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The JWT signature verification failed. Check the signing key and the algorithm.
Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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The RFUAV DATASET

This repository contains the RFUAV dataset, presented in the paper "RFUAV: A Benchmark Dataset for Unmanned Aerial Vehicle Detection and Identification". 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.

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}, 
}
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Paper for surdhum/RFUAV