Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs by Yinqing Zhen, Qingyun Yan

“…The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. …”
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Urban Canopy Parameters’ Computation and Evaluation in an Indian Context Using Multi-Platform Remote Sensing Data by Kshama Gupta, Bhoomika Ghale, Ashutosh Bhardwaj, Anshika Varshney, Shweta Khatriker, Vinay Kumar, Prasun Kumar Gupta, Pramod Kumar

“…Performance evaluation of computed UCPs against a 3D reference geodatabase showed high prediction accuracy for most UCPs, with overall biases, mean absolute error, and root-mean-square error values significantly better than 1 m, with strong correlation (0.8–0.9). …”
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Assessing above ground biomass of Wunbaik Mangrove Forest in Myanmar using machine learning and remote sensing data by Win Sithu Maung, Satoshi Tsuyuki, Takuya Hiroshima, San San Htay

“…The results revealed that the RF model outperformed the other models, yielding a higher coefficient of determination (R2) of 0.33 and a Root Mean Square Error (RMSE) of 32.05 Mg ha−1. We then set up different scenarios to improve the performance of the RF model for AGB estimation. …”
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Control and Real-Time Monitoring of Autonomous Underwater Vehicle Through Underwater Wireless Optical Communication by Dongwook Jung, Rouchen Zhang, Hyunjoon Cho, Daehyeong Ji, Seunghyen Kim, Hyeungsik Choi

“…For attitude control of the AUV, an attitude heading reference system (AHRS) and a depth sensor are installed. …”
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Efficient Techniques for Solution of Complex Computational Tasks in Building Physics by Václav Kočí, Jiří Maděra, Tomáš Krejčí, Jaroslav Kruis, Robert Černý

“…For materials homogenization, the best results are achieved when k = 0.9 in Lichtenecker’s mixing rule is assumed, which corresponds to a nearly parallel arrangement of the block. The root mean square error (RMSE) of relative humidity (RH) and temperature distribution is only 0.992% and 0.566°C, respectively. …”
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AI-enabled driver assistance: monitoring head and gaze movements for enhanced safety by Sayyed Mudassar Shah, Gan Zengkang, Zhaoyun Sun, Tariq Hussain, Khalid Zaman, Abdullah Alwabli, Amar Y. Jaffar, Farman Ali

“…The proposed system achieves superior precision, with a weighted accuracy of 95.02% and Root Mean Square Errors of 2.23 and 1.68 for vertical and horizontal gaze estimation, respectively, validated on the MPII-Gaze and DG-Unicamp datasets. …”
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Inhibition of human N myristoyltransferase 1 as a strategy to suppress cancer progression driven by myristoylation by Mohammed Merae Alshahrani

“…MM/GBSA binding energy calculations identified 24,289,547 as the most favorable inhibitor (ΔG_total = − 102.72 kcal/mol), exceeding the reference compound. Structural superposition with initially docked poses yielded RMSD < 1.25 Å, indicating conformational fidelity. …”
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Exploring the differences in atmospheric mesoscale kinetic energy spectra between AI based and physics based models by Zongheng Li, Jun Peng, Lifeng Zhang, Hanyan Wu, Wei Zhang, Juan Zhu

“…This paper compares mesoscale kinetic energy spectra from an 11-day experiment simulated by a novel AI-based model (Pangu) and a physics-based model (MPAS), using ERA5 reanalysis as a reference. Based on the commonly used evaluation metrics of latitude weighted root mean square error (RMSE) and anomaly correlation coefficient (ACC), the AI-based model has better short to medium-range weather forecasting skill compared to the physics-based model. …”
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Optimization of grading rings for 1000 kV dry-type air-core shunt reactor based on hybrid RBFNN–Kriging surrogate model by Yiqin Liu, Liang Xie, Dongyang Li, Yunpeng Liu, Kexin Liu, Gang Liu

“…This improvement is quantitatively validated through reduced absolute errors and lower root-mean-square error metrics. After the optimization, the maximum electric field strength is substantially reduced, proving the effectiveness of the hybrid surrogate model. …”
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UV Hyperspectral Imaging and Chemometrics for Honeydew Detection: Enhancing Cotton Fiber Quality by Mohammad Al Ktash, Mona Knoblich, Frank Wackenhut, Marc Brecht

