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21 pages, 5239 KiB  
Article
Influence of Tropical Cyclones and Cold Waves on the Eastern Guangdong Coastal Hydrodynamics: Processes and Mechanisms
by Yichong Zhong, Fusheng Luo, Yunhai Li, Yunpeng Lin, Jia He, Yuting Lin, Fangfang Shu and Binxin Zheng
J. Mar. Sci. Eng. 2024, 12(12), 2148; https://doi.org/10.3390/jmse12122148 (registering DOI) - 25 Nov 2024
Abstract
In response to the intensification of global warming, extreme weather events, such as tropical cyclones (TCs) and cold waves (CWs) have become increasingly frequent near the eastern Guangdong coast, significantly affecting the structure and material transport of coastal waters. Based on nearshore-measured and [...] Read more.
In response to the intensification of global warming, extreme weather events, such as tropical cyclones (TCs) and cold waves (CWs) have become increasingly frequent near the eastern Guangdong coast, significantly affecting the structure and material transport of coastal waters. Based on nearshore-measured and remote sensing reanalysis data in the winter of 2011 and summer of 2012 on the eastern Guangdong coast, this study analyzed the nearshore hydrodynamic evolution process, influencing mechanism, and marine environmental effects under the influence of TCs and CWs, and further compared the similarities and differences between the two events. The results revealed significant seasonal variations in the hydrological and meteorological elements of the coastal waters, which were disrupted by the passage of TCs and CWs. The primary influencing factors were TC track and CW intensity. The current structure changed significantly during the TCs and CWs, with the TC destroying the original upwelling current and the CW affecting the prevailing northeastward current. Wind is one of the major forces driving nearshore hydrodynamic processes. According to the synchronous analysis of research data, the TC-induced water level rise is primarily attributed to the combined effects of wind stress curl and the Ekman effect, whereas the water level rise associated with CW is primarily linked to the Ekman effect. The water transport patterns during the TC and CW differed, with transport concentrated on the right side of the TC track and within the coastal strong-wind zones, respectively. Additionally, the temporal frequency domain of wavelet analysis highlighted the distinct nature of TC and CW signals, with 1–3 d and 4–8 d, respectively, and with TC signals being short-lived and rapid compared to the more sustained CW signals. This study enhances our understanding of the response of coastal hydrodynamics to extreme weather events on the eastern Guangdong coast, and the results can provide references for disaster management and protection of nearshore ocean engineering under extreme events. Full article
(This article belongs to the Section Physical Oceanography)
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Figure 1

Figure 1
<p>(<b>a</b>) Topographic map of the NSCS (GCC signifies the Guangdong Coastal Current; SCSWC signifies the Warm Current of the South China Sea; SCS, TWS, and NW Pacific signify the South China Sea, Taiwan Strait, and Northwest Pacific Ocean, respectively; the red box signifies the study area; blue, green, and yellow dots signify the tracks of TCs Talim and Doksuri, and the color and size of the dots signify the maximum wind speed and minimum central pressure of TCs, respectively; the blue arrows signify the direction of the CW). (<b>b</b>) Topographic map of the study area (red dots signify the location of each seabed-based observation station (W1 and W2)); the magenta inverted triangle signifies the location of the land meteorological observation station (F1); and the abscissa and ordinate axes of the coordinate system of station W1, W2, and F1 are along the shore (u’) and perpendicular to the shore (v’), respectively). Water depth data were obtained from the ETOPO Global Relief Model (public access). ETOPO Global Relief Model | National Centers for Environmental Information (NCEI) (noaa.gov), access on 20 June 2023).</p>
Full article ">Figure 2
<p>(<b>a</b>–<b>i</b>) The sea surface wind field (SSWF), sea surface current field (SSCF), and sea surface height anomaly (SSHA) distribution in the NSCS during the TC from 17 June to 3 July 2012 (black, red, and magenta arrows represent wind, current, and measured current vectors, respectively, and background color represents SSHA; (<b>a</b>–<b>c</b>) and (<b>f</b>–<b>h</b>) are the periods before, during, and after T1 and T2, respectively). SSWF, SSCF, and SSHA data were obtained from NCEP Climate Forecast System Version 2 Product (public access).</p>
Full article ">Figure 3
<p>(<b>a</b>–<b>i</b>) The sea surface wind field (SSWF), sea surface current field (SSCF), and sea surface height anomaly (SSHA) distribution in the NSCS during the CW from 5 November to 27 December 2011 (black, red, and magenta arrows represent wind, current vectors, and measured current vectors, respectively, and background color represents SSHA; (<b>a</b>–<b>c</b>), (<b>d</b>–<b>f</b>), and (<b>g</b>–<b>i</b>) are the periods before, during, and after C1, C2, and C3, respectively). SSWF, SSCF, and SSHA data were obtained from NCEP Climate Forecast System Version 2 Product (public access).</p>
Full article ">Figure 4
<p>Curves of summer and winter wind vector, air temperature, and air pressure at F1 station. (<b>a</b>) Summer wind vector. (<b>b</b>) Summer air temperature and air pressure. (<b>c</b>) Winter wind vector. (<b>d</b>) Winter air temperature and air pressure (the size and color of the vector arrow represent the wind speed, and the direction of the arrow represents the wind direction; red line represents the air temperature, and the blue line represents the air pressure; the red boxes in summer data represent TCs, and the blue boxes in winter data represent CWs).</p>
Full article ">Figure 5
<p>Alongshore current velocity at stations W1 (<b>a</b>) and W2 (<b>b</b>); cross-shore current velocity at stations W1 (<b>c</b>) and W2 (<b>d</b>); echo intensity at stations W1 (<b>e</b>) and W2 (<b>f</b>) (the black dashed line represents the instrument change time; the white area represents the missing data; the magenta line represents the residual water level (RWL); the blue line represents the alongshore wind speed (AW); the black line represents the bottom-water temperature (BWT); and the red boxes represent the TC events).</p>
Full article ">Figure 6
<p>(<b>a</b>) Alongshore current velocity at stations W1 and W2. (<b>b</b>) cross-shore current velocity at stations W1 and W2. (<b>c</b>) echo intensity at stations W1 and W2 (the black dashed line represents the instrument change time; the white area represents the missing data; the magenta line represents the residual water level (RWL); the blue line represents the alongshore wind speed (AW); the black line represents the bottom-water temperature (BWT); and the blue boxes represent the CW events).</p>
Full article ">Figure 7
<p>Alongshore residual current velocity at stations W1 (<b>a</b>) and W2 (<b>b</b>) in summer; cross-shore residual current velocity at stations W1 (<b>c</b>) and W2 (<b>d</b>) in summer (the red boxes represent the TC events).</p>
Full article ">Figure 8
<p>Alongshore residual current velocity at stations W1 (<b>a</b>) and W2 (<b>c</b>) in winter; cross-shore residual current velocity at stations W1 (<b>b</b>) and W2 (<b>d</b>) in winter (the blue boxes represent the CW events; the black dashed line represents the instrument change time).</p>
Full article ">Figure 9
<p>Wavelet analysis transform of alongshore surface current at station W2 in summer. (<b>a</b>) Measured current CWT; (<b>b</b>) residual current CWT; (<b>c</b>) WTC of the wind and surface current; (<b>d</b>) XWT of the wind and surface current (the thick lines represent areas that have passed a 95% significance level test. The colors of the subfigure represent the signal energy. The relative phase relationship is also depicted in the last two panels with in-phase pointing to the right and anti-phase pointing to the left, and if the former leads the latter by 90°, it will point straight downward. The former represents wind, and the latter represents the current).</p>
Full article ">Figure 10
<p>Wavelet analysis transform of alongshore surface current at stations W1 and W2 in winter. (<b>a</b>) Measured current CWT; (<b>b</b>) residual current CWT; (<b>c</b>) WTC of the wind and surface current; (<b>d</b>) XWT of the wind and surface current (the thick lines represent areas that have passed a 95% significance level test. The colors of the subfigure represent the signal energy. The relative phase relationship is also depicted in the last two panels with in-phase pointing to the right and anti-phase pointing to the left, and if the former leads the latter by 90°, it will point straight downward. The former represents wind, and the latter represents the current).</p>
Full article ">Figure 11
<p>Ekman volume transport composite distribution in the NSCS. (<b>a</b>) During T1; (<b>b</b>) relaxation stage of TCs; (<b>c</b>) during T2; (<b>d</b>) during C1; (<b>e</b>) relaxation stage of CWs; (<b>f</b>) during C2; (<b>g</b>) Ekman volume transport curve in the study area. The red circles represent the TC circles; white lines represent the TC tracks; red boxes represent the TCs; and blue boxes represent the CWs.</p>
Full article ">Figure 12
<p>Schematic of impacts of TCs and CWs on coastal marine environment in the NSCS. (<b>a</b>) T1 and T2 represent different tracks; red and yellow circles represent the radius of T1 and T2, respectively; the black arrows represent the mixing caused by wind stress. (<b>b</b>) C1, C2, and C3 represent the different intensities of the CW; the blue arrow and wind direction symbol represent the CW direction and wind speed. The horizontal background color represents the water depth. Water depth data were obtained from the ETOPO Global Relief Model (public access).</p>
Full article ">
24 pages, 8579 KiB  
Article
Research on Directional Elements of Two-Terminal Weak-Feed AC Systems with a Negative Sequence Control Strategy
by Yan Li, Wentao Yang, Xiaofang Wu, Runbin Cao, Weihuang Huang, Faxi Peng and Junjie Hou
Electronics 2024, 13(23), 4647; https://doi.org/10.3390/electronics13234647 (registering DOI) - 25 Nov 2024
Abstract
It has become a typical scenario in power systems that renewable energy power supply is connected to an AC system through flexible DC transmission. However, since both sides of the AC line are power electronic converters, the negative sequence suppression strategy will be [...] Read more.
