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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Inductive System Monitors Tasks

The Inductive Monitoring System (IMS) software developed at Ames Research Center uses artificial intelligence and data mining techniques to build system-monitoring knowledge bases from archived or simulated sensor data. This information is then used to detect unusual or anomalous behavior that may indicate an impending system failure. Currently helping analyze data from systems that help fly and maintain the space shuttle and the International Space Station (ISS), the IMS has also been employed by data classes are then used to build a monitoring knowledge base. In real time, IMS performs monitoring functions: determining and displaying the degree of deviation from nominal performance. IMS trend analyses can detect conditions that may indicate a failure or required system maintenance. The development of IMS was motivated by the difficulty of producing detailed diagnostic models of some system components due to complexity or unavailability of design information. Successful applications have ranged from real-time monitoring of aircraft engine and control systems to anomaly detection in space shuttle and ISS data. IMS was used on shuttle missions STS-121, STS-115, and STS-116 to search the Wing Leading Edge Impact Detection System (WLEIDS) data for signs of possible damaging impacts during launch. It independently verified findings of the WLEIDS Mission Evaluation Room (MER) analysts and indicated additional points of interest that were subsequently investigated by the MER team. In support of the Exploration Systems Mission Directorate, IMS is being deployed as an anomaly detection tool on ISS mission control consoles in the Johnson Space Center Mission Operations Directorate. IMS has been trained to detect faults in the ISS Control Moment Gyroscope (CMG) systems. In laboratory tests, it has already detected several minor anomalies in real-time CMG data. When tested on archived data, IMS was able to detect precursors of the CMG1 failure nearly 15 hours in advance of the actual failure event. In the Aeronautics Research Mission Directorate, IMS successfully performed real-time engine health analysis. IMS was able to detect simulated failures and actual engine anomalies in an F/A-18 aircraft during the course of 25 test flights. IMS is also being used in colla

Source record↗
NASA NTRSDate not supplied

Feasibility Study to Interactive Workshop: Building End-user Capacity to Integrate Earth Observation Data into Federally Endangered Atlantic Salmon (Salmo salar) Habitat Monitoring in Main

Changes in temperature and precipitation patterns, along with alterations in land cover threaten ongoing conservation efforts for Federally Endangered Atlantic salmon (Salmo salar) in Maine. Earth observation data offers a unique perspective for habitat monitoring that can complement habitat restoration and conservation activity on the ground. As a dual capacity building program, the NASA DEVELOP National Program strives to build the capacity of program participants by leveraging Earth observation data to address environmental concerns across the globe, while also building capacity in partner organizations to integrate Earth observation data into their decision making practices. Between September 2021 and August 2022, three NASA DEVELOP teams demonstrated the feasibility of utilizing NASA Earth observations including Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), Terra MODIS, Landsat 5 Thematic Mapper (TM), Landsat 8 Operation Land Imager (OLI), and Shuttle Radar Topography Mission (SRTM) in conjunction with Sentinel-2 MultiSpectral Instrument (MSI) to assess temperature, precipitation, and land use land cover (LULC) over time throughout salmon habitat in Maine. While the first two teams completed projects that were categorized as NASA DEVELOP’s traditional feasibility projects, the third and final project team generated resources and planned an interactive workshop to transfer project methods to end-user organizations. Ultimately, the goal of this work was to not only inform the partner’s ongoing salmon population recovery and habitat restoration initiatives but provide tools that allow partner organizations to continue integrating Earth observation data into their work beyond their partnership with the program. This project serves as a case study within the NASA DEVELOP Program and provides lessons learned for moving beyond traditional feasibility studies to more interactive partner engagement and knowledge transfer practices.

Nicole Ramberg-Pihl↗
NASA NTRS1998

Applications of Artificial Neural Networks in Structural Engineering with Emphasis on Continuum Models

The use of continuum models for the analysis of discrete built-up complex aerospace structures is an attractive idea especially at the conceptual and preliminary design stages. But the diversity of available continuum models and hard-to-use qualities of these models have prevented them from finding wide applications. In this regard, Artificial Neural Networks (ANN or NN) may have a great potential as these networks are universal approximators that can realize any continuous mapping, and can provide general mechanisms for building models from data whose input-output relationship can be highly nonlinear. The ultimate aim of the present work is to be able to build high fidelity continuum models for complex aerospace structures using the ANN. As a first step, the concepts and features of ANN are familiarized through the MATLAB NN Toolbox by simulating some representative mapping examples, including some problems in structural engineering. Then some further aspects and lessons learned about the NN training are discussed, including the performances of Feed-Forward and Radial Basis Function NN when dealing with noise-polluted data and the technique of cross-validation. Finally, as an example of using NN in continuum models, a lattice structure with repeating cells is represented by a continuum beam whose properties are provided by neural networks.

