The first German research satellite AZUR
Project AZUR organizational structure and postlaunch problems, discussing onboard experiments
Von Eckardstein, D.↗
SEARCH · Search NASA
Results for “azure”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Ruggedizing a Commercial Depth Camera for Novel Lunar Exploration.
The novel contribution of this instrument is to take the first depth images on the moon. A commercial Kinect camera from Microsoft has been ruggedized for a CLPS mission to the Lunar South Pole. The 12-megapixel color camera is combined with a 1-megapixel color time-of-flight (ToF) depth sensor. The solid-state depth sensor/LIDAR provides greater resolution, a wider field of view, pixel binning, and reduced power consumption. The high-resolution data collected from the mission can be used to construct a near-field virtual environment of the lunar surface for scientific applications. The depth camera can provide a 360-degree view of the target area by combining ToF data with RGB imagery and rover turning. The unit was evaluated for space flight compliant materials and parts at NASA Ames. The microphone array, RF shield, front face, and outer body parts were removed to reduce mass. Plastic parts were replaced with vacuum-compatible materials; manufactured cables were added to properly interface with the host rover, built by Lunar Outpost. The environmental testing was performed on the Kinect with Random Vibration/Sine Testing (Fig 1a), per GEVS (NASA’s General Environmental Verification Standard for spaceflight launch survival). Thermal Vacuum testing cycled the instrument between expected hot (+ 85 C) and cold survival temperatures, as well as +50 C and -25 C operational temps (Fig 1b). The instrument functioned nominally at the conclusion of vibration and thermal cycling tests. The Azure Kinect is manifested on the Nova-C lander which flies on the Intuitive Machines mission IM-2, landing at the lunar south pole for a mission duration of 14 days (one lunar daylight cycle). The unit will be integrated to the MAPP (Mobile Autonomous Prospecting Platform) rover at Lunar Outpost. The Azure Kinect with ToF feature will improve the resolution of lunar geology data near the south pole and enable ground-based VR experience of details of the lunar surface (Fig 1c).
V. Jha↗
Machine Learning for Extravehicular Mobility Unit (EMU) Glove Inspections
The Extravehicular Mobility Unit (EMU) Glove Machine Learning Inspection project utilizes machine learning to expedite the inspection, analysis, and recommendation for continued use of space suit gloves post spacewalks. Today, ISS glove photos are individually reviewed by a team of experts to determine the conditions of space suit gloves. For this project the Microsoft Azure platform is used to perform Automated Machine Learning (AutoML) to detect issues with tagged images from previous Extravehicular Activities (EVA’s) to build a predictive model. The model analyzes a test image and deems the glove GO or NO-GO for additional EVA’s. The goal for this ML project is to decrease the time spent reviewing images by ground personnel and crewmembers in high frequency EVA locations such as the Moon and Mars. For destinations such as the Moon and Mars the goal is to give crew autonomy in determining glove conditions with limited support from Earth. This paper will outline the results to date and future work needed to expand the capability for in-situ recommendations.
EVA↗
AP-8 trapped proton environment for solar maximum and solar minimum
Data sets from Ov-3 and Azur indicate a need for improvement in models of the stably trapped proton flux with energies between 0.1 and 400 MeV. Two computer accessible models are described: AP8MAX and AP8MIN. The models are presented in the form of nomographs, B-L plots, R-lambda plots, and equatorial radial profiles. Nomographs of the orbit-integrated fluxes are also discussed. The models are compared with each other, with the data, and with previous AP models. Requirements for future improvements include more complete data coverage and periodic comparisons with new data sets as they become available. The machine-sensible format in which the models are available are described.
