esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress Estimation Using the Acoustoelastic Effect of Surface Waves in Weak Anisotropic Materials
## 4 Specimens and Experiments
### Experiments for Stress Estimation  
The stress estimation set-up is shown in Figure 13. The experimental set-up is similar to the acoustoelastic coefficient measurement except for the velocity measurement direction. The surface wave velocities were measured in three directions with difference of 45\({}^{\circ}\), where the angle \(\theta\) was set to 30\({}^{\circ}\). This measurement condition corresponds to the Case 2B in the numerical simulation. Stress was applied by tensile loading from 7500 N to 22,500 N at an increment of 2500 N. Specimens SA.2 and SB.2 were used for the stress estimation.  
The stress estimation results of the hot-rolled (SA.2 specimen) and extruded (SB.2 specimen) aluminum plates are plotted in Figure 14 and the detailed values are given in Tables 5 and 6, respectively. For the hot-rolled aluminum specimen, the estimated results match the given stresses and angle well. Similarly, the stress and angle estimations for the extruded specimens also exhibit a good agreement with the applied values even though this specimen has a larger anisotropy rate than the hot-rolled  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & \(K_{1}\) **[1/MPa]** & \(K_{2}\) **[1/MPa]** \\ \hline A.1 & \(-132.6\times 10^{-6}\) & \(30.5\times 10^{-6}\) \\ B.1 & \(-124.0\times 10^{-6}\) & \(39.7\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of the acoustoelastic coefficients for specimens SA.1 and SB.1.  
Figure 12: Change rates of the TOF with respect to the applied stress in specimen SB.1 for measurements of (**a**) \(K_{1}\) and (**b**) \(K_{2}\).  
Figure 13: Experimental set-up for the proposed stress estimation.  
specimen. The maximum error was 5.14 MPa at a given stress of 90.92 MPa in the hot-rolled specimen, and the other errors were less than 5.00 MPa for both specimens. As a result, it was confirmed that stresses can be estimated in weakly anisotropic materials using the proposed method. Note that the experiments were carried out for the case where the stress in the thickness direction was uniform only to verify the proposed method. If the stress varies with depth, surface waves of different wavelengths can be used to probe the stress field at different depths.
# A Sparse Multiclass Motor Imagery EEG Classification Using 1D-ConvResNet
## 3 Results
### Subject-Independent Classification  
We performed the subject-independent classification where training, validation, and testing were performed on the MI data of different subjects (see Figure 9). Testing the model on data corresponding to different subjects from the trained data is challenging, as differences in the subjects' motor skills may result in variations in imagining the movements. The diagonal cells of the confusion matrix (Figure 9a) show that the model achieves 100% classification accuracy on the MI of the right fist. The areas under the ROC curves and the precision-recall curves also validate these results; the subject-independent classification accuracy is 96.6%.
## 2 Materials and Methods
This was carried out to have all files in resemblance with DPG captures that are devoid of surface scans ensuring a close number of point clouds between all registrations. After this, the scans were assessed and compared by the same software. For trueness, each scan of each group was compared with the reference. However, for precision, the scans of each group were superimposed randomly over each other [41]. Each alignment entailed two steps where the initial alignment option was carried out first to proceed to the final rough alignment through the best fit option ensuring a top-notch merging (Figure 4) [42; 43; 44; 45; 46; 47; 48]. Fifteen superimpositions were conducted for each group, for a total of thirty alignments for each of precision and trueness. the software allowed engendering a colorimetric map (Figure 5). This map was generated for the ISB where the blue color was for inward defects, red for outward excesses, and green for minimal deformities. The surface tolerance was set with the scale ranging from a maximum deviation of +50 to \(-\)50 \(\upmu\)m. This value was determined according to the maximum clinically acceptable framework misfit tolerance [50]. All of the data collected were included in datasheets used for statistical analysis. The sample size was determined adequate for the analysis by a professional statistician. The appropriate significance was set at 0.05 and the power level was set at 0.80. A sample size of 15 in each group would detect a significant difference with a standardized effect size of 1.080. The statistical analysis was performed with IBM SPSS Statistics (version 26.0, New York, NY, USA). The level of significance was set at \(p\) value \(\leq\) 0.05. Kolmogorov-Smirnov and Shapiro-Wilk tests were used to assess the normal distribution of quantitative variables. Repeated-measure analyses of variance followed by univariates analysis and Bonferroni multiple comparisons tests were performed to compare the 3D deviation and angular deviations of precision and trueness between impression techniques and within ISBs. The Bonferroni test was performed to prevent data from incorrectly appearing to be statistically significant during multiple comparison testing since each comparison can impact other results creating multiple false positives.
