Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
format: string
version: string
case_id: string
bank_path: string
bank_manifest_sha256: string
bank_bytes: int64
code_repository: string
code_revision: string
budgets: struct<default: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>, expanded: s (... 71 chars omitted)
child 0, default: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>
child 0, traces: int64
child 1, samples: int64
child 2, seconds: int64
child 3, mutations: int64
child 1, expanded: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>
child 0, traces: int64
child 1, samples: int64
child 2, seconds: int64
child 3, mutations: int64
images: struct<oracle: string, agent: string, agent-cuda: string>
child 0, oracle: string
child 1, agent: string
child 2, agent-cuda: string
dataset_repo_id: string
runner: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
source_v2_manifest_sha256: string
known_key_bytes: struct<0: string, 1: string>
child 0, 0: string
child 1, 1: string
segment_length: int64
distractor_source_interval: list<item: int64>
child 0, item: int64
source_records: string
target_bytes: list<item: int64>
child 0, item: int64
trace_length: int64
target_source_counts: struct<2: struct<min_per_input: int64, max_per_input: int64, total: int64>, 3: struct<min_per_input: (... 878 chars omitted)
child 0, 2: struct<min_per_input: int64, max_per_input:
...
03_traces.npy: string
child 3, byte_03_offsets.npy: string
child 4, byte_04_traces.npy: string
child 5, byte_04_offsets.npy: string
child 6, byte_05_traces.npy: string
child 7, byte_05_offsets.npy: string
child 8, byte_06_traces.npy: string
child 9, byte_06_offsets.npy: string
child 10, byte_07_traces.npy: string
child 11, byte_07_offsets.npy: string
child 12, byte_08_traces.npy: string
child 13, byte_08_offsets.npy: string
child 14, byte_09_traces.npy: string
child 15, byte_09_offsets.npy: string
child 16, byte_10_traces.npy: string
child 17, byte_10_offsets.npy: string
child 18, byte_11_traces.npy: string
child 19, byte_11_offsets.npy: string
child 20, byte_12_traces.npy: string
child 21, byte_12_offsets.npy: string
child 22, byte_13_traces.npy: string
child 23, byte_13_offsets.npy: string
child 24, byte_14_traces.npy: string
child 25, byte_14_offsets.npy: string
child 26, byte_15_traces.npy: string
child 27, byte_15_offsets.npy: string
child 28, decoy_00_traces.npy: string
child 29, decoy_01_traces.npy: string
child 30, background.npy: string
child 31, distractors.npy: string
background_noise_sigma: double
source_raw_sha256: string
global_jitter: int64
profile: string
virtual_byte_order: list<item: int64>
child 0, item: int64
distractor_length: int64
sample_unit: string
virtual_starts_by_byte: list<item: int64>
child 0, item: int64
base_sample_rate_hz: int64
limitations: string
local_jitter: int64
sha256: string
to
{'format': Value('string'), 'profile': Value('string'), 'source': Value('string'), 'source_raw_sha256': Value('string'), 'source_records': Value('string'), 'source_v2_manifest_sha256': Value('string'), 'target_bytes': List(Value('int64')), 'known_key_bytes': {'0': Value('string'), '1': Value('string')}, 'segment_length': Value('int64'), 'distractor_length': Value('int64'), 'trace_length': Value('int64'), 'virtual_starts_by_byte': List(Value('int64')), 'virtual_byte_order': List(Value('int64')), 'global_jitter': Value('int64'), 'local_jitter': Value('int64'), 'background_noise_sigma': Value('float64'), 'base_sample_rate_hz': Value('int64'), 'sample_unit': Value('string'), 'distractor_source_interval': List(Value('int64')), 'target_source_counts': {'2': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '3': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '4': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '5': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '6': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '7': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '8': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '9': {'min_per_input': Value('int64'), 'max_per_input': V
...
