Browsing by Author "Lange, T."
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- ItemThe Polarimetric and Helioseismic Imager on Solar Orbiter(Les Ulis : EDP Sciences , 2020) Solanki, S.K.; del Toro Iniesta, J.C.; Woch, J.; Gandorfer, A.; Hirzberger, J.; Alvarez-Herrero, A.; Appourchaux, T.; Martínez Pillet, V.; Pérez-Grande, I.; Sanchis Kilders, E.; Schmidt, W.; Garranzo-García, D.; Laguna, H.; Martín, J.A.; Navarro, R.; Villanueva, J.; Núñez Peral, A.; Royo, M.; Sánchez, A.; Silva-López, M.; Fourmond, J.-J.; Berkefeld, Th.; Ruiz de Galarreta, C.; Bouzit, M.; Hervier, V.; Le Clec'h, J.C.; Szwec, N.; Chaigneau, M.; Buttice, V.; Volkmer, R.; Dominguez-Tagle, C.; Philippon, A.; Baumgartner, J.; Boumier, P.; Le Cocguen, R.; Baranjuk, G.; Bell, A.; Heidecke, F.; Maue, T.; Blanco Rodríguez, J.; Nakai, E.; Scheiffelen, T.; Sigwarth, M.; Soltau, D.; Domingo, V.; Fiethe, B.; Ferreres Sabater, A.; Gasent Blesa, J.L.; Rodríguez Martínez, P.; Osorno Caudel, D.; Bosch, J.; Casas, A.; Carmona, M.; Gómez Cama, J.M.; Herms, A.; Roma, D.; Guan, Y.; Alonso, G.; Gómez-Sanjuan, A.; Piqueras, J.; Torralbo, I.; Lange, T.; Michel, H.; Michalik, H.; Bonet, J.A.; Fahmy, S.; Müller, D.; Zouganelis, I.; Deutsch, W.; Busse, D.; Fernandez-Rico, G.; Grauf, B.; Gizon, L.; Heerlein, K.; Kolleck, M.; Lagg, A.; Meller, R.; Müller, R.; Schühle, U.; Staub, J.; Enge, R.; Albert, K.; Alvarez Copano, M.; Beckmann, U.; Bischoff, J.; Frahm, S.; Germerott, D.; Guerrero, L.; Löptien, B.; Meierdierks, T.; Oberdorfer, D.; Papagiannaki, I.; Ramanath, S.; Bellot Rubio, L.R.; Schou, J.; Werner, S.; Yang, D.; Zerr, A.; Bergmann, M.; Bochmann, J.; Heinrichs, J.; Meyer, S.; Monecke, M.; Müller, M.-F.; Cobos Carracosa, J.P.; Sperling, M.; Álvarez García, D.; Aparicio, B.; Balaguer Jiménez, M.; Girela, F.; Hernández Expósito, D.; Herranz, M.; Labrousse, P.; López Jiménez, A.; Orozco Suárez, D.; Ramos, J.L.; Barandiarán, J.; Vera, I.; Bastide, L.; Campuzano, C.; Cebollero, M.; Dávila, B.; Fernández-Medina, A.; García Parejo, P.This paper describes the Polarimetric and Helioseismic Imager on the Solar Orbiter mission (SO/PHI), the first magnetograph and helioseismology instrument to observe the Sun from outside the Sun-Earth line. It is the key instrument meant to address the top-level science question: How does the solar dynamo work and drive connections between the Sun and the heliosphere? SO/PHI will also play an important role in answering the other top-level science questions of Solar Orbiter, as well as hosting the potential of a rich return in further science. SO/PHI measures the Zeeman effect and the Doppler shift in the FeI 617.3nm spectral line. To this end, the instrument carries out narrow-band imaging spectro-polarimetry using a tunable LiNbO_3 Fabry-Perot etalon, while the polarisation modulation is done with liquid crystal variable retarders (LCVRs). The line and the nearby continuum are sampled at six wavelength points and the data are recorded by a 2kx2k CMOS detector. To save valuable telemetry, the raw data are reduced on board, including being inverted under the assumption of a Milne-Eddington atmosphere, although simpler reduction methods are also available on board. SO/PHI is composed of two telescopes; one, the Full Disc Telescope (FDT), covers the full solar disc at all phases of the orbit, while the other, the High Resolution Telescope (HRT), can resolve structures as small as 200km on the Sun at closest perihelion. The high heat load generated through proximity to the Sun is greatly reduced by the multilayer-coated entrance windows to the two telescopes that allow less than 4% of the total sunlight to enter the instrument, most of it in a narrow wavelength band around the chosen spectral line.
- ItemPrediction of solar particle events with SRAM-based soft error rate monitor and supervised machine learning(Amsterdam [u.a.] : Elsevier Science, 2020) Chen, J.; Lange, T.; Andjelkovic, M.; Simevski, A.; Krstic, M.This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM). © 2020 The Authors