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1. Dataset of night-time emissions of the Earth in the near UV range (290-430 nm), with 6.3 km resolution in the latitude range -51.6

Author

L. Marcelli, K. Bolmgren, D. Barghini, M. Battisti, C. Blaksley, S. Blin, A. Belov, M. Bertaina, M. Bianciotto, F. Bisconti, G. Cambiè, F. Capel, M. Casolino, I. Churilo, M. Crisconio, C. De La Taille, T. Ebisuzaki, J. Eser, F. Fenu, M.A. Franceschi, C. Fuglesang, A. Golzio, P. Gorodetzky, H. Kasuga, F. Kajino, P. Klimov, V. Kuznetsov, M. Manfrin, G. Mascetti, W. Marszal, H. Miyamoto, A. Murashov, T. Napolitano, H. Ohmori, A. Olinto, E. Parizot, P. Picozza, L.W. Piotrowski, Z. Plebaniak, G. Prevot, E. Reali, G. Romoli, M. Ricci, N. Sakaki, K. Shinozaki, J. Szabelski, Y. Takizawa, V. Vagelli, G. Valentini, M. Vrabel, and L. Wiencke

Subjects
UV terrestrial emissions, UV maps, ISS, Mini-EUSO, Earth observation from space, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

The data presented in this article are related to the research paper entitled “Observation of night-time emissions of the Earth in the near UV range from the International Space Station with the Mini-EUSO detector” (Remote Sensing of Environment, Volume 284, January 2023, 113336, https://doi.org/10.1016/j.rse.2022.113336).The data have been acquired with the Mini-EUSO detector, an UV telescope operating in the range 290-430 nm and located inside the International Space Station. The detector was launched in August 2019, and it has started operations from the nadir-facing UV-transparent window in the Russian Zvezda module in October 2019. The data presented here refer to 32 sessions acquired between 2019-11-19 and 2021-05-06. The instrument consists of a Fresnel-lens optical system and a focal surface composed of 36 multi-anode photomultiplier tubes, each with 64 channels, for a total of 2304 channels with single photon counting sensitivity. The telescope, with a square field-of-view of 44°, has a spatial resolution on the Earth surface of 6.3 km and saves triggered transient phenomena with a temporal resolution of 2.5 µs and 320 µs. The telescope also operates in continuous acquisition at a 40.96 ms scale.In this article, large-area night-time UV maps obtained processing the 40.96 ms data, taking averages over regions of some specific geographical areas (e.g., Europe, North America) and over the entire globe, are presented. Data are binned into 0.1° × 0.1° or 0.05° × 0.05° cells (depending on the scale of the map) over the Earth's surface. Raw data are made available in the form of tables (latitude, longitude, counts) and .kmz files (containing the .png images). These are – to the best of our knowledge – the highest sensitivity data in this wavelength range and can be of use to various disciplines.

Published
2023
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5. Investigation of Microstructure and Corrosion Resistance of Ti-Al-V Titanium Alloys Obtained by Spark Plasma Sintering

Author

Nokhrin, Aleksey, Andreev, Pavel, Boldin, Maksim, Chuvil'deev, Vladimir, Chegurov, Mikhail, Smetanina, Ksenia, Gryaznov, Mikhail, Shotin, Sergey, Nazarov, Artem, Shcherbak, Gleb, Murashov, Artem, and Nagicheva, Galina

Subjects
Physics - Applied Physics
Abstract

The research results of the microstructure and corrosion resistance of Ti and Ti-Al-V Russian industrial titanium alloys obtained by spark plasma sintering (SPS) are described. Investigations of the microstructure, phase composition, hardness, tensile strength, electrochemical corrosion resistance and hot salt corrosion of Ti-Al-V titanium alloy specimens were carried out. It was shown that the alloy specimens have a uniform highly dense microstructure and high hardness values. The studied alloys also have high resistance to electrochemical corrosion during tests in acidic aqueous solution causing the intergranular corrosion as well as high resistance to the hot salt corrosion. The assumption that the high hardness of the alloys as well as the differences in the corrosion resistance of the central and lateral parts of the specimens are due to the diffusion of carbon from the graphite mold into the specimen surface was suggested., Comment: 28 pages, 3 tables, 13 figures, 61 references

