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All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Analysis of DSN software anomalies

A categorized data base of software errors which were discovered during the various stages of development and operational use of the Deep Space Network DSN/Mark 3 System was developed. A study team identified several existing error classification schemes (taxonomies), prepared a detailed annotated bibliography of the error taxonomy literature, and produced a new classification scheme which was tuned to the DSN anomaly reporting system and encapsulated the work of others. Based upon the DSN/RCI error taxonomy, error data on approximately 1000 reported DSN/Mark 3 anomalies were analyzed, interpreted and classified. Next, error data are summarized and histograms were produced highlighting key tendencies.

Galorath, D. D.↗
NASA NTRS1991

Engineering derivatives from biological systems for advanced aerospace applications

The present study consisted of a literature survey, a survey of researchers, and a workshop on bionics. These tasks produced an extensive annotated bibliography of bionics research (282 citations), a directory of bionics researchers, and a workshop report on specific bionics research topics applicable to space technology. These deliverables are included as Appendix A, Appendix B, and Section 5.0, respectively. To provide organization to this highly interdisciplinary field and to serve as a guide for interested researchers, we have also prepared a taxonomy or classification of the various subelements of natural engineering systems. Finally, we have synthesized the results of the various components of this study into a discussion of the most promising opportunities for accelerated research, seeking solutions which apply engineering principles from natural systems to advanced aerospace problems. A discussion of opportunities within the areas of materials, structures, sensors, information processing, robotics, autonomous systems, life support systems, and aeronautics is given. Following the conclusions are six discipline summaries that highlight the potential benefits of research in these areas for NASA's space technology programs.

Winfield, Daniel L.↗
NASA NTRS2019

Orbital Debris Ontology, Terminology, and Knowledge Modeling

The looming threat orbital debris poses to assets in orbit demands solutions. As the orbital population grows, so does this hazard, but so does the sea of data. The problem is also an opportunity for interdisciplinary innovation and cooperation. This paper focuses on the data and information management aspect of developing solutions for a sustainable and safe orbital space environment. The corresponding author’s in-progress work to develop an orbital debris domain ontology is summarized in order to discuss knowledge modeling for this domain. Methodological approaches of this effort can also contribute to standards efforts and address terminological and policy questions. Leveraging the growing volumes of orbital debris and space situational awareness (SSA) data will create a more complete picture of the orbital space environment. Part of the solution will be: consistent and correct data interpretation, sharing orbital debris and SSA data in one form or another, terminology development & harmonization, and knowledge or domain modeling. To facilitate this, [Rovetto, 2015/16] discussed ontology development for the orbital debris domain. This paper lists concepts from that paper, and subsequently developed concepts [2-9]. Ontology engineering is an interdisciplinary field related to knowledge representation and reasoning in artificial intelligence, semantic technologies and the so-called semantic web. An ontology is effectively a computable and semantically rich terminology that presents a knowledge or domain model for a topic area. Expressions of knowledge or assertions are stored using formally defined term. This knowledge base is reasoned over to yield answers to queries, among other things. Ontologies have been developed in knowledge-based projects across various disciplines, and used for such things as search engines, chatbots, enterprise knowledge graphs, etc. Ontologies support: interoperability, automated reasoning, data sharing and integration, data search and retrieval, and communicating the meaning of data. The Orbital Debris Ontology (ODO), and related ontologies [Rovetto & Kelso 2016] [Rovetto 2016, 2017], were proposed to help achieve this. ODO, for instance, is intended as a domain ontology that can be used across federated databases, offering an explicitly specified set of concepts describing the orbital debris domain. Its meaning-rich taxonomy will provide a sharable semantics for orbital debris data to, in part, consistently communicate the meaning of data to both humans and machines, and tag data elements in space object catalogs to help afford inference tasks, decision support, knowledge discovery, and information integration. ODO and the SSA ontology (SSAO) is part of the overall Orbital Space Domain Ontology concept, which is conceived as a broader domain reference ontology. It aims to provide a knowledge representation structure of the orbital space environment, a common semantic model, and develop a sharable terminology. Collectively this will provide common meaning for datasets, a high-level taxonomy or classification for orbital space objects, and thus means to characterize space objects. Ongoing efforts have included using visualizations, R, JSON-LD, and contemporary semantic technologies. Potential applications and interdisciplinary partnerships include web-based platforms, web apps, visualizations, and academia projects. Community input and participation may yield a more widely understood domain model as well as facilitate terminological standards. For example, the proposed conceptual, terminological and ontological analysis may contribute to such efforts as the Space Debris Mitigation Requirements in the International Standards Organization by developing more precise, consistent and coherent terms and definitions. Projects that seek to develop in-house ontologies can use ODO and related ontologies as domain reference ontologies. This paper was developed independent of author affiliations. Readers are encouraged to contact corresponding author(1) with general interest and potential opportunities to support or realize the described project.

