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By NASA
Peering through the window of the SpaceX Dragon Endeavour spacecraft, NASA astronaut Matthew Dominick captured this image on Oct. 7, 2024 of the SpaceX Dragon Freedom spacecraft as vivid green and pink aurora swirled through Earth’s atmosphere while the International Space Station soared 273 miles above the Indian Ocean.
Visit Dominick’s photography on station to experience the wonders of space through his eyes, enriched by his remarkable journey of orbiting the Earth 3,760 times.
To see a short-term forecast of the location and intensity of the next aurora check this link: Aurora – 30 Minute Forecast and also NASA’s Guide to Finding and Photographing Auroras.
Image Credit: NASA/Matthew Dominick
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By European Space Agency
Global warming is driving the rapid melting of the Greenland Ice Sheet, contributing to global sea level rise and disrupting weather patterns worldwide. Because of this, precise measurements of its changing shape are of critical importance for adapting to climate change.
Now, scientists have delivered the first measurements of the Greenland Ice Sheet’s changing shape using data from ESA's CryoSat and NASA's ICESat-2 ice missions.
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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
Video: 00:04:04 English Paxi explores ice
Join Paxi on an adventure to the North and South poles, to learn more about ice and its role in keeping Earth cool.
Italian Paxi osserva il ghiaccio
Unisciti a Paxi in un'avventura ai poli Nord e Sud, per saperne di più sul ghiaccio e sul suo ruolo nel mantenere la Terra fresca.
German Paxi erforscht das Eis
Begleiten Sie Paxi auf ein Abenteuer zum Nord- und Südpol, um mehr über Eis und seine Rolle bei der Kühlung der Erde zu erfahren.
French Paxi explore la glace
Rejoignez Paxi dans une aventure aux pôles Nord et Sud, pour en savoir plus sur la glace et son rôle dans le refroidissement de la Terre.
Spanish Paxi explora el hielo
Únete a Paxi en una aventura a los polos Norte y Sur, para aprender más sobre el hielo y su papel en mantener la Tierra fría.
Portuguese Paxi explora o gelo
Junte-se a Paxi numa aventura aos pólos Norte e Sul, para aprender mais sobre o gelo e o seu papel na manutenção da Terra fresca.
Greek Ο Πάξι εξερευνά τον πάγο
Ελάτε μαζί με τον Paxi σε μια περιπέτεια στο Βόρειο και το Νότιο Πόλο, για να μάθετε περισσότερα για τον πάγο και το ρόλο του στη διατήρηση της ψύξης της Γης.
Polish Paxi bada lód
Dołącz do Paxi podczas przygody na biegunie północnym i południowym, aby dowiedzieć się więcej o lodzie i jego roli w chłodzeniu Ziemi.
Swedish Paxi utforskar is
Följ med Paxi på ett äventyr till Nord- och Sydpolen för att lära dig mer om is och dess roll för att hålla jorden sval.
Norwegian Paxi utforsker is
Bli med Paxi på et eventyr til Nord- og Sydpolen for å lære mer om is og dens rolle i å holde jorden kjølig.
Danish Paxi udforsker is
Tag med Paxi på eventyr til Nord- og Sydpolen for at lære mere om is og dens rolle i at holde Jorden kølig.
Romanian Paxi explorează gheață
Alăturați-vă lui Paxi într-o aventură la polii Nord și Sud, pentru a afla mai multe despre gheață și rolul său în menținerea Pământului rece.
Finnish Paxi tutkii jäätä
Lähde Paxin mukaan seikkailulle pohjois- ja etelänavoille ja opi lisää jäästä ja sen roolista maapallon viileänä pitämisessä.
Estonian Paxi avastab jääd
Liitu Paxiga seiklusel põhja- ja lõunapoolusele, et õppida rohkem jääst ja selle rollist Maa jahedana hoidmisel.
Czech Paxi zkoumá led
Vydejte se s Paxi na dobrodružnou výpravu na severní a jižní pól, abyste se dozvěděli více o ledu a jeho úloze při udržování chladu na Zemi.
Dutch Paxi onderzoekt ijs
Ga mee met Paxi op avontuur naar de Noord- en Zuidpool om meer te leren over ijs en de rol die ijs speelt bij het koel houden van de aarde.
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By NASA
The Fresh Eyes on Ice team receives the C. Peter Magrath exemplary project award from the Association of Public and Land-grant Universities. H. Buurman Congratulations to the Fresh Eyes on Ice project, which received a C. Peter Magrath exemplary project award from the Association of Public and Land-grant Universities! The award recognizes programs that demonstrate how colleges and universities have redesigned their learning, discovery, and engagement missions to deepen their partnerships and achieve broader impacts in their communities.
“Thank you to all of you for making this project what it is.” said Fresh Eyes on Ice project lead Research Professor Katie Spellman from the University of Alaska, Fairbanks. “We couldn’t do it without you.”
Fresh Eyes on Ice tracks changes in the timing and thickness of ice throughout Alaska and the circumpolar north. You can get involved by downloading the GLOBE Observer app and taking photos of ice conditions using the GLOBE Land Cover protocol.
Fresh Eyes on Ice is supported by the Navigating the New Arctic Program of the U.S. National Science Foundation and the NASA Citizen Science for Earth Systems Program.
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Last Updated Dec 05, 2024 Related Terms
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