Kapania, Rakesh K.↗
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↗
NASA NTRS2018

Integrated Data Visualization for Human Missions

Interplanetary missions produce exceptionally large and complex volumes of data that can be extremely difficult to navigate. This is especially true for human missions. This project seeks to develop an integrated data visualization environment that builds on the success of Apollo17.org allowing for integration of operational, engineering and scientific data while also preserving the context under which it was collected during the mission. We will build a product that integrates existing data from the Neutral Buoyancy Lab (NBL) to improve data visualization for extra vehicular activity and interplanetary missions.

Regberg, Aaron B.↗
NASA NTRS1999

Some Continuum Aspects of Data Assimilation

A long-sought goal in data assimilation is to build more fully the time dimension into the data assimilation process. Truly four-dimensional data assimilation requires evolving second-moment information, namely the estimation error covariance, along with the state estimate itself. The continuum evolution equations for the estimation error covariance constitute a system of partial differential equations in six space dimensions, forced by a model error covariance and by the observations themselves. The high dimensionality of this system poses a difficult computational problem. Numerous methods have been proposed for approximate, discrete solution of this system of equations. Four-dimensional variational schemes solve these equations indirectly, while schemes based on Kalman filtering solve the equations more directly, usually on a small subspace of the full six-dimensional space. In both cases, most proposed solution methods are not derived from the continuum covariance evolution equations themselves. Instead, most methods simply inherit the discretization used for evolving the state estimate, either in the form of a discrete tangent linear model or, in the case of ensemble schemes, in the form of a fully nonlinear discrete model. In this lecture we show that solutions of the continuum covariance evolution equations possess simple properties that are not readily satisfied upon discretization of the equations. we give numerical examples illustrating that "inherited" discretizations sometimes fail to produce meaningfully accurate solutions. Finally, we suggest alternative discretization methods that may yield more faithful approximate solutions of the continuum problem.

Cohn, Stephen E.↗
NASA NTRS2018

Building a Quality Controlled Database of Meteorological Data from NASA Kennedy Space Center and the United States Air Force's Eastern Range

The National Aeronautics and Space Administration's (NASA) Marshall Space Flight Center (MSFC) Natural Environments Branch (EV44) has provided atmospheric databases and analysis in support of space vehicle design and day-of-launch operations for NASA and commercial launch vehicle programs launching from the NASA Kennedy Space Center (KSC), co-located on the United States Air Force's Eastern Range (ER) at the Cape Canaveral Air Force Station. The ER complex is one of the most heavily instrumented sites in the United States with over 31 towers measuring various atmospheric parameters on a continuous basis. An inherent challenge with large sets of data consists of ensuring erroneous data is removed from databases, and thus excluded from launch vehicle design analyses. EV44 has put forth great effort in developing quality control (QC) procedures for individual meteorological instruments, however no standard QC procedures for all databases currently exists resulting in QC databases that have inconsistencies in variables, methodologies, and periods of record. The goal of this activity is to use the previous efforts by EV44 to develop a standardized set of QC procedures from which to build meteorological databases from KSC and the ER, while maintaining open communication with end users from the launch community to develop ways to improve, adapt and grow the QC database. Details of the QC procedures will be described. As the rate of launches increases with additional launch vehicle programs, it is becoming more important that weather databases are continually updated and checked for data quality before use in launch vehicle design and certification analyses.

