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Using Fast Lyapunov Indicators with 4D Poincare Maps to Identify Transforming Quasi-Periodic Orbits
No full textFast Lyapunov Indicator (FLI) maps and Poincarè maps are two common methods for analyzing complex dynamical systems. These maps are complicated to assess for the spatial circular restricted three-body problem due to the high number of dimensions. This paper proposes a method of using FLI in conjunction with4D Poincarè maps to more easily search for spatial periodic and quasi-periodic trajectories. The use of FLI to filter trajectories displayed in a 4D Poincarè map allows for the benefits of the Poincarè map to be realized while taking advantage of the ability of FLI to distinguish between stable and unstable trajectories
Structure from Motion with Planar Homography Estimation: A Real-time Low-bandwidth, High-resolution Variant for Aerial Reconnaissance
No full textWe propose a new algorithm variant for Structure from Motion (SfM) to enable real-time image processing of scenes imaged by aerial drones. Our new SfM variant runs in real-time at 4 Hz equating to an 80× computation time speed-up compared to traditional SfM and is capable of a 90% size reduction of original video imagery, with an added benefit of presenting the original two-dimensional (2D) video data as a three-dimensional (3D) virtual model. This opens many potential applications for a real-time image processing that could make autonomous vision–based navigation possible by completely replacing the need for a traditional live video feed. The 3D reconstruction that is generated comes with the added benefit of being able to generate a spatially accurate representation of a live environment that is precise enough to generate global positioning system (GPS) coordinates from any given point on an imaged structure, even in a GPS-denied environment
Strength Measurement of the E_α^lab = 830 keV Resonance in the Ne 22 ( α , n ) Mg 25 Reaction using a Stilbene Detector
Get PDFThe interplay between the 22Ne (,)26Mg reaction and the competing 22Ne(,)25Mg reaction determines the efficiency of the latter as a neutron source at the temperatures of stellar helium burning. In both cases, the rates are dominated by the -cluster resonance at 830 keV. This resonance plays a particularly important role in determining the strength of the neutron flux for both the weak and main process as well as the process. Recent experimental studies based on transfer reactions suggest that the neutron and -ray strengths for this resonance are approximately equal. In this study, the 22Ne (,) 25Mg resonance strength has been remeasured and found to be similar to the previous direct studies. This reinforces an 830 keV resonance strength that is approximately a factor of 3 larger for the 22Ne (,) 25Mg reaction than for the 22Ne (,) 26Mg reaction
Hub and Spoke Modeling to Support Agile Combat Employment
No full textLarge, fixed installations, traditionally have been the primary means of operating for the U.S. Air Force and enabling its mission to generate airpower. However, policies and strategy today must be updated in order to create a more agile and dispersed force, particularly in the Indo-Pacific in order to meet China\u27s pacing challenge as identified in the 2022 National Defense Strategy. Using a more optimized site selection methodology that leverages a hybrid approach of both rank-based and mathematical approaches can provide insights into the most viable locations for basing strategics across the Indo-Pacific
Nowcasting Solar EUV Irradiance with Photospheric Magnetic Fields and the MgII Index Figures, Scripts, and Data
No full textMalware Classification through Abstract Syntax Trees and L-moments
Get PDFThe ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation of code, enabling a deeper understanding of its inherent behaviors and patterns. This approach aims to detect not only known malware but also uncover new, previously unidentified threats. A comprehensive comparison with traditional static analysis methods shows that this approach excels in key performance metrics such as accuracy, precision, recall, and F1 score. These results demonstrate the significant potential of combining L-moments derived from network analysis with ASTs in enhancing malware detection. While static analysis remains an essential tool in cybersecurity, the integration of L-moments and advanced network analysis offers a more effective and efficient response to the dynamic landscape of cyber threats. This study paves the way for future research, particularly in extending the use of L-moments and network analysis into additional areas
Enhancing The Resilience of Space Systems against Ransomware Attacks
