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Free, publicly-accessible full text available January 1, 2023
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In the mid-ninth century, an earthquake triggered a landslide that blocked the narrow gorge of the Jhelum River where it exits the Kashmir Valley. The landslide impounded a lake that extended ≈100 km along the floor of the valley, implying an impounded volume of ≤21 km 3 , flooding the capital, Srinagar, and much agricultural land. An engineered breach of the landslide was contrived by a Medieval engineer resulting in the catastrophic release of flood waters. Using reasonable assumptions we calculate the probable minimum drainage time of this Medieval flood (<4 days) and maximum downstream surge velocities (≈12 m/s). These would have been sufficientmore »Free, publicly-accessible full text available October 4, 2022
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Free, publicly-accessible full text available September 1, 2022
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Free, publicly-accessible full text available November 1, 2022
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Free, publicly-accessible full text available November 1, 2022
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Free, publicly-accessible full text available August 2, 2022
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Self-driving vehicles are very susceptible to cyber attacks. This paper aims to utilize a machine learning approach in combating cyber attacks on self-driving vehicles. We focus on detecting incorrect data that are injected into the data bus of vehicles. We will utilize the extreme gradient boosting approach, as a promising example of machine learning, to classify such incorrect information. We will discuss in details the research methodology, which includes acquiring the driving data, preprocessing it, artificially inserting incorrect information, and finally classifying it. Our results show that the considered algorithm achieve accuracy of up to 92% in detecting the abnormalmore »