The Triumph of Astronaut L. Gordon Cooper, Jr. and the Faith 7, May 15-16, 1963
Ma-9 mission - flight of faith 7 spacecraft
FAITH 7↗
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Radar tracking ship performance during MA-9
Radar tracking ship performance during Faith 7 - Mercury /MA-9/ flight and evaluation for critical reentry tracking and manned space flight applications
RADAR TRACKING↗
Feature Detection Systems Enhance Satellite Imagery
In 1963, during the ninth orbit of the Faith 7 capsule, astronaut Gordon Cooper skipped his nap and took some photos of the Earth below using a Hasselblad camera. The sole flier on the Mercury-Atlas 9 mission, Cooper took 24 photos - never-before-seen images including the Tibetan plateau, the crinkled heights of the Himalayas, and the jagged coast of Burma. From his lofty perch over 100 miles above the Earth, Cooper noted villages, roads, rivers, and even, on occasion, individual houses. In 1965, encouraged by the effectiveness of NASA s orbital photography experiments during the Mercury and subsequent Gemini manned space flight missions, U.S. Geological Survey (USGS) director William Pecora put forward a plan for a remote sensing satellite program that would collect information about the planet never before attainable. By 1972, NASA had built and launched Landsat 1, the first in a series of Landsat sensors that have combined to provide the longest continuous collection of space-based Earth imagery. The archived Landsat data - 37 years worth and counting - has provided a vast library of information allowing not only the extensive mapping of Earth s surface but also the study of its environmental changes, from receding glaciers and tropical deforestation to urban growth and crop harvests. Developed and launched by NASA with data collection operated at various times by the Agency, the National Oceanic and Atmospheric Administration (NOAA), Earth Observation Satellite Company (EOSAT, a private sector partnership that became Space Imaging Corporation in 1996), and USGS, Landsat sensors have recorded flooding from Hurricane Katrina, the building boom in Dubai, and the extinction of the Aral Sea, offering scientists invaluable insights into the natural and manmade changes that shape the world. Of the seven Landsat sensors launched since 1972, Landsat 5 and Landsat 7 are still operational. Though both are in use well beyond their intended lifespans, the mid-resolution satellites, which provide the benefit of images detailed enough to reveal large features like highways while still broad enough for global coverage, continue to scan the entirety of the Earth s surface. In 2012, NASA plans to launch the Landsat Data Continuity Mission (LDCM), or Landsat 8, to extend the Landsat program s contributions to cartography, water management, natural disaster relief planning, and more.
Source record↗
Mercury-Atlas 9 Press Kit
Preparations for ma-9 manned spaceflight - mercury project news release
FAITH 7↗
MERCURY PROJECT SUMMARY INCLUDING RESULTS OF THE FOURTH MANNED ORBITAL FLIGHT, MAY 15 AND 16, 1963
Mercury project summary - results of fourth manned orbital flight
FAITH 7↗
Topological Aspects of the FAITH Experiment
This slide presentation reviews the following issues (1) What is relationship between surface pressure extrema and singular points? (2) Does every singular point in a pattern of skin friction lines occur at a surface pressure extremum? (and/or vice versa?) (3) Can this relationship be generalized to all geometries? (4) FAITH Project (5) Ongoing effort at NASA Ames Experimental AeroPhysics Branch (6) Multi-parameter wind tunnel investigation of flow around obstacle (7) Acquire data for CFD validation, optimization and (8) Relationship between FAITH and topology projects
Tobak, Murray↗
Optimization Study of the Ames 0.5 Two-Stage Light Gas Gun
There is a need for more faithful simulation of space debris impacts on various space vehicles. Space debris impact velocities can range up to 14 km/sec and conventional two-stage light gas guns with moderately heavy saboted projectiles are limited to launch velocities of 7-8 km/sec. Any increases obtained in the launch velocities will result in more faithful simulations of debris impacts. It would also be valuable to reduce the maximum gun and projectile base pressures and the gun barrel erosion rate. In this paper, the results of a computational fluid dynamics (CFD) study designed to optimize the performance of the NASA Ames 0.5' gun by systematically varying seven gun operating parameters are reported. Particularly beneficial effects were predicted to occur if (1) the piston mass was decreased together with the powder mass and the hydrogen fill pressure and (2) the pump tube length was decreased. The optimum set of changes in gun operating conditions were predicted to produce an increase in muzzle velocity of 0.7-1.0 km/sec, simultaneously with a substantial decrease in gun erosion. Preliminary experimental data have validated the code predictions. Velocities of up to 8.2 km/sec with a 0.475 cm diameter saboted aluminum sphere have been obtained, along with large reductions in gun erosion rates.
