esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Proposed Models  
#### 2.4.1 Model with Wind  
As mentioned above, several studies evidently showed that there is no best option, as we could not apply a unique temperature model for all PV module technologies. However, it was clear that the Sandia (King) model is frequently on the top list of matching temperature models. Besides, the Sandia model, unlike other temperature models, only takes on-site parameters such as plane-of-array (POA) irradiance, wind speed, and installation method into consideration. This reduction of input parameters leads to a simple implementation since it does not require many input parameters and a deep understanding of module technologies and materials.  
As PV thermal models usually describe the module temperature based on the assumption of thermal equilibrium, the thermal mass of the module has a high impact on the responding time of the changes in the temperature [26]. Moreover, [PERSON] et al. proved that the temperature prediction of a PV module is strongly affected by its own thermal inertia [9]. However, literature studies showed that Sandia is one of the models that is least affected by meteorological conditions. In other words, the changes in its empirical efficiencies are insignificant under different weather conditions and PV technologies [9; 18; 26]. Nevertheless, it was recognized that this model usually performed well under the low intensity of POA irradiance and less fluctuation of weather conditions; otherwise, it could suffer performance loss in practical implementation.  
In this research, therefore, in order to improve the calculation of the module back-surface temperature, a modification of the Sandia model (Equation (2)) by adding a correction term was put forward, based on the idea of the relevance between module thermal inertia and interval sampling, presented by the following Equation:  
\[T_{m}=T_{a}+\Delta T_{ref}^{*}\frac{G_{g}}{G_{0}}Exp\left(-\frac{W_{s}}{W_{ref }}\right)-\Delta T_{ref}\frac{\Delta G_{g}}{G_{0}}f(\Delta t), \tag{11}\]  
where \(\Delta G_{g}(t)=\left[G_{g}(t)-G_{g}(t-1)\right]\) is a dimensional constant describing the difference of POA irradiance between two consecutive sampling points (\(\Delta G_{g}=0\) W/m\({}^{2}\) at \(t=0\)); \(\Delta t\) is the time recording interval in minutes; and \(\Delta T_{ref}^{*}\) and \(W_{ref}\) are empirical coefficients depending on module installation configuration and are equivalent to the coefficients \(a\) an \(b\) in the Sandia model from Ref. [3] and Equation (2). With \(\Delta T_{ref}^{*}=G_{0}e^{a}\) and \(W_{ref}=-\frac{1}{b}\), this correction term becomes exactly the wind correction of Equation (2).
## 2 Fractional-in-Space Parabolic Equation
### Finite Element Approximation in Space  
For the discretization in space of the \((-\Delta)^{\alpha}\) operator, we use a finite element scheme as outlined by [PERSON] in [21]. Let us consider an admissible triangulation on \(\Omega\) with \(N_{\mathcal{T}}\) as number of linear conforming elements. We will denote with \(\{\varphi_{1},\ldots,\varphi_{N}\}\subset\mathbb{V}_{h}\) the nodal basis corresponding to the internal nodes \(\{x_{1},\ldots,x_{N}\}\). The basis functions have the form \(\varphi_{i}(h_{j})=\delta_{i}^{j},i,j\in[1,N]\), where \(\delta\) is the Kronecker delta. Thus, the \((-\Delta)^{\alpha}\) operator (1) is approximated by the fractional stiffness matrix \(K=(K_{ij})\in\mathbb{R}^{N\times N}\) such that  
\[K_{ij}=\frac{C(d,\alpha)}{2}\left\langle\varphi_{i},\varphi_{j}\right\rangle_{ H^{\alpha}(\Omega)}.\]  
where \(H^{\alpha}(\Omega)\) is the following Sobolev space  
\[H^{\alpha}(\Omega)=\{v\in L^{2}(\Omega):|v|_{H^{\alpha}(\Omega)}<\infty\}.\]  
It is defined with the Aronszajn-Slobodeckij seminorm\[|v|_{H^{k}(\Omega)}=\left(\iint_{\Omega\times\Omega}\frac{(v(x)-v(y))^{2}}{|x-y|^{ d+2\alpha}}dxdy\right)^{\frac{1}{2}}.\]  
The integrals that must be calculated in order to compute \(K_{ij}\) are carried out over the unbounded domain \(\mathbb{R}^{d}\). A ball-shaped domain \(B\) is introduced around \(\Omega\) to facilitate their calculations. The distance between the boundary \(\partial\Omega\) and the compliment \(B^{\complement}\) is discussed in [21]. An auxiliary admissible triangulation \(\mathcal{T}_{A}\) is introduced on \(B\setminus\Omega\) with \(n_{A}\) elements. We will denote with \(n_{\tilde{\mathcal{T}}}=n+n_{A}\) the number of elements on the combined triangulation \(\tilde{\mathcal{T}}=\mathcal{T}\cup\mathcal{T}_{A}\). Thus, the coefficients of \(K\) can be written as  
