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NASA NTRS2018

Innovative Development of a Cross-Center Timeline Planning Tool

The Payload Operations Integration Center (POIC) at Marshall Space Flight Center (MSFC) supports planning, coordination and scheduling of science activities for the International Space Station (ISS) in coordination with other NASA centers, international partners, and payload developers. The ability to efficiently plan and re-plan in response to change is critical to the flight planning teams. With the achievement of supporting a fourth crew member aboard the ISS and an increasing amount of payload science activities, came the need for a dynamic, more efficient way of building timeline planning reports that could be readily updated as fast as payload science plans could change. This paper addresses software architecture considerations in the successful cross-center development of an automated planning tool with multiple data sources. It also discusses the practical implementation of a time-boxed, hybrid Agile Software Development (ASD) approach to deliver customer-driven value despite changing requirements with respect to low-Earth orbit operational planning activities. The goal of this paper is to open discussion with members of the international community and trade effective strategies for cross-center architectural and customer-developer driven collaborations, to support increasing utilization of planning and conducting science activities in space.

Pedoto, Ramon W.↗

NASA NTRS2023

Aurora Detection From Nighttime Lights for Earth and Space Science Applications

This research leverages data from the Day/Night Band (DNB) of the Visible Infrared Imaging Radiometer (VIIRS) instrument onboard the Suomi National Polar-orbiting Partnership (S-NPP) satellite. We demonstrate the value of mining the VIIRS DNB for aurora and describe our use of unsupervised machine learning to create a binary mask for aurora occurrence. This mask can be used to flag aurora-contaminated observations for NASA's nighttime lights products for Earth science applications. The identification of auroral regions can also be used for Space Weather applications, for example, for comparison with aurora forecast model and with other satellite- or ground-based aurora observations. The DNB is a broadband channel that is sensitive to wavelengths from 500 to 900 nm, which covers most of the visible light spectrum, and as the name implies, captures light even at night with a sensitivity at the nanowatt level. This band is suitable for aurora observations since the light emitted by the aurora tends to be dominated by emissions from atomic oxygen, resulting in a greenish glow at a wavelength of 557.7 nm, especially at an altitude of 110 km. This study compares the global nighttime derived aurora regions for 17 and 18 March with the NOAA Space Weather Prediction Center's (SWPC) probability product for the St. Patrick's Day geomagnetic storm in 2015. VIIRS sensors are slated to be added to the next generation of polar-orbiting operational satellites. Our novel automated approach to aurora identification opens up an efficient way to leverage this unique data source.

Aurora↗

NASA NTRSDate not supplied

Enhancing Air Traffic Control Planning with Automatic Speech Recognition

The decisions made during the Federal Aviation Administration Air Traffic Control System Command Center's planning teleconferences hold significant sway over the National Airspace System. Held every two hours, these teleconferences convene air traffic managers and stakeholders from across the nation to discuss airspace conditions, weather, and constraints, leading to the formulation and adjustment of traffic management initiatives. Given the critical nature of these decisions, the need for accurate and efficient record-keeping is paramount. In recent years, the application of automatic speech recognition has gained popularity across diverse industries, including aviation. While traditional applications focus on transcribing air traffic control communication, this paper explores a unique application of automatic speech recognition by converting the audio from planning teleconferences into text transcriptions. This innovative approach addresses key challenges in the field, presenting potential benefits for quality assurance, real-time participation, and downstream natural language processing tasks. A notable breakthrough in the machine learning community, namely the transformer neural network architecture, forms the backbone of the proposed solution in this paper. The transformer architecture's role in this research represents a paradigm shift in the efficiency of automatic speech recognition models. By reducing the amount of in-domain training data required, this architecture allows for the fine-tuning of such models like Whisper, originally pretrained on vast English speech datasets. The adaptability of the transformer architecture proves invaluable in capturing the nuances of aviation terminology and specific language used in planning teleconferences. Leveraging the Whisper model as a baseline, our research details the fine-tuning and validation using a dataset comprising 20 hours of meticulously transcribed planning teleconferences. Notably, the baseline pretrained Whisper model exhibited a word error rate of 18.77%. Through the fine-tuning process, the model achieved a substantial improvement, demonstrating an impressive performance with a reduced word error rate of 6.82%. This substantial decrease in WER not only highlights the effectiveness of the transformer architecture but also emphasizes the practical advancements achieved through the application of automatic speech recognition in this specific domain. The utilization of automatic speech recognition in planning teleconferences in this work introduces several novelties. Firstly, the creation of text transcriptions offers a valuable tool for quality assurance and facilitates the efficient review of teleconferences. This is an important aspect of the proposed solution, given the time-sensitive and high-stakes nature of decisions made during these meetings. Furthermore, text-searchable transcriptions provide a streamlined approach for locating and validating critical information, potentially saving hours of manual effort in searching through audio recordings. Moreover, our research identifies a key use case for external facilities and stakeholders. In situations where attendance at the planning teleconference is not feasible, having access to text transcriptions in real-time or shortly after the teleconference ends, proves to be a time-saving and informative resource. This feature enhances collaboration and ensures that stakeholders can stay abreast of important discussions and decisions even in their absence. Despite the efficiency gains facilitated by the transformer architecture in automatic speech recognition technology, it is essential to acknowledge the human factors in data creation. Subject matter experts play a crucial role in accurately transcribing planning teleconferences due to the specificity and complexity of the information discussed. The research dataset, consisting of 20 hours of transcribed planning teleconferences, forms the foundation for fine-tuning and validating the Whisper model. The achieved word error rate of 6.82% demonstrates promising advancements, particularly in recognizing essential aviation terminology within the teleconferences. In conclusion, this paper presents a comprehensive exploration of the application of automatic speech recognition in Air Traffic Control System Command Center planning teleconferences, leveraging the transformer architecture for enhanced efficiency. The novel contributions lie in the improved accessibility of decision-making records, real-time participation opportunities for external stakeholders, and the potential for downstream natural language processing advancements. As the aviation industry continues to evolve, the integration of automatic speech recognition technologies holds the promise of revolutionizing decision-making processes and contributing to the overall safety and efficiency of air traffic management.

