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Search Results (4,138)

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17 pages, 23136 KiB  
Article
Analysis of an Axial Field Hybrid Excitation Synchronous Generator
by Junyue Yu, Shushu Zhu and Chuang Liu
Energies 2024, 17(24), 6329; https://doi.org/10.3390/en17246329 - 16 Dec 2024
Abstract
An axial field hybrid excitation synchronous generator (AF-HESG) is proposed for an independent power supply system, and its electromagnetic performance is studied in this paper. The distinguishing feature of the proposed generator is the addition of static magnetic bridges at both ends to [...] Read more.
An axial field hybrid excitation synchronous generator (AF-HESG) is proposed for an independent power supply system, and its electromagnetic performance is studied in this paper. The distinguishing feature of the proposed generator is the addition of static magnetic bridges at both ends to place the field windings and the use of a sloping surface to increase the additional air-gap cross-sectional area. The advantage of the structure is that it achieves brushless excitation and improves the flux-regulation range. The structure and magnetic circuit characteristics are introduced in detail. Theoretical analysis of the flux-regulation principle is conducted by studying the relationship between field magnetomotive force, rotor reluctance, and air-gap flux density. Quantitative calculation is performed using a magnetomotive force (MMF)-specific permeance model, and the influence of the main parameters on the air-gap flux density and flux-regulation range is analyzed. Subsequently, magnetic field, no-load, and load characteristics are investigated through three-dimensional finite element analysis. The loss distribution is analyzed, and the temperature of the generator under rated conditions is simulated. Finally, a 30 kW, 1500 r/min prototype is developed and tested. The test results show good flux-regulation capability and stable voltage output performance of the proposed generator. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Sectional view of the AF-HESG.</p>
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<p>Diagram of various components of the AF-HESG.</p>
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<p>Schematic of the AF-HESG system.</p>
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<p>Magnetic circuit of AF-HESG.</p>
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<p>Equivalent magnetic circuit and electrical circuit of the AF-HESG.</p>
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<p>Relationship between armature flux direction and field flux. (<b>a</b>) Case 1. (<b>b</b>) Case 2. (<b>c</b>) Case 3.</p>
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<p>MEC of the AF-HESG. (<b>a</b>) Case 1. (<b>b</b>) Case 3.</p>
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<p>Ideal air-gap MMF waveform.</p>
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<p>Calculation results of the MMF-specific permeance model. (<b>a</b>) Air-gap MMF. (<b>b</b>) Air-gap permeance. (<b>c</b>) Air-gap flux density.</p>
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<p>Relationship of the air-gap flux density and field current.</p>
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<p>The influence of main parameters on air gap. (<b>a</b>) Air-gap MMF generated by PM <span class="html-italic">F</span><sub>g_PM</sub>. (<b>b</b>) Air-gap MMF generated by FW <span class="html-italic">F</span><sub>g_<span class="html-italic">f</span></sub>. (<b>c</b>) Air-gap flux density <span class="html-italic">B</span><sub>g</sub>. (<b>d</b>) Flux-regulation range <span class="html-italic">k<sub>f</sub></span>.</p>
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<p>The influence of main air-gap length on air-gap flux density, MMF, and permeance.</p>
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<p>Magnetic field distributions with different <span class="html-italic">I<sub>f</sub></span>. (<b>a</b>) <span class="html-italic">I<sub>f</sub></span> = 0 A. (<b>b</b>) <span class="html-italic">I<sub>f</sub></span> = 2 A. (<b>c</b>) <span class="html-italic">I<sub>f</sub></span> = 6 A.</p>
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<p>The air-gap flux density distribution under different <span class="html-italic">I<sub>f</sub></span>.</p>
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<p>Output voltage under different <span class="html-italic">I<sub>f</sub></span>. (<b>a</b>) No-load voltage. (<b>b</b>) Load voltage.</p>
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<p>Harmonic of no-load and load output voltage.</p>
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<p>No-load characteristics.</p>
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<p>External characteristics under different field currents.</p>
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<p>Regulation characteristics (<span class="html-italic">U</span><sub>out</sub> = 220 V).</p>
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<p>Component of loss on the rated conditions.</p>
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<p>Steady-state temperature field of each component.</p>
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<p>Prototype and test platform.</p>
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<p>Measured no-load output voltage waveform.</p>
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<p>Measured load output voltage waveform.</p>
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<p>Comparison of no-load characteristics at different speeds.</p>
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<p>Comparison of external characteristics.</p>
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<p>Comparison of regulation characteristics.</p>
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16 pages, 3708 KiB  
Article
Suppression of Strong Cultural Noise in Magnetotelluric Signals Using Particle Swarm Optimization-Optimized Variational Mode Decomposition
by Zhongda Shang, Xinjun Zhang, Shen Yan and Kaiwen Zhang
Appl. Sci. 2024, 14(24), 11719; https://doi.org/10.3390/app142411719 - 16 Dec 2024
Viewed by 79
Abstract
To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and [...] Read more.
To effectively separate strong cultural noise in Magnetotelluric (MT) signals under strong interference conditions and restore the true forms of apparent resistivity and phase curves, this paper proposes an improved method for suppressing strong cultural noise based on Particle Swarm Optimization (PSO) and Variational Mode Decomposition (VMD). First, the effects of two initial parameters, the decomposition scale K and penalty factor α, on the performance of variational mode decomposition are studied. Subsequently, using the PSO algorithm, the optimal combination of influential parameters in the VMD is determined. This optimal parameter set is applied to decompose electromagnetic signals, and Intrinsic Mode Functions (IMFs) are selected for signal reconstruction based on correlation coefficients, resulting in denoised electromagnetic signals. The simulation results show that, compared to traditional algorithms such as Empirical Mode Decomposition (EMD), Intrinsic Time Decomposition (ITD), and VMD, the Normalized Cross-Correlation (NCC) and signal-to-noise ratio (SNR) of the PSO-optimized VMD method for suppressing strong cultural noise increased by 0.024, 0.035, 0.019, and 2.225, 2.446, 1.964, respectively. The processing of field data confirms that this method effectively suppresses strong cultural noise in strongly interfering environments, leading to significant improvements in the apparent resistivity and phase curve data, thereby enhancing the authenticity and reliability of underground electrical structure interpretations. Full article
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<p>Time-domain waveform of simulated strong cultural noise. Horizontal coordinates indicate sampling points and vertical coordinates indicate amplitude. (<b>a</b>) Clean signal; (<b>b</b>) simulated impulse noise signal; (<b>c</b>) simulated square noise signal; (<b>d</b>) simulated triangle noise signal; (<b>e</b>) simulated periodic noise signal.</p>
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<p>Convergence result of parameter iteration.</p>
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<p>Three-dimensional representation of simulated signal decomposition. Different colors represent different IMFs, blue means initial signal IMF0, orange means IMF1, green means IMF2, and so on.</p>
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<p>Comparison of simulated signal before and after denoising. Horizontal coordinates indicate sampling points and vertical coordinates indicate amplitude. The first line in blue indicates the initial signal, the second line in red indicates the extracted noise signal, and the third line in blue indicates the reconstructed denoised signal. (<b>a</b>) Comparison of simulated impulse noise signal; (<b>b</b>) comparison of simulated square noise signal; (<b>c</b>) comparison of simulated triangle noise signal; (<b>d</b>) comparison simulated periodic noise signal.</p>
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<p>Denoising representation of time series signal of field MT data. Horizontal coordinates indicate sampling points and vertical coordinates indicate amplitude. The first line in blue indicates the initial signal, the second line in red indicates the extracted noise signal, and the third line in blue indicates the reconstructed denoised signal. (<b>a</b>) Field signal; (<b>b</b>) noise contours extracted using the VMD method; (<b>c</b>) reconstructed signal.</p>
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<p>Comparison of the resistivity and phase curves before and after denoising. The black curves represent impedance Zxy and the red curves represent impedance Zyx. (<b>a</b>) Before denoising; (<b>b</b>) after denoising.</p>
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<p>Resistivity curve comparison using impedance Zxy as an example.</p>
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14 pages, 3682 KiB  
Project Report
Portable Arbitrary Pulse Generator for Driving Microcoils for Micromagnetic Neurostimulation
by Robert P. Bloom, Renata Saha, Zachary Sanger, Walter C. Low, Theoden I. Netoff and Jian-Ping Wang
Instruments 2024, 8(4), 55; https://doi.org/10.3390/instruments8040055 - 16 Dec 2024
Viewed by 126
Abstract
Micromagnetic stimulation (μMS) is a promising branch of neurostimulation but without some of the drawbacks of electrical stimulation. Microcoil (μcoil)-based magnetic stimulation uses small micrometer-sized coils that generate a time-varying magnetic field, which, as per Faraday’s Laws of Electromagnetic Induction, induces an electric [...] Read more.
