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Search Results (766)

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34 pages, 7354 KiB  
Article
Analysis of High-Frequency Sea-State Variability Using SWOT Nadir Measurements and Application to Altimeter Sea State Bias Modelling
by Estelle Mazaleyrat, Ngan Tran, Laïba Amarouche, Douglas Vandemark, Hui Feng, Gérald Dibarboure and François Bignalet-Cazalet
Remote Sens. 2024, 16(23), 4361; https://doi.org/10.3390/rs16234361 - 22 Nov 2024
Viewed by 261
Abstract
The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly [...] Read more.
The 1-day fast-sampling orbit phase of the Surface Water Ocean Topography (SWOT) satellite mission provides a unique opportunity to analyze high-frequency sea-state variability and its implications for altimeter sea state bias (SSB) model development. Time series with 1-day repeat sampling of sea-level anomaly (SLA) and SSB input parameters—comprising the significant wave height (SWH), wind speed (WS), and mean wave period (MWP)—are constructed using SWOT’s nadir altimeter data. The analyses corroborate the following key SSB modelling assumption central to empirical developments: the SLA noise due to all factors, aside from sea state change, is zero-mean. Global variance reduction tests on the SSB model’s performance using corrected SLA differences show that correction skill estimation using a specific (1D, 2D, or 3D) SSB model is unstable when using short time difference intervals ranging from 1 to 5 days, reaching a stable asymptotic limit after 5 days. It is proposed that this result is related to the temporal auto- and cross-correlations associated with the SSB model’s input parameters; the present study shows that SSB wind-wave input measurements take time (typically 1–4 days) to decorrelate in any given region. The latter finding, obtained using unprecedented high-frequency satellite data from multiple ocean basins, is shown to be consistent with estimates from an ocean wave model. The results also imply that optimal time-differencing (i.e., >4 days) should be considered when building SSB model data training sets. The SWOT altimeter data analysis of the temporal cross-correlations also permits an evaluation of the relationships between the SSB input parameters (SWH, WS, and MWP), where distinct behaviors are found in the swell- and wind-sea-dominated areas, and associated time scales are less than or on the order of 1 day. Finally, it is demonstrated that computing cross-correlations between the SLA (with and without SSB correction) and the SSB input parameters offers an additional tool for evaluating the relevance of candidate SSB input parameters, as well as for assessing the performance of SSB correction models, which, so far, mainly rely on the reduction in the variance of the differences in the SLA at crossover points. Full article
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Figure 1

Figure 1
<p>(<b>a</b>) Global means of the SWOT nadir SLA collinear differences as a function of the time interval considered for the SLA differences. The examined SLA types are SLA_uncorr (no SSB applied), SLA_corr1D (application of SSB = −3.2% SWH), SLA_corr2D (with J3 GDR-F 2D SSB table), and SLA_corr3D (with J3 GDR-F 3D SSB table); (<b>b</b>) same as in (<b>a</b>) but for the global variance; (<b>c</b>) global variance reduction as a function of the considered time interval obtained when one computes var(∆SLA_corr) minus var(∆SLA_uncorr). Negative values indicate an improvement in the SLA precision resulting from the application of the SSB correction. Higher reduction magnitudes indicate greater model skill.</p>
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<p>(<b>a</b>) Map showing the two locations from SWOT nadir pass 28 (40°S and 20°N) associated with the ACFs shown in (<b>b</b>,<b>c</b>); (<b>b</b>) autocorrelation functions (with associated 95% confidence intervals as dotted lines) of the five considered SSB input-related parameters at the 40°S location; (<b>c</b>) same as in (<b>b</b>) but for the 20°N location.</p>
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<p>Maps of the decorrelation time scales: (<b>a</b>) SWH_alti; (<b>b</b>) WS_alti; (<b>c</b>) MWP_model.</p>
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<p>Decorrelation time scales of SWH_alti with respect to the mean sea-state conditions covered by the SWOT fast-sampling nadir dataset: (<b>a</b>–<b>c</b>) density plots of the decorrelation time scales with respect to (<b>a</b>) (mean SWH_alti and mean WS_alti); (<b>b</b>) (mean_SWH_alti and mean MWP_model); (<b>c</b>) (mean MWP_model and mean WS_alti). The 3D space associated with the mean sea-state conditions (SWH, WS, and MWP) was binned, and the number of occurrences (i.e., count) pertaining to a specific bin is color-coded. (<b>d</b>–<b>f</b>) Same as in (<b>a</b>–<b>c</b>), except the decorrelation time scale values (rather than the number of their occurrences) are color-coded.</p>
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<p>Average latitudinal variations (in red) and associated dispersions (in blue) of the decorrelation time scales of the five considered SSB input-related parameters. The zonal averages were computed using 3° latitudinal bands, and the displayed dispersions correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math> one standard deviation.</p>
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<p>Average latitudinal variations (in red) and associated dispersions (in blue) of the mean values of the five considered SSB input-related parameters. The zonal averages were computed using 3° latitudinal bands, and the displayed dispersions correspond to <math display="inline"><semantics> <mrow> <mo>±</mo> </mrow> </semantics></math> one standard deviation.</p>
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<p>Cross-correlation functions (with the associated 95% confidence intervals as dotted lines) of the three considered SSB input-related parameters combinations at the (<b>a</b>) 40°S and (<b>b</b>) 20°N locations from SWOT nadir pass 28, as shown in <a href="#remotesensing-16-04361-f002" class="html-fig">Figure 2</a>a. For each of the three (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> <mo>,</mo> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>) combinations, the correlations associated with positive time delays inform on whether <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> is a predictor of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>, whereas the correlations at negative lags indicate whether <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> is a predictor of <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>X</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Maps of cross-correlation values for the three SSB input-related combinations mentioned in <a href="#remotesensing-16-04361-t002" class="html-table">Table 2</a> at time delays equal to (<b>a</b>–<b>c</b>) 0 day and (<b>d</b>–<b>f</b>) +1 day. Non-significant correlations (i.e., falling within the 95% confidence interval) were removed from all maps, leaving an empty [−0.2, 0.2] range.</p>
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<p>Maps of the cross-correlation values for the six (SSB input-related parameter, SLA) combinations mentioned in <a href="#remotesensing-16-04361-t003" class="html-table">Table 3</a> at 0 day. The maps shown in (<b>a</b>–<b>c</b>) (resp., (<b>d</b>–<b>f</b>)) are associated with combinations involving SLA_uncorr (resp., SLA_corr2D). Non-significant correlations (i.e., falling within the 95% confidence interval) were removed from all maps, leaving an empty [−0.2, 0.2] range.</p>
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<p>Comparison of the matching between the DTs of SWH_alti and SWH_model determined using the (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>, (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>h</mi> <mi>a</mi> <mi>l</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>, (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>, and (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> definitions. For each plot, a linear regression is shown in red (with the associated fitted linear relationship and the Pearson correlation coefficient indicated in the top left corner) and the bisector in blue.</p>
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<p>Maps of decorrelation time scales for SWH_alti computed using the (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mi>h</mi> <mi>a</mi> <mi>l</mi> <mi>f</mi> </mrow> </msub> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>L</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> definitions.</p>
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<p>Autocorrelation maps of WS_alti: (<b>a</b>) +1 day; (<b>b</b>) +2 days; (<b>c</b>) +3 days.</p>
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12 pages, 3320 KiB  
Article
Numerical Study of Homogenous/Inhomogeneous Hydrogen–Air Explosion in a Long Closed Channel
by Jiaqing Zhang, Xianli Zhu, Yi Guo, Yue Teng, Min Liu, Quan Li, Qiao Wang and Changjian Wang
Fire 2024, 7(11), 418; https://doi.org/10.3390/fire7110418 - 18 Nov 2024
Viewed by 327
Abstract
Hydrogen is regarded as a promising energy source for the future due to its clean combustion products, remarkable efficiency and renewability. However, its characteristics of low-ignition energy, a wide flammable range from 4% to 75%, and a rapid flame speed may bring significant [...] Read more.
