Residual air inflated systems for CubeSats

This paper presents a case for the use of residual air inflated (RAI) structures on CubeSat platforms, focusing on the development of a high altitude, de-orbiting device that utilises this deployment method. Residual air inflation relies on small pockets of air, trapped within a sealed membrane, expanding when the structure is exposed to vacuum. This expansion of trapped air then inflates the membrane. The application of this deployment method for a technology demonstrator, to be flown on a European sounding rocket in March 2014, shall be discussed. The demonstrator is a proposed passive, high altitude, end-of-life, deorbiting system that utilises solar radiation pressure. The development of this device from analysis and design through to construction shall be covered with the particular challenges present on a CubeSat platform discussed. The paper shall conclude by proposing possible applications of CubeSat based RAI structures and deployment mechanisms, focusing on the potential for deployment mechanisms and debris capture structures

Eigenvector-based community detection for identifying information hubs in neuronal networks

Eigenvectors of networked systems are known to reveal central, well-connected, network vertices. Here we expand upon the known applications of eigenvectors to define well-connected communities where each is associated with a prominent vertex. This form of community detection provides an analytical approach for analysing the dynamics of information flow in a network. When applied to the neuronal network of the nematode Caenorhabditis elegans, known circuitry can be identified as separate eigenvector-based communities. For the macaque's neuronal network, community detection can expose the hippocampus as an information hub; this result contradicts current thinking that the analysis of static graphs cannot reveal such insights. The application of community detection on a large scale human connectome (around 1.8 million vertices) reveals the most prominent information carrying pathways present during a magnetic resonance imaging scan. We demonstrate that these pathways can act as an effective unique identifier for a subject's brain by assessing the number of matching pathways present in any two connectomes

StrathSat-R : Deploying inflatable CubeSat structures in micro gravity

This paper presents the concepts, objectives and design of a student-led sounding rocket experiment which shall test novel inflatable devices in space conditions. This experiment is envisaged as the first step towards developing a CubeSat programme at the University of Strathclyde, which can exploit the novel concepts developed and the technical skills gained. The experiment itself aims to test novel, student developed, inflatable space structures in micro gravity and reduced pressure conditions. It consists of three distinct sections, the ejection housing on the rocket and the two ejectable modules that are based on CubeSat architecture. Shortly before reaching apogee, the two modules are ejected from the rocket and will deploy their own inflating structure during free flight. After landing, the ejectable modules recovery will rely upon a GPS position relayed to the team from the module by Globalstar transmission and a RF beacon for tracking with the recovery helicopter. The two modules carry two different structures resulting in distinct mission objectives: The aim of FRODO is to deploy an experimental passive de-orbiting system for high altitude spacecraft which will in the future utilise solar radiation pressure for orbit removal. The aim of SAM is to serve as a technology demonstrator for the residual air deployment method of a smart bio-inspired space structure. This paper contains details about the science objectives of the mission and how they will be achieved, its experimental design and the management of the student-led project

Eigenvector alignment : assessing functional network changes in amnestic mild cognitive impairment and Alzheimer's disease

Eigenvector alignment, introduced herein to investigate human brain functional networks, is adapted from methods developed to detect influential nodes and communities in networked systems. It is used to identify differences in the brain networks of subjects with Alzheimer’s disease (AD), amnestic Mild Cognitive Impairment (aMCI) and healthy controls (HC). Well-established methods exist for analysing connectivity networks composed of brain regions, including the widespread use of centrality metrics such as eigenvector centrality. However, these metrics provide only limited information on the relationship between regions, with this understanding often sought by comparing the strength of pairwise functional connectivity. Our holistic approach, eigenvector alignment, considers the impact of all functional connectivity changes before assessing the strength of the functional relationship, i.e. alignment, between any two regions. This is achieved by comparing the placement of regions in a Euclidean space defined by the network's dominant eigenvectors. Eigenvector alignment recognises the strength of bilateral connectivity in cortical areas of healthy control subjects, but also reveals degradation of this commissural system in those with AD. Surprisingly little structural change is detected for key regions in the Default Mode Network, despite significant declines in the functional connectivity of these regions. In contrast, regions in the auditory cortex display significant alignment changes that begin in aMCI and are the most prominent structural changes for those with AD. Alignment differences between aMCI and AD subjects are detected, including notable changes to the hippocampal regions. These findings suggest eigenvector alignment can play a complementary role, alongside established network analytic approaches, to capture how the brain's functional networks develop and adapt when challenged by disease processes such as AD

