esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modelling and Numerical Methods  
### Projection Method  
For incompressible viscous flows, the Navier-Stokes equations written in non-conservative form are  
\[\frac{\partial u_{i}}{\partial x_{i}}=0, \tag{1}\]  
\[\frac{\partial u_{i}}{\partial t}=-u_{j}\frac{\partial u_{i}}{\partial x_{j}}- \frac{\partial p}{\partial x_{i}}+\
u\frac{\partial^{2}u_{i}}{\partial x_{j} \partial x_{j}}+F_{i}, \tag{2}\]  
where \(t\) denotes time, \(u_{i}\) the \(i\)-th component of the fluid velocity, \(x_{i}\) the \(i\)-th component of the position vector, \(p\) the effective kinematic pressure, \(\
u\) the kinematic viscosity, and \(F_{i}\) the component of a body force such as gravity. The [PERSON] summation convention is used here, i.e., the summation is taken over repeated indices.  
For convenience and the purpose of demonstration, the pressure-correction projection method adopted here to solve Equations (1) and (2) in time consists of two fractional steps. At the first step, the intermediate velocity is computed as follows:  
\[u_{i}^{*}=u_{i}^{n}+\frac{3}{2}\Delta tR_{i}^{n}-\frac{1}{2}\Delta tR_{i}^{n-1}, \tag{3}\]  
where  
\[R_{i}^{n}=-u_{j}^{n}\frac{\partial u_{i}^{n}}{\partial x_{j}}+\
u\frac{ \partial^{2}u_{i}^{n}}{\partial x_{j}\partial x_{j}}+F_{i}^{n}\]  
and  
\[R_{i}^{n-1}=-u_{j}^{n-1}\frac{\partial u_{i}^{n-1}}{\partial x_{j}}-\frac{ \partial p^{n-1}}{\partial x_{i}}+\
u\frac{\partial^{2}u_{i}^{n-1}}{\partial x _{j}\partial x_{j}}+F_{i}^{n-1}.\]  
Equation (3) is obtained by integrating the momentum equations forward in time using the second-order Adams-Bashforth scheme while dropping the pressure gradient term in \(R_{i}^{n}\). Then, in the second step, \(u_{i}^{*}\) is corrected to give the updated velocity \(u_{i}^{n+1}\) as  
\[u_{i}^{n+1}=u_{i}^{*}-\frac{3}{2}\Delta t\frac{\partial p^{n}}{\partial x_{i }}, \tag{4}\]  
for which the dropped pressure gradient term is added back. The pressure field is determined to ensure that the updated velocity field satisfies the incompressibility. By taking the divergence of Equation (4) and requiring that \(u_{i}^{n+1}\) is a divergence-free vector field, we obtain the Poisson equation for the pressure field:  
\[\frac{\partial^{2}p^{n}}{\partial x_{i}\partial x_{i}}=\frac{2}{3\Delta t} \frac{\partial u_{i}^{*}}{\partial x_{i}}. \tag{5}\]  
Projecting Equation (4) on the outward unit normal vector \(\mathbf{n}\) of the boundary \(\Gamma\), we obtain the Neumann boundary condition for \(p\), i.e.,  
For a wall boundary, due to the non-penetration condition (\(\mathbf{u}\cdot\mathbf{n}=0\)), we have  
\[\left(\frac{\partial p}{\partial\mathbf{n}}\right)^{n}=\frac{2}{3\Delta t}[u_{i}^{*} n_{i}]_{\Gamma}. \tag{8}\]
# Heat Transfer in Thermoelectric Generators for Waste Energy Recovery in Piston Engines
## 2 Materials and Methods
### CFD Simulations
#### 2.1.2 Turbulence  
A RANS (Reynolds-Averaged Navier-Stokes) approach was followed. The realizable k-\(\epsilon\) model [25] was selected. This model has the same turbulent kinetic energy equation as the standard k-\(\epsilon\) model but includes an improved equation for \(\epsilon\). Compared to the standard k-\(\epsilon\) model, it shows better performance for flows involving planar and round jets (predicts round jet spreading correctly), boundary layers under strong adverse pressure gradients or separation, rotation, recirculation, and/or strong streamline curvature [25]. Standard wall functions were used.
