Table-driven configuration and formatting of telemetry data in the Deep Space Network

With a restructured software architecture for telemetry system control and data processing, the NASA/Deep Space Network (DSN) has substantially improved its ability to accommodate a wide variety of spacecraft in an era of 'better, faster, cheaper'. In the new architecture, the permanent software implements all capabilities needed by any system user, and text tables specify how these capabilities are to be used for each spacecraft. Most changes can now be made rapidly, outside of the traditional software development cycle. The system can be updated to support a new spacecraft through table changes rather than software changes, reducing the implementation, test, and delivery cycle for such a change from three months to three weeks. The mechanical separation of the text table files from the program software, with tables only loaded into memory when that mission is being supported, dramatically reduces the level of regression testing required. The format of each table is a different compromise between ease of human interpretation, efficiency of computer interpretation, and flexibility

Processing AIRS Scientific Data Through Level 3

The Atmospheric Infra-Red Sounder (AIRS) Science Processing System (SPS) is a collection of computer programs, known as product generation executives (PGEs). The AIRS SPS PGEs are used for processing measurements received from the AIRS suite of infrared and microwave instruments orbiting the Earth onboard NASA's Aqua spacecraft. Early stages of the AIRS SPS development were described in a prior NASA Tech Briefs article: Initial Processing of Infrared Spectral Data (NPO-35243), Vol. 28, No. 11 (November 2004), page 39. In summary: Starting from Level 0 (representing raw AIRS data), the AIRS SPS PGEs and the data products they produce are identified by alphanumeric labels (1A, 1B, 2, and 3) representing successive stages or levels of processing. The previous NASA Tech Briefs article described processing through Level 2, the output of which comprises geo-located atmospheric data products such as temperature and humidity profiles among others. The AIRS Level 3 PGE samples selected information from the Level 2 standard products to produce a single global gridded product. One Level 3 product is generated for each day s collection of Level 2 data. In addition, daily Level 3 products are aggregated into two multiday products: an eight-day (half the orbital repeat cycle) product and monthly (calendar month) product

Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

This paper presents a Kernel Entity Salience Model (KESM) that improves text understanding and retrieval by better estimating entity salience (importance) in documents. KESM represents entities by knowledge enriched distributed representations, models the interactions between entities and words by kernels, and combines the kernel scores to estimate entity salience. The whole model is learned end-to-end using entity salience labels. The salience model also improves ad hoc search accuracy, providing effective ranking features by modeling the salience of query entities in candidate documents. Our experiments on two entity salience corpora and two TREC ad hoc search datasets demonstrate the effectiveness of KESM over frequency-based and feature-based methods. We also provide examples showing how KESM conveys its text understanding ability learned from entity salience to search

Chemistry and Kinematics of the Late-Forming Dwarf Irregular Galaxies Leo A, Aquarius, and Sagittarius DIG

We present Keck/DEIMOS spectroscopy of individual stars in the relatively isolated Local Group dwarf galaxies Leo A, Aquarius, and the Sagittarius dwarf irregular galaxy. The three galaxies—but especially Leo A and Aquarius—share in common delayed star formation histories (SFHs) relative to many other isolated dwarf galaxies. The stars in all three galaxies are supported by dispersion. We found no evidence of stellar velocity structure, even for Aquarius, which has rotating H i gas. The velocity dispersions indicate that all three galaxies are dark-matter-dominated, with dark-to-baryonic mass ratios ranging from 4.4_(-0.8)^(+1.0) (SagDIG) to 9.6_(-1.8)^(+2.5) (Aquarius). Leo A and SagDIG have lower stellar metallicities than Aquarius, and they also have higher gas fractions, both of which would be expected if Aquarius were further along in its chemical evolution. The metallicity distribution of Leo A is inconsistent with a closed or leaky box model of chemical evolution, suggesting that the galaxy was pre-enriched or acquired external gas during star formation. The metallicities of stars increased steadily for all three galaxies, but possibly at different rates. The [α/Fe] ratios at a given [Fe/H] are lower than that of the Sculptor dwarf spheroidal galaxy, which indicates more extended SFHs than Sculptor, consistent with photometrically derived SFHs. Overall, the bulk kinematic and chemical properties for the late-forming dwarf galaxies do not diverge significantly from those of less delayed dwarf galaxies, including dwarf spheroidal galaxies

