Create README.md

README.md

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+# WiCount
+
+The description is generated by Grok3.
+
+## Dataset Description
+
+- **Repository:** [CSI-BERT2/WiCount at main · RS2002/CSI-BERT2](https://github.com/RS2002/CSI-BERT2/tree/main/WiCount)
+- **Paper:** [CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing](https://arxiv.org/abs/2412.06861)
+- **Contact:** [[email protected]](mailto:[email protected])  
+- **Collectors:** Zijian Zhao, Tingwei Chen  
+- **Organization:** AI-RAN Lab (hosted by Prof. Guangxu Zhu) in SRIBD, CUHK(SZ)  
+- **Dataset Summary:**
+  The WiCount dataset contains synchronized Channel State Information (CSI), Received Signal Strength Indicator (RSSI), and timestamp data collected using ESP32-S3 devices for WiFi-based people number estimation in a meeting room scenario. The dataset includes samples for estimating the number of people (0–3) in the environment.
+- **Tasks:** People Number Estimation
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance is a `.csv` file representing a 60-second sample with the following columns:  
+
+- **seq**: Row number of the entry.  
+- **timestamp**: UTC+8 time of data collection.  
+- **local_timestamp**: ESP32 local time.  
+- **rssi**: Received Signal Strength Indicator.  
+- **data**: CSI data with 104 numbers representing 52 subcarriers, where each subcarrier's complex CSI value is computed as `a[2i] + a[2i+1]j`.  
+- **Other columns**: Additional ESP32 device information (e.g., MAC, MCS details).
+
+### Data Fields
+
+| Field Name      | Description                                                  |
+| --------------- | ------------------------------------------------------------ |
+| seq             | Row number of the entry                                      |
+| timestamp       | UTC+8 time of data collection                                |
+| local_timestamp | ESP32 local time                                             |
+| rssi            | Received Signal Strength Indicator                           |
+| data            | CSI data (104 numbers, representing 52 subcarriers as complex values) |
+| Other columns   | Additional ESP32 metadata (e.g., MAC address, MCS details)   |
+
+### Data Splits
+
+The dataset is organized by the number of people (0–3), with each folder containing `.csv` files corresponding to the number of people present in the environment:  
+
+- **Folders**: 0, 1, 2, 3 (representing the number of people).
+
+## Dataset Creation
+
+### Curation Rationale
+
+The dataset was created to facilitate research on WiFi-based people number estimation using low-cost ESP32-S3 devices, enabling applications in smart environments, occupancy monitoring, and crowd management.
+
+### Source Data
+
+- Initial Data Collection:
+
+  Data was collected in an indoor meeting room with a single transmitter and multiple receivers using ESP32-S3 devices. The setup included:  
+
+  - **Frequency Band:** 2.4 GHz  
+  - **Bandwidth:** 20 MHz (52 subcarriers)  
+  - **Protocol:** 802.11n  
+  - **Waveform:** OFDM  
+  - **Sampling Rate:** ~100 Hz  
+  - **Antenna Configuration:** 1 antenna per device  
+  - **Environment:** Indoor with walls and a soft pad to prevent volunteer injuries.
+
+- **Who are the source data producers?**
+  The data was collected by researchers, with volunteers present in a controlled meeting room environment.
+
+### Annotations
+
+- **Annotation Process:**
+  Each `.csv` file is stored in a folder labeled with the number of people present (0–3). No additional manual annotations were provided.  
+- **Who are the annotators?**
+  The dataset creators labeled the data based on the experimental setup.
+
+### Personal and Sensitive Information
+
+The dataset does not contain personally identifiable information, as it focuses on the number of people (0–3) without associating specific identities or biometric data beyond CSI patterns.
+
+## Citation
+
+```bibtex
+@article{zhao2024mining,
+  title={CSI-BERT2: A BERT-inspired Framework for Efficient CSI Prediction and Classification in Wireless Communication and Sensing},
+  author={Zhao, Zijian and Meng, Fanyi and Lyu, Zhonghao and Li, Hang and Li, Xiaoyang and Zhu, Guangxu},
+  journal={arXiv preprint arXiv:2412.06861},
+  year={2024}
+}
+```