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At least 181 records · Page 10

A Cybersecurity Threat Profile for a Connected Lighting System

In anticipation of improved energy performance and cost savings, cities and building owners are increasingly considering “smart lighting initiatives” that aim to convert their collection of simple luminaires (i.e., lighting fixtures) into an intelligent connected lighting system (CLS) capable of remotely monitoring energy consumption and fault conditions, and possibly implementing adaptive lighting schemes. The U.S. Department of Energy (DOE) has set an national goal of tripling the energy efficiency and demand flexibility of the buildings sector by 2030, relative to 2020 levels 1. It is forecast that connected lighting systems can contribute to that goal by delivering 125 TWh of annual energy savings by 2035 2, equivalent to the annual output of 50 typical (500 MW) power plants. However, these energy savings and the DOE goal are put at significant risk if connected technologies are not adopted due to real or perceived cybersecurity concerns. Connected IoT devices such as these have historically been rife with vulnerabilities which sometimes put security considerations secondary to functionality and operability. What are the cybersecurity threats that will impact these systems, as formerly banal luminaires transition into intelligent connected devices that collect information about themselves, their surrounding environment, and possibly us? In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement. Fifty-seven threats were identified. Among our key findings: (1) 65% (37/57) of the threats did not involve the luminaires, but rather the other components needed to communicate with and manage them; (2) 63% (36/57) of the threats could have been mitigated through manufacturer-implemented defensive techniques or “controls”; and (3) 23% (13/57) of the threats were dependent on the network configuration. Recommendations based on the results of this work are made to key stakeholder groups. Notably, lighting technology developers are advised to address all threats that can be reasonably controlled with baked-in technology solutions (e.g., encryption or authentication controls), and employ some form of secure supply chain management and tracking where other parts (e.g., sensors, microprocessors) of a luminaire must also be built and manufactured with the proper security controls in place. Developers should also review threats involving assets not developed in-house to understand how connectivity with other devices will affect their product during system operation and determine if a compensating control for a defense-in-depth strategy will be needed. Finally, those interested in deploying CLS should compare the differences between cloud and on-premise models to determine which is more suitable for their needs and the abilities of their security team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Web Monitoring of EOS Front-End Ground Operations, Science Downlinks and Level 0 Processing

This paper addresses the efforts undertaken and the technology deployed to aggregate and distribute the metadata characterizing the real-time operations associated with NASA Earth Observing Systems (EOS) high-rate front-end systems and the science data collected at multiple ground stations and forwarded to the Goddard Space Flight Center for level 0 processing. Station operators, mission project management personnel, spacecraft flight operations personnel and data end-users for various EOS missions can retrieve the information at any time from any location having access to the internet. The users are distributed and the EOS systems are distributed but the centralized metadata accessed via an external web server provide an effective global and detailed view of the enterprise-wide events as they are happening. The data-driven architecture and the implementation of applied middleware technology, open source database, open source monitoring tools, and external web server converge nicely to fulfill the various needs of the enterprise. The timeliness and content of the information provided are key to making timely and correct decisions which reduce project risk and enhance overall customer satisfaction. The authors discuss security measures employed to limit access of data to authorized users only.

Cordier, Guy R.↗

Medical Database Accomplishments and Lessons Learned - 2021

The Medical Database (MD) is a virtual repository consisting of two software components: the Medical Item Database (MedID) and the Evidence Library (EL). MedID consists of engineering data and associated information for specific medical resource items (e.g., pharmaceutical, medical devices, and supporting components), while the EL is a tool which provides all of the medical evidence necessary. The Medical Database will serve as the single “source of truth” for the Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) tool suite for both medical evidence and medical resource engineering data. It will be used in conjunction with the IMPACT tool suite to inform research prioritizations and perform systematic trade study evaluations using probabilistic risk assessment and simulated human spaceflight missions to aid stakeholders in making informed decisions during Pre-Phase-A planning of human spaceflight missions. The IMPACT project is conducted under the Exploration Medical Capability (ExMC) element of the Human Research Program (HRP), within NASA’s Human Exploration and Operations Mission Directorate. Over the past year, the MedID software has been successfully merged with the Evidence Library into one cohesive Medical Database with two independent user interface experiences for modifying either clinical evidence or resource engineering data. As MD has evolved substantially over the past year, a number of challenges have been encountered and overcome along the way. A number of ‘lessons learned’ and practical/logistical realizations have emerged which will be detailed in the forthcoming Medical Database presentation.