# Can Biochar Improve the Sustainability of Animal Production?
## 5 Manure Storage and Treatment
### Biochar and Manure Anaerobic Digestion  
Anaerobic digestion has been an important waste management and bioenergy treatment for liquid swine waste and poultry litter. Here lies another opportunity during these storage and treatment steps to reduce odors and emissions by incorporating biochar.  
Anaerobic digestion produces \(\sim\)45-75% CH\({}_{4}\) and the remaining as CO\({}_{2}\); however, small amounts of NH\({}_{3}\) are also produced and cause stress on the system since NH\({}_{3}\) inhibits methanogenesis. [PERSON] et al. [118] investigated the use of HCl acidified, rice-straw biochar to reduce this stress during pig-waste anaerobic digestion. Treatments included 2, 5, 10, and 15 g biochar added to 800 mL pig wastewater and brewery sludge (a 7:3 ratio).  
Biochar addition improved the digestion process as demonstrated through a decrease in the VFA production and an increase in the total biogas production. The higher biochar concentrations resulted in a greater decrease in ammonia-N due to the high surface area and large adsorption capacity. The overall biogas production increased from 1705 mL for the control to 2306 mL for the 15 g treatment. The methane content in the biogas remained constant for all treatments at \(\sim\)58-59%. The increased biochar addition also reduced the lag time, or time from the start of reaction to the production of biogas, indicating that biochar increased the rate of degradation.  
[PERSON] et al. [119] studied the effect of biochar pyrolysis temperature, porosity, and surface area on enhancement of anaerobic digestion of cow manure. Cow manure and mushroom bran biochar were both produced at 400-600 \({}^{\circ}\)C. Anaerobic digestion inoculum was mixed with 1.524 g of biochar and 265 mL of water. Unlike [PERSON] et al. [118], the biochar enhancement did not reduce the lag time; however, both treatments did increase the cumulative biogas production. The optimal methane production was achieved in the 400 \({}^{\circ}\)C manure biochar and 550 \({}^{\circ}\)C mushroom biochar treatments.  
The 400 \({}^{\circ}\)C manure biochar increased methane production by 81.3% compared to the control by increasing the overall gas production. The 550 \({}^{\circ}\)C mushroom biochar increased methane production by 77.6% due to a shortened fermentation period. The biochar surface area and porosity was linked to the pyrolysis temperature but not to the gas production. It is hypothesized that the biochar enhanced the interactions between methanogenic and acetogenic bacteria, which resulted in a decrease in the relative abundance of other bacteria.  
All of these studies are reinforced by [PERSON] et al.'s [120] meta-analysis of 27 studies to examine the most important biochar parameters for influence on the methanogenic performance. Statistical analysis of these studies indicated that the biochar feedstock, pyrolysis temperature, and concentration in the digester were the most influential over the pH, size, surface area, and methanogen species.
# Theoretical Investigation for Angle Measurement Based on Femtosecond Maker Fringe
###### Abstract  
This paper proposes to utilize a femtosecond Maker fringe for angular measurement to expand the measurement range by using the characteristic of the multiple visible peaks in the Maker fringe. Four different z-cut nonlinear materials and three different polarization combinations of SHG were considered in the study, and various theoretical results are calculated for both the intensity-based angle measurement and the frequency-domain angle measurement. As a result, the p-s polarization shows a significant angle dependence in the range of \(\pm\)20\({}^{\circ}\) compared with the other polarization. In addition, the BBO and KDP are superior to the other investigated materials because of the relatively higher sensitivity and visibility. The refractive index difference was introduced in this paper, and it was applied to explain the angle measurement performance of the Maker fringe successfully.  
angle measurement; second harmonic generation; Maker fringe; intensity-based angle measurement; frequency-domain angle measurement +
Footnote †: journal:  
applied sciences
## 3 Results and Discussion  
### Proton and Neutron Irradiations  
Proton irradiation experiments were comprised of exposing five placards to varying proton beams. The placards featured one specimen of each of the three categories of FBGs installed in a strain relieved manner in a designated target. Spectra of each FBG specimen were collected before, during and after irradiation to assess the impact of the irradiation on two key parameters: changes to the reflected power of the FBG, labelled as Radiation Induced Absorption (RIA), and changes to the characteristic  
Figure 3: Diagram of custom FBG-based temperature probe and pressure transducer developed for isotope production experiments.  
