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At least 1,207 records · Page 67

Extreme Problem Solving: The New Challenges of Deep Space Exploration

On the International Space Station today, the crew has the near real-time support of a large group of system experts on the ground when dealing with problems on-board. For exploration beyond Low Earth Orbit, however, intermittent and delayed communication with ground will force small crews to take the lead in responding to vehicle anomalies. Enabling a flight crew of roughly four astronauts to perform the job that has traditionally been done by a ground crew of over 80 experts will require a fundamental rethinking of human-systems integration. Through observations of anomaly resolution processes, interviews with system experts and astronauts, and analyses of problem-solving models, we have identified the capabilities that are not currently available on-board but will be needed to enable safe exploration further away from Earth. These include increased data access, just-in-time training tools and technologies, and troubleshooting decision support. Important questions remain on how these technologies can be designed and implemented for increased crew autonomy. We present this critical challenge for deep space exploration to the human-computer interaction research community to reflect on the areas identified by our needs analysis and contemplate how they might be manifested as solutions.

autonomy↗

Behavioral Indicators in Air Traffic Control: Detecting and Preventing Performance Decline

Air traffic controllers are responsible for the safety and efficiency of air traffic and therefore must maintain a consistently high standard of performance. However, performance can be negatively affected by factors such as workload and fatigue, potentially leading to performance decline and performance-related incidents. Real-time identification of negative influences would facilitate timely implementation of supportive strategies prior to performance decline. The current study aimed to explore the concept of ‘behavioral indicators’ to identify when a controller was reaching a performance limit. A second aim was to capture behavioral indicators associated with performance influencing factors. A total of 65 controllers spanning Tower, Approach and Enroute facilities across the United States of America were interviewed. Findings revealed that controllers were familiar with the concept of behavioral indicators, and that indicators were associated with specific performance-influencing factors. Implications for implementing behavioral indicators training in control environments are discussed.

air traffic control↗

Behavioral Indicators: How You Know When You are Losing the Flick and What to Do About It?

Air traffic controllers are responsible for the safety and efficiency of air traffic and therefore must maintain a consistently high standard of performance. However, performance can be negatively affected by factors such as workload and fatigue, potentially leading to performance decline and performance-related incidents. Real-time identification of negative influences would facilitate timely implementation of supportive strategies prior to performance decline. The current study aimed to explore the concept of ‘behavioral indicators’ to identify when a controller was reaching a performance limit. A second aim was to capture behavioral indicators associated with performance influencing factors. A total of 65 controllers spanning Tower, Approach and Enroute facilities across the United States of America were interviewed. Findings revealed that controllers were familiar with the concept of behavioral indicators, and that indicators were associated with specific performance-influencing factors. Implications for implementing behavioral indicators training in control environments are discussed.

behavioral indicators↗

LMI Automated Air Cargo Operations Market Research and Forecast

Air cargo companies and aircraft manufacturers are making significant investments to enable the movement of cargo via various levels of automated aircraft, such as aircraft with simplified operations requiring a pilot, remotely monitored or piloted aircraft, and fully autonomous aircraft. These investments will enable greater utilization of aircraft while unlocking new air markets traditionally served by ground transportation only. Many cargo companies and aerospace experts envision an operating environment where a single pilot can remotely pilot numerous aircraft for significant increases in aircraft utilization. The future operating environment is also expected to include air cargo companies flying smaller aircraft from airports and distribution centers outside of major U.S. cities directly to city centers, avoiding congested roads and increasing the velocity of cargo shipments, particularly those that are high-value, time-sensitive, and security sensitive (e.g., pharmaceuticals). These are just a few benefits and use cases cargo companies and aerospace experts see with the advancement of automated aircraft. To better understand industry’s direction, NASA asked the LMI team to research the forecasted market, timeline, risks, and opportunities for integrating unmanned air cargo vehicles into the National Airspace System (NAS) for the development and prioritization of the NASA Air Traffic Management Exploration’s research portfolio. To begin the market assessment, we gathered data via numerous interviews with key stakeholders and subject matter experts and literature reviews. We then incorporated the data into a custom-developed systems dynamics model and visualization dashboard. The systems dynamics model classifies the size of the market (e.g., overall fleet size of automated aircraft) for four distinct use cases over the next 20 years. The model projects the year in which various types of automated aircraft will enter the commercial cargo market based on our team’s collective research on when the aircraft will become viable due to manufacturing and certification timelines and the lifespan of current, traditional aircraft in service, to name a few factors. While this report defines our team’s estimated timeline of entry and growth, the model is dynamic—it enables NASA users to change variables based on future-year events. If the necessary technology does not mature in accordance with our assumptions, then NASA can change the entry of service point to a future year to evaluate the changes in market size in the out years. This flexibility will be key to deciding when and how NASA should invest in various areas.

