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  • A deep learning database and network for focusing guided wave defect detection Since the paper is being submitted, the database set will be published after the paper is accepted. Database set information:The defects are classified as three types and specimens with no defect are also included. In the established database set, the defect depth ranges from 10% to 50%, with 10% intervals. In addition, the radius of the pinhole defect ranges from 0.5 mm to 3 mm, and the sizes of the crack defect range from 1×5 mm2 to 2×10 mm2, and the sizes of the corrosion defect range from 5×5 mm2 to 10×10 mm2. Each defect contains 1500 signal data, and the ratio of the training, validation, and test data sets are divided into 6:2:2 in this work. The data storage format and explain the descriptive data (take the pinhole defect signal with a radius of 3 mm and a depth of 10% as an example). The data set has a total of 48,060,000 signal value data and contains detailed information about defects. No. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 ··· Data -1 1 0 0 3 10 0 0 0 -2 0 0 0 0 ··· Title: Development of frequency-mixed point-focusing SH guided wave EMAT for defect inspection using deep neural network Author: Hongyu Sun, Songling Huang, State Key of Power Systems, Department of Electrical Engineering, Tsinghua University, Beijing, 10084, China. Email: sunhy18@mails.tsinghua.edu.cn If you use our code and database set, please cite our paper [however, not published]. NOTICE: Reviewers can obtain the data set password from the end of the paper's Abstract to run the code. Development environment: TensorFlow 2.2 CUDA 10.1 Python 3.7
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  • The encroachment of the agrarian frontier constitutes Nicaragua’s biggest environmental challenge as the major cause of complex processes of ecosystems degradation. I use the ESA-CCI land cover maps dataset and the UNCCD land cover classes (UNCCD-LC) to estimate its geospatial trend in terms of land cover changes and transitions at the country-level and the areas encompassing Bosawas and Río San Juan biosphere reserves from 1992 to 2018. The ESA-CCI land cover maps generated at 300 m spatial resolution on an annual basis from 1992 to 2015 in TIF format (raster layers) were collected from the official website of the ESA-CCI (https://www.esa-landcover-cci.org/). The coordinate reference system of the maps is a geographic coordinate system based on the World Geodetic System 84 (WGS84) reference ellipsoid. The processing of the geospatial data was carried out using QGIS. The 36 ESA-CCI land cover classes (CCI-LC) were re-classified to the 7 UNCCD-LC (i.e., tree-covered, grassland, cropland, wetland, artificial, other land, and water body). The ESA-CCI land cover maps were clipped to the country-level (Nicaragua) using the vector layers in SHP format collected from the official website of GADM (https://gadm.org/), and at the biosphere reserve-level (Bosawas y Río San Juan) using the vector layers in SHP format collected from the official website of Protected Planet (https://www.protectedplanet.net/). A pixel count approach was used to estimate each UNCCD-LC area. STATA was used to calculate UNCCD-LC area estimates in squared kilometers, each pixel counting for 300 m x 300 m which is the ESA-CCI land cover maps spatial resolution.
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  • Data from a behavioral study including the factors 'distractor number' (0,1,or n), cue-target SOA (0 va 300 ms), and presentation mode (separated vs superimposed streams). Data from an ERP experiment including the factors 'cue-alone vs cue+target', 'hit vs miss', and electrode position. PsychoPy Presentation Code and Stimulus Material Results of a randomization test (repeated t-tests for randomly selected subsamples, including a control with random assignment of conditions=
    Data Types:
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  • microarray data for hearts exposed to TAC and so on
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  • The dataset contains mammography with benign and malignant masses. Images in this dataset were first extracted 106 masses images from INbreast dataset, 53 masses images from MIAS dataset, and 2188 masses images DDSM dataset. Then we use data augmentation and contrast-limited adaptive histogram equalization to preprocess our images. After data augmentation, Inbreast dataset has 7632 images, MIAS dataset has 3816 images, DDSM dataset has 13128 images. In addition, we also integrate INbreast, MIAS, DDSM together. All the images were resized to 227*227 pixels.
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  • This supplementary materials contain the supplementary information Table S1 and Figure S1, as well as the R scripts and data files to perform the analyses.
    Data Types:
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  • Data tables and supplementary figures.
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  • The presented magnetic dataset were collected above a set of weapons buried at 0.6 m, 1.2 m, and 1.8 m depth while the MATLAB simulation codes were used to estimate the magnetization, length, centre, azimuth and plunge of the buried firearms assuming an anomaly caused by a long magnetic dipole. Datasets and MATLAB scripts are related to the following peer reviewed articles submitted by the contributors for review and publication: 1. K. O. Doro, E. A. Deng, C.-G. Bank, Gradient magnetometer datasets and MATLAB numerical code for simulating buried firearms at a controlled field site. Data in Brief. Submitted 2. E. A. Deng, K. O. Doro, C.-G. Bank, Suitability of magnetometry to detect clandestine buried firearms from a controlled field site and numerical modeling, Forensic Science International. In Press. https://doi.org/10.1016/j.forsciint.2020.110396 For a detailed description of these data, refer to Doro et al article in Data in Brief while for more on the study, refer to Deng et al article in Forensic Science International.
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  • This article is a compressive analysis of the job satisfaction and so work-based subjective well-being (SWB) of internal migrants across four Chinese conurbations. Various aspects of work-based SWB are considered, specifically satisfaction with income, skill acquisition, potential realization, working conditions and the overall levels of job satisfaction, thus incorporating both hedonic and eudaimonic measures of SWB. Our analysis controls for many personal, social and economic attributes, such as age, gender, education and income. In particular, we focus our analysis on the income effect, theorize two contrasting effects (signal and envy) of relative income, and analyze which effect dominates. We contrast the effect of Hukou status and find that those with non-agricultural Hukou expect to be more integrated into the local societies than the cohort with agricultural Hukou. In particular, we consider impacts from social networks on SWB and find that the cohort with agricultural Hukou disproportionally rely on referral from friends or family to obtain jobs. Finally, the decision to become self-employed amongst migrants is also investigated. We find that the cohort of self-employed migrants are amongst the worst educated and have lower SWB.
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  • The proposed dataset aims to provide a number of wind turbine blade images for testing and training purposes. The featured datasets can be used for testing and evaluation of Structure from Motion algorithms for 3D reconstruction, as data for machine learning algorithms for detecting damaged areas on blades or for quantifying blade surface roughness. Images are taken using Canon 5Ds DSLR camera with resolution of 8688 x 5792. The images come with EXIF data, containing additional information about the used capturing settings. The dataset is separated into two parts: - A dataset containing 5 wind turbine blade patches of areas of different surface structure. Two of the patches are of damaged areas, two of the patches are of rough areas, without pronounced surface deformations and one of the patches is a reference area, that does not contain any roughness or damages. The final patch contains the whole of the blade's edge area and is comprised of a mix of severely damaged areas, areas of small roughness and clear areas. The dataset also contains ground truth microscopy data for two of the damaged patches. The ground truth is scaled to absolute scale. - A dataset containing images of a small blade segment. The blade has been sand blasted, to imitate prolonged real world use. The images are taken both outdoor and indoor. The indoor images contain patches specifying areas of interest - one containing a rough patch and one containing damages. The outdoor images are focused on the whole blade and do not contain patches. The dataset also contains ground truth microscopy data for the two patches, which is scaled to absolute scale.
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