Published
2024
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6. Corrosion fatigue crack initiation in ultrafine-grained near-a titanium alloy PT7M prepared by Rotary Swaging

Author

Chuvil'deev, V. N., Kopylov, V. I., Berendeev, N. N., Murashov, A. A., Nokhrin, A. V., Grayznov, M. Yu., Shadrina, I. S., Tabachkova, N. Yu., Likhnitskii, C. V., Kotkov, D. N., and Tryaev, P. V.

Subjects
Condensed Matter - Materials Science
Abstract

The study focuses on corrosion fatigue processes taking place in an ultrafine-grained (UFG) near-a-titanium alloy Ti-2.5Al-2.6Zr (Russian industrial name PT7M) used in nuclear engineering. UFG structure formed with Rotary Swaging is found to increase resistance to corrosion fatigue. Parameters of the Basquin's equation are defined and the slope of the fatigue curve Sa-lg(N) is shown to depend (nonmonotonic dependence) on the UFG alloy annealing temperature. This effect can be explained with the patterns of microstructural evolution in a UFG alloy PT7M during annealing: (1) reduced density of lattice dislocations, (2) precipitation and dissolution of zirconium nanoparticles, (3) release of a''-phase particles causing internal stress fields along interphase (a-a'')-boundaries, and (4) intensive grain growth at elevated annealing temperatures. It is shown that the fatigue crack closure effect manifested as changing internal stress fields determined using XRD method may be observed in UFG titanium alloys., Comment: 54 pages, 2 tables, 19 figures, 64 referances

Published
2024
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7. ELVES Measurements in the “UV Atmosphere” (Mini-EUSO) Experiment Onboard the ISS and Their Reconstruction

Author

Sharakin, S., Barghini, D., Battisti, M., Belov, A., Bertaina, M., Bianciotto, M., Bisconti, F., Blaksley, C., Blin, S., Cambiè, G., Capel, F., Casolino, M., Ebisuzaki, T., Eser, J., Fenu, F., Franceschi, M. A., Golzio, A., Gorodetsky, P., Kajino, F., Kasuga, H., Klimov, P., Manfrin, M., Marcelli, L., Marszal, W., Miyamoto, H., Mignone, M., Murashov, A., Napolitano, T., Ohmori, H., Olinto, A., Parizot, E., Picozza, P., Piotrowski, L. W., Plebaniak, Z., Prévôt, G., Reali, E., Ricci, M., Romoli, G., Sakaki, N., Shinozaki, K., Szabelski, J., De La Taille, C., Takizawa, Y., Vrábel, M., Wiencke, L., and Zotov, M.

Published
2024
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23. An end-to-end calibration of the Mini-EUSO detector in space

Author

Battisti, M., Bertaina, M., Parizot, E., Abrate, M., Barghini, D., Belov, A., Bisconti, F., Blaksley, C., Blin, S., Capel, F., Casolino, M., Churilo, I., Coretti, A.G., Crisconio, M., De La Taille, C., Ebisuzaki, T., Fenu, F., Franceschi, M.A., Fuglesang, C., Gallian, S., Gardiol, D., Golzio, A., Gorodetzky, P., Kajino, F., Kasuga, H., Klimov, P., Kungel, V., Kuznetsov, V., Manfrin, M., Marcelli, L., Marszał, W., Mascetti, G., Mignone, M., Miyamoto, H., Murashov, A., Napolitano, T., Ohmori, H., Olinto, A., Picozza, P., Piotrowski, L.W., Plebaniak, Z., Prévôt, G., Reali, E., Reynaud, F., Ricci, M., Romoli, G., Sharakin, S., Shinozaki, K., Szabelski, J., Takizawa, Y., Vagelli, V., Valentini, G., Vrabel, M., Wiencke, L., and Zotov, M.