Sawyer, D. M.↗
On the energy dependence of the radial diffusion coefficient and spectra of inner radiation belt particles - Analytic solutions and comparison with numerical results
A theoretical method by which the energy dependence of the radial diffusion coefficient may be deduced from spectral observations of the particle population at the inner edge of the earth's radiation belts is presented. This region has previously been analyzed with numerical techniques; in this report an analytical treatment that illustrates characteristic limiting cases in the L shell range where the time scale of Coulomb losses is substantially shorter than that of radial diffusion (L approximately 1-2) is given. It is demonstrated both analytically and numerically that the particle spectra there are shaped by the energy dependence of the radial diffusion coefficient regardless of the spectral shapes of the particle populations diffusing inward from the outer radiation zone, so that from observed spectra the energy dependence of the diffusion coefficient can be determined. To insure realistic simulations, inner zone data obtained from experiments on the DIAL, AZUR, and ESRO 2 spacecraft have been used as boundary conditions. Excellent agreement between analytic and numerical results is reported.
Westphalen, H.↗
Comparison of LASSO and GPS time transfers
The LASSO is a technique which should allow the comparison of remote atomic clocks with sub-nanosecond precision and accuracy. The first successful time transfer using LASSO has been carried out between the Observatoire de la Cote d'Azur in France and the McDonald Observatory in Texas, United States. This paper presents a preliminary comparison of LASSO time transfer with GPS common-view time transfer.
Lewandowski, W.↗
(T2L2) Time Transfer by Laser Link
T2L2 (Time Transfer by Laser Link) is a new generation time transfer experiment based on the principles of LASSO (Laser Synchronization from Synchronous Orbit) and used with an operational procedure developed at OCA (Observatoire de la Cote d'Azur) during the active intercontinental phase of LASSO. The hardware improvements could lead to a precision better than 10 ps for time transfer (flying clock monitoring or ground based clock comparison). Such a package could fly on any spacecraft with a stable clock. It has been developed in France in the frame of the PHARAO project (cooled atom clock in orbit) involving CNES and different laboratories. But T2L2 could fly on any spacecraft carrying a stable oscillator. A GPS satellite would be a good candidate, as T2L2 could allow to link the flying clock directly to ground clocks using light, aiming to important accuracy checks, both for time and for geodesy. Radioastron (a flying VLBI antenna with a H-maser) is also envisioned, waiting for a PHARAO flight. The ultimate goal of T2L2 is to be part of more ambitious missions, as SORT (Solar Orbit Relativity Test), aiming to examine aspects of the gravitation in the vicinity of the Sun.
Veillet, Christian↗
Using Cloud-Based Storage Technologies for Earth Science Data
Cloud based infrastructure may offer several key benefits of scalability, built in redundancy and reduced total cost of ownership as compared with a traditional data center approach. However, most of the tools and software systems developed for NASA data repositories were not developed with a cloud based infrastructure in mind and do not fully take advantage of commonly available cloud-based technologies. Object storage services are provided through all the leading public (Amazon Web Service, Microsoft Azure, Google Cloud, etc.) and private (Open Stack) clouds, and may provide a more cost-effective means of storing large data collections online. We describe a system that utilizes object storage rather than traditional file system based storage to vend earth science data. The system described is not only cost effective, but shows superior performance for running many different analytics tasks in the cloud. To enable compatibility with existing tools and applications, we outline client libraries that are API compatible with existing libraries for HDF5 and NetCDF4. Performance of the system is demonstrated using clouds services running on Amazon Web Services.
