In solar cycle 24, the strongest geomagnetic storm took place on March 17, 2015, when the geomagnetic activity index was as high as -223 nT. To verify the impact that the storm had on the Global Navigation Satellite System (GNSS)’s positioning accuracy and precision, we used 30-s observations from 15 reference stations located in Central Europe. For each of them, we applied kinematic precise point positioning (PPP) using gLAB software for the day of the storm and, for comparison, for a selected quiet day (13 March 2015). Based on the conducted analyses, we found out that the position root mean square (RMS) values on the day of the geomagnetic storm were significantly high and amounted to several dozen centimeters. The average RMS for the altitude coordinates was 0.58 m between 12:00 and 24:00 (GPS time), and 0.37 and 0.26 m for directions North and East, respectively. The compromised accuracy level was caused by a sudden decrease in the number of satellites used for calculations. This was due to a high number of cycle slips (CSs) detected during this period. The occurrence of these effects was strictly correlated with the appearance of traveling ionospheric disturbances (TIDs). This was proven by analyzing changes in the total electron content (TEC) estimated for each station–satellite pair.
<p>The International GNSS Service (IGS) global ionospheric maps (GIMs) are one of the primary sources of information on the ionospheric state. They are used in many research and GNSS positioning applications. IGS GIMs are created using the weighted average of the products derived from the selected IAAC. This method allows for efficient mapping of the state of the ionosphere, especially on days without major disruptions. However, ionospheric disturbances could be more problematic to map correctly. To improve GIMs quality, we used a machine learning (ML) approach to combine individual IAAC GIMs into one product. We used total electron content (TEC) data from Jason altimetric satellite with a 5-minute interval as reference. To improve the modeling, we used auxiliary parameters such as solar and geomagnetic indices, e.g., F10.7 index. The training process was performed on the 2005-2020 dataset.&#160;</p><p>This study presents some preliminary results of VTEC modeling using the ML approach. We show inter-validation and inter-comparison with IGS GIMs, and Jason-derived VTEC. We also used pseudorange code and carrier phase single-frequency GNSS observations to show positioning accuracy improvement achieved using ML-based GIMs. For this purpose, we used 34 evenly distributed IGS stations for the selected calm period and strong geomagnetic storms. The results showed that for both calm and stormy days, the differences between the coordinates obtained from our model and those using the IGS product were up to a few centimeters for most stations for the northern and eastern components of the topocentric coordinates. Additionally, for altitude, we noticed accuracy improvement for most stations during the storm periods relative to results obtained using the final IGS product.&#160;&#160;</p>
W artykule opisano wdrażanie aplikacji internetowych wspomagających zarządzanie przedsiębiorstwem, w oparciu o wdrożenia w poszczególnych biurach podróży. Przedstawiono różne typy aplikacji internetowych z analizą zalet i wad ich użycia. W analizie zastosowanometodę sondażu diagnostycznego. Wyniki badań wykazały, że najpopularniejszą aplikacją stosowaną przez biura podróży jest CRM. Przedsiębiorstwa wykazały, że aplikacje były wdrożone bez opóźnień, a główną korzyĞcią ich posiadania jest wzrost sprzedaży.
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