It has become a typical scenario in power systems that renewable energy power supply is connected to an AC system through flexible DC transmission. However, since both sides of the AC line are power electronic converters, the negative sequence suppression strategy will be put into the converters at both ends during the asymmetric fault, which causes fundamental changes in the fault characteristics of the system, which is reflected in the two-terminal weak-feed characteristics, leading to the decline of traditional protection performance and affecting the safe operation of the system. Therefore, this paper presents a directional element of a double-ended weakly fed AC system with a negative sequence control strategy. Firstly, the characteristics of the negative sequence impedance under the negative sequence suppression strategy are analyzed when the AC line has asymmetric faults. Secondly, the difference in negative sequence impedance amplitude is analyzed. Finally, the direction element is constructed by the method of de-wave trend analysis The proposed scheme can realize the rapid identification of fault directions at both ends. The simulation results show that the proposed scheme is suitable for a two-terminal weak-feed AC system and can operate reliably under 300 Ω transition resistance and 20 dB noise interference. Full article
(This article belongs to the Special Issue Advanced Online Monitoring and Fault Diagnosis of Power Equipment)
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Figure 1

Figure 1
<p>Topology of large-scale renewable energy station through flexible and straight grid connection system.</p>
Full article ">Figure 2
<p>The negative sequence impedance angle change rule under different control objectives. (<b>a</b>) Suppress reactive power fluctuations (<span class="html-italic">k</span><sub>pq =</sub> 1); (<b>b</b>) Suppression of active power fluctuations (<span class="html-italic">k</span><sub>pq</sub> = −1).</p>
Full article ">Figure 3
<p>The negative sequence impedance amplitude under different control objectives. (<b>a</b>) Suppress reactive power fluctuations (<span class="html-italic">k</span><sub>pq</sub> = 1); (<b>b</b>) Suppression of active power fluctuations (<span class="html-italic">k</span><sub>pq</sub> = −1).</p>
Full article ">Figure 3 Cont.
<p>The negative sequence impedance amplitude under different control objectives. (<b>a</b>) Suppress reactive power fluctuations (<span class="html-italic">k</span><sub>pq</sub> = 1); (<b>b</b>) Suppression of active power fluctuations (<span class="html-italic">k</span><sub>pq</sub> = −1).</p>
Full article ">Figure 4
<p>Additional network diagram of the fault negative sequence fault at <span class="html-italic">f</span><sub>1</sub> (<b>a</b>) PV; (<b>b</b>) MMC.</p>
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<p>Additional network diagram of the fault negative sequence fault at <span class="html-italic">f</span><sub>3</sub> (<b>a</b>) PV; (<b>b</b>) MMC.</p>
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<p>Additional network diagram of the fault negative sequence fault at <span class="html-italic">f</span><sub>3</sub> (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 7
<p>Negative sequence impedance amplitude measurements at the protection installation during fault at <span class="html-italic">f</span><sub>1</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 8
<p>Negative sequence impedance amplitude measurements at the protection installation during fault at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 9
<p>Negative sequence impedance amplitude measurements at the protection installation during fault at <span class="html-italic">f</span><sub>5</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 10
<p>To deal with the fluctuation trend before and after contrast. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 11
<p>Flow chart of the directional element scheme.</p>
Full article ">Figure 12
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>1</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 13
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 14
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>5</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 15
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>1</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 16
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 17
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>5</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 18
<p>Calculation results of <span class="html-italic">α</span><sub>M</sub> at different fault locations.</p>
Full article ">Figure 19
<p>Calculation results of <span class="html-italic">α</span><sub>N</sub> at different fault locations.</p>
Full article ">Figure 20
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>1</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 21
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 22
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) result at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 23
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>1</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 24
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>3</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 25
<p>Fault <span class="html-italic">F</span>(<span class="html-italic">h</span>) results at <span class="html-italic">f</span><sub>5</sub>. (<b>a</b>) PV; (<b>b</b>) MMC.</p>
Full article ">Figure 26
<p>Calculation results of <span class="html-italic">α</span><sub>M</sub> at different fault locations.</p>
Full article ">Figure 27
<p>Calculation results of <span class="html-italic">α</span><sub>M</sub> at different fault locations.</p>
Full article ">
18 pages, 3781 KiB  
Article
A Multiscale Model to Assess Bridge Vulnerability Under Extreme Wave Loading
by Umberto De Maio, Fabrizio Greco, Paolo Lonetti and Paolo Nevone Blasi
J. Mar. Sci. Eng. 2024, 12(12), 2145; https://doi.org/10.3390/jmse12122145 - 25 Nov 2024
Viewed by 163
Abstract
A multiscale model is proposed to assess the impact of wave loading on coastal or inland bridges. The formulation integrates various scales to examine the effects of flooding actions on fluid and structural systems, transitioning from global to local representation scales. The fluid [...] Read more.
A multiscale model is proposed to assess the impact of wave loading on coastal or inland bridges. The formulation integrates various scales to examine the effects of flooding actions on fluid and structural systems, transitioning from global to local representation scales. The fluid flow was modeled using a turbulent two-phase level set formulation, while the structural system employed the 3D solid mechanics theory. Coupling between subsystems was addressed through an FSI formulation using the ALE moving mesh methodology. The proposed model’s validity was confirmed through comparisons with numerical and experimental data from the literature. A parametric study was conducted on wave load characteristics associated with typical flood or tsunami scenarios. This included verifying the wave load formulas from existing codes or refined formulations found in the literature, along with assessing the dynamic amplification’s effects on key bridge design variables and the worst loading cases involving bridge uplift and horizontal forces comparable to those typically used in seismic actions. Furthermore, a parametric study was undertaken to examine fluid flow and bridge characteristics, such as bridge elevation, speed, inundation ratio, and bearing system typology. The proposed study aims to identify the worst-case scenarios for bridge deck vulnerability. Full article
(This article belongs to the Special Issue Analysis and Design of Marine Structures)
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Figure 1

Figure 1
<p>General model and multiscale formulation for the fluid and structural systems.</p>
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<p>Multiscale model: fluid (2D) and structural (3D) systems.</p>
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<p>Schematic test configuration GM (<b>a</b>) and definition of the Reduced Model (RM) geometry (<b>b</b>).</p>
Full article ">Figure 4
<p>Comparisons between experimental, numerical [<a href="#B38-jmse-12-02145" class="html-bibr">38</a>], and proposed model results in terms of (<b>a</b>) horizontal and (<b>b</b>) vertical forces.</p>
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<p>Mesh discretization of Global Model (GM) and Reduced Model (RM): mesh discretization with details around the bridge.</p>
Full article ">Figure 6
<p>DAFs of midspan centroid displacements (V<sub>2</sub>, V<sub>3</sub>) and hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. inundation ratio <span class="html-italic">H</span>* at fixed inlet speed ratio equal to <span class="html-italic">U</span>* = 0.5 (<b>a</b>) and <span class="html-italic">U</span>* = 0.7 (<b>b</b>).</p>
