As development of CubeSat based architectures increase, methods of deploying constellations of CubeSats are required to increase functionality of future systems. Given their low cost and quickly increasing launch opportunities, large numbers of CubeSats can easily be developed and deployed in orbit. However, as secondary payloads, CubeSats are severely limited in their options for deployment into appropriate constellation geometries. This thesis examines the current methods for deploying cubes and proposes new and efficient geometries using secondary launch opportunities. Due to the current deployment hardware architecture, only the use of different launch opportunities, deployment direction, and deployment timing for individual cubes in a single launch are explored. The deployed constellations are examined for equal separation of Cubes in a single plane and effectiveness of ground coverage of two regions. The regions examined are a large near-equatorial zone and a medium sized high latitude, high population density zone. Results indicate that simple deployment strategies can be utilized to provide significant CubeSat dispersion to create efficient constellation geometries. The same deployment strategies can be used to develop a multitude of differently dispersed constellations. Different launch opportunities can be utilized to tailor a constellation for a specific region or mission objective. Constellations can also be augmented using multiple launch opportunities to optimize a constellation towards a specific mission or region. The tools developed to obtain these results can also be used to perform specific analysis on any region in order to optimize future constellations for other applications.

Uncrewed aerial vehicles (UAVs), commonly known as drones, have become increasingly prevalent in various applications. However, the localization and payload detection of drones is crucial for ensuring safety and security. This thesis proposes a vision-based solution using deep learning techniques to address these challenges. Existing solutions like radars and acoustic sensors have limitations, including high costs, limited accuracy, and the need for prior knowledge of the drone's model. Normal radars lack angle estimation accuracy and rely on known micro-Doppler features for payload detection, while acoustic sensors are restricted to close ranges for payload analysis. In contrast, cameras offer a cost-effective alternative as they have become widely available and can capture visual data. In addition, due to resource constraints, mounting multiple sensors on the UAV along with the camera is impractical, making reliance on cameras alone essential for addressing the mentioned problems. Recent advancements in deep learning algorithms enable regression and classification tasks, making vision data a promising choice for solving drone localization and payload detection problems. The proposed solution leverages convolutional neural networks (CNNs) for regression tasks, estimating the distance of a drone from the captured image. The CNN takes a cropped image within the drone's bounding box as input and outputs the estimated distance. Additionally, the drone's azimuth and elevation angles have been estimated based on its position in the captured image using a simple pinhole model for the camera. Also, the ResNet and EfficientNet classifiers could accurately classify drones as loaded or unloaded, even without prior knowledge of their shape. Due to a scarcity of publicly available datasets, this study developed the first air-to-air simulated dataset specifically for the classification of loaded versus unloaded drones. To evaluate the performance of the proposed solution, both simulated and experimental tests were conducted. The results showcased promising accuracy, with a root mean square error (RMSE) of less than 10 meters for distance estimation and an RMSE of less than 3 degrees for angle estimation. Furthermore, the payload detection problem was effectively addressed, achieving a classification accuracy of over 85\% for distinguishing between loaded and unloaded drones using the trained network based on the simulated dataset. The numerical highlights demonstrate the effectiveness of using camera sensors for 3D localization, with accurate distance and angle estimations. The high accuracy achieved in payload classification showcases the potential of the proposed solution for detecting drone payloads at distances up to 100 meters. These results pave the way for enhanced safety and security in drone environments.

This study investigates the potential military utility and key priorities of employing extra-large unmanned underwater vehicles (XLUUVs) in the complex and challenging environment of the Baltic Sea. Focusing on payload benefits, technological advancements, and military utility, the research employs a theoretical framework based on concept analysis, systems engineering and military utility. Data is collected and analyzed using a mixed-methods approach including literature review and scenario analysis. The findings suggest that XLUUVs could complement and extend existing defense systems in the Baltic Sea region, demonstrating proficiency in handling diverse missions with factors of flexibility, endurance, adaptability, and modular design. The study highlights the importance of further research and development in areas such as autonomy, communication technologies, and systems integration.

