Results for “cluster characterization” — Page 22

NASA NTRS2018

Modeling the Distribution and Type of High-Latitude Natural Wetlands for Methane Studies

High latitude (>50N) natural wetlands emit a substantial amount of methane to the atmosphere, and are located in a region of amplified warming. Northern hemisphere high latitudes are characterized by cold climates, extensive permafrost, poor drainage, short growing seasons, and slow decay rates. Under these conditions, organic carbon accumulates in the soil, sequestering CO2 from the atmosphere. Methanogens produce methane from this carbon reservoir, converting stored carbon into a powerful greenhouse gas. Methane emission from wetland ecosystems depends on vegetation type, climate characteristics (e.g, precipitation amount and seasonality, temperature, snow cover, etc.), and geophysical variables (e.g., permafrost, soil type, and landscape slope). To understand how wetland methane dynamics in this critical region will respond to climate change, we have to first understand how wetlands themselves will change and therefore, what the primary controllers of wetland distribution and type are. Understanding these relationships permits data-anchored, physically-based modeling of wetland distribution and type in other climate scenarios, such as paleoclimates or future climates, a necessary first step toward modeling wetland methane emissions in these scenarios. We investigate techniques and datasets for predicting the distribution and type of high latitude (>50N) natural wetlands from a suite of geophysical and climate predictors. Hierarchical clustering is used to derive an empirical methane-centric wetland model. The model is applied in a multistep process first to predict the distribution of wetlands from relevant geophysical parameters, and then, given the predicted wetland distribution, to classify the wetlands into methane-relevant types using an expanded suite of climate and biogeophysical variables. As the optimum set of predictor variables is not known a priori, the model is applied iteratively, and each simulation is evaluated with respect to observed high-latitude wetlands.

Romanski, Joy↗

NASA NTRS2017

Genetic Correlation with the DNA Repair Assay in Mice Exposed to High-LET

We hypothesize that DNA damage induced by high local energy deposition, occurring when cells are traversed by high-LET (Linear Energy Transfer) particles, can be experimentally modeled by exposing cells to high doses of low-LET. In this work, we validate such hypothesis by characterizing and correlating the time dependence of 53BP1 radiation-induced foci (RIF) for various doses and LET across 72 primary skin fibroblast from mice. This genetically diverse population allows us to understand how genetic may modulate the dose and LET relationship. The cohort was made on average from 3 males and 3 females belonging to 15 different strains of mice with various genetic backgrounds, including the collaborative cross (CC) genetic model (10 strains) and 5 reference mice strains. Cells were exposed to two fluences of three HZE (High Atomic Energy) particles (Si 350 megaelectronvolts per nucleon, Ar 350 megaelectronvolts per nucleon and Fe 600 megaelectronvolts per nucleon) and to 0.1, 1 and 4 grays from a 160 kilovolt X-ray. Individual radiation sensitivity was investigated by high throughput measurements of DNA repair kinetics for different doses of each radiation type. The 53BP1 RIF dose response to high-LET particles showed a linear dependency that matched the expected number of tracks per cell, clearly illustrating the fact that close-by DNA double strand breaks along tracks cluster within one single RIF. By comparing the slope of the high-LET dose curve to the expected number of tracks per cell we computed the number of remaining unrepaired tracks as a function of time post-irradiation. Results show that the percentage of unrepaired track over a 48 hours follow-up is higher as the LET increases across all strains. We also observe a strong correlation between the high dose repair kinetics following exposure to 160 kilovolts X-ray and the repair kinetics of high-LET tracks, with higher correlation with higher LET. At the in-vivo level for the 10-CC strains, we observe that drops in the number of T-cells and B-cells found in the blood of mice 24 hours after exposure to 0.1 gray of 320 kilovolts X-ray correlate well with slower DNA repair kinetics in skin cells exposed to X-ray. Overall, our results suggest that repair kinetics found in skin is a surrogate marker for in-vivo radiation sensitivity in other tissue, such as blood cells, and that such response is modulated by genetic variability.

cosmic radiation↗

NASA NTRS2001

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗

NASA NTRSDate not supplied

Comparison of Daily, Monthly (Lunar), Yearly, Decadal, Quarter-Century, Half-Century, and Centurial Shoreline Change Rates at the Kennedy Space Center, Cape Canaveral, Florida, USA

