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NASA NTRS2004

Challenges of Moving IPG into Production

Over the past 5-6 years, NASA has been developing the Information Power Grid and has a persistent testbed currently based on GT2.4.2. This presentation will begin with an overview of IPG status and services, discuss key milestones in IPG development, and present early as well as expected applications. The presentation will discuss some of the issues encountered in developing a grid including the tension between providing centralized and distributed computing. These issues also affect how the grid is moved into full production. Finally, the presentation will provide current plans for moving IPG into full production, including gaining broad user input, developing acceptance criteria from the production operations group, planning upgrades, and training users.

Schulbach, Cathy↗

NASA NTRS2006

DCS-Neural-Network Program for Aircraft Control and Testing

A computer program implements a dynamic-cell-structure (DCS) artificial neural network that can perform such tasks as learning selected aerodynamic characteristics of an airplane from wind-tunnel test data and computing real-time stability and control derivatives of the airplane for use in feedback linearized control. A DCS neural network is one of several types of neural networks that can incorporate additional nodes in order to rapidly learn increasingly complex relationships between inputs and outputs. In the DCS neural network implemented by the present program, the insertion of nodes is based on accumulated error. A competitive Hebbian learning rule (a supervised-learning rule in which connection weights are adjusted to minimize differences between actual and desired outputs for training examples) is used. A Kohonen-style learning rule (derived from a relatively simple training algorithm, implements a Delaunay triangulation layout of neurons) is used to adjust node positions during training. Neighborhood topology determines which nodes are used to estimate new values. The network learns, starting with two nodes, and adds new nodes sequentially in locations chosen to maximize reductions in global error. At any given time during learning, the error becomes homogeneously distributed over all nodes.

Jorgensen, Charles C.↗

NASA NTRS2010

Telescience Support Center Data System Software

The Telescience Support Center (TSC) team has developed a databasedriven, increment-specific Data Require - ment Document (DRD) generation tool that automates much of the work required for generating and formatting the DRD. It creates a database to load the required changes to configure the TSC data system, thus eliminating a substantial amount of labor in database entry and formatting. The TSC database contains the TSC systems configuration, along with the experimental data, in which human physiological data must be de-commutated in real time. The data for each experiment also must be cataloged and archived for future retrieval. TSC software provides tools and resources for ground operation and data distribution to remote users consisting of PIs (principal investigators), bio-medical engineers, scientists, engineers, payload specialists, and computer scientists. Operations support is provided for computer systems access, detailed networking, and mathematical and computational problems of the International Space Station telemetry data. User training is provided for on-site staff and biomedical researchers and other remote personnel in the usage of the space-bound services via the Internet, which enables significant resource savings for the physical facility along with the time savings versus traveling to NASA sites. The software used in support of the TSC could easily be adapted to other Control Center applications. This would include not only other NASA payload monitoring facilities, but also other types of control activities, such as monitoring and control of the electric grid, chemical, or nuclear plant processes, air traffic control, and the like.

