Workload Presets

Presets are complete workload definitions you can use immediately with POST /v1/plan. Each defines a multi-step pipeline with resource requirements, dependencies, security policies, and optimization objectives. List all presets:

curl https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets \
  -H "Origin: https://rotastellar.com"

On-Board ML Inference

All computation on-board. Captures 2GB of sensor data, runs ML inference, and downlinks only the 10.5MB encrypted result — a 190:1 data reduction.

Property	Value
ID	`onboard-ml-inference`
Category	ml-inference
Steps	4 (capture, preprocess, inference, encrypt)
All on-board	Yes
Data flow	2,000 MB → 10.5 MB
Policy	`min_latency`, 3-orbit deadline, 99% confidence

Pipeline: Sensor Capture → Data Preprocessing & Calibration → ML Model Inference → Encrypt Results

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/plan \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{"satellite_id": "25544", "preset_id": "onboard-ml-inference"}'

Split Learning Pipeline

Bidirectional training. Satellite runs the first 3 neural network layers (feature extraction, 40:1 reduction), downlinks 36.75MB of activations. Ground trains the remaining layers and uplinks 5.25MB of updated weights.

Property	Value
ID	`split-learning`
Category	ml-training
Steps	9 (capture → feature extraction → compress → encrypt → train backend → compress weights → encrypt → deploy)
Downlink	36.75 MB (activations)
Uplink	5.25 MB (weights)
Policy	`balanced`, 6-orbit deadline, 95% confidence
Security	`confidential`, authenticated uplink, key rotation every 12 orbits

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/plan \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{"satellite_id": "25544", "preset_id": "split-learning"}'

Earth Observation with QA

Captures 5GB of imagery, runs on-board quality assurance to discard bad frames and cloudy scenes, compresses to 400MB, applies Reed-Solomon FEC and AES-256, then downlinks 560MB across multiple ground station passes.

Property	Value
ID	`earth-observation-qa`
Category	earth-observation
Steps	8 (capture → QA → cloud filter → JPEG2000 compress → FEC encode → encrypt → ground validation → archive)
Data flow	5,000 MB → 560 MB transferred
Multi-pass downlink	Yes
Policy	`max_reliability`, 8-orbit deadline, 95% confidence

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/plan \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{"satellite_id": "25544", "preset_id": "earth-observation-qa"}'

Federated Learning

Privacy-preserving distributed training. The satellite trains locally on 500MB of data, computes and sparsifies gradients (top-k, 90% zeros), downlinks 3.7MB. Ground aggregates via FedAvg and uplinks 5.8MB updated global model. Raw data never leaves the satellite.

Property	Value
ID	`federated-learning`
Category	ml-training
Steps	10 (local train → gradients → sparsify → compress → encrypt → aggregate → compress model → encrypt → deploy)
Downlink	3.7 MB (sparse gradients)
Uplink	5.8 MB (global model)
Policy	`balanced`, 6-orbit deadline, 95% confidence
Security	`confidential`, authenticated uplink, key rotation every 12 orbits

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/plan \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{"satellite_id": "25544", "preset_id": "federated-learning"}'

Resilient Store-and-Forward Relay

Receives 100MB from a remote sensor during one pass, applies Reed-Solomon erasure coding (rate 2/3 — any 2-of-3 blocks reconstruct), buffers on-board, and transmits during a different ground pass.

Property	Value
ID	`resilient-store-forward`
Category	relay
Steps	5 (uplink receive → integrity check → erasure coding → encrypt & buffer → ground decode)
Data transferred	157.5 MB (with erasure coding overhead)
Policy	`max_reliability`, 4-orbit deadline, 99% confidence

curl -X POST https://rotastellar-cae.subhadip-mitra.workers.dev/v1/plan \
  -H "Content-Type: application/json" \
  -H "Origin: https://rotastellar.com" \
  -d '{"satellite_id": "25544", "preset_id": "resilient-store-forward"}'

Comparison

Preset	Steps	Data Reduction	Downlink	Uplink	Objective	Deadline
On-Board ML Inference	4	190:1	10.5 MB	—	min_latency	3 orbits
Split Learning	9	40:1	36.75 MB	5.25 MB	balanced	6 orbits
Earth Observation QA	8	9:1	560 MB	—	max_reliability	8 orbits
Federated Learning	10	135:1	3.7 MB	5.8 MB	balanced	6 orbits
Store-and-Forward	5	1:1.6 (expansion)	157.5 MB	—	max_reliability	4 orbits

Need something different? Define a custom workload.

Getting Started

Planning Tools

Orbital Intelligence

CAE

Operator Agent

Mission Control

Orbital Runtime

Orbital Sim

Distributed Compute

SDKs

Resources

Workload Presets

Workload Presets

On-Board ML Inference

Split Learning Pipeline

Earth Observation with QA

Federated Learning

Resilient Store-and-Forward Relay

Comparison

​Workload Presets

​On-Board ML Inference

​Split Learning Pipeline

​Earth Observation with QA

​Federated Learning

​Resilient Store-and-Forward Relay

​Comparison

Workload Presets

On-Board ML Inference

Split Learning Pipeline

Earth Observation with QA

Federated Learning

Resilient Store-and-Forward Relay

Comparison