The article describes the procedure for transformation between old and new horizontal geodetic datum in Bosnia and Herzegovina. Two triangle-based methods were used for transformation, which are based on irregular and regular triangular network. For development of transformation models two set of points were used, one for developing models (around 1200 points), and other for testing (around 850 points). Prior to development, all points were tested at presence of outliers, and outliers are marked in the points database. Results shows that large part of distortions in old triangulation network can be modeled with used methods. Maximal positional standard deviations with best model are 4.5 and 6.4 cm for two sets of points, respectively, while maximal positional discripencies are 30 and 40 cm for two sets of points. Each method has some advantages and disadvantages which are shown in this article. It is shown that the number, spatial distribution and quality of input data are crucial for development of highly accurate transformation model. Also, as an important contribution of this work, some problematic areas with irregular distortions are identified. Finally, some recommendations are given for improvement of developed models.

: T ime series data of GNSS point positioning are considerably used for the purpose of geophysical research. The velocity estimates and their uncertainties deriv e from time series data of GNSS point positioning affected by seasonal signals and the stochastic noise, contained in the series. D ata cleaning of GNSS time series is a prerequisite for the noise characterization and analysing. In this article one point positioning of time series was analysed in four different periods during the five year interval. The noise characteristics were estimated for all periods. By applying Lomb - Scargle algorithm the comparable results were also provided. Lomb - Scargle algorithm used to estimate the spectral strength density of unequal sampled data is a typical tool for this kind of analysis. S pectral indices have been estimated before cleaning data and after removing linear, annual and semi - annual signals and outliers. T he spectral indices estimated from time series data of GNSS point positioning were located in the area of fractional Gaussian noises , and stationary stochastic process was described for the whole research time period.

<p>The ionosphere is the dominant source of the errors in the Global Navigation Satellite Systems &#160;(GNSS), which causes delays and degradation of the GNSS signal. These errors have an impact on many terrestrial and space applications that rely on GNSS. The key parameter for the study of the ionosphere is the Total Electron Content (TEC). In an effort to eliminate the impact of delayed GNSS signal caused by the ionospheric refraction on the accuracy of GNSS positioning and navigation, the researchers made significant advances and began other ionospheric research. This paper studies the variability of GNSS derived TEC values in the International quiet and disturbed days, but also in periods when three tropical-like cyclones in the Mediterranean developed. However, the term tropical-like cyclone distinguishes tropical cyclones developing outside the tropics (like in the Mediterranean Basin) from those developing inside the tropics. Mediterranean tropical cyclones, known as a Medicane, show no difference to other tropical cyclones and can be developed into a hurricane.</p><p>Hence, the variability of GNSS derived TEC values time series were analyzed during periods when three Medicanes happened in the fall of 2014, 2016, 2017. Data from eight GNSS stations of the European Permanent Network (EPN) were used and TEC calculations were performed using the VShell program. The results demonstrated that the TEC variability is reflected in daily variations within one month, for three different years of consideration. When the state of the ionosphere was disturbed by external influences, such as the space weather storms, the results demonstrated extreme changes in the number of electrons in the ionosphere. Variations of the TEC and parameter VTEC*sigma were analyzed in the weeks before and after three subtropical cyclones in the Mediterranean Sea, recorded in November 2014, November 2016 and November 2017. Special attention was given to the time series analysis of the variable VTEC*sigma for the GNSS stations located nearby the area where the Medicane developed and stations in regions away from the storm.</p><p>The results demonstrated higher VTEC values derived from GNSS stations in the area of the storm on the storm days, as well as the days before and after. Also, the results for the storm in November 2014 showed higher VTEC values compared to the other two tropical-like cyclones. The recorded events of space weather are in correlation with the days when three analyzed Medicanes developed. Therefore, it is difficult to distinguish whether the TEC variability was caused by the space weather storm or the Medicane.</p>