Signal Processing
The data part is the beneficial supplementary part of the article of Complex Theory and Batch Processing in Mechanical Systemic Data Extraction. It is including 2 parts. One is the about the original designed period. Another is the experimental data from 9 virtual experiments. It serves for the higher efficiency of ABRF.
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In conventional particle beam microscopy, knowledge of the beam current is essential for accurate micrograph formation and sample milling. This generally necessitates offline calibration of the instrument. In this work, we establish that beam current can be estimated online, from the same secondary electron count data that is used to form micrographs.
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This is a data set of identification for nonlinear flow characteristics of paper ''Identification for nonlinear flow characteristics of main steam regulating valves by mining special segments from industrial big data ''.
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Accurate flood delineation is crucial in many disaster management tasks, including, but not limited to: risk map production and update, impact estimation, claim verification, or planning of countermeasures for disaster risk reduction. Open remote sensing resources such as the data provided by the Copernicus ecosystem enable to carry out this activity, which benefits from frequent revisit times on a global scale. In the last decades, satellite imagery has been successfully applied to flood delineation problems, especially considering Synthetic Aperture Radar (SAR) signals.
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The data is planned to be used in thunderstorm movement path prediction.
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We present the simulated and real data used in the experiments of the MCA-SAR method. The simulated data is the simulation echo of the existing SAR images, and the real data was collected by RADARSAT-1 in Vancouver on June 16, 2002, which is the public data taken from the book [1].
[1] Cumming I G, Wong F H. Digital Signal Processing of Synthetic Aperture Radar Data: Algorithms and Implementation [M]. London, UK: Artech House, 2005.
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Ground-to-air (GA) communication using unmanned aerial vehicles (UAVs) has gained popularity in recent years and is expected to be part of 5G networks and beyond. However, the GA links are susceptible to frequent blockages at millimeter wave (mmWave) frequencies. During a link blockage, the channel information cannot be obtained reliably. In this work, we provide a novel method of channel prediction during the GA link blockage at 28 GHz.
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The dataset contains results of the paper being submitted.
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The EegDot data set collected using a Cerebus neural signal acquisition equipment involed thirteen odor stimulating materials, five of which (smelling like rose (A), caramel (B), rotten (C), canned peach (D), and excrement (E)) were selected from the T&T olfactometer (from the Daiichi Yakuhin Sangyo Co., Ltd., Japan) and the remaining eight from essential oils (i.e., mint (F), tea tree (G), coffee (H), rosemary (I), jasmine (J), lemon (K), vanilla (L) and lavender (M)).
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The EegDoc data set collected using a Cerebus neural signal acquisition equipment involved 2 types of odors (smelling like roses and rotten odors), each with 5 concentrations. Five concentrations of the rose odor are expressed as A10-3.0 (A30), A10-3.5 (A35), A10-4.0 (A40), A10-4.5 (A45) and A10-5.0 (A50), and five concentrations of the rotten odor are expressed as C10-4.0 (C40), C10-4.5 (C45), C10-5.0 (C50), C10-5.5 (C55) and C10-6.0 (C60).
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