Search NASA⌕ Search

SEARCH · Search NASA

Results for “scenario data”

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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Perceptions of Climate Risk and Use of Climate Risk Information by Natural Resource Conservation Stakeholders Participating in ADVANCE Projects in Asia and Latin America

Integrating climate risk information into resilience-building activities in the field is important to ensure that adaptation is based on the best available science. Despite this, many challenges exist when developing, communicating, and incorporating climate risk information. There are limited resources on how stakeholders perceive risks, use risk information, and what barriers exist to limit knowledge integration. This paper seeks to define the following: 1) What do conservation stakeholders consider to be the most significant climate risks they face now and possibly in the future? 2) What have been the most significant barriers to their using climate risk information? 3) What sources and types of knowledge would be most useful for these managers to overcome these barriers? A survey was conducted among stakeholders (n = 224) associated with World Wildlife Fund projects in tropical and subtropical countries. A very high proportion of stakeholders used climate risk information and yet faced integration-related challenges, which included too much uncertainty and the lack of a relevant scale for planning. The main factors preventing the use of climate risk information in decision-making were unavailability of climate risk information, no or limited financial or human resources available to respond, lack of organizational mandate or support, and no or limited institutional incentives. Comparing perceived current and future risks revealed a decline in concern for some future climate hazards. Survey respondents identified scientific reports, climate scientists, and online sources as the most useful information sources of climate risk information, while (i) maps and illustrations; (ii) scenarios format; and (iii) data tables, graphs, and charts were identified as user-friendly formats.

Climate risk↗
NASA NTRSDate not supplied

Integration of the Cardiovascular Clinical Risk Prediction Model Astro-CHARM Into the NASA Radiation Risk Model

Space radiation poses an increased risk for cardiovascular diseases (CVD) during and after spaceflight, that should be quantified to plan future space missions such as the flight to Mars. The radiation-induced CVD risk is evaluated using a linear no-threshold dose response excess relative risk (ERR) model, so that the risk is function of the radiation dose and of the background (i.e., non-exposed) incidence rate. The background CVD risk can be evaluated using clinical prediction models (CPMs). The first CPMs developed in the 1960’s have identified CVD risk factors such as blood pressure, diabetes, cholesterol, and smoking. In this work, the steps used to integrate the CVD risk provided by a CPM into the NASA Radiation Risk Model are described. Simulation results calculated using available data for relevant mission scenarios are also shown, and the challenges in CVD risk predictions are discussed.

I Plante↗
NASA NTRS2024

Integration of the Cardiovascular Risk Model Astro-CHARM in the NASA Radiation Risk Model

Space radiation poses an increased risk for cardiovascular diseases (CVD) during and after spaceflight, that should be quantified to plan future space missions such as the flight to Mars. The radiation-induced CVD risk is evaluated using a linear no-threshold dose response excess relative risk (ERR) model, so that the risk is function of the radiation dose and of the background (i.e., non-exposed) incidence rate. The background CVD risk can be evaluated using clinical prediction models (CPMs). The first CPMs developed in the 1960’s have identified CVD risk factors such as blood pressure, diabetes, cholesterol, and smoking. In this work, the steps used to integrate the CVD risk provided by a CPM into the NASA Radiation Risk Model are described. Simulation results calculated using available data for relevant mission scenarios are also shown, and the challenges in CVD risk predictions are discussed.