Micromagnetic stimulation (μMS) is a promising branch of neurostimulation but without some of the drawbacks of electrical stimulation. Microcoil (μcoil)-based magnetic stimulation uses small micrometer-sized coils that generate a time-varying magnetic field, which, as per Faraday’s Laws of Electromagnetic Induction, induces an electric field on a conductive surface. This method of stimulation has the advantage of not requiring electrical contact with the tissue; however, these μcoils are not easy to operate. Large currents are required to generate the required magnetic field. These large currents are too large for standard test equipment to provide, and additional power amplifiers are needed. To aid in the testing and development of micromagnetic stimulation devices, we have created a compact single-unit test setup for driving these devices called the µCoil Driver. This unit is designed to drive small inductive loads up to ±8 V at 5 A and 10 kHz. Full article
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<p>(<b>a</b>) µCoil Driver front panel. (<b>b</b>) µCoil Driver back panel. (<b>c</b>) µCoil Driver unit. (<b>d</b>) µCoil Driver internals.</p>
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<p>(<b>a</b>) µCoil Driver functional diagram. (<b>b</b>) Waveform generation section. (<b>c</b>) Power amplifier section. (<b>d</b>) PCB sections: (<b>i</b>) battery charger, (<b>ii</b>) digital section, (<b>iii</b>) analog section, and (<b>iv</b>) display.</p>
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<p>(<b>a</b>) Waveform parameter definitions (help menu). (<b>b</b>) Waveform selection menu. (<b>c</b>) Sinusoidal output. (<b>d</b>) Three burst outputs. (<b>e</b>–<b>g</b>) arbitrary waveform outputs: triangle, square, and sin, respectively.</p>
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<p>(<b>a</b>) Circuit model of inductor parasitics, which can be used to calculate output current waveform. (<b>b</b>–<b>d</b>) Plots of measured output voltage and current as well as the predicted current waveforms for various inductive loads. (<b>b</b>,<b>c</b>) show a couple of μcoils with values typical to those used in micromagnetic stimulation. These coils show a primarily resistive behavior. (<b>d</b>) shows a larger inductive load whose impedance is largely due to the coils inductance, resulting in a notable phase shift.</p>
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<p>(<b>a</b>) Experimental test setup consisting of PC, µCoil Driver, oscilloscope, and MagPen (<b>b</b>) µCoil Driver output on, with oscilloscope monitoring the output current. (<b>c</b>) Surgery setup with MagPen µcoil placed over rat sciatic nerve.</p>
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17 pages, 10949 KiB  
Article
Research on the Detection Method for Feeding Metallic Foreign Objects in Coal Mine Crushers Based on Reflective Pulsed Eddy Current Testing
by Benchang Meng, Zezheng Zhuang, Jiahao Ma and Sihai Zhao
Appl. Sci. 2024, 14(24), 11704; https://doi.org/10.3390/app142411704 - 15 Dec 2024
Viewed by 447
Abstract
In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction [...] Read more.
In response to the difficulties and poor timeliness in detecting feeding metallic foreign objects during high-yield continuous crushing operations in coal mines, this paper proposes a new method for detecting metallic foreign objects, combining pulsed eddy current testing with the Truncated Region Eigenfunction Expansion (TREE) method. This method is suitable for the harsh working conditions in coal mine crushing stations, which include high dust, strong vibration, strong electromagnetic interference, and low temperatures in winter. A model of the eddy current field of feeding metallic foreign objects in the truncated region is established using a coaxial excitation and receiving coil with a Hall sensor. The full-cycle time-domain analytical solution for the induced voltage and magnetic induction intensity of the reflective field under practical square wave signals is obtained. Simulation and experimental results show that the effective time range, peak value, and time to peak of the received voltage and magnetic induction signals can be used to classify and identify the size, thickness, conductivity, and magnetic permeability of feeding metallic foreign objects. Experimental results meet the actual needs for removing feeding metallic foreign objects in coal mine sites. This provides core technical support for the establishment of a predictive fault diagnosis system for crushing equipment. Full article
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<p>Structure diagram of the open-pit coal mine crushing station (1—Mining Truck, 2—Ore Receiving Hopper, 3—Plate Feeder, 4—Protective Steel Structure, 5—Electrical Control Room, 6—Detection Probes Array, 7—Dual-roll Screening Crusher, and 8—Belt Conveyor).</p>
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<p>Structure diagram of the dual-roll screening crusher (1—Wear Plates for Front and Side Walls, 2—Crusher Tooth Rolls, 3—Drive Motor, 4—Hydraulic Coupling, 5—Reducer, and 6—Coupling).</p>
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<p>Side view of the truncated region of (<b>a</b>) the single-turn coil, and (<b>b</b>) the rectangular cross-section coaxial excitation and receiving coils with Hall sensors.</p>
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<p>Typical PEC signals with non-ferromagnetic metals; (<b>a</b>) receiving coil voltage signals; (<b>b</b>) magnetic induction signals of Hall sensor.</p>
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<p>Typical PEC signals with ferromagnetic metals; (<b>a</b>) receiving coil voltage signals; (<b>b</b>) magnetic induction signals of Hall sensor.</p>
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<p>Single-probe testing experiment; (<b>a</b>) experimental platform; (<b>b</b>) block diagram of the system.</p>
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<p>Detailed view of single-probe and samples; (<b>a</b>) bottom view of the single-probe; (<b>b</b>) seven test samples for experiment.</p>
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<p>PEC differential signals of alloy steel 42CrMo with different thicknesses; (<b>a</b>) receiving coil differential voltage signals; (<b>b</b>) magnetic induction differential signals of Hall sensor.</p>
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<p>Relationship between key characteristic quantities of PEC differential signals and the thicknesses of alloy steel 42CrMo; (<b>a</b>) peak voltage and its corresponding time to peak; (<b>b</b>) peak magnetic inductance and its corresponding time to peak.</p>
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<p>Three-dimensional surface plots between key characteristics of pulsed eddy current differential voltage signals and the conductivity and thickness of non-ferromagnetic metals; (<b>a</b>) peak voltage; (<b>b</b>) time to peak.</p>
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<p>Three-dimensional surface plots between key characteristics of pulsed eddy current differential magnetic inductance signals and the conductivity and thickness of non-ferromagnetic metals; (<b>a</b>) peak magnetic inductance; (<b>b</b>) time to peak.</p>
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<p>Field experiment platform with the multi-probe array.</p>
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<p>Dual <span class="html-italic">Y</span>-axis plot of PEC differential signals and time for the effective detection interval in the field experiment.</p>
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17 pages, 5303 KiB  
Article
Carbon Soil Mapping in a Sustainable-Managed Farm in Northeast Italy: Geochemical and Geophysical Applications
by Gian Marco Salani, Enzo Rizzo, Valentina Brombin, Giacomo Fornasari, Aaron Sobbe and Gianluca Bianchini
Environments 2024, 11(12), 289; https://doi.org/10.3390/environments11120289 - 14 Dec 2024
Viewed by 334
Abstract
Recently, there has been increasing interest in organic carbon (OC) certification of soil as an incentive for farmers to adopt sustainable agricultural practices. In this context, this pilot project combines geochemical and geophysical methods to map the distribution of OC contents in agricultural [...] Read more.
