5,498 research outputs found
Multiple solutions for asteroid orbits: Computational procedure and applications
We describe the Multiple Solutions Method, a one-dimensional sampling of the six-dimensional orbital confidence region that is widely applicable in the field of asteroid orbit determination. In many situations there is one predominant direction of uncertainty in an orbit determination or orbital prediction, i.e., a ``weak'' direction. The idea is to record Multiple Solutions by following this, typically curved, weak direction, or Line Of Variations (LOV). In this paper we describe the method and give new insights into the mathematics behind this tool. We pay particular attention to the problem of how to ensure that the coordinate systems are properly scaled so that the weak direction really reflects the intrinsic direction of greatest uncertainty. We also describe how the multiple solutions can be used even in the absence of a nominal orbit solution, which substantially broadens the realm of applications. There are numerous applications for multiple solutions; we discuss a few problems in asteroid orbit determination and prediction where we have had good success with the method. In particular, we show that multiple solutions can be used effectively for potential impact monitoring, preliminary orbit determination, asteroid identification, and for the recovery of lost asteroids
Synthesis And Characterization Of Polyynes End-Capped By Biphenyl Groups ({\Alpha},{\Omega}-Biphenylpolyynes)
Stable polyyne chains terminated with biphenyl end groups
(a,u-biphenylpolyynes) were synthesized in a single step through a simple
procedure by using the Cadiot-Chodkiewicz reaction conditions. The
a,ubiphenylpolyynes were separated through HPLC analysis and identified by
means of their electronic absorption spectra. The a,u-biphenylpolyynes were
studied by FT-IR and Raman spectroscopy and the spectral interpretation was
supported with DFT calculations. A peculiarly low reactivity of
a,u-biphenylpolyynes with ozone was observed.Comment: The research leading to these results has received funding from the
European Research Council Consolidator Grant EspLORE (ERC-2016-CoG Grant
No.724610
Survey of irrigation efficiencies on horticultural properties in the Peel-Harvey catchment
A detailed efficiency survey of about 30 per cent of the irrigated horticultural area in the Peel-Harvey catchment revealed that only two out of 20 growers operated at the recommended efficiency levels. In addition it was found that the expenses associated with inefficiency were such that 12 out of 18 farmers would be able to recover improvement costs within one year of operation
Low-frequency modes in the Raman spectrum of sp-sp2 nanostructured carbon
A novel form of amorphous carbon with sp-sp2 hybridization has been recently
produced by supersonic cluster beam deposition showing the presence in the film
of both polyynic and cumulenic species [L. Ravagnan et al. Phys. Rev. Lett. 98,
216103 (2007)]. Here we present a in situ Raman characterization of the low
frequency vibrational region (400-800 cm-1) of sp-sp2 films at different
temperatures. We report the presence of two peaks at 450 cm-1 and 720 cm-1. The
lower frequency peak shows an evolution with the variation of the sp content
and it can be attributed, with the support of density functional theory (DFT)
simulations, to bending modes of sp linear structures. The peak at 720 cm-1
does not vary with the sp content and it can be attributed to a feature in the
vibrational density of states activated by the disorder of the sp2 phase.Comment: 15 pages, 5 figures, 1 tabl
A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing
Physics-Informed Neural Networks (PINNs) have gained popularity in solving
nonlinear partial differential equations (PDEs) via integrating physical laws
into the training of neural networks, making them superior in many scientific
and engineering applications. However, conventional PINNs still fall short in
accurately approximating the solution of complex systems with strong
nonlinearity, especially in long temporal domains. Besides, since PINNs are
designed to approximate a specific realization of a given PDE system, they lack
the necessary generalizability to efficiently adapt to new system
configurations. This entails computationally expensive re-training from scratch
for any new change in the system. To address these shortfalls, in this work a
novel sequential meta-transfer (SMT) learning framework is proposed, offering a
unified solution for both fast training and efficient adaptation of PINNs in
highly nonlinear systems with long temporal domains. Specifically, the
framework decomposes PDE's time domain into smaller time segments to create
"easier" PDE problems for PINNs training. Then for each time interval, a
meta-learner is assigned and trained to achieve an optimal initial state for
rapid adaptation to a range of related tasks. Transfer learning principles are
then leveraged across time intervals to further reduce the computational
cost.Through a composites autoclave processing case study, it is shown that SMT
is clearly able to enhance the adaptability of PINNs while significantly
reducing computational cost, by a factor of 100
Assessing radiative transfer models trained by numerical weather forecasts using sun-tracking radiometric measurements for satellite link characterization up to W band
Radio communications, and in particular Earth-to-satellite
links, are worldwide used for delivering digital services.
The bandwidth demand of such services is increasing
accordingly to the advent of more advanced applications
(e.g., multimedia services, deep-space explorations, etc.)
thus pushing the scientific community toward the
investigation of channel carriers at higher frequencies.
When using carrier frequencies above X band, the main
drawback is how to tackle the impact of tropospheric
processes (i.e., rain, cloud, water vapor). This work
assesses the joint use of weather forecast models, radiative
transfer models and Sun-tracking radiometric
measurements to explore their potential benefits in
predicting path attenuation and sky noise temperature for
slant paths at frequencies between K and W band, thus
paving the way to the optimization of satellite link-budgets
Damage Prediction in Woven and Non-woven Fabric Composites
This chapter presents a step-by-step review on different damage prediction approaches for woven and non-woven fabric composites. First, the characteristics of woven and non-woven fabrics are distinguished one from another, suggesting more complex analyses required for non-woven fabrics. Then, the subsequent sub-sections are geared toward a comparison of different approaches utilized in predicting the mechanical behavior and damage mechanisms of these composites at various material scales including micro, meso, and macro. The merits and demerits of each approach with regard to practicality, accuracy, effectiveness, and characterization expense are discussed. Moreover, using recent experimental evidences, the chapter aims to highlight a number of inherent complexities in the interlaced architecture of woven composites, which may not be precisely taken into account by the damage models originally developed for non-woven and unidirectional composites. Finally, two illustrative examples on the effect of the aforementioned complexities on the mechanical behavior of woven composites are presented in more detail, through some recent works of the authors
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