In the region of Uppland, a shortage of electric power during cold days has emerged during the past years by virtue of the electrification of the society and industry in general. As a result, a power flexibility market managed by the CoordiNet project has commenced to hopefully create a more reliant, eco-friendly and accessible electricity supply. Uppsala kommun wishes to participate in the market but needs a solution for communication between market and technology and smart control methods. In this project, the solution to the problem, consisting of a mobile app, API, database, server and deep learning model, almost meets the requirements to participate on the market this autumn. With more time and resources, the product can hopefully be completed, enabling both economic and city growth in the region.

The number of man-made space objects is dramatically growing nowadays. The continuous monitoring and studying of these objects are necessary to keep their number under control and ensure safe space operations. With respect thereto, international guidelines recommend decongesting the most populated space regions from satellites arriving at the end of their operational lifetime by performing post-mission disposal strategies. In general, a satellite is considered to be functional if it is still performing periodic manoeuvres to stay within the orbital operation configuration. This study presents a promising method to detect historical in-plane manoeuvrers of satellites on a geostationary orbit (GEO). Since a manoeuvrer changes the orbital state of the spacecraft, its effect can be detected by comparing the observed data to a reference evolution. In this case, the  model is represented by the dynamical model STELA  based on a semi-analytical theory. The observed data is provided by the public American space object catalogue. The Two-line element (TLE) database contains the orbital state of each tracked object, however, not all six orbital parameters are interesting to study in terms of in- plane manoeuvrers. The evolution of the longitude and of the eccentricity vector is immediately affected by a manoeuvre that changes the shape or the size of an orbit. Within the longitude analysis, the manoeuvre epoch is estimated by focusing on the manoeuvre strategy. An operational spacecraft usually performs a manoeuvre as soon as the longitude motion threatens to violate the operational deadband. Consequently, the longitude evolution follows a parabolic motion. Two polynomial curves of second degree are laid over the observation: the first curve is derived from a simplified dynamical model and the second curve is directly obtained through a Least Squares (LS) fitting method. The discrepancy between the LS and physical fitted parabolas gives an indication on the quality of the input data, that is to say, of the TLEs. The detected manoeuvre epoch must be companioned by a confidential parameter that denotes the time range around the estimated epoch in which the manoeuvre is expected to have happened. The manoeuvre interval is then forwarded to the eccentricity analysis where the manoeuvrer epoch is estimated more precisely by studying the divergence between the observed and expected eccentricity vector evolution. The latter is propagated with STELA after having estimated the area-to-mass ratio that is needed in order to model the perturbation effects accurately upon which the performance of the dynamical reference model strongly depends. As soon as the observed eccentricity vector deviates significantly from the expected evolution, the epoch and the velocity ΔV of the manoeuvre can be recovered, too.

Background: Glioblastoma (GBM) is the most common and malignant primary brain tumor. It is a rapidly progressing tumor that infiltrates the adjacent healthy brain tissue and is difficult to treat. Despite modern treatment including surgical resection followed by radiochemotherapy and adjuvant chemotherapy, the outcome remains poor. The median overall survival is 10-12 months. Neuroimaging is the most important diagnostic tool in the assessment of GBMs and the current imaging standard is contrast-enhanced magnetic resonance imaging (MRI). Positron emission tomography (PET) has been recommended as a complementary imaging modality. PET provides additional information to MRI, in biological behavior and aggressiveness of the tumor. This study aims to investigate if the combination of PET and MRI can improve the diagnostic assessment of these tumors. Patients and methods: In this study, 22 patients fulfilled the inclusion criteria, diagnosed with GBM, and participated in all four 18F-fluorothymidine (FLT)-PET/MR examinations. FLT-PET/MR examinations were performed preoperative (baseline), before the start of the oncological therapy, at two and six weeks into therapy. Optimization of an adaptive thresholding algorithm, a batch processing pipeline, and image feature extraction algorithms were developed and implemented in MATLAB and the analyzing tool imlook4d. Results: There was a significant difference in radiochemotherapy treatment response between long-term and short-term survivors’ tumor volume in MRI (p<0.05), and marginally significant (p<0.10) for maximum standard uptake value (SUVmax), PET tumor volume, and total lesion activity (TLA). Preoperative short-term survivors had on average larger tumor volume, higher SUV, and total lesion activity (TLA). The overall trend seen was that long-term survivors had a better treatment response in both MRI and PET than short-term survivors.  During radiochemotherapy, long-term survivors displayed shrinking MR tumor volume after two weeks, and almost no remaining tumor volume was left after six weeks; the short-term survivors display marginal tumor volume reduction during radiochemotherapy. In PET, long-term survivors mean tumor volumes start to decrease two weeks into radiochemotherapy. Short-term survivors do not show any PET volume reduction two and six weeks into radiochemotherapy. For patients with more or less than 200 days progression-free survival, PET volume and TLA were significantly different, and MR volume only marginally significant, suggesting that PET possibly could have added value. Conclusion: The combination of PET and MRI can be used to predict radiochemotherapy response between two and six weeks, predicting overall survival and progression-free survival using MR and PET volume, SUVmax, and TLA. This study is limited by small sample size and further research with greater number of participants is recommended.

