metadata/README.md

WAVE BENDER IDE - Training Dataset

Dataset Overview

Generated by WAVE BENDER IDE v5.0 - Web-based Drone Telemetry & SLAM Training Dataset Generator

📊 Dataset Statistics:

• Total Telemetry Points: 12,544
• Training Duration: 125.4 seconds
• Sample Rate: 100 Hz
• Training Epochs: 0
• Obstacles Detected: 2
• Avoidance Maneuvers: 0
• Training Progress: 8%

🚀 Loading the Dataset in Hugging Face

CORRECT WAY - Load each dataset separately:

from datasets import Dataset, DatasetDict
import json

# Load telemetry data (separate dataset)
telemetry_dataset = Dataset.from_json("telemetry/telemetry.jsonl")

# Load SLAM data (separate datasets)
obstacles_dataset = Dataset.from_json("slam/obstacles.json")
detections_dataset = Dataset.from_json("slam/detections.json")
avoidances_dataset = Dataset.from_json("slam/avoidances.json")

# Load training data (separate datasets)
epochs_dataset = Dataset.from_json("statistics/epochs.json")
stats_dataset = Dataset.from_json("statistics/summary.json")

# Create DatasetDict with SEPARATE datasets (no schema conflicts!)
dataset_dict = DatasetDict({
    'telemetry': telemetry_dataset,
    'slam_obstacles': obstacles_dataset,
    'slam_detections': detections_dataset,
    'slam_avoidances': avoidances_dataset,
    'training_epochs': epochs_dataset,
    'statistics': stats_dataset
})

✅ NO ARROWINVALID ERRORS - Why this works:

1. Separate Files: Each data type is in its own file with consistent schema
2. Separate Datasets: Each file is loaded as a separate Dataset
3. No Schema Mixing: Different schemas don't conflict because they're separate
4. Always Valid: Empty arrays still have consistent schemas

📁 Directory Structure

wave_bender_dataset.zip/
├── telemetry/
│   ├── telemetry.jsonl     # Telemetry data (JSON Lines)
│   ├── telemetry.csv       # Telemetry data (CSV)
│   └── telemetry_schema.json
├── slam/
│   ├── obstacles.json      # Obstacle definitions
│   ├── detections.json     # Detection events
│   ├── avoidances.json     # Avoidance maneuvers
│   └── training_params.json
├── statistics/
│   ├── epochs.json         # Epoch progression
│   └── summary.json        # Statistics summary
├── graphs/
│   └── graph_data.json
├── metadata/
│   ├── dataset_card.json
│   └── README.md
└── huggingface_loader.py

🎯 Training Configuration

• Complexity: 7/10
• Noise Level: 2.5/5
• Frequency: 1.8 Hz
• Center Region Training: Yes
• Dynamic Obstacles: Yes
• Avoidance Training: Yes

🔧 Fixed ArrowInvalid Errors

This export structure completely eliminates ArrowInvalid errors by:

1. Separating different schema types into different directories
2. Never mixing schemas in the same file
3. Always providing consistent schemas, even for empty data
4. Using unique dataset_id fields to prevent confusion

📝 Citation

@dataset{wave_bender_dataset_2024,
  title = {WAVE BENDER IDE v5.0 - Drone Telemetry & SLAM Training Dataset},
  author = {webXOS},
  year = {2024},
  url = {https://huggingface.co/datasets/webxos/wave_bender_dataset},
  note = {Synthetic dataset for drone autonomy training}
}

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Generated: 2026-01-08T01:43:26.624Z WAVE BENDER IDE v5.0 | Hugging Face Compatible | ArrowInvalid Fixed