Space radiation↗
NASA NTRSDate not supplied

Flight Deck Design of a Hybrid Turbine/Electric Aircraft

The SUbsonic Single Aft eNgine (SUSAN) program is being developed as a sustainable subsonic regional aircraft. NASA is conducting trade studies for this future hybrid single aisle passenger aircraft. The goal of reducing emission levels by 50% will be enabled through an advanced design. An aft mounted single turbine engine provides thrust and electric power to drive 16 wing mounted electric engines. Using the 16 counter-rotating electric fans will create an efficient, very high bypass ratio, engine. Electric engines allow for distributed thrust, reducing control surfaces and provide a greater dynamic response. Batteries provide boost for takeoff and climb, allowing a smaller, more efficient, turbine engine. As part of the SUSAN effort, two pilot-in-the-loop studies were conducted at NASA Langley Research Center in Virginia to explore flight deck design issues. In particular: how many throttles are required/desired for a 17-engine aircraft, does the hybrid electric design change the answer, what are the requirements for electric engine displays, what system pages are required, and what information is required to monitor the batteries. Objective and subjective data were collected after each scenario and at the end of the day with a guided set of questionnaires from sixteen crews paired by airline. Data along with pilot comments were used to improve the throttle and engine displays between the studies and used to make the final recommendations on flight deck configurations. This paper provides final design recommendations for the SUSAN flight deck and justification for each recommendation.

PAI↗
DOE OSTI2025

Rancor Integrated Procedure System (RIPS): A Computer-Based Procedure Platform for Advanced Reactor Research

The Rancor Microworld Simulator is a simplified, pressurized water, small modular reactor simulator that includes a multi-unit plant model server, an advanced digital human-machine control interface, and the Rancor Integrated Procedure System (RIPS). Rancor provides a research and development tool that can be used for collecting operator performance data and for prototyping concepts of operations (ConOps) for advanced reactor development. RIPS is meant as a research tool and includes many unique features: (1) RIPS has a robust procedure authoring system. (2) RIPS has the capability to run any of the three IEEE-Std-1786 computer-based procedure types. (3) RIPS can be configured to take on the look and feel of different vendors’ computer-based procedure systems for the purpose of developing and evaluating different ConOps for plant upgrades or new builds. (4) RIPS includes the capability for logging operator procedure use, including integrating procedure logs with Rancor simulator logs, thereby allowing automated data collection of operator scenario runs. (5) RIPS integrates with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a dynamic human reliability analysis environment that creates a digital human twin or virtual operator to mimic reactor operator performance. (6) RIPS includes support for automation of plant monitoring and control functions. While RIPS is explicitly built into Rancor, it may also be used with full-scope training simulators. This functionality allows RIPS to be used for existing plants and advanced reactors under development.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

IM3 Phase 2 Official Simulations: GCAM-Demeter-SELECT Annualized Land Use and Land Cover, Wood Harvest and Fertilization Data with Dynamic Urbanization Harmonized to CLM Land Definitions at 0.125 Degrees

Annualized land use land cover data, including wood harvest and fertilizer use data from the Global Change Analysis Model (GCAM) downscaled to 0.125 degrees for couping with the Community Land Model (CLM). GCAM here refers to GCAM-USA v5.3.im3 which has an enhanced electricity sector and an updated data system needed to represent regional to local scale dynamics. Data is also harmonized with future urbanization projections from the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Projections/Data are generated using the demeter land use and land cover downscaling model. Original projections were generated at 0.05 degrees before being aggregared to 0.125 degrees. Projections are available for 8 alternative scenarios. Two versions of final data are included- one with managed forests or harvested forest area per pixel broken out and one with the same aggregated into total forests. Following folders are included: demeter_78_PFT_output:This is the final output of dynamic land use land cover change for 78 PFTs as required by CLM raw_outputs_incl_managed_forest: This is the final output but with managed forests broken out as a different PFT. Essentially a 79th PFT is added. wood_harvest_outputs: Wood harvest output per pixel in gC/m2 fertilization_outputs: Fertilizer use per pixel in gN/m2 Each NetCDF file in each folder represents a projection for a separate year, scenario. Land use outputs are organized as PFT level data saved as subdata. Link to GCAM version used- https://data.msdlive.org/records/yb23g-44274 Link to SELECT documentation -https://www.sciencedirect.com/science/article/pii/S1364815219301707 Link to CLM documentation- - https://www.cesm.ucar.edu/models/clm In case of questions contact- kanishka.narayan@pnnl.gov

GCAM-USA↗
DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Challenges and Opportunities for Electric Utility Modeling and Asset Valuation Frameworks: Case Study on Valuing New Pumped Storage Hydropower

