SP-LCC — a dataset on the structure and properties of lignin-carbohydrate complexes from hardwood by Marie Alopaeus, Matthias Stosiek, Daryna Diment, Joakim Löfgren, MiJung Cho, Jarl Hemming, Teija Tirri, Andrey Pranovich, Patrik C. Eklund, Davide Rigo, Mikhail Balakshin, Chunlin Xu, Patrick Rinke

“…The resulting database, termed SP-LCC, includes structural information extracted from nuclear magnetic resonance measurements (NMR) and data on the molar mass distribution, antioxidant activity, glass transition temperature, thermal degradation, and surface tension. …”
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Dataset exploring the atomic scale structure and ionic dynamics of polyanion sodium cathode materials by Martin Hoffmann Petersen, Jin Hyun Chang, Arghya Bhowmik, Juan Maria García Lastra

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A dataset for surface defect detection on complex structured parts based on photometric stereo by Lin Wu, Yu Ran, Li Yan, Yixing Liu, You Song, Dongran Han

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Band structure database of layered intercalation compounds with various intercalant atoms and layered hosts by Naoto Kawaguchi, Kiyou Shibata, Teruyasu Mizoguchi

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NEBULA101: an open dataset for the study of language aptitude in behaviour, brain structure and function by Alessandra Rampinini, Irene Balboni, Olga Kepinska, Raphael Berthele, Narly Golestani

“…The NEBULA101 dataset offers brain structural, diffusion-weighted, task-based and resting-state MRI data, alongside extensive linguistic and non-linguistic behavioural measures to explore the complex interaction of language and cognition in a highly multilingual sample. …”
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6Li(10B,9Be)7Be reaction mechanisms. Structure and interaction of 9Be + 7Be nuclei by A. T. Rudchik, A. A. Rudchik, O. O. Chepurnov, K. Rusek, K. W. Kemper, E. I. Koshchy, S. Yu. Mezhevych, Val. M. Pirnak, O. A. Ponkratenko, A. Stolarz, R. Siudak, A. P. Ilyin, Yu. M. Stepanenko, V. V. Uleshchenko, Yu. O. Shyrma

“…Thus, the information about the 9Be + 7Be optical potential, the basic reaction mechanisms and the 9Be and 7Be nuclei structures were deduced.…”
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4′-(2-Methoxyphenyl)-2,2′:6′,2′′-terpyridine by Eric M. Njogu, David O. Juma, Sizwe J. Zamisa, Bernard Omondi, Vincent O. Nyamori

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6-Chloro-3-[4-(hexyloxy)phenyl]-[1,2,4]triazolo[4,3-b]pyridazine by Jasmin Preis, Dieter Schollmeyer, Heiner Detert

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Integrated GNSS and InSAR Analysis for Monitoring the Shoulder Structures of the MOSE System in Venice, Italy by Massimo Fabris, Mario Floris

“…GNSS data were collected from 36 continuous GNSS (CGNSS) stations located at the corners of the emerged shoulder structures in the Treporti, San Nicolò, Malamocco, and Chioggia barriers. …”
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Condition Assessment of Tower and Mast Structures Monitored within One Cluster under Changing Environments by Changping Li, Xinteng Ma, Yang Liu

“…To ensure the safety of tower and mast structures, an effective measurement is to establish a simple structural health monitoring (SHM) system for each tower structure to obtain continuous deformation data of all structures. …”
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Mapping urban green structures using object-based analysis of satellite imagery: A review by Shivesh Kishore Karan, Bjørn Tobias Borchsenius, Misganu Debella-Gilo, Jonathan Rizzi

“…Urban green structures (UGS) play important roles in enhancing urban ecosystems by providing benefits such as mitigating the urban heat island effect, improving air quality, supporting biodiversity, and aiding in stormwater management. …”
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Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering by Thea Denell, Lauri Himanen, Markus Scheidgen, Claudia Draxl

“…Abstract With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. …”
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A digital twin framework with MobileNetV2 for damage detection in slab structures by Duong Huong Nguyen, Huan Nguyen, Xiaohong Gao

“…To verify the proposed framework, we present a case study of slab structure using deflection measurement as input data. …”
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New Method of Impact Localization on Plate-like Structures Using Deep Learning and Wavelet Transform by Asaad Migot, Ahmed Saaudi, Victor Giurgiutiu

“…This paper presents a new methodology for localizing impact events on plate-like structures using a proposed two-dimensional convolutional neural network (CNN) and received impact signals. …”
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OAC-HAS: outsourced access control with hidden access structures in fog-enhanced IoT systems by Jiale Zhang, Zhen Cheng, Xiang Cheng, Bing Chen

“…In this paper, we propose an outsourced access control scheme with hidden access structures, named OAC-HAS, in fog-enhanced IoT systems. …”
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A Conditional Simulation Method for Predicting Wind Pressure Fields of Large-Span Spatial Structures by Fangjin Sun, Tiantian Liu, Daming Zhang, Zhonghao Xu

“…Considering the complexity of wind pressure fields of large-span spatial structures, a simplified nonparametric method based on conditional simulation is proposed to predict the unknown pressures using the existing data. …”
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Modeling of settlement of shallow-founded rocking structures using explainable physics-guided machine learning by Sivapalan Gajan, Christopher Kantor

“…Rocking foundation is an unorthodox seismic design philosophy of structures that enhances the performance of structures by absorbing and dissipating seismic energy into soil. …”
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MaxComp: Predicting single-cell chromatin compartments from 3D chromosome structures. by Yuxiang Zhan, Francesco Musella, Frank Alber

“…To address this, we present MaxComp, an unsupervised method, for inferring single-cell A/B compartments based on 3D geometric considerations in single-cell chromosome structures-derived either from multiplexed FISH-omics imaging or 3D structure models derived from Hi-C data. …”
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Spatiotemporal Deformation Prediction Model for Retaining Structures Integrating ConvGRU and Cross-Attention Mechanism by Yanyong Gao, Zhaoyun Xiao, Zhiqun Gong, Shanjing Huang, Haojie Zhu

“…With the exponential growth of engineering monitoring data, data-driven neural networks have gained widespread application in predicting retaining structure deformation in foundation pit engineering. …”
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Novel Automatic Classification Method for Geological Structures in Carbonate Formations Based on Electrical Imaging Logging by FU Yafeng, HUANG Ke, ZHU Hanbin, WANG Hui, ZHANG Xu, ZHAO Jie, XIAO Ni

“…Finally, these features are used to train an extreme gradient boosting (XGBoost) model, enabling the automatic classification of different geological structures. Validation using actual carbonate logging data shows that the classification accuracy of this method exceeds an 80% agreement rate with core observations. …”
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