Recently, there has been increasing interest in organic carbon (OC) certification of soil as an incentive for farmers to adopt sustainable agricultural practices. In this context, this pilot project combines geochemical and geophysical methods to map the distribution of OC contents in agricultural fields, allowing us to detect variations in time and space. Here we demonstrated a relationship between soil OC contents estimated in the laboratory and the apparent electrical conductivity (ECa) measured in the field. Specifically, geochemical elemental analyses were used to evaluate the OC content and relative isotopic signature in collected soil samples from a hazelnut orchard in the Emilia–Romagna region of Northeastern Italy, while the geophysical Electromagnetic Induction (EMI) method enabled the in situ mapping of the ECa distribution in the same soil field. According to the results, geochemical and geophysical data were found to be reciprocally related, as both the organic matter and soil moisture were mainly incorporated into the fine sediments (i.e., clay) of the soil. Therefore, such a relation was used to create a map of the OC content distribution in the investigated field, which could be used to monitor the soil C sequestration on small-scale farmland and eventually develop precision agricultural services. In the future, this method could be used by farmers and regional and/or national policymakers to periodically certify the farm’s soil conditions and verify the effectiveness of carbon sequestration. These measures would enable farmers to pursue Common Agricultural Policy (CAP) incentives for the reduction of CO2 emissions. Full article
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<p>(<b>a</b>) Location of the sampling area (MB), in the Northeast sector of the municipality of Ferrara in the Emilia–Romagna region (Northeastern Italy); (<b>b</b>) the hazel orchard–grassland field before the geochemical and geophysical investigation of 19 October 2021; (<b>c</b>) soil sampling locations represented by light blue dots; (<b>d</b>) at each location, a sample was collected and mixed with five aliquots of soil per square probed at a depth of 0–30 cm; (<b>e</b>) geophysical measurements were indicated with red dots and georeferenced with an internal GPR; and (<b>f</b>) a Profiler EMP-400 (GSSI) was used to acquire the Hp and Hs electromagnetic fields at different positions.</p>
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<p>Elemental and isotopic composition of the total carbon (TC), organic carbon (OC), and inorganic carbon (IC) fractions of the soil samples.</p>
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<p>Boxplots of the (<b>a</b>) LOI 105 °C, (<b>b</b>) LOI 550 °C, (<b>c</b>) LOI 1000 °C, (<b>d</b>) TC, (<b>e</b>) OC, (<b>f</b>) IC, (<b>g</b>) δ<sup>13</sup>C<sub>TC</sub>, and (<b>h</b>) δ¹³C<sub>OC</sub> of the samples divided into three classes based on their aspect in the field and OC/IC ratio (see the text for details). In each box plot, the black line represents the median. Letters below the box plots represent the results of the Tukey post hoc test. Different letters denote significant differences between classes. The one-way ANOVA results are also reported (** <span class="html-italic">p</span> &lt; 0.001; *** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Spatial variability and distribution of the ECa values obtained from the EMI acquisition field survey using three different frequencies: (<b>a</b>) 16, (<b>b</b>) 14, and (<b>c</b>) 10 kHz.</p>
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<p>The elemental TC contents and δ¹³C<sub>TC</sub> of MB samples and average elemental TC contents and δ¹³C<sub>TC</sub> recognized as deposits from the paleochannel and levee of the easternmost Padanian plain soils, as studied by Natali et al. [<a href="#B36-environments-11-00289" class="html-bibr">36</a>] and Salani et al. [<a href="#B37-environments-11-00289" class="html-bibr">37</a>].</p>
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<p>OC/IC (in logarithmic scale) versus (<b>a</b>) δ<sup>13</sup>C<sub>TC</sub> shows a strong negative correlation; the insets reproduce the relationships between OC/IC, (<b>b</b>) δ<sup>13</sup>C<sub>IC</sub>, and (<b>c</b>) δ<sup>13</sup>C<sub>OC</sub>.</p>
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<p>Principal Component Analysis (PCA) for δ<sup>13</sup>CTC, OC, IC, TC, and ECa (measured at 10 kHz), clustered in Class I (green dots and dash-dotted line ellipse), Class II (yellow triangles and solid line ellipse), and Class III (red squares and dashed line ellipse).</p>
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<p>Linear regression graphics used to observe the relationships between the ECa measured at 10 kHz and (<b>a</b>) OC, (<b>b</b>) OC/IC, and (<b>c</b>) δ<sup>13</sup>C<sub>TC</sub>. The data are represented as green dots, yellow triangles, and red squares, for Class I, Class II, and Class III, respectively. The regression line (in black) and relative equation, R<sup>2</sup> value, and 95% confidence intervals (the red curves) are provided for each plot.</p>
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<p>Predictive maps realized using ordinary kriging for (<b>a</b>) the OC values, (<b>b</b>) the ECa values measured at 10 kHz, and cokriging to predict (<b>c</b>) a new OC surface, with the OC values and the ECa values at 10 kHz as a covariate variable. The legend values for each map represent a quantile classification.</p>
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14 pages, 2096 KiB  
Article
Resource-Efficient FPGA Architecture for Real-Time RFI Mitigation in Interferometric Radiometers
by Adrian Perez-Portero, Jorge Querol and Adriano Camps
Sensors 2024, 24(24), 8001; https://doi.org/10.3390/s24248001 (registering DOI) - 14 Dec 2024
Viewed by 324
Abstract
Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical [...] Read more.
Interferometric radiometers operating at L-band, such as ESA’s SMOS mission, enable crucial Earth observations providing high-resolution measurements of soil moisture, ocean salinity, and other geophysical parameters. However, the increasing electromagnetic spectrum utilization has led to significant Radio Frequency Interference (RFI) challenges, particularly critical given the sensors’ fine temperature resolution requirements of less than 1 K. This work presents the hardware implementation of an advanced RFI detection and mitigation algorithm specifically designed for interferometric radiometers, targeting future L-band missions. The implementation processes 1-bit quantized signals at 57.69375 MHz from multiple receivers, employing time-frequency analysis and polarimetric detection techniques while optimizing Field Programmable Gate Array (FPGA) resource utilization. Novel optimization strategies include overclocked processing cores operating at 230.775 MHz, efficient resource sharing through operation serialization, and strategic memory management. The system achieves real-time processing capabilities while maintaining detection probabilities above 63% with false alarm rates below 1% for typical interference scenarios. Performance validation using synthetic datasets demonstrates robust operation across various RFI conditions, making this implementation suitable as part of the RFI detection and mitigation efforts for future interferometric radiometer missions beyond SMOS. Full article
(This article belongs to the Special Issue Sensors for Space Applications)
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<p>Effects of 1-bit quantization on a 15,000 K chirp RFI signal with thermal noise. (<b>a</b>) Original unquantized spectrogram showing the chirp’s natural frequency progression. (<b>b</b>) After 1-bit quantization, revealing harmonic generation and aliasing effects, among others.</p>
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<p>Simplified overview of the RFI Mitigation algorithm implementation showing the three main processing stages and data flow paths. Configurable or external inputs are shown with red arrows.</p>
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<p>Observable Generation block diagram detailing the processing chain from 1 to bit inputs through windowing and FFT stages to truncated and equalized outputs. The different clock domains are higlighted in red and orange.</p>
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<p>RFI Detection architecture showing parallel computation of statistical and polarimetric parameters in time and frequency domains. The detection logic combines multiple metrics to generate blanking masks for RFI mitigation. Configurable or external inputs, and debug outputs, are shown with red arrows.</p>
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<p>PMS Blanking implementation illustrating the application of blanking masks to both high-speed signals and PMS measurements. Rate conversion and gain correction stages ensure proper synchronization and calibration.</p>
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<p>Fixed-point implementation of radiometric data processing chain. The diagram shows bit-width evolution through signal processing stages. Block colors indicate inputs and outputs (blue), lossless processing (gray), and precision loss: green for low, yellow for medium, and orange for significant precision reduction.</p>
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<p>Main sources of precision loss in the Observable Generation block. (<b>a</b>) Comparison of the full-precision and fixed-point values after windowing. (<b>b</b>) Comparison of the full-precision and fixed-point values after FFT.</p>
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<p>Comparison of the Simulink OR Mask (<b>left</b>) with the full-precision MATLAB reference (<b>right</b>).</p>
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<p>Comparison of the Simulink AND Mask (<b>left</b>) with the full-precision MATLAB reference (<b>right</b>).</p>
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14 pages, 6443 KiB  
Article
Modularized Reconfigurable Functional Electromagnetic Surfaces Using Tightly Coupled Antennas and Back-Loaded Radio Frequency Circuits
by Boyu Sima, Jiayi Gong, Zhenghu Xi, Shunli Zhang, Ziling Li, Tao Wang, Guoxiao Cheng, Huangyan Li, Xiang Wang, Jianpeng Wang and Zhiyuan Zong
Micromachines 2024, 15(12), 1490; https://doi.org/10.3390/mi15121490 - 12 Dec 2024
Viewed by 414
Abstract
This paper presents a modularized reconfigurable functional electromagnetic surface (MRFES) for broadband absorption and polarization conversion by using tightly coupled dipole antennas (TCDA) and back-loaded radio frequency (RF) circuits (BLRFC). A dual-polarized antenna array with tight coupling and wide angular scanning characteristics is [...] Read more.
