Numerical Modelling of Underground Water Pipelines exposed to Seismic Loading

The essential factor that must get the interest by the engineers during the primary design stage of underground pipes is understanding mechanism of damage during earthquakes. The attention during design period increased due to the increment of seismic catastrophes throughout the few past decades. Therefore, finite element procedure was used for studying the seismic performance of buried pipes. PLAXIS-2D program was using for simulating the seismic performance of buried pipes using earthquake motion of single frequency. The response of both seismic vertical displacement, and acceleration of the buried pipe were simulated. The experiments of shaking table for two models of buried pipe in dry case that surrounded with sand and gravel were compared with numerical simulation results. According to the obtained results, the amplification of seismic wave raised considerably from the buried pipe base to the pipe crown, the biggest amplification occurred in the highest point of the pipe model. It can be noticed that Plaxis-2D software provides an accurate method for the prediction of seismic behaviour of buried pipe due to the obvious compatibility between the results of experiments and numerical simulation.

Effects of Dual-Frequency Ultrasound-Assisted Thawing Technology on Thawing Rate, Quality Properties and Microstructure of Large Yellow Croaker (Pseudosciaena crocea)

This research evaluated the effects of dual-frequency ultrasound-assisted thawing (UAT) on the thawing time, physicochemical quality, water-holding capacity (WHC), microstructure, and moisture migration and distribution of large yellow croaker. Water thawing (WT), refrigerated thawing (RT), and UAT (single-frequency: 28 kHz (SUAT-28), single-frequency: 40 kHz (SUAT-40), dual-frequency: 28 kHz and 40 kHz (DUAT-28/40)) were used in the current research. Among them, the DUAT-28/40 treatment had the shortest thawing time, and ultrasound significantly improved the thawing rate. It also retained a better performance from the samples, such as color, texture, water-holding capacity and water distribution, and inhibited disruption of the microstructure. In addition, a quality property analysis showed that the pH, total volatile basic nitrogen (TVB-N), and K value were the most desirable under the DUAT-28/40 treatment, as well as this being best for the flavor of the samples. Therefore, DUAT-28/40 treatment could be a possible thawing method because it improves the thawing rate and maintains the quality properties of large yellow croaker.

Gauss process regression for real-time ionospheric delay estimation from GNSS observations

The number of devices equipped with global satellite positioning has exceeded seven billion recently. There are a wide variety of receivers regarding their accuracy and reliability. Low cost, multi-frequency units have been released on the market latterly; however, the number of single-frequency receivers is still significant. Since their measurements are influenced by ionospheric delay, accurate ionosphere models are of utmost importance to reduce the effect. This paper summarizes how Gauss process regression (GPR) can be applied to derive near real-time regional ionosphere models using raw Global Navigation Satellite System (GNSS) observations of permanent stations. While Gauss process is widely used in machine learning, GPR is a nonparametric, Bayesian approach to regression. GPR has several benefits for ionosphere monitoring since it is quite robust and efficient to derive a grid model from data available in irregular set of ionospheric pierce points. The corresponding instrumental delays are estimated by a parallel Kalman filter. The presented algorithm can be applied near real-time, however the results are offline calculated and are compared to two high quality TEC map products. Based on the analysis, the accuracy of the GPR modell is in 2 TECu range. The developed methods could be efficiently applied in the field of autonomous vehicle navigation with meeting both accuracy and integrity requirements.

Critical Review of Basic Methods on DoA Estimation of EM Waves Impinging a Spherical Antenna Array

Direction-of-arrival (DoA) estimation of electromagnetic (EM) waves impinging on a spherical antenna array in short time windows is examined in this paper. Reflected EM signals due to non-line-of-sight propagation measured with a spherical antenna array can be coherent and/or highly correlated in a snapshot. This makes spectral-based methods inefficient. Spectral methods, such as maximum likelihood (ML) methods, multiple signal classification (MUSIC), and beamforming methods, are theoretically and systematically investigated in this study. MUSIC is an approach used for frequency estimation and radio direction finding, ML is a technique used for estimating the parameters of an assumed probability distribution for given observed data, and PWD applies a Fourier transform to the capture response and produces them in the frequency domain. Although they have been previously adapted and used to estimate DoA of EM signals impinging on linear and planar antenna array configurations, this paper investigates their suitability and effectiveness for a spherical antenna array. Various computer simulations were conducted, and plots of root-mean-square error (RMSE) against the square root of the Cramér–Rao lower bound (CRLB) were generated and used to evaluate the performance of each method. Numerical experiments and results from measured data show the degree of appropriateness and efficiency of each method. For instance, the techniques exhibit identical performance to that in the wideband scenario when the frequency f = 8 GHz, f = 16 GHz, and f = 32 GHz, but f = 16 GHz performs best. This indicates that the difference between the covariance matrix of the signal is coherent and that the steering vectors of signals impinging from that angle are small. MUSIC and PWD share the same problems in the single-frequency scenario as in the wideband scenario when the delay sample d = 0. Consequently, the DoA estimation obtained with ML techniques is more suitable, less biased, and more robust against noise than beamforming and MUSIC techniques. In addition, deterministic ML (DML) and weighted subspace fitting (WSF) techniques show better DoA estimation performance than the stochastic ML (SML) technique. For a large number of snapshots, WSF is a better choice because it is more computationally efficient than DML. Finally, the results obtained indicate that WSF and ML methods perform better than MUSIC and PWD for the coherent or partially correlated signals studied.