Data↗
LIPA: Lunar Ice Perception Algorithm
The highest concentration of Lunar water-ice stores exists within the Permanently Shadowed Regions (PSRs) of the Lunar South Pole. As such, the ability to locate in situ water-ice stores in an accurate, systematic, and safe manner will prove vital for future Lunar activities which rely on hydrogen-based resources. Here we show how the strong absorptive properties of ice can be exploited (by coupling robotics, infrared imaging techniques, and machine learning) so that surface frost located in PSRs can be easily differentiated from the surrounding frozen regolith. Testbeds which simulate an icy lunar landscape were created and then imaged using a mid-wave infrared (MWIR) camera system mounted to a robotic arm (UR5e). Testbeds were imaged under two filter modes (1) wide band mode: whereby imagery captured filled a spectral range of 3.0 - 5.0 μm and (2) narrow band mode: whereby imagery captured were confined to a single central wavelength (CWL) of 3.15 ± 0.03 μm. A CWL of 3.15 μm was chosen due to the highly absorptive nature of ice at that specific wavelength. Images produced under both camera modes were processed in MATLAB. Narrow band images (NB) were subtracted from their wide band (WB) counterparts to produce differenced images (DI) which clearly demonstrated the spatial extent of ice (e.g., WB – NB = DI). Differenced images were used to train a Microsoft Azure model to discriminate between frozen regolith which did and did not contain ice. These works prove promising for future in situ resource utilization (ISRU) missions which employ robotics in combination with camera systems to advance science objectives (e.g., locate water-ice in frozen regolith) on the lunar surface.
Ane Slabic↗
Artificial Intelligence (AI) Methods for Augmenting the IMPACT Tool Evidence Library
Development of the Evidence Library for use with the IMPACT probability risk assessment tool took several years and involved a staggering amount of effort from a multi-disciplinary team. A very significant amount of the labor effort to collect, assess and finalize the Clinical Finding Form (CliFF) for each of the 119 medical conditions was provided by physician subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team. Many AI tools such as ChatGPT are excellent at summarizing large amounts of information and the current project was initiated to determine how such tools might streamline laborious processes, e.g., review and summarization of many scientific research publications, to execute key steps more efficiently in the process of developing CliFFs. The process for collecting the evidence which is found in the CliFFs is well documented in the Evidence Library Methods document (ELM; HRP-48036*). Using ELM and the CliFF development instructions as a guideline, a team of developers is leveraging Microsoft Azure AI tools and services along with open-source frameworks, to construct an AI-assisted automated pipeline. This pipeline is designed to search, retrieve, and process the necessary data sources, and ultimately help generate the final version of a CliFF. Currently, the large language model evaluates the relevance of each source material to spaceflights, either as direct evidence or as an analog. Additionally, the model assists in extracting keywords and generating brief summaries to enhance augmented retrieval and search processes in later stages of CliFF development. Once the data is ready, the model can perform semantic search and retrieval, generating and extracting valuable information for the CliFF. For instance, it can handle epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. The steps that required reading and summarizing articles were viewed as providing the greatest return on investment since large language models are very efficient and accurate in summarizing large amounts of text. Since labor effort to complete the original CliFF was not recorded with sufficient granularity, comparisons with an AI tool-generated CliFF will provide merely an approximation of time saved. Upon completion of the process, the CliFF for the medical condition “appendicitis” generated with the support of AI-based methods will serve as a proof-of-concept and will be compared to the original appendicitis CliFF to determine if use of the tools resulted in content and conclusory similarity. Based upon the results from face validation of the two CliFFs, modifications to the process will be made if necessary and additional condition CliFFs will be evaluated. Ultimately, CliFFs for the entire set of medical conditions will be created with the assistance of AI tools. Depending on the cost savings realized, CliFFs for additional medical conditions can be created to expand the Evidence Library. Future direction includes specifying the characteristics of the reviewer (prompting the AI tools to generate output assuming the reviewer is a sub-specialist physician, or nurse or EMT/medic) to determine if the effects on AI-generated output are different based on knowledge, skills and abilities. *Exploration Medical Capability Evidence Library Methods, HRP-48036 Rev A, July 2022.
