esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Coating Performance Test
#### 3.2.3 Coating Shear Strength Analysis  
The fracture behavior of ceramic coatings under tensile loading may exhibit different failure modes, typically characterized by: (1) interfacial failure at the substrate-coating interface, (2) cohesive failure within the coating, or (3) mixed-mode failure involving both coating and substrate materials. This study systematically evaluated the bonding strength of ceramic coatings cured at 400 \({}^{\circ}\)C. Test specimens were prepared according to two lap joint configurations shown in Figure 3a,b. Shear strength testing was conducted using an electronic universal testing machine. Figure 3a was employed to characterize the adhesive properties between the coating and substrate, while Figure 3b was used to evaluate the cohesive properties of the coating itself. The corresponding test results are presented in Table 5.  
From the experimental data, in the specimens cured at 400 \({}^{\circ}\)C, the shear strength of the coating itself with a temperature increase rate of 5 \({}^{\circ}\)C/min was lower, while the shear strength of the coating with a temperature increase rate of 2 \({}^{\circ}\)C/min was significantly higher. The analysis shows that under the conditions of a temperature increase rate of 5 \({}^{\circ}\)C/min and a curing temperature of 400 \({}^{\circ}\)C, the internal structure of the coating is more loose and porous, resulting in insufficient bonding between the ceramic particles, which reduces the bonding strength of the coating.
## 4 Proposed Scheme
### Control Plane Initialization  
The scheme utilizes \(F_{p}\) as the finite field over a large prime number \(p\), with \(p\) denoting the field's size. The parameters \((a,b)\in F_{p}\) define the elliptic curve \(E:y^{2}=x^{3}+ax+b\ mod\ p\). A group \(G\) is generated from \(E\) in the system, where \(P\) serves as the generator and \(q\) represents the prime order of \(E\). Table 1 presents additional notations and definitions employed in the scheme.  
(1) **GC Initialization:** By randomly selecting the master key \(s\) from \(Z_{q}\), the GC derives its public key as \(P_{pub}=s\cdot P\) and computes \(\alpha=s\cdot h(P_{pub})\). Subsequently, the GC chooses the hash function \(h:G\to Z_{q}^{*}\), \(H0:G\times\{0,1\}^{*}\to Z_{q}^{*}\), \(H1:\{0,1\}^{*}\to Z_{q}^{*}\), \(H2:\{0,1\}^{*}\times\{0,1\}^{*}\times\{0,1\}^{*}\times G\times G\to Z_{q}^{*}\). The value of \(\alpha\) is transmitted to LCs, while \(\{P_{pub},p,q,a,b,P,H0,H1,H2\}\) is published as the system parameters. In order to ensure the overall security of the system, the GC must keep the hash function \(h\) confidential.  
(2) **LC Initialization:** After receiving \(\alpha\) via a secure channel in the control plane, \(LC_{i}\) computes \(l_{i}=\alpha\cdot NLC_{i}\) as its secret key, where \(NLC_{i}\) is a unique number of each LC and a list of them is only stored in the GC. Since \(s\) is unknown to any third party and each \(NLC_{i}\) also stays secret, it is also difficult to compute \(l_{i}\) based on public parameters. Next, it calculates \(L_{i}=l_{i}\cdot P\) to generate its public key, and adds \(L_{i}\) into the GC's system parameters. Finally, \(\{P_{pub},p,q,a,b,P,H0,H1,H2,L_{i}\}\) is the local system parameters of this specific area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Definitions** \\ \hline \(GC\) & The Global Controller \\ \(LC_{i}\) & Local Controller \(i\) \\ \(OLD_{i}\) & The i-th online learning device \\ \(G\) & An additive cyclic group \\ \(P,q\) & The generator and order of \(G\) \\ \(p\) & The size of a field \\ \(P_{pub},s\) & A public and private key of GC \\ \(NLC_{i}\) & The number of \(NLC_{i}\) \\ \(ID_{i}\) & The identity of \(OLD_{i}\) \\ \(PID_{i}\) & The pseudo identity of \(OLD_{i}\) \\ \(PK_{i},sk_{i}\) & A public and private key of \(OLD_{i}\) \\ \(M\) & A message \\ \(msg\) & An encapsulated message \\ \(\sigma\) & The signature of a message \\ \(T_{t}\) & The time stamp \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and definitions.
# Tourism Demand Forecasting Based on an LSTM Network and Its Variants
## 2 Methods
### GRU Network  
The GRU network proposed by [PERSON] et al. [31] is a modified LSTM model with two gates, so that each cyclic unit can adaptively seize the dependencies of different time scales. Different from the LSTM network, the GRU structure is not so uncomplicated, but its usefulness has not been reduced, and sometimes even a little better than LSTM [32].  