This paper presents a modularized reconfigurable functional electromagnetic surface (MRFES) for broadband absorption and polarization conversion by using tightly coupled dipole antennas (TCDA) and back-loaded radio frequency (RF) circuits (BLRFC). A dual-polarized antenna array with tight coupling and wide angular scanning characteristics is designed. By loading different RF circuits on the back side of the antenna array’s ground plane, switchable broadband absorption and polarization conversion functions are achieved. The design adopts modularization to facilitate the replacement of back-loaded RF circuits for diverse electromagnetic (EM) control functions. The final design of the tightly coupled antenna array has a thickness of 13.437 mm and a size of 119.5 mm × 119.5 mm. It works in a bandwidth range of 4.14–13 GHz. Upon loading the absorption circuit board, a broadband absorbing electromagnetic (EM) surface is formed, achieving dual-polarization absorption within a bandwidth of 4.14–12.4 GHz. With the polarization conversion circuit board attached, polarization conversion effects are realized within a bandwidth of 4.4–12.9 GHz. Both simulations and experiments verify that the designed EM surface possesses modular reconfigurable functions for broadband absorption/polarization conversion. The proposed design scheme holds promising prospects for applications in active stealth, adaptive camouflage, intelligent communication and other fields. Full article
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<p>Three-dimensional schematics diagram of the proposed MRFES.</p>
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<p>Equivalent structural diagram of the proposed MRFES for (<b>a</b>) absorption and (<b>b</b>) polarization conversion.</p>
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<p>(<b>a</b>) The 3D diagram of the TCDA unit structure, (<b>b</b>) the top view of the second layer, (<b>c</b>) the third layer, and (<b>d</b>) the fourth layer.</p>
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<p>The simulated |<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math>| results of the TCDA unit.</p>
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<p>(<b>a</b>) The 3D diagram of the proposed absorptive EM surface structure, (<b>b</b>) the absorptive circuit board, and (<b>c</b>) top view of the absorptive circuit board.</p>
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<p>(<b>a</b>) The simulated magnitude of <span class="html-italic">S</span><sub>11</sub> of the absorptive EM surface and (<b>b</b>) the simulated absorptivity.</p>
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<p>The wide-angle scanning simulation at 0°–40°. (<b>a</b>) TE polarization and (<b>b</b>) TM polarization.</p>
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<p>(<b>a</b>) The 3D diagram of the polarization conversion EM surface structure, (<b>b</b>) the 3D diagram of the polarization conversion circuit board and (<b>c</b>) the top view of the polarization conversion circuit board, where <span class="html-italic">a<sub>v</sub></span><sub>1</sub> = 0.93 mm, <span class="html-italic">b<sub>v</sub></span><sub>1</sub> = 1.2 mm, <span class="html-italic">W<sub>m</sub></span><sub>2</sub> = 1.2 mm, <span class="html-italic">a<sub>m</sub></span><sub>2</sub> = 2.3 mm, <span class="html-italic">b<sub>m</sub></span><sub>2</sub> = 1.5 mm.</p>
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<p>(<b>a</b>) The simulated results of the co-polarized and cross-polarized reflection of the polarization conversion EM surface. (<b>b</b>) The simulated results of PCR.</p>
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<p>Photo of the fabricated prototype. (<b>a</b>) Front view of the TCDA array, (<b>b</b>) back view of the TCDA array, (<b>c</b>) full structure of the MRFES, (<b>d</b>) side view of the MRFES, (<b>e</b>) front view of the absorption circuit board, (<b>f</b>) front view of the polarization conversion board.</p>
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<p>The measurement environment.</p>
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<p>Comparison of the simulated and measured reflections of MRFES in absorbing state under (<b>a</b>) TE polarized (y-pol) incidence and (<b>b</b>) TM polarized (x-pol) incidence.</p>
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<p>(<b>a</b>) Comparison of the measured and simulated results of the MRFES in polarization conversion state for co-polarized reflection, (<b>b</b>) the cross-polarized reflection, and (<b>c</b>) The PCR.</p>
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20 pages, 1842 KiB  
Article
Assessment and Management of Risks from Occupational Exposure to Electromagnetic Fields (0 Hz to 300 GHz): A Compass to Keep the Right Course Through European and Italian Regulations
by Laura Filosa and Vanni Lopresto
Safety 2024, 10(4), 104; https://doi.org/10.3390/safety10040104 - 12 Dec 2024
Viewed by 542
Abstract
This paper outlines the specific provisions of Italian legislation regarding workers’ exposure to electromagnetic fields (EMFs) from 0 Hz to 300 GHz compared to the minimum health and safety requirements set in European Directive 2013/35/EU. In particular, the path to be followed to [...] Read more.
This paper outlines the specific provisions of Italian legislation regarding workers’ exposure to electromagnetic fields (EMFs) from 0 Hz to 300 GHz compared to the minimum health and safety requirements set in European Directive 2013/35/EU. In particular, the path to be followed to assess and manage occupational exposure to EMFs is outlined in relation to the distinction between ‘professional’ and ‘non-professional’ exposure of workers, as well as to the precautionary limits regarding exposures from power lines (50 Hz) and broadcast and telecommunication fixed systems (100 kHz–300 GHz) established by Italian regulations. The reasons underlying such an approach—mainly relying on the intent to reconcile scientific evidence with risk perception in public opinion—are analysed and discussed with the aim of increasing the knowledge of national regulatory provisions on occupational risk assessment, which may be more stringent than the requirements envisaged by international guidelines and community regulations. Full article
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<p>Italian regulatory framework regarding the protection of workers and the general public from EMF exposures.</p>
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<p>Flowchart of the EMF exposure assessment process under the Italian regulatory framework. Note—PL: power lines at grid frequency (50 Hz); BTFS: broadcast and telecommunication fixed systems (100 kHz–300 GHz).</p>
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<p>Zoning of the workplace as defined by CEI 106-45 for power lines at 50 Hz [<a href="#B29-safety-10-00104" class="html-bibr">29</a>].</p>
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<p>Zoning of the workplace as defined by CEI 106-45 for BTFS (100 kHz–300 GHz) [<a href="#B29-safety-10-00104" class="html-bibr">29</a>].</p>
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31 pages, 8270 KiB  
Article
Application of the FDTD Method to the Analysis of Electromagnetic Wave Propagation in Systems with Concrete and Reinforced Concrete
by Agnieszka Choroszucho, Tomasz Szczegielniak, Dariusz Kusiak and Anna Jaskot
Energies 2024, 17(24), 6252; https://doi.org/10.3390/en17246252 - 11 Dec 2024
Viewed by 330
Abstract
Wireless communication very often causes problems due to its quality. Problems with the network are very important when installing wireless networks inside buildings. The reason is the effects created during the propagation of electromagnetic waves inside rooms due to, among other factors, the [...] Read more.