Robert J. Rovetto↗
NASA NTRS2012

The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

Ancel, Ersin↗
NASA NTRS2003

Enabling knowledge discovery: taxonomy development for NASA

This white paper provides the background for why it is important to take the next steps with the NASA taxonomy including test and validation, XML schema development, integration with the FirstGov federal search engine, the OneNASA portal and its supporting web content management system.

taxonomy metadata classification schema NASA porta↗
NASA NTRS1988

Overview and classification of meteorites

Before interpreting properties of meteorites in terms of putative processes and conditions in the early solar system, it is necessary to understand just what sort of objects meteorites are. Such understanding begins with classification. In this chapter, the current taxonomy of meteorites is summarized, and it is shown how certain stone meteorites, the chondrites, possess chemical and petrographic features that make them potentially attractive as probes of the early solar system. The prevalence of secondary alteration effects, often capable of perturbing the primitive record even in chondrites, is also emphasized.

Sears, Derek W. G.↗
NASA NTRS2004

Mineralogical Variations Among High Albedo E-Type Asteroids: Implications for Asteroid Igneous Processes

The link between the E-type asteroids and the enstatite achondrites (aubrites) was first proposed for the original E-asteroid, 44 Nysa. The association was based on the high albedos and the featureless spectra shared by the E-asteroids and the aubrites. Among the plausible geologic and meteoritic materials, only enstatite (the magnesium end-member of the pyroxene solid solution series) is sufficiently abundant to comprise asteroid-sized bodies. However, the presence of a weak 0.89 m absorption feature in the spectrum of 44 Nysa indicates that its pyroxene contains a small amount of Fe(2+) but still substantially more than any aubrite present in the meteorite collection. The original E-class was defined based on its high albedo and flat to slightly reddish spectrum. In the absence of albedo data, the E-type was degenerate with the M- and P-types, and together these were designated as X-types. Recently, a taxonomy has been proposed to identify E-types in the absence of albedo data. In this newer classification system three subdivisions of the X-type have been proposed, including Xc, Xe and Xk. Of nine albedo-defined E-types [d], this newer non-albedo based taxonomy produced the following classifications: X-1 asteroid; Xc-2 asteroids; Xe-5 asteroids; and Xk-1 asteroid. Although the Xe subtype includes the largest number of albedo-defined E-types, most of the remaining 24 Xe-types can be excluded based on their low measured IRAS albedos, ranging from 0.116 to 0.329, which are below the lower albedo limit of the E-class (0.34) and substantially below that of the lowest albedo an actual E-type asteroid (0.41). The present discussion will be limited to unambiguous E-type asteroids determined on albedo criteria.

Gaffey, Michael J.↗
NASA NTRS1978

Physical observations and taxonomy of asteroids

Physical asteroid observations are summarized and the classification scheme to describe asteroid surfaces in relation to mineralogical composition is detailed. The principle classes, distinguished on the basis of a number of parameters involving albedo and color, are called C, S, and M.

Morrison, D.↗
NASA NTRS2016

A Taxonomy-Based Approach to Shed Light on the Babel of Mathematical Models for Rice Simulation

For most biophysical domains, differences in model structures are seldom quantified. Here, we used a taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes for each key were identified, and models were categorised into five clusters using a binary similarity measure and the unweighted pair-group method with arithmetic mean. Principal component analysis was performed on model outputs at four sites. Results indicated that (i) differences in structure often resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs. User subjectivity during calibration may have hidden expected relationships between model structure and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences model performance.

model parameterisation↗
NASA NTRS1985

Chemical compositional study of 35 iron meteorites and its application in taxonomy

Structural and compositional data are reported as a guide to the classification of 35 iron meteorites. The Xinjiang iron meteorite, previously classified as III AB, is reclassified as III E on the basis of its lower Ga/Ni and Ge/Ni ratios, its wider, swollen kamacite bands, and the ubiquitous presence of haxonite, (Fe,Ni)22C. The Dongling (III CD) appears not to be a new meteorite, but to be paired with the Nantan. Four Antarctic iron meteorites, IAB Allan Hills A77250, A77263, A77289, and A77290, are classified as a paired meteorite because of their similarities in structure and in concentrations of various elements. It is shown that Cu shares certain properties with Ga and Ge, which makes them excellent taxonomic parameters.

