The subject of this thesis is the novel Das fliegende Klassenzimmer (1933) written by the German writer Erich Kästner and its two Czech translations from Marta Třísková (1935) and Jitka Fučíková (1961). The beginning of the theoretical part describes the author's personality and his work. Afterwards, the literary, cultural and historical context are explained, followed by other aspects connected with the German source text. The theoretical part also includes the translation theory, on which the thesis is based, and the specifics of translating children's literature. The empirical part consists of a translation analysis of the original and the Czech versions and concludes with a comparison of both translations.

The subject of this thesis is the novel Das fliegende Klassenzimmer (1933) written by the German writer Erich Kästner and its two Czech translations from Marta Třísková (1935) and Jitka Fučíková (1961). The beginning of the theoretical part describes the author's personality and his work. Afterwards, the literary, cultural and historical context are explained, followed by other aspects connected with the German source text. The theoretical part also includes the translation theory, on which the thesis is based, and the specifics of translating children's literature. The empirical part consists of a translation analysis of the original and the Czech versions and concludes with a comparison of both translations.

This thesis presents the design of an autopilot capable of flying small unmanned aerial vehicles with wingspans less then 21 inches. The autopilot is extremely small and lightweight allowing it to fit in aircraft of this size. The autopilot features an advanced, highly autonomous flight control system with auto-launch and auto-landing algorithms. These features allow the autopilot to be operated by a wide spectrum of skilled and unskilled users. Innovative control techniques implemented in software, coupled with light weight, robust, and inexpensive hardware components were used in the design of the autopilot.

Compared with the terrestrial network (TN), which can only support limited covered areas, satellite communication (SC) can provide global coverage and high survivability in case of an emergency like an earthquake. Especially low-earth orbit (LEO) satellites, as a promising technology, which is integral to achieving the goal of global seamless coverage and reliable communication, catering to 6G’s communication requirements. Nevertheless, the swift movement of the LEO satellites poses a challenge: frequent handovers are inevitable, compromising the quality of service (QoS) of users and leading to discontinuous connectivity. Moreover, considering LEO satellite connectivity for different flying vehicles (FVs) when coexisting with ground terminals, an efficient satellite handover decision control and mobility management strategy is required to reduce the number of handovers and allocate resources that align with different user requirements. With the development of machine learning (ML) methods, which can greatly enhance system performance and automation, reinforcement learning (RL), as a sub-field in ML has been employed to optimize decision control. Due to the challenges of dimensionality explosion and the propensity for traditional Q-learning algorithms to get trapped in local minima, deep learning has been introduced with RL. In this thesis, the high-dimensionality user-satellite network is constructed including the LEO constellation from the ephemeris data, different types of flying vehicles such as aircraft and drones, and ground terminals. Two mathematical optimization models named the traditional low handover model and network utility model when considering the full criteria including the remaining visible time, downlink (DL) carrier-to-interference-plus-noise ratio (CINR) and the available idle channels are formulated. In this way, a novel satellite handover strategy based on Multi-Agent Reinforcement Learning (MARL) and game theory named Nash-SAC has been proposed to solve these problems. From the simulation results, compared with different benchmarks such as the traditional Q-learning algorithm, Maximum available channel (MAC)-based strategy, and Maximum instantaneous signal strength (MIS)-based strategy, Nash-SAC can effectively reduce the number of satellite handovers by over 16% close to the lower limit, and the blocking rate by over 18%. Moreover, Nash-SAC can greatly improve the network utility of the whole system by up to 48% and cater to different users’ requirements, providing reliable and robust connectivity for both FVs and ground terminals. / Jämfört med det markbundna nätet (TN), som endast kan stödja begränsade täckta områden, kan satellitkommunikation (SC) ge global täckning och hög överlevnad vid en nödsituation som en jordbävning. Speciellt lågjordiga satelliter (LEO), som en lovande teknik, som är integrerad för att uppnå målet om global sömlös täckning och tillförlitlig kommunikation, tillgodose 6G:s kommunikationskrav. Icke desto mindre utgör LEO-satelliternas snabba förflyttning en utmaning: täta överlämningar är oundvikliga, vilket äventyrar användarnas tjänstekvalitet och leder till kontinuerlig uppkoppling. Med tanke på LEO:s satellitanslutning för olika flygande fordon när de samexisterar med markterminaler krävs dessutom en effektiv strategi för kontroll av satellitöverlämning och mobilitetshantering för att minska antalet överlämningar och fördela resurser som överensstämmer med olika användarkrav. Med utvecklingen av maskininlärningsmetoder (ML), som avsevärt kan förbättra systemprestanda och automation, har förstärkningsinlärning (RL), som ett delområde i ML använts för att optimera beslutskontrollen. På grund av utmaningarna med dimensionsexplosion och benägenheten för traditionella Q-inlärningsalgoritmer att fastna i lokala minimi har djupinlärning introducerats med RL. I denna avhandling konstrueras det högdimensionella användarsatellitnätet inklusive LEO-konstellationen från ephemerisdata, olika typer av flygande fordon såsom flygplan och drönare samt markterminaler. Två matematiska optimeringsmodeller kallas den traditionella lågöverlämningsmodellen och nätverksbruksmodellen när man beaktar de fullständiga kriterierna inklusive återstående synliga tiden, nedlänk (DL) carrier-to-interferens-plus-noise ratio (CINR) och tillgängliga inaktiva kanaler formuleras. På detta sätt har en ny satellitöverlämningsstrategi baserad på Multi-Agent Reinforcement Learning (MARL) och spelteori vid namn Nash-SAC föreslagits för att lösa dessa problem. Från simuleringsresultaten, jämfört med olika riktmärken såsom den traditionella Q-learning algoritmen, Maximal available channel (MAC)-baserad strategi och Maximal instantaneous signalstyrka (MIS)-baserad strategi, kan Nash-SAC effektivt minska antalet satellitöverlämningar med över 16% nära den nedre gränsen och blockeringshastigheten med över 18%. Dessutom kan Nash-SAC avsevärt förbättra nätverksnyttan i hela systemet med upp till 48% och tillgodose olika användares krav, vilket ger tillförlitlig och robust anslutning för både flygande fordon och markterminaler.

