esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Parametric Study of SHSS Evaluation Methods
### Effect of Mesh Size  
It is usually considered that finer mesh with parabolic elements gives more accurate results as compared to relatively coarser mesh with linear elements, especially where stresses are highly non-linear. However, after analysing the results under these SHSS evaluation methods, this statement is not justified. Undoubtedly, the 1-CH20 element gave higher WTSs as compared to other elements, but as we moved away from the weld-toe, the stress values were reduced rapidly, and this rapid reduction in stress did affect the SHSS results, especially for surface stress calculation methods (Figure 8a-f). Consequently, for the 1-CH20 element, SSE and Haibach methods gave lower SHSS results as compared to other methods. Moreover, 1.0 and 2.0 mm CH20 elements had identical stress results at readout points, hence 1/2-CH20 elements SHSS results were not influenced by mesh size. However, for 1/2-CH8 elements, the 2-CH8 element gave higher stress results at 0.4\(t\) as compared to the 1-CH8 element, which increased SHSS for QSE and LSE 410 methods, while in the LSE 515 method, the stress results converged at 0.5\(t\), which is why SHSSs for both mesh sizes were identical in this method (Figures 8a-f and 9a,b). The Xiao and Yamada method showed a substantial increase in SHSS results for 2.0 mm solid elements (2-CH20/8) as compared to 1.0 mm elements (1-CH20/8), which was more evident at the deck-toe (Figure 9a,b).  
Figure 9: Comparison of SHSS evaluation methods considering different mesh techniques used in this study. (**a**) SHSS results at the deck-toe. (**b**) SHSS results at the rib-toe.  
The CH20E/8E elements had similar stress results for both mesh sizes (1.0/2.0 mm), therefore, their SHSS results were also identical. Likewise, the 1/2-CQS/4 shell elements had the same stress results for the finer mesh (1.0/2.0 mm), hence, their SHSS results were also alike (Figure 8a-f). However, CQ4 elements gave scattered stress results at the deck-toe for the coarser mesh (4.0/8.0 mm), therefore, the 4-CQ4 element had high results, while the 8-CQ4 elements had quite low SHSS results, as shown in Figure 8e. Hence, coarser mesh size (4.0/8.0 mm) should be avoided for SHSS evaluation, especially for linear shell elements (CQ4).
# Subcooled Flow Boiling Heat Flux Enhancement Using High Porosity Sintered Fiber
###### Abstract  
Passive methods to increase the heat flux on the subcooled flow boiling are extremely needed on modern cooling systems. Many methods, including treated surfaces and extended surfaces, have been investigated. Experimental research to enhance the subcooled flow boiling using high sintered fiber attached to the surface was conducted. One bare surface (0 mm) and four porous thickness (0.2, 0.5, 1.0, 2.0 mm) were compared under three different mass fluxes (200, 400, and 600 kg\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) and three different inlet subcooling temperature (70, 50, 30). Deionized water under atmospheric pressure was used as the working fluid. The results confirmed that the porous body can enhance the heat flux and reduce the wall superheat temperature. However, higher porous thickness presented a reduction in the heat flux in comparison with the bare surface. Bubble formation and pattern flow were recorded using a high-speed camera. The bubble size and formation are generally smaller at higher inlet subcooling temperatures. The enhancement in the heat flux and the reduction on the wall superheat is attributed to the increment on the nucleation sites, the increment on the heating surface area, water supply ability through the porous body, and the vapor trap ability.  