Asset valuation by electric utilities is becoming increasingly difficult in the rapidly changing electric sector. Rapid deployment of variable generation and inverter-based storage systems along with uncertain demand growth, climate, policies, and other factors create a challenging environment for understanding the value proposition of a new potential asset. This report describes an effort between the Tennessee Valley Authority (TVA) and three U.S. Department of Energy laboratories to perform a detailed review of utility modeling and analysis practices for asset valuation and identify challenges and opportunities for advancing its methods into the future. It focuses on a case study of new potential pumped storage hydropower (PSH) because of growing interest in new PSH capacity to provide energy balancing, firm capacity, and a range of ancillary services. Staff from the DOE labs conducted systematic interviews about current practices in capacity expansion modeling, production-cost modeling, hydrological modeling, and transmission stability modeling while also discussing how scenario analysis is conducted and how models and data are integrated. The effort resulted in a set of model, integration, and scenario recommendations that could be valuable to TVA, other utilities, system operators, and other stakeholders conducting integrated grid analysis. Individual model recommendations suggest exploring computational tradeoffs with detail and resolution across spatiotemporal structure, supply- and demand-side details, transmission overlays, market interactions, and ancillary services. Automated processes to pass data between models and conduct larger scenario suites could also enhance valuation practices by enabling a more consistent study of asset value across a broader range of uncertain future grid conditions where PSH could be particularly valuable. TVA and other industry stakeholders can learn from and adapt applied research-grade methods developed by DOE laboratories and other research institutions to improve decision making and accelerate progress towards a reliable, economic, sustainable energy system.

13 HYDRO ENERGY↗
NASA NTRS1995

Task Analytic Models to Guide Analysis and Design: Use of the Operator Function Model to Represent Pilot-Autoflight System Mode Problems

Task-analytic models structure essential information about operator interaction with complex systems, in this case pilot interaction with the autoflight system. Such models serve two purposes: (1) they allow researchers and practitioners to understand pilots' actions; and (2) they provide a compact, computational representation needed to design 'intelligent' aids, e.g., displays, assistants, and training systems. This paper demonstrates the use of the operator function model to trace the process of mode engagements while a pilot is controlling an aircraft via the, autoflight system. The operator function model is a normative and nondeterministic model of how a well-trained, well-motivated operator manages multiple concurrent activities for effective real-time control. For each function, the model links the pilot's actions with the required information. Using the operator function model, this paper describes several mode engagement scenarios. These scenarios were observed and documented during a field study that focused on mode engagements and mode transitions during normal line operations. Data including time, ATC clearances, altitude, system states, and active modes and sub-modes, engagement of modes, were recorded during sixty-six flights. Using these data, seven prototypical mode engagement scenarios were extracted. One scenario details the decision of the crew to disengage a fully automatic mode in favor of a semi-automatic mode, and the consequences of this action. Another describes a mode error involving updating aircraft speed following the engagement of a speed submode. Other scenarios detail mode confusion at various phases of the flight. This analysis uses the operator function model to identify three aspects of mode engagement: (1) the progress of pilot-aircraft-autoflight system interaction; (2) control/display information required to perform mode management activities; and (3) the potential cause(s) of mode confusion. The goal of this paper is twofold: (1) to demonstrate the use of the operator functio model methodology to describe pilot-system interaction while engaging modes And monitoring the system, and (2) to initiate a discussion of how task-analytic models might inform design processes. While the operator function model is only one type of task-analytic representation, the hypothesis of this paper is that some type of task analytic structure is a prerequisite for the design of effective human-automation interaction.