Wireless communication very often causes problems due to its quality. Problems with the network are very important when installing wireless networks inside buildings. The reason is the effects created during the propagation of electromagnetic waves inside rooms due to, among other factors, the construction of the walls and the building materials used. At the stage of network design, it is possible to use numerical methods, which allow for multivariate and fast analysis. This article presents a multivariate analysis of the impact of the variability in the electrical parameters of concrete and reinforced concrete on the propagation of electromagnetic waves and the value of the electric field intensity. The subject of the analysis was a wall composed of a homogeneous material (concrete) or non-homogeneous material (concrete with reinforcement). In the case of the homogeneous wall, the analysis was performed taking into account four electric permittivity values and a wide range of conductivity values. The analysis was performed at two frequencies used in wireless communication (2.4 GHz and 5 GHz). The analysis was performed using the time method based on Maxwell’s equations—the finite difference time domain method (FDTD). The results of the numerical analysis were compared with the results obtained from the presented analytical relationships. In the next step, four models and calculations were obtained for systems with a reinforced concrete wall, taking into account the variability in the spacing between the bars, the diameter of the reinforcement and the number of rows of reinforcement. The analysis of complex systems was performed at a frequency of 2.4 GHz. The aim of the presented analysis was to check how the change in the value of the electric permittivity of concrete affects the values of the field intensity and its effect on the analysis of systems composed of concrete with reinforcement. In the case of concrete, it was observed that, for conductivity above 0.9 S/m, regardless of the electric permittivity, all characteristics had a similar course. For low concrete loss, the greatest differences in the electric field intensity were observed at a frequency of 2.4 GHz rather than at 5 GHz. On the other hand, the analysis of systems with reinforced concrete showed, among other aspects, that models with two rows of bars and spacing of 0.15 m, regardless of the reinforcement diameter, were characterized by lower values of the electric field intensity compared to the variants with one row of bars. Full article
(This article belongs to the Special Issue Applications of Electromagnetism in Energy Efficiency)
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<p>Discretization of Maxwell’s equations for the region: (<b>a</b>) three-dimensional, (<b>b</b>) two-dimensional (TMz variant) with consideration of a shifted differential grid.</p>
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<p>Determination of the values of the <span class="html-italic">E<sub>z</sub></span> components in step <span class="html-italic">n</span> +1 for the Yee cell with indices <span class="html-italic">i</span>, <span class="html-italic">j</span>, <span class="html-italic">k</span>.</p>
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<p>Perpendicular incidence of the EM wave on the concrete wall.</p>
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<p>Variants of reinforced concrete walls: (<b>a</b>) 1b_L15, (<b>b</b>) 1b_L20, (<b>c</b>) 2b_L15, (<b>d</b>) 2b_L20.</p>
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<p>Comparison of the instantaneous distributions of the electric field intensity in the analyzed area with a wall of <span class="html-italic">σ</span> = 0.00195 S/m: (<b>a</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 5, (<b>b</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 6, (<b>c</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 7, (<b>d</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 8.</p>
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<p>Maximum values of the <span class="html-italic">E<sub>z</sub></span> component behind a wall composed of concrete: (<b>a</b>) 2.4 GHz, (<b>b</b>) 5 GHz.</p>
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<p>Propagation of EM wave through a concrete wall with the following parameters: <span class="html-italic">ε</span><sub>r</sub>′ = 5 i <span class="html-italic">σ</span> = 0.00195 S/m.</p>
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<p>Propagation of EM wave through a concrete wall with the following parameters: <span class="html-italic">ε</span><sub>r</sub>′ = 6 i <span class="html-italic">σ</span> = 0.00195 S/m.</p>
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<p>Propagation of EM wave through a concrete wall with the following parameters: <span class="html-italic">ε</span><sub>r</sub>′ = 7 i <span class="html-italic">σ</span> = 0.00195 S/m.</p>
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<p>Propagation of EM wave through a concrete wall with the following parameters: <span class="html-italic">ε</span><sub>r</sub>′ = 8 i <span class="html-italic">σ</span> = 0.00195 S/m.</p>
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<p>EM wave propagation in successive steps through a reinforced concrete wall (<span class="html-italic">fi</span> = 0.01 m, <span class="html-italic">L</span> = 0.15 m) with the parameters <span class="html-italic">ε</span><sub>r</sub>′ = 6 and <span class="html-italic">σ</span> = 0.00195 S/m for the frequency of 2.4 GHz.</p>
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<p>EM wave propagation in successive steps through a reinforced concrete wall (<span class="html-italic">fi</span> = 0.01 m, <span class="html-italic">L</span> = 0.2 m) with the parameters <span class="html-italic">ε</span><sub>r</sub>′ = 6 and <span class="html-italic">σ</span> = 0.00195 S/m for the frequency of 2.4 GHz.</p>
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<p>Relationship between reinforcement diameter and max(<span class="html-italic">E</span><sub>z</sub>), at different conductivity values, for model 1b_L15 and concrete with <span class="html-italic">ε</span><sub>r</sub>′: (<b>a</b>) 5, (<b>b</b>) 6, (<b>c</b>) 7, (<b>d</b>) 8.</p>
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<p>Relationship between reinforcement diameter and max(<span class="html-italic">E</span><sub>z</sub>), at different conductivity values, for model 1b_L20 and concrete with <span class="html-italic">ε</span><sub>r</sub>′: (<b>a</b>) 5, (<b>b</b>) 6, (<b>c</b>) 7, (<b>d</b>) 8.</p>
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<p>Relationship between reinforcement diameter and max(<span class="html-italic">E</span><sub>z</sub>), at different conductivity values, for model 2b_L15 and concrete with <span class="html-italic">ε</span><sub>r</sub>′: (<b>a</b>) 5, (<b>b</b>) 6, (<b>c</b>) 7, (<b>d</b>) 8.</p>
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<p>Relationship between reinforcement diameter and max(<span class="html-italic">E</span><sub>z</sub>), at different conductivity values, for model 2b_L20 and concrete with <span class="html-italic">ε</span><sub>r</sub>′: (<b>a</b>) 5, (<b>b</b>) 6, (<b>c</b>) 7, (<b>d</b>) 8.</p>
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<p>Relationship between reinforcement diameter and max(<span class="html-italic">E</span><sub>z</sub>) values for four reinforced concrete wall models calculated with typical concrete parameters: (<b>a</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 5 and <span class="html-italic">σ</span> = 0.004 S/m, (<b>b</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 6 and <span class="html-italic">σ</span> = 0.00195 S/m, (<b>c</b>) <span class="html-italic">ε</span><sub>r</sub>′ = 8 and <span class="html-italic">σ</span> = 0.01 S/m.</p>
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12 pages, 4560 KiB  
Article
Proposal for an Artificial Neural Network Tool for the Process of Generating Metasurface Unit Cell Geometries
by Paulina Góra and Przemysław Łopato
Appl. Sci. 2024, 14(24), 11549; https://doi.org/10.3390/app142411549 - 11 Dec 2024
Viewed by 341
Abstract
This paper focuses on presenting an intelligent model that can generate the desired geometry of a unit cell metasurface for a given resonant frequency at which we expect the metasurface structure to work. The model consists of the use of a multilayer perceptron [...] Read more.
This paper focuses on presenting an intelligent model that can generate the desired geometry of a unit cell metasurface for a given resonant frequency at which we expect the metasurface structure to work. The model consists of the use of a multilayer perceptron and filters, which represent the output geometry of the unit cell as a 6 × 6 matrix stored in a binary state. The value 0 in the matrix denotes the dielectric substrate on which the geometry of the unit cell is built, and the value 1 denotes the blocks as the conducting parts of the unit cell metasurface. The proposed model was tested using simulation data from the Comsol Multiphysics environment. The test confirmed the effectiveness of the model, and it is possible to develop and apply it to larger and other datasets. Full article
(This article belongs to the Section Materials Science and Engineering)
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<p>The design process methodology for a unit cell generation tool metasurface for microwave energy harvesting.</p>
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<p>The design of a unit cell layer with a selected (given) geometry of conducting parts on a dielectric substrate in Comsol Multiphysics.</p>
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<p>Mesh of designed metasurface unit cell in Comsol Multiphysics.</p>
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<p>The electric field distribution on the different designed metasurface unit cells in Comsol Multiphysics.</p>
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<p>(<b>a</b>) The division of a square unit cell into 36 blocks; (<b>b</b>,<b>c</b>) 2 of the 100 geometries used in the design of the unit cell.</p>
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<p>(<b>a</b>–<b>c</b>) Examples of different resonant frequency curves depending on the shape of the unit cell geometry.</p>
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<p>(<b>a</b>) Example of the division of the unit cell blocks into two states; (<b>b</b>) example of the matrix programming notation for the same unit cell geometry as in <a href="#applsci-14-11549-f008" class="html-fig">Figure 8</a>.</p>
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<p>A simplified diagram of how the target unit cell geometry generation tool was built.</p>
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<p>The validation loss and train loss in the process of training the multilayer perceptron.</p>
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<p>The results from the multilayer perceptron. For a given frequency, a matrix of size 6 × 6 with values in the range of 0–1 was obtained. Such values were subjected to the filter, and the output produced a matrix containing only binary values.</p>
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<p>Adjusted absorption parameters: The simulation result for the absorption and S-11 parameters for the unit cell geometry generated by the smart tool. The binary version of the tested metasurface unit cell is shown on the graph.</p>
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14 pages, 1344 KiB  
Article
The Effect of Exposure to an Electromagnetic Field on Entomopathogenic Fungi
by Dariusz Roman Ropek, Krzysztof Frączek, Krzysztof Pawlak, Karol Bulski and Magdalena Ludwiczak
Appl. Sci. 2024, 14(24), 11508; https://doi.org/10.3390/app142411508 - 10 Dec 2024
Viewed by 408
Abstract
Background: The rapid development of mobile communication has caused an increase in electromagnetic field (EMF) emissions in the environment. However, there is a lack of research on the impact of EMFs on microorganisms. Thus, the aim of the study was the determine the [...] Read more.
