Standards Research Data
Intrusion Detection Systems and Prevention Systems are the most important defence tools that facilitate the network users to get rid of online threats. Because of the growing technology, the demand for the network has been increased. With the implication of IoT, Cloud and SDN, the users and the organization are highly facilitated with the accessing of the service and the data as per their requirement. However, besides the facility of those networks, there are some drawbacks due to the online threats.
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The trench gate or U-groove MOSFET (UMOSFET) has become widely adopted as a semiconductor device globally, gradually replacing the traditional double-diffused MOSFET (DMOSFET) in many applications. Evaluating the reliability of UMOSFETs regarding neutron-induced radiation effects is crucial for understanding their response to ubiquitous atmospheric neutrons. This study presents comparative experimental and computational results of Single-Event Effects induced by monoenergetic fast neutrons in UMOS and DMOS power transistors.
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Jamming devices present a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. The detection of anomalies within frequency snapshots is crucial to counteract these interferences effectively. A critical preliminary measure involves the reliable classification of interferences and characterization and localization of jamming devices.
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Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with two interference classes and one non-interference class.
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236 Views
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease, while non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, which can
progress to more severe liver diseases such as liver fibrosis, cirrhosis and hepatocellular carcinoma. Approximately 50%-70% of T2DM patients also have
NAFLD. Traditional diagnostic methods like liver biopsy have limitations, making largescale screening difficult. In the past decade, machine learning have emerged as crucial tools for assisting in NAFLD diagnosis.
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54 Views
Interference signals degrade and disrupt Global Navigation Satellite System (GNSS) receivers, impacting their localization accuracy. Therefore, they need to be detected, classified, and located to ensure GNSS operation. State-of-the-art techniques employ supervised deep learning to detect and classify potential interference signals. We fuse both modalities only from a single bandwidth-limited low-cost sensor, instead of a fine-grained high-resolution sensor and coarse-grained low-resolution low-cost sensor.
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407 Views
This is a test case for a talent intelligence evaluation benchmark dataset with rich attributes (Attributes: 11, 909, Samples: 244, 610), containing information on honors, masterpieces, projects, rankings, and other attributes. Please note that we are providing this for scientific research use only; to use the full dataset, please contact liuying.void@gmail.com.
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Jamming devices pose a significant threat by disrupting signals from the global navigation satellite system (GNSS), compromising the robustness of accurate positioning. Detecting anomalies in frequency snapshots is crucial to counteract these interferences effectively. The ability to adapt to diverse, unseen interference characteristics is essential for ensuring the reliability of GNSS in real-world applications. We recorded a dataset with our own sensor station at a German highway with eight interference classes and three non-interference classes.
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157 Views
The example involves 16 evaluation criteria, with quantitative criteria including on time delivery (C1), delivery speed (C2), accurate delivery (C3), damaged cargo proportion (C4), after-sale service (C5), clearance efficiency (C6), geographical coverage (C7), bonded warehouse support (C8), delivery price (C12), and transport cost (C13), and qualitative criteria including flexibility in delivery and operations (C9), information system (C10), information sharing (C11), reputation (C14), financial performance (C15), and R&D ability (C16).
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54 Views