Teilchendirektionalität und andere Aspekte der Strahlungsumgebung auf dem Mars

Mars as a future target of manned exploration is a place of particular interest in the solar system. Both the past and the present habitability of Mars, and the safety of astronauts working on the Martian surface, depend, among other factors, on the ionizing radiation present on its surface. As part of the Mars Science Laboratory mission, the Radiation Assessment Detector (RAD). One of the scientific goals being addressed by the instrument is to aid in the validation of particle transport models used to simulate the radiation environment on the Martian surface. This thesis aims to address this goal in particular. Global dust storms are a meteorological feature that is unique to Mars. These storms produce a striking difference in the appearance of the planet, obscuring almost all of its surface beneath a dense layer of dust. In order to predict the changes in radiation environment, simulations comparing a global dust storm to normal atmospheric conditions are performed. Additionally, an attempt is made to validate the results of the simulations using a period of enhanced dust activity that is included in the RAD measurement time. The Martian radiation environment is dominated by downward particle fluxes of Galactic Cosmic Rays and the secondary particles produced by them. Near the Martian surface, an upward-directed component composed of secondary particles produced in the Martian soil adds to the total radiation environment. In order to fully characterize the radiation environment, this upward component must be understood as well. A method for discriminating particle directionality in RAD for charged particles is developed through simulations and validated through instrument observations. The RAD instrument is unable to determine directionality for neutrons due to its design. However, since neutrons are important for dosimetry, an instrument design capable of measuring neutron directionality is described and its basic capabilities assessed.Im Sonnensystem ist der Mars als Ziel späterer bemannter Missionen von besonderem Interesse. Die Kenntnis der ionisierenden Strahlung auf dem Mars ist sowohl für die Beurteilung der früheren und heutigen Habitabilität als auch für die Sicherheit zukünftiger Astronauten von großer Bedeutung. Im Rahmen der Mission Mars Science Laboratory mißt das Instrument Radiation Assessment Detector (RAD). Eines der wissenschaftlichen Ziele des Instrumentes ist die Validierung von Teilchentransportmodellen. Dieses Ziel wird in der vorliegenden Arbeit vorrangig verfolgt. Globale Staubstürme sind ein Phänomen, das ausschließlich auf dem Mars vorkommt. Solche Stürme verursachen markante Veränderungen des Planeten, da nahezu die gesamte Oberfläche durch eine dichte Staubschicht verdeckt wird. Um Vorhersagen über diesen Einfluss machen zu können, wurden Simulationen, die einen globalen Staubsturm mit normalen Atmosphärenbedingungen vergleichen, durchgeführt. Zusätzlich wurde ein Versuch unternommen, die Simulationsergebnisse anhand einer innerhalb der RAD-Messungen liegenden Zeit mit erhöhter Staubaktivität zu validieren. Die Strahlungsumgebung auf dem Mars wird durch abwärts gerichtete Teilchenflüsse, Galactic Cosmic Rays und ihre Sekundärteilchen, dominiert. Nahe der Oberfläche existiert zusätzlich eine aufwärts gerichtete Komponente aus innerhalb des Bodens produzierten Sekundärteilchen. Um die Strahlungsumgebung vollständig zu beschreiben, ist es notwendig, diese aufwärts gerichtete Komponente zu verstehen. Hier wurde eine Methode zur Unterscheidung der Teilchenrichtung geladener Teilchen im RAD anhand von Simulationsdaten entwickelt und durch Observationsdaten validiert. Aufgrund seiner Konstruktion ist RAD nicht in der Lage, die Richtung von Neutronen zu messen. Da sie aber für die Dosimetrie relevant sind, wurde ein Instrument, das dazu in der Lage ist, vorgestellt und seine grundlegenden Eigenschaften beurteilt

The Traumschreiber System: Enabling Crowd-based, Machine Learning-driven, Complex, Polysomnographic Sleep and Dream Experiments

