Exploration and utilization of asteroids as interplanetary communication relays

There are more than 17,000 asteroids found near Earth and nearly 2 million asteroids estimated in the main belt between Mars and Jupiter. Asteroid come in diverse forms, some may hold valuable resources such as water, carbon and rare metals that may one day supply a spacefaring civilization. However, asteroids maybe also valuable as relay stations for a permanent high-speed, high-bandwidth interplanetary communication network. Asteroids are typically pock-marked with craters and grooves. Pristine craters resemble a parabolic communication antenna, but without the reflective coating or a receiver/transmitter at the focus. In this work, we evaluate two scenarios, the preliminary feasibility of setting up such a radio antenna on the Martian moon Phobos and Deimos (thought to be captured asteroids) that would act as a communication relay between the Martian system and Earth. Phobos is closer to Mars and is tidally locked. This would require two craters converted to antennas, one perpetually pointing at Mars, another pointing at Earth and a local interconnection between the two. Alternately, the relay on Deimos would need just a single crater relay station. We will then compare this communication relay to the current state-of-the-art, namely the Mars Reconnaissance Orbiter (MRO). The proposed communication antennas would be achieved by landing a swarm of CubeSats onto a crater to form the parabolic reflector. Each CubeSat has a mass of 4 kg and a volume of 3U or 3400 cc with one side forming the surface of the reflector. These CubeSats would hop, roll and fly into the crater and distribute themselves to cover maximum surface area. Each CubeSat has deployable reflectors to fill the gap between adjacent neighbors. A parabolic reflector would be able to reflect radio waves with a gap of one-tenth of the wavelength. A large 12U CubeSat would be positioned at the crater center and extend a deployable tower with a feed antenna to the focus. To achieve the current data rate of MRO, which is 4 Mbps, the power needs of a pair of 20 m(2) aperture antennas on Phobos and the interlink will be evaluated. For Deimos, a single 20 m(2) antenna will be considered. In both cases, the intent is to have an antenna gain of 50 dBi per crater. The analysis will also be extended to a 200 m(2) aperture antenna that can provide a data rate of 40 Mbps and antenna gain of 60 dBi per crater. Our approach to the mission design exploits machine learning to perform formulation, design, planning and operations. The results from these preliminary mission design studies will be used to identify a pathway towards detailed design and field studies in a simulated environment.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Dynamics and Social Clustering on Coevolving Networks

Complex networks offer a powerful conceptual framework for the description and analysis of many real world systems. Many processes have been formed into networks in the area of random graphs, and the dynamics of networks have been studied. These two mechanisms combined creates an adaptive or coevolving network -- a network whose edges change adaptively with respect to its states, bringing a dynamical interaction between the state of nodes and the topology of the network. We study three binary-state dynamics in the context of opinion formation, disease propagation and evolutionary games of networks. We try to understand how the network structure affects the status of individuals, and how the behavior of individuals, in turn, affects the overall network structure. We focus our investigation on social clustering, since this is one of the central properties of social networks, arising due to the ubiquitous tendency among individuals to connect to friends of a friend, and can significantly impact a coevolving network system. Introducing rewiring models with transitivity reinforcement, we investigate how the mechanism affects network dynamics and the clustering structure of the networks. We perform Monte Carlo simulations to explore the parameter space of each model. By applying improved compartmental formalism methods, including approximate master equations, our semi-analytical approximation generally provide accurate predictions of the final states of the networks, degree distributions, and evolution of fundamental quantities. Different levels of semi-analytical estimation are compared.Doctor of Philosoph

Artificial Intelligence for Complex Network: Potential, Methodology and Application

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field.Comment: 51 pages, 4 figures, 10 table

Statistical Physics of Opinion and Social Conflict

The rise and development of opinion groups, just as their clash in social conflict, are notoriously difficult to study due to a complex interplay between structure and dynamics. The intricate feedback between psychological and sociological processes, tied with an ample variability of individual traits, makes these systems challenging both intellectually and methodologically. Yet regular patterns do emerge from the collective behavior of dissimilar people, seen in population and crime rates, in protest movements and the adoption of innovations. Statistical physics comes then as an apt and successful framework for their study, characterizing society as the common product of single wills, interactions among people and external effects. The work in this Thesis provides mathematical descriptions for the evolution of opinions in society, based on simple mechanisms of individual conduct and group influence. Such models abstract the inherent complexity of human behavior by reducing people to opinion variables spread over a network of social interactions, with variables and interactions changing in time at the pace of a handful of equations. Their macroscopic properties are interpreted as the emergence of social groups and of conflict between them due to opinion disagreement, and compared with small controlled experiments or with large online records of social activity. The extensive analysis of these models, both numerical and analytical, leads to a couple of generic observations on the link between opinion and social conflict. First, the emergence of consensual groups in society may be regulated by well-separated time scales of opinion dynamics and network evolution, and by a distribution of personality traits in the population. Our social environment can then be fragmented as more people turn against the collective mood, ultimately forming minorities as a response to external influence. Second, the exchange of views in collaborative tasks may lead not only to the rise and resolution of opinion issues, but to an intermediate state where conflicts appear periodically. In this way strife and cooperation, so much a part of human nature, can be emulated by surprisingly simple interactions among individuals

Towards a swarm robotic approach for cooperative object recognition

Social insects have inspired the behaviours of swarm robotic systems for the last 20 years. Interactions of the simple individuals in these swarms form solutions to relatively complex problems. A novel swarm robotic method is investigated for future robotic cooperative object recognition tasks. Previous multi-agent systems involve cameras and image analyses to identify objects. They cooperate only to improve their hypotheses of the shape's identity. The system proposed uses agents whose interactions with each other around the physical boundaries of the object's shape allow the distinguishing features found. The agents are a physical embodiment of the vision system, making them suitable for environments where it would not be possible to use a camera. A Simplified Hexagonal Model was developed to simulate and examine the strategies. The hexagonal cells of which can be empty, contain an agent (hBot) or part of an object shape. Initially the hBots are required to identify the valid object shapes from a set of two types of known shapes. To do this the hBots change state when in contact with an object and when touching other hBots of the same state level, where some states are only achieved when neighbouring certain object shapes. The agents are oblivious, anonymous and homogeneous. They also do not know their position or orientation and cannot distinguish between object shapes alone due to their limited sensor range. Further work increased the number of object shapes to provide a range of scenarios

Networks in cognitive science

Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex-systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the Cognitive Sciences.Postprint (author's final draft