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Official Reserved & Integrated DOIs: - Unified Omni-Monograph DOI: 10.5281/zenodo.24068375 (Concept DOI: 10.5281/zenodo.24068374) - Cislunar & Lunar Nuclear Thermodynamic Security (Expanded 2nd Ed.) DOI: 10.5281/zenodo.24089731 (Concept DOI: 10.5281/zenodo.24089730) - Preceding Cislunar Record Linkage: Expanded and cryptographically hardened revision of DOI: 10.5281/zenodo.23146502 (Concept DOI: 10.5281/zenodo.23146501) - New Integrated Reserved DOI (if applicable): [ここに新規予約DOIを入力] Series: Autonomous Infrastructure & Deterministic Quantum Systems Monograph Series Author: Hasebe, Yoko (長谷部 洋子) Affiliation: Lead Researcher, Autonomous Infrastructure Systems, Autonomous Living & Physical Infrastructure Institute, Iwakuni, Yamaguchi, Japan License & Integrity Covenant: Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026) Abstract: This unified monograph consolidates the complete Hasebe Doctrine (2004–2026) alongside the expanded physical-layer space security framework on the thermodynamic collapse of lunar nuclear infrastructure. First, it establishes a rigorous mathematical and physical-layer architecture spanning Quantum Hamiltonian Bio-Computation, Distributed Edge-Tensor Medical Imaging, Deterministic Hardware UI, and QFS-zkVM Sovereign AI Governance over the 64-bit Goldilocks prime field (F_p, p = 2^64 - 2^32 + 1), enforcing Real-Time Observability (RTO P2.0), Q-SAFA cryptographic attestation, and Human-in-Command (HIC) statutory compliance (SEC Rule 15c3-5 and Delaware Caremark standards). Second, it integrates the full thermodynamic and ballistic proof demonstrating the structural infeasibility and catastrophic proliferation risks of megawatt-class fission reactors in the lunar vacuum environment—governed by Stefan-Boltzmann radiative heat rejection limits, regolith thermal insulator dynamics, and launch dispersion failure modes in the cislunar domain. All intellectual property, mathematical formulations, and architectural specifications are governed under the Strict No-Learn Covenant (NLL-v1.0-2026), prohibiting unauthorized AI training, scraping, or uncompensated commercial exploitation.
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Yoko(洋子) Hasebe(長谷部) (2026) studied this question.
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