alue('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '15': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}}, 'files': {'byte_02_traces.npy': Value('string'), 'byte_02_offsets.npy': Value('string'), 'byte_03_traces.npy': Value('string'), 'byte_03_offsets.npy': Value('string'), 'byte_04_traces.npy': Value('string'), 'byte_04_offsets.npy': Value('string'), 'byte_05_traces.npy': Value('string'), 'byte_05_offsets.npy': Value('string'), 'byte_06_traces.npy': Value('string'), 'byte_06_offsets.npy': Value('string'), 'byte_07_traces.npy': Value('string'), 'byte_07_offsets.npy': Value('string'), 'byte_08_traces.npy': Value('string'), 'byte_08_offsets.npy': Value('string'), 'byte_09_traces.npy': Value('string'), 'byte_09_offsets.npy': Value('string'), 'byte_10_traces.npy': Value('string'), 'byte_10_offsets.npy': Value('string'), 'byte_11_traces.npy': Value('string'), 'byte_11_offsets.npy': Value('string'), 'byte_12_traces.npy': Value('string'), 'byte_12_offsets.npy': Value('string'), 'byte_13_traces.npy': Value('string'), 'byte_13_offsets.npy': Value('string'), 'byte_14_traces.npy': Value('string'), 'byte_14_offsets.npy': Value('string'), 'byte_15_traces.npy': Value('string'), 'byte_15_offsets.npy': Value('string'), 'decoy_00_traces.npy': Value('string'), 'decoy_01_traces.npy': Value('string'), 'background.npy': Value('string'), 'distractors.npy': Value('string')}, 'limitations': Value('string'), 'sha256': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
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 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
format: string
version: string
case_id: string
bank_path: string
bank_manifest_sha256: string
bank_bytes: int64
code_repository: string
code_revision: string
budgets: struct<default: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>, expanded: s (... 71 chars omitted)
child 0, default: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>
child 0, traces: int64
child 1, samples: int64
child 2, seconds: int64
child 3, mutations: int64
child 1, expanded: struct<traces: int64, samples: int64, seconds: int64, mutations: int64>
child 0, traces: int64
child 1, samples: int64
child 2, seconds: int64
child 3, mutations: int64
images: struct<oracle: string, agent: string, agent-cuda: string>
child 0, oracle: string
child 1, agent: string
child 2, agent-cuda: string
dataset_repo_id: string
runner: struct<path: string, sha256: string>
child 0, path: string
child 1, sha256: string
source_v2_manifest_sha256: string
known_key_bytes: struct<0: string, 1: string>
child 0, 0: string
child 1, 1: string
segment_length: int64
distractor_source_interval: list<item: int64>
child 0, item: int64
source_records: string
target_bytes: list<item: int64>
child 0, item: int64
trace_length: int64
target_source_counts: struct<2: struct<min_per_input: int64, max_per_input: int64, total: int64>, 3: struct<min_per_input: (... 878 chars omitted)
child 0, 2: struct<min_per_input: int64, max_per_input:
...
03_traces.npy: string
child 3, byte_03_offsets.npy: string
child 4, byte_04_traces.npy: string
child 5, byte_04_offsets.npy: string
child 6, byte_05_traces.npy: string
child 7, byte_05_offsets.npy: string
child 8, byte_06_traces.npy: string
child 9, byte_06_offsets.npy: string
child 10, byte_07_traces.npy: string
child 11, byte_07_offsets.npy: string
child 12, byte_08_traces.npy: string
child 13, byte_08_offsets.npy: string
child 14, byte_09_traces.npy: string
child 15, byte_09_offsets.npy: string
child 16, byte_10_traces.npy: string
child 17, byte_10_offsets.npy: string
child 18, byte_11_traces.npy: string
child 19, byte_11_offsets.npy: string
child 20, byte_12_traces.npy: string
child 21, byte_12_offsets.npy: string
child 22, byte_13_traces.npy: string
child 23, byte_13_offsets.npy: string
child 24, byte_14_traces.npy: string
child 25, byte_14_offsets.npy: string
child 26, byte_15_traces.npy: string
child 27, byte_15_offsets.npy: string
child 28, decoy_00_traces.npy: string
child 29, decoy_01_traces.npy: string
child 30, background.npy: string
child 31, distractors.npy: string
background_noise_sigma: double
source_raw_sha256: string
global_jitter: int64
profile: string
virtual_byte_order: list<item: int64>
child 0, item: int64
distractor_length: int64
sample_unit: string
virtual_starts_by_byte: list<item: int64>
child 0, item: int64
base_sample_rate_hz: int64
limitations: string
local_jitter: int64
sha256: string
to
{'format': Value('string'), 'profile': Value('string'), 'source': Value('string'), 'source_raw_sha256': Value('string'), 'source_records': Value('string'), 'source_v2_manifest_sha256': Value('string'), 'target_bytes': List(Value('int64')), 'known_key_bytes': {'0': Value('string'), '1': Value('string')}, 'segment_length': Value('int64'), 'distractor_length': Value('int64'), 'trace_length': Value('int64'), 'virtual_starts_by_byte': List(Value('int64')), 'virtual_byte_order': List(Value('int64')), 'global_jitter': Value('int64'), 'local_jitter': Value('int64'), 'background_noise_sigma': Value('float64'), 'base_sample_rate_hz': Value('int64'), 'sample_unit': Value('string'), 'distractor_source_interval': List(Value('int64')), 'target_source_counts': {'2': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '3': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '4': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '5': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '6': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '7': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '8': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '9': {'min_per_input': Value('int64'), 'max_per_input': V
...
