esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed GRU-Attention Architecture and Technical Indicators Normalization  
### The Proposed GRU-Attention Prediction Model  
This section introduces the proposed GRU-Attention prediction model. The system architecture of this research is shown in Figure 4. The model input is a set of 3D tensors of time series: the dimensions represent daily transaction data (opening, closing, highest, lowest, and trading volume), time sequence length, and the batch size. The architecture can be regarded as a target function \(P(Y_{1},\dots,Y_{T^{\prime}}\mid X_{1},\dots,X_{\rm T})=\prod_{t=1}^{T^{ \prime}}p(Y_{t}\mid c,Y_{1},\dots,Y_{t-1})\), where \(X_{1},\dots,X_{\rm T}\) are input tensors, \(Y_{1},\dots,Y_{T^{\prime}}\) are labels, \(c\) represents the context vector. The hidden layer contains a GRU structure of 128 neurons. After outputting to the Attention layer, the Decode vector is generated, and finally the probability of the target category is output by the fully connected layer and the Softmax function.  
As shown in Figure 3, the sequence length and batch size are adjustable parameters, and StockVec is the daily trading attributes of the input stock. The encoder of the model is the GRU-RNN architecture. The GRU receives the input and executes the calculations of Formulas (6)-(9), and then enter the Attention Layer to calculate the context vector of each timestep. This research adopts the Luong style attention [43]; the detailed operations are as follows:  
\[\alpha_{ts}=\frac{e^{score(h_{t},\widetilde{h}_{s})}}{\sum_{s^{\prime}=1}^{S _{s^{\prime}}}e^{score(h_{t},\widetilde{h}_{s^{\prime}})}} \tag{10}\]  
\[c_{t}=\sum_{s}\alpha_{ts}\widetilde{h}_{s} \tag{11}\]  
\[a_{t}=f(c_{t},h_{t})=\tanh(W_{c}[c_{t};h_{t}]) \tag{12}\]  
\[score(h_{t},\widetilde{h}_{s})=h_{t}^{T}W\widetilde{h}_{s} \tag{13}\]  
Figure 4: The overall architecture of proposed GRU-Attention framework.  
Attention Weights \(\alpha_{ts}\): Use the hidden state \(h_{t}\) of the current decoder to perform a score operation on the hidden states of entire encoder to obtain the importance of \(h_{t}\) to each \(\vec{h}_{s}\) via Softmax function.  
Context Vector \(c_{t}\): Use attention weight \(\alpha_{ts}\) and original \(\vec{h}_{s}\) to perform weight averaging.  
Attention Vector \(a_{t}\): Concatenation the context vector and decoder of the hidden state and perform nonlinear transformation.  
After the encoding vector is generated through GRU-Attention, the latent features are retained through a set of fully connected layers and the class probability is output through Softmax [44], which predicts the probability for the \(j\)-th class from input vector X:  
\[P(output=j|X)=\frac{e^{X^{T}w_{j}}}{\sum_{s=1}^{S}e^{X^{T}w_{s}}} \tag{14}\]
# Optimal Fleet Transition Modeling for Sustainable Inland Waterways Transport
## 2 Materials and Methods
\[Minimize\ Z=\sum_{i}\sum_{j}X_{i,\ j} \tag{1}\]  
where  
* \(Z\) is the total number of vessels,
* \(X_{i,j}\) is a binary variable that equals 1 if vessel \(i\) on route \(j\) is used and 0 otherwise. The model is subject to the following constraints:
* Each route must be covered by at least one vessel. The constraint is shown in Equation (2). \[\sum_{i}X_{i,\ j}\geq 1\ \ \ \ \ for\ all\ routes\ j\] (2)
* The state of charge of a vessel must be sufficient to cover a route (see Equation (3)), where \(S_{i,j}\) is the state of charge of vessel \(i\) on route \(j\) and \(D_{j}\) is the minimum required state of charge to complete route \(j\). \(D_{j}\) is given by the product between the specific energy consumption and the distance of route \(j\). \[S_{i,j}\geq\ D_{j}\] (3)  
This model provides a practical tool for planning the transition to sustainable inland waterways transport in local lakes. It can be adapted to different settings by adjusting the parameters and constraints according to the specific characteristics of the lake, the boats, and the routes. The optimization problem in this study can be formulated as a mixed-integer linear program, with the decision variables representing the number of vessels and their assignments to routes, and constraints enforcing requirements like route coverage and vessel charge levels. This problem structure represents the ideal mathematical formulation for finding the globally optimal fleet size and assignments. However, the algorithm used to solve this optimization problem in this article is a greedy heuristic, rather than an exact optimization technique. The greedy heuristic operates by sequentially making local decisions to assign the best available vessel to each route in each time step. While the greedy approach finds feasible solutions quickly, it does not guarantee finding the true global optimum. The tradeoff is between solution quality and computational efficiency. Alternative algorithms like the primal-dual interior-point algorithm [34], the interior point method [35], or the branch-and-bound algorithm [36] could solve different optimization problems exactly but require significantly more computation. The greedy algorithm leverages elements of dynamic programming, keeping track of vessel states over time to enable better sequential decisions.
