The dataset is intended to cover core issues pertaining to the area of a traffic optimization via RET motors inside the antenna on the mobile base station system (BSS). The principle of RET operation was already known to scientists; however, the use of a machine learning and big data provides the possibility of creation an autonomous system, which control RET system.
The datasets were originally crawled from the Meetup.com by authors of this work[1]. It consists of two part:
This dataset is created for rope crossings type classification. Crossings were divided into upper and lower types. Data were collected under four different backgrounds.
This dataset was created for Deformable Linear Objects(DLOs) segmentation and crossings classification under complex background.
RGB images, overlap maps, gradient maps are included for segmentation task. The quantity of DLOs range from one to three.
Upper and lower type of crossings are defined for classification task.
The dataset contains short video clips of four shoulder exercises.
The videos are labeled as either correct or incorrect.
<p>This multilingual Twitter dataset spans over 2 years from October 2019 to the end of 2021, including 3 months before the outbreak of the COVID-19 pandemic.</p>
Multi-label event classification label of each sample-document is done with nine bits. The first bit signifies whether an event is present or absent with 1 or 0 respectively. The remaining eight bits signifies presence or absence of (i) covid, (ii) flood, (iii) storm, (iv) heavy rain, (v) cloudburst, (vi) landslide, (vii) earthquake, (viii) Tsunami with 1 or 0. The location and the impact sentence classification labeling are similar.
A synthetic data for low power (P ≤10 mW) InGaAsP MQW-DFB lasers operating at a wavelength (λ) ranging from 1.53 to 1.57 µm at a case temperature laying between -40 ℃ to 85 ℃ with side mode suppression ratio of more than 35 dB is generated and can be used for laser lifetime prediction using machine learning based approaches.
The dataset includes processed sequences of optical time domain reflectometry (OTDR) traces incorporating different types of fiber faults namely fiber cut, fiber eavesdropping (fiber tapping), dirty connector and bad splice. The dataset can be used for developping ML-based approaches for optical fiber fault detection, localization, idenification, and characterization.
The disaster-news healline generation dataset (news_articles_and _titles) contains a set of disaster-news articles and their headlines/titles. This dataset may be used to develop a method to generate a good quality headline for a disaster-news article.
