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16 pages, 2963 KiB  
Article
A Mechanism for Slow Electrostatic Solitary Waves in the Earth’s Plasma Sheet
by Gurbax Singh Lakhina and Satyavir Singh
Plasma 2024, 7(4), 904-919; https://doi.org/10.3390/plasma7040050 - 27 Nov 2024
Viewed by 905
Abstract
An analysis of the Magnetospheric Multiscale (MMS) spacecraft data shows the presence of slow electrostatic solitary waves (SESWs) in the Earth’s plasma sheet, which have been interpreted as slow electron holes (SEHs). An alternative mechanism based on slow ion-acoustic solitons is proposed for [...] Read more.
An analysis of the Magnetospheric Multiscale (MMS) spacecraft data shows the presence of slow electrostatic solitary waves (SESWs) in the Earth’s plasma sheet, which have been interpreted as slow electron holes (SEHs). An alternative mechanism based on slow ion-acoustic solitons is proposed for these SESWs. The SESWs are observed in the region where double humped ion distributions and hot electrons co-exist. Our theoretical model considers the plasma in the SESW region to consist of hot electrons with a vortex distribution, core Maxwellian protons drifting parallel to the magnetic field, B and beam protons drifting anti-parallel to B. Parallel propagating nonlinear ion-acoustic waves are studied using the Sagdeev pseudopotential technique. The analysis yields four types of modes, namely, two slow ion-acoustic (SIA1 and SIA2) solitons and two fast ion-acoustic (FIA1 and FIA2) solitons. All solitons have positive potentials. Except the FIA1 solitons which propagate parallel to B; the other three types propagate anti-parallel to B. Good agreement is found between the amplitudes of electrostatic potential, the electric field, the widths and speed of SIA1 and SIA2 solitons, and the observed properties of SESWs by the MMS spacecraft. Full article
Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Variation in <math display="inline"><semantics> <mrow> <mi>F</mi> <mo>(</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math>, the second derivative of the Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> at <math display="inline"><semantics> <mrow> <mi>ϕ</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, versus the Mach number <span class="html-italic">M</span> for the normalized plasma sheet plasma parameters: <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>c</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0.75</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>n</mi> <mrow> <mi>b</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>0.25</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>c</mi> </msub> </semantics></math> = 3.4, <math display="inline"><semantics> <msub> <mi>σ</mi> <mi>b</mi> </msub> </semantics></math> = 1.0, <math display="inline"><semantics> <msub> <mi>v</mi> <mrow> <mi>c</mi> <mn>0</mn> </mrow> </msub> </semantics></math> = 0.48, and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mrow> <mi>b</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>5.09</mn> </mrow> </semantics></math>. R1, R2, R3, and R4 are the four real roots, two are fast ion-acoustic (R1 and R4) modes, and two are the slow ion-acoustic (R2 and R3) modes. The location of the minimum of the proton velocity distribution function (VDF) is shown as a short vertical red line on the <span class="html-italic">M</span>-axis. Slow ion-acoustic (R2 and R3) modes lie close to the red line.</p>
Full article ">Figure 2
<p>Properties of slow ion-acoustic (SIA1) solitons propagating anti-parallel to <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> for plasma sheet plasma parameters in <a href="#plasma-07-00050-f001" class="html-fig">Figure 1</a> and for <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>. Panel (<b>a</b>) shows variation in Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> versus electrostatic potential, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>, for Mach numbers <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>2.87</mn> <mo>,</mo> <mo>−</mo> <mn>2.88</mn> <mo>,</mo> <mo>−</mo> <mn>2.9</mn> <mo>,</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>2.91</mn> </mrow> </semantics></math> for curves 1, 2, 3, and 4, respectively. Curve 4 shows that SIA1 solitons do not exist for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>2.91</mn> </mrow> </semantics></math>. Panel (<b>b</b>) shows SIA1 soliton electric potential (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>2.87</mn> <mo>,</mo> <mo>−</mo> <mn>2.88</mn> </mrow> </semantics></math>, and −2.9 for curves 1, 2, and 3, respectively. Panel (<b>c</b>) shows SIA1 soliton electric field (<span class="html-italic">E</span>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>2.87</mn> </mrow> </semantics></math>, −2.88, and −2.9 for curves 1, 2, and 3, respectively.</p>
Full article ">Figure 3
<p>Properties of slow ion-acoustic solitons (SIA2) propagating anti-parallel to <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> for plasma sheet plasma parameters in <a href="#plasma-07-00050-f001" class="html-fig">Figure 1</a> and for <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>. Panel (<b>a</b>) shows variation in Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> versus electrostatic potential, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>, for Mach numbers <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>3.26</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>3.25</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>3.24</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>3.235</mn> </mrow> </semantics></math> for curves 1, 2, 3, and 4, respectively. Curve 4 shows that SIA2 solitons do not exist for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>3.235</mn> </mrow> </semantics></math> or below. Panel (<b>b</b>) shows SIA2 soliton electric potential (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>3.26</mn> <mo>,</mo> <mo>−</mo> <mn>3.25</mn> </mrow> </semantics></math>, and −3.24 for curves 1, 2, and 3, respectively. Panel (<b>c</b>) shows SIA2 soliton electric field (<span class="html-italic">E</span>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>3.26</mn> <mo>, </mo> <mo>−</mo> <mn>3.25</mn> </mrow> </semantics></math>, and −3.24 for curves 1, 2, and 3, respectively.</p>
Full article ">Figure 4
<p>Properties of fast ion-acoustic solitons (FIA1) propagating parallel to <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> for plasma sheet plasma parameters of <a href="#plasma-07-00050-f001" class="html-fig">Figure 1</a> and for <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>. Panel (<b>a</b>) shows variation in Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> versus electrostatic potential, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>, for Mach numbers <span class="html-italic">M</span> = 3.8, 3.82, 3.83, and 3.84 for curves 1, 2, 3, and 4, respectively. Curve 4 shows that solitons do not exist for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>≥</mo> </mrow> </semantics></math> 3.84. Panel (<b>b</b>) shows soliton electric potential (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) profiles for <span class="html-italic">M</span> = 3.8, 3.82, and 3.83 for curves 1, 2, and 3, respectively. Panel (<b>c</b>) shows FIA1 soliton electric field (<span class="html-italic">E</span>) profiles for <span class="html-italic">M</span> = 3.8, 3.82, and 3.83 for curves 1, 2, and 3, respectively.</p>
Full article ">Figure 5
<p>Properties of fast ion-acoustic (FIA2) solitons propagating anti-parallel to <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> for plasma sheet plasma parameters given in <a href="#plasma-07-00050-f001" class="html-fig">Figure 1</a> and for <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math>. Panel (<b>a</b>) shows variation in Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> versus electrostatic potential, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>, for Mach numbers <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>6.9</mn> <mo>,</mo> <mo>−</mo> <mn>6.91</mn> <mo>,</mo> <mo>−</mo> <mn>6.915</mn> </mrow> </semantics></math>, and −6.92 for curves 1, 2, 3, and 4, respectively. Curve 4 shows that FIA2 solitons do not exist for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>6.92</mn> </mrow> </semantics></math> and above. Panel (<b>b</b>) shows FIA2 soliton electric potential (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>6.9</mn> <mo>,</mo> <mo>−</mo> <mn>6.91</mn> </mrow> </semantics></math>, and −6.915 for curves 1, 2, and 3, respectively. Panel (<b>c</b>) shows FIA2 soliton electric field (<span class="html-italic">E</span>) profiles for <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>6.9</mn> <mo>,</mo> <mo>−</mo> <mn>6.91</mn> </mrow> </semantics></math>, and −6.9153 for curves 1, 2, and 3, respectively.</p>
Full article ">Figure 6
<p>Properties of slow ion-acoustic (SIA1) solitons propagating anti-parallel to <math display="inline"><semantics> <mi mathvariant="bold">B</mi> </semantics></math> for plasma sheet plasma parameters in <a href="#plasma-07-00050-f001" class="html-fig">Figure 1</a> and for Mach number <math display="inline"><semantics> <mrow> <mi>M</mi> <mo>=</mo> <mo>−</mo> <mn>2.89</mn> </mrow> </semantics></math>. Panel (<b>a</b>) shows variation in Sagdeev pseudopotential <math display="inline"><semantics> <mrow> <mi>S</mi> <mo>(</mo> <mi>ϕ</mi> <mo>,</mo> <mi>M</mi> <mo>)</mo> </mrow> </semantics></math> versus electrostatic potential, <math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>, for trapping parameters <math display="inline"><semantics> <mi>β</mi> </semantics></math> = 0.01, 0.1, 0.5, and 1.0 for curves 1, 2, 3, and 4, respectively. Panel (<b>b</b>,<b>c</b>) show SIA1 soliton electric potential (<math display="inline"><semantics> <mi>ϕ</mi> </semantics></math>) and electric field (<span class="html-italic">E</span>) profiles for the same values of <math display="inline"><semantics> <mi>β</mi> </semantics></math> as in Panel (<b>a</b>) for curves 1, 2, 3, and 4, respectively.</p>
Full article ">
15 pages, 823 KiB  
Article
Acoustic Drift: Generating Helicity and Transferring Energy
by Andrey Morgulis
Axioms 2024, 13(11), 767; https://doi.org/10.3390/axioms13110767 - 4 Nov 2024
Viewed by 627
Abstract
This article studies the general properties of the Stokes drift field. This name is commonly used for the correction added to the mean Eulerian velocity for describing the averaged transport of the material particles by the oscillating fluid flows. Stokes drift is widely [...] Read more.
