1. SISN
  2. All Volumes and Issues
  3. Volume 14, 2023
  4. Prototype of Intellectual System for Research of Space Weather Parameters

Prototype of Intellectual System for Research of Space Weather Parameters

SISN.
2023;
Volume 14
: pp. 348 - 356
https://doi.org/10.23939/sisn2023.14.348
Authors:
  1. Danylo Ivantyshyn
  2. Yevhen Burov
1
Lviv Polytechnic National University, Ukraine
2
Lviv Polytechnic National University, Information Systems and Networks Department

An analysis of the state of space weather research has been conducted, based on which the main problem has been identified and its relevance has been justified. Monitoring, researching, and forecasting space weather conditions receive significant attention in developed countries around the world. Despite significant progress in addressing this issue, the structure of solar-terrestrial connections is not fully understood, and the risks associated with space weather are increasing as the key aspects of our lives become increasingly technologically advanced. Today, in the structure of solar-terrestrial connections the influence of solar activity on the Earth’s lower atmosphere, including atmospheric infrasound and the electric field, remains insufficiently studied. This problem requires an examination of complex interactions that occur when different types of disturbances propagate through the Sun-Earth environment. Based on the developed generalized architecture of an intelligent system for researching space weather parameters, a prototype of this system has been proposed, and its functionality has been determined and developed. The prototype of the intelligent system is a client-server system built on the basis of server software, user software, and application software. The functionality of the intelligent system includes data collection, their preliminary processing, data processing, and visualization of the investigated signals. Data processing for space weather parameters includes spectral analysis of experimental data implemented using windowed Fourier transform and wavelet transform, as well as correlation-regression analysis, which allows for the investigation of the relationship between variables with the aim of identifying unknown causal connections. The intelligent system for researching space weather parameters will help identify new connections in the structure of solar-terrestrial interactions and study the impact of space factors on the Earth’s troposphere. The provided examples illustrate the results of processing experimental data for space weather parameters.

intellectual system
Architecture
prototype
space weather
solar-terrestrial connections
  1. Hapgood, M., et al. (2021). Development of space weather reasonable worst-case scenarios for the UK national risk assessment. Space Weather, 19(4), e2020SW002593. https://doi.org/10.1029/2020SW002593.
  2. Buzulukova N., Tsurutani B. (2022), Space Weather: From solar origins to risks and hazards evolving in time. Front. Astron. Space Sci., 9:1017103. https://doi.org/10.3389/fspas.2022.1017103.
  3. Pulkkinen, T. (2007). Space Weather: Terrestrial Perspective. Living Rev. Sol. Phys., 4, 1.  https://doi.org/10.12942/lrsp-2007-1.
  4. Singh, A. et al. (2021). Physics of Space Weather Phenomena: A Review. Geosciences, 11, 286.  https://doi.org/10.3390/geosciences11070286.
  5. Sharpe, et al. (2017). Verification of Space Weather Forecasts Issued by the Met Office Space Weather Operations Centre: Verification of MOSWOC Forecasts. Space Weather, 15(10). DOI: 10.1002/2017SW001683.
  6. Temmer, M. (2021). Space weather: the solar perspective. Living Rev. Sol. Phys., 18(4).  https://doi.org/10.1007/s41116-021-00030-3.
  7. Oliveira, D. M., Zesta, E. (2019). Satellite orbital drag during magnetic storms. Space Weather, 17, 1510– 1533. DOI: 10.1029/2019SW002287.
  8. Hapgood, M., Liu, H., Lugaz, N. (2022). SpaceX—sailing close to the space weather? Space weather, 20, e2022SW003074. DOI: 10.1029/2022SW003074.
  9. Bain, H, et al. (2023). NOAA Space Weather Prediction Center Radiation Advisories for the International Civil Aviation Organization. Space Weather, 21. 10.1029/2022SW003346.
  10. Vaičiulis, V. et al. (2021). Associations between Space Weather Events and the Incidence of Acute Myocardial Infarction  and Deaths from Ischemic Heart Disease. Atmosphere, 12, 306.  https://doi.org/10.3390/atmos12030306.
  11. Chaban, et al. (2021). Negative Effect of High-Level Infrasound on Human Myocardial Contractility: In- Vitro Controlled Experiment. Noise & health, 23, 57–66. 10.4103/nah.NAH_28_19.
  12. Gopalswamy, N. (2022) The Sun and Space Weather. Atmosphere, 13, 1781.  https://doi.org/10.3390/atmos13111781.
  13. Vadakke Veettil S., et al. (2019). The ionosphere prediction service prototype for GNSS users. J. Space Weather Space Clim., 9, A41. https://doi.org/10.1051/swsc/2019038.
  14. Cheng, F. et al. (2021). A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge   Discovery   From   Building   Operational   Data.   Frontiers    in    Energy    Research,    Vol.    9. DOI: 10.3389/fenrg.2021.652801.
  15. Veres O. M. (2015). The ontology of data cleaning. Bulletin of the Lviv Polytechnic National University. Series: Information systems and networks, No. 814, 237–245.
  16. Lozynsky, A., et al. (2023). Advances in Data Reduction Techniques to Solve Power Spectrum Estimation Problems for Emerging Wireless Networks. Lecture Notes in Electrical Engineering, Vol. 965. Springer, Cham. https://doi.org/10.1007/978-3-031-24963-1_34.
  17. Scholl, S. (2021). Fourier, Gabor, Morlet or Wigner: Comparison of Time-Frequency Transforms. arXiv, arXiv:2101.06707.
  18. Merry, R. J. E. (2005). Wavelet theory and applications: a literature study. DCT rapporten; Vol. 2005.053. Technische Universiteit Eindhoven.
  19. Devore, J. L., Berk, K. N., Carlton, M. A. (2021). Regression and Correlation. In: Modern Mathematical Statistics with Applications. Springer Texts in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-030-55156-8_1.