esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-View Multi-Attention Graph Neural Network for Traffic Flow Forecasting
## 3 Methodology
### Prediction Network Module  
#### 3.3.1 Multi-Graph Convolution Module  
Based on the four predefined diagrams based on a priori knowledge (presented in Section 3.2), we designed the following multi-layer spatial convolution for each representation based on spectral graph theory. The GCN model in this paper uses an efficient layered-propagation rule based on a first-order approximation of spectral convolution on a graph. Experimental results on a large network dataset show that the GCN model can encode the graph structure and node characteristics in a useful semi-supervised classifier [12]; thus, our application of this model for predicting station traffic in metro networks is reasonable and effective, and results of this research in Section 4 again confirm this.  
\[H_{ne}^{(l+1)}=ReLU\left(\widetilde{D}_{ne}^{-\frac{1}{2}}\widetilde{A}_{ne} \widetilde{D}_{ne}^{-\frac{1}{2}}H_{ne}^{(l)}W_{ne}^{(l)}\right) \tag{5}\]  
where \(W_{ne}^{(l)}\) represents the learnable neural layer, \(\widetilde{A}_{ne}=A_{ne}+I\) and \(\widetilde{D}_{s,il}=\sum\limits_{j}\widetilde{A}_{s,ijl}\). \(H_{ne}^{(0)}=X\in\mathbb{R}^{n\times f}\), where \(X\) denotes the feature matrix of all metro stations. Here, \(f\) denotes the feature dimension, \(H_{ne}^{(l)}\in\mathbb{R}^{n\times d}\) represents the \(I\)-th layer's output. The dimension of the hidden state for the potential representation of all metro points is denoted by \(d\). Similarly, we can write the formalization of the convolution of the other three graphs.  
\[H_{sr}^{(l+1)}=ReLU\left(\widetilde{D}_{sr}^{-\frac{1}{2}}\widetilde{A}_{sr} \widetilde{D}_{sr}^{-\frac{1}{2}}H_{sr}^{(l)}W_{sr}^{(l)}\right) \tag{6}\]  
\[H_{di}^{(l+1)}=ReLU\left(\widetilde{D}_{di}^{-\frac{1}{2}}\widetilde{A}_{di} \widetilde{D}_{di}^{-\frac{1}{2}}H_{di}^{(l)}W_{di}^{(l)}\right) \tag{7}\]  
\[H_{cs}^{(l+1)}=ReLU\left(\widetilde{D}_{cs}^{-\frac{1}{2}}\widetilde{A}_{cs} \widetilde{D}_{cs}^{-\frac{1}{2}}H_{cs}^{(l)}W_{cs}^{(l)}\right) \tag{8}\]  
In reality, these four representations are not completely unrelated. Therefore, we designed a generic GCN for convolution operations using parameter-sharing strategies. where ave denotes the average. We formally define the dissemination plan through the following operations:  
\[H_{c}^{(l+1)}=ave\left(H_{cne}^{(l+1)}+H_{csr}^{(l+1)}+H_{cdi}^{(l+1)}+H_{ccs }^{(l+1)}\right) \tag{9}\]  
Then we concatenate the features from the convolution of the five graphs as follows:  
\[\mathbf{H}_{1}=att\left(H_{ne}^{(l+1)},H_{sr}^{(l+1)},H_{di}^{(l+1)},H_{cs}^{(l+1)},H_{c}^{(l+1)}\right) \tag{10}\]  
where \(att\) denotes the attention mechanism, \(\mathbf{H}_{1}\) represents the output feature of the GCNs. Eventually, \(\mathbf{H}_{1}\) will be entered into the output layer along with the \(\mathbf{H}_{2}\) mentioned in the next section, and the output will be the predicted target shape through the full connection layer.
## 3 The Time Source Techniques  
With an understanding of the various applications in smart grids that rely on precise time synchronization, it becomes essential to investigate the time source techniques that underpin these applications. Selecting an appropriate time source requires a thorough evaluation of factors such as accuracy, resilience, reliability, security, flexibility, and cost. The following section will explore these time source techniques in detail, highlighting both established methods and emerging advancements.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Application** & **Time Requirements** \\ \hline SCADA & Every 4–6 s reporting rate \\ Digital Fault Recorder & 2 ms \\ Sequence of events recorder & 50 μs to 2 ms \\ Protective relays & 1 ms \\ Lightning correlation & 1 ms \\ Traveling wave fault location & 100 ns to 1 μs \\ Synchrophasors & 1 μs \\ \hline
**Communication protocols** & \\ \hline Substation local area network communication protocols & 100 μs to 1 ms \\ Substation LANs & 1 μs \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time requirements in power system [32].
