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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Advancing Air Mobility: Few-Shot Learning in Airspace Research and Development

The advancement of Air Mobility, particularly in the context of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), represents a transformative shift in aviation's role in modern society. A comprehensive understanding of requirement consistency is paramount for fostering interoperability, standardization, and cost-effectiveness within airspace systems. This paper introduces a novel approach utilizing a pretrained Sentence Transformers model and few-shot learning to address this crucial aspect task of flagging potentially inconsistent requirements. Few-shot learning supports the development of this future through ensuring the accuracy and consistency of identified requirements with little human oversight. This approach offers a promising solution to the challenges of requirement consistency identification in airspace systems. By harnessing the power of advanced NLP techniques with fine-tuned models, stakeholders can enhance efficiency, accuracy, and scalability; ultimately fostering improved interoperability, standardization, and cost-effectiveness in airspace management.

Natural Language Processing↗
NASA NTRSDate not supplied

Evaluating Machine Learning Approaches to Plume Tracking

On July 15, 2022, the Hunga Tonga-Hunga Ha’apai (HTHH) submarine volcano erupted, propelling trace gasses and ash through the troposphere and up into the stratosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using imagery from NASA’s Earth Observing System, including MODIS aerosol products and OMI sulfur dioxide products, this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline, establishes a framework for systematically and rapidly studying natural disasters, including additional volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of NASA’s Earth Observation and remote sensing data, this work shows how AI and open science can accelerate research and generate actionable results, even for unprecedented events. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions, and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters in a changing world.

machine learning↗
NASA NTRSDate not supplied

Tracking the Hunga Tonga-Hunga Ha’apai Eruption Stratospheric Aerosol and Trace Gas Plumes Using Machine Learning

On January 15, 2022, the Hunga Tonga-Hunga Ha’apai (hereafter, Hunga Tonga) submarine volcano had an explosive eruption that thrusted ash, gases, and water vapor through the troposphere into the stratosphere and mesosphere. Previous studies manually tracked the aerosol and trace gas plumes over time across different positions in the southern hemisphere. Using data retrieved from low earth orbiting satellite instruments (e.g., OMPS, OMI, and CALIPSO), this research demonstrates how open-source machine learning (ML) models, like Meta’s Segment Anything Model (SAM), with prompt engineering can perform automatic plume tracking following the Hunga Tonga eruption. This extensible methodology, and modular data processing and modeling pipeline using NASA Earthdata and Openscapes, establishes a framework for systematically and rapidly studying extreme events, including volcanic eruptions and large-scale wildfires. By combining advanced machine learning techniques, such as SAM’s zero-shot learning, with large volumes of remote sensing data, this work demonstrates how AI and open science can accelerate research and generate actionable results. The tools and technologies presented here can help translate earth science to action from NASA’s current and future Earth observing satellite missions (e.g., the Atmosphere Observing System (AOS)), and assist researchers and stakeholders in understanding, mapping, and responding to natural disasters and extreme events in a changing world.

David M. Giles↗
NASA NTRS2025

A Preliminary Study on the Feasibility of Large Language Models for Detecting Micro-Behaviors Among Team Members in Space Missions

