Collision detection (CD) is a key capability of carrier sense multiple access (CSMA) based medium access control (MAC) protocol. Applying CD, the transmitter can abort transmission immediately so that the power can be saved. This technique does not need the peer receiver to give feedback on whether there is a packet collision, and hence, the overall overhead is significantly low. The challenge, however, is to operate in transmit time and instantly detect the week colliding signal in the presence of strong self-interference (SI).

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Instant collision detection (CD) can be achieved at the transmitter side more efficiently. To detect the collision, though, the device has to overcome the strong self-interference (SI) in such a way that it can listen to the channel in transmit time. This capability is feasible by in-band full-duplex (IBFD) technology, which allows two nodes to communicate concurrently over the same frequency channel. Recent works have shown the network-level benefits of using IBFD for collision detection, in the sense of power efficiency, throughput, and delay performance. By any means, the performance of these MAC protocols highly depends on the rapidity and precision of the CD method, although the collision detection in this context has still not been investigated thoroughly. By leveraging multiple hidden convolutional layers, modern machine learning techniques have confirmed their effectiveness in a wide range of applications, such as automatic image recognition, and network optimization. Motivated by its remarkable success in various fields as well as its real-time functionality, in this work we investigate whether a convolutional neural network (CNN) can be exploited to accelerate CD without sacrificing the detection accuracy. Meanwhile, we realize that the CD problem can be mapped to traditional SNR estimation problem. When there is a collision, the signal SNR will drop. Lots of domain knowledge are there with regard to signal demodulation and SNR estimation. On the contrary, CNN could be regarded as a kind of domain-specific knowledge less method. It will be interesting to see the performance comparison between the two methodologies. This kind of comparison will inspire the research community to study further about how should we combine the domain-specific knowledge (DSK) with CNN. Besides, to encourage future studies, we offer free access to the dataset and programs in IEEE DataPort, which allows researchers to reproduce our results out of the box or investigate different approaches.

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This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.

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A fully-labeled C++03 program dataset provides a unique resource to evaluate model checkers in respect to language coverage. To tackle modern aspects of the C++ language, a large-scale benchmark dataset includes more than 1,500 C++03-compliant programs, which cover different aspects of the language, including exception handling, templates, inheritance, polymorphism, the standard template library, and object-oriented design.

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The paper deals with fractional-slot permanent magnet synchronous machines

(FSPMSMs) equipped with phases made up of one coil parallel branches, with emphasis on their

faculty to reject the harmonic currents circulating in the loops yielded by the phase parallel branches.

These exhibit attractive potentialities, especially their enhanced open-circuit fault tolerance capability.

Furthermore, these topologies are suitably-adapted for low-voltage power supply that makes them

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Blockchain is currently envisioned as a promising technology for enabling new applications, e.g., authenticity of diploma, supplying chain finance, and automatical transaction. Smart contract is a key function in blockchain to enable fair exchanges for values within de-centralized trust. However, as the smart contract can be automatically executed together with token transferring, hackers usually exploit vulnerabilities in smart contracts for gain possible profits in terms of digital currencies.SVM is employed for detecting unknown vulnerabilities to improve the recognition rate.

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This dataset is the experimental results for the manuscript titled “Measuring and improving communication robustness of networks”.

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The dataset is used to test the performance of encryption model

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The reliability of In-Si-O (ISO) thin-film transistors (TFT) to bias voltage-stress and photo-stress is studied. The ISO TFTs were developed with a fully photolithographic process, with a maximum temperature of 200 C. The TFTs typically showed a mobility of 5.03 cm$^2$/Vs, a threshold voltage, $V_{th}$, of -0.16 V and a subthreshold swing of 312 mV/dec. The TFTs were biased for up to four hours at different temperatures and illumination. Threshold voltage shifts were observed and modeled.

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The original data set is from the official website of NASA, the four bearings operation data of the 2nd in IMS data set were used in article experiment.

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The original data set is from the official website of NASA, the four bearings operation data of the 2nd in IMS data set were used in article experiment.

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