Significant technological advances were made toward utilizing the Hale telescope for receiving the faint laser communication signals transmitted from an optical transceiver on a spacecraft orbiting Mars. The so-called Palomar receive terminal design, which would have supported nominal downlink data rates of 1-30 Mbps, is described. Testing to validate technologies for near-Sun (3deg from edge of solar disc) daytime operations is also discussed. Finally, a laboratory end-to-end link utilizing a 64-ary pulse-position modulated photon-counting receiver and decoder that achieved predicted near-capacity (within 1.4 dB) performance is described.

Model-based analysis is a well-established approach to assess the influence of several factors on system availability within the context of system structure. Prevalent availability models in the literature consider all failures to be equivalent in terms of their consequences on system services. In other words, all the failures are assumed to be of the same level of severity. In practice, failures are typically classified into multiple severity levels, where failures belonging to the highest severity level cause a complete loss of service, while failures belonging to levels below the highest level enable the system to operate in a degraded mode. This makes it necessary to consider the influence of failure severities on system availability. In this paper we present a Markov model which considers failure severities of the components of the system in conjunction with its structure. The model also incorporates the repair of the components. Based on the model, we derive a closed form expression which relates system availability to the failure and repair parameters of the components. The failure parameters in the model are estimated based on the data collected during acceptance testing of a satellite system. However, since adequate data are not available to estimate the repair parameters, the closed form expressions are used to assess the sensitivity of the system availability to the repair parameters

This paper describes research performed to augment the capabilities of the statistical modeling and estimation of reliability functions for systems (SMERFS) software suite. Enhancements to the Schneidewind software reliability model, which features optimal selection of failure data for predicting failure counts, were made to enable clearer interpretation of that model's results. These enhancements included the addition of risk assessment measures and modifications to the model's optimal point selection procedure. An analysis of an actual data set from a NASA Goddard Space Flight Center project that was used to validate these additions to SMERFS is included.