The reliability analysis allows to estimate the system's probability of detecting and identifying outlier. Failure to identify an outlier can jeopardise the reliability level of a system. Due to its importance, outliers must be appropriately treated to ensure the normal operation of a system. The system models are usually developed from certain constraints. Constraints play a central role in model precision and validity. In this work, we present a detailed optical investigation of the effects of the hard and soft constraints on the reliability of a measurement system model. Hard constraints represent a case in which there exist known functional relations between the unknown model parameters, whereas the soft constraints are employed for the case where such functional relations can slightly be violated depending on their uncertainty. The results highlighted that the success rate of identifying an outlier for the case of hard constraints is larger than soft constraints. This suggested that hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. After identifying and removing possible outliers, one should set up the soft constraints to propagate the uncertainties of the constraints during the data processing. This recommendation is valid for outlier detection and identification purpose.

In this paper we evaluate the effects of hard and soft constraints on the Iterative Data Snooping (IDS), an iterative outlier elimination procedure. Here, the measurements of a levelling geodetic network were classified according to the local redundancy and maximum absolute correlation between the outlier test statistics, referred to as clusters. We highlight that the larger the relaxation of the constraints, the higher the sensitivity indicators MDB (Minimal Detectable Bias) and MIB (Minimal Identifiable Bias) for both the clustering of measurements and the clustering of constraints. There are circumstances that increase the family-wise error rate (FWE) of the test statistics, increase the performance of the IDS. Under a scenario of soft constraints, one should set out at least three soft constraints in order to identify an outlier in the constraints. In general, hard constraints should be used in the stage of pre-processing data for the purpose of identifying and removing possible outlying measurements. In that process, one should opt to set out the redundant hard constraints. After identifying and removing possible outliers, the soft constraints should be employed to propagate their uncertainties to the model parameters during the process of least-squares estimation.

This paper deals with an analysis of cartographical studies, the real estate cadastre, and its practical implementation, as well as the introduction of cartography into different education modules in university-level studies in Montenegro. There is a discussion of the development, production, and creativity in the fields of cartography and real estate cadastre over time, cartographical projection, scientific results, and recent changes such as advanced computer- and satellite-based technologies, GIS, cartographical visualization, and digital cartography. The impact of these changes on cartographical studies at the University of Montenegro is considered. Particular attention is given to analyses of cartography and the cadastre in institutions, and their connection with the development of cartography teaching modules of Geography, Geodesy and Geoinformatics at the University of Montenegro. The integrated analysis also covers the results of the questionnaire and the significance of the geo-topographical and cartographical heritage of Montenegro, with the aim of carrying these out. It can be seen that the tasks solved by using maps have tended to become more complex and that the cartographical methods employed in this have been always directed towards Montenegro’s most prominent and most urgent problems, including those that appear in the area of education.

ITRF input data which are integrated by GNSS, SLR, VLBI, DORIS combination centers are considered to be relatively high-quality and accurate solutions. However, when utilizing these inputs, one still needs to identify outliers, rescale inaccurate covariance matrix and evaluate the precision of the observed datum information. To achieve the above-mentioned objectives, we propose a terrestrial reference frame (TRF) stacking approach to establish the single technical reference frameworks for the ITRF2014 and ITRF2020 datasets of all four technologies. As a result, roughly 0.5% or less of the SLR observations are identified as outliers, while the ratio of DORIS, GNSS, and VLBI observations are below 1%, around 2%, and ranging from 1% to 1.2%, respectively. The post-rescaling covariance scale factors are 25.07, 27.25, 18.84, 6.98 for GNSS, SLR, VLBI, and DORIS in ITRF2014 datasets, and 8.95, 14.9, 16.8, 7.78 in ITRF2020 datasets, respectively. It is shown that the consistency between the SLR scale and ITRF has improved, increasing from around -5mm in ITRF2014 datasets to approximately -1mm in ITRF2020 datasets. The scale velocity derived from fitting the VLBI scale parameter series with all epochs in ITRF2020 datasets differs by approximately 0.21mm/year from the velocity obtained by fitting the data up to 2013.75 because of the scale drift of VLBI at around 2013. The decreasing standard deviations of the Polar motion parameter (XPO, YPO) offsets between Stacking TRFs and 14C04 (20C04) indicating an improvement in the precision of polar motion observations for all of the four techniques. From the perspective of the Weighted Root Mean Square in station coordinates, the measurement precision of GNSS, SLR, and DORIS techniques has improved, while VLBI shows no significant change.

This paper shows the development of a simultaneous tri-band (S: 2.2 - 2.7 GHz, X: 7.5 - 9 GHz and Ka: 28 - 33 GHz) low-noise cryogenic receiver for geodetic Very Long Baseline Interferometry (geo-VLBI) which has been developed by the technical staff of Yebes Observatory (IGN) laboratories in Spain. The receiver was installed in the first radio telescope of the Red Atlántica de Estaciones Geodinámicas y Espaciales (RAEGE) project, which is located in Yebes Observatory, in the frame of the VLBI Global Observing System (VGOS). After this, the receiver was borrowed by the Norwegian Mapping Autorithy (NMA) for the commissioning of two VGOS radiotelescopes in Svalbard (Norway). A second identical receiver was built for the Ishioka VGOS station of the Geospatial Information Authority (GSI) of Japan, and a third one for the second RAEGE VGOS station, located in Santa María (Açores Archipelago, Portugal). The average receiver noise temperatures are 21, 23 and 25 Kelvin and the measured antenna efficiencies are 70%, 75% and 60% in S-band, X-band and Ka-band, respectively.