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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Provenance in Data Interoperability for Multi-Sensor Intercomparison

As our inventory of Earth science data sets grows, the ability to compare, merge and fuse multiple datasets grows in importance. This requires a deeper data interoperability than we have now. Efforts such as Open Geospatial Consortium and OPeNDAP (Open-source Project for a Network Data Access Protocol) have broken down format barriers to interoperability; the next challenge is the semantic aspects of the data. Consider the issues when satellite data are merged, cross-calibrated, validated, inter-compared and fused. We must match up data sets that are related, yet different in significant ways: the phenomenon being measured, measurement technique, location in space-time or quality of the measurements. If subtle distinctions between similar measurements are not clear to the user, results can be meaningless or lead to an incorrect interpretation of the data. Most of these distinctions trace to how the data came to be: sensors, processing and quality assessment. For example, monthly averages of satellite-based aerosol measurements often show significant discrepancies, which might be due to differences in spatio- temporal aggregation, sampling issues, sensor biases, algorithm differences or calibration issues. Provenance information must be captured in a semantic framework that allows data inter-use tools to incorporate it and aid in the intervention of comparison or merged products. Semantic web technology allows us to encode our knowledge of measurement characteristics, phenomena measured, space-time representation, and data quality attributes in a well-structured, machine-readable ontology and rulesets. An analysis tool can use this knowledge to show users the provenance-related distrintions between two variables, advising on options for further data processing and analysis. An additional problem for workflows distributed across heterogeneous systems is retrieval and transport of provenance. Provenance may be either embedded within the data payload, or transmitted from server to client in an out-of-band mechanism. The out of band mechanism is more flexible in the richness of provenance information that can be accomodated, but it relies on a persistent framework and can be difficult for legacy clients to use. We are prototyping the embedded model, incorporating provenance within metadata objects in the data payload. Thus, it always remains with the data. The downside is a limit to the size of provenance metadata that we can include, an issue that will eventually need resolution to encompass the richness of provenance information required for daata intercomparison and merging.

Lynnes, Chris↗
NASA NTRS2010

Automatic Review of Abstract State Machines by Meta Property Verification

A model review is a validation technique aimed at determining if a model is of sufficient quality and allows defects to be identified early in the system development, reducing the cost of fixing them. In this paper we propose a technique to perform automatic review of Abstract State Machine (ASM) formal specifications. We first detect a family of typical vulnerabilities and defects a developer can introduce during the modeling activity using the ASMs and we express such faults as the violation of meta-properties that guarantee certain quality attributes of the specification. These meta-properties are then mapped to temporal logic formulas and model checked for their violation. As a proof of concept, we also report the result of applying this ASM review process to several specifications.

Arcaini, Paolo↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well-formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA's proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗
NASA NTRS2016

MBSE-Driven Visualization of Requirements Allocation and Traceability

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA’s proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

Model Based Systems Engineering↗
NASA NTRS2023

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally-distributed single-catchment locations for a 10-year experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF systematically increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results also demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Soil moisture↗
NASA NTRSDate not supplied

Non-Gaussian Ensemble Filtering and Adaptive Inflation for Soil Moisture Data Assimilation

The rank histogram filter (RHF) and the ensemble Kalman filter (EnKF) are assessed for soil moisture estimation using perfect model (identical twin) synthetic data assimilation experiments. The primary motivation is to gauge the impact on analysis quality attributable to the consideration of non-Gaussian forecast error distributions. Using the NASA Catchment land surface model, the two filters are compared at 18 globally distributed single-catchment locations for a 10-yr experiment period. It is shown that both filters yield adequate estimates of soil moisture, with the RHF having a small but significant performance advantage. Most notably, the RHF consistently increases the normalized information contribution (NIC) score of the mean absolute bias by 0.05 over that of the EnKF for surface, root-zone, and profile soil moisture. The RHF also increases the NIC score for the anomaly correlation of surface soil moisture by 0.02 over that of the EnKF (at a 5% significance level). Results additionally demonstrate that the performance of both filters is somewhat improved when the ensemble priors are adaptively inflated to offset the negative effects of systematic errors.