This article studies the general properties of the Stokes drift field. This name is commonly used for the correction added to the mean Eulerian velocity for describing the averaged transport of the material particles by the oscillating fluid flows. Stokes drift is widely known mainly in connection with another feature of oscillating flows known as steady streaming, which has been and remains the focus of a multitude of studies. However, almost nothing is known about Stokes drift in general, e.g., about its energy or helicity (Hopf’s invariant). We address these quantities for acoustic drift driven by simple sound waves with finite discrete Fourier spectra. The results discover that the mean drift energy is partly localized on a certain resonant set, which we have described explicitly. Moreover, the mean drift helicity turns out to be completely localized on the same set. We also present several simple examples to discover the effect of the power spectrum and positioning of the spectral atoms. It is revealed that tuning them can drastically change both resonant and non-resonant energies, zero the helicity, or even increase it unboundedly. Full article
(This article belongs to the Special Issue Fluid Dynamics: Mathematics and Numerical Experiment)
Show Figures

Figure 1

Figure 1
<p>This figure illustrates the drift energy that arises from configuration (<a href="#FD26-axioms-13-00767" class="html-disp-formula">26</a>)–(<a href="#FD29-axioms-13-00767" class="html-disp-formula">29</a>) when the number of atoms tends to <span class="html-italic">∞</span> along a sequence of the doubled primes. The distribution of power spectrum over the atoms is the normal periodic one (right panel) or the uniform one (left panel). The graphs depict the total mean drift energy and its resonant or non-resonant fractions vs. the geometric parameter <math display="inline"><semantics> <mi>β</mi> </semantics></math>. Recall that the drift includes no uniform component for the uniform power spectrum distribution (see Remark 1).</p>
Full article ">Figure 2
<p>This figure displays samples of 20 level sets of the stream function (<a href="#FD59-axioms-13-00767" class="html-disp-formula">59</a>) for the drift field emergent from 22 atoms configuration defined by equalities (<a href="#FD26-axioms-13-00767" class="html-disp-formula">26</a>)–(<a href="#FD29-axioms-13-00767" class="html-disp-formula">29</a>) for equal amplitudes, <math display="inline"><semantics> <msub> <mi>φ</mi> <mi>j</mi> </msub> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mi>π</mi> <mo>/</mo> <mn>10</mn> </mrow> </semantics></math>. Upper-left, upper-right, lower-left, and lower-right frames display the samples taken for <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0</mn> <mo>,</mo> <mn>1.69</mn> <mo>,</mo> <mn>3.38</mn> <mo>,</mo> <mn>5.08</mn> </mrow> </semantics></math>, correspondingly. So, the sampling interval is approximately eight times smaller then the period. The next semi-period demonstrates the reverse changes.</p>
Full article ">
21 pages, 6832 KiB  
Article
A SAW Wireless Passive Sensing System for Rotating Metal Parts
by Yue Zhou, Jing Ding, Bingji Wang, Feng Gao, Shurong Dong and Hao Jin
Sensors 2024, 24(20), 6703; https://doi.org/10.3390/s24206703 - 18 Oct 2024
Viewed by 733
Abstract
Passive wireless surface acoustic wave (SAW) sensors are very useful for on-site monitoring of the working status of machines in complex environments, such as high-temperature rotating objects. For rotating parts, it is difficult to realize real-time and continuous monitoring because of the unstable [...] Read more.
Passive wireless surface acoustic wave (SAW) sensors are very useful for on-site monitoring of the working status of machines in complex environments, such as high-temperature rotating objects. For rotating parts, it is difficult to realize real-time and continuous monitoring because of the unstable sensing signal caused by the continuous change of the relative position of the rotating part to the sensor and shielding of the signal. In our SAW sensing system, we propose a loop antenna integrated with the rotating part to obtain a stable sensing signal owing to its omnidirectional radiation pattern. Methodologies for determining the antenna dimension, system operating frequency, and procedures for designing a SAW sensor tag are discussed in this paper. By fully utilizing the influence of metal rotor on antenna performance, the antenna needs no impedance matching elements while it provides sufficient gain, which equips the antenna with nearly zero temperature drift at a wide temperature-sensing range. Experimental verification results show that this sensing system can greatly improve the stability of the sensing signal significantly and can achieve a temperature sensing accuracy of ~1 °C at different rotational speeds, demonstrated by the feasibility of the loop antenna for monitoring the working status of rotating metal parts. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) System configuration diagram: installation of the loop antenna, SAW sensor tags, and reader; (<b>b</b>) Hardware architecture of the reader.</p>
Full article ">Figure 2
<p>(<b>a</b>) The geometric dimensions of a loop antenna; Real (<b>b</b>) and imaginary (<b>c</b>) part of the loop antenna impedance in free space with different values of <span class="html-italic">β</span> and a normalized circumference <span class="html-italic">C<sub>λ</sub></span>.</p>
Full article ">Figure 3
<p>(<b>a</b>) The top view projection and (<b>b</b>) the three-dimensional view of the simulation model together with the simulated surface current distribution of the model when <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>–<b>f</b>) The relationship between normalized antenna radiation resistance, rotor height (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>), and rotor diameter (<math display="inline"><semantics> <mrow> <mi>c</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>) of four loop antenna with <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1,2</mn> <mo>,</mo> <mn>3,4</mn> </mrow> </semantics></math>, respectively; (<b>g</b>) Antenna radiation resistance and (<b>h</b>) normalized antenna self-resonant frequently at different normalized rotor diameters (<math display="inline"><semantics> <mrow> <mi>c</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>); (<b>i</b>) Simulated return loss (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> parameter) and impedance spectrum (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Z</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> parameter) of the 400 MHz loop antenna integrated with a rotor; (<b>j</b>) Estimation error of Equation (4) for the loop antenna integrated with the rotor model; Magnetic field distribution around the loop antenna (<b>k</b>) with the rotor model and (<b>l</b>) without the rotor model when <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 3 Cont.
<p>(<b>a</b>) The top view projection and (<b>b</b>) the three-dimensional view of the simulation model together with the simulated surface current distribution of the model when <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>; (<b>c</b>–<b>f</b>) The relationship between normalized antenna radiation resistance, rotor height (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>), and rotor diameter (<math display="inline"><semantics> <mrow> <mi>c</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>) of four loop antenna with <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1,2</mn> <mo>,</mo> <mn>3,4</mn> </mrow> </semantics></math>, respectively; (<b>g</b>) Antenna radiation resistance and (<b>h</b>) normalized antenna self-resonant frequently at different normalized rotor diameters (<math display="inline"><semantics> <mrow> <mi>c</mi> <mo>/</mo> <mi>a</mi> </mrow> </semantics></math>); (<b>i</b>) Simulated return loss (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> parameter) and impedance spectrum (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Z</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> parameter) of the 400 MHz loop antenna integrated with a rotor; (<b>j</b>) Estimation error of Equation (4) for the loop antenna integrated with the rotor model; Magnetic field distribution around the loop antenna (<b>k</b>) with the rotor model and (<b>l</b>) without the rotor model when <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>.</p>
Full article ">Figure 4
<p>Far-field antenna gain on azimuth angle (red solid line) and elevation angle (blue dot-dash line) of the loop antenna in free space (<b>a</b>–<b>d</b>) and the loop antenna integrated with a turbine rotor (<b>e</b>–<b>h</b>) at <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1,2</mn> <mo>,</mo> <mn>3,4</mn> </mrow> </semantics></math>; (<b>i</b>–<b>l</b>) Near-field Poynting energy flow at <math display="inline"><semantics> <mrow> <mi>k</mi> <mo>=</mo> <mn>1,2</mn> <mo>,</mo> <mn>3,4</mn> </mrow> </semantics></math> on three different mounting positions for the SAW sensor tag: on the surface of the turbine rotor close to the loop antenna (green dot-dash line); on the top surface of the turbine blades (red solid line); on the bottom surface of the turbine blades (blue solid line).</p>
Full article ">Figure 5
<p>(<b>a</b>) Architecture for the SAW sensor tag in this work; (<b>b</b>) Geometric parameters of the PIFA together with its simulated surface current distribution; (<b>c</b>) Simulated radiation pattern and (<b>d</b>) impedance spectrum of the proposed PIFA; (<b>e</b>) Tilted radiation pattern with polarization mismatch simulated by the asymmetric PIFA structure in [<a href="#B26-sensors-24-06703" class="html-bibr">26</a>] for contrast, where the red solid line represents the azimuth angle, and the blue dashed line represents the elevation angle. (<b>f</b>) Geometry size of IDTs and reflectors in the SAW resonator; (<b>g</b>) Impedance spectrum of the SAW resonator simulated by p-matrix; (<b>h</b>) The processing of laser direct writing; (<b>i</b>) Photograph of the SAW resonator under microscope.</p>
Full article ">Figure 5 Cont.