The investigation to understand the subsurface and its features has long been asubject of interest for various fields, including fields such as archaeology and infras-tructure projects. However, traditional excavation methods are often costly and time-consuming. In their place, alternative techniques such as borehole drilling, which isitself expensive, and ground-penetrating radar (GPR), which produces a good butdistorted image, have gained popularity. Nonetheless, the limitations of each methodimpede them from meeting the requirements of subsurface exploration. This Mas-ter’s thesis introduces an approach combining these two methods to overcome theirlimitations and enhance their accuracy to understand the subsurface.This thesis aims to demonstrate the feasibility and effectiveness of integratingborehole drilling and GPR for subsurface exploration. Specifically, the integrationof borehole with GPR-profiles will be examined to enhance their practicality andaccuracy, meaning that this thesis will investigate the utilization of borehole datato update and adjust GPR-profiles, thereby providing more precise and informativedata for further analysis.The findings of this work indicate that combining borehole drilling and GPR-profiling to improve and update the accuracy of the GPR-profiles is entirely feasibleand results in a substantially improved subsurface exploration capability. Further,the outcomes of this thesis suggest that the integrated approach can generate amore precise representation of the underground structure. Ultimately, the proposedintegration of borehole drilling and GPR-profiling presents a promising approach toenhance the accuracy and efficiency of subsurface exploration and has the potentialto be valuable in a wide range of fields.i

Neuromorphic computing applications could be made faster and more power efficient by emulating the function of a biological synapse. Non-conventional spintronic devices have been proposed that demonstrate synaptic behavior through domain wall (DW) driving. In this work, current induced domain wall motion has been studied through micromagnetic simulations. We investigate the synaptic behavior of a head to head domain wall driven by a spin polarized current in permalloy (Py) nanostrips with shape anisotropy, where triangular notches have been modeled to account for edge roughness and provide pinning sites for the domain wall. We seek optimal material parameters to keep the critical current density for driving the domain wall at order 1011 A/m2.

This study had the purpose to further the use of industrial lasers in the manufacturing of hairpin electric motors by optimizing the process of contacting the hairpins. A problem with laser beam welding of copper is the porosity created in the process which can lead to increased resistance of the welded region along with degraded mechanical properties. By experiment this study aimed to find the optimal parameters to reduce the porosity while maintaining all other requirements for the weld. The track of achieving a satisfactory simulation was done to minimize the need of physical experiments which can be argued as a sustainable development aspect. A set of parameters was found that achieved a low volume of pores, a sufficient weld depth and a desirable bead geometry. Some pores still remained, mostly at the endpoint of the laser path which could be caused by the laser shut-off leading to a keyhole collapse enclosing some pores in that region. The simulation showed promising results in welding depth and melt region. Further work on ramping the laser power at the endpoint could be beneficial for eliminating the remaining porosity as well as refining the simulation in terms of porosity.

Many transition metal oxides exhibit a Metal-Insulator Transition (MIT). This is an interesting phenomenon where the material undergoes a structural phase transition, and the electronic charge is redistributed in the crystal lattice. The crystallographic, optical, electrical, and magnetic properties are drastically changed when a material undergoes a MIT. Vanadium oxides are thermochromic materials, which means that the optical properties change with temperature, closely connected to the MIT. Vanadium sesquioxide (V2O3) experiences a MIT at the transition temperature (Tt) 160-170 K. Below the transition temperature, it is a monoclinic ferromagnetic insulator, and above the transition temperature it becomes a paramagnetic rhombohedral corundum-type metal. In the insulating phase, infrared (IR) light is transmitted, whereas in the metallic phase, IR light is reflected. The aim of this Master thesis is to improve the methodology to produce V2O3 thin films, characterize them, and study how different parameters affect the structural and optical properties of the film. V2O3 thin films were synthesized by sol-gel and deposited by spin-coating on sapphire substrates. Thereafter, the resulting films underwent an annealing process in a reducing atmosphere to achieve crystalline V2O3. The obtained crystal phase was examined by X-ray diffraction. The surface structure and morphology were studied with an optical microscope and scanning electron microscope. The optical transmittance in the IR region was examined by Fourier transform infrared spectroscopy. At last, a laser test was performed on one of the samples. In conclusion, a majority of the thin films consisted of polycrystalline V2O3. The MIT was studied with the optical hysteresis loop, which showed great difference among the different samples studied. The thin film that exhibited best optical properties showed a maximum transmission of 66 % below the Tt, and a minimum transmission of 6 % above the Tt. In comparison to this one, the other samples exhibited lower transmission and a decrease in Tt. This difference was attributed to the different morphologies and crystal orientations of the samples.

