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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Sleep, Sleepiness, and Performance Across Three In-Flight Bunk Rest Opportunities

Introduction: Airline pilots are required to take a rest break in a bunk during long-haul flights in an effort to reduce sleepiness during critical phases of flight. It is unclear, however, whether each rest break affords equal opportunity for sleep. We aimed to characterize sleep, sleepiness, and performance outcomes across three in-flight rest breaks during long-haul flights. Methods: Thirty-seven pilots wore actiwatches and completed sleep diaries for approximately two weeks while flying a variety of long-haul routes (n=126 flights). Self-reported in-flight bunk rest (BR) periods were used to set rest intervals and sleep was estimated within these intervals using actigraphy software (wake threshold set to medium). Pilots provided Karolinska Sleepiness Scale ratings (KSS) and performed a 5-minute psychomotor vigilance task (PVT) before landing. A linear mixed-effects model with participant included as a random effect and allowed to vary by intercept was used to assess differences between BR opportunities. Results : The majority (97%, n=122) of bunk rest periods contained sleep (as estimated by actigraphy). The mean (+/- standard deviation) sleep duration for the first, middle, and third BR opportunity was 152.8 (69.7), 149.2 (44.1), 125.2 (44.9) minutes, respectively. There was a significant effect of BR opportunity for sleep duration (F2,54 = 3.747, p=.03) and KSS (F2,44 = 7.869, p=.001). Bonferroni adjusted planned pairwise contrasts revealed that pilots using the third BR obtained significantly less sleep than in the first BR (p=.029). KSS ratings prior to landing were higher for the third BR compared to both the first (p=.001) and middle BR (p=.017). There were no significant differences for PVT speed or lapses (all p>05). Conclusion: These results suggest that the last rest break is associated with shorter sleep, lower alertness, and no differences in performance relative to the other rest breaks. Further analysis is required to determine whether the higher KSS ratings following the third rest break are associated with sleep inertia, or whether other factors may be involved.

sleepiness↗
NASA NTRS2021

FRB131104 Swift/BAT Data Revisited: No Evidence of a Gamma-Ray Counterpart

We present a reanalysis of the Swift Burst Alert Telescope (BAT) data around the radio detection of FRB131104. Possible evidence of a gamma-ray counterpart was presented by DeLaunay et al. However, based on our analysis using all the available BAT data, no significant emission is found in either the temporal or the image domain. We place a 5σ fluence upper limit of 3.3 × 10−6 erg cm−2 and 2.7 × 10−6 erg cm−2 (15–150 keV) with an integration time of 300 s assuming a simple power-law spectrum with photon index of −1.2 and −2.0, respectively. Our result does not support the association of this FRB with a high-energy counterpart, in agreement with growing observational evidence that most FRBs are not associated with catastrophic events such as gamma-ray bursts.

T Sakamoto↗
NASA NTRS2021

Modeling the Prompt Optical Emission of GRB 180325A: The Evolution of a Spike from the Optical to Gamma Rays

The transition from prompt to afterglow emission is one of the most exciting and least understood phases in gamma-ray bursts (GRBs). Correlations among optical, X-ray, and gamma-ray emission in GRBs have been explored, to attempt to answer whether the earliest optical emission comes from internal and/or external shocks. We present optical photometric observations of GRB 180325A collected with the TAROT and RATIR ground-based telescopes. These observations show two strong optical flashes with separate peaks at ∼50 and ∼120 s, followed by a temporally extended optical emission. We also present X-rays and gamma-ray observations of GRB 180325A, detected by the Burst Alert Telescope and X-ray Telescope, on the Neil Gehrels Swift observatory, which both observed a narrow flash at ∼80 s. We show that the prompt gamma-ray and X-ray early emission shares similar temporal and spectral features consistent with internal dissipation within the relativistic outflow (e.g., by internal shocks or magnetic reconnection), while the early optical flashes are likely generated by the reverse shock that decelerates the ejecta as it sweeps up the external medium.

R. L. Becerra↗
NASA NTRS2021

Perceive: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures

National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns. This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated model scan match or outperform existing manual practices.

