994 resultados para Space surveillance


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A mechanics based linear analysis of the problem of dynamic instabilities in slender space launch vehicles is undertaken. The flexible body dynamics of the moving vehicle is studied in an inertial frame of reference, including velocity induced curvature effects, which have not been considered so far in the published literature. Coupling among the rigid-body modes, the longitudinal vibrational modes and the transverse vibrational modes due to asymmetric lifting-body cross-section are considered. The model also incorporates the effects of aerodynamic forces and the propulsive thrust of the vehicle. The effects of the coupling between the combustion process (mass variation, developed thrust etc.) and the variables involved in the flexible body dynamics (displacements and velocities) are clearly brought out. The model is one-dimensional, and it can be employed to idealised slender vehicles with complex shapes. Computer simulations are carried out using a standard eigenvalue problem within h-p finite element modelling framework. Stability regimes for a vehicle subjected to propulsive thrust are validated by comparing the results from published literature. Numerical simulations are carried out for a representative vehicle to determine the instability regimes with vehicle speed and propulsive thrust as the parameters. The phenomena of static instability (divergence) and dynamic instability (flutter) are observed. The results at low Mach number match closely with the results obtained from previous models published in the literature.

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Much of our understanding and management of ecological processes requires knowledge of the distribution and abundance of species. Reliable abundance or density estimates are essential for managing both threatened and invasive populations, yet are often challenging to obtain. Recent and emerging technological advances, particularly in unmanned aerial vehicles (UAVs), provide exciting opportunities to overcome these challenges in ecological surveillance. UAVs can provide automated, cost-effective surveillance and offer repeat surveys for pest incursions at an invasion front. They can capitalise on manoeuvrability and advanced imagery options to detect species that are cryptic due to behaviour, life-history or inaccessible habitat. UAVs may also cause less disturbance, in magnitude and duration, for sensitive fauna than other survey methods such as transect counting by humans or sniffer dogs. The surveillance approach depends upon the particular ecological context and the objective. For example, animal, plant and microbial target species differ in their movement, spread and observability. Lag-times may exist between a pest species presence at a site and its detectability, prompting a need for repeat surveys. Operationally, however, the frequency and coverage of UAV surveys may be limited by financial and other constraints, leading to errors in estimating species occurrence or density. We use simulation modelling to investigate how movement ecology should influence fine-scale decisions regarding ecological surveillance using UAVs. Movement and dispersal parameter choices allow contrasts between locally mobile but slow-dispersing populations, and species that are locally more static but invasive at the landscape scale. We find that low and slow UAV flights may offer the best monitoring strategy to predict local population densities in transects, but that the consequent reduction in overall area sampled may sacrifice the ability to reliably predict regional population density. Alternative flight plans may perform better, but this is also dependent on movement ecology and the magnitude of relative detection errors for different flight choices. Simulated investigations such as this will become increasingly useful to reveal how spatio-temporal extent and resolution of UAV monitoring should be adjusted to reduce observation errors and thus provide better population estimates, maximising the efficacy and efficiency of unmanned aerial surveys.

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There are several good reasons why Earth and Space Science should be a part of any science curriculum. Nearly everything we do each day is connected in some way to the Earth: to its land, oceans, atmosphere, plants and animals. By 2025, eight billion people will live on Earth. If we are to continue extracting resources to maintain a high quality of life, then it is important that our children are scientifically literate in a way that allows them to exploit the Earth’s resources in a sustainable way. People who understand how earth systems work can make informed decisions and may be able to help resolve issues surrounding clean water, urban planning and development, global climate change and the use and management of natural resources.

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Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising methodology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of this approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labelling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means clustering. The results show the algorithm delivers consistent decision boundaries that classify the field into three clusters, one for each crop health level as shown in Figure 1. The methodology presented in this paper represents a venue for further esearch towards automated crop damage assessments and biosecurity surveillance.