Large-language models (LLMs) have been recently used for spoken language understanding (SLU) to infer meaning and semantics from speech in tasks such as speaker intent and sentiment classification. Due to being trained on large amounts of data, and their ability to understand context and relationships between words, LLMs are competent, enabling them to generalize across tasks without requiring many task-specific training samples. This research examines the feasibility of few-shot learning in LLMs for detecting subtle, brief, and possibly unconscious interactions between team members, called ``micro-behaviors," and provides insights into the appropriate design of LLMs for this task. Our data came from 5 teams participating in a 45-day mission at the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA). More specifically we used data collected from team interaction battery (TIB) tasks teams performed five times in-mission which comprise an average 1.5 hours of conversation data per day. Micro-behaviors were coded according to an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). We explore the ability of LLMs to detect the presence and intensity of micro-behaviors. We examine employing and fine-tuning readily available LLMs (i.e., RoBERTa, DistilBERT), as well as prompting state-of-the-art sequence classification models (i.e., Llama-2, Llama-3). In a total of 13,058 conversational turns (17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls), we compute the macro F1-score of the 3-way micro-behavior classification task (i.e., classifying among uplifting, discouraging, and neutral; 33% chance). Results indicate that the RoBERTa model achieves a F1-score of 36.2% (uplift: 43.3% precision (P), 15.1% recall (R); discourage: 20% P, 0.5% R). These results significantly improve when we augment the data via paraphrasing in the RoBERTa model, reaching a 41.2% macro F1-score (uplift: 37.7% P, 86.3% R; discourage: 3.5% P, 1.8% R). Finally, the Llama-2 model with 3-shot prompting yields 38% macro F1-score (uplift: 28.7% P, 20% R; discourage: 7.2% P, 18% R), which is slightly better compared to the RoBERTa model without data augmentation, highlighting the effectiveness of sequence classification models in detecting minority classes with a small sample size. Findings indicate that LLMs hold potential to detect subtle behaviors in conversations, which could be valuable in assessing team behavior in space exploration missions. Future studies will evaluate the performance of different LLM prompting strategies or fine-tuning methods.

Ankush Raut↗
NASA NTRSDate not supplied

AI Foundation Models for Science: An Open Collaborative Initiative

Foundation Models (FMs), AI models designed to replace task-specific models, are increasingly being recognized for their versatility across numerous downstream applications. These models, trained using self-supervised techniques on any type of sequence data, circumvent the need for large annotated datasets, a major bottleneck in traditional AI model development. FMs can be applied to downstream tasks using few-shot learning and fine-tuning, significantly reducing the need for large labeled training datasets and computational resources. However, the development of FMs requires substantial resources, including access to data and compute power, expertise in the latest models, and specialized scientific knowledge for systematic evaluation. It is challenging for a single group to possess all these capabilities. To address this, NASA IMPACT has initiated an open collaborative effort, leveraging partnerships with the private sector and other groups within and outside NASA, to jointly build FMs. The overarching goal is to develop a consistent and collaborative approach to building FMs for high-value science datasets. This initiative has fostered collaboration within NASA and with external partners, including IBM Research, Clark University, DOE’s ORNL, ESA, and USGS. The effort focuses on identifying key datasets with a wide range of downstream applications, pretraining and building FMs using modified transformer architectures, evaluating compute infrastructure needs, and sharing models, pretraining and fine-tuning code, and data with the community. Furthermore, it aims to train the Earth science community to fine-tune these models for various downstream applications. Our initial effort resulted in the creation of a 100 million parameter HLS Geospatial Model within six months, which was released on HuggingFace. We are now expanding our scope to include data from weather and climate models and investigating multimodal models. We invite those interested in participating in this effort to join us by sharing their use cases, expertise, or data.

Rahul Ramachandran↗
NASA NTRS1993

Classification of multispectral image data by the Binary Diamond neural network and by nonparametric, pixel-by-pixel methods

The classification of multispectral image data obtained from satellites has become an important tool for generating ground cover maps. This study deals with the application of nonparametric pixel-by-pixel classification methods in the classification of pixels, based on their multispectral data. A new neural network, the Binary Diamond, is introduced, and its performance is compared with a nearest neighbor algorithm and a back-propagation network. The Binary Diamond is a multilayer, feed-forward neural network, which learns from examples in unsupervised, 'one-shot' mode. It recruits its neurons according to the actual training set, as it learns. The comparisons of the algorithms were done by using a realistic data base, consisting of approximately 90,000 Landsat 4 Thematic Mapper pixels. The Binary Diamond and the nearest neighbor performances were close, with some advantages to the Binary Diamond. The performance of the back-propagation network lagged behind. An efficient nearest neighbor algorithm, the binned nearest neighbor, is described. Ways for improving the performances, such as merging categories, and analyzing nonboundary pixels, are addressed and evaluated.