Rolf Reichle↗
NASA NTRS1994

Taking advantage of ground data systems attributes to achieve quality results in testing software

During the software development life cycle process, basic testing starts with the development team. At the end of the development process, an acceptance test is performed for the user to ensure that the deliverable is acceptable. Ideally, the delivery is an operational product with zero defects. However, the goal of zero defects is normally not achieved but is successful to various degrees. With the emphasis on building low cost ground support systems while maintaining a quality product, a key element in the test process is simulator capability. This paper reviews the Transportable Payload Operations Control Center (TPOCC) Advanced Spacecraft Simulator (TASS) test tool that is used in the acceptance test process for unmanned satellite operations control centers. The TASS is designed to support the development, test and operational environments of the Goddard Space Flight Center (GSFC) operations control centers. The TASS uses the same basic architecture as the operations control center. This architecture is characterized by its use of distributed processing, industry standards, commercial off-the-shelf (COTS) hardware and software components, and reusable software. The TASS uses much of the same TPOCC architecture and reusable software that the operations control center developer uses. The TASS also makes use of reusable simulator software in the mission specific versions of the TASS. Very little new software needs to be developed, mainly mission specific telemetry communication and command processing software. By taking advantage of the ground data system attributes, successful software reuse for operational systems provides the opportunity to extend the reuse concept into the test area. Consistency in test approach is a major step in achieving quality results.

Sigman, Clayton B.↗
NASA NTRS2007

A Comparison of Two Balance Calibration Model Building Methods

Simulated strain-gage balance calibration data is used to compare the accuracy of two balance calibration model building methods for different noise environments and calibration experiment designs. The first building method obtains a math model for the analysis of balance calibration data after applying a candidate math model search algorithm to the calibration data set. The second building method uses stepwise regression analysis in order to construct a model for the analysis. Four balance calibration data sets were simulated in order to compare the accuracy of the two math model building methods. The simulated data sets were prepared using the traditional One Factor At a Time (OFAT) technique and the Modern Design of Experiments (MDOE) approach. Random and systematic errors were introduced in the simulated calibration data sets in order to study their influence on the math model building methods. Residuals of the fitted calibration responses and other statistical metrics were compared in order to evaluate the calibration models developed with different combinations of noise environment, experiment design, and model building method. Overall, predicted math models and residuals of both math model building methods show very good agreement. Significant differences in model quality were attributable to noise environment, experiment design, and their interaction. Generally, the addition of systematic error significantly degraded the quality of calibration models developed from OFAT data by either method, but MDOE experiment designs were more robust with respect to the introduction of a systematic component of the unexplained variance.

DeLoach, Richard↗
DOE OSTI2025

Understanding the impacts of inorganic species in woody biomass for preprocessing and pyrolysis–A review

Woody biomass represents an abundant resource for sustainable biofuels, biochemicals, and bioproducts. Technologies for converting woody biomass have been established for decades, and research consistently highlights the critical role of inorganic species and ash plays in feedstock handling and conversion processes, including equipment plugging, corrosion, and catalyst deactivation. A thorough understanding of the variability, transport behavior, and downstream impact of inorganic species in woody biomass is essential for defining feedstock quality specifications and developing effective management strategies for conversion processes. This review compiles critical information in five main sections: 1) inorganic species concentration in woody biomass, based on anatomical fractions and their sources of variability; 2) technique features for quantifying inorganic elemental chemical analysis; 3) impacts of inorganic species on biomass preprocessing; 4) impacts of inorganic species on pyrolysis, and 5) mitigation strategies. Additionally, this review explores future challenges and opportunities in addressing the impacts of inorganic species on biomass quality. These insights aim to support the sustainable development of the biomass-to-bioenergy pipeline and ensure high-quality lignocellulosic feedstocks for efficient downstream conversions. The findings offer valuable guidance to policy makers, industry stakeholders, and researchers in developing effective strategies for managing inorganic species in woody biomass and fostering the sustainable processes for lignocellulosic biorefineries.