air traffic management↗

Behavioral Indicators in Air Traffic Control: Detecting and Preventing Performance Decline

Air traffic controllers are responsible for the safety and efficiency of air traffic and therefore must maintain a consistently high standard of performance. However, performance can be negatively affected by factors such as workload and fatigue, potentially leading to performance decline and performance-related incidents. Real-time identification of negative influences would facilitate timely implementation of supportive strategies prior to performance decline. The current study aimed to explore the concept of ‘behavioral indicators’ to identify when a controller was reaching a performance limit. A second aim was to capture behavioural indicators associated with performance influencing factors. A total of 65 controllers spanning Tower, Approach and En-route facilities across the united states of America were interviewed. Findings revealed that controllers were familiar with the concept of behavioural indicators, and that indicators were associated with specific performance influencing factors. Implications for implementing behavioral indicators training in control environments are discussed.

behavioral indicators↗

Tarda an Unusual Carbonaceous Chondrite Meteorite Fall from Morocco

A fireball with a SW to NE trajectory was widely witnessed from the towns of Alnif, Zagora, Tazarine, and Rich in southern Morocco on the 25th of August 2020, around 2:30 pm Moroccan time (GMT+1). The fall was followed immediately by a search campaign by hundreds of Moroccan hunters and curious people in the area of the likely fall zone. On the following day, the first piece was found close to the village of Tarda between Goulmima and Errachidia. A field mission was conducted, and several eyewitnesses were interviewed. They describe a yellow and bright, barrel-sized fireball with green edges lasting ~3s, and accompanied by a whistling sound, followed by multiple detonations. The meteor produced a thick trail of white smoke that remained suspended for several minutes. The fireball was reported to release little pieces around its edges [1].

H. Chennaoui Aoudjehane↗

Argentina Food Security & Agriculture: Crop Monitoring and Forecasting for Argentina using NASA Satellite Observations

Early harvest information helps guide agricultural commodity assessments in Argentina, providing valuable planning information to identify potentially food-insecure regions, anticipate transportation and storage demands, predict price fluctuations, and project commodity trends. However, crop yield estimates are currently subjective, based on interviews with qualified informants (i.e., farmers, agribusiness actors). In partnership with the Buenos Aires Grain Exchange, we leveraged Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Soil Moisture Active Passive (SMAP), and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) NASA Earth observations to develop a Google Earth Engine (GEE) toolset to monitor vegetation growth. The first component of the toolset produces spatial and temporal maps of temperature, precipitation, soil moisture, and the Normalized Difference Vegetation Index (NDVI), allowing users to visualize the influence of the region’s climate and weather. Next, we developed an autoregressive model to predict NDVI several months in advance. Lastly, we created a linear regression model of crop yield and NDVI for soybeans, corn, and wheat, and input the forecasted NDVI to generate a predicted crop yield output. The NDVI forecasting model produced accurate predictions at two, four, and six months when examining the most recent growing season. In the crop yield model, soybeans exhibited moderately strong correlation, wheat had consistent weak correlation, and corn varied from weak to strong correlation depending on zone. This information is vital for vegetation growth monitoring by identifying areas of high growth and allocating resources to areas of lower growth to efficiently maximize crop yields.