Hydrogen is regarded as a promising energy source for the future due to its clean combustion products, remarkable efficiency and renewability. However, its characteristics of low-ignition energy, a wide flammable range from 4% to 75%, and a rapid flame speed may bring significant explosion risks. Typically, accidental release of hydrogen into confined enclosures can result in a flammable hydrogen–air mixture with concentration gradients, possibly leading to flame acceleration (FA) and deflagration-to-detonation transition (DDT). The current study focused on the evolutions of the FA and DDT of homogenous/inhomogeneous hydrogen–air mixtures, based on the open-source computational fluid dynamics (CFD) platform OpenFOAM and the modified Weller et al.’s combustion model, taking into account the Darrieus–Landau (DL) and Rayleigh–Taylor (RT) instabilities, the turbulence and the non-unity Lewis number. Numerical simulations were carried out for both homogeneous and inhomogeneous mixtures in an enclosed channel 5.4 m in length and 0.06 m in height. The predictions demonstrate good quantitative agreement with the experimental measurements in flame-tip position, speed and pressure profiles by Boeck et al. The characteristics of flame structure, wave evolution and vortex were also discussed. Full article
(This article belongs to the Special Issue Fire Numerical Simulation)
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Figure 1
<p>Schematic of the computational domain.</p>
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<p>Distribution of vertical concentration in the channel for 35% hydrogen–air mixture.</p>
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<p>Comparison between the predicted and measured flame-tip positions (35% homogenous/inhomogeneous mixture in smooth and obstructed channels); (<b>a</b>) homogenous, smooth tube; (<b>b</b>) homogenous, obstructed tube; (<b>c</b>) inhomogeneous, smooth tube; (<b>d</b>) inhomogeneous, obstructed tube.</p>
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<p>Comparison between the predicted and measured flame-tip speeds in the smooth channel.</p>
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<p>Comparison between the predicted and measured flame-tip speeds in the obstructed channel.</p>
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<p>Comparison of the predicted and measured pressure profiles at specified probe locations (x = 0.4 m, x = 2.3 m, x = 3.2 m, x = 4.1 m); (<b>a</b>) 0.4 m; (<b>b</b>) 2.3 m; (<b>c</b>) 3.2 m; (<b>d</b>) 4.1 m.</p>
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<p>Predicted contours of temperature during the initial flame propagation.</p>
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<p>Comparison between the predicted contours of temperature of the smooth channel and the obstructed channel during FA and DDT.</p>
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<p>The predicted contours of temperature during DDT.</p>
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<p>Comparison between the predicted contours of pressure of the smooth channel and the obstructed channel during FA and DDT.</p>
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<p>Comparison between the predicted contours of H<sub>2</sub> mass fractions of the smooth channel and the obstructed channel during FA and DDT.</p>
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18 pages, 7221 KiB  
Article
Investigation of the Effective Numerical Model for Seismic Response Analysis of Concrete-Faced Rockfill Dam on Deep Overburden
by Chuan Tang, Yongqian Qu, Degao Zou and Xianjing Kong
Water 2024, 16(22), 3257; https://doi.org/10.3390/w16223257 - 13 Nov 2024
Viewed by 383
Abstract
The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes [...] Read more.
The construction of high rockfill dams on deep overburden in seismically active regions poses significant challenges. Currently, there are no standardized guidelines for defining the computational domain range in seismic analysis, necessitating the establishment of a universally applicable computational domain range that optimizes the balance between computational accuracy and efficiency. This has critical engineering implications for the seismic analysis of rockfill dams on deep overburden. This study employed the seismic wave input method to consider the dynamic interaction between the dam, overburden, and infinite domain. A systematic investigation was conducted on a concrete-faced rockfill dam (CFRD) constructed on deep overburden, considering the influences of overburden thickness, dam height, overburden properties, soil layer configuration, ground motion intensity, and the frequency content of the seismic waves. The acceleration response and seismic deformation of the dam were analyzed. Subsequently, the computational domain range corresponding to various levels of acceptable engineering precision was established. The results indicated that the lateral boundary length should extend a minimum distance equal to the sum of 3 times the overburden depth and 1.2 times the maximum dam height. Additionally, the depth below the overburden–bedrock interface should extend at least 1.2 times the maximum dam height. This study provides a crucial foundation for determining the optimal computational domain range in the seismic analysis of rockfill dams constructed on deep overburden. Full article
(This article belongs to the Special Issue Research Advances in Hydraulic Structure and Geotechnical Engineering)
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Figure 1
<p>Viscous-spring artificial boundary.</p>
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<p>Viscous-spring interface element.</p>
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<p>Finite element mesh for the CFRD and overburden system.</p>
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<p>Schematic of the foundation truncation range and resulting representative locations for the CFRD and overburden system.</p>
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<p>Normalized horizontal acceleration time history: (<b>a</b>) Artash; (<b>b</b>) Gushui; (<b>c</b>) Houziyan; (<b>d</b>) Jilintai; (<b>e</b>) Jiyin; (<b>f</b>) Lianghekou.</p>
Full article ">Figure 5 Cont.
<p>Normalized horizontal acceleration time history: (<b>a</b>) Artash; (<b>b</b>) Gushui; (<b>c</b>) Houziyan; (<b>d</b>) Jilintai; (<b>e</b>) Jiyin; (<b>f</b>) Lianghekou.</p>
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<p>Normalized vertical acceleration time history: (<b>a</b>) Artash; (<b>b</b>) Gushui; (<b>c</b>) Houziyan; (<b>d</b>) Jilintai; (<b>e</b>) Jiyin; (<b>f</b>) Lianghekou.</p>
Full article ">Figure 6 Cont.
<p>Normalized vertical acceleration time history: (<b>a</b>) Artash; (<b>b</b>) Gushui; (<b>c</b>) Houziyan; (<b>d</b>) Jilintai; (<b>e</b>) Jiyin; (<b>f</b>) Lianghekou.</p>
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<p>Acceleration spectrum.</p>
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<p>Distribution of horizontal peak acceleration at selected positions for various depths below the overburden–bedrock interface: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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<p>Distribution of vertical peak acceleration at selected positions for various depths below the overburden–bedrock interface: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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<p>Acceleration response spectrum at crest for various depths below the overburden–bedrock interface.</p>
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<p>Distribution of horizontal peak acceleration at selected positions for various lateral boundary lengths: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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<p>Distribution of vertical peak acceleration at selected positions for various lateral boundary lengths: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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<p>Average error of horizontal peak acceleration for various lateral boundary lengths: (<b>a</b>) overburden thickness; (<b>b</b>) dam height; (<b>c</b>) shear modulus coefficient of overburden K; (<b>d</b>) seismic intensity; (<b>e</b>) input motion; (<b>f</b>) soil layer configuration.</p>
Full article ">Figure 13 Cont.
<p>Average error of horizontal peak acceleration for various lateral boundary lengths: (<b>a</b>) overburden thickness; (<b>b</b>) dam height; (<b>c</b>) shear modulus coefficient of overburden K; (<b>d</b>) seismic intensity; (<b>e</b>) input motion; (<b>f</b>) soil layer configuration.</p>
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<p>Average error of vertical peak acceleration for various lateral boundary lengths: (<b>a</b>) overburden thickness; (<b>b</b>) dam height; (<b>c</b>) shear modulus coefficient of overburden K; (<b>d</b>) seismic intensity; (<b>e</b>) input motion; (<b>f</b>) soil layer configuration.</p>
Full article ">Figure 14 Cont.
<p>Average error of vertical peak acceleration for various lateral boundary lengths: (<b>a</b>) overburden thickness; (<b>b</b>) dam height; (<b>c</b>) shear modulus coefficient of overburden K; (<b>d</b>) seismic intensity; (<b>e</b>) input motion; (<b>f</b>) soil layer configuration.</p>
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<p>Average error and the proportion of areas within error levels for various lateral boundary lengths: (<b>a</b>) horizontal acceleration; (<b>b</b>) vertical acceleration.</p>
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<p>Average error and its proportion of all cases within error levels of crest settlement for various lateral boundary lengths.</p>
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<p>Finite element mesh for the planned CFRD and overburden system.</p>
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<p>Normalized acceleration time history: (<b>a</b>) horizontal; (<b>b</b>) vertical.</p>
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<p>Acceleration spectrum for the site-specific motion.</p>
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<p>Distribution of horizontal peak acceleration at selected positions for cases where L = 3D and L = 10D: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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<p>Distribution of vertical peak acceleration at selected positions for cases where L = 3D and L = 10D: (<b>a</b>) vertical direction; (<b>b</b>) horizontal direction.</p>
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18 pages, 5665 KiB  
Article
Performance Characteristics of Newly Developed Real-Time Wave Measurement Buoy Using the Variometric Approach
by Chen Xue, Jingsong Guo, Shumin Jiang, Yanfeng Wang, Yanliang Guo and Jie Li
J. Mar. Sci. Eng. 2024, 12(11), 2032; https://doi.org/10.3390/jmse12112032 - 10 Nov 2024
Viewed by 511
Abstract
Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements [...] Read more.
Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument. Full article
(This article belongs to the Section Physical Oceanography)
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Figure 1
<p>Internal structure of buoy device.</p>
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<p>Actual appearance of the buoy.</p>
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<p>Process flowchart for obtaining wave displacement.</p>
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<p>(<b>a</b>) Contour map of October 2020 testing locations (The area marked with a red star is our testing area); (<b>b</b>) More specific satellite map of the test position (6.6 km from land); (<b>c</b>) Photograph of the buoy test site.</p>
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<p>Example comparisons of wave displacement time series calculated using the variometric approach for displacement analysis stand-alone engine (VADASE) method and the post-processed kinematic (PPK) method based on measurement results from the same global navigation satellite system (GNSS) buoy. The observations were made on 28 October 2020. From (<b>a</b>–<b>c</b>), the east-west, north-south, and vertical displacements are shown. Displacements obtained via the VADASE method are indicated by the black solid lines; the counterparts obtained by the PPK method are represented by the red dashed lines.</p>
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<p>Example comparison of wave energy spectra estimates obtained from the VADASE and PPK methods and the Datawell buoy. The shadows along the lines represent the errors for a 95% confidence interval.</p>
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<p>Significant wave height (Hs) as shown in figure (<b>a</b>), mean wave period (Tm) as shown in figure (<b>b</b>), and peak direction (D) as shown in figure (<b>c</b>) from the precise point positioning (PPP) method vs. the VADASE method results based on the same buoy. The black lines represent the ideal correlation regression lines in each case.</p>
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<p>Comparison of the bulk wave parameter estimates from the Datawell buoy vs. the buoy using the VADASE method for the significant wave height (Hs) as shown in figure (<b>a</b>), the mean wave period (Tm) as shown in figure (<b>b</b>), and the peak direction (D) as shown in figure (<b>c</b>). The black lines represent the ideal correlation regression lines. The statistical parameters are the Pearson coefficient of determination (coef) and the RMSE.</p>
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<p>Temporal variations in the bulk wave parameter estimates from the Datawell buoy vs. the buoy using the VADASE method, comprising the significant wave height (Hs) as shown in figure (<b>a</b>) and the mean wave period (Tm) as shown in figure (<b>b</b>). The black rhombic dots are the results from the VADASE buoy, and the blue round dots are the results from the Datawell buoy.</p>
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<p>(<b>a</b>) Contour map of November 2022 testing locations (The area marked with a red star is our testing area); (<b>b</b>) More specific satellite map of the test position (16.61 km from land); (<b>c</b>) Photograph of the buoy test site.</p>
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<p>Results of sea testing in Laoshan Bay: wave height time series (the blue and red lines represent the self-developed buoy results, and the black line represents the Datawell DWR-G4 wave buoy results).</p>
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<p>Results of sea testing in Laoshan Bay: mean period time series (the blue and red lines represent the self-developed buoy results, and the black line represents the Datawell DWR-G4 wave buoy results).</p>
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<p>Results of sea testing in Laoshan Bay: the dominant wave direction time series (the blue and red lines are the self-developed buoy results, and the black line represents the Datawell DWR-G4 wave buoy results. The arrow direction represents the dominant wave direction D in each case, and each arrow length represents the significant wave height Hs).</p>
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<p>Comparison of Joint North Sea Wave Project (Jonswap) wave power spectra at different sampling frequencies under identical input signal conditions.</p>
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<p>(<b>a</b>) Comparison of average wave periods calculated from power spectra measured at different frequencies as a function of wind speed; (<b>b</b>) magnified view of the conventional wind speed range (where the red box in (<b>a</b>) indicates the magnified region).</p>
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24 pages, 4671 KiB  
Article
On the Nearshore Significant Wave Height Inversion from Video Images Based on Deep Learning
by Chao Xu, Rui Li, Wei Hu, Peng Ren, Yanchen Song, Haoqiang Tian, Zhiyong Wang, Weizhen Xu and Yuning Liu
J. Mar. Sci. Eng. 2024, 12(11), 2003; https://doi.org/10.3390/jmse12112003 - 7 Nov 2024
Viewed by 407
Abstract
Accurate observation of nearshore waves is crucial for coastal safety. In this study, the feasibility of extracting wave information from wave video images captured by shore-based cameras using deep learning methods was explored, focusing on inverting nearshore significant wave height (SWH) from instantaneous [...] Read more.
Accurate observation of nearshore waves is crucial for coastal safety. In this study, the feasibility of extracting wave information from wave video images captured by shore-based cameras using deep learning methods was explored, focusing on inverting nearshore significant wave height (SWH) from instantaneous wave video images. The accuracy of deep learning models in classifying wind wave and swell wave images was investigated, providing reliable classification results for SWH inversion research. A classification network named ResNet-SW for wave types with improved ResNet was proposed. On this basis, the impact of instantaneous wave images, meteorological factors, and oceanographic factors on SWH inversion was evaluated, and an inversion network named Inversion-Net for SWH that integrates multiple factors was proposed. The inversion performance was significantly enhanced by the specialized models for wind wave and swell. Additionally, the inversion accuracy and stability were further enhanced by improving the loss function of Inversion-Net. Ultimately, time series inversion results were synthesized from the outputs of multiple models; the final inversion results yielded a mean absolute error of 0.04 m and a mean absolute percentage error of 8.52%. Despite certain limitations, this method can still serve as a useful alternative for wave observation. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Data collection point (Xiaomai Island buoy and offshore buoy).</p>
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<p>(<b>a</b>) The wave rose diagram for the samples in the dataset. (<b>b</b>) The distribution of significant wave height among samples in the dataset. The significant wave height data used in this study have accuracy levels of 0.1 m. To analyze the distribution of the data, the proportion of samples for each wave height value relative to the total number of samples was computed.</p>
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<p>ReNet-SW network architecture.</p>
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<p>Inversion-Net network architecture.</p>
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<p>The wave video images with wave type label, among them, Wind wave (<b>a</b>–<b>c</b>) represent labeled wind wave samples, while Swell (<b>a</b>–<b>c</b>) represent labeled swell samples.</p>
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<p>Deep learning training process with constraint condition.</p>
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<p>The classification confusion matrices of various mainstream deep learning algorithms on the test set of wind wave and swell classification datasets.</p>
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<p>Classification confusion matrix of ReNet-SW.</p>
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<p>Pearson correlation heatmap of various meteorological and oceanographic factors and SWH.</p>
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<p>The inversion results on the test set. (<b>a</b>) The inversion results of Inversion-Net model trained on the significant wave height inversion dataset. (<b>b</b>) The inversion results of the specialized models for wind wave and swell trained on the wind wave inversion dataset and the swell inversion dataset, and their inversion results were ultimately combined. The samples within the two red dashed lines represent those that meet the requirements for operational observations. The blue solid line denotes the fitted curve of the inversion results, and the red shaded area surrounding the blue line represents the 95% confidence interval.</p>
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<p>Time series plot of the observed significant wave height and the inversion results for different models. The green dotted line represents the inversion results of the ResNet-SW model, the orange dotted line represents the inversion results of the Inversion-Net model, and the red dotted line represents the inversion results synthesized from multiple models.</p>
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17 pages, 12605 KiB  
Article
Dynamics of Barred Coast at Different Temporal Scales (by the Example of Vistula Spit in the Baltic Sea)
by Dmitry Korzinin and Igor Leont’yev
Water 2024, 16(21), 3124; https://doi.org/10.3390/w16213124 - 1 Nov 2024
Viewed by 611
Abstract
According to fundamental concepts, the morphodynamic system of an accumulative sandy coast with underwater bars exhibits cyclic behavior across various time scales. This raises the question: which factor is more significant for the dynamics of a given coast—individual storms or seasonal changes in [...] Read more.
According to fundamental concepts, the morphodynamic system of an accumulative sandy coast with underwater bars exhibits cyclic behavior across various time scales. This raises the question: which factor is more significant for the dynamics of a given coast—individual storms or seasonal changes in wave activity? While observations and studies addressing this issue have primarily been conducted on oceanic coasts, there is a lack of comparable data for fetch-limited areas. Monitoring of the bottom topography along the west coast of Vistula Spit (Baltic Sea) revealed a cyclic behavior in morphology, transitioning from a straightened external bar to its connection with the shore. Analysis of field measurement results indicated that seasonal variations in wave intensity do not significantly impact coastal relief. Furthermore, it was found that the complete cycle of underwater bar evolution lasts approximately two years, during which the coast profile maintains a stable shape at the stage of the straightened external bar. The identification of the primary factor influencing coastal evolution can be characterized by the Dean number (Ω), which combines wave parameters (wave height and period) with sediment fall velocity. Utilizing ERA5 wave reanalysis data, we compared the variability of Ω values on both annual and monthly scales. The analysis revealed that for the section of the coast under consideration, there is no clearly dominant evolutionary factor; rather, the coast is influenced approximately equally by individual storm events and seasonal fluctuations in wave energy. Modeling storm-induced bed profile deformations using the CROSS-PB model demonstrated that the position of the external underwater bar remains nearly constant even during intense and prolonged storms. It is concluded that under specific conditions—determined by a combination of sediment size, coastal slope, and wave regime characteristics—the coast can remain stable, exhibiting minimal response to relatively strong storms and seasonal variations in wave energy. Such coasts are characterized by an absence of a dominant evolutionary factor as indicated by fluctuations in the Dean parameter, allowing their morphodynamic cycles to span several seasons. This type of morphodynamics in coastal accumulative relief appears to be typical for conditions found in fetch-limited areas, such as regional and semi-closed seas. Full article
(This article belongs to the Section Oceans and Coastal Zones)
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<p>Vistula Spit and the location of the study area.</p>
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<p>Relief of the coastal zone within the study site “Baltiysk-2019”.</p>
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<p>Typical coastal profile at the “Baltiysk-2019” study site (based on measurements taken in May 2019).</p>
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<p>Characteristics of the underwater coastal slope relief at the “Baltiysk-2019” study site, as identified from Google Earth satellite images. The white dotted lines indicate the visible outlines of the underwater bars.</p>
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<p>(<b>a</b>) Preparation of a boat for measurements of the coastal bottom relief of the Vistula Spit; (<b>b</b>) measurement of the beach and surf zone relief on the Vistula Spit.</p>
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<p>(<b>a</b>) Division of the coastal zone into sectors based on the methods used and the frequency of surveys (as described in the text); (<b>b</b>) examples of sea tracks and beach profiles from one of the surveys.</p>
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<p>Location of the point for obtaining ERA5 reanalysis data and point of wave measurements relative to the “Baltiysk-2019” study site.</p>
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<p>The gradual displacement of the underwater bar towards the shore, starting in May 2019 until the bar transformation into an accumulative terrace adjacent to the beach in September 2021. The new generation of the underwater bar in March 2022, and bar dynamics up to the final stage of a field survey in August 2023.</p>
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<p>Relationship between the main stages of accumulative coast evolution (as described in the text) and the observed planned outlines of the coastal relief.</p>
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<p>Change of stages of evolution of the accumulative coast during the observation period.</p>
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<p>Chronogram showing the changes in hourly wave height (<math display="inline"><semantics> <mrow> <mi>H</mi> <mi>s</mi> </mrow> </semantics></math>) values based on ERA5 reanalysis data from 21 May to 7 November 2019, including the storm events studied.</p>
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<p>Results of the simulation for storm 1 and storm 2.</p>
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<p>Results of the simulation for storm 3 and storm 4.</p>
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<p>Chronogram of changes in the values <math display="inline"><semantics> <mrow> <mi>H</mi> <mi>s</mi> </mrow> </semantics></math> between 7 March and 23 March 2020 (at the top) and dynamics of coastal profile between these dates.</p>
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24 pages, 39384 KiB  
Article
Wave–Tide–Surge Interaction Modulates Storm Waves in the Bohai Sea
by Yue Ma, Zhiliang Liu, Zhichao Dong, Bo Zhao, Wenjia Min and Ying An
J. Mar. Sci. Eng. 2024, 12(11), 1932; https://doi.org/10.3390/jmse12111932 - 28 Oct 2024
Viewed by 557
Abstract
Typhoons, extratropical cyclones, and cold fronts cause strong winds leading to storm surges and waves in the Bohai Sea. A wave–flow coupled numerical model is established for storm events observed in 2022 caused by three weather systems, to investigate how storm waves are [...] Read more.
