Results for “Label Space” — Page 7

DOE OSTI2025

Advanced Modeling of Beam Physics and Performance Optimization for Nuclear Physics Colliders

High energy colliders provide a critical tool in nuclear physics study by probing the fundamental structure and dynamics of matter. To maximize the potential of scientific discovery in nuclear physics study, it is important to optimize the parameters of these colliders to attain the best performance. The performance of a collider is typically measured by its integrated luminosity of colliding beams since the probability of a new event is proportional to the integrated luminosity. However, the achievable luminosity is limited by the electromagnetic interactions (beam-beam effects) of two colliding beams at higher energy, and the interplay between the space-charge effects and the beam-beam effects at lower energy. To achieve the best performance of a collider means to attain the highest luminosity of the collider with optimized collider parameters. Optimizing the collider’s machine parameters is both computationally and experimentally expensive. A fast and robust computational framework including beam-beam and space-charge effects will be critical to attaining the best performance of the collider. In this project, we will study the beam dynamics challenges, specifically the interplay of the space-charge and the beam-beam effects, and the machine tuning models for maximizing the performance of RHIC experiments. We will develop an advanced modeling framework based on first-principles physical simulations, lattice models and the state-of-the-art machine learning methods and apply this framework to performance improvement of the RHIC in operation. We will build data manipulation packages to connect the simulation data and the experimental data with the framework, develop a self-consistent hybrid model of space-charge and beam-beam effects, study underlying physics mechanisms, build surrogate models using the labeled data, integrate the models into the advanced modeling framework, and apply the framework to RHIC luminosity (STAR and sPHENIX) optimization. The success of this project would substantially improve the performance of existing and future colliders and increase the opportunity for scientific discovery.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Variational Quantum Circuits to Prepare Low Energy Symmetry States

We explore how to build quantum circuits that compute the lowest energy state corresponding to a given Hamiltonian within a symmetry subspace by explicitly encoding it into the circuit. We create an explicit unitary and a variationally trained unitary that maps any vector output by ansatz A(α → ) from a defined subspace to a vector in the symmetry space. The parameters are trained varitionally to minimize the energy, thus keeping the output within the labelled symmetry value. The method was tested for a spin XXZ Hamiltonian using rotation and reflection symmetry and H 2 Hamiltonian within S z = 0 subspace using S 2 symmetry. We have found the variationally trained unitary gives good results with very low depth circuits and can thus be used to prepare symmetry states within near term quantum computers.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

NASA NTRS2006

Spaceflight Effects on Hemopoiesis of Lower Vertebrates Flown on Foton-M2

Intact and operated newts Pleumdeles waltl flown on Foton-M2 for 16 days were used to study the effects of spaceflight as well as tail amputation and lensectomy on their hemopoiesis. The flight did not produce noticeable changes in the peripheral blood of nonoperated newts. However, in operated animals, the number of lymphocytes increased whereas that of neutrophils decreased. There were no morphological differences in hemopoietic organs (liver and spleen) between flown non-operated and operated animals or their controls. However, in both non-operated and operated newts the liver weight and the number of hemopoietic cells in it increased. In contrast to nonoperated newts, space-flown mammals typically showed significant changes in blood cell counts. Experiments with BrdU incorporation revealed labeled cells in the hemopoietic area of the liver as well as in blood and spleen. This observation gives evidence that the BrdU label can be used to study proliferation of hemopoietic cells.

Domaratskaya, E. I.↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

DOE OSTI2022

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants

Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Real-space chirality from crystalline topological defects in the Kitaev spin liquid

We show that certain crystalline topological defects in the gapless Kitaev honeycomb spin liquid model generate a chirality and Majorana fermion orbital magnetization that depends in a universal manner on their emergent flux. Focusing on 5–7 dislocations as building blocks, consisting of pentagon and heptagon disclinations, we identify the Kitaev bond label configurations that preserve solvability. By computing two formulations of local markers M(r) we find that the 5 and 7 lattice defects generate a real-space contribution to Chern number and an associated Majorana fermion orbital magnetization proportional to M(r). The sign of the M(r) contribution from each 5/7 defect, i.e. its q M = ± 1 chirality, is determined by the defect Frank angle sign F and emergent gauge field flux W = ± i through the expression q M = − iFW. Remarkably, though lattice curvature and torsion can interplay with the surrounding gapless background to modify the profile of M(r), its sign q M is determined locally, implying that crystalline defects in the Kitaev spin liquid can generate a robust and observable chirality.

