Hazard Prediction

The HazardPredictor identifies upcoming orbital hazards — eclipse transitions, South Atlantic Anomaly (SAA) traversals, and thermal excursions — and schedules checkpoints so that in-progress computation can survive them without data loss.

Why this matters — An eclipse boundary can cut available power by 95% in seconds. Without predictive checkpointing, any in-flight step that spans an eclipse transition risks silent data corruption or abrupt termination.

The 9-Phase Algorithm

The HazardPredictor runs a 9-phase pipeline over the planning horizon:

Phase	Name	Description
1	Orbit propagation	Keplerian + J2 perturbation model propagates the satellite state at 10-second intervals
2	Eclipse detection	Cylindrical shadow model identifies sunlit/eclipse transitions
3	Eclipse boundary refinement	Binary search narrows each transition to sub-second precision
4	SAA detection	Point-in-polygon test against the SAA boundary polygon for each propagation point
5	SAA window construction	Clusters SAA detections into contiguous traversal windows
6	Thermal prediction	Projects component temperatures using power dissipation and orbital thermal environment
7	Thermal excursion detection	Flags windows where predicted temperature exceeds component limits
8	Checkpoint scheduling	Places checkpoints before each hazard with sufficient margin for state serialization
9	Checkpoint merge	Consolidates checkpoints that fall within a configurable merge window to reduce overhead

Hazard Types

Eclipse Boundaries

Detected using the cylindrical shadow model — the same model used in the CAE orbital environment builder. The predictor identifies both eclipse entry (sunlit-to-shadow) and eclipse exit (shadow-to-sunlit) transitions.

Parameter	Value
Detection model	Cylindrical Earth shadow
Propagation	Keplerian + J2 secular perturbations
Time resolution	Sub-second (binary search refinement)
Margin	Configurable, default 30 seconds before transition

South Atlantic Anomaly

The SAA is a region of elevated radiation over the South Atlantic where the inner Van Allen belt dips closest to Earth. Sensitive electronics (GPUs, FPGAs) experience elevated single-event upset rates during SAA traversals.

Parameter	Value
Detection method	Point-in-polygon against SAA boundary contour
Boundary model	AP-8/AE-8 derived, updated annually
Altitude scaling	Contour expands at lower altitudes
Typical duration	10-20 minutes per traversal for LEO

Thermal Excursions

The thermal predictor models component temperature based on solar flux, Earth albedo, bus power dissipation, and radiator capacity. Excursions are flagged when any component is predicted to exceed its operational limit.

Parameter	Value
Thermal model	Single-node lumped parameter per component
Heat sources	Solar flux, Earth IR, internal dissipation
Heat sinks	Radiator panels, thermal mass
Warning threshold	5 degrees C below operational limit

Checkpoint Scheduling

For each detected hazard, the predictor inserts a checkpoint at hazard_start - margin - serialization_time. If two hazards are close together (within the merge window), their checkpoints are consolidated into one.

Timeline:
  |---step running---|--ckpt--|--margin--|==ECLIPSE==|---step resumes---|
                     ^                    ^
                checkpoint            hazard start

The checkpoint includes full step state: intermediate buffers, model weights, progress counters, and RNG state. The agent serializes this to on-board storage before the hazard arrives.

API Usage

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/hazards \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{
    "satellite_id": "25544",
    "prediction_hours": 24,
    "hazard_types": ["eclipse", "saa", "thermal"],
    "checkpoint_margin_s": 30,
    "merge_window_s": 60
  }'

Request Parameters

Parameter	Type	Required	Default	Description
`satellite_id`	string	Yes	—	NORAD catalog ID
`prediction_hours`	number	No	12	Prediction horizon
`hazard_types`	string[]	No	all	Filter to specific hazard types
`checkpoint_margin_s`	number	No	30	Seconds of margin before each hazard
`merge_window_s`	number	No	60	Merge checkpoints closer than this