09 BIOMASS FUELS↗
NASA NTRS1987

Simulation of fluid flows during growth of organic crystals in microgravity

Several counter diffusion type crystal growth experiments were conducted in space. Improvements in crystal size and quality are attributed to reduced natural convection in the microgravity environment. One series of experiments called DMOS (Diffusive Mixing of Organic Solutions) was designed and conducted by researchers at the 3M Corporation and flown by NASA on the space shuttle. Since only limited information about the mixing process is available from the space experiments, a series of ground based experiments was conducted to further investigate the fluid dynamics within the DMOS crystal growth cell. Solutions with density differences in the range of 10 to the -7 to 10 to the -4 power g/cc were used to simulate microgravity conditions. The small density differences were obtained by mixing D2O and H2O. Methylene blue dye was used to enhance flow visualization. The extent of mixing was measured photometrically using the 662 nm absorbance peak of the dye. Results indicate that extensive mixing by natural convection can occur even under microgravity conditions. This is qualitatively consistent with results of a simple scaling analysis. Quantitave results are in close agreement with ongoing computational modeling analysis.

Roberts, Gary D.↗
NASA NTRS2007

A Return to Innovative Engineering Design, Critical Thinking and Systems Engineering

I believe we are facing a critical time where innovative engineering design is of paramount importance to the success of our aerospace industry. However, the very qualities and attributes necessary for enhancing, educating, and mentoring a creative spirit are in decline in important areas. The importance of creativity and innovation in this country was emphasized by a special edition of the Harvard Business Review OnPoint entitled: "The Creative Company" which compiled a series of past and present articles on the subject of creativity and innovation and stressed its importance to our national economy. There is also a recognition of a lack of engineering, critical thinking and problem-solving skills in our education systems and a trend toward trying to enhance those skills by developing K-12 educational programs such as Project Lead the Way, "Science for All Americans", Benchmarks 2061 , etc. In addition, with respect to spacecraft development, we have a growing need for young to mid-level engineers with appropriate experience and skills in spacecraft design, development, analysis, testing, and systems engineering. As the Director of Engineering at NASA's Johnson Space Center, I realized that sustaining engineering support of an operational human spacecraft such as the Space Shuttle is decidedly different than engineering design and development skills necessary for designing a new spacecraft such as the Crew Exploration Vehicle of the Constellation Program. We learned a very important lesson post Columbia in that the Space Shuttle is truly an experimental and not an operational vehicle and the strict adherence to developed rules and processes and chains of command of an inherently bureaucratic organizational structure will not protect us from a host of known unknowns let alone unknown unknowns. There are no strict rules, processes, or procedures for understanding anomalous results of an experiment, anomalies with an experimental spacecraft like Shuttle, or in the conceptual design of a spacecraft. Engineering design is as much an art as it is a science. The critical thinking skills necessary to uncover lurking problems in an experimental design and creatively develop solutions are some of the same skills necessary to design a new spacecraft. Thus, I believe engineers unfamiliar with or removed from design and development need time to transition and develop the required skill set to be effective spacecraft designers. I believe the creative process necessary in design can be enhanced and even taught as early as grades K-12 and should continue to be nurtured and developed at the university level and beyond. I am going to present a strategy for developing learning teams to address complex multidisciplinary problems and to creatively develop solutions to those problems rapidly at minimal cost. I will frame a real problem, the development of on-orbit thermal protection system repair of the Space Shuttle, and step through the series of skills necessary to enhance the creative process. The case study I will illustrate is based on a real project, the R&D Reinforced Carbon-Carbon (RCC) Repair Team's development of on-orbit repair concepts for damaged Space Shuttle RCC nose cap and/or leading edges.