Full article ">Figure 7
<p>DAFs of midspan centroid displacements (V<sub>2</sub>, V<sub>3</sub>) and hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. inlet speed factor <span class="html-italic">U</span>* at fixed inundation ratio equal to <span class="html-italic">H</span>* = 3 (<b>a</b>) or <span class="html-italic">H</span>* = 4 (<b>b</b>).</p>
Full article ">Figure 8
<p>DAFs of midspan centroid transverse displacements V<sub>2</sub> (<b>a</b>) and V<sub>3</sub> (<b>b</b>) vs. deformability parameter (<span class="html-italic">s</span>*) for different values of inlet speed (<span class="html-italic">U</span>* = 0.3; 0.5; 0.7).</p>
Full article ">Figure 9
<p>Time histories of the vertical reaction force R<sub>3</sub> normalized on the value under dead loads (Rg) for different values of inundation ratios <span class="html-italic">H</span>* (<b>a</b>) and inlet speed ratios equal to <span class="html-italic">U</span>* = 0.7 (<b>a</b>) and <span class="html-italic">U</span>* = 0.5 (<b>b</b>).</p>
Full article ">Figure 10
<p>Time histories of the transverse reaction force (R<sub>2</sub>) normalized on the total weight of the deck (M<sub>b</sub>g) for different values of inlet speed ratio <span class="html-italic">U</span>* (<b>a</b>) and inundation ratios <span class="html-italic">H</span>* (<b>b</b>).</p>
Full article ">Figure 11
<p>Maximum displacements along transverse (V<sub>2</sub>) or vertical (V<sub>3</sub>) normalized on the bridge length (<span class="html-italic">L<sub>B</sub></span>) vs. support stiffness ratio (<span class="html-italic">K</span>/<span class="html-italic">K<sub>ISO</sub></span>) for different values of inlet speed ratios (<span class="html-italic">U</span>*) at a fixed inundation ratio (<span class="html-italic">H</span>*).</p>
Full article ">Figure 12
<p>Time histories of the vertical and transverse displacements (V<sub>3</sub>, V<sub>2</sub>) normalized on the maximum value: comparisons between isolated (I) or classical bridge configuration.</p>
Full article ">Figure 13
<p>Reaction forces along transverse (R<sub>2</sub>) or vertical (R<sub>3</sub>) normalized girder weight (M<sub>b</sub>g) or hydrodynamic forces (F<sub>2</sub>, F<sub>3</sub>) vs. support stiffness ratio (<span class="html-italic">K</span>/<span class="html-italic">K<sub>ISO</sub></span>) at a fixed inundation ratio (<span class="html-italic">H</span>*) and inlet speed at <span class="html-italic">U</span>* = 0.5 (<b>a</b>) and <span class="html-italic">U</span>* = 0.5 (<b>b</b>).</p>
Full article ">Figure A1
<p>Maximum tsunami-induced forces calculated by the present model with different mesh sizes in the main calculation domain.</p>
Full article ">
12 pages, 5737 KiB  
Article
Modeling of 2-D Periodic Array of Dielectric Bars with a Low Reflection Angle for a Wind Tunnel High-Power Microwave Experiment
by Rong Bao, Yang Tao and Yongdong Li
Appl. Sci. 2024, 14(23), 10876; https://doi.org/10.3390/app142310876 - 24 Nov 2024
Viewed by 268
Abstract
Two-dimensional periodic dielectric bars have potential applications in high-power microwave (HPM) radiation effect experiments performed in wind tunnels. Such a bar is designed to consist of two types of dielectric materials, and two lined-up blocks can be considered as a period along the [...] Read more.
Two-dimensional periodic dielectric bars have potential applications in high-power microwave (HPM) radiation effect experiments performed in wind tunnels. Such a bar is designed to consist of two types of dielectric materials, and two lined-up blocks can be considered as a period along the bar. Under plane excitation, the theoretical period length of the beat wave pattern fits well with the simulation result, which requires modifying the previously presented field-matching method. The phase distribution on the cross-section can be non-uniform when two different guiding modes are excited independently and propagate along different materials. Directional reflection with a low reflection angle can be obtained by reasonably choosing the parameters of the dielectric array. The designed array can decrease the returned-back microwave power toward the microwave source by 6 dB according to the numerical simulation, which included the wind tunnel, the input antenna, the test target, and the reflect array in one model. Full article
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Figure 1

Figure 1
<p>Illustration of HPM radiation effect experiment system [<a href="#B1-applsci-14-10876" class="html-bibr">1</a>].</p>
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<p>Illustration of the 2-D dielectric bar reflect array.</p>
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<p>Incident microwave after reflection: (<b>a</b>) <span class="html-italic">E<sub>y</sub></span> component on the <span class="html-italic">x</span>o<span class="html-italic">y</span> plane, (<b>b</b>) power flow on the <span class="html-italic">x</span>o<span class="html-italic">y</span> plane and (<b>c</b>) simulation model of the test target.</p>
Full article ">Figure 4
<p>Propagation in the dielectric bar waveguide along the −<span class="html-italic">x</span> direction: (<b>a</b>) illustration of geometry and (<b>b</b>) simulation model using periodic boundary. <span class="html-italic">w</span> is the width of the bars in the <span class="html-italic">y</span> direction; <span class="html-italic">l</span><sub>1</sub> and <span class="html-italic">l</span><sub>2</sub> are the sizes of the high-permittivity and low-permittivity materials, respectively; and <span class="html-italic">ε</span><sub>l</sub> and <span class="html-italic">ε</span><sub>h</sub> are the permittivities of the materials and <span class="html-italic">ε</span><sub>l</sub> &lt; <span class="html-italic">ε</span><sub>h</sub>.</p>
Full article ">Figure 5
<p>Electric field distribution under plane wave excitation at different frequencies: (<b>a</b>) electric field at 2 GHz, (<b>b</b>) electric field at 4 GHz, (<b>c</b>) electric field at 6 GHz, (<b>d</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 2 GHz, (<b>e</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 4 GHz, and (<b>f</b>) field strength of <span class="html-italic">E<sub>y</sub></span> component at 6 GHz.</p>
Full article ">Figure 6
<p>Region division for the theoretical analysis.</p>
Full article ">Figure 7
<p>Simulated average norm of the electric field in the <span class="html-italic">x</span>o<span class="html-italic">z</span> plane (<b>a</b>) at 8.7 GHz, (<b>b</b>) at 9.2 GHz, and (<b>c</b>) at 9.7 GHz.</p>
Full article ">Figure 8
<p>Amplitude simulation model of (<b>a</b>) the array without metal wall and simulated power density distributions at (<b>b</b>) 7.7 GHz, (<b>c</b>) 8.7 GHz, and (<b>d</b>) 9.7 GHz.</p>
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<p>Simulated phase distribution using the model in <a href="#applsci-14-10876-f008" class="html-fig">Figure 8</a> at (<b>a</b>) 7.7 GHz, (<b>b</b>) 8.7 GHz, and (<b>c</b>) 9.7 GHz.</p>
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<p>Radiation pattern of structure in <a href="#applsci-14-10876-f008" class="html-fig">Figure 8</a> with (<b>a</b>) <span class="html-italic">h</span> = 40 mm, (<b>b</b>) <span class="html-italic">h</span> = 45 mm, and (<b>c</b>) <span class="html-italic">h</span> = 50 mm.</p>
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<p>Radiation pattern of the reflect-array structure: (<b>a</b>) simulation model of the reflect array, (<b>b</b>) radiation pattern of the electric field, (<b>c</b>) radiation pattern with ‘phi’ = 0°, (<b>d</b>) radiation pattern with ‘phi’ = 90°, (<b>e</b>) phase distribution on the interface when <span class="html-italic">g</span> is 13.5 mm, and (<b>f</b>) distribution of the amplitude of the <span class="html-italic">E<sub>y</sub></span> component on the interface when <span class="html-italic">g</span> is 17.5 mm.</p>
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<p>Simulation of the experimental setup in a wind tunnel: (<b>a</b>) simulation model, (<b>b</b>) outgoing power vs. frequency without reflect array, and (<b>c</b>) outgoing power vs. frequency with reflect array.</p>
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13 pages, 4997 KiB  
Article
Numerical Study on the Influence of Drift Angle on Wave Properties in a Two-Layer Flow
by Xiaoxing Zhao, Liuliu Shi and Eryun Chen
J. Mar. Sci. Eng. 2024, 12(12), 2139; https://doi.org/10.3390/jmse12122139 - 23 Nov 2024
Viewed by 246
Abstract
This study examines the influence of drift angle on the wave and flow field generated by a submarine navigating through a density-stratified fluid. Employing a numerical methodology, this research computed the viscous flow field around the SUBOFF bare hull under conditions of oblique [...] Read more.