<p>(<b>a</b>) Architecture for the SAW sensor tag in this work; (<b>b</b>) Geometric parameters of the PIFA together with its simulated surface current distribution; (<b>c</b>) Simulated radiation pattern and (<b>d</b>) impedance spectrum of the proposed PIFA; (<b>e</b>) Tilted radiation pattern with polarization mismatch simulated by the asymmetric PIFA structure in [<a href="#B26-sensors-24-06703" class="html-bibr">26</a>] for contrast, where the red solid line represents the azimuth angle, and the blue dashed line represents the elevation angle. (<b>f</b>) Geometry size of IDTs and reflectors in the SAW resonator; (<b>g</b>) Impedance spectrum of the SAW resonator simulated by p-matrix; (<b>h</b>) The processing of laser direct writing; (<b>i</b>) Photograph of the SAW resonator under microscope.</p>
Full article ">Figure 6
<p>Experimental equipment setup and results: (<b>a</b>) Diagram and (<b>b</b>) experimental setup of the metallic turbine model; (<b>c</b>) S-parameters of used antennas and the SAW sensor; Measured <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Z</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> of (<b>d</b>) the loop antenna integrated with a rotor model, (<b>e</b>) PIFA, (<b>f</b>) SAW resonator, (<b>g</b>) SAW resonator integrated with PIFA; (<b>h</b>) Measured <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> of the PIFA at various temperatures; (<b>i</b>) Sensing accuracy of the SAW sensor tag over a wide temperature range compared to the thermocouple; RSSI data output of one frequency sweep and its fitting results at (<b>j</b>) static test with the proposed loop antenna, (<b>k</b>) rotation test with loop antenna, and (<b>l</b>) static test with the commercial dipole antenna; (<b>m</b>) Temperature and (<b>n</b>) power measured at different placement angles during the static test; (<b>o</b>) Eight repeat experiments of temperature and power measured during the dynamic test of the turbine model; The temperature measured by the SAW sensor tag under constant power heating (<b>p</b>) at different rotational speeds and (<b>q</b>) step changes in rotational speed from 140 rpm to 710 rpm; (<b>r</b>) The temperature measured by the SAW sensor tag during periodic switching of the heater at a rotational speed of 560 rpm.</p>
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<p>Experimental equipment setup and results: (<b>a</b>) Diagram and (<b>b</b>) experimental setup of the metallic turbine model; (<b>c</b>) S-parameters of used antennas and the SAW sensor; Measured <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>Z</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> of (<b>d</b>) the loop antenna integrated with a rotor model, (<b>e</b>) PIFA, (<b>f</b>) SAW resonator, (<b>g</b>) SAW resonator integrated with PIFA; (<b>h</b>) Measured <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>S</mi> </mrow> <mrow> <mn>11</mn> </mrow> </msub> </mrow> </semantics></math> of the PIFA at various temperatures; (<b>i</b>) Sensing accuracy of the SAW sensor tag over a wide temperature range compared to the thermocouple; RSSI data output of one frequency sweep and its fitting results at (<b>j</b>) static test with the proposed loop antenna, (<b>k</b>) rotation test with loop antenna, and (<b>l</b>) static test with the commercial dipole antenna; (<b>m</b>) Temperature and (<b>n</b>) power measured at different placement angles during the static test; (<b>o</b>) Eight repeat experiments of temperature and power measured during the dynamic test of the turbine model; The temperature measured by the SAW sensor tag under constant power heating (<b>p</b>) at different rotational speeds and (<b>q</b>) step changes in rotational speed from 140 rpm to 710 rpm; (<b>r</b>) The temperature measured by the SAW sensor tag during periodic switching of the heater at a rotational speed of 560 rpm.</p>
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18 pages, 18528 KiB  
Article
Data Poisoning Attack against Neural Network-Based On-Device Learning Anomaly Detector by Physical Attacks on Sensors
by Takahito Ino, Kota Yoshida, Hiroki Matsutani and Takeshi Fujino
Sensors 2024, 24(19), 6416; https://doi.org/10.3390/s24196416 - 3 Oct 2024
Viewed by 3173
Abstract
In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is [...] Read more.
In this paper, we introduce a security approach for on-device learning Edge AIs designed to detect abnormal conditions in factory machines. Since Edge AIs are easily accessible by an attacker physically, there are security risks due to physical attacks. In particular, there is a concern that the attacker may tamper with the training data of the on-device learning Edge AIs to degrade the task accuracy. Few risk assessments have been reported. It is important to understand these security risks before considering countermeasures. In this paper, we demonstrate a data poisoning attack against an on-device learning Edge AI. Our attack target is an on-device learning anomaly detection system. The system adopts MEMS accelerometers to measure the vibration of factory machines and detect anomalies. The anomaly detector also adopts a concept drift detection algorithm and multiple models to accommodate multiple normal patterns. For the attack, we used a method in which measurements are tampered with by exposing the MEMS accelerometer to acoustic waves of a specific frequency. The acceleration data falsified by this method were trained on an anomaly detector, and the result was that the abnormal state could not be detected. Full article
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<p>Autoencoder-based anomaly detector.</p>
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<p>Overview of ELM.</p>
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<p>Overview of concept drift detection algorithm. (<b>a</b>) Trained centroids are sequentially calculated during training. (<b>b</b>) Test centroids are sequentially calculated during inference. (<b>c</b>) When concept drift occurs, the test centroid moves away from the train centroid. (<b>d</b>) When the test centroid exceeds the threshold, a concept drift is detected and a new instance is created. The new instance computes its own train centroid from the latest data (training data).</p>
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<p>Expected drift rate behavior. (<b>a</b>) Concept drift does not occur; (<b>b</b>) Concept drift occurs.</p>
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<p>Behavior of multi-instance on-device learning anomaly detector.</p>
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<p>Behavior of anomaly detector without attack.</p>
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<p>Behavior of anomaly detector with data poisoning attack.</p>
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<p>Experimental setup. (<b>a</b>) Overall setup; (<b>b</b>) Cooling fan and speaker.</p>
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<p>Block diagram of experimental setup.</p>
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<p>Observed frequency spectrum while both cooling fans are stopped.</p>
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<p>Relationship between irradiated acoustic wave frequency (in audible range), observed peak frequency, and amplitude.</p>
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<p>Relationship between irradiated acoustic wave frequency (in ultrasonic range), observed peak frequency, and amplitude.</p>
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<p>Effects of sound pressure for observed peak amplitude (frequency of acoustic waves: 3000 Hz).</p>
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<p>Samples of observed data. (<b>a</b>) Normal state; (<b>b</b>) Abnormal state; (<b>c</b>) Poisoned state.</p>
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<p>Error and drift rate without data poisoning attack.</p>
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<p>Error and drift rate with data poisoning attack.</p>
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22 pages, 1364 KiB  
Article
Signal Denoising Method Based on EEMD and SSA Processing for MEMS Vector Hydrophones
by Peng Wang, Jie Dong, Lifu Wang and Shuhui Qiao
Micromachines 2024, 15(10), 1183; https://doi.org/10.3390/mi15101183 - 24 Sep 2024
Viewed by 3554
Abstract
The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones [...] Read more.
The vector hydrophone is playing a more and more prominent role in underwater acoustic engineering, and it is a research hotspot in many countries; however, it also has some shortcomings. For the mixed problem involving received signals in micro-electromechanical system (MEMS) vector hydrophones in the presence of a large amount of external environment noise, noise and drift inevitably occur. The distortion phenomenon makes further signal detection and recognition difficult. In this study, a new method for denoising MEMS vector hydrophones by combining ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) is proposed to improve the utilization of received signals. First, the main frequency of the noise signal is transformed using a Fourier transform. Then, the noise signal is decomposed by EEMD to obtain the intrinsic mode function (IMF) component. The frequency of each IMF component in the center further determines that the IMF component belongs to the noise IMF component, invalid IMF component, or pure IMF component. Then, there are pure IMF reserved components, removing noisy IMF components and invalid IMF components. Finally, the desalinated IMF reconstructs the signal through SSA to obtain the denoised signal, which realizes the denoising processing of the signal, extracting the useful signal and removing the drift. The role of SSA is to effectively separate the trend noise and the periodic vibration noise. Compared to EEMD and SSA separately, the proposed EEMD-SSA algorithm has a better denoising effect and can achieve the removal of drift. Following that, EEMD-SSA is used to process the data measured by Fenhe. The experiment is carried out by the North University of China. The simulation and lake test results show that the proposed EEMD-SSA has certain practical research value. Full article
(This article belongs to the Special Issue MEMS Sensors and Actuators: Design, Fabrication and Applications)
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<p>Flowchart of the EEMD-SSA algorithm.</p>
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<p>Original signal of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>IMF signal and corresponding spectrum of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>IMF signal and corresponding spectrum of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>The denoising result of EEMD algorithm of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>The denoising result of SSA algorithm of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>The denoising result of SSA algorithm of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>Time domain signals of different methods of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>Comparison of denoising results of different algorithms of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>Comparison of denoising results of different algorithms of Equation (<a href="#FD8-micromachines-15-01183" class="html-disp-formula">8</a>).</p>
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<p>Original signal of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>IMF signal and corresponding spectrum of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>The denoising result of EEMD algorithm of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>The denoising result of SSA algorithm of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>Time-domain signals of different methods of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>Comparison of denoising results of different algorithms of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with −10 dB.</p>
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<p>Original signal of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>IMF signal and corresponding spectrum of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>The denoising result of EEMD algorithm of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>The denoising result of SSA algorithm of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>Time domain signals of different methods of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>Comparison of denoising results of different algorithms of Equation (<a href="#FD9-micromachines-15-01183" class="html-disp-formula">9</a>) with 10 dB.</p>
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<p>Experimental process.</p>
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<p>Measured signal of MEMS hydorphone.</p>
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<p>IMF signal and corresponding spectrum of measured signal.</p>
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<p>The denoising result of EEMD algorithm of measured signal.</p>
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<p>The denoising result of SSA algorithm of measured signal.</p>
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<p>Comparison of denoising results of different algorithms of measured signal.</p>
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12 pages, 10478 KiB  
Article
Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar Buoy
by Xinquan Xiong, Wei Fan, Yongchuang Shi, Zuli Wu, Shenglong Yang, Wenjie Xu, Shengchi Yu and Yang Dai
Appl. Sci. 2024, 14(13), 5516; https://doi.org/10.3390/app14135516 - 25 Jun 2024
Viewed by 1125
Abstract
To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting [...] Read more.
