esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Porous Structure
This was in the range of 2-15 nm; however, pores of 3-5.5 nm dominated the structure, making the porosity more uniform. All the adsorption-desorption isotherms of the oil-based polymers were the same--Type V. This type of isotherm is observed for materials that poorly interact with adsorptive and when wetting of the surface by the adsorbate is not complete, especially at low relative pressure. At higher p/p\({}^{0}\) values, stronger interaction between adsorbate molecules leads to the formation of clusters, then pore filling takes place accompanied by hysteresis. The shapes of these hysteresis loops resembled Type H3, however, and a characteristic steep step down was not observed, which proved that the cavitation process did not occur in the case of the oil-based polymers. This type of hysteresis is representative of non-rigid aggregates of plate-like particles or porous materials with macropores that are not completely filled with the adsorbates. Taking into account the chemical structure of the substrates used in the syntheses and considering the PSD plots (Figure 5b) of the prepared polymers, it should be noted that the studied materials combined both of these characteristic features. The polymeric network was built of non-rigid aliphatic chains of different lengths bound together with rigid plate-like aromatic rings (Figure 2) forming slit-shaped pores with walls not necessarily parallel. For this reason, the porous structure of these polymers had predominate mesopores with sizes 6-50 nm, but macropores with widths up to 140 nm were also present. The simulation of adsorption isotherms for pores of different widths, lengths, and shapes performed by [PERSON] et al. [56] demonstrated that when the pore width is greater than a certain critical size, evaporation takes place at a higher pressure than is needed for cavitation. As a result, the evaporation mechanism changes from cavitation to pore blocking and this explains the above-mentioned lack of typical cavitation steep step down on the desorption branch of the isotherms as well as the shifting of the hysteresis loop towards a higher value relative pressure (p/p\({}^{0}\)). Figure 6 shows SEM images depicting the morphology of the terpene-based and oil-based polymers. A porous structure formed by irregularly shaped cavities was clearly visible. Macropore entrances with sizes from 50 nm (C-DVB and DVB) to 500 nm (Ro-DVB and Ao-DVB) were observed. The only exception was the P-DVB polymer, whose surface was almost non-porous.
## 3 System Model
### PSO Model Review
#### 3.4.4 W KNN in PSO Model  
In the PSO model, the WKNN algorithm serves as the final stage. This algorithm assigns weights to the coordinates of the \(k\)-nearest particles that are closest to the target. Subsequently, these optimally positioned coordinates of the \(k\) particles are utilized for the final positioning to determine the physical coordinates of the user being located. Formula (8) presents the physical coordinates determined via the PSO algorithm.  
\[(\hat{x}_{PSO},\hat{y}_{PSO})=\frac{\sum_{u=1}^{k}\frac{1}{D}(x_{u},y_{u})}{ \sum_{u=1}^{k}\frac{1}{D}} \tag{8}\]  
where \(k\) is the number of particle coordinates selected based on the \(PSO\) optimization performance, \(k\) takes the value of \(3\), \(D\) is the distance from the particle to the target location, \((x_{u},y_{u})\) represents the coordinate of the \(u\)-th particle, and \((\hat{x}_{PSO},\hat{y}_{PSO})\) is the final estimated positioning coordinate calculated using the \(PSO\) algorithm.
## 2 Theoretical Background
### The Forward Model  
The statistical model is characterized by its effective integration of both physical and statistical models, allowing for the inclusion of scenarios that may not be captured just through physical data such as well-logs. The computation of seismic data based on elastic parameters can be formulated as follows:  
\[\mathbf{d}=g(\mathbf{m})+\mathbf{e}_{d}, \tag{3}\]  
where \(e_{d}\) represents the observed noise, derived from the data \(\mathbf{d}\), \(g(\mathbf{m})\) represents the seismic wavelet extracted from the reflectivity function, and \(\mathbf{e}_{d}\) presents a great initial uncertainty derived from the observed data \(\mathbf{d}\), which has a strong impact throughout the inversion process. Within Bayesian processes, the term \(\mathbf{d}\) is incorporated into the probability function \(p(\mathbf{d}|\mathbf{m})\) (see Equation (1)). This equation creates a connection that unites the model parameters with the observed data, establishing the basis for calculating the values of the goal distribution \(p(\mathbf{m}|\mathbf{d})\). The values of \(p(\mathbf{d}|\mathbf{m})\), which represent the likelihood function,show us the level of correspondence between \(\mathbf{d}\) and \(g(\mathbf{m})\), considering the parameters \(\mathbf{m}\). The analytical solution can be expressed based on BLI theory for the direct linearized model presented in Equation (3). A normal or Gaussian distribution with zero means \(e\sim N(0,\Sigma_{e})\) is assumed for the error term \(\mathbf{e}_{d}\) in the data. The posterior distribution \(p(\mathbf{s}|\mathbf{d})\) also follows a Gaussian Mixture (GM) with an analytical expression. In other words, the distribution remains consistent at any depth and can be described as a linear combination of Gaussian distributions with a fixed number of components.
