Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data Science”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Management and International Sorption Model Collaboration (M4SF-23LL010302062-NEA-TDB)

This progress report (Level 4 Milestone Number M4SF-23LL010302062) summarizes research conducted at Lawrence Livermore National Laboratory (LLNL) within the Crystalline International Collaborations Activity Number SF-23LL01030206. The activity is focused on our long-term commitment of engaging our partners in international nuclear waste repository research. This includes participation in the Nuclear Energy Agency Thermochemical Database (NEA-TDB) Project and development of methodologies for integrating US and international thermodynamic databases for use in SFWST Generic Disposal System Assessment (GDSA) efforts. A continuing focus for FY23 efforts has been to support the US participation in the NEA-TDB effort (Mavrik Zavarin replaced Cindy Atkins-Duffin on the NEA-TDB Management Board (MB) and Executive Group (EG)) and developing mechanisms for integration of NEA-TDB thermochemical data with LLNL’s SUPCRTNE thermodynamic database that supports the SFWST GDSA activities. This effort is coordinated with the Argillite work package SUPCRTNE database development efforts. The goal is to provide a downloadable database that will be hosted on a LLNL website which integrates NEA-TDB data into the LLNL SUPCRTNE database where appropriate. As part of our international activities, we continue our effort to integrate international sorption databases into L-SCIE (Zavarin et al., 2022b). We presented opportunities to include sorption in the next phase of NEA-TDB efforts at the April 2023 EG meeting in Paris. FY23 efforts focused on ensuring interoperable database development across multiple international database development activities. The overall goal is to produce an open source database that can be shared and integrated with multiple nuclear waste programs internationally and harness modern data science workflows and algorithms to incorporate these new approaches into reactive transport and performance assessment models. In collaboration with our Helmholtz Zentrum Dresden Rossendorf partners, we recently demonstrated the power of FAIR open source databases by fitting iron oxide (hydrous ferric oxide, goethite, hematite, and magnetite) protolysis constants to all available L-SCIE data. The results were submitted as a manuscript to J. Colloid Interface Science. This work will inform future metal sorption studies on a variety of iron oxides in order to discern the most appropriate acidity constants and surface complexation modeling constructs to account for pH-dependent mineral surface charge behavior. This work also explored automated surface complexation model development workflows in order to generate higher throughput model input files for a more facile incorporation into GDSA activities.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
NASA NTRS2015

The Role of the NASA Global Hawk Link Module as an Information Nexus For Atmospheric Mapping Missions

The Link Module described in this paper was developed for the NASA Uninhabited Aerial System (UAS) Global Hawk Pacific Mission (GloPAC) Airborne Science Campaign; four flights of 30 hour duration, supporting the Aura Validation Experiment (AVE). It was used again during the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth Science field experiment to better understand how tropical storms form and develop into major hurricanes. In these missions, the Link Module negotiated all communication over the high bandwidth Ku satellite link, archived all the science data from onboard experiments in a spatially enabled database, routed command and control of the instruments from the Global Hawk Operations Center, and re-transmitted select data sets directly to experimenters control and analysis systems. The availability of aggregated information from collections of sensors, and remote control capabilities, in real-time, is revolutionizing the way Airborne Science is being conducted. The Link Module NG now being flown in support of the NASA Earth Venture missions, the Hurricane and Severe Storm Sentinel (HS3) mission, and Airborne Tropical Tropopause Experiment (A TTREX) mission, has advanced data fusion technologies that are further advancing the Scientific productivity, flexibility and robustness of these systems. On-the-fly traffic shaping has been developed to allow the high definition video, used for critical flight control segments, to dynamically allocate variable bandwidth on demand. Historically, the Link Module evolved from the instrument and communication interface controller used by NASA's Pathfinder and Pathfinder plus solar powered UAS's in the late 1990' s. It later was expanded for use in the AIRDAS four channel scanner flown on the NASA Altus UAS, and then again to a module in the AMS twelve channel multispectral scanner flying on the NASA (Predator-b) Ikhana UAS. The current system is the answer to the challenges imposed by extremely long duration UASs, with on-board multi-instrument (>= 12) Sensor Webs.