Background: The rapid development of mobile communication has caused an increase in electromagnetic field (EMF) emissions in the environment. However, there is a lack of research on the impact of EMFs on microorganisms. Thus, the aim of the study was the determine the effect of exposure to 900 and 1800 MHz electromagnetic fields on the entomopathogenic fungi (EPFs) Beauveria bassiana, Cordyceps fumosorosea, and Metarhizium anisopliae. Methods: The entomopathogenic fungi developed under exposure to an EMF for seven days. After the termination of exposure, the linear colony growth, sporulation, gemination, and pathogenicity of the EPFs were investigated. Results: The effect of EMFs on B. bassiana, C. fumosorosea, and M. anisopliae depended on the EMF frequency and the tested fungus species. Exposure to the 900 MHz frequency stimulated the growth of the mycelium and the pathogenicity of the entomopathogenic fungi, whereas the 1800 MHz electromagnetic field inhibited sporulation and spore germination. Conclusions: The exposure to the 900 MHz frequency stimulated the development of the mycelium of all tested species and the pathogenicity of C. fumosorosea. The sporulation and germ tube length of the entomopathogenic fungi were stimulated by the 900 MHz frequency. The 1800 MHz electromagnetic field inhibited the sporulation and spore germination of B. bassiana. Full article
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<p>The effect of exposure to 900 and 1800 MHz EMFs on the colony growth (after seven days, reverse side) of the entomopathogenic fungi <span class="html-italic">B. bassiana</span>. ((<b>a</b>)—control; (<b>b</b>)—900 MHz; (<b>c</b>)—1800 MHz; bar = 1 cm).</p>
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<p>The effect of exposure to 900 and 1800 MHz EMFs on the colony growth (after seven days, reverse side) of the entomopathogenic fungi <span class="html-italic">C. fumosorosea</span>. ((<b>a</b>)—control; (<b>b</b>)—900 MHz; (<b>c</b>)—1800 MHz; bar = 1 cm).</p>
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<p>The effect of exposure to 900 and 1800 MHz EMFs on the colony growth (after seven days, reverse side) of the entomopathogenic fungi <span class="html-italic">M. anisopliae</span>. ((<b>a</b>)—control; (<b>b</b>)—900 MHz; (<b>c</b>)—1800 MHz; bar = 1 cm).</p>
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<p>The effect of exposure to 900 and 1800 MHz EMFs on the colony growth rate (T—linear growth coefficient) of the entomopathogenic fungi <span class="html-italic">B. bassiana</span>, <span class="html-italic">C. fumosorosea,</span> and <span class="html-italic">M. anisopliae</span>. a, b, c—bars indicated with the same letter do not differ significantly (<span class="html-italic">p</span> ≤ 0.05). I—standard deviation.</p>
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21 pages, 4915 KiB  
Review
A Review of Cascaded Metasurfaces for Advanced Integrated Devices
by Lingyun Zhang, Zeyu Zhao, Leying Tao, Yixiao Wang, Chi Zhang, Jianing Yang, Yongqiang Jiang, Huiqi Duan, Xiaoguang Zhao, Shaolong Chen and Zilun Wang
Micromachines 2024, 15(12), 1482; https://doi.org/10.3390/mi15121482 - 10 Dec 2024
Viewed by 559
Abstract
This paper reviews the field of cascaded metasurfaces, which are advanced optical devices formed by stacking or serially arranging multiple metasurface layers. These structures leverage near-field and far-field electromagnetic (EM) coupling mechanisms to enhance functionalities beyond single-layer metasurfaces. This review comprehensively discusses the [...] Read more.
This paper reviews the field of cascaded metasurfaces, which are advanced optical devices formed by stacking or serially arranging multiple metasurface layers. These structures leverage near-field and far-field electromagnetic (EM) coupling mechanisms to enhance functionalities beyond single-layer metasurfaces. This review comprehensively discusses the physical principles, design methodologies, and applications of cascaded metasurfaces, focusing on both static and dynamic configurations. Near-field-coupled structures create new resonant modes through strong EM interactions, allowing for efficient control of light properties like phase, polarization, and wave propagation. Far-field coupling, achieved through greater interlayer spacing, enables traditional optical methods for design, expanding applications to aberration correction, spectrometers, and retroreflectors. Dynamic configurations include tunable devices that adjust their optical characteristics through mechanical motion, making them valuable for applications in beam steering, varifocal lenses, and holography. This paper concludes with insights into the potential of cascaded metasurfaces to create multifunctional, compact optical systems, setting the stage for future innovations in miniaturized and integrated optical devices. Full article
(This article belongs to the Special Issue Terahertz and Infrared Metamaterial Devices, 3nd Edition)
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<p>Categories and applications of cascaded metasurfaces. Reproduced with permission. Copyright 2016, published by Springer Nature [<a href="#B63-micromachines-15-01482" class="html-bibr">63</a>]. Reproduced with permission. Copyright 2023, published by John Wiley and Sons [<a href="#B64-micromachines-15-01482" class="html-bibr">64</a>]. Reproduced with permission. Copyright 2021, published by American Chemical Society [<a href="#B65-micromachines-15-01482" class="html-bibr">65</a>]. Reproduced with permission. Copyright 2024, published by the Institute of Optics and Electronics [<a href="#B55-micromachines-15-01482" class="html-bibr">55</a>]. Reproduced with permission. Copyright 2018, published by Springer Nature [<a href="#B66-micromachines-15-01482" class="html-bibr">66</a>].</p>
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<p>(<b>a</b>) A generic structure consisting of four cascaded metasurfaces (electric sheet admittances) separated by dielectric layers. In general, the sheet admittances are anisotropic such that <span class="html-italic">x</span>- and <span class="html-italic">y</span>-polarized light can be controlled independently [<a href="#B68-micromachines-15-01482" class="html-bibr">68</a>]. (<b>b</b>) Broadband polarization conversion in reflection. Schematic (<b>left</b>) and microscopic image (<b>right</b>) of a metamaterial linear polarization converter [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>]. (<b>c</b>) Broadband polarization conversion in transmission. Schematic of the unit cell of the metamaterial linear polarization converter, in which a normally incident <span class="html-italic">x</span>-polarized wave is converted into a <span class="html-italic">y</span>-polarized one [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>]. (<b>d</b>) Schematic geometry of a twisted metamaterial. Each layer is rotated by a constant angle compared to its immediate neighbor. The transfer matrix of each twisted unit cell can be obtained by suitably rotating the transfer matrix of the first unit cell. A twisted unit cell consists of a propagation length <span class="html-italic">d</span> in free space and an ultrathin metasurface in the middle [<a href="#B72-micromachines-15-01482" class="html-bibr">72</a>]. (<b>e</b>) Schematic model of the cascaded metasurface device and its meta-atom design. The meta-atom consists of three functional structure layers that are a reflection layer, a filtering layer, and two transmission layers [<a href="#B73-micromachines-15-01482" class="html-bibr">73</a>]. (<b>a</b>) Reprinted/adapted with permission from Ref. [<a href="#B68-micromachines-15-01482" class="html-bibr">68</a>]. Copyright 2013, American Institute of Physics (AIP). (<b>b</b>) Reprinted/adapted with permission from Ref. [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>]. Copyright 2013, American Association for the Advancement of Science (AAAS). (<b>c</b>) Reprinted/adapted with permission from Ref. [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>]. Copyright 2013, American Association for the Advancement of Science (AAAS). (<b>d</b>) Reprinted/adapted with permission from Ref. [<a href="#B72-micromachines-15-01482" class="html-bibr">72</a>]. Copyright 2014, American Physical Society (APS). (<b>e</b>) Reprinted/adapted with permission from Refs. [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>,<a href="#B73-micromachines-15-01482" class="html-bibr">73</a>]. Copyright 2019, Optical Society of America (OSA).</p>