Camarda, Charles J.↗
NASA NTRS2016

Characterization of Evidence for Human System Risk Assessment

Understanding the kinds of evidence available and using the best evidence to answer a question is critical to evidenced-based decision-making, and it requires synthesis of evidence from a variety of sources. Categorization of human system risks in spaceflight, in particular, focuses on how well the integration and interpretation of all available evidence informs the risk statement that describes the relationship between spaceflight hazards and an outcome of interest. A mature understanding and categorization of these risks requires: 1) sufficient characterization of risk, 2) sufficient knowledge to determine an acceptable level of risk (i.e., a standard), 3) development of mitigations to meet the acceptable level of risk, and 4) identification of factors affecting generalizability of the evidence to different design reference missions. In the medical research community, evidence is often ranked by increasing confidence in findings gleaned from observational and experimental research (e.g., "levels of evidence"). However, an approach based solely on aspects of experimental design is problematic in assessing human system risks for spaceflight. For spaceflight, the unique challenges and opportunities include: (1) The independent variables in most evidence are the hazards of spaceflight, such as space radiation or low gravity, which cannot be entirely duplicated in terrestrial (Earth-based) analogs, (2) Evidence is drawn from multiple sources including medical and mission operations, Lifetime Surveillance of Astronaut Health (LSAH), spaceflight research (LSDA), and relevant environmental & terrestrial databases, (3) Risk metrics based primarily on LSAH data are typically derived from available prevalence or incidence data, which may limit rigorous interpretation, (4) The timeframe for obtaining adequate spaceflight sample size (n) is very long, given the small population, (5) Randomized controlled trials are unattainable in spaceflight, (6) Collection of personal and environmental data on the astronaut population may create opportunities for advanced analytics and human-environment modeling that goes beyond that achieved in isolated experimental designs; and (7) Translation of relevant research to operations is a complex, transdisciplinary enterprise in which the approach must apply across the physical, biological, behavioral, and social sciences. The approach to synthesizing evidence must address both source and fidelity of data, and reflect the most general attributes of quality of evidence in science and engineering: reliability and validity. The authors are developing a two-factor approach which includes the various kinds of evidence required to understand risks and for the integrated interpretation of all evidence that is essential to develop standards and countermeasures. A unified framework for aggregating and assessing different kinds of evidence provides a consistent, traceable, evidence-based decision-making process to translate research to operations in an environment where engineers, scientists, physicians, and managers all engage in analyzing the trade space of vehicle design, standards, requirements and solutions for spaceflight.

Steinberg, S. L.↗
NASA NTRS2016

A New Look at Data Usage by Using Metadata Attributes as Indicators of Data Quality

This study reviews the key metrics (users, distributed volume, and files) in multiple ways to gain an understanding of the significance of the metadata. Characterizing the usability of data by key metadata elements, such as discipline and study area, will assist in understanding how the user needs have evolved over time. The data usage pattern based on product level provides insight into the level of data quality. In addition, the data metrics by various services, such as the Open-source Project for a Network Data Access Protocol (OPeNDAP) and subsets, address how these services have extended the usage of data. Over-all, this study presents the usage of data and metadata by metrics analyses, which may assist data centers in better supporting the needs of the users.

metadata↗
NASA NTRS2008

A Scenario-Based Process for Requirements Development: Application to Mission Operations Systems

The notion of using operational scenarios as part of requirements development during mission formulation (Phases A & B) is widely accepted as good system engineering practice. In the context of developing a Mission Operations System (MOS), there are numerous practical challenges to translating that notion into the cost-effective development of a useful set of requirements. These challenges can include such issues as a lack of Project-level focus on operations issues, insufficient or improper flowdown of requirements, flowdown of immature or poor-quality requirements from Project level, and MOS resource constraints (personnel expertise and/or dollars). System engineering theory must be translated into a practice that provides enough structure and standards to serve as guidance, but that retains sufficient flexibility to be tailored to the needs and constraints of a particular MOS or Project. We describe a detailed, scenario-based process for requirements development. Identifying a set of attributes for high quality requirements, we show how the portions of the process address many of those attributes. We also find that the basic process steps are robust, and can be effective even in challenging Project environments.