# Seventy-Five Years since the Point-Contact Transistor: Germanium Revisited
## 4 Defect Processes in Ge
### Acceptor Atoms  
The main difference in acceptor atoms (B, indium (In), gallium (Ga) and aluminum (Al)) to the donor atoms considered above is that they do not migrate at a fast rate in the lattice [70; 93; 94; 95; 96; 97; 98; 99]. This is because B repels vacancies, whereas In, Ga and Al are only weakly bound to vacancies [43]. For B, it was determined by the proton irradiation experiments conducted by [PERSON] et al. [94] that an interstitial mechanism is prevalent. Under equilibrium conditions, self-interstitials have a negligible concentration, but proton irradiation forms a supersaturation of self-interstitials that enhances B diffusion and suppresses vacancy-mediated diffusion [94]. These experimental results, in conjunction with the DFT study by [PERSON] et al. [100], provide evidence for an interstitials mediated diffusion mechanism  
Figure 4: The \(\mathrm{F_{4}}V\) cluster in Ge. The black circle represents the vacancy, the light blue circles F atoms and the yellow circles Ge atoms.  
Figure 3: A schematic representation of the As\({}_{n}V\) clusters (n = 1–4). Cubes represent the vacancies, and white and black circles represent the Ge and As atoms, respectively [35].  
for B. On the other hand, In, Ga and Al can diffuse via the vacancy mechanism but with considerably higher activation enthalpies of diffusion as compared to the P, As, and Sb atoms considered above [43; 59; 101; 102]. This is also evident in Figure 3, where the \(p\)-type dopants have considerably lower diffusivities as compared to the \(n\)-type dopants.  
From a technological perspective, the limited diffusion of B and the acceptable activation level can lead to the formation of efficient \(p\)-type regions for devices, taking advantage of the higher hole mobility of Ge as compared to Si.
## 2 Principles of New Measurement Methods
### UAS-Based Deformation Measurement  
UAS-based surveying is an efficient way to collect data even from very large areas. In recent years, UAS-based surveying has become one of the popular methods due to its low cost, efficiency, and accuracy. In some cases, it is the only method to collect data when an area is remote or hard to reach. This method can provide terrain information in the form of a point cloud or an orthomosaic image. Several researchers have used this technique to monitor and measure the changes in multiple different cases. [PERSON] et al. [21] utilized UAS for post-fire surveying to detect the areas of sliding potential. In addition, multiple researchers, [PERSON] et al. [22], [PERSON] et al. [23], and [PERSON] [24], have utilized an UAS-based method to generate the digital topographic map, collect the deformation data, and analyze the potential for slope failure. In this research, UAS surveying is used to monitor the potential movement of slopes, with a focus on surface deformation measurements only.  
The technique needs to have ground reference points visible to a UAS. Then, a UAS flies through the target area with a camera that takes high-resolution images with a dedicated GNSS receiver (Global Navigation Satellite System) to enable the PPK (Post  
Figure 2: Cross section of fiber optic cable for physical strain and temperature measurement.  
Processing Kinematic) technique to obtain centimeter-level positioning. The required base station should be set up to correct the GNSS data of the drone; in this study, the UAS platform was WingtraOne. After the flights, aerial images, UAS-based Rinex file, the estimated base station position were obtained from OPUS (Online Positioning User Service), and the base station Rinex file was needed to process together to add the geographic information to imagery data at the centimeter level. At the end of this procedure, a 3D point cloud of the surveyed area was developed, as shown in Figure 3 as an example. The grainy image in the figure is due to the discrete nature of the data in the point cloud, highlighting the extremely high-resolution points that represent the digitized scene (containing the 3D coordinate information at the centimeter level typically).  
