Refine dataset card and remove irrelevant tags

This PR refines the dataset card by:

- Removing irrelevant `task_categories` (image-classification, text-classification, robotics).
- Adding a direct link to the GitHub repository for code and usage instructions.
- Clarifying the dataset description to emphasize its focus on RF-based UAV detection and identification.

README.md

@@ -1,36 +1,32 @@
----
-license: apache-2.0
-task_categories:
-- image-classification
-- text-classification
-- audio-classification
-- robotics
-language:
-- en
-- zh
-tags:
-- signal processing
----
-
-
-<h1 style="text-align:center;"> The RFUAV DATASET </h1>
-
-## Abstract
-
-The official dataset repository for our paper, *"[RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://arxiv.org/abs/2503.09033)"*, can be accessed here. RFUAV offers a comprehensive benchmark dataset for Radio-Frequency (RF)-based drone detection and identification.
-  
-![pic.1](./abstract/profile.png)
-
-More detailed usage see our [GitHub](https://github.com/kitoweeknd/RFUAV)
-
-## Citation
-
-    @misc{shi2025rfuavbenchmarkdatasetunmanned,
-          title={RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification}, 
-          author={Rui Shi and Xiaodong Yu and Shengming Wang and Yijia Zhang and Lu Xu and Peng Pan and Chunlai Ma},
-          year={2025},
-          eprint={2503.09033},
-          archivePrefix={arXiv},
-          primaryClass={cs.RO},
-          url={https://arxiv.org/abs/2503.09033}, 
-    }
+---
+language:
+- en
+- zh
+license: apache-2.0
+task_categories:
+- audio-classification
+tags:
+- signal-processing
+- drone-detection
+- drone-identification
+- rf-signal
+---
+
+<h1 style="text-align:center;"> The RFUAV DATASET </h1>
+
+This repository contains the RFUAV dataset, presented in the paper "[RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification](https://huggingface.co/papers/2503.09033)". RFUAV provides approximately 1.3 TB of raw frequency data collected from 37 distinct UAVs, offering a comprehensive benchmark for radio-frequency-based drone detection and identification.  The dataset addresses limitations of existing datasets by providing a diverse range of drone types, sufficient data volume, coverage across various signal-to-noise ratios (SNR), and open-access evaluation tools.
+
+For detailed usage instructions, preprocessing methods, model evaluation tools, and code examples, please refer to the [GitHub repository](https://github.com/kitoweeknd/RFUAV).
+
+
+## Citation
+
+    @misc{shi2025rfuavbenchmarkdatasetunmanned,
+          title={RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification}, 
+          author={Rui Shi and Xiaodong Yu and Shengming Wang and Yijia Zhang and Lu Xu and Peng Pan and Chunlai Ma},
+          year={2025},
+          eprint={2503.09033},
+          archivePrefix={arXiv},
+          primaryClass={cs.RO},
+          url={https://arxiv.org/abs/2503.09033}, 
+    }