esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Analysis  
### CvT Model Incorporating Convolution Exhibits Excellent Performance in Facial Expression Recognition  
The results in Table 7 clearly demonstrate that the CvT model (79.12%) significantly outperforms the ViT model (48%) in facial expression recognition for children with autism.  
Building upon the established research on the facial expression characteristics of children with autism discussed in the Related Works Section, our findings confirm that models incorporating both local and global feature processing mechanisms perform better for this specific population. The CvT architecture, with its integration of convolutional operations, appears particularly suited for capturing the unique facial expression patterns exhibited by children with ASD, where emotional cues may manifest differently compared to typically developing children.  
Children with autism exhibit reduced dynamic variability in their entire face and display a more balanced intensity of facial movements [9], as compared with typically developing children. [PERSON] et al. [35] discovered, via computational analysis, that children with autism have diminished complexity in all facial regions when experiencing sadness, whereas certain facial regions display atypical dynamics during moments of happiness. In order to accurately predict the emotional states of children with autism, it is crucial to analyze both the intricate features of their entire face and the distinct features of certain facial regions.  
The CvT model synergistically integrates the advantages of CNN's local feature recognition with the transformer's global information processing capability, enabling collection of both local details and the overall structural characteristics of facial expressions. Compared with the ViT model, which mainly relies on global information processing, the CvT model shows better performance in dealing with tasks with special expression features such as those of children with autism. [PERSON] et al. [PERSON] et al. (2018) proposed a network structure including convolution and ViT for face forgery detection task, which raises the accuracy to more than 75%. It also demonstrates the efficacy of utilizing convolution and a transformer in combination, consistent with the findings of this research. Hence, when constructing a classification model for facial expression images of children with autism, it is necessary to thoroughly consider the differences in children's facial expressions. In addition, incorporating the concept of convolution into the model enables it to effectively focus on the fine-grained features of the image.
## 3 Results and Discussion
### Average Nusselt Number and Friction Factor Correlations  
Based on the results obtained from the numerical investigations of the 49 ribbed tube cases, the following correlations for the average Nusselt number and friction factor were obtained. The correlations are limited to ribbed tubes, where \(\rm e_{1}/d=0.1\), \(\rm p/d=1\), and \(\rm w/d=0.05\). The average Nusselt number can be correlated as:  
\[\rm Nu_{av}=0.1365\;(e_{2}/e_{1})^{0.01544}\;Re^{0.6766}\;Pr^{0.3604} \tag{15}\]  
which has an \(\rm R^{2}\) value of 0.997. The maximum deviation of \(\rm Nu_{av}\) is 7.5% at \(\rm Re=10{,}000\), \(\rm Pr=3.42\), and \(\rm e_{2}/e_{1}=1\), while the average deviation of all values is 2.2%. The exponent of the Prandtl number is 0.3604, which is in the range between 0.3 and 0.4, as found in many previous studies [9; 12].  
Likewise, the friction factor correlation can be presented as:  
\[\rm f=0.5422\;exp[0.4192(e_{2}/e_{1})]\;Re^{-0.03093} \tag{16}\]  
The friction factor changes exponentially with the zigzag ratio \(\rm e_{2}/e_{1}\). The \(\rm R^{2}\) value is 0.88. The maximum deviation is 7.5% at \(\rm Re\) value of 10,000, and \(\rm e_{2}/e_{1}=1\), while the average deviation is 4.1%.
## 2 Literature Review  
### Lattice Structures and Material Homogenization  
Lattice structures are generally composed of replicas of a unit cell that are continuous, uniformly distributed and fill the space. The reason why lattice structures attract the attention of engineers is that they can retain good mechanical performance while reducing material usage and weight. For this reason, lattice structures are used for energy absorption, heat transfer and vibration damping applications [2]. Additive manufacturing (AM) has also widened the application range of lattice structures. The manufacturing freedom of AM has promoted the use of lattice structures for materializing the results of structural optimization [6, 7, 8, 9] and for biomedical applications in orthopedics and tissue engineering [10,11].  
Material homogenization seeks the equivalent Young's modulus and Poisson's ratio to produce a homogeneous structure that resembles the displacements field of the lattice domain. Material homogenization suppresses the geometrical complexity associated with lattice domains. Therefore, lighter FEA meshes are obtained, and consequently, the computational cost and time requirements for FEA simulations are reduced [6,12].  