Salu, Yehuda↗
NASA NTRS1980

A low-cost forward fairing for the Bell Long Ranger Helicopter

A description is presented of work concerned with determining the effects of long-term flight service on advanced composite helicopter airframe components. The helicopter chosen for the program is the Long Ranger Model 206L. The components to be evaluated include the baggage door, litter door, vertical fin, and forward fairing. Only the vertical fin is classified as primary structure. Loss of any of the other components will not compromise safety of the aircraft. Attention is given to the program objectives, the design of the forward fairing, the fabrication procedures, the exterior surface, the cocure procedure, material tests, and initial cost-tracking. The considered program demonstrates the ability to produce an acceptable fairing by the 'one-shot' cocured process and, based on learning curve experience, production costs will be low. The low-temperature 200 F cure does not affect the structural properties to an unacceptable degree. A method for obtaining a smooth, exterior painted surface for Kevlar/epoxy fabric has been developed.

Zinberg, H.↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was implemented to offer an efficient framework for determining material characteristics from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enable parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM). SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography or other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
NASA NTRSDate not supplied

The Porous Microstructure Analysis (PuMA) software

The open-source Porous Microstructure Analysis (PuMA) software was created to offer an efficient framework for determining material properties from 3D microstructures. Its development was inspired by progress in X-ray microtomography, an imaging technology that captures the internal structure of materials in 3D, and even in a 4D temporal context. Over recent years, this method has transformed the domain of materials science due to its capability to non-destructively examine material microstructures while presenting digital data about their geometrical details. It has provided insights into materials relevant to several NASA missions, including heatshields, parachute fabrics, meteorites, and other advanced composites. PuMA, in its current version 3, delivers an array of features, spanning from basic geometric insights of a microstructure to intricate anisotropic thermo-elastic and chemical behavior. Specifically, the software evaluates morphological attributes (specific surface area, volume fractions, mean intercept lengths, orientation) and physical characteristics (conductivity, elasticity, permeability, and tortuosity). Additionally, it can model material degradation processes, such as oxidation and surface chemistry interactions. The software can generate synthetic microstructures, from straightforward geometrical designs to intricate woven and non-woven geometries. Coupling material generation and characterization enables parametric studies and sensitivity analysis to optimize the microstructural performance and inform design decisions and reliability assessment based on uncertainty quantification. A recent addition to PuMA includes the TomoSAM plugin, devised to incorporate the cutting-edge Segment Anything Model (SAM) into our image segmentation workflow. SAM is a promptable deep learning model that can identify objects and create image masks in a zero-shot manner, based only on a few user clicks. The synergy between these tools aids in the segmentation of complex 3D datasets from tomography and other imaging techniques, which would otherwise require a laborious manual segmentation process.

Tomography↗
NASA NTRS2006

Impact Testing of Orbiter Thermal Protection System Materials

This viewgraph presentation reviews the impact testing of the materials used in designing the shuttle orbiter thermal protection system (TPS). Pursuant to the Columbia Accident Investigation Board recommendations a testing program of the TPS system was instituted. This involved using various types of impactors in different sizes shot from various sizes and strengths guns to impact the TPS tiles and the Leading Edge Structural Subsystem (LESS). The observed damage is shown, and the resultant lessons learned are reviewed.