Both computed tomography (CT) and magnetic resonance imaging (MRI) have a purpose for radiotherapy. But having two imaging sessions brings uncertainty which makes it beneficial to create synthetic CT (sCT) images from MR images. In this work a Generative Adversarial Network (GAN) was designed and implemented for sCT generation. The purpose of the work was to broaden the understanding of how variation in training data affects a model’s performance on generating sCTs. This was done by increasing the training sets with patients with artifacts, female patients and synthetic MR contrasts. Eight different machine learning models with varying training data were trained and evaluated. Four models were trained using T2-weighted data only while the other four used both real T2-weighted images and synthetic T1 (sT1) images in their training sets. The models were evaluated on the pixel value difference between the CT and the resulting sCTs using a mean absolute error (MAE) evaluation. Afterwards, dose calculations were made with patients’ treatment plans on both their CTs and their corresponding sCTs and compared doses to some of the structures. Finally the models were compared based on their performance on synthetic MR contrasts. This means I used a contrast transfer model to change the contrast from a T1-weighted image to a synthetic T2 or from a T2-weighted image to a synthetic T1 and then generated sCT images from the synthetic contrasts. These experiments showed that when I increased the models’ training sets with relevant patients MAE decreased between the CT and a generated sCT. Importantly, this was also true for our models trained on sT1s when evaluating on T1 weighted images. Increasing the size of the datasets also increased the performance in a treatment planning purpose and it also decreased the difference when evaluating the models on original MR images and synthetic MR images. In conclusion an improved performance was shown for models evaluated on images with artifacts, female patients and other MR contrast when including more images from those image types in the training dataset.

Mars’ climate has undergone many massive changes over the course of it’s lifetime. In order toestablish how Mars lost the vast majority of its water, we must be able to understand how Marsis losing its atmosphere today. By understanding the current escape rates of H and D and theprocesses that control them, we can extrapolate back in time to model the escape rates under pastconditions. By using the Exospheric General Model (EGM) developed by researchers at LATMOS,Sorbonne University, I have simulated the density profiles and escape rates of H, D, related isotopesand particles due to collisions with hot oxygen particles in the Martian exosphere at the currentepoch at mean solar activity. By adding H and D to the model and implementing changes to theprogram between simulations, I have improved the accuracy of the escape rate of these particlesfrom Mars in the EGM. While my results for H, H2 and HD reflect what has been observed fromin-situ Martian Satellite, MAVEN, future work is needed to include the solar wind interaction for Din the model, as it has been shown to be significant and has been left out of this work.

Qubits (quantum bits) are what runs quantum computers, like a bit in classical computers. Quantum gates are used to operate on qubits in order to change their states. As such they are what ”programmes” a quantum computer. An unfortunate side effect of quantum physics is that coupling a quantum system (like our qubits) to an outside environment will lead to a certain loss of information. Reducing this decoherence effect is thus vital for the function of a quantum computer. Geometric quantum computation is a method for creating error robust quantum gates by using so called geometric phases which are solely reliant on the geometry of the evolution of the system. The purpose of this project has been to develop physical schemes of geometric entangling two-qubit gates along the Schmidt sphere, a geometric construct appearing in two-qubit systems. Essentially the overall aim has been to develop new schemes for implementing robust entangling quantum gates solely by means of interactions intrinsic to the computational systems. In order to create this gate four mutually orthogonal states were defined which together spanned the two-qubit state space. Two of the states were given time dependent variables containing a total of two angles,which were used to parameterize the Schmidt sphere. By designing an evolution for these angles that traced out a cyclical evolution along geodesic lines a quantum gate with exclusively geometric phases could be created. This gate was dubbed the ”Schmidt gate” and could be shown to be entangling by analyzing a change in the concurrence of a two qubit system. Two Hamiltonians were also defined which when acted upon the predefined system of states would give rise to the aforementioned evolution on the Schmidt sphere. The project was successful in creating an entangling quantum gate which could be shown by looking at difference in the concurrence of the input and output state of a two-qubit system passing through the gate.