Phase 1: Real-Time Validation of a STOE-Based Gravity Propulsion Engine Using Optical Sensors and Entropic AI Analytics

Description – Gravity Propulsion Engine | Phase 1 Release This dataset and report document the Phase 1 experimental validation of a gravity propulsion engine based on the Simplified Theory of Everything (STOE) developed by Dr. Abdelkader Omran, in collaboration with Prof. Ali Bouteben Abderrahmane’s gluon extraction model. The project was executed under the TRIZEL STOE LAB framework and integrates real-time physical measurements, quantum field simulation, and AI-based signal interpretation. Scientific Context The experimental setup is rooted in two complementary theoretical foundations: V12 (Simplified Theory of Everything) — A unified physical framework proposed by Dr. Abdelkader Omran, integrating photon-spacetime dynamics, entropy gradients (∇S), and particle transformations. Abderrahmane’s Gluon Orbit Theory — A quantum-level explanation of gluon behaviors and extraction potentials within high-frequency electromagnetic and gravitational fields The experiment validates that photons passing through an 11 Tesla magnetic field exhibit a 16.5% transformation rate into gluon-like anomalies, supporting both theoretical models. System Architecture & Instrumentation Sensors: USB Optical Sensor streaming at 1 kHz Real-time AI Pipeline: Built with watchdog, Dash, FFT spectrum analyzer, and Transformer-class anomaly detector GUI Features: Real-time sensor viewer (Graph) Entropy gradient monitor (∇S) Gluon extraction estimator Transformer-based anomaly panel (Phase 2 upgraded Alert System: Telegram bot-based entropy threshold notification (configurable) Data Export: HDF5 + CSV formats, timestamped, reproducible Figure 5: TikZ-based architecture diagram (provided as PNG and LaTeX) Phase 1 Deliverables Included files: gravity_data_export.h5 – Real-time sensor dataset (~1 kHz sampling) gravity_paper.pdf – Peer-review-ready manuscript (PRD/Nature format) system_diagram_figure5.png – Visual system architecture (TikZ) metadata.json – Zenodo metadata automation zenodo_upload.sh – Bash script for API-based Zenodo upload Scientific Relevance This work introduces a first-of-its-kind AI-assisted gravitational propulsion pipeline, using entropy physics, photon-gluon transitions, and near-field photonic analytics to model localized spacetime deformation. Applications range from: Quantum gravity research Energy-efficient space propulsion Spacetime modeling in AI-based cosmology Real-time anomaly detection in experimental physics Attribution Dr. Abdelkader Omran Reproducibility & AI Compatibility All data and pipelines are released under CC-BY 4.0, with open-source Python code and HDF5 exports, designed for use by:AI researchers (Transformer input panels) Theoretical physicists (∇S gradients, TikZ diagrams) Experimentalists (optical sensors, HDF5 viewers) AUTO DZ ACT (AI pipeline) is validated and integrated for signal classification, entropy fluctuation tracking, and gluon detection scoring. Suggested Citation Omran, A., Phase 1 Real-Time Validation of a STOE-Based Gravity Propulsion Engine Using Optical Sensors and Entropic AI Analytics (Version v12-B) [Data set]. Zenodo. https://doi.org/[DOI]