The potential hazard of staphylococci and micrococci to human subjects in a life support systems evaluator while on a simulated GT-7 mission Final report, 19 Sep. - 1 Nov. 1965

Potential hazard of staphylococci and micrococci to human subjects in life support system

Migration Trends in US History, 19th-21st Century

Poster exhibit that details student project examining trends in migration to and throughout United States in 19th-21st Centuries

The potential hazard of staphylococci and micrococci to human subjects in a life support systems evaluator and on a diet of precooked freeze dehydrated foods

Distribution, and hazards of indigenous microbial populations in humans during prolonged space flight simulatio

On the Determination of Poisson Statistics for Haystack Radar Observations of Orbital Debris

A convenient and powerful method is used to determine if radar detections of orbital debris are observed according to Poisson statistics. This is done by analyzing the time interval between detection events. For Poisson statistics, the probability distribution of the time interval between events is shown to be an exponential distribution. This distribution is a special case of the Erlang distribution that is used in estimating traffic loads on telecommunication networks. Poisson statistics form the basis of many orbital debris models but the statistical basis of these models has not been clearly demonstrated empirically until now. Interestingly, during the fiscal year 2003 observations with the Haystack radar in a fixed staring mode, there are no statistically significant deviations observed from that expected with Poisson statistics, either independent or dependent of altitude or inclination. One would potentially expect some significant clustering of events in time as a result of satellite breakups, but the presence of Poisson statistics indicates that such debris disperse rapidly with respect to Haystack's very narrow radar beam. An exception to Poisson statistics is observed in the months following the intentional breakup of the Fengyun satellite in January 2007

Fluctuations and massive separation in three-dimensional shock-wave/boundary-layer interactions

Shock-wave unsteadiness was observed in rapidly compressed supersonic turbulent boundary layer flows with significant separation. A Mach 2.85 shock-wave/turbulent boundary layer flow was set up over a series of cylinder-flare bodies in the High Reynolds Number Channel 1. The transition from fully attached to fully separated flow was studied using axisymmetric flares with increasing compression angles. In the second phase, the 30 deg flare was inclined relative to the cylinder axis, so that the effect on a separated flow of increasing 3 dimensionality could be observed. Two 3-D separated cases are examined. A simple conditional sampling technique is applied to the data to group them according to an associated shock position. Mean velocities and turbulent kinetic energies, computed from the conditionally samples data, are compared to those from the unsorted data and to computed values. Three basic questions were addressed: can conditional sampling be used to provide snapshots of the flow; are averaged turbulence quantities dominated by the bimodal nature of the interaction; and is the shock unsteadiness really important to computational accuracy

The Characteristics and Consequences of the Break-up of the Fengyun-1C Spacecraft

The intentional break-up of the Fengyun-1C spacecraft on 11 January 2007 via hypervelocity collision with a ballistic object created the most severe artificial debris cloud in Earth orbit since the beginning of space exploration. More than 900 debris on the order of 10 cm or greater in size have been identified by the U.S. Space Surveillance Network (SSN). The majority of these debris reside in long-lived orbits. The NASA Orbital Debris Program Office has conducted a thorough examination of the nature of the Fengyun-1C debris cloud, using SSN data for larger debris and special Haystack radar observations for smaller debris. These data have been compared with the NASA standard satellite break-up model for collisions, and the results are presented in this paper. The orbital longevity of the debris have also been evaluated for both small and large debris. The consequent long-term spatial density effects on the low Earth orbit (LEO) regime are then described. Finally, collision probabilities between the Fengyun-1C debris cloud and the resident space object population of 1 January 2007 have been calculated. The potential effect on the growth of the near-Earth satellite population is presented

Evidence of platelet activation in multiple sclerosis

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Statistical Estimation of Orbital Debris Populations with a Spectrum of Object Size

Orbital debris is a real concern for the safe operations of satellites. In general, the hazard of debris impact is a function of the size and spatial distributions of the debris populations. To describe and characterize the debris environment as reliably as possible, the current NASA Orbital Debris Engineering Model (ORDEM2000) is being upgraded to a new version based on new and better quality data. The data-driven ORDEM model covers a wide range of object sizes from 10 microns to greater than 1 meter. This paper reviews the statistical process for the estimation of the debris populations in the new ORDEM upgrade, and discusses the representation of large-size (greater than or equal to 1 m and greater than or equal to 10 cm) populations by SSN catalog objects and the validation of the statistical approach. Also, it presents results for the populations with sizes of greater than or equal to 3.3 cm, greater than or equal to 1 cm, greater than or equal to 100 micrometers, and greater than or equal to 10 micrometers. The orbital debris populations used in the new version of ORDEM are inferred from data based upon appropriate reference (or benchmark) populations instead of the binning of the multi-dimensional orbital-element space. This paper describes all of the major steps used in the population-inference procedure for each size-range. Detailed discussions on data analysis, parameter definition, the correlation between parameters and data, and uncertainty assessment are included