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At least 73 records · Page 4

A Direct Broadcast Operations Concept for the HyspIRI Mission

HyspIRI is evaluating a X-band Direct Broadcast (DB) capability that would enable data to be delivered to ground stations virtually as it is acquired. However the HyspIRI VSWIR and TIR instruments will produce 1 Gbps data while the DB capability is 15 M bps for an approximate 60x oversubscription. In order to address this data volume mismatch a DB concept has been developed that determines which data to downlink based on both: 1. the type of surface the spacecraft is overflying and 2. onboard processing of the data to detect events. For example when the spacecraft is overflying polar regions it might downlink a snow/ice product. Additionally the onboard software will search for thermal signatures indicative of a volcanic event or wild fire and downlink summary information (extent, spectra) when detected, thereby reducing data volume.

Chien, Steve A.↗

Modeling the Transport Processes of a Pair of Solar Energetic Particle Events Observed by Parker Solar Probe near Perihelion

We present model calculations of the transport processes of solar energetic particles in the corona and interplanetary medium for two events detected by Parker Solar Probe near its second perihelion on 2019 April 2 and April 4. In the 2019 April 2 event, the <100 keV proton differential intensity measured by the ISOIS EPI-Lo instrument increased by more than a factor of 10 above the pre-event intensity, whereas the ~1 MeV proton differential intensity detected by the EPI-Hi instrument did not show any intensity enhancement. In the 2019 April 4 event, the ~1 MeV proton intensity showed an increase of more than a factor of 100 above the pre-event intensity, but the <100 keV proton intensity enhancement was rather low and gradual. The observational fact that only the 2019 April 4 event displayed a high-energy intensity enhancement indicates that the associated acceleration process in the second event was more energetic than the first event. However, the gradual and low enhancement of the low-energy proton intensity in the second event is at odds with the scenario. In this paper, we investigate the injection and transport processes of protons in the corona and interplanetary magnetic fields with numerical model simulations. Our model calculations suggest that the gradual and low rise of the low-energy protons in the April 4 event was probably due to the different diffusion conditions in these two events and the fact that the spacecraft was moving away from the source region during these two events.

Solar energetic particles↗

Interplanetary Type II Radio Bursts from Wind/WAVES and Sustained Gamma-Ray Emission from Fermi/LAT: Evidence for Shock Source

We present quantitative evidence that interplanetary type II radio bursts and sustained gamma-ray emission (SGRE) events from the Sun are closely related. Out of about 30 SGRE events reported in Share et al. we consider 13 events that had a duration exceeding 5 hr to exclude any are-impulsive phase gamma-rays. The SGRE duration also has a linear relation with the ending frequency of the bursts. The synchronism between the ending times of SGRE and the type II emission strongly supports the idea that the same shock accelerates electrons to produce type II bursts and protons (>300 MeV) that propagate from the shock to the solar surface to produce SGRE via pion decay. The acceleration of high-energy particles is confirmed by the associated solar energetic particle (SEP) events detected at Earth and/or at the Solar Terrestrial Relations Observatory spacecraft. Furthermore, the presence of >300 MeV protons is corroborated by the fact that the underlying coronal mass ejections (CMEs) had properties identical to those associated with ground-level enhancement events: they had speeds of >2000 km s1 and all were full-halo CMEs. Many SEP events did not have detectable flux at Earth in the >300 MeV energy channels, presumably because of poor magnetic connectivity.

Gopalswamy, Nat↗

Probable detection of solar neutrons by ground-level neutron monitors during STIP interval 16

The third solar neutron event detected by Earth-orbiting spacecraft was observed during STIP Interval XVI. The solar flare beginning at 2356 UT on 24 April l984 produced a variety of emissions including gamma rays and solar neutrons. The neutrons were observed by the SMM satellite and the neutron-decay protons were observed on the ISEE-3 spacecraft. Between 0000 and 0010 UT on 25 April an increase of 0.7 and 1.7 percent was recorded by neutron monitors at Tokyo (Itabashi) and Morioka, Japan. These stations were located about 42 degrees from the sub-solar point, and consequently, these is approximately 1400 grams of atmosphere between the incident neutrons at the top of the atmosphere and their detection on the Earth's surface. Nevertheless, the time coincidence of a small increase in the total counting rate of two independent neutron monitors indicates the presence of solar neutrons with energies greater than 400 MeV at the top of the Earth's atmosphere. The small increases in the counting rate emphasize the difficulty in identifying similar events using historical neutron monitor data.

Shea, M. A.↗

Standing hydromagnetic waves observed by ISEE 1 and 2 - Radial extent and harmonic

Direct measurements of the spatial extent of the resonant hydromagnetic waves associated with Pc 4 and 5 magnetic pulsations made by the closely spaced ISEE 1 and 2 satellites are presented together with ISEE 1 determinations of the harmonic of the resonant waves. The use of two satellites in similar orbits, which makes it possible to distinguish between spatial and temporal features, has shown the resonant region widths to extend over about 0.2 to 1.6 L shells for three events detected on the dayside between L = 4 and L = 7. The two events for which plasma density data was available occurred at plasma density gradients in the vicinity of the plasmapause. The standing wave harmonic was determined by the combination of two techniques: the comparison of the observed wave period with that predicted by standing wave theory, and the comparison of the phases of the observed wave electric and magnetic field. The two events analyzed are found to be second harmonic oscillations, suggesting internal generation in the magnetosphere by a bounce resonant mechanism.