GRU eradicates the cell state and applies the hidden state to transmit information. Another distinction between GRU and LSTM is that the forget gate and input gate in LSTM are combined into an update gate. Figure 1c shows the architecture of GRU cell. The mathematical operations inside the GRU neurons are shown as follows [31]:  
Reset gate:  
\[r_{t}=\sigma(\mathbf{W}_{r}\cdot[h_{t-1},x_{t}]+b_{r}) \tag{7}\]  
Update gate:  
\[z_{t}=\sigma(\mathbf{W}_{z}\cdot[h_{t-1},x_{t}]+b_{z}) \tag{8}\]  
Process input:  
\[\widetilde{h}_{t}=\tanh\bigl{(}\mathbf{W}_{\widetilde{h}}\cdot[r_{t}\times h_ {t-1},x_{t}]+b_{\widetilde{h}}\bigr{)} \tag{9}\]  
Output:  
\[h_{t}=(1-z_{t})\times h_{t-1}+z_{t}\times\widetilde{h}_{t} \tag{10}\]  
where \(b_{r}\) is the bias vector of the reset gate. Its function is the same as that in the LSTM, that is, the smaller \(r_{t}\) is, the less information passes. \(b_{z}\) and \(b_{\widetilde{h}}\) are the bias of the update gate and cell state, respectively. \(\mathbf{W}_{r},\mathbf{W}_{z}\) and \(\mathbf{W}_{\widetilde{h}}\) are the weight matrices of the reset gate, update gate, and cell state, respectively.  
Mean squared error was used as the loss function and \"Adam\" optimizer [33] was used to find the optimum weights for the networks. All the models were implemented using Keras in Python [34], and a Tensorflow backend [35]. In this study, during the training stage, all possible configurations of manually defined parameter subsets were tried to choose parameters for the network.  
The data was normalized to be between 0 and 1. After using LSTM, Bi-LSTM and GRU models to make predictions, the predicted data was inverted and restored to the original state. Equation (11) describes the function used in this study to normalize the dataset:  
\[x^{\prime}{}_{t}=\frac{x_{t}-x_{\min}}{x_{\max}-x_{\min}} \tag{11}\]  
where \(x_{t}\) is the input time series, \(x^{\prime}{}_{t}\) is the normalized time series, and \(x_{\min}\) and \(x_{\max}\) are the minimum and maximum values of the time series respectively.  
In order to evaluate the forecasting performances of the model, the root mean squared error (RMSE) was used:  
\[\text{RMSE}=\sqrt{\frac{1}{M}\sum_{m=1}^{M}(y_{m}-y_{m}^{*})^{2}} \tag{12}\]  
where \(y_{m}\) and \(y_{m}^{*}\) are the observed and predicted values, respectively; \(M\) is the number of data samples.
## 5 Discussion
### Pragmatics  
In SmartSync, and more generally in the methodologies we presented in Section 2.2, the user is required to align the model elements manually. Although this seems like a both-ersome and time-expensive step, we believe that it is almost impossible to do otherwise in  
Figure 17: Diagrams for the \(A^{\prime}\) and \(B^{\prime}\) S2 ML models of the fixed wing drone at the final comparison step.  
general. Even though models have syntax and semantics that allow them to be interpreted by a computer, they also carry a meaning that only makes sense to humans. This meaning is called pragmatics. Pragmatic models, such as the MBSE models, carry a meaning that is lost if model elements are replaced with abstract names, such as described in [33]. The interested reader may find more information on pragmatics in the book [34].  
The SmartSync tool deals with the semantic aspect of models by creating a form of a dictionary--the CSV file. In the general definition of consistency that we have given in this paper, we do not force the consistency relation to consider pragmatics. Yet, SmartSync still deals with pragmatics, so we should explain how that makes sense in the S2 ML + Cat Framework.  
One way to include pragmatics in the S2 ML + Cat framework is the idea that we should build the biggest common skeleton to both models that respects the user's dictionary. This can be achieved by constructing a model that represents the dictionary. Injections towards this model from both \(A_{comp}\) and \(B_{comp}\) and then building the pullback of \(A_{comp}\) and \(B_{comp}\) will allow representing the alignment of objects by the user. Therefore, as depicted in Figure 18, the A' and B' categories would be the pullback of \(A_{comp}\) and \(B_{comp}\).  
