esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### Experiments  
A series of experiments is carried out to measure skin deformation during various facial expressions based on a 3D-DIC system. This system comprises two CCD cameras (acA2500-20 gm, Basler, Ahrensburg, Germany) and two illuminants to record human facial expressions, as illustrated in Figure 1a. Before the experiments, calibration procedures using [PERSON]'s method ensured three-dimensional reconstruction, meeting micro-level deformation measurement requirements [29].  
Figure 1: (**a**) Experimental setup, (**b**) the cropped images of facial skin with irregular spots, and (**c**) relevance between facial expressions and Aus [30,31].  
Before the experiments, healthy volunteers (20-30 years old) without skin diseases, burns, or surgical scars participated in this study. All participants provided written informed consent, and the experimental protocol was approved by the Ethics Committee of Tianjin Hospital of China. Irregular speckles are painted on the facial skin of human subjects using black skincare products. This procedure aims to enhance optical contrast and tracking accuracy.  
A standardized procedure is implemented for facial expression experiments. Firstly, a pre-experiment is carried out. A neutral state is important to ensure high consistency both within and across subjects. To minimize physiological variability and ensure measurement consistency, participants undergo a 30 min acclimatization period in a controlled environment (25 \({}^{\circ}\)C) to achieve a relaxed baseline state. Secondly, trained professionals guide participants to produce six basic facial expressions: anger, disgust, fear, happiness, sadness, and surprise. These expressions are standardized based on the Facial Action Coding System (FACS), as proposed by [PERSON] and [PERSON] [30; 31], which classifies facial muscle movements into distinct Action Units (AUs). In this study, the relevance between facial expressions and AUs is generated with some references [30; 31] and shown in Figure 1c. The systematic identification of AUs ensured repeatable and anatomically consistent facial expressions. Subsequently, the experimental system captured high-resolution image sequences (1600 \(\times\) 1200 pixels, 8-bit) at 30 frames per second, recording the transition from a neutral expression to the target expression states. The collected image sequences are pre-processed and segmented, ensuring optimal feature extraction for displacement analysis. Finally, the 3D-DIC technique is applied to compute full-field displacement distributions across the facial surface by referencing the neutral expression as an undeformed state. Using the 3D-DIC method, the subset size is set to 41 \(\times\) 41 pixels, with a step size of 5 pixels, optimizing spatial resolution while maintaining computational efficiency. To ensure the reliability of measurements, the regions with a correlation coefficient above 0.98 are considered valid for displacement calculation.
## 6 Conclusions  
This study proposes a new numerical framework to investigate the combustion of pyrolysis materials. A detailed three-dimensional pyrolysis model is coupled with fireFoam, a numerical solver for combustion processes. The pyrolysis model captures the physics in the material region and describes the interaction between a multi-phase reactive material with a multi-species reactive gas mixture. The description is done at the macroscopic scale, where the governing equations are derived from upscaling theories. FireFoam is used to describe the environment region. Its governing equations are derived from the Navier-Stokes equations after applying the LES filtering process and using the Favre mean variables. The two models are coupled at the material-environment interface, where specific conditions for velocity, pressure, temperature and species concentration are imposed. Two applications are considered to illustrate the capabilities of the tool. The first one considers the combustion of two different gaseous species with the implementation of two separate finite-rate combustion reactions. Results show how the two generated flames differ between each other in terms of temperature and reaction times. The behavior of the flames is strongly influenced by the pyrolysis species considered in the model. The second case illustrates the combustion of a wood log. Results illustrate how gaseous species are produced with time through the pyrolysis of the wood log and how these species enter in the environmental region to start and sustain the flame. Thanks to the flame and to the temperature increase, the pyrolysis processes speed up and the species concentrations continuously change in time according to the rate of reactions. Once the wood is fully pyrolyzed, the fire consumes all the fuels and extinguishes by itself. The puffing effect can be observed during the evolution of the simulation: the fire exhibits periodic fluctuations that follow each other with a numerical frequency that is approximately the same as the theoretical one. The numerical framework that was developed during this study is available in the Porous material Analysis Toolbox based on OpenFoam (PATO) released Open Source by NASA (www.pato.ac (accessed on 14 September 2021)) [34]. Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, writing--review and editing, visualization, supervision, project administration, funding acquisition: H.S., A.A.-S., J.L., F.R. All authors have read and agreed to the published version of the manuscript. This research was funded by a PhD grant awarded by Arts et Metiers Institute of Technology. Not applicable. All the data are included within the article. The research of H.S. was sponsored by a PhD grant awarded by Arts et Metiers Institute of Technology. The authors declare no conflict of interest. **Nomenclature**