Robust assessment of EEG connectivity patterns in Mild Cognitive Impairment and Alzheimer's disease

The prevalence of dementia, including Alzheimer's disease (AD), is on the rise globally with screening and intervention of particular importance and benefit to those with limited access to healthcare. Electroencephalogram (EEG) is an inexpensive, scalable, and portable brain imaging technology that could deliver AD screening to those without local tertiary healthcare infrastructure. We study EEG recordings of sporadic mild cognitive impairment (MCI) and prodromal familial, early-onset, AD subjects for the same working memory tasks using high- and low-density EEG, respectively. A challenge in detecting electrophysiological changes with EEG is that noise and volume conduction effects are common and disruptive to functional connectivity analysis. It is known that the imaginary part of coherency (iCOH) can mitigate against volume conduction when generating functional connectivity networks. We aim to expose topological differences in these connectivity networks with a global network measure, eigenvector alignment (EA), that is shown to be robust to targeted alterations in the connectivity network; emulating the erasure of true instantaneous activity (zero or π-phase) by iCOH. The assessment of alignments establishes the relationship between EEG channels from the similarity of their connectivity patterns. Significant alignments, versus random null models, are found to be consistent across frequency ranges (delta, theta, alpha, and beta) for the working memory tasks - aided by the relative consistency of iCOH connectivities - in order to reveal network structure. For high-density EEG recordings, stark differences in the control and sporadic MCI results are observed with the control group demonstrating far more consistent alignments. These differences are also detected by comparing the significant correlation and iCOH connectivities, again in reference to random null models, where only EA suggests a notable difference in network topology when comparing subjects with sporadic MCI and prodromal familial AD. The consistency of alignments, across frequency ranges, provides a measure of confidence in EA's detection of topological structure, an important aspect that marks this approach as a promising direction for developing a reliable test for early onset AD

Dynamical influence driven space system design

Complex networks are emerging in low-Earth-orbit, with many thousands of satellites set for launch over the coming decade. These data transfer networks vary based on spacecraft interactions with targets, ground stations, and other spacecraft. While constellations of a few, large, and precisely deployed satellites often produce simple, grid-like, networks. New small-satellite constellations are being deployed on an ad-hoc basis into various orbits, resulting in complex network topologies. By modelling these space systems as flow networks, the dominant eigenvectors of the adjacency matrix identify influential communities of ground stations. This approach provides space system designers with much needed insight into how differing station locations can better achieve alternative mission priorities and how inter-satellite links are set to impact upon constellation design. Maximum flow and consensus-based optimisation methods are used to define system architectures that support the findings of eigenvector-based community detection

Identifying effective sink node combinations in spacecraft data transfer networks

Complex networks are emerging in low-Earth-orbit as the communication architectures of inter-linked space systems. These data transfer networks vary based on spacecraft interaction with targets and ground stations, which respectively represent source and sink nodes for data flowing through the network. We demonstrate how networks can be used to identify effective sink node selections that, in combination, provide source coverage, high data throughput, and low latency connections for intermittently connected, store-and-forward space systems. The challenge in this work is to account for the changing data transfer network that varies significantly depending on the ground stations selected -- given a system where data is downlinked by spacecraft at the first opportunity. Therefore, passed-on networks are created to capture the redistribution of data following a sink node's removal from the system, a problem of relevance to traffic management in a variety of flow network applications. Modelling the system using consensus dynamics, enables sink node selections to be evaluated in terms of their source coverage and data throughput. While restrictions in the depth of propagation when defining passed-on networks, ensures the optimisation implicitly rewards lower latency connections. This is a beneficial by-product for both space system design and store-and-forward data networks in general. The passed-on networks also provide an insight into the relationship between sink nodes, with eigenvector embedding-based communities identifying sink node divisions that correspond with differences in source node coverage