Apart from predicting macro-mechanical properties ([PERSON]'s and [PERSON]'s moduli), material homogenization has enabled the study of periodic strut-like lattice structures built via AM, considering the defects caused during the manufacturing with AM and the stiffening in the joints of the structure [13,14]. These studies are, however, limited to strut-like lattice structures. Material homogenization has also been successfully integrated with topology optimization to produce optimal designs of lattice structures suitable for AM [6,12]. However, since the homogeneous and lattice domains have notorious geometrical differences and stresses/strains depend on the geometry, the stress-strain behavior of the homogeneous domain does not resemble the one of the lattice domain.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 3 Target Tracking Based on Sliding Model Control
### Guidance Law  
In the subsection, the guidance command \(\omega_{\psi}\) is designed for Equation (5) by using the NFTSM manifold given in Equation (15). From Definition 2, the control goal of this study is to regulate \(\tilde{\psi}_{a}=-f(r)\) to zero. According to Equation (15), we can redefine a new \(f(r)=\alpha_{2}sig^{\beta_{2}}(\ell)+\alpha_{3}\sigma(\ell)\), where \(\ell=\arctan{(k_{1}r)}\), \(k_{1}>0\). From Equation (8), the NFTSM manifold \(s\) defined in Equation (15) can be designed as follows:  
\[s=\left\{\begin{array}{ll}\tilde{\psi}_{a}+\alpha_{2}sig^{\beta_{2}}(\ell) +\alpha_{3}sig^{\beta_{3}}(\ell),&|\ell|>\eta,\\ \tilde{\psi}_{a}+\alpha_{2}sig^{\beta_{2}}(\ell)+\alpha_{3}(r_{1}\ell+r_{2} sig^{2}(\ell)),&|\ell|\leq\eta,\end{array}\right. \tag{17}\]  
where \(\eta\) being a small positive constant. These coefficients \(\alpha_{2}>0\), \(\alpha_{3}>0\), \(\beta_{2}\geq 1\), \(0<\beta_{3}<1\), \(r_{1}=(2-\beta_{3})\eta^{\beta_{3}-1}\), \(r_{2}=(\beta_{3}-1)\eta^{\beta_{3}-2}\) are gains to be designed.  
To make the UAV state quickly converge to the manifold \(s\) defined in Equation (17) from the initial states, we select the fast reaching law as follows:  
\[\dot{s}=-k_{2}s-k_{3}sig^{\gamma_{1}}(s), \tag{18}\]  
where \(k_{2}>0\), \(k_{3}>0\) and \(0<\gamma_{1}<1\).  
**Definition 6**.: _Using the Equations (16)-(18), the NFTSM guidance law is proposed as follows:_  
\[\omega_{\psi}=\left\{\begin{aligned} \dot{\lambda}+\hat{\psi}_{a}^{d}- \frac{(\alpha_{2}\beta_{2}|\ell|^{\beta_{2}-1}+\alpha_{3}\beta_{3}|\ell|^{\beta _{3}-1})}{1+k_{1}^{2}r^{2}}k_{1}\dot{r}-k_{2}s-k_{3}sig^{\gamma_{1}}(s),& |\ell|\leq\eta,\\ \dot{\lambda}+\hat{\psi}_{a}^{d}-\frac{(\alpha_{2}\beta_{2}|\ell|^ {\beta_{2}-1}+\alpha_{3}(r_{1}+2r_{2}\ell))}{1+k_{1}^{2}r^{2}}k_{1}\dot{r}-k_{ 2}s-k_{3}sig^{\gamma_{1}}(s),&|\ell|\leq\eta.\end{aligned}\right. \tag{19}\]  
From Equation (7), \(\hat{\psi}_{a}^{d}\) can be expressed as  
\[\hat{\psi}_{a}^{d}=\frac{(\hat{v}_{t}\sin\bar{\psi}_{t}+v_{t}\cos\bar{\psi}_{ t}(\omega_{w}-\dot{\lambda}))-\frac{\hat{v}_{a}}{\hat{v}_{a}}(v_{t}\sin\bar{ \psi}_{t}-v_{w}\sin\bar{\psi}_{w})}{\sqrt{v_{a}^{2}-(v_{t}\sin\bar{\psi}_{t}- v_{w}\sin\bar{\psi}_{w})^{2}}}, \tag{20}\]  
where \(\hat{v}_{a}=0\) according to the hypothesis that the airspeed of UAV does not change.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 1 Introduction  
Within the next decade, Additive Manufacturing (AM) is expected to become the primary process for creating complex geometries. Achieving that expectation, however, will still require four new technologies that include (1) AM software for high-complexity product design and engineering, (2) AM machines for high-quality and low-cost product fabrication, (3) AM sensors for in situ monitoring of various AM processes, and (4) controllers that can analyze big data and make optimal decisions in real-time [1].  