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 4 Experiment and Result Analysis  
In order to verify the effectiveness of the proposed algorithm, experiments were carried out.  
Table 2 lists the threshold parameters in the experiment. Power threshold is denoted by \(\delta_{p}\), noise variance threshold is \(\delta_{n}\), cluster threshold is \(Thr_{c}\), and threshold in event pairing (i.e., forward-backward pairing) are \(\varepsilon_{p}\).
## 3 Controller Design
### Adaptive Robust Term
\[\hat{\epsilon}=-\beta(\lambda\dot{\epsilon}^{\lambda-1}\cdot s+\widetilde{ \epsilon}\widetilde{\epsilon}) \tag{30}\]  
The adaptive robust term can improve control performance by compensating neural network approximation errors, although Equation (30) satisfied the [PERSON] theorem, the adaptive robust term contains many error-related terms, which can suffer convergence and convergence speed. Tuning is not easy due to the characteristic of the term of \(\dot{\epsilon}^{\lambda-1}\). Therefore, we substitute a constant term that satisfies Equation (31) by using the fact that it is always a positive. \[\dot{\epsilon}^{\lambda-1}<C\text{ and }\left|\dot{\epsilon}^{\lambda-1} \cdot k\cdot s\cdot\text{sgn}(s)+\sigma\widetilde{W}^{T}\widetilde{W}+ \widetilde{\epsilon}\widetilde{\epsilon}^{T}\widetilde{\epsilon}\right|>\left| \widetilde{\epsilon}^{T}\lambda\cdot s(\dot{\epsilon}^{\lambda-1}-C)\right| \tag{31}\]  
Equation (32) is the adaptive robust term update law replaced by a constant term. Next, we combine the reverse saturation function as Equation (33) with the adaptive robust term. The reverse saturation function generates outputs larger than the threshold value and removes outputs less than the threshold value. Therefore, phenomena such as chattering can be prevented by removing the additional control input in steady state through the reverse saturation function. \[\dot{\hat{\epsilon}}=-\beta(\lambda\cdot C\cdot s+\widetilde{\epsilon}\widetilde {\epsilon}) \tag{32}\]  
\[SAT_{rev}(u)= \begin{cases}-|u|&\text{if}:\quad u<-N\\ 0&\text{if}:-N\leq u\leq N\\ +|u|&\text{if}:\quad u>N\end{cases}  \tag{33}\]  
Finally, we can obtain the proposed control input as Equation (34). The control flow is in Figure 4. Figure 4 is the controller block diagram using Equation (34). The controller creates an error signal using the reference signal and the output, and generates the output of the network and the adaptive robust term output. Finally, the generated outputs are integrated with the NTSMC controller and applied to the snake robot's head. \[u=(C+G)+M\cdot\{\tilde{x}_{d}+\alpha\dot{e}/\lambda\dot{e}^{\lambda-1}+\underbrace {k\cdot\text{sgn}(s)}_{\text{Switching term}}-\underbrace{\dot{d}(\bullet)\Lambda}_{\text{RBF NN output}}-\underbrace{SAT_{\text{rev}}(\dot{e})}_{\text{Adaptive robust term}} \tag{34}\]
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 2 Experimental Procedure  
In the current experimental study, the mechanical response of corroded embedded steel reinforcing bars under dynamic loadings was studied to simulate the seismic behavior of steel reinforcement in existing corroded RC structures. For this reason, the experimental procedure was divided in two phases. Primarily, 34 reinforced concrete specimens were cast and subsequently corrosion tests via imposed current density were carried out. Upon completion of the corrosion process, the corrosion damage of all specimens was estimated and thereafter mechanical tests on non-corroded and corroded steel bars were performed under dynamic loadings imposing gradually increased deformations to study their non-linear hysteretic behavior.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 2 Formation Mechanisms of Benzene and PAHs
### Formation Mechanisms of PAHs  
Over the past two decades, significant efforts have been directed towards elucidating PAH formation mechanisms. These efforts have resulted in the development of various reaction kinetic pathways, which have been rigorously validated against data obtained from diverse combustion environments to better understand PAH formation. The formation mechanisms include the hydrogen abstraction and acetylene addition (HACA) mechanism, the hydrogen abstraction and vinyl acetylene addition (HVA) mechanism, formation mechanisms with radicals, and the hydrogen abstraction and phenylacetylene addition (HAPaA) mechanism. Among these, the HACA mechanism stands as a classical and widely utilized pathway for PAH formation. However, in fuel blends where vinyl acetylene is abundant, the hydrogen abstraction and vinyl acetylene addition (HAVA) mechanism may prevail over HACA due to favorable reaction kinetics. Among formation mechanisms with radicals, methyl addition cyclization (MAC), although not competitive with the HACA mechanism, plays a crucial role in augmenting the PAH chain. Similarly, the