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25 pages, 3822 KiB  
Article
Doppler Compensation Techniques for M-Ary Sequence Spread Spectrum Signals Based on Correlation Cost Factors in Mobile Underwater Acoustic Communication
by Yubo Han, Shuping Han, Heng Zhao, Yaohui Hu, Jingfeng Xu and Gang Yang
J. Mar. Sci. Eng. 2024, 12(12), 2151; https://doi.org/10.3390/jmse12122151 - 25 Nov 2024
Abstract
Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received [...] Read more.
Unlike terrestrial radio, the speed of sound in the ocean is relatively slow, which results in mobile underwater M-ary spread spectrum communication typically exhibiting significant and variable multipath effects along with strong Doppler effects, leading to rapid carrier phase shifts in the received signal that severely impact decoding accuracy. This study aims to address the issue of rapid carrier phase shifts caused by significant time-varying Doppler shifts during mobile underwater M-SS communication. This paper innovatively proposes a method for updating matched filters based on correlation cost factors. By calculating the correlation cost factors for each received symbol, the method guides the direction of Doppler estimation and updates the matched filters. After identifying the optimal match, the received symbols are shifted, correlated, and decoded. Simulation and sea trial results indicate that this method demonstrates higher computational efficiency and improved decoding accuracy compared to traditional Doppler estimation matched filters under low signal-to-noise ratio conditions, and exhibits greater robustness under complex motion conditions. Full article
(This article belongs to the Section Ocean Engineering)
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Figure 1

Figure 1
<p>Underwater acoustic communication system model.</p>
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<p>Doppler estimation process.</p>
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<p>Frame structure.</p>
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<p>Time-varying channel impulse response (TVCIR) of KAU1 and KAU2, including both a 3D representation of the channel and its 2D projections.</p>
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<p>The BER curves for different reception methods under two types of channels with varying motion states are presented: (<b>a</b>) represents the BER curve for constant velocity motion under the KAU1 channel; (<b>b</b>) represents the BER curve for constant velocity motion under the KAU2 channel; (<b>c</b>) represents the BER curve for variable velocity motion under the KAU1 channel; (<b>d</b>) represents the BER curve for variable velocity motion under the KAU2 channel.</p>
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<p>(<b>a</b>) illustrates the variation in Doppler factors during variable velocity motion under two types of channels; (<b>b</b>) depicts the changes in acceleration during variable velocity motion under both channels (with identical acceleration variations).</p>
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<p>Map of sea trial scope of Laoshan Bay.</p>
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<p>(<b>a</b>) is the speed of the first group processes recorded by BDS. (<b>b</b>) is the first group processes channel impulse response.</p>
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<p>The accuracy rates of each frame obtained by different methods: (<b>a</b>,<b>b</b>) correspond to the first sea trial, (<b>c</b>,<b>d</b>) correspond to the second sea trial, and (<b>e</b>,<b>f</b>) correspond to the third sea trial.</p>
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<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (first group).</p>
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<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 32 and 33 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 32 and 33 by FDE-MSS (first frame of first group).</p>
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<p>(<b>a</b>) is the speed of the second group processes recorded by BDS, (<b>b</b>) is the second group processes channel impulse response.</p>
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<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (second group).</p>
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<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 3 and 4 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 3 and 4 by FDE-MSS (third frame of second group).</p>
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<p>(<b>a</b>) is the speed of the third group processes recorded by BDS, (<b>b</b>) is the third group processes channel impulse response.</p>
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<p>(<b>a</b>) is the CFO compensation estimated by CCF-MSS, (<b>b</b>) is the CFO compensation estimated by BDS output speed, (<b>c</b>) is the CFO compensation estimated by FDE-MSS (third group).</p>
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<p>(<b>a</b>,<b>c</b>) is despreading correlation peaks of symbols 21 and 31 by CCF-MSS, (<b>b</b>,<b>d</b>) is despreading correlation peaks of symbols 21 and 31 by FDE-MSS (thirteen frame of third group).</p>
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<p>BER curves of sea trial data with random ocean noise added under different motion states.</p>
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11 pages, 2800 KiB  
Article
A Data-Assisted and Inter-Symbol Spectrum Analysis-Based Speed Estimation Method for Radiated Signals from Moving Sources
by Gaohui Liu and Boquan Chen
Appl. Sci. 2024, 14(23), 10869; https://doi.org/10.3390/app142310869 - 24 Nov 2024
Viewed by 290
Abstract
Aiming at the problem of estimating the speed of M-ary Phase Shift Keying (MPSK) communication radiated sources and their carrying platform targets, this paper proposes a data-assisted and inter-symbol spectrum analysis-based speed estimation method for MPSK communication radiated sources. The method first demodulates [...] Read more.
Aiming at the problem of estimating the speed of M-ary Phase Shift Keying (MPSK) communication radiated sources and their carrying platform targets, this paper proposes a data-assisted and inter-symbol spectrum analysis-based speed estimation method for MPSK communication radiated sources. The method first demodulates a signal-carrying message symbol from the received MPSK signal; then segments the signal according to the symbol synchronization information and the symbol period; and then compensates the phase of the symbol waveform corresponding to the message data according to the demodulated message symbol; finally combines the phase-compensated symbol waveform data into a two-dimensional matrix and finds the Doppler frequency of the data at the same sampling moment of different symbols using the vertical Fourier transform to obtain the moving target speed. The speed measurement accuracy and anti-noise performance of the method are analyzed through simulation experiments, and the simulation results show that the speed measurement accuracy of the method is 98.5%. Full article
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Figure 1
<p>Schematic diagram of the speed estimation algorithm.</p>
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<p>Schematic diagram of the signal segmentation principle.</p>
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<p>Schematic diagram of transmission signal.</p>
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<p>Signal segmentation simulation diagram.</p>
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<p>Signal arrangement simulation diagram.</p>
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<p>Vertical FFT simulation of the unbalanced initial phase.</p>
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<p>Vertical FFT simulation diagram of the velocity 100 m/s after the initial equilibrium phase.</p>
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<p>Schematic diagram of the signal segmentation principle.</p>
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<p>Relationship between actual speed measurement accuracy and symbol period.</p>
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<p>SNR −10 dB speed 100 m/s simulation diagram.</p>
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<p>Simulation diagram of 100 m/s when there is one demodulation error within 10 symbols.</p>
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<p>Simulation diagram of 100 m/s when there is one demodulation error in 100 symbols.</p>
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22 pages, 2772 KiB  
Article
A Low-Cost Communication-Based Autonomous Underwater Vehicle Positioning System
by Raphaël Garin, Pierre-Jean Bouvet, Beatrice Tomasi, Philippe Forjonel and Charles Vanwynsberghe
J. Mar. Sci. Eng. 2024, 12(11), 1964; https://doi.org/10.3390/jmse12111964 - 1 Nov 2024
Viewed by 692
Abstract
Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the [...] Read more.
Underwater unmanned vehicles are complementary with human presence and manned vehicles for deeper and more complex environments. An autonomous underwater vechicle (AUV) has automation and long-range capacity compared to a cable-guided remotely operated vehicle (ROV). Navigation of AUVs is challenging due to the high absorption of radio-frequency signals underwater and the absence of a global navigation satellite system (GNSS). As a result, most navigation algorithms rely on inertial and acoustic signals; precise localization is then costly in addition to being independent from acoustic data communication. The purpose of this paper is to propose and analyze the performance of a novel low-cost simultaneous communication and localization algorithm. The considered scenario consists of an AUV that acoustically sends sensor or status data to a single fixed beacon. By estimating the Doppler shift and the range from this data exchange, the algorithm can provide a location estimate of the AUV. Using a robust state estimator, we analyze the algorithm over a survey path used for AUV mission planning both in numerical simulations and at-sea experiments. Full article
(This article belongs to the Special Issue Autonomous Marine Vehicle Operations—2nd Edition)
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Figure 1
<p>Positioning algorithm.</p>
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<p>Dynamic of the AUV.</p>
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<p>Architecture of the communication frame.</p>
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<p>Architecture of the communication decoder.</p>
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<p>Global architecture of the simulation script.</p>
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<p>Noise statistics added for each mesurements. The central mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The outliers are plotted individually using a red cross marker symbol.</p>
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<p>Two-dimensional top view of the averaged positioning estimation in comparison to the actual AUV position in black.</p>
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<p>Statistical performance results of the simulated positioning system as a function of mission time and the UWA communication period <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>c</mi> <mi>o</mi> <mi>m</mi> </mrow> </msub> </semantics></math>. <b>Up</b>: averaged positioning error (% etd), <b>middle</b>: error as a percentage of the traveled distance, and <b>down</b>: root mean square error (RMSE).</p>
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<p>Map of trajectory #1 made by the boat.</p>
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<p>Trajectory #1 (<b>left</b> ), trajectory #2 (<b>right</b>).</p>
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<p>Trajectory #3 (<b>left</b>), trajectory #4 (<b>right</b>).</p>
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<p>Experimental performance of positioning system for the 4-th selected trajectory as function of mission time. <b>Up</b>: averaged positioning error, and <b>down</b>: error dispersion (RMSE).</p>
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12 pages, 621 KiB  
Article
Maximum Doppler Shift Identification Using Decision Feedback Channel Estimation
by Yudai Handa, Hiroya Hayakawa, Riku Tanaka, Kosuke Tamura, Jaesang Cha and Chang-Jun Ahn
Electronics 2024, 13(20), 4113; https://doi.org/10.3390/electronics13204113 - 18 Oct 2024
Viewed by 538
Abstract
This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler [...] Read more.
