This thesis investigated a tip-over stability of mobile boom cranes with swinging payloads. Base and crane motion presents a tip-over problem. Attaching complex payloads further complicates the problem. They study began with a single-pendulum payload to analyze a tip-over stability characteristics under different conditions. A simple tip-over prediction model was developed with a goal of limiting a computational cost to a minimum. The stability was characterized by a tip-over stability margin method. The crane's tip-over stability was also represented by the maximum possible payload it can carry throughout the workspace. In a static stability analysis, mobile boom crane was assumed to be stationary, thus with no payload swing. The study provided basic understanding on the relationship between tip-over stability and boom configuration. In a pseudo-dynamic stability analysis, the method incorporated payload swing into the analysis by adding estimated maximum payload swing due to motions. To estimate the angles, differential equations of motions of payload swings were derived. The thesis extended the study to a double-pendulum payload. The maximum swing angles estimated in the single-pendulum case were directly applied to the double-pendulum case. To validate the analytical methods, a full dynamic multi-body simulation model of a mobile boom crane was developed. The predictions from the previous analysis were verified by the simulation results. The prediction model and the analytical methods in the thesis provide a significant tool for practical application of tip-over stability analysis on mobile boom cranes. The experimental results increase the confidence of the study's accuracy.

Buffer overflow vulnerabilities continue to prevail and the sophistication of attacks targeting these vulnerabilities is continuously increasing. As a successful attack of this type has the potential to completely compromise the integrity of the targeted host, early detection is vital. This thesis examines generic approaches for detecting executable payload attacks, without prior knowledge of the implementation of the attack, in such a way that new and previously unseen attacks are detectable. Executable payloads are analysed in detail for attacks targeting the Linux and Windows operating systems executing on an Intel IA-32 architecture. The execution flow of attack payloads are analysed and a generic model of execution is examined. A novel classification scheme for executable attack payloads is presented which allows for characterisation of executable payloads and facilitates vulnerability and threat assessments, and intrusion detection capability assessments for intrusion detection systems. An intrusion detection capability assessment may be utilised to determine whether or not a deployed system is able to detect a specific attack and to identify requirements for intrusion detection functionality for the development of new detection methods. Two novel detection methods are presented capable of detecting new and previously unseen executable attack payloads. The detection methods are capable of identifying and enumerating the executable payload’s interactions with the operating system on the targeted host at the time of compromise. The detection methods are further validated using real world data including executable payload attacks.

A literature study was performed on a new type of cancer medicine: antibody drug conjugates, or ADCs. These consist of a monoclonal antibody, chemically linked to a cytotoxic agent. What makes them unique is their selective toxicity against cancer cells. The first approval of such a pharmaceutical was in the year 2000, with three or four available in different regions of the world today. In the range of 50 registered drugs in clinical development were found, by major and minor corporations. These have been presented in a table in the appendix according to their properties such as type of linker, cytotoxin, development status etc. Furthermore, a detailed study has been done of the chemistry of the linker conjugation as well as an attempt at studying the ADC market. Finally, the mentioned strengths of the drug were compared to its weaknesses, mainly instability and otherwise poor pharmacokinetics. The main conclusion is that these drugs are expected to play a major role in oncology in the future.

With the increase in mobile broadband services the operators are gaining profits by providing high speed Internet access over the mobile network. On the other side they are also facing challenges to give QoS guarantee to the customers. In this thesis we investigate the impact of data rate and payload size on One Way Delay (OWD) and packet loss over TCP performance in 3G networks. Our goal is to evaluate the OWD and packet loss characteristics with respect to payload size and data rate from the collected network level traces. To collect these traces an experimental testbed is setup with Endace Data Acquisition and Generation (DAG) cards, for accurate measurements Endace DAG cards together with Global Positioning System (GPS) synchronization is implemented. The experiments are conducted for three different Swedish mobile operator networks and further the statistics of OWD measurements and packet loss for different data rates and payload sizes are evaluated. Our results indicate that the minimal OWD occurred at higher data rates and also shows a high delay variability. The packet loss has much impact on higher data rates and larger payload sizes, as the packet loss increases with the increase in data rate and payload size.