addition of phenyl radicals (PAC) contributes to the enhancement of 6-ring membered aromatics within the polycyclic aromatic chain. Furthermore, the presence of vinyl radicals, originating from ethylene, promotes the hydrogen abstraction and vinyl radical addition (HAVA *) mechanisms, particularly under moderate temperature conditions, facilitating the formation of naphthalene and larger PAHs such as fluoranthene. However, it is crucial to acknowledge that all these pathways exhibit limitations in the formation of pre-condensed aromatic hydrocarbons. Consequently, the recently proposed HAPaA mechanism has emerged to address this challenge, specifically targeting the formation of pre-condensed aromatic hydrocarbons. A summarized overview of the diverse pathways involved in PAH formation is provided in the schematic diagram presented in Figure 4.
# Scheduling for Media Function Virtualization
## 5 Evaluation
### Evaluation Setup  
Studio architectures have stringent performance requirements such as lossless and high-speed (multi-Tbps) switching, support for point-to-multipoint communication, etc. In addition to fulfilling these requirements, data-center topologies, such as fat-tree, are also easier to upgrade for high-quality media formats [2]. Therefore, the MFVi considered for evaluation is arranged in the fat-tree topology. The topology consisting of \(\kappa\) pods, where each pod has \((\kappa/2)^{2}\) server nodes, \(\kappa/2\) access layer switches, and \(\kappa/2\) aggregate layer switches and the core layer contains \((\kappa/2)^{2}\) switches. The total number of server nodes in the topology are \(\kappa^{3}/4\). We assume \(\kappa=8\), i.e., there are a total of 128 servers, with each server node containing 20 cores. All the devices (switches and server nodes) in the MFVi physical network are equipped with 10 GbE interfaces.  
As mentioned earlier, media services are represented via VMF-FGs. The focus of this paper is on VMF-FG scheduling; we therefore pay attention to the resource allocation aspects, not to the specific VMF-FG functionality. To this end, the VMF-FG considered for the evaluation is shown in Figure 5. It consists of 11 VMFs (and 6 endpoints) and 16 virtual links. The VMF-FG is considered in three media formats: (i) HD @ 30 fps, (ii) HD @ 60 fps, and (iii) FHD @ 30 fps. The subsampling and sample encoding of the VMF-FG are assumed to be 4:2:2 and 10 bits, respectively. These media formats are typically employed in today's media production environments [3].  
The other parameters and their corresponding values (range) for media service requests, MFVi, and VMFs are listed in Table 2.
## 3 Our Model
### Model Overview  
The ATKGRM consists of a generator and a discriminator. In the generator, a new long- and short-term interest model is adopted to model user features and item features and generate high-quality candidate item sets. In the discriminator, to better discriminate candidate items, we use the popularity-based sampling strategy accurately to obtain the user's positive items and negative items and compare the user's scores on the positive items, candidate items, and negative items to identify candidate items more accurately. The performance of the overall adversarial model is improved by a high-quality generator and a high-quality discriminator.  
As shown in Figure 1, for generator G, in order to better mine user features and generate candidate items for users, user features are divided into two sections. To better mine item features, it is critical to reasonably capture the item information in the knowledge graph. First, an item-centric knowledge graph is constructed at the item embedding layer to obtain the vector representations of historical items and target items. Then, in the user modeling layer, the user representation \(\mathbf{u}\) consists of the user short-term feature \(\mathbf{u}_{short}\) and the user long-term feature \(\mathbf{u}_{long}\). It filters the L historical items of the user's recent interaction, integrating the corresponding location coding information and inputting it into the self-attention mechanism to obtain the potential relation between the user's recent interaction items. The output result takes the mean operation to obtain the user's short-term interest. Location coding is added to obtain the effect of the item's time information. The long-term interests of users can be obtained through training based on all historical interaction items. The user representation \(\mathbf{u}\) is obtained by combining \(\mathbf{u}_{short}\) and \(\mathbf{u}_{long}\) through a multilayer perceptron (MLP). Finally, the user score of the target item \(\mathbf{y}_{k}\) (\(\mathbf{k}=1,\,2,\,\dots,\,\text{K}\)) is predicted by the inner product, and the high-quality candidate item set is obtained by the sorting score (Rank).  