Brenton, James C.↗
NASA NTRS1993

surf3d: A 3-D finite-element program for the analysis of surface and corner cracks in solids subjected to mode-1 loadings

A computer program, surf3d, that uses the 3D finite-element method to calculate the stress-intensity factors for surface, corner, and embedded cracks in finite-thickness plates with and without circular holes, was developed. The cracks are assumed to be either elliptic or part eliptic in shape. The computer program uses eight-noded hexahedral elements to model the solid. The program uses a skyline storage and solver. The stress-intensity factors are evaluated using the force method, the crack-opening displacement method, and the 3-D virtual crack closure methods. In the manual the input to and the output of the surf3d program are described. This manual also demonstrates the use of the program and describes the calculation of the stress-intensity factors. Several examples with sample data files are included with the manual. To facilitate modeling of the user's crack configuration and loading, a companion program (a preprocessor program) that generates the data for the surf3d called gensurf was also developed. The gensurf program is a three dimensional mesh generator program that requires minimal input and that builds a complete data file for surf3d. The program surf3d is operational on Unix machines such as CRAY Y-MP, CRAY-2, and Convex C-220.

Raju, I. S.↗
NASA NTRS2022

A New Machine Learning Based Analysis for Improving Satellite Retrieved Atmospheric Composition Data: OMI SO2 as an Example

Despite recent progress, satellite retrievals of anthropogenic SO2 still suffer from relatively low signal-tonoise ratios. In this study, we demonstrate a new machine learning data analysis method to improve the quality of satellite SO2 products. In the absence of large ground-truth datasets for SO2, we start from SO2 slant column densities (SCDs) retrieved from the Ozone Monitoring Instrument (OMI) using a data-driven, physically based algorithm and calculate the ratio between the SCD and the root mean square (rms) of the fitting residuals for each pixel. To build the training data, we select presumably clean pixels with small SCD / rms ratios (SRRs) and set their target SCDs to zero. For polluted pixels with relatively large SRRs, we set the target to the original retrieved SCDs. We then train neural networks (NNs) to reproduce the target SCDs using predictors including SRRs for individual pixels, solar zenith, viewing zenith and phase angles, scene reflectivity, and O3 column amounts, as well as the monthly mean SRRs. For data analysis, we employ two NNs: (1) one trained daily to produce analyzed SO2 SCDs for polluted pixels each day and (2) the other trained once every month to produce analyzed SCDs for less polluted pixels for the entire month. Test results for 2005 show that our method can significantly reduce noise and artifacts over background regions. Over polluted areas, the monthly mean NN-analyzed and original SCDs generally agree to within ±15 %, indicating that our method can retain SO2 signals in the original retrievals except for large volcanic eruptions. This is further confirmed by running both the NN-analyzed and original SCDs through a topdown emission algorithm to estimate the annual SO2 emissions for ∼ 500 anthropogenic sources, with the two datasets yielding similar results. We also explore two alternative approaches to the NN-based analysis method. In one, we employ a simple linear interpolation model to analyze the original SCD retrievals. In the other, we develop a PCA–NN algorithm that uses OMI measured radiances, transformed and dimension-reduced with a principal component analysis (PCA) technique, as inputs to NNs for SO2 SCD retrievals. While the linear model and the PCA–NN algorithm can reduce retrieval noise, they both underestimate SO2 over polluted areas. Overall, the results presented here demonstrate that our new data analysis method can significantly improve the quality of existing OMI SO2 retrievals. The method can potentially be adapted for other sensors and/or species and enhance the value of satellite data in air quality research and applications.

Can Li↗
NASA NTRS1995

Recent results from Long Duration Exposure Facility materials testing

The overall goals of the Long Duration Exposure Facility (LDEF) investigations, established by the Materials Special Investigation Group (MSIG) prior to LDEF retrieval, are to provide useful engineering data to people designing and building spacecraft, and secondarily, to obtain data of potential interest to materials researchers. The specific objectives are to support predictions of materials lifetimes under the various low earth orbit (LEO) environments to determine how long the material will physically survive; to estimate the engineering performance lifetimes of these same materials under specific LEO exposures; to identify materials and processes by which given materials degrade; and to provide insights into development of new, more inherently LEO environmentally resistant materials. To achieve the established objectives, two criteria were established to select which materials had the highest priorities for analyses. The first criteria was to examine materials which are still being used on new spacecraft. The second priority was to examine materials with multiple exposure locations on LDEF, because this provided the opportunity to develop predictions on how a material will behave as the exposure environment varies. The goals as defined led to the identification of silverized Teflon (Ag/FEP), chromic acid anodized aluminum, and certain thermal control paints used on LDEF as the material types of highest priority for examination. Ag/FEP was chosen because of an excellent a/e ratio (extremely low), and extensive flight history. Also influencing the decision was the fact that the Hubble Space Telescope uses Ag/FEP as its primary passive thermal control system (and was launched shortly after LDEF retrieval) and that a number of other spacecraft are using or considering this material for use. The various forms of Ag/FEP were used on virtually every side of the LDEF except the earth end. The potential uses of this material, and the location distribution and therefore, exposure conditions, have provided the rationale and opportunity for a comprehensive study of this material.