porous body; subcooled flow boiling; fibrous metal; heat flux enhancement; bubble behavior; flow pattern Article
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 5 Applications
### Material Science
#### 5.3.2 Materials for Electrochemistry  
IR fs-LMS was applied to obtain the 3D chemical imaging of the Cu-Sn-Pb (lead bronzes: CuSn10 Pb10, CuSn7 Pb15, and CuSn5 Pb20), model bronze alloys [159]. These materials were identified as high-performance cathode materials for electro-organic synthesis (dehalogenation, deoxygenation) of industrially relevant building blocks. The quantitative and spatially resolved element analysis of such cathode materials can be helpful in understanding the observed differences in the electrochemical reactivity and stability of these alloys. The element analysis showed specific composition correlations among the major elements (Cu, Sn, and Pb). On selected spots on the sample, the minor elements including Ni, Zn, Ag, and Sb, and trace elements C, P, Fe, and As were quantified. Inductively coupled plasma collision/reaction interface mass spectrometry (ICP-CRI-MS) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) reference measurements were conducted in parallel. The element analyses showed significant chemical inhomogeneity in all three ternary bronze alloys with profound local deviations from their nominal bulk compositions and indicated further differences in the nature and origin of this composition inhomogeneity.
# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 2 HSI Inpainting Model and Mechanism
### HSI Inpainting Mechanism
Therefore, we introduce a robust objective function to enhance the robustness of the image inpainting process against noise and outliers, i.e., we convert Equation (4) into an optimization of the following minimization problem:  
\[\underset{\mathbf{\alpha}_{i}}{\text{min}}\sum_{i}\|\mathbf{\Psi}_{i}\mathbf{\alpha}_{i}- \mathbf{y}_{o,i}\|_{1}^{1}+\lambda\|\mathbf{\alpha}_{i}\|_{1}. \tag{10}\]  
According to previous discussions [25; 26], the robust function error term \(\|\mathbf{\Psi}_{i}\mathbf{\alpha}_{i}-\mathbf{y}_{o,i}\|_{1}^{1}\) makes the data fit less susceptible to the influence of noise or outliers. The robust sparse coding \(\hat{\mathbf{\alpha}}_{i}\) is obtained by solving the optimization problem given by Equation (10) and reconstructing the unobserved parts of each pixel spectral vector to obtain the following:  
\[\hat{\mathbf{x}}_{i}=\mathbf{D}\hat{\mathbf{\alpha}}_{i},\ i=1,\ldots,n_{s}. \tag{11}\]  
Therefore, the inpainted HSI is expressed as \(\hat{\mathbf{X}}=[\hat{\mathbf{x}}_{1},\hat{\mathbf{x}}_{2},\ldots,\hat{\mathbf{x}}_{n_{s}}]\). The process described above uses the sparse representation method to inpaint the hyperspectral remote sensing image. The most critical steps are the acquisition of the spectral dictionary \(\mathbf{D}\) and the solution of the robust sparse coding \(\hat{\mathbf{\alpha}}_{i}\).
# The Emerging Energy Internet: Architecture, Benefits, Challenges, and Future Prospects
## 4 Role of Energy Internet in Smart Grid
### Energy Internet as a Peak Load Optimizer
The objective function of this model is as follows:  
\[\textit{minimize}\quad C_{grid}+C_{m}+C_{RES}\ -B_{grid} \tag{3}\]  
where \(C_{grid}\) is the total grid energy usage cost of the all end-users, which is a function of RTP on an hourly basis and the grid energy used for charging. \[C_{grid}=\sum_{t=1}^{24}RTP_{i}(t)\cdot E_{L}^{i}(t)+\sum_{t=1}^{24}RTP_{i}( t)\cdot E_{ES,t}^{i}\cdot\delta_{grid,t} \tag{4}\]  
Here, \(RTP_{i}(t)\) represents the RTP at the \(t\)-th hour on day \(i\); \(E_{L}^{i}(t)\) is the amount of the gird energy used by the end-users at the \(t\)-th hour on day \(i\); \(E_{ES,t}^{i}\) is the amount of the grid energy used by end-users to charge at the \(t\)-th hour on day \(i\); \(\delta_{grid,t}\) is a binary decision variable that decides whether end-users use the grid energy at the t-th hour to charge electricity (i.e., \(\delta_{grid,t}=1\) represents power charging; \(\delta_{grid,t}=0\) represents power discharging). The energy generated by Wind and PV can be used by customers directly or else they can store it in the DESDs. The maintenance cost of energy storage facilities of all end-users is given by \(C_{m}\), charging cost \(C_{RES}\) is of all end-user's charging their generated renewable energies is given by  
\[ \begin{array}{c}C_{RES}=\sum C_{i}\\ (i=\textit{Wind or PV or fuel cell or Bio mass, etc.})\end{array}  \tag{5}\]  
where, \(C_{wind}=M_{wind}\cdot N\) is the cost of wind energy generation, \(C_{PV=}\ M_{PV}\cdot S_{PV}\) is the cost of PV energy generation, \(M_{wind}\), \(M_{PV}\) is the maintenance cost of the relevant equipment per day, \(N\) is the total number of wind turbine generators, and SPV is the total area of solar power generating equipment. The income \(B_{grid}\) represents a function of the RTP tariff and selling the energy from the user's own energy storage facilities to the grid. Therefore, the optimized EMS solution of Equations (3)-(5) adopted the EI framework, and considered the RTP influence based on the demand-supply balance and energy market data with the involvement of renewable energy sources, which is possible in EI and FREEDM architecture.