The combined data set makes it possible to conduct a comparison of the coordinates of point clouds from multiple different times. This will allow one to compute the spatial differences through a technique called change detection. Once fully processed, the point cloud data set was extremely dense due to the high-resolution cameras flow in modern-day UAS platforms flying at above-ground altitudes that are close to the ground. The actual overview of point clouds and derived deformation profiles are shown and discussed in the following sections.
## Materials and Methods  
### Sample  
We investigated a commercial-grade AlGaN-based 265 nm-emitting DUV LED. Its sample structure is shown in Figure 1. The strains of AlGaN layers grown on a sapphire substrate are strong and have a high dislocation density due to the difference in the lattice constant and the thermal expansion coefficient. Therefore, an AlN buffer layer between the sapphire substrate and the AlGaN layer was included to minimize the strain and dislocation density of the AlGaN to be grown atop.  
We grew n-type AlGaN layers on the AlN buffer with an Al composition ratio of 0.7. Multi-quantum wells (MQWs) composed of Al\({}_{0.7}\)Ga\({}_{0.3}\)N barriers and Al\({}_{0.53}\)Ga\({}_{0.47}\)N quantum wells were grown on top of the n-Al\({}_{0.7}\)Ga\({}_{0.3}\)N layer. An electron-blocking layer was placed above the MQWs to block the electron overflow. A p-AlGaN layer was grown on this layer with an Al composition ratio of 0.7, the same as the n-AlGaN layer. The final layer grown was a p-GaN contact layer for p-contact, used as a flip-chip structure to collect the emitted light in the sapphire substrate side.  
Figure 2 shows the electroluminescence spectra measured at 10 mA forward current at 300 K and 80 K with a duty cycle of 15 %. At 300 K, the band-edge signal at 265 nm was dominant, and the external quantum efficiency at a maximum output power of 1 mW was measured to be 1%.  
Figure 1: A schematic diagram of the AlGaN-related 265 nm flip-chip deep-ultraviolet (DUV) light-emitting diode (LED) sample.
## 3 Results and Discussion  
### Comparative Analysis of Algorithm Performance Combining Sequence and Structural Features  
This section provides an in-depth discussion of the performance of the three algorithms based on different feature combinations in grain protein function prediction, focusing on the impact of structural features on the algorithm performance. Four types of grains were selected as research subjects, and the performances of different algorithms on the three types of protein function ontologies were analyzed. The results from the five experiments were averaged for each set of experimental data. The experimental results are presented in Table 2.  
0.044 and 0.009 lower than those of MCNN-1D(S), respectively, and the MCC value of MCNN-1D(S+SS) was 0.056 lower than that of MCNN-1D(S) in the CC dataset. Overall, the effectiveness of the MCNN-1D(S+SS) algorithm was verified, demonstrating that secondary structure information is effective in predicting grain protein function.  
The MMSNet algorithm adds structural branch and contact map features based on MCNN-1D(S+SS). The experimental results showed that the prediction performance of the model was the best when combined with structural contact map features. For the three ontology datasets of the four grains, the MMSNet algorithm outperformed the other two algorithms in terms of at least three evaluation metrics. In particular, MMSNet performed better on the japonica protein dataset, with an AvgRe value only 0.012 lower than that of MCNN-1D (S+SS) on the CC dataset and an AvgPr value higher than those of the other two algorithms. This may be due to the larger sample size of the Japonica protein dataset, which provided richer training information. The advantage of its data volume compared to the other three grains is that it may reduce the risk of overfitting, thereby allowing the model to generalize better to unseen data. In terms of the critical metric Fmax, the MMSNet algorithm outperformed the other two algorithms in all three sub-datasets of the four grains.  
This demonstrated the critical role of algorithm innovation in improving the performance of grain protein function prediction models. At the same time, the excellent performance of the MMSNet algorithm powerfully demonstrates the potential of combining multidimensional biological data with advanced computing technology in the field of grain protein function prediction. It should be noted that the wheat protein dataset, with the smallest sample size, resulted in more unsatisfactory outcomes for the model across the three sub-datasets. This phenomenon reflects the limitations of data scarcity in model training.