AM has a major problem, however. Even given optimal process parameters and even under ideal machining conditions, the quality of AM products is still highly variable [2]. There is variability in the powdered material, the build process, the sensor data, and the control functions, among others. To reduce the overall part-quality uncertainty that results from that variability, we believe a new architectural approach to AM process control is needed.  
In this paper, we focus on (1) defining a reference functional architecture for monitoring and controlling AM process with an example of powder bed fusion system and (2) giving an example of how to implement that architecture. Section 2 describes our research platforms as well as current, commercially available AM processes, equipment, and sensors. Section 3 provides our view of what AM process control is all about--namely part-quality control. Section 4 provides a high-level, functional view of our proposed, two-loop, control architecture and provides a more detailed description ofthe functions in those control loops. It also describes the major offline functions, typically executed in the cloud, that interact with those control functions. Section 5 provides a detailed description of our example, control problem, which involves melt-pool, shape analysis, and classification using the clustering method. Section 6 provides a summary and a short description of future work.
## 3 Results and Discussion
### Pseudoisocyanine  
When the PIC aqueous solution was irradiated with 633 nm cw laser light, an absorption increase of \(\Delta A\sim 10^{-5}\) was observed around 390 nm, as shown in Figure 2a. On the other hand, the PIC/PVA sample did not exhibit the absorption change, as can be seen in Figure 2b. These results indicate that the aggregation state was changed by laser irradiation, as discussed in Ref. [38]. In general, PIC molecules form J-aggregates, which have the resonance energy peak at 573 nm [42] and do not form H-aggregates when simply dissolved in water. However, it is reported that, when surfactants are mixed, PIC molecules form H-aggregates and an absorption peak at 430 nm appears [48]. The observed state (390 nm) is much higher in energy than the H aggregates reported for PIC in Reference [48], and this result suggests that aggregation states that do not exist in thermal equilibrium are generated by laser irradiation. The PIC J-aggregate has an aggregation distance of 0.8 nm and an aggregation angle of 24.8 [49]. From Equations (1) and (2), the absorption wavelength of the H aggregate is estimated to be 438 nm (where the aggregation angle = 90\({}^{\circ}\)), which is close to the value in Ref. [48]. Under the same conditions, when there is an absorption resonance at 390 nm, the aggregation distance is estimated to be 0.6 nm.  
For the PIC sample, the ratio of the optical gradient force potential to the thermal motion energy is \(7.8\times 10^{-8}\) for the PIC's polarizability volume (671 A\({}^{3}\)) and the irradiation intensity (1.1 kW/cm\({}^{2}\)). The peak monomer absorbance is approximately 0.25 in Figure 2a, so the experimental aggregation ratio is \(\Delta A/A=4\times 10^{-5}\). Therefore, the aggregation efficiency is estimated to be 500.
## 2 Materials and Methods
The sample was put on a carbon conductive tape in order to ensure the conductivity of the experiments. For both SEM and SEM-EDS analysis, the electron beam was set to 10 kV, with 10 mm work distance. High-resolution transmission electron microscopy (HR-TEM) was performed using an EFTEM Jeol 2200 FS microscope (Jeol, Tokyo, Japan). A 200 keV acceleration voltage was used for the measurement. Elemental maps and EDS spectra were acquired with an X-MaxN 80 TS SDD detector from Oxford Instruments (Oxfordshire, UK). The sample preparation was attained by drop-casting the suspension (1 mg mL\({}^{-1}\) in water) on a TEM grid (Cu; 200 mesh; Formvar/carbon) and then drying in a vacuum dryer at 25 \({}^{\circ}\)C and p/p\({}^{0}\) = 0.2. Simultaneous thermal analysis combined with mass spectroscopy (STA-MS) was performed using the Setsys Evolution apparatus from Setaram in a temperature range up to 650 \({}^{\circ}\)C. The measurements were performed in a dynamic helium atmosphere with a flow rate of 50 mL/min and a heating rate of 5 \({}^{\circ}\)C/min. An OmniStar mass spectrometer (MS) (Pfeiffer Vacuum GmbH, A\(\underline{\mathrm{\SIUnitSymbolMicro}}\)lar, Germany) was used to analyze gases that evolved during the heating. Fourier transform infrared (FT-IR) spectroscopy was used for the identification of chemical bonds in the hardened MOB samples. Mid-infrared spectra were analyzed on a Nicolet 6700 spectrometer (Thermo Fisher Scientific, Waltham, MA, USA), using the attenuated total reflectance (ATR) technique after 32 scans. The wavenumbers ranged from 4000 cm\({}^{-1}\) to 400 cm\({}^{-1}\), with the spectral resolution of 4 cm\({}^{-1}\). Samples were homogenized in an agate grinding mortar. Figure 2: Samples (**a**) MOB-318, (**b**) MOB-518. The scale bar is in cm.
