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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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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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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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283 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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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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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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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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447 Views
The "Multilabel Extremism Classification Tweets Dataset" dataset contains user comments annotated with labels including toxic, severe toxic, obscene, threat, insult, and identity hate. Designed for multi-label classification, this dataset is valuable for researchers focused on detecting online extremism and toxicity across multiple languages. It enables the development of NLP models for content moderation, hate speech detection, and extremism identification.
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209 Views
The "Multi-Label Extremism and Jihadism Classification Tweets Dataset" dataset is a multilingual resource designed for multi-label classification of online extremism and toxic behavior, including extremism and jihadism. Each comment is annotated with labels indicating the presence of various extremism traits: toxic, severe toxic, obscenity, threats, insults, identity hate, and jihadi content.
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The accurate distinction between line-of-sight (LOS) and non-line-of-sight (NLOS) propagation channels is paramount for precise distance measurement within ultra-wideband (UWB) indoor localization systems. In complex and dynamic environments, such as those encountered in the indoor positioning of autonomous mobile robots or vehicles, UWB signal propagation is particularly susceptible to NLOS conditions.
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567 Views