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<p>The dissertation research was conducted to examine articles, research, and studies that have been collected in recent years to understand energy management for general aviation airplane pilots. The experiment was broken down into four phases with control and treatment groups which have evaluated the real-world problem of energy management in aviation. The four phases were to fly a flight profile, evaluate the energy state of the airplane within the flight by video, fly the same flight profile again, and a post-flight interview with the pilots. The idea of this experiment was to recognize the lack of understanding in energy management in pilots, build a conceptual model, and lastly verify and validate Phase II of the model by utilizing previous studies and research. Additionally, the three main goals were to assess the ability to interpret energy management, assess the ability to control the aircraft, and lastly, to interview for perception of energy management. The data was collected on the flight training device’s G1000, and the researcher analyzed the data using R, Minitab, Excel, and NVivo. The research provided ideas for creating a future model to evaluate energy management, validated by testing Phase II of the model to understand assessing energy management in real time, and interviewed pilots on their experiences with energy management, identified gaps in general aviation research, and suggested methods of how to facilitate understanding of energy management.</p>

In human-computer systems, such as supervisory control systems, large volumes of incoming and complex information can degrade overall system performance. Strategically integrating automation to offload tasks from the operator has been shown to increase not only human performance but also operator efficiency and safety. However, increased automation allows for increased task complexity, which can lead to high cognitive workload and degradation of situational awareness. Adaptive automation is one potential solution to resolve these issues, while maintaining the benefits of traditional automation. Adaptive automation occurs dynamically, with the quantity of automated tasks changing in real-time to meet performance or workload goals. While numerous studies evaluate the relative performance of manual and adaptive systems, little attention has focused on the implications of selecting particular invoking or revoking strategies for adaptive automation. Thus, evaluations of adaptive systems tend to focus on the relative performance among multiple systems rather than the relative performance within a system. This study takes an intra-system approach specifically evaluating the relationship between cognitive workload and situational awareness that occurs when selecting a particular invoking-revoking strategy for an adaptive system. The case scenario is a human supervisory control situation that involves a system operator who receives and interprets intelligence outputs from multiple unmanned assets, and then identifies and reports potential threats and changes in the environment. In order to investigate this relationship between workload and situational awareness, discrete event simulation (DES) is used. DES is a standard technique in the analysis iv of systems, and the advantage of using DES to explore this relationship is that it can represent a human-computer system as the state of the system evolves over time. Furthermore, and most importantly, a well-designed DES model can represent the human operators, the tasks to be performed, and the cognitive demands placed on the operators. In addition to evaluating the cognitive workload to situational awareness tradeoff, this research demonstrates that DES can quite effectively model and predict human cognitive workload, specifically for system evaluation. This research finds that the predicted workload of the DES models highly correlates with well-established subjective measures and is more predictive of cognitive workload than numerous physiological measures. This research then uses the validated DES models to explore and predict the cognitive workload impacts of adaptive automation through various invoking and revoking strategies. The study provides insights into the workload-situational awareness tradeoffs that occur when selecting particular invoking and revoking strategies. First, in order to establish an appropriate target workload range, it is necessary to account for both performance goals and the portion of the workload-performance curve for the task in question. Second, establishing an invoking threshold may require a tradeoff between workload and situational awareness, which is influenced by the task’s location on the workload-situational awareness continuum. Finally, this study finds that revoking strategies differ in their ability to achieve workload and situational awareness goals. For the case scenario examined, revoking strategies based on duration are best suited to improve workload, while revoking strategies based on revoking thresholds are better for maintaining situational awareness.

In recent years, the rapid advancement of Unmanned Aircraft Systems (UAS) has led to an increasingly complex National Airspace System (NAS), necessitating a comprehensive understanding of factors that impact pilot visual acquisition and detection of other aircraft (including manned fixed-wing, rotorcraft, and UAS). The objective of this study is to investigate factors that affect pilot performance in visually acquiring and detecting other manned-fixed wing aircraft and manned rotorcraft using a multi-method approach, incorporating qualitative and quantitative data analysis. A diverse sample of pilots with varying flight experience participated in the study. Participants were exposed to a series of flight test scenarios in a high-fidelity flight test campaign using different flight paths and detecting different types of aircraft, designed to replicate real-world airspace encounters with other aircraft. Post-flight interviews were conducted, and situational awareness questionnaires and NASA Task Load Index (NASA-TLX) were administered to capture insights on the pilots’ experiences. The goal was to determine the level at which aircraft characteristics, test subjects’ situational awareness and workload, flight conditions, and environmental conditions influenced visual acquisition and detection. All interviews were subjected to several cycles of meticulous coding and subcoding processes to discern both individual and co-occurring factors affecting visual detection capabilities. Additionally, a rigorous statistical analysis was executed on the data derived from the situational awareness questionnaires and NASA-TLX to extract quantitative insights into pilot-centric metrics influencing visual detection. The amalgamated results from both the qualitative and quantitative analyses were synthesized to construct a comprehensive representation of all variables influencing visual detection, in addition to delineating the parallels between factors that affect visual acquisition in both manned fixed-wing and rotorcraft detection scenarios.