The Kennedy Space Center (KSC) is North America’s premier spaceport and provides crucial access to space for both government and private entities. The National Aeronautics and Space Administration (NASA) facilities are clustered along a 12 km active shoreline at Cape Canaveral, Florida, USA, with the entire NASA and US Space Force station (SF) shoreline stretching over 32 km (Figure 1). Several Launch Complexes (LC’s) including the modern heavy lift facilities are only a few tens of meters from the active shoreline and are imminently threatened by continued sea level rise and ongoing coastal erosion. The space center was built on the northern part of Cape Canaveral. Images collected in 1943 show that Apollo Heavy Lift Launch Complexes were built in swales on ground that was at or below sea level with Launch Complex 39B constructed on a paleo inlet (Figures 2 and 3). Today the Heavy Lift Launch Complexes are ~220 meters from the shoreline with the area between the complexes retreating at the highest rates along the cape (Figure 4). To better understand the intersection of environmental concerns, limited budgets for shoreline protection, and sheltering critical infrastructure; NASA, The University of Florida, United States Geological Survey (USGS), National Park Service (NPS) and contractors have and continue to conduct numerous studies to characterize and understand the shoreline evolution and rates of change at KSC. This project aims to better understand and visualize the temporal and spatial variability of change along the NASA shoreline beyond a simple point to point solution.

Richard MacKenzie III↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2002

Autophosphorylation-dependent inactivation of plant chimeric calcium/calmodulin-dependent protein kinase

Chimeric calcium/calmodulin dependent protein kinase (CCaMK) is characterized by the presence of a visinin-like Ca(2+)-binding domain unlike other known calmodulin- dependent kinases. Ca(2+)-Binding to the visinin-like domain leads to autophosphorylation and changes in the affinity for calmodulin [Sathyanarayanan P.V., Cremo C.R. & Poovaiah B.W. (2000) J. Biol. Chem. 275, 30417-30422]. Here, we report that the Ca(2+)-stimulated autophosphorylation of CCaMK results in time-dependent loss of enzyme activity. This time-dependent loss of activity or self-inactivation due to autophosphorylation is also dependent on reaction pH and ATP concentration. Inactivation of the enzyme resulted in the formation of a sedimentable enzyme due to self-association. Specifically, autophosphorylation in the presence of 200 microm ATP at pH 7.5 resulted in the formation of a sedimentable enzyme with a 33% loss in enzyme activity. Under similar conditions at pH 6.5, the enzyme lost 67% of its activity and at pH 8.5, 84% enzyme activity was lost. Furthermore, autophosphorylation at either acidic or alkaline reaction pH lead to the formation of a sedimentable enzyme. Transmission electron microscopic studies on autophosphorylated kinase revealed particles that clustered into branched complexes. The autophosphorylation of wild-type kinase in the presence of AMP-PNP (an unhydrolyzable ATP analog) or the autophosphorylation-site mutant, T267A, did not show formation of branched complexes under the electron microscope. Autophosphorylation- dependent self-inactivation may be a mechanism of modulating the signal transduction pathway mediated by CCaMK.

