Artificial Intelligence

This dataset comprises three degrees of freedom (3 DOF) sensory data and simulation data collected from a Kinova robotic arm. The sensory data includes real-time measurements from the robotic arm’s joint positions, velocities, and torques, providing a detailed account of the arm’s dynamic behavior. The dataset also includes simulated data generated using a high-fidelity physics engine, accurately modeling the Kinova arm’s kinematics and dynamics under various operational scenarios.

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This dataset has been meticulously curated to evaluate the efficiency of Retrieval-Augmented Generation (RAG) pipelines in both retrieval and generative accuracy, with a particular focus on scenarios involving overlapping contexts. The dataset comprises two primary components: Motor data and Employee data. The Motor dataset includes master data of various motor models along with their corresponding manuals, linked by the motor's model name. Similarly, the Employee dataset encompasses employee master data and associated policy documents, linked by department.

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This dataset is designed for the reconstruction of images of underground potato tubers using received signal strength (RSS) measurements collected by a ZigBee wireless sensor network. It includes RSS data from sensing areas of various sizes, environments with different layouts, and soils with varying moisture levels. The measurements were obtained from 9 potato tubers of differing sizes and shapes, which were buried in two distinct positions within the sensing area.

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We construct the triple-modality dataset, VGG-sound+, comprising image-text-audio data. Based on VGG-sound, VGG-sound+ consists of 200,000 audio-visual data entries categorized as video data, including metadata label- ing the category of each video clip. We define the image-text-audio triplet modalities of VGG-sound+ as the dataset Di = (Ii , Ti , Ai), where Ii represents an image snap- shot of the video, Ti denotes a textual description of the video, and Ai signifies the audio clip.

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In this paper, two datasets for text classification were primarily used in the experiments: AG News and IMDB. The AG News dataset is a widely used four-class news dataset, including four categories: World News, Sports News, Business News, and Technology News. The dataset contains a total of 120,000 samples, with 114,000 samples in the training set and the remaining 6,000 samples in the test set. The IMDB dataset is a movie review dataset used for sentiment analysis, primarily for binary classification tasks, i.e., positive and negative reviews.

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This graph illustrates the visualization trend of a subset of the dataset I have uploaded, which comprises 6500*9 data points. The dataset consists of nine columns representing underwater speed (UWS), underwater course (UWC), depth below the surface (DBS), rate of change in speed (RCS), rate of change in course (RCC), rate of change in depth (RCD), trend A and B of vibrational signals (TVS_A, TVS_B) and electromagnetic noise trend (TEN) recorded by the AUV.

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Acquiring imagery of extraterrestrial planetary surfaces has consistently posed a significant challenge. Moreover, the availability of publicly accessible, annotated lunar datasets remains exceedingly sparse. To address this gap in the field, we have developed the Real Chang'e Lunar Landscape Dataset. The images in this dataset were captured by the landing cameras of the Chang'e-3, Chang'e-4, and Chang'e-5 lunar mission probes, as well as the panoramic and terrain cameras carried by the Yutu-1 and Yutu-2 lunar rovers used during the missions.

 

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Acquiring imagery of extraterrestrial planetary surfaces has consistently posed a significant challenge. Moreover, the availability of publicly accessible, annotated lunar datasets remains exceedingly sparse. To address this gap in the field, we have developed the Real Chang'e Lunar Landscape Dataset. The images in this dataset were captured by the landing cameras of the Chang'e-3, Chang'e-4, and Chang'e-5 lunar mission probes, as well as the panoramic and terrain cameras carried by the Yutu-1 and Yutu-2 lunar rovers used during the missions.

 

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Acquiring imagery of extraterrestrial planetary surfaces has consistently posed a significant challenge. Moreover, the availability of publicly accessible, annotated lunar datasets remains exceedingly sparse. To address this gap in the field, we have developed the Real Chang'e Lunar Landscape Dataset. The images in this dataset were captured by the landing cameras of the Chang'e-3, Chang'e-4, and Chang'e-5 lunar mission probes, as well as the panoramic and terrain cameras carried by the Yutu-1 and Yutu-2 lunar rovers used during the missions.

 

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This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather. This is an optical flow dataset which covers multiple independent moving object samples under various adverse weather.

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