4,042 research outputs found

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation

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    Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors. To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach. A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk. The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D

    The Expedition PS129 of the Research Vessel POLARSTERN to the Weddell Sea in 2022

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    The Expedition PS133/2 of the Research Vessel POLARSTERN to the Scotia Sea in 2022

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    The Eruption of Disruption: The Manifestation of Disrupting whiteness in Secondary Social Studies in Appalachia

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    This phenomenological dissertation explores the lived experiences of secondary social studies educators situated in the Appalachian region. Hermeneutic phenomenology was used as a philosophical and methodological approach to gather insights into this phenomenon. Interviews were conducted with three educators to capture their experiences from their childhoods, to their teaching careers, and into their current personal lives. These experiences were analyzed using a Whole-Part-Whole process to understand how they came to disrupt whiteness, the ways they did so, and their understanding of the impact disrupting whiteness for creating learning environments, developing curriculum and making instructional decisions. The findings revealed how these educators came to recognize the importance of acknowledging differences and race, and how they faced and navigated instances of racism and racist structures within the education system. The use of physics as a metaphor highlighted how educators disrupted whiteness through spatial disruptions, curriculum design, advocacy and activism, and creating an environment for students to question their understanding of racism. The implications for social studies education suggest the importance of directly exposing racist foundations, providing educators with instructional tools to disrupt problematic ideologies, and utilizing important resources. As teacher education continues to evolve, a focus on tapping into students\u27 lived experiences can help students move closer to addressing the phenomenon in their future classrooms. Finally, an important part of growth could be seen within educators’ discomfort and reflection. White people can begin their journey toward dismantling white supremacy by examining their privilege and power

    Historical Burdens on Physics

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    When learning physics, one follows a track very similar to the historical path of the evolution of this science: one takes detours, overcomes superfluous obstacles and repeats mistakes, one learns inappropriate concepts and uses outdated methods. In the book, more than 200 articles present and analyze such obsolete concepts methods. All articles have the same structure: 1. subject, 2. deficiencies, 3. origin, 4. disposal. The articles had originally appeared as columns in various magazines. Accordingly, we had tried to write them in an easily understandable way

    Safe Multi-Agent Reinforcement Learning with Quantitatively Verified Constraints

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    Multi-agent reinforcement learning is a machine learning technique that involves multiple agents attempting to solve sequential decision-making problems. This learn- ing is driven by objectives and failures modelled as positive numerical rewards and negative numerical punishments, respectively. These multi-agent systems explore shared environments in order to find the highest cumulative reward for the sequential decision-making problem. Multi-agent reinforcement learning within autonomous systems has become a prominent research area with many examples of success and potential applications. However, the safety-critical nature of many of these potential applications is currently underexplored—and under-supported. Reinforcement learn- ing, being a stochastic process, is unpredictable, meaning there are no assurances that these systems will not harm themselves, other expensive equipment, or humans. This thesis introduces Assured Multi-Agent Reinforcement Learning (AMARL) to mitigate these issues. This approach constrains the actions of learning systems during and after a learning process. Unlike previous multi-agent reinforcement learning methods, AMARL synthesises constraints through the formal verification of abstracted multi- agent Markov decision processes that model the environment’s functional and safety aspects. Learned policies guided by these constraints are guaranteed to satisfy strict functional and safety requirements and are Pareto-optimal with respect to a set of op- timisation objectives. Two AMARL extensions are also introduced in the thesis. Firstly, the thesis presents a Partial Policy Reuse method that allows the use of previously learned knowledge to reduce AMARL learning time significantly when initial models are inaccurate. Secondly, an Adaptive Constraints method is introduced to enable agents to adapt to environmental changes by constraining their learning through a procedure that follows the styling of monitoring, analysis, planning, and execution during runtime. AMARL and its extensions are evaluated within three case studies from different navigation-based domains and shown to produce policies that meet strict safety and functional requirements

    Designing data-aided demand-driven user-centric architecture for 6G and beyond networks

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    Despite advancements in capacity-enhancing technologies like massive MIMO (multiple input, multiple output) and intelligent reflective surfaces, network densification remains crucial for significant capacity gains in future networks such as 6G. However, network densification increases interference and power consumption. Traditional cellular architectures struggle to minimize these without compromising service quality or capacity, which necessitates a shift to a user-centric radio access network (UC-RAN). The UC-RAN approach offers additional degrees of freedom to ease the spectral-energy efficiency interlock while improving the service quality. However, its increased degrees of freedom make its optimal design and operation more challenging. This dissertation introduces four novel approaches for UC-RAN optimal design and operation. The objectives include mitigating interference, reducing power consumption, ensuring diverse user/vertical service quality, facilitating proactive network operation, risk-aware optimization, adopting an open radio access network, and enabling universal coverage. First, we construct an analytical framework to assess the effects of incorporating Coordinated Multipoint (CoMP) technology into UC-RAN to reduce interference and power consumption. We use stochastic geometry tools to derive expressions for network-wide coverage, spectral efficiency, and energy efficiency as a function of UC-RAN Configuration and Optimization Parameters (COPs), including data base station densities and user-centric service zone sizes. While the analytical framework provides insightful performance analysis that can guide overall system design, it cannot fully capture the dynamics of a UC-RAN system to enable optimal operation. Next, we present a Deep Reinforcement Learning (DRL) based method to dynamically orchestrate the UC-RAN service zone size to satisfy varying application demands of various service verticals during its operation. We define a novel multi-objective optimization problem that fairly optimizes otherwise conflicting key performance indicators (KPIs). DRL's practical adaptation by the industry remains thwarted by the risk it poses to the safe operation of a live network. To address this challenge, we propose a digital twin-enabled approach to enrich the DRL-based optimization framework, ensuring risk-aware COP optimization. We use Open Radio Access Network standards-based simulations to show that the proposed risk-aware DRL framework can maximize system-level KPIs while maintaining safe operational requirements. Lastly, we propose a hybrid model of aerial and terrestrial UC-RAN deployment to ensure universal coverage. We assess the impact of aerial base station parameters on system-level KPIs, providing a quantitative analysis of the advantages of a hybrid over a solely terrestrial UC-RAN. We develop a robust multi-objective function solvable via our DRL-based framework to balance and optimize these KPIs in a hybrid UC-RAN. Our extensive analytical and system-level simulation results suggest that these contributions can foster the much-needed paradigm shift towards demand-driven, elastic, and user-centric architecture in emerging and future cellular networks
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