First ever in situ observations of Venus’ polar upper atmosphere density using the tracking data of the Venus Express Atmospheric Drag Experiment (VExADE)

Icarus ◽  
2012 ◽  
Vol 217 (2) ◽  
pp. 831-838 ◽  
Author(s):  
P. Rosenblatt ◽  
S.L. Bruinsma ◽  
I.C.F. Müller-Wodarg ◽  
B. Häusler ◽  
H. Svedhem ◽  
...  
Keyword(s):  
Upper Atmosphere ◽  
Tracking Data ◽  
Atmospheric Drag ◽  
Atmosphere Density ◽  
In Situ Observations ◽  
Venus Express

Thermal behaviour of ram-facing instruments during deep dives into a planetary atmosphere: The case of Daedalus/CWS

2020 ◽  
Author(s):  
Johan De Keyser ◽  
Marius Echim ◽  
Sylvain Ranvier ◽  
Thomas Chambon ◽  
Björn Ordoubadian ◽  
...  
Keyword(s):  
Upper Atmosphere ◽  
Wind Instruments ◽  
Atmospheric Drag ◽  
Atmospheric Density ◽  
Present Contribution ◽  
The Earth ◽  
Thermal Simulations ◽  
Heat Loads ◽  
Satisfactory Manner

<p>Spacecraft that aim to study the atmosphere of a planetary object through in situ sampling face the problem of strong atmospheric drag. In order not to compromise mission lifetime, the orbit can be designed so that repeated deep dives into the upper atmosphere are performed to sample atmospheric density, pressure, and composition down to relatively low altitudes. During such deep dives, ram-facing instruments, such as ion and neutral wind instruments, in particular are exposed to a severe heating flux.</p><p>The present contribution focuses on the particular case of the neutral Cross-Wind Sensor (CWS) under study for the Daedalus Earth Explorer 10 mission led by ESA, which will sample the Earth’s upper atmosphere during its perigee passes at an altitude currently planned to be in the 110 to 140 km range. Thermal simulations are presented that show the transient heat loads on the CWS instrument. It is shown that, with an appropriate materials choice, these heat loads can be dealt with in a satisfactory manner.</p>

scholarly journals In situ observations of waves in Venus’s polar lower thermosphere with Venus Express aerobraking

2016 ◽  
Vol 12 (8) ◽  
pp. 767-771 ◽  
Author(s):  
Ingo C. F. Müller-Wodarg ◽  
Sean Bruinsma ◽  
Jean-Charles Marty ◽  
Håkan Svedhem
Keyword(s):  
Lower Thermosphere ◽  
In Situ Observations ◽  
Venus Express

In situ observations of electromigration induced void behavior

1995 ◽  
Vol 53 ◽  
pp. 244-245
Author(s):  
T. Marieb ◽  
J. C. Bravman ◽  
P. Flinn ◽  
D. Gardner ◽  
M. Madden
Keyword(s):  
Electron Microscopy ◽  
Void Nucleation ◽  
Plan View ◽  
Transmission Electron ◽  
Nucleation Sites ◽  
Microelectronics Industry ◽  
In Situ Observations ◽  
Backscatter Electron Detector ◽  
Voltage Scanning

Electromigration and stress voiding have been active areas of research in the microelectronics industry for many years. While accelerated testing of these phenomena has been performed for the last 25 years[1-2], only recently has the introduction of high voltage scanning electron microscopy (HVSEM) made possible in situ testing of realistic, passivated, full thickness samples at high resolution.With a combination of in situ HVSEM and post-testing transmission electron microscopy (TEM) , electromigration void nucleation sites in both normal polycrystalline and near-bamboo pure Al were investigated. The effect of the microstructure of the lines on the void motion was also studied.The HVSEM used was a slightly modified JEOL 1200 EX II scanning TEM with a backscatter electron detector placed above the sample[3]. To observe electromigration in situ the sample was heated and the line had current supplied to it to accelerate the voiding process. After testing lines were prepared for TEM by employing the plan-view wedge technique [6].

scholarly journals In situ observations of coral spawning and spawn slick at Lankayan Island, Sabah, Malaysia

2021 ◽  
Vol 51 (1) ◽  
Author(s):  
Sze Hoon Gan ◽  
Zarinah Waheed ◽  
Fung Chen Chung ◽  
Davies Austin Spiji ◽  
Leony Sikim ◽  
...  
Keyword(s):  
Coral Spawning ◽  
In Situ Observations

scholarly journals Snow Depth Fusion Based on Machine Learning Methods for the Northern Hemisphere

2021 ◽  
Vol 13 (7) ◽  
pp. 1250
Author(s):  
Yanxing Hu ◽  
Tao Che ◽  
Liyun Dai ◽  
Lin Xiao
Keyword(s):  
Machine Learning ◽  
Northern Hemisphere ◽  
Snow Depth ◽  
Learning Algorithm ◽  
Random Forest Regression ◽  
Machine Learning Methods ◽  
Long Time ◽  
In Situ Observations ◽  
Input Variables

In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth datasets and in situ observations. A total of 29,565 in situ observations were used to train and optimize the machine learning algorithm. A total of five gridded snow depth datasets—Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) snow depth, Global Snow Monitoring for Climate Research (GlobSnow) snow depth, Long time series of daily snow depth over the Northern Hemisphere (NHSD) snow depth, ERA-Interim snow depth and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) snow depth—were used as input variables. The first three snow depth datasets are retrieved from passive microwave brightness temperature or assimilation with in situ observations, while the last two are snow depth datasets obtained from meteorological reanalysis data with a land surface model and data assimilation system. Then, three machine learning methods, i.e., Artificial Neural Networks (ANN), Support Vector Regression (SVR), and Random Forest Regression (RFR), were used to produce a fused snow depth dataset from 2002 to 2004. The RFR model performed best and was thus used to produce a new snow depth product from the fusion of the five snow depth datasets and auxiliary data over the Northern Hemisphere from 2002 to 2011. The fused snow-depth product was verified at five well-known snow observation sites. The R2 of Sodankylä, Old Aspen, and Reynolds Mountains East were 0.88, 0.69, and 0.63, respectively. At the Swamp Angel Study Plot and Weissfluhjoch observation sites, which have an average snow depth exceeding 200 cm, the fused snow depth did not perform well. The spatial patterns of the average snow depth were analyzed seasonally, and the average snow depths of autumn, winter, and spring were 5.7, 25.8, and 21.5 cm, respectively. In the future, random forest regression will be used to produce a long time series of a fused snow depth dataset over the Northern Hemisphere or other specific regions.

Preliminarily study of Saturn’s upper atmosphere density by observing Cassini plunging via China’s deep space station

2020 ◽  
Vol 20 (7) ◽  
pp. 102
Author(s):  
Lue Chen ◽  
Jin-Song Ping ◽  
Xiang Liu ◽  
Na Wang ◽  
Jian-Feng Cao ◽  
...  
Keyword(s):  
Space Station ◽  
Upper Atmosphere ◽  
Deep Space ◽  
Atmosphere Density
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