For discriminator D, first, to better identify the candidate items generated by generator G, the popularity-based sampling strategy is used to accurately obtain the positive items and negative items from the interaction item sets and the noninteraction item sets. Then, the inner product of the user representation \(\mathbf{u}\) and the positive item vector \(\mathbf{e}_{p}\), the negative item vector \(\mathbf{e}_{n}\), and the candidate item vector \(\mathbf{e}_{c}\) are taken to obtain the scores \(\mathbf{y}_{wp}\), \(\mathbf{y}_{wn}\), and \(\mathbf{y}_{wc}\). Finally, the scores are input into the adversarial training loss function.  
Figure 1: Illustration of the proposed ATKGRM model.
## 3 Results and Discussion
### Performance Appraisal of the Artificial Intelligence Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Calibration**} \\ \hline
**Models** & \(\mathbf{R^{2}}\) & **R** & **MSE** & **RMSE** & **MAPE** \\ \hline SVR & 0.4736 & 0.6953 & 0.0082 & 0.0906 & 0.0978 \\ SVR-PSO & 0.6496 & 0.8100 & 0.0043 & 0.0657 & 0.0823 \\ SVR-HHO & 0.8470 & 0.9218 & 0.0009 & 0.0293 & 0.0291 \\ \hline \multicolumn{4}{c}{**Verification**} \\ \hline  & \(\mathbf{R^{2}}\) & **R** & **MSE** & **RMSE** & **MAPE** \\ \hline SVR & 0.8244 & 0.9080 & 0.0041 & 0.0637 & 0.0853 \\ SVR-PSO & 0.8498 & 0.9218 & 0.0031 & 0.0556 & 0.0546 \\ SVR-HHO & 0.8534 & 0.9238 & 0.0014 & 0.0368 & 0.0116 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Findings of the appraisal of the single and hybrid models for predicting GRG. Figure 19: Radar plot for GRG in the (**a**) calibration and (**b**) verifications stages. Figure 20: _Cont._  
An overall comparison of the novel AI models in this study was carried out using a two-dimensional Taylor diagram, as depicted in Figure 21. The Taylor diagram describes and sums up many statistical metrics, such as RMSE, R, and standard deviation, between the actual and predicted values. This diagram can explain the goodness of fit of different techniques when compared with other models. Hence, this diagram can be visualized as a series of points against a polar plot. As noticed in Figure 21, the SVR model exhibited lower mimicking of the data pattern, with an R value = 47%. However, when the SVR-PSO and SVR-HHO models were compared, it was seen that their learning capability outperformed that of the SVR model by 16% and 22%, respectively. With respect to the prediction capability of the GRG, the performance of the SVR-HHO model was slightly higher than that of the SVR-PSO model by just 0.2%. These findings culminated in the conclusion that SVR-PSO and SVR-HHO models are sufficient in capturing the complex and non-linear trends of the GRG. Figure 20: Scatter plot for (**a**) SVR, (**b**) SVR–PSO, and (**c**) SVR–HHO models for all datasets. Nevertheless, taking into consideration the appraisal performance metrics, the simulated findings provided adequate ground for a high level of prediction superiority of all the artificial intelligence techniques in spite of the better predictive ability exhibited by the SVR technique.
