esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Vertical Shear Behaviors of an Immersion Joint with Steel Shear Keys
## 5 Vertical Shear Failure of the Joint
### Damage and Failure of the Joint  
The damage phenomena can be observed in the end of the test. As shown in Figure 14a, the VSKA in the middle of the right sidewall of element 2 has fallen off from the embedded anchor plate. The other steel shear keys are still fixed on the elements. Figure 14b shows that the rubber bearing fixed on the fallen shear key is compressed until it burst, whereas the others are compressed but not broken. It can be concluded from the above observations that the shear keys of the right sidewall share more shear force than the others during the vertical loading process. Moreover, all the bolts of the fallen shear key are cut off, whereas no obvious deformations or cracks in the body of the fallen shear key or the other shear keys are found, indicating that the bolts contribute much to the shear capacity of a steel shear key. Furthermore, plastic extensions of the bolts are observed on the fracture surfaces in Figure 14c. Hence, it can be concluded that the bolts experience a short plastic state before complete failure, which conforms to the short period of the plastic failure state in the shear force-displacement curve in Figure 13 (i.e., stage 4).  
Figure 13: Shear force–displacement curve of the joint in the destruction test.  
These observations indicate that the failure of the joint is a single failure of one shear key on the sidewall, not a simultaneous failure of all the shear keys. In other words, the shear keys share different shear forces at the same time during the vertical loading process. When an individual shear key carries more shear force, the failure of that single shear key occurs, leading to the failure of the entire joint.  
Generally, the shear keys are thought to evenly share the vertical shear force in the immersion joint. However, the results of the experiment show a specific and different conclusion, which challenges the general assumption in conventional designs.
## 4 Discussion
### Limitations and Future Directions  
While the present study offers valuable insights into the effectiveness of the TMT-VR as a neuropsychological assessment tool, a few limitations should be considered, which also pave the way for future research. Although the sample size was adequate for the statistical analyses conducted, this study primarily involved healthy young adults with relatively high levels of education. This demographic homogeneity, while useful for establishing baseline performance, limits the generalizability of the findings to other populations, such as older adults or individuals with cognitive impairments. Future studies should aim to include a more diverse sample, encompassing a broader age and education range, as well as clinical populations, to better understand the TMT-VR's applicability and reliability across various groups. Once validated with older adults, TMT-VR could provide accessible cognitive assessments tailored to this population's specific needs. Furthermore, although the primary focus of this initial study was on user experience and interaction modes, these findings lay the groundwork for subsequent studies that can evaluate the cognitive validity of the TMT-VR more directly. Lastly, future research could incorporate feature importance analysis to identify which attributes have the greatest impact on performance across interaction modes.
## 2 Theory
### PIRNN Forward Modeling
\begin{split} v_{x}^{k+\frac{1}{2}}[ix+\frac{1}{2},iz]=\frac{ \Delta t[p^{k}[ix+1,iz]-p^{k}[ix,iz]]}{\Delta x}+v_{x}^{k-\frac{1}{2}}[ix- \frac{1}{2},iz]\\ p_{x}^{k+1}[ix,iz]=v^{2}[ix,iz]\Delta t\frac{v_{x}^{k+\frac{1}{2}}[ ix+\frac{1}{2},iz]-v_{x}^{k+\frac{1}{2}}[ix-\frac{1}{2},iz]}{\Delta x}+p_{x}^{k }[ix,iz]\end{split} . \tag{7}\]  
From (7), in each time loop, we first use the pressure \(p^{k}\) at time \(k\Delta t\) and the particle velocity \(v_{x}^{k-\frac{1}{2}}\) at time \((k-\frac{1}{2})\Delta t\) to calculate the particle velocity \(v_{x}^{k+\frac{1}{2}}\) at time \((k+\frac{1}{2})\Delta t\). Then, based on the calculated particle velocity \(v_{x}^{k+\frac{1}{2}}\) at time \((k+\frac{1}{2})\Delta t\) and the pressure \(p_{x}^{k}\) at time \(k\Delta t\), we compute the pressure \(p_{x}^{k+1}\) for the next time step. In this process, each time the wave equation is solved, the wave field data from the previous time steps, \(k\Delta t\) and \((k-\frac{1}{2})\Delta t\), is utilized. This process enables seismic forward