Published
2025
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24. The EUSO-SPB2 fluorescence telescope for the detection of Ultra-High Energy Cosmic Rays

Author

Adams, James H., Jr., Allard, Denis, Alldredge, Phillip, Anchordoqui, Luis, Anzalone, Anna, Battisti, Matteo, Belov, Alexander A., Bertaina, Mario, Bertone, Peter F., Blin-Bondil, Sylvie, Burton, Julia, Cafagna, Francesco S., Casolino, Marco, Černý, Karel, Christl, Mark J., Colalillo, Roberta, Crawford, Hank J., Creusot, Alexandre, Cummings, Austin, Diesing, Rebecca, Nola, Alessandro Di, Ebisuzaki, Toshikazu, Eser, Johannes, Ferrarese, Silvia, Filippatos, George, Finch, William W., Flaminio, Flavia, Fornaro, Claudio, Fuehne, Duncan, Fuglesang, Christer, Garg, Diksha, Golzio, Alessio, Guarino, Fausto, Guépin, Claire, Heibges, Tobias, Judd, Eleanor G., Klimov, Pavel A., Krizmanic, John F., Kungel, Viktoria, Kupari, Luke, Kuznetsov, Evgeny, Manfrin, Massimiliano, Marszał, Włodzimierz, Matthews, John N., Mese, Marco, Meyer, Stephan S., Mignone, Marco, Miyamoto, Hiroko, Murashov, Alexey S., Nachtman, Jane M., Olinto, Angela V., Onel, Yasar, Osteria, Giuseppe, Panico, Beatrice, Parizot, Ètienne, Paul, Tom, Pech, Miroslav, Perfetto, Francesco, Piotrowski, Lech W., Plebaniak, Zbigniew, Posligua, Jonatan, Prévôt, Guillaume, Przybylak, Marika, Reardon, Patrick, Reno, Mary Hall, Ricci, Marco, Sarazin, Fred, Schovánek, P., Scotti, Valentina, Shinozaki, Kenji, Soriano, Jorge F., Stillwell, Ben K., Szabelski, Jacek, Takizawa, Yoshiyuki, Trofimov, Daniil, Unel, Fredrik, Valore, Laura, Venters, Tonia M., Watts, John, Jr., Wiencke, Lawrence, Wistrand, Hannah, and Young, Roy

Published
2025
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25. Refined STACK-CNN for Meteor and Space Debris Detection in Highly Variable Backgrounds

Author

Leonardo Olivi, Antonio Montanaro, Mario Edoardo Bertaina, Antonio Giulio Coretti, Dario Barghini, Matteo Battisti, Alexander Belov, Marta Bianciotto, Francesca Bisconti, Carl Blaksley, Sylvie Blin, Karl Bolmgren, Giorgio Cambie, Francesca Capel, Marco Casolino, Igor Churilo, Marino Crisconio, Christophe De La Taille, Toshikazu Ebisuzaki, Johannes Eser, Francesco Fenu, George Filippatos, Massimo Alberto Franceschi, Christer Fuglesang, Alessio Golzio, Philippe Gorodetzky, Fumiyoshi Kajino, Hiroshi Kasuga, Pavel Klimov, Viktoria Kungel, Vladimir Kuznetsov, Massimiliano Manfrin, Laura Marcelli, Gabriele Mascetti, Wlodzimierz Marszal, Marco Mignone, Hiroko Miyamoto, Alexey Murashov, Tommaso Napolitano, Hitoshi Ohmori, Angela Olinto, Etienne Parizot, Piergiorgio Picozza, Lech Wiktor Piotrowski, Zbigniew Plebaniak, Guillaume Prevot, Enzo Reali, Marco Ricci, Giulia Romoli, Naoto Sakaki, Sergei Sharakin, Kenji Shinozaki, Jacek Szabelski, Yoshiyuki Takizawa, Valerio Vagelli, Giovanni Valentini, Michal Vrabel, Lawrence Wiencke, and Mikhail Zotov

