اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدNumerical removal of water-vapor effects from THz-TDS measurements
158
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
One source of disturbance in a pulsed T-ray signal is attributed to ambient water vapor. Water molecules in the gas phase selectively absorb T-rays at discrete frequencies corresponding to their molecular rotational transitions. This results in prominent resonances spread over the T-ray spectrum, and in the time domain the T-ray signal is observed as fluctuations after the main pulse. These effects are generally undesired, since they may mask critical spectroscopic data. So, ambient water vapor is commonly removed from the T-ray path by using a closed chamber during the measurement. Yet, in some applications a closed chamber is not applicable. This situation, therefore, motivates the need for another method to reduce these unwanted artifacts. This paper presents a study on a computational means to address the problem. Initially, a complex frequency response of water vapor is modeled from a spectroscopic catalog. Using a deconvolution technique, together with fine tuning of the strength of each resonance, parts of the water-vapor response are removed from a measured T-ray signal, with minimal signal distortion.
قيم البحث
اقرأ أيضاً
We numerically investigate the effect of non-condensable gas inside a vapor bubble on bubble dynamics, collapse pressure and pressure impact of spherical and aspherical bubble collapses. Free gas inside a vapor bubble has a damping effect that can we
aken the pressure wave and enhance the bubble rebound. To estimate this effect numerically, we derive and validate a multi-component model for vapor bubbles containing gas. For the cavitating liquid and the non-condensable gas, we employ a homogeneous mixture model with a coupled equation of state for all components. The cavitation model for the cavitating liquid is a barotropic thermodynamic equilibrium model. Compressibility of all phases is considered in order to capture the shock wave of the bubble collapse. After validating the model with an analytical energy partitioning model, simulations of collapsing wall-attached bubbles with different stand-off distances are performed. The effect of the non-condensable gas on rebound and damping of the emitted shock wave is well captured.
We report on the measurements of telluric water vapor made with the instrument FIFI-LS on SOFIA. Since November 2018, FIFI-LS has measured the water vapor overburden with the same measurement setup on each science flight with about 10 data points thr
oughout the flight. This created a large sample of 469 measurements at different locations, flight altitudes and seasons. The paper describes the measurement principle in detail and provides some trend analysis on the 3 parameters. This presents the first systematic analysis with SOFIA based on in situ observations.
We report on a method and an associated open source software, Fit@TDS, working on an average personal computer. The method is based on the fitting of a time-trace data of a terahertz time-domain-spectroscopy system enabling the retrieval of the refra
ctive index of a dielectric sample and the resonance parameters of a metasurface (quality factor, absorption losses, etc.). The software includes commonly used methods where the refractive index is extracted from frequency domain data. However, these methods are limited, for instance in case of a high noise level or when an absorption peak saturates the absorption spectrum bringing the signal to the noise level. Our software allows to use a new method where the refractive indices are directly fitted from the time-trace. The idea is to model a material or a metamaterial through parametric physical models (Drude-Lorentz model and time-domain coupled mode theory) and to implement the subsequent refractive index in the propagation model to simulate the time-trace. Then, an optimization algorithm is used to retrieve the parameters of the model corresponding to the studied material/metamaterial. In this paper, we explain the method and test it on fictitious samples to probe the feasibility and reliability of the proposed model. Finally, we used Fit@TDS on real samples of high resistivity silicon, lactose and gold metasurface on quartz to show the capacity of our method
Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of
Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. The trained model was evaluated on more than 1500 hours of recorded data. It achieves a root mean square error (RMSE) of 0.098 mm for a forecasting interval of 5 minutes in the future, and outperforms the naive approach for a lead-time of up to 40 minutes.
Ice-water, water-vapor interfaces and ice surface are studied by molecular dynamics simulations with the SPC/E model of water molecules having the purpose to estimate the profiles of electrostatic potential across the interfaces. We have proposed a m
ethodology for calculating the profiles of electrostatic potential based on a trial particle, which showed good agreement for the case of electrostatic potential profile of the water-vapor interface of TIP4P model calculated in another way. The measured profile of electrostatic potential for the pure ice-water interface decreases towards the liquid bulk region, which is in agreement with simulations of preferential direction of motion of Li$^{+}$ and F$^{-}$ solute ions at the liquid side of the ice-water interface. These results are discussed in connection with the Workman-Reynolds effect.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
