esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Analysis of the Behaviour of Piled Raft Foundations in Loose Sand
## 3 Results and Discussion
### Influence of Pile Distance  
Within the experiment, the influence of pile spacing on the relationship between load and settlement in a piled raft foundation system was investigated, as well as the distribution of forces between piles and raft.  
The diagram in Figure 33 shows that pile spacing has an effect on the relationship between load and settlement of this combined foundation system. This influence is much more pronounced when the spacing is e/d = 3 or smaller when compared to models with pile spacing of e/d = 4 and 5. Smaller pile spacing provides significant improvement in the performance of the piled raft foundation, and, accordingly, increased spacing of the piles leads to increased settlement of the foundation. Pile spacing also affects the distribution of forces between the piles and the raft.  
Figure 34 shows the effect of a group of piles. It can be observed that the shape of the curve is approximately the same for pile lengths of 10d and 20d, while the curve for pile length of 40d has a slightly different shape. Moreover, the effect of the pile group at a pile distance of 3d is much more pronounced, about 2.72 times larger, when compared to the pile distance of 4d. For piles of lengths 20d and 10d, the effect of the pile group at a distance of 3d is, in relation to a pile distance of 4d, about 1.55 times greater for a length of 20d and about 1.45 times greater for a length of 10d. It was also observed that, with a pile length of 40d at distances greater than 5d, the group effect is lost and the piles start to behave as individual piles, while, for piles of 10d and 20d length, the group effect can still be observed. Similar conclusions were reached by [PERSON] et al. in their work [65].  
Figure 32: Load distribution graph for the piled raft L/d = 40, e/d = 5.  
Figure 34: The effect of the group of 2 \(\times\) 2 piles for piles length L = 40d.  
Figure 35: Diagram “force–displacement” for the piled raft L = 40d, e/d = 3;4;5.  
Figure 37: Settlements of pile groups with length of L = 10d in relation to pile distances at the load of 100 N.  
Figure 35: Settlements of pile groups with length of L = 40d in relation to pile distances at the load of 350 N.
## 8 Experimental Results and Analysis
### Slant Distance Correction  
In underwater acoustic positioning, the large azimuth angle of the sound ray will lead to the underwater sound waves diverge during the incident process, so the azimuth angle control under \(70^{\circ}\)[27; 28; 29; 30]. The experiments here are based on the environment of the simulation experiment. In the simulation experiment, the azimuth angle and elevation angle of the USBL transmitter are both set to \(30^{\circ}\), \(40^{\circ}\), \(45^{\circ}\). Therefore, we placed the target respectively at a depth of 10, 50, 100, 150, and 200 m from the USBL transmitter. For simulation purposes, we calculate the true slant range of the target at different depths by Equation (29) in simulation experiment and compare the true slant distance with the corrected slant range. We used Equation (25) for the traditional method iterations, and Equation (17) for the constant sound speed ray tracing method iterations, and compare the obtained results with the results of the proposed combined ray tracing method. The results are shown in Table 1.  
\[l_{true}=\sqrt{H^{2}+X^{2}} \tag{29}\]  
where \(l_{true}\) represents the slant distance of the underwater target in simulation experiment. Figure 14: The experimental results at the threshold of 0.1. In Table 1, the calculation iterations parameter denotes the number of times the horizontal displacement of each layer was added due to the division of the sound speed profile layers. Figure 15 shows the simulated sound ray tracing model at 45\({}^{\circ}\). It can be seen that the sound ray tracing model under the traditional method is completely a straight line. At the beginning of the constant sound ray tracing method and the combined ray tracing method, the sound ray tracing almost overlaps, but gradually becomes curved later. It can be seen from Figure 15 that in the sound line iteration, the model of sound line propagation under the traditional method has always been a straight line. However, the M1 and M2 methods have different degrees of bending. The error analysis was used to evaluate the comparison of positioning error between different ray tracing methods. The error was defined as  
\[\varepsilon=\frac{|l_{true}-l_{new}|}{l_{true}}\cdot 100\% \tag{30}\]  
where \(l_{true}\) represents the true slant distance, \(l_{new}\) represents the new slant distance obtained by the three methods. The comparison of the accuracy of different ray tracing methods, calculated using Equation (30), is given in Table 2, where M1 represents the constant sound ray tracing method, and M2 represents the combined ray tracing method.