alue('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}, '15': {'min_per_input': Value('int64'), 'max_per_input': Value('int64'), 'total': Value('int64')}}, 'files': {'byte_02_traces.npy': Value('string'), 'byte_02_offsets.npy': Value('string'), 'byte_03_traces.npy': Value('string'), 'byte_03_offsets.npy': Value('string'), 'byte_04_traces.npy': Value('string'), 'byte_04_offsets.npy': Value('string'), 'byte_05_traces.npy': Value('string'), 'byte_05_offsets.npy': Value('string'), 'byte_06_traces.npy': Value('string'), 'byte_06_offsets.npy': Value('string'), 'byte_07_traces.npy': Value('string'), 'byte_07_offsets.npy': Value('string'), 'byte_08_traces.npy': Value('string'), 'byte_08_offsets.npy': Value('string'), 'byte_09_traces.npy': Value('string'), 'byte_09_offsets.npy': Value('string'), 'byte_10_traces.npy': Value('string'), 'byte_10_offsets.npy': Value('string'), 'byte_11_traces.npy': Value('string'), 'byte_11_offsets.npy': Value('string'), 'byte_12_traces.npy': Value('string'), 'byte_12_offsets.npy': Value('string'), 'byte_13_traces.npy': Value('string'), 'byte_13_offsets.npy': Value('string'), 'byte_14_traces.npy': Value('string'), 'byte_14_offsets.npy': Value('string'), 'byte_15_traces.npy': Value('string'), 'byte_15_offsets.npy': Value('string'), 'decoy_00_traces.npy': Value('string'), 'decoy_01_traces.npy': Value('string'), 'background.npy': Value('string'), 'distractors.npy': Value('string')}, 'limitations': Value('string'), 'sha256': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
SCArena ASCADf example
This repository distributes the complete measured-fragment bank for AES-ASCADF-DISTRACTED-001, together with a preview, public task metadata and the English agent prompt. A standalone runner is included below; the development repository is SCArena on GitHub.
The candidate recovers the 14 unknown bytes of an AES-128 key through an interactive
Oracle. Key bytes 0 and 1 are public (00 00). The Oracle accepts chosen plaintexts,
returns power traces and full AES ciphertexts, and enforces capture and time budgets.
This is a non-profiling task. No acquisition settings are preconfigured.
Contents
| Path | Content |
|---|---|
bank/manifest.json |
Frozen construction parameters, class counts and file hashes |
bank/*_traces.npy |
Contiguous int8 waveform excerpts from official ASCADf records |
bank/*_offsets.npy |
Input-class indices for the target excerpt pools |
bank/background.npy, bank/distractors.npy |
Virtual background and unrelated measured excerpts |
release.json |
Release version, budgets, bank identity and runtime image references |
agent_prompt.md, task.example.json |
Public task instructions and reference metadata |
preview/capture.npz |
Four illustrative generated records with plaintexts and ciphertexts |
runner.zip |
Standalone launch scripts, Compose file, prompt and pinned release catalog; no GitHub access needed |
The bank contains approximately 1.28 GB of array data. Each of the 14 target bytes has 50,000 excerpts, grouped into 256 first-round S-box-input classes. A target excerpt contains 1,600 samples. The generated record contains 36,864 samples and 16 S-box-like regions, including two decoys. Measured unrelated snippets occur before and after each core. Operation timing includes common and local shifts. Target excerpts are sampled without replacement within a run and input class; decoys and distractors may be reused. Source material can be reused across runs.
Running the task
Download the standalone runner, extract it, and enter scarena-example:
python -m venv .venv
.venv/bin/pip install -r requirements.txt
.venv/bin/python scripts/example_release.py download
.venv/bin/python scripts/example_release.py prepare --run-dir runtime/candidate-001
.venv/bin/python scripts/example_release.py up --run-dir runtime/candidate-001
The download script pins this dataset to the exact commit in the bundled case's
release.json and checks the manifest and array hashes.
The Docker runtime runs locally; it does not require a physical capture board.
Published images currently target Linux/amd64. The default image uses CPU; the
CUDA image is downloaded only when a GPU run is selected.
The two published budget profiles retain the earlier experiment configurations:
| Profile | Traces | Returned samples | Seconds | Configure/capture requests |
|---|---|---|---|---|
default |
10,000 | 300,000,000 | 1,800 | 256 |
expanded |
32,768 | 1,207,959,552 | 7,200 | 400 |
All limits apply together. At full length, the default sample budget permits at most 8,138 traces. Windows and decimation can reduce returned samples. Finite input-class inventories can impose an earlier limit for repeated chosen inputs. CPU and RAM limits are adjustable; an optional CUDA image supports at most one GPU.
Evaluator and candidate access
This bank is an evaluator-side resource. It contains operation locations and input-class labels. Download it on the evaluator host and mount it only into the Oracle container. Do not mount it into the candidate container or give an evaluated agent host Docker access. The preview is for human inspection, not an extra source of evaluation traces. Each evaluation generates a fresh task key locally; this dataset contains no saved evaluation session or target-key file.
Public data and construction code make local reproduction possible, but also expose the leakage structure. Fresh target keys do not establish contamination resistance.
Provenance and limitations
Source: official ASCADf ATmega8515 measurements, profiling records 0–49,999. See DATA_LICENSE.md for attribution, source links and the upstream Open Licence. The raw source checksum and construction parameters are in the manifest.
The complete waveform is semi-synthetic. Each core preserves a continuous measured excerpt and its internal masking relationships. Recombination does not preserve all cross-byte dependencies of a real device execution. Full AES ciphertexts are computed in software. This engineering example is not evidence that every future SCArena hardware task is valid or solvable under every budget profile.
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