This study examines the influence of drift angle on the wave and flow field generated by a submarine navigating through a density-stratified fluid. Employing a numerical methodology, this research computed the viscous flow field around the SUBOFF bare hull under conditions of oblique shipping maneuvers. The analytical framework relies on the Reynolds-Averaged Navier–Stokes (RANS) equations, supplemented by the Re-Normalization Group (RNG) k-ε turbulence model and the Volume of Fluid (VOF) method. The initial phases of this study involved verifying grid convergence and the accuracy of the numerical methods used. Subsequently, numerical simulations were performed across a spectrum of drift angles while maintaining a fixed Froude number of Fn = 0.5, with submergence depths set at 1.1 D and 2.0 D. The analysis focused on the wave profiles at both the free surface and the internal surface. The results indicate that the presence of a drift angle produces significant alterations in the characteristics of the free surface and internal surface when compared with straight-ahead motion. Specifically, the asymmetry in the flow field is enhanced, and the variability in the roughness of the free surface is pronounced. Full article
(This article belongs to the Section Ocean Engineering)
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<p>Schematic of the DARPA SUBOFF bare hull model.</p>
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<p>Schematic of the computational domain.</p>
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<p>Grids in the vertical central plane.</p>
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<p>Grids in proximity to the submarine’s surface.</p>
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<p>Rankine ovoid model.</p>
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<p>Comparison of numerical and experimental results [<a href="#B24-jmse-12-02139" class="html-bibr">24</a>].</p>
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<p>Free surface wave of the Rankine ovoid.</p>
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<p>Distribution of free surface waves at a submergence depth of h = 1.1 D.</p>
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<p>Distribution of free surface waves at a submergence depth of h = 2.0 D.</p>
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<p>Free surface wave profiles at different submergence depths.</p>
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<p>Internal surface wave profiles at different submergence depths.</p>
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<p>Lateral waveforms at different streamwise locations.</p>
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<p>Distribution of surface pressure along the length of the submarine within the horizontal center plane.</p>
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<p>Distribution of surface pressure along the length of the submarine within the vertical center plane.</p>
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<p>Distributions of the convergence and divergence of surface velocity at the free surface.</p>
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10 pages, 4742 KiB  
Article
Tellurium Photonic Crystal-Based Terahertz Polarization Splitter Using a Diamond-Shaped Ferrite Pillar Array
by Haiping Zhang, Zhifeng Zeng and Yong Wang
Crystals 2024, 14(12), 1015; https://doi.org/10.3390/cryst14121015 - 23 Nov 2024
Viewed by 307
Abstract
A T-shaped photonic crystal waveguide was designed with square lattice tellurium photonic crystals. A diamond-shaped ferrite pillar array was inserted in the junction of the waveguide to make a novel terahertz polarization splitter. Both transverse electric and transverse magnetic modes were numerically investigated [...] Read more.
A T-shaped photonic crystal waveguide was designed with square lattice tellurium photonic crystals. A diamond-shaped ferrite pillar array was inserted in the junction of the waveguide to make a novel terahertz polarization splitter. Both transverse electric and transverse magnetic modes were numerically investigated by the plane wave expansion method, which used complete photonic band gaps covering from 0.138 THz to 0.144 THz. In this frequency domain of the fully polarized band gaps, the transmission efficiency of the photonic crystal waveguide was up to −0.21 dB and −1.67 dB for the transverse electric and transverse magnetic modes, respectively. Under the action of a DC magnetic field, the THz waves were rotated 90 degrees by the diamond-shaped ferrite pillar array. Transverse electric waves or transverse magnetic waves can be separated by a polarization isolator (six smaller tellurium rods) from the fixed waves. The characteristics of the designed polarization splitter were analyzed by the finite element method, and its transmission efficiency was optimized to 95 percent by fine-tuning the radii of the thirteen ferrite pillars. A future integrated communication network of sky–earth–space will require fully polarized devices in the millimeter and terahertz wavebands. The envisaged polarization splitter has a unique function and provides a promising method for the realization of fully polarized 6G devices. Full article
(This article belongs to the Special Issue Metamaterials and Their Devices)
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<p>The tridimensional figure of the PC-based PBS.</p>
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<p>The detailed structural parameters in a plane figure for the designed PBS.</p>
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<p>The PBGs of the SLTRAs for both the TE and TM modes: three TE-mode PBGs (marked in blue) and two TM-mode PBGs (marked in red) around the normalized frequency domain from 0.15(<span class="html-italic">a</span>/<span class="html-italic">λ</span>) to 0.41(<span class="html-italic">a</span>/<span class="html-italic">λ</span>).</p>
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<p>The transmission characteristics of the T-shaped PCW for the TE mode in the increasing frequency domain.</p>
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<p>The transmission characteristics of the T-shaped PCW for the TM mode in the increasing frequency domain.</p>
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<p>The transmission characteristics of the PBS: (<b>a</b>) the planar view of the transmission path for the TE mode; (<b>b</b>) the altitudinal view; and (<b>c</b>) the transmission efficiency and isolation of the PBS for TE waves in the frequency domain from 136 to 144 GHz.</p>
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<p>The transmission characteristics of the PBS: (<b>a</b>) the planar view of the transmission path for the TM mode; (<b>b</b>) the altitudinal view; and (<b>c</b>) the transmission efficiency and isolation of the PBS for TM waves in the frequency domain from 136 to 144 GHz.</p>
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14 pages, 17097 KiB  
Article
Enhancing Deep Sleep Induction Through a Wireless In-Ear EEG Device Delivering Binaural Beats and ASMR: A Proof-of-Concept Study
by Elke Hestermann, Kristiaan Schreve and David Vandenheever
Sensors 2024, 24(23), 7471; https://doi.org/10.3390/s24237471 - 22 Nov 2024
Viewed by 329
Abstract
This study presents the development of a wireless in-ear EEG device designed to monitor brain activity during sleep and deliver auditory stimuli aimed at enhancing deep sleep. The device records EEG signals and plays a combined auditory stimulus consisting of autonomous sensory meridian [...] Read more.
This study presents the development of a wireless in-ear EEG device designed to monitor brain activity during sleep and deliver auditory stimuli aimed at enhancing deep sleep. The device records EEG signals and plays a combined auditory stimulus consisting of autonomous sensory meridian response (ASMR) and 3 Hz binaural beats at a 60:30 dB ratio, intended to promote delta wave activity and non-rapid eye movement (NREM) stage 3 sleep. Fifteen participants completed this study, which included two consecutive nights: a baseline night and a testing night. Participants were divided into an experimental group, which received the combined ASMR and binaural beat stimulus, and a control group, which received only ASMR. The combined stimulus was delivered upon entering NREM stage 2 and replaced by ASMR when NREM stage 3 was reached. Results showed that the experimental group experienced an increase in NREM 3 sleep, a decrease in NREM 2 sleep, and a slight increase in NREM 3 latency compared to the baseline night. Although the findings are promising, further testing with a larger sample size is required to confirm the device’s potential to enhance sleep quality and promote delta activity in the brain. Full article
(This article belongs to the Special Issue Advances on EEG-Based Sensing and Imaging: 2nd Edition)
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<p>Design of earpiece. Part (<b>a</b>) illustrates the earpiece where the section in red represents the part inserted inside the ear canal, and the light gray shows the placement of the in-ear EEG, bias and reference electrodes depending on the ear. Part (<b>b</b>) demonstrates how the earpiece would be inserted in the ear.</p>
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<p>The earpiece design with a close-up of the earpiece on the (<b>left</b>) and how it fits in the ear on the (<b>right</b>).</p>
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<p>System architecture and the linkage between the devices.</p>
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<p>Final design of in-ear EEG device.</p>
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<p>Front and back view of how the participants wore the in-ear EEG device.</p>
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<p>Flow diagram of the EEG data filtering procedure.</p>
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<p>Unfiltered, raw EEG data of one channel.</p>
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<p>Obtained EEG sleep data from the designed in-ear EEG device.</p>
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<p>Comparison between the baseline, experimental, and control groups, where an asterisk (*) indicates statistical significance. The error bars present the standard deviation relative to the mean.</p>
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34 pages, 7354 KiB  
Article
Analysis of High-Frequency Sea-State Variability Using SWOT Nadir Measurements and Application to Altimeter Sea State Bias Modelling
by Estelle Mazaleyrat, Ngan Tran, Laïba Amarouche, Douglas Vandemark, Hui Feng, Gérald Dibarboure and François Bignalet-Cazalet
Remote Sens. 2024, 16(23), 4361; https://doi.org/10.3390/rs16234361 - 22 Nov 2024
Viewed by 288
Abstract
The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly [...] Read more.