Machine-Learning Space Applications on SmallSat Platforms with TensorFlow

Due to their attractive benefits, which include affordability, comparatively low development costs, shorter development cycles, and availability of launch opportunities, SmallSats have secured a growing commercial and educational interest for space development. However, despite these advantages, SmallSats, and especially CubeSats, suffer from high failure rates and (with few exceptions to date) have had low impact in providing entirely novel, market-redefining capabilities. To enable these more complex science and defense opportunities in the future, small-spacecraft computing capabilities must be flexible, robust, and intelligent. To provide more intelligent computing, we propose employing machine intelligence on space development platforms, which can contribute to more efficient communications, improve spacecraft reliability, and assist in coordination and management of single or multiple spacecraft autonomously. Using TensorFlow, a popular, open-source, machine-learning framework developed by Google, modern SmallSat computers can run TensorFlow graphs (principal component of TensorFlow applications) with both TensorFlow and TensorFlow Lite. The research showcased in this paper provides a flight-demonstration example, using terrestrial-scene image products collected in flight by our STP-H5/CSP system, currently deployed on the International Space Station, of various Convolutional Neural Networks (CNNs) to identify and characterize newly captured images. This paper compares CNN architectures including MobileNetV1, MobileNetV2, Inception-ResNetV2, and NASNet Mobile

Eight Year Climatologies from Observational (AIRS) and Model (MERRA) Data

We examine climatologies derived from eight years of temperature, water vapor, cloud, and trace gas observations made by the Atmospheric Infrared Sounder (AIRS) instrument flying on the Aqua satellite and compare them to similar climatologies constructed with data from a global assimilation model, the Modern Era Retrospective-Analysis for Research and Applications (MERRA). We use the AIRS climatologies to examine anomalies and trends in the AIRS data record. Since sampling can be an issue for infrared satellites in low earth orbit, we also use the MERRA data to examine the AIRS sampling biases. By sampling the MERRA data at the AIRS space-time locations both with and without the AIRS quality control we estimate the sampling bias of the AIRS climatology and the atmospheric conditions where AIRS has a lower sampling rate. While the AIRS temperature and water vapor sampling biases are small at low latitudes, they can be more than a few degrees in temperature or 10 percent in water vapor at higher latitudes. The largest sampling biases are over desert. The AIRS and MERRA data are available from the Goddard Earth Sciences Data and Information Services Center (GES DISC). The AIRS climatologies we used are available for analysis with the GIOVANNI data exploration tool. (see, http://disc.gsfc.nasa.gov)

Glycemic Benefits with Adherence to testosterone therapy in men with hypogonadism and type 2 diabetes mellitus.

BACKGROUND: While previous studies have demonstrated testosterone\u27s beneficial effects on glycemic control in men with hypogonadism and Type 2 Diabetes, the extent to which these improvements are observed based on the degree of treatment adherence has been unclear. OBJECTIVES: To evaluate the effects of long-term testosterone therapy in A1C levels in men with Type 2 Diabetes Mellitus and hypogonadism, controlling for BMI, pre-treatment A1C, and age among different testosterone therapy adherence groups. MATERIALS AND METHODS: We performed a retrospective analysis of 1737 men with diabetes and hypogonadism on testosterone therapy for 5 years of data from 2008-2018, isolating A1C, lipid panels, and BMI results for analysis. Subjects were categorized into adherence groups based on quartiles of the proportion of days covered (\u3e 75% of days, 51-75% of days, 26-50% of days and 0-25% of days), with \u3e75% of days covered considered adherent to therapy. RESULTS: Pre-treatment median A1C was 6.8%. Post-treatment median A1C was 7.1%. The adherent group, \u3e75%, was the only group notable for a decrease in A1C, with a median decrease of -0.2 (p = 0.0022). BMI improvement was associated with improved post-treatment A1C (p = 0.007). When controlling for BMI, age, and pre-treatment A1C, the \u3e75% adherence group was associated with improved post-treatment A1C (p \u3c 0.001). DISCUSSION: When controlling for all studied variables, testosterone adherence was associated with improved post-treatment A1C. The higher the initial A1C at the initiation of therapy, the higher the potential for lowering the patient\u27s A1C with \u3e75% adherence. Further, all groups showed some reduction in BMI, which may indicate that testosterone therapy may affect A1C independent of weight loss. CONCLUSION: Even when controlling for improved BMI, pre-treatment A1C, and age, testosterone positively impacted glycemic control in diabetes patients with hypogonadism, with the most benefit noted in those most adherent to therapy (\u3e75%)