---
language: en
license: apache-2.0
tags:
- ionic-simulation
- synthetic-data
- neural-networks
- threejs
- webgl
- chemistry
- biology
- 3D
- art
- climate
- ion
- python
- Electron
- physics
- science
- scintillation
- oceanography
- molecular-dynamics
datasets:
- IonicOceanSyntheticDataset
task_categories:
- time-series-forecasting
- tabular-regression
- tabular-classification
---

<div style="
    background: #00FF00;
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    margin: 2rem 0;
    font-family: 'Fira Code', 'Courier New', monospace;
    color: #00FF00;
    border-radius: 0 8px 8px 0;
">
    <pre style="
        font-size: 12px;
        line-height: 1.2;
        margin: 0;
        overflow-x: auto;
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    ">
                   ___           ___                        ___           ___           ___           ___           ___           ___     
       ___        /  /\         /  /\           ___        /  /\         /  /\         /  /\         /  /\         /  /\         /  /\    
      /__/\      /  /::\       /  /::|         /__/\      /  /::\       /  /::\       /  /::\       /  /::\       /  /::\       /  /::|   
      \__\:\    /  /:/\:\     /  /:|:|         \__\:\    /  /:/\:\     /  /:/\:\     /  /:/\:\     /  /:/\:\     /  /:/\:\     /  /:|:|   
      /  /::\  /  /:/  \:\   /  /:/|:|__       /  /::\  /  /:/  \:\   /  /:/  \:\   /  /:/  \:\   /  /::\ \:\   /  /::\ \:\   /  /:/|:|__ 
   __/  /:/\/ /__/:/ \__\:\ /__/:/ |:| /\   __/  /:/\/ /__/:/ \  \:\ /__/:/ \__\:\ /__/:/ \  \:\ /__/:/\:\ \:\ /__/:/\:\_\:\ /__/:/ |:| /\
  /__/\/:/~~  \  \:\ /  /:/ \__\/  |:|/:/  /__/\/:/~~  \  \:\  \__\/ \  \:\ /  /:/ \  \:\  \__\/ \  \:\ \:\_\/ \__\/  \:\/:/ \__\/  |:|/:/
  \  \::/      \  \:\  /:/      |  |:/:/   \  \::/      \  \:\        \  \:\  /:/   \  \:\        \  \:\ \:\        \__\::/      |  |:/:/ 
   \  \:\       \  \:\/:/       |__|::/     \  \:\       \  \:\        \  \:\/:/     \  \:\        \  \:\_\/        /  /:/       |__|::/  
    \__\/        \  \::/        /__/:/       \__\/        \  \:\        \  \::/       \  \:\        \  \:\         /__/:/        /__/:/   
                  \__\/         \__\/                      \__\/         \__\/         \__\/         \__\/         \__\/         \__\/    
    </pre>
</div>  

# IONICOCEAN
by webXOS 

*THIS DATASET WAS CREATED USING IONICSPHERE. Ionicsphere.html is available for download in the /generator/ folder.*

*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:**
```javascript
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:**
```javascript
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:
```bibtex
@dataset{ionicocean,
  title={Ionicocean Dataset},
  author={webXOS]
  year={2026},
  publisher={webXOS},
  url={webxos.netlify.app}
}
```

### License
Apache 2.0