Cable logging will become more important as harvesting shifts to greater annual proportions on steep terrain in New Zealand. The costs of cable logging are considerably higher than that of conventional ground-based methods. Improving cost-effectiveness has been identified as key to ensuring the forestry industry remains cost competitive in the international market. This thesis focuses on ways to better understand and improve cable logging methods by specifically focusing on rigging configurations. The investigation was conducted through a comprehensive literature review, an industry survey to establish current use and preferences, a Delphi survey with experts to establish actual advantages and disadvantages, scale model testing to establish some fundamental knowledge of tension to deflection relationship, and finally a series of targeted case studies to establish both productivity and skyline tension in actual operations. Each of these aspects of the research topic employed different methodology. The literature review highlighted the most relevant research relating to cable logging world-wide spanning nearly a century. Various research papers, manuals, books and computer software were summarized. While many aspects of cable yarding operations have been investigated, much of it focusing on various aspects of operational efficiency through case studies, there is very limited information with regard to rigging configurations. The survey of 50 cable logging practitioners determined what rigging configurations were commonly used in New Zealand. It includes their perceived advantages and disadvantages for varying levels of deflection, but also for specific scenarios such as pulling away from native forest boundaries and flying logs over a stream. Results showed that there were many conflicting perceptions about rigging configuration options. Using an expert panel, a Delphi process was used to derive consensus on what advantages were truly unique to each configuration. This allowed the longer lists of perceived advantages from the industry survey to be pared down to a concise list of ad/disadvantages that will be used in the updating of the Best Practice Guidelines for Cable Logging. To increase our fundamental understanding of tension / payload / deflection relationships, an experiment was conducted in a controlled environment. Using a model yarder in a lab and continuous tension and video recording devices, the dynamic skyline behavior of three similar configurations were tested: North Bend, South Bend and Block in the Bight. The tensions were compared by use of a two-way analysis of variance, which indicated configuration and choker length were significant variables in some but not all of the dynamic load tests. Results also showed that some configurations performed better than others in minimizing the shock loads due to dropping into full suspension, impact with ground objects, and breakout during bridling. Finally, a series of eight studies were conducted on targeted logging operations where relevant stand and terrain parameters were related to the continuous skyline tension monitoring, and recording of productivity through time study. The three targeted configurations included (1) North Bend, (2) Standing skyline using a motorized slack-pulling carriage and (3) a live skyline using a motorized grapple carriage. Results showed that peak and average tensions, as well as amplification factors and the payload to tension relationship, varied between configurations. The study also showed that tensions could be collected to compute measures of payload and tension efficiency, which provided insight into operational performance. The safe working load was exceeded in 53% of all cycles studied and across seven of eight study sites and 14 of 16 spans. Cycle times were significantly different between rigging configurations and that production information could be used to compute measures of labor and energy consumption as well as payload and tension efficiency; which also provide insight into operational performance. The industry should give serious consideration to the use of tension monitors. Tension monitors have many benefits and have the potential to improve cable logging operations in New Zealand. Monitoring tensions can help one learn new techniques or methods (i.e. rigging configurations), help improve payload analysis software for future planning and help evaluate new technology and machinery.

A deorbiting drag device is being designed and built by the University of Toronto Institute for Aerospace Studies/Space Flight Laboratory (UTIAS/SFL) to be demonstrated on the Canadian Advanced Nanospace eXperiment 7 (CanX-7) satellite. CanX-7 will address the growing issue of space debris by designing a drag sail device that will be demonstrated for cubesat-sized satellites. Mission analysis done to ensure the drag device functions properly and deorbits within the required lifetime is performed while varying different properties such as drag coefficient, effective drag area, and solar cycle variations. The design evolution of the device is documented and the chosen design, along with several stages of prototyping, is described. The individual components that make up the device are described as are preliminary numerical analyzes. Finally, the test plan required for the device is described with several deployment experiments and risk reduction tests documented.

A deorbiting drag device is being designed and built by the University of Toronto Institute for Aerospace Studies/Space Flight Laboratory (UTIAS/SFL) to be demonstrated on the Canadian Advanced Nanospace eXperiment 7 (CanX-7) satellite. CanX-7 will address the growing issue of space debris by designing a drag sail device that will be demonstrated for cubesat-sized satellites. Mission analysis done to ensure the drag device functions properly and deorbits within the required lifetime is performed while varying different properties such as drag coefficient, effective drag area, and solar cycle variations. The design evolution of the device is documented and the chosen design, along with several stages of prototyping, is described. The individual components that make up the device are described as are preliminary numerical analyzes. Finally, the test plan required for the device is described with several deployment experiments and risk reduction tests documented.