Magnetic properties and materials↗

DOE OSTI2025

Physics-informed latent neural operator for real-time predictions of time-dependent parametric PDEs

Deep operator network (DeepONet) has shown significant promise as surrogate models for systems governed by partial differential equations (PDEs), enabling accurate mappings between infinite-dimensional function spaces. However, when applied to systems with high-dimensional input-output mappings arising from large numbers of spatial and temporal collocation points, these models often require heavily overparameterized networks, leading to long training times. Latent DeepONet addresses some of these challenges by introducing a two-step approach: first learning a reduced latent space using a separate model, followed by operator learning within this latent space. While efficient, this method is inherently data-driven and lacks mechanisms for incorporating physical laws, limiting its robustness and generalizability in data-scarce settings. Here, in this work, we propose PI-Latent-NO, a physics-informed latent neural operator framework that integrates governing physics directly into the learning process. Our architecture features two coupled DeepONets trained end-to-end: a Latent-DeepONet that learns a low-dimensional representation of the solution, and a Reconstruction-DeepONet that maps this latent representation back to the physical space. By embedding PDE constraints into the training via automatic differentiation, our method eliminates the need for labeled training data and ensures physics-consistent predictions. The proposed framework is both memory and compute-efficient, exhibiting near-constant scaling with problem size and demonstrating significant speedups over traditional physics-informed operator models. We validate our approach on a range of parametric PDEs, showcasing its accuracy, scalability, and suitability for real-time prediction in complex physical systems.

Latent representations↗

NASA NTRS2005

Hybrid Power Management

An engineering discipline denoted as hybrid power management (HPM) has emerged from continuing efforts to increase energy efficiency and reliability of hybrid power systems. HPM is oriented toward integration of diverse electric energy-generating, energy-storing, and energy-consuming devices in optimal configurations for both terrestrial and outer-space applications. The basic concepts of HPM are potentially applicable at power levels ranging from nanowatts to megawatts. Potential applications include terrestrial power-generation, terrestrial transportation, biotechnology, and outer-space power systems. Instances of this discipline at prior stages of development were reported (though not explicitly labeled as HPM) in three prior NASA Tech Briefs articles: "Ultracapacitors Store Energy in a Hybrid Electric Vehicle"(LEW-16876), Vol. 24, No. 4 (April 2000), page 63; "Photovoltaic Power Station With Ultracapacitors for Storage" (LEW-17177), Vol. 27, No. 8 (August 2003), page 38; and "Flasher Powered by Photovoltaic Cells and Ultracapacitors" (LEW-17246), Vol. 24, No. 10 (October 2003), page 37. As the titles of the cited articles indicate, the use of ultracapacitors as energy-storage devices lies at the heart of HPM. An ultracapacitor is an electrochemical energy-storage device, but unlike in a conventional rechargeable electrochemical cell or battery, chemical reactions do not take place during operation. Instead, energy is stored electrostatically at an electrode/electrolyte interface. The capacitance per unit volume of an ultracapacitor is much greater than that of a conventional capacitor because its electrodes have much greater surface area per unit volume and the separation between the electrodes is much smaller. Power-control circuits for ultracapacitors can be simpler than those for batteries, for two reasons: (1) Because of the absence of chemical reactions, charge and discharge currents can be greater than those in batteries, limited only by the electrical resistances of conductors; and (2) whereas the charge level of a battery depends on voltage, temperature, age, and load condition, the charge level of an ultracapacitor, like that of a conventional capacitor, depends only on voltage.

Eichenberg, Dennis↗

NASA NTRS2007

The Use of Heavy Ion Radiation as an Analog for Space Radiation Environment and Its Effects on Drug Stability

While it is common knowledge that electromagnetic radiation such as x-rays and gamma rays affect physical-chemical characteristics (PC) of compounds in addition to their toxic and mutagenic effects on biological systems, there are no reports on the effects of cosmic radiation encountered during space missions on stability of pharmaceuticals. Alterations in PC of drug formulations can adversely affect treatment with medications in space. Preliminary evaluation of stability and shelf-life of select pharmaceuticals (12) flown on space missions revealed that 37% and 40% of the formulations failed to meet USP requirements after shuttle and ISS flights, respectively. Based on these results, the current investigation is designed to examine the effect of proton (P) and heavy ion (Fe) radiation on 20 pharmaceutical preparations flown aboard the shuttle and ISS. The objectives of this project are: 1) Examine susceptibility of pharmaceuticals to short acute bouts of high intensity ionizing radiation species encountered during space flights; 2) Estimate extent of degradation of susceptible formulations as a function of intensity of each beam (P & Fe); and 3) compare and contrast the effects of single beam irradiation to that of a combined beam (P + Fe) that simulates space craft environment on drug stability. Irradiations were conducted at the Brookhaven National Laboratories (BNL) with beam strengths of 10 cGy, 10 or 50Gy of P and Fe beams separately. Preliminary evaluation of results revealed a reduction in the chemical content of label claim ranging 12-55 % for Augmentin, 7% for promethzine tablets and 9% for ciprofloxacin ointment. These results are in agreement, although less in magnitude than those observed during space flight and after gamma irradiation.