To reduce costs associated with the detection and population assessment of Antarctic krill and diversify the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection. Full article
(This article belongs to the Section Marine Science and Engineering)
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<p>Buoy. (<b>a</b>) Buoy design. (<b>b</b>) Photo of the buoy.</p>
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<p>Buoy. (<b>a</b>) Buoy design. (<b>b</b>) Photo of the buoy.</p>
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<p>The workflow diagram of the buoy.</p>
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<p>Krill distribution.</p>
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<p>Line chart of ambient air temperature.</p>
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<p>Line chart of sea surface temperature.</p>
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<p>Echo intensity comparison chart.</p>
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<p>Target intensity (dB) echogram. (<b>a</b>) No Antarctic krill. (<b>b</b>) Antarctic krill.</p>
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22 pages, 6460 KiB  
Article
Ocean-Current-Motion-Model-Based Routing Protocol for Void-Avoided UASNs
by Zhicheng Tan, Yun Li, Haixin Sun, Shaohua Hong and Shanlin Sun
J. Mar. Sci. Eng. 2024, 12(4), 537; https://doi.org/10.3390/jmse12040537 - 24 Mar 2024
Viewed by 1083
Abstract
An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, [...] Read more.
An increasing number of scholars are researching underwater acoustic sensor networks (UASNs), including the physical layer, the protocols of the routing layer, the MAC layer, and the cross-layer. In UASNs, the ultimate goal is to transmit data from the seabed to the surface, and a well-performed routing protocol can effectively achieve this goal. However, the nodes in the network are prone to drift, and the topology is easily changed because of the movement caused by ocean currents, resulting in a routing void. The data cannot be effectively aggregated to the sink terminal on the surface. Thus, it is extremely important to determine how to find an alternative node as a relay node after node drift and how to rebuild a reliable transmission path. Although many relay routing protocols have been proposed to avoid routing voids, few of them consider the relay node selection between the outage probability and the ocean current model. Therefore, we propose an ocean current motion model based routing (OCMR) protocol to avoid the routing void in UASNs. We predicted underwater node movement based on the ocean current motion model and designed a protection radius to construct a limited search coverage based on the optimal outage probability; then, the node with the best fitness value within the protection radius was selected as the alternative relay node using an improved WOA. In OCMR, the problem of the routing void caused by ocean current motion is effectively suppressed. The simulation results show that, compared with VBF, HH-VBF, and QELAR, the proposed OCMR platform performs well in terms of the PDR (packet delivery ratio), average end-to-end delay, and average energy consumption. Full article
(This article belongs to the Special Issue Technological Oceanography Volume II)
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<p>Routing diagram of UASNs.</p>
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<p>Velocity perpendicular to the coast.</p>
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<p>Velocity along the coast.</p>
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<p>Protection radius diagram.</p>
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<p>Comparison of convergence rates of WOA and IWOA.</p>
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<p>(<b>a</b>) spave diagram of search area. (<b>b</b>) planform of search area.</p>
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<p>(<b>a</b>) Routing strategy diagram (single-hop process). (<b>b</b>) Routing strategy diagram (multi-hop process).</p>
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<p>Comparison of PDR with different numbers of nodes.</p>
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<p>Comparison of average end-to-end delay with a different number of nodes.</p>
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<p>Comparison of average energy consumption with different number of nodes.</p>
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<p>Comparison of PDR with different maximum node velocities.</p>
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<p>Comparison of average end-to-end delay with different maximum node velocities.</p>
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<p>Comparison of average energy consumption with different maximum node velocities.</p>
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<p>Comparison of PDR with different initial node energies.</p>
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<p>Comparison of average end-to-end delay with different node initial energies.</p>
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<p>Comparison of average energy consumption with different node initial energies.</p>
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22 pages, 7667 KiB  
Article
Altimeter Calibrations in the Preliminary Four Years’ Operation of Wanshan Calibration Site
by Wanlin Zhai, Jianhua Zhu, Hailong Peng, Chuntao Chen, Longhao Yan, He Wang, Xiaoqi Huang, Wu Zhou, Hai Guo and Yufei Zhang
Remote Sens. 2024, 16(6), 1087; https://doi.org/10.3390/rs16061087 - 20 Mar 2024
Viewed by 1264
Abstract
In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment [...] Read more.
In order to accomplish the calibration and validation (Cal/Val) of altimeters, the Wanshan calibration site (WSCS) has been used as a calibration site for satellite altimeters since its completion in August 2019. In this paper, we introduced the WSCS and the dedicated equipment including permanent GNSS reference stations (PGSs), acoustic tide gauges (ATGs), and dedicated GNSS buoys (DGB), etc. placed on Zhi’wan, Wai’ling’ding, Dan’gan, and Miao’Wan islands of the WSCS. The PGSs data of Zhi’wan and Wai’ling’ding islands were processed and analyzed using the GAMIT/GLOBK (Version 10.7) and Hector (Version 1.9) software to define the datum for Cal/Val of altimeters in WSCS. The DGB was used to transfer the datum from the PGSs to the ATGs of Zhi’wan, Wai’ling’ding, and Dan’gan islands. Separately, the tidal and mean sea surface (MSS) corrections are needed in the Cal/Val of altimeters. We evaluated the global/regional tide models of FES2014, HAMTIDE12, DTU16, NAO99jb, GOT4.10, and EOT20 using the three in situ tide gauge data of WSCS and Hong Kong tide gauge data (No. B329) derived from the Global Sea Level Observing System. The HAMTIDE12 tide model was chosen to be the most accurate one to maintain the tidal difference between the locations of the ATGs and the altimeter footprints. To establish the sea surface connections between the ATGs and the altimeter footprints, a GPS towing body and a highly accurate ship-based SSH measurement system (HASMS) were used to measure the sea surface of this area in 2018 and 2022, respectively. The global/regional mean sea surface (MSS) models of DTU 2021, EGM 2008 (mean dynamic topography minus by CLS_MDT_2018), and CLS2015 were accurately evaluated using the in situ measured data and HY-2A altimeter, and the CLS2015 MSS model was used for Cal/Val of altimeters in WSCS. The data collected by the equipment of WSCS, related auxiliary models mentioned above, and the sea level data of the hydrological station placed on Dan’gan island were used to accomplish the Cal/Val of HY-2B, HY-2C, Jason-3, and Sentinel-3A (S3A) altimeters. The bias of HY-2B (Pass No. 375) was −16.7 ± 45.2 mm, with a drift of 0.5 mm/year. The HY-2C biases were −18.9 ± 48.0 mm with drifts of 0.0 mm/year and −5.6 ± 49.3 mm with −0.3 mm/year drifts for Pass No. 170 and 185, respectively. The Jason-3 bias was −4.1 ± 78.7 mm for Pass No. 153 and −25.8 ± 85.5 mm for Pass No. 012 after it has changed its orbits since April 2022, respectively. The biases of S3A were determined to be −16.5 ± 46.3 mm with a drift of −0.6 mm/year and −9.8 ± 30.1 mm with a drift of 0.5 mm/year for Pass No. 260 and 309, respectively. The calibration results show that the WSCS can commercialize the satellite altimeter calibration. We also discussed the calibration potential for a wide swath satellite altimeter of WSCS. Full article
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<p>Satellite altimeter calibration sites and experiments. The red triangulars represent the operational in situ calibration sites. The blue dots represent the altimeter calibration campaigns.</p>
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<p>Detailed view of the facilities of WSCS and nearby related stations. The pink triangles represent the combined stations of PGSs and ATGs. The yellow triangles represent the station of PGSs on Miao’wan island and Hong Kong (HKWS), respectively. The coral circle represents the laboratory of WSCS on Gui’shan island. The B329 tide gauge station of GCOS in Hong Kong were also included in this research (coral triangular).</p>
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<p>Time-series of the coordinates of ZWAN and WLDD. The blue dots represent the raw coordinate processed by GAMIT/GLOBK. The red lines represent the results after removing the noise and geometric modelling.</p>
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<p>Definition of the datum of the ATGs using the DGB. Panels (<b>a</b>–<b>i</b>) show the comparisons between the ATGs and the DGB from 2019–2021. The magenta lines in (<b>h</b>) represent the SSH of the DGB jumped suddenly. This may be caused by weak GNSS satellite coverage, multipath, or changes in the floating position (Reprinted/adapted with permission from Ref. [<a href="#B19-remotesensing-16-01087" class="html-bibr">19</a>]).</p>
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<p>Sea surface measurements along the ATG of Zhi’wan island and HY-2B orbits. Image (<b>a</b>) is the GPS towing-body used in the campaign of 2018. Image (<b>b</b>) is the principle of SSH measurements for HASMS. Image (<b>c</b>) is the displacement of the HASMS and the data acquisition and the GNSS receiver were placed in the cabin. Image (<b>d</b>) is the measured sea surface of this area using the ordinary kriging interpolation of the measured lines in ArcGIS 10.4.</p>
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<p>Comparisons between the DGB and the HAMSM. The distance between the two equipment is less than 100 m.</p>
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<p>Error distributions between the in situ sea surface measurements and DTU 2021 (<b>a</b>), EGM2008 plus CLS_MDT_2018 (<b>b</b>), and CLS 2015 (<b>c</b>).</p>