This paper introduces a new method for estimating the maximum Doppler shift using decision feedback channel estimation (DFCE). In highly mobile environments, which are expected to be covered beyond 5G and 6G systems, the relative movement between the transmitter and receiver causes Doppler shifts. This leads to inter-carrier interference (ICI), significantly degrading communication quality. To mitigate this effect, systems that estimate the maximum Doppler shift and adaptively adjust communication parameters have been extensively studied. One of the most promising methods for maximum Doppler shift estimation involves inserting pilot signals at both the beginning and end of the packet. Although this method achieves high estimation accuracy, it introduces significant latency due to the insertion of the pilot signal at the packet’s end. To address this issue, this paper proposes a new method for rapid estimation using DFCE. The proposed approach compensates for faded signals using channel state information obtained from decision feedback. By treating the compensated signal as a reference, the Doppler shift can be accurately estimated without the need for pilot signals at the end of the packet. This method not only maintains high estimation accuracy but also significantly reduces the latency associated with conventional techniques, making it well-suited for the requirements of next-generation communication systems. Full article
(This article belongs to the Special Issue 5G and 6G Wireless Systems: Challenges, Insights, and Opportunities)
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Figure 1
<p>This diagram shows the transmitted packets used in the conventional methods.</p>
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<p>This diagram compares the transmitted packets used in the conventional and proposed methods.</p>
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<p>This diagram illustrates the principle of the DFCE.</p>
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<p>Detection accuracy when varying the parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math>.</p>
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<p>Detection time when varying the parameter <math display="inline"><semantics> <mi>γ</mi> </semantics></math>.</p>
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<p>Simulation results with the conventional method for various Eb/No values.</p>
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<p>Simulation results with the proposed method for various Eb/No values.</p>
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<p>Simulation results of processing time differences.</p>
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28 pages, 10257 KiB  
Article
Thomson Scattering and Radiation Reaction from a Laser-Driven Electron
by Ignacio Pastor, Luis Roso, Ramón F. Álvarez-Estrada and Francisco Castejón
Photonics 2024, 11(10), 971; https://doi.org/10.3390/photonics11100971 - 17 Oct 2024
Viewed by 704
Abstract
We investigate the dynamics of electrons initially counter-propagating to an ultra-fast ultra-intense near-infrared laser pulse using a model for radiation reaction based on the classical Landau–Lifshitz–Hartemann equation. The electrons, with initial energies of 1 GeV, interact with laser fields of up to [...] Read more.
We investigate the dynamics of electrons initially counter-propagating to an ultra-fast ultra-intense near-infrared laser pulse using a model for radiation reaction based on the classical Landau–Lifshitz–Hartemann equation. The electrons, with initial energies of 1 GeV, interact with laser fields of up to 1023 W/cm2. The radiation reaction effects slow down the electrons and significantly alter their trajectories, leading to distinctive Thomson scattering spectra and radiation patterns. It is proposed to use such spectra, which include contributions from harmonic and Doppler-shifted radiation, as a tool to measure laser intensity at focus. We discuss the feasibility of this approach for state-of-the-art and near-future laser technologies. We propose using Thomson scattering to measure the impact of radiation reaction on electron dynamics, thereby providing experimental scenarios for validating our model. This work aims to contribute to the understanding of electron behavior in ultra-intense laser fields and the role of radiation reaction in such extreme conditions. The specific properties of Thomson scattering associated with radiation reaction, shown to be dominant at the intensities of interest here, are highlighted and proposed as a diagnostic tool, both for this phenomenon itself and for laser characterization in a non-intrusive way. Full article
(This article belongs to the Special Issue Photon-Photon Collision Using Extreme Lasers)
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Figure 1
<p>Schematic representation of the studied process. Initially, the laser and the electron bunch are counter-propagating. The laser moves towards the positive Z-axis, and the electron bunch moves initially in the opposite direction. The upper figure shows the initial position with the electron bunch centered at <math display="inline"><semantics> <mrow> <msub> <mi>z</mi> <mn>00</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> and the laser pulse peak at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>24</mn> <msub> <mi>λ</mi> <mn>0</mn> </msub> </mrow> </semantics></math> (19.2 <math display="inline"><semantics> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Neglecting the interaction before this point is a reasonable assumption. Since the electron’s initial speed is very close to <span class="html-italic">c</span>, the electron would cross the laser pulse close to <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mo>−</mo> <mn>12</mn> <msub> <mi>λ</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. However, for extreme intensities, as we will describe, the trajectory of the electrons will be significantly modified.</p>
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<p>Schematic representation of the TS radiation pattern. Electrons driven exclusively by the laser polarization radiate mainly in the YZ plane (<b>a</b>). However, electrons driven by the laser magnetic field radiate mostly in the XY plane (<b>b</b>). Comparing Thomson radiation along these two planes provides relevant information on RR-induced dynamics. The YZ pattern, driven by the laser electric field, corresponds to even harmonics of the fundamental frequency. The XY radiation, due to coupling with the laser magnetic field, also shows strong even harmonics. Relativistic distortion of these donut shapes is not depicted for simplicity.</p>
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<p>Spectral location of the Thomson scattered main peak (i.e., <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>norm</mi> </msub> <mo>=</mo> <msub> <mi>ω</mi> <mi>peak</mi> </msub> <mo>/</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> </mrow> </semantics></math>), X-polarization, spectra averaged for an electron sample aimed at the central part of the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> laser pulse, including RR, as a function of <math display="inline"><semantics> <msub> <mi>θ</mi> <mi>s</mi> </msub> </semantics></math>. Scattering is computed in the YZ plane. Notice the extremely fast increase in the (blue-shifted) peak frequency with the scattering angle as the angle approaches <math display="inline"><semantics> <mi>π</mi> </semantics></math> (this corresponds to light scattered counter-propagating with respect to the laser).</p>
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<p>Integrated spectral power, X-polarization in red and the other orthogonal quadrature in blue, averaged for an electron sample aimed at the central part of the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> laser pulse, including RR, as a function of the scattering angle. Scattering plane is YZ. Notice that for a scattering angle of <math display="inline"><semantics> <mrow> <mi>π</mi> <mo>/</mo> <mn>2</mn> </mrow> </semantics></math>, the light is nearly completely polarized along the X-axis, and notice how the two orthogonal contributions become more balanced as the scattering angle approaches <math display="inline"><semantics> <mi>π</mi> </semantics></math>.</p>
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<p>Schematic representation of the angles where the TS spectra have been calculated. Labels (<b>a</b>–<b>i</b>) refer to the directions of observation of the spectra shown in the next figure.</p>
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<p>Averaged TS spectra including RR for several scattering angles in the YZ plane; see main text for details. (<b>a</b>–<b>i</b>) Scattering angles are, respectively, <math display="inline"><semantics> <mrow> <mn>64</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>72</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>80</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>88</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>96</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>104</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>120</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>126</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>127</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>. Observe that the last one, (<b>i</b>), corresponds to scattering almost perfectly counter-propagating to the laser pulse. The colors red and blue are the same as in the previous figure, with quadrature <math display="inline"><semantics> <msub> <mi>q</mi> <mn>1</mn> </msub> </semantics></math> in red (polarization along the electric field) and quadrature <math display="inline"><semantics> <msub> <mi>q</mi> <mn>2</mn> </msub> </semantics></math> in blue.</p>
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<p>Projection of a sample trajectory onto the XZ plane, including RR. The electron is initially located at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, moves from right to left, strongly interacts with the laser pulse around <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>12</mn> </mrow> </semantics></math>, and gets deflected, eventually becoming a free electron. The center of the laser pulse was initially at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>24</mn> </mrow> </semantics></math> and propagates from left to right along the Z axis.</p>
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<p>Time evolution of the relativistic <math display="inline"><semantics> <mi>γ</mi> </semantics></math> factor (<b>a</b>) and the normalized velocity (<b>b</b>) for the sample trajectory in <a href="#photonics-11-00971-f007" class="html-fig">Figure 7</a>. The significant reduction in the <math display="inline"><semantics> <mi>γ</mi> </semantics></math> factor (i.e., the electron energy) due to RR is evident, along with substantial perturbations in <math display="inline"><semantics> <msub> <mi>v</mi> <mi>x</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math>. Color code in (<b>b</b>): red, green, and blue correspond to <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, respectively.</p>
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<p>Radiated electric field at the detector in the time domain (<b>a</b>) and a close-up (<b>b</b>) displaying the spike structure under the radiated pulse envelope. The scattering plane is YZ. The electric field component parallel to the X axis is shown in red, while the other component is in blue. In (<b>b</b>), the component along the scattering vector is also displayed in green and is negligible compared to the other components.</p>
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<p>Asymptotic momentum components for counter-propagating 1 GeV electrons, including RR. (<b>a</b>–<b>c</b>) correspond respectively to the asymptotic distributions of <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math>. The sample contains 4096 electrons randomly distributed up to <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.25</mn> <msub> <mi>w</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <msub> <mi>w</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>4</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>. The laser is in the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> mode, X polarized, with a peak intensity of <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>22</mn> </msup> <mspace width="0.166667em"/> <msup> <mrow> <mi mathvariant="normal">W</mi> <mo>/</mo> <mi>cm</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>.</p>
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<p>Asymptotic kinetic energy for counter-propagating 1 GeV electrons with RR. The sample includes 4096 electrons randomly distributed up to <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.25</mn> <mo>×</mo> <msub> <mi>w</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, with <math display="inline"><semantics> <mrow> <msub> <mi>w</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>4</mn> <mo> </mo> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>. The laser is in the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> mode, X polarized, with a peak intensity of <math display="inline"><semantics> <mrow> <mn>5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>22</mn> </msup> <mspace width="0.166667em"/> <msup> <mrow> <mi mathvariant="normal">W</mi> <mo>/</mo> <mi>cm</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>. A strong reduction in kinetic energy is predicted under these conditions with RR. Contour plots of iso-energy show an approximate circular symmetry around the laser propagation axis.</p>
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<p>Integrated spectral power: X-polarization in red and the orthogonal quadrature (q2) in blue, averaged over electron samples (typically 2048 or 4096 at each scattering angle) aimed at the central part of the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> laser pulse, including RR, as a function of the scattering angle. The scattering plane is YZ. Notice the rapid increase in integrated power with the scattering angle.</p>
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<p>Averaged TS spectra. The scattering plane is YZ. Scattering angles for (<b>a</b>–<b>i</b>) are respectively <math display="inline"><semantics> <mrow> <mn>64</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>68</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>72</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>76</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>80</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>84</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>88</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>92</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>96</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>. A spectrum extending up to <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>/</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>≥</mo> <mn>3000</mn> </mrow> </semantics></math> is obtained for <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>96</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>.</p>
