esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 8 Undistorted Airy Pulses
### Physical Accelerating Pulse  
Clearly, the main problem with the Airy function is that it is not a normalizable function, i.e., it has infinite energy and can have no physical realization.  
One option to normalize the airy function is to multiply the pulse by an exponent [36]:  
\[A(z=0,t)=A_{0}\exp(at)\text{Ai}(t/\theta) \tag{90}\]  
In which case, the pulse dynamics can be derived with the aid of Equations (8) and (88), yielding:  
\[A(z,t)=A_{0}Ai\left[\frac{t-i\beta_{2}az}{\theta}-\left(\frac{\beta_{2}z}{2 \theta^{2}}\right)^{2}\right]\exp\left[-i\frac{\beta_{2}}{2}a^{2}z+at-i\frac{ \beta_{2}z(t-i\beta_{2}az)}{2\theta^{3}}+\frac{i}{12}\left(\frac{\beta_{2}z}{ \theta^{2}}\right)^{3}\right] \tag{91}\]  
In Figure 26, the dynamics of this pulse is presented. A spatiotemporal presentation of the pulse's intensity is presented in Figure 27. This pulse has a finite energy (it can be normalized), but as a consequence, the pulse decays in the z-direction as well. Since  
\[|A(z,t)|^{2}=|A_{0}|^{2}\bigg{|}Ai\left[\frac{t-i\beta_{2}az}{\theta}-\left( \frac{\beta_{2}z}{2\theta^{2}}\right)^{2}\right]\bigg{|}^{2}\exp\left(2 at-a \frac{(\beta_{2}z)^{2}}{\theta^{3}}\right) \tag{92}\]  
the pulse's confinement within the time-scale \(a^{-1}\) \"costs\" resultant confinement in the \(z\)-direction, i.e., the pulse decays within a distance of \(z\cong\sqrt{\theta^{3}/a}/\beta_{2}\).  
Figure 25: Presentation of the temporal dynamics of the accelerating Airy pulse (Equation (88)). On the left, the real and imaginary components of the pulse’s field are presented (for the final distance of \(\zeta=\beta_{2}z/\theta^{2}=1\)), and on the right, the pulse’s intensity (\(|A(t,z)|^{2}\)) is presented (for the final distance of \(\zeta=\beta_{2}z/\theta^{2}=2\)).
## 5 Results and Discussions
The y-axis corresponds to the predicted wavelength peak of the DBN model. From the results, it can be seen that the DBN model can also accurately predict the marked peak wavelengths of fixed FBG and three different interferences.
## 6 Discussion
### Comparison of Performance
For Group B, similar results can be seen in Tables 6 and 7 that ASLM-LHS and ASLM-AP outperform all the baselines under all measurements in 1/10 Reddit, 1/80 Last.fm, and Tmall datasets. For the Tmall dataset, the evaluation result of ASLM-LHS shown in Table 7 improves 70.86%, 49.06%, and 114.50% in R@10, R@20, and MRR@20 scores compared with the SWIWO-I method, respectively. Please note that, for the Tmall dataset, as shown in Table 1, there is scarcely enough sessions for each user (namely, 3.99 per user) and number of average session length (2.99, per user) compared with those in Reddit and Last.fm. For 1/10 Reddit and 1/80 Last.fm, since the user history information is reduced dramatically compared with the full Reddit and Last.fm datasets, most of the models' performances significantly decrease accordingly. However, both ASLM models still outperform the strongest baselines, as shown in Table 6. It demonstrates that ASLM can reach better performance even when it is severely short of both long-term and short-term user behavior. When \(K\) changes from 5 to 20, for the Reddit and 1/10 Reddit datasets, there is a downward trend in the relative scores of ASLM-LHS and ASLM-AP. For the Last.fm dataset, there is an upward trend in those of ASLM-LHS. The reason for the upward trend is that there is more abundant user history information in the Last.fm dataset, and, with the increase of \(K\), ASLM-LHS still has the potential to further capture the context information implied by the Last.fm dataset, which results in some space to improve its performance. In terms of the two versions of ASLM (ASLM-AP using average pooling and ASLM-LHS using the last hidden state), Tables 5-7 show that, for the Reddit dataset, the test performance of ASLM-LHS has an obvious advantage compared to that of ASLM-AP, while they are at the same level for the other four datasets. As mentioned above, the model's subsequent training process benefits from the last hidden state method by receiving the valuable context information from history sessions.
