AI 中文总结
研究针对卫星云原生运行时缺少地面开源工具链的问题，提出卫星任务编译器这一四阶段管道，能依据规则检查并编译任务计划为相关工件，经多种方式验证，还向AI代理暴露管道，有相关工具及发布协议。
AI 中文摘要
卫星上的云原生运行时正通过多种途径出现，但都假定执行的工作流工件来自地面，且未发布开源地面工具链。我们提出了卫星任务编译器，它是一个四阶段管道，可弥补这一差距。该管道根据从公共ORCHIDE材料派生的Pydantic模式解析计划，根据包含十个拒绝规则的OPA/Rego政策包进行评估，编译为类型化的WorkflowIntent中间表示，并呈现为支持动态资源分配的Argo工作流DAG和Kueue作业清单。我们将上行前损失事件分为四个严重程度等级，验证通过多种方式进行，六个模型上下文协议工具将管道暴露给人工智能代理。编译器在EUPL-1.2下发布。
英文摘要
Onboard cloud-native runtimes for satellites are emerging on multiple tracks (ORCHIDE, Axiom Space's AxDCU-1, Kepler's Jetson nodes), but each assumes that the workflow artifacts it executes arrive from the ground. ORCHIDE's architecture document D3.1 states explicitly that "only the Deferred Phase is part of the ORCHIDE scope," and no open-source ground-side toolchain has been released by the consortium. We present Satellite Mission Compiler, a four-stage pipeline that addresses this gap: it takes a human-authored mission plan, checks it against machine-checkable structural and policy rules, and compiles it into the container-workflow artifacts that cloud-native satellite runtimes consume. The pipeline parses the plan against a Pydantic schema derived from public ORCHIDE materials, evaluates it against an OPA/Rego policy package of ten deny rules with documented provenance, compiles it into a typed WorkflowIntent intermediate representation, and renders it as Argo Workflow DAGs and Kueue Job manifests with Dynamic Resource Allocation (DRA) support. We classify pre-uplink loss events into four severity tiers tied to specific schema and policy checks, and anchor the layered-validation design in the safety reading of defense-in-depth (NASA-STD-8739.8B) rather than the security reading (NIST SP 800-53). The implementation is validated by golden translation evaluations, argo lint, an in-process baseline that reproduces OPA's decisions, and live single-node cluster submission, including a DRA-backed GPU admission cascade on Kueue v0.17.3 (re-validated on v0.18.3) and, on v0.18.3, a unified GPU+CPU device-class quota with a scheduler-level accelerator fallback. Six Model Context Protocol (MCP) tools expose the pipeline to AI agents. The compiler is released under EUPL-1.2 (DOI 10.5281/zenodo.21228150).
Comments13 pages. Extended version of the paper accepted at IEEE SMC-IT/SCC 2026 (Space Mission Challenges for Information Technology / Space Computing Conference); adds a unified GPU+CPU DRA quota and a scheduler-level accelerator fallback re-validated on Kueue v0.18.3
