esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
When these bubbles collapse, they generate extreme localized conditions, such as high temperature and pressure, facilitating the production of nanobubbles. This method is widely used due to its ability to generate stable nanobubbles with high efficiency. Hydrodynamic cavitation involves the formation of nanobubbles through rapid pressure changes within a liquid. This is typically achieved by forcing the liquid through a constricted or specially designed orifice, nozzle, or Venturi tube. As the liquid velocity increases at the constriction, the pressure decreases, causing the dissolved gas to nucleate and form microbubbles. These bubbles subsequently collapse due to downstream pressure recovery, generating nanobubbles. This method is advantageous for large-scale applications due to its simplicity and energy efficiency. In the porous-material diffusion method, gas is introduced into a liquid through a porous membrane or material with nanometer-scale pores. The gas diffuses through the pores and forms bubbles as it enters the liquid phase. By controlling the pore size and flow rate, the size distribution and concentration of the nanobubbles can be precisely controlled. This method is particularly effective for generating monodisperse nanobubbles and is commonly used in applications requiring controlled gas dissolution. In this research, as shown in Figure 1, we used a carbon porous material to generate nanobubbles. The solvent exchange method generates nanobubbles by exploiting differences in solubility between two miscible liquids. In this process, a liquid containing dissolved gas is mixed with a second solvent in which the gas has lower solubility. As the solubility equilibrium shifts, gas molecules are forced out of the solution, leading to the nucleation and stabilization of nanobubbles. This method is often used for producing nanobubbles in specific solvents or for applications requiring precise control over bubble stability and composition. Each of these methods has distinct advantages and is selected based on the desired application, efficiency, and scalability. Although numerous studies and reports have demonstrated the effectiveness of nanobubbles in applications such as cleaning and purification [29, 34, 35, 36, 37], research on other potential applications remains limited [38]. To date, researchers have reported the promotion of seed germination in agriculture using ozone nanobubbles, the preservation of seafood freshness using nitrogen and oxygen nanobubbles, and the cooling effects of nanobubbles in cutting processes. Given this context, this research investigates whether the free radicals generated by nanobubbles can actively degrade or modify organic matter, thereby serving as a potential pretreatment step in liquefaction technology. In addition, polyurea films were prepared from liquefaction products of wood meal pretreated with ozone nanobubbles, and a comparative analysis of the physical properties was conducted between these films and those derived from liquefaction products of untreated wood meal.
## 4 Discussion  
In spite of the reduced number of clinical reports, the findings of this review underscore the transformative potential of fully digital workflows in the fabrication of maxillofacial soft prostheses. By integrating advanced imaging, computer-aided design (CAD), and direct additive manufacturing techniques, these workflows address longstanding challenges associated with traditional prosthesis fabrication, including high costs, prolonged production times, and limited customization.  
This comprehensive exploration highlights the ability of digital workflows to deliver prostheses with enhanced fit, functionality, and aesthetic outcomes, while significantly reducing patient appointments and the overall production timeline [16]. These advancements are particularly relevant for patients with complex craniofacial defects resulting from oncological pathologies, where affordability and timely intervention are crucial for improving quality of life [22].  
Furthermore, the reviewed studies demonstrate the feasibility of utilizing innovative materials, such as silicone-based elastomers [10,13,15] and flexible resins [12,16], which mimic the mechanical and aesthetic properties of human soft tissues. The integration of these materials into digital workflows enables the production of patient-specific prostheses with high precision, paving the way for a more accessible and efficient approach to maxillofacial rehabilitation.  
To delve deeper into the advancements and challenges of this evolving field, it is essential to examine the specific aspects of data acquisition and design, the manufacturing process, and the material characteristics that collectively shape the success and feasibility of digital workflows in maxillofacial prosthetics.  
The goal of this scoping review was to identify the current state of the art in direct additive manufacturing for soft maxillofacial prostheses, highlight existing drawbacks and limitations, and propose future development directions to optimize clinical outcomes and broaden accessibility.