Flying can constitute as much as a third of an academic institution’s total emissions and there’s a growing concern among researchers around their academic travel contributing to global warming. This paper is part of KTH FLIGHT, a research project for decreased CO2-emissions in flight-intensive organizations through creation and testing of practical tools. It is a Research through Design work that provides new knowledge about a previously largely unexplored implementation area of emission trading systems like Cap and Trade. These have previously helped reduce emissions at lower costs than tax based systems in various settings but little research exists in terms of design and important factors for a university to promote more sustainable travel patterns. This paper reveals challenges and opportunities regarding implementation and shows current feasibility in implementing in a Swedish university (KTH), and provides suggestions for choosing suitable users and direction of future research. / Flygresor kan utgöra upp till en tredjedel av en akademisk institutions totala utsläpp och det finns en växande oro bland forskare kring att deras resande bidrar till den globala uppvärmningen. Denna rapport är en del av KTH FLIGHT, ett forskningsprojekt med syfte att minska CO2 -utsläpp i flygintensiva organisationer genom att skapa och testa praktiska verktyg. Detta är ett Research through Design-arbete som bidrar med ny kunskap om ett tidigare till stor del outforskat område för implementeringar av utsläppshandelssystem såsom Cap and Trade. Dessa har i olika sammanhang tidigare bidragit till minskade utsläpp med lägre kostnader än skattebaserade system. Men i dagsläget finns det lite forskning gällande dess design och viktiga faktorer för att främja mer hållbara resvanor på ett universitet. Denna uppsats visar på utmaningar och möjligheter vid en implementering och visar aktuell genomförbarhet i ett svenskt universitet (KTH), samt ger förslag på val av lämpliga användare samt inriktning för framtida forskning.

Цель работы – разработка системы управления высокоточной опорно-поворотной платформы. В работе представлен анализ требований к системе управления опорно-поворотной платформы, произведены расчеты и оценка моментов опорно-поворотной платформы, произведен выбор оборудования, входящего в состав системы опорно-поворотной платформы. / The aim of the dissertation is to develop a control system for a precise mounting-rotating platform. The dissertation presents an analysis of the requirements for the control system of the mounting-rotating platform, calculations and assessment of the torques of the mounting-rotating platform are performed, the selection of equipment that is part of the system of the mounting-rotating platform is performed.