We need to precisely define the dictionary if we want to operate the computation of the pullback or even prove that it does always exist. An idea for that would be to build it from \(A_{comp}\) and \(B_{comp}\) with these constraints:  
* \(Ob(Dictionary)\) is a set of blocks, ports and connections, such that we have \(p:Ob(A_{comp})\)\(\longrightarrow\)\(Ob(Dictionary)\), \(q:Ob(B_{comp})\)\(\longrightarrow\)\(Ob(Dictionary)\) such that for each \(x\in Ob(A_{comp})\) and \(y\in Ob(B_{comp})\), we have \(p(x)=q(x)\) if and only if the elements have been aligned by the user;
* \(p\) and \(q\) are injective functions;
* We give the dictionary the form of a model by building the morphisms needed to have images of the morphisms of \(A_{comp}\) and \(B_{comp}\) when we enrich \(p\) and \(q\) to make them functors.  
We can make it so that this category is unique by choosing the symbols for the ports judiciously, but it is not important, as any such category would suffice to represent that we aligned the objects of \(A_{comp}\) and \(B_{comp}\).  
Although we do not care for the category structure in the dictionary, we can build it. The components over the objects of the \(p\) and \(q\) functors carry important information, i.e., the alignment of model elements.
# A Novel Deep Learning Method for Detecting Strawberry Fruit
## 3 Experiments And Results
### Experimental Results and Analysis
#### 3.2.2 Experiments of the Triplet Attention Mechanism  
The attention mechanisms were embedded in the last two layers of R-YOLO's neck network to better extract crucial features. We conducted comparative experiments on various types of attention mechanisms, including channel attention mechanisms, such as SE, and channel and spatial attention mechanisms, such as CBAM and the triplet. The experimental results for different attention mechanisms are shown in Table 4.  
According to the results in Table 4, all the attention mechanisms improved the detection accuracy of R-YOLO. Given an input image, the channel attention focused on what was meaningful. It considered each channel of a feature map as a feature detector, contributing more to the classification. In this study, the primary challenge was to accurately identify the target class, and thus, the channel attention contributed more to the model's accuracy. This explained why the SE attention mechanism, which only included the channel attention part, achieved a significant performance improvement [32]. Contrarily, the spatial attention component complemented the channel attention by emphasizing the precise localization of informative parts, which led to further detection accuracy improvements. As a result, the channel and spatial attention mechanisms achieved a better performance than SE. Since the triplet attention can capture the cross-dimensional interaction of the channel and spatial dimension, it achieved the highest detection precision rate, \(mAP_{0.5}\), \(mAP_{0.75}\), and \(mAP_{0.5,0.95}\) metrics. Therefore, we selected the triplet attention mechanism to enhance the model's feature extraction.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **\(\mathbf{P}\) (\%)** & **\(\mathbf{R}\) (\%)** & \(\mathbf{mAP_{0.5}}\) (\%)** & \(\mathbf{mAP_{0.75}}\) (\%) & \(\mathbf{mAP_{0.50.95}}\) & **FPS** \\ \hline R-YOLO & 89.05 & 78.50 & 86.58 & 62.75 & 54.87 & **154** \\ R-YOLO + SE & 89.69 & 79.44 & 88.02 & 62.24 & 55.35 & 149 \\ R-YOLO + CBAM & 89.49 & **81.26** & 88.54 & 62.07 & 55.80 & 137 \\ R-YOLO + coordinate & 89.96 & 80.48 & 88.24 & 62.14 & 55.48 & 141 \\ R-YOLO + NAM & 89.84 & 80.40 & 88.10 & 62.36 & 55.06 & 152 \\ R-YOLO + SGE & 89.37 & 80.02 & 87.82 & 62.78 & 54.93 & 141 \\ R-YOLO + SimAM & 90.34 & 80.52 & 89.02 & 63.19 & 56.07 & 139 \\ R-YOLO + triplet & **91.43** & 81.05 & **89.21** & **63.46** & **56.29** & 145 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of R-YOLO with various attention mechanisms.