Degani, Asaf↗
DOE OSTI2021

Experimental Analysis of the Effects of Simulator Complexity on Human Performance

Human Reliability Analysis predicts accidents caused by human errors and is an important factor in Probabilistic Safety Assessment that comprehensively evaluates the safety of nuclear power plants. This study compares and analyzes the human performance of nuclear power plant operators according to the simulator complexity as part of the HRA data collection support method development project conducted by Idaho National Laboratory. This experiment was conducted by setting two types of simulators and scenarios as independent variables. The data collected by conducting the experiments in two different simulators were analyzed using an analysis of variance test and a correlation analysis, and four human performance charts were derived.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background: Consortium on Nuclear Security Technologies (CONNECT) (Q2 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore supervised machine learning (ML) algorithms for development of a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm for detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Added value of site load measurements in probabilistic lifetime extension: a Lillgrund case study

Site-specific fatigue estimation is an essential part of wind turbine lifetime extension, with various methods depending on data availability. The present study compares probabilistic lifetime extension assessment results for rotor blades with and without load measurements. It also addresses two key questions in such assessments: the applicability of the Frandsen model for estimating waked turbulence under complex and mixed wake conditions and the extrapolation of mid-term data over longer time periods. The case study wind turbine is SWT-2.3-93, located at the edge of the Lillgrund wind farm, situated in the Øresund Strait between Denmark and Sweden. The turbine is extensively instrumented, with 5 years of data available from its supervisory control and data acquisition (SCADA) system. Although the Frandsen turbulence estimates deviate in a different manner from measurements at below- and above-rated mean wind speeds, the model remains a conservative approach for fatigue load prediction and reliability. In the current case study, the site-specific assessment using strain gauge measurements yields a 33 % higher annual fatigue reliability index after 35 years compared to a scenario based on the Frandsen estimation combined with ambient environmental data and a generic aeroelastic model. The results also demonstrate that the sensitivity of fatigue reliability to load uncertainty is negligible when load measurements are used directly but relatively high when relying on the Frandsen model in combination with a generic aeroelastic model. Overall, the high variability of the lifetime extension in different scenarios of data availability and accuracy shows the importance and added value of high-quality measurements combined with wind-farm-level SCADA and a model updated in real time (digital twins).

17 WIND ENERGY↗
NASA NTRS1990

Gravity field improvement using GPS data from Topex/Poseidon - A covariance analysis

A covariance analysis is performed using a realistic scenario for processing 10 days of GPS data, to obtain the expected improvement to the GEM-T2 gravity field. The gravity bin technique has been refined to compute the covariance matrix associated with the spherical harmonic gravity field. It is shown that the GPS data from one ten-day arc of Topex/Poseidon with no a priori can improve medium degree and order (3-26) sigmas for the parameters in the GEM-T2 gravity field by more than an order of magnitude.

Bertiger, Willy I.↗
DOE OSTI2022

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗
NASA NTRS1994

Mars Pathfinder mission operations concepts

The Mars Pathfinder Project plans a December 1996 launch of a single spacecraft. After jettisoning a cruise stage, an entry body containing a lander and microrover will directly enter the Mars atmosphere and parachute to a hard landing near the sub-solar latitude of 15 degrees North in July 1997. Primary surface operations last for 30 days. Cost estimates for Pathfinder ground systems development and operations are not only lower in absolute dollars, but also are a lower percentage of total project costs than in past planetary missions. Operations teams will be smaller and fewer than typical flight projects. Operations scenarios have been developed early in the project and are being used to guide operations implementation and flight system design. Recovery of key engineering data from entry, descent, and landing is a top mission priority. These data will be recorded for playback after landing. Real-time tracking of a modified carrier signal through this phase can provide important insight into the spacecraft performance during entry, descent, and landing in the event recorded data is never recovered. Surface scenarios are dominated by microrover activity and lander imaging during 7 hours of the Mars day from 0700 to 1400 local solar time. Efficient uplink and downlink processes have been designed to command the lander and microrover each Mars day.

