The data includes the prediction values of photovoltaic and load power, as well as the parameters of the test system.

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Parallel fractional hot-deck imputation (P-FHDI) is a general-purpose, assumption-free tool for handling item nonresponse in big incomplete data by combining the theory of FHDI and parallel computing. FHDI cures multivariate missing data by filling each missing unit with multiple observed values (thus, hot-deck) without resorting to distributional assumptions. P-FHDI can tackle big incomplete data with millions of instances (big-n) or 10, 000 variables (big-p).

This repository includes three types of data: incomplete data with massive instances (big-n data), incomplete data with many variables (big-p data), incomplete data with tremendous instances and high dimensionality (ultra data). The repository has synthetic data and practical data from various scientific domains. Overall, there exist seven big-n datasets, four big-p datasets, and ten ultra datasets. For instructions, see Readme files in the dataset folder for the step-by-step use of UP-FHDI with different types of incomplete datasets.

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Database for a moderation of technological acceptance research

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This is a dataset is an example of a distribution of 20 correlated Bernoulli random variables.

Q_joint ... is 5 cells each consists of the joint distributions of 4,8,12,16,20 bits, respectively. The dimension of each cell is 2^n X 1, .e., a vertical column and n=4,8,12,16,20.

Q_conditional... is 5 cells each consists of the conditional distributions of 4 bits given 0, 4, 8,12,16 bits, respectively. In other words, 1:4 bits, 5:8 bits given 1:4 bits, 9:12 bits given 1:8 bits, 13:16 bits given 1:12 bits, 17:20 given 1:16 bits. The dimension of each cell is 2^4=16 X 2^n, i.e., a vertical column and n=4,8,12,16.

Q_ marginal... is 5 cells each consists of the marginal distributions of each 4 consecutive bits, i.e., 1:4 - 5:8 - 9:12 - 13:16 - 17:20, respectively. The dimension of each cell is 16 X 1, i.e., q vertical column.

Also, a MATLAB code is uploaded to extract conditional and marginal distributions from any given discrete distribution.

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This work provides the measurement data of sixteen high frequency (HF) radio frequency identification (RFID) transponder (tag) chips. In particular, the chip input impedance was characterized versus chip input voltage at 13.56 MHz. The measurement is based on the radio frequency current-voltage impedance measurement method and achieves, compared to previous work, higher measurement accuracy of lower than 1.5 %. The accompanying publication provides additional details.

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1.Visualization of convolutional neural network layers for one participant at ROI 301 * 301

2.Convolutional neural network structure analysis in Matlab

3.Convolutional neural network Matlab code

4.Videos of brightness mode (B-mode) ultrasound images from two participants during the recorded walking trials at 5 different speeds

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This is the result found by multi-tree-search.</p>

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Extensive experimental measurement campaigns of more than **30,000 data points** of end-to-end latency measurements for the following network architecture schemes is available:

- Unlicensed IoT (
**standalone****LoRa**) - Cellular IoT (
**standalone****LTE-M**) - Concatenated IoT (
**LoRa interfaced with LTE-M**)

Download *Data.zip* to access all relevant files for the open data measurements.

** Related Paper**:

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The data are associated with a submitted journal paper.

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