## 3 Experiments
### Chicken (Gallus Domesticus): Age-Dependent Stress Condition Classification
#### 3.3.2 Stress and Diet detection  
A second study on poultry vocalizations is also implemented, focused on determining whether vocalization patterns could be used to differentiate dietary conditions (meat _vs_. vegetable fed), stress _vs_. nonstress conditions (human presence being the stress stimulus), and age of the animals (young, 0-4 weeks, _vs_. mature, 5-6 weeks). Results for stress classification are shown in Table 8. All accuracies are above 90%, and indicate that within these conditions, human presence stress is relatively easy to detect. The slightly higher accuracy for mature vocalizations may be simply correlated with age, in that vocalization patterns tend to be more stable and established in older individuals, or may be due to learned reactions or preferences. \begin{table}
\begin{tabular}{l c} \hline \hline \multicolumn{2}{c}{**Accuracy**} \\ \hline
1 month & 60.5\% \\
2 months & 62.0\% \\
3 months & 72.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overall 6 condition classification accuracy for 1, 2, and 3 month old age groups. \begin{table}
\begin{tabular}{|c|c|c|c|c|c|c|c|} \hline \hline \multicolumn{2}{c|}{} & \multicolumn{5}{c|}{**Classification**} \\ \cline{3-8} \multicolumn{2}{c|}{} & \multicolumn{1}{c|}{**Control**} & \multicolumn{1}{c|}{**H**} & \multicolumn{1}{c|}{**HP**} & \multicolumn{1}{c|}{**H + HP**} & \multicolumn{1}{c|}{**C**} & \multicolumn{1}{c|}{**H + C**} \\ \hline \multirow{5}{*}{**V.**} & **Control** & 97 & 0 & 2 & 0 & 0 & 0 \\ \cline{2-8}  & **H** & 0 & 39 & 7 & 12 & 7 & 0 \\ \cline{2-8}  & **HP** & 5 & 1 & **70** & 0 & 20 & 0 \\ \cline{2-8}  & **H + HP** & 0 & 12 & 0 & **24** & 0 & 6 \\ \cline{2-8}  & **C** & 7 & 2 & 2 & 0 & **34** & 1 \\ \cline{2-8}  & **H + C** & 3 & 1 & 0 & 11 & 9 & **19** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sample condition classification confusion matrix, for 3 month old age group. Accuracy 72.4% (_vs_. chance accuracy 25.3%). In comparison, determining whether or not diet has impact on vocalization patterns is a much more difficult classification task. Table 9 presents the results on this question. The main observation is that diet does impact vocalization patterns with accuracies high enough to confirm significance in all cases. Additionally, it can be seen that the nonstressed condition vocalizations are much easier to discriminate than the stress condition vocalizations, and that more mature animals are easier to differentiate than young animals. This indicates that diet-related vocalization differences are diminished by stress but increase with age. Maximum accuracy, for mature animals in the nonstressed condition, are relatively easy to discriminate on the basis of diet, at 89.2%. Looking comprehensively at all the poultry experiments, the main overall conclusion is that while vocalization patterns increase in consistency and differentiability with age, stress conditions can be differentiated across all age levels. \begin{table}
# Safety Assessment of Gas Pipelines Crossing River through Hydrodynamic Analysis
## 2 Materials and Methods
### Hydrodynamic Safety Assessment of Buried Gas Pipelines in Riverbeds
occurring over hours. For the short-term riverbed change analysis, this study utilizes the two-dimensional models RMA2 and SED2D provided by SMS. These models were selected because they are capable of simulating detailed two-dimensional hydrodynamic and sediment transport processes, which are essential for capturing the localized effects of extreme flood events on riverbed morphology. RMA2 handles the hydrodynamic flow field, while SED2D simulates sediment transport and bed change. The integration within SMS provides a flexible and user-friendly platform for setting up complex geometries and boundary conditions. SMS also offers advanced mesh generation and visualization tools, which are crucial for accurately representing the study area's topography and interpreting the simulation results. These features make RMA2 and SED2D particularly suitable for a short-term, high-resolution analysis of riverbed changes during extreme events. Through these assessments of long-term and short-term riverbed changes, the safety of the buried gas pipelines is ensured, and a maintenance system is established based on the results. This procedure represents a systematic approach to guaranteeing the safety of buried gas pipelines by comprehensively analyzing and preparing for both long-term and short-term changes in riverbed conditions.
## 1 Introduction
First, can WAAS achieve 20 m Vertical Alert Limit (VAL) (_i.e._, Required Navigation Performance (RNP) of APV II) [6] over conterminous United States (CONUS) with improved modeling and Final Operational Capability (FOC) infrastructure? Second, can WAAS be a CAT I (12 m VAL, RNP of CAT I) [6] system or possibly achieve 10 m VAL over CONUS with the additional new civil frequencies and satellites from Galileo? Accordingly, this paper is organized as follows: Section 2 discusses the system analysis assumptions. The performance of the upgraded WAAS is discussed in Section 3. Section 4 discusses the performance of the L1-L5 dual-frequency WAAS. The possible benefit from the improved signal model is discussed in Section 5. In Section 6, the dual-mode WAAS (GPS and Galileo) is investigated. Section 7 presents a summary and some concluding remarks.
