esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of the Composition of the Activator on Mechanical Characteristics of a Geopolymer
## 3 Test Results
### Tensile Strain  
Some specimens were instrumented with strain gauges to evaluate the strains up to failure, during the bending tensile tests. It is assumed that failure would correspond to the point of maximum load applied to the specimen. The results are shown in Figure 3. The presented margin of error of the data is computed from the standard error of the mean (or alternatively from the product of the standard deviation of the data and the inverse of the square root of the sample size, which is the same) and it is typically approximately twice the standard deviation--the half-width of a 95% confidence interval.  
As can be seen, Type A mixes have, on average, slightly lower values than those of Type B, except for 1:1.5, for which the strain value is far below the others (290 \(\mu\)\(\varepsilon\)). The possible reason given before for the explanation of low tensile strength values, i.e., some excess of liquid, does not seem to fully explain the difference in this case. The variability of the results would help to explain some relatively high variations.  
For Type B mixes, the composition with the highest strain corresponds to 1:1.75 (414 \(\mu\)\(\varepsilon\)) and the lowest to 1:2.5 (318 \(\mu\)\(\varepsilon\)). It also appears that most of the mixes have strains higher than that of the Base Mix, except for 1:2.25 and 1:2.5. However, the values did not show any direct relationship to the activator composition. For Type B mixes, the standard deviation shows a maximum value of 108 \(\mu\)\(\varepsilon\), revealing the significant variability of the results.  
For OPC mortars, the tensile strain measured at failure was 118 \(\mu\)\(\varepsilon\), which is clearly lower than those obtained for the different compositions of geopolymer specimens (the maximum standard deviation was found to be 21 \(\mu\)\(\varepsilon\)). On average, OPC mortars have values approximately one third of those of geopolymers. This means that the geopolymers seem significantly more deformable at tensile failure. These results are very important and have great impact on the structural behaviour  
Figure 3: Strains at failure for Type A and Type B mixes.  
of the members. The structures could be more adaptable to the imposed deformations (for instance differential soil settlements), but the deformations of the slabs could be a critical aspect in the structural design (as known, the deformation of the slabs must not exceed some values specified in standards to prevent cracking of non-structural components of the construction, such as partition walls, for instance).
## 3 Results and Discussion
### Thermal Properties of the Studied Polymers
The DTG curves presented in Figure 4b clearly indicate that although two stages of a relatively mild course have been distinguished, the process is much more complex. In all DTG plots of materials prepared with the mixture of decanol and benzyl alcohol as porogens, only two stages can be perceived. Their rates are higher than those for the corresponding polymers in the series with toluene, indicating the more rapid course of their decomposition [23]. Figure 3: The influence of the applied pore-forming diluents on the thermal behavior of the analyzed polymers under a helium and air atmosphere. TG curves of the toluene series in He (**a**) and in the air (**b**), and the mixture of alcohols series in He (**c**) and in the air (**d**). In order to evaluate if the applied pore-forming diluents had any influence on the thermal stability-prepared materials, the TG curves of the polymers with the same monomer ratios were in pairs superimposed on the same graph (Figure 11). In the range of polymer decomposition temperatures for analyses carried out under He (Figure 11,b), no significant differences can be noticed, except the one for the DVB-GMA 1:2 copolymers, of which this obtained with toluene turned out to be slightly less thermally stable than that with the mixture of alcohols. Under the air atmosphere (Figure 11,d) in terms of weight loss up to approximately 20%, no differences were recorded, with the exception of DVB-GMA t and DVB-GMA db as before. Above this value, the polymers of the db series decomposed easier than those prepared in toluene. The performed analyses show that the chemical composition of polymers and the degree of their cross-linking determine their thermal properties to the greatest extent. The impact of the used pore-forming solvents seems to be insignificant, especially since the observed differences concern the range of temperatures in which the decomposition of the polymer under the air atmosphere is advanced (20% of the samples decomposed).
