Ionospheric Electron Density and Temperature Profiles Using Ionosonde-like Data and Machine Learning

This research investigated and optimized the separation of carbon dioxide (CO2) from natural gas in an adsorption column filled with grafted-beam nanofiber adsorbent. The main purpose of using ANN and RSM models in this manuscript is to compare these two methods in predicting the CO2 adsorption capacity. In other words, it was made to find a suitable model that has the highest agreement with the experimental data. Also, the other purpose of using the RSM model is to detect the optimized empirical conditions. Moreover, two common ANN models are applied in this work, including a multilayer perceptron (MLP) and radial basis function (RBF). The novelties of this work are explained as follows: (1) detecting the optimized synthesis factors of NF-PAN/PUGMA sorbent which possess the highest CO2 adsorption capacity with the help of response surface methodology (RSM), (2) studying the simultaneous interaction of synthesis parameters on the CO2 adsorption capacity with the help of both RSM and artificial neural networks (ANNs), (3) testing two types of ANN models including multilayer perceptron (MLP) and radial basis function (RBF) to predict the effect of monomer volume percentage and irradiation dose on the CO2 adsorption capacity. Indeed, finding the best model (ANN or RSM) can help engineers in practical applications predict CO2 adsorption capacity using NF-PAN/PUGMA under different conditions without incurring high-priced chemical materials, electricity, or human resources. The validation results were examined using correlation coefficients (R2) of RSM, RBF, and MLP models. The correlation coefficients for the RSM, RBF, and MLP models were 0.9910, 0.9949, and 0.9968, respectively. Additionally, the average absolute relative deviation (AARD) values for the RBF and MLP models were 0.00046512 and 0.00045511, respectively, indicating that the MLP model is better than the RBF model. To identify the optimal network structure, the trial-and-error method was conducted for MLP and RBF models. The number of neurons was found at 12 and 45 for MLP and RBF, respectively. The optimized effective parameters were obtained using RSM: 25.80% GMA, 66.45% amine, and an irradiation intensity of 28kGy.

Longitudinal behaviors of the IRI-B parameters of the equatorial electron density profiles retrieved from FORMOSAT-3/COSMIC radio occultation measurements

Efficient global optimization of aerodynamic shapes with the high-fidelity method is of great importance in the design process of modern aircrafts. In this study, modifications are made to the particle swarm optimization (PSO) algorithm and the radial basis function (RBF) model for further improvements of efficiency and accuracy of optimization. Specifically, a PSO algorithm with randomly distributed cognitive and social parameters, exponential decrease of maximum velocity and inertia weight, and periodic mutation of particle position is proposed. Furthermore, a nonuniform shape parameters strategy is introduced for the RBF surrogate model. Validations on test functions show that the new PSO has remarkable speed of convergence, and the new RBF model has superior approximation accuracy. The PSO algorithm and RBF model are then combined to construct the computational fluid dynamics (CFD)–based optimization framework. Finally, optimizations of transonic airfoil and supersonic launch vehicle are perfo...

Electron density and temperature profiles in the mid latitude ionosphere are derived from the ”resonance cone” in the radiation pattern of high‐frequency point antennas aboard a sounding rocket. By comparing the shapes for reversed wave propagation direction it is possible to study electron drift motion and field‐aligned beams. The data from the COREX‐I (Cooperative Resonance Cone Experiment) experiment show close agreement of electron density and temperature profiles from resonance cones with results from independent instruments. There was no indication of a substantial temperature anomaly atEregion altitude during this flight. An unexpected electron drift can be interpreted as electron Hall current in the presheath of the negatively charged payload. Field‐aligned beams, which have been suggested in connection with the temperature anomaly, were not detected.

It is clear that the edge plasma plays a crucial role in global tokamak confinement. This paper is a report on simulations of a new drift wave type instability driven by the electron temperature gradient in tokamak scrapeoff-layers (SOL). A 2d fluid code has been developed in order to explore the anomalous transport in the boundary plasmas. The simulation consists of a set of fluid equations for the vorticity {nabla}{sub {perpendicular}}{sup 2}{phi}, the electron density n{sub c} and the temperature T{sub c} in a shearless plasma slab confined by a uniform, straight magnetic field B{sub z} with two divertor (or limiter) plates intercepting the magnetic field. The model has two regions separated by a magnetic separatrix: in the edge region inside the separatrix, the model is periodic along the magnetic field while in the SOL region outside the separatrix, the magnetic field is taken to be of finite length with model boundary conditions at diverter plates. The simulation results show that the observed linear instability agrees well with theory, and that a saturated state of turbulence is reached. In saturated turbulence, clear evidence of the expected long-wavelength mode penetration into the edge is seen, an inverse cascade of wave energy is observed. The simulation results also show that amplitudes of potential and the electron temperature fluctuations are somewhat above and the heat flux are somewhat below those of the simplest mixing-length estimates, and furthermore the large-scale radial structures of fluctuation quantities indicate that the cross-field transport is not diffusive. After saturation, the electron density and temperature profiles are flattened. A self-consistent simulation to determine the microturbulent SOL electron temperature profile has been done, the results of which reasonably agree with the experimental measurements.

