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Communications and telemetry subsystem analyses for Manned Orbital Research Laboratory system improvement study
Handling of waste products on spacecraft to include identification, potential for on-board utilization, pretreatment requirements for storage, and disposal concepts - Vol.1
Usability studies demonstrate that web users are notoriously impatient, spending as little as 15 seconds on a home page. How do you get users to stay long enough to understand a citizen science project? How do you get users to complete complex citizen science tasks online? Image Detective, a citizen science project originally developed by scientists and science engagement specialists at the NASA Johnson Space center to engage the public in the analysis of images taken from space by astronauts to help enhance NASA's online database of astronaut imagery, partnered with the CosmoQuest citizen science platform to modernize, offering new and improved options for participation in Image Detective. The challenge: to create a web interface that builds users' skills and knowledge, creating engagement while learning complex concepts essential to the accurate completion of tasks. The project team turned to usability testing for an objective understanding of how users perceived Image Detective and the steps required to complete required tasks. A group of six users was recruited online for unmoderated and initial testing. The users followed a think-aloud protocol while attempting tasks, and were recorded on video and audio. The usability test examined users' perception of four broad areas: the purpose of and context for Image Detective; the steps required to successfully complete the analysis (differentiating images of Earth's surface from those showing outer space and identifying common surface features); locating the image center point on a map of Earth; and finally, naming geographic locations or natural events seen in the image. Usability test findings demonstrated that the following best practices can increase participation in Image Detective and can be applied to the successful implementation of any citizen science project: (1) Concise explanation of the project, its context, and its purpose; (2) Including a mention of the funding agency (in this case, NASA); (3) A preview of the specific tasks required of participants; (4) A dedicated user interface for the actual citizen science interaction. In addition, testing revealed that users may require additional context when a task is complex, difficult, or unusual (locating a specific image and its center point on a map of Earth). Video evidence will be made available with this presentation.
This final report covers the period from April 1994 through March 1998. The proposed research was organized under four main tasks. Those tasks were: (1) Investigate the vertical and horizontal velocity structures within and adjacent to thin and subvisual cirrus; (2) Investigate the lowest 1 km of the PBL and develop algorithms for processing pulsed Doppler lidar data obtained from single shots into regions of significant inhomogeneities in Beta and U; (3) Participate in OSSEs including those designed to establish shot density requirements for meso-gamma scale phenomena with quasi-persistent locations (e.g., jets, leewaves, tropical storms); and (4) Participate in the planning and execution of an airborne mission to measure winds with a pulsed CO2 Doppler lidar. Over the four year period of this research contract, work on all four tasks has yielded significant results which have led to 38 professional presentations (conferences and publications) and have been folded into the science justification for an approved NASA space mission, SPARCLE (SPAce Readiness Coherent Lidar Experiment), in 2001. Also this research has, through Task 4, led to a funded proposal to work directly on a NASA field campaign, CAMEX III, in which an airborne Doppler wind lidar will be used to investigate the cloud-free circulations near tropical storms. Monthly progress reports required under this contract are on file. This final report will highlight major accomplishments, including some that were not foreseen in the original proposal. The presentation of this final report includes this written document as well as material that is better presented via the internet (web pages). There is heavy reference to appended papers and documents. Thus, the main body of the report will serve to summarize the key efforts and findings.
