Standards Research Data
Kubernetes is a tool that facilitates rapid deployment of software. Unfortunately, configuring Kubernetes is prone to errors.
Configuration defects are not uncommon and can result in serious consequences. This paper reports an empirical study about
configuration defects in Kubernetes with the goal of helping practitioners detect and prevent these defects. We study 719 defects that
we extract from 2,260 Kubernetes configuration scripts using open source repositories. Using qualitative analysis, we identify 15
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1.Cora dataset is derived from a multi-group citation network, and the two-group subgraphs are selected for tasks such as graph neural network node classification. The dataset contains sparse Bag-of-Words feature vectors as node attributes, and the labels are mostly academic paper topic categories or fields. This subgraph focuses on the influence of graph structure and node characteristics on model prediction, which provides a reliable experimental benchmark for the research of multi-step adversarial attacks and defense strategies.
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On-demand polarization control of electromagnetic waves is the fundamental element of modern optics. Its interest has recently been expanded in the terahertz (THz) range for coherent excitation of collective quasiparticles in matters, triggering a wide variety of non-trivial intriguing physics, e.g., anharmonicity, nonlinear coupling, and metastability. Wavelength tunability in THz polarization control is fundamentally important for the resonant excitation of collective modes.
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Repeated Route Naturalistic Driving Dataset (R2ND2) is a dual-perspective dataset for driver behavior analysis constituent of vehicular data collected using task-specific CAN decoding sensors using OBD port and external sensors, and (b) gaze-measurements collected using industry-standard multi-camera gaze calibration and collection system. Our experiment is designed to consider the variability associated with driving experience that depends on the time of day and provides valuable insights into the correlation of these additional metrics on driver behavior.
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In-vehicle networks are responsible for safety-critical control applications, depending on data communication between electronic control units, and most are based on the CAN protocol. A huge amount of data is necessary for reliability, safety, and cybersecurity analysis in today's automotive solutions, especially to feed machine learning models. It is relevant to provide comprehensive datasets about CAN communication and different driving situations, which represents a lack in recent research because most public datasets are very limited.
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Abstract—
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Both the original and processed 2019 datasets share the same column structure, with similar data fields. However, there are differences in data formatting and units:
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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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