DEVELOP Tech Paper↗

Secondary Science Teachers’ Implementation of a Curricular Intervention When Teaching With Global Climate Models

In the past decade, emphasis on promoting “climate literacy” in K-16 science classrooms has increased. Teachers play a critical role in cultivating these opportunities, especially in secondary science classrooms. However, most prior climate education research has focused on students and student learning; little is known about how teachers implement climate-focused curricular interventions. Here, we report findings from a concurrent mixed methods, multiple-case study of four secondary science teachers’ implementation of a new, NGSS-aligned, model-centric climate curriculum module grounded in the use of a data-driven, computer-based climate modeling tool—Easy Global Climate Model (EzGCM). We employ multiple data sources, including video-recorded classroom observations, interviews, and instructional artifacts, and both qualitative and quantitative analyses, to investigate how teachers implemented the curriculum. Findings show that, overall, teachers implemented the curriculum in ways that were less model-centric than designed, placing greater emphasis on EzGCM itself rather than using the model to investigate Earth’s changing climate. Additionally, we present detailed single-case studies of each participant teacher that highlight differences in teachers’ implementation of the curriculum module and their reasoning for making observed instructional decisions. This research sheds light on the design of secondary science learning environments by illustrating the varied ways teachers implement a climate-focused curriculum to support students’ developing climate literacy. This has important implications for the design of climate-focused curriculum and supports for teachers.

Secondary science teaching↗

Behavioral Indicators in Air Traffic Controllers: How Do You Know When You Are Working at The Edge of Performance?

Air traffic controllers are responsible for the safety and efficiency of air traffic and therefore must maintain a consistently high standard of performance. However, performance can be negatively affected by factors such as workload and fatigue, potentially leading to performance decline and performance-related incidents. Real-time identification of negative influences would facilitate timely implementation of supportive strategies prior to performance decline. The current study aimed to explore the concept of ‘behavioral indicators’ to identify when a controller was reaching a performance limit. A second aim was to capture behavioral indicators associated with performance influencing factors. A total of 65 controllers spanning Tower, Approach and Enroute facilities across the United States of America were interviewed. Findings revealed that controllers were familiar with the concept of behavioral indicators, and that indicators were associated with specific performance-influencing factors. Implications for implementing behavioral indicators training in control environments are discussed.

behavioral indicators↗

State of the Industry: UAS Sensor Review

This report is a follow on from HF Designworks’ 2018 report on state of the present and state of the art sensor technologies as they related to Ground Based Detect and Avoid (GBDAA) systems for Unmanned Aircraft Systems (UAS). In this update, HF Designworks again looks to UAS test sites, manufacturers, vendors, and users to identify the latest sensor technologies covering not just GBDAA, but additional UAS sensor types; including airborne. For this latest report, the following sensor types were reviewed, as well as a few that don’t quite fit within the following categories: Electro-Optical / Infrared (EO/IR), LiDAR, RADAR, Radiofrequency (RF) scanner, Acoustic, and Other. From our interviews and research with various organizations and individuals, we found both interesting new technologies as well as improvements to many technologies first reviewed in 2018. Compared with our 2018 findings, many of the new technologies have helped produce sensors that are smaller, lighter, require less power, and have better range and resolution than those discussed in 2018. However, challenges still exist. There is no single currently available and FAA approved sensor suite that can adequately provide complete detect and avoid capabilities to meet the needs of UAS beyond visual line of sight (BVLOS) in the civilian airspace.