# Efficient Open-Set Recognition for Interference Signals Based on Convolutional Prototype Learning
## 4 Proposed Method
### Feature Extractor and Prototype Classifier
#### 4.1.3 Hybrid Attention and Feature Reuse Net  
Combining with the feature reuse structure and hybrid attention module, the proposed feature exactor for interference signals, i.e., HAFRNet, is shown in Figure 4.  
HAFRNet consists of ten one-dimensional convolution layers, three HDAMs, four one-dimensional average pooling layers and one FC layer. For simplicity, we did not draw the BatchNormlization layer and ReLu layer before each convolution layer in Figure 4, which can be defined as Equations (12) and (13), respectively.  
\[BN(x)=a\frac{x-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\varepsilon}}+b \tag{12}\]  
\[\text{ReLu}(x)=\max(0,x) \tag{13}\]  
where \(\mu_{B}\) and \(\sigma_{B}\) are the mean and variance of the samples in the mini-batch, respectively, \(a\) and \(b\) are learnable parameters and \(\varepsilon\) is a small constant added to the mini-batch variance for numerical stability.  
The input of HAFRNet is a one-dimensional noisy interference signal, \(x\), while its output is a 16-dimensional feature representation. The shortcut in Figure 4 is the feature reuse structure, as shown in Figure 2, which is a channel-wise concatenation. The convolution layer parameter meanings are the size, type, number and stride of the convolution kernel, respectively. For example, [7 conv1d, 32, /2] means the convolution layer uses one-dimensional convolution with 32 convolution kernels whose size is 7 \(\times\) 1 and stride is 2. The average pooling layer parameter meanings are the type, size and stride of the pooling kernel, respectively. For example, [1 dAvgPool, 4, /2] means the average pooling layer uses one-dimensional average pooling with a pooling kernel whose size is 4 \(\times\) 1, and the stride is 2. The compression rate of the HDAM is 8. The FC layer parameter is the number of neurons in the output layer.