## 4 Development of Machine Learning Models
### Performance Evaluation Metrics  
The values associated with coefficient of determination (\(R^{2}\)), adjusted \(R^{2}\), and Root Mean Square error (RMSE) are used for evaluating the performance of the prediction models. Smaller values for the errors and higher values of \(R^{2}\) indicate the better performance of the models.  
In case of classification models, the performance of the models was evaluated using accuracy, precision, recall, and F1 score. These metrics were defined based on the confusion charts, which display the actual damage states and the predicted damage states by the classification models. The diagonal elements of the matrix represent cases accurately classified by the algorithms, while the off-diagonal elements indicate misclassified cases. Additionally, the confusion chart includes row-normalized and column-normalized values, which provides the percentage of accurately classified samples relative to the total number of samples.  
Accuracy is defined as the percentage of correctly predicted damage states relative to the total number of damage states or samples analysed. Precision is measured as the ratio of true positives to the total number of positives, either true or false; this value is represented by column-normalized values. This metric reflects the ability of the model to classify damage states while minimizing false positives accurately. Recall, indicated by row-normalized values, is the ratio of correctly classified data to the total number of actual cases. Recall is crucial in seismic damage classification, as it ensures the correct identification of severely damaged structures, a situation in which misclassification may lead to significant consequences. Higher values of accuracy, precision, and recall indicate superior performance of the machine learning algorithm.  
In order to fully examine the effectiveness of the classification model, both recall and precision should be assessed. However, tuning the model to increase recall may result in a decrease in precision, and vice versa. As a trade-off, the F1-score is commonly used to examine the accuracy of the model. The F1-score is the weighted harmonic mean of the precision and recall, and it is given by the following:  
\[\text{F1}=2\bigg{(}\frac{\text{Precision}\;*\;\text{Recall}}{\text{Precision}+ \text{Recall}}\bigg{)} \tag{3}\]
## 2 Related Works

Table 1: Comparison of the most recent related works. Moreover, the placement technique was considered to enhance the service continuity as proposed in Reference [24], which maps the service chains with primary-backup redundant scheme in the telecom operator network to avoid large-scale network failures. Similarly, the work in Reference [49] exploited path diversity in the physical network to enhance the survivability of service chains with minimum redundancy by multi-path link embedding. Further, deploying the service requests with protection schemes was proved to increase the embedding cost where the case in Reference [24] was mapping longer paths. In addition, the authors of Reference [49] claimed that mapping more than 5 paths to protect one virtual node and one virtual link increases the embedding cost. In this paper we considered this issue and studied the impact of increasing the number of paths in the service request on the performance the deployed schemes. Furthermore, the complexity of NFV-RA problem has affected the proposed strategies to solve the problem, where heuristic solutions were proposed for small and large scale physical networks while the exact (mathematical) solutions were proposed to solve the problem in small scale physical and simple service requests. For example exact solutions (such as ILP) were proposed in Reference [11] to find the optimal solutions in small-scale physical networks and simple/forking service requests while large-scale only solved in heuristic. In the exact schemes, the placement problems were formulated as ILP as in References [11; 12; 36; 37; 49] and even more, a Mixed ILP were formulated in References [27; 34; 35; 45; 47]. Mathematical based solution also used within heuristic solutions, for example, Reference [50] used matrix based method and Reference [38] used Linear Programs (LP) method in their heuristic solutions. It is worth to mention that the best average execution time for the ILP in large scale physical network (with 110 nodes) was 459.519 s as in Reference [12]. The embedding cost of the heuristic scheme was 1.25 of the optimal solution in Reference [47] in which we got a better ratio. Furthermore, the proposed path mapping approach has reduced the ratio between heuristic scheme execution time and exact scheme execution time compared to the reported ratio in Reference [37], which was from 65 to 3500.