modeling using RNNs, where each layer of the RNN is designed to calculate and store the wave field information at a specific time step. This structure maps the temporal evolution of the wave field onto the hierarchical structure of the RNN. Specifically, each layer of the RNN represents the state of the wave field at a particular time step, including pressure and particle velocity. Within this framework, each RNN layer stores the current wave field information and receives the output from the previous layer as input. This design effectively simulates the time dependency of the wave equation, where the solution at each time step depends on the state from the previous time step. The aforementioned process is based on PIRNNs for seismic forward modeling, forming the foundation for seismic inversion. To construct an architecture supporting seismic inversion, the PIRNN forward modeling process is shown in Figure 1, where the velocity model is input into the RNN structure, and forward modeling is performed step by step through the SGFD RNN operator to finally obtain the output VSP shot-gather. Here, \(\text{Input}(i)\) represents the wave field information stored in the RNN unit at time step \(i\), and \(\text{Output}(i)\) represents the simulated shot-gather record generated at time step \(i-1\) (recording local wave field information using a VSP observation system). During this process, the subsurface medium parameters are incorporated as trainable parameters within each SGFD RNN operator. Consequently, the loss calculation includes gradient information with respect to these parameters. This enables the neural network to compute the gradient of the objective function with respect to the subsurface parameters through backpropagation, iteratively updating these parameters. The specific principles underlying this process will be introduced in the next subsection.
## 3 Results
### Heat Transfer Coefficient in the Tropical Island Region  
#### 3.3.1 Daily Average Heat Transfer Coefficient  
Relation between Heat Transfer Coefficients, Wind Speeds, and Rain Hours  
By combining the CHTC predictive model derived from full-scale measurements and daily mean wind speeds in the tropical island region, the daily average CHTC throughout the year can be calculated. Then, the daily average EHTC can be obtained by adopting the simplified calculation model described above. Figure 11 shows the variation relationship between daily average heat transfer coefficients, wind speeds, and rain hours. As can be seen, the variation trend of CHTC is coincident with wind speed, and the daily average CHTCs are all concentrated in the range of 10-60 W/(m\({}^{2}\cdot\)K). Furthermore, RHTC is almost impervious to wind speed and rainfall, keeping unchanged at 5.8 W/(m\({}^{2}\cdot\)K). However,  
Figure 8: Daily variation curves of average wind speeds and total rain hours in Xisha.  
Figure 10: Annual precipitation of different representative cities.  
Figure 9: Statistical result of high wind speed proportions among different cities.  
compared with CHTC and RHTC, the EHTC shows strong randomness and indeterminacy, owing to rainfall, and it has a wider changing range from 0 to 160 W/(m\({}^{2}\cdot\)K).  
The frequency distribution of daily average heat transfer coefficients in the tropical island region is presented in Figure 12. It can be seen that the CHTCs and RHTCs are all in relatively centralized distributions, but the distribution of EHTC is discrete on account of the uncertain rainfall intensity. Consequently, the total heat transfer coefficient with consideration of evaporation heat transfer (THTC-Y) is equipped with a wide distribution range of approximately 16-220 W/(m\({}^{2}\cdot\)K). Furthermore, with respect to THTC-Y, the range from 20 to 25 W/(m\({}^{2}\cdot\)K) occupies the highest frequency, with up to 110 days (30.1%). And the THTC-Y which is in the scope of 20-50 W/(m\({}^{2}\cdot\)K) accounts for a proportion of more than 80% year-round time. Additionally, considering that the THTC recommended values for winter or summer in the Chinese current standard are all less than 25 W/(m\({}^{2}\cdot\)K), and therefore, taking 25 W/(m\({}^{2}\cdot\)K) as the boundary line, it can be found that the number of days that THTC-Y is less or more than 25 W/(m\({}^{2}\cdot\)K) are 146 (40%) or 219 (60%), respectively.
## 3 The Research Results and Discussion
### Emission Characteristics  
Variation of toxic substances and soot emission in exhaust gases with a change in the boost pressure and the \(D_{b}\)/\(T\) ratio is illustrated in Figure 18.  