Vaksman, Z.↗

NASA NTRS1991

Center for Space Power, Texas A and M University

Johnson Controls is a 106 year old company employing 42,000 people worldwide with $4.7 billion annual sales. Though we are new to the aerospace industry we are a world leader in automobile battery manufacturing, automotive seating, plastic bottling, and facilities environment controls. The battery division produces over 24,000,000 batteries annually under private label for the new car manufacturers and the replacement market. We are entering the aerospace market with the nickel hydrogen battery with the help of NASA's Center for Space Power at Texas A&M. Unlike traditional nickel hydrogen battery manufacturers, we are reaching beyond the space applications to the higher volume markets of aircraft starting and utility load leveling. Though space applications alone will not provide sufficient volume to support the economies of scale and opportunities for statistical process control, these additional terrestrial applications will. For example, nickel hydrogen batteries do not have the environmental problems of nickel cadmium or lead acid and may someday start your car or power your electric vehicle. However you envision the future, keep in mind that no manufacturer moves into a large volume market without fine tuning their process. The Center for Space Power at Texas A&M is providing indepth technical analysis of all of the materials and fabricated parts of our battery as well as thermal and mechanical design computer modeling. Several examples of what we are doing with nickel hydrogen chemistry to lead to these production efficiencies are presented.

Jones, Ken↗

NASA NTRS1985

NPLOT: an Interactive Plotting Program for NASTRAN Finite Element Models

The NPLOT (NASTRAN Plot) is an interactive computer graphics program for plotting undeformed and deformed NASTRAN finite element models. Developed at NASA's Goddard Space Flight Center, the program provides flexible element selection and grid point, ASET and SPC degree of freedom labelling. It is easy to use and provides a combination menu and command driven user interface. NPLOT also provides very fast hidden line and haloed line algorithms. The hidden line algorithm in NPLOT proved to be both very accurate and several times faster than other existing hidden line algorithms. A fast spatial bucket sort and horizon edge computation are used to achieve this high level of performance. The hidden line and the haloed line algorithms are the primary features that make NPLOT different from other plotting programs.

Jones, G. K.↗

NASA NTRS1992

The role of pyridoxine as a countermeasure for in-flight loss of lean body mass

Ground based and in flight research has shown that humans, under conditions of microgravity, sustain a loss of lean body tissue (protein) and changes in several biological processes including, reductions in red blood cell mass, and neurotransmitters. The maintenance of muscle mass, the major component of lean body mass, is required to meet the needs of space station EVAs. Central to the biosynthesis of amino acids, the building blocks of protein, is pyridoxine (vitamin B-6). Muscle mass integrity requires the availability of vitamin B-6 for protein metabolism and neurotransmitter synthesis. Furthermore, the formation of red blood cells require pyridoxine as a cofactor in the biosynthesis of hemoglobin, a protein that carries oxygen to tissues. In its active form, pyridoxal-5'-phosphate (PLP), vitamin B-6 serves as a link between amino acid and carbohydrate metabolism through intermediates of glycolysis and the tricarboxylic acid cycle. In addition to its role in energy metabolism, PLP is involved in the biosynthesis of hemoglobin and neurotransmitter which are necessary for neurological functions. Alterations in pyridoxine metabolism may affect countermeasures designed to overcome some of these biochemical changes. The focus of this research is to determine the effects of microgravity on the metabolic utilization of vitamin B-6, integrating nutrition as an integral component of the countermeasure (exercise) to maintain lean body mass and muscle strength. The objectives are: 1) to determine whether microgravity effects the metabolic utilization of pyridoxine and 2) to quantitate changes in B-6 vitamer distribution in tissue and excreta relative to loss of lean body tissue. The rationale for this study encompasses the unique challenge to control biochemical mechanisms effected during space travel and the significance of pyridoxine to maintain and counter muscle integrity for EVA activities. This experiment will begin to elucidate the importance of biochemical interactions between micronutrients and the homeostasis condition of biological processes in the space environment. To address this research topic a simulated microgravity model has been developed. The experiment uses radioisotopically labelled pyridoxine administered as an oral dose to rats which are maintained by tail suspension to simulate a microgravity environment. At the termination of the study, liver, muscle, blood and urine are collected and analyzed by reverse phase high pressure liquid chromatography to determine the quantity and distribution of the B-6 vitamers in tissue and excreta relative to lean body tissue loss. Earlier studies, published by this investigator, have shown that differences in vitamer distribution among samples from experimental versus control subjects indicate changes in metabolic utilization and storage of vitamin B-6.