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<p>(<b>a</b>) Exploded view of tunable metamaterials (<b>upper left</b>). One unit cell of the tunable metamaterial, including BC-SRRs, where SRR1 is on a silicon frame and SRR2 is on a SiNx thin film (<b>upper right</b>). Simulated spectra of the individual uncoupled SRRs and broadside-coupled SRRs when <span class="html-italic">d</span> = 20 μm. (<b>lower left</b>) The surface charge distribution of the symmetric mode and antisymmetric mode of BC-SRRs (<b>lower right</b>) [<a href="#B75-micromachines-15-01482" class="html-bibr">75</a>]. (<b>b</b>) Tunable chiroptical properties of MCMs. SEM images (<b>Upper left</b>), measured and simulated CD spectra (<b>upper middle</b>), and schematic illustrations of three sets (i.e., −10° vs. 10°, −15° vs. 15°, and −20° vs. 20°, respectively) of MCMs (<b>upper right</b>). The scale bars are 1 µm. A series of CD spectra of an MCM under various rotation angles from 15° to 315° at an interval of 60°, a wheel illustration of the rotational periodicity (π/3) in the θ-dependent chiroptical properties of the MCMs, and a series of CD spectra of an MCM under the various lateral translations of the top layer of Au nanohole arrays in the <span class="html-italic">x</span> and <span class="html-italic">y</span> directions, respectively, from 0 to 500 nm with an interval of 100 nm (<b>Lower left to right</b>) [<a href="#B70-micromachines-15-01482" class="html-bibr">70</a>]. (<b>c</b>) Moiré metasurface in real space. Schematic illustration of a moiré metasurface (<b>upper</b>). The mutual twist of two closely attached metasurfaces produces a varying moiré pattern. Composition of a moiré metasurface from bottom to top: a metallic back plate, a spacer, and two closely stacked metasurface layers with a mutual twist (<b>lower</b>) [<a href="#B69-micromachines-15-01482" class="html-bibr">69</a>]. (<b>d</b>) Schematic of a bilayer metasurface stacked by two MMs with interlayer spacing <span class="html-italic">h</span> (<b>left</b>). <span class="html-italic">k</span> is the incident wavevector. Reflection (R) and transmission (T) spectra of the bilayer metasurface as a function of <span class="html-italic">h</span> (<b>right</b>). The insets correspond to the |<b><span class="html-italic">E</span></b>| distributions of points B and C, respectively [<a href="#B76-micromachines-15-01482" class="html-bibr">76</a>]. (<b>a</b>) Reprinted/adapted with permission from Ref. [<a href="#B75-micromachines-15-01482" class="html-bibr">75</a>]. Copyright 2016, Nature Publishing Group (NPG). (<b>b</b>) Reprinted/adapted with permission from Ref. [<a href="#B70-micromachines-15-01482" class="html-bibr">70</a>]. Copyright 2017, Wiley-VCH. (<b>c</b>) Reprinted/adapted with permission from Ref [<a href="#B69-micromachines-15-01482" class="html-bibr">69</a>]. Copyright 2022, American Association for the Advancement of Science (AAAS). (<b>d</b>) Reprinted/adapted with permission from Refs. [<a href="#B67-micromachines-15-01482" class="html-bibr">67</a>,<a href="#B76-micromachines-15-01482" class="html-bibr">76</a>]. Copyright 2022, Optical Society of America (OSA).</p>
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<p>(<b>a</b>) Schematic diagram of the metalens doublet [<a href="#B79-micromachines-15-01482" class="html-bibr">79</a>]. (<b>b</b>) Three-layer lens. (<b>left</b>) Artist’s view of the three-layer lens. When illuminated with white light, each layer focuses its designated part of the spectrum to a distance of 1 mm along the optical axis. (<b>right</b>) Schematic illustration of the layered structure. Each layer consists of nanodiscs with the following diameters D and separations l: DAu 1⁄4 125 nm, lAu 1⁄4 185 nm; DAg 1⁄4 85 nm, lAg 1⁄4 195 nm; DAl 1⁄4 120 nm, lAl 1⁄4 150 nm [<a href="#B86-micromachines-15-01482" class="html-bibr">86</a>]. (<b>c</b>) The multiwavelength metalens doublet (NA = 0.42) [<a href="#B84-micromachines-15-01482" class="html-bibr">84</a>]. (<b>d</b>) A monolithic planar retroreflector made of two metasurfaces. Schematic drawing of the planar retroreflector (<b>left</b>). Two metasurfaces are patterned on opposite sides of a glass substrate. Optical image of an array of retroreflectors (<b>right</b>) [<a href="#B80-micromachines-15-01482" class="html-bibr">80</a>]. (<b>e</b>) Illustrations of multiwavelength holograms (<b>upper</b>), multiwavelength waveplates (<b>central</b>), and 3D holograms (<b>lower</b>) using bilayer metasurfaces [<a href="#B87-micromachines-15-01482" class="html-bibr">87</a>]. (<b>f</b>) Working principle and inverse design realized with cascaded metasurfaces. A schematic of cascaded metasurfaces composed of numerous TiO2 nanorods (<b>upper</b>). Starting with a random phase, the optimization loop eventually converses with the target fields with a very small MSE (<b>lower</b>) [<a href="#B54-micromachines-15-01482" class="html-bibr">54</a>]. (<b>a</b>) Reproduced with permission from Ref. [<a href="#B79-micromachines-15-01482" class="html-bibr">79</a>]. Copyright 2017, Nano Lett. (<b>b</b>) Reproduced with permission from Ref. [<a href="#B86-micromachines-15-01482" class="html-bibr">86</a>]. Copyright 2017, Nat. Commun. (<b>c</b>) Reproduced with permission from Ref. [<a href="#B84-micromachines-15-01482" class="html-bibr">84</a>]. Copyright 2018, Nano Lett. (<b>d</b>) Reproduced with permission from Ref. [<a href="#B80-micromachines-15-01482" class="html-bibr">80</a>]. Copyright 2017, Nat. Photonics. (<b>e</b>) Reproduced with permission from Ref. [<a href="#B87-micromachines-15-01482" class="html-bibr">87</a>]. Copyright 2019, Light: Sci. Appl. (<b>f</b>) Reproduced with permission from Ref. [<a href="#B54-micromachines-15-01482" class="html-bibr">54</a>]. Copyright 2023, Nat. Commun.</p>
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<p>(<b>a</b>) Schematic of moiré metasurface operation [<a href="#B65-micromachines-15-01482" class="html-bibr">65</a>]. Reproduced with permission. Copyright 2021, American Chemical Society. (<b>b</b>) Three uniformly illuminated facial fluorescence images at different depths and processed with HiLo [<a href="#B65-micromachines-15-01482" class="html-bibr">65</a>]. (<b>c</b>) Schematic of the varifocal metalens based intelligent fluorescence endo-microscopy [<a href="#B105-micromachines-15-01482" class="html-bibr">105</a>]. Reproduced with permission. Copyright 2024, John Wiley and Sons. (<b>d</b>) Scheme of the zooming imaging doublet consisting of two metasurfaces. The focal length of the doublet changes continuously when varying the relative angle θ. For θ &gt; 0, the doublet works as a positive lens, and for θ &lt; 0, as a negative lens [<a href="#B106-micromachines-15-01482" class="html-bibr">106</a>]. Reproduced with permission. Copyright 2024, John Wiley and Sons. (<b>e</b>) Rotational multiplexing method for cascaded metasurface holography, adapted from Wei et al. [<a href="#B26-micromachines-15-01482" class="html-bibr">26</a>]. Reproduced with permission. Copyright 2024, John Wiley and Sons. (<b>f</b>) By rotating multiple metasurfaces, the focal spot can be dynamically controlled in both two-dimensional (2D) and three-dimensional (3D) spaces [<a href="#B102-micromachines-15-01482" class="html-bibr">102</a>]. Reproduced with permission. Copyright 2023, The American Association for the Advancement of Science.</p>
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<p>(<b>a</b>) SEM and optical images of a MEMS-actuated Alvarez metalens. The right side shows the actuated focal displacement and applied voltage over time. The general trend of the actuated displacement follows the square of the applied voltage [<a href="#B112-micromachines-15-01482" class="html-bibr">112</a>]. Reproduced with permission. Copyright 2020, Springer Nature. (<b>b</b>) Behavior of the Alvarez lens in response to x displacement, adapted from Zhan et al. [<a href="#B113-micromachines-15-01482" class="html-bibr">113</a>]. Plot of focal length dependence on displacement. Larger displacements result in a more rapidly varying phase profile, corresponding to a lens with a smaller focal length [<a href="#B113-micromachines-15-01482" class="html-bibr">113</a>]. Reproduced with permission. Copyright 2017, Springer Nature. (<b>c</b>) Schematics of the decentered microlens array-based BSS [<a href="#B119-micromachines-15-01482" class="html-bibr">119</a>]. Reproduced with permission. Copyright 2022, American Chemical Society. (<b>d</b>) Illustration of diffraction pattern switch by using cascaded metasurfaces, by moving one piece in the <span class="html-italic">x</span> or <span class="html-italic">y</span> direction, the corresponding diffraction orders can be switched [<a href="#B120-micromachines-15-01482" class="html-bibr">120</a>]. Reproduced with permission. Copyright 2024, John Wiley and Sons. (<b>e</b>) Schematic of the zoom metalens. Inset: detailed layout of the zoom metalens [<a href="#B121-micromachines-15-01482" class="html-bibr">121</a>]. Reproduced with permission. Copyright 2024, American Chemical Society. (<b>f</b>) Fabrication process summary and schematic illustration of the imaging setup using a regular glass lens and the tunable doublet. The image formed by the doublet is magnified and re-imaged using a custom-built microscope with a ×55 magnification onto an image sensor. A simplified fabrication process of a lens on a membrane (<b>left</b>), a simplified fabrication process of the lens on the glass substrate (<b>middle</b>), and schematics of the bonding process (<b>right</b>) [<a href="#B83-micromachines-15-01482" class="html-bibr">83</a>]. Reproduced with permission. Copyright 2018, American Chemical Society.</p>
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18 pages, 12837 KiB  
Article
Geophysical Frequency Domain Electromagnetic Field Simulation Using Physics-Informed Neural Network
by Bochen Wang, Zhenwei Guo, Jianxin Liu, Yanyi Wang and Fansheng Xiong
Mathematics 2024, 12(23), 3873; https://doi.org/10.3390/math12233873 - 9 Dec 2024
Viewed by 469
Abstract
Simulating electromagnetic (EM) fields can obtain the EM responses of geoelectric models at different times and spaces, which helps to explain the dynamic process of EM wave propagation underground. EM forward modeling is regarded as the engine of inversion. Traditional numerical methods have [...] Read more.
