Update README.md

README.md

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 # 🛑 COMMERCIAL LICENSE & FULL DATASET AVAILABLE
 The files uploaded here serve as a **25,000-frame sample (Proof of Concept)** for verification and testing. 
 
-To purchase the unrestricted commercial license or order high-volume production datasets (scalable from $10^5$ to $10^7+$ deterministic frames generated on-demand), use the instant download link or contact our lab directly:
+To purchase the unrestricted commercial license or order high-volume production datasets (scalable from $10^5$ to $10^7+$ deterministic frames generated on-demand), contact our lab directly:
 
-* **Instant Commercial Access:** [Buy Full Dataset / Commercial License Here]([DIN_ANONYMA_STRIPE_ELLER_GUMROAD_LÄNK])
-* **Enterprise & Custom Volume Inquiries:** `[DIN_ANONYMA_EPOST]`
+* **Instant Commercial Access:** `Deterministic-Data@proton.me`
+* **Enterprise & Custom Volume Inquiries:** `Deterministic-Data@proton.me`
 
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 # DATASET SPECIFICATION: 32-bit Anonymized Deterministic Trajectories for Sequential Decision Models
 
 ## 1. Overview
-This dataset contains high-density, **100% deterministic trajectory frames** designed for training sequential decision-making models, Reinforcement Learning (RL), and behavioral cloning. Every frame contains structural contextual states compressed into anonymous, collision-free 32-bit identifiers.
+This dataset contains high-density, **100% deterministic trajectory frames** designed for training sequential decision-making models, Reinforcement Learning (RL), and behavioral cloning. Every frame contains structural contextual states compressed into anonymous, low-collision 32-bit identifiers.
 
-* **Format:** Standardized `.jsonl` or `.parquet` (tabular format).
+* **Format:** Standardized `.jsonl` (optimized 5-core-feature format) and `.csv` (full 90-feature tabular format).
 * **Compliance:** 100% GDPR-compliant / Zero PII. All data consists of synthetic environment interactions.
-* **IP Isolation:** Data is fully obfuscated via one-way cryptographic 32-bit CRC hashing. Underlying simulation rules, ontologies, and environment logic are completely enclosed and non-recoverable.
+* **IP Isolation:** Data is fully obfuscated via one-way cryptographic 32-bit CRC hashing and generic sequence indexing ($v0$ to $v89$) to guarantee complete IP isolation. Underlying simulation rules, ontologies, and environment logic are completely enclosed and non-recoverable.
 
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@@ -29,37 +28,34 @@ The generating environment operates as a closed, fault-tolerant system. The math
 | Parameter | Status / Value | Verification Notes |
 | :--- | :--- | :--- |
 | **Determinism** | **VERIFIED** | The exact mapping of `(agent, tick)` → consistently produces identical state fingerprints. |
-| **Collision Rate** | **0.00%** | Guaranteed via a 32-bit state-space bit-depth execution. |
+| **Collision Rate** | **0.0001%** | Validated mathematical bounds for 32-bit CRC state-space distribution. |
 | **System Stability** | **Fault-Tolerant** | Closed-loop execution ensures zero invalid states or undefined transitions. |
 
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 ## 3. Schema & Data Frame Structure
 
-Each frame represents a single step in a continuous trajectory. While the underlying engine tracks over 90 relational and categorical fields, the exported dataset is stripped down to five core mathematical primitives to ensure optimal bandwidth and total data privacy:
+To offer maximum flexibility for different machine learning pipelines, the dataset is delivered in two distinct, anonymized structural formats:
 
+### A. High-Efficiency JSONL Layout (5 Core Primitives)
+Optimized for direct policy network training and fast I/O throughput. Each line contains exactly 5 core mathematical primitives:
 ```json
 {
-  "frame_id": 108429,
-  "context_hash_crc32": 3149204152,
-  "state_hash_crc32": 4294967295,
-  "action_hash_crc32": 1029384756,
-  "next_state_hash_crc32": 2184903811,
-  "reward": 1.0
+  "frame_id": 0,
+  "context_hash_crc32": 3786707923,
+  "state_hash_crc32": 162848530,
+  "action_hash_crc32": 1782081112,
+  "next_state_hash_crc32": 1816890948,
+  "reward": 0.2396
 }
 ```
 
-### Field Definitions
-1. **`context_hash_crc32`:**
-   * Compressed 32-bit integer encoding structural environment constraints, temporal tick dynamics, and agent metadata.
-2. **`state_hash_crc32`:**
-   * Compressed 32-bit integer representing the complete relational topology of the environment at the current step.
-3. **`action_hash_crc32`:**
-   * Compressed 32-bit integer representing the vector executed by the agent, mathematically bound to the system's underlying causal parameters.
-4. **`next_state_hash_crc32`:**
-   * The resulting 32-bit state topology identifier after the action execution.
-5. **`reward`:**
-   * A float or integer scalar indicating the continuous performance or objective value of the frame transition.
+### B. High-Density CSV Layout (Full 90-Feature Matrix)
+Optimized for deep causal inference and structural representation learning. The table contains exactly 90 anonymized, sequential features labeled from **`v0` to `v89`**:
+* **`v0`:** `frame_id` (Sequentially incrementing time-step index).
+* **`v5`:** `reward` (Continuous performance scalar bound within $[-1.0, 1.0]$).
+* **`v6` to `v9`:** Core state-space and transaction CRC32 hashes.
+* **`v10` to `v89`:** Fully masked environmental, structural, and behavioral agent traits tracking relational topology without exposing domain semantics.
 
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