This monthly report summarizes the work performed under contract NAS8-00114 for Marshall Space Flight Center in the following tasks: 1) Purchase Order No. H-32831D, Task Order 001A, GPB Program Software Oversight; 2) Purchase Order No. H-32832D, Task Order 002, ISS EXPRESS Racks Software Support; 3) Purchase Order No. H-32833D, Task Order 003, SSRMS Math Model Integration; 4) Purchase Order No. H-32834D, Task Order 004, GPB Program Hardware Oversight; 5) Purchase Order No. H-32835D, Task Order 005, Electrodynamic Tether Operations and Control Analysis; 6) Purchase Order No. H-32837D, Task Order 007, SRB Command Receiver/Decoder; and 7) Purchase Order No. H-32838D, Task Order 008, AVGS/DART SW and Simulation Support
As part of Task 1, we have started by testing our modeling capabilities by reproducing isothermal DFIT simulations presented in the literature. Once satisfied with the results we have started by targeting the modeling of the DFITs at conducted at well 58-32. We have a identified a specific test (cycle 4 in zone 2) as the most interesting to be model with GEOS hydraulic fracturing module. Thus, we have first produced results with an isothermal model and adjusted model parameters to get a satisfying match with field pressure data. The, we have added thermal effects and compared the modeling results with and without thermal effects to estimate how thermal effects may influence test interpretation. Models seem to suggest that, for small volumes of fluid, thermal effects are moderate. In Task 2, we have adapted GEOS phase-field formulation to be able to simulate near-wellbore hydraulic fracture nucleation and propagation. We have devised a novel formulation that, compared to other existing ones, incorporates rock strengths. We have submitted a journal publication about our work. We are currently employing this phase-field formulation to model the experiments taking place at U Pitt and help us understand the effect of various parameters. In Task 3, we have built a model of the region surround well 16A and have started modeling stage 3 stimulation because of its simpler planar geometry. After calibrating simulation parameters using known analytical solutions, we have simulated the stage 3 stimulation using our isothermal hydraulic fracturing module, varying the permeability field, the stress conditions including different physics to get a better understanding of the numerical challenges and of the effects of varying these parameters on the simulation results. In Task 4 laboratory experimentation, a set of specialized drilling and injection tools has been customized and constructed to accommodate an inclined well with an orientation of up to 30 degrees relative to material anisotropy or principal stress axes. These inclined samples have also undergone thermal stress and hydraulic fracturing at a temperature of 190 degrees Celsius. Furthermore, both vertical and deviated sampling testing setups enable an extended analysis of post-peak pressure behaviors, facilitating post-test pressure analyses such as the G-function, step rate, and fracture reopening measurements. Thus, the key components of in-situ stress estimation can be extracted and validated through our experiment, providing a solid foundation for validating existing in-situ stress estimation theories or proposing new ones. Simultaneously, we are integrating computer vision techniques with traditional experimental fracture observation methods such as multi-overcore/slicing and water-penetration fracture observation. This combination will prove beneficial in populating the hydraulic fracture patterns database, generated under challenging EGS conditions. This approach aims to deepen our understanding of the complexities in EGS reservoirs and pave the way for future data-driven investigations. Additionally, PITT has also equipped the ELE International compression machine, which is now prepared for conducting indirect tensile and fracture toughness tests. These tests will aid in characterizing how rock fabrics influence the resulting fracture patterns. Additionally, we have completed the required personnel training and gained access to Scanning Electron Microscopy (SEM) and Energy Dispersive Spectroscopy (EDS) for conducting more detailed characterization and analysis of rock fabrics, as well as the examination of thermal and hydraulically induced fracture patterns. Thus, the PITT team has effectively demonstrated the capabilities of our experimental apparatuses in exploring the thermal effects, well deviation angles, material anisotropy, and operational choices (such as circulation rate, injection fluid viscosity, and injection rate) and their impact on pressure responses and fracture trajectories under the Utah FORGE conditions.
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.
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.
Low pressure fuel turbopump turbine labyrinth send tip rubbing analysis, gas dynamic analysis, and HPFTP blade crack and blade impact are presented.