## 2 Materials and Methods
### Mechanical Properties
#### 2.1.2 Specim Preparation and Conditioning  
Specimens were sampled from boxes with nominal densities of 22 and 23 kg/m\({}^{3}\) provided by Tempra. The cross-sectional area of specimens used in the compression test was 50 mm \(\times\) 50 mm and the thickness was 22 mm within a precision of \(\pm\)3%. For the flexure test, the key was to maintain the aspect ratio of specimens; the ratio for thickness-to-width-to-length was 1:4:12. Five specimens were used in the compression test and four specimens in the flexure test. A total of 54 specimens were prepared from the bottom of EPS boxes; 30 for the compression test and 24 for the flexure test. As it was impossible to achieve the height with a minimum of 25 mm specified in ASTM D 1621, two-thirds of the specimens preserved their original thickness and were only cut on four lateral sides, marked as EPS22 and EPS23. ASTM D 1621 suggested preparing specimens with a band saw or any method involving the use of abrasives; however, in industrial practice, specimens cut with a hot wire are commonly adopted due to their convenience. The major difference between the two methods is how they affect the surface; hot-wire cutting will cut through the material by fusing the plastic in contact, hence creating surface tension, which is likely to affect testing results, while cutting with a fine table saw will not. The two cutting methods are demonstrated in Figure 3.  
Figure 2: Mechanical properties test setups for EPS. (**a**) Testing instrument. (**b**) Compression test setup. (**c**) Flexure test setup.  
To further study the influence of cutting methods, one-third of the specimens were cut on all six faces and therefore consumed 1 mm from the top and bottom areas, resulting in a final thickness of 20 mm, marked as EPS22*. A summary of specimen specification is shown in Table 1. The sampling process of this test can be further improved by referring to the ASTM sampling practice.  
All specimens went through conditioning in a standard laboratory atmosphere in accordance with Procedure A of ASTM D 618-13 for not less than 88 h prior to testing [29]. Saturated \(Mg(NO_{3})_{2}\) solution was placed in a transparent plastic container to create a closed space with constant humidity of 50 \(\pm\) 5% [30], and a UX100-011A Temp/RH data logger from Onset (**Onset:** Company specialized in the data logger and monitoring solution in Bourne, USA) was placed inside to monitor if the temperature was 23 \(\pm\) 2 \({}^{\circ}\)C. All specimens were laid on a wire screen frame that was lifted up for over 25 mm to obtain adequate air circulation on all sides. The conditioning environment could be improved by replacing the container with an automatically controlled environmental test chamber.
## 3 Results and Discussion  
This section presents the results obtained by the two-step DGA classifier model. First, it was evaluated that the metrics obtained by the model trained with 70% of the examples contained in the dataset and tested with 30% of the examples. To make it possible to evaluate the impact of character-level embedding on the metrics obtained, the same tests were carried out with an altered version of the proposed model, called Model 1, removing the embedding layer. This model without the embedding layer is called Model 2. Also, to assess the model's performance in relation to the state-of-the-art DGA classification efforts, five other models available in recent works were implemented, being trained and tested on the same dataset. In a second moment, Model 1 was subjected to the incremental training process to increase the class, as presented in Section 2.3.
## 3 Feature-Engineering
### Feature Extraction
#### 3.2.2 Dynamic Features  
The extracted dynamic features are _Pearson correlation coefficients, crossing-the-mean count, outlier-occurrence count_, and _outlier indicator_. _Correlation coefficient_ is computed between each pair of vital signs within each window. For this feature, it is necessary to be applied to the _z-score_ of the vital signs. _Crossing-the-mean count_ of a vital sign is determined by counting the number of times that the recorded vital sign crosses its mean value within each window. This feature indicates the abrupt changes in the vital sign from one observation to another. _Outlier-occurrence count_ is computed by counting the number of outliers detected within each window. An outlier is detected by the statistical definition: any point outside the range \(\mu\pm 3\sigma\) for a normally distributed variable is an outlier. For this feature, it is not expected to work with the vital sign of oxygen saturation \((SpO_{2})\) as it is negatively skewed, however, it will be tested as a feature to prove the concept. Finally, the _outlier indicator_ is determined by the difference between the mean and the median of the records within each window.  