Subjects
Neural network applications, space technology, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract

In this article, we present cutting-edge machine learning-based techniques for the detection and reconstruction of meteors and space debris in the Mini-EUSO experiment, a detector installed on board of the International Space Station, and pointing toward the Earth. We base our approach on a recent technique, the STACKing method plus Convolutional Neural Network (STACK-CNN), originally developed as an online trigger in an orbiting remediation system to detect space debris. Our proposed method, the refined-STACKing method plus convolutional neural network (R-Stack-CNN), makes the STACKing method plus convolutional neural network (STACK-CNN) more robust, thanks to a random forest that learns the temporal development of these events in the camera. We prove the flexibility of our method by showing that it is sensitive to any space object that moves linearly in the field of view. First, we search small space debris, never observed by Mini-EUSO. Due to the limiting statistics, also in this case, no debris were found. However, since meteors produce signals similar to space debris but they are much more frequent, the R-Stack-CNN is adapted to identify such events while avoiding the numerous false positives of the Stack-CNN. Results from real data show that the R-Stack-CNN is able to find more meteors than a classical thresholding method and a new method of two neural networks. We also show that the method is also able to accurately reconstruct speed and direction of meteors with simulated data.

Published
2024
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37. Complex multidimensional fuzzy models of monitoring and rehabilitation processes for patients with uneven observation interval

Author

V. V. Skalozub, V. M. Horiachkin, І. V. Klymenko, and O. V. Murashov

Subjects
analysis and forecasting, complex fuzzy models, uneven interval, rehabilitation, monitoring, multiparametric processes, Transportation engineering, TA1001-1280
Abstract

Purpose. The work is devoted to the development of mathematical models and methods of fuzzy modeling of multidimensional time series (CDM) for the processes of monitoring and rehabilitation of patients with uneven intervals between observations. CDM takes into account the system properties and unity of the components of the studied processes by forming combined/complex multidimensional fuzzy models (CFTS). Methodology. The implementation of CFTS models takes into account the intrinsic features of these processes. The peculiarity of CFTS is that uneven intervals of observations, as well as other parameters, reflect the systemic unity of the controlled process, rather than the externally established form of observations, regulations. SFTS generalize models of multidimensional fuzzy time series of order n with m input parameters and one output parameter, i.e., different components may have different order of prehistory n, individual parameters may be measured by different types of data and forms of uncertainty. Findings. The article presents a comprehensive improved structure of CFTS models of order n with m input and one output parameter, which is adapted to the properties of monitoring and rehabilitation processes with uneven observation intervals. To form the SFTS, a step-by-step procedure is proposed that allows forming the composition of parameters. An example of modeling the process of rehabilitation of patients with diabetes based on the SFTS is presented, which demonstrates its differences and effectiveness. The comparative properties of SFTS and FTS models are presented. Originality. The development of CDM models and methods for monitoring and rehabilitation processes at uneven intervals is obtained, and complex SFTS models are formed. The difference between the SFTS is that the component of uneven intervals is represented as other multiparameters m, which can have different order of prehistory n, as well as different types of data and forms of uncertainty. A procedure for the step-by-step formation of the composition of the parameters of SFTS models is proposed. Practical value. SFTS models ensure the implementation of multiparameter monitoring and rehabilitation processes with uneven observation intervals, simplify the structure and reduce the number of relational relations, and eliminate the conflict of product rules in case of ensuring the required accuracy of results. The example of modeling the rehabilitation process for diabetics with such parameters as sugar level, interval between observations, and blood pressure has confirmed the reliability and practical significance of CFTS models.

Published
2023
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