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 4 The FedIter-HT Algorithm
The statistical bias term \(g_{3}(x^{*})\) can be much smaller than \(g_{1}(x^{*})\) in Theorem 1 because \(g_{3}(x^{*})\) only depends on the norm of \(\
abla f(x^{*})\) restricted to the support \(\widetilde{\mathcal{I}}\) of size \(2N\tau+\tau^{*}\). Because the norm of the gradient is a dominating term in \(g_{1}\) and \(g_{3}\), slightly increasing \(K\) does not significantly vary the statistical bias terms (when \(d\gg 2N\tau+\tau^{*}\))._  
Using the results in Theorem 2, we can further derive Corollary 3 to specify the number of rounds required to achieve a given estimation precision. **Corollary 3**. : _If all the conditions in Theorem 2 hold, for a given \(\epsilon>0\), the FedIter-HT requires the most \(T\leq C_{2}\log(\frac{\|x_{0}-x^{*}\|}{\epsilon})\) rounds to obtain_  
\[E[\|x_{T}-x^{*}\|^{2}]\leq\epsilon+g_{3}(x^{*}),\]  
_where \(C_{2}=-(\log(\theta_{2}))^{-1}\)._  
Because \(g_{3}(x^{*})=O(\|\pi_{\mathcal{I}}(\
abla f(x^{*}))\|^{2})\), and we also know \(\|\pi_{\mathcal{I}}(\
abla f(x^{*}))\|^{2}\leq(2N\tau+\tau^{*})^{2}\|\
abla f (x^{*})\|_{\infty}^{2}\) and \(2N\tau+\tau^{*}\ll d\) in high dimensional statistical problems, the result in Corollary 3 gives a tighter bound than the one obtained in Corollary 1. Similarly, we also obtain a tighter upper bound for the convergence performance of the objective function \(f(x)\). Corollary 4: _If all the conditions in Theorem 2 hold, let \(\Delta_{2}=l_{s}\|x_{0}-x^{*}\|^{2}\), and \(g_{4}(x^{*})=O(\|\pi_{\mathcal{I}}(\
abla f(x^{*}))\|^{2})\), we have_  
\[E[f(x_{T})-f(x^{*})]\leq\theta_{2}^{T}\Delta_{2}+g_{4}(x^{*}).\]  
The proof details can be found in Appendix A.7. The theorem and corollaries developed in this section only depend on the \(l_{s}\)-restricted smoothness and \(\rho_{s}\)-restricted strong convexity, where \(s=2\tau+\tau^{*}\), which are the same conditions used in the analysis of existing IHT methods. Moreover, \(\|\pi_{\mathcal{I}}(\
abla f(x^{*}))\|\leq(2N\tau+\tau^{*})\|\
abla f(x^{*})\|_ {\infty}\), which means \(g_{3}(x^{*})\) and \(g_{4}(x^{*})\) are \(O((2N\tau+\tau^{*})^{2}\|\
abla f(x^{*})\|_{\infty}^{2})\), where \(2N\tau+\tau^{*}\) is the size of support \(\mathcal{I}\). Therefore, our results match the current best-known upper bound for the statistic bias term compared with the results for traditional IHT methods.