With the accumulating of the strong earthquakes records, it becomes practicable to achieve the more accurate attenuation relationships. Based on the seismic records of West American, the Radial Basis Function (RBF) and Back Propagation (BP) artificial neural networks model are respectively constructed for three-dimensional seismic parameters attenuation relationship. The RBF model is nice fitting for the training data, although it has great errors on other tested points. While the BP model is not good than the RBF model for the training data, it possesses a better consecutive property in the whole area. It is a proper neural network model for the problem. After training with the selected records, the Neural Networks (NN) shows a good fitting with the training records. And it is easy to construct three-dimensional model to predict the attenuation relationship. In order to demonstrate the efficiency of the presented methodology, the contrast is discussed for the results of the BP model and three typical traditional attenuation formulae.

In this paper, we describe a wavelet-based approach to multiresolution stochastic image modeling. The basic idea here is that a complex random field, e.g., one with long range and nonlinear spatial correlations, can be decomposed into several less complex random fields. This is done by defining a random field in each resolution of a wavelet expansion. Experiments, performed for the multiresolution AR (autoregressive) and RBF (radial basis function) models, have produced promising results. Specifically, the wavelet-AR model captures long range correlation better than the single resolution AR model, and for both the wavelet AR and RBF models, random fields in the wavelet domain do appear to be simpler to model than those on the finest resolution.

<p>Ionosphere is one of the main errors in the signal propagation of global navigation system satellite (GNSS), and it is also the key issue of space weather. The International Reference Ionosphere (IRI) is the most important empirical model described the ionospheric characteristics, and it provides the monthly averages of electron densities and vertical total electron content (VTEC) in the altitude range of 50km-2000km. The IRI-2016 model is the latest version. But some studies showed that the accuracy of the IRI model is not high enough in China due to the use of fewer data sources. This paper will assess the performance of IRI-2016 model in China, and a modified IRI 2016 model by adjusting the driving parameters IG and RZ index of IRI2016 model with GNSS TEC data are also investigated. In this contribution, GNSS data from the Crustal Movement Observation Network of China (CMONC) are used to estimate TEC values, and the ionosonde data from three stations are used as references for the ionospheric electron densities. Three ionosonde stations are located at Beijing (BP440, 40.3°N/116.2°E), Wuhan (WU430, 30.5°N/114.4°E) and Sanya (SA418, 18.3°N/ 109.6°E). The above data respectively cover a period of 6 days in the high year (2015) and low year (2019) of solar activity.</p><p>The study shows that the biggest reason for the difference (DTEC) between GPS-TEC and IRI2016-TEC in China is that the poor estimation of NmF2 and hmF2 by IRI model, and the driving parameters IG and RZ index of IRI2016 can be updated by constraining DTEC. Finally, the performance of the modified IRI-2016 model is improved by the updated IG and RZ indexes as the short-term driving values of ionospheric parameters. The analysis show that the modified IRI-2016 model is more accurate at estimating both the TEC and the electron density profile than the original model.</p>

Design and characterization of the polychromators for JT-60SA Thomson scattering systems

The electron density and temperature profiles measured by the Thomson scattering diagnostic on the COMPASS tokamak are used for estimation of electron kinetic energy, energy confinement time, and effective charge number Z(eff). Data are compared with the line-integrated electron density measured by a microwave interferometer in an ohmically heated plasma with a circular cross section. An error analysis of both electron temperature and density are performed by two methods-a constant chi-square boundaries method and a Monte Carlo simulation, determining asymmetrical error bars for the electron temperature.

The international reference ionosphere (IRI) models have been widely used for correcting the ionospheric scintillations at different altitude levels. An evaluation on the performance of VTEC correction from IRI models (version 2007, 2012 and 2016) over Sukkur, Pakistan (27.71º N, 68.85º E) is presented in this work. Total Electron Content (TEC) from IRI models and GPS in 2019 over Sukkur region are compared. The main aim of this comparative analysis is to improve the VTEC in low latitude Sukkur, Pakistan. Moreover, this study will also help us to identify the credible IRI model for the correction of Global Positioning System (GPS) signal in low latitude region in future. The development of more accurate TEC finds useful applications in enhancing the extent to which ionospheric influences on radio signals are corrected. VTEC from GPS and IRI models are collected between May 1, 2019 and May 3, 2019. Additionally, Dst and Kp data are also compared in this work to estimate the geomagnetic storm variations. This study shows a good correlation of 0.83 between VTEC of GPS and IRI 2016. Furthermore, a correlation of 0.82 and 0.78 is also recorded for IRI 2012 and IRI 2007 respectively, with VTEC of GPS. The IRI TEC predictions and GPS-TEC measurements for the studied days reveal the potential of IRI model as a good candidate over Pakistan.

