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This data ia small sample set from the purchase order data catalog of state of california publicaly available at https://catalog.data.gov/dataset/purchase-order-data. From this source, we take a sample of 50,000 entries. The data provides comprehensive details about various purchase orders issued during this period. The dataset comprises 32 columns, capturing information about each purchase order. This dataset is rich in information and provides a valuable resource for item UNSPSC categorization.
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148 Views
Sea-rail intermodal transportation is an essential infrastructure in global supply chains nowadays. Since the yard is the interface between sea and land, optimizing the transportation process in yards is of significant interest for increasing transportation efficiency.
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135 Views
Experiment Details:
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1679 Views
Traditional magnetic localization methods based on mathematical model and optimization algorithm often fail to achieve the global optimum due to their dependency on initial values of pose parameters. Despite deep learning can potentially address these limitations, the existing methods are restricted to capture both global position distribution and local spatial features, leading to diminished performance in tasks required precise pose information.
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82 Views
It is a dataset containing sentence segments from cutomer reviews about mobile phone from different sources like Amazon, Flipkart, Tweeter and some existing datasets. It contains more than 1000 records tagged with one of the five aspect categories battery, camera, display, price and processor. Whether a sentence segment has sentiment expression (subjective/ objective) is also tagged and the sentiment orientation (positive/ negative/ neutral) of each sentence segment is assigned. Explicit or implicit presence of aspect is also maintained.
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250 Views
The data used in this work is collected using the AirBox Sense system developed to detect six air pollutants, ambient temperature, and ambient relative humidity. The pollutants are Nitrogen Dioxide (NO2), surface Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2), Particulate Matter (PM2.5, and PM10). The sensors monitor these pollutants in real-time and store them in a cloud-based platform using a cellular module. Data are collected every 20 seconds, producing 4320 readings each day.
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289 Views
This dataset contains Wi-Fi sensing data using Channel State Information (CSI) for various sleep disturbance parameters, from respiratory disturbances, to motion-based disturbances from posture shifts, leg restlessness and confusional arousals.The Wi-Fi CSI data was collected using the Wi-Fi module on the ESP32 Microcontroller units using the esp32-csi-tool.The Wi-Fi CSI respiratory disturbance data is accompanied by respiration belt data taken with the Wi-Fi measurements simultaneously using the Neulog NUL-236 respiration belt logger as ground truth.
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1193 Views
This data was collected using a Bosch Parking Lot Sensor (TPS110 EU) placed in a time-limited public parking space over a period of two months. Each time the sensor detected a change in the parking status, it transmitted the new state via The Things Stack LoRaWAN network to the server.
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95 Views
This dataset has been measured from the User Equipment (UE) using an Automated Guided Vehicle (AGV). The collected metrics include the radio information measured by the modem, and the localization information obtained from the AGV's navigation system based on LiDAR technology.
The AGV is configured to follow a loop movement from the south to the north of the laboratory at 1 m/s speed. The BTS is a commercial cell publicly available on mmWave, but no external users were connected to mmWave during the experiments.
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235 Views
Data Collection Period: Both datasets cover the period from July 1, 2022, to July 31, 2023. This one-year span captures a full cycle of seasonal variations, which are critical for understanding and forecasting air quality trends.
Data Characteristics
- Temporal Resolution: The data is recorded at 15-minute intervals, offering detailed temporal resolution.
- Missing Data: Both datasets contain missing values due to sensor malfunctions or communication issues. These missing values were handled using imputation techniques as part of the preprocessing phase.
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457 Views