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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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By NASA
3 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
LISTER (Lunar Instrumentation for Subsurface Thermal Exploration with Rapidity) is one of 10 payloads flying aboard the next delivery for NASA’s CLPS (Commercial Lunar Payload Services) initiative. The instrument is equipped with a drilling system and thermal probe designed to dig into the lunar surface. Photo courtesy: Firefly Aerospace Earth’s nearest neighboring body in the solar system is its Moon, yet to date humans have physically explored just 5% of its surface. It wasn’t until 2023 – building on Apollo-era data and more detailed studies made in 2011-2012 by NASA’s automated GRAIL (Gravity Recovery and Interior Laboratory) mission – that researchers conclusively determined that the Moon has a liquid outer core surrounding a solid inner core.
As NASA and its industry partners plan for continued exploration of the Moon under Artemis in preparation for future long-duration missions to Mars, improving our understanding of Earth’s 4.5-billion-year-old Moon will help teams of researchers and astronauts find the safest ways to study and live and work on the lunar surface.
That improved understanding is the primary goal of a state-of-the-art science instrument called LISTER (Lunar Instrumentation for Subsurface Thermal Exploration with Rapidity), one of 10 NASA payloads flying aboard the next delivery for the agency’s CLPS (Commercial Lunar Payload Services) initiative and set to be carried to the surface by Firefly Aerospace’s Blue Ghost 1 lunar lander.
Developed jointly by Texas Tech University in Lubbock and Honeybee Robotics of Altadena, California, LISTER will measure the flow of heat from the Moon’s interior. Its sophisticated pneumatic drill will penetrate to a depth of three meters into the dusty lunar regolith. Every half-meter it descends, the drilling system will pause and extend a custom-built thermal probe into the lunar regolith. LISTER will measure two different aspects of heat flow: thermal gradient, or the changes in temperature at various depths, and thermal conductivity, or the subsurface material’s ability to let heat pass through it.
“By making similar measurements at multiple locations on the lunar surface, we can reconstruct the thermal evolution of the Moon,” said Dr. Seiichi Nagihara, principal investigator for the mission and a geophysics professor at Texas Tech. “That will permit scientists to retrace the geological processes that shaped the Moon from its start as a ball of molten rock, which gradually cooled off by releasing its internal heat into space.”
Demonstrating the drill’s effectiveness could lead to more innovative drilling capabilities, enabling future exploration of the Moon, Mars, and other celestial bodies.. The science collected by LISTER aims to contribute to our knowledge of lunar geology, improving our ability to establish a long-term presence on the Moon under the Artemis campaign.
Under the CLPS model, NASA is investing in commercial delivery services to the Moon to enable industry growth and support long-term lunar exploration. As a primary customer for CLPS deliveries, NASA aims to be one of many customers on future flights. NASA’s Marshall Space Flight Center in Huntsville, Alabama, manages the development of seven of the 10 CLPS payloads carried on Firefly’s Blue Ghost lunar lander.
Learn more about CLPS and Artemis at:
https://www.nasa.gov/clps
Alise Fisher
Headquarters, Washington
202-358-2546
Alise.m.fisher@nasa.gov
Corinne Beckinger
Marshall Space Flight Center, Huntsville, Ala.
256-544-0034
corinne.m.beckinger@nasa.gov
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Last Updated Dec 18, 2024 EditorBeth RidgewayContactCorinne M. Beckingercorinne.m.beckinger@nasa.govLocationMarshall Space Flight Center Related Terms
Commercial Lunar Payload Services (CLPS) Artemis Marshall Space Flight Center Explore More
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By NASA
4 Min Read NASA Finds ‘Sideways’ Black Hole Using Legacy Data, New Techniques
Image showing the structure of galaxy NGC 5084, with data from the Chandra X-ray Observatory overlaid on a visible-light image of the galaxy. Chandra’s data, shown in purple, revealed four plumes of hot gas emanating from a supermassive black hole rotating “tipped over” at the galaxy’s core. Credits: X-ray: NASA/CXC, A. S. Borlaff, P. Marcum et al.; Optical full image: M. Pugh, B. Diaz; Image Processing: NASA/USRA/L. Proudfit NASA researchers have discovered a perplexing case of a black hole that appears to be “tipped over,” rotating in an unexpected direction relative to the galaxy surrounding it. That galaxy, called NGC 5084, has been known for years, but the sideways secret of its central black hole lay hidden in old data archives. The discovery was made possible by new image analysis techniques developed at NASA’s Ames Research Center in California’s Silicon Valley to take a fresh look at archival data from the agency’s Chandra X-ray Observatory.
Using the new methods, astronomers at Ames unexpectedly found four long plumes of plasma – hot, charged gas – emanating from NGC 5084. One pair of plumes extends above and below the plane of the galaxy. A surprising second pair, forming an “X” shape with the first, lies in the galaxy plane itself. Hot gas plumes are not often spotted in galaxies, and typically only one or two are present.
The method revealing such unexpected characteristics for galaxy NGC 5084 was developed by Ames research scientist Alejandro Serrano Borlaff and colleagues to detect low-brightness X-ray emissions in data from the world’s most powerful X-ray telescope. What they saw in the Chandra data seemed so strange that they immediately looked to confirm it, digging into the data archives of other telescopes and requesting new observations from two powerful ground-based observatories.
