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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"

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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.
PropertyValue
IDonboard-ml-inference
Categoryml-inference
Steps4 (capture, preprocess, inference, encrypt)
All on-boardYes
Data flow2,000 MB → 10.5 MB
Policymin_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"}'

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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.
PropertyValue
IDsplit-learning
Categoryml-training
Steps9 (capture → feature extraction → compress → encrypt → train backend → compress weights → encrypt → deploy)
Downlink36.75 MB (activations)
Uplink5.25 MB (weights)
Policybalanced, 6-orbit deadline, 95% confidence
Securityconfidential, 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"}'

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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.
PropertyValue
IDearth-observation-qa
Categoryearth-observation
Steps8 (capture → QA → cloud filter → JPEG2000 compress → FEC encode → encrypt → ground validation → archive)
Data flow5,000 MB → 560 MB transferred
Multi-pass downlinkYes
Policymax_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"}'

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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.
PropertyValue
IDfederated-learning
Categoryml-training
Steps10 (local train → gradients → sparsify → compress → encrypt → aggregate → compress model → encrypt → deploy)
Downlink3.7 MB (sparse gradients)
Uplink5.8 MB (global model)
Policybalanced, 6-orbit deadline, 95% confidence
Securityconfidential, 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"}'

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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.
PropertyValue
IDresilient-store-forward
Categoryrelay
Steps5 (uplink receive → integrity check → erasure coding → encrypt & buffer → ground decode)
Data transferred157.5 MB (with erasure coding overhead)
Policymax_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"}'

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Comparison

PresetStepsData ReductionDownlinkUplinkObjectiveDeadline
On-Board ML Inference4190:110.5 MB—min_latency3 orbits
Split Learning940:136.75 MB5.25 MBbalanced6 orbits
Earth Observation QA89:1560 MB—max_reliability8 orbits
Federated Learning10135:13.7 MB5.8 MBbalanced6 orbits
Store-and-Forward51:1.6 (expansion)157.5 MB—max_reliability4 orbits
Need something different? Define a custom workload.