# Link Prediction Based on Feature Mapping and Bi-Directional Convolution
## 3 Model Details
### Decoding Layer  
In our experiments, we employed bi-directional convolution as part of the decoder, aiming to analyze the features of global embedding and bi-directional coding. By employing bidirectional coding, we give the model a stronger edge in predicting entities. Subsequent experimental results demonstrated that bi-directional coding shows good results in improving the performance of the model, and the scoring function of the decoding layer is shown by the following equation.  
\[h_{triple}=concat[h_{\perp},d_{r},t_{\perp},t_{\perp},d_{r},t_{\perp},h_{\perp}] \tag{14}\]  
\[f(t_{ij}^{k})=(\parallel_{m=1}^{\Omega}ReLU(h_{triple}*w^{m}))W \tag{15}\]  
where \(w^{m}\) denotes the convolution filter, \(\Omega\) denotes the hyperparameter of the number of filters used, \(*\) is the convolution operation, and \(W\) denotes the linear transformation matrix used to calculate the final score of the triple.  
The r-drop training method is used while decoding due to the randomness of the dropout, and although the same model is passed twice, it is also slightly different in the forward passing process. r-drop makes each batch of data pass through the forward neural network before and after two times to acquire two different distributions. The KL dispersion between the two distributions generated by the r-drop is continuously reduced during the training process, which in turn improves the robustness of the model. The loss function used in the model is shown below.  
\[L=\sum_{t_{ij}^{k}\in\{S\cup S^{\prime}\}}log(1+exp(l_{t_{ij}^{k}}\cdot f(t_{ ij}^{k})))+\frac{\lambda}{2}\parallel W\parallel_{2}^{2} \tag{16}\]  
\[l_{t_{ij}^{k}}=\begin{cases}1&for\quad t_{ij}^{k}\in S\\ -1&for\quad t_{ij}^{k}\in S^{\prime}\end{cases} \tag{17}\]  
where \(L\) is the loss function of the decoding layer; \(L\) consists of the softplus function and the \(L2\) regularization, where the coefficient of the \(L2\) regularization is \(\lambda/2\); and \(S\) is the triple of positive samples while \(S^{\prime}\) is the triple of negative samples.
## 5 Experiments and Discussion
### Discussion  
First, our experimental results indicate that EIAFM significantly improves model performance in providing recommendations. During the model design process, as described in Section 5.3.4, we conducted relevant ablation experiments to explore the impact of the different aspects of feature interaction on model performance. Our experimental results suggest that simultaneously capturing explicit and implicit high-order interactions in recommendation models and introducing an attention mechanism to capture low-order interactions is effective. This approach aligns with the design principles of state-of-the-art related models; thus, we based our model architecture on this design.  
Second, in exploring the model's hyperparameters, as described in Section 5.3.3, we experimented with different numerical values to investigate their impact on model performance. This process helped us to select appropriate hyperparameter values. It is important to note that these experiments were conducted based on our training dataset; for future applications, parameter values may need to be adjusted according to the scale of the dataset.  
Additionally, as observed in Section 5.3.2, while the EIAFM model shows improved recommendation performance, it increases model training time and the number of parameters. However, this increase is not significant compared to mainstream models. Therefore, EIAFM is applicable to the Service Recommendation of Industrial Software Components task.  
Finally, the results of this experiment are of significant importance in the field of service recommendation. They offer a new approach that simultaneously captures explicit and implicit high-order interactions and introduces an attention mechanism to capture low-order interactions, providing valuable insights.  