Following long-duration exploration transits, adaptive changes in sensorimotor function may impair the crew's ability to safely perform manual control tasks such as operating pressurized rovers. Postflight performance will also be influenced by the level of preflight skill proficiency they have attained. The purpose of this study was to characterize the acquisition of skills in a motion-based rover simulation over multiple sessions, and to investigate the effects of varying the simulation scenarios. METHODS: Twenty healthy subjects were tested in 5 sessions, with 1-3 days between sessions. Each session consisted of a serial presentation of 8 discrete tasks to be completed as quickly and accurately as possible. Each task consisted of 1) perspective-taking, using a map that defined a docking target, 2) navigation toward the target around a Martian outpost, and 3) docking a side hatch of the rover to a visually guided target. The simulator utilized a Stewart-type motion base (CKAS, Australia), single-seat cabin with triple scene projection covering 150 deg horizontal by 50 deg vertical, and joystick controller. Subjects were randomly assigned to a control group (tasks identical in the first 4 sessions) or a varied-practice group. The dependent variables for each task included accuracy toward the target and time to completion. RESULTS: The greatest improvements in time to completion occurred during the docking phase. The varied-practice group showed more improvement in perspective-taking accuracy. Perspective-taking accuracy was also affected by the relative orientation of the rover to the docking target. Skill acquisition was correlated with self-ratings of previous gaming experience. DISCUSSION: Varying task selection and difficulty will optimize the preflight acquisition of skills when performing novel operational tasks. Simulation of operational manual control will provide functionally relevant evidence regarding the impact of sensorimotor adaptation on early surface operations and what countermeasures are needed. Learning Objective: The use of a motion-based simulation to investigate decrements in the proficiency to operate pressurized rovers during early surface operations of space exploration missions, along with the acquisition of skill proficiency needed during the preflight phase of the mission.
A description is given of a three dimensional hand force capability model for the seated operator and a biomechanical model for analysis of symmetric sagittal plane activities. The models are used to simulate and study human strengths for one and two handed tasks in zero gravity. Specific conditions considered include: (1) one hand active, (2) both hands active but with different force directions on each, (3) body bracing situations provided by portable foot restraint when standing and lap belt when seated, (4) static or slow movement tasks with maximum length of 4 seconds and a minimum rest of 5 minutes between exertions, and (5) wide range of hand positions relative to either the feet or bisection of a line connecting the hip centers. Simulations were also made for shirt sleeved individuals and for the male population strengths with anthropometry matching that of astronauts.
This dataset contains GSE yearly load profiles for each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6).  GSE load profile of a sample day across different airport categories under charging scenario 1.
This dataset contains the required number of chargers for GSE at each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6).
This dataset contains the required number of vehicles for each GSE type at each airport under six charging scenarios: (1) charging when the battery state of charge is insufficient for the next service using 40-kW chargers (S1); (2) charging when the battery state of charge is insufficient for the next service using 20-kW chargers (S2); (3) charging starts immediately after each GSE completes a service task using 40-kW chargers (S3); (4) charging starts immediately after each GSE completes a service task using 20-kW chargers (S4); (5) charging during off-peak hours using 40-kW chargers (S5); and (6) charging during off-peak hours using 20-kW chargers (S6).  Number of GSE vehicles per GSE type and chargers required for different airport categories under charging scenario 1.
The wind shears program (WISP) supports the collection of data on magnetic tape for permanent storage or analysis. The document structure provides: (1) the hardware and software configuration required to execute the WISP system and start up procedure from a power down condition; (2) data collection task, calculations performed on the incoming data, and a description of the magnetic tape format; (3) the data display task and examples of displays obtained from execution of the real time simulation program; and (4) the raw data dump task and examples of operator actions required to obtained the desired format. The procedures outlines herein will allow continuous data collection at the expense of real time visual displays.