Figure 17: Dependence of the indicated power on the change in \(p_{in}\) and \(D_{b}\)/\(T\) ratio.  
Figure 18: Variation of toxic substances and soot content in diesel engine exhaust gases with the boost pressure and \(D_{b}\)/\(T\) ratio change: (**a**) NO\({}_{X}\); (**b**) soot; (**c**) CO; (**d**) HC.  
The reasons for the decrease in temperature-related NO\({}_{\rm x}\) emissions include the growth in the boost pressure and the decrease in the time of ignition delay. The boost pressure value rises, which leads to air mass growth; therefore, \(\alpha\) goes up, and as a result, \(T_{\rm max}\) (Figure 13b) and NO\({}_{\rm x}\) (Figure 16) decrease. At the high boost pressure, the geometry of the combustion chamber has a slight effect on the production of NO\({}_{\rm x}\), while at the low boost pressure, the effect of the combustion chamber geometrical parameters on the variation of NO\({}_{\rm x}\) is significant (at \(p_{in}\) = 0.15 MPa). The NO\({}_{\rm x}\) production depends on the \(D_{b}\)/\(T\) ratio and reaches the minimum value of NO\({}_{\rm x}\) at a \(D_{b}\)/\(T\) ratio of about 5.0. The NO\({}_{\rm x}\) value decreases when the ratio \(D_{b}\)/\(T\) = (3.2 \(\div\) 4.5) and is almost unchanged with the ratio \(D_{b}\)/\(T\) = (4.5 \(\div\) 5.6) and continues to decrease when the ratio \(D_{b}\)/\(T\) = (7.8 \(\div\) 10). Variation of soot is opposite to the NO\({}_{\rm x}\) change. Except for \(p_{in}\) = 0.15 MPa, the NO\({}_{\rm x}\) value increases with the ratio \(D_{b}\)/\(T\) = (7.8 \(\div\) 10), and soot and CO are almost unchanged when the ratio \(D_{b}\)/\(T\) = (4.5 \(\div\) 10).  
As the boost pressure goes up, more fuel concentrates in a smaller volume, and so the fuel density in the volume of the spray increases, which increases the resistance time of the spray. As a result, less air can be available to burn the injected fuel completely with a cleaner exhaust, with the cloud of burning fuel (Figure 15) smaller at the end of combustion, especially when \(D_{b}\) = 90 mm. This, in turn, leads to an increase in CO, HC, and soot (Figure 18).
###### Abstract  
Deep learning has been successfully showing promising results in plant disease detection, fruit counting, yield estimation, and gaining an increasing interest in agriculture. Deep learning models are generally based on several millions of parameters that generate exceptionally large weight matrices. The latter requires large memory and computational power for training, testing, and deploying. Unfortunately, these requirements make it difficult to deploy on low-cost devices with limited resources that are present at the fieldwork. In addition, the lack or the bad quality of connectivity in farms does not allow remote computation. An approach that has been used to save memory and speed up the processing is to compress the models. In this work, we tackle the challenges related to the resource limitation by compressing some state-of-the-art models very often used in image classification. For this we apply model pruning and quantization to LeNet5, VGG16, and AlexNet. Original and compressed models were applied to the benchmark of plant seedling classification (V2 Plant Seedlings Dataset) and Flavia database. Results reveal that it is possible to compress the size of these models by a factor of 38 and to reduce the FLOPs of VGG16 by a factor of 99 without considerable loss of accuracy.  
deep learning models; compression; agriculture; models pruning; models quantization 1
## 5 Conclusions  
In this paper, we proposed a defect detection system using synthetic defect data based on stacked convolutional autoencoders. The proposed autoencoder is trained by using only non-defect data and synthetic defect data generated by using the characteristics of defects described from the knowledge of experts. To verify the performance of our method, we compared the performance of the proposed system with U-Net trained with actual defect data, and showed that the proposed system using only non-defect data and synthetic data can detect actual defects comparably with that using real defect data. This method can be applied to many industrial and medical applications such as cancer detection using the knowledge of experienced doctors.  
As a defect detection system using synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts, our system has a limitation of the low detection-rate of unknown defects. However, the limitation is also shared with defect detection system based on real defect data. The system can be improved by iteration of the whole process. If the human expert forgets to mention a type of defect and the system makes some errors, the expert can add the type and some related types.  