wavelength (i.e., Bragg wavelength) of the FBG, labelled as Radiation Induced Shift (RIS). Overall, no statistically significant effect was observed due to proton irradiation on any of the test placards or in any of the three categories of FBGs. All variations in reflectivity or characteristic wavelength were either attributed to observed fluctuations in ambient air or placard temperature in the experimental environment or typical fluctuations seen in FBG testing that were insignificant for the purpose of this experiment.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 1 Introduction
### Original Contributions of this Paper  
Aiming at further reduce the overall loss of EDS and explore the feasibility of new motor control object, EDS loss model considering copper loss, iron loss, and inverter loss was established in this paper. Then, this paper studied the influence on EDS based on a new control object current: Angle \(\beta\) (the angle between stator current \(i_{s}\) and d-axis) and proposed a novel energy optimization control strategy based on \(\beta\) look-up table for EDS. The original contributions of this paper are as follows:  
1. The influence of current angle \(\beta\) on EDS efficiency was studied and the general rule between \(\beta\) and EDS efficiency was obtained in this paper, which provides a reference for future research.
2. The optimization range and the calculation amount of \(\beta\) to get the best EDS efficiency in the whole operating range was reduced by GSS search algorithm.
3. An energy optimization control strategy based on \(\beta\) look-up table was proposed in this paper. Compared with the traditional control strategy, the proposed control strategy can make EDS run over the whole vehicle operational range at an efficient point without affecting the performance and improve the efficiency of EDS.
## 3 Method or Methodology
### Dual Mutual-Feedback Component  
The dual mutual-feedback component of the \(t\)-th iteration is shown in Figure 4. Pink represents the up-projection unit, and blue represents the down-projection unit. Then, we unfold the two back-projection units. The upward arrows represent the up-sampling operation, and the downward arrows represent the down-sampling operation. The pink arrows connect to an up-projection unit, and the blue arrows connect to a down-projection unit. Then, we use mutual learning (black arrows) between the two back-projection units to exchange information. Finally, the outputs of the two units are fed back into each other in the next iteration to realize dual mutual feedback.  
In the dual mutual-feedback component of the \(t\)-th iteration, we define the HR features as \(H_{1}^{t}\), \(H_{2}^{t}\) and \(H_{3}^{t}\), and the LR features as \(L_{1}^{t}\), \(L_{2}^{t}\) and \(L_{3}^{t}\). We use \(f_{CRB}\) to represent the operations of CRB. The dual mutual-feedback procedure is as follows:  
\[L_{1}^{t}=\bigg{\{}\begin{array}{ll}f_{CRB}(L_{in})&t=1\\ f_{CRB}([L_{in},L_{out}^{t-1}])&t\geq 2\end{array}\bigg{\}}, \tag{7}\]  
\[H_{1}^{t}=\bigg{\{}\begin{array}{ll}f_{CRB}(H_{in})&t=1\\ f_{CRB}([H_{in},H_{out}^{t-1}])&t\geq 2\end{array}\bigg{\}}, \tag{8}\]  
\[\bigg{\{}\begin{array}{ll}L_{2}^{t}=f_{CRB}([L_{1}^{t},f_{down}(H_{1}^{t})]) \\ H_{2}^{t}=f_{CRB}([H_{1}^{t},f_{up}(L_{1}^{t})])\end{array}\bigg{\}}, \tag{9}\]  
\[\bigg{\{}\begin{array}{ll}L_{3}^{t}=f_{CRB}([L_{2}^{t},f_{down}(H_{2}^{t})]) \\ H_{3}^{t}=f_{CRB}([H_{2}^{t},f_{up}(L_{2}^{t})])\end{array}\bigg{\}}, \tag{10}\]  
\[\bigg{\{}\begin{array}{ll}L_{out}^{t}=f_{CRB}(L_{2}^{t}+f_{down}(H_{3}^{t}-H_ {1}^{t}))\\ H_{out}^{t}=f_{CRB}(H_{2}^{t}+f_{up}(L_{3}^{t}-L_{1}^{t}))\end{array}\bigg{\}}. \tag{11}\]
## 4 Characterizing Spectral Efficiency Dynamics
### Metrics of Interest  
We now proceed characterizing the spectral efficiency at time instant \(t\), i.e., \(S_{E}(t)\) that can be written as  
\[S_{E}(t)=\log_{2}\biggl{(}1+\frac{P_{T}G_{A}(t)G_{U}(t)L(y,t)}{N_{0}B+I_{M}} \biggr{)}, \tag{22}\]  
where \(G_{A}(t)\) and \(G_{U}(t)\) are the BS and UE antenna gains at time \(t\) (see (11)), and \(L(y,t)\) is the path loss at \(t\) defined in (5). By substituting (18) and (16) into (11) and (5) further into (22), we arrive at (15), where \(\rho_{U,V}\) and \(\rho_{A,V}\) are the directivity functions at BS and UE, respectively, that can be found using the antenna radiation patterns at UE and BS and misalignment angles in (17) and (19).  
