Update dataset card

README.md

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+---
+dataset_info:
+  features:
+    - name: sample_id
+      dtype: string
+    - name: energy
+      sequence: float32
+      length: 3384
+    - name: n_freqs
+      dtype: int32
+    - name: n_dirs
+      dtype: int32
+    - name: source
+      dtype: string
+    - name: station
+      dtype: string
+    - name: n_anchors
+      dtype: int32
+    - name: anchors_json
+      dtype: string
+    - name: Hs
+      dtype: float32
+    - name: Tp
+      dtype: float32
+    - name: Dp
+      dtype: float32
+    - name: total_energy
+      dtype: float32
+  splits:
+    - name: train
+      num_examples: 100964
+    - name: validation
+      num_examples: 5532
+    - name: test
+      num_examples: 5529
+license: cc-by-4.0
+task_categories:
+  - image-to-image
+tags:
+  - oceanography
+  - wave-spectrum
+  - compression
+  - diffusion-model
+---
+
+# ATLAS-WDS: Wave Directional Spectrum Dataset
+
+海浪方向谱压缩回传训练数据集。
+
+## 数据格式
+
+每条记录包含一个 **47×72 能量矩阵**（展平为 3384 维 float32 数组）
+及对应的 **斜高斯锚点参数**。
+
+## 快速加载
+
+```python
+from datasets import load_dataset
+import numpy as np, json
+
+ds = load_dataset("wuff-mann/ATLAS-WDS", split="train", streaming=True)
+
+for sample in ds:
+    # 还原能量矩阵
+    energy = np.array(sample["energy"], dtype=np.float32).reshape(
+        sample["n_freqs"], sample["n_dirs"])  # (47, 72)
+    # 锚点参数
+    anchors = json.loads(sample["anchors_json"])
+    # 物理参数
+    Hs, Tp, Dp = sample["Hs"], sample["Tp"], sample["Dp"]
+```
+
+## 三阶段训练使用
+
+```python
+# Stage 1: cLDM 预训练 — 只用能量矩阵
+for sample in ds:
+    matrix = np.array(sample["energy"]).reshape(47, 72)
+
+# Stage 2: Swin 编码器 — 矩阵 + 锚点
+for sample in ds:
+    matrix = np.array(sample["energy"]).reshape(47, 72)
+    anchors = json.loads(sample["anchors_json"])
+
+# Stage 3: 端到端对齐 — 仅真实数据
+ds_real = ds.filter(lambda x: x["source"] != "synthetic")
+```