NASA · NASA-843 · record R0448

Curiosities of a Systems Engineer

National Aeronautics and Space Administration · GSFC: Goddard Space Flight Center

Overview

Development stage: In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.
High-impact designation: Not high-impactSource: c) Not high-impact
Impact justification: Not reported
Start date: Not reported
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Science
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected benefits

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.

System outputs

GPP ( Gross Primary Productivity ) across entire TEMPO footprint for all daylight hours

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Not reported
Custom-developed code: Not reported
Public source code: Not reported

Sourcing

How it was built: Not reported
Vendor (as reported): Not reported
Vendors (standardized): None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Not reported
Privacy Impact Assessment: Not reported
Authorization to Operate: Not reported
Demographic features: Not reported
Training and evaluation data: Not reported
Federal Data Catalog: Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingNo answer reported

AI impact assessmentNo answer reported

Independent reviewNo answer reported

Ongoing monitoringNo answer reported

Operator trainingNo answer reported

Fail-safeNo answer reported

Appeal processNo answer reported

User and public consultationNo answer reported

Potential impacts and how they were identified

Not reported