Sturms, Francis M., Jr.↗
DOE OSTI2025

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection

Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection Description This dataset contains input and output data for the manuscript Mongird, K. et al. (under review) titled "Energy Infrastructure Futures: A Multiscale Evaluation of Projected Power Plant Siting Across the Western Interconnection". Input data corresponds to gridded spatial siting attributes that are necessary to conduct a random forest machine learning analysis of siting feature importance. Output data includes SHAP feature analysis outputs, and classification report values. For data on power plant siting results referred to in the manuscript, please refer to the CERF: IM3 Projected Western US Power Plant Locations data download page. The downloadable data includes values for eight different future scenarios for the Western US. The scenarios include combinations of two Shared Socioeconomic Pathways (SSP3 and SSP5) with four high-resolution climate projections specific to the United States (see, https://tgw-data.msdlive.org/). These climate projections include "hotter" and "cooler" variants for two Representative Concentration Pathways (RCP4.5 and RCP8.5). The resulting eight simulations are: rcp45cooler_ssp3 rcp45cooler_ssp5 rcp45hotter_ssp3 rcp45hotter_ssp5 rcp85cooler_ssp3 rcp85cooler_ssp5 rcp85hotter_ssp3 rcp85hotter_ssp5 Technical Information The dataset includes two sets of data files: (1) CERF gridded siting parameters and (2) Feature analysis outputs and classification reports. All downloadable data is in csv file format. Files with x/y coordinate information use the Albers Equal Area Conic projection (ESRI:102003). 1. CERF Gridded Siting Parameters This directory provides a balanced sample of gridded CERF siting parameters data for eight different scenarios for the Western US through 2055, seven different technologies, and eight timesteps. This data serves as input to the feature analysis. It contains the following parameters. region_name - name of region (i.e., state) sited - binary value representing whether the grid cell received a siting of that technology type (1=True) rcp - binary value representing scenario resource concentration pathway (0 = RCP4.5, 1 = RCP8.5) ssp - binary value representing scenario shared socioeconomic pathway (0 = SSP3, 1 = SSP5) climate - binary value representing cooler (0) or hotter (1) GCM forcing tech_name - generation technology name sited_year - year that values correspond to transmission_cost - cost of transmission interconnection pipeline_cost - cost of natural gas pipeline interconnection interconnection_cost - total interconnection cost (sum of transmission cost and gas pipeline cost) lmp - associated locational marginal value ($/MWh) associated with the grid cell, timestep, scenario, and technology xcoord - x-coordinate of location ycoord - y-coordinate of location 2a. Feature Analysis Output The dataset includes the feature analysis shap output for locational marginal price and interconnection cost. It contains the following parameters. technology - generator technology name scenario - name of scenario feature - name of feature, either locational_marginal_price or interconnection_cost value - the mean of absolute value of SHAP values for given feature 2b. Feature Analysis Classification Report This download includes the classification report associated with each random forest model. The dataset contains the following parameters. technology - generation technology name scenario - name of scenario test - one of precision (the proportion of predicted positives that are actually correct), recall (the proportion of actual positives that were correctly identified), f1-score (the harmonic mean of precision and recall) 0.0 - value of test for classification of 0 (grid cell not chosen for siting) 1.0 - value of test for classification of 1 (grid cell chosen for siting) accuracy - accuracy of model (i.e., fraction of all predictions that were right) macro avg - Simple average of test values for all classes weighted avg - Weighted average of test values for all classes, weighted based on Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall [Pacific Northwest National Labor↗
NASA NTRS2000

Protocol Architecture Model Report

NASA's Glenn Research Center (GRC) defines and develops advanced technology for high priority national needs in communications technologies for application to aeronautics and space. GRC tasked Computer Networks and Software Inc. (CNS) to examine protocols and architectures for an In-Space Internet Node. CNS has developed a methodology for network reference models to support NASA's four mission areas: Earth Science, Space Science, Human Exploration and Development of Space (REDS), Aerospace Technology. This report applies the methodology to three space Internet-based communications scenarios for future missions. CNS has conceptualized, designed, and developed space Internet-based communications protocols and architectures for each of the independent scenarios. The scenarios are: Scenario 1: Unicast communications between a Low-Earth-Orbit (LEO) spacecraft inspace Internet node and a ground terminal Internet node via a Tracking and Data Rela Satellite (TDRS) transfer; Scenario 2: Unicast communications between a Low-Earth-Orbit (LEO) International Space Station and a ground terminal Internet node via a TDRS transfer; Scenario 3: Multicast Communications (or "Multicasting"), 1 Spacecraft to N Ground Receivers, N Ground Transmitters to 1 Ground Receiver via a Spacecraft.

Dhas, Chris↗