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Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

40 Years of Processing Pieces of Space

This year marks the 40th year anniversary for the Antarctic Search for Meteorite (ANSMET) program. In 1976, the ANSMET program led the first expedition to Antarctica. The ANSMET program is a US-led field-based science project that recovers meteorite samples from Antarctica. Once a year from late November to late January, a field team consisting of 8 to 12 people, spends 6-8 weeks camping on the ice and collecting meteorites. Since 1976, more than 22,000 meteorite samples have been recovered. These meteorites come from asteroids, planets and other bodies of the solar system. Once collected, the Antarctic meteorites are shipped to NASA/Johnson Space Center (JSC) Houston, TX. in a refrigerated truck and are kept frozen to minimize oxidation until they are ready for initial processing. In Antarctica each meteorite is given a field tag which consists of numbers, once in the lab, this is replaced by an official tag, consisting of the Antarctic field location and year collected. The types and numbers of meteorites that have been classified include 849 carbonaceous chondrites, 135 enstatites, 512 achondrites, 64 stony, 115 irons, 48 others (27 R chondrites, 7 ungrouped), 6,161 H chondrites, 7,668 L chondrites, and 4,589 LL chondrites. Although 80-85 percent of the collected meteorites fall in the ordinary chondrite group, the other approximately 15 percent represent rare types of achondrites and carbonaceous chondrites. These rare meteorites include 25 lunar meteorites, 15 Martian meteorites, scores of various types of carbonaceous chondrites, and unique achondrites. The Antarctic meteorites that have been collected are processed in the Meteorite Processing Lab at JSC in Houston, TX. Initial processing of the meteorites begins with thawing/drying the meteorites in a nitrogen glove box for 24 to 48 hours. The meteorites are then photographed, measured, weighed and a description of the interior and exterior of each meteorite is written. The meteorite is broken and a representative sample, either a 1-3 gram chip or thin section is sent to the Smithsonian Institution for classification. After Antarctic meteorites have been classified and approved by the Nomenclature Committee of the Meteoritical Society, they are announced in the Antarctic Meteorite Newsletter, which is published twice per year (fall and spring) so that scientists may review which meteorites are available to study. Requests for Antarctic Meteorite samples are welcomed from research scientists, regardless of their current state of funding for meteorite studies. Since its inception over 3,300 requests have been made for pieces of these meteorites and over 400 investigators worldwide are active in the study of meteorites.. Research on these samples has been published in more than1500 peer reviewed articles; a listing of papers for any meteorite sample can be generated by accessing http://curator.jsc.nasa.gov/antmet/referencesearch.cfm. Antarctic meteorite samples requested by scientists are prepared several different ways. Most samples are prepared as chips, either using a rock splitter or using a chisel and chipping bowl. In special situations, a researcher may request a meteorite slab in which case the samples are cut using a diamond-bladed bandsaw inside of a dry nitrogen glove box. The meteorites are always cut in a 100 percent liquid-free environment. Additionally, thin/thick sections of Antarctic meteorites are also prepared at JSC. The meteorite thin section lab at JSC can prepare standard 30-micron thin sections, thick sections of variable thickness (100 to 200 microns), or demountable sections using superglue, all section are prepared without using water. Although many of the techniques used back in the '70's are still used today, advances in computers, software, databases, available tools and instrumentation have helped to streamline and shorten the duration of the classification process. In conjunction with present day missions to asteroids and other planets, meteorite studies have not only led to a better understanding of the complex histories of these bodies but have also tied certain meteorite groups to particular asteroid bodies. New meteorite discoveries by the ANSMET program provide a cost effective method for obtaining samples of previously unsampled bodies, allowing scientists to learn more about the origin, composition, and evolution of the solar system. Preservation in our cleanrooms at NASA allows material to be archived for future generations and advances in instrumentation and analysis.