# Extended Geometry and Probability Model for GNSS+ Constellation Performance Evaluation
## 2 Methods
### Calculation of DOPs and Satellite Visibility
\begin{array}{l}PDOP=\sqrt{Q_{\mathit{UIEN}}(1,1)+Q_{\mathit{UIEN}}(2,2)+Q _{\mathit{UIEN}}(3,3)}\ \text{position DOP}\\ HDOP=\sqrt{Q_{\mathit{UIEN}}(2,2)+Q_{\mathit{UIEN}}(3,3)}\ \text{horizontal DOP}\\ VDOP=\sqrt{Q_{\mathit{UIEN}}(1,1)}\ \text{vertical DOP}\\ TDOP=\sqrt{Q_{\mathit{UIEN}}(4,4)}\ \text{time DOP}\\ NDOP=\sqrt{Q_{\mathit{UIEN}}(3,3)}\ \text{north DOP}\\ EDOP=\sqrt{Q_{\mathit{UIEN}}(2,2)}\ \text{east DOP}\end{array} \right.. \tag{33}\]  
Obtained from the above expression, the correlation, \(\rho_{\mathit{Uclk}}\), of station geodetic height between receiver clock difference follows the expression of:  
\[\rho_{\mathit{Uclk}}=\frac{Q_{\mathit{BUEN}}(1,4)}{\sqrt{Q_{\mathit{UIEN}}(1,1)}\sqrt{Q_{\mathit{UIEN}}(4,4)}}. \tag{34}\]  
In addition, the ratio, \(\mathit{NE}\), of the positioning precision in the longitude component to the latitude component can be written as:  
\[\mathit{NE}=\frac{\sqrt{Q_{\mathit{UIEN}}(3,3)}}{\sqrt{Q_{\mathit{UIEN}}(2,2)}}. \tag{35}\]  
In summary, we can establish an observation equation based on all grids or points above CEA, and the probability of satellites in this grid is regarded as the weight of this observation equation. Then, the normal equation of the GNSS+ constellation can be given by:  
\[N_{bb}^{\mathit{GNSS+}}=N_{bb}^{\mathit{MEO}}+N_{bb}^{\mathit{LEO}}+N_{bb}^{ \mathit{GEO}}+N_{bb}^{\mathit{IGSO}}+N_{bb}^{\mathit{HEO}}+N_{bb}^{\mathit{ QZO}}, \tag{36}\]  
where \(N_{bb}^{\mathit{MEO}}\), \(N_{bb}^{\mathit{LEO}}\), \(N_{bb}^{\mathit{GEO}}\), \(N_{bb}^{\mathit{IGSO}}\), \(N_{bb}^{\mathit{QZO}}\), and \(N_{bb}^{\mathit{HEO}}\) can be given by Equation (25).
## 3 Results and Discussion  
A typical small aspect ratio all-movable fin is studied in this paper; a diagram of this is shown in Figure 2, and the relevant parameters are shown in Table 1. Unless otherwise specified, \(\delta_{\rm B}=0.1^{\circ}\), \(\delta_{\rm T}=0.1^{\circ}\), the integration time step is \(0.001\) s, and the initial conditions \(\mathbf{x}(0)\) of the time-domain integration are set to \([10\delta_{\rm B},10\delta_{\rm T},0.0,0.0]^{\rm T}\). The air density and Mach are \(0.122\) kg/m\({}^{3}\) and \(3.0\), respectively. The mode shapes are shown in Figure 3, and the linear flutter results obtained using the root-locus method are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Description** & **Value** \\ \hline \(m\) & Mass & \(5.49\) kg \\ \(l_{xx}\) & Mass moment of inertia & \(9.28\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(l_{yy}\) & Mass moment of inertia & \(3.39\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(l_{xy}\) & Mass moment of inertia & \(1.18\times 10^{-2}\) kg \(\cdot\) m\({}^{2}\) \\ \(K_{\rm B}\) & Bending spring stiffness & \(5.0\times 10^{3}\) N \(\cdot\) m/rad \\ \(K_{\rm T}\) & Torsional spring stiffness & \(2.0\times 10^{3}\) N \(\cdot\) m/rad \\ \([\mathbf{\varepsilon},\mathbf{g}]\) & Center-of-gravity position & \([0.013\) m, \(0.111\) m] \\ \hline \end{tabular}
\end{table}
Table 1: Structure parameters of the all-movable fin.