The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points. Full article
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<p>(<b>a</b>) Global means of the SWOT nadir SLA collinear differences as a function of the time interval considered for the SLA differences. The examined SLA types are SLA_uncorr (no SSB applied), SLA_corr1D (application of SSB = −3.2% SWH), SLA_corr2D (with J3 GDR-F 2D SSB table), and SLA_corr3D (with J3 GDR-F 3D SSB table); (<b>b</b>) same as in (<b>a</b>) but for the global variance; (<b>c</b>) global variance reduction as a function of the considered time interval obtained when one computes var(∆SLA_corr) minus var(∆SLA_uncorr). Negative values indicate an improvement in the SLA precision resulting from the application of the SSB correction. Higher reduction magnitudes indicate greater model skill.</p>
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<p>(<b>a</b>) Map showing the two locations from SWOT nadir pass 28 (40°S and 20°N) associated with the ACFs shown in (<b>b</b>,<b>c</b>); (<b>b</b>) autocorrelation functions (with associated 95% confidence intervals as dotted lines) of the five considered SSB input-related parameters at the 40°S location; (<b>c</b>) same as in (<b>b</b>) but for the 20°N location.</p>
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<p>Maps of the decorrelation time scales: (<b>a</b>) SWH_alti; (<b>b</b>) WS_alti; (<b>c</b>) MWP_model.</p>
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<p>Decorrelation time scales of SWH_alti with respect to the mean sea-state conditions covered by the SWOT fast-sampling nadir dataset: (<b>a</b>–<b>c</b>) density plots of the decorrelation time scales with respect to (<b>a</b>) (mean SWH_alti and mean WS_alti); (<b>b</b>) (mean_SWH_alti and mean MWP_model); (<b>c</b>) (mean MWP_model and mean WS_alti). The 3D space associated with the mean sea-state conditions (SWH, WS, and MWP) was binned, and the number of occurrences (i.e., count) pertaining to a specific bin is color-coded. (<b>d</b>–<b>f</b>) Same as in (<b>a</b>–<b>c</b>), except the decorrelation time scale values (rather than the number of their occurrences) are color-coded.</p>
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<p>Average latitudinal variations (in red) and associated dispersions (in blue) of the decorrelation time scales of the five considered SSB input-related parameters. The zonal averages were computed using 3° latitudinal bands, and the displayed dispersions correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math> one standard deviation.</p>
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<p>Average latitudinal variations (in red) and associated dispersions (in blue) of the mean values of the five considered SSB input-related parameters. The zonal averages were computed using 3° latitudinal bands, and the displayed dispersions correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math> one standard deviation.</p>
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<p>Cross-correlation functions (with the associated 95% confidence intervals as dotted lines) of the three considered SSB input-related parameters combinations at the (<b>a</b>) 40°S and (<b>b</b>) 20°N locations from SWOT nadir pass 28, as shown in <a href="#remotesensing-16-04361-f002" class="html-fig">Figure 2</a>a. For each of the three (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>) combinations, the correlations associated with positive time delays inform on whether <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> is a predictor of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, whereas the correlations at negative lags indicate whether <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> is a predictor of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Maps of cross-correlation values for the three SSB input-related combinations mentioned in <a href="#remotesensing-16-04361-t002" class="html-table">Table 2</a> at time delays equal to (<b>a</b>–<b>c</b>) 0 day and (<b>d</b>–<b>f</b>) +1 day. Non-significant correlations (i.e., falling within the 95% confidence interval) were removed from all maps, leaving an empty [−0.2, 0.2] range.</p>
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<p>Maps of the cross-correlation values for the six (SSB input-related parameter, SLA) combinations mentioned in <a href="#remotesensing-16-04361-t003" class="html-table">Table 3</a> at 0 day. The maps shown in (<b>a</b>–<b>c</b>) (resp., (<b>d</b>–<b>f</b>)) are associated with combinations involving SLA_uncorr (resp., SLA_corr2D). Non-significant correlations (i.e., falling within the 95% confidence interval) were removed from all maps, leaving an empty [−0.2, 0.2] range.</p>
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<p>Comparison of the matching between the DTs of SWH_alti and SWH_model determined using the (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>h</mi> <mi>a</mi> <mi>l</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> definitions. For each plot, a linear regression is shown in red (with the associated fitted linear relationship and the Pearson correlation coefficient indicated in the top left corner) and the bisector in blue.</p>
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<p>Maps of decorrelation time scales for SWH_alti computed using the (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>h</mi> <mi>a</mi> <mi>l</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> definitions.</p>
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<p>Autocorrelation maps of WS_alti: (<b>a</b>) +1 day; (<b>b</b>) +2 days; (<b>c</b>) +3 days.</p>
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21 pages, 7993 KiB  
Article
Incorporating Non-Equlibrium Ripple Dynamics into Bed Stress Estimates Under Combined Wave and Current Forcing
by Raúl P. Flores, Sabine Rijnsburger, Saulo Meirelles, Alexander R. Horner-Devine, Alejandro J. Souza and Julie D. Pietrzak
J. Mar. Sci. Eng. 2024, 12(12), 2116; https://doi.org/10.3390/jmse12122116 - 21 Nov 2024
Viewed by 259
Abstract
We present direct measurements of seafloor ripple dimensions, near-bed mean flow Reynolds stresses and near-bed turbulent sediment fluxes on a sandy inner shelf subjected to strong wave and tidal current forcing. The measurements of ripple dimensions (height, wavelength) and Reynolds stresses are used [...] Read more.
We present direct measurements of seafloor ripple dimensions, near-bed mean flow Reynolds stresses and near-bed turbulent sediment fluxes on a sandy inner shelf subjected to strong wave and tidal current forcing. The measurements of ripple dimensions (height, wavelength) and Reynolds stresses are used to evaluate the performance of a methodology for the incorporation of non-equilibrium ripple dynamics into the calculations of the drag exerted by the bed on the overlying flow (i.e., the bed stress) using a boundary layer model for wave–current interaction. The methodology is based on the simultaneous use of existing models for the time-dependent evolution of ripple geometry and for the wave–current boundary layer that enable a continuous feedback between bottom drag and small-scale seabed morphology, which determines seabed roughness. The model-data comparison shows good agreement between modeled and measured bed stresses and bedform dimensions. Moreover, the proposed methodology is shown to give better results than combining the wave–current interaction model and standard equilibrium ripple predictors, both in terms of bed stresses and ripple dimensions. The near-bed turbulent vertical sediment fluxes show good correlation with the combined wave–current stresses and are used as a proxy for the resuspension of fine sediments (d < 64 μm) from the sandy seabed matrix. Implications for the modeling of the resuspension processes and erosional fluxes are discussed in light of our findings. Full article
(This article belongs to the Section Coastal Engineering)
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<p>Field site, instrumentation and grain size. (<b>a</b>) Bathymetry. The red square indicates the location of the 12 m site. (<b>b</b>) Photo of the benthic frame deployed in the 12 m site. (<b>c</b>) Volumetric grain size distribution at the 12 m site.</p>
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<p>Experimental conditions. (<b>a</b>) Wind speed. (<b>b</b>) Tidal elevation. (<b>c</b>) Near-bottom currents at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math> mab. (<b>d</b>) Significant wave height. (<b>e</b>) Wave period.</p>
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<p>Bedforms and wave forcing. (<b>a</b>) Bedform height. (<b>b</b>) Bedform wavelength. (<b>c</b>) Bedform steepness. (<b>d</b>) Representative bottom wave orbital velocity. (<b>e</b>) Representative wave period. (<b>f</b>–<b>j</b>) Seafloor images corresponding to times indicated in panel (<b>a</b>).</p>
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<p>Bedform predictors. (<b>a</b>) Bedform height. Measured data (black line), GM82 (blue line), WH94 (green line) and PG09 (red line) predictors. (<b>b</b>) Bedform wavelength. Measured data (black line), GM82 (blue line), WH94 (green line) and PG09 (red line) predictors.</p>
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<p>Friction velocity predictions versus measured data. (<b>a</b>) Bottom roughness derived from measured ripple dimensions. (<b>b</b>) Bottom roughness derived from median grain size, <math display="inline"><semantics> <msub> <mi>D</mi> <mn>50</mn> </msub> </semantics></math>. In panels (<b>a</b>,<b>b</b>), the gray dots correspond to the raw data, and black squares correspond to the binned-averaged values. The dashed red line corresponds to the 1:1 line.</p>
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<p>Friction velocity predictions versus measured data. (<b>a</b>) Modeled current friction velocity using the GM82 ripple dimension predictions and the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] bottom boundary layer model versus the measured wave-filtered current friction velocity. (<b>b</b>) Modeled current friction velocity using the WH94 ripple dimension predictions and the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] bottom boundary layer model versus the measured wave-filtered current friction velocity. (<b>c</b>) Modeled current friction velocity using the PG09 ripple dimension predictions and the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] bottom boundary layer model versus the measured wave-filtered current friction velocity. In all panels, the gray dots correspond to the raw data, and the black squares represent the binned-averaged values. The dashed red line corresponds to the 1:1 line.</p>
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<p>(<b>a</b>) Measured (black line) and predicted (red line) bedform height. (<b>b</b>) Measured (black line) and predicted (red line) bedform wavelength. (<b>c</b>) Modeled current fiction velocity using the Soulsby et al. [<a href="#B13-jmse-12-02116" class="html-bibr">13</a>] ripple evolution model and Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] versus measured current friction velocity. In panel (<b>c</b>), the gray dots represent the raw data, and the black squares represent the binned-averaged data, the dashed red line corresponds to the 1:1 line.</p>
