Cordgrass performance in different zones at the salt marsh site in 2009.

<p>(A) number of stems (B) maximum plant height and (C) number of inflorescences. Data are means + 1SE. ND indicates no data. Bars sharing a capital letter were not significantly different from one another (nonparametric multiple comparisons, Steel-test). <i>P</i>-values from ANOVAs or Wilcoxon tests investigating the effect of neighbors are indicated above the bar group for low marsh transplants.</p

Cordgrass performance in different zones at the estuarine site in 2010.

<p>(A) number of stems (B) maximum plant height (C) number of inflorescences and (D) biomass. Data are means + 1SE. ND indicates no data. Bars sharing a capital letter were not significantly different from one another (nonparametric multiple comparisons, Steel-test). <i>P</i>-values from ANOVAs or Wilcoxon tests investigating the effect of neighbors are indicated above each bar group for low marsh and high marsh transplants.</p

Cordgrass performance in different flooding treatments in the common garden experiment.

<p>(A) number of stems and maximum plant height and (B) biomass. Data are means + 1SE (<i>n</i> = 6). <i>P</i>-values from ANOVAs or Wilcoxon tests are indicated.</p

Physical stresses and vegetation cover in different zones at the salt marsh and estuarine sites.

<p>Data are means ± 1SE. Sample sizes are 8 for salinity, moisture, and 15 for vegetation cover. Flooding frequency (shown as a range) was estimated based on previous studies and field observations.</p><p>Statistical analyses were conducted using generalized linear models. Gamma regressions with log link were used for salinity and moisture while a Poisson regression with log link was used for vegetation cover. -, no data. Flooding indicates the percentage of days flooded in a year.</p

Map showing study sites in the Yellow River Delta, northern China.

<p>Cordgrass was artificially planted at Wuhaozhuang in the 1980s, and has invaded the estuarine site. Cordgrass was collected at Wuhaozhuang and transplanted into different zones (along the dotted lines) at the salt marsh and estuarine sites.</p

All data files are available from the figshare database.

(https://doi.org/10.6084/m9.figshare.24151551). (DOCX)</p

Experimental data comparison table.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.</div

Network block.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.</div

Active sonar reverberation suppression change graph.

Active sonar reverberation suppression change graph.</p

Loss curve.

Reverberation is the primary background interference of active sonar systems in shallow water environments, affecting target position detection accuracy. Reverberation suppression is a signal processing technique used to improve the clarity and accuracy of echo by eliminating the echoes, reverberations, and noise that occur during underwater propagation.This paper proposes an end-to-end network structure called the Reverberation Suppression Network (RS-U-Net) to suppress the reverberation of underwater echo signals. The proposed method effectively improves the signal-to-reverberation ratio (SRR) of the echo signal, outperforming existing methods in the literature. The RS-U-Net architecture uses sonar echo signal data as input, and a one-dimensional convolutional network (1D-CNN) is used in the network to train and extract signal features to learn the main features. The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. The weights of reverberation suppression tasks are initialized with an auto-encoder, which effectively uses the training time and improves performance. By comparing with the experimental pool data, it is found that the proposed method can improve the reverberation suppression by about 2 dB compared with other excellent methods.</div