Curiosities of a Systems Engineer
Description
AI has helped enable me as a systems engineer to dive deep into subjects outside my expertise. Helping bridging the gap between specialists and generalists. This includes helping me identifying what appears to be an optimal 5 node constellation for a GNSS like constellation around the Moon focused on the Lunar South Pole. Which happens to be the most optimal 6 node constellation with one node removed. Another project has been an auditory "game" demonstrating how the brain builds correlations between different signals played in the separate ears. This demonstration uses an auditory rendition of the GPS gold codes and builds an intuition on phase offset and doppler offsets. The last "ongoing" side project has been using AI to create a software GNSS receiver leveraging the intuitions gained from auditory demo. I have learned a few prompting tricks along the way that I would like to share.
Detailed example
GPP ( Gross Primary Productivity ) across entire TEMPO footprint for all daylight hours
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
TEMPO spectra seem to contain enough information to predict GPP as accurately as the combination of MODIS and MERRA-2 data, despite the absence of infrared information.
Controls / human review
ATO: Not reported; PIA: Not published