Typhoons, extratropical cyclones, and cold fronts cause strong winds leading to storm surges and waves in the Bohai Sea. A wave–flow coupled numerical model is established for storm events observed in 2022 caused by three weather systems, to investigate how storm waves are modulated by wave–tide–surge interaction (WTSI). Wave response is basically controlled by water level change in coastal areas, where bottom friction or breaking dominates the energy dissipation, and determined by the current field in deep water by altering whitecapping. Wave height increases/decreases are induced by positive/negative water level or obtuse/acute wave–current interaction angle, leading to six types of field patterns for significant wave height (Hs) responses. For the three storm events, Hs basically changed within ±5% in central deep water, while the maximum increase/decrease reached 160%/−60% in the coastal area of Laizhou Bay/Liaodong Bay. Based on maximum Hs and its occurrence time, WTSI modulation is manifested as the superposition effect of wave–tide and wave–surge interactions in both space and time scales, and occurrence time depends more on tide than surge for all three storms. The enhancement/abatement of WTSI modulation happens for consistent/opposite changing trends of wave–tide and wave–surge interaction, with the ultimate result showing the side with a higher effect. Full article
(This article belongs to the Section Physical Oceanography)
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<p>Bathymetry of the Bohai Sea (in the terrestrial reference system CGCS2000) with large port names (red), tidal stations (green circles), and satellite trajectories (grey circles).</p>
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<p>The modeling frame and data exchange in this study.</p>
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<p>Comparison of (<b>a</b>) pressure <span class="html-italic">p</span>, (<b>b</b>) eastward wind component <span class="html-italic">u</span>, and (<b>c</b>) northward wind component <span class="html-italic">v</span> at QHD location between buoy measurement (Buoy-<span class="html-italic">p</span>, -<span class="html-italic">u</span>, -<span class="html-italic">v</span>), original (ERA5-<span class="html-italic">p</span>, -<span class="html-italic">u</span>, -<span class="html-italic">v</span>) and modified ERA5 data (ERA5-<span class="html-italic">um</span>, -<span class="html-italic">vm</span>) with error indices (CC, RMSE, and Bias defined as Equations (1)–(3)).</p>
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<p>Wind modification: (<b>a</b>–<b>c</b>) comparison of wave parameters at QHD-Buoy using different (<span class="html-italic">u</span>,<span class="html-italic">v</span>) input; (<b>d</b>) factor map of Bohai Sea by Fan et al. [<a href="#B17-jmse-12-01932" class="html-bibr">17</a>]; (<b>e</b>,<b>f</b>) modification of ERA5-<span class="html-italic">u</span> (<span class="html-italic">ue</span>) and -<span class="html-italic">v</span> (<span class="html-italic">ve</span>) according to QHD-Buoy measurement (<span class="html-italic">ub</span>, <span class="html-italic">vb</span>).</p>
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<p>Model input wind fields during three storms (shades and arrows indicate the wind speed and direction, respectively): (<b>a1</b>–<b>a3</b>) show the wind fields at representative moments during Typhoon ‘Meihua’; (<b>b1</b>–<b>b3</b>) and (<b>c1</b>–<b>c3</b>) show those during extratropical cyclone ‘221003’ and cold front ‘221129’.</p>
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<p>Tide and surge validation: (<b>a</b>–<b>c</b>) show tidal level comparisons at representative stations of BYQ, TG, and WFG; (<b>d</b>,<b>e</b>) show comparisons of maximum storm surges at several stations during the typhoon and extratropical storm; and (<b>f</b>) storm tide comparison at WFG.</p>
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<p>Wave validation by QHD-Buoy and satellite data during three storm events: (<b>a</b>–<b>c</b>,<b>e</b>–<b>g</b>,<b>i</b>–<b>k</b>) show comparisons in terms of <span class="html-italic">Hs</span>, <span class="html-italic">Tp</span>, and <span class="html-italic">Dir</span> at QHD-Buoy; (<b>d</b>,<b>h</b>,<b>l</b>) illustrate <span class="html-italic">Hs</span> comparison between satellite and modeled results with the measuring position along the trajectories of satellites HY2B and HY2D shown in <a href="#jmse-12-01932-f001" class="html-fig">Figure 1</a> as gray circles.</p>
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<p>Significant wave height <span class="html-italic">Hs</span> (<b>a1</b>,<b>d1</b>,<b>g1</b>) and peak period <span class="html-italic">Tp</span> (<b>a2</b>,<b>d2</b>,<b>g2</b>) with background tidal level (<b>b1</b>,<b>e1</b>,<b>h1</b>) and atmospheric surge (<b>b2</b>,<b>e2</b>,<b>h2</b>), tidal current field (<b>c1</b>,<b>f1</b>,<b>i1</b>) and wind-induced current field (<b>c2</b>,<b>f2</b>,<b>i2</b>) at representative moments (black and red arrows, marked with ‘wave-<span class="html-italic">dir</span>’ and ‘vel-<span class="html-italic">dir</span>’, indicate the directions of waves and currents).</p>
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<p>Changes in <span class="html-italic">Hs</span> (<b>b</b>,<b>f</b>,<b>i</b>), <span class="html-italic">Tp</span> (<b>c</b>,<b>g</b>,<b>j</b>), and energy (<b>d</b>,<b>h</b>,<b>k</b>) induced by storm tide in terms of water level alone (<b>a</b>), current alone (<b>e</b>), and their combination at 3 October 2022 17:00 (black and red arrows, marked with ‘wave-<span class="html-italic">dir</span>’ and ‘vel-<span class="html-italic">dir</span>’, indicate the directions of waves and currents).</p>
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<p>Field modulation patterns of WTSI displayed by different <span class="html-italic">Hs</span> changes (<b>c</b>,<b>f</b>,<b>i</b>) under the combination of water level changes (<b>a</b>,<b>d</b>,<b>g</b>) and current (<b>b</b>,<b>e</b>,<b>h</b>) at three picked moments (black and red arrows, marked with ‘wave-<span class="html-italic">dir</span>’ and ‘vel-<span class="html-italic">dir</span>’, indicate the directions of waves and currents).</p>
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<p>Comparison of the changes induced by tide, surge, and storm tide: (<b>a</b>,<b>c</b>,<b>e</b>) show <span class="html-italic">Hs</span> changes at WFG, TG, and BYQ with corresponding water level conditions (<b>b</b>,<b>d</b>,<b>f</b>).</p>
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<p>Spatial patterns of changes in <span class="html-italic">Hs</span><sub>max</sub> caused by tide (<b>a</b>,<b>d</b>,<b>g</b>), surge (<b>b</b>,<b>e</b>,<b>h</b>), and storm tide (<b>c</b>,<b>f</b>,<b>i</b>) processes during storm events.</p>
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<p>Spatial patterns of changes in <span class="html-italic">t</span><sub>max</sub> caused by tide (<b>a</b>,<b>d</b>,<b>g</b>), surge (<b>b</b>,<b>e</b>,<b>h</b>), and storm tide (<b>c</b>,<b>f</b>,<b>i</b>) processes during storm events.</p>
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19 pages, 4721 KiB  
Article
Study and Analysis of the Thunder Source Location Error Based on Acoustic Ray-Tracing
by Jinyuan Guo, Caixia Wang, Jia Xu, Song Jia, Hui Yang, Zhuling Sun and Xiaobao Wang
Remote Sens. 2024, 16(21), 4000; https://doi.org/10.3390/rs16214000 - 28 Oct 2024
Viewed by 372
Abstract
Error analysis and estimation of thunder source location results is a prerequisite for obtaining accurate location results of thunder sources, which is of great significance for a deeper understanding of the physical process of lightning channel discharges. Most of the thunder source location [...] Read more.