Response Structure

{
  "satellite_id": "25544",
  "prediction_start": "2026-03-10T12:00:00Z",
  "prediction_hours": 24,
  "hazards": [
    {
      "type": "eclipse_entry",
      "start": "2026-03-10T12:34:12.847Z",
      "end": "2026-03-10T13:07:45.231Z",
      "duration_s": 2012,
      "severity": "high",
      "details": {
        "battery_soc_at_entry": 0.82,
        "predicted_soc_at_exit": 0.41
      }
    },
    {
      "type": "saa",
      "start": "2026-03-10T14:18:30.000Z",
      "end": "2026-03-10T14:32:15.000Z",
      "duration_s": 825,
      "severity": "medium",
      "details": {
        "peak_flux_ratio": 3.2,
        "upset_probability": 0.0012
      }
    },
    {
      "type": "thermal_excursion",
      "start": "2026-03-10T16:45:00.000Z",
      "end": "2026-03-10T17:02:00.000Z",
      "duration_s": 1020,
      "severity": "low",
      "details": {
        "component": "gpu",
        "predicted_temp_c": 78.5,
        "limit_c": 85
      }
    }
  ],
  "checkpoint_schedule": [
    {
      "time": "2026-03-10T12:33:22.847Z",
      "reason": "eclipse_entry",
      "hazard_index": 0,
      "serialization_budget_s": 20
    },
    {
      "time": "2026-03-10T14:17:40.000Z",
      "reason": "saa",
      "hazard_index": 1,
      "serialization_budget_s": 20
    }
  ],
  "max_safe_window_s": 6137,
  "overhead_fraction": 0.018
}

Response Fields

Field	Type	Description
`hazards`	array	All detected hazards in chronological order
`hazards[].severity`	string	`low`, `medium`, or `high` based on impact to computation
`checkpoint_schedule`	array	Recommended checkpoint times with reasons
`max_safe_window_s`	number	Longest uninterrupted compute window in the prediction horizon
`overhead_fraction`	number	Fraction of total time consumed by checkpoint serialization

The overhead_fraction helps you decide whether predictive checkpointing is worth the cost. Values below 0.05 (5%) are typical for LEO orbits with 90-minute periods.

Console Integration

The Console surfaces hazard predictions on the Hazards tab of the asset detail page:

Timeline visualization shows hazards as colored bands (red for eclipse, orange for SAA, yellow for thermal)
Checkpoint markers appear on the timeline with serialization budget indicators
The max safe window is highlighted
Clicking a hazard expands its detail panel with severity, duration, and predicted impact

Agent Integration

When the agent receives a plan with predictive checkpoints, it listens for the checkpoint.predicted event and serializes state to on-board storage at the scheduled time. See the Agent Protocol documentation for event handling details.

Orbital Primitives

Eclipse and window-aware step types

Understanding Plans

How to read plan responses including hazard data

Getting Started

Planning Tools

Orbital Intelligence

CAE

Operator Agent

Mission Control

Orbital Runtime

Orbital Sim

Distributed Compute

SDKs

Resources

Hazard Prediction

Hazard Prediction

The 9-Phase Algorithm

Hazard Types

Eclipse Boundaries

South Atlantic Anomaly

Thermal Excursions

Checkpoint Scheduling

API Usage

Request Parameters

Response Structure

Response Fields

Console Integration

Agent Integration

Orbital Primitives

Understanding Plans

​Hazard Prediction

​The 9-Phase Algorithm

​Hazard Types

​Eclipse Boundaries

​South Atlantic Anomaly

​Thermal Excursions

​Checkpoint Scheduling

​API Usage

​Request Parameters

​Response Structure

​Response Fields

​Console Integration

​Agent Integration

Orbital Primitives

Understanding Plans

Hazard Prediction

The 9-Phase Algorithm

Hazard Types

Eclipse Boundaries

South Atlantic Anomaly

Thermal Excursions

Checkpoint Scheduling

API Usage

Request Parameters

Response Structure

Response Fields

Console Integration

Agent Integration