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This paper provides a tutorial overview of the IRIDIUM@ low earth orbit (LEO) satellite system. Section I contains an introduction to the IRIDIUM@ network as well as the system specifications. Section II discusses the satellite constellation design, orbital parameters, and horizontal pointing angles between satellites. Section III introduces the idea of time dependent connectivity in a mobile network, and analyzes the cycle of network connectivity for IRIDIUM@. Section IV discusses the IRIDIUM@ Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) schemes and uses these to calculate the overall system capacity. Section V examines the call processing procedure to include user location and call set up. Finally, Section VI analyzes the network performance in terms of end-to-end delay and hop count.

A previously proposed interference-avoiding Transform Domain Communication System (TDCS) is shown capable of operating successfully in a Multiple Access Environment (MAE)[l]. The TDCS uses phase coding (mapping) generated from a Linear Feedback Shift Register (LFSR) configured to output a maximallength binary pseudorandom sequence (m-sequence). Quasi-orthogonal Basis Functions (BFs) are used in a Code Division Multiple Access (CDMA) scheme to provide private communication channels to independent user pairs in the MAE. An existing single channel TDCS model is augmented to simulate MAE interference effects on bit error performance (PB). The proposed TDCS system is simulated using MATLAB0 for system capacities up to eight channels and E n o values ranging from 0 to 9 dB. Simulated MAE TDCS bit error performance closely approximates estimated results; the error for eight channels has a mean value less than 1.7 x lo3 and a standard deviation less than 1.3 x lo5. The analysis of acquisition-related performance metrics and bit error performance through computer simulation provides a good measure of TDCS operational capabilities in a MAE.

In recent years, attack trees have been developed to describe processes by which malicious users attempt to exploit or break computer software and/or networks. Attack trees are a way of decomposing, visualizing, and determining the cost or likeliness of attacks. Similarly, Petri Nets (PNs) are graphical representations of a system or process used for modeling, formal analysis, and design verification. PNs are easy to build and simulate using a myriad of available tools. There are a number of subclasses of PNs, including Colored, Timed, Stochastic, etc. This paper focuses on the use of Generalized Stochastic PNs (GSPNs) to model and analyze Attack Trees with the ultimate goal of automating the analysis using simulation tools.The results of this simulation and analysis can be used to further refine the Attack Tree or to develop countermeasures.