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<p>MSS of the WSCS. Image (<b>a</b>) shows the orbits of HY-2A (red lines). Images (<b>b</b>,<b>c</b>) are the comparisons between the HY-2A SSH of Pass No. 190 and 203 and MSS of DTU2021, CLS2015, and EGM (MDT minus by CLS_MDT_2018), respectively.</p>
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<p>The SSH of HY-2B when it flies over the Zhi’wan island and the calibration results. Image (<b>a</b>) represents the SSH of HY-2B. The dark green lines represent the Zhi’wan island. The x dots represent the HY-2B SSH of 20 Hz data, and the circles represent the 1 Hz data. Image (<b>b</b>) shows the calibration results using the ATGs of WSCS. The red dotted line in image (<b>b</b>) represents the drift of the SSH.</p>
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<p>The SSH and the calibration results of HY-2C when it flies over the Dan’gan island. Image (<b>a</b>) represents the SSH results of the altimeter when it flies over WSCS. The dark green lines represent the Zhi’wan island. The x dots represent the HY-2B SSH of 20 Hz data, and the circles represent the 1 Hz data. Images (<b>b</b>,<b>c</b>) were the calibration results for Pass No. 185 and Pass 170, respectively. The red dotted lines in image (<b>b</b>,<b>c</b>) represent the drifts of the SSH.</p>
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<p>The SSH of Jason-3 when it flies over the WSCS. Images (<b>a</b>,<b>b</b>) are examples of the SSH before and after cycle 301 (April 2022). The red x and green dots in images (<b>a</b>,<b>b</b>) represent the 20Hz and 1Hz SSH data, respectively. The light green areas represent the small islands of the strike to Wai’ling’ding island and Zhi’wan island, respectively, which may contaminate the SSH. Image (<b>c</b>) shows the orbits of the two passes. There are about 6 small islands in the red circle that may contaminate the SSH or wet zenith delay of the altimeter. Image (<b>d</b>) shows the bias and drift (red line) of the altimeter and the light blue area represents the cycles before 301 (Pass No. 153). The red dotted line in image (<b>d</b>) represents the drift of the SSH.</p>
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<p>The SSH and the bias of S3A when it flies over the WSCS. Image (<b>a</b>) shows the example of S3A flying through the WSCS. The red dots and magenta-filled area represent the SSH of Pass No. 260 and the Zhi’wan island. The green dots and khaki-filled area represent the SSH of Pass No. 309 and the Dan’gan island. Image (<b>b</b>) shows the bias of S3A Pass No. 260 calibrated by the ATGs of the three islands of WSCS, and (<b>c</b>) shows the biases of Pass No. 309 calibrated by the hydrological station and ATG of Dan’gan island. The red dotted lines in image (<b>b</b>,<b>c</b>) represent the drifts of the SSH.</p>
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<p>The planning devices (red triangle) and the area of WSCS (blue lines). The planning devices include the moored GNSS buoy, the bottom pressure tide gauge, and the hydro-meteorological buoy, which will be placed at the footprint of HY-2B altimeter. The blue lines represent the area of WSCS, which can be used for the Cal/Val of a wide swath altimeter such as SWOT.</p>
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23 pages, 6423 KiB  
Article
Laboratory Investigations of Iceberg Melting under Wave Conditions in Sea Water
by Aleksey Marchenko and Nataliya Marchenko
J. Mar. Sci. Eng. 2024, 12(3), 501; https://doi.org/10.3390/jmse12030501 - 18 Mar 2024
Cited by 2 | Viewed by 1219
Abstract
Changes in the masses of icebergs due to deterioration processes affect the drift of icebergs and should be taken into account when assessing iceberg risks in the areas of offshore development. In 2022 and 2023, eight laboratory experiments were carried out in the [...] Read more.
Changes in the masses of icebergs due to deterioration processes affect the drift of icebergs and should be taken into account when assessing iceberg risks in the areas of offshore development. In 2022 and 2023, eight laboratory experiments were carried out in the wave tank of the University Centre in Svalbard to study the melting of icebergs in sea water under calm and rough conditions. In the experiments, the water temperatures varied from 0  to 2.2 . Cylindrical iceberg models were made from columnar ice cores with a diameter of 24 cm. In one experiment, the iceberg model was protected on the sides with plastic fencing to investigate the iceberg’s protection from melting when towed to deliver fresh water. The iceberg masses, water temperatures, and ice temperatures were measured in the experiments. The water velocity near the iceberg models was measured with an acoustic Doppler velocimeter. During the experiments, time-lapse cameras were used to describe the shapes and measure the vertical dimensions of the icebergs. Using experimental data, we calculated the horizontal dimensions of icebergs, latent heat fluxes, conductive heat fluxes inside the iceberg models, and turbulent heat fluxes in water as a function of time. We discovered the influence of surface waves and water mixing on the melt rates and found a significant reduction in the melt rates due to the lateral protection of the iceberg model using a plastic barrier. Based on the experimental data obtained, the ratio of the rates of lateral and bottom melting of the icebergs and lateral melting of the icebergs under wave conditions was parametrized depending on the wave frequency. Full article
(This article belongs to the Special Issue Recent Research on the Measurement and Modeling of Sea Ice)
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Figure 1
<p>Photograph of the wave tank with the installed equipment for experiments 1–4 (<b>a</b>). Iceberg model inside the frame with two fiber optic temperature strings, TS3 and TS4, installed inside the iceberg (<b>b</b>). Acoustic Doppler velocimeter (Sontek ADV Hydra 5 MHz, San Diego, CA, USA) with fiber optic temperature string TS4 (<b>c</b>).</p>
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<p>Installation of FBG temperature strings in iceberg (<b>a</b>). Location of ADV and TS4 measurements relative to the iceberg model (<b>b</b>).</p>
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<p>Experiment with protected iceberg.</p>
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<p>Full-scale periods (<b>a</b>) and amplitudes (<b>b</b>) of waves versus the geometrical scaling factor <math display="inline"><semantics> <mrow> <mi>λ</mi> </mrow> </semantics></math>.</p>
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<p>Water temperatures averaged over the depth in experiments 1–4 (<b>a</b>) and 5–8 (<b>b</b>). Blue lines correspond to TS1 data, and yellow lines correspond to TS2 data.</p>
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<p>Iceberg before (<b>a</b>,<b>c</b>,<b>e</b>,<b>g</b>) and after (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) experiments 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>), 3 (<b>e</b>,<b>f</b>), and 4 (<b>g</b>,<b>h</b>). Iceberg (<b>b</b>) is flipped.</p>
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<p>Thin sections of the iceberg surface after experiments 3 (<b>a</b>) and 5 (<b>b</b>). Length of the yellow strip is 5 cm.</p>
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<p>Changes in iceberg shapes, reconstructed using time-lapse images in experiments 1 (<b>a</b>), 2 (<b>b</b>), and 3 (<b>c</b>). Numbers mark the time (h) after the beginning of the experiment.</p>
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<p>Wave interaction with iceberg in experiments 2 (<b>a</b>) and 3 (<b>b</b>).</p>
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<p>Iceberg masses (<b>a</b>,<b>b</b>) and heights (<b>c</b>,<b>d</b>) measured in experiments 1–8; iceberg radiuses (<b>e</b>,<b>f</b>) calculated from the data of experiments 1–8 versus time.</p>
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<p>Temperatures measured using TS3 versus the time (<b>a</b>,<b>c</b>,<b>e</b>) and vertical coordinate <math display="inline"><semantics> <mrow> <mi>z</mi> </mrow> </semantics></math> (<b>b</b>,<b>d</b>,<b>f</b>) in experiments 1 (<b>a</b>,<b>b</b>), 2 (<b>c</b>,<b>d</b>), and 4 (<b>e</b>,<b>f</b>). Roman numerals indicate the intervals of turbulent measurements (ITMs). The times of the temperature profile measurements are indicated in hours.</p>
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<p>Temperatures measured using TS3 (solid lines) and TS4 (dashed lines) versus time (<b>a</b>,<b>c</b>,<b>e</b>,<b>f</b>) and the vertical coordinate <math display="inline"><semantics> <mrow> <mi>z</mi> </mrow> </semantics></math> (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) in experiments 5 (<b>a</b>,<b>b</b>), 6 (<b>c</b>,<b>d</b>), 7 (<b>e</b>,<b>f</b>), and 8 (<b>g</b>,<b>h</b>). The color legend for (<b>a</b>,<b>c</b>,<b>d</b>,<b>f</b>) is shown in <a href="#jmse-12-00501-f011" class="html-fig">Figure 11</a>a. Blue and yellow lines in (<b>b</b>,<b>d</b>,<b>f</b>,<b>h</b>) show temperature profiles measured, respectively, at the beginning and at the end of the experiments.</p>
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<p>Dependencies of ratios <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>R</mi> </mrow> </msub> <mo>/</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>H</mi> </mrow> </msub> </mrow> </semantics></math> (<b>a</b>) and the wave correction factor <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>α</mi> </mrow> <mrow> <mn>5</mn> </mrow> </msub> <mo>+</mo> <msub> <mrow> <mi>γ</mi> </mrow> <mrow> <mn>5</mn> </mrow> </msub> </mrow> </semantics></math> (<b>b</b>) from the dimensionless wave frequency <math display="inline"><semantics> <mrow> <mi>ϕ</mi> </mrow> </semantics></math> obtained from experiments 1–8. Blue and yellow points are calculated with <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>1</mn> <mo> </mo> <mo>℃</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>s</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mo>−</mo> <mn>0.5</mn> <mo> </mo> <mo>℃</mo> </mrow> </semantics></math>. Numbers of the experiments are pointed out.</p>
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15 pages, 4586 KiB  
Article
Development of Temperature Sensor Based on AlN/ScAlN SAW Resonators
by Min Wei, Yan Liu, Yuanhang Qu, Xiyu Gu, Yilin Wang, Wenjuan Liu, Yao Cai, Shishang Guo and Chengliang Sun
Electronics 2023, 12(18), 3863; https://doi.org/10.3390/electronics12183863 - 12 Sep 2023
Cited by 6 | Viewed by 1848
Abstract
Temperature monitoring in extreme environments presents new challenges for MEMS sensors. Since aluminum nitride (AlN)/scandium aluminum nitride (ScAlN)-based surface acoustic wave (SAW) devices have a high Q-value, good temperature drift characteristics, and the ability to be compatible with CMOS, they have become some [...] Read more.