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<p>(<b>a</b>) Measured bedform height (black line), predicted the bedform height using the GM82 predictor (as in <a href="#jmse-12-02116-f002" class="html-fig">Figure 2</a>, red line) and modeled bedform height using the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] boundary layer model in combination with the Soulsby et al. [<a href="#B13-jmse-12-02116" class="html-bibr">13</a>] model, using GM82 to estimate equilibrium bedform dimensions (blue line). (<b>b</b>) Measured bedform wavelength (black line), predicted the bedform wavelength using the GM82 predictor (as in <a href="#jmse-12-02116-f002" class="html-fig">Figure 2</a>, red line) and modeled bedform wavelength using the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] boundary layer model in combination with the Soulsby et al. [<a href="#B13-jmse-12-02116" class="html-bibr">13</a>] model, with the GM82 to estimate equilibrium bedform dimensions (blue line). (<b>c</b>) Modeled current fiction velocity using the Soulsby et al. [<a href="#B13-jmse-12-02116" class="html-bibr">13</a>] ripple evolution model and Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] (with GM82 for the equilibrium predictor) versus measured current friction velocity. In panel (<b>c</b>), the gray dots represent the raw data, and the black squares represent the binned-averaged data. The dashed red line represents the 1:1 line.</p>
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<p>Bed stresses, turbulent sediment fluxes and suspended sediment concentration. (<b>a</b>) Wave (black line) and current (red line) stresses. (<b>b</b>) Combined wave–current stresses. (<b>c</b>) Vertical turbulent sediment flux. Gray line corresponds to the raw data, and black line corresponds to a 2 h median filter. (<b>d</b>) Suspended sediment concentrations at 0.25 mab (black line) and 0.75 mab (red line).</p>
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<p>Non-equilibrium ripple dynamics. (<b>a</b>) Wave (blue line) and current (red line) Shields parameters. (<b>b</b>) Measured ripple height (black line) and modeled ripple height (blue line). (<b>c</b>) time rate of change in bedform height. Gray line represents the raw data, and red line corresponds to a 2-hr median filter.</p>
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<p>Combined wave–current bed stress versus turbulent sediment flux, for the cases where bed stress was estimated using the Grant and Madsen [<a href="#B4-jmse-12-02116" class="html-bibr">4</a>] model and estimates of bottom roughness obtained from measured bedform dimensions (black squares), <math display="inline"><semantics> <msub> <mi>d</mi> <mn>50</mn> </msub> </semantics></math> (red squares) and the Soulsby et al. [<a href="#B13-jmse-12-02116" class="html-bibr">13</a>] model. These values correspond to bin averages of the data. Dotted lines correspond to the fits to Equation (<a href="#FD38-jmse-12-02116" class="html-disp-formula">38</a>). Dashed lines correspond to the 25th and 75th percentiles for the case where measured bedforms were used in the computation of bed stresses (black squares).</p>
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24 pages, 9829 KiB  
Article
Multi-Camera Calibration Using Far-Range Dual-LED Wand and Near-Range Chessboard Fused in Bundle Adjustment
by Prayook Jatesiktat, Guan Ming Lim and Wei Tech Ang
Sensors 2024, 24(23), 7416; https://doi.org/10.3390/s24237416 - 21 Nov 2024
Viewed by 356
Abstract
This paper presents a calibration approach for multiple synchronized global-shutter RGB cameras surrounding a large capture volume for 3D application. The calibration approach uses an active wand with two LED-embedded markers waved manually within the target capture volume. Data from the waving wand [...] Read more.
This paper presents a calibration approach for multiple synchronized global-shutter RGB cameras surrounding a large capture volume for 3D application. The calibration approach uses an active wand with two LED-embedded markers waved manually within the target capture volume. Data from the waving wand are combined with chessboard images taken at close range during each camera’s intrinsic calibration, optimizing camera parameters via our proposed bundle adjustment method. These additional constraints from the chessboard are developed to overcome an overfitting issue of wand-based calibration discovered by benchmarking its 3D triangulation accuracy in an independent record against a ground-truth trajectory and not on the record used for calibration itself. Addressing this overfitting issue in bundle adjustment leads to significant improvements in both 3D accuracy and result consistency. As a by-product of this development, a new benchmarking workflow and our calibration dataset that reflects realistic 3D accuracy are proposed and made publicly available to allow for fair comparisons of various calibration methods in the future. Additionally, our experiment highlights a significant benefit of a ray distance-based (RDB) triangulation formula over the popular direct linear transformation (DLT) method. Full article
(This article belongs to the Special Issue Sensing and Processing for 3D Computer Vision: 3rd Edition)
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<p>A schematic diagram of the proposed calibration method. Each orange box represents an operation that is performed independently on each camera. Each green box represents an operation that is performed together across all the cameras. The arrows represent the way that information flows between operations.</p>
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<p>An example of a set of 30 chessboard images from a camera used in both intrinsic initialization and subsequent bundle adjustment (FusedBA only). In this step, the collection of chessboard corners can easily spread across the entire image area from a near range, as this step is performed exclusively on each camera. The user does not need to try making the chessboard visible to be registered by more than one camera.</p>
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<p>Calibration wand with red and green active markers.</p>
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<p>An example of the waving pattern using the active wand. The red and green points are the red and green active marker positions from all the time frames, respectively. The six blue points are the detected floor-touching points that are used to fit the floor plane (gray rectangle).</p>
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<p>An example plot of the number of bright pixels in a cluster (<span class="html-italic">g</span>) against the inverse of the squared distance from the triangulated position to the camera (<math display="inline"><semantics> <mrow> <mn>1</mn> <mo>/</mo> <msup> <mi>d</mi> <mn>2</mn> </msup> </mrow> </semantics></math>). Without the noises under the lower boundary, the distribution is fairly symmetrical on both sides of the fitted linear regression model.</p>
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<p>Structure of a Jacobian matrix <span class="html-italic">J</span> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>8</mn> <mo>,</mo> <mi>B</mi> <mo>=</mo> <mn>9</mn> <mo>,</mo> <mi>W</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, and each camera has 3 chessboard images. Each column represents an unknown parameter in <math display="inline"><semantics> <mover accent="true"> <mi>θ</mi> <mo>→</mo> </mover> </semantics></math>, and each row represents a residue in the residue vector (<math display="inline"><semantics> <mover accent="true"> <mi>e</mi> <mo>→</mo> </mover> </semantics></math>). The cells that are not painted with gray are always zero. This sparse structure allows for a huge acceleration in our customized forward-mode automatic differentiation. Zoom for more details in the digital version.</p>
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<p>Structure of <math display="inline"><semantics> <mrow> <msup> <mi>J</mi> <mo>⊤</mo> </msup> <mi>J</mi> <mo>+</mo> <mi>λ</mi> <mi>I</mi> </mrow> </semantics></math> when <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>=</mo> <mn>3</mn> <mo>,</mo> <mi>M</mi> <mo>=</mo> <mn>8</mn> <mo>,</mo> <mi>B</mi> <mo>=</mo> <mn>9</mn> <mo>,</mo> <mi>W</mi> <mo>=</mo> <mn>6</mn> </mrow> </semantics></math>, and each camera has 3 chessboard images. The cells that are not painted with gray are always zero. This matrix is split into matrices <math display="inline"><semantics> <mrow> <mi mathvariant="bold">A</mi> <mo>,</mo> <mi mathvariant="bold">B</mi> <mo>,</mo> <msup> <mrow> <mi mathvariant="bold">B</mi> </mrow> <mo>⊤</mo> </msup> </mrow> </semantics></math>, and <math display="inline"><semantics> <mi mathvariant="bold">D</mi> </semantics></math> according to Equation (<a href="#FD8-sensors-24-07416" class="html-disp-formula">8</a>). Zoom for more details in the digital version.</p>
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<p>The optional L frame with 4 active red markers. <span class="html-italic">L</span>0 is the guide for the origin.</p>
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<p>Top view of the experimental setup showing the spatial arrangement of marker-based cameras (Arqus M12 and Miqus M3), RGB cameras, and the hexagonal target capture area in relation to the global coordinates.</p>
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<p>An RGB camera with a varifocal lens. The three LEDs are added to support mocap-assisted calibration and the benchmarking record.</p>
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<p>Three different modes of calibration.</p>
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<p>The proposed benchmarking workflow using the error between the recovered 3D trajectory in comparison with an independent ground-truth record. This benchmarking workflow is repeated 33 times with 33 different takes of video record input (i.e., 33 takes of wand waving or 33 takes of ChArUco board waving) to test the consistency of the method. Note that the data in the blue box are kept constant to ensure fairness across different calibration methods. In particular, for mocap-assisted calibration, the method also has access to the 3D trajectory of the passive marker for the corresponding take, which is not included in this figure.</p>
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<p>Projections of a camera’s position and frustum from an opposite camera’s perspective using parameters from different calibration methods. The result from Anipose’s calibration produces the worst alignment, as the frustum center is off relative to the camera lens. INIT also exhibits noticeable misalignment, while the rest of the methods align well.</p>
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<p>Distribution of benchmark errors from 33 rounds of calibration across different variants of bundle adjustment and the two triangulation methods.</p>
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<p>From some camera perspectives, the area of wand waving in the capture volume cannot cover the majority of the FOV. In this example, the wand is only waved in the highlighted area in the middle. As a result, the intrinsic parameters fine-tuned by the BAp15 method become overfitted to the wand data and cause the projection outside the wand-waving area to be badly distorted. However, the proposed FusedBA method can still fine tune all the intrinsic parameters without the mentioned issue.</p>
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19 pages, 8825 KiB  
Article
A Fully Integrated High Linearity CMOS Dual-Band Power Amplifier for WLAN Applications in 55-Nm CMOS
by Haoyu Shen and Bin Wu
Appl. Sci. 2024, 14(23), 10768; https://doi.org/10.3390/app142310768 - 21 Nov 2024
Viewed by 332
Abstract
This paper presents a dual-band fully integrated high linearity CMOS power amplifier (PA). The PA employs a reconfigurable transformer in the input matching network to achieve low reflection coefficient across both bands, demonstrating significant flexibility in the design of dual-band power amplifiers with [...] Read more.