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.10. Benzenoids  
1H-indole was the only benzenoid found in a higher concentration in _LEV_ wine (Table 11). Benzenoids with the highest _F_-ratios were mostly much more abundant in control _SCE_ wine, including particular benzenoids from the phenylalanine metabolism and their derivatives, such as ethyl 2-phenylacetate, ethyl phenethyl ether, and 2-phenylacetaldehyde. This, together with the higher concentration of 2-phenylethanol, implies a greater expression of the responsible genes in _S. cerevisiae_ yeast. Besides the transformation of amino acid precursors and inter-conversions of benzenoids during fermentation, [PERSON] et al. [52] reported about the possibility of de novo synthesis of some of these compounds by _Hansenuspora vitae_ (which is also a non-_Saccharomyces_ yeast) without the presence of their corresponding precursors from grapes.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Co.** & **Volatile Aroma Compounds** & **ID** & **LRIexp** & **LRIlit** & **_F_-Ratio** & \multicolumn{2}{c}{**Treatment**} \\ \hline  & & & & & & _SCE_ & _LEV_ \\ \hline FL22 & \(\delta\)-Hexalactone & MS, LRI & 1804 & 1798 & 0.610 & 0.659 \(\pm\) 0.079 & 0.599 \(\pm\) 0.108 \\  & 5-Methyl-5- & & & & & & \\ FL23 & hydroxyhexanoic acid & MS & 1141 & - & 0.607 & 1.40 \(\pm\) 1.28 & 0.73 \(\pm\) 0.77 \\  & lactone & & & & & & \\ FL24 & \(\gamma\)-Valerolactone & MS, LRI & 1616 & 1617 & 0.477 & 0.231 \(\pm\) 0.130 & 0.283 \(\pm\) 0.012 \\ FL25 & Ethyl 2-fluoroate & MS, LRI & 1629 & 1628 & 0.390 & 26.15 \(\pm\) 1.50 & 27.26 \(\pm\) 2.69 \\ FL26 & Solerone & MS, LRI & 2076 & 2096 & 0.010 & 1.28 \(\pm\) 0.19 & 1.25 \(\pm\) 0.45 \\ \hline  & & & & & & & \\ \end{tabular} Abbreviations: Co.—compound’s code. ID—identification of compounds: S—retention time accordant with that of a pure standard; MS—mass spectra accordant with that from NIST 2.0, Wiley 8, and FFNSC 2 mass spectra databases from electronic libraries or the literature; LRI—linear retention index accordant with the index from the literature. Compounds with only MS in the ID column were considered tentatively identified. LRIexp—experimental linear retention index; LRIlit—linear retention index from the literature. _SCE_—_Saccharomyces cerevisiae_ (control, pure culture); _LEV_—_Lachancancancanc thermoidemars_ (sequentially inoculated; fermentation finished by _S. cerevisiae (SCE)_ inoculated at 2 vol 5* ethanol). Different superscript lowercase letters in a row represent statistically significant differences among two investigated wines determined by one-way ANOVA and least significant difference test (LSD) at \(p\) < 0.05.  
\end{table}
Table 11: Concentrations (\(\mathrm{\SIUnitSymbolMicro g/L}\)) of benzenoids found in Malvazija istarska white wines produced using different yeasts determined by untargeted two-dimensional gas chromatography with time-of-flight mass spectrometry (GC\(\times\)GC/TOF-MS) sorted by decreasing Fisher’s _F_-ratio.
## 1 Introduction
### Mitigation  
Mitigation refers to actions taken to decrease the likelihood that an emergency will occur and reduce its impact should it occur. Geographical Information Systems (GIS) are often used to identify geographical areas of high risk that would need to be prioritised during an emergency, and disaster databases are used for research and risk analysis, informing policy making and emergency planning.
## 1 Introduction
However, most of the study sites concentrate in the western countries except a few remote areas like the Arctic tundra, Siberian taiga, the Amazonian forest and Sahelian savannah. For the drylands ecosystems of Central Asia, knowledge on carbon fluxes is still very poor. The challenge for the modellers working in this region, therefore, is to find appropriate methods for estimating carbon sequestration without undertaking the expensive and often not realizable ecosystem flux measurements. In grasslands of Central Asia and Kazakhstan, ecologists have measured annual NPP through direct sampling of biomass growth increment for decades. However, most of these studies only provide results of local small-scale measurements from different decades that are not feasible for producing carbon stocks maps [28-33]. This study describes and tests a satellite data-based model for mapping total carbon sequestration over a semi-arid grassland-dominated region in Central Kazakhstan. This model employs the algorithm of Monteith and combines remote sensing data from SPOT-Vegetation satellite and ground inventory data on biomass. The approach presented in the study expands the algorithm of Monteith incorporating ecosystem respiration. Light use efficiency was estimated from the ground-based data using a simple empirical approach. Results were considered in relation to design of algorithms for satellite-based monitoring of the Kazakhstan-wide carbon cycle.