Temperature monitoring in extreme environments presents new challenges for MEMS sensors. Since aluminum nitride (AlN)/scandium aluminum nitride (ScAlN)-based surface acoustic wave (SAW) devices have a high Q-value, good temperature drift characteristics, and the ability to be compatible with CMOS, they have become some of the preferred devices for wireless passive temperature measurement. This paper presents the development of AlN/ScAlN SAW-based temperature sensors. Three methods were used to characterize the temperature characteristics of a thin-film SAW resonator, including direct measurement by GSG probe station, and indirect measurement by oscillation circuit and antenna. The temperature characteristics of the three methods in the range of 30–100 °C were studied. The experimental results show that the sensitivities obtained with the three schemes were −28.9 ppm/K, −33.6 ppm/K, and −29.3 ppm/K. The temperature sensor using the direct measurement method had the best linearity, with a value of 0.0019%, and highest accuracy at ±0.70 °C. Although there were differences in performance, the characteristics of the three SAW temperature sensors make them suitable for sensing in various complex environments. Full article
(This article belongs to the Special Issue MEMS/NEMS Sensors: Advances, Trends and Challenges)
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Figure 1
<p>(<b>a</b>) TCF measurement system block diagram, (<b>b</b>) oscillation circuit measurement system block diagram, and (<b>c</b>) wireless measurement system block diagram.</p>
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<p>(<b>a</b>) Three-dimensional model of AlN/ScAlN thin film SAW resonator, (<b>b</b>) comparison of simulation results of three different types of films, (<b>c</b>) simulated impedance curve and device surface displacement diagram at <span class="html-italic">f<sub>s</sub></span> of the AlN/ScAlN composite thin-film SAW resonator, (<b>d</b>) simulation of resonator impedance variation with temperature.</p>
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<p>(<b>a</b>) Fabrication process of AlN/ScAlN thin-film SAW resonator, (<b>b</b>) micrograph of the SAW resonator, and (<b>c</b>) cross-section image of the SAW resonator.</p>
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<p>(<b>a</b>) Oscillator circuit diagram, (<b>b</b>) test curve and mBVD model fitting result graph, (<b>c</b>) time domain simulation diagram of oscillator, and (<b>d</b>) frequency domain simulation diagram of oscillator.</p>
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<p>Circuit diagram of wireless passive sensor system.</p>
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<p>(<b>a</b>,<b>b</b>) Direct measurement system, (<b>c</b>) S parameter test curve, and (<b>d</b>) impedance curve of AlN/ScAlN SAW resonator.</p>
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<p>(<b>a</b>) Scatter diagrams and fitting curves of the relationship between frequency and temperature measured by direct measurement method, (<b>b</b>) measured temperature accuracy of measurement by network analyzer, and (<b>c</b>) the test curve of the sensor resolution, showing the <span class="html-italic">f<sub>s</sub></span> values at the three temperatures of 70 °C, 70.1 °C, and 70.2 °C were 446.873 MHz, 446.874 MHz, and 446.875 MHz, respectively.</p>
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<p>(<b>a</b>) Oscillation circuit test system and the PCB board of oscillator, and (<b>b</b>) oscillator output spectrogram.</p>
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<p>(<b>a</b>) Scatter diagrams and fitting curves of the relationship between frequency and temperature measured by oscillator circuit, (<b>b</b>) accuracy of temperature measurement by oscillator circuit, and (<b>c</b>) the test curve of the sensor resolution, showing the output signal frequencies at the three temperatures of 70 °C, 70.1 °C, and 70.2 °C were 446.449 MHz, 446.448 MHz, and 446.446 MHz, respectively.</p>
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<p>(<b>a</b>) Wireless sensor test system and radio request unit, and (<b>b</b>) excitation and response signal test results diagram of wireless sensor measurement system.</p>
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<p>(<b>a</b>) Scatter diagrams and fitting curves of the relationship between frequency and temperature measured by wireless test system, (<b>b</b>) measured temperature accuracy of wireless test system, (<b>c</b>) the test curve of the sensor resolution, showing the output signal frequencies at the three temperatures of 61 °C, 63 °C, and 65 °C were 445.766 MHz, 445.719 MHz, and 445.671 MHz, respectively, and (<b>d</b>) the relationship between the maximum peak-to-peak value of the response signal and the distance of the antenna transmission distance.</p>
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24 pages, 11778 KiB  
Article
Atmospheric and Ionospheric Effects of La Palma Volcano 2021 Eruption
by Hanshuo Zhang, Kaiguang Zhu, Yuqi Cheng, Dedalo Marchetti, Wenqi Chen, Mengxuan Fan, Siyu Wang, Ting Wang, Donghua Zhang and Yiqun Zhang
Atmosphere 2023, 14(8), 1198; https://doi.org/10.3390/atmos14081198 - 26 Jul 2023
Cited by 3 | Viewed by 2035
Abstract
On 19 September 2021, La Palma volcano (Canarias Archipelagos) started an eruption that persisted until 13 December 2021. Despite the Volcano Explosive Index (VEI) being estimated equal to 3, corresponding to not so powerful eruption, the long eruption activity posed much scientific interest [...] Read more.