Satterwhite, C. E.↗

Congruence of Clusters Defined By Whole Genome Sequencing and MALDI-TOF for Bacteria Isolated From Cleanrooms

Introduction: Oligotrophic conditions can render cleanrooms inhospitable to microbes. Despite these constraints, fungi and bacteria are frequently isolated from surfaces in astromaterials cleanrooms at the Johnson Space Center. Bacillus species are of particular concern because endospores belonging to this genus are resilient and can affect astromaterials. Current monitoring programs rely on 16S rRNA sequencing and the VITEK2 Compact system. These methods have limited power to resolve Bacillus species. Matrix-assisted laser desorption - time of flight mass spectrometry (MALDI-TOF MS), provides a rapid, low cost, method of identifying bacterial isolates and has a higher resolution than 16S rRNA sequencing, particularly for Bacillus species; however, few studies have compared this method to the industry gold standard, whole genome sequencing (WGS). Methods: Based on 16S rRNA classification, we selected 14 isolates for analysis with MALDI-TOF and WGS. Mass spectra were generated with MALDI-TOF MS and processed with custom scripts to identify clusters of closely related isolates and calculate a matrix of pairwise cosine similarity scores. Hybrid Illumina and Nanopore sequencing were used to generate draft genomes. Pairwise similarity scores were calculated from these genomes based on the average amino acid identity (AAI) predicted from single copy core genes. Congruence of clustering between these methods, was assessed by calculating adjusted Rand and Wallace coefficients. Results: Clusters of species generated from MALDI-TOF MS showed good agreement of phylotypes generated with WGS. Pairs of strains that were > 94% similar to each other in terms of predicted amino acid sequences consistently showed cosine similarities of mass spectra > 0.65 and, of the 9 clusters identified with WGS, 8 were identical with MALDI-TOF. This corresponds to an adjusted Rand index of 0.95 and a 95% confidence interval of 0.80 – 1.00 for adjusted Wallace coefficients. The only discordance was for a pair of isolates that were classified as Paenibacillus species. This pair showed relatively high similarity (0.84) in terms of MALDI-TOF MS but only 85% similarity in terms of AAI. Conclusion: This study shows that MALDI-TOF and WGS exhibit a similar ability to delineate Bacillus species isolated from cleanrooms and taxonomic units described by these two methods are consistent with one another. Since MALDI-TOF MS is low in cost and high in throughput, this approach appears to be an ideal option for routine microbial monitoring and identifying Bacillus species.

Farnaz Mazhari↗

Use of non-subjective analysis of lens transparency in experimental radiation cataract research

Historically the major impediment to radiation cataract follow-up has been the necessarily subjective nature of assessing the degree of lens transparency. This has spurred the development of instruments which produce video images amenable to digital analysis. One such system, the Zeiss Scheimpflug slit lamp measuring system (SLC), was incorporated into our ongoing studies of radiation cataractogenesis. It was found that the Zeiss SLC measuring system has high resolution and permits the acquisition of reproducible images of the anterior segment of the eye. Our results, based on about 650 images of lenses followed over a period of 91 weeks of radiation cataract development, showed that the changes in the light scatter of the lens correlated well with conventional assessment of radiation cataracts with the added advantages of objectivity, permanent and transportable records and linearity as cataracts become more severe. This continuous data acquisition, commencing with cataract onset, can proceed through more advanced stages. The SLC exhibits much greater sensitivity reflected in a continuously progressive severity thereby avoiding the artifactual plateaus in staging which occur using conventional scoring methods.

NASA Discipline Number 04-10↗

Development and Provision of Functional Foods to Promote Health on Long-Duration Space Missions