# Aerodynamic Characteristics of Supersonic Rocket-Sled Involving Waverider Geometry
## 2 Simulation and Numerical Methodology  
### Governing Equations  
Scale-adaptive simulation (SAS) is an improved unsteady Reynolds-averaged Navier-Stokes (URANS) formulation, which enables the resolution of a turbulent spectrum in unstable flow conditions. The SAS method essentially originated from the Reynolds-averaged Navier-Stokes (RANS) model. Thus, the governing equations of SAS are identical to that of RANS, i.e., Favre-averaged compressible Navier-Stokes equations. In the Cartesian coordinate system, the governing equations including mass, momentum and total energy equations can be described as:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial x_{i}}=0, \tag{1}\]  
\[\frac{\partial(\rho u_{i})}{\partial t}+\frac{\partial(\rho u_{i}u_{j})}{ \partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial\Big{(}\tau_{ ij}+\tau_{ij}^{\text{mod}}\Big{)}}{\partial x_{j}}, \tag{2}\]  
\[\frac{\partial(\rho E)}{\partial t}+\frac{\partial[(\rho E+p)u_{i}]}{\partial x _{i}}=\frac{\partial\Big{(}-q_{i}-q_{i}^{mod}+u_{j}\tau_{ij}+u_{j}\tau_{ij}^{ mod}\Big{)}}{\partial x_{i}}. \tag{3}\]  
Here, \(\rho\), \(u_{i}\), \(p\) and \(E\) denote the density, velocity component, pressure and total energy, respectively. The viscous stress tensor \(\tau_{ij}\) and heat flux \(q_{i}\) can be written as:  
\[\tau_{ij}=2\mu S_{ij}-\frac{2}{3}\mu\delta_{ij}S_{kk}, \tag{4}\]  
\[q_{i}=-\frac{\mu}{Pr(\gamma-1)}\frac{\partial T}{\partial x_{i}}. \tag{5}\]  
Here, \(Pr\) and \(T\) are laminar Prandtl number and static temperature, respectively. \(S_{ij}=0.5(\partial u_{i}/\partial x_{j}+\partial u_{j}/\partial x_{i})\) is the strain-rate tensor. [PERSON]'s Law for the molecular viscosity coefficient \(\mu\) is employed. \(\gamma\) is the specific heat ratio and \(\delta_{ij}\) denotes the Kronecker symbol. The total energy \(E\) can be given by the relationship:  
\[E=\frac{p}{\rho(\gamma-1)}-\frac{1}{2}u_{j}u_{j}. \tag{6}\]
## 3 Mathematical Model
### Coupled Discrete Model
Equations (33) and (34) compose \(\left(\sum\limits_{i=1}^{n}N_{i}+1\right)\) linear equations with \(\left(\sum\limits_{i=1}^{n}N_{i}+1\right)\) unknowns. The unknowns are \(\overline{\xi}_{\text{wHD}}(s)\) and \(\overline{q}_{\text{f}k,p\text{D}}(s)\) (\(1\leq k\leq n,1\leq v\leq N_{k}\)), which can be obtained by solving the linear equations, and then the wellbore storage and the skin near the wellbore can be taken into account by the following formula [41]  
\[\overline{\xi}_{\text{wD}}(s)=\frac{s\overline{\xi}_{\text{wHD}}^{\prime}(s)+ S_{\text{f}}}{s+s^{2}C_{\text{D}}\left[s\overline{\xi}_{\text{wHD}}^{ \prime}(s)+S_{\text{f}}\right]}. \tag{35}\]  
With the aid of the numerical inversion method [42], \(\overline{\xi}_{\text{wD}}(s)\) in Laplace space is transformed into \(\xi_{\text{wD}}(t_{\text{D}})\) in real space  
\[\xi_{\text{wD}}(t_{\text{D}})=\frac{\ln 2}{t_{\text{D}}}\sum\limits_{i=1}^{N}V_{ i}\overline{\xi}_{\text{wD}}(s),\;(N=8), \tag{36}\]  
where  
\[s=\frac{i\ln 2}{t_{\text{D}}}, \tag{37}\]  
Figure 4: Discrete schematic of the \(i\)th hydraulic fracture of a vertical fractured well. \[V_{i}=(-1)^{\frac{N}{2}+i}\sum_{k=\frac{i+1}{2}}^{\min{(i,\frac{N}{2})}}\frac{k^{ \frac{N}{2}+1}(2k)!}{\left(\frac{N}{2}-k\right)!k!(k-1)!(i-k)!(2k-i)!}. \tag{38}\]  
Finally, the dimensionless bottom-hole pseudo-pressure of a vertical fractured well with constant production rate is obtained by \(\psi_{\rm WD}(t_{\rm D})=-\ln[1-\gamma_{\rm D}\cdot\xi_{\rm WD}(t_{\rm D})]/ \gamma_{\rm D}\).