## 3 Results and Discussion
### CNT/Si Devices with Films of Similar Transmittance
Both fast-filtered films produced \(\sigma_{\text{dc}}\):\(\sigma_{\text{OP}}\) ratios significantly lower than the thicker fast-filtered films in Table 3 which is expected given the thinness of the films. This indicates that the slow-filtered films were superior thin conductive films to the thin fast-filtered films. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Fast-Filtered Films** & **Slow Gravity Filtered Films** \\ \hline PCE (\%) & **5.38** (4.21 \(\pm\) 1.01) & **5.39** (4.86 \(\pm\) 0.76) \\ \hline \(\text{J}_{\text{sc}}\) (mA cm\({}^{-2}\)) & **23.47** (22.77 \(\pm\) 0.62) & **25.21** (24.17 \(\pm\) 1.47) \\ \hline \(\text{V}_{\text{oc}}\) (V) & **0.451** (0.440 \(\pm\) 0.009) & **0.501** (0.497 \(\pm\) 0.0064) \\ \hline FF & **0.53** (0.417 \(\pm\) 0.098) & **0.43** (0.40 \(\pm\) 0.042) \\ \hline Ideality & **3.72** (3.82 \(\pm\) 0.16) & **2.03** (2.74 \(\pm\) 0.92) \\ \hline Saturated Current Density (mA cm\({}^{-2}\)) & **0.0950** (0.143 \(\pm\) 0.046) & **0.00068** (0.0087 \(\pm\) 0.013) \\ \hline Series Resistance (\(\Omega\)) & **13.3** (113 \(\pm\) 34.2) & **103** (259 \(\pm\) 254) \\ \hline Shunt Resistance (\(\Omega\)) & **2820** (1482 \(\pm\) 1172) & **41,300** (14,360 \(\pm\) 23,331) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Device data for fast and slow-filtered films formed from 500 \(\upmu\)L aliquots of CNT suspension. Best performing values are bolded, with the average and error in brackets. Figure 7: J/V properties for devices with slow and fast-filtered SWCNT films from 500 \(\upmu\)L aliquots. On the macroscale, films produced via the slow filtration method were less homogeneous than with standard filtration methods, with small areas of high CNT density surrounded by larger areas of low CNT density. The data in Appendix C clearly shows that over smaller areas the slow-filtered films are uniform. Fast-filtered films, on the other hand, were homogeneous on the macroscale to the naked eye. For improved reproducibility for the slow filtered films, some degree of stirring during gravity filtration could be used. However, this may also decrease the benefits seen on the microscale for slow filtration as it could disrupt the natural ordering of the CNTs in suspension. Alternatively, the use of a controllable low vacuum system instead of gravity filtration may lead to improved macroscale homogeneity. The low vacuum would ensure that a uniform and controllable filtration rate, which would provide tunable alignment and film thickness. The importance of the filtration rate was demonstrated by [PERSON] et al. [34].
## 3 Methodology
### Rain Streak Extraction Network (RSENet)
#### 3.3.2 Spatial Attention Mechanism  
The spatial attention mechanism (SAM) [24; 25] assigns various weights to different regions of the input feature map, reflecting the relevance of each region. The Atlas entry attribute \(F\in R^{C\times H\times W}\) undergoes both greatest division and even division procedures, and the effects are summed, before passing through a convolution layer to extract a single-channel feature map. After that, a sigmoid activation function is used on this feature map to produce the spatial attention map \(M_{s}\in R^{1\times H\times W}\) as shown in Equation (6).  
\[M_{s}=\sigma(Conv(Concate[MP(F_{SAM}^{i}),AP(F_{SAM}^{i})])) \tag{6}\]  
where \(Conv\) symbolizes the process of convolution, \(MP\) and \(AP\) represent the operations of maximum pooling and average pooling, respectively, and \(Concate\) denotes the concatenation along the channel dimension.  
\[F_{SAM}^{0}=F_{SAM}^{i}\otimes M_{s} \tag{7}\]  
The final output is obtained through carrying out a duplication of elements within \(F_{SAM}^{i}\) and \(M_{s}\) as shown in Equation (7).  