Simulating electromagnetic (EM) fields can obtain the EM responses of geoelectric models at different times and spaces, which helps to explain the dynamic process of EM wave propagation underground. EM forward modeling is regarded as the engine of inversion. Traditional numerical methods have certain limitations in simulating the EM responses from large-scale geoelectric models. In recent years, the emerging physics-informed neural networks (PINNs) have given new solutions for geophysical EM field simulations. This paper conducts a preliminary exploration using PINN to simulate geophysical frequency domain EM fields. The proposed PINN performs self-supervised training under physical constraints without any data. Once the training is completed, the responses of EM fields at any position in the geoelectric model can be inferred instantly. Compared with the finite-difference solution, the proposed PINN performs the task of geophysical frequency domain EM field simulations well. The proposed PINN is applicable for simulating the EM response of any one-dimensional geoelectric model under any polarization mode at any frequency and any spatial position. This work provides a new scenario for the application of artificial intelligence in geophysical EM exploration. Full article
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<p>The workflow of the proposed PINN.</p>
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<p>A set of three-layer geoelectric models. (<b>a</b>) low-resistance model (Simple Model 1); (<b>b</b>) uniform half-space model (Simple Model 2); (<b>c</b>) high-resistance model (Simple Model 3).</p>
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<p>Training loss curves of Simple Model 1 (top line) at 1 Hz (<b>a</b>), 10 Hz (<b>b</b>), 100 Hz (<b>c</b>), and 1000 Hz (<b>d</b>); Simple Model 2 (middle line) at 1 Hz (<b>e</b>), 10 Hz (<b>f</b>), 100 Hz (<b>g</b>), and 1000 Hz (<b>h</b>); and Simple Model 3 (bottom line) at 1 Hz (<b>i</b>), 10 Hz (<b>j</b>), 100 Hz (<b>k</b>), and 1000 Hz (<b>l</b>). The blue, orange, and green curves represent <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>p</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>l</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>, respectively.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the electric field response of Simple Model 1 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the electric field response of Simple Model 2 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the electric field response of Simple Model 3 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the magnetic field response of Simple Model 1 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the magnetic field response of Simple Model 2 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the magnetic field response of Simple Model 3 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>The two multi-layer geoelectric models. (<b>a</b>) Complex Model 1. (<b>b</b>) Complex Model 2.</p>
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<p>The training loss curves of Complex Model 1 (top line) at 1 Hz (<b>a</b>), 10 Hz (<b>b</b>), 100 Hz (<b>c</b>), and 1000 Hz (<b>d</b>) and Complex Model 2 (bottom line) at 1 Hz (<b>e</b>), 10 Hz (<b>f</b>), 100 Hz (<b>g</b>), and 1000 Hz (<b>h</b>). The blue, orange, and green curves represent <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>t</mi> <mi>o</mi> <mi>t</mi> <mi>a</mi> <mi>l</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>p</mi> <mi>d</mi> <mi>e</mi> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> <mi>o</mi> <mi>s</mi> <mi>s</mi> </mrow> <mrow> <mi>l</mi> <mi>b</mi> </mrow> </msub> </mrow> </semantics></math>, respectively.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the electric field response of Simple Model 1 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the electric field response of Simple Model 2 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the magnetic field response of Complex Model 1 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>Comparison of the simulation results of the proposed PINN and FD for the magnetic field response of Complex Model 2 at (<b>a</b>,<b>b</b>) 1 Hz; (<b>c</b>,<b>d</b>) 10 Hz; (<b>e</b>,<b>f</b>) 100 Hz; and (<b>g</b>,<b>h</b>) 1000 Hz.</p>
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<p>The multi-frequency PINN.</p>
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<p>The prediction electric field responses at (<b>a</b>,<b>b</b>) 1.25 Hz; (<b>c</b>,<b>d</b>) 15.8 Hz; (<b>e</b>,<b>f</b>) 79.4 Hz; (<b>g</b>,<b>h</b>) 398.1 Hz.</p>
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<p>The prediction magnetic field responses at (<b>a</b>,<b>b</b>) 1.25 Hz; (<b>c</b>,<b>d</b>) 15.8 Hz; (<b>e</b>,<b>f</b>) 79.4 Hz; (<b>g</b>,<b>h</b>) 398.1 Hz.</p>
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14 pages, 306 KiB  
Article
The Quantum Electromagnetic Field in the Weyl–Wigner Representation
by Emilio Santos
Universe 2024, 10(12), 452; https://doi.org/10.3390/universe10120452 - 9 Dec 2024
Viewed by 337
Abstract
The quantum electromagnetic (EM) field is formulated in the Weyl–Wigner representation (WW), which is equivalent to the standard Hilbert space one (HS). In principle, it is possible to interpret within WW all experiments involving the EM field interacting with macroscopic bodies, the latter [...] Read more.
The quantum electromagnetic (EM) field is formulated in the Weyl–Wigner representation (WW), which is equivalent to the standard Hilbert space one (HS). In principle, it is possible to interpret within WW all experiments involving the EM field interacting with macroscopic bodies, the latter treated classically. In the WW formalism, the essential difference between classical electrodynamics and the quantum theory of the EM field is just the assumption that there is a random EM field-filling space, i.e., the existence of a zero-point field with a Gaussian distribution for the field amplitudes. I analyze a typical optical test of a Bell inequality. The model admits an interpretation compatible with local realism, modulo a number of assumptions assumed plausible. Full article
(This article belongs to the Special Issue Quantum Field Theory, 2nd Edition)
11 pages, 4199 KiB  
Article
Experimental Study on the Propulsion Performance of Laser Ablation Induced Pulsed Plasma
by Hang Song, Jifei Ye, Ming Wen, Haichao Cui and Wentao Zhao
Aerospace 2024, 11(12), 1013; https://doi.org/10.3390/aerospace11121013 - 9 Dec 2024
Viewed by 437
Abstract
This study investigates the influence of electromagnetic fields on the propulsion performance of laser plasma propulsion. Based on the principle of pulsed plasma thrusters, an electromagnetic field is utilized to accelerate laser plasma, achieving enhanced propulsion performance. This approach represents a novel method [...] Read more.
This study investigates the influence of electromagnetic fields on the propulsion performance of laser plasma propulsion. Based on the principle of pulsed plasma thrusters, an electromagnetic field is utilized to accelerate laser plasma, achieving enhanced propulsion performance. This approach represents a novel method for the electromagnetic enhancement of laser propulsion performance. In this paper, pulsed plasma thrusters induced by laser ablation are employed. The generated plasma is subjected to the Lorentz force under the influence of an electromagnetic field to obtain higher speed, thus increasing impulse and specific impulse. An experimental platform for laser-ablation plasma electromagnetic acceleration was constructed to explore the enhancement effect of discharge characteristics and propulsion performance. The results demonstrate that increased laser energy has little effect on discharge characteristics, while the trend of propulsion performance parameters initially rises and then declines. After coupling the electromagnetic field, the propulsion performance is significantly enhanced, with stronger electromagnetic fields yielding more pronounced effects. Full article
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<p>Schematic diagram.</p>
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<p>Schematic diagram of the experimental apparatus.</p>
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<p>Laser (<b>left</b>) and high-voltage power supply (<b>right</b>).</p>
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<p>Physical diagram of the micro impulse measuring device based on the torsion pendulum test bench.</p>
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<p>Torsion pendulum displacement change curve.</p>
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<p>Discharge current change curve: (<b>a</b>) the laser energy E = 730 mJ; (<b>b</b>) the laser energy E = 358 mJ.</p>
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<p>(<b>a</b>) Impulse change curve with charging voltage; (<b>b</b>) impulse change curve with laser energy.</p>
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<p>(<b>a</b>) Impulse coefficient change curve with charging voltage; (<b>b</b>) impulse coefficient change curve with laser energy.</p>
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<p>(<b>a</b>) Specific impulse change curve with charging voltage; (<b>b</b>) specific impulse change curve with laser energy.</p>
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<p>(<b>a</b>) Efficiency change curve with charging voltage; (<b>b</b>) efficiency change curve with laser energy.</p>
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