Kerr, Justin↗
NASA NTRS1999

Lesson Plan Prototype for International Space Station's Interactive Video Education Events

The outreach and education components of the International Space Station Program are creating a number of materials, programs, and activities that educate and inform various groups as to the implementation and purposes of the International Space Station. One of the strategies for disseminating this information to K-12 students involves an electronic class room using state of the art video conferencing technology. K-12 classrooms are able to visit the JSC, via an electronic field trip. Students interact with outreach personnel as they are taken on a tour of ISS mockups. Currently these events can be generally characterized as: Being limited to a one shot events, providing only one opportunity for students to view the ISS mockups; Using a "one to many" mode of communications; Using a transmissive, lecture based method of presenting information; Having student interactions limited to Q&A during the live event; Making limited use of media; and Lacking any formal, performance based, demonstration of learning on the part of students. My project involved developing interactive lessons for K-12 students (specifically 7th grade) that will reflect a 2nd generation design for electronic field trips. The goal of this design will be to create electronic field trips that will: Conform to national education standards; More fully utilize existing information resources; Integrate media into field trip presentations; Make support media accessible to both presenters and students; Challenge students to actively participate in field trip related activities; and Provide students with opportunities to demonstrate learning

Zigon, Thomas↗
NASA NTRS1981

Climate model postprocessing

The development of new postprocessing software of the climate modeling group is summarized. Code, test, and perform simulations with global general circulation models are described. The models improve understanding and ability to predict the vagaries of weather and climate. To learn from and utilize the model results, it is necessary to create elaborate postprocessing software to allow analysis of the large volume of data produced. The models produce sigma history tapes. The sigma history records are interpolated to pressure history records, which are written on a pressure history tape. The model results are analyzed on pressure surfaces, with snap shots or time averages.

Abeles, J.↗
NASA NTRS2006

Providing Housing, Food and Medical Support for 25,000 Katrina Evacuees with 12 Hours Notice: The Harris County Medical Support of the Superdome Evacuees

Hurricane Katrina was responsible for trapping 25,000 people in the New Orleans Superdome and isolating many others throughout Louisiana and Mississippi. The transport of these evacuees to the Reliant Park (Houston, Texas) used 500 buses each containing about 55 people. Processing the arriving evacuees included addressing their health status and medical needs as follows: an initial triage at disembarkation, a secondary triage in the Reliant Astrodome and Center, and definitive clinical care in the Reliant Arena "Katrina" Clinic. Baylor College of Medicine (BCM) physicians boarded buses and identified the sickest for emergency transport to Harris County Hospital District (HCHD) hospitals. BCM departments represented included pediatrics, family and community medicine, internal medicine, radiology, obstetrics and gynecology, orthopedics, surgery, and psychiatry. Astrodome and Center triage was managed by BCM physicians and staffed by HCHD Nurses and volunteers from Texas and beyond. The Reliant Astrodome, Center and Arena reached peak headcounts of 15,000,4500, and 2500, respectively Most evacuees visiting the triage sites in the Astrodome and Center were treated using "over-the-counter" medications with the remaining being transported to the "Katrina" clinic. The clinic was equipped with a lab, pharmacy, digital X-ray, and ultrasound machines in addition to electronic patient records created using 80 computer terminals. The Katrina clinic saw more than 15,000 patients during 15 days of operations (2,000 on the first full day), administered 10,000 tetanus shots, and filled thousands of prescriptions. At the peak of operations, the clinic saw 150 patients/hour with 25 physicians scheduled for each 12-hour shift. Approximately 900 people were transported to hospital emergency rooms. Within 3 weeks of arriving at the Reliant Park facilities, more than 90% of the families found permanent housing, enrolled children in schools, and found work. Using data obtained from manual and electronic medical records, this presentation will document the major milestones and lessons learned from this extraordinary project to help the Katrina evacuees.