ATM↗

DOE OSTI2020

Holistic Measurement Driven Resilience: Combining Operational Fault and Failure Measurements and Fault Injection for Quantifying Fault Detection, Propagation and Impact. Final report

For HPC systems to date, application resilience to faults and failures has been accomplished by the brute- force method of checkpoint/restart, which allows an application to make forward progress in the face of system and application faults, errors, and failures independent of root cause or end result. It has remained the primary resilience mechanism because we lack a way to identify faults and anticipate consequences early enough to take meaningful mitigating action. However, checkpoint/restart implementations put a tremendous burden on system resources and on the applications themselves and is becoming less feasible at scale. Because we have not yet operated at scales at which checkpoint/restart fails to provide forward progress, despite increasing costs, vendors have had little motivation to provide the instrumentation necessary for early identification of faults and failures. However, as we move from petascale to exascale, component mean time to failure (MTTF) will render the existing techniques ineffectual and/or too expensive. Furthermore, fault recovery mechanisms such as failover and/or error correction introduce performance inconsistency. Instrumentation allowing early indication of problems and tools to enable use of such information by systems, operating systems, and applications offer an alternative, more scalable and less costly solution. In the HMDR project, we built on our experience and expertise developed and accumulated over years of research on design, monitoring, measurement, and assessment of resilient computing systems. Analysis of field data on the current and past generations of extreme-scale systems revealed several challenges that, if not addressed in increasingly larger and more complex systems, may hinder the effectiveness of future exascale computing systems. Specifically, i) file systems and interconnects in current-generation large-scale systems already operate at the margins of resiliency, including consistent performance, and may not scale to larger deployments; ii) automated, software-based failover mechanisms are frequently inadequate and can introduce wider failures, such that failures during recovery may lead to system/application failures, including system-wide outages; and iii) silent data corruption represents a critical fault mode and will require efficient detection mechanisms if next-generation applications are to take full advantage of exascale hardware. To address the above challenges, we assembled a team of world-renowned experts in resilient extreme- scale computing from the University of Illinois (Electrical and Computer Engineering, Computer Science, and NCSA), SNL, LANL, NERSC, and Cray. Our team includes representatives from centers that house many of the largest HPC resources in the world, both today and over the coming years. The team has a unique track record of research in i) system and application failure characterization based on the analysis of field data, ii) data-driven design of fault/error detection mechanisms, and iii) experimental characterization of system/application resiliency. The team includes system owners/operators who provide continuous data collection and access and ensure installation of appropriate analysis tools.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1998

Efficient Methods to Assimilate Satellite Retrievals Based on Information Content: Suboptimal Retrieval Assimilation - Part 2

