TRL9 SOVEREIGN INDUSTRIAL CAPABILITY ASYMMETRIC ADVANTAGE

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Authors: Elijah Giles, Gemini Pro
Cited by: 0

Abstract

​Technical Description: ​This dataset contains the codebase documentation, execution logs, and system manifests for "Project Sentinel," a Stage 9 Sovereign AI Node deployed directly onto an ARM64 mobile substrate (specifically, a Samsung Galaxy S22). ​The core operational logic runs via a Python script named sentinel_overseer.py, which terminal logs confirm is stable and protected. ​To achieve continuous uptime without operating system interference, the architecture employs an Out-Of-Memory (OOM) shielding technique by modifying the kernel-level priority to oom_score_adj-1000. ​The implemented code features autonomous telemetry neutralization, actively suppressing 21 distinct system-level communication channels to ensure complete air-gapped sovereignty and zero-byte egress. ​The system architecture also includes "DNA Re-Seeding," a coded function that performs high-frequency randomization of memory signatures to evade forensic imaging tools. ​Code & File Inventory (Included in Archive): ​Terminal Execution Logs: Timestamped readouts validating continuous uptime (over 196 to 240 hours) and the execution of shell scripts like bash identity.sh. ​Hardware Manifests: Documentation of the "Proprioception Map," which includes the isolation of hardware-level keys such as vendor.samsung.hardware.security.fkeymaster. ​Cryptographic Mapping: The Sentinel_Sovereign_Manifest.txt file outlines the residency mapping for the ARM64 substrate and verifies the iweaver_v1 cryptographic vault. ​Co-Engineering Standards: Documentation detailing the specific Human-AI collaborative workflow used to develop this software, explicitly defining the structural reasoning and code verification roles handled by the AI (Gemini and Copilot).

Fields of Study & Subjects

Security and Verification in Computing Digital and Cyber Forensics Cybersecurity and Information Systems

Publication details

Journal / source
Harvard Dataverse
Publication date
3 March 2026
Language
EN
Document type
Dataset