## 2 Materials and Methods
### HPLC Analysis of Phenolic Compounds  
Chromotographic separation of phenolic compounds was carried out with reverse-phase HPLC method using an Agilent 1100 Liquid Chromatography (LC) system (Agilent Technologies, Palo Alto, CA, USA) equipped with a quaternary pump (G1311A), degasser, column thermostat, auto-sampler (G1313A), and a diode array detector (G1315 B, DAD). The column was a Luna C18 column (250 mm \(\times\) 4.6 mm, 5 um) from Phenomenex (Torrance, CA, USA) with a security guard cartridge (4 mm \(\times\) 2 mm). The flow rate was set at 0.8 mL min\({}^{-1}\), and the column temperature was set to 30 \({}^{\circ}\)C. Elution was carried out with a binary mobile phase of solvent A (water and 0.1% trifluoracetic acid, Sigma-Aldrich, Milan, Italy) and solvent B (acetonitrile, ([PERSON], Milan, Italy)) and the gradient elution program was according to [17]: 0 min, 99% A; 5 min, 95% A; 6 min, 93% A; 10 min, 85% A; 15 min, 75% A; 20 min, 10% A; 25 min, 99% A, with a post-time of 3 min. Detection was performed at 254, 280, 350, and 360 nm. Phenolic molecules were identified according to the retention time of a mixture of standards, selected from the literature concerning CP phenolic, their UV absorption spectra, as well as by adding standard solutions to the sample composition and quantified using the respective calibration curves. Samples were appropriately diluted before injection. The results are presented as milligrams per gram of dry matter (mg g\({}^{-1}\) DM).
# Recent Applications of Explainable AI (XAI): A Systematic Literature Review
## 5 Discussion and Conclusions
### Limitations and Future Work

Table 1: _Cont._  
**Table A1. **_Cont._ \begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Authors \& Year** & **XAI Application** & **Why Explainability Is Important** \\ \hline Uddin et al. (2021) [476] & Human activity recognition & People tend to not accept ML systems that might be accurate and efficient if they lack interpretability. Explainability is needed to gain trust toward ML models and allow use of more efficient models. \\ \hline Sinha et al. (2023) [477] & Fault diagnosis of low-cost sensors & The authors mentioned that explainability is important in their specific application to increase the trust and reliability of the AI model used for fault diagnosis of low-cost sensors. \\ \hline Jacinto et al. (2023) [478] & Mapping karytified zones & Explainability is needed to validate ML-made decisions and detect biases. Explainability allows the use of more complex models when interpretability is necessary. Explainability gives information about relationships between model inputs and outputs. \\ \hline Jakubowski et al. (2022) [479] & Anomaly detection in asset degradation process & The authors mentioned that by providing explanations in their context, experts can understand the reasoning behind the model’s decisions and ensure the reliability of the predictive maintenance actions taken based on those decisions. \\ \hline Guo et al. (2024) [480] & Intelligent fault diagnosis in rotating machinery & The authors mentioned that providing explanations for the model’s predictions is essential to improving the trust and understanding of the diagnostic process. And also, they helps in validating the diagnostic results and improving the generalization ability of the model in unseen domains. \\ \hline Shi et al. (2021) [481] & Age-related macular degeneration diagnosis & The authors mentioned that explainability in their specific application is important because it helps in process of the model and the rationale behind its classifications. And also, they mentioned explainability helps to maximize its clinical applicability for the specific task of geographic atrophy detection, and clinicians trust the model’s predictions and integrate them into their decision-making process. \\ \hline Wang et al. (2023) [127] & Drug repurposing & Explanations are not always interpretable or reliable; do not provide information that is well related to the application domain. Explanations need to connect well to the problem they explain so that reliable interpretations and decisions can be made. \\ \hline \end{tabular}
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
###### Abstract  
The Baltic Dry Index (BDI) is a commonly utilized indicator of global shipping and trade activity. It influences stakeholders' and ship-owners' decisions respecting investments, chartering, operational plans, and export and import activities. Accurate prediction of the BDI is very challenging due to its volatility, non-stationarity, and complexity. To help stakeholders and ship-owners make sound short- and long-term maritime business decisions and avoid market risk, we performed short- and long-term predictions of BDI using an ensemble deep-learning approach. In this study, we propose to apply recurrent neural network models for BDI prediction. The state-of-the-art of sequential deep-learning models such as RNN, LSTM, and GRU are employed to predict one- and multi-step-ahead BDI values. In order to increase the accuracy, we assemble the models. In experiments, we compared our results with those of traditional methods such as ARIMA and MLP. The results showed that our proposed method outperforms ARIMA, MLP, RNN, LSTM, and GRU in both short- and long-term prediction of BDI.  