## 3 Samples and Methods
A Rock-Eval 6 pyrolyzer, manufactured by Vinci Technologies, was used for pyrolysis. The samples were crushed into 0.07-0.15 mm, and then weighed and loaded onto sample crucibles, which were put into a pyrolysis oven. The pyrolyzed samples were put into an oxidation oven. Determination parameters: free hydrocarbon (S1) was analyzed at 300 \({}^{\circ}\)C for 3 min to detect the free hydrocarbon content in the sample (mg/g); pyrolysis hydrocarbon (S2) was detected at a temperature rate of 25 \({}^{\circ}\)C/min between 300 and 650 \({}^{\circ}\)C to detect the pyrolysis hydrocarbon content in the sample (mg/g). T\({}_{\rm max}\) is the pyrolysis temperature corresponding to the highest point of the S2 peak (\({}^{\circ}\)C). S3 is the amount of CO\({}_{2}\) produced by the pyrolysis of organic matter (mg CO\({}_{2}\)/g Rock), which is generally used to calculate the oxygen index. The pyrolyzable carbon (PC; from the pyrolysis stage), residual carbon (RC; from the oxidation stage), total organic (TOC; sum of RC and PC), hydrogen index [HI; (S2/TOC) \(\times\) 100], oxygen index [OI; (S3/TOC) \(\times\) 100], oil saturation index [OSI; (S1/TOC) \(\times\) 100] were determined and calculated. The analysis and calculation procedures were carried out in accordance with the national standard GB/T 18602-2012. Correlations among the variables were evaluated by the R\({}^{2}\), the F-test, the T-test, and the \(p\)-value. In general, the degree of the variables' statistical correlation (R\({}^{2}\)) was judged by the magnitude of the correlation coefficient (1.0-0.8: very strong correlation, 0.8-0.6: strong correlation, 0.6-0.4: moderate correlation, 0.4-0.2: weak correlation, 0.2-0.0: very weak or no correlation); the F-test was used for the overall regression equation significance test; the T-test was used to determine the significance of the coefficient of variation; and the \(p\)-value was used as a measure of the T-test and F-test [67; 68; 69; 70]. In simple linear regression, the square of the T-statistic of the T-test for the explanatory variables is equal to the F-test for the overall significance of the equation of the F-statistic, and the two tests are equivalent [63; 64; 65; 66]. We measured the correlation and significance of the regression equation using the T-statistic, \(p\)-value, and R\({}^{2}\): T-absolute value \(>\) 1.65, \(p\)\(<\) 0.10 regression coefficient is significant at the 10% level; T-absolute value \(>\) 1.96, \(p\)\(<\) 0.05 regression coefficient is significant at the 5% level; T-absolute value \(>\) 2.58, \(p\)\(<\) 0.01 regression coefficient is significant at the 1% level [67; 68; 69; 70].
## 5 Conclusions  
This work described the derivation of a series of DSPs to describe the incongruent dissolution of C-S-H gel from the jennite and tobermorite-II end-members provided in the cemdata07 database. While solid solution modelling is possible in PHREEQ, it carries an inherent time and computational load. By using the DSP C-S-H gel solubility models, the computational demand and time were significantly lower and were often completed in less than a minute with no loss in accuracy. In the current work, it was demonstrated that only one of the DSPs derived to model the C-S-H gel phase was required as the Ca/Si and H/Si ratios were suitable for the cement blends herein. While the use of solid solutions and the derivation of DSPs was a thermodynamically credible approach to simulate the incongruent solubility behaviour of C-S-H gel, they were only needed for hydrating cements incorporating SCMs or those undergoing chemical attack.  
It was shown that PHREEQ could be used to accurately model cement hydration for Portland cement systems with various amounts of limestone. In these simulations, we predicted the governing dissolution/precipitation reactions accounting for oxide components dissolved in the clinker phases and the represented time-dependent oversaturation and alkali binding to C-S-H by defining the input for PHREEQ in a spreadsheet and pasting it into the input file, which was more efficient, giving the user full control.  
Conceptualization, N.H. and M.T.; methodology, N.H.; software, D.K. and N.H.; validation, M.T.; formal analysis, N.H. and D.K.; investigation, N.H.; data curation, N.H.; writing--original draft preparation, N.H.; writing--review and editing, M.T. and D.K. All authors have read and agreed to the published version of the manuscript. Funding Funding to publish this work was from a US-Ireland grant trifunded by the National Science Foundation (NSF, 1805818), Science Foundation Ireland (SFI, 17/US/3424) and the Department for the Economy of Northern Ireland (DfE, ISI 127). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Acknowledgments: The authors would like to acknowledge the advice provided by [PERSON] during the production of this paper. Conflicts of Interest: The authors declare no conflict of interest.  
Figure 5: Pore solution chemistry produced by the PHREEQ model using [20] for **(a)** OPC and **(b)** 4% limestone binder (LOTH represents the measured data from [20]). **(a)** OPC pore chemistry; **(b)** 4% limestone pore chemistry.