Pippin, H. Gary↗
NASA NTRS2015

Finding Atmospheric Composition (AC) Metadata

The Atmospheric Composition Portal (ACP) is an aggregator and curator of information related to remotely sensed atmospheric composition data and analysis. It uses existing tools and technologies and, where needed, enhances those capabilities to provide interoperable access, tools, and contextual guidance for scientists and value-adding organizations using remotely sensed atmospheric composition data. The initial focus is on Essential Climate Variables identified by the Global Climate Observing System CH4, CO, CO2, NO2, O3, SO2 and aerosols. This poster addresses our efforts in building the ACP Data Table, an interface to help discover and understand remotely sensed data that are related to atmospheric composition science and applications. We harvested GCMD, CWIC, GEOSS metadata catalogs using machine to machine technologies - OpenSearch, Web Services. We also manually investigated the plethora of CEOS data providers portals and other catalogs where that data might be aggregated. This poster is our experience of the excellence, variety, and challenges we encountered.Conclusions:1.The significant benefits that the major catalogs provide are their machine to machine tools like OpenSearch and Web Services rather than any GUI usability improvements due to the large amount of data in their catalog.2.There is a trend at the large catalogs towards simulating small data provider portals through advanced services. 3.Populating metadata catalogs using ISO19115 is too complex for users to do in a consistent way, difficult to parse visually or with XML libraries, and too complex for Java XML binders like CASTOR.4.The ability to search for Ids first and then for data (GCMD and ECHO) is better for machine to machine operations rather than the timeouts experienced when returning the entire metadata entry at once. 5.Metadata harvest and export activities between the major catalogs has led to a significant amount of duplication. (This is currently being addressed) 6.Most (if not all) Earth science atmospheric composition data providers store a reference to their data at GCMD.

metadata search↗
NASA NTRS1984

An integrated GIS/remote sensing data base in North Cache soil conservation district, Utah: A pilot project for the Utah Department of Agriculture's RIMS (Resource Inventory and Monitoring System)

A basic geographic information system (GIS) for the North Cache Soil Conservation District (SCD) was sought for selected resource problems. Since the resource management issues in the North Cache SCD are very complex, it is not feasible in the initial phase to generate all the physical, socioeconomic, and political baseline data needed for resolving all management issues. A selection of critical varables becomes essential. Thus, there are foud specific objectives: (1) assess resource management needs and determine which resource factors ae most fundamental for building a beginning data base; (2) evaluate the variety of data gathering and analysis techniques for the resource factors selected; (3) incorporate the resulting data into a useful and efficient digital data base; and (4) demonstrate the application of the data base to selected real world resoource management issues.

Wheeler, D. J.↗
NASA NTRSDate not supplied

Quality Control Methods of Tower Data at Kennedy Space Center’s Launch Complex 39-B and the USAF Cape Canaveral 500 Foot Tower

The National Aeronautics and Space Administration (NASA) has long used meteorological data from weather towers located at Kennedy Space Center (KSC) and the United States Air Force’s Eastern Range(ER) in support of their various launch vehicles and climate studies. Some of the most valuable data is gathered at the Launch Complex 39-B (LC39-B) and the 500 foot tower located approximately two miles west of LC39-B and three miles north of the Vehicle Assembly Building (VAB). The data from LC39-B gives NASA Space Launch System (SLS) engineers valuable insight into the weather the vehicle can expect at the pad, while the 500 foot tower has a longer period of record and has been used by the Shuttle and SLS programs. However, numerous data quality (DQ) control issues have arisen when utilizing this data for analysis. In addition, instrumentation tends to deteriorate faster than normal due to the corrosive nature of the high salt content in the air. NASA’s Natural Environments branch has developed several QC databases of these towers, but these studies were for fixed periods of records. EV44 identified a need for a continually updated and QC’ed database of tower data not only to provide along term QC’ed database for vehicle and climate analyses, but also to provide the capability to investigate recent weather events such as downbursts and other high wind events. This study follows the methods developed in prior Natural Environment QC tower databases, which includes variable specific QC thresholds and checks used to generate QC flags. However, this study developed new techniques such as the development and design of the graphical user interface (GUI) for manual verification, and the creation of the final monthly QC files.

