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The demand for poultry products will keep rising as the world's population rises. One of the most significant and rapidly expanding economic sectors of India's agriculture sector is poultry. To meet this demand, increasing housing and managing more chicken birds is one potential technique to boost productivity. It will become more challenging for producers to keep track of the health, production, and welfare conditions of all of their birds as a result of this technique, labour shortages, and escalating biosecurity measures.
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This article provides an introduction to the field of datasets, including their types, characteristics, and applications. Datasets refer to collections of data that have been organized for specific purposes. They can come in various forms, including structured data, unstructured data, and semi-structured data. Each type of dataset has its own unique characteristics and uses. For example, structured data typically includes datasets that have been organized into tables and rows, such as spreadsheets or databases, while unstructured data typically includes text, images, and videos.
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Iman Sharafaldin et al. generated the real time network traffic and these are made available at the Canadian Institute of Cyber security Institute website. The team of researchers published the network traffic data and has made the dataset publicly available in both PCAP and CSV formats. The network traffic data is generated during two days. Training Day was on January 12th, 2018 and Testing Day was on March 11th, 2018.
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This Named Entities dataset is implemented by employing the widely used Large Language Model (LLM), BERT, on the CORD-19 biomedical literature corpus. By fine-tuning the pre-trained BERT on the CORD-NER dataset, the model gains the ability to comprehend the context and semantics of biomedical named entities. The refined model is then utilized on the CORD-19 to extract more contextually relevant and updated named entities. However, fine-tuning large datasets with LLMs poses a challenge. To counter this, two distinct sampling methodologies are utilized.
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Experimental measurement data was obtained utilizing RCbenchmark 1780 with full-range PWM signals. Measurements were made for two series of setups.
First series is related to low-voltage setups using the following T-MOTOR components: - motors: MN4014 400Kv, MN5212 340Kv, MN501-S 360Kv, U7 280Kv, MN6007 320Kv, P60 340Kv, MN701-S 280Kv; - ESC: Air 40A, Flame 40A, Flame 70A, Alpha 60A, Flame 100A; - propellers: P17×5.8, P18×6.1, P20×6, P22×6.6, P24×7.2, G26×8.5; - battery: 6-cell (6S) Lithium polymer (LiPo).
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This data provides the traffic data transmission and reception at Wikipedia's six data centers (Eqiad, Codfw, Esams, Ulsfo, Eqsin, and Drmrs) in Wikitech.
- Eqiad : Data center located in Ashburn, USA
- Codfw : Data Center in Carrollton, Texas, USA
- Esams : Data center located in Amsterdam, The Netherlands
- Ulsfo : Data Center located in San Francisco
- Eqsin : Data Center located in Singapore
- Drmrs : Data Center located in Marseille, France.
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The training data consists of data from various faults from five individual configurations, while the testing data is blind and is from one individual configuration of the rock drill. A final validation data set will be from two individual configurations from the rock drill and the labels are blind.
The training data set contains data from 11 different fault classification categories, in which 10 are different failure modes and one class is from the healthy/no fault condition.
Each file follows the naming convention of data_{sensor}{individual impact cycle number}.csv.
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This dataset extracts the entropy of each of the PE sections of benign and ransomware reports to be used for detecting ransomware. Several machine learning classifiers were trained on this dataset such as Decision Tree, Random Forest, KNN, XGBoost and Naive Bayes. From the results, PE entropy can accurately detect ransomware with a decision tree classifier yielding the overall best result with a 98.8% accuracy and an AUC of 0.969. The latency with the prediction of the decision tree classifier was extremely quick with a result of 1.509 milliseconds.
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Sensor arrays are ubiquitous. They capture images in digital cameras, record the swipes of our fingers on the screens of our phones and tablets, or map pressure distribution over an area. Soft capacitive sensor arrays have been proposed to make electronic pressure-sensing skins capable of identifying the location and intensity of touch. However, large arrays of those sensors remain challenging to produce, as they require high-resolution patterning of electrodes and routing of long and thin electrical connections.
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IMUs have gained popularity for tracking joint kinematics due to their portability and versatility. However, challenges such as limited accuracy, lack of real-time data analysis, and complex sensor-to-segment calibration procedures have hindered their widespread use. To address these limitations, we developed a portable system that integrates four IMUs to collect treadmill walking data, with ground truth values obtained from a Motion Capture System.
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