In this study, a multi-branch remaining network incorporating circuit and geographic focus processes is introduced in the deraining network. The suggested method demonstrates the following advantages in deraining accomplishment, and the findings from the investigation are presented later in the paper.  
Improvement in rain trace removal: in the rain trace removal task on the Rain1200 dataset, the peak signal-to-noise Ratio (PSNR) of MSWT-SSIDA reached 34.86 dB, which is a 5.24 dB improvement over the network that did not incorporate the channel and spatial attention mechanisms. Additionally, the structural similarity index (SSIM) increased by 0.085, reaching 0.961.  
Improvement in computational efficiency: under the same hardware conditions, RSENet's inference speed is 20% faster than the model without attention mechanisms, reducing the processing time per image by approximately 15 milliseconds.  
Subsequent experimental results show that the mechanisms of spatial and channel attention provide significant advantages in single-image deraining tasks, enabling better preservation of image details, improved deraining performance, and enhanced computational efficiency, while also increasing the stability of the model.
# Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
###### Abstract  
In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) _atmospheric parameters_ such as the timing and spatial extent of monsoon cloud cover; and, b) _phenological parameters_, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as referencing/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.  
spatiotemporal; daily MODIS; monsoon; vegetation phenology; EOF; temporal mixture model +
Footnote †: journal: Remote Sensing, 2019, 11, 1203; doi:10.3390/rs11101203
# Detection of Carrageenanen in Cheese Using
## 3 Results and Discussion
### Lectin Histochemistry
#### 3.2.2 Limit of Detection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Carrageenanan Type**} & **Carrageenanan** & **Carrageenanan** & **L*a*b*a** & **b*a** \\  & & **[mg kg\({}^{-1}\)]** & & & \\ \hline LD1A & \(\kappa\) & 100 & 81.31 \(\pm\) 6.06 \({}^{\text{de}}\) & \(-12.66\pm 4.82\) \({}^{\text{a}}\) & 38.12 \(\pm\) 7.82 \({}^{\text{bc}}\) \\ LD1B & \(\kappa\) & 1000 & 86.62 \(\pm\) 4.58 \({}^{\text{bc}}\) & \(-12.89\pm 2.33\) \({}^{\text{a}}\) & 35.18 \(\pm\) 5.76 \({}^{\text{cd}}\) \\ LD1C & \(\kappa\) & 10,000 & 88.28 \(\pm\) 4.20 \({}^{\text{ab}}\) & \(-18.44\pm 1.59\) \({}^{\text{f}}\) & 31.80 \(\pm\) 6.18 \({}^{\text{ef}}\) \\ LD2A & \(\iota\) & 100 & 26.2 \(\pm\) 3.67 \({}^{\text{c}}\) & \(-13.30\pm 1.16\)\({}^{\text{ab}}\) & 40.52 \(\pm\) 4.15 \({}^{\text{b}}\) \\ LD2B & \(\iota\) & 1000 & 81.51 \(\pm\) 4.62 \({}^{\text{d}}\) & \(-15.89\pm 2.63\)\({}^{\text{de}}\) & 36.63 \(\pm\) 6.55 \({}^{\text{cd}}\) \\ LD2C & \(\iota\) & 10,000 & 86.60 \(\pm\) 3.77 \({}^{\text{bc}}\) & \(-16.59\pm 3.72\)\({}^{\text{e}}\) & 35.42 \(\pm\) 6.33 \({}^{\text{cd}}\) \\ LD3A & \(\lambda\) & 100 & 89.71 \(\pm\) 2.57 \({}^{\text{a}}\) & \(-14.49\pm 1.19\)\({}^{\text{bc}}\) & 36.32 \(\pm\) 5.64 \({}^{\text{cd}}\) \\ LD3B & \(\lambda\) & 1000 & 76.57 \(\pm\) 3.18 \({}^{\text{f}}\) & \(-14.88\pm 1.86\)\({}^{\text{cd}}\) & 34.16 \(\pm\) 4.88 \({}^{\text{de}}\) \\ LD3C & \(\lambda\) & 10,000 & 87.99 \(\pm\) 4.64 \({}^{\text{ab}}\) & \(-13.83\pm 2.08\)\({}^{\text{ab}}\) & 29.48 \(\pm\) 5.55 \({}^{\text{f}}\) \\ LD40 & Control & 0 & 78.95 \(\pm\) 5.76 \({}^{\text{e}}\) & \(-14.15\pm 3.67\)\({}^{\text{abc}}\) & 43.64 \(\pm\) 4.11 \({}^{\text{a}}\) \\ \hline \hline \end{tabular}  
* Note: Mean values denoted by different letters at individual column are significantly different (\(p<0.05\)). Data are expressed as mean value \(\pm\) standard deviation.  
