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IONICOCEAN

by webXOS

THIS DATASET WAS CREATED USING IONICSPHERE. FREE to use. LINK: webxos.netlify.app/IONICSPHERE

Trains synthetic data sets generated from ionic ocean simulations.

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.

SPECS

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

Training Summary

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

Dataset Information

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

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

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.

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.

Exmple Usage Instructions

1. EXAMPLE: Load Model in TensorFlow.js:

async function loadModel() {
    const model = await tf.loadLayersModel('tfjs_model/model.json');
    const weights = await fetch('tfjs_model/weights.bin');
    // Load weights and make predictions
}

2. EXAMPLE: Analyze Particle Data:

const data = JSON.parse(particleDataJson);
const positions = data.positions;  // Array of position frames
const velocities = data.velocities; // Array of velocity frames

3. EXAMPLE: Reproduce Simulation:

• Use Three.js with provided particle data
• Apply same physics parameters
• Feed data into neural network for stability predictions

Citation

If you use this data in research, please cite:

@dataset{ionic_sphere_2025,
  title={Ionic Ocean Dataset},
  author={webXOS]
  year={2025},
  publisher={webXOS},
  url={webxos.netlify.app}
}

License

Apache 2.0