@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/core/result.py

@@ -0,0 +1,65 @@
+"""
+Coverage result aggregation and statistics.
+"""
+
+from dataclasses import dataclass
+from typing import List
+
+
+@dataclass
+class PointResult:
+    lat: float
+    lon: float
+    rsrp: float
+    distance: float
+    path_loss: float
+    terrain_loss: float
+    building_loss: float
+    diffraction_loss: float
+    has_los: bool
+    model_used: str
+
+    def to_dict(self) -> dict:
+        return {
+            "lat": self.lat, "lon": self.lon,
+            "rsrp": self.rsrp, "distance": self.distance,
+            "path_loss": self.path_loss, "terrain_loss": self.terrain_loss,
+            "building_loss": self.building_loss, "diffraction_loss": self.diffraction_loss,
+            "has_los": self.has_los, "model_used": self.model_used,
+        }
+
+
+@dataclass
+class CoverageResult:
+    points: List[PointResult]
+    stats: dict
+    computation_time: float
+    models_used: List[str]
+
+    def to_dict(self) -> dict:
+        return {
+            "points": [p.to_dict() for p in self.points],
+            "count": len(self.points),
+            "stats": self.stats,
+            "computation_time": round(self.computation_time, 2),
+            "models_used": self.models_used,
+        }
+
+
+def compute_stats(points: List[PointResult]) -> dict:
+    if not points:
+        return {"min_rsrp": 0, "max_rsrp": 0, "avg_rsrp": 0,
+                "los_percentage": 0, "total_points": 0}
+
+    rsrp_values = [p.rsrp for p in points]
+    los_count = sum(1 for p in points if p.has_los)
+
+    return {
+        "min_rsrp": min(rsrp_values),
+        "max_rsrp": max(rsrp_values),
+        "avg_rsrp": sum(rsrp_values) / len(rsrp_values),
+        "los_percentage": los_count / len(points) * 100,
+        "total_points": len(points),
+        "points_with_buildings": sum(1 for p in points if p.building_loss > 0),
+        "points_with_terrain_loss": sum(1 for p in points if p.terrain_loss > 0),
+    }