This paper presents a dual-band fully integrated high linearity CMOS power amplifier (PA). The PA employs a reconfigurable transformer in the input matching network to achieve low reflection coefficient across both bands, demonstrating significant flexibility in the design of dual-band power amplifiers with high output powers. Additionally, a detailed design methodology for the dual-band matching network is introduced. By utilizing this methodology, the PA has been designed using 55 nm CMOS technology. For continuous-wave operation, the PA achieves a saturated power (Psat) of 28.03 dBm and 27.5–28.2 dBm, with power-added efficiency (PAE) of 33.2% and 24.6–31.1%, in the 2.4 GHz and 5 GHz WLAN bands, respectively. Concurrently, the PA power cells, which employ multi-gate transistor (MGTR) technology, achieve an intermodulation distortion (IMD3) of below 30 dBc at an output power of 15 dBm in both the 2.4 GHz and 5 GHz WLAN bands. The proposed PA outperforms other dual-band or multi-band PAs in terms of output power and exhibits great potential for WLAN applications. Full article
(This article belongs to the Special Issue Applications of Wireless and Mobile Communications)
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<p>Simplified schematic of output matching circuit.</p>
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<p>Simulated power contours and PAE contours in 2.4 and 5 GHz bands. (<b>a</b>) 2.4 GHz. (<b>b</b>) 5.5 GHz.</p>
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<p>Reflection coefficient contours of output matching network. (<b>a</b>) 2.45 G. (<b>b</b>) 5.5 G.</p>
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<p>Insertion loss contours of output matching network. (<b>a</b>) 2.4 G. (<b>b</b>) 5.5 G.</p>
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<p>Layout of output transformer.</p>
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<p>On-resistance and Coff of switch transistor versus size.</p>
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<p>Achieved insertion loss of the output matching network. (<b>a</b>) 2.4 GHz. (<b>b</b>) 5 GHz.</p>
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<p>Reconfigurable input matching circuit.</p>
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<p>Simplified schematic of the reconfigurable transformer.</p>
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<p>Simplified schematic of the tunable inductor.</p>
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<p>(<b>a</b>) Equivalent inductance and quality factor versus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>b</b>) Equivalent quality factor versus <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>R</mi> </mrow> <mrow> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>The layout of the reconfigurable transformer.</p>
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<p>S-parameter comparison with fixed transformer. (<b>a</b>) Same <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math>. (<b>b</b>) Larger <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>i</mi> <mi>n</mi> <mi>p</mi> </mrow> </msub> </mrow> </semantics></math> in fixed transformer.</p>
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<p>The complete schematic of the proposed power amplifier.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>g</mi> </mrow> <mrow> <mi>m</mi> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math> vs. gate voltage.</p>
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<p>Comparison of IMD3 with and without MGTR.</p>
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<p>Schematic of integrated circuits. (<b>a</b>) Common source. (<b>b</b>) Common gate.</p>
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<p>(<b>a</b>) Layout of proposed PA. (<b>b</b>) Layout of test chip.</p>
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<p>Simulated S-parameters. (<b>a</b>) 2.4 G band. (<b>b</b>) 5 G band.</p>
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<p>Simulated power gain and PAE versus output power. At (<b>a</b>) 2.4 and (<b>b</b>) 5.5 GHz modes.</p>
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<p>Simulated <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi>s</mi> <mi>a</mi> <mi>t</mi> </mrow> </msub> </mrow> </semantics></math> and PAE at different frequencies.</p>
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<p>Simulated IMD3 versus output. At (<b>a</b>) 2.4 and (<b>b</b>) 5.5 GHz modes.</p>
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13 pages, 46604 KiB  
Article
Human Activity Recognition Based on Point Clouds from Millimeter-Wave Radar
by Seungchan Lim, Chaewoon Park, Seongjoo Lee and Yunho Jung
Appl. Sci. 2024, 14(22), 10764; https://doi.org/10.3390/app142210764 - 20 Nov 2024
Viewed by 307
Abstract
Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, [...] Read more.
Human activity recognition (HAR) technology is related to human safety and convenience, making it crucial for it to infer human activity accurately. Furthermore, it must consume low power at all times when detecting human activity and be inexpensive to operate. For this purpose, a low-power and lightweight design of the HAR system is essential. In this paper, we propose a low-power and lightweight HAR system using point-cloud data collected by radar. The proposed HAR system uses a pillar feature encoder that converts 3D point-cloud data into a 2D image and a classification network based on depth-wise separable convolution for lightweighting. The proposed classification network achieved an accuracy of 95.54%, with 25.77 M multiply–accumulate operations and 22.28 K network parameters implemented in a 32 bit floating-point format. This network achieved 94.79% accuracy with 4 bit quantization, which reduced memory usage to 12.5% compared to existing 32 bit format networks. In addition, we implemented a lightweight HAR system optimized for low-power design on a heterogeneous computing platform, a Zynq UltraScale+ ZCU104 device, through hardware–software implementation. It took 2.43 ms of execution time to perform one frame of HAR on the device and the system consumed 3.479 W of power when running. Full article
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<p>Data collection setup.</p>
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<p>Configuration of dataset classes and their corresponding point clouds: (<b>a</b>) Stretching; (<b>b</b>) Standing; (<b>c</b>) Taking medicine; (<b>d</b>) Squatting; (<b>e</b>) Sitting chair; (<b>f</b>) Reading news; (<b>g</b>) Sitting floor; (<b>h</b>) Picking; (<b>i</b>) Crawl; (<b>j</b>) Lying wave hands; (<b>k</b>) Lying.</p>
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<p>Overview of the proposed HAR system.</p>
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<p>Proposed classification network.</p>
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<p>Training and test loss curve and accuracy curve: (<b>a</b>) Training and test loss curve; (<b>b</b>) Training and test accuracy curve.</p>
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<p>Confusion matrix.</p>
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<p>Environment used for FPGA implementation and verification.</p>
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11 pages, 8301 KiB  
Article
A 2-D Fully Polarized Van Atta Array Based on Wide-Beam Tri-Polarized Antennas
by Jicheng Pan, Lei Chen, Shuangdi Zhao and Tianling Zhang
Micromachines 2024, 15(11), 1400; https://doi.org/10.3390/mi15111400 - 20 Nov 2024
Viewed by 286
Abstract
This paper proposes a 2-D fully polarized Van Atta array, which consists of four tri-polarized antenna elements. The tri-polarized antenna element comprises a monopole antenna and a low-profile microstrip antenna that widens the beam by folding four electric walls. This configuration enables the [...] Read more.
