feat: Telegram OSINT map layer, Osiris intel ports, and maritime settings

Add Telegram OSINT with hourly incremental t.me scraping, metro geocoding
separate from news centroids, threat-intercept popup UI with inline media,
and HTML markers above alert boxes so pins stay clickable. Expose GFW_API_TOKEN
in onboarding and Settings Maritime; harden GFW/CCTV/geo fetchers. Port Osiris-
derived recon, SCM, entity graph, malware/cyber feeds, sanctions, and submarine
cable layers with tests and documentation.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/services/scm/__init__.py

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+"""Supply-chain risk overlay."""

backend/services/scm/suppliers.py

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+"""SCM supplier risk overlay (Osiris port, uses in-memory dashboard data)."""
+from __future__ import annotations
+
+import math
+from datetime import datetime, timezone
+from typing import Any
+
+from services.fetchers._store import _data_lock, _mark_fresh, get_latest_data_subset_refs, is_any_active, latest_data
+from services.network_utils import fetch_with_curl
+
+SUPPLIERS: list[dict[str, Any]] = [
+    {"id": "sup-tsmc-hsinchu", "name": "TSMC Fab 12 (Tier 1)", "city": "Hsinchu", "country": "Taiwan", "lat": 24.774, "lng": 120.992, "category": "Semiconductor"},
+    {"id": "sup-tsmc-tainan", "name": "TSMC Fab 14 (Tier 1)", "city": "Tainan", "country": "Taiwan", "lat": 23.111, "lng": 120.273, "category": "Semiconductor"},
+    {"id": "sup-sec-giheung", "name": "Samsung Electronics (Tier 1)", "city": "Giheung", "country": "South Korea", "lat": 37.221, "lng": 127.098, "category": "Semiconductor"},
+    {"id": "sup-sk-icheon", "name": "SK Hynix (Tier 1)", "city": "Icheon", "country": "South Korea", "lat": 37.256, "lng": 127.483, "category": "Semiconductor"},
+    {"id": "sup-sony-kumamoto", "name": "Sony Semiconductor (Tier 2)", "city": "Kikuyo", "country": "Japan", "lat": 32.883, "lng": 130.825, "category": "Electronics"},
+    {"id": "sup-mlcc-murata", "name": "Murata MLCC (Tier 2)", "city": "Izumo", "country": "Japan", "lat": 35.361, "lng": 132.756, "category": "Electronics"},
+    {"id": "sup-bosch-stuttgart", "name": "Bosch Auto Parts (Tier 1)", "city": "Stuttgart", "country": "Germany", "lat": 48.815, "lng": 9.176, "category": "Automotive"},
+    {"id": "sup-zf-bavaria", "name": "ZF Friedrichshafen (Tier 1)", "city": "Friedrichshafen", "country": "Germany", "lat": 47.662, "lng": 9.489, "category": "Automotive"},
+    {"id": "sup-valeo-paris", "name": "Valeo R&D (Tier 2)", "city": "Paris", "country": "France", "lat": 48.878, "lng": 2.308, "category": "Automotive"},
+    {"id": "sup-magna-celaya", "name": "Magna Assembly (Tier 2)", "city": "Celaya", "country": "Mexico", "lat": 20.525, "lng": -100.814, "category": "Automotive"},
+    {"id": "sup-denso-monterrey", "name": "Denso Corp (Tier 1)", "city": "Monterrey", "country": "Mexico", "lat": 25.772, "lng": -100.174, "category": "Automotive"},
+    {"id": "sup-catl-ningde", "name": "CATL Battery HQ (Tier 1)", "city": "Ningde", "country": "China", "lat": 26.666, "lng": 119.544, "category": "Battery"},
+    {"id": "sup-byd-shenzhen", "name": "BYD Gigafactory (Tier 1)", "city": "Shenzhen", "country": "China", "lat": 22.684, "lng": 114.341, "category": "Battery"},
+    {"id": "sup-panasonic-nevada", "name": "Panasonic Giga (Tier 1)", "city": "Sparks", "country": "US", "lat": 39.539, "lng": -119.439, "category": "Battery"},
+]
+
+
+def _distance_km(lat1: float, lng1: float, lat2: float, lng2: float) -> float:
+    dx = (lng1 - lng2) * math.cos(math.radians((lat1 + lat2) / 2))
+    dy = lat1 - lat2
+    return math.sqrt(dx * dx + dy * dy) * 111.32
+
+
+def _seismic_risk_level(distance_km: float, magnitude: float) -> str | None:
+    """Meaningful fab impact only — ignore routine micro-quakes (e.g. Taiwan M3.x)."""
+    if magnitude < 4.5:
+        return None
+    if magnitude >= 6.0 and distance_km <= 200:
+        return "CRITICAL"
+    if magnitude >= 5.5 and distance_km <= 75:
+        return "CRITICAL"
+    if magnitude >= 5.0 and distance_km <= 100:
+        return "HIGH"
+    if magnitude >= 4.5 and distance_km <= 40:
+        return "HIGH"
+    return None
+
+
