Update README.md

README.md

@@ -17,85 +17,234 @@ source_datasets:
 - generated
 task_categories:
 - time-series-forecasting
-- reinforcement-learning
-- other
 task_ids:
 - multivariate-time-series-forecasting
-paperswithcode_id: null
 ---
 
-# Dataset Card for **DBbun / Extreme_Environment_Generator**
+# DBbun / Extreme_Environment_Generator
 
 ## Table of Contents
+- [Dataset Summary](#dataset-summary)  
+- [Environments](#environments)  
+- [Dataset Structure](#dataset-structure)  
+  - [State Table](#state-true)  
+  - [Sensor Table](#sensors)  
+  - [Failure Table](#failures)  
+  - [Layer Profile Table](#layer-profile)  
+  - [Environment Config Table](#environment-config)  
+  - [Summary Table](#summary)  
+- [Sensors](#detailed-sensor-descriptions)  
+- [State Fields](#detailed-state-fields)  
+- [Use Cases](#use-cases)  
+- [Limitations](#limitations)  
+- [License](#license)  
+- [Citation](#citation)  
 
-- [**Dataset Summary**](#dataset-summary)  
-- [**Supported Tasks and Leaderboards**](#supported-tasks-and-leaderboards)  
-- [**Languages**](#languages)  
-- [**Dataset Structure**](#dataset-structure)  
-  - [**Data Instances**](#data-instances)  
-  - [**Data Fields**](#data-fields)  
-  - [**Data Splits**](#data-splits)  
-- [**Dataset Creation**](#dataset-creation)  
-  - [**Curation Rationale**](#curation-rationale)  
-  - [**Source Data**](#source-data)  
-  - [**Simulated Procedure**](#simulated-procedure)  
-- [**Bias, Risks, and Limitations**](#bias-risks-and-limitations)  
-- [**Additional Information**](#additional-information)  
-  - [**Usage**](#usage)  
-  - [**Citation Information**](#citation-information)  
-  - [**License**](#license)  
-  - [**Contact**](#contact)  
-
----
 
 ## Dataset Summary
 
-**Extreme_Environment_Generator** is a family of three synthetic datasets created by DBbun.  
-All three datasets are generated from a single physics-inspired simulator and share the **same schema**, making them directly comparable for cross-environment benchmarking.
+**Extreme_Environment_Generator** is a large synthetic dataset produced by a configurable Python simulator created by **DBbun**.  
+It generates multi-sensor time-series data from extreme physical environments inspired by:
 
-The three environments are:
+- deep-Earth subsurface  
+- subsea environments beneath oceans  
+- deep ice-world and icy-planet structures  
 
-- **Deep Subsurface** – a hot, high-pressure interior with crust, mantle, and core-like layers  
-- **Subsea** – an oceanic and seafloor environment with water/sediment upper layers and crust–mantle transition  
-- **Ice World** – a cold, ice-dominated environment inspired by icy planets and deep terrestrial ice layers  
+Each generated scenario includes:
 
-For each environment, the simulator produces:
+- the true physical state of the environment over time  
+- corrupted/noisy sensor measurements  
+- layer and material profiles  
+- environmental configuration  
+- rare failure events  
+- scenario-level summary statistics  
 
-- A “true” physical state over time (`state_true`)  
-- Noisy, drifting, dropout-prone sensor readings (`sensors`)  
-- Layer definitions (`layer_profile`)  
-- Scenario configuration (`environment_config`)  
-- Rare catastrophic failure events (`failures`)  
-- Scenario-level aggregates (`summary`)  
+The dataset is designed for machine learning research, including:
 
-The dataset is designed for research in:
+- multivariate forecasting  
+- sensor fusion  
+- anomaly and rare-event detection  
+- robustness testing under noise, drift, and missing data  
+- reinforcement learning in extreme synthetic environments  
+- foundation model pretraining for time series  
 
-- Multivariate time-series forecasting  
-- Sensor fusion and multimodal learning  
-- Anomaly and rare-event detection  
-- Robustness and uncertainty under noise/drift  
-- Reinforcement learning in synthetic physics  
-- Educational and demonstrative use in synthetic data generation  
 
----
+## Environments
 
-## Supported Tasks and Leaderboards
+### 1. Deep Subsurface
+A high-pressure, high-temperature environment inspired by Earth’s crust, mantle, outer core, and inner core.  
+Includes 5 fixed layers from 0 km to 6371 km depth.
 
-**Supported tasks:**
+### 2. Subsea
+Includes water, sediment, and crust layers, followed by deep subsurface structure.  
+Lower temperatures and pressures in early depth stages, transitioning to mantle-like profiles.
 