Figure 4: A flow chart illustrating the feature extraction process including the three feature categories (i.e., statistical, dynamic and physiological) and the sequence of the process marked by the evaluation steps. Also, in the process, the investigation is applied to the false negative patients only.
# Inverse Coupled Simulated Annealing for Enhanced OSPF Convergence in IoT Networks
## 4 Conclusions  
In the context of the current evolution of IoT network structures from small-scale distributed systems to large-scale hierarchical collaboration between backbone and access networks, the dynamic changes in backbone node connections and the surge in service demands pose significant challenges. The sluggish fault detection speed leads to a substantial reduction in the effective service transmission time. This paper proposes an OSPF dynamic routing convergence method based on reverse coupled simulated annealing (OSPF-ICSA). This method utilizes the statistical characteristics of Hello packets to acquire the link status and characterizes the node state based on the aggregated features of the link status, providing data support for the reverse coupled simulated annealing algorithm regarding the network conditions. Furthermore, the Hello packet is improved and an OSPF interval synchronization and node state transmission mechanism is designed to synchronize the sending intervals and fault detection times of nodes within the same subnet, while sharing the node state of nodes. Building on this foundation, the reverse coupled simulated annealing algorithm is introduced to collaboratively optimize the Hello packet sending interval and fault detection time. Experimental results demonstrate that OSPF-ICSA exhibits outstanding performance across various fault scenarios, particularly in four key indicators: fault detection time, detection accuracy, routing overhead, and packet delivery rate. This algorithm effectively addresses the trade-off between routing convergence time and routing overhead, achieving an optimized convergence speed while significantly reducing resource consumption. The aim is for OSPF-ICSA to provide a new perspective on routing convergence in large-scale hierarchical IoT network topologies. In future research, we plan to integrate machine learning, large models, and other cutting  
Figure 11: Comparison of link failure count vs. packet delivery rate.  
Figure 12: Comparison of node failure count vs. packet delivery rate.  
edge algorithms to achieve more efficient and rapid routing convergence, ushering in a new era of network optimization.  
Conceptualization, methodology, and software, C.P.; validation, formal analysis, and investigation, H.L.; resources, data curation, and writing--original draft preparation, H.S.; writing--review and editing, visualization, and supervision, Y.W.; project administration, and funding acquisition, L.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 61931004.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 5 Simulation Results--BER Performance
### Channel with Constant Parameters  
BER performance for different bit rates \(R\) in the case of applying algorithm (9) are shown in Figure 4 and Table 3. The same figure shows the potential BER without intersymbol interference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Number of Additions** & **Number of Multiplications** & **Memory Consumption** \\ \hline Proposed algorithm with & & & \\ decision feedback with & & & \\ evaluation of subsequent & & & \\ symbols in accordance with & & & \\ the maximum likelihood ratio & & & \\ criterion (9) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity of (9) for \(T_{\mathrm{s}}=8T\) and \(R=1.42/T\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BER** & \(\mathbf{10^{-2}}\) & \(\mathbf{10^{-3}}\) & \(\mathbf{10^{-4}}\) \\ \hline \(R=1.11/T\) & 4.34 dB & 6.79 dB & 8.38 dB \\ \hline \(R=1.25/T\) & 4.63 dB & 6.94 dB & 8.44 dB \\ \hline \(  
First of all, let us compare the BER at a bit rate \(R=1.25/T\) with the potential BER. It can be seen that the energy losses are no more than 0.1 dB at \(BER=10^{-4}\). When the bit rate is increased to values above the Nyquist barrier by a factor of two (\(R=2/T\)), energy losses are equal to 4.5 dB at \(BER=10^{-4}\).  
Figure 5 and Table 4 show the BER dependences for high bit rates. We can see that the energy costs for \(R=2.5/T\) are equal to 20 dB for \(BER=7\times 10^{-3}\). The energy consumption is equal to 8 dB at the same BER value for \(R=2/T\). It can be stated that when the data rate increases above the Nyquist barrier by 2.5 times, energy losses increase significantly and amount to more than 15 dB relative to \(R=1/T\).  
Figure 4: BER performance. Channel with constant parameters. Processing algorithm (9).  
Figure 5: BER performance. Channel with constant parameters. Processing algorithm (9) in case of high rates.  