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Symposium co-ordinated by The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) Purpose Global monitoring of the price and affordability of foods, meals and diets is urgently needed. There are major methodological challenges in developing robust, cost-effective, standardized, and policy relevant tools, pertinent to nutrition, obesity, and diet-related non-communicable diseases and their inequalities. There is increasing pressure to take into account environmental sustainability. Changes in price differentials and affordability need to be comparable between and within countries and over time. Robust tools could provide baseline data for monitoring and evaluating structural, economic and social policies at the country/regional and household levels. INFORMAS offers one framework for consideration.

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We explore the semi-classical structure of the Wigner functions ($\Psi $(q, p)) representing bound energy eigenstates $|\psi \rangle $ for systems with f degrees of freedom. If the classical motion is integrable, the classical limit of $\Psi $ is a delta function on the f-dimensional torus to which classical trajectories corresponding to ($|\psi \rangle $) are confined in the 2f-dimensional phase space. In the semi-classical limit of ($\Psi $ ($\hslash $) small but not zero) the delta function softens to a peak of order ($\hslash ^{-\frac{2}{3}f}$) and the torus develops fringes of a characteristic 'Airy' form. Away from the torus, $\Psi $ can have semi-classical singularities that are not delta functions; these are discussed (in full detail when f = 1) using Thom's theory of catastrophes. Brief consideration is given to problems raised when ($\Psi $) is calculated in a representation based on operators derived from angle coordinates and their conjugate momenta. When the classical motion is non-integrable, the phase space is not filled with tori and existing semi-classical methods fail. We conjecture that (a) For a given value of non-integrability parameter ($\epsilon $), the system passes through three semi-classical regimes as ($\hslash $) diminishes. (b) For states ($|\psi \rangle $) associated with regions in phase space filled with irregular trajectories, ($\Psi $) will be a random function confined near that region of the 'energy shell' explored by these trajectories (this region has more than f dimensions). (c) For ($\epsilon \neq $0, $\hslash $) blurs the infinitely fine classical path structure, in contrast to the integrable case ($\epsilon $ = 0, where $\hslash $ )imposes oscillatory quantum detail on a smooth classical path structure.

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This paper shows that by using only symbolic language phrases, a mobile robot can purposefully navigate to specified rooms in previously unexplored environments. The robot intelligently organises a symbolic language description of the unseen environment and “imagines” a representative map, called the abstract map. The abstract map is an internal representation of the topological structure and spatial layout of symbolically defined locations. To perform goal-directed exploration, the abstract map creates a high-level semantic plan to reason about spaces beyond the robot’s known world. While completing the plan, the robot uses the metric guidance provided by a spatial layout, and grounded observations of door labels, to efficiently guide its navigation. The system is shown to complete exploration in unexplored spaces by travelling only 13.3% further than the optimal path.

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Incursions of plant pests and diseases pose serious threats to food security, agricultural productivity and the natural environment. One of the challenges in confidently delimiting and eradicating incursions is how to choose from an arsenal of surveillance and quarantine approaches in order to best control multiple dispersal pathways. Anthropogenic spread (propagules carried on humans or transported on produce or equipment) can be controlled with quarantine measures, which in turn can vary in intensity. In contrast, environmental spread processes are more difficult to control, but often have a temporal signal (e.g. seasonality) which can introduce both challenges and opportunities for surveillance and control. This leads to complex decisions regarding when, where and how to search. Recent modelling investigations of surveillance performance have optimised the output of simulation models, and found that a risk-weighted randomised search can perform close to optimally. However, exactly how quarantine and surveillance strategies should change to reflect different dispersal modes remains largely unaddressed. Here we develop a spatial simulation model of a plant fungal-pathogen incursion into an agricultural region, and its subsequent surveillance and control. We include structural differences in dispersal via the interplay of biological, environmental and anthropogenic connectivity between host sites (farms). Our objective was to gain broad insights into the relative roles played by different spread modes in propagating an invasion, and how incorporating knowledge of these spread risks may improve approaches to quarantine restrictions and surveillance. We find that broad heuristic rules for quarantine restrictions fail to contain the pathogen due to residual connectivity between sites, but surveillance measures enable early detection and successfully lead to suppression of the pathogen in all farms. Alternative surveillance strategies attain similar levels of performance by incorporating environmental or anthropogenic dispersal risk in the prioritisation of sites. Our model provides the basis to develop essential insights into the effectiveness of different surveillance and quarantine decisions for fungal pathogen control. Parameterised for authentic settings it will aid our understanding of how the extent and resolution of interventions should suitably reflect the spatial structure of dispersal processes.

