23 July 2020
Three space tracking ships of China's Yuanwang provided maritime monitoring support for China's first Mars probe launch on 23 July. They were all positioned in the Pacific Ocean when the CZ-5 rocket took off from Wenchang cosmodrome at 04:41 UTC
About six minutes after the liftoff, Yuanwang-6 detected and locked its targets. The other two tracking ships, Yuanwang-5 and Yuanwang-7, then took turns to complete their missions. The monitoring process lasted nearly 30 minutes.
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24 July 2020
Chinese space authorities are inviting the general public to share their excitement about the successful launch of the Tianwen 1 Mars probe mission by holding an official global naming campaign for the country's first Mars rover on 24 July. The global naming campaign event was hosted by the Lunar Exploration and Space Program Centre of China's National Space Administration in collaboration with the Tianwen 1 project contractors including the China Academy of Space Technology and China Academy of Launch Vehicle Technology. China's search engine Baidu will undertake the naming activity on its Baidu App. The rover naming campaign will be open until 12 August, Baidu said in a press release.
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24 July 2020
China's Mars mission Tianwen 1 is not designed to compete for space leadership; in fact, the universe is big enough for any country's exploration and cooperation, Wang Wenbin, Chinese Foreign Ministry spokesperson said on 24 July during the Regular Press Conference of the Foreign Ministry. He was answering the question by Beijing Media Network that foreign media believe Tianwen 1 is aimed at outer space dominance.
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Link to the transcript from the 24 July Regular Press Conference of the Ministry of Foreign Affairs of the PRC

22 July 2020
A joint team of researchers from the State Key Laboratory of Satellite Ocean Environment Dynamics, the CAS Key Laboratory of Ocean Circulation and Waves, the Center for Ocean Mega-Science, and the College of Marine Sciences of the Shanghai Ocean University, developed the satellite data-driven deep learning model for forecasting the evolution of sea surface temperature associated with the tropical instability wave. Their study was inspired by available satellite remote sensing data and advancements in deep learning technology. They have explored key marine data and established a deep learning model to conduct oceanic forecasts. The research result was published in the journal Science Advances.
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