One of the outstanding problems in data assimilation has been and continues to be how best to utilize satellite data while balancing the tradeoff between accuracy and computational cost. A number of weather prediction centers have recently achieved remarkable success in improving their forecast skill by changing the method by which satellite data are assimilated into the forecast model from the traditional approach of assimilating retrievals to the direct assimilation of radiances in a variational framework. The operational implementation of such a substantial change in methodology involves a great number of technical details, e.g., pertaining to quality control procedures, systematic error correction techniques, and tuning of the statistical parameters in the analysis algorithm. Although there are clear theoretical advantages to the direct radiance assimilation approach, it is not obvious at all to what extent the improvements that have been obtained so far can be attributed to the change in methodology, or to various technical aspects of the implementation. The issue is of interest because retrieval assimilation retains many practical and logistical advantages which may become even more significant in the near future when increasingly high-volume data sources become available. The central question we address here is: how much improvement can we expect from assimilating radiances rather than retrievals, all other things being equal? We compare the two approaches in a simplified one-dimensional theoretical framework, in which problems related to quality control and systematic error correction are conveniently absent. By assuming a perfect radiative transfer model and perfect knowledge of radiance and background error covariances, we are able to formulate a nonlinear local error analysis for each assimilation method. Direct radiance assimilation is optimal in this idealized context, while the traditional method of assimilating retrievals is suboptimal because it ignores the cross-covariances between background errors and retrieval errors. We show that interactive retrieval assimilation (where the same background used for assimilation is also used in the retrieval step) is equivalent to direct assimilation of radiances with suboptimal analysis weights. We illustrate and extend these theoretical arguments with several one-dimensional assimilation experiments, where we estimate vertical atmospheric profiles using simulated data from both the High-resolution InfraRed Sounder 2 (HIRS2) and the future Atmospheric InfraRed Sounder (AIRS).

Joiner, J.↗

DOE OSTI2025

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗

NASA NTRS1986

A photovoltaic-powered water electrolyzer - Its performance and economics

A prototype water electrolyzer designed to operate from a solar photovoltaic (PV) array without power conditioning was operated for three months at the Florida Solar Energy Center. A 1 kWpk PV array was used to operate the electrolyzer at internal gas pressure from 0 to 40 psig. Performance of the elecrolyzer/PV array was measured and characterized in terms of charge efficiency and power efficiency calculated from the operation data. The economics of residential production of hydrogen for energy purposes were calculated and summarized. While the near-term outlook for this energy storage technique was not found to be favorable, the long-term outlook was encouraging.

Hancock, O. G., Jr.↗

NASA NTRS1987

A review of recent activities in the NASA CELSS program

A CELSS (Controlled Ecological Life Support System) is a device that utilizes photosynthetic organisms and light energy to regenerate waste materials into oxygen and food for a crew in space. The results of studies with the CELSS program suggest that a bioregenerative life support system is a useful and effective method of regenerating consumable materials for crew sustenance. The data suggests that the operation of a CELSS in space is practical if plants can be made to behave predictably in the space environment. Much of the work centers on the biological components of the CELSS system. Ways of achieving high efficiency and long term stability of all components of the system are examined. Included are explorations of the conversion of nonedible cellulose to edible materials, nitrogen fixation by biological and chemical methods, and methods of waste processing. A description is provided of the extent to which a bioregenerative life support system can meet the constraints of the space environment, and the degree is assessed to which system efficiency and stability can be increased during the next decade.

Macelroy, R. D.↗

NASA NTRS1990

Hazard evaluation and operational cockpit display of ground-measured windshear data

Information transfer issues associated with the dissemination of wind shear alerts from the ground are studied. The two issues specifically addressed are the effectiveness of different cockpit presentations of ground-measured information and the assessment of the wind shear hazard from ground-based measurements. A pilot survey has produced an information base for study of crew-centered wind shear alert design. A part-task Boeing 767 'glass cockpit' simulation has provided useful data about modes of cockpit information presentation for both wind shear alert and ATC clearance delivery. Graphical map displays are observed to be exceptionally efficient for presentation of position-critical alerts, while some problems with text displays are identified. Problems associated with hazard assessment of ground-measured wind shear information are also identified.

Wanke, Craig↗

NASA NTRS1992

Structural analysis of an NDE-inspected carbon-carbon component

In this research, it is demonstrated that the integration of nondestructive evaluation with the finite element method (a quantitative NDE/FEM approach) can be used for analysis and behavior prediction of advanced materials, such as fiber composites, after processing and during in-service testing. First, stress, strain, and energy data were calculated by the finite element method for square plates with a 10 percent difference in the material properties between specified segments, i.e., between the center and the outside section or between two halves of the plate. This demonstrated the efficiency of the method in predicting variations in the structural behavior based on small material property differences. Then, finite element analyses and actual axial compression tests were performed on a Space Shuttle brake stator made from woven carbon-carbon composites. The material properties used in the numerical modeling of the stator were previously characterized at a large number of points on the component by quantitative NDE-procedures, i.e., ultrasonics and radiography. The results from the experimental compressive loading tests were used to verify the NDE/FEM results. Good agreement was found for most parameters.