During long-duration NASA space missions, such as proposed missions to Mars, astronauts may experience negative physiological effects such as bone loss. Functional foods such as high-lycopene, high-flavonoids and high-omega-3 products and fruits and vegetables may mitigate the negative effects of spaceflight on physiological factors including the bone health of crewmembers. Previous studies showed that current ISS provisions provide high-lycopene and high-omega-3 food items but the variety is limited, which could promote menu fatigue. Bioactive compounds can degrade like other chemical compounds and lose functionality. The native concentrations and stability of bioactive compounds have never been determined in spaceflight foods, and adequate information is not available for commercial products for the storage durations required for space exploration (5 years). The purpose of this task is to develop new spaceflight foods that are high in omega-3 fatty acids, lycopene, or flavonoids, identify commercial products with these bioactive compounds that meet spaceflight requirements, and define the stability of these nutrients in storage to enable purposeful functional food incorporation into the space food system. The impact of storage temperature on the stability of lutein, lycopene, beta-carotene, omega-3 fatty acids, phenolics, anthocyanins and sterols is being studied in 12 ISS menu items stored at three different temperatures (4, 21, 35 degree C) over 2 years. Additionally, nutrient and quality stability are being assessed on a larger food set stored at 21 degree C over 2 years that contains twelve newly developed foods, 10 commercial products repackaged to spaceflight requirements, and another 5 current ISS menu items expected to be good sources of omega-3 fatty acids, lycopene, or flavonoids. All items were shipped overnight to the Linus Pauling Institute at Oregon State University (Corvalis, OR) after processing and 1-year of storage and analyzed for bioactive compound concentrations. Sensory evaluation was conducted on the newly developed functional foods and commercial products with untrained panelists (n is greater than or equal to 25) using a 9-point Hedonic scale to test sensory attributes and overall acceptability after processing and 1-year of storage (21 degree C). Repeat nutritional and sensory analyses will be conducted in the same foods after the 2-year storage period is completed. The stability of bioactive compounds in the selected foods was dependent on storage temperature and food matrix. Omega-3 showed excellent stability in the analyzed products after 1-year of storage, regardless of the storage temperature; phenolic compounds also showed good stability. Lycopene was more stable in oil-based products rather than water-based products because of the protection that lipids offer to lycopene molecules. Also, lycopene was more stable in freeze-dried products than in high moisture foods. The 12 newly developed functional foods showed good overall acceptability in sensory attributes after processing (average score 7.2 out of 9.0) and maintained sensory quality through 1-year (21 degree C); the overall acceptability was on average 7.1 after storage. Similar behavior was observed for the 10 commercial products after 1 year. The developed products are good sources of omega-3 (both plant and marine), vegetables (7 vegetable-based products), and good sources of carotenoids, such as the Curry Pumpkin Soup and the Sweet and Savory Kale. Nine of the new products, such as Mango Salad, Pickled Beets, and Braised Red Cabbage, are rich in phenolic compounds. Stability of most of the studied nutrients seems to be adequate after 1-year of storage in most of the tested foods. However, storage temperature of the food must be considered during long-duration space missions to achieve stability of all nutrients. Likewise, more information is needed regarding nutrient retention after 2-years of storage to identify nutritional gaps that may be expected over the 5-year shelf life required for a Mars mission. New developed products will be filling a gap in the current space food system to minimize menu fatigue, provide specific nutrients to reduce the negative effects of long-duration space missions and maintain crew members' health. Information about bioactive compounds in developed products after 1-year and 2-year of storage will provide the knowledge base for further product development.

Bermudez-Aguirre, D.↗

Multi-Variate LSTM Prediction of Alaska Magnetometer Chain Utilizing a Coupled Model Approach

During periods of rapidly changing geomagnetic conditions electric fields form within the Earth’s surface and induce currents known as geomagnetically induced currents(GICs), which interact with unprotected electrical systems our society relies on. In this study, we train multi-variate Long-Short Term Memory neural networks to predict magnitude of north-south component of the geomagnetic field (|BN|) at multiple ground magnetometer stations across Alaska provided by the SuperMAG database with a future goal of predicting geomagnetic field disturbances. Each neural network is driven by solar wind and interplanetary magnetic field inputs from the NASA OMNI database spanning from 2000–2015 and is fine tuned for each station to maximize the effectiveness in predicting |BN|. The neural networks are then compared against multivariate linear regression models driven with the same inputs at each station using Heidke skill scores with thresholds at the 50, 75, 85, and 99 percentiles for |BN|. The neural network models show significant increases over the linear regression models for |BN| thresholds. We also calculate the Heidke skill scores for d|BN|/dt by deriving d|BN|/dt from |BN| predictions. However, neural network models do not show clear outperformance compared to the linear regression models. To retain the sign information and thus predict BN instead of |BN|, a secondary so-called polarity model is utilized. The polarity model is run in tandem with the neural networks predicting geomagnetic field in a coupled model approach and results in a high correlation between predicted and observed values for all stations. We find this model a promising starting point for a machine learned geomagnetic field model to be expanded upon through increased output time history and fast turnaround times.

Matthew Blandin↗

Computational Transport Modeling of High-Energy Neutrons Found in the Space Environment