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<p>Comparison of TS spectra (X-quadrature in red, q2-quadrature in blue) with (<b>a</b>) and without (<b>b</b>) RR. The scattering angle is <math display="inline"><semantics> <mrow> <mn>72</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>. The shape, amplitude, and power ratio of the two quadratures are altered by RR, which can help distinguish and detect RR signatures in the spectra. See more details in the main text.</p>
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<p>Projection of a sample trajectory onto the X-Z plane, including RR. The electron, initially at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, moves from right to left, interacts strongly with the laser pulse around <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>11</mn> </mrow> </semantics></math>, and is deflected nearly <math display="inline"><semantics> <msup> <mn>45</mn> <mo>∘</mo> </msup> </semantics></math> from its initial direction. Note the reversal in electron motion after deceleration (curly part moving towards the positive side of the <span class="html-italic">z</span>-axis).</p>
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<p>Time evolution of the relativistic <math display="inline"><semantics> <mi>γ</mi> </semantics></math> factor (<b>a</b>) and normalized velocity (<b>b</b>) for the trajectory shown in <a href="#photonics-11-00971-f015" class="html-fig">Figure 15</a>. The strong reduction in <math display="inline"><semantics> <mi>γ</mi> </semantics></math> due to RR is evident, along with substantial perturbations in <math display="inline"><semantics> <msub> <mi>v</mi> <mi>x</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math>, and to a lesser extent in <math display="inline"><semantics> <msub> <mi>v</mi> <mi>y</mi> </msub> </semantics></math>. Note that <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math> reverses from counter-propagating to co-propagating at various points in the trajectory. In (<b>b</b>), the color code is as follows: red, green, and blue represent <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, respectively.</p>
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<p>Radiated electric field at the detector in the time domain (<b>a</b>) and a close-up view (<b>b</b>) showing the structure of spikes under the radiated pulse envelope. The scattering plane is Y-Z. The electric field component parallel to the X-axis is shown in red, while the other component is shown in blue. In (<b>b</b>), the component along the scattering vector is also displayed (in green), which is negligible compared to the other components.</p>
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<p>Three-dimensional asymptotic distributions of <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math> (<b>a</b>), <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math> (<b>b</b>), and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math> (<b>c</b>) as functions of the initial transverse position of the electrons. The panels show 4096 sample electrons, uniformly and randomly distributed up to <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.25</mn> <msub> <mi>w</mi> <mn>0</mn> </msub> </mrow> </semantics></math>.</p>
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<p>Asymptotic distribution of <math display="inline"><semantics> <msub> <mi>E</mi> <mi>kin</mi> </msub> </semantics></math> projected onto the X-axis. The panel includes 4096 sample electrons, uniformly and randomly distributed up to <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.25</mn> <mo>×</mo> <msub> <mi>w</mi> <mn>0</mn> </msub> </mrow> </semantics></math>. RR accounts for up to 98.5% loss of the initial kinetic energy for electrons near the center of the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> mode laser axis.</p>
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<p>Averaged TS spectra. The scattering plane is YZ, and scattering angles for (<b>a</b>–<b>i</b>) are, respectively, <math display="inline"><semantics> <mrow> <mn>48</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>56</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>60</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>64</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>68</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>72</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>76</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>80</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>96</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>. An extremely broad spectrum, extending well beyond <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>/</mo> <msub> <mi>ω</mi> <mn>0</mn> </msub> <mo>≥</mo> <mn>15</mn> <mo>,</mo> <mn>000</mn> </mrow> </semantics></math>, is obtained for <math display="inline"><semantics> <mrow> <msub> <mi>θ</mi> <mi>s</mi> </msub> <mo>=</mo> <mn>96</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math>.</p>
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<p>Visualization of the scattering process on a unit momentum sphere. Each point on the sphere’s surface represents the asymptotic direction of electron scattering; dot colors indicate asymptotic energy, with dark blue representing minimum and red representing maximum energy.</p>
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<p>Projection of a sample trajectory onto the XZ plane, including RR. The electron is initially located at <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math>, moves from right to left, strongly interacts with the laser pulse around <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>/</mo> <msub> <mi>λ</mi> <mn>0</mn> </msub> <mo>=</mo> <mo>−</mo> <mn>10.5</mn> </mrow> </semantics></math>, and is deflected at an angle larger than <math display="inline"><semantics> <msup> <mn>90</mn> <mo>∘</mo> </msup> </semantics></math> with respect to its initial direction. The <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math> (or <math display="inline"><semantics> <msub> <mi>p</mi> <mi>z</mi> </msub> </semantics></math>) component changes from counter-propagating to co-propagating. The thick gray line indicates the initial position of the electron bunch. For this extreme intensity, the electron trajectory is reversed.</p>
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<p>Time evolution of the relativistic <math display="inline"><semantics> <mi>γ</mi> </semantics></math> factor (<b>a</b>) and of the normalized velocity (<b>b</b>) for the sample trajectory in <a href="#photonics-11-00971-f022" class="html-fig">Figure 22</a>. The strong reduction in <math display="inline"><semantics> <mi>γ</mi> </semantics></math> due to RR is apparent, as well as the large perturbations on <math display="inline"><semantics> <msub> <mi>v</mi> <mi>x</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math>, and also to some extent on <math display="inline"><semantics> <msub> <mi>v</mi> <mi>y</mi> </msub> </semantics></math>. Note that <math display="inline"><semantics> <msub> <mi>v</mi> <mi>z</mi> </msub> </semantics></math> asymptotically reverses from counter-propagating to co-propagating. Color code in (<b>b</b>) is red, green, and blue corresponding to <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>c</mi> </mrow> </semantics></math>, respectively.</p>
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<p>Radiated electric field at the detector in the time domain (<b>a</b>) and a close-up (<b>b</b>) displaying the structure of the spikes under the radiated pulse envelope. The scattering plane is YZ. The electric field component parallel to the X axis is displayed in red, the other one in blue. In (<b>b</b>), the component along the scattering vector is also displayed (in green), showing it to be negligible compared to the other components. Note the highly asymmetric shape of the electric pulse envelope in this particular case.</p>
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<p>Three-dimensional asymptotic normalized momentum components for counter-propagating 1 GeV electrons. (<b>a</b>–<b>c</b>) correspond respectively to the asymptotic distributions of <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>x</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>y</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mi>z</mi> </msub> <mo>/</mo> <mi>m</mi> <mi>c</mi> </mrow> </semantics></math>. The sample includes 4096 electrons randomly distributed up to <math display="inline"><semantics> <mrow> <msub> <mi>ρ</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.25</mn> <mo>×</mo> <msub> <mi>w</mi> <mn>0</mn> </msub> </mrow> </semantics></math>, where <math display="inline"><semantics> <msub> <mi>w</mi> <mn>0</mn> </msub> </semantics></math> is 4 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m. The laser is in the <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> mode, X polarized, with a peak intensity of <math display="inline"><semantics> <mrow> <msup> <mn>10</mn> <mn>23</mn> </msup> <mspace width="0.166667em"/> <msup> <mrow> <mi mathvariant="normal">W</mi> <mo>/</mo> <mi>cm</mi> </mrow> <mn>2</mn> </msup> </mrow> </semantics></math>. Close to the center of the laser <math display="inline"><semantics> <msub> <mi>TEM</mi> <mn>00</mn> </msub> </semantics></math> mode, RR accounts for up to 99 percent energy loss.</p>
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<p>Asymptotic velocity distribution (final velocity of the electrons) depicted over the unit sphere. The pink circle is the equator of the unit sphere and the gray circle crosses the unit sphere poles. The value of the final velocity is given by the color scale. The orange dotted ribbon indicates the multiple trajectories with asymptotic speeds that have been reversed, i.e., points towards the positive values of <span class="html-italic">z</span>.</p>
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<p>A comparison of TS spectra (X-quadrature in red, q2-quadrature in blue) with (<b>a</b>) and without (<b>b</b>) RR. The scattering angle is <math display="inline"><semantics> <mrow> <mn>28</mn> <mi>π</mi> <mo>/</mo> <mn>128</mn> </mrow> </semantics></math> in this case. The shape, amplitude, and power ratio of the two quadratures are altered by RR, which can help distinguish and detect RR signatures in the spectra. See more details in the main text.</p>
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17 pages, 6781 KiB  
Communication
An Iterative Orthogonal Frequency Division Multiplexing Receiver with Sequential Inter-Carrier Interference Canceling Modified Delay and Doppler Profiler for an Underwater Multipath Channel
by Suguru Kuniyoshi, Shiho Oshiro, Rie Saotome and Tomohisa Wada
J. Mar. Sci. Eng. 2024, 12(10), 1712; https://doi.org/10.3390/jmse12101712 - 27 Sep 2024
Viewed by 573
Abstract
In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was [...] Read more.
In 2023, we proposed the modified delay and Doppler profiler (mDDP) as an inter-carrier interference (ICI) countermeasure for underwater acoustic orthogonal frequency division multiplexing (OFDM) mobile communications in a multipath environment. However, the performance improvement in the computer simulation and pool experiments was not significant. In a subsequent study, the accuracy of the channel transfer function (CTF), which is the input for the mDDP channel parameter estimation, was considered insufficient. Then a sequential ICI canceling mDDP was devised. This paper presents simulations of underwater OFDM communications using an iterative one- to three-step mDDP. The non-reflective pool experiment conditions are a two-wave multipath environment where the receiving transducer moves at a speed of 0.25 m/s and is subjected to a Doppler shift in the opposite direction. As NumCOL, the number of taps in the multitap equalizer which removes ICI, was increased, the bit error rate (BER) of 0.0526661 at NumCOL = 1 was significantly reduced by a factor of approximately 45 to a BER of 0.0011655 at NumCOL = 51 for the sequential ICI canceling mDDP. Full article
(This article belongs to the Section Ocean Engineering)
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<p>UWA communication under multipath Doppler condition.</p>
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<p>OFDM signal after the shrink and expansion processing.</p>
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<p>Block diagram of previous receiver.</p>
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<p>Time–frequency representation of OFDM signal of transmitter and receiver.</p>
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<p>Block diagram of proposed iterative multistep mDDP receiver.</p>
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<p>Flowchart of iterative multistep modified delay and Doppler profiler.</p>
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<p>Computer simulation model assuming reverse two-path multipath condition.</p>
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<p>Delay time and DUR in simulation.</p>
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<p>Result of computer simulation assuming time dependencies of measured BER and constellation (real part of complex).</p>
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<p>Result of computer simulation assuming constellations in moving period.</p>
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<p>Comparison of simulated BER.</p>
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<p>BER vs. CNR of conventional and iterative mDDP on NumCOL = 21.</p>
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<p>Non-reflective pool experiment environment.</p>
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<p>The received spectrum on RX transducer.</p>
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<p>Results of pool experiment environment time dependencies of measured BER and constellation (real part of complex).</p>
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<p>BER comparison of non-reflective pool experiment.</p>
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19 pages, 9732 KiB  
Article
Improved Methods for Retrieval of Chlorophyll Fluorescence from Satellite Observation in the Far-Red Band Using Singular Value Decomposition Algorithm
by Kewei Zhu, Mingmin Zou, Shuli Sheng, Xuwen Wang, Tianqi Liu, Yongping Cheng and Hui Wang
Remote Sens. 2024, 16(18), 3441; https://doi.org/10.3390/rs16183441 - 17 Sep 2024
Viewed by 821
Abstract
Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected [...] Read more.