“…Additionally, partial least squares regression (PLS-R) was utilized to predict the honeydew content for each pixel in hyperspectral images, achieving a cross-validation coefficient of determination <i>R</i>² = 0.75 and root mean square error of RMSE = 0.8 for the honey model. …”
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Effects of nickel, lead, and copper stress on the growth and biochemical responses of Aegilops tauschii seedlings by Ning Wang, Hao Chen, Yaowu Tian

“…This study investigated the effects of different concentrations (0, 100, 200, and 300 mg·kg–1) of nickel, lead, and copper stress on the growth and biochemical responses of Aegilops tauschii seedlings, to provide a reference for research on the mechanism of invasion and screening potential sources of wheat tolerance genes. …”
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Overvoltage Simulation Analysis and Suppression of Breaking in a 35 kV Shunt Reactor by Jing Chen, Xiaoyue Chen, Siying Feng, Xinmeng Liu, Qin Liu

“…It is concluded that the equivalent chopping current of the other two phases caused by the continuous reignition of the first open phase is the root cause of the high-amplitude interphase overvoltage. …”
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Partitioned Soil Water Balance and Its Link With Water Uptake Strategy Under Apple Trees in the Loess‐Covered Region by Peijun Shi, Haoqi Gai, Zhi Li

“…Therefore, the soil water deficit was the cumulative effects of root water uptake. The methods for SWB partitioning provides technical support for similar studies, and the findings are helpful to better understand the hydrological processes in the thick loess deposition.…”
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Collection, Storage and Protein Extraction Method of Gingival Crevicular Fluid for Proteomic Analysis by Harraa S. Mohammed-Salih, Hayder F. Saloom

“…Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. …”
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A Brassica exon array for whole-transcript gene expression profiling. by Christopher G Love, Neil S Graham, Seosamh O Lochlainn, Helen C Bowen, Sean T May, Philip J White, Martin R Broadley, John P Hammond, Graham J King

“…The 135 k Brassica Exon Array was validated by quantifying transcriptome differences between leaf and root tissue from a reference Brassica rapa line (R-o-18), and categorisation by Gene Ontologies (GO) based on gene orthology with Arabidopsis thaliana. …”
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Solar-Blind Ultraviolet Four-Quadrant Detector and Spot Positioning System Based on AlGaN Diodes by Longfei Peng, Shangqing Li, Yong Huang, Yang Yang

“…This research provides a reference for the application of solar-blind UV 4QDs in positioning, alignment, and monitoring scenarios, thereby holding significant practical implications.…”
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Informative Path Planning Using Physics-Informed Gaussian Processes for Aerial Mapping of 5G Networks by Jonas F. Gruner, Jan Graßhoff, Carlos Castelar Wembers, Kilian Schweppe, Georg Schildbach, Philipp Rostalski

“…For a fixed maximum travel distance, a path is iteratively computed, reducing the flight distance by up to 98% while maintaining an average root-mean-square error of less than 6 dBm when compared to the measurement trials.…”
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Exploring a minimal Convolutional Linear-Regression Model for Urban Land Surface Temperature estimation by Matteo Piccardo, Emanuele Massaro, Luca Caporaso, Alessandro Cescatti, Grégory Duveiller

“…CLRM achieves robust accuracy, with a mean absolute error (MAE) of approximately 1.5 °C, a root mean square error (RMSE) typically below 2 °C - aligning with the error ranges reported for LST downscaling methods in the literature. …”
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Steady-State Model Enabled Dynamic PEMFC Performance Degradation Prediction via Recurrent Neural Network by Qiang Liu, Weihong Zang, Wentao Zhang, Yang Zhang, Yuqi Tong, Yanbiao Feng

“…The processed dataset, comprising model-calculated reference outputs under dynamic conditions synchronized with filtered operational parameters, served as inputs for a recurrent neural network (RNN). …”
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Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations by Yifan Zhu, Fei Guo, Xiaohong Zhang, Wentao Yang

“…The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. …”
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