Let us evaluate algorithm (9) in comparison with known processing algorithms. In Figure 6 and Table 5, the dependence of the BER on the ratio \(E_{b}/N_{0}\) for the bit rate \(R=1.42/T\) for various processing algorithms (Table 1) is shown.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{R}\) & \(\mathbf{2.00/T}\) & \(\mathbf{2.10/T}\) & \(\mathbf{2.22/T}\) & \(\mathbf{2.50/T}\) \\ \hline \(E_{b}/N_{0}\) & & & & \\ \((BER=10^{-2})\) & 8.34 dB & 9.10 dB & 12.93 dB & 17.61 dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: BER performance (values of \(E_{b}/N_{0}\) for fixed BER). Channel with constant parameters. Processing algorithm (9) in case of high rates.  
Figure 6: BER performance for different algorithms for \(R=1.42/T\) and \(\beta=0.2\).  
Proposed algorithm (9) provides an energy gain compared to the SSSSE [18] and SSSgbKSE [18] algorithms. DDSIC [24] provides a gain of less than 0.4 dB in the area \(E_{b}/N_{0}=\) (6-10) dB. However, at higher values of \(E_{b}/N_{0}\), proposed algorithm (9) allows one to achieve better values of energy costs (up to 4 dB). Note that this case (\(E_{b}/N_{0}>10\) dB) is common for 5G and 6G communication systems.
## 3 Results and Discussion
### Water Demand Projection
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Season** & \multicolumn{3}{c}{**RCP 4.5**} & \multicolumn{3}{c}{**RCP 6.0**} & \multicolumn{3}{c}{**RCP 8.5**} \\ \hline  & **2020s** & **2050s** & **2080s** & **2020s** & **2050s** & **2080s** & **2020s** & **2050s** & **2080s** \\ \hline Dry season & & & & & & & & & \\ Jan & 1.8 & 1.7 & 1.8 & 1.4 & 1.3 & 1.2 & 0.1 & 0.7 & \(-\)0.1 \\ Feb & 2.5 & 1.9 & 2.2 & 2.0 & 0.9 & \(-\)0.6 & 0.8 & 0.4 & 0.2 \\ Mar & \(-\)1.9 & \(-\)2.5 & \(-\)2.9 & \(-\)2.7 & \(-\)3.5 & \(-\)4.6 & \(-\)4.5 & \(-\)4.9 & \(-\)6.5 \\ Apr & \(-\)2.0 & \(-\)2.2 & \(-\)1.6 & \(-\)2.3 & 6.4 & 5.3 & \(-\)2.9 & \(-\)2.9 & \(-\)3.5 \\ May & 8.1 & 8.1 & 8.1 & 7.6 & 8.4 & 8.0 & 7.4 & 7.1 & 7.2 \\ June & 9.5 & 9.7 & 10.2 & 9.2 & 12.2 & 12.6 & 9.1 & 8.8 & 9.1 \\ \hline Wet Season & & & & & & & & & \\ Jul & \(-\)0.34 & \(-\)0.6 & \(-\)0.6 & \(-\)0.3 & \(-\)0.8 & \(-\)0.6 & \(-\)2.0 & \(-\)1.4 & \(-\)1.2 \\ Aug & \(-\)0.02 & \(-\)0.2 & \(-\)0.3 & \(-\)0.4 & \(-\)1.0 & \(-\)1.5 & \(-\)1.7 & \(-\)1.3 & \(-\)1.2 \\ Sep & \(-\)2.22 & \(-\)2.4 & \(-\)2.4 & \(-\)2.4 & \(-\)3.6 & \(-\)3.6 & \(-\)4.2 & \(-\)4.4 & \(-\)4.7 \\ Oct & \(-\)0.68 & \(-\)0.7 & \(-\)0.6 & \(-\)0.9 & 7.4 & 7.3 & \(-\)2.2 & \(-\)2.2 & \(-\)2.1 \\ Nov & 9.3 & 9.8 & 9.8 & 9.5 & 10.2 & 10.0 & 8.6 & 8.5 & 8.8 \\ Dec & 10.7 & 10.8 & 11.5 & 10.9 & 13.4 & 13.0 & 9.3 & 9.7 & 9.7 \\ \hline \hline \end{tabular} Note: Water supply \(>\) Water demand (Excess +), Water supply \(<\) Water demand (Shortage -).  
\end{table} 
Table 3: Projected excess/shortage water supply under RCP scenarios 4.5, 6.0, and 8.5 for the periods 2010–2039, 2040–2069, and 2070–2099. Figure 13: Projected daily available discharges for irrigation supply, actual water demand and recommended irrigation supply from multi-models for RCP 8.5 scenario from 1 January to 31 December 2020.