Error analysis and estimation of thunder source location results is a prerequisite for obtaining accurate location results of thunder sources, which is of great significance for a deeper understanding of the physical process of lightning channel discharges. Most of the thunder source location algorithms are based on the simplified model of the straight-line propagation of acoustic waves to determine the location of the thunder source; however, the acoustic wave is affected by the inhomogeneity of the atmosphere medium in the propagation process and its acoustic ray will be bent. Temperature and humidity are the main factors affecting the vertical distribution of the velocity of sound in the atmosphere, therefore, it is necessary to study the changes in location errors under the models of uniform vertical distribution of temperature only and uniform vertical distribution of humidity only. This paper focuses on the theory of acoustic ray-tracing in neglecting the presence of the wind and the acoustic attenuation and the theoretical derivation of the location error of thunder source inversion for the three models is carried out by using MATLAB R2019b programming. Then, simulation analysis and comparative study on the variation law of thunder source location error with the height of the source, ground temperature, ground humidity, and array position under the three models are carried out. The results of the study show that the maximum location error can be obtained from the straight-line propagation model, the location error obtained from the model of uniform vertical distribution of temperature only is the second, and the location error obtained from the model of uniform vertical distribution of humidity only is the least and can be negligible compared to the first two models. In the trend of error variation, the variation of location error with temperature and humidity is relatively flat in the first two models; however, the variation of location error with the height of the thunder source is more drastic, which can be more than 80%. The location error obtained from the array inversion closer to the thunder source increases linearly with the height of the thunder source, the location error obtained from the more distant array inversion shows a fast-decreasing trend at the height of the thunder source from 500 to 3500 m, and a flat trend above 3500 m. The location error varies relatively smoothly with the height of the thunder source, the ground temperature, and the ground humidity in the model of uniform vertical distribution of humidity only. In addition, the position of the array also has an important effect on lightning location. The further the horizontal distance from the source, the greater the location error will be obtained in the first two models, and when the thunder source is at a low height and detected at a long distance, the location error will be very large, so relevant data should be modified in actual observation. Full article
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Graphical abstract

Graphical abstract
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<p>Direction of acoustic ray propagation corresponding to the model of temperature variation with height in windless atmosphere [<a href="#B32-remotesensing-16-04000" class="html-bibr">32</a>].</p>
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<p>Schematic diagram of the acoustic ray trajectory. <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">X</mi> <mn>0</mn> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">Z</mi> <mn>0</mn> </msub> </mrow> </semantics></math> represent the horizontal and vertical position of the initial thunder source, respectively. <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mn>0</mn> </msub> </mrow> </semantics></math> represents the initial grazing angle. <math display="inline"><semantics> <mi>X</mi> </semantics></math> and <math display="inline"><semantics> <mi>Z</mi> </semantics></math> represent the horizontal and vertical position of the acoustic ray in propagation. <math display="inline"><semantics> <mi>α</mi> </semantics></math> represents the propagation direction of the acoustic wave at any height.</p>
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<p>The theoretical trajectory of the acoustic ray and trajectory of inversion. <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">X</mi> <mi mathvariant="normal">n</mi> </msub> </mrow> </semantics></math> denotes the array position, <math display="inline"><semantics> <mi mathvariant="normal">H</mi> </semantics></math> denotes the theoretical height of the thunder source, <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">n</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> denotes the height of the inversion result for each array, <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="sans-serif">α</mi> <mi mathvariant="normal">n</mi> </msub> </mrow> </semantics></math> denotes the initial grazing angle corresponding to the array, <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="sans-serif">α</mi> <mi mathvariant="normal">n</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> denotes the receiving grazing angle.</p>
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<p>Schematic diagram of the RLE based on straight-line propagation model. <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>n</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> denotes the receiving grazing angle, <math display="inline"><semantics> <mrow> <msub> <mi>X</mi> <mi>n</mi> </msub> </mrow> </semantics></math> denotes the position of the array, <math display="inline"><semantics> <mrow> <msubsup> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">n</mi> <mo>′</mo> </msubsup> </mrow> </semantics></math> denotes inversion height.</p>
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<p>The variation of RLE with HTS in straight-line propagation model. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GT in straight-line propagation model. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GRH in straight-line propagation model. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with HTS in the model of uniform vertical distribution of temperature only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GT in the model of uniform vertical distribution of temperature only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GRH in the model of uniform vertical distribution of temperature only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with HTS in the model of uniform vertical distribution of humidity only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GT in the model of uniform vertical distribution of humidity only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE with GRH in the model of uniform vertical distribution of humidity only. Six curves represent the variation trend of the corresponding array position.</p>
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<p>The variation of RLE of different vertical distribution models with HTS. (<b>a</b>) shows the variation of RLE at the array position of 500 m, (<b>b</b>) shows the variation of RLE at the array position of 3500 m, (<b>c</b>) shows the variation of RLE at the array position of 8000 m.</p>
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<p>The variation of RLE of different vertical distribution models with GT. (<b>a</b>) shows the variation of RLE at the array position of 500 m, (<b>b</b>) shows the variation of RLE at the array position of 3500 m, (<b>c</b>) shows the variation of RLE at the array position of 8000 m.</p>
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<p>The variation of RLE of different vertical distribution models with GRH. (<b>a</b>) shows the variation of RLE at the array position of 500 m, (<b>b</b>) shows the variation of RLE at the array position of 3500 m, (<b>c</b>) shows the variation of RLE at the array position of 8000 m.</p>
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<p>The variation of relative change of the grazing angle with HTS.</p>
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24 pages, 15956 KiB  
Article
Dynamics of Sandy Shorelines and Their Response to Wave Climate Change in the East of Hainan Island, China
by Wei Xu, Shenliang Chen, Hongyu Ji, Taihuan Hu, Xiaojing Zhong and Peng Li
J. Mar. Sci. Eng. 2024, 12(11), 1921; https://doi.org/10.3390/jmse12111921 - 28 Oct 2024
Viewed by 717
Abstract
Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor [...] Read more.
Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor their responses to wave climate change driven by climate variability. This study focuses on the open sandy coast and assesses shoreline evolutionary dynamics in response to wave climate variability over a 30-year period from 1994 to 2023, using an open-source software toolkit that semi-automatically identify the shorelines (CoastSat v2.4) and reanalysis wave datasets (ERA5). The shorelines of the study area were extracted from CoastSat, and then tidal correction and outlier correction were performed for clearer shorelines. Combining the shoreline changes and wave conditions derived from ERA5, the dynamics of the shorelines and their response to wave climate change were further studied. The findings reveal that the average long-term shoreline change rate along the eastern coast of Hainan Island is 0.03 m/year, with 44.8% of transects experiencing erosion and 55.2% showing long-term accretion. And distinct evolutionary patterns emerge across different sections. Interannual variability is marked by alternating erosion and siltation cycles, while most sections of the coast experiences clear seasonal fluctuations, with accretion typically occurring during summer and erosion occurring in winter. El Niño–Southern Oscillation (ENSO) cycles drive changes in parameters including significant wave height, mean wave period, wave energy flux, and mean wave direction, leading to long-term changes in wave climate. The multi-scale behavior of the sandy shoreline responds distinctly to the ongoing changes in wave climate triggered by ENSO viability, with El Niño events typically resulting in accretion and La Niña periods causing erosion. Notably, mean wave direction is the metric most closely linked to changes in the shoreline among all the others. In conclusion, the interplay of escalating anthropogenic activities, natural processes, and climate change contributes to the long-term evolution of sandy shorelines. We believe this study can offer a scientific reference for erosion prevention and management strategies of sandy beaches, based on the analysis presented above. Full article
(This article belongs to the Section Coastal Engineering)
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Figure 1
<p>(<b>a</b>) Study area (depicted by the black box) located on the Qionghai–Wanning Coast of eastern Hainan, with the locations of the wave reanalysis grid points (E1, E2, E3); (<b>b</b>) detailed view of the four studied sections; (<b>c</b>) an offshore artificial island in Boao; (<b>d</b>) Wanquan River mouth; and (<b>e</b>) Xiaohai Lagoon inlet.</p>
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<p>Representative photographs of the different beaches in the study area: (<b>a</b>) Xiaohai, (<b>b</b>) Hele-Zhengmenhai (HZ), (<b>c</b>) Yudaitan, and (<b>d</b>) Boao.</p>
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<p>Monthly and annual averaged variations in the Multivariate ENSO Index (MEI) from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events).</p>
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<p>Outputs from the CoastSat tool: (<b>a</b>) RGB image of Xiaohai ROI from Landsat 8; (<b>b</b>) output of image classification where each pixel is labeled as “sand”, “water”, “whitewater”, or “other”; (<b>c</b>) grayscale image of the MNDWI pixel values; and (<b>d</b>) histogram showing the probability density function of MNDWI values for each of the four labeled classes and Otsu’s thresholding specific to the sand–water interface.</p>
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<p>The details of the four studied sections and the associated transects: (<b>a</b>) Xiaohai, (<b>b</b>) Hele-Zhengmenhai (HZ), (<b>c</b>) Yudaitan, and (<b>d</b>) Boao.</p>
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<p>Time series of shoreline change along transect No. 171 at HZ.</p>