Hamilton, Douglas↗
NASA NTRS2015

Strain Gage Loads Calibration Testing with Airbag Support for the Gulfstream III SubsoniC Research Aircraft Testbed (SCRAT)

This paper describes the design and conduct of the strain-gage load calibration ground test of the SubsoniC Research Aircraft Testbed, Gulfstream III aircraft, and the subsequent data analysis and results. The goal of this effort was to create and validate multi-gage load equations for shear force, bending moment, and torque for two wing measurement stations. For some of the testing the aircraft was supported by three airbags in order to isolate the wing structure from extraneous load inputs through the main landing gear. Thirty-two strain gage bridges were installed on the left wing. Hydraulic loads were applied to the wing lower surface through a total of 16 load zones. Some dead-weight load cases were applied to the upper wing surface using shot bags. Maximum applied loads reached 54,000 lb. Twenty-six load cases were applied with the aircraft resting on its landing gear, and 16 load cases were performed with the aircraft supported by the nose gear and three airbags around the center of gravity. Maximum wing tip deflection reached 17 inches. An assortment of 2, 3, 4, and 5 strain-gage load equations were derived and evaluated against independent check cases. The better load equations had root mean square errors less than 1 percent. Test techniques and lessons learned are discussed.

flight tests↗
NASA NTRS2004

U.S. Centennial of Flight Commision: Born of Dreams - Inspired by Freedom

The U.S. Centennial of Flight Commission developed and maintained a public web site that included activities related to the centennial of flight celebration and the history of aviation. The web site, www.centennialofflight.gov, was continually updated with educational and historical information, events, sights and sounds, and Commission information from its inception to June 2004. This DVD contains a 'snap shot' of the web site as of April 2004. The Web site on this DVD can be enjoyed without an Internet connection although in some places, you will be given links to online content. DVD content includes: 1) About the Commission - Information on the legislation, the Commissioners and Advisory Board members, news, the National Plans, meeting minutes and status reports; 2) Calendar of Events - A comprehensive list of activities, symposiums, exhibits, air shows, educational activities and more that took place through March 2004; 3) Wright Brothers History - The Library of Congress bibliography of Wright-related resources as well as the Chronology and Flight Log; the Brunsman articles; interactive learning modules from The Wright Experience; short informative essays and a series of links to other Wright brothers information sources. 4) History of Flight - Essays and images on the history of flight; 5) Sights and Sounds - Images, movies and special collections that capture the accomplishments of the Wright brothers and others who made significant contributions throughout the history of aviation and aerospace. As part of the NASA Art Program, a centennial song, 'Way Up There,' was commissioned; 6) Licensed Products - View collections of souvenirs and gift items to commemorate the 100th anniversary of the first powered flight; 7) Education - Resources that will help educators and their students celebrate 100 years of flight. Teachers can download Wright brothers posters and a Centennial of Flight bookmark, view live Web casts, and access an Educational Resources Center Matrix representing more than 50 government, industry and labor organizations promoting aviation and aerospace education.

