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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 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
iss071e650763 (Sept. 14, 2024) — The long exposure photograph taken by NASA astronaut Matthew Dominick shows star trails, streaks of city lights, and two Roscosmos crew ships, the Soyuz MS-26 docked to the Rassvet module (foreground) and the Soyuz MS-25 (background) docked to the Prichal docking module, as the International Space Station orbited 265 miles above central China.NASA Space Station trajectory data is now available to the public!
This data, called an ephemeris, is generated by the ISS Trajectory Operations and Planning Officer (TOPO) flight controllers in the Mission Control Center at NASA’s Johnson Space Center. TOPO keeps track of where the ISS is, where it is going to be, and most importantly makes sure it isn’t at risk of colliding with other objects in space. At ISS’s altitude, a very thin atmosphere is still present. This thin atmosphere creates drag and over time can cause TOPO’s predicted ISS trajectory to accumulate error. Because of this, TOPO updates the predicted trajectory approximately three times a week, so the ISS Flight Control Team has the best trajectory estimate possible. An accurate trajectory is essential for maintaining communications links, planning visiting vehicle rendezvous, and ensuring ISS’s path is clear of any potential collisions.
The links above and below are to the most current posted ephemeris. The ephemeris is in the CCSDS Orbital Ephemeris Message (OEM) standard and is available in .txt and .xml file formats. Each file contains header lines with the ISS mass in kg, drag area in m2, and drag coefficient used in generating the ephemeris. The header also contains lines with details for the first and last ascending nodes within the ephemeris span. Following this is a listing of upcoming ISS translation maneuvers, called “reboosts,” and visiting vehicle launches, arrivals, and departures.
After the header, ISS state vectors in the Mean of J2000 (J2K) reference frame are listed at four-minute intervals spanning a total length of 15 days. During reboosts (translation maneuvers), the state vectors are reported in two-second intervals. Each state vector lists the time in UTC; position X, Y, and Z in km; and velocity X, Y, and Z in km/s.
Orbit Ephemeris Message (OEM)
https://nasa-public-data.s3.amazonaws.com/iss-coords/current/ISS_OEM/ISS.OEM_J2K_EPH.txt https://nasa-public-data.s3.amazonaws.com/iss-coords/current/ISS_OEM/ISS.OEM_J2K_EPH.xml Users of this data should monitor this page for information regarding any future changes to the file format. Past data postings can be found archived on data.nasa.gov by searching “ISS COORDS.”
NOTE: NASA is providing this information for use by the general public. The OEM data format is supported natively by many commercial spaceflight software applications. Please consult your application’s support documentation for specific details on how to deploy this data.
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