## 5 Simulation Analysis
### Sensitivity Analysis  
The sensitivity analysis is carried out by exploring the effect of the weighting parameters \(\omega_{1}\), \(\omega_{2}\), and \(\omega_{3}\). Considering the absolute numerical value of each part of the reward, the alternative values of each parameter is set to \([1,3,5]\), \([1,2,3]\), and \([4,7,10]\). More precisely, a grid-based search can be programmed for the 27 situations. For each situation, an agent is trained in 300 episodes, and then 20 rounds of simulation are conducted to evaluate the performance of the agent.  
Table 2 shows the metrics performance for different combinations of the parameters. It can be seen that an increase of \(\omega_{1}\) and \(\omega_{2}\) can improve the corresponding indicators to a certain extent in general, while the mean jerk is not sensitive to the change of \(\omega_{3}\). Nevertheless, the increase of \(\omega_{2}\) does not necessarily reduce the travel time on some occasions. We infer that the weighting parameter with a high value encourages the agent to choose a more aggressive and greedy strategy, and this kind of strategy may be limited by the uncertain environment. The same is true of \(\omega_{1}\). Besides, the mutual restriction of the three parameters will also exert an unexpected influence on the vehicle. Therefore, a compromising scheme is selected in this study. Concurrently, this provides plasticity for the control law. We can choose the parameter configuration with low travel time to improve the traffic efficiency if the travel is sensitive to the travel time. Similarly, we can also choose the configuration with low energy consumption to save electricity to the greatest extent. From Table 2, we can conclude that the energy consumption can be reduced by 31.73%\(\sim\)45.90%, while the sacrifice of travel time is 25%\(\sim\)98%.
## 3 Results
### Single Number Acoustic Index \(\alpha_{w}\)  
Based on the above results, the weighted sound absorption coefficients \(\alpha_{w}\) derived from the SSRR measurements were calculated. These single indices are useful for an immediate and practical comparison of the performance of different conditions. The higher the \(\alpha_{w}\) values, the better the material capability in sound absorption. Their values normally range from 0 to 1, with 1 meaning 100% sound absorption.  
The weighted sound absorption coefficient \(\alpha_{w}\) is derived from practical sound absorption coefficients, \(\alpha_{p}\) which is calculated as an average of the one-third octave sound absorption coefficients within the octave in accordance with ISO 11,654 [41]. Weighted sound absorption coefficient \(\alpha_{w}\) can be obtained with the reference curve (\(\alpha_{250}=0.8\); \(\alpha_{500}=1\); \(\alpha_{1000}=1\); \(\alpha_{2000}=1\); \(\alpha_{4000}=0.9\)), which is shifted in steps of 0.05 towards the \(\alpha_{p}\) values until the sum of unfavorable deviations is less or equal to 0.10. The unfavorable deviations occur when the measured value is lower than the value of the reference curve. Finally, the weighted sound absorption coefficient is the value of the adjusted reference curve at 500 Hz.  
Table 3 shows that there are a few differences among the single indices within each concrete data. It is evident from these values that the highest performance is obtained for panel type D. The \(\alpha_{w}\) values for the single layer of type D samples become significant (>0.40) for a thickness of 60 mm. The single layers of 20 mm and 40 mm present an improvement of the \(\alpha_{w}\) values when they are mounted with an air gap behind (\(\alpha_{w}=0.50\)). This mounting condition performance is further improved when the air gap is filled with a porous material. It can be noticed that when the entire gap is filled with rock wool (50 mm), the highest \(\alpha_{w}\) is obtained. A significant improvement due to the air gap is also obtained for sample C, while a slight improvement is reported for sample A. Conversely, depending on the sample thicknesses, sample B values of \(\alpha_{w}\) are either not affected or reduced when the air gap is added at the back of the 20 mm and 40 mm thick layers, respectively.
