Artificial Intelligence
Soft robots are a promising area of research due to their potential use in various applications. Learning the kinematics of soft robots is crucial for their advancement and application. This dataset is designed to provide training data for the development of machine learning models that can learn the kinematics of soft robots with different actuation types. The dataset includes the positional data of three soft robots, specifically the simulated pneumatic soft robot, simulated tendon-driven soft robot, and real-world tendon-driven soft robot.
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A CNC adapter was utilized together with the software established as part of the GRBL project to operate the CNC adapter, and two data sets were produced for the physical model in order to build the linear and circular motion models. The parameters for motion quantity, motion duration, and feed rate are in the data set.
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The historical dataset of the meteorological variables was recorded by the Mexican National Water Council (Comisión Nacional del Agua, CONAGUA) ground station located at the city of Mérida, Yucatán, Mexico; the following variables were found: temperature (T), vapor pressure (P), and relative humidity (H). The range dates of the records were from January 1, 2000 to September 30, 2018, where there is a daily record of temperatures with minimum, maximum, and average units, resulting in nine readings provided for each day.
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Table Detection is a fundamental task for visually rich document understanding. However, popular public datasets widely used in related studies have inherent limitations, including noisy and inconsistent samples, limited training samples and limited data sources.
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The advancements in the field of telecommunications have resulted in an increasing demand for robust, high-speed, and secure connections between User Equipment (UE) instances and the Data Network (DN). The implementation of the newly defined 3rd Generation Partnership Project 3GPP (3GPP) network architecture in the 5G Core (5GC) represents a significant leap towards fulfilling these demands. This architecture promises faster connectivity, low latency, higher data transfer rates, and improved network reliability.
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There is a growing trend towards combining mathematics and computer science education. Although there are surely synergies between the two disciplines, has sufficient thought been given to the benefit of fostering independently the unique skillsets they offer in order to best harness these synergies?
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