This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R 2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R 2 = 0.83) and a RMSE of 0.32 m 2 /m 2 (12% of mean value).

The massification of higher education in Poland means that many students choose this educational pathway to improve their chances for a good job. Therefore, the labour market outcomes of graduates provide an important perspective for future students, higher education institutions, as well as decision makers at the national level. The Polish Graduate Tracking System (ELA), based on administrative data, is designed to monitor graduates' outcomes in the labour market by type of studies, higher education institution, as well as individual curricula. Results of the first two years of graduate tracking show that the outcomes vary by study area, but also change over time. While in the first months after graduation, aspects such as prior experience in the labour market and place of residence have a substantial effect on employment chances, in the longer run, they lose their importance relative to other factors.

One of the most challenging aspects of obtaining detailed and accurate land-use and land-cover (LULC) maps is the availability of representative field data for training and validation. In this manuscript, we evaluate the use of the Eurostat Land Use and Coverage Area frame Survey (LUCAS) 2018 data to generate a detailed LULC map with 19 crop type classes and two broad categories for woodland and shrubland, and grassland. The field data were used in combination with Copernicus Sentinel-2 (S2) satellite data covering Europe. First, spatially and temporally consistent S2 image composites of (1) spectral reflectances, (2) a selection of spectral indices, and (3) several bio-geophysical indicators were created for the year 2018. From the large number of features, the most important were selected for classification using two machine-learning algorithms (support vector machine and random forest). Results indicated that the 19 crop type classes and the two broad categories could be classified with an overall accuracy (OA) of 77.6%, using independent data for validation. Our analysis of three methods to select optimum training data showed that by selecting the most spectrally different pixels for training data, the best OA could be achieved, and this already using only 11% of the total training data. Comparing our results to a similar study using Sentinel-1 (S1) data indicated that S2 can achieve slightly better results, although the spatial coverage was slightly reduced due to gaps in S2 data. Further analysis is ongoing to leverage synergies between optical and microwave data.

BAROQUE POLISH IN THE INTERNET OR THE ELECTRONIC DICTIONARY OF POLISH IN THE 17TH AND 18TH CENTURIES In 2021, the Electronic Dictionary of Polish Language of the 17th and 18th Centuries (e-SXVII) was given the 4th edition Award of the Linguistics Committee of the Polish Academy of Sciences for outstanding scientific achievements in the field of linguistics. The award was granted in the category of team work carried out by Prof. Włodzimierz Gruszczyński PhD together with the Studio of the History of Polish Language of the 17th and 18th Centuries IJP PAN under his management, where e-SXVII is being created. The dictionary has been recognized as “an achievement in producing linguistic resources or tools”. The article reports on the history of the studio and introduces the evolution of conceptual works, especially after the decision to suspend further publication of SXVII in paper form (2004) and transform it into the first Polish dictionary created initially in the electronic form. It also deals with detailed issues regarding the canon of sources, structure and content (shape) of individual elements of the entry and media structure, and relations with other digital collections (especially the Electronic Corpus of Polish Texts from the 17th and 18th Centuries). The authors of the article also describe an important IT tool created for the needs of this dictionary.