Ali Al↗
Artificial Intelligence (AI) Methods for Automating the Impact Tool Evidence Library
INTRODUCTION: The development of the Evidence Library for use with the IMPACT (Informing Mission Planning via Analysis of Complex Tradespaces) probability risk assessment tool involved a multilayered, time intensive process of data collection and analysis by subject matter experts from the Exploration Medical Capability (ExMC) Element Clinical and Science Team to produce clinical findings forms (CliFFs) for 120 medical conditions. Artificial Intelligence Large Language Models (LLMs) can be leveraged to facilitate this process, thus reducing labor and time. TOPIC: CliFFs contain information about medical conditions as they pertain to spaceflight. This includes condition definitions, incidence data, crew task impairment estimates caused by conditions, treatment protocols and references to literature used for gathering condition evidence. Guided by the Evidence Library Methods document and the CliFF development instructions, a team has leveraged Microsoft Azure AI services and open-source documentation to construct an AI-assisted automated pipeline for CliFF development. This process is designed to search, retrieve, and evaluate the applicable data, and ultimately generate a completed CliFF. The LLM evaluates the relevance of each of the source materials to spaceflight, either as direct evidence or as an analog. The model extracts keywords and generates brief summaries to enhance search and retrieval in later stages of CliFF development. For instance, it can calculate epidemiological statistical data, such as incidence rates and the likelihood of best or worst-case scenarios. APPLICATION: Large Language Models (LLMs) can efficiently summarize large amounts of text. Leveraging this technology will automate data retrieval and evidence gathering for medical databases, like the IMPACT tool, by aiding in the labor-intensive process of analyzing large bodies of literature and organizing it into a formatted document like a CliFF. This added efficiency will enable expeditious expansion of the Evidence Library with additional medical conditions and update previous CLiFFs as new technology becomes available.
Ali Al↗
Hydrology Copilot: A Cloud-Native Ai System for Hydrological Data Analysis
The emergence of AI-driven Earth observation systems promises to broaden access to petabyte-scale geospatial data beyond domain specialists. However, translating this vision into operational scientific infrastructure requires addressing fundamental challenges in data virtualization, code transparency, and domain-specific reasoning. We present Hydrology Copilot, a cloud-native AI framework for natural-language-driven analysis of Earth observation data. To demonstrate operational capabilities at scale, we implement the system using NASA's North American Land Data Assimilation System version 3 (NLDAS-3), which provides surface meteorological forcing and land-surface model output across North and Central America at 1-km resolution, from which drought diagnostics are derived. The system integrates five core contributions: (1) scalable data virtualization using Kerchunk-based cloud optimized access, achieving a 1.5 to 4.6 times improvement in I/O latency across benchmark queries spanning regional single-day extractions (4.6 times speedup) to continental monthly aggregations (1.5 times speedup); (2) transparent code generation through Microsoft Azure AI Foundry agents that expose executable Python workflows for scientific verification; (3) persistent conversational memory enabling multi-turn analytical discourse across sessions; (4) intelligent query validation that enforces dataset boundaries and resolves ambiguous requests before execution; and (5) a multi-agent architecture coordinating query parsing, code generation, and visualization. We evaluate the system through drought-monitoring workflows, demonstrating reliable code generation, accurate results validated against reference computations and the operational U.S. Drought Monitor, and efficient operation across increasingly complex tasks. By bridging natural-language interfaces with rigorous hydrological analysis, Hydrology Copilot advances beyond proof-of-concept demonstrations to provide a deployable framework for operational Earth science applications.
Data virtualization↗
Dose--response of initial G2-chromatid breaks induced in normal human fibroblasts by heavy ions
PURPOSE: To investigate initial chromatid breaks in prematurely condensed G2 chromosomes following exposure to heavy ions of different LET. MATERIAL AND METHODS: Exponentially growing human fibroblast cells AG1522 were irradiated with gamma-rays, energetic carbon (13 keV/ microm, 80 keV/microm), silicon (55 keV/microm) and iron (140 keV/microm, 185keV/microm, 440keV/microm) ions. Chromosomes were prematurely condensed using calyculin-A. Initial chromatid-type and isochromatid breaks in G2 cells were scored. RESULTS: The dose response curves for total chromatid breaks were linear regardless of radiation type. The relative biological effectiveness (RBE) showed a LET-dependent increase, peaking around 2.7 at 55-80keV/microm and decreasing at higher LET. The dose response curves for isochromatid-type breaks were linear for high-LET radiations, but linear-quadratic for gamma-rays and 13 keV/microm carbon ions. The RBE for the induction of isochromatid breaks obtained from linear components increased rapidly between 13keV/microm (about 7) and 80keV/microm carbon (about 71), and decreased gradually until 440 keV/microm iron ions (about 66). CONCLUSIONS: High-LET radiations are more effective at inducing isochromatid breaks, while low-LET radiations are more effective at inducing chromatid-type breaks. The densely ionizing track structures of heavy ions and the proximity of sister chromatids in G2 cells result in an increase in isochromatid breaks.