# In Vitro Evaluation of the Strength of Dentin Replacement in Complex Posterior Tooth Restoration
## 3 Results
### Tensile Strength Analysis  
Table 1 expresses the descriptive statistics table of tensile strength in fiber dentin replacement, non-fiber dentin replacement, and flowable composite group, while the box plot is shown in Figure 3.  
Statistical analysis of the tensile strength indicated a \(F\) (2,33) = 1.916, \(p\) (0.163) > 0.05, suggesting the absence of significant differences in the tensile strength among the three groups. However, the fiber dentin replacement group exhibited the highest average tensile strength than those noted for the non-fiber dentin replacement and flowable composite groups, with a value of 24.47 MPa.
## 4 Materials and Methods
### Test Architecture and Coding Libraries  
In order to train and test our network, we used the Python programming language, version 3.7, and the PyTorch library, version 1.7. Both the programming language and libraries were chosen for ease of use, as they are amongst the most common approaches in the field. All the validations were performed on a machine containing an Intel I9-9900k processor and an NVIDIA RTX2080 graphics card.
# T-Splines for Isogeometric Analysis of the Large Deformation of Elastoplastic Kirchhoff-Love Shells
## 3 Formulations for the Large Deformation of Elastoplastic Kirchhoff-Love Shells
### Variational Formulation  
The equilibrium equation is based on the principle of virtual work:  
\[\begin{split}\delta W^{\mathrm{u}}_{\mathrm{int}}& =\int_{\Omega_{0}}\delta\mathbf{E}:\mathbf{S}dV=\int_{A_{0}} \left(\delta\mathbf{\varepsilon}:\mathbf{N}+\delta\mathbf{\kappa}:\mathbf{M}\right) dA,\\ \delta W^{\mathrm{u}}_{\mathrm{ext}}&=\int_{A_{0} }\delta\mathbf{u}\cdot\mathbf{t}dA,\end{split} \tag{36}\]  
where \(\delta W^{\mathrm{u}}_{\mathrm{int}}\) and \(\delta W^{\mathrm{u}}_{\mathrm{ext}}\) are the sum of internal and external work, respectively; \(\varepsilon\) and \(\kappa\) are the tensorial form of strain components defined in Equation (28); and \(\Omega_{0}\) and \(A_{0}\) denote the initial integrating domain of the shell and the midsurface, with \(\mathbf{f}\) being the external load.  
The Newton-Raphson method [54; 55] can be used to solve Equation (36) with residual vector:  
\[\mathbf{R}^{\mathrm{u}}=\mathbf{F}^{\mathrm{u}}_{\mathrm{ext}}-\mathbf{F}^{ \mathrm{u}}_{\mathrm{int}}. \tag{37}\]  
\(\mathbf{F}^{\mathrm{u}}_{\mathrm{ext}}\) is the vector of external load. The internal nodal force vector \(\mathbf{F}^{\mathrm{u}}_{\mathrm{int}}\) can be obtained by the derivatives of \(\delta W^{\mathrm{u}}_{\mathrm{int}}\) with regards to nodal displacements \(\mathbf{u}\). Its components are computed as  
\[F^{\mathrm{u}}_{\mathrm{int}}=\int_{A_{0}}\left(\frac{\partial\mathbf{\varepsilon }}{\partial u_{r}}:\mathbf{N}+\frac{\partial\mathbf{\kappa}}{\partial u_{r}}: \mathbf{M}\right)dA. \tag{38}\]  
where \(u_{r}\) is the \(r\)-th component of the nodal displacement vector. The components of the tangent stiffness matrix \(\mathbf{K}^{\mathrm{u}}\) take the form of  
\[K^{\mathrm{u}}_{rs}=\int_{A_{0}}\left(\frac{\partial\mathbf{\varepsilon}}{\partial u _{r}}:\frac{\partial\mathbf{N}}{\partial u_{s}}+\frac{\partial^{2}\mathbf{ \varepsilon}}{\partial u_{r}\partial u_{s}}:\mathbf{N}+\frac{\partial\mathbf{ \kappa}}{\partial u_{r}}:\frac{\partial\mathbf{M}}{\partial u_{s}}+\frac{ \partial^{2}\mathbf{\kappa}}{\partial u_{r}\partial u_{s}}:\mathbf{M}\right)dA. \tag{39}\]  
Finally, the incremental displacement vector \(\Delta\mathbf{u}\) shall be solved via the linearized equation system:  
\[\mathbf{K}^{\mathrm{u}}\Delta\mathbf{u}=\mathbf{R}^{\mathrm{u}}. \tag{40}\]