\[K_{ij}=\frac{C(d,\alpha)}{2}\sum_{l=1}^{n_{\tilde{\mathcal{T}}}}\left(\sum_{k= 1}^{n_{\tilde{\mathcal{T}}}}I_{l,k}^{i,j}+2I_{l}^{i,j}\right),\quad l,k\in[1,n _{\tilde{\mathcal{T}}}].\]  
The integrals \(I\) and \(J\) are  
\[\begin{split} I_{l,k}^{i,j}&=\int_{T_{l}}\int_{T_{m }}\frac{\Big{(}\varphi_{i}(x)-\varphi_{i}(y)\Big{)}\Big{(}\varphi_{j}(x)- \varphi_{j}(y)\Big{)}}{|x-y|^{d+2\alpha}}dxdy\\ I_{l}^{i,j}&=\int_{T_{l}}\int_{B^{\complement}} \frac{\varphi_{i}(x)\varphi_{j}(x)}{|x-y|^{d+2\alpha}}dydx.\end{split} \tag{2}\]  
Computing \(I_{l,k}^{i,j}\) and \(I_{k}^{i,j}\) is a numerically complex task. For this purpose, specialized methods and algorithms are utilized. Specifically, singular integrands are involved in the computation of \(I\), while \(J\) is calculated over an unbounded domain. The derivation of the \(I\) and \(J\) integrals as well as the theoretical analysis of the described finite element scheme is given in [21].
## 3 Modeling
### Model Calibration and Validation
#### 3.3.2 Model Validation  
Figure 6 compares the compressive strength and elastic modulus of granite obtained from the experimental and numerical simulations at different temperatures. As shown in Figure 6a, the uniaxial compressive strength obtained through numerical simulation follows a trend consistent with the experimental results as temperature varies. At temperatures below 200 \({}^{\circ}\)C, the high-temperature effect leads to an increase in the uniaxial compressive strength of granite. Between 300 \({}^{\circ}\)C and 500 \({}^{\circ}\)C, the uniaxial compressive strength gradually decreases with increasing temperature. At 600 \({}^{\circ}\)C, the compressive strength significantly decreases. As shown in Figure 6b, currently, there is no effective method in PFC to simulate mineral transformation and chemical reactions during the heating process under real-time high-temperature conditions for granite. Consequently, the deformation of particles after 400 \({}^{\circ}\)C is underestimated, resulting in elastic modulus values higher than those obtained from experimental results [40].  
Figure 7 compares the final failure modes obtained from experiments and numerical simulations at different temperatures. It can be observed that under uniaxial compression, the samples primarily fail in shear, and the fragments of the failed samples are mainly in the form of strips and blocks. With increasing temperature, the number of fine particles produced by fracture gradually increases. Similarly, in the numerical simulation, there are two distinct oblique crack bands in the failed samples, forming an X-shaped conjugate shear failure along the axial loading direction. As the temperature rises, the distribution of thermal damage-induced microcracks becomes more dispersed, and a greater number of cracks begin to propagate and combine, forming a network of microcracks around the mineral crystals. Through the above comparison, it can be observed that the numerical simulation results are in good agreement with the experimental results, which to some extent accurately reflect the mechanical properties of granite under real-time high-temperature conditions.  
Figure 6: Comparison of test results: (**a**) uniaxial compressive strength; (**b**) elastic modulus.
## 3 Discussion of Results
### Effect of Glass on 28 Day Concrete Exposed to Elevated Temperatures (550 \({}^{\circ}\)C)  
The glass blended concrete (C\({}_{80}\)G\({}_{20}\)) exposed to elevated temperature lost 22.6%, 16.8% and 7.4% of strength in C\({}_{90}\)G\({}_{10}\). C\({}_{80}\)G\({}_{20}\) and C\({}_{70}\)G\({}_{30}\), respectively, whereas OPC concrete (C\({}_{100}\)G\({}_{0}\)) lost about 28.2% of strength, as shown in Figure 16. The strength retention is due to microstructural stability and thermal absorption by glass. The residual density (Figure 17) of the sample was 4.2% of pre-thermal treatment in C\({}_{80}\)G\({}_{20}\). This further increased to 4.9% and 5.0% in C\({}_{90}\)G\({}_{10}\) and C\({}_{70}\)G\({}_{30}\), respectively, while OPC concrete lost 16.9 wt.% in density. This result implies that the use of glass particles in concrete in fire-resistant structures such as kitchens and incinerators can be effective because of the propensity to improve the durability of concrete structures.  
