The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: RuntimeError
Message: Failed to open input buffer: Invalid data found when processing input
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1953, in __iter__
batch = formatter.format_batch(pa_table)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
batch = self.python_features_decoder.decode_batch(batch)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2147, in decode_batch
decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1409, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/features/audio.py", line 204, in decode_example
audio = AudioDecoder(f, stream_index=self.stream_index, sample_rate=self.sampling_rate)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/torchcodec/decoders/_audio_decoder.py", line 64, in __init__
self._decoder = create_decoder(source=source, seek_mode="approximate")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/torchcodec/decoders/_decoder_utils.py", line 45, in create_decoder
return core.create_from_file_like(source, seek_mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/torchcodec/_core/ops.py", line 151, in create_from_file_like
return _convert_to_tensor(_pybind_ops.create_from_file_like(file_like, seek_mode))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Check out the documentation for more information.
This dataset provides 3D XCT reconstruction results for eight retired and one fresh LiFePO4 (LFP) prismatic batteries. These samples were manufactured by REPT BATTERO Energy Co., Ltd. in 2021 and sourced from Dongfeng E70 electric vehicles. Each battery has a nominal voltage of 3.2 V and a rated capacity of 135 Ah (dimensions: 148 mm × 80 mm × 105 mm). Compared to the fresh control sample (136.7 Ah), the retired batteries exhibit varying degrees of degradation, with measured capacities ranging from 110.2 to 122.4 Ah, corresponding to a capacity loss of approximately 10–20%.
For detailed metadata, please refer to the file sample_info.txt, which lists the specific capacity values and the corresponding 3D reconstruction parameters for each battery sample.
The non-destructive characterization was performed using a Nikon XTH 255/320 LC industrial XCT system equipped with a high-sensitivity flat panel detector. The scans were conducted at a voltage of 205 kV and a current of 200 μA, capturing approximately 2500 projection images over a 360° rotation to visualize the internal structural deformation.
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