Quality Control↗
NASA NTRS2001

An Approach to Application of Multispectral Sensors, using AVIRIS Data

High spatial resolution multispectral/hyperspectral sensors are being developed by private industry with science/research customers as end users. With an increasingly wide range of sensor choices, it is important for the remote sensing science community and commercial community alike to understand the trade-offs between ground sample distance (GSD), spectral resolution, and signal-to-noise ratio (SNR) in selecting a sensor that will best meet their needs. High spatial resolution hyperspectral imagery and super resolution multispectral charge-coupled device imagery can be used to develop prototypes of proposed data acquisition systems without building new systems or collecting large sets of additional data. By using these datasets to emulate proposed and existing systems, imaging systems may be optimized to meet customer needs in a virtual environment. This approach also enables one to determine, a priori, whether an existing dataset will be useful for a given application.

Warner, Amanda↗
NASA NTRS2005

NASA Remote Sensing Data in Earth Sciences: Processing, Archiving, Distribution, Applications at the GES DISC

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of the major Distributed Active Archive Centers (DAACs) archiving and distributing remote sensing data from the NASA's Earth Observing System. In addition to providing just data, the GES DISC/DAAC has developed various value-adding processing services. A particularly useful service is data processing a t the DISC (i.e., close to the input data) with the users' algorithms. This can take a number of different forms: as a configuration-managed algorithm within the main processing stream; as a stand-alone program next to the on-line data storage; as build-it-yourself code within the Near-Archive Data Mining (NADM) system; or as an on-the-fly analysis with simple algorithms embedded into the web-based tools (to avoid downloading unnecessary all the data). The existing data management infrastructure at the GES DISC supports a wide spectrum of options: from data subsetting data spatially and/or by parameter to sophisticated on-line analysis tools, producing economies of scale and rapid time-to-deploy. Shifting processing and data management burden from users to the GES DISC, allows scientists to concentrate on science, while the GES DISC handles the data management and data processing at a lower cost. Several examples of successful partnerships with scientists in the area of data processing and mining are presented.

Leptoukh, Gregory G.↗
NASA NTRS2021

Montana Water Resources II: Enhancing a Moisture Index for Drought and Flood Monitoring in the Missouri River Basin

The Missouri River Basin is a major global breadbasket, containing large amounts of agricultural land. Recent devastating weather events have motivated regional organizations to dedicate efforts to drought and flood monitoring and early warning systems. The DEVELOP team partnered with the following organizations: Montana Climate Office, NOAA National Weather Service (NWS) Missouri Basin River Forecast Center, NOAA Regional Climate Services of the Central Region, NOAA Physical Sciences Laboratory, and the US Army Corps of Engineers' Missouri River Basin Water Management Division. The team collaborated with the partners in their efforts to monitor flood and drought by enhancing a composite moisture index (CMI) for the Missouri River Basin. The CMI leverages NASA Earth observations to derive snow cover data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) mission and snow water equivalent and snowdepth datasets from the NOAA NWS National Operational Hydrologic Remote Sensing Center’s Snow Data Assimilation System (SNODAS). Building upon this framework, the team added groundwater storage data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions, soil moisture data from Soil Moisture Active Passive (SMAP), and United States Geological Survey in situ streamflow data to the CMI. To test the tool’s validity, the team compared the CMI results to historically extreme dry and extreme wet years, March 2017 and March 2019, respectively. The CMI accurately reflects both 2017 and 2019 climate conditions in the Missouri River Basin. The refined CMI enhances the understanding of antecedent soil moisture conditions and improves flood and drought forecasting in the Missouri River Basin before the growing season.

Chloe Schneider↗
NASA NTRS2013

Towards a Decision Support System for Space Flight Operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The discrepancy of the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the three increments of this vision. These increments include (1) hardware and software system components and interfaces with the NASA ground system, (2) uncertainty management and (3) re-planning and automated execution. Each of these increments provide value independently; but some may also enable building of a subsequent increment.

Meshkat, Leila↗
NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