\end{table} 
Table 3: Limit of detection for CIE L*a*b* system. The results also confirmed a statistically significant difference between the control and the samples containing carrageenanan additions (\(p<0.05\)) for b* parameter. L* and a* did not differ statistically for some low addition concentrations or for 1% \(\lambda\)-carrageenanan addition. The reason for this is the lower colorability of \(\lambda\)-carrageenanan. For quantitative methods, LoD is expressed as 3 times the noise. For quantitative lectin histochemistry, LoD was determined to be 43.64 \(\pm\) 12.33 of b* value.
# Semantic Lidar-Inertial SLAM for Dynamic Scenes
## 3 Our Method
### Joint Optimization  
Equation (7) provides the constraints between the state of reference frame and the states of following frames. The transformation from the frames to be optimized (\(\alpha\in\{p+1,\cdots,i,j\}\)) to the reference frame \(p\) is computed by the chain rule:  
\[\mathbf{T}_{Lx}^{Lp}=\mathbf{T}_{L}^{B}\mathbf{T}_{Ba}^{W}\mathbf{T}_{Bp}^{W-1} \mathbf{T}_{L}^{B-1}=\begin{bmatrix}\mathbf{R}_{Lx}^{Lp}&\mathbf{p}_{Lx}^{Lp}\\ 0&1\end{bmatrix} \tag{8}\]  
where \(\mathbf{T}_{L}^{B}\) denotes the extrinsic parameters from lidar to IMU. \(\mathbf{T}_{Ba}^{W}\) and \(\mathbf{T}_{Bp}^{W}\) are the states of the body in frames \(\alpha\) and \(p\).  
As the window slides, the reference frame also changes. For each \(\alpha\in\{p+1,\cdots,i,j\}\) and \(m=[\mathbf{x},\mathbf{n},d]\in\mathbf{m}_{Lx}\), the residual of Equation (7) can be represented by the distance from point to plane:  
\[r_{1}\Big{(}m,\mathbf{T}_{Lp}^{W},\mathbf{T}_{Lx}^{W},\mathbf{T}_{L}^{B} \Big{)}=\mathbf{n}^{T}\Big{(}\mathbf{R}_{Lx}^{Lp}\mathbf{x}+\mathbf{p}_{Lx}^{Lp}\Big{)}+d \tag{9}\]  
To obtain the states from frame \(p\) to frame \(j\), we adopt a joint non-linear optimization and marginalization strategy. As shown in Figure 4, the number of states to be estimated is fixed in the window from \(\mathbf{X}_{Bp}^{W}\) to \(\mathbf{X}_{Bj}^{W}\). If new constraints appear, the window will include new states and marginalize old states. The entirety of the states in the window can be defined as:  
\[\mathbf{X}=\left\{\mathbf{X}_{Bp}^{W},\cdots,\mathbf{X}_{Bj}^{W},\mathbf{T}_{ L}^{B}\right\} \tag{10}\]  
State \(\mathbf{X}\) can be estimated by minimizing the whole cost function:  
\[\min_{\mathbf{X}}\frac{1}{2}\left\{\sum_{\begin{subarray}{c}m\in\mathbf{m}_{ Lx}\\ \alpha\in\{p+1,\cdots,j\}\end{subarray}}\|r_{1}(m,\mathbf{X})\|^{2}+\|r_{2}( \mathbf{X})\|^{2}+\sum_{\beta\in\{p,\cdots,j-1\}}\|r_{3}\Big{(}z_{\beta+1}^{ B},\mathbf{X}\Big{)}\|^{2}\right\} \tag{11}\]  