Our immediate future work includes enhancing the performance with larger and various learning datasets and an enhanced defect generating unit since the performance of our system can vary widely with different learning and test datasets. We can also improve the performance by solving the problem of low recall and precision when the difference between the input image and the restored image is very small. As one of the solutions, we may set a threshold in the tolerance according to the applications such that we can decide the limit of small defects that is allowed in a specific application. Finally, we will apply semisupervised learning which is based on the use of a large amount of unlabeled data but also employs a small amount of labeled data which may be available as in [22].  
Figure 5: Performance of the proposed method: (**a**) initial input image; (**b**) ground truth; (**c**) results of our method; and (**d**) results of method using U-Net.  
**Author Contributions**: Conceptualization, Y.-J.H.; methodology, Y.-J.H.; software, Y.-J.H.; validation, Y.-J.H.; formal analysis, Y.-J.H.; investigation, Y.-J.H.; resources, Y.-J.H.; data curation, Y.-J.H.; writing--original draft preparation, Y.-J.H.; writing--review and editing, H.-J.Y.; visualization, Y.-J.H.; supervision, H.-J.Y.; project administration, H.-J.Y.; funding acquisition, H.-J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (2020R1A2C1007081).  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 RS Applications in Ocean
### Ocean Surface Wind (OSW)
#### 3.1.1 Microwave Radiometer
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**RS System (Passive/Active)** & **RS System (Type)** & **Advantage** & **Disadvantage** \\ \hline \multirow{4}{*}{Passive} & \multirow{2}{*}{Microwave radiometer} & Appropriate efficiency in high wind speeds, large-scale coverage & Low accuracy for OSW direction estimation in low wind speeds, coarse spatial resolution \\  & & Higher spatial and temporal resolution, less sensitivity & Inadequate number of satellites, need more investigation and validation \\  & & GNSS-R & \begin{tabular}{c} High spatial resolution, applicable \\ coarse spatial resolution \\ \end{tabular} \\  & & GNSS-R & \begin{tabular}{c} High spatial resolution, less sensitivity \\ atmospheric attenuation, low-cost, \\ low weight, low power needs for \\ receivers, unique sensing geometry \\ High spatial resolution, applicable \\ at both low and high wind speeds \\ \end{tabular} & \begin{tabular}{c} Inadequate number of satellites, need more investigation \\ and validation \\ \end{tabular} \\  & \multirow{2}{*}{SAR} & \multirow{2}{*}{\begin{tabular}{c} Speckle noise issue, challenging \\ preprocessing steps \\ Coarse spatial resolution, \\ signal in high wind \\ speeds, \\ \end{tabular} } \\  & & \begin{tabular}{c} Good efficiency in low wind speeds, \\ global coverage \\ \end{tabular} & \
\begin{tabular}{c} Speckle noise issue, challenging \\ preprocessing steps \\ Coarse spatial resolution, \\ saturated signal in high wind \\ speeds, rain contamination \\ \end{tabular} \\  & \multirow{2}{*}{HF radar} & Reasonable accuracy at different \\  & & wind speeds, large-scale coverage &
\begin{tabular}{c} Availability of OSW data only at \\ specific coastal locations where \\ the HF radar has been installed \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Different RS systems for OSW estimation along with their advantages and disadvantages.
## 3 Theoretical Analysis and Proof
### Experiments with Different Datasets
#### 4.3.1 Sentinel-2
Although EAEND model (10) also experiences some interference, its identification accuracy can still be maintained at a Kappa value of 0.951829, and its OA, AA, and PP values are almost the same as those in the noise-free \(\psi(t)=0\) environment, demonstrating that EAEND model (10) indeed has excellent noise-resistance performance. Subsequently, NRI model (28) and ETZNN model (29) show that a large amount of noise exists in the images and the images appear blurred to varying degrees. These conditions are also reflected in the relevant numerical results in Table 1, with the RE of ETZNN model (29) reaching \(10^{6}\). This suggests that ETZNN model (29) is not suitable for the task of sea ice identification.