ASRS↗
NASA NTRSDate not supplied

Using Landsat 8 Satellite Imagery to Analyze Biogeochemical Constituents in the Waters of the San Francisco Bay Area and Beyond

The ocean's coastal zones play a key role in our planet's health and mitigate the adverse effects of climate change. Building on the success of NASA satellite imagery in mapping land cover changes around the globe in response to climate change, deforestation, and natural disasters, I have worked to determine how aquatic reflectance images from the Landsat 8 sensor could be correlated with biogeochemical constituents within the waters of the San Francisco Bay Area and California coastline. By analyzing Landsat 8 satellite imagery from the years 2013-2020 in collaboration with data from the United States Geological Survey (USGS), San Francisco Estuary Institute (SFEI), and universities involved with the Harmful Algal Bloom Monitoring and Alert Program (HABMAP), I investigated which areas of the San Francisco Bay and California coastline demonstrated significant correlations with values extracted from the satellite imagery. Using a Chlorophyll Index (CI) ratio calculated from two Landsat 8 satellite reflectance bands, it was found that there is a positive correlation between the CI and calculated oxygen, suspended particulate matter, and silicate from several USGS sampling points. In contrast, there is a significant negative correlation between the CI and salinity, nitrite, and phosphate at those same locations. Moreover, large suspected harmful algal blooms (HABs) along the California coastline seen in the Landsat 8 imagery were corroborated with HAB data from various institutions. Understanding the effectiveness of Landsat 8 aquatic reflectance images will allow scientists to predict HABs and other nutrient cycling that may be harmful to aquatic ecosystems around the world.

Landsat↗
NASA NTRS2021

Positive Train Control (PTC) Study: An Analysis of PTC-Related Reports Submitted to the Confidential Close Call Reporting System (C3RS)

A supplemental analysis of reports submitted to the voluntary Confidential Close Call Reporting System (C3RS) was conducted to identify the potential operational risks associated with the integration and operation of Positive Train Control (PTC) systems. This study identified four areas that may merit further investigation by FRA and rail carriers: System/Paperwork synchronization, training, PTC acceptance, and alerting mechanisms.

C3RS↗
NASA NTRSDate not supplied

Lunar Search & Rescue Applications of Lunar GNSS

Accurate lunar navigation and timing knowledge provides for the development of safety-critical services in the cislunar and lunar surface domain. Currently under development, the Goddard Space Flight Center’s (GSFC) Search and Rescue Mission Office is investigating and integrating search and rescue (SAR) capability into planned and future lunar communication and navigation interfaces. Lunar Search and Rescue (LunaSAR) development has a stated end-goal for assured, reliable, and timely indication of distress events for a wide variety of lunar surface users, including government-sponsored, commercial, and international users. LunaSAR performance requirements are modelled after the current terrestrial Cospas-Sarsat distress notification system, leveraging an internationally robust global navigation satellite system (GNSS) ecosystem as a core element of survivor locating capability. This presentation will discuss NASA’s work to develop user-focused distress messaging capabilities including infusion of example sensor data for triggering of automated distress alerts coupled with location-tagging. Additionally, the presentation will examine overall message structures, rotating fields for use in bi-directional distress messaging, and specific use cases based on NASA’s lunar exploration and lunar communication relay architectures. Modelling and simulation of LunaSAR use by individual lunar explorers will be discussed, based on notional industry and government design reference missions and mission considerations. Results from GSFC-funded Internal Research and Development (IRAD) efforts will be detailed, including successful distress message formulation simulating the ingestion of example legacy space suit telemetry fields. Hardware-in-the-loop testing using high-reliability software defined radio (SDR) modules serve as an example of IRAD successes and the framework for technical requirements. Architectural development and technical evolution from 2020 to 2021 included alignment of LunaSAR distress waveforms with ongoing NASA LunaNet interoperability development, as well as engagement with NASA Lunar Spectrum authorities for allocation of UHF-band distress frequencies on the lunar surface. S-Band and UHF-band transmission characteristics will be detailed, along with band-specific applications of each emission type. Additionally, examples of ingestion and formatting of GNSS signals (using historical terrestrial National Marine Electronics Association-formatted GNSS data) will be detailed, underscoring lunar user needs for a common lunar GNSS receiver output message framework. Maturity and ability to support evolving lunar exploration goals has been demonstrated and will be detailed, with maturity gaps such as position, navigation, and timing (PNT) and lunar reference frames identified within the context of distress message generation. Provision of LunaSAR services for lunar surface users represents a new era of ensured safety for lunar explorers and builds off of forty years of the Cospas-Sarsat program, underscoring the importance of lunar GNSS for safety-critical applications and growing interest in safe, reliable lunar surface operations. Enabled by new GNSS systems being developed by government and industry partners, NASA will continue to evolve and integrate lunar GNSS types into distress message generation, with a focus on compact and efficient message transmission over various lunar communication links. When fielded, LunaSAR will be the first dedicated search and rescue notification system employed on another celestial body. Robust lunar navigation and timing services form the core of LunaSAR capabilities, allowing for system syncing with time-dominant sensors, and high-accuracy location of those in distress while engaged in lunar surface activities.