On 19 September 2021, La Palma volcano (Canarias Archipelagos) started an eruption that persisted until 13 December 2021. Despite the Volcano Explosive Index (VEI) being estimated equal to 3, corresponding to not so powerful eruption, the long eruption activity posed much scientific interest in this natural hazard event. In this paper, we searched for possible effects of this eruption on the atmosphere and ionosphere, investigating the climatological archive and Swarm magnetic satellite data. In particular, we explored Aerosol, Sulphur Dioxide and Carbon Monoxide concentrations in the atmosphere identifying both the direct emissions from the volcano as well as the plume that drifted toward West-South-West and was reinforced during the eruption period. The vertical profile of temperature from the Saber satellite was analysed to search for the possible presence of acoustic gravity waves induced by volcanic activity. Compared with the year before without eruption in the areas, a lot of Saber profiles present an Energy Potential very much higher than the previous year, proposing the presence of Acoustic Gravity Waves (AGW) induced by volcano eruption activity. We also identified Swarm magnetic disturbances on the day of the eruption and in November. The mechanism of coupling could be different for the latter one, as there is no evidence for AGW. They may be due to a more complex of physical and chemical alterations that propagate from the lower atmosphere to the upper one into the ionosphere. Full article
(This article belongs to the Special Issue State-of-the-Art in Gravity Waves and Atmospheric-Ionospheric Physics)
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<p>Map of the localisation of the earthquakes from 19 September 2021 until 31 December 2021 in La Palma Island.</p>
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<p>Earthquake depth versus origin time. The colour and size of each dot represents the earthquake magnitude.</p>
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<p>Temperature vertical profile in Kelvin (black solid curve) with 3rd order polynomial fitted value (red dashed curve), perturbation temperature Tp (solid black curve) around reference level (blue dashed line), Brunt Vaisala Frequency N<sup>2</sup> in rad/S<sup>2</sup>, calculated potential energy Ep in J/kg, and threshold Ep (The orange dotted line is the mean of previous years, and the purple dotted line is the mean plus two standard deviations) from 30 to 50 km on 19 September 2021, 21 September 2021 and 25 September 2021.</p>
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<p>Distribution maps of AOT on 19 September 2021, 21 September 2021 and 25 September 2021. The blue line indicates coastline.</p>
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<p>Temperature vertical profile in Kelvin (black solid curve) with 3rd order polynomial fitted value (red dashed curve), perturbation temperature Tp (solid black curve) around reference level (blue dashed line), Brunt Vaisala Frequency N<sup>2</sup> in rad/S<sup>2</sup>, calculated potential energy Ep in J/kg, and threshold Ep (The orange dotted line is the mean of previous years, and the purple dotted line is the mean plus two standard deviations) from 30 to 50 km on 9 October 2021, 20 October 2021 and 24 October 2021.</p>
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<p>Distribution maps of AOT on 9 October 2021, 20 October 2021 and 24 October 2021. The blue line indicates the coastline.</p>
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<p>Temperature vertical profile in Kelvin (black solid curve) with 3rd order polynomial fitted value (red dashed curve), perturbation temperature Tp (solid black curve) around reference level (blue dashed line), Brunt Vaisala Frequency N2 in rad/S2, calculated potential energy Ep in J/kg, and threshold Ep (The orange dotted line is the mean of previous years, and the purple dotted line is the mean plus two standard deviations) from 30 to 50 km on 2 November 2021, 10 November 2021 and 13 November 2021.</p>
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<p>Distribution maps of AOT on 2 November 2021, 10 November 2021 and 13 November 2021. The blue line indicates the coastline.</p>
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<p>Variations over time of maximum value and extension area (half-maximum area) of Aerosol, SO<sub>2</sub> and CO were recorded in the studied region. Time series are plotted from 19 September to 31 December 2021. The time series of the estimation of the area of the plume is performed only during the eruption, i.e., from 19 September to 13 December 2021.</p>
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<p>Map of the interpolated maximum value of Aerosol, SO<sub>2</sub> and CO. The time of acquisition of the data is represented with colour. The blue line indicates the coastline.</p>
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<p>Time series of aerosol from 19 September to 31 December 2021 compared with historical time series. The years of 1980 and 1987 have been automatically excluded for the presence of outliers in the original data.</p>
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<p>Time series of SO<sub>2</sub> from 19 September to 31 December 2021 compared with historical time series.</p>
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<p>Time series of CO from 19 September to 31 December 2021 compared with historical time series.</p>
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<p>Swarm Alpha magnetic data was acquired on 19 September 2021 at 22:41 UT (centre of the shown track) for X-North, Y-East, and Z-center components and F, the scalar intensity of geomagnetic field residuals. In the map, the green star represents the position of La Palma volcano and the brown line is the ground projection of satellite orbit. The red line underlines the section of the Swarm magnetic track, which is identified as anomalous by the MASS algorithm.</p>
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<p>Swarm Bravo magnetic data acquired on 21 September 2021 at 22:34 UT (center of the shown track) for X-North, Y-East, Z-center components and F the scalar intensity of geomagnetic field residuals. In the map, the green star represents the position of La Palma volcano and the brown line is the ground projection of satellite orbit (the red section indicates some unusual data quality flags). The red line underlines the section of the Swarm magnetic track, which is identified as anomalous by the MASS algorithm. More investigations of this track are provided in <a href="#app3-atmosphere-14-01198" class="html-app">Appendix B</a>.</p>
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<p>Swarm Bravo magnetic data was acquired on 28 September 2021 above Las Canarias Archipelagos. A green star marked the position of the volcano. X (North), Y (East) and Z (centre) components were measured by VFM, while F was measured by ASM payloads. In the map, the green star represents the position of La Palma volcano and the brown line is the ground projection of satellite orbit (red section indicates some unusual data quality flags). The red line underlines the section of the Swarm magnetic track, which is identified as anomalous by the MASS algorithm.</p>
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<p>Possible mechanisms of lithosphere atmosphere and ionosphere Coupling (LAIC) in the occasion of volcano eruption. The image has been readapted from Marchetti et al. [<a href="#B5-atmosphere-14-01198" class="html-bibr">5</a>] and shows a chemical-physical channel (<b>A</b>), electromagnetic channel (<b>B</b>) or Acoustic Gravity Wave AGW (<b>C</b>) channel.</p>
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<p>Distribution maps of SO<sub>2</sub> and CO on 19 September 2021, 21 September 2021 and 25 September 2021. The blue line indicates the coastline.</p>
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<p>Distribution maps of SO<sub>2</sub> and CO on 9 October 2021, 20 October 2021 and 24 October 2021. The blue line indicates the coastline.</p>
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<p>Distribution maps of SO<sub>2</sub> and CO on 2 November 2021, 10 November 2021 and 13 November 2021. The blue line indicates the coastline.</p>
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<p>Swarm B original data (version 602) in NEC frame acquired on 21 September 2021. The quality flags are also represented.</p>
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19 pages, 5534 KiB  
Article
Hybrid Indoor Positioning System Based on Acoustic Ranging and Wi-Fi Fingerprinting under NLOS Environments
by Zhengyan Zhang, Yue Yu, Liang Chen and Ruizhi Chen
Remote Sens. 2023, 15(14), 3520; https://doi.org/10.3390/rs15143520 - 12 Jul 2023
Cited by 6 | Viewed by 2269
Abstract
An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and [...] Read more.
An accurate indoor positioning system (IPS) for the public has become an essential function with the fast development of smart city-related applications. The performance of the current IPS is limited by the complex indoor environments, the poor performance of smartphone built-in sensors, and time-varying measurement errors of different location sources. This paper introduces a hybrid indoor positioning system (H-IPS) that combines acoustic ranging, Wi-Fi fingerprinting, and low-cost sensors. This system is designed specifically for large-scale indoor environments with non-line-of-sight (NLOS) conditions. To improve the accuracy in estimating pedestrian motion trajectory, a data and model dual-driven (DMDD) model is proposed to integrate the inertial navigation system (INS) mechanization and the deep learning-based speed estimator. Additionally, a double-weighted K-nearest neighbor matching algorithm enhanced the accuracy of Wi-Fi fingerprinting and scene recognition. The detected scene results were then utilized for NLOS detection and estimation of acoustic ranging results. Finally, an adaptive unscented Kalman filter (AUKF) was developed to provide universal positioning performance, which further improved by the Wi-Fi accuracy indicator and acoustic drift estimator. The experimental results demonstrate that the presented H-IPS achieves precise positioning under NLOS scenes, with meter-level accuracy attainable within the coverage range of acoustic signals. Full article
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<p>Framework of the proposed H-IPS.</p>
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<p>(<b>a</b>) Structure of the deep learning framework. (<b>b</b>) Structure of the GRU.</p>
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<p>Schematic diagram of NLOS evaluation.</p>
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<p>Trajectories under different handheld modes.</p>
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<p>Performance comparison of Wi-Fi matching.</p>
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<p>Prediction errors comparison of MLP and GRU.</p>
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<p>Synchronization between the smartphone and acoustic stations.</p>
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<p>Test Route in Office Scene.</p>
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<p>Test route in the office scene.</p>
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<p>Experimental site.</p>
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<p>Comparison between different compensation approaches.</p>
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<p>Trajectory comparison of different combinations.</p>
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<p>Positioning errors of different structures.</p>
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19 pages, 7164 KiB  
Article
An Inversion Method for Geoacoustic Parameters in Shallow Water Based on Bottom Reflection Signals
by Zhuo Wang, Yuxuan Ma, Guangming Kan, Baohua Liu, Xinghua Zhou and Xiaobo Zhang
Remote Sens. 2023, 15(13), 3237; https://doi.org/10.3390/rs15133237 - 23 Jun 2023
Cited by 6 | Viewed by 1665
Abstract
The inversion method based on the reflection loss-grazing angle curve is an effective tool to obtain local underwater acoustic parameters. Because geoacoustic parameters vary in sensitivity to grazing angle, it is difficult to get accurate results in geoacoustic parameter inversion based on small-grazing-angle [...] Read more.