## 2 Generalities of Fatigue Analysis  
Since the data from the accumulated fatigue damage analysis allow us to determine how the mechanical elements are weakened over time, then the main points considered in fatigue analysis are: (i) load, (ii) component geometry, (iii) material properties, (iv) stress-life analysis and (v) fatigue life analysis. However, fatigue analysis is performed by considering that the fatigue life depends on the average value and on the amplitude of the fluctuating loads [16]. However, since several mechanical components are subjected to fluctuating load conditions, then fatigue analysis is required in order to estimate their lifetime [17; 18]. On the other hand, fatigue analysis requires an estimation of the cycles to failure, which is achieved when a set of n samples are tested to failure. Thus, when the fracture occurs, the corresponding cycles are recorded as the corresponding cycle to failure. In high-cycle fatigue situations, material performance is commonly characterized by an S-N curve [19], also known as Wohler's curve. The S-N is a graph of the magnitude of the alternating stress (\(\sigma_{a}\)) against the logarithmic of cycles to failure (\(N\)) [3]. Therefore, the principal stresses \(\sigma_{1}\) and \(\sigma_{2}\) values are used to determine the stress amplitude \(\sigma_{a}\) and mean stress \(\sigma_{m}\) values, which are used in fatigue analysis. Then the \(\sigma_{a}\) and \(\sigma_{m}\) values are both used in a failure theory criterion to determine the corresponding equivalent stress \(\sigma_{\alpha}\) value used in [PERSON]'s equation and, thus, determine the \(N\) value for the expectedcycles to failure [20]. Because cycles to failure occur due to weakness of the strength, they can be represented by the weakest link theory represented by the Weibull distribution, which offers the smallest extreme value distribution [14]. The most useful properties of the Weibull distribution to represent fatigue data are its stability property (referred to as stability to changes of the location parameter), its stability by using the minimum operations, and its stability under scale transformation (see [14] chapter 2 for details).  
Since the Weibull stress \((\beta_{s}\), \(\eta_{s})\) parameters used in the proposed method are both estimated directly from the applied principal stresses \(\sigma_{1}\) and \(\sigma_{2}\) values [21,22], let us present how they are determined from the static stress analysis.
## 5 Classification of Harmonic Emissions Profiles
### Implementation of Support Vector Machines
#### 5.3.2 Determination of Optimal Parameters  
A selection of optimal parameters for the regularisation parameter, \(C\), and the kernel parameter, \(\gamma\), is required before the application of SVMs with a RBF kernel. A simple grid search, using k-fold cross-validation to find the two best parameters (C, \(\gamma\)) within a wide enough parameter range [37], is applied to each of the SVMs. The two parameter ranges \(C=\big{\{}10^{-1},\dots,\,10^{2}\big{\}}\) and \(\gamma=\big{\{}10^{-2},\dots,\,10^{1}\big{\}}\) are suitable for the given classification problem.  
A cross-validation is applied for each combination of (C, \(\gamma\)), dividing the training set into \(k\) equally sized subgroups. The training is applied \(k\)-times, whereas each time one group is tested against the remaining groups to determine the error rate. The result of the grid search for optimal parameters (C, \(\gamma\)) of the classifier SVM-1 is displayed in Figure 8.  
The training results of SVM-1 with the optimal parameters in Figure 9a had a classification rate of 96.3%, with one misclassification.  
Selecting suboptimal parameters may result in perfect classification rates of 100% for the training, as displayed in Figure 9b. Future classifications will very likely lead to many misclassifications due to the overfitting to the training samples.  
Figure 8: Grid search for the optimal parameters of the classifier SVM-1 using a cross-validation with 10 subgroups (\(k=1\)); error rate in p.u.
## 3 GPR Image Characteristics Analysis
### Image Processing and Enhancement  
Based on the forward simulation output of GprMax, Figure 15a shows the image of the void defect after direct wave removal. Between the travel times of 2 ns and 5 ns, no anomalous reflection waves were observed, indicating the absence of anomalous defects in this region. Between the travel times of 5 ns and 7 ns, anomalous reflections occurred, with discontinuous in-phase axes and misaligned reflection waves in adjacent traces. Strong anomalous reflection waves occurred at 7 ns, as well as at the upper interface of the void area, exhibiting similar characteristics to a \"hyperbola\".  
Figure 14: Void defect instantaneous frequency features.  
Figure 13: Void defect instantaneous phase features.  
Figure 15: Image processing of void defect.  
