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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 Space Force
The mission successfully achieved a complex effort across multiple Space Force organizations to pull an existing GPS III satellite from storage, accelerate integration and launch vehicle readiness, and rapidly process for launch.
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By European Space Agency
Video: 00:11:10 In 2024, ESA continued to drive Europe’s innovation and excellence in space, equipping the continent with advanced tools and knowledge to address global and local challenges. The year saw pioneering missions, cutting-edge satellites and the pivotal restoration of Europe’s independent access to space.
The first Ariane 6 launch was perhaps ‘the’ highlight of the year but it was only one of many achievements. We saw the last Vega launch and then the return to flight of Vega-C, the more powerful, upgraded version carrying Sentinel-1C.
Far away in our Solar System, the ESA/JAXA BepiColombo spacecraft performed twoMercury flybys in 2024, needed so that it can enter orbit around Mercury in 2026. Juice also performed a crucial gravity assist, this time becoming the first spacecraft to conduct a Moon-Earth double flyby on its way to Jupiter.
Twenty years after ESA’s Rosetta was launched and 10 years since its historic arrival at the comet 67P/Churyumov-Gerasimenko, we launched another spacecraft to a small body, the Hera planetary defence mission to investigate asteroid Dimorphos.
2024 was an important year for Europe’s Galileo constellation which continued to expand with the launch of four new satellites and an updated Galileo ground system. The year also saw the launch of ESA’s Proba-3 mission: two precision formation-flying satellites forming a solar coronagraph to study the Sun’s faint corona.
In human spaceflight, Europe continues to contribute to science from the ISS as Andreas Mogensen’s Huginn mission continued into 2024. Andreas even met up in space with ESA project astronaut Marcus Wandt who was launched on his Muninn mission, making it the first time two Scandinavians were in space together.
Meanwhile the latest class of ESA astronauts completed basic training and graduated in April. Two of them, Sophie and Raphaël, were then assigned to long-duration missions to the ISS in 2026.
We made crucial steps for Europe in gaining access to the Moon: the inauguration of our LUNA facility with DLR, and the delivery of a third European Service Module for NASA’s Orion spacecraft as part of the Artemis programme.
Europe is also contributing to the international Lunar Gateway and developing and ESA lunar lander called Argonaut. These landers will rely on ESA Moonlight, the programme to establish Europe’s first dedicated satellite constellation for lunar communication and navigation.
As 2024 draws to a close, ESA’s achievements this year have reinforced Europe’s role in space. ESA’s journey continues to explore new frontiers, shaping the space landscape for generations to come.
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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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