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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
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
This article is from the 2024 Technical Update
Autonomous flight termination systems (AFTS) are being progressively employed onboard launch vehicles to replace ground personnel and infrastructure needed to terminate flight or destruct the vehicle should an anomaly occur. This automation uses on-board real-time data and encoded logic to determine if the flight should be self-terminated. For uncrewed launch vehicles, FTS systems are required to protect the public and governed by the United States Space Force (USSF). For crewed missions, NASA must augment range AFTS requirements for crew safety and certify each flight according to human rating standards, thus adding unique requirements for reuse of software originally intended for uncrewed missions. This bulletin summarizes new information relating to AFTS to raise awareness of key distinctions, summarize considerations and outline best practices for incorporating AFTS into human-rated systems.
Key Distinctions – Crewed v. Uncrewed
There are inherent behavioral differences between uncrewed and crewed AFTS related to design philosophy and fault tolerance. Uncrewed AFTS generally favor fault tolerance against failure-to-destruct over failing silent
in the presence of faults. This tenet permeates the design, even downto the software unit level. Uncrewed AFTS become zero-fault-to-destruct tolerant to many unrecoverable AFTS errors, whereas general single fault
tolerance against vehicle destruct is required for crewed missions. Additionally, unique needs to delay destruction for crew escape, provide abort options and special rules, and assess human-in-the-loop insight, command, and/or override throughout a launch sequence must be considered and introduces additional requirements and integration complexities.
AFTS Software Architecture Components and Best-Practice Use Guidelines
A detailed study of the sole AFTS currently approved by USSF and utilized/planned for several launch vehicles was conducted to understand its characteristics, and any unique risk and mitigation techniques for effective human-rating reuse. While alternate software systems may be designed in the future, this summary focuses on an architecture employing the Core Autonomous Safety Software (CASS). Considerations herein are intended for extrapolation to future systems. Components of the AFTS software architecture are shown, consisting of the CASS, “Wrapper”, and Mission Data Load (MDL) along with key characteristics and use guidelines. A more comprehensive description of each and recommendations for developmental use is found in Ref. 1.
Best Practices Certifying AFTS Software
Below are non-exhaustive guidelines to help achieve a human-rating
certification for an AFTS.
References
NASA/TP-20240009981: Best Practices and Considerations for Using
Autonomous Flight Termination Software In Crewed Launch Vehicles
https://ntrs.nasa.gov/citations/20240009981 “Launch Safety,” 14 C.F.R., § 417 (2024). NPR 8705.2C, Human-Rating Requirements for Space Systems, Jul 2017,
nodis3.gsfc.nasa.gov/ NASA Software Engineering Requirements, NPR 7150.2D, Mar 2022,
nodis3.gsfc.nasa.gov/ RCC 319-19 Flight Termination Systems Commonality Standard, White
Sands, NM, June 2019. “Considerations for Software Fault Prevention and Tolerance”, NESC
Technical Bulletin No. 23-06 https://ntrs.nasa.gov/citations/20230013383 “Safety Considerations when Repurposing Commercially Available Flight
Termination Systems from Uncrewed to Crewed Launch Vehicles”, NESC
Technical Bulletin No. 23-02 https://ntrs.nasa.gov/citations/20230001890 View the full article
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By NASA
6 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
This animation shows data taken by NASA’s PACE and the international SWOT satellites over a region of the North Atlantic Ocean. PACE captured phytoplankton data on Aug. 8, 2024; layered on top is SWOT sea level data taken on Aug. 7 and 8, 2024. NASA’s Scientific Visualization Studio One Earth satellite can see plankton that photosynthesize. The other measures water surface height. Together, their data reveals how sea life and the ocean are intertwined.
The ocean is an engine that drives Earth’s weather patterns and climate and sustains a substantial portion of life on the planet. A new animation based on data from two recently launched missions — NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) and the international Surface Water and Ocean Topography (SWOT) satellites — gives a peek into the heart of that engine.
Physical processes, including localized swirling water masses called eddies and the vertical movement of water, can drive nutrient availability in the ocean. In turn, those nutrients determine the location and concentration of tiny floating organisms known as phytoplankton that photosynthesize, converting sunlight into food. These organisms have not only contributed roughly half of Earth’s oxygen since the planet formed, but also support economically important fisheries and help draw carbon out of the atmosphere, locking it away in the deep sea.
“We see great opportunity to dramatically accelerate our scientific understanding of our oceans and the significant role they play in our Earth system,” said Karen St. Germain, director of the Earth Science Division at NASA Headquarters in Washington. “This visualization illustrates the potential we have when we begin to integrate measurements from our separate SWOT and PACE ocean missions. Each of those missions is significant on its own. But bringing their data together — the physics from SWOT and the biology from PACE — gives us an even better view of what’s happening in our oceans, how they are changing, and why.”