The inversion method based on the reflection loss-grazing angle curve is an effective tool to obtain local underwater acoustic parameters. Because geoacoustic parameters vary in sensitivity to grazing angle, it is difficult to get accurate results in geoacoustic parameter inversion based on small-grazing-angle data in shallow water. In addition, the normal-mode model commonly used in geoacoustic parameter inversion fails to meet the needs of accurate local sound field simulation as the influence of the secant integral is ignored. To solve these problems, an acoustic data acquisition scheme was rationally designed based on a sparker source, a fixed vertical array, and ship drifting with the swell, which could balance the trade-off among signal transmission efficiency and signal stability, and the actual local acoustic data at low-to-mid frequencies were acquired at wide grazing angles in the South Yellow Sea area. Furthermore, the bottom reflection coefficients (bottom reflection losses) corresponding to different grazing angles were calculated based on the wavenumber integration method. The local seafloor sediment parameters were then estimated using the genetic algorithm and the bottom reflection loss curve with wide grazing angles, obtaining more accurate local acoustic information. The seafloor acoustic velocity inverted is cp=1659 m/s and the sound attenuation is αp=0.656 dB/λ in the South Yellow Sea. Relevant experimental results indicate that the method described in this study is feasible for local inversion of geoacoustic parameters for seafloor sediments. Compared with conventional large-scale inversion methods, in areas where there are significant changes in the seabed sediment level, this method can obtain more accurate local acoustic features within small-scale areas. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Graphical abstract

Graphical abstract
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<p>Horizontally stratified environment.</p>
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<p>Map of the area around the survey station. S1 is the location of the survey station.</p>
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<p>Sound velocity profile (the blue line) at the survey station.</p>
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<p>Configuration schematic of the marine experimental devices.</p>
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<p>Schematic of the geometric relationship between the source and receivers.</p>
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<p>Curves of bottom reflection loss versus grazing angle for different seafloor acoustic velocities.</p>
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<p>Curves of bottom reflection loss versus grazing angle for different seafloor acoustic attenuation values.</p>
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<p>Schematic of the marine environment used for inversion.</p>
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<p>Theoretical reflection coefficient curve (the blue line) for the marine environment.</p>
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<p>Theoretical reflection loss curve (the dashed line) for the marine environment.</p>
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<p>Comparison between the inversion results (red asterisks) and the theoretical modeling results (green solid line).</p>
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<p>Schematics of the posteriori probability distributions of inverted acoustic P-wave velocity and sound attenuation.</p>
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<p>Some of the valid signals in channel 1 on Card1.</p>
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<p>Comparison among the results of filtering based on the wavelet transform: (<b>a</b>) original signal; (<b>b</b>) low-pass-filtered signal based on the wavelet transform; (<b>c</b>) spectrum of the original signal; (<b>d</b>) spectrum of the low-pass-filtered signal based on the wavelet transform.</p>
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<p>Comparison among the results of filtering based on the wavelet transform: (<b>a</b>) original signal; (<b>b</b>) low-pass-filtered signal based on the wavelet transform; (<b>c</b>) spectrum of the original signal; (<b>d</b>) spectrum of the low-pass-filtered signal based on the wavelet transform.</p>
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<p>Calculating eigenrays based on the ray model (horizontal distance = 100 m; depth of the receiving point = 30 m).</p>
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<p>Calculating response times of eigenray signals based on the ray model (horizontal distance = 100 m; depth of the receiving point = 30 m).</p>
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<p>Actual reflection coefficients.</p>
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<p>Actual reflection losses.</p>
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<p>Inverted reflection loss curve and actual reflection loss curve.</p>
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<p>Posterior probability distributions of inversion parameters.</p>
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16 pages, 9669 KiB  
Article
Water Temperature Reconstruction via Station Position Correction Method Based on Coastal Acoustic Tomography Systems
by Pan Xu, Shijie Xu, Fenyuan Yu, Yixin Gao, Guangming Li, Zhengliang Hu and Haocai Huang
Remote Sens. 2023, 15(8), 1965; https://doi.org/10.3390/rs15081965 - 7 Apr 2023
Cited by 3 | Viewed by 1689
Abstract
Underwater acoustic tomography is an advanced technology in water environment observation. Sound propagation duration between transceivers is used for underwater parameter distribution profile reconstruction in the inverse problem. The key points of acoustic tomography are accurate station distance and time synchronization. Two methods [...] Read more.
Underwater acoustic tomography is an advanced technology in water environment observation. Sound propagation duration between transceivers is used for underwater parameter distribution profile reconstruction in the inverse problem. The key points of acoustic tomography are accurate station distance and time synchronization. Two methods are introduced in this study for sound station position correction. The direct signal transmission correction (DSC) method corrects the multi-peak (expect direct ray) travel time via the travel time difference between different sound rays and reference direct ray. The ray-model position correction (RMC) method calculates exact station position by the station drift distance obtained from transceiver depth variations to correct direct ray travel time; the other multi-peak travel time is revised by the corrected direct ray travel time. A water temperature observation experiment was carried out in a reservoir using coastal acoustic tomography (CAT) systems to verify the flexibility of these two methods. Multi-ray arrival peaks are corrected using DSC and RMC methods; water temperature inversion results in a 2D vertical profile are obtained. The reliability of the method is proved by comparison with temperature depth sensor (TD) data. The methods improve the quality of initial data and can be attempted for further water environment observation in acoustic tomography observation studies. Full article
(This article belongs to the Special Issue Remote Sensing for Surface Water Monitoring)
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<p>Three-station position correction model: (<b>a</b>) shows the transceiver drift processing; (<b>b</b>) shows the projection of station drift in horizontal direction; and (<b>c</b>) shows station depth variation in the vertical section.</p>
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<p>Experiment setting: (<b>a</b>) is the map of Huangcai Reservoir and adjacent regions; experiment settings are magnified in (<b>b</b>), which shows the position of CAT stations (S1, S2, S3, and S4) and the position of TD array; (<b>c</b>) is the transmission mode between different stations; (<b>d</b>) is the table of station depth and distance; (<b>e</b>) is the sound speed profile measured by CTD during the experiment.</p>
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<p>Multi-peak identification of S1 to S3: (<b>a</b>,<b>b</b>) are the results of peak extraction from S1 to S3 and S3 to S1 after correlation. The abscissa axis is the travel times of signals; the ordinate axis is the time of sending signals; the peak height is the SNR value. Circles in different colors represent different travel times of the acoustic arrival signals and also match different sound ray paths. Red circles denote first peaks, green circles denote second peaks, and blue circles denote third peaks.</p>
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<p>Multi-peak identification of S1 to S4. (<b>a</b>,<b>b</b>) are the results of peak extraction from S1 to S4 and S4 to S1 after correlation. The meanings of coordinate axis and circles are same as in <a href="#remotesensing-15-01965-f003" class="html-fig">Figure 3</a>.</p>
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<p>Multi-peak identification of S3 to S4. (<b>a</b>,<b>b</b>) are the results of peak extraction from S3 to S4 and S4 to S3 after correlation. The meanings of coordinate axis and circles are same as in <a href="#remotesensing-15-01965-f003" class="html-fig">Figure 3</a>.</p>
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<p>Ray simulation and launch angle: (<b>a</b>) shows the sound ray structure of three station pairs. (<b>b</b>–<b>d</b>) are the relationships between S1 to S3, S1 to S4, and S3 to S4, respectively. The abscissa axis is the launch angle of signals; the ordinate axis is the corresponding ray length. The colors of rays and circles correspond to those in the multi-peak identification.</p>
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<p>Travel time correction of S1 to S3 via two methods. (<b>a</b>–<b>c</b>) are the initial travel time results of first peak, second peak, and third peak during the entire sampling period, respectively. Red lines denote the transmission from S1 to S3, and black lines denote the transmission from S3 to S1. (<b>d</b>–<b>f</b>) are the corrected travel time of first peak, second peak, and third peak via the two methods (direct signal transmission correction and ray-model position correction), respectively. The dotted lines denote DSC, and real lines denote RMS.</p>
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<p>Layer-averaged water temperature comparison of S1 to S3: (<b>a</b>) shows the temperature inversion results by initial travel time at three layers; (<b>b</b>) shows the temperature inversion results by corrected travel time at three layers.</p>
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<p>Temperature inversion comparison: (<b>a</b>,<b>b</b>) are the inversion temperature results compared with the TD data at the same depths of 5.5 m and 18 m, respectively; the red lines indicate S1–S3 inversion results, blue lines indicate S1–S4 inversion results, and black lines indicate TD temperature variations; (<b>c</b>–<b>h</b>) are the 2D water temperature color maps at 23:10, 23:25, and 23:30 of S1–S3 and S1–S4.</p>
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9 pages, 2105 KiB  
Article
Underwater Positioning System Based on Drifting Buoys and Acoustic Modems
by Pablo Otero, Álvaro Hernández-Romero, Miguel-Ángel Luque-Nieto and Alfonso Ariza
J. Mar. Sci. Eng. 2023, 11(4), 682; https://doi.org/10.3390/jmse11040682 - 23 Mar 2023
Cited by 8 | Viewed by 2497
Abstract
GNSS (Global Navigation Satellite System) positioning is not available underwater due to the very short range of electromagnetic waves in the sea water medium. In this article a LBL (Long Base Line) acoustic repeater system of the GNSS positioning is presented. The system [...] Read more.
GNSS (Global Navigation Satellite System) positioning is not available underwater due to the very short range of electromagnetic waves in the sea water medium. In this article a LBL (Long Base Line) acoustic repeater system of the GNSS positioning is presented. The system is hyperbolic, i.e., based on time differences and it does not need very accurate atomic clocks to synchronize repeaters. The system architecture and system calculations that demonstrate the feasibility of the solution are presented. The system uses four buoys that sequentially transmit their position and the time of the instant of transmission, for which they are equipped with GNSS receivers and acoustic modems. The buoys can be fixed or even drifting, but they are inexpensive devices, which pose no hazard to navigation and can be easily and quickly deployed for a specific underwater mission. The multilateration algorithm used in the receiver is presented. To simplify the algorithm, the depth of the receiver, measured by a depth sensor, is used. Results are presented for the position error of an underwater vehicle due to its displacement during the transmission frame of the four buoys. Full article
(This article belongs to the Special Issue Navigation and Localization for Autonomous Marine Vehicles)
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<p>System sketch.</p>
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<p>Frame structure.</p>
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<p>Error when the receiver is moving along a parallel.</p>
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<p>Error when the receiver is moving along a meridian.</p>
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