## 5 Conclusions  
In this study, the capability of FEM tools to simulate the thermal behavior of lattice structures through solid bodies is analyzed. Based on the obtained results, the following conclusions are determined.  
1. It is feasible to simulate the thermal behavior of lattice structures by means of solid blocks composed of fictitious materials whose properties have been adapted accordingly. An error rate below 2% is obtained between the simulated lattice and the solid block composed of the fictitious material. 1. The density of the fictitious material is proportional to the fill percentage of the lattice structure. 2. The specific heat of the fictitious material remains constant. 3. The effective thermal conductivity of the fictitious material needs to be adjusted based on the fill percentage and the shape of the lattice structure.
2. Thanks to the developed methodology, the computational cost required for the simulation of lattice structures can be reduced by up to 90%.
3. The convection coefficient is approximated as a linear function with regard to the temperature. The more stretched the lattice, the lower the resistance that it poses to the airflow and, therefore, the higher the convection coefficient. In order to consider the equivalent convection in the solid body, it has to be escalated according to the area ratio.
4. Once the convection coefficient has been adjusted experimentally, the thermal simulation of the lattice structures presents the same behavior as in the experimental tests.
5. Lastly, the obtained results open the door to the design of anisotropic components based on lattice structures and their topological optimization once the effective conductivity of a lattice structure is characterized.  
Conceptualization, J.I.A. and A.L.; methodology, J.I.A.; software, J.I.A., X.A. and J.L.; validation, G.G., J.I.A., S.S., L.G., X.A. and J.L.; formal analysis, X.A. and J.L.; investigation, S.S., L.G., J.A., X.A. and J.L.; resources, A.L.; data curation, X.A. and J.L.; writing--original draft preparation, J.I.A.; writing--review and editing, all authors; funding acquisition, J.I.A. and A.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basque Government through the ELKARTEK program, grant number KK-2023/00096, and by the MCIN/AEI/10.13039/501100011033/ and ERDF A way of making Europe, grant PID2022-141946 OB-C21.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Carbon from Bagasse Activated with Water Vapor and Its Adsorption Performance for Methylene Blue
## 1 Introduction
By increasing the steam activation time, an increase in surface area followed by an increase in aromatic surface groups and a decrease in polar/nonpolar surface groups was recently reported [29]; however, the interaction mechanism was not developed. In this research work, carbonized bagasse was activated by flowing water vapor as an activation agent under various temperatures. The aim of this research work was to explore the possibility, through steam activation, of preparing activated carbon from bagasse--a solid waste of sugar factories. The resulting activated-carbons were characterized by several techniques. Furthermore, the activated-carbon with the highest surface area was tested for methylene blue adsorption to understand its adsorption capability. The interaction mechanism between the carbonaceous surface and methylene blue was also proposed here.
## 2 Materials and Methods
### Performance Metrics  
The predictive performance of the developed model was evaluated using several statistical evaluation parameters. In this research, the evaluation parameters were computed based on the values of the actual output variable (bearing capacity) from the database and on the bearing capacity predicted by the developed MPR model. Although several evaluation parameters can be used to assess the model's performance, this research considers two key assessment parameters: the Root-Mean-Squared Error (RMSE) and the coefficient of determination (\(R^{2}\)). These parameters have been widely used in previous studies and are considered sufficient for assessing the prediction performance of the models. RMSE is a measure of the difference between the predicted values and the actual values. It is used to evaluate the performance of a model, specifically for regression problems. It is calculated by taking the square root of the mean of the squared differences between the predicted values and the actual values. The RMSE of the model is computed using Equation (4). The smaller the RMSE, the better the model is at predicting the target variable. RMSE is sensitive to outliers, so it is important to look at other metrics as well, especially when the data set has outliers.  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}^{obs}-y_{l}^{pre} \right)^{2}} \tag{4}\]  
It is important to note that RMSE has the same unit as the target variable, which makes it easy to interpret and compare to other models.  