Hurricanes↗
NASA NTRS2019

Cloud Giovanni: Reining in Costs and Improving Performance with Analytical Data Stores Using Scalable Serverless Architecture

Giovanni is the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure developed at NASA GES DISC which provides a simple and intuitive way to visualize, analyze, and access vast amounts of Earth science data. It receives large number of user requests each day for a variety of analysis and visualization services, which leads to the big data challenge of serving gradually increasing large data volumes with diverse statistical algorithms. We hereby propose a multi-dimensional accumulation method which provides fast and cost-efficient cloud analysis for diverse services including both area averaging and time averaging. This method involves the weighted volume integration over multiple variable dimensions (time and space), and is implemented in AWS using Athena providing serverless and highly scalable data analysis. Compared to the standard method, this approach dramatically reduced the computational time by order of magnitude with a minimal AWS cost incurred. For example, for a benchmark of 10-year area averaging over the 1x1 degree daily variable, the computational time was reduced from minutes to seconds, and the Athena cost is only $5 for 100,000 requests.

Zhang, Hailiang↗
NASA NTRS2012

An Information NEXUS: The NASA Global Hawk Link Module

The Link Module described in this paper was first developed for the NASA Global Hawk Pacific Mission (GloPAC), four flights of 30 hour duration, supporting the Aura Validation Experiment (AVE). Its second use was during the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth Science field experiment to better understand how tropical storms form and develop into major hurricanes. In these missions, the Link module negotiated all communication over the high bandwidth Ku satellite link, archived al the science data from onboard experiments in a spatially enable database, routed command and control of the instruments from the Global Hawk Operations Center, and retransmitted select data sets directly to experimenters control and analysis systems. The availability of aggregated information from collections of sensors, and remote control capabilities, in real-time, is revolutionizing the way Airborne Science is being conducted. Also described is the next generation Link Module now being designed and tested to support the NASA Earth Venture missions, the Hurricane and Severe Storm Sentinel (HS3) mission, and Airborne Tropical Tropopause Experiment (ATTREX) mission. Advanced data fusion technologies being developed will further advance the Scientific productivity, flexibility and robustness of these systems. Historically, the Link module evolved from the instrument and communication interface controller used by NASA's Pathfinder and Pathfinder plus solar powered UAS's in the late 1990's. It later was expanded for use in the AIRDAS four channel scanner flown on the NASA Altus UAS, and then again to a module in the AMS twelve channel multispectral scanner flying on the NASA (Predator-b) Ikhana UAS. The current system is the next step in the evolution, a multi board system packaged in a Curtiss Wright MIL-spec, flight qualified enclosure.

Remote Sensing↗
DOE OSTI2022

Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems

Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1981

Dynamics Explorer launch

Simultaneously launched from the WSMC, two satellites are to be placed into polar, copolar orbit in order to acquire data on the coupling phenomena between Earth's lower thermosphere and the magnetosphere, as part of the Solar-Terrestrial Program. The mission sequence, instruments, and science data processing system are described as well as the characteristics of the Delta 3913 launch vehicle, and payload separation staging.

Source record↗
NASA NTRS1989

Data system interprobability

Software was developed and tested which allows an end-user to build a book data object in an interactive fashion. The electronic book model was developed for the Library front-end of the DAVID (Distributed Access View Integrated Database) system. It is a complex data object which is capable of holding data of different types and formats. After the task, the plan was to assess the Book model and investigate the possibility of it serving as a knowledge base for a expert system front-end to the DAVID system. For the rest of the fellowship period, data system interoperability was examined. Specifically, a plan was prepared titled Implementations of the Land Analysis System (LAS) to Meet UNEP/GRID Requirements. This plan is aimed at reaching the goal of building one virtual global change information system which will link existing and future data systems allowing earth and space scientists to learn about, locate, access, and correlate data of interest regardless of its physical location. The plan proposed the construction of a master directory (MD) for the European data systems that is equivalent in structure to NASA's MD. The MD is to contain brief, high-level information about existing Earth and space science data sets sufficient for a user to know where data of interest reside and how to go about getting additional information. The plan also outlined the tasks that would be involved to electronically connect the MD system to the various discipline data systems (e.g., LAS) in order to achieve general interroperabilty, that is, the abilty to perform cross-systems searches and exchange of and access to distributed information.