MOS↗
NASA NTRS2004

Software Quality Assurance Metrics

Software Quality Assurance (SQA) is a planned and systematic set of activities that ensures conformance of software life cycle processes and products conform to requirements, standards and procedures. In software development, software quality means meeting requirements and a degree of excellence and refinement of a project or product. Software Quality is a set of attributes of a software product by which its quality is described and evaluated. The set of attributes includes functionality, reliability, usability, efficiency, maintainability, and portability. Software Metrics help us understand the technical process that is used to develop a product. The process is measured to improve it and the product is measured to increase quality throughout the life cycle of software. Software Metrics are measurements of the quality of software. Software is measured to indicate the quality of the product, to assess the productivity of the people who produce the product, to assess the benefits derived from new software engineering methods and tools, to form a baseline for estimation, and to help justify requests for new tools or additional training. Any part of the software development can be measured. If Software Metrics are implemented in software development, it can save time, money, and allow the organization to identify the caused of defects which have the greatest effect on software development. The summer of 2004, I worked with Cynthia Calhoun and Frank Robinson in the Software Assurance/Risk Management department. My task was to research and collect, compile, and analyze SQA Metrics that have been used in other projects that are not currently being used by the SA team and report them to the Software Assurance team to see if any metrics can be implemented in their software assurance life cycle process.

McRae, Kalindra A.↗
NASA NTRS1992

SIMBAD quality-control

The astronomical database SIMBAD developed at the Centre de donnees astronomiques de Strasbourg presently contains 760,000 objects (stellar and non-stellar). It has the unique characteristic of being structured specifically for astronomical objects. All types of heterogeneous data (bibliographic references, measurements, and sets of identification) are connected with each object. The attributes that define quality of the database include the following. Reliability: cross-identification should not rely upon just exact values object coordinates. It also means that information attached to one simple object should be consistent. The existing data must be controlled in order to start with a reliable base and to cross-identify new data assuring the quality as data grows. Exhaustivity: delays between publication of new informations and their inclusion in the database should be as short as possible. The integrity of the database has to be maintained as data accumulates. Taking the amount of data into consideration and the rate of new data production, it is necessary to use automatic methods. One of the possibilities is to use multivariate data analysis. The factor-space is a n-dimensional relevancy space which is described by the n-axes representing a set of n subject matter headings; the words and phrases can be used to scale the axes and the documents are then a vector average of the terms within them. The application reported herein is based on the NASA-STI bibliographical database. The selected data concern astronomy, astrophysics, and space radiation (102,963 references from 1975 to 1991 included 8070 keywords). The F-space is built from this bibliographical data. By comparing the F-space position obtained from the NASA-STI keywords with the F-space position obtained from the SIMBAD references, the authors will be able to show whether it is possible to retrieve information with a restricted set of words only. If the comparison is valid, this will be a way to enter bibliographic information in the SIMBAD quality control process. Furthermore, it is possible to connect the physical measurements of stars from SIMBAD to literature concerning these stars from the NASA-STI abstracts. The physical properties of stars (e.g. UBV colors) are not randomly distributed. Stars are distributed among different clusters in a physical parameter space. The authors will show that there are some relations between this classification and the literature concerning these objects clusters in a factor space. They will investigate the nature of the relationship between the SIMBAD measurements and the bibliography. These would be new relationships that are not pre-established by an astronomer. In addition, the bibliography could be neutral information that can be used in combination with the measured parameters.

Lesteven, Soizick↗
DOE OSTI2025

Performance Evaluation of Weather@home2 Simulations over West African Region

Weather and climate forecasting, using climate models, have become essential tools and life-savers in the West African region; in spite of the fact that climate models do not fully comply with attributes of forecast qualities—RASAP: reliability, association, skill, accuracy, and precision. The objective of this paper is to quantitatively evaluate, in comparison to CRU and ERA5 datasets, the RASAP compliance-level of the weather@home2 modeling system (w@h2). Findings from some statistical evaluations show that, to a moderately significant extent, w@h2 model provides useful information during the monsoon seasons; skills to capture the Little Dry Season over the Guinea zone; predictive skills for the onset season; ability to reproduce all the annual characteristics of the surface maximum air temperature over the region; as well as skill to detect heat waves that usually ravage West Africa during the boreal spring. The model displays traces of attributes that are needed for seasonal climate predictions and applications. Deficiencies in the quantitative reproducibility point to the facts that the model does provide a reliability akin to that of regional climate models. This paper further furnishes a prospective user with information on whether the model might be “useful or not” for a particular application.

West Africa↗