curl https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets \
-H "Origin: https://rotastellar.com"
{
"presets": [
{
"id": "onboard-ml-inference",
"name": "On-Board ML Inference",
"description": "Run ML inference on-board to achieve 190:1 data reduction before downlink...",
"category": "ml-inference",
"steps": 4,
"onboard_steps": 4,
"ground_steps": 0,
"total_compute_s": 170,
"data_flow": {
"initial_capture_mb": 2000,
"final_output_mb": 10.5,
"overall_reduction": "190:1"
},
"needs_downlink": true,
"needs_uplink": false,
"security": {
"encryption": "aes256",
"data_classification": "restricted",
"allowed_ground_stations": null,
"require_authenticated_uplink": true,
"key_rotation_orbits": 24
},
"policy": {
"objective": "min_latency",
"deadline_orbits": 3,
"max_data_loss_fraction": 0.001,
"min_delivery_confidence": 0.99
}
}
],
"count": 5
}
List available workload presets with metadata
curl https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets \
-H "Origin: https://rotastellar.com"
{
"presets": [
{
"id": "onboard-ml-inference",
"name": "On-Board ML Inference",
"description": "Run ML inference on-board to achieve 190:1 data reduction before downlink...",
"category": "ml-inference",
"steps": 4,
"onboard_steps": 4,
"ground_steps": 0,
"total_compute_s": 170,
"data_flow": {
"initial_capture_mb": 2000,
"final_output_mb": 10.5,
"overall_reduction": "190:1"
},
"needs_downlink": true,
"needs_uplink": false,
"security": {
"encryption": "aes256",
"data_classification": "restricted",
"allowed_ground_stations": null,
"require_authenticated_uplink": true,
"key_rotation_orbits": 24
},
"policy": {
"objective": "min_latency",
"deadline_orbits": 3,
"max_data_loss_fraction": 0.001,
"min_delivery_confidence": 0.99
}
}
],
"count": 5
}
https://rotastellar-cae.subhadip-mitra.workers.dev
ā No API key required.curl https://rotastellar-cae.subhadip-mitra.workers.dev/v1/presets \
-H "Origin: https://rotastellar.com"
{
"presets": [
{
"id": "onboard-ml-inference",
"name": "On-Board ML Inference",
"description": "Run ML inference on-board to achieve 190:1 data reduction before downlink...",
"category": "ml-inference",
"steps": 4,
"onboard_steps": 4,
"ground_steps": 0,
"total_compute_s": 170,
"data_flow": {
"initial_capture_mb": 2000,
"final_output_mb": 10.5,
"overall_reduction": "190:1"
},
"needs_downlink": true,
"needs_uplink": false,
"security": {
"encryption": "aes256",
"data_classification": "restricted",
"allowed_ground_stations": null,
"require_authenticated_uplink": true,
"key_rotation_orbits": 24
},
"policy": {
"objective": "min_latency",
"deadline_orbits": 3,
"max_data_loss_fraction": 0.001,
"min_delivery_confidence": 0.99
}
}
],
"count": 5
}