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<p>Validation of satellite-derived shoreline positions vs. in situ shoreline positions.</p>
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<p>Long-term shoreline change rates (1994–2023) using Theil-Sen estimator.</p>
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<p>Spatial distribution of shoreline change rates from 1994 to 2023.</p>
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<p>Spatiotemporal evolution of the cross-shore distance along the sandy coast from 1994 to 2023 (initially set at 0 m).</p>
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<p>Annual averaged variations in cross-shore shoreline distance from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events).</p>
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<p>Cross-shore distance monthly average and standard deviation of transect No.60.</p>
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<p>Time series of cross-shore shoreline change along (<b>a</b>) transect No.518, and (<b>b</b>) transect No.506 at Boao (the start of artificial island construction is indicated by black lines).</p>
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<p>Time series of cross-shore shoreline change along transect No. 103 at Xiaohai (the start of artificial island construction is indicated by black lines).</p>
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<p>Monthly average (<b>a</b>) significant wave height, (<b>b</b>) mean wave period, and (<b>c</b>) wave energy flux at E1, E2, and E3 sites.</p>
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<p>Multi-year average (<b>a</b>) significant wave height, (<b>b</b>) mean wave period, and (<b>c</b>) wave energy flux at E1, E2, and E3 sites during 30 years.</p>
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<p>Annual averaged variations in (<b>a</b>) significant wave heights, (<b>b</b>) mean wave periods, and (<b>c</b>) wave energy flux at E1, E2, and E3 sites during the period from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events).</p>
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<p>Monthly rose charts of mean wave direction and wave energy flux at the E2 from 1994 to 2023: (<b>a</b>–<b>l</b>) display the average monthly wave energy flux and direction from January to December.</p>
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<p>Annual averaged variation in mean wave direction (+ is clockwise; − is counterclockwise) relative to the overall average at E2 from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events).</p>
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<p>Annual averaged variation of 95% exceedance wave energy flux at E1, E2, and E3 sites from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events).</p>
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<p>Rose chart of extreme wave direction at E2 from 1994 to 2023.</p>
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<p>Annual averaged variation in extreme wave direction (+ is clockwise; − is counterclockwise) relative to the overall average at E2 from 1994 to 2023 (light red shading denotes El Niño events, and light blue shading denotes La Niña events.</p>
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<p>Comparison between annual average mean wave direction (blue line) and cross-shore shoreline distance (yellow dot) from 1994 to 2023.</p>
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22 pages, 3270 KiB  
Article
The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions
by Marina Tautan, Maria Zoran, Roxana Radvan, Dan Savastru, Daniel Tenciu and Alexandru Stanciu
Atmosphere 2024, 15(10), 1230; https://doi.org/10.3390/atmos15101230 - 15 Oct 2024
Viewed by 614
Abstract
This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, [...] Read more.
This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. Full article
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<p>Location of the investigated metropolitan areas Wuhan (China), Milan (Italy), Madrid (Spain), London (UK), and Bucharest (Romania).</p>
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<p>Temporal distribution of the daily mean ground level of ozone concentrations in the investigated metropolises during 1 January 2020–15 June 2020.</p>
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<p>Temporal patterns of the daily mean ground level of nitrogen dioxide concentrations in the investigated metropolises from 1 January 2020 to 15 June 2020.</p>
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<p>Temporal patterns of the daily mean Air Quality Index in the investigated metropolises during 1 January 2020–15 June 2020.</p>
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<p>Temporal patterns of the daily mean AOD in the investigated metropolises from 1 January 2019 to 15 June 2020.</p>
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<p>Temporal patterns of the daily new COVID-19 cases (DNCs) in the investigated metropolises from 1 January 2019 to 15 June 2020.</p>
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<p>Temporal patterns of the total COVID-19 cases recorded during January 2020–15 June 2020 in the investigated metropolises.</p>
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<p>Temporal patterns of the total COVID-19 deaths recorded during January 2020–15 June 2020 in the investigated metropolises.</p>
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16 pages, 3710 KiB  
Article
Experimental Analysis of Terahertz Wave Scattering Characteristics of Simulated Lunar Regolith Surface
by Suyun Wang and Kazuma Hiramatsu
Remote Sens. 2024, 16(20), 3819; https://doi.org/10.3390/rs16203819 - 14 Oct 2024
Cited by 1 | Viewed by 630
Abstract
This study investigates terahertz (THz) wave scattering from a simulated lunar regolith surface, with a focus on the Brewster feature, backscattering, and bistatic scattering within the 325 to 500 GHz range. We employed a generalized power-law spectrum to characterize surface roughness and fabricated [...] Read more.
This study investigates terahertz (THz) wave scattering from a simulated lunar regolith surface, with a focus on the Brewster feature, backscattering, and bistatic scattering within the 325 to 500 GHz range. We employed a generalized power-law spectrum to characterize surface roughness and fabricated Gaussian correlated surfaces from Durable Resin V2 using 3D printing technology. The complex dielectric permittivity of these materials was determined through THz time-domain spectroscopy (THz-TDS). Our experimental setup comprised a vector network analyzer (VNA) equipped with dual waveguide frequency extenders for the WR-2.2 band, transmitter and receiver modules, polarizing components, and a scattering chamber. We systematically analyzed the effects of root-mean-square (RMS) height, correlation length, dielectric constant, frequency, polarization, and observation angle on THz scattering. The findings highlight the significant impact of surface roughness on the Brewster angle shift, backscattering, and bistatic scattering. These insights are crucial for refining theoretical models and developing algorithms to retrieve physical parameters for lunar and other celestial explorations. Full article
(This article belongs to the Special Issue Future of Lunar Exploration)
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<p>The geometry of wave scattering from rough surface.</p>
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<p>The rough surface samples are designed with specified RMS heights and correlation lengths.</p>
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<p>The measured dielectric constant of the material by THz-TDS.</p>
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<p>The roughness validation of one selected rough surface with an RMS height of 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a correlation length of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p>
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<p>The experiment configuration.</p>
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<p>The polarizer consists of three reflectors.</p>
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<p>The comparison between the simulated and experimental HH and VV reflections from a flat surface with a dielectric constant of <math display="inline"><semantics> <mrow> <mn>2.597</mn> <mo>+</mo> <mi>j</mi> <mn>0.165</mn> </mrow> </semantics></math>.</p>
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<p>The frequency effect on THz scattering from rough surface.</p>
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<p>The correlation length effect on THz scattering from rough surface. (<b>a</b>) <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>) <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p>
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<p>The RMS height effect on THz scattering from rough surface. (<b>a</b>) <span class="html-italic">l</span> = 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math> (<b>b</b>) <span class="html-italic">l</span> = 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math>.</p>
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<p>Comparison of bistatic scattering from flat and rough surfaces with RMS heights of 0.5<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed correlation length of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p>
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<p>Comparison of bistatic scattering from flat and rough surfaces with RMS heights of 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 0.08<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed correlation length of 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p>
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<p>Comparison of bistatic scattering from the flat surface and rough surface with different correlation lengths of 2<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, 4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and 6<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed RMS height of 0.8<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at the incident angle of 30°, 45° and 60° for HH and VV polarizations.</p>
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<p>Comparison of bistatic scattering from rough surfaces with correlation lengths of 1.5<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, 1<math display="inline"><semantics> <mi>λ</mi> </semantics></math>, and 0.4<math display="inline"><semantics> <mi>λ</mi> </semantics></math> and a fixed RMS height of 0.1<math display="inline"><semantics> <mi>λ</mi> </semantics></math> at incident angles of 30°, 45°, and 60° for both HH and VV polarizations.</p>
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<p>Comparison of three different incident angles 30°, 45°, and 60° for HH and VV polarizations from a Gaussian correlated surface of <span class="html-italic">l</span> = 1.0 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.1 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. ((<b>left</b>): VV polarization, (<b>right</b>): HH polarization).</p>
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<p>Comparison of three different incident angles 30°, 45°, and 60° for HH and VV polarizations from a Gaussian correlated surface of <span class="html-italic">l</span> = 2.0 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>, <math display="inline"><semantics> <mi>σ</mi> </semantics></math> = 0.5 <math display="inline"><semantics> <mi>λ</mi> </semantics></math>. ((<b>left</b>): VV polarization, (<b>right</b>): HH polarization).</p>
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15 pages, 920 KiB  
Article
Capillary Blood Docosahexaenoic Acid Levels Predict Electrocardiographic Markers in a Sample Population of Premenopausal Women
by Breno P. Casagrande, George Sherrard, Mike S. Fowler, Débora Estadella and Allain A. Bueno
J. Clin. Med. 2024, 13(19), 5957; https://doi.org/10.3390/jcm13195957 - 7 Oct 2024
Viewed by 868
Abstract
Introduction: The relationship between blood N-3 polyunsaturated fatty acid (PUFA) levels and cardiovascular health is known, but direct evidence that N-3 PUFA levels influence electrocardiographic (ECG) parameters is non-existent. In the study described herein, we investigated the relationship between anthropometric biomarkers and [...] Read more.
