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Data Engineering

Space traffic coordination needs an evidence market, not another data feed

More orbital data does not automatically produce a better collision warning.

A useful warning depends on the quality and timing of observations, the model used to estimate an object's orbit, the uncertainty attached to that estimate, the way several sources are combined, and whether a satellite operator can act on the result. Two providers can observe the same object and produce different answers without either behaving irrationally.

That is what made a small 2024 procurement by the Office of Space Commerce (OSC) more interesting than the original announcement suggested. OSC, part of the National Oceanic and Atmospheric Administration (NOAA), hired Kayhan Space and SpaceNav to evaluate the accuracy, consistency, and quality of commercial space situational awareness (SSA) products created by three other companies. The work supported a limited pathfinder for the emerging Traffic Coordination System for Space (TraCSS).

The government was not only buying data. It was buying an independent way to learn which data and services were useful, under which conditions, and according to which evidence.

Cloud-to-Edge AI Is a Decision-Placement Problem

Originally published in 2024; substantially revised in 2026 to deepen the analysis and incorporate additional sources.

“Cloud to edge” is often presented as an infrastructure continuum: train and aggregate centrally, then move inference closer to users and sensors. That description is technically sound but strategically incomplete.

The important design question is not where the compute happens. It is where data becomes knowledge, where a recommendation becomes a decision, and which parts of that chain must continue to function when connectivity, latency, power, or trust changes.

Cloud-to-edge AI is a decision-placement problem expressed through architecture.

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