Hubble Space Telescope image of galaxy NGC 5084’s core. A dark, vertical line near the center shows the curve of a dusty disk orbiting the core, whose presence suggests a supermassive black hole within. The disk and black hole share the same orientation, fully tipped over from the horizontal orientation of the galaxy.NASA/STScI, M. A. Malkan, B. Boizelle, A.S. Borlaff. HST WFPC2, WFC3/IR/UVIS. The surprising second set of plumes was a strong clue this galaxy housed a supermassive black hole, but there could have been other explanations. Archived data from NASA’s Hubble Space Telescope and the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile then revealed another quirk of NGC 5084: a small, dusty, inner disk turning about the center of the galaxy. This, too, suggested the presence of a black hole there, and, surprisingly, it rotates at a 90-degree angle to the rotation of the galaxy overall; the disk and black hole are, in a sense, lying on their sides.
The follow-up analyses of NGC 5084 allowed the researchers to examine the same galaxy using a broad swath of the electromagnetic spectrum – from visible light, seen by Hubble, to longer wavelengths observed by ALMA and the Expanded Very Large Array of the National Radio Astronomy Observatory near Socorro, New Mexico.
“It was like seeing a crime scene with multiple types of light,” said Borlaff, who is also the first author on the paper reporting the discovery. “Putting all the pictures together revealed that NGC 5084 has changed a lot in its recent past.”
It was like seeing a crime scene with multiple types of light.
Alejandro Serrano Borlaff
NASA Research Scientist
“Detecting two pairs of X-ray plumes in one galaxy is exceptional,” added Pamela Marcum, an astrophysicist at Ames and co-author on the discovery. “The combination of their unusual, cross-shaped structure and the ‘tipped-over,’ dusty disk gives us unique insights into this galaxy’s history.”
Typically, astronomers expect the X-ray energy emitted from large galaxies to be distributed evenly in a generally sphere-like shape. When it’s not, such as when concentrated into a set of X-ray plumes, they know a major event has, at some point, disturbed the galaxy.
Possible dramatic moments in its history that could explain NGC 5084’s toppled black hole and double set of plumes include a collision with another galaxy and the formation of a chimney of superheated gas breaking out of the top and bottom of the galactic plane.
More studies will be needed to determine what event or events led to the current strange structure of this galaxy. But it is already clear that the never-before-seen architecture of NGC 5084 was only discovered thanks to archival data – some almost three decades old – combined with novel analysis techniques.
The paper presenting this research was published Dec. 18 in The Astrophysical Journal. The image analysis method developed by the team – called Selective Amplification of Ultra Noisy Astronomical Signal, or SAUNAS – was described in The Astrophysical Journal in May 2024.
For news media:
Members of the news media interested in covering this topic should reach out to the NASA Ames newsroom.
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Last Updated Dec 18, 2024 Related Terms
Black Holes Ames Research Center Ames Research Center's Science Directorate Astrophysics Chandra X-Ray Observatory Galaxies Galaxies, Stars, & Black Holes Galaxies, Stars, & Black Holes Research General Hubble Space Telescope Marshall Astrophysics Marshall Science Research & Projects Marshall Space Flight Center Missions NASA Centers & Facilities Science & Research Supermassive Black Holes The Universe Explore More
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By European Space Agency
A multi-orbit constellation of about 300 satellites that will deliver resilient, secure and fast communications for EU governments, European companies and citizens will be put in orbit after two contracts were confirmed today in Brussels.
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By NASA
At Goddard Space Flight Center, the GSFC Data Science Group has completed the testing for their SatVision Top-of-Atmosphere (TOA) Foundation Model, a geospatial foundation model for coarse-resolution all-sky remote sensing imagery. The team, comprised of Mark Carroll, Caleb Spradlin, Jordan Caraballo-Vega, Jian Li, Jie Gong, and Paul Montesano, has now released their model for wide application in science investigations.
Foundation models can transform the landscape of remote sensing (RS) data analysis by enabling the pre-training of large computer-vision models on vast amounts of remote sensing data. These models can be fine-tuned with small amounts of labeled training and applied to various mapping and monitoring applications. Because most existing foundation models are trained solely on cloud-free satellite imagery, they are limited to applications of land surface or require atmospheric corrections. SatVision-TOA is trained on all-sky conditions which enables applications involving atmospheric variables (e.g., cloud or aerosol).
SatVision TOA is a 3 billion parameter model trained on 100 million images from Moderate Resolution Imaging Spectroradiometer (MODIS). This is, to our knowledge, the largest foundation model trained solely on satellite remote sensing imagery. By including “all-sky” conditions during pre-training, the team incorporated a range of cloud conditions often excluded in traditional modeling. This enables 3D cloud reconstruction and cloud modeling in support of Earth and climate science, offering significant enhancement for large-scale earth observation workflows.
With an adaptable and scalable model design, SatVision-TOA can unify diverse Earth observation datasets and reduce dependency on task-specific models. SatVision-TOA leverages one of the largest public datasets to capture global contexts and robust features. The model could have broad applications for investigating spectrometer data, including MODIS, VIIRS, and GOES-ABI. The team believes this will enable transformative advancements in atmospheric science, cloud structure analysis, and Earth system modeling.
The model architecture and model weights are available on GitHub and Hugging Face, respectively. For more information, including a detailed user guide, see the associated white paper: SatVision-TOA: A Geospatial Foundation Model for Coarse-Resolution All-Sky Remote Sensing Imagery.
Examples of image reconstruction by SatVision-TOA. Left: MOD021KM v6.1 cropped image chip using MODIS bands [1, 3, 2]. Middle: The same images with randomly applied 8×8 mask patches, masking 60% of the original image. Right: The reconstructed images produced by the model, along with their respective Structural Similarity Index Measure (SSIM) scores. These examples illustrate the model’s ability to preserve structural detail and reconstruct heterogeneous features, such as cloud textures and land-cover transitions, with high fidelity.NASAView the full article
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