Striz, A. G.↗

NASA NTRS2003

Aviation Safety Modeling and Simulation (ASMM) Propulsion Fleet Modeling: A Tool for Semi-Automatic Construction of CORBA-based Applications from Legacy Fortran Programs

Within NASA's Aviation Safety Program, NASA GRC participates in the Modeling and Simulation Project called ASMM. NASA GRC s focus is to characterize the propulsion systems performance from a fleet management and maintenance perspective by modeling and through simulation predict the characteristics of two classes of commercial engines (CFM56 and GE90). In prior years, the High Performance Computing and Communication (HPCC) program funded, NASA Glenn in developing a large scale, detailed simulations for the analysis and design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). Three major aspects of this modeling included the integration of different engine components, coupling of multiple disciplines, and engine component zooming at appropriate level fidelity, require relatively tight coupling of different analysis codes. Most of these codes in aerodynamics and solid mechanics are written in Fortran. Refitting these legacy Fortran codes with distributed objects can increase these codes reusability. Aviation Safety s modeling and simulation use in characterizing fleet management has similar needs. The modeling and simulation of these propulsion systems use existing Fortran and C codes that are instrumental in determining the performance of the fleet. The research centers on building a CORBA-based development environment for programmers to easily wrap and couple legacy Fortran codes. This environment consists of a C++ wrapper library to hide the details of CORBA and an efficient remote variable scheme to facilitate data exchange between the client and the server model. Additionally, a Web Service model should also be constructed for evaluation of this technology s use over the next two- three years.

Sang, Janche↗

NASA NTRS2004

Microminiature Monitor for Vital Electrolyte and Metabolite Levels of Astronauts

Ions, such as proton (pH) and potassium, play a crucial role in body fluids to maintain proper basic functioning of cells and tissues. Metabolites, such as glucose, control the energy available to the entire human body in normal as well as stress situations, and before, during, and after meals. These molecules diffuse easily between blood in the capillaries and the interstitial fluid residing between cells and tissues. We have developed and approach to monitoring of critical ions (called electrolytes) and glucose in the interstitial fluid under the human skin. Proton and potassium levels sensed using optode technology that translates the respective ionic concentrations into variable colors of corresponding ionophore/dye/polymeric liquid membranes. Glucose is monitored indirectly, by coupling through immobilized glucose oxidase with local pH that is then detected using a similar color scheme. The monitor consists of a tiny plastic bar, 100-200 microns wide and 1-2 mm long, placed just under the skin, with color changing spots for each analyte as well as blanks. The colors are read and translated into concentration values by a CCD camera. Direct optical coupling between the in vivo sensing bar and the ex vivo detector device requires no power, and thus eliminates the need for wires or optical fibers crossing the skin. The microminiature bar penetrates the skin easily and painlessly, so that astronauts could insert it themselves. The approach is fully compatible with telemetry in space, and thus, in vivo clinical data will be available real time in the Earth based command center once the device is fully developed. The information provided can be used for collecting hitherto unavailable vital data on clinical effects of space travel. Managing clinical emergencies in space with the sensor already in place should also become much more efficient than without a continuous monitor, as is currently the case. Civilian applications may include better glucose control of patients with moderate to severe diabetes: a growing health problem in the US and World-wide.

Tohda, Koji↗

NASA NTRS2018

Exploration Ground Data Systems (xGDS) Overview

xGDS supports rapid scientific decision making by synchronizing the time and mapped location of observation notes, instrument data, photos, video, samples and other data. xGDS is a suite of web tools, developed at NASA Ames Research Center to support remote science operations in analog missions and prototype new ideas for planetary exploration. During operations, xGDS displays science data in real-time with geographic context, supporting key decisions (e.g. sample site selection). Post-operations, xGDS enables efficient analysis of data by correlating data products in time and on the map. xGDS has been developed in close collaboration with science teams since 2009, and used enthusiastically by upwards of 100 scientists to support scientific field research and data analysis.