Solar-induced chlorophyll fluorescence (SIF) is highly correlated with photosynthesis and can be used for estimating gross primary productivity (GPP) and monitoring vegetation stress. The far-red band of the solar Fraunhofer lines (FLs) is close to the strongest SIF emission peak and is unaffected by chlorophyll absorption, making it suitable for SIF intensity retrieval. In this study, we propose a retrieval window for far-red SIF, significantly enhancing the sensitivity of data-driven methods to SIF signals near 757 nm. This window introduces a weak O2 absorption band based on the FLs window, allowing for better separation of SIF signals from satellite spectra by altering the shape of specific singular vectors. Additionally, a frequency shift correction algorithm based on standard non-shifted reference spectra is proposed to discuss and eliminate the influence of the Doppler effect. SIF intensity retrieval was achieved using data from the GOSAT satellite, and the retrieved SIF was validated using GPP, enhanced vegetation index (EVI) from the MODIS platform, and published GOSAT SIF products. The validation results indicate that the SIF products provided in this study exhibit higher fitting goodness with GPP and EVI at high spatiotemporal resolutions, with improvements ranging from 55% to 129%. At low spatiotemporal resolutions, the SIF product provided in this study shows higher consistency with EVI and GPP spatially. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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<p>Singular vectors in the forward model and the state vector of Fs within two retrieval windows: (<b>a</b>) FLs band, (<b>b</b>) joint retrieval for FLs-O<sub>2</sub> absorption bands.</p>
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<p>The identical set of spectra before and after wavenumber correction. For the sake of clarity, only a limited portion of the FTS-Band1 spectrum is displayed.</p>
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<p>Scatter plot and goodness-of-fit (R<sup>2</sup>), Pearson correlation coefficient (P) of monthly SIF products with GPP and VI for January 2019 at 0.1° spatial resolution.</p>
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<p>Goodness-of-fit (GOF) of the two SIF products with GPP and EVI at 0.1° spatial resolution and monthly scale from January 2018 to June 2020. The shaded part corresponds to December 2018 when SIF retrieves too few results.</p>
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<p>Pearson correlation coefficients (<span class="html-italic">p</span>-values) of the two SIF products with GPP and EVI at 0.1° spatial resolution and monthly scale from January 2018 to June 2020. The shaded part corresponds to December 2018 when SIF retrieves too few results.</p>
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<p>Scatter plot and goodness-of-fit (R), Pearson correlation coefficient (P) of 2019 annual SIF products with GPP and VI at 0.1° spatial resolution.</p>
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<p>Scatter plot and goodness-of-fit (R), Pearson correlation coefficient (P) of 2019 annual SIF products with GPP and VI at 2° spatial resolution.</p>
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<p>Intensity distribution of the two 2019 annual mean SIF products and 2019 annual mean EVI, GPP in 0.1° grid units. To facilitate observation, morphological dilation was applied to the SIF intensity distribution images.</p>
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<p>Intensity distribution of the two 2019 annual solar-normalized SIF products and annual mean EVI, GPP in 2° grid units.</p>
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<p>Maps of the mean 2019 annual intensity distribution for the two SIF products at 2° grid cells and of the mean annual results for EVI at 0.1° grid cells. The color–intensity relationship is the same for each row of subplots. The maps contain four regions: Northern South America, the United States and southern Canada, Western Europe, and southern Africa.</p>
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<p>Slope and GOF of the linear fit between the SIF retrieval results from the combined retrieval window (O<sub>2</sub> absorption and FLs bands) and FLs band alone with GPP/EVI.</p>
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<p>The first six singular vectors and the seventh to eighth singular vectors obtained from the spectra of the training set before and after frequency shift correction.</p>
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<p>Scatter plots and linear fitting results of retrieval outcomes with GPP and EVI before and after satellite spectral frequency shift correction in January 2019.</p>
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<p>The intensity distribution of the existing daily average SIF (<b>a</b>) and the proposed daily average SIF (<b>b</b>) is presented for the entire year of 2019 under 10.5 Km × 10.5 Km spatial resolution. Additionally, the intensity distribution of monthly MODIS Enhanced Vegetation Index (EVI) (<b>c</b>) under 0.5° grid cells and annual GPP (<b>d</b>) under 500 m SIN grid is displayed for the entire year of 2019.</p>
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19 pages, 5157 KiB  
Article
Underwater Acoustic Orthogonal Frequency-Division Multiplexing Communication Using Deep Neural Network-Based Receiver: River Trial Results
by Sabna Thenginthody Hassan, Peng Chen, Yue Rong and Kit Yan Chan
Sensors 2024, 24(18), 5995; https://doi.org/10.3390/s24185995 - 15 Sep 2024
Viewed by 756
Abstract
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits [...] Read more.
In this article, a deep neural network (DNN)-based underwater acoustic (UA) communication receiver is proposed. Conventional orthogonal frequency-division multiplexing (OFDM) receivers perform channel estimation using linear interpolation. However, due to the significant delay spread in multipath UA channels, the frequency response often exhibits strong non-linearity between pilot subcarriers. Since the channel delay profile is generally unknown, this non-linearity cannot be modeled precisely. A neural network (NN)-based receiver effectively tackles this challenge by learning and compensating for the non-linearity through NN training. The performance of the DNN-based UA communication receiver was tested recently in river trials in Western Australia. The results obtained from the trials prove that the DNN-based receiver performs better than the conventional least-squares (LS) estimator-based receiver. This paper suggests that UA communication using DNN receivers holds great potential for revolutionizing underwater communication systems, enabling higher data rates, improved reliability, and enhanced adaptability to changing underwater conditions. Full article
(This article belongs to the Special Issue Advanced Acoustic Sensing Technology)
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<p>Structure of a neuron.</p>
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<p>ReLU and leaky ReLU [<a href="#B26-sensors-24-05995" class="html-bibr">26</a>].</p>
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<p>NN training process.</p>
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<p>Architecture of an LSTM layer.</p>
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<p>Architecture of the CNN-based receiver.</p>
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<p>Shelly Jetty, Western Australia.</p>
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<p>Frame structure.</p>
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<p>Block diagram of the transmitter.</p>
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<p>Block diagram of the receiver.</p>
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<p>Architecture of the LSTM-based receiver.</p>
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<p>River trial setup.</p>
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<p>BER performance of the proposed NN-based receiver.</p>
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<p>Tank setup.</p>
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<p>Indoor tank channel profile.</p>
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<p>River trial 1 channel profile.</p>
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<p>BER performance of the NN-based receivers in river trial 1.</p>
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<p>River trial 2 channel profile.</p>
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<p>BER performance of the NN-based receivers in river trial 2 with 1000 packets for testing, 4 layers and 200 epochs.</p>
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<p>BER performance of the NN-based receivers with 2000 packets for training, 2000 packets for testing in river trial 2.</p>
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18 pages, 7384 KiB  
Article
Characteristics Analysis of Acoustic Doppler Current Profile Measurements in Northeast Taiwan Offshore
by Chung-Ru Ho, Kai-Ho Cheng, Zhe-Wen Zheng, Hung-Jen Lee and Tai-Wen Hsu
J. Mar. Sci. Eng. 2024, 12(9), 1632; https://doi.org/10.3390/jmse12091632 - 12 Sep 2024
Viewed by 540
Abstract
A comprehensive study was conducted at a wave energy device test site located off the northeastern coast of Taiwan to assess the influence of oceanic currents on experimental equipment. A bottom-mounted 600 kHz acoustic Doppler current profiler, equipped with integrated temperature and pressure [...] Read more.
