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-# ⚙️ RFT Adaptive Computing Kernel — Technical Notes (v1.0)
-
-The **Rendered Frame Theory (RFT) Adaptive Computing Kernel** serves as a universal stability and noise-control framework designed for computational environments spanning **CPU, GPU, and TPU architectures**.  
-It measures and adjusts harmonic balance parameters *(QΩ and ζ_sync)* across system workloads to maintain coherence even under fluctuating or high-noise conditions.
-
----
-
-## 🧠 Core Purpose
-The kernel evaluates and self-adjusts processing stability by simulating perturbations within computational cycles.  
-It applies Rendered Frame Theory’s harmonic laws to map **energy, timing, and coherence** between data operations.
-
-Each run outputs:
-- **QΩ (Harmonic Stability):** Represents amplitude-based consistency across compute cycles.  
-- **ζ_sync (Synchronization Coherence):** Represents phase-alignment and temporal coherence.  
-- **Status:** `nominal`, `perturbed`, or `critical` depending on noise scale and recovery behavior.
-
----
-
-## ⚡ Operational Domains
-The system supports multiple adaptive profiles:
-| Profile | Description |
-|----------|--------------|
-| **AI / Neural** | Evaluates drift under training noise, backpropagation irregularities, or floating-point jitter. |
-| **SpaceX / Aerospace** | Simulates vibration and latency perturbations found in avionics and telemetry systems. |
-| **Energy / RHES** | Models electrical grid fluctuations and frequency stabilization under dynamic loads. |
-| **Extreme Perturbation** | Pushes systems to their operational noise limits to identify breakdown thresholds. |
-
----
-
-## 🧮 Internal Algorithmic Overview
-- **Adaptive Baseline:** Maintains a moving equilibrium between QΩ and ζ_sync to resist instability.  
-- **Dynamic Weighting:** Each domain uses a tuned ratio of stability-to-coherence importance.  
-- **Noise Injection:** Synthetic σ values (0.00–0.30) emulate hardware, data, or environmental perturbations.  
-- **Bounded Validation:** All metrics capped at 0.99 to prevent saturation artifacts and false harmonics.  
-- **Soft Learning Memory:** Gradual convergence toward prior equilibrium to simulate recovery intelligence.
-
----
-
-## 🧰 Usage Notes
-1. Select a **System Profile** and **Noise Distribution** (gauss/uniform).  
-2. Adjust the **Noise Scale (σ)** to simulate conditions.  
-3. Press **Run Simulation**.  
-4. Observe QΩ and ζ_sync behavior — repeated runs with constant σ show adaptive learning trends.
-
----
-
-## 📊 Interpretation Summary
-| Status | Description | Typical QΩ Range | ζ_sync Range |
-|---------|--------------|------------------|--------------|
-| **Nominal** | Stable harmonic equilibrium | 0.82–0.89 | 0.75–0.88 |
-| **Perturbed** | Transitional adaptive response | 0.79–0.84 | 0.70–0.82 |
-| **Critical** | Instability threshold reached | <0.78 | <0.70 or >0.90 |
-
----
-
-## 🔐 Legal & Verification
-All metrics are generated within sealed internal algorithms protected under **RFT-IPURL v1.0** (UK/Berne Convention).  
-SHA-512 timestamps are applied to safeguard integrity and originality of simulation logic.
-
-**Author:** Liam Grinstead  
-**Affiliation:** Rendered Frame Theory Systems (RFTSystems)  
-**DOI:** [https://doi.org/10.5281/zenodo.17466722](https://doi.org/10.5281/zenodo.17466722)  
-**License:** RFT-IPURL v1.0 — Research validation use only.