## 5 Estimation and Simulations
### Simulation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{n} & \multirow{2}{*}{**Est.**} & \multicolumn{3}{c}{**Set I: \(\delta\) = 0.5, \(\beta\) = 2.4, \(\alpha\) = 3.6**} & \multicolumn{3}{c}{**Set II: \(\delta\) = 3.2, \(\beta\) = 2.7, \(\alpha\) = 3.8**} \\ \cline{3-8}  & & **MLE** & **MSE** & **Bias** & **MLE** & **MSE** & **Bias** \\ \hline \multirow{3}{*}{25} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.5067 & 5.922 & 1.007 & 2.932 & 4.041 & \(-\)0.268 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.754 & 0.707 & 0.354 & 2.715 & 0.381 & 0.015 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.626 & 0.780 & 0.026 & 4.237 & 0.811 & 0.437 \\ \hline \multirow{3}{*}{50} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.431 & 5.088 & 0.931 & 2.904 & 3.893 & \(-\)0.295 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.645 & 0.384 & 0.245 & 2.660 & 0.275 & \(-\)0.039 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.495 & 0.598 & \(-\)0.105 & 4.178 & 0.684 & 0.378 \\ \hline \multirow{3}{*}{75} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.458 & 4.680 & 0.928 & 2.978 & 3.791 & \(-\)0.222 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.608 & 0.298 & 0.238 & 2.643 & 0.201 & \(-\)0.057 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.443 & 0.481 & \(-\)0.157 & 4.094 & 0.612 & 0.293 \\ \hline \multirow{3}{*}{100} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.461 & 4.513 & 0.910 & 2.919 & 3.396 & \(-\)0.281 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.614 & 0.273 & 0.214 & 2.639 & 0.182 & \(-\)0.061 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.429 & 0.430 & \(-\)0.170 & 4.093 & 0.551 & 0.283 \\ \hline \multirow{3}{*}{200} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.112 & 2.531 & 0.611 & 3.045 & 2.730 & \(-\)0.156 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.533 & 0.157 & 0.143 & 2.656 & 0.119 & \(-\)0.044 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.511 & 0.284 & \(-\)0.106 & 4.019 & 0.424 & 0.219 \\ \hline \multirow{3}{*}{300} & \multirow{3}{*}{\(\hat{\delta}\)} & 1.112 & 1.767 & 0.442 & 3.069 & 2.412 & \(-\)0.131 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.543 & 0.114 & 0.098 & 2.654 & 0.099 & \(-\)0.046 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.494 & 0.223 & \(-\)0.079 & 3.962 & 0.336 & 0.162 \\ \hline \multirow{3}{*}{400} & \multirow{3}{*}{\(\hat{\delta}\)} & 0.886 & 1.457 & 0.387 & 3.123 & 1.921 & \(-\)0.078 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.481 & 0.098 & 0.081 & 2.665 & 0.078 & \(-\)0.034 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.519 & 0.196 & \(-\)0.080 & 3.923 & 0.263 & 0.123 \\ \hline \multirow{3}{*}{500} & \multirow{3}{*}{\(\hat{\delta}\)} & 0.788 & 1.112 & 0.288 & 3.156 & 1.780 & \(-\)0.044 \\ \cline{2-8}  & & \(\hat{\beta}\) & 2.463 & 0.078 & 0.063 & 2.669 & 0.069 & \(-\)0.031 \\ \cline{2-8}  & & \(\hat{\alpha}\) & 3.548 & 0.167 & \(-\)0.052 & 3.904 & 0.233 & 0.104 \\ \hline \end{tabular}
\end{table} 
Table 2: SRs of the NGLog–Weib distribution for Set I and Set II.
## 4 Design
### Execution of the Smart Broker System  
#### 4.5.1 The Continuous Execution  
The last quality value \(q_{\tau}^{w-1}\) of the data, streams is determined at any point in time \(t_{u}\). This value can be different for each topic or group of topics. Parallelly, during each interval \(\lambda\), \(TDQAM\) collects an amount of data up to the equivalent of \(\lambda/\delta\), where \(\delta\) is the intended publishing interval. Meanwhile, the \(TDM\) continuously monitors the arrival time of data with respect to \(\delta\) within the interval \(\lambda\) and the \(D_{\tau}\) scores are available to the \(TRS\) after every sub-interval of \(\delta\). The \(TRS\) (Algorithm 1 and Figure 4) calculates the reputation score of the  
Figure 3: Illustration of the relationship as vectors between three topics (**a**) normal and (**b**,**c**) when the relationship varies in the window. Quality is calculated based on the raw vector difference.  
topic after every sub-interval using the previous quality score \(\left(q_{\tau}^{\text{up}-1}\right)\). When the largest of the windows in the groups of topics elapses, \(TRS\) receives the new quality score \(\left(q_{\tau}^{\text{up}}\right)\) from _TDQAM_ and calculates the reputation score by using current scores \(\left(D_{u}\text{ and }q_{\tau}\right)\).  