Strength and aerobic capacity are predictors of astronaut performance for extravehicular activities (EVA) during exploration missions. It is expected that astronauts will self‐select a pace below their ventilatory threshold (VT). PURPOSE: To determine the percentage of VT that subjects self‐select for prolonged occupational tasks. METHODS: Maximal aerobic capacity and a variety of lower‐body strength and power variables were assessed in 17 subjects who climbed 480 rungs on a ladder ergometer and then completed 10 km on a treadmill as quickly as possible using a self‐selected pace. The tasks were performed on 4 days, with a weighted suit providing 0% (suit fabric only), 40%, 60%, and 80% of additional bodyweight (BW), thereby altering the strength to BW ratio. Oxygen consumption and heart rate were continuously measured. Repeated measures ANOVA and post‐hoc comparisons were performed on the percent of VT values under each suited condition. RESULTS: Subjects consistently self‐paced at or below VT for both tasks and the pace was related to suit weight. At the midpoint for the ladder climb the 80% BW condition elicited the lowest metabolic cost (‐19+/-14% below VT), significantly different than the 0% BW (‐3+/-16%, P=0.002) and the 40% BW conditions (‐5+/-22%, P=0.023). The 60% BW condition (‐13+/-19%) was different than the 40% BW condition (P=0.034). Upon completion of the ladder task there were no differences among the conditions (0%BW: 3+/-18%; 40%BW: 3+/-21%; 60%BW: ‐ 8+/-25%; 80%BW: ‐10+/-18%). All subjects failed to complete 5km at 80%BW. At the midpoint of the treadmill test the three remaining conditions were all significantly different (0%BW: ‐20+/-15%; 40%BW: ‐ 33+/-15%; 60%BW: ‐41+/-19%). Upon completion of the treadmill test the 60% BW condition (‐38+/-12%) was significantly different than the 40% BW (‐28+/-15%, P=0.024). CONCLUSIONS: Decreasing relative strength results in progressive and disproportionate decreases (relative to VT) in self‐selected pacing during long‐duration activities. Thus, during prolonged, endurance‐type activities, large reductions in strength cause notable performance decrements despite no changes in aerobic capacity. These data highlight the importance of both aerobic capacity and muscle strength to the performance of prolonged EVA in exploration mission scenarios.
OBJECTIVE: Task-related EEG is sensitive to changes in cognitive state produced by increased task difficulty and by transient impairment. If task-related EEG has high test-retest reliability, it could be used as part of a clinical test to assess changes in cognitive function. The aim of this study was to determine the reliability of the EEG recorded during the performance of a working memory (WM) task and a psychomotor vigilance task (PVT). METHODS: EEG was recorded while subjects rested quietly and while they performed the tasks. Within session (test-retest interval of approximately 1 h) and between session (test-retest interval of approximately 7 days) reliability was calculated for four EEG components: frontal midline theta at Fz, posterior theta at Pz, and slow and fast alpha at Pz. RESULTS: Task-related EEG was highly reliable within and between sessions (r0.9 for all components in WM task, and r0.8 for all components in the PVT). Resting EEG also showed high reliability, although the magnitude of the correlation was somewhat smaller than that of the task-related EEG (r0.7 for all 4 components). CONCLUSIONS: These results suggest that under appropriate conditions, task-related EEG has sufficient retest reliability for use in assessing clinical changes in cognitive status.
The Human Research Program (HRP) Grant Lifecycle process can be described using information from several government authoritative sources with overlapping generalizations. It is the responsibility of the HRP Program Planning & Control (PP&C) Office and the Data Management Integration Office (DMIO)to interpret this information into a cohesive process that can be communicated to the human research organization. PP&C and DMIO have been exploring training opportunities to facilitate understanding through the form of infographics. This communication tool combines eye-catching visuals and text making complex information and data more digestible and shareable. The infographic poster tells a short story about a federally awarded grant and is intended to help both the new and experienced HRP workforce understand the timeline for a research procurement and where their specific tasks fall within the 4 phases of the grant lifecycle. The Pre-Award, Award, Post-Award, and Closeout phases are overlayed with business swimlanes so HRP stakeholders can see where they fit into the timeline rather than working in an isolated part. This includes the Chief Scientist Office (CSO), PP&C, the Data Management Integration Office (DMIO), the Principal Investigator (PI), the Element stakeholders, the LSDA Archivists, and the Research & Operations Integration (ROI) team along with the stakeholders in the Human Health and Performance Directorate. Additionally, the poster introduces (1) the HRP Data Integration Schedule identified in the HRP Data Management Plan HRP-48047, Table 8-2 and (2) a Smartsheet Solution to automate the grants tracking business process. Emphasis is given to the Data Integration Schedule which is the basis for the milestone tasks that are to be completed during the Post-Award phase in the grant lifecycle. The poster is also an opportunity to present the Smartsheet Solution, a software collaboration and work management tool used to assign tasks and track project progress. This PP&C FY24 effort will facilitate an end-to-end solution to track grants and provide insight to the health of HRP grant research procurements using metrics, reports, and dashboards.