See Table 6 for a list of the abbreviations used in this article.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mode With Varied Components** & **Logloss** & **AUC** \\ \hline
**LC** & 0.413 & 0.9035 \\ \hline
**AC** & 0.4726 & 0.8933 \\ \hline
**EC** & 0.3897 & 0.918 \\ \hline
**LC + AC** & 0.4053 & 0.9106 \\ \hline
**LC + EC** & 0.3881 & 0.9104 \\ \hline
**LC + IC** & 0.3686 & 0.9187 \\ \hline
**AC + EC** & 0.3811 & 0.9138 \\ \hline
**AC + IC** & 0.3701 & 0.9165 \\ \hline
**EC + IC** & 0.3622 & 0.9211 \\ \hline
**IC + EC + AC** & 0.3501 & 0.9261 \\ \hline
**IC + AC + LC** & 0.3605 & 0.9239 \\ \hline
**IC + EC + LC** & 0.3595 & 0.9242 \\ \hline
**AC + EC + LC** & 0.3754 & 0.9155 \\ \hline
**LC + AC + EC + IC** & **0.3476** & **0.9281** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Contrasting models with varied components in EIAFM.
## 4 Results and Discussion
### Validation
#### 4.3.2 The Probability of Classification Model Success  
In order to evaluate the probability of the success of this classification model, the confidence interval (CI) was calculated for probability of population success with the 95% CI (\(\alpha\)). In line with different authors, for this analysis, it was accepted that probability of success would be distributed as the Binomial Function [68; 69; 70; 71]. In this case study, bearing in mind the results found in Section 4.2, there were 300 study samples and 289 accepted samples, which corresponds to an acceptance percentage (\(\hat{p}\)) of 96.3%. Moreover, the number of rejected samples was 11 out of 300, which corresponds to a rejection percentage (\(\hat{q}\)) of 3.7%.  
Considering that \(n\cdot\hat{p}=300\cdot 0.963=288.9>5\) and \(n\cdot\hat{q}=300\cdot 0.037=11.10>5\), it was possible for the Binomial Function, defined as B(n, \(\hat{p}\)) = (300, 0.963), to come close to a Normal Distribution [72], N(n,\(\sqrt{\frac{\hat{q}\cdot\hat{p}}{n}}\)) = (300, 0.0111), in such a way that the CI can be obtained with Equation (1):  
\[\left(\hat{p}-Z_{\frac{n}{2}}\cdot\sqrt{\frac{\hat{q}\cdot\hat{p}}{n}};\hat{p} +Z_{\frac{n}{2}}\cdot\sqrt{\frac{\hat{q}\cdot\hat{p}}{n}}\right) \tag{1}\]  
Equation (1) considers that \(Z_{\alpha/2}=1.96\), with \(\alpha/2=0.025\)[48] by substituting the following values in Equation (1):  
\[(0.963-0.0111;0.963+0.0111) \tag{2}\]  
Figure 10: Model of the classification of degree of corrosion and attack type. Degree of corrosion (red lines) according to the limits set out by Standard UNE 112072:2011 [64].  
Therefore, it can be stated that the CI for the model's probability success was \(p\) = 0.963 \(\pm\) 0.0111. This means that the real percentage of the model's success with a 95% CI was between 95.19% and 97.41%.
## 3 Spray Development from Nozzle Orifices
### Near Nozzle Velocity Measurement with the Structural Image Velocimetry
The velocity distribution in the radial direction of the spray (Figure 16b) shows the high value in the center and the low value at the periphery in both positions at 5 mm downstream (red solid line) and 10 mm downstream (red dotted line). This tendency on the radial velocity profiles fit well with the common understanding of spray velocity distribution. The velocity on the spray axis (red solid line) is shown in Figure 16c. The velocity from 4 mm downstream the injector is well comparable to the expectation. In [41], a comparison is done with other approaches to determine the spray velocity (from multiple illumination with X-ray scattering and from L2F measurement discussed below) and with the expectation from the measured flow rates and from the Bernoulli equation (assuming no friction and no cavitation), within about 20 percent, if the slope of the decreasing part is extrapolated to the nozzle exit. It is found that for Diesel sprays up to 100 MPa, the difference between the approaches results in exit velocities, even within 10 percent. Near the nozzle region the SIV velocity is reduced compared to the expected axial profile. Here, obviously, the spray structure is too dense and not dispersed enough for the SIV application to be discriminated. We define the applicable range, therefore, from the range where the axial profile decreases continually, while the range very near to the nozzle is marked out (gray background, having a width of 4 mm for 150 MPa and 2-3 mm for up to 100 MPa [41]). The radial profiles of the SIV values are very near to the expectation  
Figure 16: Results from high speed SIV at 150 MPa injection pressure: (**a**) Velocity field; (**b**) Velocity distribution in radial direction of spray (red) at two downstream positions of 5 and 10 mm, together with measured standard deviation (black); (**c**) Velocity on spray axis (red) with measured standard deviation (black). in Figure 16b. [PERSON] [42] shows that the X-ray velocimetry gives too high velocities in the border regions of radial distributions, which can be understood with the property of X-ray velocimetry that small droplets below approx. 8 \(\upmu\)m are not visible there (depending on the pixel resolution), which is consistent with the assumption that these are more dominant in the outer spray part. Standard deviations (black lines in Figure 16b,c) are almost constant for the entire spray. It seems that this quantity depends essentially on the correlation window size being constant in this evaluation. It is likely that the application range of the SIV technique can be expanded with more applied adjustments of the correlation window size and higher spatial resolution images.