It is expected that the data will arrive at regular intervals of \(\delta\), but if there are delays, then the \(D_{\tau}\) will gradually start dropping until it reaches \(\mu\), after which this drift score will hit 0. At the start of the window \(\lambda\), the drift time reference (\(t_{0}^{\lambda}\)) is reset. If the data arrives back on track, this could help recover the score in a new window. Figure 4 illustrates the execution of the smart broker modules for a specific window and topic.  
It is possible that due to the drift in time, the data for the quality calculation may be affected as not enough data may be available in the corresponding window \(w\) for peer data streams. In this case, the drift score will become 0, and the quality score cannot be calculated. If the group that contains the affected topic has more than three topics, then the affected topics are not accounted for in Equations (12) and (13). If it is just three topics, then it is not possible at all, and the smart broker will keep using the last successfully calculated quality score.  
As with any conventional data broker, the smart broker too does not need to consider data that is older than the oldest running window across all topic pairs. Thus, while the smart broker needs to use a little more data, it can still be as fast as the conventional broker.
## 4 Parametric Analysis
### Bending Stiffness  
The effects of the bending stiffness on the building response are shown in Figure 13. The building settlement slightly increases with the increase in the bending stiffness (Figure 13a), indicating that the bending stiffness has little effect on the building settlement. Given by the larger bending stiffness, the rotation angles of the left half of the building gradually increase while those of the right half of the building gradually decrease (Figure 13b). The increase in the bending stiffness results in sharply increased bending moments (Figure 13c), which demonstrates that a stiffer building suffers much larger bending moments than those of a relatively flexible building. The magnitude of the increase in the bending moments decreases when the bending stiffness increases. The maximum shear forces also increase as the bending stiffness increases from 500 to 2500 MN\(\cdot\)m\({}^{2}\) (Figure 13d). Additionally, the \(y\)-coordinates of the maximum positive and negative shear forces are fixed at 4.0 m and decrease from 17.0 to 16.0 m, respectively.  
Figure 12: Building response with different soil Poisson’s ratios: (**a**) settlement; (**b**) rotation angle; (**c**) bending moment; (**d**) shear force.
## 2 Materials and Methods
### Controlled Experiment
#### 2.1.2 Estimation of Error  
To determine the positioning error associated with Atlas PTs, we compared the recorded (observed) coordinates to reference coordinates determined in real-time using the Arrow 100. The Atlas PTs receive single frequency (L1) signals from the United States GPS system and are capable of static horizontal accuracies of less than 2.5 m 50% of the time and less than 5 m 90% of the time. The Arrow 100 (also single frequency) is a multi-constellation receiver utilizing GLONASS and BeiDou in addition to GPS. Differential correction with a Satellite Based Augmentation System (SBAS), which is the Wide Area Augmentation System (WAAS) in the United States, enables it to achieve accuracies less than 60 cm. After the four Atlas PTs were situated in the arrangement described above, the Arrow 100 was placed at each Atlas PT position where it recorded coordinates and position dilution of precision (PDOP) values (see Table 1). After sampling, we projected all data to the Universal Transverse Mercator (UTM) projection, which has units in meters, using ArcGIS 10.3 software (Version 10.3.1, 2015, Esri, Redlands, CA, USA). We then calculated the horizontal error for each observation (each 1 second-interval transmission) as the hypotenuse distance between the two sets of UTM casting (denoted \(UTM_{e}\)) and UTM northing (denoted \(UTM_{n}\)) points (actual--observed), as shown in Equation (1). Observed coordinates were retrieved from unit B's transmission log of all four Atlas PT positions over the sample period.  
\[Error=e=\sqrt{\left(act.UTM_{e}-obs.UTM_{e}\right)^{2}+\left(act.UTM_{n}-obs.UTM _{n}\right)^{2}} \tag{1}\]  
We determined if this error varied by stand, cover, or individual transponder unit using an Analysis of Variance (ANOVA), and then we identified significant sources of variation among stands and transponders using a Bonferroni multiple comparison test.  
