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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KNET Seismic Ground Motion & Building Response Dataset (MDOF)

Ground motion records from the K-NET strong-motion network (Japan), paired with numerically simulated floor acceleration responses for 250 multi-degree-of-freedom (MDOF) shear-building configurations. Used to train and evaluate Fourier Neural Operator (FNO) models for seismic structural response prediction.

Dataset Summary

Property	Value
Ground motion records	3,474 (K-NET)
Amplitude scale factors per GM	57
Building configurations	250 MDOF shear buildings
Signal length	3,000 time steps (60 s @ 50 Hz)
GM file format	HDF5 (`.h5`)
Building response format	HDF5 (`.h5`), one file per building

File Structure

MDOF/
├── All_GMs/
│   └── GMs_knet_3474_AF_57.h5        # Ground motion inputs
└── knet-250/
    └── Data/
        └── fno/
            ├── Blg_F6_18m_IM7_st0.h5.h5       # Floor acc. response

Ground Motion File (`GMs_knet_3474_AF_57.h5`)

Each entry stores the scaled acceleration time series for one GM × scale-factor combination.

HDF5 Key pattern	Shape	Description
`gm_{i}/af_{j}/data`	`(3000,)`	Scaled acceleration (m/s²), 50 Hz
`gm_{i}/af_{j}/pga`	scalar	Peak ground acceleration (m/s²)
`gm_{i}/metadata`	attrs	Station, event, magnitude, etc.

Building Response Files (`Blg_F6_18m_IM7_st0.h5.h5`)

Each file stores the simulated structural response for all GM × scale combinations for one building.

HDF5 Key	Shape	Description
`response/gm_{i}/af_{j}/floor_acc`	`(n_floors, 3000)`	Floor acceleration (m/s²)
`building/attributes`	attrs	Number of floors, periods, damping, etc.
`building/damage_state/gm_{i}/af_{j}`	scalar int	HAZUS damage state (0–4)

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Refer to github repo and codes.

License

Creative Commons Attribution 4.0 (CC BY 4.0)

Downloads last month: 10

Total file size:

38.5 GB