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NASA NTRS2019

IRIS Observations of Magnetic Interactions in the Solar Atmosphere Between Preexisting and Emerging Magnetic Fields. II. UV Emission Properties

Multiwavelength ultraviolet (UV) observations by the Interface Region Imaging Spectrograph satellite in active region NOAA 12529 have recently pointed out the presence of long-lasting brightenings, akin to UV bursts, and simultaneous plasma ejections occurring in the upper chromosphere and transition region during secondary flux emergence. These signatures have been interpreted as evidence of small-scale, recurrent magnetic reconnection episodes between the emerging flux region (EFR) and the preexisting plage field. Here we characterize the UV emission of these strong, intermittent brightenings and study the surge activity above the chromospheric arch filament system (AFS) overlying the EFR. We analyze the surges and the cospatial brightenings observed at different wavelengths. We find an asymmetry in the emission between the blue and red wings of the Si iv λ1402 Å and Mg ii k λ2796.3 lines, which clearly outlines the dynamics of the structures above the AFS that form during the small-scale eruptive phenomena. We also detect a correlation between the Doppler velocity and skewness of the Si iv λλ1394 and 1402 line profiles in the UV burst pixels. Finally, we show that genuine emission in the Fe xii λ1349.4 line is cospatial to the Si iv brightenings. This definitely reveals a pure coronal counterpart to the reconnection event.

Guglielmino, Salvo L.↗
NASA NTRS2019

Forecasting the Ambient Solar Wind with Numerical Models. I. On the Implementation of an Operational Framework

The ambient solar wind conditions in interplanetary space and in the near-Earth environment are determined by activity on the Sun. Steady solar wind streams modulate the propagation behavior of interplanetary coronal mass ejections and are themselves an important driver of recurrent geomagnetic storm activity. The knowledge of the ambient solar wind flows and fields is thus an essential component of successful space weather forecasting. Here, we present an implementation of an operational framework for operating, validating, and optimizing models of the ambient solar wind flow on the example of Carrington Rotation 2077. We reconstruct the global topology of the coronal magnetic field using the potential field source surface model (PFSS) and the Schatten current sheet model (SCS) and discuss three empirical relationships for specifying the solar wind conditions near the Sun, namely the Wang–Sheeley (WS) model, the distance from the coronal hole boundary model (DCHB), and the Wang-Sheeley–Arge (WSA) model. By adding uncertainty in the latitude about the sub-Earth point, we select an ensemble of initial conditions and map the solutions to Earth by the Heliospheric Upwind eXtrapolation (HUX) model. We assess the forecasting performance from a continuous variable validation and find that the WSA model most accurately predicts the solar wind speed time series (RMSE≈83 km/s). We note that the process of ensemble forecasting slightly improves the forecasting performance of all solar wind models investigated. We conclude that the implemented framework is well suited for studying the relationship between coronal magnetic fields and the properties of the ambient solar wind flow in the near-Earth environment.

Reiss, Martin A.↗
NASA NTRS2018

Solar Irradiance Variability, Influenced by r Modes

A spectrum of the four-decade solar irradiance record has a prominent cluster of power for periodicities near 1 yr. Correlating irradiance with a bandpass filter showed that periodicity values were not constant, but varied sinusoidally with each cycle lasting 14 ± 1 yr. The large modulation amplitude makes solar frequencies ≥1 yr(exp −1) hard to detect at the solar surface. After removing the modulation, a Lomb–Scargle spectrum exposed two true periodicities: 1.006 and 0.920 yr. They are interpreted as the synodic rotation periods of r modes of lowest angular degree (ℓ = 1). The first propagates in the stable interior and the second in the convective envelope perturbed by its several flow fields. The rotational beat period of the two modes is about 10.9 yr. This is close to the average length of a solar cycle and possibly controls this average. The 1.006 yr periodicity dominates most of the filtered irradiance record but an abrupt change to about 0.8 yr occurs in mid-2010. Also found was evidence for higher-degree r modes (ℓ = 2 to 8) and a curious sawtooth modulation with a recurrence period of 2.6 yr.