-- **Time-series forecasting**  
-  - Predict future physical states or sensor readings given historical sequences.  
-- **Anomaly / rare-event detection**  
-  - Identify unstable states, catastrophic events, or abnormal patterns in the time series.  
-- **Sensor fusion**  
-  - Learn from multiple correlated sensor channels (including fictional sensors) to infer hidden state or stability.  
-- **Robust ML and stress-testing**  
-  - Evaluate model behavior under noise, drift, and missing data.  
-- **Reinforcement learning and control (sim2sim)**  
-  - Use the sequences as environment rollouts or offline RL training data.  
-- **Foundation model pretraining (time-series)**  
-  - Pretrain sequence models on rich synthetic physical dynamics.  
+### 3. Ice World
+Inspired by icy moons and exoplanets.  
+Includes multi-kilometer ice shells, ice–rock transition, and deep interior structure.
+
+All three environments use the same schema, allowing direct cross-environment comparison.
+
+
+## Dataset Structure
+
+### state_true
+Ground-truth physical state at every time step.
+
+| Field | Description | Unit |
+|-------|-------------|------|
+| scenario_id | scenario index | — |
+| time_sec | time since start | seconds |
+| depth_km | simulated depth | km |
+| true_temperature_C | real internal temperature | °C |
+| true_pressure_GPa | real pressure | GPa |
+| true_density_kg_m3 | real density | kg/m³ |
+| true_phase | solid/liquid/plastic | — |
+| true_seismic_noise | baseline seismic activity | arbitrary |
+| true_vibration_level | structural vibration | arbitrary |
+| stability_score | stability (0–1) | — |
+| rare_event_flag | True if an extreme event occurred | bool |
+
+
+### sensors
+Corrupted sensor readings derived from state_true.
+
+| Sensor | Field Prefix | Description |
+|--------|--------------|-------------|
+| Temperature | temperature_ | noisy, drifting temperature |
+| Pressure | pressure_ | noisy pressure with dropout |
+| Accelerometer | accel_ | acceleration in g |
+| Seismic | seismic_ | seismic noise + dropout |
+| Quantum Resonance | quantum_resonance_ | fictional high-sensitivity sensor |
+| Neutrino Flux | neutrino_flux_ | fictional deep-environment reading |
+| Spin Coherence | spin_coherence_ | fictional material spin measurement |
+| Gravity Wave | gravity_wave_ | fictional micro-gravity perturbations |
+
+All sensors include controlled noise, drift, and dropout behavior.
+
+
+### failures
+Rare catastrophic or near-catastrophic events.
+
+| Field | Description |
+|--------|-------------|
+| scenario_id | scenario index |
+| time_sec | event time |
+| depth_km | event depth |
+| event_type | “thermal_spike”, “pressure_surge”, etc. |
+| severity | 0–1 normalized severity |
+
+
+### layer_profile
+Static description of each environment’s internal layers.
+
+| Field | Description |
+|--------|-------------|
+| scenario_id | scenario index |
+| layer_id | layer index |
+| name | layer name |
+| depth_start_km | start depth |
+| depth_end_km | end depth |
+| material_name | material |
+| density_kg_m3 | perturbed density |
+| phase | solid/plastic/liquid |
+
+
+### environment_config
+The full configuration of each scenario (planet, sensors, perturbations, etc.).
+
+### summary
+High-level statistics per scenario, including max temperature, average pressure, number of failures, and stability metrics.
+
+
+## Detailed Sensor Descriptions
+
+### Temperature Sensor
+- Gaussian noise in °C  
+- Linear drift over time  
+- Occasional dropout  
+
+### Pressure Sensor
+- Multiplicative noise (%)  
+- Dropout probability  
+- Tracks large pressure gradients  
+
+### Accelerometer
+- Measures vibration and acceleration  
+- Useful for instability detection  
+
+### Seismic Sensor
+- Low-frequency seismic noise  
+- Sensitive to layer boundaries and rare events  
+
+### Quantum Resonance Sensor (fictional)
+- Extremely small fluctuations  
+- Useful for ML models requiring additional modality  
+
+### Neutrino Flux Sensor (fictional)
+- Sensitive to density changes  
+- Inspired by high-energy particle flux  
+
+### Spin Coherence Sensor (fictional)
+- Measures microscopic oscillatory behavior  
+- Correlates weakly with phase transitions  
+
+### Gravity-Wave Sensor (fictional)
+- Very small signal  
+- Produces ultra-low-level noise  
+- Adds complexity to multimodal fusion tasks  
+
+
+## Detailed State Fields
+
+### true_temperature_C
+Real physical temperature at depth, following the environment’s temperature profile.
+
+### true_pressure_GPa
+Real pressure increasing with depth.
+
+### true_density_kg_m3
+Density of the current layer (with ±5% random perturbation).
+
+### true_phase
+Categorical:
+- solid  
+- plastic  
+- liquid  
+
+### true_seismic_noise
+Base seismic level.
+
+### true_vibration_level
+Simulated mechanical vibration.
+
+### stability_score
+A scalar between 0 and 1 representing the local stability of the environment.
+
+### rare_event_flag
+Boolean indicating whether a rare catastrophic event occurred at that time step.
+
+
+## Use Cases
+
+- Multimodal sensor fusion  
+- Long-horizon forecasting  
+- Rare event detection  
+- Model robustness evaluation  
+- Foundation-model pretraining for time series  
+- Reinforcement learning in synthetic physical environments  
+- Synthetic data research  
+- Instrumentation testbeds  
+
+
+## Limitations
+
+- Physics is approximate and synthetic  
+- Sensor noise is parameterized, not empirically derived  
+- Rare events are artificially triggered  
+- Layer boundaries are simplified  
 
----
 
+## Citation
 
+- Kartoun, U. (2025). Extreme_Environment_Generator (DBbun LLC). Synthetic extreme-environment dataset for multimodal time-series research.