The International Space Station provides a high-quality of microgravity and extended exposure time which makes it a platform of choice for microgravity research. In order to increase accessibility of onboard experimentation, Space Applications Services will soon launch the ICE Cubes facility as part of its ICE Cubes Service. The facility is foreseen to host standardized plug-and-play payload cubes to reduce overall cost and procedure time required to install payloads on the station. To remotely support the facility it is decided to develop a utility cube named Cube-Zero that will be launched and installed with the facility on the station. This thesis work included the complete design, development and documentation of the cube. The thesis started by conducting a preliminary needs and market study from which two specific purposes were defined for the cube. In addition to its original function of support-utility, the cube is tasked to be a technical commercial demonstrator for the service. This led to the conceptual design of the cube as a multidisciplinary framework able to host two user-defined experiment modules. The preliminary concept was further refined in this paper and with support of prototypes, simulations and analyses led to a final functional design for the Cube-Zero. The work is concluded with the manufacturing of an engineering model of the cube. The model is fully operational, can support the test of the facility before launch and can demonstrate to users its versatility and ease of use in operating any kind of experiment module. Eventually, the information gathered in this thesis report will support future users into developing their own Cube-Zero payload module and guide Space Applications Services into manufacturing, testing and operating the Cube-Zero protoflight model. / ICE Cubes

Network-based attacks against computer systems are a common and increasing problem. Attackers continue to increase the sophistication and complexity of their attacks with the goal of removing sensitive data or disrupting operations. Attack detection technology works very well for the detection of known attacks using a signature-based intrusion detection system. However, attackers can utilize attacks that are undetectable to those signature-based systems whether they are truly new attacks or modified versions of known attacks. Anomaly-based intrusion detection systems approach the problem of attack detection by detecting when traffic differs from a learned baseline. In the case of this research, the focus was on a relatively new area known as payload anomaly detection. In payload anomaly detection, the system focuses exclusively on the payload of packets and learns the normal contents of those payloads. When a payload's contents differ from the norm, an anomaly is detected and may be a potential attack. A risk with anomaly-based detection mechanisms is they suffer from high false positive rates which reduce their effectiveness. This research built upon previous research in payload anomaly detection by combining multiple techniques of detection in a layered approach. The layers of the system included a high-level navigation layer, a request payload analysis layer, and a request-response analysis layer. The system was tested using the test data provided by some earlier payload anomaly detection systems as well as new data sets. The results of the experiments showed that by combining these layers of detection into a single system, there were higher detection rates and lower false positive rates.

In this thesis we study some storage loading problems motivated from several practical contexts, under different types of uncertainty on the items’ data. To have robust stacking solutions against the data uncertainty, we apply the concepts of strict and adjustable robustness. We first give complexity results for various storage loading problems with stacking constraints, and point out some interesting settings in which the adjustable robust problems can be solved more efficiently than the strict ones. Then we propose different solution algorithms for the robust storage loading problems, and figure out which algorithm performs best for which data setting. We also propose a robust optimization framework dealing with storage loading problems under stochastic uncertainty. In this framework, we offer several rule-based ways of scenario generation to derive different uncertainty sets, and analyze the trade-off between cost and robustness of the robust stacking solutions. Additionally, we introduce a novel approach in dealing with stability issues of stacking configurations. Our key idea is to impose a limited payload on each item depending on its weight. We then study a storage loading problem with the interaction of stacking and payload constraints, as well as uncertainty on the weights of items, and propose different solution approaches for the robust problems.

Inkjet printing is an attractive patterning technology that has received tremendous interest as a mass fabrication method for a variety of electronic devices due to its manufacturing exibility and low-cost feature. However, the printing facilities that are being used, especially the inkjet printer, are very expensive. This thesis introduces an extremely cost-friendly inkjet printing method using a printer that costs less than $100. In order to verify its reliability, linearly and circularly polarized (CPd) planar and conformal microstrip antennas were fabricated using this printing method, and their measurement results were compared with copper microstrip antennas. The result shows that the printed microstrip antennas have similar performances to those of the copper antennas except for lower efficiency. The effects of the conductivity and thickness of the ink layer on the antenna properties were studied, and it is found that the conductivity is the main factor affecting the radiation efficiency, though thicker ink yields more effective antennas. This thesis also presents the detailed antenna design for a sub-payload. The sub-payload is a cylindrical structure with a diameter of six inches and a height of four inches. It has four booms coming out from the surface, which are used to measure the variations of the energy flow into the upper atmosphere in and around the aurora. The sub-payload has two types of antennas: linearly polarized (LPd) S-band antennas and right-hand circularly polarized (RHCPd) GPS antennas. Each type of antenna has various requirements to be fully functional for specific research tasks. The thesis includes the design methods of each type of antenna, challenges that were confronted, and the possible solutions that were proposed. As a practical application, the inkjet printing method was conveniently applied in validating some of the antenna designs.

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.