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Data from NASA's Stratospheric Observatory for Infrared Astronomy (SOFIA) was used to create a water distribution map of the area near the moon's south pole. <br/><br/>Credit: NASA's Goddard Space Flight Center Scientific Visualization Studio/Ernie Wright | edited by Space.com's Steve Spaleta<br/>Music: Stay in Orbit by Victor Lundberg / courtesy of Epidemic Sound

Water Distribution Near Moon’s South Pole Mapped Using NASA SOFIA Data

⏲ 1:6 👁 135K

KPTV FOX 12 Oregon

Portland firefighters prepare for warm temperatures and the threat of wildfires

⏲ 3 minutes 2 seconds 👁 122

FirstEnergy

FirstEnergy Recognizes Family of Linemen at Ohio Edison

⏲ 2 minutes 57 seconds 👁 1.6K

More Severe Weather , Forecast for Texas Following , String of Extreme Storms.<br/>'The Independent' reports that nearly a quarter <br/>of a million people remain without power <br/>in Texas following a series of severe storms. .<br/>The National Weather Service has warned <br/>that more heavy rain and dangerous <br/>winds are forecast for the area. .<br/>According to utility company Oncor, <br/>work to restore power remains ongoing. .<br/>Heavy rain, thunder, lightning and <br/>flooding are impacting several of <br/>the communities where Oncor teams <br/>are working to restore these outages, Oncor website, via 'The Independent'.<br/>Storms on May 28 killed a 16-year-old <br/>worker in the Houston suburb of Magnolia. .<br/>In Houston, streets remain flooded from <br/>the same storm, as more severe thunderstorm <br/>watches were put into effect on the morning of May 31. .<br/>According to National Weather Service <br/>meteorologist Josh Weiss, the May 31 storm system , \

More Severe Weather Forecast for Texas Following String of Extreme Storms

⏲ 1:30 👁 515K

KRON 4

PG&E rewards program aimed to get customers to save power

⏲ 2 minutes 11 seconds 👁 407

opgvideos

Nuclear energy: the core of Ontario's power

⏲ 1 minute 43 seconds 👁 3.8K

On May 29, 1919, a total solar eclipse helped to prove Albert Einstein's theory of general relativity. <br/><br/>Einstein's theory suggested that gravity could bend light by curving the fabric of spacetime. To prove it, he thought it would be a great idea to look at background stars that appear very close to the sun. This isn't possible without a total solar eclipse, because the sun is way too bright. During totality, the sun is completely covered by the moon, which makes it possible to see stars that are usually invisible during the daytime. According to relativity, the stars would appear to be in slightly different locations in the sky. Einstein was planning to test his theory during a total solar eclipse on August 21, 1914, but World War I broke out less than a month before and ruined his plans. For another total solar eclipse in 1919, the British astronomer Sir Arthur Eddington organized two similar expeditions to Africa and Brazil. During totality, they mapped the positions of the stars near the sun to see if they appeared to have shifted. The difference in the stars' positions was tiny, but it was just enough to finally prove Einstein's theory.

OTD In Space – May 29: Einstein's Theory Of Relativity Tested With Total Solar Eclipse

⏲ 1:13 👁 55K

KENS 5: Your San Antonio News Source

CPS Energy's new interactive power outage map

⏲ 1 minute 47 seconds 👁 237

WKBN27

First Energy releases statement on continued power outages throughout the Valley

⏲ 3 minutes 56 seconds 👁 368

Welcome to Session 14 of our Open RAN series! In this session, we'll introduce supervised machine learning and its application in designing intelligent systems for Open RAN.<br/><br/><br/>Understanding Supervised Machine Learning:<br/>Supervised machine learning is a type of machine learning where the algorithm learns from labeled data. It involves training a model on a dataset that contains input-output pairs, where the input is the data and the output is the corresponding label or target variable. The algorithm learns to map inputs to outputs by finding patterns in the data. In Open RAN, supervised learning can be used for tasks such as predicting network performance based on historical data.<br/><br/>Types of Supervised Machine Learning:<br/>There are two main types of supervised machine learning: classification and regression. In classification, the algorithm learns to categorize data into predefined classes or categories. For example, it can classify network traffic into different application types (e.g., video streaming, web browsing). Regression, on the other hand, involves predicting continuous values or quantities. It is used when the output variable is a real or continuous value, such as predicting the signal strength of a network connection.<br/><br/>Binary and Multi-Class Classification:<br/>Binary classification involves categorizing data into two classes or categories. For example, it can be used to classify network traffic as either malicious or benign. Multi-class classification, on the other hand, involves categorizing data into more than two classes. It can be used to classify network traffic into multiple application types (e.g., video streaming, social media, email).<br/><br/>Regression in Machine Learning:<br/>Regression is a supervised learning technique used for predicting continuous values or quantities. It involves fitting a mathematical model to the data, which can then be used to make predictions. In Open RAN, regression can be used for tasks such as predicting network latency, throughput, or coverage based on various input variables such as network parameters, traffic patterns, and environmental conditions.<br/><br/>Subscribe to \

Session 14: Introduction to Supervised Machine Learning in Open RAN

⏲ 4:28 👁 40K

Eversource

New Outage Map Tutorial

⏲ 1 minute 12 seconds 👁 4.7K

CNBC Television

First Energy 'working very hard' to restore power quickly after Hurricane Ida

⏲ 5 minutes 41 seconds 👁 240

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