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First Use of Cosmic Lens to Probe Dark Energy
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By European Space Agency
In a world first, ESA and Telesat have successfully connected a Low Earth Orbit (LEO) satellite to the ground using 5G Non-Terrestrial Network (NTN) technology in the Ka-band frequency range, marking a crucial step towards making space-based connections as simple as using a mobile phone.
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By Space Force
The new squadron, which falls under Space Delta 11, marks a critical milestone in advancing the Space Force’s ability to test, train, and prepare for cyber threats in the contested space domain.
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By NASA
1 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
NASA’s X-59 quiet supersonic research aircraft completed its first maximum afterburner test at Lockheed Martin’s Skunk Works facility in Palmdale, California. This full-power test, during which the engine generates additional thrust, validates the additional power needed for meeting the testing conditions of the aircraft. The X-59 is the centerpiece of NASA’s Quesst mission, which aims to overcome a major barrier to supersonic flight over land by reducing the noise of sonic booms.Lockheed Martin Corporation/Garry Tice NASA completed the first maximum afterburner engine run test on its X-59 quiet supersonic research aircraft on Dec. 12. The ground test, conducted at Lockheed Martin’s Skunk Works facility in Palmdale, California, marks a significant milestone as the X-59 team progresses toward flight.
An afterburner is a component of some jet engines that generates additional thrust. Running the engine, an F414-GE-100, with afterburner will allow the X-59 to meet its supersonic speed requirements. The test demonstrated the engine’s ability to operate within temperature limits and with adequate airflow for flight. It also showed the engine’s ability to operate in sync with the aircraft’s other subsystems.
The X-59 is the centerpiece of NASA’s Quesst mission, which seeks to solve one of the major barriers to supersonic flight over land by making sonic booms quieter. The X-59’s first flight is expected to occur in 2025.
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Last Updated Dec 20, 2024 EditorDede DiniusContactMatt Kamletmatthew.r.kamlet@nasa.gov Related Terms
Aeronautics Aeronautics Research Mission Directorate Armstrong Flight Research Center Commercial Supersonic Technology Integrated Aviation Systems Program Low Boom Flight Demonstrator Quesst (X-59) Supersonic Flight Explore More
2 min read NASA, Notre Dame Connect Students to Inspire STEM Careers
Article 4 hours ago 2 min read NASA Flight Rerouting Tool Curbs Delays, Emissions
Article 4 hours ago 3 min read Atmospheric Probe Shows Promise in Test Flight
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Quesst: The Vehicle
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By NASA
Hubble Space Telescope Hubble Home Overview About Hubble The History of Hubble Hubble Timeline Why Have a Telescope in Space? Hubble by the Numbers At the Museum FAQs Impact & Benefits Hubble’s Impact & Benefits Science Impacts Cultural Impact Technology Benefits Impact on Human Spaceflight Astro Community Impacts Science Hubble Science Science Themes Science Highlights Science Behind Discoveries Hubble’s Partners in Science Universe Uncovered Explore the Night Sky Observatory Hubble Observatory Hubble Design Mission Operations Missions to Hubble Hubble vs Webb Team Hubble Team Career Aspirations Hubble Astronauts News Hubble News Hubble News Archive Social Media Media Resources Multimedia Multimedia Images Videos Sonifications Podcasts E-books Online Activities Lithographs Fact Sheets Glossary Posters Hubble on the NASA App More 35th Anniversary 2 min read
Hubble Spies a Cosmic Eye
This NASA/ESA Hubble Space Telescope image features the spiral galaxy NGC 2566. ESA/Hubble & NASA, D. Thilker This NASA/ESA Hubble Space Telescope image features the spiral galaxy NGC 2566, which sits 76 million light-years away in the constellation Puppis. A prominent bar of stars stretches across the center of this galaxy, and spiral arms emerge from each end of the bar. Because NGC 2566 appears tilted from our perspective, its disk takes on an almond shape, giving the galaxy the appearance of a cosmic eye.
As NGC 2566 appears to gaze at us, astronomers gaze right back, using Hubble to survey the galaxy’s star clusters and star-forming regions. The Hubble data are especially valuable for studying stars that are just a few million years old; these stars are bright at the ultraviolet and visible wavelengths to which Hubble is sensitive. Using these data, researchers can measure the ages of NGC 2566’s stars, which helps piece together the timeline of the galaxy’s star formation and the exchange of gas between star-forming clouds and the stars themselves.
Hubble regularly teams up with other astronomical observatories to examine objects like NGC 2566, including the NASA/ESA/CSA James Webb Space Telescope. Webb data complements Hubble’s by going beyond the infrared wavelengths of light Hubble can see, better defining areas of warm, glowing dust. At even longer wavelengths, the Atacama Large Millimeter/submillimeter Array (ALMA) of 66 radio telescopes that work together can capture detailed images of the clouds of gas and dust in which stars form. Together, Hubble, Webb, and ALMA provide an overview of the formation, lives, and deaths of stars in galaxies across the universe.
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Hubble Focus E-Book: Galaxies through Space and Time
Facebook logo @NASAHubble @NASAHubble Instagram logo @NASAHubble Media Contact:
Claire Andreoli (claire.andreoli@nasa.gov)
NASA’s Goddard Space Flight Center, Greenbelt, MD
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Last Updated Dec 19, 2024 Editor Andrea Gianopoulos Location NASA Goddard Space Flight Center Related Terms
Astrophysics Astrophysics Division Galaxies Goddard Space Flight Center Hubble Space Telescope Spiral Galaxies Keep Exploring Discover More Topics From NASA
Hubble Space Telescope
Since its 1990 launch, the Hubble Space Telescope has changed our fundamental understanding of the universe.
Hubble’s Partners in Science
Hubble’s Night Sky Challenge
Hubble’s Galaxies
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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