Exploration Medical Capability↗

Application of Fault Management Theory to the Quantitative Selection of a Launch Vehicle Abort Trigger Suite

The theory of System Health Management (SHM) and of its operational subset Fault Management (FM) states that FM is implemented as a "meta" control loop, known as an FM Control Loop (FMCL). The FMCL detects that all or part of a system is now failed, or in the future will fail (that is, cannot be controlled within acceptable limits to achieve its objectives), and takes a control action (a response) to return the system to a controllable state. In terms of control theory, the effectiveness of each FMCL is estimated based on its ability to correctly estimate the system state, and on the speed of its response to the current or impending failure effects. This paper describes how this theory has been successfully applied on the National Aeronautics and Space Administration's (NASA) Space Launch System (SLS) Program to quantitatively estimate the effectiveness of proposed abort triggers so as to select the most effective suite to protect the astronauts from catastrophic failure of the SLS. The premise behind this process is to be able to quantitatively provide the value versus risk trade‐off for any given abort trigger, allowing decision makers to make more informed decisions. All current and planned crewed launch vehicles have some form of vehicle health management system integrated with an emergency launch abort system to ensure crew safety. While the design can vary, the underlying principle is the same: detect imminent catastrophic vehicle failure, initiate launch abort, and extract the crew to safety. Abort triggers are the detection mechanisms that identify that a catastrophic launch vehicle failure is occurring or is imminent and cause the initiation of a notification to the crew vehicle that the escape system must be activated. While ensuring that the abort triggers provide this function, designers must also ensure that the abort triggers do not signal that a catastrophic failure is imminent when in fact the launch vehicle can successfully achieve orbit. That is, the abort triggers must have low false negative rates to be sure that real crew‐threatening failures are detected, and also low false positive rates to ensure that the crew does not abort from non‐crew‐threatening launch vehicle behaviors. The analysis process described in this paper is a compilation of over six years of lessons learned and refinements from experiences developing abort triggers for NASA's Constellation Program (Ares I Project) and the SLS Program, as well as the simultaneous development of SHM/FM theory. The paper will describe the abort analysis concepts and process, developed in conjunction with SLS Safety and Mission Assurance (S&MA) to define a common set of mission phase, failure scenario, and Loss of Mission Environment (LOME) combinations upon which the SLS Loss of Mission (LOM) Probabilistic Risk Assessment (PRA) models are built. This abort analysis also requires strong coordination with the Multi‐Purpose Crew Vehicle (MPCV) and SLS Structures and Environments (STE) to formulate a series of abortability tables that encapsulate explosion dynamics over the ascent mission phase. The design and assessment of abort conditions and triggers to estimate their Loss of Crew (LOC) Benefits also requires in‐depth integration with other groups, including Avionics, Guidance, Navigation and Control(GN&C), the Crew Office, Mission Operations, and Ground Systems. The outputs of this analysis are a critical input to SLS S&MA's LOC PRA models. The process described here may well be the first full quantitative application of SHM/FM theory to the selection of a sensor suite for any aerospace system.

Lo, Yunnhon↗

Prairie Horizon Carbon Management Hub

The Energy & Environmental Research Center (EERC), with support from the North Dakota Industrial Commission and the U.S. Department of Energy, evaluated the potential for the development of a large-scale carbon management hub (CMH) in Stark County, North Dakota. The objective of the Prairie Horizon CMH project is to build public confidence to benefit and accelerate commercial carbon capture, utilization, and storage (CCUS) deployment. Findings from this investigation can facilitate project development by informing early-stage decision-making related to site selection, stakeholder engagement, understanding risks, and permitting readiness.

02 PETROLEUM↗

Remote Sensing of In-Flight Icing Conditions: Operational, Meteorological, and Technological Considerations

