Application of ultra-wideband data transmission systems for monitoring of natural and man-made objects by I. I. Zabenkov, D. V. Arkhipenkov

“…This article provides a comparative analysis of communication systems with OFDM modulation the advantages and disadvantages of the various systems on the probability of error (BER), proposed method of increasing noise immunity and bandwidth of ultra-wideband radio data transmission.…”
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Sea surface temperature retrieval from MSG/SEVIRI data in the Baltic Sea area by Monika Woźniak, Adam Krężel

“…SSTs determined on the basis of AVHRR data were used in the regression analysis instead of in situ data. …”
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Data-driven assessment of corrosion in reinforced concrete structures embedded in clay dominated soils by Shahbaz Ahmad, Siraj Ahmad, Sabih Akhtar, Faraz Ahmad, Mujib Ahmad Ansari

“…Two ANN architectures, Feedforward Backpropagation (FFBP) and Cascadeforward Backpropagation (CFBP), were developed and trained using 72 experimental data points extracted from the literature. The FFBP model outperformed CFBP in terms of predictive accuracy, achieving a correlation coefficient (R) of 0.998, a mean absolute percentage error (MAPE) of 30.43%, and a root mean square error (RMSE) of 0.071 during testing. …”
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Simulation and Empirical Studies of Long Short-Term Memory Performance to Deal with Limited Data by Khusnia Nurul Khikmah, Kusman Sadik, Khairil Anwar Notodiputro

“…The best performance of the method is determined by testing the analysis of variance against the mean absolute percentage error. …”
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Characterization of High-Speed Steels—Experimental Data and Their Evaluation Supported by Machine Learning Algorithms by Manfred Wiessner, Ernst Gamsjäger

“…These differently heat-treated steel samples are also investigated by dilatometric analysis and by metallographic analysis in order to label the samples accordingly. …”
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Quantifying uncertainties in satellite NO₂ superobservations for data assimilation and model evaluation by P. Rijsdijk, P. Rijsdijk, P. Rijsdijk, H. Eskes, A. Dingemans, A. Dingemans, K. F. Boersma, K. F. Boersma, T. Sekiya, K. Miyazaki, S. Houweling, S. Houweling

“…When the resolution mismatch is not properly bridged, the final analysis based on the satellite data may be degraded. …”
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De novo clustering of long-read amplicons improves phylogenetic insight into microbiome data by Yan Hui, Dennis Sandris Nielsen, Lukasz Krych

“…LACA controls the average error rate of corrected sequences below 1% for the Oxford Nanopore Technologies (ONT) R9.4.1 and ONT R10.3 data, 0.2% for ONT R10.4.1, and 0.1% for high-accuracy ONT Duplex and Pacific Biosciences (PacBio) circular consensus sequencing (CCS) data in both simulated 16S rRNA and real 16-23S rRNA amplicon datasets. …”
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Industrial data-driven machine learning soft sensing for optimal operation of etching tools by Feiyang Ou, Henrik Wang, Chao Zhang, Matthew Tom, Sthitie Bom, James F. Davis, Panagiotis D. Christofides

“…A statistical analysis method involving point-biserial correlation and the Mean Absolute Error (MAE) difference score is introduced to select the optimal candidate datasets for aggregation, further improving the effectiveness of data aggregation. …”
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Winter Wheat Canopy Height Estimation Based on the Fusion of LiDAR and Multispectral Data by Hao Ma, Yarui Liu, Shijie Jiang, Yan Zhao, Ce Yang, Xiaofei An, Kai Zhang, Hongwei Cui

“…The mean absolute error values were 0.006 m, 0.011 m, and 0.011 m, respectively. …”
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Comparison of Oceansat-2 Scatterometer Wind Data with Global Moored Buoys and ASCAT Observation by Jungang Yang, Jie Zhang

“…In this study, the historic archived OSCAT wind vectors are compared with the global moored buoys’ wind observations, including the U.S. National Data Buoy Center (NDBC), the Tropical Atmosphere Ocean (TAO), the Pilot Research Moored Array in the Tropical Atlantic (PIRATA), the Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction (RAMA), and Advanced Scatterometer (ASCAT) wind data in the same period of OSCAT by calculating the statistical parameters, namely, the root mean square error (RMSE), bias (mean of residuals), and correlation coefficient (R) between the collocated data. …”
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Evaluation of gait speed over time in adults with arthritis: Data from the osteoarthritis initiative by Vishal Vennu, Harsh Misra

“…Methods: We performed a secondary analysis using data from the osteoarthritis initiative. …”
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Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach by Mohamed Elkattan

“…The proposed neural network with tuned design factors was also evaluated under noisy conditions. Error analysis verified the effectiveness of the proposed neural network design in inverting electromagnetic data to derive induced polarization parameters.…”
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Estimation of monthly flow data with artificial intelligence in basins with scarce information in the Colombian Orinoquía by Camilo Ocampo-Marulanda, Teresita Canchala Nastar, Yesid Carvajal-Escobar, Lina Cerón-Arámburo

“…This study proposes a method for imputing monthly streamflow data from 1985 to 2020 by applying Nonlinear Principal Component Analysis (NLPCA) combined with CHIRPS precipitation data. …”
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Design and realization of compressor data abnormality safety monitoring and inducement traceability expert system. by Yuan Wang, Shaolin Hu

“…The results show that this method effectively overcomes the problems of false alarms and missed alarms based on fixed threshold alarm methods, and achieves 100% classification of two types of faults: non starting of the drive machine and low oil pressure by constructing a PCA (Principal Component Analysis)-SPE (Square Prediction Error)-CNN (Convolutional Neural Network) classifier. …”
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Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning by Wangsong Xie

“…And for 1200 test samples, the average error of data risk prediction of the proposed method is 0.0217%. …”
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Hybrid machine learning for flood prediction: comparing CHIRPS satellite and ground station data by Agustina Rachmawardani, Budhy Kurniawan, Sastra Kusuma Wijaya, Ardhasena Sopaheluwakan, Marzuki Sinambela

“…In contrast, the ANFIS model without PCA yielded higher errors, with RMSE 6.3, MAE 6.2, and R2 0.74. Training with CHIRPS satellite data resulted in significantly higher errors (RMSE 30.14, MAE 24.05, R2 0.42). …”
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Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks by Stefan Hildebrand, Sandra Klinge

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Variability of the Lake Onega ice coverage in the period 2000-2018 according to the satellite data by V. N. Baklagin

“…On average for this period, from 5 to 15 values were annually obtained, which was not enough for a comprehensive analysis of the ice coverage variability. In this paper, for the frst time, a daily series of values of the ice coverage of the Lake Onega for the period 2000–2018 had been formed basing on the results of a combined analysis of the following satellite data sets: NSIDC, NOAA NESDIS, corrected by data of the satellite MODIS sensor. …”
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Diffusion Filters for Variational Data Assimilation of Sea Surface Temperature in an Intermediate Climate Model by Xuefeng Zhang, Dong Li, Peter C. Chu, Lianxin Zhang, Wei Li

“…The GDF can retrieve successfully the longwave information over the whole analysis domain and the shortwave information over data-dense regions. …”
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Double weighted combat data quality evaluation method based on CVF optimized FAHP by Jianwei Wang, Chengsheng Pan, Qing Zhang

“…The double-weighted evaluation theory combines these two tiers of weights to produce the final assessment. Analysis of the experimental results indicates that the proposed method reduces the mean squared error to 5.35 when compared to results obtained using FAHP, interval intuitionistic fuzzy methods, and artificial neural networks, bringing it closer to actual standard values. …”
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