare the performance of machine learning (ML) and deep learning (DL) methods to predict more than one agronomic variable, such as days to maturity (DM), plant height (PH), and grain yield (GY). As these variables are important to developing an overall precision farming model, we propose a machine learning approach to predict DM, PH, and GY for soybean cultivars based on multispectral bands. The field experiment considered 524 genotypes of soybeans in the 2017/2018 and 2018/2019 growing seasons and a multitemporal–multispectral dataset collected by embedded sensor in an unmanned aerial vehicle (UAV). We proposed a multilayer deep learning regression network, trained during 2000 epochs using an adaptive subgradient method, a random Gaussian initialization, and a 50% dropout in the first hidden layer for regularization. Three different scenarios, including only spectral bands, only vegetation indices, and spectral bands plus vegetation indices, were adopted to infer each variable (PH, DM, and GY). The DL model performance was compared against shallow learning methods such as random forest (RF), support vector machine (SVM), and linear regression (LR). The results indicate that our approach has the potential to predict soybean-related variables using multispectral bands only. Both DL and RF models presented a strong (r surpassing 0.77) prediction capacity for the PH variable, regardless of the adopted input variables group. Our results demonstrated that the DL model (r = 0.66) was superior to predict DM when the input variable was the spectral bands. For GY, all machine learning models evaluated presented similar performance (r ranging from 0.42 to 0.44) for each tested scenario. In conclusion, this study demonstrated an efficient approach to a computational solution capable of predicting multiple important soybean crop variables based on remote sensing data. Future research could benefit from the information presented here and be implemented in subsequent processes related to soybean cultivars or other types of agronomic crops.

NASA's Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar mission which will produce near global (51.6°S to 51.6°N) maps of forest structure and above‐ground biomass density during its 2‐year mission. GEDI uses a waveform simulator for calibration of algorithms and assessing mission accuracy. This paper implements a waveform simulator, using the method proposed in Blair and Hofton (1999; https://doi.org/10.1029/1999GL010484), and builds upon that work by adding instrument noise and by validating simulated waveforms across a range of forest types, airborne laser scanning (ALS) instruments, and survey configurations. The simulator was validated by comparing waveform metrics derived from simulated waveforms against those derived from observed large‐footprint, full‐waveform lidar data from NASA's airborne Land, Vegetation, and Ice Sensor (LVIS). The simulator was found to produce waveform metrics with a mean bias of less than 0.22 m and a root‐mean‐square error of less than 5.7 m, as long as the ALS data had sufficient pulse density. The minimum pulse density required depended upon the instrument. Measurement errors due to instrument noise predicted by the simulator were within 1.5 m of those from observed waveforms and 70–85% of variance in measurement error was explained. Changing the ALS survey configuration had no significant impact on simulated metrics, suggesting that the ALS pulse density is a sufficient metric of simulator accuracy across the range of conditions and instruments tested. These results give confidence in the use of the simulator for the pre‐launch calibration and performance assessment of the GEDI mission. Plain Language Summary NASA's Global Ecosystem Dynamics Investigation (GEDI) will be the first spaceborne lidar optimized for forest measurement and will produce a range of near‐global forest products. This paper assesses the accuracy of the GEDI simulator, which underpins the pre‐launch calibration of GEDI's data products. Key Points GEDI's simulator has been validated and found accurate enough for pre‐launch calibration activities The uncertainties of the simulator have been quantified and ALS beam density identified as a sufficient measure of accuracy Interesting quirks of full‐waveform metrics have been highlighted and investigated

Soil erosion and groundwater resources are two fundamental global concerns intricately linked through various hydrological and morphometric processes. Morphometrics with soil erosion assessment is crucial for managing hydrological processes and implementing preventative strategies. Utilizing Geographical Information system and Remote Sensing techniques, morphometric, morphotectonic, and soil erosion susceptibility in the tectonically active Hunza-Nagar catchment were explored, spanning 1455.05 km 2 with elevations from 1763–7697 m above sea level. With this motive, linear, areal, and relief morphometric variables were investigated. Analysis of the linear aspects indicated the sub-dendritic drainage pattern with streams ordered from 1 to 4th order. The calculated parameters recorded huge variations, including stream length of 384.92 km, bifurcation ratio of 1.65, drainage density of 2.65 km/km 2 , drainage intensity of 0.25 km −1 , drainage texture of 0.49, stream frequency of 0.07 km −2 and form factor of 0.41, respectively. The circulatory ratio of 0.46 indicates structural influence, elongation ratio of 0.72 reflects moderate to steep slopes with low flood regimes, length of overland flow of 1.33 km shows high infiltration and shape index of 2.47 underscores a higher risk of soil erosion in the catchment. Soil erosion susceptibility analysis was conducted using the XGBoost model, renowned for its proficiency in predictive modeling and classification tasks. The model was trained and tested on a dataset comprising factors pertinent to soil erosion dynamics. Subsequently, the trained model was applied to assess soil erosion susceptibility across the study area. The final Susceptibility map was classified from low to very high susceptible zones. Confusion matrix and Receiving operative characteristic curve (ROC) were used to validate the model. These results offer crucial insights into geohydrological characteristics, supporting global conservation efforts in conservation of natural resources and soil practices.

The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m2 , we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m2 , we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area. accuracy; deep learning; Earth observation; Sentinel-2; validation

In the context of the International GNSS Service (IGS), several IGS Ionosphere Associated Analysis Centers have developed different techniques to provide global ionospheric maps (GIMs) of vertical total electron content (VTEC) since 1998. In this paper we present a comparison of the performances of all the GIMs created in the frame of IGS. Indeed we compare the classical ones (for the ionospheric analysis centers CODE, ESA/ESOC, JPL and UPC) with the new ones (NRCAN, CAS, WHU). To assess the quality of them in fair and completely independent ways, two assessment methods are used: a direct comparison to altimeter data (VTEC-altimeter) and to the difference of slant total electron content (STEC) observed in independent ground reference stations (dSTEC-GPS). The main conclusion of this study, performed during one solar cycle, is the consistency of the results between so many different GIM techniques and implementations.

Satellite laser ranging (SLR) to low Earth orbiters (LEOs) provides optical distance measurements with mm-to-cm-level precision. SLR residuals, i.e., differences between measured and modeled ranges, serve as a common figure of merit for the quality assessment of orbits derived by radiometric tracking techniques. We discuss relevant processing standards for the modeling of SLR observations and highlight the importance of line-of-sight-dependent range corrections for the various types of laser retroreflector arrays. A 1–3 cm consistency of SLR observations and GPS-based precise orbits is demonstrated for a wide range of past and present LEO missions supported by the International Laser Ranging Service (ILRS). A parameter estimation approach is presented to investigate systematic orbit errors and it is shown that SLR validation of LEO satellites is not only able to detect radial but also along-track and cross-track offsets. SLR residual statistics clearly depend on the employed precise orbit determination technique (kinematic vs. reduced-dynamic, float vs. fixed ambiguities) but also reveal pronounced differences in the ILRS station performance. Using the residual-based parameter estimation approach, corrections to ILRS station coordinates, range biases, and timing offsets are derived. As a result, root-mean-square residuals of 5–10 mm have been achieved over a 1-year data arc in 2016 using observations from a subset of high-performance stations and ambiguity-fixed orbits of four LEO missions. As a final contribution, we demonstrate that SLR can not only validate single-satellite orbit solutions but also precise baseline solutions of formation flying missions such as GRACE, TanDEM-X, and Swarm.