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Droidware is an Android malware dataset developed at the Cybersecurity Lab, GLA University, India. It comprises 253,527 applications, including 129,950 benign and 123,577 malicious samples. The dataset captures 68 features extracted from function call graphs, permissions, and Java source code, providing a comprehensive view of Android malware behavior. This latest and up-to-date dataset supports the training of AI-based malware detection models, aiding in the development of robust malware classification and threat mitigation strategies for cybersecurity research.
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This dataset comprises volatile organic compound (VOC) profiles collected from blood culture broth samples using an electronic nose (E-nose) system. The samples include cultures positive for Candida spp., including C. albicans, C. glabrata, C. tropicalis, among others, as well as negative control samples.
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Bitcoin(₿) is a cryptocurrency invented in 2008 by an unknown person or group of people using the pseudonym Satoshi Nakamoto. The currency began use in 2009 when its implementation was released as open-source software.
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74 Views
A rapid growth of wireless communication networks, particularly in 5G Non-Standalone (NSA) deployments, has necessitated advanced multiple access techniques to enhance spectral efficiency, interference management, and energy optimization [1-3]. Rate-Splitting Multiple Access (RSMA) has arisen as a strong candidate to replace conventional Non-Orthogonal Multiple Access (NOMA) by efficiently splitting user data into common and private components. [1-2].
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27 Views
This dataset comprises a collection of CSV files containing paired time-series measurements essential for nonlinear compensation research in electrochemical seismometers (MET). Each CSV file, named according to specific magnitude-frequency combinations (magX_freqY.csv), contains two columns: 'origin' representing the original system response and 'target' representing the desired compensated output.
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21 Views
At-sea testing of underwater acoustic communication systems requires resources unavailable to the wider research community, and researchers often resort to simplified channel models to test new protocols. The present dataset comprises in-situ hydrophone recordings of communications and channel probing waveforms, featuring an assortment of popular modulation formats. The waveforms were transmitted in three frequency bands (4-8 kHz, 9-14 kHz, and 24-32 kHz) during an overnight experiment in an enclosed fjord environment, and were recorded on two hydrophone receivers.
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116 Views
This study presents a deep learning-based framework for detecting vehicle deceleration patterns using Ultra-Wideband (UWB) Channel Impulse Response (CIR) analysis. Unlike traditional GPS or IMU-based systems, which struggle in GPS-denied environments such as tunnels, the proposed method leverages UWB CIR signal variations to classify two key driving behaviors: rapid deceleration and gradual deceleration. All data were collected from real-world experiments using UWB devices installed on actual vehicles at a professional highway testing site.
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This dataset presents longitudinal measurements of plant growth characteristics under varying lighting conditions. Collected for 30 individual plants, the data spans multiple time points and includes key variables such as plant fresh weight (g), plant height (mm), plant width (mm), and number of leaves. Each measurement is associated with a specific plant, date (DD/MM/YYYY), experimental group, and a lighting recipe defined by the percentage composition of Red, Green, and Blue light.
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The data covers the period from January 4, 2021, to August 16, 2023. It includes the carbon trading prices from the Hubei carbon market and other relevant feature data that may influence carbon prices. The feature data has undergone preliminary screening and consists of Brent crude oil prices, natural gas prices, Rotterdam coal prices, EU Emission Allowances, the China Securities 300 Index, and the Euro exchange rate.
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This dataset was built as part of our study MentalAgora: A Gateway to Advanced Personalized Care in Mental Health through Multi-Agent Debating and Attribute Control. The dataset was sourced from mental health-related posts in Reddit Mental Health Dataset and tagged with responses from mental health professionals to selected posts. For more details on building the dataset, please see the paper.
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