fix(controller): re-arm window_drop and expose kernel_datapath

Three fixes from live-calibrating the kernel impairment filters on an
isolated instance (Docker-to-Docker veth link, per-sequence evidence):

- update_filters skipped an unchanged filters dict as a no-op, so a
  one-shot window_drop outage that had already passed could never be
  re-armed: re-applying the same values produced no datapath command at
  all. A PUT still carrying window_drop now reconciles, restarting the
  schedule from the apply as documented. Measured: identical re-apply
  moves the outage window from seq 50-70 to 70-91 (arm at +8s of the
  new apply).

- gemodel parameter semantics were documented backwards in the
  validation rules, available_filters, the MCP/copilot descriptions and
  the feature docs. Measured against the kernel (per-direction counters,
  offered = sent + dropped): p is the good-to-bad transition chance, r
  the bad-to-good transition, 1-h the loss chance in the bad state (the
  good state loses nothing) - mean one-way loss p/(p+r)*(1-h), e.g.
  [10,30,100] -> 24.8%/25.7% measured vs 25% predicted, [5,45,100] ->
  10.8% both ways vs 10%.

- kernel_datapath was silently stripped from every REST/MCP link payload
  by the response model (LinkBase carried no such field), so clients
  could not tell a kernel link from a relay one.

Also verified on the same setup: the 3-argument window_drop form works
exactly as specified ([3000,5000,100] -> a single seq 31-55 outage,
[20000,6000,100] -> seq 115-144), and both generations of the command
builder emit the parameters unpadded.

Adds an API test for the field and controller tests for the no-op skip
and the window_drop re-arm exemption.

gns3server/utils/packet_filter_validation.py

@@ -210,12 +210,15 @@ def validate_filter_parameters(filter_type: str, values: List[Any]) -> None:
             "units": ["%", "%", "packets"],
         },
         "gemodel": {
-            # Gilbert-Elliot loss model, tc semantics: p = loss probability in
-            # the bad state, r = loss probability in the good state, 1-h =
-            # probability of moving from the good to the bad state.
+            # Gilbert-Elliot loss model, tc semantics (verified against the
+            # kernel on kernel-datapath links): p = probability of moving
+            # from the good to the bad state (PGAP), r = probability of
+            # moving back (PGBP), 1-h = loss probability in the bad state
+            # (the good state loses nothing). Steady-state mean loss is
+            # p/(p+r) * (1-h) per direction.
             "params_count": (1, 3),
             "ranges": [(0, 100), (0, 100), (0, 100)],
-            "names": ["p (bad-state loss)", "r (good-state loss)", "1-h (good-to-bad transition)"],
+            "names": ["p (good-to-bad transition)", "r (bad-to-good transition)", "1-h (bad-state loss)"],
             "units": ["%", "%", "%"],
         },
         "seed": {"params_count": (1, 1), "ranges": [(0, 4294967295)], "names": ["Seed"], "units": [""]},