Singer, H. J.↗

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↗

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↗

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↗

GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run

We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00 UTC. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we present 39 candidate gravitational wave events. At this threshold, we expect a contamination fraction of less than 10%. Of these, 26 candidate events were reported previously in near real-time through GCN Notices and Circulars; 13 are reported here for the first time. The catalog contains events whose sources are black hole binary mergers up to a redshift of ~ 0.8, as well as events whose components could not be unambiguously identified as black holes or neutron stars. For the latter group, we are unable to determine the nature based on estimates of the component masses and spins from gravitational wave data alone. The range of candidate event masses which are unambiguously identified as binary black holes (both objects ≥ 3 M⨀) is increased compared to GWTC-1, with total masses from ∼ 14M⨀ for GW190924 021846 to ∼ 150M⨀ for GW190521. For the first time, this catalog includes binary systems with significantly asymmetric mass ratios, which had not been observed in data taken before April 2019. We also find that 11 of the 39 events detected since April 2019 have positive effective inspiral spins under our default prior (at 90% credibility), while none exhibit negative effective inspiral spin. Given the increased sensitivity of Advanced LIGO and Advanced Virgo, the detection of 39 candidate events in ∼26 weeks of data (∼1.5 per week) is consistent with GWTC-1.

Gravitational wave detection↗

Near-Earth Asteroids Detection Rate with LISA

The LISA space mission, designed to monitor low frequency gravitational waves, is also sensitive to passages of asteroids nearby one of its three spacecrafts. We report the expected rate of detections of asteroid passages, using the known catalog of asteroids and a modeled population. The method adopted consists of determining for each known asteroid the critical encounter distance capable of producing a detectable event, and then computing the rate of encounters within this distance. Results are then scaled to the modeled population using its differential distribution in absolute magnitude, correcting for selection effects. We find that an average of 2.0 +/- 0.1 events per year at a signal-to-noise ratio of 1 will be detected by LISA, including all the asteroids in the modeled population with absolute magnitude H < 22, roughly equivalent to all asteroids with a diameter larger than 100 m.

Tricarico, P.↗

SREDD: Framework to facilitate MLopS related to discovery of commercial satellite data

SREDD or Super Resolution Event Detection Dashboard is a comprehensive data discovery platform designed to facilitate the identification of multiple events. It is equipped to handle data of varying spatial resolutions and from various vendors, acquired by NASA’s Commercial Smallsat Data Acquisition (CSDA) Program, making it a convenient centralized hub for searching event-related information.

Sujit Roy↗

Analyzing Responses of Chemical Sensor Arrays

NASA is developing a third-generation electronic nose (ENose) capable of continuous monitoring of the International Space Station s cabin atmosphere for specific, harmful airborne contaminants. Previous generations of the ENose have been described in prior NASA Tech Briefs issues. Sensor selection is critical in both (prefabrication) sensor material selection and (post-fabrication) data analysis of the ENose, which detects several analytes that are difficult to detect, or that are at very low concentration ranges. Existing sensor selection approaches usually include limited statistical measures, where selectivity is more important but reliability and sensitivity are not of concern. When reliability and sensitivity can be major limiting factors in detecting target compounds reliably, the existing approach is not able to provide meaningful selection that will actually improve data analysis results. The approach and software reported here consider more statistical measures (factors) than existing approaches for a similar purpose. The result is a more balanced and robust sensor selection from a less than ideal sensor array. The software offers quick, flexible, optimal sensor selection and weighting for a variety of purposes without a time-consuming, iterative search by performing sensor calibrations to a known linear or nonlinear model, evaluating the individual sensor s statistics, scoring the individual sensor s overall performance, finding the best sensor array size to maximize class separation, finding optimal weights for the remaining sensor array, estimating limits of detection for the target compounds, evaluating fingerprint distance between group pairs, and finding the best event-detecting sensors.

Zhou, Hanying↗

Estimates of SEU rates from heavy ions in devices exhibiting dual-node susceptibility

A single-event effect (SEE) occurs when a single ionizing particle passing through an electronic device produces a detectable event in the device. The type of SEE most frequently discussed in the literature is a "single-node upset" problem in which an upset at a given node depends only on conditions seen by that node. In contrast, some devices are designed so that an upset becomes a "dual-node" problem in which a device upset depends on conditions simultaneously seen by two nodes. This report derives a method for estimating heavy-ion induced upset rates for the dual-node problem.