The high charge and high energy (HZE) particle radiation environment in space interacts with spacecraft materials and the human body to create a population of neutrons encompassing a broad kinetic energy spectrum. As an HZE ion penetrates matter, there is an increasing chance of fragmentation as penetration depth increases. When an ion fragments, secondary neutrons are released with velocities up to that of the primary ion, giving some neutrons very long penetration ranges. These secondary neutrons have a high relative biological effectiveness, are difficult to effectively shield, and can cause more biological damage than the primary ions in some scenarios. Ground-based irradiation experiments that simulate the space radiation environment must account for this spectrum of neutrons. Using the Particle and Heavy Ion Transport Code System (PHITS), it is possible to simulate a neutron environment that is characteristic of that found in spaceflight. Considering neutron dosimetry, the focus lies on the broad spectrum of recoil protons that are produced in biological targets. In a biological target, dose at a certain penetration depth is primarily dependent upon recoil proton tracks. The PHITS code can be used to simulate a broad-energy neutron spectrum traversing biological targets, and it account for the recoil particle population. This project focuses on modeling a neutron beamline irradiation scenario for determining dose at increasing depth in water targets. Energy-deposition events and particle fluence can be simulated by establishing cross-sectional scoring routines at different depths in a target. This type of model is useful for correlating theoretical data with actual beamline radiobiology experiments. Other work exposed human fibroblast cells to a high-energy neutron source to study micronuclei induction in cells at increasing depth behind water shielding. Those findings provide supporting data describing dose vs. depth across a water-equivalent medium. This poster presents PHITS data suggesting an increase in dose, up to roughly 10 cm depth, followed by a continual decrease as neutrons come to a stop in the target.

Cox, Brad↗

Diabatic Initialization of Mesoscale Models in the Southeastern United States: Can 0 to 12h Warm Season QPF be Improved?

It is well known that numerical warm season quantitative precipitation forecasts lack significant skill for numerous reasons. Some are related to the model--it may lack physical processes required to realistically simulate convection or the numerical algorithms and dynamics employed may not be adequate. Others are related to initialization-mesoscale features play an important role in convective initialization and atmospheric observation systems are incapable of properly depicting the three-dimensional stability structure at the mesoscale. The purpose of this study is to determine if a mesoscale model initialized with a diabatic initialization scheme can improve short-term (0 to 12h) warm season quantitative precipitation forecasts in the Southeastern United States. The Local Analysis and Prediction System (LAPS) developed at the Forecast System Laboratory is used to diabatically initialize the Pennsylvania State University/National center for Atmospheric Research (PSUNCAR) Mesoscale Model version 5 (MM5). The SPORT Center runs LAPS operationally on an hourly cycle to produce analyses on a 15 km covering the eastern 2/3 of the United States. The 20 km National Centers for Environmental Prediction (NCEP) Rapid Update Cycle analyses are used for the background fields. Standard observational data are acquired from MADIS with GOES/CRAFT Nexrad data acquired from in-house feeds. The MM5 is configured on a 140 x 140 12 km grid centered on Huntsville Alabama. Preliminary results indicate that MM5 runs initialized with LAPS produce improved 6 and 12h QPF threat scores compared with those initialized with the NCEP RUC.

Lapenta, William M.↗

Functional Mobility Performance and Balance Confidence in Older Adults after Sensorimotor Adaptation Training

Research indicates a main contributor of injury in older adults is from falling. The decline in sensory systems limits information needed to successfully maneuver through the environment. The objective of this study was to determine if prolonged exposure to the realignment of perceptual-motor systems increases adaptability of balance, and if balance confidence improves after training. A total of 16 older adults between ages 65-85 were randomized to a control group (walking on a treadmill while viewing a static visual scene) and an experimental group (walking on a treadmill while viewing a rotating visual scene). Prior to visual exposure, participants completed six trials of walking through a soft foamed obstacle course. Participants came in twice a week for 4 weeks to complete training of walking on a treadmill and viewing the visual scene for 20 minutes each session. Participants completed the obstacle course after training and four weeks later. Average time, penalty, and Activity Balance Confidence Scale scores were computed for both groups across testing times. The older adults who trained, significantly improved their time through the obstacle course F (2, 28) = 9.41, p < 0.05, as well as reduced their penalty scores F (2, 28) = 21.03, p < 0.05, compared to those who did not train. There was no difference in balance confidence scores between groups across testing times F (2, 28) = 0.503, p > 0.05. Although the training group improved mobility through the obstacle course, there were no differences between the groups in balance confidence.