<p dir="ltr">The Global Navigation Satellite System (GNSS) is heavily relied on for the navigation of Earth satellites. For satellites in cislunar space and beyond, GNSS is not readily available. As a result, other sources such as NASA's Deep Space Network (DSN) must be relied on for navigation. However, DSN is overburdened and can only support a small number of satellites at a time. Furthermore, communication with external sources can become interrupted or deprived in these environments. Given NASA's current efforts towards cislunar space operations and the expected increase in cislunar satellite traffic, there will be a need for more autonomous navigation options in cislunar space and beyond.</p><p dir="ltr">In this thesis, a navigation system capable of accurate and computationally efficient orbit determination in these communication-deprived environments is proposed and investigated. The emphasis on computational efficiency is in support of cubesats which are constrained in size, cost, and mass; this makes navigation even more challenging when resources such as GNSS signals or ground station tracking become unavailable.</p><p dir="ltr">The proposed navigation system, which is called GRAVNAV in this thesis, involves a two-satellite formation orbiting a planet. The primary satellite hosts an Extended Kalman Filter (EKF) and is capable of measuring the relative position of the secondary satellite; accurate attitude estimates are also available to the primary satellite. The relative position measurements allow the EKF to estimate the absolute position and velocity of both satellites. In this thesis, the proposed navigation system is investigated in the two-body and three-body problems.</p><p dir="ltr">The two-body analysis illuminates the effect of the gravity model error on orbit determination performance. High-fidelity gravity models can be computationally expensive for cubesats; however, celestial bodies such as the Earth and Moon have non-uniform and highly-irregular gravity fields that require complex models to describe the motion of satellites orbiting in their gravity field. Initial results show that when a second-order zonal harmonic gravity model is used, the orbit determination accuracy is poor at low altitudes due to large gravity model errors while high-altitude orbits yield good accuracy due to small gravity model errors. To remedy the poor performance for low-altitude orbits, a Gravity Model Error Compensation (GMEC) technique is proposed and investigated. Along with a special tuning model developed specifically for GRAVNAV, this technique is demonstrated to work well for various geocentric and lunar orbits.</p><p><br></p><p dir="ltr">In addition to the gravity model error, other variables affecting the state estimation accuracy are also explored in the two-body analysis. These variables include the six Keplerian orbital elements, measurement accuracy, intersatellite range, and satellite formation shape. The GRAVNAV analysis shows that a smaller intersatellite range results in increased state estimation error. Despite the intersatellite range bounds, semimajor axis, measurement model, and measurement errors being identical for both orbits, the satellite formation shape also has a strong influence on orbit determination accuracy. Formations that place both satellites in different orbits significantly outperform those that place both satellites in the same orbit.</p><p dir="ltr">The three-body analysis primarily focuses on characterizing the unique behavior of GRAVNAV in Near Rectilinear Halo Orbits (NRHOs). Like the two-body analysis, the effect of the satellite formation shape is also characterized and shown to have a similar impact on the orbit determination performance. Unlike the two-body problem, however, different orbits possess different stability properties which are shown to significantly affect orbit determination performance. The more stable NRHOs yield better GRAVNAV performance and are also less sensitive to factors that negatively impact performance such as measurement error, process noise, and decreased intersatellite range.</p><p dir="ltr">Overall, the analyses in this thesis show that GRAVNAV yields accurate and computationally efficient orbit determination when GMEC is used. This, along with the independence of GRAVNAV from GNSS signals and ground-station tracking, shows that GRAVNAV has good potential for navigation in cislunar space and beyond.</p>

The pursuit of precision and flexibility in satellite missions has led to an increased number of formation flying missions being developed. These systems consist of multiple satellites flying at close distances (from a few kilometres to a few meters) to achieve common objectives. This master thesis delves into the domain of the Guidance, Navigation and Control (GNC) for formation flying satellite systems, aiming to propose a novel architecture of different sets of sensors capable of determining absolute and relative positioning of the formation, ensuring mission success. This research begins by providing an overall status of existing and tested in-space systems. It will be complemented with novel and other systems already tested and promising new technologies in development. The thesis then delves into the design of an absolute and a relative Extended Kalman Filter (EKF) for distributed Synthetic Aperture Radar (SAR) systems implemented as part of an in-house simulator. Concluding with the results when using simulated Global Navigation Satellite Systems (GNSS) data as the filter input. Finally, the thesis will be completed with a trade-off analysis of the sensor systems, which could be used in formation-flying satellite systems in the near future. The outcome of this thesis is a novel proposal of a set of sensors to be brought to space navigation, with a corresponding detailed trade-off analysis. Additionally, to validate some of the sensor systems, an EKF is proposed, implemented and tested with the results from an in-house formation flying simulator. This master thesis report is the outcome of the work done during an internship at the Microwave and Radar Institute of the Deutsche Zentrum für Luft- und Raumfahrt e.V. (DLR) – German Aerospace Center – in Oberpfaffenhofen, Bavaria, Germany.

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