A comprehensive study was conducted at a wave energy device test site located off the northeastern coast of Taiwan to assess the influence of oceanic currents on experimental equipment. A bottom-mounted 600 kHz acoustic Doppler current profiler, equipped with integrated temperature and pressure sensors, was deployed at a depth of approximately 31 m. This study, spanning from 6 June 2023 to 11 May 2024, recorded ocean current profiles by assembling data from 50 pings every 10 min, with a resolution of one meter per depth layer. The findings reveal that variations in water levels were predominantly influenced by the M2 tidal constituent, followed by the O1, K1, and S2 tides. Notably, seawater temperature fluctuations at the seabed were modulated by tides, especially the M2 tide. A significant drop in seawater temperature was also observed as the typhoon passed through the south of Taiwan. In terms of sea surface currents, the measured maximum current speed was 71.89 cm s−1, but the average current speed was only 15.47 cm s−1. Tidal currents indicated that the M4 and M2 tides were the most significant, with semimajor axes and inclination angles of 8.48 cm s−1 and 102.60°, and 7.00 cm s−1 and 97.76°, respectively. Seasonally, barotropic tidal currents were the strongest in winter. Additionally, internal tides were identified, with the first baroclinic mode being dominant. The zero-crossing depths varied between 14 and 18 m. During the summer, the M2 baroclinic tidal current displayed characteristics of the second baroclinic mode, with zero-crossing depths at approximately 7 m and 22 m. This node aligns with results from the empirical orthogonal function analysis and correlates with the depths’ significant shifts in seawater temperature as measured by a conductivity, temperature, and depth instrument. Despite the velocities of internal tides not being strong, the directional variance between surface and bottom flows presents critical considerations for the deployment and operation of moored wave energy devices. Full article
(This article belongs to the Special Issue Ocean Observations)
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<p>The location of the ADCP deployed.</p>
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<p>Time series of sea level.</p>
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<p>Power spectrum of sea level.</p>
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<p>Time series of seawater temperature at the depth of the sensor.</p>
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<p>The path of the typhoon approaching Taiwan during the measuring period. The asterisks in the figure are the ADCP deployment locations. Typhoon path data are taken from the National Centers for Environmental Information, National Oceanic and Atmospheric Administration at <a href="https://www.ncei.noaa.gov/products/international-best-track-archive" target="_blank">https://www.ncei.noaa.gov/products/international-best-track-archive</a> (accessed on 1 September 2024).</p>
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<p>Power spectrum of seawater temperature.</p>
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<p>Time series of ocean current in summer (<b>a</b>), autumn (<b>b</b>), winter (<b>c</b>), and spring (<b>d</b>). Only one month per season is plotted to avoid overcrowding the plot.</p>
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<p>Tidal ellipses for the main tidal constituents of each layer throughout the entire measurement time (<b>a</b>) and during summer (<b>b</b>), autumn (<b>c</b>), winter (<b>d</b>), and spring (<b>e</b>).</p>
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<p>Tidal ellipses for barotropic current in the M2, S2, K1, O1, M4, and MS4 tidal constituents throughout the entire measurement time (<b>a</b>), and in summer (<b>b</b>), autumn (<b>c</b>), winter (<b>d</b>), and spring (<b>e</b>).</p>
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<p>The baroclinic tidal ellipses at each layer of six tidal constituents for the entire measurement period (<b>a</b>), summer (<b>b</b>), autumn (<b>c</b>), winter (<b>d</b>), and spring (<b>e</b>).</p>
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<p>Seawater temperature profile measured by CTD on 11 May 2024.</p>
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<p>EOF analysis of the first baroclinic mode of the M2 and M4 tides over the entire measurement period (<b>a</b>) and in summer (<b>b</b>), autumn (<b>c</b>), winter (<b>d</b>), and spring (<b>e</b>). The <span class="html-italic">u</span> component is in blue and the <span class="html-italic">v</span> component is in red, with the baroclinic tidal ellipses superimposed.</p>
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<p>The power spectrum of surface current.</p>
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<p>Rotary spectra for counterclockwise (CCW, solid line) and clockwise (CW, dashed line) in the frequencies of M2, S2, M4, and MS4 tidal constituents throughout the entire measurement time (<b>a</b>), in summer (<b>b</b>), autumn (<b>c</b>), winter (<b>d</b>), and spring (<b>e</b>).</p>
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24 pages, 11255 KiB  
Article
On-Orbit Wavelength Calibration Error Analysis of the Spaceborne Hyperspectral Greenhouse Gas Monitoring Instrument Using the Solar Fraunhofer Lines
by Yulong Guo, Cailan Gong, Yong Hu, Fuqiang Zheng and Yunmeng Liu
Remote Sens. 2024, 16(18), 3367; https://doi.org/10.3390/rs16183367 - 10 Sep 2024
Viewed by 600
Abstract
Accurate on-orbit wavelength calibration of the spaceborne hyperspectral payload is the key to the quantitative analysis and application of observational data. Due to the high spectral resolution of general spaceborne hyperspectral greenhouse gas (GHG) detection instruments, the common Fraunhofer lines in the solar [...] Read more.
Accurate on-orbit wavelength calibration of the spaceborne hyperspectral payload is the key to the quantitative analysis and application of observational data. Due to the high spectral resolution of general spaceborne hyperspectral greenhouse gas (GHG) detection instruments, the common Fraunhofer lines in the solar atmosphere can be used as a reference for on-orbit wavelength calibration. Based on the performances of a GHG detection instrument under development, this study simulated the instrument’s solar-viewing measurement spectra and analyzed the main sources of errors in the on-orbit wavelength calibration method of the instrument using the solar Fraunhofer lines, including the Doppler shift correction error, the instrumental measurement error, and the peak-seek algorithm error. The calibration accuracy was independently calculated for 65 Fraunhofer lines within the spectral range of the instrument. The results show that the wavelength calibration accuracy is mainly affected by the asymmetry of the Fraunhofer lines and the random error associated with instrument measurement, and it can cause calibration errors of more than 1/10 of the spectral resolution at maximum. A total of 49 Fraunhofer lines that meet the requirements for calibration accuracy were screened based on the design parameters of the instrument. Due to the uncertainty of simulation, the results in this study have inherent limitations, but provide valuable insights for quantitatively analyzing the errors of the on-orbit wavelength calibration method using the Fraunhofer lines, evaluating the influence of instrumental parameters on the calibration accuracy, and enhancing the accuracy of on-orbit wavelength calibration for similar GHG detection payloads. Full article
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<p>Super-Gaussian functions of different orders <span class="html-italic">n</span> and the other parameters of the function are as follows: <math display="inline"><semantics> <mrow> <mi>a</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>λ</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> <mo>=</mo> <mn>2060.0</mn> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mi>F</mi> <mi>W</mi> <mi>H</mi> <mi>M</mi> <mo>=</mo> <mn>0.1</mn> </mrow> </semantics></math>. When <math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math>, the function is identical to a general Gaussian function.</p>
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<p>Positions of 21 typical Fraunhofer lines in solar reference irradiance spectra of the three bands, where the positions of Fraunhofer lines are marked by a red “x”.</p>
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<p>Simulations of the ideal instrumental solar observation values in the WCO<sub>2</sub> band, where the ILS was simulated using a general Gaussian function with FWHM = 0.08 nm: (<b>a</b>) the high-resolution Kurucz solar irradiance spectrum in the WCO<sub>2</sub> band; (<b>b</b>) simulated instrumental solar measurement DN values.</p>
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<p>Flow chart of the simulation to calculate instrumental solar measurement spectrum.</p>
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<p>Simulation spectrum of the instrumental solar observation in the SCO<sub>2</sub> band before (red line) and after (blue line) adding measurement uncertainty, where SNR = 750:1@8.3 × 10<sup>19</sup> <math display="inline"><semantics> <mrow> <mi mathvariant="normal">p</mi> <mi mathvariant="normal">h</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">o</mi> <mi mathvariant="normal">n</mi> <mo>·</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>·</mo> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> <mo>·</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> <mi mathvariant="normal">r</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> <mo>·</mo> <msup> <mrow> <mi mathvariant="sans-serif">μ</mi> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> <mo>=</mo> </mrow> </semantics></math>0.2%.</p>
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<p>Peak-seek using the Gaussian fitting algorithm to the inverted normalized simulated measurement Fraunhofer lines at 1596.446 nm in the WCO<sub>2</sub> band: the blue dots represent the inverted normalized simulated measurement values; the black line is the fitted Gaussian function curve; the red “+” is the peak position of fitted Gaussian function curve; and the blue dashed line marks the actual position of the Fraunhofer absorption peaks in the reference spectrum. The peak-seek position of the measured Fraunhofer line at 1596.446 nm is 1596.4427 nm, and the offset of peak-seek is 0.0033 nm.</p>
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<p>The flow of the on-orbit wavelength calibration method based on the solar Fraunhofer lines.</p>
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<p>The wavelength shifts at the central wavelength of different spectral channels in the three bands caused by the Doppler effect. The Doppler shifts of the starting and ending wavelengths of the three bands are 0.000327 nm, 0.000677 nm, and 0.000910 nm, respectively.</p>
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<p>The average Doppler shift correction error of the three bands caused by ±5% relative velocity calculation when <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>r</mi> <mi>e</mi> <mi>l</mi> </mrow> </msub> <mo>=</mo> <mn>7.0</mn> <mo> </mo> <mi mathvariant="normal">k</mi> <mi mathvariant="normal">m</mi> <mo>·</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> (red “x”).</p>
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<p>Peak-seek algorithm applied to the simulated measurement of Fraunhofer lines at 1612.039 nm and 1613.034 nm in the WCO<sub>2</sub> band: (<b>a</b>) the high-resolution Kurucz solar spectrum at 1612.039 nm and 1613.034 nm; (<b>b</b>) the simulated measurement spectra at the ILS with a general Gaussian function and FWHM = 0.08 nm; (<b>c</b>) peak-seek algorithm using Gaussian fitting to the normalized simulated measurement of Fraunhofer lines.</p>
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<p>The peak-seek errors (blue squares) of Fraunhofer lines at 1612.039 nm and 1613.034 nm fluctuate around the mean value (red dashed line) affected by the shift in the central wavelength position of each sampling point.</p>
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<p>The peak-seek errors at 1612.039 nm when (<b>a</b>) FWHM = 0.10 nm and (<b>b</b>) FWHM = 0.06 nm. The systematic error caused by the asymmetry of the Fraunhofer line decreases from 0.0138 nm to 0.0093 nm as the spectral resolution changes from 0.10 nm to 0.06 nm.</p>
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<p>Variation in the peak-seek error of the 7 Fraunhofer lines in the WCO<sub>2</sub> band with spectral resolution, which changes from 0.06 nm to 0.12 nm.</p>
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<p>Variation in the peak-seek error of the 7 Fraunhofer lines in the WCO<sub>2</sub> band with the super-Gaussian function order of ILS, which changes from 1.5 to 3.5.</p>
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<p>Comparison of the maximum peak-seek errors (“x”) of the 21 Fraunhofer lines (<b>a</b>) before and (<b>b</b>) after removing the average systematic error in three bands.</p>
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<p>Comparison of the maximum peak-seek errors (“x”) of the 21 Fraunhofer lines (<b>a</b>) before and (<b>b</b>) after removing the average systematic error in three bands.</p>
