The evidence is proven or strong, but this capability still needs a capability-specific ESP32 or Rust runtime adapter.
Intended capability
Validate this target only after a capability-specific adapter and ground-truth study exist: Detects slow water leaks inside bathroom walls, under floors, and behind kitchen lines weeks before visible damage or mold, per room, from the existing mesh.
This describes the intended outcome. Readiness is needs edge adapter, evidence is class B, and a catalog mapping or recording is not proof of this outcome at a real site.
Solution blueprint
See the environment before installing it.
This exact kit is one of 191 first-class designs. It includes geometry, objects, nodes, wording, scenarios, installation, limitations, and catalog-bound readiness.
No rendered revision is available yet.
Bundled recording
A related Hospitality scene
This is one recorded vertical scenario. It is not separate validation of every capability in the catalog.
01 The physics
Water ingress raises local eps_r from ~2 (dry drywall) toward 80, increasing path delay and attenuation for every ray transiting that wall section; the change is spatially localized and monotonic over days — a signature nothing else in a hotel produces.
02 Shared processing path
CIR super-resolved delay drift on wall-crossing paths -> RFF/NeRF differential eps_r map to localize the wet voxel -> compartment CUSUM over days/weeks (119-day drift control proven) to separate leak trends from HVAC humidity cycles.
This is a capability design path. Components may be shared with other catalog entries; it is not presented as a unique algorithm.
03 Validation plan
Own experiment: controlled wetting of a drywall test section between ESP32 pairs, tracking delay/eps_r drift vs moisture-meter ground truth; ESP32-wall dataset for baseline wall-transit characterization.
04 Commercial hypothesis
Leak insurance for the hotel chief engineer: 'room 412's shower wall started absorbing water 9 days ago' — sold against five-figure mold remediation claims.