unmanned aircraft systems↗

Automation & Autonomy Standards and Guidelines for Space Vehicles

The NASA Office of the Chief Health and Medical Officer (OCHMO) requested HRP conduct a survey of industry and government standards and guidelines for the interaction of automation/autonomy with humans. Guidance for verification testing of human-automation designs was also requested. A rapid response was requested so that the deliverables could be provided as part of a future NASA solicitation for automated systems for HLS. The project began with a broad review and analysis of prior NASA work in this area, as well as external standards related to human interaction with automation/autonomy. The team reviewed approximately 200 documents, and interviewed multiple industry and government subject matter experts (SMEs). “Gold standard” documents were selected for focus and key categories were identified. A cross-walk of standards was created, and common standards and guidelines were selected and rephrased (where necessary) to be information-rich, concise, and usable. Verification guidelines and examples were also developed. The final report includes an introduction to automation and autonomy, selected standards and guidelines, verification method guidance, and research gaps. In addition to the report, the team delivered a list of candidate standards for autonomous vehicles, as well as a list of candidate standards for a future NASA-STD-3001 update on automation/autonomy. The presentation will describe the rapid response project approach and methods, examples of standards and guidelines identified and delivered to OCHMO, and research needed to develop additional automation/autonomy design guidance where gaps currently exist.

automation↗

NASA’s Bio-inspired Broadband Acoustic Absorber: Experiences at the 2021 FedTech Startup Studio

Motivated by the need to reduce aircraft engine noise, NASA’s Bio-inspired Broadband Acoustic Absorber (“Bioliner”) is patented technology that can be developed into multifunctional structures that can absorb sound and might also transfer heat and/or carry a load. Bioliner was one of 12 technologies from federal and university labs that were selected for the FedTech Startup Studio 2021:1 National Aeronautics and Space Administration (NASA),2,3 National Institute of Standards and Technology (NIST),4 National Nuclear Security Administration (NNSA),5 and Northeastern University.6 From August to December 2021, FedTech coached teams of entrepreneurs who explored the commercial potential of the bioliner by performing customer discovery interviews. Results of that investigation supported the claim that the bioliner might have broad commercial potential, beyond aerospace use

acoustic absorber, noise, liner↗

xTM - ATM Coordination - Knowledge Elicitation Results: xTM Operational Domain

Crown Consulting Inc. has been tasked by NASA to elicit information from operators, vehicle manufacturers, and service suppliers on the expected xTM-ATC coordination. For this presentation, first, an introduction for the task and the process is discussed. The second part of the presentation will go through the results which are broken up into the participant company types that we interviewed to elicit feedback on the issues / barriers associated with the introduction of new vehicles and their system (called xTM) into the National Airspace System. The company types are HAPS (Balloons/HALE), sUAS, Large UAS, UAM, Supersonics, and Supporting Technology Providers.

xTM-ATM coordination↗

The Virginia Earth System Science Scholars (VESSS) Experience: Inspiring High School Students in Stem Through Mentorship and Collaborative Skills Development

Since 2016, the Virginia Earth System Science Scholars (VESSS) program offered by the Virginia Space Grant Consortium (VSGC) has provided an interactive science, technology, engineering, and mathematics learning experience for Virginia high school juniors and seniors. The VESSS program was created through a partnership with VSGC, NASA Langley Research Center (LaRC), and Hampton University to engage students in learning about Earth’s climate change and its global impacts. High school students from all areas of Virginia learn about Earth science and climate change in a 16 week online course focused on the Earth’s surface and interior, atmospheric composition, weather, water energy cycles, and climate variability. Students that complete the online course are invited to a no-cost seven-day residential summer academy at NASA LaRC where students are divided into teams based on four of the major spheres of study for Earth: atmosphere, biosphere, hydrosphere, and lithosphere. Each team is guided by a Master Teacher and teams of undergraduate student interns who were also previous VESSS scholars. Students interview for team positions and are given a role reflecting an actual satellite design team position such as project scientist, research scientist, budget analyst, launch and orbit analyst, contamination analyst, and education and public outreach specialist. The students spend a week mentored by NASA and industry scientists and engineers as they learn how to select science goals and objectives, select science instruments, balance a mission budget, determine the mission launch location and orbit, and communicate the importance of the mission to the public. Each team produces a mission proposal, and at the end of the week, each team presents their portion of the mission to a panel of experts, followed by a question and answer period. Through this week students learn teamwork, communication, time management, meeting management, and research skills. Project work is supplemented by team-building activities, NASA facility tours, and lectures by acclaimed guest speakers such as Dr. Christine Darden, a NASA “Hidden Figure.” Students have conveyed through course evaluations and testimonials not only the vast personal learning and growth they achieved, but also the new perspectives they gained on future STEM career fields and opportunities to explore.