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<p>The peak-seek obtained by Gaussian fitting (<b>a</b>) before and (<b>b</b>) after adding noise at the 2045.000 nm Fraunhofer line. The peak-seek error increases from 0.0008 nm to 0.0027 nm due to the influence of the noise signal (SNR = 750), and thus the random error due to noise is 0.0019 nm.</p>
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<p>Peak-seek errors of the Fraunhofer lines at (<b>a</b>) 2056.96 nm and (<b>b</b>) 2063.533 nm before (left figure) and after (right figure) removing the systematic error. The blue data points denote the peak-seek position error values of 400 experiments.</p>
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<p>Peak-seek errors of the Fraunhofer lines at (<b>a</b>) 2045.0 nm, (<b>b</b>) 2054.431 nm, and (<b>c</b>) 2074.254 nm when the SNR is 800, 650, and 500, where the red line is the boundary of FWHM/10.</p>
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<p>Variation in the peak-seek error of the 7 Fraunhofer lines in the SCO<sub>2</sub> band with SNR changing from 500 to 850.</p>
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<p>Variation in the peak-seek error of the Fraunhofer lines in the SCO<sub>2</sub> band with the inter-channel relative radiometric calibration uncertainty changing from 0.05% to 0.3%.</p>
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<p>Peak-seek obtained using the (<b>a</b>) center-of-mass algorithm and (<b>b</b>) cubic spline fitting algorithm to the normalized simulated measurement Fraunhofer lines at 1596.446 nm in the WCO<sub>2</sub> band.</p>
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<p>The peak-seek errors of the Fraunhofer lines (<b>a</b>) before and (<b>b</b>) after removing the systematic error in three bands using the Gaussian fitting (red “x”), center-of-mass (blue “x”), and cubic spline fitting (green “x”) algorithms.</p>
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<p>The peak-seek errors of Fraunhofer lines at 1612.039 nm and 1613.034 nm affected by the shift in the center wavelength position of each sampling point using the cubic spline fitting algorithm. The red dashed line is the reference line of the average value of peak-seek errors (blue squares).</p>
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<p>The peak-seek errors of the Fraunhofer lines in three bands using the super-Gaussian function fitting algorithm with the corresponding order of ILS: (<b>a</b>) the order of the super-Gaussian function is 2.5; (<b>b</b>) the order of the super-Gaussian function is 3.0. The peak-seek errors are after removing the systematic error.</p>
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<p>Selection of Fraunhofer lines in the three bands according to the systematic error: the black “x” marks indicate that the peak-seek systematic error of the Fraunhofer line is &gt;FWHM/10; the blue triangle marks indicate that the systematic error is &lt;FWHM/10 and &gt;FWHM/20; the red five-pointed star marks indicate that the systematic error is &lt;FWHM/20.</p>
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<p>The random errors of peak-seek positions of 19 Fraunhofer lines in the SCO<sub>2</sub> band (SNR = 700, uncertainty = 0.2%): the blue dashed line is the reference line of FWHM/10, and the position of Fraunhofer line is marked by a green “x” if the random error is less than FWHM/10, while it is marked by a red “x” if the random error is greater than FWHM/10.</p>
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9 pages, 5034 KiB  
Communication
A Reduction in the Rotational Velocity Measurement Deviation of the Vortex Beam Superposition State for Tilted Object
by Hongyang Wang, Yinyin Yan, Zijing Zhang, Hao Liu, Xinran Lv, Chengshuai Cui, Hao Yun, Rui Feng and Yuan Zhao
Photonics 2024, 11(7), 679; https://doi.org/10.3390/photonics11070679 - 21 Jul 2024
Viewed by 760
Abstract
In measuring object rotational velocity using vortex beam, the incident light on a tilted object causes spectral broadening, which significantly interferes with the identification of the true rotational Doppler shift (RDS) peak. We employed a velocity decomposition method to analyze the relationship between [...] Read more.
In measuring object rotational velocity using vortex beam, the incident light on a tilted object causes spectral broadening, which significantly interferes with the identification of the true rotational Doppler shift (RDS) peak. We employed a velocity decomposition method to analyze the relationship between the spectral extremum and the central frequency shift caused by the object tilt. Compared with the linear growth trend observed when calculating the object rotational velocity using the frequency peak with the maximum amplitude, the central frequency calculation method effectively reduced the deviation rate of the RDS and velocity measurement value from the true value, even at large tilt angles. This approach increased the maximum tilt angle for a 1% relative error from 0.221 to 0.287 rad, representing a 29.9% improvement. When the tilt angle was 0.7 rad, the velocity measurement deviation reduction rate can reach 5.85%. Our work provides crucial support for achieving high-precision rotational velocity measurement of tilted object. Full article
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<p>(<b>a</b>) Side view of a tilted object illuminated by a vortex beam. Two schematic diagrams of the vortex beam at the emission plane and tilted object plane. (<b>b</b>) Velocity decomposition diagram of point N in the object plane coordinate system. (<b>c</b>) Velocity decomposition diagram of point N in the global coordinate system. The <span class="html-italic">v<sub>Y</sub></span> direction is perpendicular to the <span class="html-italic">X-Z</span> plane.</p>
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<p>(<b>a</b>) Three-dimensional graph of the RDS at varying tilt angles <span class="html-italic">γ</span> and polar angles <span class="html-italic">θ</span>. (<b>b</b>) The graph of the RDS at varying polar angles <span class="html-italic">θ</span>. <span class="html-italic">θ</span> is in the range of [0, 2π].</p>
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<p>(<b>a</b>) Curve of the frequency extremum and center frequency shift varying with the tilt angle. (<b>b</b>) Curve of the average and maximum frequency shift deviation rates <span class="html-italic">η</span><sub>avg</sub> and <span class="html-italic">η</span><sub>max</sub> varying with the tilt angle.</p>
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<p>Spectrum graph corresponding to tilt angles: <span class="html-italic">γ</span> = (<b>a</b>) 0, (<b>b</b>) 0.17, (<b>c</b>) 0.34, and (<b>d</b>) 0.50 rad.</p>
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<p>(<b>a</b>) Growth curve of <span class="html-italic">δ</span><sub>1</sub>, <span class="html-italic">δ</span><sub>2</sub>, and extreme value difference (Δ<span class="html-italic">f</span><sub>SD</sub>) with increasing tilt angles. The purple dotted line represents a 1% relative error. (<b>b</b>) The RDS or velocity measurement relative error reduction rate <span class="html-italic">δ</span><sub>Δ</sub> with increasing tilt angles.</p>
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13 pages, 1595 KiB  
Article
Preconception Physical Exercise Is Associated with Phenotype-Specific Cardiovascular Alterations in Women at Risk for Gestational Hypertensive Disorders
by Pauline Dreesen, Pauline Volders, Dorien Lanssens, Sandy Nouwen, Birgit Vrancken, Febe Janssen, Bert O. Eijnde, Dominique Hansen, Michael Ceulemans, Adelheid Soubry and Wilfried Gyselaers
J. Clin. Med. 2024, 13(14), 4164; https://doi.org/10.3390/jcm13144164 - 16 Jul 2024
Viewed by 1133
Abstract
Background/Objectives: Gestational hypertensive disorders (GHD) pose significant maternal and fetal health risks during pregnancy. Preconception physical exercise has been associated with a lower incidence of GHD, but insights into the cardiovascular mechanisms remain limited. This study aimed to evaluate the effect of [...] Read more.
Background/Objectives: Gestational hypertensive disorders (GHD) pose significant maternal and fetal health risks during pregnancy. Preconception physical exercise has been associated with a lower incidence of GHD, but insights into the cardiovascular mechanisms remain limited. This study aimed to evaluate the effect of preconception physical exercise on the complete cardiovascular functions of women at risk for GHD in a subsequent pregnancy. Methods: A non-invasive hemodynamics assessment of arteries, veins, and the heart was performed on 40 non-pregnant women at risk for developing GHD in a subsequent pregnancy. Measurements of an electrocardiogram Doppler ultrasound, impedance cardiography and bio-impedance spectrum analysis were taken before and after they engaged in physical exercise (30–50 min, 3×/week, 4–6 months). Results: After a mean physical exercise period of 29.80 weeks, the total peripheral resistance (TPR), diastolic blood pressure and mean arterial pressure decreased in the total study population, without changing cardiac output (CO). However, in 42% (9/21) of women categorized with high or low baseline CO (>P75 or <P25 resp.), a shift in CO was observed towards the normal reference interquartile range (P25–P75). This was associated with improved hepatic venous and central arterial hemodynamic functions. Similar changes in TPR occurred in 38% (11/29) of women classified as having low or high baseline TPR. Conclusions: As in pregnancy, output- or resistance-dominant cardiovascular profiles already exist prior to conception. This study illustrates that preconception physical exercise shifts high or low CO and/or TPR towards the normal midrange, allowing women at risk for GHD to start a subsequent pregnancy with a more gestation-adaptable cardiovascular system. Full article
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<p>Standardized validated cardiovascular assessment protocol. The non-invasive techniques used are (from left to right): Electrocardiogram-Doppler ultrasound and bio-impedance spectrum analysis in a supine position, followed by impedance cardiography in a supine and a standing position. The resulting parameters are presented with each technique, respectively.</p>
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<p>Overview of the total study population included in the statistical analysis. CV: cardiovascular; PC: preconception; PCPS: preconception post-sport.</p>
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<p>CO and TPR levels evaluated before and after preconception physical exercise advice for each CV profile. The study population (n = 40) was categorized into (<b>a</b>) low (n = 12), normal (n = 19), or high (n = 9) baseline CO profiles, and (<b>b</b>) high (n = 21), normal (n = 11), or low (n = 8) baseline TPR profiles. Data are presented as boxplots with a median and IQR. The green bar represents the normal reference range (P25–P75) for CO and TPR of non-pregnant women, respectively. CO: cardiac output; TPR: total peripheral resistance; PC: preconception; PCPS: preconception post-sport. * <span class="html-italic">p</span> &lt; 0.05. ° Outlier. Red * extreme outlier.</p>
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<p>Change in CO following the preconception physical exercise advice at the subject level. The individual change in CO between the preconception visit and the post-sport measurement is presented in function of the weeks between the measurements for women with (<b>a</b>) a low baseline CO profile and (<b>b</b>) a high baseline CO profile. Each colored dot represents an individual subject. Each colored line refers to the change in CO for an individual subject. The black horizontal lines represent the upper and lower limit of the normal reference interquartile range (P25–P75). CO: cardiac output.</p>
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<p>Change in TPR following the preconception physical exercise advice at the subject level. The individual change in TPR between the preconception visit and the post-sport measurement is presented in function of the weeks between the measurements for women with (<b>a</b>) a low baseline TPR profile and (<b>b</b>) a high baseline TPR profile. Each colored dot represents an individual subject. Each colored line refers to the change in TPR for an individual subject. The black horizontal lines represent the upper and lower limit of the normal reference interquartile range (P25–P75). TPR: total peripheral resistance.</p>
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27 pages, 1079 KiB  
Article
A PLL-Based Doppler Method Using an SDR-Receiver for Investigation of Seismogenic and Man-Made Disturbances in the Ionosphere
by Nazyf Salikhov, Alexander Shepetov, Galina Pak, Vladimir Saveliev, Serik Nurakynov, Vladimir Ryabov and Valery Zhukov
Geosciences 2024, 14(7), 192; https://doi.org/10.3390/geosciences14070192 - 16 Jul 2024
Viewed by 836
Abstract
The article describes in detail the equipment and method for measuring the Doppler frequency shift (DFS) on an inclined radio path, based on the principle of the phase-locked loop using an SDR receiver for the investigation of seismogenic and man-made disturbances in the [...] Read more.
