Optimal design of acoustic metamaterials for noise suppression by the frequency division in military equipment by Shaohua Bi, Fei Yang, Xinmin Shen, Wenqiang Peng, Xiaocui Yang, Qin Yin, Xiaonan Zhang, Heng Zhang, Dong Wang, Jingguo Chen, Zhiming Li

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Integration of ground-based and remote sensing data with deep learning algorithms for mapping habitats in Natura 2000 protected oak forests by Lucia Čahojová, Ivan Jarolímek, Barbora Klímová, Michal Kollár, Michaela Michalková, Karol Mikula, Aneta A. Ožvat, Denisa Slabejová, Mária Šibíková

“…A dataset was selected for the training of a deep learning algorithm called the Natural Numerical Network on the basis of the analysis results. …”
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Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence by Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, Francesca Ardito, Anna Maria Stellacci, Afwa Thameur, Sergio Ruggieri, Sabina Tangaro, Marcello Mastrorilli, Nicola Sanitate, Simone Pietro Garofalo

“…Different machine learning (ML) algorithms were trained and compared using spectral band data and calculated vegetation indices (VIs) as predictors. …”
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The Prevalence of Star-forming Clumps as a Function of Environmental Overdensity in Local Galaxies by Dominic Adams, Hugh Dickinson, Lucy Fortson, Kameswara Mantha, Vihang Mehta, Jürgen Popp, Claudia Scarlata, Chris Lintott, Brooke Simmons, Mike Walmsley

“…The resulting sample of 41,445 u -band bright clumps in 34,246 galaxies is the largest sample of clumps yet assembled. …”
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Development and Validation of a Nomogram for Predicting Long-Term Net Adverse Clinical Events in High Bleeding Risk Patients Undergoing Percutaneous Coronary Intervention by Junyan Zhang, Zhongxiu Chen, Ran Liu, Yuxiao Li, Hongsen Zhao, Yanning Li, Minggang Zhou, Hua Wang, Chen Li, Li Rao, Yong He

“…The cohort was randomly divided into training and internal validation sets in a ratio of 7:3. …”
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Quantitative evaluation of the wide-field fundus photographs in eyes with severe stage 3 and stage 4A retinopathy of prematurity by Sadik Etka Bayramoglu, Nihat Sayin, Gurkan Erdogan

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Prediction of Lard in Palm Olein Oil Using Simple Linear Regression (SLR), Multiple Linear Regression (MLR), and Partial Least Squares Regression (PLSR) Based on Fourier-Transform... by Siong Fong Sim, Min Xuan Laura Chai, Amelia Laccy Jeffrey Kimura

“…The absorption bands at 3006 cm−1, 2852 cm−1, 1117 cm−1, 1236 cm−1, and 1159 cm−1 were identified as the marker bands. …”
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Spectral enhancement of PlanetScope using Sentinel-2 images to estimate soybean yield and seed composition by Supria Sarkar, Vasit Sagan, Sourav Bhadra, Felix B. Fritschi

“…The MKSF was trained using PS and S2 image pairs from different growth stages and predicted the potential VNIR1 (705 nm), VNIR2 (740 nm), VNIR3 (783 nm), SWIR1 (1610 nm), and SWIR2 (2190 nm) bands from the PS images. …”
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Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study by Gordon Alderink, Diana McCrumb, David Zeitler, Samhita Rhodes

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assessment of land-cover change in South part of Lake Urmia using satellite imagery by Khadijeh Mikaeli Hajikandi, Behrooz Sobhani, Saeid Varamesh

“…Land use/cover maps in the two studied years were provided using Maximum Likelihood Classifier (MLC) algorithm applied on two series data including spectral bands (data series 1) also spectral bands and filter texture layer (data series 2) and six categories of land use/cover containing Irrigated Farmland, Dry Farmland, garden, rangeland, bare land and water bodies were distinguished.. …”
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Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs by Eryang Chen, Ruichun Chang, Kaibo Shi, Ansheng Ye, Fang Miao, Jianghong Yuan, Ke Guo, Youhua Wei, Yiping Li