Elaine Seasly↗

Environmental Justice Needs-Assessment For Health & Air Quality: Assessing the Needs and Capacity of Organizations Working Towards Health and Environmental Justice Related to Health and Air Quality

Health and air quality are concerns in environmental justice (EJ) work, as air contaminants and their adverse health effects inequitably impact communities that are often underrepresented in research and litigation. Previously, the NASA DEVELOP National Program partnered with organizations that focus on a variety of health and air quality concerns and is now committed to partnering directly with EJ organizations to address inequities in health and air quality through the application of Earth observations. To support the establishment of future partnerships, DEVELOP is working to identify potential partners for further EJ research in the U.S. regarding health and air quality. This project conducted a needs assessment with EJ organizations to quantify their EJ involvement, geospatial capacity, and interest in collaborating with DEVELOP. A broad literature review identified multiple factors influencing health and air quality for potential DEVELOP projects: agriculture, heat, wildfires, industry and transportation infrastructure, and miscellaneous factors (e.g., burning, urban heat island effect, smoke plumes, truck exhaust, and noise). The needs assessment showed that 22% of the organizations interviewed were primarily concerned with transportation infrastructure, 21% with industry, 19% with heat, 10% with miscellaneous factors (e.g., resource mining, COVID-19), 8% with wildfires, 7% with agriculture, and 6% with resource mining. This will help inform the DEVELOP Program's pursuit of EJ projects relating to health and air quality. The team also created a directory for communities impacted by air pollutants to connect with geospatial resources and NASA Applied Sciences personnel for future EJ health and air quality work.

Elizabeth Duran↗

Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, and Psychosocial Adaptions within a Team

The Risk of Performance and Behavioral Health Decrements Due to Inadequate Cooperation, Coordination, Communication, and Psychosocial Adaptation within a Team (the Team Risk) is primarily performance-focused, with a secondary emphasis on behavioral health outcomes resulting from team performance and interpersonal interactions. Monitoring tools, measures, and countermeasures are aimed at enhancing team processes and team composition configurations to optimize team performance and functioning. Long-duration exploration missions (LDEMs) will include major challenges that could affect team performance, including social isolation, physical confinement, a small and diverse crew, communication delays between crew and ground, limited or no crew rotation or evacuation options, limited or no resupply, and a high-consequence environment. Each of these conditions will affect the crew’s coordination, cooperation, psychological well-being, and performance. Although the International Space Station (ISS) remains important for studies that require spaceflight testing and validation, the current conditions on the ISS do not adequately mimic the exploration environment that is required for National Aeronautics and Space Administration (NASA) teams research, and thus access to terrestrial or ground-based analogs of LDEM conditions is paramount. The emphasis on analogs for research is reflected in this updated evidence review of the Team Risk, and includes data from studies conducted at isolated, confined, extreme (ICE) environments (e.g., Antarctic stations), and from several mission simulation analogs such as the Human Exploration Research Analog (HERA) (HERA Experiment Information Package, 2014), also known as isolated, confined, controlled (ICC) environments. These studies have characterized many team factors regarding LDEMs, and the Team Risk has now matured from risk characterization to focusing more on countermeasure development. Because spaceflight evidence for team-level research is lacking, no reliable data is available to quantify the impact of team-level variables on individual and team-level outcomes during spaceflight missions. Until recently, no systematic attempt had been undertaken to measure the performance effects of team cohesion, team composition, team training, or team-related psychosocial adaptation during spaceflight. The Team Risk is a relatively young research area for NASA, with substantial growth only since the 2000s, and with limited access to spaceflight performance data. As a result, spaceflight evidence is lacking to identify specifically what team composition, level of training, amount of cohesion, or quality of psychosocial adaptation is necessary to reduce the risk of performance errors in space. However, astronaut journals and interviews and reports from spaceflight subject matter experts (SMEs) provide testimonies that team performance during spaceflight is important for mission success and to maintain crew health. Team spaceflight data is now being collected as part of the Spaceflight Standard Measures task (Clement, 2021)—a set of core measurements related to many human spaceflight risks that are collected from astronauts before, during, and after long-duration missions. The team-related standard measures focus on team cohesion, team performance, group living, team climate, and team processes. Collection of standard measures data is ongoing and published data is not yet available. Finally, although spaceflight evidence is lacking, evidence gleaned from ground studies and spaceflight analog studies will help close the gaps outlined in the Team Risk. Ground-based studies provide quantitative evidence for team functioning in ICE environments. Academic research on teams has produced dozens of meta-analyses that can be used to understand the general relationships among team inputs (e.g., team member characteristics and skills, job context), team processes, and emergent states (e.g., coordination, communication, cooperation, cohesion, trust, shared cognition), and team outcomes (e.g., effectiveness, errors, adaptation). Teams are complex, incorporating individual characteristics of team members, but also existing at a level that is greater than the sum of its parts. Therefore, the Team Risk must be integrated with other individual-focused NASA Human Research Program (HRP) risks, including Behavioral Medicine (BMed), Sleep, and Human-Systems Integration Architecture (HSIA), and emerging research indicates more integration may needed between the Team Risk and the physiologically oriented risks. Much of this integration occurs through the Human Systems Risk Board (HSRB). A lack of team functioning may be a stressor in some circumstances, but the team often acts as a countermeasure. For example, support for team leaders and teammates can facilitate individual functioning and encourage psychological and physically healthy behaviors and attitudes. However, more research is needed regarding teams during LDEMs and the remaining gaps in the research are described in the current report.