NASA Discipline Plant Biology↗

NASA NTRS2011

NASA Tech Briefs, January 2011

The topics include: 1) Distributed Aerodynamic Sensing and Processing Toolbox; 2) Collaborative Supervised Learning for Sensor Networks; 3) Hazard Detection Software for Lunar Landing; 4) Onboard Nonlinear Engine Sensor and Component Fault Diagnosis and Isolation Scheme; 5) Network-Capable Application Process and Wireless Intelligent Sensors for ISHM; 6) Interface Supports Multiple Broadcast Transceivers for Flight Applications; 7) FPGA Sequencer for Radar Altimeter Applications; 8) Miniature Sapphire Acoustic Resonator - MSAR; 9) Process-Hardened, Multi-Analyte Sensor for Characterizing Rocket Plume Constituents; 10) SAD5 Stereo Correlation Line-Striping in an FPGA; 11) Hybrid Composite Cryogenic Tank Structure; 12) Nanoscale Deformable Optics; 13) Reliability-Based Design Optimization of a Composite Airframe Component; 14) Zinc Oxide Nanowire Interphase for Enhanced Lightweight Polymer Fiber Composites; 15) Plasma Igniter for Reliable Ignition of Combustion in Rocket Engines; 16) Wire Test Grip Fixture; 17) A Sub-Hertz, Low-Frequency Vibration Isolation Platform; 18) Carbon Nanofibers Synthesized on Selective Substrates for Nonvolatile Memory and 3D Electronics; 19) Nanoparticle/Polymer Nanocomposite Bond Coat or Coating; 20) High-Resolution Wind Measurements for Offshore Wind Energy Development; 21) Spring Tire; 22) Marsviewer 2008; 23) Mission Services Evolution Center Message Bus; 24) Major Constituents Analysis for the Vehicle Cabin Atmosphere Monitor; 25) Astronaut Health Participant Summary Application; 26) Adaption of the AMDIS Method to Flight Status on the VCAM Instrument; 27) Natural Language Interface for Safety Certification of Safety-Critical Software; 28) Cryogenic Caging for Science Instrumentation; 29) Wide-Range Neutron Detector for Space Nuclear Applications; 30) In Situ Guided Wave Structural Health Monitoring System; 31) Multiplexed Energy Coupler for Rotating Equipment; 32) Attitude Estimation in Fractionated Spacecraft Cluster Systems; 33) Full Piezoelectric Multilayer-Stacked Hybrid Actuation/Transduction Systems; 34) Active Flow Effectors for Noise and Separation Control; 35) Method and System for Temporal Filtering in Video Compression Systems; 36) Apparatus for Measuring Total Emissivity of Small, Low-Emissivity Samples; 37) Multiple-Zone Diffractive Optic Element for Laser Ranging Applications; 38) Simplified Architecture for Precise Aiming of a Deep-Space Communication Laser Transceiver; 39) Two-Photon-Absorption Scheme for Optical Beam Tracking; 40) High-Sensitivity, Broad-Range Vacuum Gauge Using Nanotubes for Micromachined Cavities; 41) Wide-Field Optic for Autonomous Acquisition of Laser Link; 42) Extracting Zero-Gravity Surface Figure of a Mirror; 43) Modeling Electromagnetic Scattering From Complex Inhomogeneous Objects; 44) Visual Object Recognition and Tracking of Tools; 45) Method for Implementing Optical Phase Adjustment; 46) Visual SLAM Using Variance Grid Maps; 47) Rapid Calculation of Spacecraft Trajectories Using Efficient Taylor Series Integration; 48) Efficient Kriging Algorithms; 49) Predicting Spacecraft Trajectories by the WeavEncke Method; 50) An Augmentation of G-Guidance Algorithms; 51) Comparison of Aircraft Icing Growth Assessment Software; 52) Silicon-Germanium Voltage-Controlled Oscillator at 105 GHz; 53) Estimation of Coriolis Force and Torque Acting on Ares-1; 54) Null Lens Assembly for X-Ray Mirror Segments; and 55) High-Precision Pulse Generator.

Source record↗

NASA NTRS1996

Spatial orientation of semicircular canals and afferent sensitivity vectors in pigeons

Rotational head motion in vertebrates is detected by the semicircular canal system, whose innervating primary afferent fibers carry information about movement in specific head planes. The semicircular canals have been qualitatively examined over a number of years, and the canal planes have been quantitatively characterized in several animal species. The present study first determined the geometric relationship between individual semicircular canals and between the canals and the stereotactic head planes in pigeons. Stereotactic measurements of multiple points along the circumference of the bony canals were taken, and the measured points fitted with a three-dimensional planar surface. Direction normals to the plane's surface were calculated and used to define angles between semicircular canal pairs. Because of the unusual shape of the anterior semicircular canals in pigeons, two planes, a major and a minor, were fitted to the canal's course. Calculated angle values for all canals indicated that the horizontal and posterior semicircular canals are nearly orthogonal, but the anterior canals have substantial deviations from orthogonality with other canal planes. Next, the responses of the afferent fibers that innervate each of the semicircular canals to 0.5 Hz sinusoidal rotation about an earth-vertical axis were obtained. The head orientation relative to the rotation axis was systematically varied so that directions of maximum sensitivity for each canal afferent could be determined. These sensitivity vectors were then compared with the canal plane direction normals. The afferents that innervated specific semicircular canals formed homogeneous clusters of sensitivity vectors in different head planes. The horizontal and posterior afferents had average sensitivity vectors that were largely co-incident with the innervated canal plane direction normals. Anterior canal afferents, however, appeared to synthesize contributions from the major and minor plane components of the bony canal structure to produce a resultant sensitivity vector that was positioned between the canal planes. Calculated angles between the average canal afferent sensitivity vectors revealed that direction orthogonality is preserved at the afferent signal level, even though deviations from canal plane orthogonality exist.