Figure 15b shows the forward simulation after undergoing signal gain processing. The image filtered strong reflection waves such as direct waves, reducing the disparity between strong and weak reflection waves and allowing weak reflection waves to be better visualized. This provides a more detailed representation of the image characteristics of the void defect. According to the radar simulation image after gain processing, the reflection signals from the upper and lower interfaces of the inflated void were strengthened, and it was possible to obtain the interlayer information of the subgrade structures and the boundary information and image characteristics of the target anomalous body. This enabled the estimation of the size, position, and boundaries of the defect.  
Comparing the images before and after processing, the original radar image could only clearly identify the upper surface of the defect area, failing to accurately identify the lower surface. As indicated by the red line in the figure, after gain processing, the electromagnetic wave was reflected at the lower boundary at a travel time of 18 ns, allowing the identification of the lower boundary of the loose defect area. Based on the height of the loose area, and assuming that the relative dielectric constant of the filling medium in the loose area was 4, the propagation speed of the electromagnetic wave in the porous loose defect was deduced to be 0.25 m/ns.  
By eliminating direct waves and applying a signal gain, the response characteristics of the GPR image of the subgrade defect could be enhanced, improving the accuracy of GPR detection and the interpretation of subgrade defects.
## 2 Numerical Model  
LES in the EllipSys3D code are used. LES resolve the largest and most energetic eddies and only the smallest eddies are modelled, here using the subgrid-scale model from Ta-Phoue [18].  
The turbines are introduced into the simulations using an actuator disc method (ACD) based on airfoil data [19]. A generator-torque controller is used to adjust each turbine's rotational speed in the variable flow in a realistic way [20]. Both the ACD and the atmosphere i.e., turbulence and wind shear are introduced using body forces in the incompressible Navier Stokes (NS) equations formulated in vector notation, see Equation (1). In this equation \(\mathbf{U}\) is the velocity, \(\mathbf{P}\) the pressure, \(t\) the time, \(\rho\) the density of air and \(\
u\) is the kinematic viscosity, and \(\
u_{SGS}\) is the subgrid scale viscosity. The bar represent the filtered parameters. The coordinate system used in the article is \(\mathbf{x}=(z,x,y)\) with the velocities \(\mathbf{U}=(w,u,v)\) for the streamwise, spanwise and vertical directions. The body forces \(\mathbf{f}\) that are added in the domain comes from the ACD (\(\mathbf{f}_{ACD}\)), from the FBL (\(\mathbf{f}_{FBL}\)) and from the turbulence (\(\mathbf{f}_{turb}\)) as described in [10]. The Coriolis correction defined in Equations (3) and (4) can be added as \(\mathbf{f}_{Corr_{mn}}\). The code is non-dimensional (normalized with respect to the rotor radius \((R)\) and the undisturbed velocity at hub height (\(U_{0}\))).  
\[\frac{\partial\overline{\mathbf{U}}}{\partial t}+\overline{\mathbf{U}}\cdot \
abla\overline{\mathbf{U}}=-\frac{1}{\rho}\
abla\overline{\mathbf{P}}+\frac {1}{\rho}\mathbf{f}_{ACD}+\frac{1}{\rho}\mathbf{f}_{FBL}+\frac{1}{\rho} \mathbf{f}_{turb}+\mathbf{f}_{Corr_{mn}}+\
abla\Big{[}(\
u+\
u_{SGS})*\
abla \overline{\mathbf{U}}\Big{]},\ \ \
abla\cdot\overline{\mathbf{U}}=0 \tag{1}\]  
The production (P) is calculated, according to Equation (2), as the angular velocity of the rotor \((\Omega_{rotor})\), which adjust to the flow using the generator-torque controller, times the rotor torque. The rotor torque is calculated as the area integral over the rotor area (\(A\)) of the local forces in the tangential direction per unit area calculated in the ACD model (\(f_{ACD_{data}}\)) times the local radius (\(r\)). In the results the relative production (\(P_{rel}\)) is presented which is \(P\) normalized with the production for the first turbine in the row of turbines (\(P_{1}\)).  
\[P=\Omega_{rotor}\cdot\int\int_{A}f_{ACD_{data}}rdA \tag{2}\]
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
## 5 Reward Design
### Extra Terms  
When composing the reward, certain metrics can complement the objective function [75; 184; 195; 246; 247]. This can be performed to influence the agent behavior to account for costs [115; 225; 267] or lateness [71; 173; 208], for example.  
