37,504 research outputs found
The ARIEL Instrument Control Unit design for the M4 Mission Selection Review of the ESA's Cosmic Vision Program
The Atmospheric Remote-sensing Infrared Exoplanet Large-survey mission
(ARIEL) is one of the three present candidates for the ESA M4 (the fourth
medium mission) launch opportunity. The proposed Payload will perform a large
unbiased spectroscopic survey from space concerning the nature of exoplanets
atmospheres and their interiors to determine the key factors affecting the
formation and evolution of planetary systems. ARIEL will observe a large number
(>500) of warm and hot transiting gas giants, Neptunes and super-Earths around
a wide range of host star types, targeting planets hotter than 600 K to take
advantage of their well-mixed atmospheres. It will exploit primary and
secondary transits spectroscopy in the 1.2-8 um spectral range and broad-band
photometry in the optical and Near IR (NIR). The main instrument of the ARIEL
Payload is the IR Spectrometer (AIRS) providing low-resolution spectroscopy in
two IR channels: Channel 0 (CH0) for the 1.95-3.90 um band and Channel 1 (CH1)
for the 3.90-7.80 um range. It is located at the intermediate focal plane of
the telescope and common optical system and it hosts two IR sensors and two
cold front-end electronics (CFEE) for detectors readout, a well defined process
calibrated for the selected target brightness and driven by the Payload's
Instrument Control Unit (ICU).Comment: Experimental Astronomy, Special Issue on ARIEL, (2017
EChO Payload electronics architecture and SW design
EChO is a three-modules (VNIR, SWIR, MWIR), highly integrated spectrometer,
covering the wavelength range from 0.55 m, to 11.0 m. The baseline
design includes the goal wavelength extension to 0.4 m while an optional
LWIR module extends the range to the goal wavelength of 16.0 m.
An Instrument Control Unit (ICU) is foreseen as the main electronic subsystem
interfacing the spacecraft and collecting data from all the payload
spectrometers modules. ICU is in charge of two main tasks: the overall payload
control (Instrument Control Function) and the housekeepings and scientific data
digital processing (Data Processing Function), including the lossless
compression prior to store the science data to the Solid State Mass Memory of
the Spacecraft. These two main tasks are accomplished thanks to the Payload On
Board Software (P-OBSW) running on the ICU CPUs.Comment: Experimental Astronomy - EChO Special Issue 201
S-PRAC: Fast Partial Packet Recovery with Network Coding in Very Noisy Wireless Channels
Well-known error detection and correction solutions in wireless
communications are slow or incur high transmission overhead. Recently, notable
solutions like PRAC and DAPRAC, implementing partial packet recovery with
network coding, could address these problems. However, they perform slowly when
there are many errors. We propose S-PRAC, a fast scheme for partial packet
recovery, particularly designed for very noisy wireless channels. S-PRAC
improves on DAPRAC. It divides each packet into segments consisting of a fixed
number of small RLNC encoded symbols and then attaches a CRC code to each
segment and one to each coded packet. Extensive simulations show that S-PRAC
can detect and correct errors quickly. It also outperforms DAPRAC significantly
when the number of errors is high
Harnessing high altitude solar power
As an intermediate solution between Glaser's satellite solar power (SSP) and ground-based photovoltaic (PV) panels, this paper examines the collection of solar energy using a high-altitude aerostatic platform. A procedure to calculate the irradiance in the medium/high troposphere, based on experimental data, is described. The results show that here a PV system could collect about four to six times the energy collected by a typical U.K.-based ground installation, and between one-third and half of the total energy the same system would collect if supported by a geostationary satellite (SSP). The concept of the aerostat for solar power generation is then briefly described together with the equations that link its main engineering parameters/variables. A preliminary sizing of a facility stationed at 6 km altitude and its costing, based on realistic values of the input engineering parameters, is then presented
Harnessing data flow and modelling potentials for sustainable development
Tackling some of the global challenges relating to health, poverty, business and the environment is known to be heavily dependent on the flow and utilisation of data. However, while enhancements in data generation, storage, modelling, dissemination and the related integration of global economies and societies are fast transforming the way we live and interact, the resulting dynamic, globalised and information society remains digitally divided. On the African continent, in particular, the division has resulted into a gap between knowledge generation and its transformation into tangible products and services which Kirsop and Chan (2005) attribute to a broken information flow. This paper proposes some fundamental approaches for a sustainable transformation of data into knowledge for the purpose of improving the peoples' quality of life. Its main strategy is based on a generic data sharing model providing access to data utilising and generating entities in a multi disciplinary environment. It highlights the great potentials in using unsupervised and supervised modelling in tackling the typically predictive-in-nature challenges we face. Using both simulated and real data, the paper demonstrates how some of the key parameters may be generated and embedded in models to enhance their predictive power and reliability.
Its main outcomes include a proposed implementation framework setting the scene for the creation of decision support systems capable of addressing the key issues in society. It is expected that a sustainable data flow will forge synergies between the private sector, academic and research institutions within and between countries. It is also expected that the paper's findings will help in the design and development of knowledge extraction from data in the wake of cloud computing and, hence, contribute towards the improvement in the peoples' overall quality of life. To void running high implementation costs, selected open source tools are recommended for developing and sustaining the system.
Key words: Cloud Computing, Data Mining, Digital Divide, Globalisation, Grid Computing, Information Society, KTP, Predictive Modelling and STI
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