## 3 Methodology  
### Signal Separation Based on Knowledge Representation Framework  
The signal separation method based on knowledge representation consists of the following three steps. First, knowledge extraction is performed based on the received observation signal, including the scenario of which type of knowledge representation the signal belongs to, and the number of target source signals is determined based on the signal itself, thereby determining the number of network branches. Then, the signals are represented using the knowledge representation framework. Finally, knowledge is applied by forming a knowledge-guided loss function between the results predicted by the neural network and the knowledge representation, guiding the neural network to achieve signal separation. The entire process is illustrated in Figure 3.
## 7 Finite Element Study of Optimal Process Parameters
### Establishment of the Simulation Experiment  
To study the optimal combination of outer die taper angle, friction coefficient, and drawing speed for wall thickness uniformity, three independent factors were selected: outer die taper angle (Factor A), friction coefficient (Factor B), and drawing speed (Factor C). These factors were analyzed using an orthogonal experiment, with three levels set for each factor. The specific parameters are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Drawing Speed (m/s)** & **Wall Thickness Uniformity** & **Wall Thickness (mm)** \\ \hline
0.6 & 0.106\% & 2.165 \\
0.7 & 0.096\% & 2.164 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Wall thickness and wall thickness uniformity under different drawing speeds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Factors**} \\ \cline{2-4}
**Level** & **A** & **B** & **C** \\ \cline{2-4}  & **Outer Die Angle/\({}^{\circ}\)** & **Drawing Speed/m/s** & **Friction Coefficient** \\ \hline
1 & 13 & 0.3333 & 0.0125 \\
2 & 14 & 0.6667 & 0.025 \\
3 & 15 & 1.3333 & 0.05 \\
4 & 16 & 2.6667 & 0.06 \\
5 & 17 & 5.3333 & 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Simulation parameters.  
Based on the above three parameters, a simulation was designed. With five values for each parameter, a total of \(5^{3}=125\) simulation sets would be required, making the experiment workload quite large; therefore, stratified sampling was chosen to design the parameter combinations. Considering that neural network learning requires a large amount of data, 75 parameter combinations were selected through stratified sampling to conduct the simulation experiments, as shown in Figure 16.  
The stratified sampling results are evenly distributed within the sample range with strong randomness. Based on the sampled data, finite element simulations were conducted and the results from these simulations were used for neural network prediction.
## 4 Discussion
### Interpretation of SAR-SLs  
Figure 20 summarizes our conceptual model, which partially explains the differences and similarities between the ASC and DESC SAR-SLs at the study sites. For a beach-dune-cliff system oriented west to east and different total water levels, we expect DESC SAR-SL to delineate the wet-dry beach boundary, the cliff/dune top, or the cliff/dune backshore. For the same beach-dune-cliff system, ASC SAR-SLs are more likely to delineate the wet-dry beach boundary or the cliff/dune toe, with the main reason being the presence or absence of a shadow area between the water and cliff/dune for the DESC and ASC orbits, respectively. In this figure, we have assumed a constant radar look direction and changed the water level to illustrate the different interpretations of the shoreline mapped. The radar look direction is not constant but also changes with the distance between the satellite and the feature on the ground. This simple conceptual model illustrates the importance of performing SAR visibility analysis (e.g., identifying the areas that can be affected by shadowing) for a better informed interpretation of the features being mapped.
## 6 Experiments and Discussion
### Description of the Algorithms Employed for Comparison  
Many researches [42; 43] show that the UKF has a higher accuracy than the EKF, so we directly employ existing adaptive UKF schemes to conduct the comparison experiments. All the compared schemes with the same altitude aiding strategy [27] are briefly described below:  
(1) Standard tightly-coupled integration (STC)  
A standard UKF integration filter is used to fuse INS results and GNSS measurements, and **R** is set to a fixed value. Four satellites are selected by picking their GDOP ranked least.  