NASA Discipline Neuroscience↗

NASA NTRS1991

Development of an electrostatic propulsion engine using sub-micron powders as the reaction mass

Asteroid sample return missions would benefit from development of an improved rocket engine. Chemical rockets achieve their large thrust with high mass consumption rate (dm/dt) but low exhaust velocity; therefore, a large fraction of their total mass is fuel. Present day ion thrusters are characterized by high exhaust velocity, but low dm/dt; thus, they are inherently low thrust devices. However, their high exhausy velocity is poorly matched to typical mission requirements and therefore, wastes energy. A better match would be intermediate between the two forms of propulsion. This could be achieved by electrostatically accelerating solid powder grains, raising the possibility that interplanetary material could be processed to use as reaction mass. An experiment to study the charging properties of sub-micron sized powder grains is described. If a suitable material can be identified, then it could be used as the reaction mass in an electrostatic propulsion engine. The experiment employs a time of flight measurement to determine the exhaust velocity (v) of various negatively charged powder grains that were charged and accelerated in a simple device. The purpose is to determine the charge to mass ratio that can be sustained for various substances. In order to be competitive with present day ion thrusters, a specific impulse (v/g) of 3000 to 5000 seconds is required. Preliminary results are presented. More speculatively, there are some mission profiles that would benefit from collection of reaction mass at the remote asteroid site. Experiments that examine the generation of sub-micron clusters by electrostatic self-disruption of geologically derived material are planned.

Herbert, F.↗

NASA NTRS2000

Optical Characterization of Bulk ZnSeTe Solid Solutions

Optical characterization was performed on wafers sliced from crystals of ZnSe, ZnTe, and ZnSe(1-x)Te(x)(0 less than x less than 0.4) grown by physical vapor transport. Energy band gaps at room temperature were determined from optical transmission measurements on 11 wafers. A best fit curve to the band gap versus composition x data gives a bowing parameter of 1.45. This number lies between the value of 1.23 determined previously on ZnSeTe bulk crystals and the value of 1.621 reported on ZnSeTe epilayers. Low-temperature photoluminescence (PL) spectra were measured on six samples. The spectra of ZnSe and ZnTe were dominated by near band edge emissions and no deep donor-acceptor pairs were observed. The PL spectrum exhibited a broad emission for each of the ZnSe(1-x)Te(x) samples, 0.09 less than x less than 0.39. For x=0.09, this emission energy is about 0.2 eV lower than the band gap energy measured at low temperature. As x increases the energy discrepancy gradually decreases and reduces to almost zero at x=0.4. The single broad PL emission spectra and the spectra measured as a function of temperature were interpreted as being associated with the exciton bound to Te clusters because of the high Te content in these samples.

Su, Ching-Hua↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRSDate not supplied

An Approach to Identifying Aspects of Positive Pilot Behavior within the Aviation Safety Reporting System