Wakim, Nagi T.↗
NASA NTRS2003

A Virtual Bioinformatics Knowledge Environment for Early Cancer Detection

Discovery of disease biomarkers for cancer is a leading focus of early detection. The National Cancer Institute created a network of collaborating institutions focused on the discovery and validation of cancer biomarkers called the Early Detection Research Network (EDRN). Informatics plays a key role in enabling a virtual knowledge environment that provides scientists real time access to distributed data sets located at research institutions across the nation. The distributed and heterogeneous nature of the collaboration makes data sharing across institutions very difficult. EDRN has developed a comprehensive informatics effort focused on developing a national infrastructure enabling seamless access, sharing and discovery of science data resources across all EDRN sites. This paper will discuss the EDRN knowledge system architecture, its objectives and its accomplishments.

knowledge systems↗
NASA NTRS2010

Modeling Martian Dust Using Mars-GRAM

Engineering-level atmospheric model widely used for diverse mission applications. Mars-GRAM s perturbation modeling capability is commonly used, in a Monte-Carlo mode, to perform high fidelity engineering end-to-end simulations for entry, descent, and landing (EDL). From the surface to 80 km altitude, Mars-GRAM is based on NASA Ames Mars General Circulation Model (MGCM). Mars-GRAM and MGCM use surface topography from Mars Global Surveyor Mars Orbiter Laser Altimeter (MOLA), with altitudes referenced to the MOLA areoid, or constant potential surface. Traditional Mars-GRAM options for representing the mean atmosphere along entry corridors include: TES Mapping Years 1 and 2, with Mars-GRAM data coming from MGCM model results driven by observed TES dust optical depth TES Mapping Year 0, with user-controlled dust optical depth and Mars-GRAM data interpolated from MGCM model results driven by selected values of globally-uniform dust optical depth. Mars-GRAM 2005 has been validated against Radio Science data, and both nadir and limb data from the Thermal Emission Spectrometer (TES).

Justh, Hilary L.↗
NASA NTRS2017

VIIRS Thermal Emissive Bands L1B Calibration Uncertainty

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument on-board the Suomi National Polar-orbiting Partnership (S-NPP) spacecraft. The S-NPP launched in October 2011 and it has been collecting valuable Earth science data with VIIRS and four other instruments for more than five years. The VIIRS Characterization Support Team (VCST) of the National Aeronautics and Space Administration (NASA) Science Investigator-led Processing Systems (SIPS) is designed to support the VIIRS sensor pre-launch geometric and radiometric characterization and to access on-orbit long-term Level-1B (L1B) calibration and performance. This paper reviews the VIIRS thermal emissive bands (TEB), covering wavelengths from 3.7 to 12.0 μm, L1B radiometric calibration algorithms and presents the calibration uncertainty methodology which will be implanted in the L1B processing software. Discussions will be focused on the key uncertainty parameters and the application in L1B.

S-NPP VIIRS TEB Uncertainty↗
NASA NTRS2018

In-Flight Operation of the Dawn Ion Propulsion System Through Completion of the Final Orbit Around Dwarf Planet Ceres