Problem constraints can also be used as extra penalty terms [267; 292; 301], allowing infeasible solutions to be explored while also increasing the state space, for example, exceeding resource limits [258; 261] or missing entity due dates [44; 213].  
Extra terms can make approaches more general with the flexibility they bring, allowing penalties and secondary objectives to further guide the agent. However, balancing multiple terms becomes of extra importance, as extra terms can dominate the primary reward function and introduce biases.
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 3 Results and Discussion
### Evaluation of Bioactivities of Microalgal Extracts
#### 3.1.1 Antioxidant Activity
(POC)} & W & 52.5 \(\pm\) 3.1 & n.d. & 34.7 \(\pm\) 0.2 & n.d. \\  & ET & 62.5 \(\pm\) 0.3 & 0.144 \(\pm\) 0.004  & 71.7 \(\pm\) 0.1 & 0.0854 \(\pm\) 0.0140  \\  & EA & 82.4 \(\pm\) 2.0 & 0.337 \(\pm\) 0.013  & 47.6 \(\pm\) 0.5 & n.d. \\ \hline \multirow{3}{*}{_Nannochloropsis_ sp. (NANNO) & W & 72.0 \(\pm\) 0.3 & 0.621 \(\pm\) 0.018  & 83.2 \(\pm\) 0.1 & 0.279 \(\pm\) 0.014  \\  & ET & 43.5 \(\pm\) 0.6 & n.d. & 19.7 \(\pm\) 0.5 & n.d. \\  & EA & 3.6 \(\pm\) 0.40 & n.d. & 4.03 \(\pm\) 0.1 & n.d. \\ \hline \multirow{3}{*}{_Tetraselmis striata_ CTP4 (TCTP4)} & W & 31.6 \(\pm\) 0.7 & n.d. & 96.6 \(\pm\) 0.7  & 0.385 \(\pm\) 0.003  \\  & ET & 54.4 \(\pm\) 6.5 & n.d. & 3.23 \(\pm\) 0.15 & n.d. \\  & EA & 39.0 \(\pm\) 5.6 & n.d. & 33.2 \(\pm\) 0.4 & n.d. \\ \hline \multirow{3}{*}{_Tisochrysis lute_ (TIL) & W & 72.6 \(\pm\) 0.4 & 0.184 \(\pm\) 0.003  & 79.0 \(\pm\) 1.9 & 0.403 \(\pm\) 0.013  \\  & ET & 85.5 \(\pm\) 0.1 & 0.228 \(\pm\) 0.008  & 75.0 \(\pm\) 0.1 & 0.207 \(\pm\) 0.006  \\  & EA & 67.8 \(\pm\) 0.5 & 0.297 \(\pm\) 0.009  & 42.7 \(\pm\) 1.0 & n.d. \\ \hline \multirow{3}{*}{_Pharocductulum_ tricruratum_ (PHA) & W & 62.8 \(\pm\) 1.5 & n.d. & 75.2 \(\pm\) 0.9 & 0.508 \(\pm\) 0.005  \\  & ET & 74.4 \(\pm\) 0.6 & 0.358 \(\pm\) 0.008  & 58.8 \(\pm\) 1.4 & n.d. \\  & EA & 65.0 \(\pm\) 0.9 & 0.737 \(\pm\) 0.025  & 11.8 \(\pm\) 0.1 & n.d. \\ \hline \multirow{3}{*}{_Skeletonema_ sp. (SKE) & W & 74.0 \(\pm\) 0.5 & 0.434 \(\pm\) 0.012  & 63.8 \(\pm\) 0.9 & n.d. \\  & ET & 87.1 \(\pm\) 0.1 & 0.195 \(\pm\) 0.007  & 53.4 \(\pm\) 0.1 & n.d. \\  & EA & 76.3 \(\pm\)0.1 & 0.487 \(\pm\) 0.030  & 42.4 \(\pm\) 0.6 & n.d. \\ \hline \multirow{3}{*}{Spirulina (SPR)} & W & 74.0 \(\pm\) 0.5 & n.d. & 1.71 metals (copper, cadmium, iron, among others), due to their affinity towards metal ions. Although metals are vital for the normal functioning of the human body, an imbalance of metals in the biological matrix is known to generate free radicals through the Fenton and Haber-Weiss reactions and are often implicated in neurodegenerative diseases [6; 46]. Chelation mainly occurs when a ligand (e.g., cyclic peptides, ionophores, siderophores, phytochelatins, flavonoids) has at least two donor groups, which link to a central metal atom/ion through an acyclic or ring-like coordination bond and form stable complexes [47]. The chelation ability of compounds is influenced by their structure, functional groups, conjugated double bonds, electron donor groups and/or acceptor sites [48]. Therefore, our findings suggest that hexane fractions from both _Porphyridium_ sp. and _Skeletonema_ sp. may be sources of metal chelating compounds, which could play a significant role in antioxidant and neuroprotective mechanisms.