Wolff, Charles L.↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRSDate not supplied

A Framework for the Analysis of Deep Neural Networks in Autonomous Aerospace Applications using Bayesian Statistics

Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.

Deep Neural networks↗
NASA NTRSDate not supplied

Small Space Craft Materials Evaluation of Commercial Off The Shelf (COTS) Parts

The proliferation of inexpensive small spacecraft involves increasing use of Commercial Off The Shelf (COTS) parts. Often the composition, origin, provenance and/or properties of these parts are unspecified.The use of these materials complicates the evaluation of component suitability for low outgassing, material properties and even functionality. These problems will drive: a) the development of a sequence of gateway acceptability tests that can be used to reject parts before contaminating the usual Thermal Vacuum Chamber (TVAC) and other test equipment; b)the creation of a database for COTS parts that are acceptable for spacecraft use; c) a course of flight hardware training for novice spacecraft builders to limit the recurrence of common mistakes; d)fostering a more experienced, mature and sophisticated community of spacecraft developers that can determine if a proposed approach or material use is appropriate.An especially common spacecraft material issue involves the appropriate and inappropriate use of silicones. This presentation will provide examples of strategic methods used to mitigate the risks of COTS parts.

Small Spacecraft↗
NASA NTRS2021

Loss of Control Detection for Commercial Transports Using Conditional Variational Autoencoders

This work describes a detector for the loss of control of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. We demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is experiencing a flight state change is approaching by measuring a shift in the sampling Gaussian distributions of the latent space. We provide an analysis of its applicability to flight data from a NASA generic commercial transport-type aircraft.

Newton H Campbell↗
NASA NTRS2020

Increasing Activity in T CrB Suggests Nova Eruption Is Impending

Estimates of the accretion rate in symbiotic recurrent novae (RNe) often fall short of theoretical expectations by orders of magnitude. This apparent discrepancy can be resolved if the accumulation of mass by the white dwarf(WD)is highly sporadic, and most observations are performed during low states. Here we use a re-analysis of archival data from the Digital Access to a Sky Century @Harvard survey to argue that the most recent nova eruption in symbiotic RN T CrB, in 1946, occurred during—and was therefore triggered by—a transient accretion high state. Based on similarities in the optical light curve around 1946 and the time of the prior eruption, in 1866,we suggest that the WD in T CrB accumulates most of the fuel needed to ignite the thermonuclear runaways (TNRs) during accretion high states. A natural origin for such states is dwarf-nova like accretion-disk instabilities, which are expected in the presumably large disks in symbiotic binaries. The timing of the TNRs in symbiotic RNe could thus be set by the stability properties of their accretion disks. T CrB is in the midst of an accretion high state like the ones we posit led to the past two nova eruptions. Combined with the approach of the time at which a TNR would be expected based on the 80 yr interval between the prior two novae (2026 ± 3), the current accretion high state increases the likelihood of a TNR occurring in T CrB in the next few years.

Gerardo J M Luna↗
NASA NTRSDate not supplied

Network Jets as the Driver of Counter-streaming Flows in a Solar Filament

We investigate the driving mechanism of counter-streaming flows in a solar filament, using EUV images from SDO/AIA, line of sight magnetograms from SDO/HMI, IRIS SJ images, and H-alpha data from GONG. We find that: (i) persistent counter-streaming flows along adjacent threads of a small (100” long) solar filament is present; (ii) both ends of the solar filament are rooted at the edges of magnetic network flux lanes; (iii) recurrent small-scale jets (also known as network jets) occur at both ends of the filament; (iv) some of the network jets occur at the sites of flux cancelation between the majority-polarity flux and merging minority-polarity flux patches; (v) these multiple network jets clearly drive the counter-streaming flows along the adjacent threads of the solar filament for ~2 hours with an average speed of 70 km/s; (vi) some the network jets show base brightenings, analogous to the base brightenings of coronal jets; and (vii) the filament appears wider (4”) in EUV images than in H-alpha images (2.5”), consistent with previous studies. Thus, our observations show that counter-streaming flows in the filament are driven by network jets and possibly these driving network jet eruptions are triggered by flux cancelation.