Remote-sensing systems that map aircraft icing conditions in the flight path from airports or aircraft would allow icing to be avoided and exited. Icing remote-sensing system development requires consideration of the operational environment, the meteorological environment, and the technology available. Operationally, pilots need unambiguous cockpit icing displays for risk management decision-making. Human factors, aircraft integration, integration of remotely sensed icing information into the weather system infrastructures, and avoid-and-exit issues need resolution. Cost, maintenance, power, weight, and space concern manufacturers, operators, and regulators. An icing remote-sensing system detects cloud and precipitation liquid water, drop size, and temperature. An algorithm is needed to convert these conditions into icing potential estimates for cockpit display. Specification development requires that magnitudes of cloud microphysical conditions and their spatial and temporal variability be understood at multiple scales. The core of an icing remote-sensing system is the technology that senses icing microphysical conditions. Radar and microwave radiometers penetrate clouds and can estimate liquid water and drop size. Retrieval development is needed; differential attenuation and neural network assessment of multiple-band radar returns are most promising to date. Airport-based radar or radiometers are the most viable near-term technologies. A radiometer that profiles cloud liquid water, and experimental techniques to use radiometers horizontally, are promising. The most critical operational research needs are to assess cockpit and aircraft system integration, develop avoid-and-exit protocols, assess human factors, and integrate remote-sensing information into weather and air traffic control infrastructures. Improved spatial characterization of cloud and precipitation liquid-water content, drop-size spectra, and temperature are needed, as well as an algorithm to convert sensed conditions into a measure of icing potential. Technology development also requires refinement of inversion techniques. These goals can be accomplished with collaboration among federal agencies including NASA, the FAA, the National Center for Atmospheric Research, NOAA, and the Department of Defense. This report reviews operational, meteorological, and technological considerations in developing the capability to remotely map in-flight icing conditions from the ground and from the air.

Ryerson, Charles C.↗

Active Swarm Resiliency in the HelioSwarm Mission

Designed to observe plasma turbulence dynamics in solar wind over a distributed volume of space, the HelioSwarm mission comprises a primary chief spacecraft and eight smaller deputy satellites in uniquely assigned “loops” of periodic relative motion in a P/2 lunar resonant orbit. If one or more deputies fail, this multi-satellite architecture facilitates resiliency for science goals through repositioning of satellites to contingency loops. This strategy of Active Swarm Resiliency mitigates risk by modeling quantitative results ahead of time for mission operators to make informed decisions. Responsive actions meet minimum science objectives based on past and predicted system performance, an approach with applications to future missions with similar architecture and requirements.

Fault Management↗

Active Swarm Resiliency in the HelioSwarm Mission

Designed to observe plasma turbulence dynamics in solar wind over a distributed volume of space, the HelioSwarm mission comprises a primary chief spacecraft and eight smaller deputy satellites in uniquely assigned “loops” of periodic relative motion in a P/2 lunar resonant orbit. If one or more deputies fail, this multi-satellite architecture facilitates resiliency for science goals through repositioning of satellites to contingency loops. This strategy of Active Swarm Resiliency mitigates risk by modeling quantitative results ahead of time for mission operators to make informed decisions. Responsive actions meet minimum science objectives based on past and predicted system performance, an approach with applications to future missions with similar architecture and requirements.

Fault Management↗

San José Urban Development: Quantifying Canopy Cover and Land Surface Temperature in San José to Identify Future Tree Planting Sites

The urban heat island effect refers to the phenomenon of substantially increased temperatures in urban areas compared to their surrounding suburban or rural counterparts. The City of San José’s Department of Parks, Recreation and Neighborhood Services (PRNS) and Department of Transportation (DOT) work to mitigate the UHI effect through urban forestry initiatives. The PRNS and DOT partnered with NASA DEVELOP to identify areas in need of tree plantings. We examined land surface temperature (LST) throughout the city using data from the Thermal Infrared Sensor (TIRS) and TIRS-2 on NASA’s Landsat 8 and 9 satellites from 2013 to 2024. We also assessed canopy cover in parks using LiDAR data collected in 2020 for the United States Geological Survey’s 3D Elevation Program, and measured vegetation greenness using the Normalized Difference Vegetation Index (NDVI) with PlanetScope imagery from 2018 to 2024. We evaluated social and environmental factors that influence the distribution of heat event impacts by creating a heat vulnerability index. We found that heat is concentrated in urban areas and that poor vegetation health is associated with high LST. We also found that socially vulnerable communities are disproportionately located in areas of high environmental risk. These analyses allow the partners to prioritize tree plantings in parks near areas of high social and environmental risk. We determined that Earth observations can be used to inform urban forestry decision making, but because methodologies for using LiDAR to assess canopy cover vary greatly, it is difficult to make comparisons across different canopy cover assessments.

land surface temperature↗

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗

Hawai‘i Island Disasters: Using NASA Earth Observations to Assess Coastal Flood Risk with Measures of Land Cover Change, Flood Extent, and Vulnerability for Adaptation and Mitigation Planning on Hawai‘i Island