The article describes in detail the equipment and method for measuring the Doppler frequency shift (DFS) on an inclined radio path, based on the principle of the phase-locked loop using an SDR receiver for the investigation of seismogenic and man-made disturbances in the ionosphere. During the two M7.8 earthquakes in Nepal (25 April 2015) and Turkey (6 February 2023), a Doppler ionosonde detected co-seismic and pre-seismic effects in the ionosphere, the appearances of which are connected with the various propagation mechanisms of seismogenic disturbance from the lithosphere up to the ionosphere. One day before the earthquake in Nepal and 90 min prior to the main shock, an increase in the intensity of Doppler bursts was detected, which reflected the disturbance of the ionosphere. A channel of geophysical interaction in the system of lithosphere–atmosphere–ionosphere coupling was traced based on the comprehensive monitoring of the DFS of the ionospheric signal, as well as of the flux of gamma rays in subsoil layers of rocks and in the ground-level atmosphere. The concept of lithosphere–atmosphere–ionosphere coupling, where the key role is assigned to ionization of the atmospheric boundary layer, was confirmed by a retrospective analysis of the DFS records of an ionospheric signal made during underground nuclear explosions at the Semipalatinsk test site. A simple formula for reconstructing the velocity profile of the acoustic pulse from a Dopplerogram was obtained, which depends on only two parameters, one of which is the dimension of length and the other the dimension of time. The reconstructed profiles of the acoustic pulses from the two underground nuclear explosions, which reached the height of the reflection point of the sounding radio wave, are presented. Full article
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<p>The disposition scheme of radio transmitters whose signals are used for the Doppler sounding of the ionosphere, relative to the radio receivers (RX) in the points with Doppler installations at the Institute of Ionosphere and Radiopolygon Orbita.</p>
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<p>Functional scheme of the receiving part of the hardware-software complex for measuring the Doppler frequency shift of the ionospheric radio signal using the SDR receiver. See the text for explanations.</p>
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<p>Comparison of the real characteristic (<span class="html-italic">R</span>) of the PLL conversion of the Doppler equipment with the ideal linear characteristic (<span class="html-italic">I</span>). The units along the horizontal axis are the number <span class="html-italic">N</span> of the succeeding frequency tunings (see text). The plot is taken from the publication Salikhov and Somsikov, 2014 [<a href="#B33-geosciences-14-00192" class="html-bibr">33</a>].</p>
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<p>Schematic of the testing facility to check the operation of the hardware-software complex of Doppler measurements in the imitated conditions of multipath receive.</p>
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<p>The vector diagram of the resulting oscillation in a sum of two sinusoidal signals. Designations: <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">1</mn> </mrow> </semantics></math>—the vector of the larger-amplitude beam, <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">2</mn> </mrow> </semantics></math>—the vector of the smaller beam, <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">3</mn> </mrow> </semantics></math>—the resulting vector, which equals to the geometric sum of the vectors <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">2</mn> </mrow> </semantics></math>, modulated in phase (frequency) and amplitude by the frequency of the beating.</p>
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<p>The change in the resulting frequency, per a cycle of the smaller-amplitude vector <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">2</mn> </mrow> </semantics></math>, as measured with the different ratios <span class="html-italic">K</span> between the amplitudes of the two sinusoids <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">1</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi mathvariant="bold-italic">A</mi> <mn mathvariant="bold">2</mn> </mrow> </semantics></math>.</p>
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<p>The measurement error of the difference frequency <math display="inline"><semantics> <msub> <mi>F</mi> <mi>d</mi> </msub> </semantics></math> determined for the various amplitude ratios <span class="html-italic">K</span>.</p>
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<p>Receiving a two-beam ionospheric signal by the hardware-software complex of Doppler measurements in realistic conditions. <span class="html-italic">1</span>—the interfering signal at the output of PLL, <span class="html-italic">2</span>—the variation of the Doppler shift of the larger amplitude signal selected by the low-pass filter (<span class="html-italic">Filter #2</span> in <a href="#geosciences-14-00192-f002" class="html-fig">Figure 2</a>).</p>
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<p>Two samples of the short-term Doppler bursts reflecting the ionization flashes in the ionosphere. The vertical axis is expressed in the units of Doppler frequency <math display="inline"><semantics> <msub> <mi>F</mi> <mi>D</mi> </msub> </semantics></math>.</p>
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<p>The disposition scheme of the radio path of Doppler measurements and of the seismic network stations of the National Nuclear Center in Makanchi (MAKZ, MK31) and Karatau (KKAR), in relation to the epicenter of the 25 April 2015 M7.8 earthquake. <span class="html-italic">Tx</span>—the radio transmitter (44.15944° N, 86.89917° E), <span class="html-italic">Rx</span>—the receiver (43.05831° N, 76.97361° E). The red oval indicates the projection of the reflection point of the sounding radio wave (sub-ionospheric point).</p>
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<p>The response of the ionosphere to the M7.8 earthquake in Nepal on 25 April 2015 registered by the Doppler ionosonde on the “Urumqi—Radiopolygon Orbita ” radio path. <span class="html-italic">1</span>—the original measurement data; <span class="html-italic">2</span>—same data after application of the 10 points running average filter.</p>
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<p>The fragments of the Doppler shift record and of the seismograms of Z-component written on 25 April 2015 at different distances from the epicenter, at the stations in Makanchi (MAKZ, MK31), Karatau (KKAR), and Almaty (KNDC). The scale of the horizontal axis is expressed in seconds passed since the moment of the earthquake.</p>
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<p>The propagation trajectory of the sounding wave of Doppler measurements on the radio path “Urumqi—Radiopolygon Orbita ” in the time of the M7.8 Nepal earthquake.</p>
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<p>The estimated altitude profile of the sound speed (<b>left</b>), and the calculated arrival moment of an acoustic wave at the different heights in the ionosphere (<b>right</b>) in the time of the M7.8 Nepal earthquake.</p>
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<p>The response in the Doppler frequency shift of ionospheric signal to the <math display="inline"><semantics> <mrow> <mi>M</mi> <mn>7.8</mn> </mrow> </semantics></math> earthquake in Turkey on 6 February 2023, as detected at the radio path “Kuwait—Institute of Ionosphere (Almaty)”. Thin curve—original measurements, bold curve—same data smoothed by a 10 points running average filter. The vertical line indicates the beginning of the ionospheric disturbance in the reflection point of radio waves at 01:34:12 UTC. The plot is taken from the publication Salikhov et al., 2023 [<a href="#B26-geosciences-14-00192" class="html-bibr">26</a>].</p>
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<p>Intensity of the short-term Doppler bursts detected by the modified method of Doppler measurements before and in the time of the M7.8 Nepal earthquake. The scale of the abscissa axis is expressed in thousands of the seconds passed since the beginning of the day in local time; each distribution covers the time interval of ∼18 h. Along the ordinate axis, the counting rate of short-term Doppler bursts <math display="inline"><semantics> <mrow> <mi>Imp</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> is recorded; the curves are displaced in vertical direction for convenience of comparison.</p>
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<p>The outburst of the flux of (50–200) keV gamma rays detected 40 m underground in the borehole prior to the 30 December 2017 M4.2 earthquake, together with the simultaneous negative anomaly in the Doppler frequency shift of ionospheric signal. The moment of the earthquake (EQ) is indicated by a vertical line. The counting rate of gamma rays, <math display="inline"><semantics> <msub> <mi>R</mi> <mi>γ</mi> </msub> </semantics></math>, is expressed in the units of the amount of pulses obtained from the gamma detector in 10 s. The data on the Doppler frequency <math display="inline"><semantics> <msub> <mi>f</mi> <mi>D</mi> </msub> </semantics></math>, in Hz, are presented with daily averaging.</p>
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<p>Comparison of the gamma ray flux variations measured during the period of 22–27 December 2017 at a depth of 40 m in the borehole (<span class="html-italic">1</span>), and at the surface of the ground (<span class="html-italic">2</span>). The counting rates of gamma radiation <math display="inline"><semantics> <msub> <mi>R</mi> <mi>γ</mi> </msub> </semantics></math> are expressed as the amount of detector pulses per one second. Two vertical lines mark the mutually corresponding bursts of gamma ray intensity, which were successively appearing, at first in the borehole and then in the ground-level atmosphere.</p>
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<p>Two responses in the Doppler frequency shift <math display="inline"><semantics> <msub> <mi>f</mi> <mi>D</mi> </msub> </semantics></math> of ionospheric signal detected at the 75 kt underground nuclear explosion on 19 October 1989 at the Semipalatinsk test site. <span class="html-italic">1</span>—510 s after the explosion, <span class="html-italic">2</span>—1005 s after the explosion. The scale of the horizontal axis is expressed in the seconds passed since the moment of the explosion.</p>
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<p>The record of the Doppler shift measured at the underground nuclear explosion on 17 December 1988, and the restored velocity profile <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <msub> <mi mathvariant="bold-italic">r</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. The frequency of the sounding radio wave <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>7.7</mn> </mrow> </semantics></math> MHz, the altitude of the reflection point <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>237</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>225</mn> </mrow> </semantics></math> m, <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>21.3</mn> </mrow> </semantics></math> s.</p>
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<p>The record of the Doppler shift measured at the underground nuclear explosion on 19 October 1988, and the restored velocity profile <math display="inline"><semantics> <mrow> <msub> <mi>v</mi> <mi>n</mi> </msub> <mrow> <mo>(</mo> <msub> <mi mathvariant="bold-italic">r</mi> <mn>0</mn> </msub> <mo>,</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math>. The frequency of the sounding radio wave <math display="inline"><semantics> <mrow> <mi>ω</mi> <mo>=</mo> <mn>7.7</mn> </mrow> </semantics></math> MHz, the altitude of the reflection point <math display="inline"><semantics> <mrow> <mi>z</mi> <mo>=</mo> <mn>225</mn> </mrow> </semantics></math> km, <math display="inline"><semantics> <mrow> <msub> <mi>L</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>232</mn> </mrow> </semantics></math> m, <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>20.0</mn> </mrow> </semantics></math> s.</p>
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17 pages, 12797 KiB  
Article
Study on the Momentum Flux Spectrum of Gravity Waves in the Tropical Western Pacific Based on Integrated Satellite Remote Sensing and In Situ Observations
by Zhimeng Zhang, Yang He, Yuyang Song and Zheng Sheng
Remote Sens. 2024, 16(14), 2550; https://doi.org/10.3390/rs16142550 - 11 Jul 2024
Viewed by 641
Abstract
Gravity wave (GW) momentum flux spectra help to uncover the mechanisms through which GWs influence momentum transfer in the atmosphere and provide crucial insights into accurately characterizing atmospheric wave processes. This study examines the momentum flux spectra of GWs in the troposphere (2–14 [...] Read more.