where \(r_{1}(m,\mathbf{X})\), \(r_{2}(\mathbf{X})\), and \(r_{3}\Big{(}z_{\beta+1}^{B},\mathbf{X}\Big{)}\) represent the residual for point-to-plane constraints, marginalization, and IMU pre-integration constraints, respectively. The Gauss-Newton method is used to solve the function above. \(\left\|r_{2}(\mathbf{X})\right\|^{2}\) is calculated by Schur complement, which is described in [6], and \(r_{3}\Big{(}z_{\beta+1}^{B},\mathbf{X}\Big{)}\) is computed from the states and IMU pre-integration in [20].  
Figure 4: Illustration of sliding window and local map.
## 3 Optimization Problem Characterization
### QoS Constraints  
In our analysis, we consider that all the user devices in the examined MISO-NOMA cell need to satisfy a QoS requirement where the minimum rate \(R_{min}\) is required to be realised in the system [22; 23], this constraint can be expressed as follows:  
\[Log_{2}(1+SINR_{i})\geq R_{min} \tag{18}\]  
where \(SINR_{n}\) is the signal-to-interference plus noise ratio for \(i\)th user and \(R_{min}\) is the minimum required transmission rate in the examined MISO-NOMA cell. The expression in (18) can be redeveloped as follows [24]:  
\[|\mathbf{h}_{i1}+\mathbf{h}_{i2}|^{2}\rho\left(\eta_{i}-(2^{R_{min}}-1)\sum\limits_{j =1}^{i-1}\eta_{j}\right)>(2^{R_{min}}-1) \tag{19}\]  
where \(\rho\) represents the SNR and \(\eta_{j}\) is the power percentage given for \(j\)th user device.
## 2 Theoretical Model  
As shown in Figure 1, the variable splitting ratio splitter with coupling-weighted and velocity-tapered waveguides (CVW) structure is composed of three single-mode waveguides. The parameter \(G_{a}\) is the central gap with maximum coupling and \(G_{b}\) is the perturbation. In addition, the width of the narrower port of the waveguide is \(W_{a}\). The width of the wider port of the waveguide is \(W_{b}\). Then, \(H\) is the height of guide. \(L_{R}\) is the length of two outer guides. The wave propagates in the \(z\)-direction. In order to adjust the splitting ratio, a length \(L_{\rm s}\) from the center of the two outer guides is shifted in the \(z\)-direction. When the shifting length is set to zero (\(L_{\rm s}=0\)), the device is symmetric in the \(x\)-direction relative to guide 0. The width of guide 0 is (\(W_{a}\)+\(W_{b}\))/2. The guide 1 and 2 are velocity-tapered and coupling-weighted. In order to weight the device structure with tapered width and gap, the Blackman function is employed. Two adjacent guides are set tightly that power is able to be coupled by each other. The match point with the maximum coupling coefficient is set in the central position of the outer guide, in which the propagation constant mismatch is zero.  