## 3 Results and Discussion
### Electrical Characterization of Nafion112/IL-Cations Doped Membranes
Ion transport number (\(t_{i}\)) is the parameter commonly used to characterize the effect of membrane charge on the transport of ions since it represents the fraction of the electric current transported by an ion \(i\) (\(t_{i}=I_{i}/I_{\mathrm{T}}\), then \(\sum_{i}\)\(t_{i}=1\); \(t_{+}+t_{-}=1\) for single salts), and consequently, for an ideal cation-exchanger membrane \(t_{+}=1\). \(t_{+}\) values for both membranes were determined from membrane potential by the following expression [28,29]: \(t_{+}=\Delta E/\Delta E_{\mathrm{max}}\). Average values of H\({}^{+}\) transport number across Nafion-112 and Nafion112/BMIM\({}^{+}\) membranes for the whole interval of HCl concentrations are: \(<\)\(t_{+}\)(Nafion-112) \(\geq\) (0.938 \(\pm\) 0.019) and \(<\)\(t_{+}\)(Nafion112/BMIM\({}^{+}\)) \(\geq\) (0.978 \(\pm\) 0.011). According to these results only slight differences between wet samples of both kinds of membranes seems to exist, but BMIM\({}^{+}\) doping of Nafion-112 membrane slightly favours proton transport.
## 3 Results and Discussion
### Characterization of Cockleshell Powder
#### 3.1.5 Scanning Electron Microscopy (SEM) and BET Analysis  
Nitrogen adsorption-desorption isotherms were used to determine the textural parameters of porous materials using the Brunauer-Emmett-Teller method. The specific surface area of CS was 50.954 m\({}^{2}\)/g, with a pore volume equal to 0.893 mL/g and a pore radius of 151.536 A.  
Based on the IUPAC classification of adsorption isotherms, the BET revealed the following:  
The N2 isotherm resembled type III, with a high adsorption capacity, while indicating the presence of larger pore-size distributions, narrower mesopores, and larger micropores.  
Figure 5: XRD of cockleshell powder before and after adsorption of DR-81.  
The small hysteresis in the N2 adsorption-desorption isotherm indicates the capillary condensation phenomenon, which indicates a mesoporous nature [36].  
From Figure 6a, it can be observed that the shells present an irregular and non-uniform shape. Therefore, according to the literature, the shells have a needle [37; 38] and plate structure with parallel cleavages [26].  
From Figure 6b, it can be observed that the initially empty sites of CS become full; this can be explained by an agglomeration of DR 81 molecules after adsorption onto the solid surface of CS.  