Non-NASA Center↗
The evolution of chromosomal instability in Chinese hamster cells: a changing picture?
PURPOSE: To investigate the kinetics of chromosomal instability induced in clones of Chinese hamster cells following X-irradiation. MATERIALS AND METHODS: X-irradiated clones of GM10115, human-hamster hybrid cells containing a single human chromosome 4 (HC4), have been previously established. These clones were defined as unstable if they contained > or = three subpopulations of cells with unique rearrangements of HC4 as detected by FISH. Stable and unstable clones were analysed by FISH and Giemsa staining at various times post-irradiation. RESULTS: While most of the stable clones continued to show chromosomal stability of HC4 over time, one became marginally unstable at approximately 45 population doublings post-irradiation. Clones exhibiting chromosomal instability had one of several fates. Many of the unstable clones were showed similar levels of instability over time. However, one unstable clone became stable with time in culture, while another became even more unstable over time. Cytogenetic analyses of all clones after Giemsa staining indicated that in some clones the hamster chromosomes were rearranged independent of HC4, demonstrating increased frequencies of chromatid breaks and dicentric chromosomes. The majority of the unstable clones also had higher yields of chromatid gaps. CONCLUSIONS: These data demonstrate the dynamic nature of chromosomal instability as measured by two different cytogenetic assays.
Non-NASA Center↗
Autonomous Lunar Solar Array (ALSA) 2018: Big Idea Challenge Interns
NASA has aimed its sights towards sending humans back to the moon to develop and test technologies needed for future deep space exploration. Permanent power generation structures will play a key role in establishing habitat and in-situ resource utilization (ISRU) structures. Our group, part of the NASA Big Idea Challenge, came up with a concept for a solar array large enough to power a habitat, ISRU activities, and rover exploration in permanently shadowed lunar craters at the lunar poles.
McNatt, Jeremiah↗
Head in the clouds, Feet on the ground - Machine Learning for xEVA Glove Inspections
Explore the source record for details and available documents.
EMU↗
Machine Learning for EMU Glove Inspections: Overview, Testing and Results
Developed a data pipeline utilizing a commercial Machine Learning platform to potentially improve the speed in which recommendations are made for continued use of EMU gloves on ISS; demonstrating the use of ISS as a testbed for further, deep space applications.
EMU↗
SatCORPS Hybridized Cloud Product Data Storage: The Design of a Hybrid Data Repository That Leverages the Strengths of the Cloud and the Data Center
There is a strong demand for the near real time NASA Langley Satellite ClOud and Radiation Property retrieval System (SatCORPS) products. As important as real-time information, archived copies of the products form the basis for targeted research focusing on specific events or conditions. To make these SatCORPS products available for downloading, the SatCORPS group has developed a number of tools and technologies to create a hybrid data storage system that leverages the strengths of both cloud and on-premises resources. In this work, we describe the technologies the group uses to marshal disparate data repositories and materialize them into a single searchable overview and give a broad description of the organization of the dataset. As with any implementation, the strengths, weaknesses and constraints surrounding the components establish priorities and provide insight where trade-offs are necessary. We further describe the design and architecture underpinning our hybrid data repository and delivery system.
AWS↗