Gilbert, Joyce A.↗

NASA NTRS2017

Silicon Power MOSFETs

Recent work for the NASA Electronic Parts and Packaging Program Power MOSFET task is presented. The Task technology focus, roadmap, and partners are given. Recent single-event effect test results on commercial, automotive, and radiation hardened trench power MOSFETs are summarized with an emphasis on risk of using commercial and automotive trench-gate power MOSFETs in space applications.

trench-gate MOSFET↗

NASA NTRS2017

Total Dose Survivability of Hubble Electronic Components

A total dose analysis for exposure of electronic parts at the box level is presented for the Hubble Space Telescope. This was done using solid angle sectoring/3-dimensional ray trace and Monte Carlo radiation transport simulations. Results are discussed in terms of parts that are potential total dose concerns.

Monte Carlo radiation transport simulation↗

NASA NTRS1999

Energy expenditure and balance during spaceflight on the space shuttle

The objectives of this study were as follows: 1) to measure human energy expenditure (EE) during spaceflight on a shuttle mission by using the doubly labeled water (DLW) method; 2) to determine whether the astronauts were in negative energy balance during spaceflight; 3) to use the comparison of change in body fat as measured by the intake DLW EE, 18O dilution, and dual energy X-ray absorptiometry (DEXA) to validate the DLW method for spaceflight; and 4) to compare EE during spaceflight against that found with bed rest. Two experiments were conducted: a flight experiment (n = 4) on the 16-day 1996 life and microgravity sciences shuttle mission and a 6 degrees head-down tilt bed rest study with controlled dietary intake (n = 8). The bed rest study was designed to simulate the flight experiment and included exercise. Two EE determinations were done before flight (bed rest), during flight (bed rest), and after flight (recovery). Energy intake and N balance were monitored for the entire period. Results were that body weight, water, fat, and energy balance were unchanged with bed rest. For the flight experiment, decreases in weight (2.6 +/- 0.4 kg, P < 0.05) and N retention (-2. 37 +/- 0.45 g N/day, P < 0.05) were found. Dietary intake for the four astronauts was reduced in flight (3,025 +/- 180 vs. 1,943 +/- 179 kcal/day, P < 0.05). EE in flight was 3,320 +/- 155 kcal/day, resulting in a negative energy balance of 1,355 +/- 80 kcal/day (-15. 7 +/- 1.0 kcal. kg-1. day-1, P < 0.05). This corresponded to a loss of 2.1 +/- 0.4 kg body fat, which was within experimental error of the fat loss determined by 18O dilution (-1.4 +/- 0.5 kg) and DEXA (-2.4 +/- 0.4 kg). All three methods showed no change in body fat with bed rest. In conclusion, 1) the DLW method for measuring EE during spaceflight is valid, 2) the astronauts were in severe negative energy balance and oxidized body fat, and 3) in-flight energy (E) requirements can be predicted from the equation: E = 1.40 x resting metabolic rate + exercise.

Non-NASA Center↗

DOE OSTI2025

Fourier-based three-dimensional multistage transformer for aberration correction in multicellular specimens

High-resolution tissue imaging is often compromised by sample-induced optical aberrations that degrade resolution and contrast. Although wavefront sensor-based adaptive optics (AO) can measure these aberrations, such hardware solutions are typically complex, expensive to implement and slow when serially mapping spatially varying aberrations across large fields of view. Here we introduce AOViFT (adaptive optical vision Fourier transformer)—a machine learning-based aberration sensing framework built around a three-dimensional multistage vision transformer that operates on Fourier domain embeddings. AOViFT infers aberrations and restores diffraction-limited performance in puncta-labeled specimens with substantially reduced computational cost, training time and memory footprint compared to conventional architectures or real-space networks. We validated AOViFT on live gene-edited zebrafish embryos, demonstrating its ability to correct spatially varying aberrations using either a deformable mirror or postacquisition deconvolution. By eliminating the need for the guide star and wavefront sensing hardware and simplifying the experimental workflow, AOViFT lowers technical barriers for high-resolution volumetric microscopy across diverse biological samples.

Alshaabi, Thayer [Howard Hughes Medical Institute,↗

NASA NTRS1998

STS-96: Expedition Crew #2 and 4 Work in Node #1 at the SSPF

Live footage of the crewmembers of STS-96, Commander Kent V. Rominger, Pilot Rick D. Husband, Mission Specialists Ellen Ochoa, Tamara E. Jernigan, Daniel T. Barry, Julie Payette, and Valery Ivanovich Tokarev, shows them in the node of the vehicle at the Space Station Processing Facility (SSPF). Scenes include the engineer explaining and the crew asking questions as to what certain labels mean. Footage also includes the crew observing the nose of the vehicle.

Source record↗