Lauren Blackwell Landon↗

Overview of JAXA and NASA Collaboration on Manned and Unmanned Aircraft Operations Integration in Disaster Response

Natural disasters have increased in frequency and intensity. With that increase has come an increased focus on improving disaster response efforts. As part of this focus, the Japan Aerospace Exploration Agency (JAXA) and the National Aeronautics and Space Administration (NASA) have been in collaboration since 2016 to explore improved airspace management for disaster response through the integration of manned aircraft and small Unmanned Aircraft Systems (UAS). The approach to this integration has been through leveraging JAXA’s Disaster Relief Aircraft Information Sharing Network (D-NET) system for the coordinated management of manned aircraft assets combined with NASA’S UAS Traffic Management (UTM) system for the management of UAS assets. To drive the innovation in improved response, JAXA and NASA have successfully connected respective systems and conducted a series of live flight tests first as part of a large-scale disaster drill and later in dedicated research flights. In each case, the D-NET and UTM systems were connected and exchanged data in real-time, which supported the planning and tracking of manned and unmanned operations. Through these test events, a number of key findings were derived through data analysis and stakeholder interviews with respect to the improvements offered by the integrated systems as well as key considerations for situation awareness of pilots regarding other operations in the airspace and flight path management.

disaster response↗

Data Science and the Knowledge Discovery Adventure

This talk will cover the important steps involved in the data science and knowledge discovery process: • Initial fact gathering (interview domain experts, review reports, articles, state-of-the-art) • Identify the problem (prediction, classification, statistical analysis, etc.) • Survey supporting data sources • Understand the data (numerical, categorical, text, sampling rate, data quality issues, etc.) • Selecting relevant features and sources • Acquire the data (set up agreements with the data stewards, APIs to download, etc.) • Merge data sources (temporal, spatial, common key, other ontologies...) • Feature Engineering (non linear domain knowledge or physics-based relationships) • Build data processing pipeline (may need to tap into data stream, develop parallel processing algorithm, federated learning etc.) • Build model and test (tune hyper-parameters, cross validation.) • Analyze/Validate results (do the results make sense. Does it answer the original question). • Deploy/Publish (Monitor and assess benefits)

Data science↗