Navdeep K Panesar↗
NASA NTRSDate not supplied

Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

This work describes a detector for the loss of control condition of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. First, we demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA’s dynamically-scaled generic transport model (GTM) aircraft.

Loss of control↗
NASA NTRS2020

Seven Centuries of Reconstructed Brahmaputra River Discharge Demonstrate Underestimated High Discharge and Flood Hazard Frequency

The lower Brahmaputra River in Bangladesh and Northeast India often floods during the monsoon season, with catastrophic consequences for people throughout the region. While most climate models predict an intensified monsoon and increase in flood risk with warming, robust baseline estimates of natural climate variability in the basin are limited by the short observational record. Here we use a new seven-century (1309–2004 C.E) tree-ring reconstruction of monsoon season Brahmaputra discharge to demonstrate that the early instrumental period (1956–1986 C.E.) ranks amongst the driest of the past seven centuries (13th percentile). Further, flood hazard inferred from the recurrence frequency of high discharge years is severely underestimated by 24–38% in the instrumental record compared to previous centuries and climate model projections. A focus on only recent observations will therefore be insufficient to accurately characterise flood hazard risk in the region, both in the context of natural variability and climate change.

tree-ring reconstruction↗
NASA NTRSDate not supplied

STEREO Observations of Stream Interaction Regions in 2007-2016:Relationship with Heliospheric Current Sheets, Solar Cycle Variations, and Dual Observations

We have conducted a survey of 575 slow-to-fast stream interaction regions (SIRs) using STEREOA/B data in 2007-2016 (Jian et al., 2019). Among 518 well-defined SIRs, 54% are associated with heliospheric current sheet (HCS) crossings, and 34% are without any HCS crossing. The other 12% of the SIRs often occur in association with magnetic sectors shorter than 3 days. The SIRs with HCS crossings have slightly slower speeds but higher maximum number densities, magnetic field strengths, dynamic pressures, and total pressures than the SIRs without an HCS. The iron charge state is higher throughout the SIRs with an HCS than the SIRs without an HCS, by about 1/3 charge unit. In contrast with the comparable phases of solar cycle 23,slightly more SIRs and higher recurrence rates are observed in the years 2009-2016 of cycle 24,with a lower HCS association rate, possibly attributed to persistent equatorial coronal holes and more pseudostreamers in this recent cycle. The solar wind speed, peak magnetic field and pressures of SIRs are all lower in this cycle, but the weakening is less than for the comparable background solar wind parameters. Before STEREO B lost contact in 2014 October, 151 SIR pairs were observed by the twin spacecraft. Of the dual observations, the maximum speed is the best correlated of the plasma parameters. We have obtained a sample of plasma parameter differences analogous to those that would be observed for a mission at Lagrange point 4 or 5.By studying several cases with large discrepancies between the dual observations, we have also investigated the effects of HCS relative location, tilt of stream interface, and small transients on the SIR properties.

Lan K Jian↗
NASA NTRS2020

Interannual Variability in the Thermal Habitat of Alexandrium catenella in the Bay of Fundy and the Implications of Climate Change