Self-consistent simulation of the impedance matching network for single frequency capacitively coupled plasma

Matching networks are of vital importance for capacitively coupled plasmas to maximize the power transferred to the plasma discharge. The nonlinear interaction between the external circuit and plasma has to be considered to design suitable matching networks. To study the effect of the matching circuit, we coupled PIC/MC model and nonlinear circuit equations based on Kirchhoff’s laws, in a fully nonlinear and self-consistent way. The single-frequency capacitively coupled discharge with ”L”-Type matching networks are simulated. Fully self-consistently results of circuit and plasma parameters are presented and then power absorbed by the plasma and efficiency are calculated. With the tune of the matching network, the efficiency can reach 28.7 %, leading to higher potential as well as higher electron density at fixed source voltage. Besides, only very small components of the third harmonics are found in the plasma voltage and current while surface charge densities have multiple harmonics on account of the strong plasma nonlinearity. Finally, the effects of matching capacitors on discharge are analyzed, results show that smaller Cm1 and Cm2 of 500 pF to 1000 pF may be a proper choice for better matching, resulting in higher voltage across the CCP, and thus higher electron density and power absorption efficiency are obtained.

Mosaicking Weather Radar Retrievals from an Operational Heterogeneous Network at C and X Band for Precipitation Monitoring in Italian Central Apennines

Meteorological radar networks are suited to remotely provide atmospheric precipitation retrieval over a wide geographic area for severe weather monitoring and near-real-time nowcasting. However, blockage due to buildings, hills, and mountains can hamper the potential of an operational weather radar system. The Abruzzo region in central Italy’s Apennines, whose hydro-geological risks are further enhanced by its complex orography, is monitored by a heterogeneous system of three microwave radars at the C and X bands with different features. This work shows a systematic intercomparison of operational radar mosaicking methods, based on bi-dimensional rainfall products and dealing with both C and X bands as well as single- and dual-polarization systems. The considered mosaicking methods can take into account spatial radar-gauge adjustment as well as different spatial combination approaches. A data set of 16 precipitation events during the years 2018–2020 in the central Apennines is collected (with a total number of 32,750 samples) to show the potentials and limitations of the considered operational mosaicking approaches, using a geospatially-interpolated dense network of regional rain gauges as a benchmark. Results show that the radar-network pattern mosaicking, based on the anisotropic radar-gauge adjustment and spatial averaging of composite data, is better than the conventional maximum-value merging approach. The overall analysis confirms that heterogeneous weather radar mosaicking can overcome the issues of single-frequency fixed radars in mountainous areas, guaranteeing a better spatial coverage and a more uniform rainfall estimation accuracy over the area of interest.

A Real-time Breast Hyperthermia Monitoring Scheme Based on Processing of Microwave Scattering Parameters with Deep Learning

This study proposes a new method based on deep learning to determine whether the temperature values ​​are at an appropriate level during the use of microwave hyperthermia method in the treatment of breast cancer. To implement our method, we utilize the temperature dependent dielectric properties of biological tissues to generate the heating scenarios that simulates the thermal behavior of biological tissue during the breast cancer hyperthermia treatment. Using the temperature-dependent dielectric properties we designated corresponding temperature thresholds, next, we labeled the malignant tumor region and the healthy tissue region in accordance with the pre-determined thresholds. In addition, scattering problems are solved based on treatment (hot or heated) and pre-treatment (cool) scenarios. Using the difference between hot and cool states, we train, test, and validate the CNN. Our main purpose in the project is to determine whether the tissue is heated in the desired temperature region using only the single frequency differential scattered electric field data.

A Real-time Breast Hyperthermia Monitoring Scheme Based on Processing of Microwave Scattering Parameters with Deep Learning

This study proposes a new method based on deep learning to determine whether the temperature values ​​are at an appropriate level during the use of microwave hyperthermia method in the treatment of breast cancer. To implement our method, we utilize the temperature dependent dielectric properties of biological tissues to generate the heating scenarios that simulates the thermal behavior of biological tissue during the breast cancer hyperthermia treatment. Using the temperature-dependent dielectric properties we designated corresponding temperature thresholds, next, we labeled the malignant tumor region and the healthy tissue region in accordance with the pre-determined thresholds. In addition, scattering problems are solved based on treatment (hot or heated) and pre-treatment (cool) scenarios. Using the difference between hot and cool states, we train, test, and validate the CNN. Our main purpose in the project is to determine whether the tissue is heated in the desired temperature region using only the single frequency differential scattered electric field data.