“…Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. …”
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Application of Machine Learning for Radiowave Propagation Modeling Below 6 GHz by Mohammud Z. Bocus, Afzal Lodhi

“…This paper presents the application of supervised learning and use of fully connected neural network (FCNN) for the development of a path specific propagation model for frequencies below 6 GHz. The model has been trained and tested against an extensive measurement dataset capturing several areas and the diverse topography of the UK. …”
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Early Detection of Multiwavelength Blazar Variability by Hermann Stolte, Jonas Sinapius, Iftach Sadeh, Elisa Pueschel, Matthias Weidlich, David Berge

“…It is capable of detecting various types of anomalies in real-world, multiwavelength light curves, ranging from clear high states to subtle correlations across bands. Based on unsupervised anomaly detection and clustering methods, we differentiate source variability from noisy background activity, without the need for a labeled training data set of flaring states. …”
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SVDDD: SAR Vehicle Target Detection Dataset Augmentation Based on Diffusion Model by Keao Wang, Zongxu Pan, Zixiao Wen

“…In response to this issue, this paper collects SAR images of the Ka, Ku, and X bands to construct a labeled dataset for training Stable Diffusion and then propose a framework for data augmentation for SAR vehicle detection based on the Diffusion model, which consists of a fine-tuned Stable Diffusion model, a ControlNet, and a series of methods for processing and filtering images based on image clarity, histogram, and an influence function to enhance the diversity of the original dataset, thereby improving the performance of deep learning detection models. …”
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An atmospheric correction method for Himawari-8 imagery based on a multi-layer stacking algorithm by Menghui Wang, Donglin Fan, Hongchang He, You Zeng, Bolin Fu, Tianlong Liang, Xinyue Zhang, Wenhan Hu

“…To address the lack of training data, 10,000 Rayleigh-corrected reflectance samples were synthesized for six Himawari-8 bands, using simulated water-leaving, which cover different optically complex water properties through a radiative transfer, and aerosol reflectance data under different geometrical conditions. …”
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Respiratory disease detection in lung auscultation with convolutional neural networks and CVAE augmentation by D.V. Panaskin, S.H. Stirenko, D.S. Babko

“…The stage of data preprocessing includes discretization to 4kHz frequency, as well as filtering of frequency bands that do not carry information value for the task. …”
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Harm minimisation for self-harm: a cross-sectional survey of British clinicians’ perspectives and practices by Alexandra Pitman, Nicola Morant, Faraz Mughal, Sarah L Rowe, Aishah Madinah Haris, Evelina Bakanaite

“…Commonly recommended techniques were snapping rubber bands on one’s wrist and squeezing ice. Other techniques, such as teaching use of clean instruments when self-harming, were less likely to be recommended. …”
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Measurement-Based Prediction of mmWave Channel Parameters Using Deep Learning and Point Cloud by Hang Mi, Bo Ai, Ruisi He, Anuraag Bodi, Raied Caromi, Jian Wang, Jelena Senic, Camillo Gentile, Yang Miao

“…Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz frequency bands. The major differences include higher path loss and sparser multipath components (MPCs), resulting in more significant time-varying characteristics in mmWave channels. …”
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Fault Diagnosis Method for Rotating Machinery Based on Hierarchical Amplitude-Aware Permutation Entropy and Pairwise Feature Proximity by Ling Shu, Jinxing Shen, Xiaoming Liu

“…By constructing high and low-frequency operators, this method can extract the features of different frequency bands of time series simultaneously, so as to avoid the issue of information loss. …”
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Scale-Dependent Signal Identification in Low-Dimensional Subspace: Motor Imagery Task Classification by Qingshan She, Haitao Gan, Yuliang Ma, Zhizeng Luo, Tom Potter, Yingchun Zhang

“…While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. …”
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