Rahman, Hasan↗

NASA NTRS2016

Estimating the Rate of Occurrence of Renal Stones in Astronauts

Changes in urine chemistry, during and post flight, potentially increases the risk of renal stones in astronauts. Although much is known about the effects of space flight on urine chemistry, no inflight incidence of renal stones in US astronauts exists and the question "How much does this risk change with space flight?" remains difficult to accurately quantify. In this discussion, we tackle this question utilizing a combination of deterministic and probabilistic modeling that implements the physics behind free stone growth and agglomeration, speciation of urine chemistry and published observations of population renal stone incidences to estimate changes in the rate of renal stone presentation. The modeling process utilizes a Population Balance Equation based model developed in the companion IWS abstract by Kassemi et al. (2016) to evaluate the maximum growth and agglomeration potential from a specified set of urine chemistry values. Changes in renal stone occurrence rates are obtained from this model in a probabilistic simulation that interrogates the range of possible urine chemistries using Monte Carlo techniques. Subsequently, each randomly sampled urine chemistry undergoes speciation analysis using the well-established Joint Expert Speciation System (JESS) code to calculate critical values, such as ionic strength and relative supersaturation. The Kassemi model utilizes this information to predict the mean and maximum stone size. We close the assessment loop by using a transfer function that estimates the rate of stone formation from combining the relative supersaturation and both the mean and maximum free stone growth sizes. The transfer function is established by a simulation analysis which combines population stone formation rates and Poisson regression. Training this transfer function requires using the output of the aforementioned assessment steps with inputs from known non-stone-former and known stone-former urine chemistries. Established in a Monte Carlo system, the entire renal stone analysis model produces a probability distribution of the stone formation rate and an expected uncertainty in the estimate. The utility of this analysis will be demonstrated by showing the change in renal stone occurrence predicted by this method using urine chemistry distributions published in Whitson et al. 2009. A comparison to the model predictions to previous assessments of renal stone risk will be used to illustrate initial validation of the model.

gravitational physiology↗

NASA NTRS2000

BOREAS TE-18 Landsat TM Physical Classification Image of the NSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Hall, Forrest G.↗

NASA NTRS2000

BOREAS TE-18 Landsat TM Physical Classification Image of the SSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Hall, Forrest G.↗

NASA NTRS2000

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the SSA

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep- 1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).

Hall, Forrest G.↗

NASA NTRS2017

Answering the Call for Model-Relevant Observations of Aerosols and Clouds

Redemann, J.↗

NASA NTRS2017

Use of A-Train Aerosol Observations to Constrain Direct Aerosol Radiative Effects (DARE) Comparisons with Aerocom Models and Uncertainty Assessments

We describe a technique for combining multiple A-Train aerosol data sets, namely MODIS spectral AOD (aerosol optical depth), OMI AAOD (absorption aerosol optical depth) and CALIOP aerosol backscatter retrievals (hereafter referred to as MOC retrievals) to estimate full spectral sets of aerosol radiative properties, and ultimately to calculate the 3-D distribution of direct aerosol radiative effects (DARE). We present MOC results using almost two years of data collected in 2007 and 2008, and show comparisons of the aerosol radiative property estimates to collocated AERONET retrievals. Use of the MODIS Collection 6 AOD data derived with the dark target and deep blue algorithms has extended the coverage of the MOC retrievals towards higher latitudes. The MOC aerosol retrievals agree better with AERONET in terms of the single scattering albedo (ssa) at 441 nm than ssa calculated from OMI and MODIS data alone, indicating that CALIOP aerosol backscatter data contains information on aerosol absorption. We compare the spatio-temporal distribution of the MOC retrievals and MOC-based calculations of seasonal clear-sky DARE to values derived from four models that participated in the Phase II AeroCom model intercomparison initiative. Overall, the MOC-based calculations of clear-sky DARE at TOA over land are smaller (less negative) than previous model or observational estimates due to the inclusion of more absorbing aerosol retrievals over brighter surfaces, not previously available for observationally-based estimates of DARE. MOC-based DARE estimates at the surface over land and total (land and ocean) DARE estimates at TOA are in between previous model and observational results. Comparisons of seasonal aerosol property to AeroCom Phase II results show generally good agreement best agreement with forcing results at TOA is found with GMI-MerraV3. We discuss sampling issues that affect the comparisons and the major challenges in extending our clear-sky DARE results to all-sky conditions. We present estimates of clear-sky and all-sky DARE and show uncertainties that stem from the assumptions in the spatial extrapolation and accuracy of aerosol and cloud properties, in the diurnal evolution of these properties, and in the radiative transfer calculations.

aerosol↗

NASA NTRSDate not supplied

Development of the X-57 Mod III/IV Piloted Simulator and Discussion of the Resultant Flighting Qualities Predictions

