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Metal fuel for carbon-free energy on Earth… and the Moon
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By NASA
Through NASA’s Artemis campaign, astronauts will land on the lunar surface and use a new generation of spacesuits and rovers as they live, work, and conduct science in the Moon’s South Pole region, exploring more of the lunar surface than ever before. Recently, the agency completed the first round of testing on three commercially owned and developed LTVs (Lunar Terrain Vehicle) from Intuitive Machines, Lunar Outpost, and Venturi Astrolab at NASA’s Johnson Space Center in Houston.NASA/Bill Stafford Venturi Astrolab’s FLEX, Intuitive Machines’ Moon RACER, and Lunar Outpost’s Eagle lunar terrain vehicle – three commercially owned and developed LTVs (Lunar Terrain Vehicle) – are pictured at NASA’s Johnson Space Center in Houston in this photo from Nov. 21, 2024.
As part of an ongoing year-long feasibility study, each company delivered a static mockup of their vehicle to Johnson at the end of September, initiated rover testing in October and completed the first round of testing in December inside the Active Response Gravity Offload System (ARGOS) test facility. Lunar surface gravity is one-sixth of what we experience here on Earth, so to mimic this, ARGOS offers an analog environment that can offload pressurized suited subjects for various reduced gravity simulations.
See how these LTVs were tested.
Image credit: NASA/Bill Stafford
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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 European Space Agency
Image: With the festive season approaching, even Earth-observing satellites are getting into the spirit, capturing a stunning compilation of European cities that resemble stars. View the full article
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By NASA
4 min read
Preparations for Next Moonwalk Simulations Underway (and Underwater)
The six SCALPSS cameras mounted around the base of Blue Ghost will collect imagery during and after descent and touchdown. Using a technique called stereo photogrammetry, researchers at Langley will use the overlapping images to produce a 3D view of the surface. Image courtesy of Firefly. Say cheese again, Moon. We’re coming in for another close-up.
For the second time in less than a year, a NASA technology designed to collect data on the interaction between a Moon lander’s rocket plume and the lunar surface is set to make the long journey to Earth’s nearest celestial neighbor for the benefit of humanity.
Developed at NASA’s Langley Research Center in Hampton, Virginia, Stereo Cameras for Lunar Plume-Surface Studies (SCALPSS) is an array of cameras placed around the base of a lunar lander to collect imagery during and after descent and touchdown. Using a technique called stereo photogrammetry, researchers at Langley will use the overlapping images from the version of SCALPSS on Firefly’s Blue Ghost — SCALPSS 1.1 — to produce a 3D view of the surface. An earlier version, SCALPSS 1.0, was on Intuitive Machines’ Odysseus spacecraft that landed on the Moon last February. Due to mission contingencies that arose during the landing, SCALPSS 1.0 was unable to collect imagery of the plume-surface interaction. The team was, however, able to operate the payload in transit and on the lunar surface following landing, which gives them confidence in the hardware for 1.1.
The SCALPSS 1.1 payload has two additional cameras — six total, compared to the four on SCALPSS 1.0 — and will begin taking images at a higher altitude, prior to the expected onset of plume-surface interaction, to provide a more accurate before-and-after comparison.
These images of the Moon’s surface won’t just be a technological novelty. As trips to the Moon increase and the number of payloads touching down in proximity to one another grows, scientists and engineers need to be able to accurately predict the effects of landings.
How much will the surface change? As a lander comes down, what happens to the lunar soil, or regolith, it ejects? With limited data collected during descent and landing to date, SCALPSS will be the first dedicated instrument to measure the effects of plume-surface interaction on the Moon in real time and help to answer these questions.
“If we’re placing things – landers, habitats, etc. – near each other, we could be sand blasting what’s next to us, so that’s going to drive requirements on protecting those other assets on the surface, which could add mass, and that mass ripples through the architecture,” said Michelle Munk, principal investigator for SCALPSS and acting chief architect for NASA’s Space Technology Mission Directorate at NASA Headquarters in Washington. “It’s all part of an integrated engineering problem.”
Under the Artemis campaign, the agency’s current lunar exploration approach, NASA is collaborating with commercial and international partners to establish the first long-term presence on the Moon. On this CLPS (Commercial Lunar Payload Services) initiative delivery carrying over 200 pounds of NASA science experiments and technology demonstrations, SCALPSS 1.1 will begin capturing imagery from before the time the lander’s plume begins interacting with the surface until after the landing is complete.
The final images will be gathered on a small onboard data storage unit before being sent to the lander for downlink back to Earth. The team will likely need at least a couple of months to
process the images, verify the data, and generate the 3D digital elevation maps of the surface. The expected lander-induced erosion they reveal probably won’t be very deep — not this time, anyway.
One of the SCALPSS cameras is visible here mounted to the Blue Ghost lander.Image courtesy of Firefly. “Even if you look at the old Apollo images — and the Apollo crewed landers were larger than these new robotic landers — you have to look really closely to see where the erosion took place,” said Rob Maddock, SCALPSS project manager at Langley. “We’re anticipating something on the order of centimeters deep — maybe an inch. It really depends on the landing site and how deep the regolith is and where the bedrock is.”
But this is a chance for researchers to see how well SCALPSS will work as the U.S. advances human landing systems as part of NASA’s plans to explore more of the lunar surface.
“Those are going to be much larger than even Apollo. Those are large engines, and they could conceivably dig some good-sized holes,” said Maddock. “So that’s what we’re doing. We’re collecting data we can use to validate the models that are predicting what will happen.”
The SCALPSS 1.1 project is funded by the Space Technology Mission Directorate’s Game Changing Development Program.
NASA is working with several American companies to deliver science and technology to the lunar surface under the CLPS initiative. Through this opportunity, various companies from a select group of vendors bid on delivering payloads for NASA including everything from payload integration and operations, to launching from Earth and landing on the surface of the Moon.
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Last Updated Dec 19, 2024 EditorAngelique HerringLocationNASA Langley Research Center Related Terms
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4 min read Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
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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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