Finally, it is also important to emphasize the cost efficiency of glass waste blended concrete: in 1 m\({}^{3}\) of concrete, a cement content of 280 kg/m\({}^{3}\) can be added to 70 kg/m\({}^{3}\) of glass waste powder to constitute a total binder of 350 kg/m\({}^{3}\). Therefore, the cost of cement could be reduced by 80% in addition to the environmental benefit of solid waste reduction.  
Figure 15: XRD diffractogram of glass blended mortar.
## 5 Discussion of the Case Study--Rwanda
For occupant presence counting, Algorithm 1 was used to synchronize two communicating IR sensors (one counting incoming users, the other one decrementing ongoing users). Its deployment, shown in Figure 4, positioned it at any known entry points of the building. The reason for this was to facilitate the knowledge of the aggregated number of occupants in a specific building, in case of an incident. The configuration and cooperation of two IR sensors were programmed using the following algorithm:  
```
Input: peopleIN, peopleOUT, IRinStatus, IRoutStatus, inPeople, outPeople, stayPeople Output: Number of stayPeople in a Room
1 Initialization of variables: assign zero to variable peopleIN, peopleOUT
2 while(true)do
3 Read IRinStatus
4 Read IRoutStatus
5 ifIRinStatus==0 then
6 RETURN inPeople \(\leftarrow\) peopleIN++
7 end if
8 ifIRoutStatus==0 then
9 RETURN outPeople \(\leftarrow\) peopleOUT++
10 end if
11 stayPeople = inPeople - outPeople
12 ifstayPeople \(\leq\)0 then
13 No people inside // No sending process as there is null data to be sent
14 else
15 RETURN stayPeople
16 Send stayPeople in the database // Send data to the database
17 endif
18 endwhile
```  
**Algorithm 1**An Algorithm for Entry Counting
# NURBS-Enhanced Meshfree Method with an Integration Subtraction Technique for Complex Topology
## 5 Conclusions  
A NURBS-enhanced meshfree method with an integration subtraction technique is developed to model holes interactively in a predesigned domain for adaptive problems. With this method, the integration domain with complex topology can be easily and exactly described by its boundaries. By combining this method with the homotopic mesh method, any holes can be directly dug out from the predesigned domain with no refinement. In addition, a more efficient moving subtraction method is expanded to speed up hole-varying problems, in which only the region between the new and old holes boundaries must be integrated. The major discoveries arising from the comparison among FEM, RPIM, RSUB, and RMOV are as follows:  
1. Both RPIM and RSUB are more accurate than FEM and show higher convergence in displacement but do not have good enough energy convergence.
2. The proposed RSUB method achieves superior accuracy on a coarse mesh and a smoother stress result with a regular hole.
3. The duplicated integration of holes for subtraction yields improvements in precision, making RSUB more efficient than the original RPIM.
4. The results of both the proposed RSUB method and RMOV in displacement and stress are in good agreement with those of FEM and RPIM but cause little oscillation in the stress field near the boundary of the irregular hole, with RMOV causing less than RSUB.  
In conclusion, the proposed integration subtraction technique not only provides a convenient mesh for complex topology but also demonstrates excellent accuracy and good efficiency. Unfortunately, its performance in energy convergence is not good, and little oscillation around an irregular hole appears in the method. Other integration methods, such as nodal integration, may improve the performance in the area of energy convergence and greatly decrease the computation cost. Moreover, a new stress recovery method can be developed to eliminate the oscillation. These are the two major aspects of planned future work to perfect the proposed method.  
Conceptualization, Z.W.; investigation, C.Y.; writing--original draft, Y.L.; writing--review & editing, X.W. All authors have read and agreed to the published version of this manuscript.  
The authors would like to acknowledge funding from the National Key Research and Development Program (2016 YFB0200703).  
The authors declare no conflict of interest.  
Figure 21: Displacement in the y-direction along the upper side of the boundary.