## 3 Methodology
### Preprocessing Steps
#### 3.2.3 Multiclass Detection  
In this phase, we will use a sample consisting of three classes (Krack, Kr00k, and Nominal), 15,000 instances, and 254 features. Due to the importance of preprocessing as we noted in the previous subsections, we have applied preprocessing steps in the chosen sample, removing the empty features and the features with constant values, in addition to replacing all the empty cells in the remaining features with NaN. Then, we applied ML algorithms using the classification linear application on MATLAB. The performance of the ML algorithms is presented in Table 8. The table presents the accuracy results using FS with NOVA algorithm techniques and without using FS.  
The accuracy results for the mentioned steps are presented in Figure 7.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Accuracy** \\ \hline Decision tree & 93.3\% \\ \hline Ensemble classifier & 83.3\% \\ \hline KNN & 86.7\% \\ \hline SVM & 96.67\% \\ \hline Kernel & 83.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of the learning algorithms after feature selection—Kr00k attack.  
Figure 6: The performance of the learning algorithms—Kr00k attack.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **Accuracy without FS** & **Accuracy FS** \\ \hline Decision tree & 57.1\% & 88.3\% \\ \hline Ensemble classifier & 44.7\% & 90.7\% \\ \hline KNN & 67.4\% & 73.3\% \\ \hline SVM & 57.1\% & 69.5\% \\ \hline Kernel & 53.1\% & 60.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: The performance of the learning algorithms—multi-class.  
The best accuracy that we achieved without using FS was for KNN 67.4%, while when we applied the ANOVA FS, the performance increased in all the used algorithms. The best accuracy was achieved by the ensemble classifier with 90.7% and for the decision tree with 88.3%.
## 4 Results and Discussions
### Pressure Fluctuation Analysis of Bladeless Region
This indicates that the pressure fluctuation intensity at 0.75 Qd is relatively large, and the flow separation at the trailing edge of the guide vane has a great influence on the pressure fluctuation in the bladeless region and the cascade channel. The pressure fluctuation intensity at 1.0 Qd and 1.25 Qd was relatively small, and the pressure fluctuation distributions in the bladeless region were mainly affected by the rotor-stator interaction at the outlet of the runner. Figure 17: Pressure fluctuation distributions at different vane heights at 0.75 Qd. Figure 18: Pressure fluctuation distributions at different vane heights at 1.0 Qd. It can be seen from the above figures that the pressure fluctuation in the bladeless region at different vane heights has significant differences, and the high-amplitude fluctuation frequency caused by rotor-stator interaction is mainly 10 \(\mathrm{f_{n}}\) and 20 \(\mathrm{f_{n}}\). Figure 20 shows the circumferential distribution of 10 \(\mathrm{f_{n}}\) and 20 \(\mathrm{f_{n}}\) amplitudes at different vane heights and under different flow rates. As can be seen from the figure, the amplitude difference of 10 \(\mathrm{f_{n}}\) at different vane heights was large, while the amplitude difference of 20 \(\mathrm{f_{n}}\) was relatively insignificant. For 10 \(\mathrm{f_{n}}\), at a small flow rate at 0.75 \(\mathrm{Q_{d}}\), the amplitudes near the middle height and the bottom ring were relatively large, while the amplitude near the roof was significantly reduced, indicating that the rotor-stator interaction near the bottom ring was relatively strong. With the increase of the flow rate, the amplitude of the middle height gradually decreased, close to that found near the roof, and the amplitude near the bottom ring was relatively large, especially at 1.25 \(\mathrm{Q_{d}}\). Except for the monitoring points near the trailing edge of the guide vanes, the amplitudes of 10 \(\mathrm{f_{n}}\) at the other monitoring points were higher than 20 \(\mathrm{f_{n}}\), indicating that the node-diameter number \(\mathrm{k_{1}}=-6\) dominated the rotor-stator interaction in the bladeless region. Figure 19: Pressure fluctuation distributions at different vane heights at 1.25 \(\mathrm{Q_{d}}\). Figure 20: _Cont._
## 3 Experiments  
### Data Description  
The efficacy of the proposed EnGS-DGR model is validated through two real-world traffic datasets:1. PEMS-BAY dataset collected by California transportation department performance measurement system (PEMS). This dataset contains the data of the bay area from 1 January 2017 to 31 May 2017, including 325 monitor nodes.
2. METR-LA dataset collected by Los Angeles County Highway ring detector. This dataset contains data from 1 March 2012 to 30 June 2012, including 207 monitor nodes.  
The monitor distribution in PEMS-BAY and METR-LA dataset are displayed in Figure 6. Meanwhile, 80% of traffic data is used as the training set and 20% as the test set.