As the County of Hawai‘i faces an increased risk of extreme flooding events, sea-level rise, and other hazards associated with climate change, the need for building geospatial capacity to make better-informed decisions is critical. The County of Hawai‘i and Arizona State University partnered with NASA DEVELOP to complete a macro-scale risk analysis for the island of Hawai‘i analyzing flooding, land cover, vulnerability, and exposure factors using Earth observations and socio-economic data. The team assessed the variation in urban coastal vulnerability around the entire island of Hawai‘i, using satellite imagery of coastal land cover typology from satellite products such as Landsat 8 Operational Land Imager (OLI), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Sentinel-1 Synthetic Aperture Radar (SAR). The team made a sharable geodatabase containing datasets modeling vulnerability to coastal flooding as well as the Hawai‘i Flood Risk Toolbox (HiFloRT) which contains multiple tools for the County to map land cover, extreme rainfall and flood extent across the Island. The end products will allow the County of Hawai‘i to establish a protocol and standard framework for the utilization of Earth observations in future planning.

Garren Kalter↗

Risk Acceptance Personality Paradigm: How We View What We Don't Know We Don't Know

The purpose of integrated hazard analyses, probabilistic risk assessments, failure modes and effects analyses, fault trees and many other similar tools is to give managers of a program some idea of the risks associated with their program. All risk tools establish a set of undesired events and then try to evaluate the risk to the program by assessing the severity of the undesired event and the likelihood of that event occurring. Some tools provide qualitative results, some provide quantitative results and some do both. However, in the end the program manager and his/her team must decide which risks are acceptable and which are not. Even with a wide array of analysis tools available, risk acceptance is often a controversial and difficult decision making process. And yet, today's space exploration programs are moving toward more risk based design approaches. Thus, risk identification and good risk assessment is becoming even more vital to the engineering development process. This paper explores how known and unknown information influences risk-based decisions by looking at how the various parts of our personalities are affected by what they know and what they don't know. This paper then offers some criteria for consideration when making risk-based decisions.

Massie, Michael J.↗

Using NASA Remotely Sensed Data to Help Characterize Environmental Risk Factors for National Public Health Applications

This project has dual goals in decision ]making activities .. Providing information to decision makers about associations between environmental exposures and health conditions in a large national cohort study. Enriching the CDC Wide ]ranging Online Data for Epidemiologic Research (WONDER) system by integrating environmental exposure data. .. Develop daily high ]quality spatial data sets of environmental variables for the conterminous U.S. for the years 2003-2008 utilizing NASA data (Objective 1). Fine Particulates (PM2.5) (NASA MODIS and EPA AQS). Land Surface Temperature (NASA MODIS). Solar Insolation and Heat ]related Products (Reanalysis Data). Link these environmental variables with public health data from a national cohort study and examine environmental health relationships (Objective 2). Cognitive Function. Hypertension. Make the environmental datasets available to public health professionals, researchers and the general public via the CDC WONDER system (Objective 3).

Al-Hamdan, Mohammad↗

Evolution and Implementation of the NASA Robotic Conjunction Assessment Risk Analysis Concept of Operations

Reacting to potential on-orbit collision risk in an operational environment requires timely and accurate communication and exchange of data, information, and analysis to ensure informed decision-making for safety of flight and responsible use of the shared space environment. To accomplish this mission, it is imperative that all stakeholders effectively manage resources: devoting necessary and potentially intensive resource commitment to responding to high-risk conjunction events and preventing unnecessary expenditure of resources on events of low collision risk. After 10 years of operational experience, the NASA Robotic Conjunction Assessment Risk Analysis (CARA) is modifying its Concept of Operations (CONOPS) to ensure this alignment of collision risk and resource management. This evolution manifests itself in the approach to characterizing, reporting, and refining of collision risk. Implementation of this updated CONOPS is expected to have a demonstrated improvement on the efficacy of JSpOC, CARA, and owner/operator resources.