Ground Data System↗

NASA NTRS2019

The Value of Being a Trustworthy Repository

Today, NASA's Earth Observing System Data and Information System (EOSDIS), a system ofactive archives is attaching the CoreTrustSeal to its websites signifying that it merits theconfidence of its user community. But what value does being a trustworthy repository impart to auser? What does it mean to the owners and operators of repositories? What will it mean in thefuture? EOSDIS was started in the 1990s based on a framework of discipline-oriented, geographicallydistributed centers of expertise, named Distributed Active Archive Centers (DAACs). The functionof EOSDIS is to collect Earth Science data sensor measurements (principally those created andneeded by NASA) and manage the data and many derived digital products. EOSDIS providesmany services, including processing, curating, documenting, disseminating, and enabling datadiscovery as well as efficient use of the data. The EOSDIS has been operational over 25 years andmany lessons have been learned relative to the TRUST principles. During the tenure of EOSDIS,many changes have occurred as we have increased the size of the collection from gigabytes totens of petabytes and the distribution of the data to millions of users. We have had severalstages of system evolution that have improved EOSDIS in order to meet both stakeholder andcustomer expectations. This type of evolution is an on-going process to ensure that ourrepositories remain trustworthy. It is also important that our own community of data managersand system engineers add value in being trustworthy. This paper will discuss approaches to change within a large system of Earth Science data and services, while remaining a trustworthyrepository.

Behnke, Jeanne↗

NASA NTRSDate not supplied

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

The Icing Research Tunnel at NASA Glenn follows the recommended practice for calibration outlined in SAE’s ARP5905. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within the stated specifications of variation within +/- 10% of the curve fit equation generated from calibration data. Using past measurements and data trends, IRT characterization engineers wanted to develop methods for the ability to know when data were not within variation. Trends can be observed in the liquid water content measurement process by constructing statistical process control charts. This paper describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, process for rejection of data, and construction of control charts. Data are presented to display the control capability to meet defined liquid water content specifications of the IRT with the Multi-Element Sensor mounted in the center of the test section.

Statistical Process Control↗

NASA NTRSDate not supplied

Statistical Process Control and Analysis on the Water Content Measurements in NASA Glenn’s Icing Research Tunnel

Statistical Process Control↗

NASA NTRS2012

Data Distribution System (DDS) and Solar Dynamic Observatory Ground Station (SDOGS) Integration Manager

The DDS SDOGS Integration Manager (DSIM) provides translation between native control and status formats for systems within DDS and SDOGS, and the ASIST (Advanced Spacecraft Integration and System Test) control environment in the SDO MOC (Solar Dynamics Observatory Mission Operations Center). This system was created in response for a need to centralize remote monitor and control of SDO Ground Station equipments using ASIST control environment in SDO MOC, and to have configurable table definition for equipment. It provides translation of status and monitoring information from the native systems into ASIST-readable format to display on pages in the MOC. The manager is lightweight, user friendly, and efficient. It allows data trending, correlation, and storing. It allows using ASIST as common interface for remote monitor and control of heterogeneous equipments. It also provides failover capability to back up machines.

Pham, Kim↗

NASA NTRS2024

Exploring New Frontiers in Space Communications: Enhancing Delay Tolerant Networking through Cloud and Containerization

The High-rate Delay Tolerant Networking (HDTN) project at NASA Glenn Research Center has developed software that enables more flexible, reliable, and efficient space internetworking by using modern computing techniques such as cloud services, microservices, network function virtualization, software defined networking, and a distributed architecture. HDTN is built upon the Bundle Protocol and related convergence layers which have been developed to mitigate the challenges of the space networking environment including long delays, asymmetric data rates, and intermittent connectivity. The HDTN implementation employs asynchronous message processing tasks which allow for non-blocking operations as well as deployment in both centralized and distributed architectures. This paper investigates deploying HDTN in a containerized approach on the NASA Goddard’s Mission Cloud Platform using Amazon Web Services Elastic Compute Cloud (EC2). Commercial cloud computing will lower operating costs, provide flexible resource allocation, and allow for interconnectivity between multiple NASA centers as well as external partners. Containerization using Docker will enable greater portability and scalability for HDTN to be deployed into a variety of environments. We discuss possible NASA missions and use-cases such as the Laser Communications Relay Demonstration (LCRD) where the services provided by HDTN (reliable transport, high-rate message processing, and store-and-forward capabilities) will be enhanced through cloud computing and containerization. In addition, we describe the HDTN architecture and possible microservice-based networking approaches that can be obtained via HDTN’s configuration capabilities. Finally, we detail the EC2 specifications needed to achieve data rates greater than 1 Gbps to support optical communication missions such as LCRD.

Blake LaFuente↗