Examensarbeten för masterexamen

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    The core mass function in the galactic center
    (2024) Kinman, Alva; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Tan, Jonathan; Petkova, Maya
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    Chemical diversity among asymptotic giant branch stars
    (2024) Brinkmalm, Johanna; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; De Beck, Elvire; De Beck, Elvire
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    Ray-tracing based atmospheric propagation simulator for a 2x2 LOS MIMO system
    (2023) Zhou, Yongan; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Eriksson, Patrick; Bao, Lei; Coldrey, Mikael
    A microwave radio system with multiple antennas is one popular technology for backhaul network deployment to reach the capability increase required for 5G and 6G. Antenna separations at transmit and receive sites should be carefully designed to ensure a proper phase relation, in this Multiple Input Multiple Output (MIMO) system with long Line-of-Sight (LoS) paths between transmitter and receiver. The LOS MIMO system may fail to operate under an extremely refractive atmosphere due to a lack of sufficient system gain which is determined by the power level and phase condition of the received sub-streams. The contribution of the thesis is to provide a simulator that can model radio’s at mospheric propagation, and it can be further used to verify real link measurement data. It is tested that the simulator has minor accuracy loss over the propagating distance concerned in this study. The simulation of electromagnetic wave propa gation is based on Forward Ray Tracing (FRT). The results demonstrate that the simulator is capable of predicting channel performance (MIMO gain, MIMO phase, etc.) for a 2-by-2 LOS MIMO system over a refractive atmosphere. The results also demonstrate that the simulator is found to be in good agreement with the lit erature and with Parabolic Equation (PE) methods, validating its potential use for predicting the outage probability for the MIMO link. This study, to the author’s best knowledge, is the first work that models the im pact of atmospheric refractivity on LOS MIMO channels using FRT. It is found that for a 2x2 LOS MIMO system the antenna separation calculated assuming free space propagation is also valid for the case of standard refractivity. For other re fraction conditions, the link will more likely experience an outage due to variation in phase condition than loss of power. In addition, atmospheric multipath may in duce random MIMO phase variation. However, the simulator cannot yet properly tackle surface-induced effects on the signals; this requires further development of the software.
  • Post
    Predicting sea surface wave and wind parameters from satellite radar images using machine learning
    (2023) Borg, Filip; Brobeck, Axel; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Eriksson, Leif; Amell, Adrià; Elyouncha, Anis
    Accurate predictions of wave and wind parameters over oceans are crucial for various marine operations. Although buoys provide accurate measurements, their deployment is limited, which necessitates the exploration of alternative data sources. Sentinel-1, a satellite mission capturing Synthetic Aperture Radar (SAR) images with high coverage, presents a promising opportunity. However, establishing the relationship between SAR images and wave/wind parameters is not straightforward. This project aims to develop a machine learning model that can effectively extract this relationship. To accomplish this, data from all available buoys measuring significant wave height and wind speed in the year 2021 were utilized. The corresponding SAR images were located, and 2 km×2 km sub-images were extracted around each buoy. From each sub-image, a set of features were extracted. These sub-images and features served as input to train machine learning models capable of predicting buoy measurements, supplemented with model data as necessary. The project presents two final deep learning models: one utilizing only the extracted features and another employing both the sub-images and features. These multi-class regression models simultaneously predict significant wave height and wind speed. The model using only features achieved a Root Mean Square Error (RMSE) of 0.553 m for significant wave height and 1.573 m/s for wind speed. The model incorporating both sub-images and features achieved an RMSE of 0.459 m for significant wave height and 1.658 m/s for wind speed. The code for the project can be found on https://github.com/SEE-GEO/sarssw.
  • Post
    Beam selection optimization algorithm for the Satcube Ku.
    (2023) Olsson, Lucas; Saber, Pouria; Chalmers tekniska högskola / Institutionen för rymd-, geo- och miljövetenskap; Chalmers University of Technology / Department of Space, Earth and Environment; Haas, Rüdiger; Johansson, Jan; Malm, Calle
    The Satcube Ku is a satellite terminal which provides internet access to a user anywhere on Earth with the help of spot beam technology. For a given position on Earth, multiple intersecting beams will be present with varying signal strength. There is the need to distinguish one beam from another in order establish a hierarchy. The purpose of this master thesis is to investigate a way to rank the available beams at a position on Earth and develop that into a new beam selection algorithm for the Satcube Ku terminal. The new algorithm must address both circular and elliptical HTS-beams as the beams are projected across the surface of the Earth. There are many limitations to this project and many different approaches have been presented. The approach is to analyze user data and geospatial data combined with provided satellite information in order to find important aspects of the problem. A five step algorithm was ultimately decided. The algorithm takes the user position as an input and gives a sorted list from highest to lowest carrier-to-noise expected in that position as output. The steps are: Step 1: Position and all available beams, Step 2: Beamcenter of beams, Step 3: CNR value at beamcenter, Step 4: CNR reduction to the user posi tion, Step 5: CNR arranged list based on projection and accuracy. The step 1 was solved using polygon conversion of the beam contours given by the terminal and then calculating which polygons the user was inside of. In step 2 the beamcenter of the beams were estimated by measuring the distance from the polygon center to a beam center position provided by Intelsat for certain beams. In step 3 the each beamcenter was given a maximum theoretical value also provided by Intelsat. In step 4 three different methods were investigated to project what the received CNR would be at the user position. The methods were Concentric Contour Contraction, Concentric Elliptical Expansion and Least square fit method. The Concentric El liptical expansion was the most appropriate method available and was used in step 5 for a test position in Gothenburg. The resulting output was limited to only IS33 and IS37 satellites due to the lack of information regarding the beam center values. The final list projections compared to actual measurements performed within 1 dB.