## 3 Results and Discussion
### Analysis of Atmospheric-Pressure Helium Dielectric-Barrier Discharges (APHeDBDs) Gas Heating
#### 3.2.2 Elastic Collision  
Figure 11 shows the contributions of elastic collisions among electrons and different background neutral gases. Although the energy exchange of each elastic collision process between an electron and a background molecule is inefficient due to large mass difference, the high collision frequency results in a remarkable contribution to the heating source as reported by other groups [9; 21; 22] in atmospheric pressure helium discharges. The collisions of electrons and helium atoms contribute to more than 99.9% of the heating source of elastic collision while the collisions of electrons and N\({}_{2}\) molecules contribute to less than 0.1% of the heating source of elastic collision since the concentration of helium atoms is much higher than that of the N\({}_{2}\) molecules.
## 3 Results and Discussion  
### Characterization of Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN Composite  
The synthesized Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composite was characterized by transmission electron microscopy (TEM), scanning electron microscopy (SEM), atomic force microscopy (AFM), energy dispersive X-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FTIR). The morphology and particle size of the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) and Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composites were first characterized by TEM. TEM images of the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) and Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\)/CN composites are shown in Figure 1a,b, respectively. From both the images, it is evident that each composites' morphology is different from the other. This is due to an additional layer of nitrogen doped carbon on the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) composite. The calculated particle size is in the range of 80-100 nm. The morphology was also justified by SEM (Figure 1c,d) and AFM (Figure 1e,f). In the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) composites, particles are visible, and they are semispherical. However, after the addition of CN, there is a clear change in shape and morphology. This is formed by calcination at higher temperatures. AFM results show the surface of the products: the Cu\({}_{2}\)O-Co\({}_{3}\)O\({}_{4}\) is rough as compared  
to hydroxyl groups, which are abundantly present on the surface of metal oxides. The FTIR results confirm the C=O bond of amide as the peaks appearing at 1668 cm\({}^{-1}\) and 1570 cm\({}^{-1}\), which indicates the stretching and bending of C=O. The peak at 1418 cm\({}^{-1}\) indicates the presence of a hydroxyl group, and the symmetrical vibration peaks at 1635.74 cm\({}^{-1}\) and 1733.65 cm\({}^{-1}\) belong to C=O and C=N bands, respectively. The peak at 1703 cm\({}^{-1}\) shows the interaction between the carbonyl group and the metal ions. The peaks for the metals' coordination with COO appear at 1560 cm\({}^{-1}\) and 1560 cm\({}^{-1}\), respectively.