Buccello-Stout, Regina R.↗

Marshall Application Realignment System (MARS) Architecture

The Marshall Application Realignment System (MARS) Architecture project was established to meet the certification requirements of the Department of Defense Architecture Framework (DoDAF) V2.0 Federal Enterprise Architecture Certification (FEAC) Institute program and to provide added value to the Marshall Space Flight Center (MSFC) Application Portfolio Management process. The MARS Architecture aims to: (1) address the NASA MSFC Chief Information Officer (CIO) strategic initiative to improve Application Portfolio Management (APM) by optimizing investments and improving portfolio performance, and (2) develop a decision-aiding capability by which applications registered within the MSFC application portfolio can be analyzed and considered for retirement or decommission. The MARS Architecture describes a to-be target capability that supports application portfolio analysis against scoring measures (based on value) and overall portfolio performance objectives (based on enterprise needs and policies). This scoring and decision-aiding capability supports the process by which MSFC application investments are realigned or retired from the application portfolio. The MARS Architecture is a multi-phase effort to: (1) conduct strategic architecture planning and knowledge development based on the DoDAF V2.0 six-step methodology, (2) describe one architecture through multiple viewpoints, (3) conduct portfolio analyses based on a defined operational concept, and (4) enable a new capability to support the MSFC enterprise IT management mission, vision, and goals. This report documents Phase 1 (Strategy and Design), which includes discovery, planning, and development of initial architecture viewpoints. Phase 2 will move forward the process of building the architecture, widening the scope to include application realignment (in addition to application retirement), and validating the underlying architecture logic before moving into Phase 3. The MARS Architecture key stakeholders are most interested in Phase 3 because this is where the data analysis, scoring, and recommendation capability is realized. Stakeholders want to see the benefits derived from reducing the steady-state application base and identify opportunities for portfolio performance improvement and application realignment.

Belshe, Andrea↗

Cybersickness Following Repeated Exposure to DOME and HMD Virtual Environments

Virtual environments (VE) offer unique training opportunities, including training astronauts to preadapt them to the novel sensory conditions of microgravity. However, one unresolved issue with VE use is the occurrence of cybersickness during and following exposure to VE systems. Most individuals adapt and become less ill with repeated interaction with VEs. The goal of this investigation was to compare motion sickness symptoms (MSS) produced by dome and head-mounted (HMD) displays and to examine the effects of repeated exposures on MSS. Sixty-one subjects participated in the study. Three experimental sessions were performed each separated by one day. The subjects performed a navigation and pick and place task in either a dome or HMD VE. MSS were measured using a Simulator Sickness Questionnaire before, immediately after, and at 1, 2, 4 and 6 hours following exposure to the VEs. MSS data were normalized by calculating the natural log of each score and an analysis of variance was performed. We observed significant main effects for day and time and a significant day by time interaction for total sickness and for each of the subscales, nausea, oculomotor and disorientation. However, there was no significant main effect for device. In conclusion, subjects reported a large increase in MSS immediately following exposure to both the HMD and dome, followed by a rapid recovery across time. Sickness severity also decreased over days, which suggests that subjects become dual-adapted over time making VE training a viable pre-flight countermeasure for space motion sickness.

Taylor, Laura C.↗

Summarizing Multiple Aspects of Triple Collocation Analysis in a Single Diagram

With the ongoing expansion of global observation networks, it is expected that we shall routinely analyze records of geophysical variables such as temperature from multiple collocated instruments. Validating datasets in this situation is not a trivial task because every observing system has its own bias and noise. Triple collocation is a general statistical framework to estimate the error characteristics in three or more observational-based datasets. In a triple colocation analysis, several metrics are routinely reported but traditional multiple-panel plots are not the most effective way to display information. A new formula of error variance is derived for connecting the key terms in the triple collocation theory. A diagram based on this formula is devised to facilitate triple collocation analysis of any data from observations, as illustrated using three aerosol optical depth datasets from the recent Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). An observational-based skill score is also derived to evaluate the quality of three datasets by taking into account both error variance and correlation coefficient. Several applications are discussed and sample plotting routines are provided.

triple collocation↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or create more accurate transcripts of air traffic management conversations. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement (LOA) which is used as the evaluation case in this paper. These applications can benefit from the use of state-of-the-art Natural Language Processing (NLP) techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of transferring pre-trained large language models to the aviation domain by adapting transformer based models using aviation datasets. This paper utilized two datasets to adapt a ‘Robustly Optimized Bidirectional Encoder Representations from Transformers Approach’ (RoBERTa) model and two down-stream classification tasks to assess its performance. These datasets are all built upon Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. The first two datasets are used for the adaptation of RoBERTa to the aviation domain and were of different sizes to assess the number of documents needed to adapt to the aviation domain. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the unsupervised adaptation. The second dataset is a separate set of LOA documents with two sets of classification labels to be used for evaluation; one at the document level and one at the line level. These down-stream evaluations allowed the measurement of improvement by adapting RoBERTa. The accuracy increased by 4-6% on both tasks and the F1 score on the class of interest increased by 4-8% from the adaptation.