## 3 Results and Discussion
### Catalytic Performance Testing  
In order to evaluate the performance of a catalyst, the protection time is a crucial fac-tor [13]. Figure 7 shows the protection times of various CeO\({}_{2}\) catalysts, and no distinct regularity in the protection time for samples with different morphologies was observed. This result may be due to the difference in the filling weight for CeO\({}_{2}\) nanomaterials with different morphologies to achieve the same bed volume. Therefore, in order to avoid the influence of catalyst loading quantity on the protection time, the mass specific treatment capacity (MSTC) of the catalyst towards DMMP was calculated and shown in Table 2. Based on the result, the MSTCs of catalysts can be arranged in the following order; 12 MCeO\({}_{2}\)nr (0.310 g\({}_{\text{DMMP}}\)/g\({}_{\text{cat}}\)) > 6 MCeO\({}_{2}\)nr (0.266 g\({}_{\text{DMMP}}\)/g\({}_{\text{cat}}\)) > 2 MCeO\({}_{2}\)np (0.222 g\({}_{\text{DMMP}}\)/g\({}_{\text{cat}}\)) > 12 MCeO\({}_{2}\)nc (0.152 g\({}_{\text{DMMP}}\)/g\({}_{\text{cat}}\)) > 6 MCeO\({}_{2}\)nc (0.085 g\({}_{\text{DMMP}}\)/g\({}_{\text{cat}}\)). As such, according to the trend, CeO\({}_{2}\) nanorods exhibited superior MSTC as compared to the irregular nanoparticles and nanocubes. Furthermore, the surface area specific treatment capacity (SSTC) of the catalyst towards DMMP was also calculated and the corresponding results are summarized in Table 2. Interestingly, the SSTC of CeO\({}_{2}\) nanocubes was higher than those of CeO\({}_{2}\) nanorods and irregular nanoparticles. This result is due to the extremely low specific surface area of CeO\({}_{2}\) nanocubes as compared to the other two morphologies. When comparing CeO\({}_{2}\) nanomaterials with the same morphology, 12 MCeO\({}_{2}\)nr and 12 MCeO\({}_{2}\)nc with larger crystal sizes possessed larger MSTC and SSTC. Thus, based on these results, the morphology and crystal size of CeO\({}_{2}\) can significantly affect its catalytic performance. Table 3 provided a brief summary of the protection times for various catalysts reported in the literature.  
Figure 7: Effects of morphology and size various CeO\({}_{2}\) on protection time with the following conditions: Reaction temperature, 300 \({}^{\circ}\)C; DMMP concentration, 5.32 g/m\({}^{3}\); GRSV, 10,000 h\({}^{-1}\); catalyst weight, 0.66 g CeO\({}_{2}\) nanocubes, 0.42 g CeO\({}_{2}\) irregular nanoparticles, and 0.42 g CeO\({}_{2}\) nanorods; size, 20–40 mesh.
## 3 Results
### Surface Roughness  
The results of the surface roughness measurements are presented in Table 5. Analysis of the results shows that with the decrease in the amount of sodium oxide in favor of the increase in potassium oxide in the glaze, the surface roughness decreases. It can be seen that the differences between glazes 1-3 are small, while a violent decrease is observed for glazes with the predominant potassium oxide content. This may show that the oxide has a different effect on the processes that take place in the glazes.  
When correlating the roughness values (Table 5) and the characteristic temperature values (Figure 2), it can be observed that with increasing potassium oxide content in the glaze composition, the values of the characteristic temperatures and the roughness values decrease. This may indicate a stronger fluxing effect of this oxide. Lowering the values of characteristic temperatures may indicate a faster process of melting and, thus, a reduction in the viscosity of the system and its better homogeneity and, therefore, greater surface smoothness.  
Analysis of the phase composition data shows that an increase in the amount of potassium oxide causes a decrease in the content of crystalline phases. These phases usually increase the roughness, especially if they are located on the surface or just below the surface of the glaze. Smoothing of such a system usually occurs when the amorphous phase is predominant. The increase in the content of the amorphous phase in the analyzed glazes could cause a decrease in the roughness in glazes with a predominance of potassium oxide.  
The decrease in surface roughness, i.e., the increase in its smoothness, is a positive aspect. Smooth surfaces are easier to clean (increased hygiene).  
Figure 14: Results of color measurements.  
\begin{table}
\begin{tabular}{c c} \hline
**Glaze** & **Ra [um]** \\ \hline Glaze 1 & 0.696 \(\pm\) 0.064 \\ Glaze 2 & 0.663 \(\pm\) 0.037 \\ Glaze 3 & 0.623 \(\pm\) 0.024 \\ Glaze 4 & 0.490 \(\pm\) 0.024 \\ Glaze 5 & 0.244 \(\pm\) 0.034 \\ \hline \end{tabular}
\end{table}
Table 5: Results of roughness measurements.