The Dawn mission, part of NASA’s Discovery Program, has as its goal the scientific exploration of the two most massive main-belt objects, Vesta and Ceres. The Dawn spacecraft was launched from the Cape Canaveral Air Force Station on September 27, 2007 on a Delta-II 7925H- 9.5 (Delta-II Heavy) rocket that placed the 1218-kg spacecraft onto an Earth-escape trajectory. On-board the spacecraft is an ion propulsion system (IPS) developed at the Jet Propulsion Laboratory for the heliocentric transfer to Vesta, orbit capture at Vesta, transfer between Vesta science orbits, departure and escape from Vesta, heliocentric transfer to Ceres, orbit capture at Ceres, transfer between Ceres science orbits, and orbit maintenance maneuvers. Full-power thrusting from December 2007 through October 2008 was used to successfully target a Mars gravity assist flyby in February 2009 that provided an additional DV of 2.6 km/s. Deterministic thrusting for the heliocentric transfer to Vesta resumed in June 2009 and concluded with orbit capture at Vesta on July 16, 2011. From July 2011 through September 2012 the IPS was used to transfer to all the different science orbits at Vesta and to escape from Vesta orbit. Cruise for a rendezvous with Ceres began in August 2012 and completed in late December 2014. From December 2014 through June 2016 the IPS was used for transiting the spacecraft to all science orbits at Ceres including the final orbit for Dawn’s primary mission, called the low altitude mapping orbit (LAMO), a circular orbit at a mean altitude above Ceres of approximately 385 km. Dawn met or exceeded all pre-launch science requirements and Dawn's prime mission concluded on June 30, 2016. Dawn subsequently received NASA approval for two extended missions at Ceres, called XM1 and XM2. During XM1 IPS was used for orbit maintenance and to transit the spacecraft to several new, higher-altitude science orbits, ending in an elliptical orbit at a maximum altitude of approximately 38,000 km. In XM2, IPS operations included transiting the Dawn spacecraft to its final, highly elliptical orbit at Ceres ranging from 35 km perigee to 4,800 km apogee. Science data acquisition will continue in XM2 until the hydrazine is exhausted, which is expected to occur between August and October 2018. Dawn has successfully completed all science goals for both the primary and XM1 extended missions. To date the IPS has been operated for approximately 51,250 hours, consumed approximately 416 kg of xenon, and provided a delta-V of almost 11.5 km/s, a record for an on-board propulsion system. The IPS performance characteristics are close to the expected performance based on analysis and testing performed pre-launch. Dawn’s IPS continues to be fully operational as of June 2018. This paper provides an overview of Dawn’s mission objectives and the results of Dawn IPS mission operations for XM1, and XM2 through June 2018.

Rayman, Marc D.↗
NASA NTRSDate not supplied

The NASA Merra-2 Reanalysis Products: Data and Tools Used for Aerosol and Air Quality Studies

The NASA Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is atmospheric reanalysis data spanning 1980 to present. It has been produced by the NASA Global Modeling and Assimilation Office (GMAO) and is distributed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). MERRA-2 data includes 100 collections of Earth system variables, mainly from the atmospheric model, such as aerosol fields and meteorological fields, radiation fields, and aerosol fields, guided by the assimilation of as many as six million observations every six hours. MERRA-2 has been one of the most popular datasets from NASA and is widely used in interdisciplinary research and applications, with increasing numbers of new users. For example, at least 7000 users accessed MERRA-2 data at GES DISC in the year 2021, ~1000 more users than in the year 2020. In this presentation, we will introduce the MERRA-2 datasets associated with aerosol and air quality studies and use a wildfire case study to demonstrate the data tools developed at GES DISC to analyze and visualize MERRA-2 data, such as Giovanni and the level 3 and level 4 subsetter, and Jupyter Python notebook. We will also update the status of cloud migration of the MERRA-2 data to Amazon Web Services (AWS).

Xiaohua Pan↗
NASA NTRSDate not supplied

Using Time Series Data Products to Support ASHRAE’s Historic and Future Climate Data Needs through NASA’s POWER Web Services

To address the data needs for resilience and sustainability of building systems within ASHRAE, the NASA Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data on a long-term, global scale. Solar data from several NASA projects and meteorological data from NASA assimilation models have been reformatted and disseminated to the public via a user friendly web GIS-enabled based data portal (https://power.larc.nasa.gov) in selectable data formats, immediately amenable to key industry wide decision support tools such as EnergyPlus. Time series of potential climate conditions from downscaled climate simulations are being made available for similar statistics and formats. An emphasis is placed upon obtaining and utilizing time series data to conduct analysis of building system performance for current and planning building systems for energy savings and greenhouse gas emissions. This presentation has the learning following learning objectives for the ASHRAE community: 1) Learn how NASA POWER’s web service suite can provide supplementary solar and surface meteorological data parameters, including a discussion of uncertainty. 2) Learn how downscaled climate scenario information can be provided to evaluate potential future requirements. The presentation is part of a forum entitled, "Weather Data for Large-Scale Building Energy Modeling".

