National Aeronautics and Space Administration (NASA), Kennedy Space Center

2019 ◽  
pp. 287-310
Author(s):  
Monte Lee Matthews
1990 ◽  
Author(s):  
MARK BUFFO ◽  
HARROLD SWEET ◽  
ROBERT AITKEN ◽  
TINA KHODADAD

2021 ◽  
Vol 9 (1) ◽  
pp. 121-132
Author(s):  
Susan John ◽  
Farid Abou-Issa ◽  
Karl H. Hasenstein

Abstract In preparation of a flight experiment, ground-based studies for optimizing the growth of radishes (Raphanus sativus) were conducted at the ground-based Advanced Plant Habitat (APH) unit at the Kennedy Space Center (KSC), Florida. The APH provides a large, environmentally controlled chamber that has been used to grow various plants, such as Arabidopsis, wheat, peppers, and now radish. In support of National Aeronautics and Space Administration (NASA)'s goals to provide astronauts with fresh vegetables and fruits in a confined space, it is important to extend the cultivation period to produce substantial biomass. We selected Raphanus sativus cv. Cherry Belle as test variety both for preliminary tests and flight experiments because it provides edible biomass in as few as four weeks, has desirable secondary metabolites (glucosinolates), is rich in minerals, and requires relatively little space. We report our strategies to optimize the growth substrate, watering regimen, light settings, and planting design that produces good-sized radishes, minimizes competition, and allows for easy harvesting. This information will be applicable for growth optimization of other crop plants that will be grown in the APH or other future plant growth facilities.


Author(s):  
V. H. Ayma ◽  
V. A. Ayma ◽  
J. Gutierrez

Abstract. Nowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms.


2021 ◽  
pp. 139-145
Author(s):  
Tamra Stambaugh ◽  
Emily Mofield

2014 ◽  
Vol 5 (2) ◽  
pp. 270-281 ◽  
Author(s):  
Eric D. Stolen ◽  
Donna M. Oddy ◽  
Mike L. Legare ◽  
David R. Breininger ◽  
Shanon L. Gann ◽  
...  

Abstract Quantifying habitat occupancy of the southeastern beach mouse Peromyscus polionotus niveiventris is important for managing this threatened species throughout its limited range. Tracking tubes were used to detect the southeastern beach mouse in coastal areas on the federal lands of the Kennedy Space Center, Cape Canaveral Air Force Station, and Canaveral National Seashore. Because this method relied on observations of footprints, detections of beach mice were confounded by the co-occurrence of cotton mice Peromyscus gossypinus, which have wider but slightly overlapping footprint widths. Mice of both species were captured and footprinted using tracking tubes to collect a database of footprints of known identity. These data were used to develop a Bayesian hierarchical model of the cutoff width at which a print could be assigned as a beach mouse with a known probability of error. Specifically, within the model, observed footprint widths were used to estimate a mean and variance of footprint width for each species, while accounting for variation between individual mice. Then, a distribution of new footprint widths was generated for each species by drawing from their modeled distributions. Finally, the new footprints were compared with a range of potential cutoff widths to evaluate the proportion of times the correct decision to exclude or accept the footprint was made. We graphically evaluated the performance of the cutoff widths and chose one that traded off between reducing false positives and retaining more correct detections for use in occupancy models. We explored the use of the cutoff width using occupancy models that allow for false-positive detections, and found that the use of the cutoff performed as expected. Over 40% of primary dune habitat on the Kennedy Space Center was occupied by beach mice during the period sampled. The proportion of vegetated habitat at a site had a negative influence on detection probability. No ecological covariates had a measurable influence on beach mouse occupancy, probably due to the limited range of environmental variation in the sampled region. The use of a cutoff for footprint width resulted in a reliable method to deal with false-positive detections in tracking tubes with small mammals and allowed the use of occupancy models that rely on certain detection.


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