A collaboration between NASA and the French space agency CNES (Centre National d’Études Spatiales), the SWOT’ satellite launched in December 2022 to measure the height of nearly all water on Earth’s surface. It is providing one of the most detailed, comprehensive views yet of the planet’s ocean and its freshwater lakes, reservoirs, and rivers.
Launched in February 2024, NASA’s PACE satellite detects and measures the distribution of phytoplankton communities in the ocean. It also provides data on the size, amount, and type of tiny particles called aerosols in Earth’s atmosphere, as well as the height, thickness, and opacity of clouds.
“Integrating information across NASA’s Earth System Observatory and its pathfinder missions SWOT and PACE is an exciting new frontier in Earth science,” said Nadya Vinogradova Shiffer, program scientist for SWOT and the Integrated Earth System Observatory at NASA Headquarters.
Where Physics and Biology Meet
The animation above starts by depicting the orbits of SWOT (orange) and PACE (light blue), then zooms into the North Atlantic Ocean. The first data to appear was acquired by PACE on Aug. 8. It reveals concentrations of chlorophyll-a, a vital pigment for photosynthesis in plants and phytoplankton. Light green and yellow indicate higher concentrations of chlorophyll-a, while blue signals lower concentrations.
Next is sea surface height data from SWOT, taken during several passes over the same region between Aug. 7 and 8. Dark blue represents heights that are lower than the mean sea surface height, while dark orange and red represent heights higher than the mean. The contour lines that remain once the color fades from the SWOT data indicate areas of the ocean with the same height, much like the lines on a topographic map indicate areas with the same elevation.
The underlying PACE data then cycles through several groups of phytoplankton, starting with picoeukaryotes. Lighter green indicates greater concentrations of this group. The final two groups are cyanobacteria — some of the smallest and most abundant phytoplankton in the ocean — called Prochlorococcus and Synechococcus. For Prochlorococcus, lighter raspberry colors represent higher concentrations. Lighter teal colors for Synechococcus signal greater amounts of the cyanobacteria.
The animation shows that higher phytoplankton concentrations on Aug. 8 tended to coincide with areas of lower water height. Eddies that spin counterclockwise in the Northern Hemisphere tend to draw water away from their center. This results in relatively lower sea surface heights in the center that draw up cooler, nutrient-rich water from the deep ocean. These nutrients act like fertilizer, which can boost phytoplankton growth in sunlit waters at the surface.
Overlapping SWOT and PACE data enables a better understanding of the connections between ocean dynamics and aquatic ecosystems, which can help improve the management of resources such as fisheries, since phytoplankton form the base of most food chains in the sea. Integrating these kinds of datasets also helps to improve calculations of how much carbon is exchanged between the atmosphere and the ocean. This, in turn, can indicate whether regions of the ocean that absorb excess atmospheric carbon are changing.
More About SWOT
The SWOT satellite was jointly developed by NASA and CNES, with contributions from the Canadian Space Agency (CSA) and the UK Space Agency. NASA’s Jet Propulsion Laboratory, managed for the agency by Caltech in Pasadena, California, leads the U.S. component of the project. For the flight system payload, NASA provided the Ka-band radar interferometer (KaRIn) instrument, a GPS science receiver, a laser retroreflector, a two-beam microwave radiometer, and NASA instrument operations. The Doppler Orbitography and Radioposition Integrated by Satellite system, the dual frequency Poseidon altimeter (developed by Thales Alenia Space), the KaRIn radio-frequency subsystem (together with Thales Alenia Space and with support from the UK Space Agency), the satellite platform, and ground operations were provided by CNES. The KaRIn high-power transmitter assembly was provided by CSA.
To learn more about SWOT, visit:
https://swot.jpl.nasa.gov
More About PACE
The PACE mission is managed by NASA Goddard Space Flight Center, which also built and tested the spacecraft and the Ocean Color Instrument, which collected the data shown in the visualization. The satellite’s Hyper-Angular Rainbow Polarimeter #2 was designed and built by the University of Maryland, Baltimore County, and the Spectro-polarimeter for Planetary Exploration was developed and built by a Dutch consortium led by Netherlands Institute for Space Research, Airbus Defence, and Space Netherlands.
To learn more about PACE, visit:
https://pace.gsfc.nasa.gov
News Media Contacts
Jacob Richmond (for PACE)
NASA’s Goddard Space Flight Center, Greenbelt, Md.
jacob.a.richmond@nasa.gov
Jane J. Lee / Andrew Wang (for SWOT)
Jet Propulsion Laboratory, Pasadena, Calif.
818-354-0307 / 626-379-6874
jane.j.lee@jpl.nasa.gov / andrew.wang@jpl.nasa.gov
2024-169
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Last Updated Dec 09, 2024 Related Terms
PACE (Plankton, Aerosol, Cloud, Ocean Ecosystem) Climate Science Oceans SWOT (Surface Water and Ocean Topography) Explore More
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