Silence can be informative.
The event gate makes a non-transmission legible: the model knows that local change stayed below a documented threshold.
A sparse sensor network reconstructs nearshore change by learning when local measurements are worth the energy required to send them.
Nearshore sensing is limited by batteries, weather, and intermittent radio. Tidal Memory evaluates a fictional event-triggered policy that compresses local history and transmits only when a measurement changes the shared coastal estimate. In the demonstration dataset, the policy preserves the reported reconstruction target while reducing radio use; all values below are illustrative rather than real research claims.
The study separates what each station remembers, when it transmits, and how the shore model combines asynchronous evidence. Select a stage to examine the corresponding mechanism.
Each station keeps a short, bounded summary of recent conditions and their uncertainty. The summary is designed for a low-power device and can be inspected without the shore model.
These are intentionally fictional demonstration findings. A real project page must replace every line with evidence traced to its supplied paper.
The event gate makes a non-transmission legible: the model knows that local change stayed below a documented threshold.
The reconstructed field includes an uncertainty band that expands as stations remain quiet or links fail.
Technicians can review the bounded station summary without replaying a hidden cloud pipeline.
The citation is complete, selectable, and local to the page. Replace this fictional record with the paper's verified bibliographic data.
@article{hart2026tidal,\n title={Tidal Memory: Learning the Coast Between Transmissions},\n author={Hart, Leona and Mendel, Ivo and Quill, Samira},\n journal={Fictional Coastal Systems Review},\n year={2026}\n}