Galileo Project Observatory Class System Architecture

TL;DR

The paper introduces the OCICP system, a comprehensive platform employing multiple sensors and subsystems to improve data quality and transparency in the scientific study of Unidentified Anomalous Phenomena.

Contribution

It presents the design, implementation, and preliminary results of a novel integrated system for systematic UAP data collection and analysis.

Findings

System can collect calibrated, high-quality data

Supports real-time and post-processing analysis

Enhances transparency and scientific rigor

Abstract

Scientific investigation of Unidentified Anomalous Phenomena (UAP) is limited by poor data quality and a lack of transparency. Existing data are often fragmented, uncalibrated, and missing critical metadata. To address these limitations, the authors present the Observatory Class Integrated Computing Platform (OCICP), a system designed for the systematic and scientific study of UAPs. OCICP employs multiple sensors to collect and analyze data on aerial phenomena. The OCICP system consists of two subsystems. The first is the Edge Computing Subsystem which is located within the observatory site. This subsystem performs real-time data acquisition, sensor optimization, and data provenance management. The second is the Post-Processing Subsystem which resides outside the observatory. This subsystem supports data analysis workflows, including commissioning, census operations, science operations, and system effectiveness monitoring. This design and implementation paper describes the system lifecycle, associated processes, design, implementation, and preliminary results of OCICP, emphasizing the ability of the system to collect comprehensive, calibrated, and scientifically sound data.