Artificial Intelligence

LDRText is a large-scale and diverse dataset that suitable for scene text image super-resolution and recognition tasks

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This article presents a dataset collected from a real process control network (PCN) to facilitate deep-learning-based anomaly detection and analysis in industrial settings. The dataset aims to provide a realistic environment for researchers to develop, test, and benchmark anomaly detection models without the risk associated with experimenting on live systems. It reflects raw process data from a gas processing plant, offering coverage of critical parameters vital for system performance, safety, and process optimization.

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This dataset is derived from Sentinel-2 satellite imagery.
The main goal is to employ this dataset to train and classify images into two classes: with trees, and without trees.
The structure of the dataset is 2 folders named: "tree" (images containing trees) and "no-trees" (images without presence of trees).
Each folder contains 5200 images of this type.

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we evaluate resource management in device-to-device (D2D) networks that can coexist with mobile cellular user equipments (CUEs). Moreover, a new smart model for joint scheduling and resource management for D2D networks is presented.

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Computer vision (CV) techniques help to perform non-destructive seed viability detection (SVD) for faster, more efficient and fairer results. However, the seed vigor dataset currently suffers from insufficient number of samples, data noise, and imbalance of positive and negative samples.

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The dataset contains 2100 different observations each having 1099 absorption data points for different types of cells. The reflection absorption data were obtained from terahertz metamaterials on top of which the cells are placed. The 2100 observations made were for varying size specimen size and for four different types of cells

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``By touching an instrument placed in the southern gallery, a miniature Spanish cruiser anchored in the fountain lake on the lower floor, 90 feet away, was blown into the air. There was no connection between the transmitter and the vessel in the lake'' reported the New York Times in 1898.

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The dataset tracks the performance of eight stock market indices, from six countries. The indices are: IPC, S\&P 500, DAX, DJIA, FTSE, N225, NDX, and CAC. The time period is from the 1st of June 2006 to the 31st of May 2023.The index and the FX data are sourced from Yahoo Finance, and the rest of the variables are retrieved from the OECD.

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The current quantitative retrieval of Aerosol Optical Depth (AOD) typically uses Top-of-atmosphere (TOA) reflectance data obtained by radiometric calibration. Errors can be introduced during the conversion of DN values to TOA reflectance, affecting the retrieval of AOD. Especially when the surface reflectance is relatively high, the conversion error will bring significant errors to the AOD retrieval, as in such cases, the contribution of aerosols to the radiation received by satellite sensors is relatively small.

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In international contexts, natural scenes may include text in multiple languages. Especially, Latin and Arabic scene character image dataset is essential for training models to accurately detect and recognize text regions within real-world images. This is crucial for applications such as text translation, image search, content analysis, and autonomous vehicles that need to interpret text in different languages.

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