Introduction: The relationship between blood N-3 polyunsaturated fatty acid (PUFA) levels and cardiovascular health is known, but direct evidence that N-3 PUFA levels influence electrocardiographic (ECG) parameters is non-existent. In the study described herein, we investigated the relationship between anthropometric biomarkers and capillary blood PUFAs with ECG outputs in a sample population of healthy pre-menopausal women. Method: Twenty-three consenting females were recruited, with the study power analysis sufficiently demonstrated. Food intake, anthropometric and cardiovascular parameters were obtained. Capillary blood was collected for fatty acid chromatographic analysis. Results: Body mass index, haematocrit, heart rate (HR), mean arterial pressure (MAP) and ECG readings all fell within healthy ranges. Principal component analysis-mediated correlations were carried out controlling for combined Components 1 (age, body fat % and waist-to-hip ratio) and 2 (height, HR and MAP) as control variables. Docosahexaenoic acid (DHA) unequivocally decreased the QRS area under the curve (AUC-QRS) regardless of the impact of control variables, with each unit increase in DHA corresponding to a 2.3-unit decrease in AUC-QRS. Mediation analysis revealed a significant overall effect of DHA on AUC-QRS, with the impact of DHA on R wave amplitude accounting for 77% of the total observed effect. Discussion: Our new findings revealed an inverse relationship between AUC-QRS with capillary blood DHA, suggesting that the association between ventricular mass and its QRS depolarising voltage is mediated by DHA. Our findings bridge a knowledge gap on the relationship between ventricular mass and ventricular efficiency. Further research will confirm whether the relationship identified in our study also exists in diseased patients. Full article
(This article belongs to the Section Cardiovascular Medicine)
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<p>Increasing values of capillary blood docosahexaenoic acid (C22:6n-3, DHA) are associated with a significant reduction in ventricular depolarisation (AUC-QRS), after accounting for variation in other common anthropometric biomarkers. The regression slope and 95% CIs (shaded interval) are based on an additive multiple linear regression model with DHA and the first two axes from a PCA of six common biomarkers as predictor variables, including Factor 1 (age, body fat % and WHR) and Factor 2 (height, HR and MPA). Only DHA showed a significant relationship with AUC-QRS; further statistical model details are provided in <a href="#jcm-13-05957-t005" class="html-table">Table 5</a>. <a href="#jcm-13-05957-f001" class="html-fig">Figure 1</a> was created with the visreg package for R [<a href="#B46-jcm-13-05957" class="html-bibr">46</a>].</p>
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<p>Graphical representation of the mediation model for the effect of DHA on AUC-QRS. C1 and C2: confounder variables, factor 1 and factor 2; (X) predictor: DHA; (Y) outcome: AUC-QRS; (M) mediator: R wave amplitude. Numbers on arrows between model components are the standardised beta values (effects). Dashed lines show non-significant paths; continuous lines show significant paths. Grey lines show control variable effects; black lines show main model effects.</p>
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14 pages, 2475 KiB  
Article
Diagnostic Accuracy of Ultrasound Imaging and Shear Wave Elastography to Discriminate Patients with Chronic Neck Pain from Asymptomatic Individuals
by Gustavo Plaza-Manzano, César Fernández-de-las-Peñas, María José Díaz-Arribas, Marcos José Navarro-Santana, Sandra Sánchez-Jorge, Carlos Romero-Morales and Juan Antonio Valera-Calero
Healthcare 2024, 12(19), 1987; https://doi.org/10.3390/healthcare12191987 - 5 Oct 2024
Viewed by 698
Abstract
Objectives: The aim of this study was to determine and compare the capability of several B-mode ultrasound (US) and shear wave elastography (SWE) metrics to differentiate subjects with chronic non-specific neck pain from asymptomatic subjects. Methods: A diagnostic accuracy study recruiting a sample [...] Read more.
Objectives: The aim of this study was to determine and compare the capability of several B-mode ultrasound (US) and shear wave elastography (SWE) metrics to differentiate subjects with chronic non-specific neck pain from asymptomatic subjects. Methods: A diagnostic accuracy study recruiting a sample of patients with chronic neck pain and asymptomatic controls was conducted. Data collection included sociodemographic information (i.e., gender, age, height, weight and body mass index), clinical information (pain intensity assessed using the Visual Analogue Scale and pain-related disability using the Neck Disability Index) and B-mode ultrasound and shear wave elastography features of the cervical multifidus muscle (cross-sectional area, perimeter, mean echo intensity, fat infiltration, shear wave speed and Young’s modulus). After analyzing between-group differences for left/right sides, cases and controls, and males and females, the area under the receiver operating characteristic (ROC) curve, the optimal cut-off point, the sensitivity, the specificity, the positive likelihood ratio (LR) and negative LR for each metric were calculated. A total of 316 individuals were recruited in this study (n = 174 cases with neck pain and n = 142 asymptomatic controls). Results: No significant differences (p > 0.05) were found between cases and controls for most variables, except for fatty infiltration, which was significantly higher in chronic neck pain cases (p < 0.001). Gender differences were significant across all US and SWE metrics (all, p < 0.001 except p = 0.015 for fatty infiltrates). A slight asymmetry was observed between the left and right sides for area (p = 0.038). No significant interactions between group, gender and side (all metrics, p > 0.008) were identified. Fatty infiltration was the most effective discriminator, with a ROC value of 0.723, indicating acceptable discrimination. The optimal cut-off point for fatty infiltration was 25.77, with a moderate balance between sensitivity (59.8%) and specificity (20.5%). However, its positive likelihood ratio (LR) of 0.75 suggests limited usefulness in confirming the condition. Conclusions: Fatty infiltration was significantly higher in individuals with chronic idiopathic neck pain compared to those without symptoms, while other muscle metrics were similar between both groups. However, since fat infiltration had moderate diagnostic accuracy and the other metrics showed poor discriminatory power, US cannot be used solely to discriminate patients with idiopathic neck pain. Full article
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<p>Ultrasound imaging of the cervical multifidus muscle acquired at the C4–C5 level: (<b>A</b>) raw image; (<b>B</b>) fat infiltration calculation; (<b>C</b>) shear wave elastography.</p>
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<p>Comparison of model performance for B-mode ultrasound (<b>A</b>) and shear wave imaging (<b>B</b>) using ROC and Precision-Recall curves. The ROC curve shows the trade-off between sensitivity and specificity for each parameter, while the Precision-Recall curve further details the performances of these parameters. The bar charts quantify the overall model quality for each parameter.</p>
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23 pages, 3379 KiB  
Article
Coastal Vulnerability Index (CVI) Assessment: Evaluating Risks Associated with Human-Made Activities along the Limassol Coastline, Cyprus
by Christos Theocharidis, Marina Doukanari, Eleftheria Kalogirou, Demetris Christofi, Christodoulos Mettas, Charalampos Kontoes, Diofantos Hadjimitsis, Athanasios V. Argyriou and Marinos Eliades
Remote Sens. 2024, 16(19), 3688; https://doi.org/10.3390/rs16193688 - 3 Oct 2024
Cited by 1 | Viewed by 1219
Abstract
Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the [...] Read more.
Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the Limassol district’s coastline, in Cyprus, on which there have been limited studies. Furthermore, an analysis is conducted utilising the Coastal Vulnerability Index (CVI) by exploiting eight key parameters: land cover, coastal slope, shoreline erosion rates, tidal range, significant wave height, coastal elevation, sea-level rise, and coastal geomorphology. These parameters were assessed utilising remote sensing (RS) data and Geographical Information Systems (GISs) along a 36.1 km stretch of coastline. The results exhibited varying risk levels of coastal vulnerability, mainly highlighting a coastal area where the Kouris River estuary is highly vulnerable. The study underscores the need for targeted coastal management strategies to address the risks associated with coastal erosion. Additionally, the CVI developed in this study can be exploited as a tool for decision makers, empowering them to prioritise areas for intervention and bolster the resilience of coastal areas in the face of environmental changes. Full article
(This article belongs to the Section Environmental Remote Sensing)
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<p>The study area with grid cells along the shoreline.</p>
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<p>A flow chart presenting the process of calculating the CVI.</p>
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<p>Vulnerability level of (<b>a</b>) land cover, (<b>b</b>) coastal slope, (<b>c</b>) coastal erosion rate, (<b>d</b>) mean tidal range, (<b>e</b>) mean significant wave height, (<b>f</b>) coastal elevation, (<b>g</b>) relative sea-level rise, and (<b>h</b>) coastal geomorphology. Pie charts indicate the percentage occupied by each rank, while the number of transects for each rank are in parentheses.</p>
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<p>ESA Worldcover and pie chart with the percentages of spatial distribution per land-cover class inside the grid cells.</p>
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<p>(<b>a</b>) Shoreline evolution of the study area by LRR (m/year) between 1963 and 2019. The pie chart indicates the percentage distribution of the LRR, while in parentheses are the numbers of transects for each rank. (<b>b</b>) Bar chart of LRR per grid cell. (<b>c</b>) Bar chart of LRR per transect ID. (<b>d</b>) Map of LRR, with the most significant erosion occurring in grid cells A5–A6. (<b>e</b>) Map of LRR showing the most significant accretion, which occurred in grid cell A4.</p>
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<p>(<b>a</b>) The CVI map of the study area. The pie chart shows the percentage distribution of the CVI, while the numbers of transects for the CVI scores are in parentheses. (<b>b</b>) Bar chart of CVI per grid cell.</p>
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<p>NSM in the study area. The more red the coastline, the more erosion it has undergone, while the bluer it is, the more deposition it has seen.</p>
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<p>Bar chart showing the number of transects ranked in CVI categories according to land-cover vulnerability score.</p>
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<p>Bar chart showing the number of transects ranked in CVI categories according to coastal slope vulnerability score.</p>
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<p>Bar chart showing the number of transects ranked in CVI categories according to coastal elevation vulnerability score.</p>
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<p>Bar chart showing the number of transects ranked in CVI categories according to coastal geomorphology vulnerability score.</p>
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<p>Bar chart showing the number of transects ranked in CVI categories according to coastal erosion vulnerability score.</p>
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23 pages, 15519 KiB  
Article
Coastal Storm-Induced Sinkholes: Insights from Unmanned Aerial Vehicle Monitoring
by Alice Busetti, Christian Leone, Amerigo Corradetti, Saverio Fracaros, Sebastian Spadotto, Pietro Rai, Luca Zini and Chiara Calligaris
Remote Sens. 2024, 16(19), 3681; https://doi.org/10.3390/rs16193681 - 2 Oct 2024
Cited by 1 | Viewed by 1083
Abstract
In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be [...] Read more.