Wang, D.↗
NASA NTRS2022

T-Rex: The NASA Technology Taxonomy Recommender System

NASA tracks over 16,000 technology projects across the Agency, from propulsion systems to software. These projects are classified according to NASA’s Technology Taxonomy to facilitate data search, extraction, and application. In 2020, the Taxonomy was revised to better align strategic goals with project technical disciplines. Manual re-classification of cur-rent and historical projects was estimated to take thousands of technologist labor hours. Instead of manual classification, our team developed T-Rex, a recommender engine, trained on just a small set of manually classified projects. T-Rex was used to classify the projects and then integrate the data into TechPort to recommend classes to users when updating projects. The system and methodology are used in other NASA projects, and T-Rex has achieved 95% accepted accuracy overall.

Space Technology↗
NASA NTRS1985

A new taxonomy for distributed computer systems based upon operating system structure

Characteristics of the resource structure found in the operating system are considered as a mechanism for classifying distributed computer systems. Since the operating system resources, themselves, are too diversified to provide a consistent classification, the structure upon which resources are built and shared are examined. The location and control character of this indivisibility provides the taxonomy for separating uniprocessors, computer networks, network computers (fully distributed processing systems or decentralized computers) and algorithm and/or data control multiprocessors. The taxonomy is important because it divides machines into a classification that is relevant or important to the client and not the hardware architect. It also defines the character of the kernel O/S structure needed for future computer systems. What constitutes an operating system for a fully distributed processor is discussed in detail.

Foudriat, E. C.↗
NASA NTRS1982

Characteristic variations in reflectance of surface soils

Surface soil samples from a wide range of naturally occurring soils were obtained for the purpose of studying the characteristic variations in soil reflectance as these variations relate to other soil properties and soil classification. A total 485 soil samples from the U.S. and Brazil representing 30 suborders of the 10 orders of 'Soil Taxonomy' was examined. The spectral bidirectional reflectance factor was measured on uniformly moist soils over the 0.52 to 2.32 micron wavelength range with a spectroradiometer adapted for indoor use. Five distinct soil spectral reflectance curve forms were identified according to curve shape, the presence or absence of absorption bands, and the predominance of soil organic matter and iron oxide composition. These curve forms were further characterized according to generically homogeneous soil properties in a manner similar to the subdivisions at the suborder level of 'Soil Taxonomy'. Results indicate that spectroradiometric measurements of soil spectral bidirectional reflectance factor can be used to characterize soil reflectance in terms that are meaningful to soil classification, genesis, and survey.

Stoner, E. R.↗
NASA NTRS2019

An Evolutionary System of Mineralogy: Proposal for A Classification of Planetary Materials Based on Natural Kind Clustering

Minerals reveal the nature of the co-evolving geosphere and biosphere through billions of years of Earth history. Mineral classification systems have the potential to elucidate this rich evolutionary story; however, the present mineral taxonomy, based as it is on idealized major element chemistry and crystal structure, lacks a temporal aspect, and thus cannot reflect planetary evolution. A complementary evolutionary system of mineralogy based on the quantitative recognition of “natural kind clustering” for a wide range of condensed planetary materials with different paragenetic origins has the potential to amplify, though not supersede, the present classification system.

Philosophy of mineralogy↗
NASA NTRS1974

Craters on Earth, Moon, and Mars - Multivariate classification and mode of origin

Testing extraterrestrial craters and candidate terrestrial analogs for morphologic similitude is treated as a problem in numerical taxonomy. According to a principal-components solution and a cluster analysis, 402 representative craters on the Earth, the Moon, and Mars divide into two major classes of contrasting shapes and modes of origin. Craters of net accumulation of material (cratered lunar domes, Martian calderas, and all terrestrial volcanoes except maars and tuff rings) group apart from craters of excavation (terrestrial meteorite impact and experimental explosion craters, typical Martian craters, and all other lunar craters). Maars and tuff rings belong to neither group but are transitional. The classification criteria are four independent attributes of topographic geometry derived from seven descriptive variables by the principal-components transformation. Morphometric differences between crater bowl and raised rim constitute the strongest of the four components.

Pike, R. J.↗
NASA NTRS1989

A three-parameter asteroid taxonomy

Broadband U, V, and x photometry together with IRAS asteroid albedos have been used to construct an asteroid classification system. The system is based on three parameters (U-V and v-x color indices and visual geometric albedo), and it is able to place 96 percent of the present sample of 357 asteroids into 11 taxonomic classes. It is noted that all but one of these classes are analogous to those previously found using other classification schemes. The algorithm is shown to account for the observational uncertainties in each of the classification parameters.

Tedesco, Edward F.↗