Datasets:

spiderpilot89
/

mars-edl-1000hz

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
trajectory_id: large_string
type: large_string
scenario: large_string
steps: int64
proof_hash: large_string
score_dispersion: double
score_final_velocity: double
score_max_deceleration_g: double
score_mission_success: bool
reasoning_context: large_string
data_point_index: int64
alt: double
phase: large_string
pos_x: double
pos_y: double
pos_z: double
rho: double
t: double
vel: double
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 2336
to
{'trajectory_id': Value('string'), 'type': Value('string'), 'scenario': Value('string'), 'steps': Value('int64'), 'proof_hash': Value('string'), 'score_dispersion': Value('float64'), 'score_final_velocity': Value('float64'), 'score_max_deceleration_g': Value('float64'), 'score_mission_success': Value('float64'), 'data_point_index': Value('int64'), 'alt': Value('float64'), 'phase': Value('string'), 'pos_x': Value('float64'), 'pos_y': Value('float64'), 'pos_z': Value('float64'), 'rho': Value('float64'), 't': Value('float64'), 'vel': Value('float64')}
because column names don't match
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 2815, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              trajectory_id: large_string
              type: large_string
              scenario: large_string
              steps: int64
              proof_hash: large_string
              score_dispersion: double
              score_final_velocity: double
              score_max_deceleration_g: double
              score_mission_success: bool
              reasoning_context: large_string
              data_point_index: int64
              alt: double
              phase: large_string
              pos_x: double
              pos_y: double
              pos_z: double
              rho: double
              t: double
              vel: double
              -- schema metadata --
              pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 2336
              to
              {'trajectory_id': Value('string'), 'type': Value('string'), 'scenario': Value('string'), 'steps': Value('int64'), 'proof_hash': Value('string'), 'score_dispersion': Value('float64'), 'score_final_velocity': Value('float64'), 'score_max_deceleration_g': Value('float64'), 'score_mission_success': Value('float64'), 'data_point_index': Value('int64'), 'alt': Value('float64'), 'phase': Value('string'), 'pos_x': Value('float64'), 'pos_y': Value('float64'), 'pos_z': Value('float64'), 'rho': Value('float64'), 't': Value('float64'), 'vel': Value('float64')}
              because column names don't match

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Zero-Trust Physics: 1000Hz Mars EDL Trajectories (G^G)

1,000 Mars Entry, Descent, and Landing (EDL) trajectories at 1000Hz — deterministic, first-principles, SHA-256 sealed at every integration step.

Generated by the G^G Rust physics engine (mars_monte_carlo). Pure CPU Rust — no external physics libraries.

Physics Model

Parameter	Value
Gravity	3.721 m/s²
Atmosphere	CO₂ exponential: `ρ = 0.020 × e^(-0.00009 × h)`
Drag	`F_d = 0.5 × ρ × v² × C_d × A` (C_d=1.2, A=10 m²)
Entry mass	1,000 kg
Fuel mass	400 kg
Retro Isp	290 s
Integration	Euler 1000Hz (dt = 1.0ms)
Phases	Atmospheric entry → powered descent → suicide burn

The underlying cliff: retro-propulsion fuel depletion during the suicide burn phase is the primary failure mode. The dataset captures the full boundary — from clean soft landings to lithobraking under adverse atmospheric density variations.

Schema

Each row is one 1.0ms integration step.

Column	Description
`trajectory_id`	Unique trajectory identifier
`type`	Trajectory type classification
`scenario`	Parameter scenario name
`steps`	Total integration steps in this trajectory
`proof_hash`	SHA-256 running hash chain seal
`score_dispersion`	Landing dispersion score
`score_final_velocity`	Final touchdown velocity score
`score_max_deceleration_g`	Peak deceleration score (g-load)
`score_mission_success`	Overall mission success probability
`data_point_index`	Step index within trajectory
`alt`	Altitude (m)
`phase`	Current EDL phase (`entry`, `descent`, `landing`)
`pos_x/y/z`	3D position vector (m)
`rho`	Atmospheric density at current altitude (kg/m³)
`t`	Elapsed time (s)
`vel`	Total velocity magnitude (m/s)

Loading

from datasets import load_dataset

dataset = load_dataset("spiderpilot89/mars-edl-1000hz", split="train")
df = dataset.to_pandas()

# Filter successful landings
landed = df[df["score_mission_success"] > 0.9]
print(f"Successful landings: {landed['trajectory_id'].nunique()}")

# Plot altitude vs velocity for one trajectory
traj = df[df["trajectory_id"] == df["trajectory_id"].iloc[0]]
print(traj[["alt", "vel", "rho", "phase"]].head(20))

Cryptographic Integrity

Every trajectory carries a proof_hash — a SHA-256 running hash chain computed during physics generation at each integration step. The chain is mathematically immutable: altering any single data point breaks the hash.

ICP Mainnet anchor: ad7wi-4aaaa-aaaad-aeijq-cai

License

MIT License — free to use, modify, and distribute with attribution.

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