+def _apply_seismic_threats(suppliers: list[dict[str, Any]], earthquakes: list[dict[str, Any]]) -> None:
+    for sup in suppliers:
+        best: tuple[str, float] | None = None
+        for eq in earthquakes:
+            lat = eq.get("lat")
+            lng = eq.get("lng") or eq.get("lon")
+            mag = float(eq.get("mag") or eq.get("magnitude") or 0)
+            if lat is None or lng is None or mag < 4.5:
+                continue
+            dist = _distance_km(sup["lat"], sup["lng"], float(lat), float(lng))
+            level = _seismic_risk_level(dist, mag)
+            if not level:
+                continue
+            severity = {"HIGH": 1, "CRITICAL": 2}
+            if best is None:
+                best = (level, mag)
+            else:
+                cur = severity[level]
+                prev = severity[best[0]]
+                if cur > prev or (cur == prev and mag > best[1]):
+                    best = (level, mag)
+        if best:
+            level, mag = best
+            if sup["risk_level"] == "NORMAL" or (
+                level == "CRITICAL" and sup["risk_level"] != "CRITICAL"
+            ):
+                sup["risk_level"] = level
+            elif level == "CRITICAL" and sup["risk_level"] == "HIGH":
+                sup["risk_level"] = "CRITICAL"
+            sup["active_threats"].append(f"SEISMIC PROXIMITY (M{mag:.1f})")
+
+
+def build_scm_payload() -> dict[str, Any]:
+    suppliers = [{**s, "risk_level": "NORMAL", "active_threats": []} for s in SUPPLIERS]
+    refs = get_latest_data_subset_refs("earthquakes", "firms_fires", "gdelt")
+
+    earthquakes = refs.get("earthquakes") or []
+    _apply_seismic_threats(suppliers, earthquakes)
+
+    fires = refs.get("firms_fires") or []
+    for sup in suppliers:
+        count = 0
+        for fire in fires:
+            lat = fire.get("lat") or fire.get("latitude")
+            lng = fire.get("lng") or fire.get("lon") or fire.get("longitude")
+            if lat is None or lng is None:
+                continue
+            if _distance_km(sup["lat"], sup["lng"], float(lat), float(lng)) < 50:
+                count += 1
+        if count:
+            if sup["risk_level"] == "NORMAL":
+                sup["risk_level"] = "HIGH"
+            sup["active_threats"].append(f"WILDFIRE PROXIMITY ({count} hotspots)")
+
+    conflicts = refs.get("gdelt") or []
+    for sup in suppliers:
+        for event in conflicts:
+            lat = event.get("lat")
+            lng = event.get("lng") or event.get("lon")
+            if lat is None or lng is None:
+                continue
+            if _distance_km(sup["lat"], sup["lng"], float(lat), float(lng)) < 100:
+                sup["risk_level"] = "CRITICAL"
+                sup["active_threats"].append("ARMED CONFLICT / RIOT")
+                break
+
+    # USGS fallback if earthquakes empty
+    if not earthquakes:
+        try:
+            resp = fetch_with_curl(
+                "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/4.5_day.geojson",
+                timeout=5,
+            )
+            if resp.status_code == 200:
+                features = resp.json().get("features") or []
+                usgs_quakes = [
+                    {
+                        "lat": f.get("geometry", {}).get("coordinates", [None, None])[1],
+                        "lng": f.get("geometry", {}).get("coordinates", [None, None])[0],
+                        "mag": f.get("properties", {}).get("mag") or 0,
+                    }
+                    for f in features
+                    if len(f.get("geometry", {}).get("coordinates") or []) >= 2
+                ]
+                _apply_seismic_threats(suppliers, usgs_quakes)
+        except Exception:
+            pass
+
+    critical = sum(1 for s in suppliers if s["risk_level"] == "CRITICAL")
+    return {
+        "suppliers": suppliers,
+        "total": len(suppliers),
+        "critical_count": critical,
+        "timestamp": datetime.now(timezone.utc).isoformat(),
+    }
+
+
+def fetch_scm_suppliers() -> dict[str, Any]:
+    if not is_any_active("scm_suppliers"):
+        return latest_data.get("scm_suppliers") or {}
+    payload = build_scm_payload()
+    with _data_lock:
+        latest_data["scm_suppliers"] = payload
+    _mark_fresh("scm_suppliers")
+    return payload