The increasing dependence on digital infrastructure leaves individuals, societies and even nations vulnerable in the case of a cyberattack. To prepare for threats and attacks, cyberattacks be simulated in environments called cyber ranges. CYBER RANGE AND TRAINING ENVIRONMENT (CRATE), Sweden's cyber range, is an example of such an environment. This environment is sophisticated and complex, but challenges remain for the operatives in how to interpret the multitude of information items that are produced during a simulation. An emerging field of study is the study of situational awareness in the cyber domain, which describes how an operative can render an accurate mental picture, which enables for quick assessment and decision-making in a situation where a multitude of data or information items are involved. An integral part of situational awareness is effective visualization. Visualization can form the linkage in the human-computer interaction and has been demonstrated in other industries and fields to facilitate situational awareness. However, the linkage between situational awareness and visualization in the unique context of a cyber range was a new topic of study. This thesis aim was to provide insight and advance the knowledge of visualization for situational awareness in the unique context of the cyber range CRATE. Conclusively, in the development of a visualization software, the abstraction levels and time frame of the information items collected should be considered. Each information item is of different relevance depending on both the operative's role and in which time frame through which the information is analyzed. A visualization technique that recognizes the abstraction level and the time frame increases the situational awareness for the operative conducting the simulation because it renders both an estimation of critical core processes, current events that are unfolding and enables for the future projection of events.

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<p>Ballistic and chemical trajectory options to Uranus are investigated for launch dates spanning 50 years. Trajectory solutions are found using STOUR, a patched conic propagator with an analytical ephemeris model. STOUR is heritage software developed by JPL and Purdue, written in FORTRAN. A total of 89 distinct gravity-assist paths to Uranus are considered, most of which will allow for a deep space maneuver (DSM) at some point along the path. For each launch year, the most desirable trajectory is identified and cataloged based on time of flight (up to 15 years), total $\Delta$V cost (DSM and capture maneuver), arrival $V_\infty$, and delivered payload. The Falcon Heavy (Recoverable), Vulcan VC6, Falcon Heavy (Expendable) and SLS Block 1B are considered to provide a range of low- to high-performance launch vehicle capabilities. A rough approximation of Starship's performance capabilities is also computed and applied to select years of launch dates. A flagship mission that delivers both a probe and an orbiter at Uranus is considered, which is approximated as a trajectory capable of delivering 2000 kg. Jupiter is unavailable as a gravity-assist body until the end of the 2020s but alternative gravity-assist paths exist, providing feasible trajectories even in years when Jupiter is not available. A rare Saturn-Uranus alignment in the late 2020's is identified which provides some such trajectory opportunities. A probe-and-orbiter mission to Uranus is feasible for a Vulcan VC6 with approximately 13 year flight times and for a Recoverable Falcon Heavy with approximately 14.5 year flight times. An Expendable Falcon Heavy reduces the time of flight to around 12.5 years and opens up `0E0U' as a gravity-assist path, while the SLS Block 1B typically offers trajectories with 10 to 11 year flight times and opens up more direct `JU' and `U' solutions. With the SLS, flight times as low as 7.5 years are possible.</p> <p>  </p> <p>A new, rapid grid search tool called GREMLINS is also outlined. This new software is capable of solving the same problems as STOUR, but improves on it in three crucial ways: an improved user-experience, more maneuver capabilities, and a more easily maintained and modified code base. GREMLINS takes a different approach to the broad search problem, forgoing $C_3$ matching in favor of using maneuvers to patch together tables of pre-computed Lambert arcs. This approach allows for vectorized computations across data frames of Lambert solutions, which can be computed much more efficiently than the for-loop style approach of past tools. Through the use of SQL tables and a two-step trajectory solving approach, this tool is able to run very quickly while still being able to handle any amount of data required for a broad search. Every line of code in GREMLINS is written in Python in an effort to make it more approachable and easier to develop for a wide community of users, as GREMLINS will be the only only grid search tool available as free and open source software. Multiple example missions and trajectory searches are explored to verify the output from GREMLINS and to compare its performance against STOUR. Despite using a slower coding language, GREMLINS is capable of performing the same trajectory searches in approximately 1/5 the runtime of STOUR, a FORTRAN-coded tool, thanks to its vectorized computations.</p>