Error was summarized for each unit and each stand as the root mean square error (RMSE), which is a measure of the difference between predicted (based on the Arrow 100) and observed (based on the Atlas PT) values.  
\[RMSE=\sqrt{\frac{\sum e^{2}}{n}} \tag{2}\]  
where \(e\) represents error as calculated in Equation (1), and \(n\) represents the sample size (number of 1-second transmissions). All calculations and statistical analyses for the study were completed using R open source statistical computing software (Version 3.3.1, 2016, The R Foundation, Vienna, Austria) and are presented in the Results section [31].
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 1 Introduction
The cooperation between dry ports and seaports directly affects the collaboration benefits of both parties and their future development trends. When conducting a cooperative assessment, we should not only consider the immediate interests of both parties but also take into account the satisfaction of cargo owners, all of which should align with the common interests of both. In order to measure the cooperative performance between dry ports and seaports, we use the balanced scorecard, which can effectively transform the strategic objectives of the seaport and dry port cooperation into performance indicators and actions at all levels, from four aspects, to achieve the balance between financial and non-financial measurement methods: the balance between long-term goals and short-term goals, the external and internal balance, the result and process balance, and the balance between management performance and operating performance. The cooperation between dry ports and seaports is a nonlinear classification problem which is difficult to study. Based on the actual situation of sea port and dry port cooperation and the basic principles and main characteristics of the support vector machine (SVM), this paper will use SVM in machine learning to evaluate the performance of cooperation between seaports and dry ports. The remainder of this paper is organized as follows. Section 2 describes collaboration between seaports and dry ports, the related method. Section 3 formulates the performance evaluation model for the cooperation between dry ports and seaports. The case studies are presented in Section 4, with the conclusions drawn in Section 5.
## 4 Proposed Method
### Codeword Embedding Module  
To effectively represent semantic and structural relationships, discrete codewords are transformed into continuous vector representations through a codeword-embedding layer [32; 33]. This embedding process generates dense, trainable feature representations that improve model learning efficiency. By mapping discrete codewords into a high-dimensional space, the embeddings enable subsequent layers to capture complex dependencies and structural patterns within the speech data [32; 33].  
For the \(x_{i,j,k}(1\leq k\leq 4)\) in the \(j\)-th speech frame of the \(i\)-th codeword sequence, the embedding process can be defined as follows:  
\[\mathbf{r}_{i,j,k}=\mathbf{E}_{i}\mathbf{F}_{one\_hot}(\mathbf{x}_{i,j,k}). \tag{8}\]  
where \(\mathbf{E}_{i}\) represents the embedding matrix for the \(i\)-th codeword sequence, and \(\mathbf{F}_{one\_hot}\) denotes the one hot representation. This mapping enables the model to treat codewords as dense vectors, facilitating the extraction of semantic patterns in later stages. By repeating the same process to other codewords in the \(i\)-th codeword sequence, we can obtain the following result:  
\[\mathbf{R}_{i}=[\mathbf{r}_{i,\mathbf{1},\mathbf{1}},\ldots,\mathbf{r}_{i,\mathbf{1},\mathbf{A}},\mathbf{r}_{ i,\mathbf{2},\mathbf{1}},\ldots,\mathbf{r}_{i,\mathbf{2},\mathbf{A}},..,\mathbf{r}_{i,\mathbf{m},\mathbf{1}}, \ldots,\mathbf{r}_{i,\mathbf{m},\mathbf{A}}]. \tag{9}\]  
Using the same operation in other codeword sequences, the mapped codeword sequences are as follows:  
\[\mathbf{R}=[\mathbf{R}_{\mathbf{1}},\mathbf{R}_{\mathbf{2}},\ldots,\mathbf{R}_{g}]. \tag{10}\]  
Figure 1: The framework of our method.  
Then, to mitigate overfitting and reduce the dimensionality of the steganalysis features, the average operation is performed on the elements within \(\mathbf{R}\).  
\[\mathbf{R}_{A}=\textit{Average}(\mathbf{R_{1}},\mathbf{R_{2}},\dots,\mathbf{R_{g}}). \tag{11}\]  
where \(\mathbf{R}_{A}\) indicates the output of the average operation, and _Average_ is the average operation.