@mytec: feat: Phase 3.0 Architecture Refactor ✅

Major refactoring of RFCP backend:
- Modular propagation models (8 models)
- SharedMemoryManager for terrain data
- ProcessPoolExecutor parallel processing
- WebSocket progress streaming
- Building filtering pipeline (351k → 15k)
- 82 unit tests

Performance: Standard preset 38s → 5s (7.6x speedup)

Known issue: Detailed preset timeout (fix in 3.1.0)

backend/app/propagation/okumura_hata.py

@@ -0,0 +1,74 @@
+"""
+Okumura-Hata empirical propagation model.
+
+Valid for:
+- Frequency: 150-1500 MHz
+- Distance: 1-20 km
+- TX height: 30-200 m
+- RX height: 1-10 m
+
+Reference: Hata (1980), "Empirical Formula for Propagation Loss
+           in Land Mobile Radio Services"
+"""
+
+import math
+from app.propagation.base import PropagationModel, PropagationInput, PropagationOutput
+
+
+class OkumuraHataModel(PropagationModel):
+
+    @property
+    def name(self) -> str:
+        return "Okumura-Hata"
+
+    @property
+    def frequency_range(self) -> tuple:
+        return (150, 1500)
+
+    @property
+    def distance_range(self) -> tuple:
+        return (100, 20000)  # Extended to 100m minimum for practical use
+
+    def calculate(self, input: PropagationInput) -> PropagationOutput:
+        f = input.frequency_mhz
+        d = max(input.distance_m / 1000, 0.1)  # km, min 100m
+        hb = max(input.tx_height_m, 1.0)
+        hm = max(input.rx_height_m, 1.0)
+
+        # Mobile antenna height correction factor
+        if input.environment == "urban" and f >= 400:
+            # Large city
+            a_hm = 3.2 * (math.log10(11.75 * hm) ** 2) - 4.97
+        else:
+            # Medium/small city
+            a_hm = (1.1 * math.log10(f) - 0.7) * hm - (1.56 * math.log10(f) - 0.8)
+
+        # Basic path loss (urban)
+        L_urban = (
+            69.55
+            + 26.16 * math.log10(f)
+            - 13.82 * math.log10(hb)
+            - a_hm
+            + (44.9 - 6.55 * math.log10(hb)) * math.log10(d)
+        )
+
+        # Environment correction
+        if input.environment == "suburban":
+            L = L_urban - 2 * (math.log10(f / 28) ** 2) - 5.4
+        elif input.environment == "rural":
+            L = L_urban - 4.78 * (math.log10(f) ** 2) + 18.33 * math.log10(f) - 35.94
+        elif input.environment == "open":
+            L = L_urban - 4.78 * (math.log10(f) ** 2) + 18.33 * math.log10(f) - 40.94
+        else:
+            L = L_urban
+
+        return PropagationOutput(
+            path_loss_db=L,
+            model_name=self.name,
+            is_los=False,
+            breakdown={
+                "basic_loss": L_urban,
+                "environment_correction": L - L_urban,
+                "antenna_correction": a_hm,
+            },
+        )