time series↗
NASA NTRS2010

Things That Work: Roles and Services of SPDF

The current Heliophysics Science Data Management Policy (HpSDMP) defines the roles of the Space Physics Data Facility (SPDF) project as a heliophysics active Final Archive (aFA), a focus for critical data infrastructure services and a center of excellence for data and ancillary information services. This presentation will highlight (1) select current SPDF activities, (2) the lessons we are continuing to learn in how to usefully serve the the heliophysics science community and (3)SPDF's programmatic emphasis in the coming year. In cooperation with the Heliophysics Virtual discipline Observatories (VxOs), we are working closely with current, and with upcoming missions such as RBSP and MMS, to define effective approaches to ensure the long-term availability and archiving of mission data, as well as how SPDF services can complement active mission capabilities. We are working to make the Virtual Space Physics Observatory (VSPO) service comprehensive in all significant and NASA relevant heliophysics data. We will highlight a new CDAWeb interface, a faster SSCWeb, availability of our data through VxO services such as Autoplot, a new capability to easily access our data from within IDL and continuing improvements to CDF including better handling of leap seconds.

McGuire, R. E.↗
DOE OSTI2022

Data-centric framework for crystal structure identification in atomistic simulations using machine learning

Atomic-level modeling performed at large scales enables the investigation of mesoscale materials properties with atom-by-atom resolution. The spatial complexity of such cross-scale simulations renders them unsuitable for simple human visual inspection. Instead, specialized structure characterization techniques are required to aid interpretation. These have historically been challenging to construct, requiring significant intuition and effort. Here we propose an alternative framework for a fundamental structural characterization task: classifying atoms according to the crystal structure to which they belong. Our approach is data-centric and favors the employment of Machine Learning over heuristic rules of classification. A group of data-science tools and simple local descriptors of atomic structure are employed together with an efficient synthetic training set. We also introduce the first standard and publicly available benchmark data set for evaluation of algorithms for crystal-structure classification. Further, it is demonstrated that our data-centric framework outperforms all of the most popular heuristic methods—especially at high temperatures when lattices are the most distorted—while introducing a systematic route for generalization to new crystal structures. Moreover, through the use of outlier detection algorithms our approach is capable of discerning between amorphous atomic motifs (i.e., noncrystalline phases) and unknown crystal structures, making it uniquely suited for exploratory materials synthesis simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Microparticle cloud imaging and tracking for data-driven plasma science

Oceans of image and particle track data encountered in plasma interactions with microparticle clouds motivate development and applications of machine-learning (ML) algorithms. A local-constant-velocity tracker, a Kohonen neural network or self-organizing map, the feature tracking kit, and U-Net are described and compared with each other for microparticle cloud datasets generated from exploding wires, dusty plasmas, and atmospheric plasmas. Particle density and the signal-to-noise ratio have been identified as two important factors that affect the tracking accuracy. Fast Fourier transform is used to reveal how U-Net, a deep convolutional neural network developed for non-plasma applications, achieves the improvements for noisy scenes. Viscous effects are revealed in the ballistic motions of the particles from the exploding wires and atmospheric plasmas. Subdiffusion of microparticles satisfying Δr 2 ∝t k (k=0.84±0.02) is obtained from the dusty plasma datasets. Microparticle cloud imaging and tracking, when enhanced with data and ML models, present new possibilities for plasma physics.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

United States CMM Insights Dataset

Geo-data science applications for critical mineral analysis, development acceleration, economic impact assessment, and project efficiency. Coverage: 10 years (2014-2023), 3 geographic levels (county, state, tract), 45 features Data Categories: - census: 30 features (e.g., asian population percentage, black population percentage, citizen voting age population percentage) - ejscreen: 3 features (e.g., P_DSLPM, P_PM25, P_PWDIS) - energyexpenditure: 12 features (e.g., Housing adjustment factor, Income adjustment factor, Monthly electricity cost)

AS↗
DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