Analysis of responsive satellite manoeuvres using graph theoretical techniques

Manoeuvrable, responsive satellite constellations that can respond to real-time events could provide critical data on-demand to support, for example, disaster monitoring and relief efforts. The authors demonstrate the feasibil-ity of such a system by expanding on a fully-analytical method for designing responsive spacecraft manoeuvres using low-thrust propulsion. This method enables responsive manoeuvre planning to provide coverage of targets on the Earth, with each manoeuvre option having a different target look angle, and requiring a different manoeuvre time and propellant cost. The trade-space for this analysis rapidly expands when considering multiple space-craft, targets and manoeuvres. To explore the trade-space efficiently, it is perceived as a graph in which connections are rapidly traversed to identify favourable routes to achieve the mission goals. The case study presented considers four satellites required to provide flyovers of two targets, with an associated graph of possible manoeuvres comprising 10726 nodes. The min-imum time solution is 2.59 days to complete both flyovers with 7.037 m/s change in velocity. Investigation of the graph highlights that selecting a good but not minimum time solution can allow the system to perform well but also have alternate options available to deal with possible errors in the manoeuvre execution, or changes in mission priorities. Restricting the prob-lem to consider only two satellites, with a smaller swath and less available propellant, reduces the graph to 510 nodes. In this case, the minimum time solution requires 9.04 m/s velocity change and takes approximately 2.59 days. The analysis also provides non-intuitive solutions, for example, that it is faster for one satellite to perform two targeting manoeuvres than for two satellites to manoeuvre simultaneously

Mapping of shifting tidal estuaries to support inshore rescue

Across the world, many coastal tidal regions are unsafe to navigate due to shifting mud and sand pushed by water currents. Ability to regularly map the current location of a channel will aid safe passage for commercial, leisure and rescue craft. This work investigates the use of synthetic aperture radar data derived from satellites to provide accurate mapping of moving channels in coastal regions. As images must be collected at low tide, data availability is assessed considering the relationship between the orbital motion of the satellites and the tides. Change detection methods are applied to suitable images to map changes in the location of navigable channels. Pixels that undergo similar changes over time (e.g. from water covered to exposed sand) are grouped together by examining the principal component of the covariance matrix, for a vector composed of pixel values from the same location at different times. The Solway Firth in Great Britain is selected as a trial site as it is exposed to some of Europe's fastest tidal movements and ranges, and hence is one of Great Britain's most treacherous stretches of coastline

Characterising gameplay and autism spectrum disorder development with swipe pattern networks

Autism spectrum disorder (ASD) is a neurodevelopmental condition affecting at least 700,000 individuals in the UK with an aggregate annual healthcare and support cost of at least £28 billion. Early identification, proceeded by therapeutic intervention, can produce significant, lifelong health and economic benefit. An ASD diagnosis currently requires a trained clinician, but there is a long and growing waiting list for such assessments. To meet demand, and create more accessible means of assessment, bespoke touchscreen games have been developed for early autism detection and trialled for children aged 2.5–6 years. These games focus on recognising ASD through detecting disorder in intentional movements. In this study, we employed a serious iPad game for young children (441 without known neurodevelopmental problems, 373 diagnosed with autism spectrum disorders, and 64 diagnosed with other neurodevelopmental disorders) that allowed for different play patterns, but where the child was encouraged toward a social aspect of gameplay (sharing food) with attractive sensory feedback. Children were encouraged to drag four pieces of food from a serving area (food zone) to deliver them to a set of four children (snap-to-plate zones) to trigger feeding animations and an audible celebration (Figure 1a). By converting gameplay swipes into a graph, we can identify – for the first time – the specific pattern signatures of autistic users. We find that autistic participants employ an indirect, two-step, sharing process in contrast to the direct, single-step, approach employed by children without neurodevelopmental disorders. These insights into the serial organisation of play actions could form the basis of effective diagnosis and tailored therapeutic interventions