webxos/ionicocean

   

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IONICOCEAN

THIS DATASET WAS CREATED USING IONICSPHERE. The Ionicsphere.html is available for download in the /generator/ folder of this repo.

The model predicts ionic stability and simulated quantum state transitions in ionic environments. Trapped-ion quantum simulators, typically involve physical hardware for tasks like entanglement measurement or Hamiltonian engineering. This dataset is desgined as a fully synthetic browser-based alternative for developers without lab access.

For more info on WEBXOS visit https://github.com/webxos

Theory

The Ionic Ocean Synthetic Dataset is a specialized dataset designed to bridge the gap between complex atmospheric physics and efficient machine learning models. The goal of this dataset is to provide high-fidelity training data for neural networks to predict ionospheric conditions—specifically electron density and signal interference—without requiring the extreme computational power of traditional physics engines.

Model Name: IonicOceanSyntheticDataset_v7.0 Version: 7.0 Export Date: 2025-12-31T00:27:29.944Z

• —Total Epochs: 3
• —Final Loss: 0.6713
• —Final Accuracy: 65.6%
• —Training Samples: 800
• —Simulation Time: 37.8s

This package contains real-time captured data from the ionic ocean simulation:

Particle Data:

• —Frames captured: 29
• —Particles per frame: 10240
• —Total position samples: 890880
• —Time range: 38s

Features Captured:

1. 1.Position (x, y, z) - normalized coordinates
2. 2.Velocity (x, y) - movement vectors
3. 3.Timestamp - simulation time
4. 4.Model state - neural network parameters at capture time

Target Phenomenon

It models an "Ionic Ocean," referring to the fluid-like behavior of ionized particles in the Earth's upper atmosphere (ionosphere). This dataset allows for the training of "surrogate models" that can predict results in real-time. Used for improving the accuracy of GNSS/GPS positioning by predicting and correcting for atmospheric delays and signal scintillation.

Technical

-Synthetic Generation: The data is algorithmically generated, using a simplified physics-based simulation.

-Spatial Coordinates: Latitude, longitude, and altitude.

-Temporal Data: Timestamps reflecting diurnal (day/night) cycles.

-Physical Parameters: Electron density, magnetic field orientation, and solar flux indices (e.g., F10.7 index).

-Format: Distributed as a tabular dataset (often in .csv or .parquet formats) to be compatible with common machine learning frameworks like PyTorch or TensorFlow.

Model Architecture

Input(5) → Dense(32, relu) → Dropout(0.2)
         → Dense(16, relu)
         → Dense(8, relu)
         → Output(1, sigmoid)

Training Configuration

• —Optimizer: Adam (learning_rate=0.001)
• —Loss Function: Binary Crossentropy
• —Batch Size: 32
• —Validation Split: 20%
• —Shuffle: True

Simulation Parameters

• —Ion Count: 10,240
• —Ocean Size: 200x200 units
• —Physics Engine: GPU.js accelerated
• —Render Engine: Three.js r128
• —Target FPS: 60

File Structure

ionicsphere_export_v7.0_*.zip/
├── model_metadata.json     # Model configuration and stats
├── training_log.json       # Loss/accuracy per epoch
├── particle_data.json      # Captured particle positions/velocities
├── README.md              # This file
├── terminal_log.txt       # CLI interaction history
└── config.json           # System configuration

License

MIT