John D.T. Nguyen

By John D. Nguyen

This chapter discusses the taxonomy of Systems-of-Systems (SOS) with a focus on space and airborne systems perspective. A discussion with a broad view of taxonomy with considerations for space and airborne systems classification, including SOS and Family-of-Systems (FOS), will be presented. The chapter defines taxonomic categories considering dimensions in the classification of space and airborne SOS based on their acquisition strategy, operational mode, and problem domain. Commercial and military acquisition strategies will be addressed along with their intentional operational modes and problem domains. The space and airborne systems discussed will be Satellite Communication (SATCOM) systems, sensing and imaging satellite systems and Positioning-Navigation-and-Timing (PNT) satellite, and military and commercial aircraft systems. The chapter provides examples on notional military SATCOM and manned aircraft systems.

Part of the book: Systems-of-Systems Perspectives and Applications

By John Nguyen

This chapter presents an overview of legacy, existing, and future advanced satellite systems for future wireless communications. The overview uses top-down approach, starting with a comparison between a typical commercial regular satellite system and a high-throughput satellite (HTS) system, following by a discussion on commonly used satellite network topologies. A discussion on the design of satellite payload architectures supporting both typical regular satellite and HTS with associated network topologies will be presented. Four satellite payload architectures will be discussed, including legacy analog bent-pipe satellite (ABPS); existing digital bent-pipe satellite (DBPS) and advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB); and future advanced regenerative on-board processing satellite (AR-OBPS) payload architectures. Additionally, various satellite system architectures using AdBP-DCBS and AR-OBPS payloads for the fifth-generation (5G) cellular phone applications will also be presented.

Part of the book: Satellite Systems

By Tien M. Nguyen, John D.T. Nguyen, My T.N. Nguyen, Charles H. Lee and Tam Nguyen

Program management (PM) complexity depends on the budget size and program types. In general, the program types can be classified into three categories, namely, defense, commercial, and civilian types. This chapter presents and discusses an approach for integrating the PM discipline areas with emerging data science and decision science1 (DDS) for any program type. Additionally, we describe the key PM areas and present a corresponding generalized model consists of a list of multiple PM discipline areas that can be tailored for any program types. To demonstrate the PM-DDS integration approach, we focus on three key PM areas and corresponding PM discipline areas related to schedule, cost, and risk management. These three discipline areas are analyzed to identify appropriate program elements that can be enhanced using existing DDS technology enablers (TEs). We also propose a flexible PM-DSS integration framework by leveraging existing machine learning operations (MLOps) framework. The proposed integration framework is expected to allow for enhancing the program planning and execution by reducing the program risk using a wide range of DDS TEs, including big data analytics, artificial intelligence, machine learning, deep learning, neural networks, and artificial intelligent.

Part of the book: Project Management