In recent decades, the scientific community has increasingly focused on extreme events linked to climate change, which are leading to more intense and frequent natural disasters. The Mediterranean can be considered a hotspot where the effects of these changes are expected to be more intense compared to other regions of the planet. Italy is not exempt; in fact, with its extensive shoreline, it is particularly vulnerable, especially to high sea levels and coastal erosions. In this framework, from late October to early November 2023, six storm surges occurred in the Gulf of Trieste (NE Italy). These events, characterized by winds from 190°N to 220°N and the significant wave height, which reached up to 1.81 m nearshore—an uncommon meteorological condition in the northern Adriatic Sea—caused the occurrence of eight coastal sinkholes and substantial damages to man-made structures. Thanks to Unmanned Aerial Vehicles (UAVs) and their derived products (high-resolution orthomosaics, Digital Elevation Models—DEMs, and point clouds), it was possible to study these features over time, enabling long-term coastal dynamics monitoring, which can be crucial for timely and effective response and restoration efforts. Full article
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<p>Recent coastal sinkhole worldwide: (<b>a</b>) Aerial view of Souter sinkhole and surrounding coastal landscape in Sunderland, England (photo credits: National Trust Images/Annapurna Mellor, Souter Lighthouse and The Leas, Tyne and Wear); (<b>b</b>) Sinkhole formed on January 2023 in Cape Kiwanda State Park, Oregon (photo credits: Oregon Parks and Recreation Department Media Hub); (<b>c</b>) Sinkhole due to a storm surge event on November 2023 in Sant’Agata di Militello (Sicily, Italy) (photo credits: “In Alto Mare-Sant’Agata di Militello” [<a href="#B49-remotesensing-16-03681" class="html-bibr">49</a>]; (<b>d</b>) Sinkhole formed at the Sistiana Bay (Friuli Venezia Giulia, Italy) after a storm surge on October 2023 (photo credits: Busetti A).</p>
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<p>Study area location: (<b>a</b>) Friuli Venezia Giulia (FVG) region; (<b>b</b>) simplified lithological map of part of the Classical Karst Region; (<b>c</b>) geomorphological map of the study area. Yellow rectangles identify SECTOR 1 and SECTOR 2, and the letters are related to the occurred sinkholes.</p>
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<p>(<b>a</b>) Fetch referred to the study area and (<b>b</b>) bathymetric model of the Gulf of Trieste and monitoring system locations: DWRG1 (13.24°E, 45.56°N, 15.2 m depth) and DWRG3 (13.52°E, 45.69°N, 9.7 m depth) wave buoys, Paloma station (13.57°E, 45.62°N, 10 m height), and Molo Sartorio (Trieste) tide gauge (13.76°E, 45.65°N). DWRG1 and DWRG3 (installed and managed by Istituto Nazionale di Oceanografia e di Geofisica Sperimentale (OGS), record semi-hourly data; Paloma (Italian acronyms of Advanced Platform Oceanographic Laboratory Adriatic Sea), installed 8 nautical miles off the coast of Trieste between Piran (Slovenia) and Grado, measure both wind speed and direction with a sampling rate of 15 min. The bathymetric model was obtained from one of the European Marine Observation and Data Network (EMODnet) of 2022 [<a href="#B88-remotesensing-16-03681" class="html-bibr">88</a>] integrated with the bathymetry provided by [<a href="#B89-remotesensing-16-03681" class="html-bibr">89</a>] and higher resolution bathymetry of the northern coastal area.</p>
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<p>Wave roses of (<b>a</b>) DWRG1 relating to the time interval 2004–2023, and (<b>b</b>) DWRG3 relating to the time interval 2007–2023.</p>
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<p>Historical (<b>a</b>,<b>b</b>) and recent (<b>c</b>,<b>d</b>) images of the damages that occurred along Barcola waterfront (a and b, photo credits: ASTS, Commissariato del Governo nella Regione Friuli Venezia Giulia, Fototeca (b. 1, ex b. 8, in riordinamento) [<a href="#B72-remotesensing-16-03681" class="html-bibr">72</a>]; c and d, photo credits: Busetti A).</p>
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<p>Graph representing the significant wave height recorded at the DWRG1 buoy (open sea, light blue dashed line) and simulated through MIKE21 Spectral Waves by ©DHI at the study area (Sistiana Mare, dark blue line). The different storm surge events are here indicated with their corresponding numbers (from 1 to 6 and refers to those of <a href="#remotesensing-16-03681-t002" class="html-table">Table 2</a> and <a href="#remotesensing-16-03681-t003" class="html-table">Table 3</a>). UAV surveys of 27 October, following the first event, and 9 November, following the whole multi-event, are evidenced (vertical red lines).</p>
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<p>Caravella Beach shaded relief and orthomosaics (SECTOR 1) comparison between two different surveys: pre-storm event of 29 September (2023) and on 27 October (2023), just after the EVENT 1. The storm surge event that occurred during the night between 26 and 27 October (2023) produced the opening of only one sinkhole, the one identified with letter C, but also caused the removal of the finest material along the beach and in particular in its western part.</p>
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<p>Orthomosaics (<b>a</b>–<b>e</b>) and shaded relief (<b>f</b>–<b>j</b>) representing sinkhole H. The scene depicted represents sinkhole H on 29 September 2023 (<b>a</b>,<b>f</b>), on 27 October 2023 (<b>b</b>,<b>g</b>), on 7 November 2023 (<b>c</b>,<b>h</b>), on 4 December 2023 (<b>d</b>,<b>i</b>) and on 27 February 2024 (<b>e</b>,<b>j</b>). (<b>k</b>) A detail of the collapsed retaining wall on 28 October.</p>
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<p>Caravella Beach shaded relief and orthomosaics (SECTOR 1) comparison between two different surveys: 4 December (2023) and 13 February (2024). The storm surge events between 31 October and 5 November (2023) caused the occurrence of most of the sinkholes in this sector (A, B, D, E, F, and G) and enlarged sinkhole C. The last acquisitions on 13 February show the ongoing restauration phase, with the leveling of the embankment behind the retaining wall and the consequent disappearance of sinkholes D, E, F, and G and the beach replenishment (mainly in front of sinkhole C).</p>
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<p>(<b>a</b>) HR 3D model of sinkhole B on 29 November 2023 (<a href="https://skfb.ly/p7U7E" target="_blank">https://skfb.ly/p7U7E</a>, accessed on 21 August 2024); (<b>b</b>) HR 3D model of sinkholes F and G on 29 November 2023 (<a href="https://skfb.ly/p7Uws" target="_blank">https://skfb.ly/p7Uws</a>, accessed on 21 August, 2024). (1) foundations of the retaining wall; (2) horizontally elongated hole; (3) collapsed material. View towards North. The ground resolutions of the models (provided by Agisoft Metashape reports) are respectively 2.86 (<b>a</b>) and 2.33 mm/pix (<b>b</b>).</p>
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<p>Three-dimension model of the sinkhole H on 29 November 2023 (<a href="https://skfb.ly/p7Uzu" target="_blank">https://skfb.ly/p7Uzu</a>, accessed on 21 August 2024). It was already partially filled with landfill material. The retaining wall is almost completely broken. View towards the north-west. The ground resolution of the model (provided by the Agisoft Metashape report) is equal to 3.34 mm/pix.</p>
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<p>Cloud-to-cloud comparison between dense point clouds of (<b>a</b>) 29 September (2023) and 27 October (2023) and (<b>b</b>) between 27 October (2023) and 4 December (2023). Highlighted in green are the areas where the wave erosion was concentrated (1); in red are the areas of accumulation of gravel sediments (2) after EVENT 1, and the wooden material accumulation (4) occurred after the last four storm events hit Caravella Beach. Number (3) identifies the linear depressions located immediately beyond the retaining wall and parallel to it.</p>
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