Search and Rescue↗
NASA NTRSDate not supplied

ACAS Xr Part Task Sim, Preliminary Experiment Design

In early 2022, the Human Autonomy Teaming Lab (NASA Ames Research Center) will conduct a manned, human-in-the-loop (HITL) simulation. This part task HITL will begin the lab’s Hazard Perception and Avoidance (HPA) technical work under NASA’s Advanced Air Mobility (AAM), Automated Flight and Contingency Management (AFCM) Sub-Project. The goals will be to assess levels of automation for manned, electric vertical takeoff and landing (eVTOL) aircraft. This simulation will test manual and automated Resolution Advisory (RA) responses and return-to-course (RTC) maneuvers for the first version of the Airborne Collision Avoidance System’s (ACAS) rotary-wing (Xr) variant. This will be conducted on a fixed-based simulator designed to fly eVTOL aircraft while maneuvering for intruding traffic. Variables for this study include levels of autonomy (i.e., manual and automated) as well as the types of alerts at the onset of conflicts (i.e., Corrective and RA). The data collected will include response times, losses of well clear, and maneuver sizes and durations as well as subjective ratings like acceptability, perceived workload, and meaningful human control. Additional details and future anticipations are also discussed.

air taxis↗
NASA NTRS2021

NASA Academy Summer 2021 Equipment Development Team Report

Wildfires have become increasingly frequent, widespread, intense, and destructive. The 2021 NASA Academy at Langley Research Center was given the task of applying NASA technology to the challenges faced by wildland firefighting professionals. The Equipment Development Team focused on developing tools that could be deployed to make the work of wildland firefighters safer and more effective. The team developed both a conceptual design and a prototype of a lightweight respirator with air cooling capabilities. The team also researched body-worn biometric and environmental sensors, identifying a suite of sensors that could predict and alert firefighters to dangerous physical and environmental conditions. This report outlines the results of these activities, and analyzes each component of the systems, identifying challenges, possible technological solutions, and their feasibility.

Kilarendha Sundling↗
NASA NTRSDate not supplied

A Survey Protocol to Assess Meaningfulness and Usefulness of Automated Topic Finding in the NASA Aviation Safety Reporting System

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database, Safety Alert Bulletins and For Your Information Notices, and the CALLBACK newsletter. Key to these publications are the timely processing (de-identification, coding and summarization) of new reports, which is currently done by ASRS expert safety analysts. Thus, we believe topic modelling could decrease effort in ASRS, if topics are comprehensible. Aim: We propose a methodology to evaluate whether automated topic finding using topic modelling provides meaningful and useful topics. Method: We extend the total error survey methodology to evaluate user topic comprehension of machine learning outputs. To accomplish this we performed a literature review to identify existing methods and define a construct for topic comprehension, utilizing existing ASRS synopsis writing practices to more precisely define meaningfulness and usefulness. Results: A survey protocol was created that addresses the limitations of other survey protocols found in the literature review, which we found lacking in rationale and clear protocol definition. Conclusion: The surveying of user understanding in machine learning outputs presents challenges due to the explosion of parameters to control for and the lack of systematic approach presented in the literature. More reproducible work and survey protocols are needed in the literature and our work is one step towards that direction.

topic finding↗
NASA NTRSDate not supplied

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗
NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗
NASA NTRSDate not supplied

Improving the transient sensitivity of AMEGO-X using single-site events

AMEGO-X is a proposed Compton/pair telescope which will image and survey the medium-energy gamma-ray sky between 100 keV and 1 GeV. It is designed to detect and characterize gamma-ray emission from multi-messenger sources. On short time scales (<100s), including information from single-site events (photoelectric effect interactions)significantly increases AMEGO-X's sensitivity and lowers its energy threshold down to 25 keV. In this presentation, we will present AMEGO-X's sensitivity to transient events such as gamma-ray bursts and magnetar giant flares, and discuss the improvements due to the inclusion of single-site events. We will also discuss AMEGO-X's ability to localize transient sources and alert the community for follow-up observations.

Henrike Fleischhack↗
NASA NTRSDate not supplied

Improving the Transient Sensitivity of AMEGO-X Using Single-site Events

AMEGO-X is a Compton/pair gamma-ray telescope, submitted to NASA's 2021 MIDEX AO, which will image and survey the medium-energy gamma-ray sky between 100 keV and 1 GeV. It is designed to detect and characterize gamma-ray emission from multi-messenger sources. On short time scales (<100s), including information from single-site events (photoelectric effect interactions) significantly increases AMEGO-X's sensitivity and lowers its energy threshold down to 25 keV. In this presentation, we will present AMEGO-X's sensitivity to transient events such as gamma-ray bursts and magnetar giant flares, and discuss the improvements due to the inclusion of single-site events. We will also discuss AMEGO-X's ability to localize transient sources and alert the community for follow-up observations.