The National Airspace System (NAS) is constantly evolving as air traffic continues to ramp up to pre-pandemic numbers and projected to grow to unprecedented levels in the coming years. As well as increasing demand to the current system, emerging operations such as Unmanned Autonomous Systems are also expected to add to complexity in the airspace. To address these issues, the industry and government agencies supporting the NAS will need to rely upon additional automation and new technologies to address future operational requirements, while continuing to be a world-leading safe transportation system. As these new technologies are implemented, the system continues to rely on human pilots and controllers in the loop to monitor the system and intervene in situations the automation cannot handle. The goal of proactively addressing safety is of foremost concern to ensure passenger confidence. The industry has implemented various Safety Monitoring Systems to identify safety risks and proactively address them before they result in a serious incident or accident. One such program is the Aviation Safety Reporting System (ASRS). ASRS is a long-established system where pilots and controllers voluntarily and anonymously report safety incidents they experienced and observed during line operations by providing rich text narratives describing the events, the environment, and conditions leading to the safety event of concern. These narratives provide insight and context around events of interest and can be used to identify emerging problems. They can trigger investigations within Flight Operational Quality Assurance or Flight Data Monitoring programs. However, this process typically focuses on the adverse events and the unsafe aspects of the operations surrounding the reported or detected events. This perspective of investigating factors that went wrong around an adverse event is commonly referred to as Safety I. Alternatively, characterizing successful actions that operators perform every day under varying conditions that keep the system within safe operating bounds is a concept referred to as Safety II. The benefit of the Safety II view is that the scope is much larger than that of Safety I since a vast majority of the operations result in successful flights. Many of the successful techniques used to manage operational threats are not documented in standard operating procedures or taught during training. They are typically acquired over time by working with experienced pilots during line operations or in many cases after experiencing a problem for the first time and reacting to it in situ, drawing from years of experience to manage the threat. In an attempt to quantify these positive actions, we are proposing an approach to extracting key behaviors within ASRS reports that can support the Safety II concept. Our analysis assumes that ASRS reports contain some descriptions of corrective actions that operators performed to prevent a situation from leading to an accident. Leveraging recent advances in Natural Language Process modeling, we have developed an approach to extract positive sentiment from reports, embed these positive statements in a vector space where they can be numerically analyzed, and clustering these statements into similar contextual categories. From these contextualized categories we can attempt to summarized and distilled aspects of the positive behavior. The goal is to identify categories of behavior that describe consistent operator techniques that supports the Safety II concept. With this information, airlines may enable learning from these positive actions, or address procedures that need to be changed to avoid having pilots implement a workaround. These insights can provide a lens into what is “going right” in the operations that may otherwise not be known widely within the community. It is envisioned that this approach can be extended to other narrative programs such as Line Operation Safety Audit or Learning Improvement Team reports where similar observed behavior can be analyzed to extract positive actions and inform the overall operations.

NLP↗

NASA NTRS2002

The Velocity Distribution of Isolated Radio Pulsars

We infer the velocity distribution of radio pulsars based on large-scale 0.4 GHz pulsar surveys. We do so by modelling evolution of the locations, velocities, spins, and radio luminosities of pulsars; calculating pulsed flux according to a beaming model and random orientation angles of spin and beam; applying selection effects of pulsar surveys; and comparing model distributions of measurable pulsar properties with survey data using a likelihood function. The surveys analyzed have well-defined characteristics and cover approx. 95% of the sky. We maximize the likelihood in a 6-dimensional space of observables P, dot-P, DM, absolute value of b, mu, F (period, period derivative, dispersion measure, Galactic latitude, proper motion, and flux density). The models we test are described by 12 parameters that characterize a population's birth rate, luminosity, shutoff of radio emission, birth locations, and birth velocities. We infer that the radio beam luminosity (i) is comparable to the energy flux of relativistic particles in models for spin-driven magnetospheres, signifying that radio emission losses reach nearly 100% for the oldest pulsars; and (ii) scales approximately as E(exp 1/2) which, in magnetosphere models, is proportional to the voltage drop available for acceleration of particles. We find that a two-component velocity distribution with characteristic velocities of 90 km/ s and 500 km/ s is greatly preferred to any one-component distribution; this preference is largely immune to variations in other population parameters, such as the luminosity or distance scale, or the assumed spin-down law. We explore some consequences of the preferred birth velocity distribution: (1) roughly 50% of pulsars in the solar neighborhood will escape the Galaxy, while approx. 15% have velocities greater than 1000 km/ s (2) observational bias against high velocity pulsars is relatively unimportant for surveys that reach high Galactic absolute value of z distances, but is severe for spatially bounded surveys; (3) an important low-velocity population exists that increases the fraction of neutron stars retained by globular clusters and is consistent with the number of old objects that accrete from the interstellar medium; (4) under standard assumptions for supernova remnant expansion and pulsar spin-down, approx. 10% of pulsars younger than 20 kyr will appear to lie outside of their host remnants. Finally, we comment on the ramifications of our birth velocity distribution for binary survival and the population of inspiraling binary neutron stars relevant to some GRB models and potential sources for LIGO.