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This paper argues that the Panopticon is an accurate model for and illustration of policing and security methods in the modern society. Initially, I overview the theoretical concept of the Panopticon as a structure of perceived universal surveillance which facilitates automatic obedience in its subjects as identified by the theorists Jeremy Bentham and Michel Foucault. The paper subsequently moves to identify how the Panopticon, despite being a theoretical construct, is nevertheless instantiated to an extent through the prevalence of security cameras as a means of sovereignly regulating human conduct; speeding is an ordinary example. It could even be contended that increasing surveillance according to the model of the Panopticon would reduce the frequency of offences. However, in the final analysis the paper considers that even if adopting an approach based on the Panopticon is a more effective method of policing, it is not necessarily a more desirable one.

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This article addresses the problem of how to select the optimal combination of sensors and how to determine their optimal placement in a surveillance region in order to meet the given performance requirements at a minimal cost for a multimedia surveillance system. We propose to solve this problem by obtaining a performance vector, with its elements representing the performances of subtasks, for a given input combination of sensors and their placement. Then we show that the optimal sensor selection problem can be converted into the form of Integer Linear Programming problem (ILP) by using a linear model for computing the optimal performance vector corresponding to a sensor combination. Optimal performance vector corresponding to a sensor combination refers to the performance vector corresponding to the optimal placement of a sensor combination. To demonstrate the utility of our technique, we design and build a surveillance system consisting of PTZ (Pan-Tilt-Zoom) cameras and active motion sensors for capturing faces. Finally, we show experimentally that optimal placement of sensors based on the design maximizes the system performance.

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High frequency, miniature, pulse tube cryocoolers are extensively used in space applications because of their simplicity. Parametric studies of inertance type pulse tube cooler are performed with different length-to-diameter ratios of the pulse tube with the help of the FLUENT (R) package. The local thermal non-equilibrium of the gas and the matrix is taken into account for the modeling of porous zones, in addition to the wall thickness of the components. Dynamic characteristics and the actual mechanism of energy transfer in pulse are examined with the help of the pulse tube wall time constant. The heat interaction between pulse tube wall and the oscillating gas, leading to surface heat pumping, is quantified. The axial heat conduction is found to reduce the performance of the pulse tube refrigerator. The thermal non-equilibrium predicts a higher cold heat exchanger temperature compared to thermal equilibrium. The pressure drop through the porous medium has a strong non-linear effect due to the dominating influence of Forchheimer term over that of the linear Darcy term at high operating frequencies. The phase angle relationships among the pressure, temperature and the mass flow rate in the porous zones are also important in determining the performance of pulse tuberefrigerator.

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This chapter provides a critical legal geography of outer Space, charting the topography of the debates and struggles around its definition, management, and possession. As the emerging field of critical legal geography demonstrates, law is not a neutral organiser of space, but is instead a powerful cultural technology of spatial production. Drawing on legal documents such as the Outer Space Treaty and the Moon Treaty, as well as on the analogous and precedent-setting legal geographies of Antarctica and the deep seabed, the chapter addresses key questions about the legal geography of outer Space, questions which are of growing importance as Space’s available satellite spaces in the geostationary orbit diminish, Space weapons and mining become increasingly viable, Space colonisation and tourism emerge, and questions about Space’s legal status grow in intensity. Who owns outer Space? Who, and whose rules, govern what may or may not (literally) take place there? Is the geostationary orbit the sovereign property of the equatorial states it supertends, as these states argued in the 1970s? Or is it a part of the res communis, or common property of humanity, which currently legally characterises outer Space? Does Space belong to no one, or to everyone? As challenges to the existing legal spatiality of outer Space emerge from spacefaring states, companies, and non-spacefaring states, it is particularly critical that the current spatiality of Space is understood and considered.