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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1914, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 771, in _write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 812, in _build_writer
self.pa_writer = pq.ParquetWriter(
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1070, in __init__
self.writer = _parquet.ParquetWriter(
^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2363, in pyarrow._parquet.ParquetWriter.__cinit__
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowNotImplementedError: Cannot write struct type 'attributes' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1342, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1925, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
shape list | data_type string | chunk_grid dict | chunk_key_encoding dict | fill_value int64 | codecs list | attributes dict | zarr_format int64 | node_type string | storage_transformers list |
|---|---|---|---|---|---|---|---|---|---|
[
300001,
16
] | uint8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
16
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
[
300001,
16
] | uint8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
16
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
[
300001,
16
] | uint8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
16
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
[
300001,
1
] | uint8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
1
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
[
300001,
1
] | uint8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
1
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
null | null | null | null | null | null | {} | 3 | group | null |
[
300001,
250000
] | int8 | {
"name": "regular",
"configuration": {
"chunk_shape": [
300001,
25
]
}
} | {
"name": "default",
"configuration": {
"separator": "/"
}
} | 0 | [
{
"name": "bytes",
"configuration": null
},
{
"name": "zstd",
"configuration": {
"level": 0,
"checksum": true
}
}
] | {} | 3 | array | [] |
null | null | null | null | null | null | {} | 3 | group | null |
ascad-v1-vk
This script downloads, extracts, and uploads the optimized ASCAD v1 Variable Key dataset to Hugging Face Hub.
Dataset Structure
This dataset is stored in Zarr format, optimized for chunked and compressed cloud storage.
Traces (/traces)
- Shape:
[300001, 250000](Traces x Time Samples) - Data Type:
int8 - Chunk Shape:
[300001, 25]
Metadata (/metadata)
- key: shape
[300001, 16], dtypeuint8 - mask: shape
[300001, 16], dtypeuint8 - plaintext: shape
[300001, 16], dtypeuint8 - rin: shape
[300001, 1], dtypeuint8 - rout: shape
[300001, 1], dtypeuint8
Leakage Analysis Targets
The following targets are available for side-channel leakage analysis on this dataset:
| Target Name | Description |
|---|---|
ciphertext |
Returns metadata['ciphertext'][:, byte_index] |
key |
Returns metadata['key'][:, byte_index] |
mask |
Returns metadata['mask'][:, byte_index] |
mask_ |
Returns metadata['mask_'][:, byte_index] |
perm_index |
Returns metadata['perm_index'][:, byte_index] |
plaintext |
Returns metadata['plaintext'][:, byte_index] |
rin |
Returns metadata['rin'][:, 0] |
rin_ |
Returns metadata['rin_'][:, 0] |
rm |
Returns metadata['rm'][:, 0] |
rm_ |
Returns metadata['rm_'][:, 0] |
rout |
Returns metadata['rout'][:, 0] |
rout_ |
Returns metadata['rout_'][:, 0] |
sbi |
Returns np.bitwise_xor(metadata['plaintext'][:, byte_index], metadata['key'][:, byte_index]) |
sbo |
Returns SBOX[Targets.sbi(metadata=metadata, byte_index=byte_index, dataset_name=dataset_name)] |
sbox_masked |
Returns metadata['sbox_masked'][:, byte_index] |
sbox_masked_with_perm |
Returns metadata['sbox_masked_with_perm'][:, byte_index] |
v1_key |
Round-0 key byte at position byte_index (= cipher key byte).key[i] where i = byte_index.The key byte is loaded unprotected from flash/ROM during AddRoundKey r=0 and XORed into the masked state. Classic first-order DPA target. |
v1_lut_idx |
maskedSbox LUT index computed during maskedSubBytes at round 1, byte byte_index.ptx[i] ^ key[i] ^ rin where i = byte_index.Computed as state[i] ^ mask[i] ^ r0 in the AVR inner loop: the per-byte mask cancels, leaving the unmasked SBI XORed with rin.Replaces: sasca_xrin from the Bronchain et al. SASCA factor graph. |
v1_masked_ptx |
State after loadAndMaskInput at byte byte_index.ptx[i] ^ mask[i] where i = byte_index.Initial masked plaintext stored in state[i] before any round key has been applied. |
v1_masked_sbi |
State entering round 1 at byte byte_index: after AddRoundKey r=0.(ptx[i] ^ key[i]) ^ mask[i] where i = byte_index.Boolean-masked plaintext XOR key value that maskedSubBytes will process. Replaces: sasca_x0 from the Bronchain et al. SASCA factor graph. |
v1_raw_out |
maskedSbox raw_out at round 1, byte byte_index: the LUT output.SBOX(ptx[i] ^ key[i]) ^ rout where i = byte_index.This is maskedSbox[lut_idx] — the value read from the masked S-Box LUT. It sits between :meth:v1_lut_idx (LUT address) and :meth:v1_sbo_mid (post-XOR-mask intermediate).Original-paper label: sbox_masked[byte_index] in the ASCAD v1 HDF5 file.Replaces: sasca_yrout from the Bronchain et al. SASCA factor graph. |
v1_sbo_masked |
Boolean-masked SBO at byte byte_index after full maskedSubBytes.SBOX(ptx[i] ^ key[i]) ^ mask[i] where i = byte_index.State value written back into state[i] at the end of the inner loop: rout has been removed and only the per-byte mask remains.Replaces: sasca_y0 from the Bronchain et al. SASCA factor graph. |
v1_sbo_mid |
Mid-SubBytes state at byte byte_index before the final rout strip.SBOX(ptx[i] ^ key[i]) ^ rout ^ mask[i] where i = byte_index.raw_out ^ masksState[i] in the AVR inner loop: the value in the register after XOR-ing the LUT output with the per-byte mask, before the final EOR r_val, r1 removes rout. |
Auto-Generated Leakage Plots
| Dataset | Target | Byte Index | Plot |
|---|---|---|---|
| ascad-v1-vk | key | 0 | ![]() |
| ascad-v1-vk | plaintext | 0 | ![]() |
| ascad-v1-vk | mask | 2 | ![]() |
| ascad-v1-vk | rin | none | ![]() |
| ascad-v1-vk | rout | none | ![]() |
Parameters Used for Generation
- HF_ORG:
DLSCA - CHUNK_SIZE_Y:
300001 - CHUNK_SIZE_X:
25 - TOTAL_CHUNKS_ON_Y:
1 - TOTAL_CHUNKS_ON_X:
10000 - NUM_JOBS:
10 - CAN_RUN_LOCALLY:
True - CAN_RUN_ON_CLOUD:
False - COMPRESSED:
True
Usage
You can load this dataset directly using Zarr and Hugging Face File System:
import zarr
from huggingface_hub import HfFileSystem
fs = HfFileSystem()
# Map only once to the dataset root
root = zarr.open_group(fs.get_mapper("datasets/DLSCA/ascad-v1-vk"), mode="r")
# Access traces directly
traces = root["traces"]
print("Traces shape:", traces.shape)
# Access plaintext metadata directly
plaintext = root["metadata"]["plaintext"]
print("Plaintext shape:", plaintext.shape)
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