The all-electric X-57 Mod III aircraft was designed to demonstrate wingtip propulsion drag reduction benefits on a high-aspect-ratio wing. The X-57 Mod IV aircraft was designed to expand on the Mod III version and demonstrate the benefits of distributed electric propulsion. This paper discusses the development of theall-electricX-57 Mod III and Mod IV fixed-base pilot-in-the-loop nonlinear simulator. The paper describes the model development and the simulator cockpit construction for uses such as flight training and flying qualities analysis. Results from a stability and flying qualities analysis for both Mod III and Mod IV are presented. These results predict that both Mod III and Mod IV are stable throughout their flight envelopes. Mod III is predicted to have satisfactory flying qualities while Mod IV is predicted to have areas of adequate flying qualities.

Ryan Wallace↗

NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗

NASA NTRS1987

TAVERNS and the space station software support environment

The Space Station Information System (SSIS) provides the data processing capability for the Space Station Program (SSP). The Software Support Environment (SSE) System for the SSP is the collection of software, procedures, standards, hardware specification, documentation, policy, and training materials. The Ada programming language was baselined by the Space Station Program Office as the language for development and maintenance of all space station software including the software of the SSE itself. The Test And Validation Environment for Remote Networked Systems (TAVERNS) is a distributed philosophy for development and validation of Ada applications software for the space station and as such is closely related to the SSE. An overview of the system is provided.

Howes, Norman R.↗

NASA NTRS2009

Constellations: A New Paradigm for Earth Observations

The last decade has seen a significant increase in the number and the capabilities of remote sensing satellites launched by the international community. A relatively new approach has been the launching of satellites into heterogeneous constellations. Constellations provide the scientists a capability to acquire science data, not only from specific instruments on a single satellite, but also from instruments on other satellites that fly in the same orbit. Initial results from the A-Train (especially following the CALIPSO/CloudSat launch) attest to the tremendous scientific value of constellation flying. This paper provides a history of the constellations (particularly the A-Train) and how the A-Train mission design was driven by science requirements. The A-Train has presented operational challenges which had not previously been encountered. Operations planning had to address not only how the satellites of each constellation operate safely together, but also how the two constellations fly in the same orbits without interfering with each other when commands are uplinked or data are downlinked to their respective ground stations. This paper discusses the benefits of joining an on-orbit constellation. When compared to a single, large satellite, a constellation infrastructure offers more than just the opportunities for coincidental science observations. For example, constellations reduce risks by distributing observing instruments among numerous satellites; in contrast, a failed launch or a system failure in a single satellite would lead to loss of all observations. Constellations allow for more focused, less complex satellites. Constellations distribute the development, testing, and operations costs among various agencies and organizations for example, the Morning and Afternoon Constellations involve several agencies within the U.S. and in other countries. Lastly, this paper addresses the need to plan for the long-term evolution of a constellation. Agencies need to have a replenishment strategy as some satellites age and eventually leave the constellation. This will ensure overlap of observations, thus providing continuous, calibrated science data over a much longer time period. Thoughts on the evolution of the A-Train will also be presented.

Kelly, Angelita C.↗

NASA NTRS1990

Distributed memory approaches for robotic neural controllers

The suitability is explored of two varieties of distributed memory neutral networks as trainable controllers for a simulated robotics task. The task requires that two cameras observe an arbitrary target point in space. Coordinates of the target on the camera image planes are passed to a neural controller which must learn to solve the inverse kinematics of a manipulator with one revolute and two prismatic joints. Two new network designs are evaluated. The first, radial basis sparse distributed memory (RBSDM), approximates functional mappings as sums of multivariate gaussians centered around previously learned patterns. The second network types involved variations of Adaptive Vector Quantizers or Self Organizing Maps. In these networks, random N dimensional points are given local connectivities. They are then exposed to training patterns and readjust their locations based on a nearest neighbor rule. Both approaches are tested based on their ability to interpolate manipulator joint coordinates for simulated arm movement while simultaneously performing stereo fusion of the camera data. Comparisons are made with classical k-nearest neighbor pattern recognition techniques.