Sleep and dreaming are important research topics. Unfortunately, the methods for researching them have several shortcomings. In-laboratory polysomnographic sleep and dream research is a costly, time-consuming and effortful endeavor, often resulting in small subject counts. Moreover, the unfamiliar sleeping environment can lead to distorted measurements as compared to the natural sleep environment at the subject’s home. Conducting sleep and dream experiments in the field by a crowd of subjects could be a solution. However, complex experiment paradigms cannot be investigated this way, because there are no tools available, which enable naive subjects to carry out complex polysomnographic studies on their own. The Traumschreiber system, which is developed and evaluated in this dissertation, offers a solution to this problem. It consists of a high-tech sleep mask and a minicomputer, and enables naive crowd subjects to perform complex polysomnographic sleep and dream experiments at home. On the one hand, it instructs the crowd subject, what to do when. On the other hand, it controls the experiment during the time the subject is asleep, analyzing the data in real-time using state-of-the art machine learning techniques. The rationale behind is to enable a big data approach to sleep and dream research, using the data recorded by a crowd of subjects for large-scale investigations about sleep and dreaming, with low costs for the researcher. After describing the development process of the Traumschreiber system, its usefulness regarding crowd-based automated polysomnographic field studies is evaluated. First, it is validated against a commercial medical poly­somnographic sleep laboratory system, demonstrating its good polysomnographic data recording capabilities – including measurements of EEG, EOG, EMG and ECG –, which enable the researcher to identify typical sleep patterns like slow waves or rapid eye movements as well as sleep stages in the recorded data. Furthermore, two field studies show, that the Traumschreiber system can be used successfully by naive subjects to conduct complex sleep experiments at their homes. This includes acoustic stimulation of the sleeping subject as well as sleep stage dependent activities of the system. The sleep staging algorithm implements a Keras/Tensorflow based neural network approach, which demonstrates the system’s readiness for state-of-the-art machine learning techniques. However, the currently used neural network is kept very simple and can determine the sleep stage not very reliably; it should be further developed and trained on more data of more subjects. The Traumschreiber system will be made available under an open source license, enabling any researcher to use, modify or further develop it. A description, how to produce arbitrarily many entities of the Traumschreiber system, is given in this dissertation and shows that one system can be produced at low costs in a short amount of time. Taken together, the Traumschreiber system is a new tool for sleep and dream research, which enables a crowd-based and machine learning-driven approach to gathering polysomnographic data from complex sleep and dream experiments

Investigating consciousness in the sleep laboratory - an interdisciplinary perspective on lucid dreaming

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Investigating consciousness in the sleep laboratory - an interdisciplinary perspective on lucid dreaming

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Leveraging Deep Neural Networks for Lucid Dream Communication via Two-Dimensional Electrooculography

This paper introduces a novel approach to lucid dream communication, leveraging electrooculographical signals for real-time, natural language messaging within dreams. Beyond traditional horizontal eye movement techniques, our methodology incorporates vertical eye movements, enhancing the expressive range of communication. Recognizing the potential for lucid dream communication in diverse settings, including home and laboratory environments with varying conditions, we explore two distinct approaches. Methodologically, we employ a grid system enabling dreamers to encode messages into eye gestures. We develop two Deep Neural Network models for classifying and decoding eye movement data. One model integrates a dynamic threshold algorithm with a convolutional neural network for gesture classification, while the other uses a static threshold algorithm and rate of change analysis for classification, followed by transformer-based decoding of grid-encoded natural language sentences. Tested in two pilot studies with awake participants, our setups exhibit promising results. In Pilot Study 1, we attained an average accuracy of 77.6% in classifying eye gestures across subjects. Pilot Study 2 demonstrates an average accuracy of 97.6% in classifying elementary gestures, sustaining a 90.3% average accuracy post-decoding, successfully recovering the original English language message in most cases. Our method exhibits an average eye typing speed of 19.7 characters per minute. Our results emphasize the practicality of real-time natural language dream communication, demonstrating its applicability in both laboratory and home settings. If successfully replicated during lucid dreaming states, this pioneering method holds the potential to pave the way for novel research on dreams, memory, and consciousness

On determining the zenith angle dependence of the Martian radiation environment at Gale Crater altitudes

We report the zenith angle dependence of the radiation environment at Gale Crater on Mars. This is the first determination of this dependence on another planet than Earth and is important for future human exploration of Mars and understanding radiation effects in the Martian regolith

Cerebral blood flow measurements with ¹⁵O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects. Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF. The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling. </jats:p

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

Cerebral blood flow (CBF) can be measured with dynamic positron emission tomography (PET) of 15O-labeled water by using tracer kinetic modelling. However, for quantification of regional CBF, an arterial input function (AIF), obtained from arterial blood sampling, is required. In this work we evaluated a novel, non-invasive approach for input function prediction based on machine learning (MLIF), against AIF for CBF PET measurements in human subjects. Twenty-five subjects underwent two 10 min dynamic 15O-water brain PET scans with continuous arterial blood sampling, before (baseline) and following acetazolamide medication. Three different image-derived time-activity curves were automatically segmented from the carotid arteries and used as input into a Gaussian process-based AIF prediction model, considering both baseline and acetazolamide scans as training data. The MLIF approach was evaluated by comparing AIF and MLIF curves, as well as whole-brain grey matter CBF values estimated by kinetic modelling derived with either AIF or MLIF. The results showed that AIF and MLIF curves were similar and that corresponding CBF values were highly correlated and successfully differentiated before and after acetazolamide medication. In conclusion, our non-invasive MLIF method shows potential to replace the AIF obtained from blood sampling for CBF measurements using 15O-water PET and kinetic modelling