## 3 Experiments  
We used the following performance criteria to evaluate the performance of the proposed model: (1) compression ratio (CR); (2) percentage RMS difference (PRD); (3) signal-to-noise ratio (SNR). The definitions and formulas of these performance criteria are as follows:  
(1) Compression Ratio (CR): It is defined as the compressed data length over the size of uncompressed data.  
\[CR=\frac{D_{\mathit{cp}}}{D_{\mathit{or}}} \tag{6}\]  
where \(D_{\mathit{or}}\) is the number of bytes of all original data. \(D_{\mathit{cp}}\) is the number of bytes of all compressed data.  
(2) Percentage RMS Difference (PRD): It represents the quality of reconstructed data in the compression. The PRD value is expected to be as low as possible for a quality compression approach.  
\[PRD(\%)=100\times\left(\frac{\sum_{i=0}^{D-1}\left(S_{o}(i)-S_{r}(i)\right)^ {2}}{\sum_{i=0}^{D-1}\left(S_{o}(i)\right)^{2}}\right)^{\frac{1}{2}} \tag{7}\]  
where \(S_{o}\) represents the original input data, \(S_{r}\) represents the reconstructed data.  
(3) Signal-to-Noise Ratio (SNR): SNR is the ratio of signal to noise. The SNR is calculated by:  
\[SNR(dB)=10\log_{10}\left(\frac{\sum_{i=0}^{D-1}\left(S_{o}(i)\right)^{2}}{ \sum_{i=0}^{D-1}\left(S_{r}(i)-S_{o}(i)\right)^{2}}\right) \tag{8}\]  
We built the Tensorflow framework on the computer with Ubuntu 16.04 LTS system and NVIDIA GeForce GTX 1080 Ti as our experimental environment, and then built the CBN-VAE model by using Tensorflow. All our experiment results were obtained using this environment.
## 4 Conclusions  
In this paper, a modified image reconstruction method for detecting the internal defects in wood using segmented propagation rays of stress wave was proposed. To verify the feasibility of the proposed method, both simulation data and experimental data were used for image reconstructions. Eight specimens with defects differing in size and position were tested, and the area and shape of the imaging results were analyzed. The presented experiments support the following conclusions:  
1. The original rays graphs are all improved significantly by the RSEN (ray segmentation by elliptical neighborhood) algorithm. Compared with the original graph of rays, the segmented graph of rays can better reflect the potential spatial distribution of defects and benefits the subsequent spatial interpolation. In addition, the RSEN algorithm is not sensitive to control coefficient \(c_{1}\) for defect patterns with single circle distribution or edge distribution. For other defects, when \(c_{1}\) is less than 0.65 or more than 0.85, the corresponding results of rays graphs begin to become worse. The coefficient \(c_{1}\) with a value in the range of 0.65-0.8 is suggested.
2. The images reconstructed by the SISE (spatial interpolation by segmented ellipse) algorithm can reflect the size and shape of defects inside wood. In addition, the SISE algorithm is also not sensitive to control coefficient \(c_{2}\) for defect patterns with single circle distribution or edge distribution. For other defects, when \(c_{2}\) is less than 0.85, the corresponding results of reconstructed images begin to become worse. The coefficient \(c_{2}\) with a value in the range of 0.85-0.95 is suggested.
3. The defective area proportion from the reconstructed image using the proposed method is closer to the actual defective area, and the contour extracted from the reconstructed image using the proposed method is much more similar to the actual contour.
4. The average accuracy of the proposed algorithm is 8.9% higher than that of the EBSI (ellipse based spatial interpolation) algorithm, and the average precision of the proposed algorithm is 12.8% higher than that of the EBSI algorithm.  
In general, compared with the results of the EBSI algorithm, the proposed algorithm not only can better reflect internal wood defects qualitatively and quantitatively, but also has a high quality of reconstructed images. However, the method overestimates the size of real defects, especially for complicated samples. We can further improve the method in future work.  
[PERSON] conceived the ideas, designed the experiments, and wrote the paper. [PERSON] performed the experiments. [PERSON] and [PERSON] analysed the data. [PERSON] revised the manuscript. This research was funded by the National Natural Science Foundation of China [U1509207] and the Zhejiang Province Key Science and Technology of China [2015C03008]. The authors declare no conflict of interest.