Sofia Engine — Scientific & Edge Intelligence Runtime

Developed by Rootcastle Engineering & Innovation
Canonical Source Repository: github.com/rootcastleco/sofia-ai
Project Homepage: rootcastle.com
DOI: 10.57967/hf/10549
Storage Bucket: huggingface.co/buckets/rootcastleengineering/sofia-bucket

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1. Overview

Sofia Engine is an open-source scientific and edge intelligence runtime developed by Rootcastle Engineering & Innovation for deterministic physical telemetry processing, industrial signal analysis, machine-health diagnostics, bounded edge inference, and safety-gated technical automation.

Operating under the engineering principle "Evidence beats claims", Sofia Engine combines:

• Scientific DSP: FFT, Welch PSD, Hilbert analytic amplitude envelope, and Fortescue 3-phase symmetrical components.
• Physical Telemetry Ingestion: Bounded, unit-bearing telemetry frames with quality status (GOOD, DEGRADED, SATURATED).
• Versioned Feature Extraction: 14+ statistical and spectral features packaged in ordered, reproducible FeatureVector contracts (Schema v3.0).
• Evidence-Based Diagnostics: Noisy-OR evidence aggregation and machine health scoring with explicit uncertainty bands ($0-100 \pm \Delta$).
• Embedded & Edge Execution: Hardened virtual machine (SofiaAsmVM), C99 zero-allocation microcontroller implementation, and experimental in-situ backpropagation.
• Safety & Policy Gating: Strict default-DENY policy engine, Nonce + TTL replay protection, and an actuation firewall that strictly isolates external LLMs from physical machinery.

[!NOTE] Canonical Pretrained Checkpoint Status: No canonical pretrained Sofia checkpoint is distributed in this release. This repository provides the Sofia model/runtime specification, machine-readable manifests, reproducible examples, and future checkpoint distribution interfaces. When pre-trained foundation models are released, they will be published with complete training datasets, evaluation reports, and SHA-256 digests.

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2. Subsystem Maturity Table

Maturity levels are assigned strictly based on verifiable implementation and test coverage:

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3. Architecture & Data Flow

flowchart LR
    S["Physical Sensors"] --> I["Telemetry Ingestion"]
    I --> Q["Validation & Signal Quality"]
    Q --> B["Bounded Buffers"]
    B --> D["Scientific DSP"]
    D --> F["Versioned Feature Vector"]
    F --> M["Inference Backends"]
    M --> E["Evidence Fusion"]
    E --> H["Diagnostics & Health"]
    H --> P["Policy Engine"]
    P --> O["Advisory / Controlled Output"]

Key Architectural Invariant: External LLMs (OpenAI, NVIDIA NIM, OpenRouter) operate exclusively as optional copilots at Layer 10. They have no read or write access to the deterministic signal processing or policy enforcement layers.

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4. Scientific Scope & Calculations

Sofia Engine implements calculations relevant to industrial engineering standards:

• Mechanical Vibration: Implements calculations relevant to ISO 10816 / ISO 20816 vibration severity evaluation (RMS, peak, crest factor, kurtosis, skewness).
• Spectral Analysis: Discrete Fourier Transform (one-sided FFT magnitude), Welch's averaged periodogram with window gain corrections, spectral centroid, spectral entropy, and spectral flatness.
• Envelope Demodulation: Hilbert transform analytic signal for bearing defect and gear-mesh modulation extraction.
• Electrical Power Quality: Implements calculations relevant to IEEE 519 and IEC 61000-4-30 analysis, including True RMS, Active/Reactive/Apparent Power, Total Harmonic Distortion (THD), and Fortescue 3-phase symmetrical components ($V_0, V_1, V_2, \text{VUF}$).
• Process Telemetry: Thermal rates of change ($dT/dt$), pulsation peak-to-peak, and fluid pressure crest factors.

Note: Software implementation does not constitute formal laboratory certification. Operational deployment requires qualified engineering calibration.

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5. Installation

Core Runtime (NumPy Only)

pip install sofia-engine

Industrial Telemetry Integrations (MQTT, Modbus, Serial)

pip install "sofia-engine[industrial]"

From Canonical Source

git clone https://github.com/rootcastleco/sofia-ai.git
cd sofia-ai
pip install -e .

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6. Reproducible Examples

Executable scripts are maintained in the examples/ directory:

• examples/basic_inference.py: Signal window packaging, 14-feature extraction, threshold anomaly detection, and evidence-based health scoring.
• examples/signal_analysis.py: FFT magnitude, Welch PSD with Parseval energy conservation check, Hilbert analytic envelope, and Fortescue symmetrical components.
• examples/edge_runtime.py: Bounded SignalFrame, Sofia Assembly VM execution, in-situ neural training step, and safe sofia.model.v1 serialization.

To execute the basic inference example:

python examples/basic_inference.py

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7. Model Manifests & Checkpoint Policy

Hugging Face distribution files are machine-readable and schema-validated:

• config.json: Runtime capabilities, Python version, and dependency requirements.
• model_manifest.json: Conforms to schemas/model.schema.json.
• runtime_manifest.json: Exact Git commit SHA, dependency versions, latency budgets, and subsystem maturity.

Checkpoint Policy

• Zero-Pickle Invariant: Model weights must be distributed in .safetensors or .npz format. Python pickle is strictly prohibited.
• Cryptographic Verification: Every model artifact must declare its lowercase SHA-256 parameter digest.
• See artifacts/README.md for full details.

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8. Empirical Performance Benchmarks

Measured on Profile A hardware (Intel64 x86_64, Windows 11, Python 3.12.10 CPython) across $N = 100$ iterations:

Complete benchmark reports and JSON schema are available in benchmarks/.

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9. Safety & Limitations

• Engineering Judgment: Sofia Engine outputs are diagnostic aids. They do not replace physical inspection or qualified engineering sign-off.
• Physical Safety Interlocks: Under no circumstance should AI or diagnostic outputs directly control machinery without external, hardware-certified safety interlocks (e.g. SIL relays).
• Sensor Calibration: Diagnostic accuracy is bounded by sensor fidelity, mounting configuration, and signal acquisition quality.
• Read docs/LIMITATIONS.md and docs/SECURITY.md for detailed boundaries.

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10. Citation

To cite Sofia Engine in scientific software, publications, or engineering documentation:

@software{sofia_engine_2026,
  author       = {{Rootcastle Engineering \& Innovation}},
  title        = {Sofia Engine: Scientific \& Edge Intelligence Runtime},
  year         = {2026},
  version      = {3.0.0a1},
  publisher    = {Hugging Face},
  doi          = {10.57967/hf/10549},
  url          = {https://huggingface.co/rootcastleengineering/sofia}
}