Arzoumanian, Z.↗

NASA NTRS2000

Optical Characterization of Bulk ZnSeTe Solid Solutions

Optical characterization was performed on wafers sliced from crystals of ZnSe, ZnTe and ZnSe (sub 1-x) Te (sub x) (0 less than x less than 0.4) grown by physical vapor transport technique. The energy band gaps at room temperature were determined from optical transmission measurements on 11 wafers. The best fit to the band gap vs. composition, x, data gives a bowing parameter of 1.336 which is between the value of 1.23 determined previously on ZnSeTe bulk crystals by reflectivity and the value of 1.621 reported on epilayers by photoconductivity. Low-temperature photoluminescence (PL) spectra were measured on 6 samples. The spectra of ZnSe and ZnTe were dominated by near band edge emissions and no deep donor-acceptor pairs were observed. The PL spectrum exhibited a broad emission for each of the ZnSe (sub 1-x) Te (sub x) samples, 0.09 less than x less than 0.39. For x = 0.09, this emission energy is about 0.2eV lower than the band gap energy measured at low temperature. As x increases the energy discrepancy gradually decreases and reduces to almost zero at x = 0.4. The single broad PL emission spectra and the spectra measured as a function of temperature were interpreted to be associated with the exciton bound to Te clusters because of the high Te content in these samples.

Su, Ching-Hua↗

NASA NTRS2019

The Simons Observatory: Science Goals and Forecasts

The Simons Observatory (SO) is a new cosmic microwave background experiment being built on CerroToco in Chile, due to begin observations in the early 2020s. We describe the scientific goals of the experiment, motivate the design, and forecast its performance. SO will measure the temperature and polarization anisotropy of the cosmic microwave background in six frequency bands centered at: 27,39, 93, 145, 225 and 280 GHz. The initial configuration of SO will have three small-aperture 0.5-mtelescopes and one large-aperture 6-m telescope, with a total of 60,000 cryogenic bolometers. Our key science goals are to characterize the primordial perturbations, measure the number of relativistic species and the mass of neutrinos, test for deviations from a cosmological constant, improve our understanding of galaxy evolution, and constrain the duration of reionization. The small aperture telescopes will target the largest angular scales observable from Chile, mapping≈10% of the sky to a white noise level of 2μK-arcmin in combined 93 and 145 GHz bands, to measure the primordial tensor-to-scalar ratio, r, at a target level ofσ(r) = 0.003. The large aperture telescope will map ≈40% of the sky at arcminute angular resolution to an expected white noise level of 6μK-arcminin combined 93 and 145 GHz bands, overlapping with the majority of the Large Synoptic Survey Telescope sky region and partially with the Dark Energy Spectroscopic Instrument. With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev–Zel’dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio. The survey will also provide a legacy catalog of 16,000 galaxy clusters and more than20,000 extragalactic sources.

Peter Ade↗

NASA NTRS2019

The Simons Observatory: science goals andforecasts

The Simons Observatory (SO) is a new cosmic microwave background experiment being built on Cerro Toco in Chile, due to begin observations in the early 2020s. We describe the scientific goals of the experiment, motivate the design, and forecast its performance. SO will measure the temperature and polarization anisotropy of the cosmic microwave background in six frequency bands centered at: 27, 39, 93, 145, 225 and 280 GHz. The initial configuration of SO will have three small-aperture 0.5-m telescopes and one large-aperture 6-m telescope, with a total of 60,000 cryogenic bolometers. Our key science goals are to characterize the primordial perturbations, measure the number of relativistic species and the mass of neutrinos, test for deviations from a cosmological constant, improve our understanding of galaxy evolution, and constrain the duration of reionization. The small aperture telescopes will target the largest angular scales observable from Chile, mapping ≈ 10% of the sky to a white noise level of 2 μK-arcmin in combined 93 and 145 GHz bands, to measure the primordial tensor-to-scalar ratio, r, at a target level of σ(r)=0.003. The large aperture telescope will map ≈ 40% of the sky at arcminute angular resolution to an expected white noise level of 6 μK-arcmin in combined 93 and 145 GHz bands, overlapping with the majority of the Large Synoptic Survey Telescope sky region and partially with the Dark Energy Spectroscopic Instrument. With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio. The survey will also provide a legacy catalog of 16,000 galaxy clusters and more than 20,000 extragalactic sources.

Peter Ade↗