###### Abstract  
The traceability of the geographical origin of coffee is a challenging issue to protect producers and consumers from the risk of fraud. A total of 162 Arabica from Peru, Colombia and Brazil, and Robusta from India, Vietnam and Uganda, espresso coffee (EC) samples of different degrees of roasting (light, medium and dark) were characterized for physico-chemical features (lipids, solids, and chlorogenic acids) and analyzed via SHS-GC/MS analysis, with the aim of discriminating the samples according to their geographical origin. Linear discriminant analysis (LDA), performed on the data of the chemical classes of the volatile organic compounds (VOCs), was able to correctly identify 97.53% of the tested samples through cross-validation. The dark roasting of the coffee beans implied a higher quantity of volatile compounds in the headspace of the EC, belonging to chemical classes of furans, esters, N-heterocyclic and sulfur compounds, reducing the differences by geographical origin. Light- and medium-roasted Robusta EC showed a major contribution of pyrazines and pyrimidines, while aldehydes, alcohols and ketones were generally more representative in Arabica samples. The quantitative distribution of volatile compounds proved to be a useful tool to discriminate samples by geographical origin.  
VOCs; physico-chemical properties; coffee authentication; coffee geographical origin; discriminant analysis; different roasting degree +
Footnote †: journal: Applied  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-000  
experienced by consumers [11], limiting the production of artifacts. Earlier studies have developed statistical models demonstrating the potential of using VOCs for the geographical origin determination of Arabica and Robusta green and roasted coffee beans [12; 13; 14]. However, the profile of volatile compounds in EC has never been used for the geographical discrimination of coffee beans. Secondly, the role of different roasting levels in the discrimination of coffee geographical origin by VOC analysis of EC has never been explored. Previously, [PERSON] et al. [15] demonstrated the discrimination ability of the physico-chemical parameters of roasted beans, such as acrylamide, caffeine, acidity and total phenolics, to cluster samples of different origins.  
Therefore, the traceability of geographical origin of coffee beans using VOCs of EC as chemical markers can be a challenging issue for producers and consumers eager for reliable criteria of authenticity. This study uses SHS-GC/MS analysis to discriminate a total of 162 samples of Arabica and Robusta EC subjected to three different levels of roasting (light, medium, and dark), of different origins (India, Vietnam, Uganda, Brazil, Colombia and Peru). A linear discriminant analysis (LDA) was performed on the major VOC chemical classes to classify coffee samples according to their geographical origin.
# Research on Leakage Location of Pipeline Based on Module Maximum Denoising
## 2 Materials and Method
### Leakage Point Location Method  
#### 2.2.1 Negative Pressure Wave Theory  
The negative pressure wave (NPW) method is a very effective and mature technology in pipeline leakage location applications. The delay of the signal is calculated by collecting leakage signals, at the head and end of the pipe, using sensors to estimate where the leak occurred. The positioning schematic is shown in Figure 1.  
Two pressure sensors are installed on the water supply pipe. The leak point is located between the two sensors. If a leak occurs, the fluid pressure at that leak point drops and this negative pressure wave change is transmitted up (towards sensor 1), and down (towards sensor 2) along the pipe. Based on the velocity of the negative pressure wave, the time difference between the two pressure sensors and the distance between the sensors, the location of the leak can be determined.  
It can be assumed that the distance between the two sensors is \(L\) and the propagation speed of the negative pressure wave is \(v\). The position of sensor 1 is set as the reference starting point. Assuming that the leakage point is located at position \(X\), and the arrival time of the sensors at both ends of the pipe for NPW are \(t_{1}\) and \(t_{2}\), respectively, the time difference can be calculated according to the point corresponding to the inflection point of  
Figure 1: Pipeline leak location diagram.  
the negative pressure wave, so as to locate the leakage point. The calculation formula is as follows:  
\[Tt=\frac{n_{1}-n_{2}}{f} \tag{15}\]  
In the formula, \(n_{1}\) and \(n_{2}\) are the locations of the inflection points where the NPW is detected, and \(f\) is the sampling rate of the acquired signal.  
The pipeline leak location formula is:  
\[L_{A}=\frac{1}{2}(L+vTt) \tag{16}\]  
Therefore, the wave velocity \(v\) of NPW and the time difference \(Tt\), between the NPW reaching the upstream and downstream, are two key factors affecting the accuracy of positioning.