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In-Orbit Commissioning of Czech Nanosatellite VZLUSAT-1 for the QB50 Mission with a Demonstrator of a Miniaturised Lobster-Eye X-Ray Telescope and Radiation Shielding Composite Materials
(Springer, 2019) Dániel V.; Inneman A.; Veřtát I.; Báča T.; Nentvich O.; Urban M.; Stehlíková V.; Sieger L.; Skala P.; Filgas R.; Zadražil V.; Linhart R.; Masopust J.; Jamroz T.; Pína L.; Maršíková V.; Mikulíčková L.; Belas E.; Pospíšil S.; Vykydal Z.; Mora Y.; Pavlica R.
This paper presents the results of in-orbit commissioning of the first Czech technological CubeSat satellite of VZLUSAT-1. The 2U nanosatellite was designed and built during the 2013 to 2016 period. It was successfully launched into Low Earth Orbit of 505 km altitude on June 23, 2017, as part of international mission QB50 onboard a PSLV C38 launch vehicle. The satellite was developed in the Czech Republic by the Czech Aerospace Research Centre, in cooperation with Czech industrial partners and universities. The nanosatellite has three main payloads. The housing is made of a composite material which serves as a structural and radiation shielding material. A novel miniaturized X-Ray telescope with lobster-eye optics and an embedded Timepix detector represents the CubeSat’s scientific payload. The telescope has a wide field of view. VZLUSAT-1 also carries the FIPEX scientific instrument as part of the QB50 mission for measuring the molecular and atomic oxygen concentration in the upper atmosphere.
Návrh a výroba biokompatibilních polymerních kompozitů s nanoplnivy pro biomedicínské aplikace
Design and Fabrication of Biocompatible Polymer Composites with Nanofillers for Biomedical Applications
(Czech Technical University in Prague, 2026-06-15) Havaldar, Darshana; Cvrček, Ladislav; Keerthiveettil Ramakrishnan, Sumesh; Velgošová, Oksana; Bačáková, Lucie
Snaha o nalezení pokročilých biomateriálů vhodných pro namáhaná kloubní spojení vedla k vývoji polymerních kompozitů s vynikajícími mechanickými vlastnostmi, odolností proti opotřebení a biokompatibilitou. Tato práce si proto kladla za cíl vyrobit a analyzovat nanokompozity na bázi ultra vysokomolekulárního polyethylenu (UHMWPE) vyztužené nemodifikovanými a modifikovanými nanoplnivy z titaničitanu barnatého (BaTiO3), s primárním důrazem na jejich mechanické, tribologické a biologické vlastnosti. V prvním kroku byly tlakovým lisováním vyrobeny nemodifikované nanokompozity UHMWPE/BaTiO3 (010 hmot.%). Následně byly studovány jejich mechanické, tribologické, povrchové vlastnosti a biokompatibilita za účelem stanovení optimálního obsahu plniva. Složení s 5 hmot.% BaTiO3 (PEBT 5) vykazovalo nejvyváženější kombinaci žádaných vlastností. Z tohoto důvodu byl konstantní obsah plniva 5 hmot.% vybrán pro studium účinku povrchové úpravy pomocí (3-aminopropyl)triethoxysilanu (APTES). Povrch BaTiO3 byl upravován s různými stupni funkcionalizace a následně smíšen s UHMWPE, čímž se dále zlepšila distribuce plniva, kompatibilita s matricí a mezifázové interakce. Modifikované kompozitní systémy (UHMWPE/m_BaTiO3) byly podrobně charakterizovány, včetně TGA a FTIR, které potvrdily přítomnost povrchově vázané silanové vrstvy bez změny krystalické struktury plniva, což bylo ověřeno pomocí XRD. Pomocí SEM pozorování bylo zjištěno, že disperze funkcionalizovaného BaTiO3 v polymerní matrici se zlepšila. Modifikované kompozity navíc vykazovaly vynikající mechanické vlastnosti ve srovnání s čistým UHMWPE i nemodifikovanými kompozity. Pro hodnocení tribologických vlastností byl vybrán optimální modifikovaný kompozit (PE_mBT4). Studie smáčivosti ukázala zvýšenou hydrofilnost modifikovaných kompozitů, zatímco in vitro stanovení bioaktivity v simulované tělní tekutině (SBF) ukázalo omezenou, ale detekovatelnou schopnost tvorby apatitu ve srovnání s bioinertním UHMWPE. Provedený výzkum prezentuje povrchovou funkcionalizaci BaTiO3 pomocí APTES jako efektivní přístup ke zlepšení disperze a mezifázové kompatibility v nanokompozitech na bázi UHMWPE. Optimalizovaný nanokompozitní systém dosahuje lepší rovnováhy mezi mechanickými vlastnostmi, tribologickým chováním a biologickou odezvou. To naznačuje jeho potenciální použitelnost v ortopedických aplikacích, což by ale vyžadovalo dlouhodobé podrobné hodnocení.