Gravity wave (GW) momentum flux spectra help to uncover the mechanisms through which GWs influence momentum transfer in the atmosphere and provide crucial insights into accurately characterizing atmospheric wave processes. This study examines the momentum flux spectra of GWs in the troposphere (2–14 km) and stratosphere (18–28 km) over Koror Island (7.2°N, 134.3°W) using radiosonde data from 2013–2018. Utilizing hodograph analysis and spectral methods, the characteristics of momentum flux spectra are discussed. Given that the zonal momentum flux spectra of low-level atmospheric GWs generally follow a Gaussian distribution, Gaussian fitting was applied to the spectral structures. This fitting further explores the seasonal variations of the zonal momentum flux spectra and the average spectral parameters for each month. Additionally, the GW energy is analyzed using SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) satellite data and compared with the results of the momentum flux spectra from radiosonde data, revealing the close negative correlation between wave energy and wave momentum for stratospheric GW changing with time. The findings indicate that the Gaussian peak shifts more eastward in both the troposphere and stratosphere, primarily due to the absorption of eastward-propagating GWs by the winter tropospheric westerly jet and critical layer filtering. The full width at half maximum (FWHM) in the stratosphere is larger than in the troposphere, especially in June and July, as the spectrum broadens due to propagation effects, filtering, and interactions among waves. The central phase speed in the stratosphere exceeds that in the troposphere, reflecting the influences of Doppler effects and background wind absorption. The momentum flux in the stratosphere is lower than in the troposphere, which is attributed to jet absorption, partial reflection, or the dissipation of GWs. Full article
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Graphical abstract

Graphical abstract
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<p>Timeheight cross-sections over the Koror station from 2013 to 2018 (<b>a</b>–<b>d</b>).</p>
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<p>Momentum flux spectrum and Gaussian fitting spectrum at the Koror station: (<b>a</b>) Tropospheric meridional, (<b>b</b>) Tropospheric zonal, (<b>c</b>) Stratospheric meridional, and (<b>d</b>) Stratospheric zonal phase speed.</p>
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<p>Vertical profiles of the seasonal average zonal wind at the Koror station for each of the four seasons.</p>
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<p>Seasonal variation in tropospheric zonal momentum flux spectra in Koror Station by Gaussian fitting.</p>
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<p>Seasonal variation in stratospheric zonal momentum flux spectra in Koror Station by Gaussian fitting.</p>
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<p>Line chart of tropospheric Gaussian fitting parameters for each month after the 6-year average of Koror Station; (<b>a</b>) peak value, (<b>b</b>) Gaussian central phase velocity, and (<b>c</b>) FWHM.</p>
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<p>Line chart of stratospheric Gaussian fitting parameters for each month after the 6-year average of Koror Station; (<b>a</b>) peak value, (<b>b</b>) Gaussian central phase velocity, and (<b>c</b>) FWHM.</p>
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<p>Distribution of stratospheric potential energy by month in the Koror region from 2013 to 2018, from SABER.</p>
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<p>Stratospheric zonal Gaussian fitting parameters for each season from 2013 to 2018 in Koror region, (<b>a</b>) peak value, and (<b>b</b>) FWHM.</p>
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17 pages, 2161 KiB  
Article
Multiparametric Ultrasound for Focal Testicular Pathology: A Ten-Year Retrospective Review
by Dean Y. Huang, Majed Alsadiq, Gibran T. Yusuf, Annamaria Deganello, Maria E. Sellars and Paul S. Sidhu
Cancers 2024, 16(13), 2309; https://doi.org/10.3390/cancers16132309 - 24 Jun 2024
Cited by 1 | Viewed by 922
Abstract
Conventional ultrasonography (US), including greyscale imaging and colour Doppler US (CDUS), is pivotal for diagnosing scrotal pathologies, but it has limited specificity. Historically, solid focal testicular abnormalities often led to radical orchidectomy. This retrospective study evaluated the utilisation of contrast-enhanced ultrasound (CEUS) and [...] Read more.
Conventional ultrasonography (US), including greyscale imaging and colour Doppler US (CDUS), is pivotal for diagnosing scrotal pathologies, but it has limited specificity. Historically, solid focal testicular abnormalities often led to radical orchidectomy. This retrospective study evaluated the utilisation of contrast-enhanced ultrasound (CEUS) and strain elastography (SE) in investigating intratesticular focal abnormalities. A total of 124 cases were analysed. This study underscored the superior diagnostic capabilities of CEUS in detecting vascular enhancement in all malignant cases, even those with undetectable vascularity by CDUS. It also highlighted the potential of CEUS in identifying distinctive vascular patterns in benign vascular tumours. Definitive confirmation of benignity could be obtained when the absence of enhancement was demonstrated on CEUS. While SE alone offered no distinctive advantage in differentiating between benign and malignant pathologies, we demonstrated that incorporating a combination of CEUS and SE into the evaluation of focal testicular abnormalities could improve diagnostic performance metrics over conventional CDUS. Our findings underscore the role of advanced ultrasound techniques in enhancing the evaluation of focal testicular abnormalities in clinical practice and could aid a shift towards testis-sparing management strategies. Full article
(This article belongs to the Special Issue Updates on Imaging of Common Urogenital Neoplasms)
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Figure 1

Figure 1
<p>MPUS of a testicular seminoma. (<b>a</b>) Greyscale US reveals a large, multiloculated hypoechoic mass (white arrow). (<b>b</b>) CDUS demonstrates that the lesion (white arrow) is vascularised. (<b>c</b>) On SE, the lesion (white arrow) exhibits uniformly hard tissue stiffness, appearing blue. (<b>d</b>) On CEUS the lesion (white arrow) shows enhancement, with late-phase washout evident on the CEUS time–intensity curve (x-axis: time; y-axis: signal intensity) (<b>e</b>). Red region of interest (ROI) = lesion; blue and green ROIs = surrounding parenchyma.</p>
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<p>MPUS of a leydig cell tumour. (<b>a</b>) Greyscale US shows small, well-defined lesions (white arrow) with homogeneous low reflectivity. (<b>b</b>) CDUS indicates that the lesion (white arrow) is highly vascularised. (<b>c</b>) SE identifies the lesion (white arrow) as mildly hard, depicted in shades of green and blue. (<b>d</b>) CEUS demonstrates a hyper-enhancing lesion (white arrow), with prolonged hyper-enhancement relative to the surrounding parenchyma in the late phase on the CEUS time–intensity curve (x-axis: time; y-axis: intensity) (<b>e</b>). Red region of interest (ROI) = lesion; blue and green ROIs = surrounding parenchyma.</p>
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<p>MPUS of a segmental infarction. (<b>a</b>) Greyscale US shows a lesion (white arrow) with a low echogenic centre and surrounding high echogenicity. (<b>b</b>) CDUS indicates that no colour Doppler signal is present within the lesion (white arrow). (<b>c</b>) On SE, this lesion (white arrow) demonstrates a predominantly green signal consistent with a “soft” lesion. (<b>d</b>) CEUS conclusively demonstrates the absence of enhancement within the central aspects of the lesion (white arrow).</p>
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<p>MPUS of a testicular lymphoma. (<b>a</b>) Greyscale ultrasound shows diffuse enlargement of the testis with ill-defined, extensive decreased echogenicity in the majority of the testis. (<b>b</b>) CDUS indicates that hypervascularity is present within the testis. (<b>c</b>) SE demonstrates a hard lesion (white arrow), which is not clearly depicted on greyscale US. (<b>d</b>) CEUS demonstrates hyper-enhancement of the lesion (white arrow).</p>
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<p>Quantitative CEUS analysis of an intratesticular abnormality. Perfusion parameters derived from the time–intensity curve (blue circles: intensity data entries) with curve-fitting (orange curve) include the following: tmax (time to peak, TTP): time needed from contrast injection to maximum intensity (s); peak value (PV): the maximum intensity on the TIC curve (arbitrary units, arb); wash-in time (WIT) (raise time): time from 5% intensity to 50% intensity (s); wash-out time (WOT): time from the peak of the TIC curve to the 50% PV value (s); inflow rate (5 s): calculated as the rate of rise in the first 5 s from t0 (arb/s); inflow rate: calculated as the rate of rise over tpeak-t0 (arb/s); outflow rate (5 s): calculated as the rate of outflow in the first 5 s from PV (arb/s); outflow rate: calculated as peak enhancement divided by WOT for the descending slope to reach a contrast signal intensity of zero or the end of the curve (arb/s); MTT (mean transit time, full width at half maximum, FWHM): The time between the half-amplitude values on each side of the maximum (s).</p>
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