Air Traffic Management↗

Visual/motion cue mismatch during a coordinated roll maneuver

The effect of a performance mismatch between the simulator visual and motion display systems on pilot performance while engaged in a compensatory tracking task, and the effect of a reduction in simulator motion scaling while maintaining constant visual scaling on pilot performance for this same task were investigated. A jet transport aircraft with motion in the roll and lateral simulator axes was used as a test vehicle for a computer simulation. The aircraft was disturbed by moderate levels of turbulence which resulted in flight path deviations in the roll and lateral axes. The task of the pilot was to maintain flight formation behind the aircraft in front of him as displayed by a video monitor located in the simulator cockpit. Experimental data consisting of pilot describing functions and pilot performance scores are presented and discussed as they relate to experimental evidence.

Shirachi, D. K.↗

Assessment of Cognitive Processing by Multiple Sclerosis Patients Using Electroencephalographic Energy Density Analysis

Recent neuropsychological studies demonstrate that cognitive dysfunction is a common symptom in patients with multiple sclerosis. In many cases the presence of cognitive impairment affects the patient's daily activities to a greater extent than would be found due to their physical disability alone. Cognitive dysfunction can have a significant impact on the quality of life of both the patient and that of their primary caregiver. Two cognitively impaired male MS patients were given a visual discrimination task before and after a one hour cooling period. The subjects were presented a series of either red or blue circles or triangles. One of these combinations, or one fourth of the stimuli, was designated as the "target" presentation. EEG was recorded from 20 scalp electrodes using a Tracor Northern 7500 EEG/ERP system. Oral and ear temperatures were obtained and recorded manually every five minutes during the one hour cooling period. The EEG ERP signatures from each series of stimuli were analyzed in the energy density domain to determine the locus of neural activity at each EEG sampling time. The first subject's ear temperature did not decrease during the cooling period. It was actually elevated approximately 0.05C by the end of the cooling period compared to his mean of control period value. In turn, Subject One's discrimination performance and cortical energy remained essentially the same after body cooling. In contrast, Subject Two's ear temperature decreased approx. 0.8C during his cooling period. Subject Two's ERROR score decreased from 12 during the precooling control period to 2 after cooling. His ENERGY value increased approximately 300%, from a precooling value of approximately 200 to a postcooling value of nearly 600.

Montgomery, Leslie D.↗

Enhancement of Cognitive Processing by Multiple Sclerosis Patients Using Liquid Cooling Technology: A Case Study

Recent neuropsychological studies demonstrate that cognitive dysfunction is a common symptom in patients with multiple sclerosis. In many cases the presence of cognitive impairment affects the patient's daily activities to a greater extent than would be found due to their physical disability alone. Cognitive dysfunction can have a significant impact on the quality of life of both the patient and that of their primary caregiver. Two cognitively impaired male MS patients were given a visual discrimination task before and after a one hour cooling period. The subjects were presented a series of either red or blue circles or triangles. One of these combinations, or one fourth of the stimuli, was designated as the "target" presentation. EEG was recorded from 20 scalp electrodes using a Tracor Northern 7500 EEG/ERP system. Oral and ear temperatures were obtained and recorded manually every five minutes during the one hour cooling period. The EEG ERP signatures from each series of stimuli were analyzed in the energy density domain to determine the locus of neural activity at each EEG sampling time. The first subject's ear temperature did not decrease during the cooling period. It was actually elevated approximately 0.05 C by the end of the cooling period compared to his mean of control period value. In turn, Subject One's discrimination performance and cortical energy remained essentially the same after body cooling. In contrast, Subject Two's ear temperature decreased approx. 0.8 C during his cooling period. Subject Two's ERROR score decreased from 12 during the precooling control period to 2 after cooling. His ENERGY value increased approximately 300%, from a precooling value of approximately 200 to a postcooling value of nearly 600. These findings might be interpreted by the following three-part hypothesis: (1) the general cognitive impairment of MS patients may be a result of low or unfocused metabolic energy conversion in the cortex; (2) such differences show up most strongly in reduced energy in the occipital region during the initial processing of the precooling period visual stimulus which may indicate impaired early visual processing; and (3) increased postcooling activation in the le ft angular gyrus may result in enhanced higher-level reasoning related to processing visual task information. By this hypothesis the superior performance of Subject Two following body cooling may be a result of increased neural activation in his early visual recognition and processing centers.

Montgomery, Leslie D.↗