Weakly-supervidsed learning with applications in biomedicial image analysis
Slabě řízené učení s aplikacemi v analýze biomedicínských obrazů
(Czech Technical University in Prague) Baručić, Denis; Kybic, Jan; Brázdil, Tomáš; Cheplygina, Veronika
Supervised learning has achieved remarkable success in biomedical image analysis, but it typically requires large amounts of fully annotated data. Acquiring such annotations is laborious and, in the biomedical domain, often requires expert knowledge. In many practical scenarios, weak annotations of various forms are easier to obtain. This thesis studies several examples of learning from data consisting of labeled groups of unlabeled instances. We first consider Multiple Instance Learning (MIL), where a group is labeled positive if it contains at least one positive instance. Building upon an existing MIL method, we propose a learning objective that explicitly incorporates instance-level supervision through pseudo-labeling. The resulting method improves instance classification performance while preserving strong group-level accuracy. Next, we address Learning from Label Proportions (LLP). In LLP, each group is annotated with the proportion of positive instances it contains. We focus on problems with small training groups and model the number of positive instances in a group using the Poisson binomial distribution. We propose an expectationmaximization algorithm for training a deep network that parametrizes this model. The method converges faster than existing deep LLP approaches while maintaining competitive predictive performance. Third, we study weakly supervised image segmentation with annotation in the form of object sizes. Our approach is based on a distance function that is non-differentiable with respect to the pixel probabilities, which prevents the application of gradient descent. Therefore, instead of optimizing the loss directly, we inject virtual noise into the system, and optimize the expected loss. The approach improves segmentation performance when only a few fully (pixel-wise) annotated images and many weakly annotated images are available. Finally, we propose a group-based formulation for recognizing chemical compounds applied to cells in microscopy images. The formulation allows the classifier to account for varying concentration and exposure conditions of the predicted compound. The resulting classification method matches or outperforms existing alternatives. Together, our contributions demonstrate that, although weakly supervised learning is inherently challenging, it can be successfully applied to practical biomedical image analysis problems.
Towards Robust Long-Term Visual Teach and Repeat Navigation
Cesta k robustní dlouhodobé vizuální navigaci
(Czech Technical University in Prague) Rozsypálek, Zdeněk; Krajník, Tomáš; Christoforis, Pablo de; Balogh, Richard
Autonomous mobile robotics is a rapidly developing field of research with many real-world applications, where the viability of mobile system deployment is heavily dependent on the ability to reliably navigate within the operational area. Autonomous navigation is a complex problem that requires advanced data processing and decision-making on multiple levels. One widely used framework is Visual Teach and Repeat (VT&R) navigation, which divides mapping and navigation into two separate processes and allows a precise definition of the robots operational space. These simplifications make VT&R a popular choice for various deployment scenarios. However, unstructured or highly dynamic environments still pose a significant challenge to most existing autonomous mobile systems. This thesis focuses on improving navigation robustness in long-term autonomous setups, where uncontrolled environments can undergo significant appearance or structural changes. We propose a novel visual model with state-of-the-art performance for horizontal image registration under time-specific environmental changes. Building on this model, we design a probabilistic state estimator that significantly improves navigation precision. We also present an alternative approach to VT&R that can substantially reduce mapping requirements. All proposed methods address common problems encountered in real-world deployments and have practical implications for the applicability of VT&R in autonomous mobile robotics.
Effect of surface preparation and adhesive type on CFRP–steel bridge joints
(Ernst & Sohn, 2026) Boutar Y.; Ryjáček P.; Hovorka J.; Jarolím T.; Montalbano A.; Tichá P.
Strengthening steel bridges using carbon fibre reinforced polymer (CFRP) laminates is an effective technique to increase load-carrying capacity and extend the service life of ageing infrastructure. The performance of CFRP strengthening systems depends strongly on the quality of the adhesive bond between steel and CFRP, making adhesive selection and surface preparation critical design parameters. This study experimentally investigates the influence of adhesive type and primer application on the bond behaviour of CFRP–steel joints manufactured using a wet-layup technique. Mild steel substrates were sandblasted, with half of the specimens receiving an additional primer coating. Three commercially available structural epoxy adhesives were used to bond CFRP fabrics to steel substrates. A total of 18 CFRP–steel double-lap joints were tested under quasi-static shear loading. Load–displacement behaviour and shear strength were evaluated. The results show that both adhesive type and primer application significantly affect joint performance. While primer application improved consistency for some adhesive systems, it did not universally increase shear strength and, in certain cases, resulted in reduced capacity. The findings highlight the adhesive-dependent nature of surface treatment effects and provide practical guidance for the design of durable CFRP strengthening systems for steel structures.