# Integrated Double-Sided Random Microlens Array Used for Laser Beam Homogenization
## 3 Fabrication of D-rMLA
Then the roughness and apertures of the prepared D-rMLA were measured by step profilometer (Stylus Profiler System, Dektak XT, Bruker, Karlsruhe, Germany). The resolution of the step profilometer is 1 A. The roughness is about 9 nm in average, and the apertures vary form 25 um to 45 um, which is consistent with the designed parameters of the masks. The profile of the sub-lens was also fitted, as shown in Figure 7. The profile of the fabricated sub-lens unit almost coincides with the ideal sphere, showing high profile accuracy. The depths of the sub-lenses vary from 2 um to 4 um. Figure 5: Fabrication process of D-rMLA: (**a**) pretreatment; (**b**) double sided exposure; (**c**) development; (**d**) chromium remove; (**e**) etching of the D-rMLA. Figure 6: Microscopic image of the D-rMLA: (**a**) crosshair alignment picture; (**b**) MLA micrograph of the first surface; (**c**) MLA micrograph of the second surface.
## 3 Experiment Setup and Evaluation
### Results of Evaluated Metrics
The detection time of an algorithm is a crucial parameter when it is used in real-time applications. In this regard, we tested the previously mentioned algorithms to assess their detection speed. We conducted 50 runs for each test image and calculated the average detection time. Figure 16 shows the average number of detections. As a result, the fastest algorithm is SSD, with an average of 0.025 s, and the slowest is Faster R-CNN, with an average of 1.32 s. The latter is expected because one-stage deep learning methods are faster than two-stage methods. All algorithm times were low,  
Figure 14: AP of the deep learning algorithms. enabling them to be used in real-time applications. The speed of the SSD algorithm is mainly attributed to using a lightweight network like MobileNetv2. Its main advantage is that it can be employed in embedded systems, which is common in practical applications in mining environments. Although it is less accurate than other analyzed deep learning algorithms, we could improve it by incorporating attention modules.
# FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints
## 4 Result and Discussion
### Experiment Settings  
Dataset. We evaluated the proposed method on 13 core classes of the ShapeNet [1] dataset. In this paper, we uniformly sampled 10,000 points on the surface of each CAD (computer-aided design, CAD) [1] model as the ground truth. For a fair comparison, we adopt the rendered images and corresponding camera parameters provided by [PERSON] et al. [8]. The data of each class is divided into a training set, a validation set, and a test set according to the ratio of 70:10:20.  
Train details. To improve training stability, we adopt the staged training approach of [PERSON] et al. [18] to train each subnet separately and then fine-tune the entire network in an end-to-end manner. In this paper, all experiments are performed on a workstation with an Intel Xeon Gold 5218R CPU and an NVIDIA GTX 3090 GPU. We train the network using the Pytorch framework and the Adam optimizer [41] (\(\beta\)1 = 0.9, \(\beta\)2 = 0.999). The framework takes as input a 224 \(\times\) 224 image \(I\) with a batch size of 32 and a spherical mesh \(G_{0}\) with 2562 vertices, and the number of local patches \(n\) = 8. We train for 300 epochs with a learning rate of 1 \(\times\) 10\({}^{-3}\) and decay to 1 \(\times\) 10\({}^{-4}\) after 100 epochs. The hyperparameter values are as follows: \(\lambda_{1}\) = 1 \(\times\) 10\({}^{-3}\), \(\lambda_{2}\) = 5 \(\times\) 10\({}^{-7}\), \(\lambda_{3}\) = 0.1, \(\lambda_{4}\) = 1 \(\times\) 10\({}^{-4}\). The pruning threshold \(\tau\) is set to 0.02, the curvature hyperparameters of the extend patch extract module are set to t\({}_{1}\) = 0.6, t\({}_{2}\) = 0.2, and the scale \(\varepsilon\) of the FAGCN is set to 0.1. During training, the local patch refinement module directly uses the output patches to calculate the loss, while in the inference stage, the output patches are stitched to the overall mesh through mean pooling.  
Evaluation Metric. In the paper, we evaluate the performance of the reconstruction results by the chamfer distance [12] and the F-score [42] with a threshold of 0.001, which is calculated between 10,000 sampling points in the generated mesh and the ground truth. The smaller the Chamfer Distance (CD) value, the better, and the larger the F-score, the better.