Edmonds, Larry D.↗

Automated Monitoring with a BCP Fault-Decision Test

The Bayesian conditional probability (BCP) technique is a statistical fault-decision technique that is suitable as the mathematical basis of the fault-manager module in the automated-monitoring system and method described in the immediately preceding article. Within the automated-monitoring system, the fault-manager module operates in conjunction with the fault-detector module, which can be based on any one of several fault-detection techniques; examples include a threshold-limit-comparison technique or the BSP or SPRT technique mentioned in the preceding article. The present BCP technique is used to evaluate a series of one or more fault-detection events for the purpose of filtering out occasional false alarms produced by many types of statistical fault-detection procedures. The BCP technique increases the probability that an automated monitoring system produces a correct decision regarding the presence or absence of a fault. Because occasional false alarms are an inevitable consequence of the SPRT, BSP, or any other statistically based fault-detection test, there is a need for a logical procedure to distinguish between true and false alarms. Heretofore, it has been common practice to make a fault decision on an ad hoc basis for example by following a multiple-observation voting strategy in which a signal is declared to be indicative of a fault if m of the last n observations produced a fault-detection alarm. The BCP technique was developed to obtain results more reliable than those afforded by a voting strategy. The BCP technique involves a test in which one applies Bayesian inference techniques to a series of one or more single-observation alarms produced by a fault-detection test. One considers the last n decisions generated by a fault-detection test in order to evaluate the conditional probability that a failure is indicated (see figure). Each new decision reached by a fault-detection test is treated as a new piece of evidence about the state of the monitored asset, and the conditional probability of failure for the system is updated on the basis of this new evidence. The conditional probability of failure is compared with a predefined limit. For a probability below the limit, the asset is declared to be healthy. For a probability above the limit, the asset is declared to be faulty.

Bickford, Randall L.↗

Using automated planning for sensorweb response

This paper describes efforts to link these science event detection systems with an automated response system to retarget remote sensing assets to observe these important but transient science events. Of course, automated mission planning is a key element of the overall tracking and response system. We describe the current prototype system which utilizes the Earth Observing One spacecraft, MODIS flying on Terra and Aqua, QuickSCAT, GOES, and AVHRR platforms as well as future plans for expansion.

sensorweb response↗

Seasonal Variations of Seismic Activity on Mars?

We analyze the sequence of seismic events of different types as recorded by the SEIS instrument of the InSight mission. After several weeks without any detection, event counts started to increase at the end of May 2019. The majority of recorded events belongs to the class of 2.4 Hz events, which prominently excite a continuously observed natural resonance frequency. Comparison with expected event counts from a constant-rate Poisson process shows a repeated, step wise increase of the event rate with time. At the same time, event amplitudes, and hence magnitudes, are found to increase as well.

Knapmeyer, M.↗

A Detailed Look at the Performance Characteristics of the Lightning Imaging Sensor

The Lightning Imaging Sensor (LIS) on board the Tropical Rainfall Measuring Mission (TRMM) effectively reached its end of life on April 15, 2015 after 17+ years of observation. Given the wealth of information in the archived LIS lightning data, and growing use of optical observations of lightning from space throughout the world, it is still of importance to better understand LIS calibration and performance characteristics. In this work, we continue our efforts to quantify the optical characteristics of the LIS pixel array, and to further characterize the detection efficiency and location accuracy of LIS. The LIS pixel array was partitioned into four quadrants, each having its own signal amplifier and digital conversion hardware. In addition, the sensor optics resulted in a decreasing sensitivity with increasing displacement from the center of the array. These engineering limitations resulted in differences in the optical emissions detected across the pixel array. Our work to date has shown a 20% increase in the count of the lightning events detected in one of the LIS quadrants, because of a lower detection threshold. In this study, we will discuss our work in progress on these limitations, and their potential impact on the group- and flash-level parameters.

Zhang, Daile↗

Type III Radio Burst Duration and SEP Events

Long-duration (>15 min), low-frequency (<14 MHz) type III radio bursts have been reported to be indicative of solar energetic particle events. We measured the durations of type III bursts associated with large SEP events of solar cycle 23. The Type III durations are distributed symmetrically at 1 MHz yielding a mean value of approximately 33 min (median = 32 min) for the large SEP events. When the SEP events with ground level enhancement (GLE,) are considered, the distribution is essentially unchanged (mean = 32 min, median = 30 min). To test the importance of type III bursts in indicating SEP events, we considered a set of six type III bursts from the same active region (AR 10588) whose durations fit the "long duration" criterion. We analyzed the coronal mass ejections (CMEs), flares, and type II radio bursts associated with the type III bursts. The CMEs were of similar speeds and the flares are also of similar size and duration. All but one of the type III bursts was not associated with a type II burst in the metric or longer wavelength domains. The burst without type II burst also lacked a solar energetic particle (SEP) event at energies >25 MeV. The 1-MHz duration of the type III burst (28 rein) is near the median value of type III durations found for gradual SEP events and ground level enhancement (GLE) events. Yet, there was no sign of SEP events. On the other hand, two other type III bursts from the same active region had similar duration but accompanied by WAVES type 11 bursts; these bursts were also accompanied by SEP events detected by SOHO/ERNE. This study suggests that the type III burst duration may not be a good indicator of an SEP event, consistent with the statistical study of Cliver and Ling (2009, ApJ ).

Gopalswamy, N.↗