Globally, harmful algal blooms (HABs) are an increasing problem. In the Gulf of Maine and Bay of Fundy, blooms of the toxic dinoflagellate Alexandrium catenella are annually recurrent phenomena. As this region is one of the most rapidly warming areas of the global ocean, an improved understanding of the mechanisms driving the initiation of local A. catenella blooms, their interannual variability and the implications of future climate change is critical to local monitoring strategies and marine resources management. A 27-year (1988–2014) time series of weekly A. catenella cell counts from the Bay of Fundy and concurrent satellite-measured sea surface temperature, freshwater discharge from the St. John River and wind-driven turbulence are compared to assess their relationship to variability in bloom phenology metrics. The mean thermal habitat associated with early detection of A. catenella is 6.5 ± 1.6°C, whereas that of bloom initiation averages 9.2 ± 1.5°C. Both thermal habitats for A. catenella are trending earlier over the study period. Bloom initiations that precede the arrival of the thermal habitat mean (occur in colder water) are associated with higher spring freshwater discharge and are generally weaker blooms. Increased spring freshwater discharge is also associated with earlier bloom initiation and earlier maximum concentration dates. No significant relationship was observed with the strength of wind-driven mixing. Removal of the mean thermal seasonal cycle shows that surface temperature anomalies have a strong negative relationship to the bloom phenology metrics and arrival of thermal habitat: warmer years are linked to earlier arrival of thermal habitats (∼12 days °C–1) and earlier detection and bloom initiation dates (∼33 days °C–1). Using these relationships and present trends in Bay of Fundy surface temperature warming over the period 1982–2019, we project the arrival dates of bloom thermal habitat and bloom phenology metrics out to the middle of this century. Based on current rates of sea surface temperature change, bloom phenology metrics (e.g., bloom initiation, early detection), can be expected to shift 1–2 months earlier in the season by mid-century. Such changes in the phenology of A. catenella blooms will need to be incorporated into both monitoring strategies and forecasting models for the region.

Andre F. Bucci↗
NASA NTRS2000

Quantum Decision Maker Theory and Simulation

A quantum device simulating the human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and of classical neural nets describing the evolution of probabilities of these processes which represent the mental dynamics.

Quantum↗
NASA NTRS1999

Quantum Decision-Maker

A quantum device simulating human decision making process is introduced. It consists of quantum recurrent nets generating stochastic processes which represent the motor dynamics, and a classical neural nets describing evolution of probabilities of these processes which represent the mental dynamics.

quantum↗
NASA NTRS1995

Ulysses Solar Wind Plasma Observations at High Southerly Latitudes

The solar wind is an ionized gas flowing from the solar corona and consisting primarily of free electrons, protons, and doubly-ionized helium. Ulysses solar wind plasma observations through -80.2 degrees solar latitude and continuing equatorward to -40.1 degrees are summarized. Recurrent high-speed streams and corotating interaction regions dominate at mid latitudes. Observation and analysis is given.

Ulysses↗
NASA NTRS2013

Towards a decision support system for space flight operations

The Mission Operations Directorate (MOD) at the Johnson Space Center (JSC) has put in place a Model Based Systems Engineering (MBSE) technological framework for the development and execution of the Flight Production Process (FPP). This framework has provided much added value and return on investment to date. This paper describes a vision for a model based Decision Support System (DSS) for the development and execution of the FPP and its design and development process. The envisioned system extends the existing MBSE methodology and technological framework which is currently in use. The MBSE technological framework currently in place enables the systematic collection and integration of data required for building an FPP model for a diverse set of missions. This framework includes the technology, people and processes required for rapid development of architectural artifacts. It is used to build a feasible FPP model for the first flight of spacecraft and for recurrent flights throughout the life of the program. This model greatly enhances our ability to effectively engage with a new customer. It provides a preliminary work breakdown structure, data flow information and a master schedule based on its existing knowledge base. These artifacts are then refined and iterated upon with the customer for the development of a robust end-to-end, high-level integrated master schedule and its associated dependencies. The vision is to enhance this framework to enable its application for uncertainty management, decision support and optimization of the design and execution of the FPP by the program. Furthermore, this enhanced framework will enable the agile response and redesign of the FPP based on observed system behavior. The differences between the anticipated system behavior and the observed behavior may be due to the processing of tasks internally, or due to external factors such as changes in program requirements or conditions associated with other organizations that are outside of MOD. The paper provides a roadmap for the four increments of this vision. These increments include (1) the existing capabilities (2) hardware and software system components and interfaces with the NASA ground system, (3) uncertainty management and (4) re-planning and automated execution. Each of these increments provides value independently; but some may also enable building of a subsequent increment.

Ruszkowski, James↗