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1. (16) Psyche: A mesosiderite-like asteroid?

   https://doi.org/10.1051/0004-6361/201834091  

   Viikinkoski, M.; Vernazza, P.; Hanuš, J.; Le Coroller, H.; Tazhenova, K.; Carry, B.; Marsset, M.; Drouard, A.; Marchis, F.; Fetick, R.; et al (November 2018, Astronomy & Astrophysics)

   null (Ed.)

   Context . Asteroid (16) Psyche is the target of the NASA Psyche mission. It is considered one of the few main-belt bodies that could be an exposed proto-planetary metallic core and that would thus be related to iron meteorites. Such an association is however challenged by both its near- and mid-infrared spectral properties and the reported estimates of its density. Aims . Here, we aim to refine the density of (16) Psyche to set further constraints on its bulk composition and determine its potential meteoritic analog. Methods . We observed (16) Psyche with ESO VLT/SPHERE/ZIMPOL as part of our large program (ID 199.C-0074). We used the high angular resolution of these observations to refine Psyche’s three-dimensional (3D) shape model and subsequently its density when combined with the most recent mass estimates. In addition, we searched for potential companions around the asteroid. Results . We derived a bulk density of 3.99 ± 0.26 g  cm −3 for Psyche. While such density is incompatible at the 3-sigma level with any iron meteorites (∼7.8 g  cm −3 ), it appears fully consistent with that of stony-iron meteorites such as mesosiderites (density ∼4.25 g  cm −3 ). In addition, we found no satellite in our images and set an upper limit on the diameter of any non-detected satellite of 1460 ± 200 m at 150 km from Psyche (0.2% × R Hill , the Hill radius) and 800 ± 200 m at 2000 km (3% × R Hill ). Conclusions . Considering that the visible and near-infrared spectral properties of mesosiderites are similar to those of Psyche, there is merit to a long-published initial hypothesis that Psyche could be a plausible candidate parent body for mesosiderites. 

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2. Gaia, Trumpler 16, and Eta Carinae

   https://doi.org/10.3847%2F2515-5172%2Faad63c  

   Davidson, Kris; Helmel, Greta; Humphreys, Roberta M. (July 2018, Research notes of the AAS)
3. The Role of Ozone Vibrational Resonances in the Isotope Exchange Reaction ¹⁶ O ¹⁶ O + ¹⁸ O → ¹⁸ O ¹⁶ O + ¹⁶ O: The Time-Dependent Picture

   https://doi.org/10.1021/acs.jpca.9b06139  

   Yuen, Chi Hong; Lapierre, David; Gatti, Fabien; Kokoouline, Viatcheslav; Tyuterev, Vladimir G. (August 2019, The Journal of Physical Chemistry A)
4. Designing a K-16 Cybersecurity Collaborative: CIPHER

   https://doi.org/10.1109/MSEC.2021.3050246  

   Sanzo, Karen L.; Paredes Scribner, Jay; Wu, Hongyi (March 2021, IEEE Security & Privacy)
5. Applying machine learning methods to detect convection using Geostationary Operational Environmental Satellite-16 (GOES-16) advanced baseline imager (ABI) data

   https://doi.org/10.5194/amt-14-2699-2021  

   Lee, Yoonjin; Kummerow, Christian D.; Ebert-Uphoff, Imme (January 2021, Atmospheric Measurement Techniques)

   Abstract. An ability to accurately detect convective regions isessential for initializing models for short-term precipitation forecasts.Radar data are commonly used to detect convection, but radars that providehigh-temporal-resolution data are mostly available over land, and the qualityof the data tends to degrade over mountainous regions. On the other hand,geostationary satellite data are available nearly anywhere and in near-realtime. Current operational geostationary satellites, the GeostationaryOperational Environmental Satellite-16 (GOES-16) and Satellite-17, provide high-spatial- and high-temporal-resolution data but only of cloud top properties; 1 min data, however, allow us to observe convection from visible andinfrared data even without vertical information of the convective system.Existing detection algorithms using visible and infrared data look forstatic features of convective clouds such as overshooting top or lumpy cloudtop surface or cloud growth that occurs over periods of 30 min to anhour. This study represents a proof of concept that artificial intelligence(AI) is able, when given high-spatial- and high-temporal-resolution data fromGOES-16, to learn physical properties of convective clouds and automate thedetection process. A neural network model with convolutional layers is proposed to identifyconvection from the high-temporal resolution GOES-16 data. The model takesfive temporal images from channel 2 (0.65 µm) and 14 (11.2 µm) asinputs and produces a map of convective regions. In order to provideproducts comparable to the radar products, it is trained against Multi-RadarMulti-Sensor (MRMS), which is a radar-based product that uses a rathersophisticated method to classify precipitation types. Two channels fromGOES-16, each related to cloud optical depth (channel 2) and cloud topheight (channel 14), are expected to best represent features of convectiveclouds: high reflectance, lumpy cloud top surface, and low cloud toptemperature. The model has correctly learned those features of convectiveclouds and resulted in a reasonably low false alarm ratio (FAR) and highprobability of detection (POD). However, FAR and POD can vary depending onthe threshold, and a proper threshold needs to be chosen based on thepurpose. 

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