Inrecent years, artificial neural networks have become the subject ofintensive research inanumber ofscientific areas. The high performance and operational speed ofneural models open up awide spectrum ofapplications invarious areas oflife sciences. Objectives pursued bymany scientists, who use neural modelling intheir research, focus - among others - onintensifying real-time calculations. This study shows the possibility ofusing Multilayer-Perceptron (MLP) and Radial Basis Function (RBF) models ofartificial neural networks for the future development ofnew methods for animal science. The process should beexplained explicitly tomake the MLP and RBF models more readily accepted bymore researchers. This study describes and recommends certain models aswell asuses forecasting methods, which are represented bythe chosen neural network topologies, inparticular MLP and RBF models for more successful operations inthe field ofanimals sciences.

In view of the problem of low accuracy in walnut mass detection caused by relatively inconstant density, this study suggested the integration of X‐ray imaging technology and image processing technology with machine learning for walnut mass detection. Using image processing technology to remove the background of walnut X‐ray image and to segment the kernels, the mass prediction models could be constructed after extracting the shape and texture of walnut characteristic parameters and the kernel shape characteristic parameters. The results revealed that when mass prediction models were built based on walnut shape characteristic parameters or constructed with the application of texture characteristic parameters, the determination coefficient (R2) were rather low, even the residual predictive deviation (RPD) less than 1.4, proving that these models were not reliable. However, when mass prediction models were built based on walnut and kernel shape characteristic parameters, the R2 for both partial least squares (PLS) and radial basis function (RBF) models were higher than 0.84. And the RPD values were 1.8133 and 1.7474, respectively. On this basis, when competitive adaptive reweighed sampling (CARS) optimized parameters were adopted, the R2 for both PLS and RBF models were higher than 0.86 and the RPD were 1.8759 and 1.8850, respectively. The result proved that the application of walnut and kernel shape characteristics can improve the accuracy of model prediction. Therefore, using X‐ray imaging technology to detect walnut mass was feasible, which could realized the prompt, accurate and nondestructive detection of walnut mass. This technology provided a novel thought for the accurate grading of walnuts.Practical ApplicationsAt present, there is a problem of similar size but large mass difference after walnut size classification, which directly affects the commodity value of walnuts. It is necessary to combine multiple characteristics to select and classify walnuts. Mass is an important grading index. With the development of machine vision technology, online detection of fruit mass has been realized. However, due to the relatively inconstant density of walnuts and the limitation of the technology, the machine vision technique cannot obtain internal information. This study used X‐ray transmission to obtain the internal information of walnuts without destroying the walnuts. At the same time, the mass detection models were established by combining the internal and external characteristics of the walnuts to achieve fast, accurate, and nondestructive testing of walnut mass. Automatic mass detection provides technical support for intelligent grading, which has important production significance and economic value for the development of the walnut industry.

The global statistical median behavior of the electron temperature (Te) in the topside ionosphere was investigated through in-situ data collected by Langmuir Probes on-board the European Space Agency Swarm satellites constellation from the beginning of 2014 to the end of 2020. This is the first time that such an analysis, based on such a large time window, has been carried out globally, encompassing more than half a solar cycle, from the activity peak of 2014 to the minimum of 2020. The results show that Swarm data can help in understanding the main features of Te in the topside ionosphere in a way never achieved before. Te data measured by Swarm satellites were also compared to data modeled by the empirical climatological International Reference Ionosphere (IRI) model and data measured by Jicamarca (12.0°S, 76.8°W), Arecibo (18.2°N, 66.4°W), and Millstone Hill (42.6°N, 71.5°W) Incoherent Scatter Radars (ISRs). Moreover, the correction of Swarm Te data recently proposed by Lomidze was applied and evaluated. These analyses were performed for two main reasons: (1) to understand how the IRI model deviates from the measurements; and (2) to test the reliability of the Swarm dataset as a new possible dataset to be included in the underlying empirical dataset layer of the IRI model. The results show that the application of the Lomidze correction improved the agreement with ISR data above all at mid latitudes and during daytime, and it was effective in reducing the mismatch between Swarm and IRI Te values. This suggests that future developments of the IRI Te model should include the Swarm dataset with the Lomidze correction. However, the existence of a quasi-linear relation between measured and modeled Te values was well verified only below about 2200 K, while for higher values it was completely lost. This is an important result that IRI Te model developers should properly consider when using the Swarm dataset.

Estimation of Lyapunov spectrum and model selection for a chaotic time series