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The field trip is divided into two sections: (1) Crater Rim Road; and (2) Chain of Craters Road. Most bibliographic references are omitted from the text, but a selected list of references to recent Hawaiian volcanic activity and to special studies is included.
Loran-C time-difference data were collected on January 9, 1979 during a flight from Athens, Ohio to Madison VOR in Connecticut, thence to Millville VOR in New Jersey, and a landing at Atlantic City NAFEC. Portions of the return trip to Athens, Ohio were also recorded. Loran-C GRI data frames were recorded using the 99600 U. S. Northeast Loran chain stations Seneca/Nantucket (TDA) and Seneca/Carolina Beach (TDB). The GRI sequence number TDA and TDB were recorded as integer numbers, with the TD's in integer microseconds. Actual time-of-day can be determined from the data start time, plus the time per GRI and the sequence number. The low cost Loran-C receiver was used to obtain the time-difference data for each GRI. Data was recorded on digital magnetic tape and post-processed into latitude and longitude using an IBM system/370 computer.
The NASA Mars Exploration Program has recently adopted a plan that includes a first Mars sample return (MSR) mission proposed for launch in 2013. Such a mission would deal with two new categories of planetary protection requirements: (1) assuring a very low probability of inadvertent release of the sample in order to provide extra protection against the extremely unlikely possibility of biological hazards in the returned material and (2) keeping the samples free of round-trip Earth organisms to facilitate confirmation of safety after return to Earth. This paper describes the planetary-protection-related technical challenges awaiting any MSR mission and describes work in progress on technology needed to meet these challenges. New technology is needed for several functions. Containment assurance requires breaking the chain of contact with Mars: the exterior of the sample container must not be contaminated with Mars material either during the loading process or during launch from the Mars surface.
Markov chain and Monte Carlo analysis techniques are applied to the simulated Space Shuttle Orbiter Ferry flights to obtain statistical distributions of flight time duration between Edwards Air Force Base and Kennedy Space Center. The two methods are compared, and are found to be in excellent agreement. The flights are subjected to certain operational and meteorological requirements, or constraints, which cause eastbound and westbound trips to yield different results. Persistence of events theory is applied to the occurrence of inclement conditions to find their effect upon the statistical flight time distribution. In a sensitivity test, some of the constraints are varied to observe the corresponding changes in the results.
This dissertation discusses several machine learning techniques to improve routing in delay tolerant networks (DTNs). These are networks in which there may be long one-way trip times, asymmetric links, high error rates, and deterministic as well as non-deterministic loss of contact between network nodes, such as interplanetary satellite networks, mobile ad hoc networks and wireless sensor networks. This work uses historical network statistics to train a multi-label classifier to predict reliable paths through the network. In addition, a clustering technique is used to predict future mobile node locations. Both of these techniques are used to reduce the consumption of resources such as network bandwidth, memory and data storage that is required by replication routing methods often used in opportunistic DTN environments. Thesis contributions include: an emulation tool chain developed to create a DTN test bed for machine learning, the network and software architecture for a machine learning based routing method, the development and implementation of classification and clustering techniques and performance evaluation in terms of machine learning and routing metrics.
A method for evaluating the probability of a Viable Earth Microorganism (VEM) contaminating a sample during the sample acquisition and handling (SAH) process of a potential future Mars Sample Return mission is developed. A scenario where multiple core samples would be acquired using a rotary percussive coring tool, deployed from an arm on a MER class rover is analyzed. The analysis is conducted in a structured way by decomposing sample acquisition and handling process into a series of discrete time steps, and breaking the physical system into a set of relevant components. At each discrete time step, two key functions are defined: The probability of a VEM being released from each component, and the transport matrix, which represents the probability of VEM transport from one component to another. By defining the expected the number of VEMs on each component at the start of the sampling process, these decompositions allow the expected number of VEMs on each component at each sampling step to be represented as a Markov chain. This formalism provides a rigorous mathematical framework in which to analyze the probability of a VEM entering the sample chain, as well as making the analysis tractable by breaking the process down into small analyzable steps.