###### Abstract  
The main objectives of the present prospective clinical study were to evaluate the marginal bone resorption and the peri-implant soft tissue conditions around narrow diameter implants, as well as the clinical performances of metal-ceramic and all-ceramic single crowns in the implant-prosthetic treatment of maxillary lateral incisors agenesis. Seventy-two patients affected by monolateral or bilateral agenesis of the maxillary lateral incisors were included in the study, and a total of 105 narrow diameter implants were inserted. The final single crowns were fabricated 4 months after the surgical procedures by means of either titanium abutments with Auro-Galvan Crowns (AGC) veneered with feldspathic ceramics or zirconia abutments with all-ceramic (alumina or zirconia) crowns layered with dedicated veneering ceramics. Both marginal bone resorption and soft tissue quality were evaluated after a maximum period of 16 years. Two independent survival curves for patients wearing AGC or all-ceramic crowns were calculated by means of Kaplan-Meier analysis, and a log-rank test was performed in order to compare these curves (\(p<0.05\)). After 16 years of clinical function, a mean marginal bone resorption of 1.2 \(\pm\) 0.61 mm and good response of peri-implant soft tissues were observed. Descriptive statistics resulted in 80% and 87.1% cumulative success rates for patients provided with AGC prostheses onto titanium abutments and all-ceramic crowns on-to zirconia abutments, respectively. The cumulative survival rate calculated for AGC group was 93.3%, whereas all-ceramic group showed a cumulative survival rate of 95.7%. No statistically significant differences were found with the log-rank tests. The overall success and survival rates calculated were 85% and 95% for all the restorations, respectively. Mucositis, abutment unscrewing, loss of retention, chipping, core fracture, and eshetic failure were the major complications reported. The implant-prosthetic approach can be considered an effective and viable solution to treat cases of maxillary lateral incisor agenesis in the long term.  
a Article
## 2 Mathematical Models Used for Route Development  
The first stage of determining the ship's passage route using the method proposed in the publication involves identifying how environmental forces interact with the ship's hull. For this purpose, capability plots can be employed, commonly used on vessels equipped with dynamic positioning systems to assess the ship's operational capability in a selected area. An example of a CP is presented in Figure 1, which comprises three layers:  
* The shape located at the center of the plot represents the ship's silhouette.
* The numbers on the outer contour represent the angle of incidence of environmental forces on the ship's hull.
* The red line indicates the maximum wind speed (in knots) that can impact the ship's hull at a given angle while keeping the designated position.  
The determination of the characteristics shown in Figure 1 begins with defining the model of the vessel based on its technical data and the model of environmental forces acting on the ship's hull.  
The impact of environmental forces on the ship's hull is divided into three categories:  
* Environmental forces originating from wind.
* Environmental forces originating from ocean currents.
* Environmental forces originating from sea waves.  
Figure 1: An example of the capability plot.
# Evolutionary Optimization for Robust Epipolar-Geometry Estimation and Outlier Detection
## 3 Problem Formulation
### Robust Estimation Problem  
The main problem of robust estimation is to find a minimal sample set of inliers, from which the fundamental matrix (the model) can be estimated correctly. To determine how correct an estimated model is, an objective function is required. In RANSAC robust estimation, this objective function is defined as the support of the estimated model. However, as mentioned in Section 2, this requires a threshold to decide whether a correspondence supports the estimated model or not. In this study, to avoid such a threshold, the concept of least trimmed squares (LTS) [49] is applied. The objective is to minimize the sum of squared residuals over a minimum number of inliers, which we would call an inlier set of minimum cardinality denoted by \(\boldsymbol{I}\). Given a candidate sample set of correspondences, a fundamental matrix, \(F\), can be estimated via Equation (2). Therefore, the inlier set of minimum cardinality, \(\boldsymbol{I}\), would be the set of \(a^{*}\) correspondences with the smallest residuals, \(d_{k}\), leading to the cost function \(\text{Cost}(F)\):  
\[\text{Cost}(F)=\sum_{k\in\boldsymbol{I}}d_{k}^{2}, \tag{5}\]  
where \(\boldsymbol{I}=\{k|d_{k}<d_{i}\text{ for all }i\
otin\boldsymbol{I}\}\). The cardinality of \(\boldsymbol{I}\), \(n^{*}\), can be hypothesized without loss of generality. For instance, one can assume that, in a dataset containing 1000 putative correspondences, at least 100 matches are inliers without having any knowledge of the errors; i.e., \(n^{*}=100\). The minimum number of inliers would be a more relaxed assumption in comparison with the approximate ratio of outliers.
## 4 Results and Discussion  
### Evaluation of Compaction Tests  
The results of compaction tests can be found in Figures 4 and 5. The compaction results followed the same trend as previous literature of adding material below the specific gravity of soil. As can be seen from the results, the MDD decreased by increasing the sawdust content. This can be attributed to the fact that soil particles were replaced by a lower specific gravity (SG). On the other hand, the OMC was reported to decrease as well. This trend is attributed to the reduction in contact surface in accordance with explanations provided by current literature [3].  
Figure 4: Compaction curves for the used mixtures (i.e., 0%, 3%, 5%, 10% and 15% sawdust).