[PERSON] and [PERSON] used both alkaline digestion methods and sonic oscillation to isolate the hyphal walls of _Allomyces macrogynus_[39]. Both types of preparation showed the walls to consist of chitin, glucan, and ash. In addition, the mechanically isolated walls contained a protein fraction whose properties and the significance thereof were not determined. Hemicellulose-type polysaccharides, pectic substances, ether-soluble lipids, and constituents gave rise to 3-0-\(\alpha\)-carboxymethyl hexosamine that was not found in the walls. The walls of plants grown for 60-70 h under the prescribed conditions contained approximately 60% chitin, 15% glucan, 10% ash, and 10% protein intimately associated with the walls. The percentage of wall material in a mycelium, as well as the percentage of chitin in the walls, increases with the chronological age of the mycelium. These percentages are not, however, affected by variations in the composition of the nutrient medium. The chitin in the walls could be hydrolyzed in the presence of chitinase or lysozyme, yet it had no detectable effect on the walls.
## 4 The Proposed Collaborative Weighted Clustering Protocol
### Collaborative Weighted Clustering Algorithm
The cluster set-up phase of the proposed protocol is a little different from that of the LEACH algorithm. A detailed description is as follows. Initially, nodes are not associated to any clusters. In order to enable the discovery of neighbors, each node sends an \"HELLO\" message to its neighbors periodically. When a node receives this message, it updates its neighbor table by adding or updating the information, which includes the address of the node, its coordinates, the number of neighbors and the value of its metric. In this application, this time is fixed at 2 min. Then, the node compares its metric with others. If its value is smaller, it awaits an INVITE message, which is sent by a cluster head inviting other nodes to join its cluster. Otherwise, this node would move to the cluster head state, and send an INVITE message to other nodes. When a non-cluster head node receives the INVITE message, it stores the information related to the cluster head in its neighbors table, and uses the virtual grid partition method to mark the cluster head node as its parent for routing. Then, it becomes a member of this cluster. On the other hand, if a node at the cluster head state receives this INVITE message, the node compares its metric with the one included in the INVITE message. If it is better, the sender would be considered as its parent, and the node would shift to the member state.
# Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Fit Mines
## 5 Discussions
### Comparison of the DNN and the Multiple Regression Analysis  
In order to compare the predictive performances of the DNN model with those of other learning methods, morning ore production were predicted through the multiple regression analysis. The 16,217 training datasets were used for multiple regression analysis model training, and 5 \(\times\) 5-fold cross validation was performed to determine the degree of multiple regression equation. Table 11 shows cross validation results performed when the degree of multiple regression equation is set to 2. The average R\({}^{2}\) for validation data was 0.91, and the average MAPE was 23.54%. The average and standard deviation of MAPE for validation data was higher than that of DNN model. According to the t-significant test results, the predictive performance differences between the DNN model and multiple regression analysis model were considered statistically significant (t-value: \(-4.09\), \(p\)-value \(<0.005\)).  
Morning ore production estimates were obtained for five days in the second week of February 2019 through multiple regression analysis. Figure 15 illustrates the results of morning ore production  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Degree of Multiple Regression Equation**} & \multirow{2}{*}{**Statistics**} & \multicolumn{6}{c}{**Round of 5-Fold Cross Validation**} & \multicolumn{1}{c}{**Total**} \\ \cline{3-8}  & & **1** & **2** & **3** & **4** & **5** & **Mean** \\ \hline \multirow{4}{*}{2} & \multirow{2}{*}{R\({}^{2}\)} & Mean & 0.90 & 0.90 & 0.90 & 0.91 & 0.92 & 0.91 \\  & & STD & 0.13 & 0.13 & 0.14 & 0.12 & 0.10 & 0.13 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{MAPE} & Mean & 23.71 & 23.47 & 23.87 & 23.37 & 23.26 & 23.54 \\  & & STD & 8.37 & 8.52 & 9.37 & 8.34 & 7.82 & 8.48 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of accuracy analysis of multiple regression analysis model for predicting morning ore production using training and validation data.  
Figure 14: Ore-production predictions performed every 30 min on 14 February, 2019.  
inference, and Table 12 shows inference error between observed and predicted values. MAPE for morning ore production inference over five days was analyzed to be about 16.37%, which was about 4.97% higher than the DNN model inference error. As the results of the inference, it was possible to predict morning ore production more accurately when using the DNN model rather than the multiple regression analysis.