Jorgensen, Charles C.↗

NASA NTRS2024

Unveiling the Transferability of PLSR Models for Leaf Trait Estimation: Lessons from a Comprehensive Analysis with a Novel Global Dataset

Leaf traits are essential for understanding many physiological and ecological processes. Partial least-squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47,393 records for >700 species and eight PFTs at 101 globally-distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R 2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55% and 7.0-33.12%) compared to nonspatial random cross-validation (NRCV). The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs and time due to biased validation schemes and provide guidance for future field sampling strategies and remote sensing applications.

Leaf traits↗

NASA NTRS2007

Effects of Daily Centrifugation on Segmental Fluid Distribution in Bed-rested Subjects

The effect of daily centrifugation on segmental fluid distribution have been studied during 21 days of 6 degree head down bedrest. One group (N=7) underwent no countermeasure while the other (N=8) received a daily, one hour, dose (2.5 gz at the foot, decreasing to 1.0 gz at the heart) of artificial gravity (AG) training on the Johnson Space Center short radius centrifuge. Fluid shifts of thoracic(VTO), abdominal (VAB), thigh (VTH), and calf (VCA) regions were measured by the tetrapolar segmental body impedance technique. Untrained subjects reduced their total volume from 18.9 plus or minus 0.5L to 17.9 plus or minus 0.9L (MN plus or minus SE, P less than 0.05) while trained subjects maintained their total volume. In untrained, control, subjects after bed rest, there was a trend toward reduced volume in all segments, with significant reductions in thigh and calf (fig, P less than 0.05). Trained subjects maintained volume in all segments. Our data indicate that artificial gravity treatment counteracts bed rest-induced hypovolemia.

Diedrich, Andre↗

NASA NTRS2011

Approximate Simulation of Acute Hypobaric Hypoxia with Normobaric Hypoxia

INTRODUCTION. Some manufacturers of reduced oxygen (O2) breathing devices claim a comparable hypobaric hypoxia (HH) training experience by providing F(sub I) O2 < 0.209 at or near sea level pressure to match the ambient O2 partial pressure (iso-pO2) of the target altitude. METHODS. Literature from investigators and manufacturers indicate that these devices may not properly account for the 47 mmHg of water vapor partial pressure that reduces the inspired partial pressure of O2 (P(sub I) O2). Nor do they account for the complex reality of alveolar gas composition as defined by the Alveolar Gas Equation. In essence, by providing iso-pO2 conditions for normobaric hypoxia (NH) as for HH exposures the devices ignore P(sub A)O2 and P(sub A)CO2 as more direct agents to induce signs and symptoms of hypoxia during acute training exposures. RESULTS. There is not a sufficient integrated physiological understanding of the determinants of P(sub A)O2 and P(sub A)CO2 under acute NH and HH given the same hypoxic pO2 to claim a device that provides isohypoxia. Isohypoxia is defined as the same distribution of hypoxia signs and symptoms under any circumstances of equivalent hypoxic dose, and hypoxic pO2 is an incomplete hypoxic dose. Some devices that claim an equivalent HH experience under NH conditions significantly overestimate the HH condition, especially when simulating altitudes above 10,000 feet (3,048 m). CONCLUSIONS. At best, the claim should be that the devices provide an approximate HH experience since they only duplicate the ambient pO2 at sea level as at altitude (iso-pO2 machines). An approach to reduce the overestimation is to at least provide machines that create the same P(sub I)O2 (iso-P(sub I)O2 machines) conditions at sea level as at the target altitude, a simple software upgrade.

Conkin, J.↗

NASA NTRSDate not supplied

Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

This work describes a detector for the loss of control condition of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. First, we demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA’s dynamically-scaled generic transport model (GTM) aircraft.

Loss of control↗