operations↗

Navigating Epistemic Uncertainty in the Management of Flash Droughts

Abstract: Flash droughts, characterized by their rapid onset, sharply contrast with the typically gradual development of traditional droughts. These events are triggered by a combination of low rainfall and high evaporation rates, driven by elevated temperatures, making them particularly challenging to predict and prepare for. As a relatively new concept, flash drought is not well understood, which introduces significant epistemic uncertainties regarding their nature and detection methods. This under-detection hinders planners' ability to effectively manage these events. Despite these uncertainties, flash droughts can have significant impacts, raising the question: how can decision-makers prepare for such events given the current knowledge gaps? To address this, we propose a methodological framework aimed at enhancing flash drought preparedness by guiding the selection of appropriate indicators based on their detection capabilities and the decision-makers' level of risk aversion. Our approach involves evaluating six different flash drought indicators and analyzing the level of agreement among them. Additionally, we consider the decision-makers' risk aversion, distinguishing between those who require consensus across all methods (risk-takers) and those who act based on a single method's indication (risk-averse). The insights gained from this study offer a pathway towards more informed decision-making processes regarding flash droughts, potentially mitigating their adverse effects through better preparedness and response strategies.

climate resilience↗

The NASA Earth Science Applied Sciences Disasters Program: Making EO Data and Expertise Available through the Disasters Mapping Portal to Inform Decision-Makers throughout the Disaster Cycle

The NASA Earth Science Applied Sciences Disasters Program promotes the use of Earth Observations (EO) to inform disaster risk reduction and resilience throughout the disaster cycle, from local to global scales, by harnessing NASA science and technology capabilities, through engagement with end users to demonstrate the value and impact of EO to support decision-making, and by supporting end users in their use of EO in decision-making while developing relationships to grow as a trusted source of relevant science and useful results. Through the NASA Earth Science Applied Sciences Disasters Program Mapping Portal, an Esri-backed hub of geospatially enabled disaster products, event-based and near-real-time products are hosted to provide end users with analysis-ready data and data services, keeping in mind their expressed EO needs. In this presentation, case studies of past events will be highlighted, showcasing how EO data and services were provided by the Program and utilized by end users during the disaster cycle. Additionally, this presentation will highlight some of the Program’s ongoing collaboration activities to assist end users in understanding and preparing for utilization of EO data from the Mapping Portal to inform their decision-making throughout the disaster cycle. Ongoing efforts to advance the science provided by the Program on the Mapping Portal through new developments and capabilities will be highlighted, and common challenges and data needs end users often encounter when using EO data and our Program’s innovative solutions to these challenges will be addressed. Furthermore, this presentation will touch on the challenges faced and the solutions created by the Mapping Portal team in managing and hosting a plethora of EO data for end users across various disciplines.

Ronan Lucey↗

Estimating the Acute Health Impacts of Fire-Originated PM 2.5 Exposure During the 2017 California Wildfires: Sensitivity to Choices of Inputs

Exposure to wildfire smoke increases the risk of respiratory and cardiovascular hospital admissions. Health impact assessments, used to inform decision-making processes, characterize the health impacts of environmental exposures by combining preexisting epidemiological concentration–response functions (CRFs) with estimates of exposure. These two key inputs influence the magnitude and uncertainty of the health impacts estimated, but for wildfire-related impact assessments the extent of their impact is largely unknown. We first estimated the number of respiratory, cardiovascular, and asthma hospital admissions attributable to fire-originated PM 2.5 exposure in central California during the October 2017 wildfires, using Monte Carlo simulations to quantify uncertainty with respect to the exposure and epidemiological inputs. We next conducted sensitivity analyses, comparing four estimates of fire-originated PM 2.5 and two CRFs, wildfire and nonwildfire specific, to understand their impact on the estimation of excess admissions and sources of uncertainty. We estimate the fires accounted for an excess 240 (95% CI: 114, 404) respiratory, 68 (95% CI: -10, 159) cardiovascular, and 45 (95% CI: 18, 81) asthma hospital admissions, with 56% of admissions occurring in the Bay Area. Although differences between impact assessment methods are not statistically significant, the admissions estimates' magnitude is particularly sensitive to the CRF specified while the uncertainty is most sensitive to estimates of fire-originated PM 2.5 . Not accounting for the exposure surface's uncertainty leads to an underestimation of the uncertainty of the health impacts estimated. Employing context-specific CRFs and using accurate exposure estimates that combine multiple data sets generates more certain estimates of the acute health impacts of wildfires.

58 GEOSCIENCES↗

Costing the satellite power system

The paper presents a methodology for satellite power system costing, places approximate limits on the accuracy possible in cost estimates made at this time, and outlines the use of probabilistic cost information in support of the decision-making process. Reasons for using probabilistic costing or risk analysis procedures instead of standard deterministic costing procedures are considered. Components of cost, costing estimating relationships, grass roots costing, and risk analysis are discussed. Risk analysis using a Monte Carlo simulation model is used to estimate future costs.

Hazelrigg, G. A., Jr.↗