Bogdanoff, D. W.↗
Old Faithful Model for Radiolytic Gas-Driven Cryovolcanism at Enceladus
A new model is presented on how chemically driven cryovolcanism might contribute to episodic outgassing at the icy moon Enceladus and potentially elsewhere including Europa and Kuiper Belt Objects. Exposed water ices can become oxidized from radiolytic chemical alteration of near-surface water ice by space environment irradiation. In contact with primordially abundant reductants such as NH3, CH4, and other hydrocarbons, the product oxidants can react exothermically to produce volatile gases driving cryovolcanism via gas-piston forces on any subsurface liquid reservoirs. Radiolytic oxidants such as H2O2 and O2 can continuously accumulate deep in icy regoliths and be conveyed by rheological flows to subsurface chemical reaction zones over million-year time scales indicated by cratering ages for active regions of Enceladus and Europa. Surface blanketing with cryovolcanic plume ejecta would further accelerate regolith burial of radiolytic oxidants. Episodic heating from transient gravitational tides, radioisotope decay, impacts, or other geologic events might occasionally accelerate chemical reaction rates and ignite the exothermic release of cumulative radiolytic oxidant energy. The time history for the suggested "Old Faithful" model of radiolytic gas-driven cryovolcanism at Enceladus and elsewhere therefore consists of long periods of chemical energy accumulation punctuated by much briefer episodes of cryovolcanic activity. The most probable sequence for detection of activity in the current epoch is a long evolutionary phase of slow but continuous oxidant accumulation over billions of years followed by continuous but variable high activity over the past 10(exp 7)-10(exp 8) years. Detectable cryovolcanic activity could then later decline due to near-total oxidation of the rheologically accessible ice crust and depletion the accessible reductant abundances, as may have already occurred for Europa in the more intense radiation environment of Jupiter's magnetosphere. Astrobiological potential of Enceladus could correspondingly be higher than at Europa due to a less extreme state of oxidation and greater residual abundance of organics.
Cooper, John F.↗
Does Collection Time Bias the Ecology of Cleanroom Air Samples?
Microbial monitoring of astromaterials collections has taken on increased importance with the return of biologically sensitive samples from the asteroids Ryugu and Bennu and the initiation of the Mars Sample Return Program. Terrestrial bacteria and fungi can alter the mineralogy and organic composition of our collections causing irreversible contamination of pristine samples and increasing the risk of false positives for life detection measurements. NASA has conducted routine microbial monitoring of its existing collections since 20181. Initial monitoring focused on surface samples collected with foam swabs. Although, airborne microbiology is often decoupled from surface microbiology in the built environment2 culture-based air sampling techniques like impactors were not compliant with existing contamination control requirements. Bringing organic rich media, gelatin or liquids into curation cleanrooms presents an unacceptable risk to pristine samples. In 2022 NASA purchased a materials complaint air sampler and began collecting air samples from the cleanrooms in addition to surface samples3. The new instrument uses an electret filter to collect samples that are suitable for cultivating organisms or for direct DNA sequencing. Preliminary DNA sequencing results appeared to indicate that longer sampling times biased the microbial community in favor of hearty, spore-forming bacteria3. We present the results of a study comparing overnight sampling (17 hours) to short (1 hour) sampling of unoccupied curation cleanrooms. The results will help us optimize our monitoring protocols and develop a more detailed inventory of the ecology of astromaterials curation cleanrooms. Methods: We analyzed 72 paired air samples from six different cleanrooms including the meteorite processing lab (ISO 7 equivalent, 16 samples), the lunar lab (ISO 6 equivalent, 10 samples), the stardust lab (ISO 5 equivalent 14 samples), the OSIRIS-REx lab (ISO 5 equivalent, 12 samples), the Hayabusa2 lab (ISO 5 equivalent, 14 samples), and the Genesis lab (ISO 4 equivalent, 6 samples). All the samples were collected with an InnovaPrep Bobcat air sampler operating at a sampling rate of 200 L/min. The sampler operates for 5 minutes out of every 20 minute period. Half of the samples were collected by filtering 3,000L (15 min. of active sampling) of air across an electret filter for one hour. The rest of the samples were collected by filtering approximately 51,000 L air across the filter overnight (~17 hours, 255 min. of active sampling). Cells were eluted from the filter using 6-7 ml of pressurized 0.15% tween 20 in PBS (phosphate buffered saline). This liquid was used to cultivate bacteria according to previously published methods1,4,5 and for DNA extraction and next generation sequencing. DNA was extracted with a Qiagen MagAttract PowerMicrobiome kit6. To identify bacteria and archaea, the 16S rRNA gene was amplified using Earth Microbiome primers for the V4 region 7. The amplified DNA was sequenced on an Illumina MiSeq using a V3 reagent kit. The resulting sequences were processed using DADA2 and QIIME2 as implemented on the EDGE bioinformatics platform8–10. Results: Only two of the 72 samples had no amplifiable DNA. Amplified DNA concentrations ranged from 2.67 – 0.272 ng/µl. The