{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.26340",
  "title": "A Generic On-Board AI Processing Chain for Earth Observation: From Application Development to Embedded Deployment and Mission-Level Orchestration",
  "summary": "The method replaces ground-station processing of full downlinked scenes with an on-board chain of int8-quantized inference on FPGA, a reusable tiler-inference-mosaicer workflow, and unikernel-packaged steps orchestrated by a cloud-native on-board orchestrator. The cost is substantial embedded-engineering effort: model quantization and hardware porting, plus adoption of specialized unikernel packaging and orchestration infrastructure, none of which runs on a conventional GPU workstation. Adoption can fail through quantization-induced accuracy loss on new tasks, hardware-specific toolchain friction, and orchestration overhead on constrained space-grade compute.",
  "rationale": "The contribution targets satellite-embedded deployment on specific FPGA hardware, so it offers no directly adoptable component for an engineer with a single 24 GB GPU or API-based production stack, though its quantization-and-modular-workflow pattern is worth tracking.",
  "technique": "On-board AI processing chain for satellite inference",
  "family": "outro",
  "recommendation": "observar",
  "published": "2026-10-19",
  "as_of": "2026-10-20",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.26340",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces ground-station processing of full downlinked scenes with an on-board chain of int8-quantized inference on FPGA, a reusable tiler-inference-mosaicer workflow, and unikernel-packaged steps orchestrated by a cloud-native on-board orchestrator. The cost is substantial embedded-engineering effort: model quantization and hardware porting, plus adoption of specialized unikernel packaging and orchestration infrastructure, none of which runs on a conventional GPU workstation. Adoption can fail through quantization-induced accuracy loss on new tasks, hardware-specific toolchain friction, and orchestration overhead on constrained space-grade compute.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26340",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Int8 quantization and porting to an AMD Versal VCK190 preserved the model's segmentation performance on the board; no multiplicative speed, memory, or cost factor is stated.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.26340",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}