Get PDFAs their relevance has increased in recent years, space systems have become nearly essential in modern life. They are integral in the operation of navigational systems, military operations, and have ushered in a new domain of scientific inquiry. Technological advances have enabled the miniaturization of components and increased the accessibility of satellites as they find new applications in the form of Cube Satellites. However, even as these advancements have brought satellites to new heights, their interconnectedness leaves them open to new cyber threats. Ransomware attacks are one of the most prominent and disruptive cyber threats to terrestrial systems, and while ground segments of space systems have faced such threats, the vulnerability of space vehicles, including CubeSats, has not been extensively explored. This research investigates the susceptibility of NASA’s core Flight System (cFS) to ransomware attacks through proof-of-concept simulations. The goal of these scenarios is to simulate cyber effects that disrupt, degrade, and deny the operational capacity of the flight software by emulating ransomware attack techniques
Quantum Circuit Reduction Using Three Layer Transposition
Get PDFThe potential of quantum computing to revolutionize critical military applications has led the US Department of Defense to recognize it as a keen interest. However, the practical implementation of these theoretical applications on physical quantum devices is currently limited by inherent reliability and accuracy issues in quantum hardware. To mitigate errors stemming from these limitations, the incorporation of software-based solutions is imperative. Quantum circuit optimization stands out as a primary method of increasing the accuracy of quantum computations. One of the key components of this approach is circuit reduction, whereby circuits are condensed to realize the same computation using fewer operations. State-of-the-art reduction schemes include the use of template matching to identify and reduce portions of a circuit. This thesis presents an algorithmic approach to quantum circuit reduction that uses layer transposition to achieve greater reductions than state-of-the-art methods. Specifically, it focuses on those transpositions of a single layer with either the preceding or the following pair of layers within a subcircuit that preserve the effect of the subcircuit. Upon executing the transposition operation and revealing a new, equivalent circuit arrangement, conventional optimization techniques are employed to identify reductions that were previously undetected. Following extensive testing methodologies, the proposed algorithm for implementing layer transposition consistently outperforms traditional optimization methods, demonstrating a statistically significant advantage in quantum cost reductions for exhaustively generated circuits and a statistically insignificant yet observable advantage for randomly generated circuits. However, these enhanced reduction capabilities come with trade-offs in terms of runtime and scalability, as the algorithm’s execution time exhibits exponential growth with increasing input size
Evaluation of VTOL-Capable Cargo UAVs for Dispersible Airfield Logistics in the USINDOPACOM AOR
Get PDFThis research models and analyzes the ability of commercial cargo UAVs to rapidly evacuate logistics from an airfield to proximal, outlying destinations, particularly in the USINDOPACOM AOR. This is a tenet of Agile Combat Employment by the USAF, which seeks to mitigate the effect of kinetic threats by near-peer adversaries. The analysis sets forth a binary linear program to minimize the total time to evacuate a fixed amount of logistics from an airfield. Parameters include the cargo UAV with its performance specifications, number of cargo loading points at the airfield, number of destinations for cargo evacuation, and subset of destinations for refueling. Model constraints ensure the integrity of cargo packages to a common destination when transported on multiple cargo UAVs; require that the destinations be utilized relatively equally; prevent multiple cargo UAVs from occupying a loading point simultaneously, and route cargo UAVs to refueling-capable destinations as needed. The CPLEX solver typically identified optimal solutions to moderately sized instances within five minutes. Analysis over a variation of cargo UAVs and instance parameters informed relevant insights for the problem and demonstrated a viable process that, when parameterized for a specific operational instance, can directly inform cargo UAV acquisition decisions and their employment for this operational concept
Reinforcement Learning for Team Based Air Combat Maneuvering Decisions with Directed Energy Weaponry
Get PDFLeveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage emergent behaviors. An illustrative experiment is designed to assess combat performance of AUCAVs, which can be used to inform future research endeavors. Qualitative analysis of learned combat maneuvers and quantitative experiments on different DEW weapon parameters offer insights into the efficacy of our RL solution procedure and its potential for informing the development of future weapon concepts