The coefficient of determination (\(R^{2}\)) is a statistical indicator that reflects the extent to which the independent variable(s) in a regression model explain the variance in the dependent variable. It ranges from 0 to 1, with a value of 1 indicating that the model completely explains the variance and a value of 0 meaning that the model explains none of the variance. The \(R^{2}\) can be determined using the following equation:  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}^{obs}-y_{l}^{pre}\right)^{2}}{\sum_{i=1}^{ n}\left(y_{i}^{obs}-y_{i}^{pre}\right)^{2}} \tag{5}\]  
where \(y_{i}^{obs}\) indicates the actual outcome of the bearing capacity, \(y_{i}^{pre}\) represents the predicted outcome, \(n\) indicates the sample count employed for modeling the bearing capacity, and \(y_{i}^{-obs}\) is the mean value of the actual outcome.
# An Adaptive Delay-Tolerant Routing Algorithm for Data Transmission in Opportunistic Social Networks
## 3 System Model Design
### Computation of Transmission Evaluation Value Based on CMFI
#### 3.4.2 The Fuzzy Inference Component  
According to 'If-Then' fuzzy rules [36], there are nine different combinations that can be generated from the fuzzy subsets between the first-fuzzy and second-fuzzy inputs (the remaining cache and remaining energy). These nine various combinations represent nine different levels of processing data packages. Moreover, neighbor nodes can be classified as three different categories based on the two-condition nine-rule fuzzy inference logic [37; 38], where active, potential and under-resourced relay nodes correspond to high (levels 1, 2, and 3), medium (levels 4, 5, and 6), and low (levels 7, 8, and 9) membership levels, respectively. The active relay nodes are the users that move frequently and have high information processing capacity with high energy and cache priority [20; 23]. On the contrary, under-resourced nodes could be considered as dead users due to their low remaining energy and cache space. Potential relay nodes may be regarded as reliable next hop users in the future. Accordingly, the source node will select those active rely nodes as its next hop to route and forward data information through the dynamic fuzzy evaluation of data processing capability. This is only a preliminary fuzzy assessment, and the final decision-making requires digital metrics, which can be determined from the De-Blurring component.
# DenseNet-Based Classification of EEG Abnormalities Using Spectrograms
## 4 Results
### Performance of Post-Processing Method  
Table 6 shows the performance of the DenseNet121-based classification method (using signal images, spectrograms and scalograms as input) after post-processing, evaluated on the training, validation, and test sets. The results show a noticeable improvement in sensitivity, specificity, precision, accuracy and F1 score, following the application of the post-processing method (Section 3.4) on signal images, spectrograms and scalograms. Figure 6 shows the confusion matrix for the test set, where spectrograms are used as the input.  
Figure 4: Training and validation loss and accuracy curves for spectrogram inputs.  
Figure 5: Training and validation loss and accuracy curves for scalogram inputs.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** & **F1 Score** & **Balanced Accuracy** \\ \hline \multirow{2}{*}{Signal Image} & Train & 54.78\% & 82.52\% & 74.30\% & 69.21\% & 63.06\% & 68.65\% \\  & Validation & 52.57\% & 79.18\% & 70.93\% & 66.10\% & 60.39\% & 65.87\% \\  & Test & 55.11\% & 81.58\% & 71.43\% & 69.53\% & 62.22\% & 68.35\% \\ \hline \multirow{2}{*}{Spectrogram} & Train & 76.06\% & 84.55\% & 81.95\% & 80.48\% & 78.90\% & 80.31\% \\  & Validation & 74.31\% & 84.81\% & 82.55\% & 79.65\% & 78.21\% & 79.56\% \\  & Test & 70.94\% & 81.24\% & 75.97\% & 76.55\% & 73.37\% & 76.09\% \\ \hline \multirow{2}{*}{Scalogram} & Train & 73.58\% & 79.14\% & 76.49\% & 76.47\% & 75.01\% & 76.36\% \\  & Validation & 71.44\% & 80.52\% & 77.99\% & 76.06\% & 74.57\% & 75.98\% \\ \cline{1-1}  & Test & 70.34\% & 78.05\% & 72.82\% & 74.54\% & 71.56\% & 74.20\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation of the DenseNet121-based classification method for identifying normal and abnormal EEG signals was conducted using signal images, spectrograms, and scalograms as separate inputs. This evaluation was performed on the training, validation and test set.