This paper proposes a 2-D fully polarized Van Atta array, which consists of four tri-polarized antenna elements. The tri-polarized antenna element comprises a monopole antenna and a low-profile microstrip antenna that widens the beam by folding four electric walls. This configuration enables the Van Atta arrays to receive and transmit arbitrarily polarized incident waves over a wider range. The measurement results indicate that the proposed Van Atta array exhibits a −5 dB radar cross-section (RCS) greater than 95° when TE-polarized waves are incident and greater than 134° when TM-polarized waves are incident, significantly surpassing the 2-D dual-polarized array. Full article
(This article belongs to the Special Issue Microwave Passive Components, 2nd Edition)
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<p>Schematic diagram of the antenna loaded with electric walls: (<b>a</b>) current relationship between vertical electric walls and main patch; (<b>b</b>) electric wall loading method; (<b>c</b>) principle of beam spreading.</p>
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<p>Antenna loaded with parasitic patches: (<b>a</b>) 3D view; (<b>b</b>) top view.</p>
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<p>Current flow on the surface of the antenna loaded with folded electric walls: (<b>a</b>) 3D view; (<b>b</b>) top view; (<b>c</b>) side view.</p>
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<p>Comparison of simulated results of antenna pattern with or without a folding wall: (<b>a</b>) E-plane pattern; (<b>b</b>) H-plane pattern.</p>
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<p>Geometry of tri-polarized antenna: (<b>a</b>) 3D view; (<b>b</b>) top view; (<b>c</b>) side view (L1 = 19 mm, L2 = 7.8 mm, L3 = 6 mm, r1 = 2 mm, r2 = 0.51 mm, r3 = 0.5 mm, dv = 2 mm, S = 0.5 mm, W1 = 2.5 mm, h = 1.5 mm, hm = 7.5 mm, Lf = 3.5 mm).</p>
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<p>Simulated and measured S-parameters of tri-polarized antennas: (<b>a</b>) reflection coefficients; (<b>b</b>) isolations.</p>
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<p>Simulated and measured radiation patterns at 9.6 GHz of the proposed antenna.</p>
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<p>Prototype of the antenna array: (<b>a</b>) 3D view; (<b>b</b>) top view.</p>
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<p>Reflection coefficients of antenna array elements: (<b>a</b>) Ant. 1; (<b>b</b>) Ant. 2; (<b>c</b>) Ant. 3; (<b>d</b>) Ant. 4.</p>
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<p>Van Atta array: (<b>a</b>) prototype of the Van Atta array; (<b>b</b>) connections for fully polarized planar Van Atta array.</p>
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<p>Schematic of the measurement system for the monostatic RCS.</p>
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<p>Measured monostatic RCS with TM incident wave: (<b>a</b>) tri-polarized antenna; (<b>b</b>) dual-polarized antenna.</p>
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<p>Measured monostatic RCS with TE incident wave: (<b>a</b>) tri-polarized antenna; (<b>b</b>) dual-polarized antenna.</p>
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12 pages, 2095 KiB  
Article
Phase Portraits and Abundant Soliton Solutions of a Hirota Equation with Higher-Order Dispersion
by Fengxia Wu, Nauman Raza, Younes Chahlaoui, Asma Rashid Butt and Haci Mehmet Baskonus
Symmetry 2024, 16(11), 1554; https://doi.org/10.3390/sym16111554 - 20 Nov 2024
Viewed by 303
Abstract
The Hirota equation, an advanced variant of the nonlinear Schrödinger equation with cubic nonlinearity, incorporates time-delay adjustments and higher-order dispersion terms, offering an enhanced approximation for wave propagation in optical fibers and oceanic systems. By utilizing the traveling wave transformation generated from Lie [...] Read more.
The Hirota equation, an advanced variant of the nonlinear Schrödinger equation with cubic nonlinearity, incorporates time-delay adjustments and higher-order dispersion terms, offering an enhanced approximation for wave propagation in optical fibers and oceanic systems. By utilizing the traveling wave transformation generated from Lie point symmetry analysis with the combination of generalized exponential differential rational function and modified Bernoulli sub-ODE techniques, several traveling wave solutions, such as periodic, singular-periodic, and kink solitons, emerge. To examine the solutions visually, parametric values are adjusted to create 3D, contour, and 2D illustrations. Additionally, the dynamic properties of the model are explored through bifurcation analysis. The exact results demonstrate that both techniques are practical and robust. Full article
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<p>Qualitative analysis.</p>
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<p>The 3D (<b>a</b>), contour (<b>b</b>), and 2D (<b>c</b>) plots of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> [Equation (<a href="#FD26-symmetry-16-01554" class="html-disp-formula">26</a>)].</p>
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<p>The 3D (<b>a</b>), contour (<b>b</b>), and 2D (<b>c</b>) plots of <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>b</mi> <mi>s</mi> </mrow> </semantics></math> [Equation (<a href="#FD26-symmetry-16-01554" class="html-disp-formula">26</a>)].</p>
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<p>The 3D (<b>a</b>), contour (<b>b</b>), and 2D (<b>c</b>) plots of <math display="inline"><semantics> <mrow> <mi>R</mi> <mi>e</mi> </mrow> </semantics></math> [Equation (<a href="#FD45-symmetry-16-01554" class="html-disp-formula">45</a>)].</p>
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<p>The 3D (<b>a</b>), contour (<b>b</b>), and 2D (<b>c</b>) plots of <math display="inline"><semantics> <mrow> <mi>A</mi> <mi>b</mi> <mi>s</mi> </mrow> </semantics></math> [Equation (<a href="#FD45-symmetry-16-01554" class="html-disp-formula">45</a>)].</p>
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19 pages, 7926 KiB  
Article
Preparation and Electromagnetic-Wave-Absorption Properties of Cement-Based Materials with Graphite Tailings and Steel Fiber
by Qian Wang, Taibing Wei, Rong Wang, Deliang Zhu, Feiyu Liu and Huawei Li
Buildings 2024, 14(11), 3685; https://doi.org/10.3390/buildings14113685 - 19 Nov 2024
Viewed by 350
Abstract
The development of functional building materials that can absorb electromagnetic radiation is important for preventing and controlling electromagnetic pollution in urban areas. In this study, cement-based electromagnetic wave (EMW)-absorbing materials were created using graphite tailings (GTs) as a conductive admixture and steel fiber [...] Read more.
The development of functional building materials that can absorb electromagnetic radiation is important for preventing and controlling electromagnetic pollution in urban areas. In this study, cement-based electromagnetic wave (EMW)-absorbing materials were created using graphite tailings (GTs) as a conductive admixture and steel fiber (SF) as an EMW absorber, which resulted in materials with a wide effective bandwidth and high reflection loss (RL). In particular, a GT–cement matrix with excellent mechanical and electrical properties was obtained. This study explored the influence mechanism of the SF content on the mechanical, electrical, and EMW-absorption properties of cement-based materials under the synergistic effect of GTs and SF. Findings demonstrate that the combination of GTs and SF notably improved the electrical and EMW-absorption characteristics of the cement-based materials. Optimal EMW-absorption properties were observed for a combination of 30% GTs and 6% SF. A developed cement-based EMW-absorbing material with a thickness of 20 mm displayed a minimum RL of −25.78 dB in the frequency range of 0.1–5 GHz, with an effective bandwidth of 0.953 GHz. Thus, the cement-based composite materials developed in this study have excellent EMW-absorption performance, which provides an effective strategy for preventing and controlling electromagnetic pollution in urban spaces. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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<p>Particle-size distribution of the GTs.</p>
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<p>Morphology of the GTs with different magnifications (×500 and ×5000).</p>
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<p>Strength and resistivity of the cement-based materials: (<b>a</b>) compressive strength, (<b>b</b>) flexural strength, and (<b>c</b>) resistivity.</p>
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<p>XRD patterns of the cement-based materials at (<b>a</b>) 3 and (<b>b</b>) 28 d.</p>
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<p>SEM images of the cement-based materials cured for 3 d: (<b>a</b>) Blank, (<b>b</b>) G10, (<b>c</b>) G20, (<b>d</b>) G30, and (<b>e</b>) G40.</p>
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<p>SEM images of the cement-based materials cured for 28 d: (<b>a</b>) Blank, (<b>b</b>) G10, (<b>c</b>) G20, (<b>d</b>) G30, and (<b>e</b>) G40.</p>
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<p>EDS results of the cement-based materials.</p>
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<p>Strength and resistivity of the cement-based materials containing GTs and SF: (<b>a</b>) compressive strength, (<b>b</b>) flexural strength, and (<b>c</b>) resistivity.</p>
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<p>Electromagnetic parameters of the cement-based materials with different SF contents: (<b>a</b>) <math display="inline"><semantics> <msup> <mi>ε</mi> <mo>′</mo> </msup> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msup> <mi>ε</mi> <mo>″</mo> </msup> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <msup> <mi>μ</mi> <mo>′</mo> </msup> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <msup> <mi>μ</mi> <mo>″</mo> </msup> </semantics></math>.</p>
Full article ">Figure 9 Cont.
<p>Electromagnetic parameters of the cement-based materials with different SF contents: (<b>a</b>) <math display="inline"><semantics> <msup> <mi>ε</mi> <mo>′</mo> </msup> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <msup> <mi>ε</mi> <mo>″</mo> </msup> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <msup> <mi>μ</mi> <mo>′</mo> </msup> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <msup> <mi>μ</mi> <mo>″</mo> </msup> </semantics></math>.</p>
Full article ">Figure 10
<p>Attenuation coefficient of the cement-based materials with different SF contents.</p>
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<p>Normalized impedance matching values of the cement-based materials with different SF contents.</p>
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<p>EMW-absorption performance of the cement-based materials with different SF contents: (<b>a</b>) RL and (<b>b</b>) minimum RL and effective bandwidth.</p>
Full article ">Figure 13
<p>Schematic of the transmission loss mechanisms of the cement-based materials containing GTs and SF.</p>
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