(2) Adaptive tightly-coupled integration (ATC)  
Residual-based adaptive estimation (RAE) is implemented to improve the performance of the UKF. For the RAE-UKF scheme [31], **R** is adaptively updated as follows:  
Figure 3: The proposed tightly-coupled architecture based on the RMNCE approach.  
\[\left\{\begin{array}{l}\mathbf{\hat{R}}_{k}=\mathbf{\hat{C}}_{pk}\ +\ \mathbf{P}_{zz,k}^{+}\\ \mathbf{\hat{C}}_{ck}=\frac{1}{N}\sum\limits_{j=j_{0}}^{k}\mathbf{r}_{j} \mathbf{r}_{j}^{T}\\ \mathbf{r}_{j}=\mathbf{Z}_{j}\ -\ \sum\limits_{i=0}^{2n}\omega_{(i)}^{(\mathrm{m}) }h\Big{[}\chi_{j,(i)}\Big{]}\\ \mathbf{P}_{zz,k}^{+}=\sum\limits_{i=0}^{2n}\omega_{(i)}^{(\mathrm{c})}\bigg{\{} h\Big{[}\chi_{k,(i)}\Big{]}\ -\ \sum\limits_{i=0}^{2n}\omega_{(i)}^{(\mathrm{m}) }h\Big{[}\chi_{k,(i)}\Big{]}\bigg{\}}\bigg{\{}h\Big{[}\chi_{k,(i)}\Big{]}\ -\ \sum\limits_{i=0}^{2n}\omega_{(i)}^{(\mathrm{m}) }h\Big{[}\chi_{k,(i)}\Big{]}\bigg{\}}^{T}\end{array}\right. \tag{34}\]  
where \(\mathbf{r}_{j}\) denotes the residual error, \(N\) is the window length for calculating the covariance of \(\mathbf{r}_{j}\), and \(\chi_{j,(i)}\) is the \(i\)-th \(n\)-dementional sigma point at step \(j\). Besides, a 5-satellite selection based on GDOP are employed in ATC.  
(3) Modified ATC (MATC)  
Modified ATC (MATC) which uses the proposed satellite selection algorithm is also implemented to help to analyze the individual contributions of the satellite selection algorithm and RMNCE-UKF.  
(4) CNR and satellite elevation based tightly-coupled integration (CNE-TC)  
In a recently published article [25], satellites with elevations lower than 10\({}^{\circ}\) or CNR lower than 30 dB-Hz are excluded, and the variance on the pseudo-range estimates is weighted as:  
\[\phi_{p}^{2}=\frac{a+b\cdot 10^{-\frac{C}{N_{0}}}}{\sin(Elev)} \tag{35}\]  
where \(\frac{C}{N_{0}}\) is the CNR value, \(Elev\) is the satellite elevation, \(a\) and \(b\) are empirical parameters, that are recommended to set \(a=1\) and \(b=281^{2}\).
# Electronic Properties of Electron-Deficient Zn(II) Porphyrins for HBr Splitting
## 3 Results and Discussion  
### Syntheses  
Porphyrin **1** was prepared as reported in Scheme 1.  
The H\({}_{2}\)TPPF\({}_{20}\) core was prepared by the [PERSON] method [26] in 62% yield, condensing pyrrole with pentafluorophenylbenzaldehyde in the presence of catalytic amounts of BF\({}_{3}\)(OEt)\({}_{2}\), and then oxidizing the porphyrinogen with 2,3-dichloro-5,6-discyano-1,4-benzoquinone.  