Henrike Fleischhack↗
NASA NTRSDate not supplied

Flight Testing of In-Time Safety Assurance Technologies for UAS Operations

Ongoing research at NASA is driven by a strategic plan defined by the Aeronautics Research Mission Directorate and a vision for future In-Time Aviation Safety Management Systems (IASMS) as described by the National Academies. In both visions, system safety awareness and provision are expanded through increased access to relevant data; integrated analysis and predictive capabilities; improved real-time detection and alerting of domain-specific hazards; decision support, and in some cases, automated risk mitigation strategies. One primary research focus is to develop means by which more timely (i.e., “in-time”) actions may be taken to mitigate precursors, anomalies, or trends that are observed during operations. In this paper, we describe such means as a collection of Services, Functions, and Capabilities (SFCs) that are supported by an underlying information system. For example, an integrated risk assessment capability is envisioned that continuously monitors safety-related metrics and margins and recommends timely operational changes. Assessment functions and/or services can be based on data analytics and predictive models derived from heterogeneous data sets that span relevant indicator metrics and their time histories. Likewise, on-board functions can identify and reduce susceptibility to precursor conditions that have led (and can lead) to aircraft loss-of-control or out-of-control accidents. This paper summarizes development and testing of such an information system tailored to hazards anticipated for future highly autonomous flight missions near and over densely populated areas. Testing is accomplished via simulation and by using small, unmanned aircraft operating over a test range at NASA’s Langley Research Center. Flight plans and test scenarios are defined to emulate several use-cases, including package delivery; reconnaissance; fire management; and urban air taxi vertiport operations. Two test phases are summarized with Phase 1 occurring in (2019-2020) and Phase 2 ongoing (2021-present). Results focus on SFC performance, technology readiness level assessment, and requirements discovery/validation. Companion papers are cited throughout for additional details on the recent testing.

safety management↗
NASA NTRSDate not supplied

Effects of Unmanned Aircraft Voice Communication Delay on En Route Air Traffic Management Operations

The present study investigated the effects of remotely piloted unmanned aircraft (UA) voice communication delay on air traffic management operations with different background sector traffic-volume levels. Three one-way UA voice delay lengths of 400, 900, and 2,000 milliseconds (ms) were tested, representing the currently estimated transmission delays for terrestrial, satellite communication (SATCOM), and long SATCOM Command and Control Link System, respectively. All delay values exceeded 390 ms, the FAA’s current requirement for the maximum communication latency in the National Airspace System. Eight retired en route air traffic controllers (ATCs) and eight remote pilots (RPs) participated in a human-inthe-loop simulation study, where a simulated UA flight transitioned through an Oakland en route low-altitude sector to a local non-towered airport. The results showed that, when the 2,000-ms voice delay was present, radio transmission step-ons and detect-and-avoid (DAA) alert level elevations increased. In addition, ATC workload, ATC acceptability for the UA, and RP acceptability for the DAA were negatively affected only when the UA had a 2,000-ms voice delay under the high traffic-volume condition, but not in the other conditions.

remotely piloted unmanned aircraft, voice delay, s↗
NASA NTRSDate not supplied

Working Time and Fatigue Management

There are physiological limits to human performance, influenced by the internal programming of the sleep homeostat and circadian rhythms. This presentation will provide information about the physiology underlying fatigue, and provide some strategies for better sleep and for managing alertness on the job. This presentation will be provided to personnel at Kennedy Space Center.

fatigue↗
NASA NTRS2022

Effects of Unmanned Aircraft Voice Communication Delay on En Route Air Traffic Management Operations

The present study investigated the effects of remotely piloted unmanned aircraft (UA) voice communication delay on air traffic management operations with different background sector traffic-volume levels. Three one-way UA voice delay lengths of 400, 900, and 2,000 milliseconds (ms) were tested, representing the currently estimated transmission delays for terrestrial, satellite communication (SATCOM), and long SATCOM Command and Control Link System, respectively. All delay values exceeded 390 ms, the FAA’s current requirement for the maximum communication latency in the National Airspace System. Eight retired en route air traffic controllers (ATCs) and eight remote pilots (RPs) participated in a human-in-the-loop simulation study, where a simulated UA flight transitioned through an Oakland en route low-altitude sector to a local non-towered airport. The results showed that, when the 2,000-ms voice delay was present, radio transmission step-ons and detect-and-avoid (DAA) alert level elevations increased. In addition, ATC workload, ATC acceptability for the UA, and RP acceptability for the DAA were negatively affected only when the UA had a 2,000-ms voice delay under the high traffic-volume condition, but not in the other conditions.

remotely piloted unmanned aircraft, voice delay, s↗