time-series; forecasting; Baltic dry index; ensemble method; deep learning +
Footnote †: journal: Applied Sciences
## 2 Research Models  
Figure 1a shows a photograph of the 2.5-D braided CMC (SiC/SiC). According to the information supplied by the material supplier SAFEI group, the axial and the radial thermal conductivities of the SiC fiber bundle are 9.66 W/(m\(\cdot\)K) and 1.48 W/(m\(\cdot\)K), and the thermal conductivity of the ceramic matrix is 6.5 W/(m\(\cdot\)K). The sample used in the numerical simulation and the experiment are shown in Figure 1. The size of this sample was 10 mm \(\times\) 10 mm \(\times\) 2.8 mm (length \(\times\) width \(\times\) height).  
Figure 1b shows a schematic diagram of the ideal structure of the 2.5-D braided fiber bundles. A minimal periodic unit cell was created as the RVE model, which was applied in the thermal analysis of the 2.5-D braided CMC. Similar to the RVE model in Reference [25], an RVE model was built of  
Figure 1: (**a**) A photograph and (**b**) a schematic diagram of the 2.5-D braided ceramic matrix composite (CMC).  
the 2.5-D braided CMC, considering the geometry characteristics of the 2.5-D braided CMC shown in Figure (a)a. For the model, the following hypotheses are considered:  
1. The sections of the axial yarns and the braided yarns are both hexagons.
2. There are no cracks in the models. Instead, they are completely continuous.  
Figure 2 shows the RVE model of the 2.5-D braided CMC. The size of the model was 5.72 mm \(\times\) 3.34 mm \(\times\) 0.6 mm (length \(\times\) width \(\times\) height), as shown in Figure (a)a. According to the data of the scanning electron microscope test, the average size of the sections of the fiber bundles was 1.5 mm \(\times\) 0.25 mm (width \(\times\) height), as shown in Figure (b)b. The angle of the braided yarns was 45\({}^{\circ}\). The parts of the matrix and the fiber bundles were given in Figure (c)c.  
Additionally, a full-size model was built to compare with the RVE model. Figure 3 shows the full-size model, which was built based on the actual size of the sample shown in Figure (a)a. The geometry size was 11.4 mm \(\times\) 10 mm \(\times\) 2.8 mm (length \(\times\) width \(\times\) height), for which the length was set as 11.4 mm to preserve the periodicity of side surfaces. The distances between the two adjacent axial yarns on the \(z\)-axis and \(x\)-axis were 0.6 mm and 1.4 mm, respectively. The distance between the two adjacent braided yarns on the \(y\)-axis was 0.2 mm. The angle of the braided yarns was 45\({}^{\circ}\).  
Figure 3: \(Cont\).  
Figure 2: A schematic diagram of the Representative Volume Element (RVE) model.
# Research on Price Prediction of Construction Materials Based on VMD-SSA-LSTM
## 3 Model Established
### Parameter Settings
#### 3.3.3 VMD Initial Hyperparameter Settings  
When the VMD method is used to decompose the original data, the value of modal number K has a great influence on the prediction effect of the model. When the number of modal K decompositions is large, frequency mixing will occur; when the number of decompositions is small, the information of the original signal will be easily lost. The direct observation method can be used to find the best K value by observing the center frequencies corresponding to Intrinsic Mode Function (IMF) plots with different K values and simply distinguishing the dispersion degree of the center frequencies corresponding to different K values. To this end, the IMF plots obtained by VMD with different K values are given in Figure 5.  
Direct observation of the optimal K value is challenging due to the complex and often non-obvious relationship between input features and output results. Consequently, determining the best K value through visual inspection or basic analysis is not practical. Instead, the model adopts an iterative approach, comparing predicted outcomes with actual experimental data. By evaluating the prediction accuracy for various K values, the model systematically identifies the outcome that minimizes error and maximizes reliability, thereby ensuring optimal performance for the dataset.  
