The original data collection comes from Sinopec Gaoqiao Petrochemical's real-time database (Honeywell PHD) and LIMS experimental database. Among them, the operating variable data comes from a real-time database, the collection time is from April 2017 to May 2020, and the number of collection operation variables is 354. From April 2017 to September 2019, the data collection frequency was 3 minutes/time; from October 2019 to May 2020, the data collection frequency was 6 minutes/time. The properties of raw materials, products and catalysts are non-operational variables. The data comes from the LIMS experimental database. The data time range is from April 2017 to May 2020. The octane number of raw materials and products is an important modeling variable, and the data collection frequency is 2 times a week. The related code is given.

Ran-binding protein 1 Leishmania infantum (strain JPCM5)

Primary equity market - GDP panel data for the period 1990-2019

A Monte Carlo framework for solving optimal control problems governed by kinetic models is presented. The focus is on a kinetic model with Keilson-Storer linear collision term and the control mechanism is an external space-dependent force. The purpose of this control is to drive an ensemble of particles to acquire a desired mean velocity and position and to reach a desired final configuration in phase space. For this purpose, a gradient-based computational strategy in the framework of Monte Carlo methods is developed. Results of numerical experiments successfully validate the proposed control framework.

This dataset contains a simplified profile of taxi pickup in New York City. The package contains three folders: data, py_code and r_code. The data folder contains the arrival profile data. The py_code folder contains the python code for the calculation of dynamic time warping. The r_code folder contains the r language code for hierarchical clustering and silhouette index calculation.

A Research on the effect of Geostatistical texture Analysis on Image Classification Accuracy Dataset

This is a dataset for Bluetooth 5.1 DoA applications with URA8 antenna arrays. It was conceived as a starting point for developing and validating DoA algorithms for real-life scenarios. Unlike other datasets, it contains Bluetooth signals with not only varying intensity of additive white Gaussian noise, but also coherent interfering signals with random DoA coordinates. The dataset is divided into two branches, one consisting of pure sinusoidal tones and the second comprised of baseband Bluetooth signals. The Python code which generated this dataset is included. Therefore, the dataset can be expanded and also modified to suit other types of signals and simulate other types of antenna arrays. Please note that this is only the first part of the dataset. The dataset is comprised of four parts, and you need to download all of them in order to recreate the entire directory structure. Part 1: http://dx.doi.org/10.17632/zjn2zxcgyz.1 Part 2: http://dx.doi.org/10.17632/gysjfpvxpm.1 Part 3: http://dx.doi.org/10.17632/nnv3vpy4y3.1 Part 4: http://dx.doi.org/10.17632/362szwsnc4.1

The 3-D resistivity model covers the central portion of NCC containing all the three major blocks (TNCO, ENCC, WNCC), which is inverted from 41 long period and 142 broadband MT sites acquired by the SINOPROBE project. Depth Coverage: 0 - 1000 km (It is reliable < 200 km); Areal Coverage: Latitude: 34N to 40N Longitude: 106.7E to 118.4E.

A set of electroencephalogram (EEG) signals data obtained from Yayasan Kita Dan Buah Hati (YKBH), Jakarta, Indonesia. The data was collected using a BrainMaker EEG machine with 19 channels. The sampling rate of the machine is 250Hz. There are 14 participants (girl = 5, boy = 9) join the data collection. A psychologist has verified 7 of them are an addiction, and 7 of them are healthy children. The EEG data were recorded through one protocol with five tasks for 10 minutes. The five tasks are baseline, emotional state, memorize task, executive task, and recall task. The obtained data is used for analyzing the pattern of pornography addiction among children. Further analysis of pre-processing, feature extraction, and classification will be applied to the data.

This is a dataset for Bluetooth 5.1 DoA applications with URA8 antenna arrays. It was conceived as a starting point for developing and validating DoA algorithms for real-life scenarios. Unlike other datasets, it contains Bluetooth signals with not only varying intensity of additive white Gaussian noise, but also coherent interfering signals with random DoA coordinates. The dataset is divided into two branches, one consisting of pure sinusoidal tones and the second comprised of baseband Bluetooth signals. The Python code which generated this dataset is included. Therefore, the dataset can be expanded and also modified to suit other types of signals and simulate other types of antenna arrays. Please note that this is only the first part of the dataset. The dataset is comprised of four parts, and you need to download all of them in order to recreate the entire directory structure. Part 1: http://dx.doi.org/10.17632/zjn2zxcgyz.1 Part 2: http://dx.doi.org/10.17632/gysjfpvxpm.1 Part 3: http://dx.doi.org/10.17632/nnv3vpy4y3.1 Part 4: http://dx.doi.org/10.17632/362szwsnc4.1