Source record↗
NASA NTRS2022

Moon to Mars (M2M) Habitation Considerations: A Snap Shot As of January 2022

The following NASA Technical Memorandum (TM) is intended to provide a snapshot in time of NASA’s current considerations (ground rules and assumptions, functional allocations, logistics) for habitation systems for the lunar surface (non-roving) and Mars transits. As NASA continues to refine the reference designs to meet the needs of an evolving architecture, it is expected that this information will also be updated as a result. Where appropriate, relevant publicly released documents will be referenced to provide further detail. NASA’s human lunar exploration plan under the Artemis program calls for achieving the goal of sending the first woman and first person of color to the surface of the Moon in the mid- 2020s and working toward sustainable exploration by the end of the decade. Working with both commercial and international partners, NASA will establish a permanent human presence on the Moon to uncover new scientific discoveries and lay the foundation for private companies to build a lunar economy. Longer duration missions on the lunar surface and in lunar orbit will also serve as a test bed for technologies to support future Mars exploration campaigns. The agency will use what we learn on the Moon to prepare for humanity's next giant leap – sending astronauts to Mars. NASA intends to establish a sustained lunar presence with the development of the Artemis Base Camp to prove technologies and capabilities that will one day enable humans to live and work on Mars, beginning with core elements including the Lunar Terrain Vehicle (LTV), the Pressurized Rover (PR), the lunar Surface Habitat (SH), power systems, and in-situ resource utilization (ISRU) systems. For in space operations and eventual transport of humans to Mars, NASA will utilize a Mars Transit Habitat (TH). Following deployment, the TH will complete a series of longer duration missions and shakedown testing while docked at Gateway, leveraging Gateway’s habitation redundancy for safety measures. Proposed Gateway-TH missions will far exceed the longest duration cislunar human missions to date. They will be the first operational readiness tests of our long-duration deep space systems, and of the split crew (two crew on the surface, two crew in space) operations that are vital to the approach for the first human Mars mission. Both the SH and TH are major architectural elements of NASA’s Moon to Mars (M2M) approach, each with very different concepts of operation. The SH is intended for use on the lunar surface as a home for astronauts, surface operations base, science facility, hub for communications, extravehicular activity (EVA) equipment repair site, waste processing facility, and supply hub. It serves as an enabler for a sustained surface presence and preparation for partial gravity operations during Mars missions. The SH will be designed to be self-sufficient for operations on the lunar surface. The SH will independently provide several functions, including its own power generation, energy storage capability, sleep quarters, hygiene areas, work areas, and dining areas. It will be capable of communicating with surface assets, orbital assets, and directly with Earth ground stations. It is planned to operate with two crew in the habitat for ~28- day stays with crew swap-outs in which the PR crew of two trades places with the habitat crew. During the swap-out, the habitat will nominally support four crew for a short period of time. For contingency scenarios, the habitat must also be capable of supporting four crew for up to 7 days. The TH will be designed to be capable of up to ~1,200-day Mars missions with the ability to carry all food and supplies needed to support a crew of four for this duration. An assumed Mars mission profile for the TH is to carry crew and supplies for ~850-day roundtrips between Earth and Mars orbit that allows 30-day stays on the Martian surface. To test the systems for this long journey, the TH will be used to extend the duration of missions at Gateway, enabling the orbiting outpost to be used as a Mars analog. These analog missions will be accomplished by attaching the TH to Gateway and conducting lunar surface operations from the TH. The TH may also need to perform free-flying shakedown missions to test out all systems prior to leaving for Mars. The habitat provides many critical functions including: a contingency airlock, crew quarters, galley, hygiene areas, safe haven capability, and science equipment. It can receive docked items from either axial end or on a radial port.

Habitat↗
NASA NTRS2004

ASK Magazine

In this issue, ASK writers explore ways to maintain their balance in their field of Project Management, and even what happens when they don't. From his own experiences. Colby Africa learned that pushing too hard can take a personal toll, even though his project was a success in the end. He looked back and asked himself. At what personal cost? Sometimes one of the most simple - and the most human way - of keeping oneself grounded is not to lose your sense of humor. Ray Morgan's story about a test flight gone bad tells how the sound of their model crashing to the ground was followed by the test team's hysterical laughter. The story, you will see, is much deeper. But the message in the example? Sometimes for no fault of our own. things just don t go as planned. One way of dealing with it is to be able to laugh at ourselves. Of course. a setback itself is not to be taken lightly, but a leader capable of lightening the moment is more likely to set a positive tone for the try, try again. Staying optimistic is important for team morale. specifically when a project is dealt a huge downsizing blow. After his project was cut significantly, Tom Sutliff was able to show his team that all was not lost and to help them focus on the fact that they still had a job to do. He had to balance the new project requirements with the fact that his team had been committed to the original prcject and would be personally affected. He stood back, got a new perspective. and upheld the positivity needed to lead them effectively. Even when you keep your chin up and work to the best of your ability, things still go wrong. It's human nature. People train for years to make it to the Olympics and blow their shot during one crucial second in the spotlight. For Marty Davis, his crucial second was when the contractor dropped his 3,000 pound spacecraft. Rather than point the finger at those around him. Marty stood up like a true leader and acknowledged what he could do better if ever in this situation again.

Laufer, Alexander↗