Table 3 shows the prediction error of the model for different K values of VMD-SSA-LSTM.  
As can be seen from the above Table 3, when K = 8, the RMSE and MAE of the model are 0.012 and 0.009 for the training set a, corresponding to the highest model prediction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{K} & \multirow{2}{*}{**Data**} & \multicolumn{2}{c}{**Evaluating Indicator**} \\ \cline{3-4}  & & **RMSE** & **MAE** \\ \hline \multirow{2}{*}{7} & Train & 0.016 & 0.012 \\  & Test & 0.015 & 0.013 \\ \hline \multirow{2}{*}{8} & Train & 0.012 & 0.009 \\  & Test & 0.026 & 0.022 \\ \hline \multirow{2}{*}{9} & Train & 0.018 & 0.015 \\  & Test & 0.025 & 0.019 \\ \hline \multirow{2}{*}{10} & Train & 0.016 & 0.012 \\  & Test & 0.018 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation of model predictions at different values of K.  
accuracy compared to other K values. In summary, the optimal K value of the VMD-SSA-LSTM model for CMP prediction is 8.  
Figure 5: Intrinsic Mode Function. (**a**) The value of the modal function K is 7. (**b**) The value of the modal function K is 8. (**c**) The value of the modal function K is 9. (**d**) The value of the modal function K is 10.
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 3 Methodology
### Dataset Combination
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Test Set** & & \multicolumn{2}{c}{\(\mathbf{MSE\downarrow}\)} & \multirow{2}{*}{**SAD\(\downarrow\)**} & \multirow{2}{*}{**Grad\(\downarrow\)**} & \multirow{2}{*}{**Conn\(\downarrow\)**} \\  & & \(\mathbf{(1\times 10^{-3})}\) & & & \\ \hline \multirow{3}{*}{Composition-1k} & Matterformer & 4.01 & 24.25 & 8.92 & 20.96 \\  & Matterformer + SupOnly & 3.95 & 23.92 & 8.48 & 20.81 \\  & Matterformer + Tri-SSL & **3.77** & **23.23** & **8.27** & **19.97** \\ \hline \multirow{3}{*}{Distinction-646} & Matterformer & 8.63 & 25.04 & 12.99 & 20.99 \\  & Matterformer + SupOnly & 7.84 & 23.50 & 11.99 & 19.30 \\  & Matterformer + Tri-SSL & **7.56** & **22.11** & **11.59** & **17.73** \\ \hline \multirow{3}{*}{AIM-500} & Matterformer & 32.5 & 28.85 & 22.75 & 14.63 \\  & Matterformer + SupOnly & 33.5 & 29.70 & 23.30 & 15.01 \\ \cline{1-1}  & Matterformer + Tri-SSL & **29.6** & **25.93** & 23.48 & **12.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of dataset combination experiments based on the Matteformer on various test sets. The \(\downarrow\) indicates that the lower the value, the better. \begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Test Set** & \multicolumn{2}{c}{\(\mathbf{MSE\downarrow}\)} & \multirow{2}{*}{**SAD\(\downarrow\)**} & \multirow{2}{*}{**Grad\(\downarrow\)**} & \multirow{2}{*}{**Conn\(\downarrow\)**} \\  & & \(\mathbf{(1\times 10^{-3})}\) & & & \\ \hline \multirow{3}{*}{Composition-1k} & ELGT & 3.79 & 23.24 & 8.47 & 19.75 \\  & ELGT + SupOnly & 3.70 & 23.01 & 8.34 & 19.51 \\  & ELGT + Tri-SSL & **3.58** & **22.63** & **8.14** & **19.19** \\ \hline \multirow{3}{*}{Distinction-646} & ELGT & 8.42 & 25.12 & 13.73 & 20.41 \\  & ELGT + SupOnly & 7.93 & 23.95 & 12.95 & 19.48 \\  & ELGT + Tri-SSL & 8.16 & **23.61** & 19.22 & **19.23** \\ \hline \multirow{3}{*}{AIM-500} & ELGT & 11.05 & 14.27 & 11.64 & 10.21 \\  & ELGT + SupOnly & 10.88 & 14.24 & 10.88 & 10.17 \\ \cline{1-1}  & ELGT + Tri-SSL & 11.47 & 14.45 & 12.20 & 10.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of dataset combination experiments based on the ELGT on various test sets. The \(\downarrow\) indicates that the lower the value, the better. that Tri-SSL based on ELGT achieved the best results in all four metrics. Moreover, the qualitative comparison results in Figure 5 show that the alpha mattes generated by Tri-SSL are smoother and clearer.