## 2 Data Set Construction
### Image Processing and Data Set  
The CCSN GW signals embedded in noise are processed, generating time-frequency plots organized in a 2D map. These 2D maps are configured to encapsulate the information present in the GW signal. The initial data consist of time-frequency representations, denoted as (TFR), defined as follows:  
\[\{TFR_{i}(t,f),s_{i}\}_{i=1}^{N}, \tag{4}\]  
Figure 2: Two examples of CCSN GW signals with different HFF slopes of 514 Hz/s and 2411 Hz/s. The left panel displays the strain signal, while the middle and right panels show the time-frequency representation and the processed 2D maps, respectively.  
where \(TFR_{i}(t,f)\in\mathbb{R}^{N_{i}\times N_{f}}\), \(t\) denotes the time, \(f\) the frequencies, and \(s_{i}\) the HFF slope associated with the number of instances in the data set, denoted as \(N\). To produce a suitable representation of \(\mathcal{D}_{set}\), each time-frequency representation is downsampled as a grayscale image. This grayscale image is set to dimension \(28\times 28\) to obtain the final two-dimensional data matrix. We produced 10,000 2D maps encoding the 100 different HFF slope values associated with CCSN GW signals. Our data set, \(\mathcal{D}_{set}\), is defined as follows:  
\[\mathcal{D}_{set}=\{X_{i},s_{i}\}_{i=1}^{N}, \tag{5}\]  
where \(X_{i}\in\mathbb{R}^{28\times 28}\) and \(s_{i}\in[500,\,5000]\).
## 2 Numerical Methods
### Resolution Convergence Test  
The resolution tests of one-dimensional detonations, as shown in Figure 3, are conducted to check the convergence of the numerical method with the present two-step reaction model. The first one is a CJ detonation with \(k_{\mathrm{R}}=2.0\) s\({}^{-1}\), and \(T_{0}=295\)K transmits into a low-temperature zone with \(T_{1}=270\) K. The second one is a CJ detonation with \(k_{\mathrm{R}}=4.0\) s\({}^{-1}\) and \(T_{0}=295\)K transmits into a high-temperature zone with \(T_{1}=320\) K. The resolution tests of two-dimensional detonations, as shown in Figure 4, are also conducted to check the convergence of cellular structures. The set-up is in accord with our following simulations as shown in Figure 2 but in a shorter length of the domain to save computation time. In the present resolution test, \(k_{\mathrm{R}}=2.0\) s\({}^{-1}\) and \(A=15\) K. The convergence of the numerical results are observed in both the one- and two-dimensional tests. A grid resolution of at least 32 pts/\(\Delta_{1}\) is found to be adequate to ensure the convergence in the present study.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \(T_{0}\) (K) & \(p_{0}\) (Pa) & \(R\) (J/kg\(\cdot\)K) & \(\dfrac{Q}{RT_{0}}\) & \(\gamma\) & **Ma** & \(\dfrac{E_{\mathrm{I}}}{RT_{\mathrm{s}}}\) & \(\dfrac{E_{\mathrm{R}}}{RT_{\mathrm{s}}}\) & \(k_{\mathrm{I}}\) (I/s) & \(k_{\mathrm{R}}\) (I/s) & \(\Delta_{1}\) (mm) & \(\Delta_{\mathrm{R}}\) (mm) & \(\chi\) \\ \hline
295 & 50 & 218.79 & 19.71 & 1.44 & 5.6 & 4.8 & 1.0 & 1.39 & 2.0 & 1.0 & 0.96 & 5.0 \\
295 & 50 & 218.79 & 19.71 & 1.44 & 5.6 & 4.8 & 1.0 & 1.39 & 3.0 & 1.0 & 0.64 & 7.5 \\
295 & 50 & 218.79 & 19.71 & 1.44 & 5.6 & 4.8 & 1.0 & 1.39 & 4.0 & 1.0 & 0.48 & 10.0 \\ \hline \end{tabular}
\end{table}
Table 1: Detonation reaction parameters.  
Figure 2: Schematic of the computation setup, (**a**) top view and (**b**) side view.