In such an adiabatic coupled-mode system, the standard coupled differential equations describing changes in the mode amplitudes [20] with distance is defined as:  
\[\frac{d\overline{a}}{dz}=-i\overline{M}\overline{a}=-i\left[\begin{array}{ccc}- \delta_{1}&\kappa_{1}&0\\ \kappa_{1}&0&\kappa_{2}\\ 0&\kappa_{2}&-\delta_{2}\end{array}\right]\cdot\left[\begin{array}{c}a_{1}\\ a_{0}\\ a_{2}\end{array}\right], \tag{1}\]  
where \(\overline{a}=\left[\begin{array}{ccc}a_{1}&a_{0}&a_{2}\end{array}\right]^{T}\) is a vector containing the amplitudes of the modes in guides 1, 0, and 2. \(\overline{M}\) is a \(3\times 3\) matrix. \(\delta_{1}\) and \(\delta_{2}\) are the dephasing terms of the structure. \(\kappa_{1}\) and \(\kappa_{2}\) are the coupling terms of the structure. Those elements of \(\overline{M}\) vary with distant \(z\).  
One particular case occurs as the shifting length is zero (\(L_{\rm s}=0\)), the proposed splitter becomes a symmetric structure which is relative to the center guide, resulting in \(\delta_{1}=\delta_{2}=-\delta\) and \(\kappa_{1}=\kappa_{2}=\kappa\). Full 3-dB power transfer from the center guide to the two outer guides can be achieved [21].  
For a small shifting length (\(L_{\rm s}\
eq 0\)), the coupled-mode amplitudes of (1) results in \(\delta_{1}\
eq\delta_{2}\) and \(\kappa_{1}\
eq\kappa_{2}\). As shown in Figure 1, the symmetry of the CVW splitter is broken by shifting guide 2. The power ratio between the two outer guides can be changed by varying the shifting length \(L_{\rm s}\).
## 1 Introduction
### Related Works
ED Net applied improved methods such as Enhanced Asymmetric Convolution Block (EAC Block) and Double Pooling Concatenated InputSE Block (DPCI_SE Block), which made ED Net achieve good experimental indicators in recognition tasks, where the accuracy rate could reach 99.23% and the recall rate could reach 99.43%. The above method is a CNN image classification based approach for wind turbine blade surface damage detection. In their proposed CNN, the authors achieved good accuracy rates of 92.85% to 99.23% on their respective prepared datasets. However, their proposed CNNs were based on VGGNet, AelxNet, ResNet, DenseNet, and ShuffleNet and focus more on the improvement of classification accuracy with little consideration for further improvement of their CNNs in terms of lightness. Although their improved methods achieve significant accuracy improvements compared to the original models, they also inevitably increase the number of parameters, with some proposed CNNs reaching 20 M or more parameters. The large number of parameters in these CNNs limits their deployment on mobile devices such as UAVs. Currently, inspection workers can use high-definition UAVs to obtain clear visual data on the surface of WTBs, and then visually identify WTB surface damage themselves, so it incorporates the inspector's subjective factors into the identification results. However, a trend has gradually formed of directly transmitting the collected visual data to mobile terminal devices, such as mobile phones, and using artificial intelligence to identify blade surface damage. The recognition accuracy rate of artificial intelligence is higher than that of human eyes, and can further reduce the burden of human work. In recent years, with the computing capacity of mobile terminals having improved significantly, the storage space can also be increasingly large. This provides base conditions for deploying CNNs in mobile terminals. However, at the same time, it still faces many challenges. For example, the large model size limits the embedding of the CNN in mobile phone software, and abundant parameters mean that mobile phones require too much time to carry out forward computation on the CNN, resulting in poor user experience. However, the lightweight nature of CNNs enables complex network models to migrate to run on devices with limited computing power. The lightweight model technology allows many complex models with high precision to be applied on more devices and expands the application field of deep learning methods. In terms of economic efficiency, the lightweight CNN model requires less computational and storage resources for deployment, thereby reducing the hardware cost and energy cost associated with deployment. Therefore, according to the actual needs, a new lightweight CNN is proposed in this article for the WTB surface damage identification.
## 6 MIMO Antenna Performance Enhancement Techniques
### Electromagnetic Bandgap (EBG) Structure  
For the transmission of electromagnetic waves, it acts like a medium. EBG structure provides capability of low mutual coupling and excellent efficiency, as in [88]. An improved scattering parameter with exhibiting wideband gap characteristics is achieved. In the desired direction far field gain pattern is achieved.