The complexation of H\({}_{2}\)TPPF\({}_{20}\) with Zn(CH\({}_{3}\)CO\({}_{2}\))\({}_{2}\):H\({}_{2}\)O afforded ZnTPPF\({}_{20}\) in almost quantitative yield. Then, ZnTPPF\({}_{20}\) was monochrominated in \(\beta\)-pyrrolic position by reaction with N-bromosuccinimide (70% yield), affording ZnTPPF\({}_{20}\)-Br, which was used for a Sonogashira's coupling with trihexylsilylacetylene, [Pd(PPh\({}_{3}\))\({}_{4}\)] and CuI, following the microwave-assisted approach (1 h, 120 \({}^{\circ}\)C) developed previously [27]. ZnTPPF\({}_{20}\)-Si was obtained at 89% yield. After removal of the protective silyl group with tetrabutylammonium fluoride in THF, ZnTPPF\({}_{20}\)-H was used for a second Sonogashira's coupling (24 h at 80 \({}^{\circ}\)C) with 4-(7-bromobenzo[1,2,5]thiadiazol-4-yl)benzaldehyde (previously prepared as reported in Scheme S1) in a triethylamine/THF mixture as the solvent and catalytic amounts of [Pd(PPh\({}_{3}\))\({}_{4}\)] and CuI, affording ZnTPPF\({}_{20}\)-BTD-CHO (28% yield). The final step was a Knoevenagel condensation between ZnTPPF\({}_{20}\)-BTD-CHO and cyanoacetic acid with piperidine as a base in a CHCl\({}_{3}\)/CH\({}_{3}\)CN mixture, affording **1** 49% yield.  
The synthesis of porphyrin **2** was accomplished as depicted in Scheme 2.  
The selective nucleophilic substitution [28] of only one of the four _para_ fluorine atoms of the pentafluorophenyl rings in the 5,10,15,20 _meso_-position of H\({}_{2}\)TPPF\({}_{20}\) core was achieved (47% yield) by reaction of the free base with 1 equivalent of sodium azide with N,N-dimethylformamide as a solvent, followed by complexation with Zn\({}^{\mathrm{II}}\) in almost quantitative yield. Then, a CuI-catalyzed Huisgen 1,3-dipolar cycloaddition [29] between ZnTPPF\({}_{19}\)-N\({}_{3}\) and 4-ethynlbenzaldehyde in a THF/CH\({}_{3}\)CN mixture with N,N-diisopropyllamine as a base gave ZnTPPF\({}_{19}\)-click-CHO (60% yield). As the final step, a Knoevenagel condensation between ZnTPPF\({}_{19}\)-click-CHO and cyanoacetic acid with piperidine as a base in a CHCl\({}_{3}\)/CH\({}_{3}\)CN mixture afforded porphyrin **2** at 80% yield.  
The new dyes were characterized by \({}^{1}\)H and \({}^{19}\)F-NMR spectroscopy and by ESI-MS spectrometry (see Experimental section).
## 5 Results and Discussion
Therefore, the kNN model is explained by fewer parameters, with respect to the SVM. The influence of the feature selection activity was evaluated by comparing the accuracy of the algorithm trained considering all the twenty-three features, or only the features in Table 4 that are in common to both the SVM and kNN, which are \(p_{3}\), \(p_{6}\), \(p_{10}\), \(p_{18}\), and \(p_{19}\). For this task, 100 different datasets containing \(N=2000\) randomly selected samples from the original database were used. Each dataset was split into two subsets with a ratio 80/20: the former was used for the training phase, while the latter was used for the computation of the testing accuracy. The accuracy of the algorithms in both the considered cases and for each set of data was recorded and the mean accuracy was computed to analyse the overall impact of the feature selection activity. The results are reported in Figure 8.  
Thus, with the help of the insights provided by the SHAP analysis, the number of relevant features could be reduced with proper justification, making the ML models more robust and easily explainable in terms of feature importance for the outcome of the model. As it can be seen from Figure 8a, the accuracy for the SVM is only 1% worse, as if it has been trained considering all the features, whereas Figure 8b shows that the kNN algorithm performs better only if the most important features are considered. This was expected, because, as cited previously, this kind of algorithm suffers from feature redundancy. Then, diagnosis accuracies are not significantly affected by the selection of only five features out of twenty-three, as long as the SHAP values are employed to select the relevant features by means of model explanations.