# FEM Analysis of Piezoelectric Resonator Polarization Process
## 2 Materials and Methods  
The presented approach is based on the numerical model of the problem, based on the physical description of piezoelectric continuum, see [17; 18]. Such a description includes elastic Equations (2), electric Equations (3) and state equations for piezoelectric material (4) and (5), relation between electric field and potential (6), relation between strain and mechanical displacement (7).  
\[\
abla\cdot\mathbf{T}=\frac{\partial\mathrm{T}_{\mathrm{ij}}}{\partial x_{j}}=0 \quad i,j=1,2,3\quad x\in\Omega, \tag{2}\]  
\[\
abla\cdot\mathbf{D}=\frac{\partial\mathrm{D}_{\mathrm{j}}}{\partial x_{j}}=0 \tag{3}\]  
\[\mathrm{T}_{\mathrm{ij}}=c_{ijkl}\cdot\mathrm{S}_{\mathrm{kl}}-e_{ijk}\cdot \mathrm{E}_{\mathrm{k}},\qquad i,j=1,2,3. \tag{4}\]  
\[\mathrm{D}_{k}=e_{kij}\cdot\mathrm{S}_{\mathrm{ij}}+\epsilon_{kj}\cdot \mathrm{E}_{\mathrm{j}}+P_{k}^{s},\qquad k=1,2,3, \tag{5}\]  
\[E_{i}=-\frac{\partial\varphi}{\partial x_{i}}, \tag{6}\]  
\[S_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right), \tag{7}\]  
where T, D, S, E, u, \(P_{k}^{s}\), c, e and \(\epsilon\) are stress, the vector of electric displacement, strain, electric field, mechanical displacement, spontaneous polarization and material tensors. \(\Omega\) denotes the volume of sample. The FEM-solution of this problem [17; 18], leads to the set of linear equations with large and sparse matrices,  
\[\left(\begin{array}{cc}\mathbf{K}&\mathbf{P}^{\mathrm{T}}\\ \mathbf{P}&\mathbf{E}\end{array}\right)\left(\begin{array}{c}\mathbf{U}\\ \mathbf{\Phi}\end{array}\right)=\left(\begin{array}{c}\mathbf{R}_{\mathbf{U }}\\ \mathbf{R}_{\mathbf{\Phi}}\end{array}\right), \tag{8}\]  
where \(\mathbf{K},\mathbf{P},\mathbf{E},\mathbf{U},\mathbf{\Phi},\mathbf{R}_{\mathbf{ U}},\mathbf{R}_{\mathbf{\Phi}}\) are elastic, piezoelectric and electric part of global matrix, displacements and potentials in nodes of finite element mesh and vector of right hand side, which contains boundary conditions. These variables allow to compute the elastic stress, electric field, electric displacement and energies.
## 4 Experimental Evaluations and Results
### Usages with BEERL
Furthermore, we evaluated antenna telecommunication metrics (RSRP, SINR, and throughput) before and after adjusting the agent reward weights as shown in Figure 12. We can see that the agent with the adjusted reward weights has a better RSRP KPI than the original agent, while SINR and throughput KPIs are slightly compromised. Since the range of the base values of the adjusted agent is low, the action selection can depend more on the contribution of input features to the reward components and less on the base values of the reward components. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Action** & **RSRP** & **SINR** & **Throughput** \\ \hline \hline Tilt-down & 9.273874 & 9.247864 & 9.758694 \\ No-tilt & 9.375855 & 9.965568 & 10.043088 \\ Tilt-up & 9.231198 & 9.528765 & 9.867805 \\ \hline \hline Mean & 9.293643 & 9.580732 & 9.889862 \\ Range & 0.144657 & 0.717704 & 0.284394 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Base value for each action and reward component at the RL level after the reward weights are adjusted to reduce the bias of the reward components. Figure 11: Explanations generated using BEERL at the RL level after the weights of the reward components (shown in parentheses) are adjusted. (**a**) Decomposed feature importance at the RL level. (**b**) Decomposed feature importance presented per reward component at the RL level.