After reviewing land based freight growth trends nationally and internationally, this thesis discusses the main parameters governing fuel consumption, as well as past approaches in modelling road and rail energy consumption. Past work on comparing these two main modes is also reviewed here. The review included ways of estimating energy consumption of a complete freight task i.e., from origin to destination. Mathematical models estimating modal energy consumption are presented in this thesis. Modal energy consumption is a complex function to be approximated in practice due to numerous variables affecting their outcome. Energy demands are particularly sensitive to changes in vehicle characteristics such as mass and size; route parameters such as grade and curvature; traffic conditions such as level of congestion; and less sensitive to ambient conditions, such as temperature and altitude. There is a large set of energy estimation models available to transportation planners. Unfortunately, unless simple relationships are established for energy estimation and modal comparison, their application in freight movement planning and corridor development becomes computationally prohibitive. This thesis describes the development of a modal freight energy comparison tool to quantify the energy advantage from mode choice, corridor development and vehicle types and loading improvements. The thesis also describes the used modelling processes and the trade-offs between model complexity and data quality. The tool developed in this thesis is based on well established relationships between energy consumption and traffic flow, route and vehicle operating characteristics for road freight movement. The rail freight component was developed from equations of motion together with parameters obtained from past studies. The relationships have been enhanced to fit the purpose of corridor level comparative analysis. The comparison tool has been implemented using a spreadsheet based approach developed specifically to calculate the total door to door energy consumption for given task options. A series of linked sheets enable the user to: specify all necessary inputs; estimate road and rail energy by trip segment. The outputs consist of trip segment energy demand and total energy efficiency of each option. A case study approach, for aiding in model development and testing, is presented. Toowoomba second range crossing in Southern Queensland, Australia (section between below Postman's Ridge and Gowrie Junction) was selected. Four options considered include existing and proposed road and rail corridors. The existing rail and road corridors could be taken as a typical poor case, with very high grades and sharp curvatures. The proposed new road section has a relaxed curvature and gradient. The section of proposed rail corridor, under consideration here, still contains a high grade section. However, the proposed track length is considerably shorter than the base-case. The new proposed train alignment was found as the most efficient mode and the existing trains as the least efficient mode when measured based on absolute expected fuel gain (litres/tonnage of freight moved). This could be attributed to the improvement in curvature and load carrying capacity. However, when the options are compared in terms of litres/1000 NTK, the new train option did not show a significant advantage. Furthermore, the developed model was applied on some simulated cases to test the functionality of other aspects of the model. The total door-to-door energy consumption and the efficiency were compared for all the simulated cases. It showed that the energy efficiency of scenarios varies exponentially with the variation in the ratio of road pickup and delivery legs to the rail line-haul length. In general, energy efficiency of the intermodal options was found to be better unless the best case of the road and the worst case of intermodal option was compared. The modelling approaches presented in the thesis and the comparison model developed in this study could be used for several purposes namely: to assess the energy (and hence greenhouse gas) implications of specific modal freight movements; to aid in the economic and environmental evaluation of transport options; and to assess the potential for energy efficiency gains from vehicle and infrastructure improvements. A number of suggested improvements to the model are also discussed.

Thesis (Master').

ITC/USA 2009 Conference Proceedings / The Forty-Fifth Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2009 / Riviera Hotel & Convention Center, Las Vegas, Nevada / The Central Test and Evaluation Investment Program (CTEIP) Integrated Network Enhanced Telemetry (iNET) program is currently developing new standards for wired-wireless local area networking (LAN-WLAN) using the Internet Protocol (IP), for use in telemetry (TM) channels, under the umbrella of the Telemetry Network System (TmNS). Some advantages of TmNS are real-time command and control of instrumentation, quick-look acquisition, data retransmission and recovery ('gapless TM' or 'PCM backfill'), data segmentation, etc. The iNET team is developing and evaluating prototypes, based on commercial 802.x and other technologies, in conjunction with Range Commander's Council (RCC) Inter-Range Instrumentation Group (IRIG) standards and standards developed under the iNET program.

A growing population is driving automatization in agricultural industry to strive for more productive arable land. Being part of this process, this work is aimed to investigate the possibility to implement sensor-based automation in a particular system called Front End Loader, which is a lifting arms that is commonly mounted on the front of a tractor. Two main tasks are considered here, namely Electronic Self Leveling (ESL) and payload estimation. To propose commercially implementable solutions for these tasks, specific objectives are set, which are: 1) to propose a controller to perform ESL under typical disturbances 2) to propose a methodology for payload estimation considering realistic estimation conditions. Lastly, aligned with these goals, 3) to propose models for the Front End Loader under consideration for derivation of solutions of the specified tasks. The self-leveling task assists farmers in maintaining the angular position of the mounted implements, e.g. a bale handler or a bucket, with respect to the ground when the loader is manually lifted or lowered. Experimental results show that different controllers are required in lifting and lowering motions to maintain the implement's angular position with a required accuracy due to principle differences in gravity impact. The gravity helps the necessary correction in lifting motion, but works against the correction in lowering motions. This led us to propose a controller with a proportional term, a discontinuous term and an on-line disturbance estimation and compensation as well as the tuning procedure to achieve a 2 degrees tracking error for lowering motions in steady state. The proposed controller shows less sensitive performance to lowering velocity, as the main disturbance, in comparison to a linear controller. The second task, payload estimation, assists farmers to work within safety range as well as to work with a weight measurement tool. A mechanical model derived based on equations of motion is improved by a pressure based friction to sufficiently accurately represent the motion of the front end loader under consideration. The proposed model satisfies the desired estimation accuracy of 2\% full scale error in a certain estimation condition domain in constant velocity regions, with off-line calibration step and off-line payload estimation step. An on-line version of the estimation based on Recursive Least Squares also fulfills the desired accuracy, while keeping the calibration step off-line.