median concentration of amplified DNA for the 1 hour samples was 0.770 ± 0.368 ng/µl. The median concentration of amplified DNA for the overnight samples was 0.877 ± 0.434 ng/µl. On average the overnight samples had slightly more sequences (58,960 vs. 59,456) and ASV’s (amplicon sequence variants) (60 vs 64.5) than the one hour samples, but these differences are not statistically significant. The most abundant ASV in every sample mapped to the genus Cupravidus. ASV’s mapping to the genuses Bacillus, Schlegelella, Thermus, and Staphylococcus were also common. Discussion and Future Work: Alpha diversity statistics like Shannon Entropy and Faith Phylogenetic Diversity are used to describe the diversity of organisms in a single sample. If a longer sampling time was biasing the data, we would expect to see a change in these diversity statistics vs. sample time. However, we did not observe this in our data. The median Shannon entropy was slightly higher for the overnight samples (3.773 vs 3.611) as was the Faith Phylogenetic Diversity (4.042 vs 3.596), but both values were within a standard deviation of each other for the two sampling times (Fig. 1). It is unlikely, that the longer sampling time is introducing bias into our data. We do observe a significant decrease in diversity when comparing the air samples by lab. The Genesis lab (ISO 4 equivalent) has a lower median number of ASV’s (45.5) than the other labs (62). Median values for Shannon Entropy (3.717 vs. 3.430) and Faith Phylogenetic Diversity (3.796 vs. 3.548) are also lower for Genesis, but those values are with one standard deviation of each other for the different sampling times. This is consistent with previous culture-based results suggesting that the environment in cleanrooms tends to select for a core group of organisms capable of surviving under dry, low nutrient, conditions. The presence of the ASV’s mapping to Cupravidus and Thermus in our sequencing blanks and controls suggests that several of the most common organisms in our samples represent contaminants from the reagents used to perform the DNA extractions and sequencing. Further work is needed to identify these contaminants, remove them from our data and recalculate the diversity statistics. This is a systematic error. Therefore, we do not expect removing the sequencing contaminants to change our conclusions. Longer air sample collection times appear to result in slightly higher diversity and do not bias the results towards “hardy” bacteria like spore-formers. Based on these preliminary results we conclude that sampling at least 3,000 liters of air is sufficient to capture the microbial diversity of cleanrooms, and that air samples can also be collected overnight without negatively impacting diversity. These results allow us to be flexible when designing microbial monitoring plans so that they do not interfere with routine lab activity. References: 1. Regberg, A. B. et al. 49th Lunar and Planetary Science Conference (2018). 2. The United States Pharmacopeial Convention. USP General Chapter <1116> (2013). 3. Regberg, A. B., et al. 54th Lunar and Planetary Science Conference (2023). 4. Regberg, A. B. et al. 53rd Lunar and Planetary Science Conference ( 2022). 5. Davis, R. E.,et al. 50th Lunar and Planetary Science Conference (2019). 6. Qiagen. MagAttract® PowerMicrobiome® DNA/RNA EP Kit Handbook. (2018). 7. Walters, W. et al. mSystems 1, (2015). 8. Callahan, B. J. et al. Nat. Methods 13, 581–583 (2016). 9. Hall, M. & Beiko, R. G. Microbiome Analysis: Methods and Protocols113–129 (Springer, 2018). 10. Philipson, C. et al. Bio-Protoc. 7, e2622 (2017).
A. B. Regberg↗
Iona Ecological Conservation: Utilizing Earth Observations to Understand Landscape Patterns and Assist in Wildlife Management in Iona National Park, Angola
Following the end of the Angolan civil war in 2002, human and livestock populations have increased exponentially within Iona National Park. An ongoing drought since 2017 has brought these people and livestock into increasing competition with local wildlife for resources – highlighting a conservation challenge that will become more entrenched as the effects of anthropogenic climate change increase. In 2019, African Parks began co-managing Iona National Park in Angola with the Angolan government, hoping to enact scientifically grounded management strategies to meet this challenge. To accomplish this, African Parks needed contemporary and historic information on the spatial distribution of landcover types within Iona and adjacent areas. We constructed and applied a Random Forest classifier in Google Earth Engine to multispectral imagery gathered from Landsat 5, 7, 8 and Sentinel-1 and 2 to meet this need. Using the classifier, we generated a time-series of land cover maps between 1990–2023, from which landscape metrics and change detection analysis were calculated to show how certain habitats and formations had changed over time. The resulting maps have producer and user’s accuracies above 87% and show four broad landcover regions within the study area. Notably, we observed a decrease in the park’s diversity as per the Shannon Diversity Index – an index that considers the richness of classes, as well the evenness of their distribution. A lack of arid specific land cover indices and ground-truthed training data from earlier years limited the accuracy and resolution of our landcover maps. However, this project still demonstrates that Earth observations can be used to form the basis of conservation policy in arid environments, where ground-truth data may be difficult to obtain or non-existent.
remote sensing↗