The intensity and frequency of hurricanes and storms increase due to climate change, leaving destruction in their trail. After a hurricane happens, it is fundamental to respond as quickly as possible, and Search and Rescue operations occur to limit human damage further. The operations take place in hostile environments and extreme situations, where speed and efficiency are essential. Indeed, rescuers must be able to have a shared mental model of the situation and act immediately. This study focuses on visualizing situational awareness in such environments to optimize decision-making.  This study has been conducted in collaboration with Frog design and Sony and is part of the broader project FindMe Tag, a system composed of a wearable device that tracks civilians’ location and biometric data. The tag is connected to an app for civilians to handle which data to share. The data is shared to the rescuers’ dashboard for optimizing Search and Rescue operations, which is the focus of this thesis.  Following a Research through Design approach, this thesis project aims to contribute to the interaction design field by proposing a way to visualize situational awareness during extreme and dynamic situations. The process resulted in a dashboard prototype to support Search and Rescue operations by providing a way to visualize data concerning civilians’ status and rescuers, communicate among teams, and encourage connectedness among civilians.

The need to visualise sets of data and networks within a company is awell-known task. In this thesis, research has been done of techniques used to visualize complex networks in order to find out if there is a generalized optimal technique that can visualize complex networks. For this purpose an application was implemented containing three different views, which were selected from the research done on the subject. As it turns out, it points toward that there is no generalized optimal technique that can be used to default visualize complex networksin a satisfactory way. A definit conclusion could not be given due to the fact that all visualization techniques which could not be evaluated within this thesis timeframe. / Behov av att visualisera data inom bolag är ett känt faktum. Denna avhandling har använt olika tekniker för att undersöka om det existerar en generell optimal teknik som kan tillämpas vid visualisering av komplexa nätverk. Vid genomförandet implementerades en applikation med tre olika vyer som valdes ut baserat på forskning inom det valda området. Resultatet visade att det inte existerar en generell optimalteknik som kan tillämpas vid visualisering av komplexa nätverk, det medför att en definitiv slutsats inte kan dras. Det på grund av att alla de visualiseringstekniker som existerar inte kunde undersökas inom examensarbetets tidsram.

With the increase in space traffic, proliferation of inexpensive launch opportunities, and interest from many countries in utilizing the space domain, threats to existing space assets are likely to increase dramatically in the coming years. The development of a system that can identify potential threats and alert space operators is vital to maintaining asset resiliency and security. The focus of this thesis is the design and evaluation of such a system. The design is comprised of the development of a classification hierarchy and the selection of machine learning models that will enable the identification of anomalous object behavior. The hierarchy is based on previous examples applied to object classification while reconsidering the assumption that a satellite may perform only one mission. The selected machine learning models perform both supervised classification of actively maneuvering objects and unsupervised identification of anomalous behavior within large satellite constellations. The evaluation process considers the independent adjustment of model hyperparameters to achieve optimal model settings. The optimal models perform both classification functions and return moderate accuracy. The system is applied to several case studies examining edge cases and what factors constitute a threatening object and what factors do not. Suggestions for improvement of the system in the future are presented. / Master of Science / The increase in space traffic, proliferation of inexpensive launch opportunities, and interest from many countries in utilizing the space domain represent existential threats to existing spacecraft and operations in low-Earth orbit. Threats to the safe operation of spacecraft are likely to increase dramatically in the coming years. The development of a system that can identify potential threats and alert space operators is vital to maintaining asset resiliency and security. The focus of this thesis is the design and evaluation of such a system. This is accomplished by identifying a system architecture through evaluating current assumptions of what missions satellites are capable of performing. Following the system-level design, modules are proposed that utilize machine learning to identify satellite behavior that is abnormal. These modules are tested and tuned with optimal parameters to deliver improved identification performance. The system is applied to several case studies examining edge cases and what factors constitute a threatening object and what factors do not. Suggestions for improvement of the system in the future are presented.