Observe that \(S_{E}(t)\) in (15) is a random variable expressed as a function of several constants, including the initial coordinates of a user \((x_{0},0)\) and two random variables \(X(t)\) and \(Y(t)\). Recall that the probability density function of a random variable \(Y\), \(w(y)\)  
Figure 6: Top view of the considered scenario.  
expressed as a function \(y=\phi(x)\) of another random variable \(X\) with the pdf \(f(x)\), is given by [51]  
\[w(y)=\sum_{\forall i}f(\psi_{i}(y))|\psi^{\prime}{}_{i}(y)|, \tag{23}\]  
where \(x=\psi_{i}(y)=\phi_{i}^{-1}(x)\) is the \(i\)-th branch of the inverse.  
One may extend (23) to the case of two random variables by replacing \(|\psi^{\prime}{}_{i}(y)|\) with the Jacobian of the transformation, while \(f(\psi_{i}(y))\) with the joint density of \(X(t)\) and \(Y(t)\) can be expressed in multiplicative form due to the assumed independence of diffusions along the \(x\)- and \(y\)-planes. However, the complexity of the function in (15) prevents one from obtaining a useful expression for \(S_{E}(t)\). To determine the mean value of \(S_{E}(t)\), we will consider the Taylor series expansion of the spectral efficiency function \(S_{E}(t)=g(x,y)\) around the means of \(X(t)\) and \(Y(t)\), \(\vec{\mu}=(E[X(t)],E[Y(t)])=(\mu_{X},\mu_{Y})\). Recall that the second-order decomposition is as follows:  
\[g(x,y) =g(\theta)+g^{\prime}{}_{x}(\theta)(x-\theta_{x})+g^{\prime}{}_{y }(\theta)(y-\theta_{y})+\] \[+\frac{1}{2}\Big{[}g^{\prime\prime}{}_{xx}(\theta)(x-\theta_{x}) ^{2}+2g^{\prime\prime}{}_{xy}(\theta)(x-\theta_{x})(y-\theta_{y})+\] \[+g^{\prime\prime}{}_{yy}(\theta)(y-\theta_{y})^{2}\Big{]}. \tag{24}\]  
Assuming \(\theta=(E[X(t)],E[Y(t)])\), we obtain  
\[E(g(X,Y))=g(\theta)+\frac{1}{2}\Big{[}g^{\prime\prime}{}_{xx}( \theta)Var(X)+\] \[+2g^{\prime\prime}{}_{xy}(\theta)Cov(X,Y)+g^{\prime\prime}{}_{yy} Var(Y)\Big{]}+O(n^{-1})=\] \[\approx g(\theta)+\frac{1}{2}\Big{[}g^{\prime\prime}{}_{xx}( \theta)Var(X)+g^{\prime\prime}{}_{yy}Var(Y)\Big{]}, \tag{25}\]  
where the latter is due to independence between \(X(t)\) and \(Y(t)\).
## 5 Performance Analysis of C-GFB  
In this section, the effects of the mechanical preloads introduced by the electrified PZT actuators on the C-GFB's static and dynamic performance are analyzed via the model prediction for C-GFB lubrication. The performance prediction model coupled with the equivalent spring model of PZT actuators and foil structures is presented. Under specified static load and rotational speed, the static performance includes the gas-film pressure and thickness distributions, and the journal equilibrium position. Meanwhile, the dynamic performance indicates the journal trajectory within the bearing clearance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Part** & **Symbol** & **Value** & **Note** \\ \hline PZT coefficient of a PZT stack [\(\mu\)m/V] & \(D_{33,1}\) & \(-0.0028\) & measured \\ PZT stack stiffness per unit area [N/m\({}^{3}\)] & \(k_{p}\) & \(1.30\times 10^{9}\) & measured \\ Holder spring stiffness per unit area [N/m\({}^{3}\)] & \(k_{sp}\) & \(3.95\times 10^{6}\) & Provided \\ Bump foil stiffness per unit area [N/m\({}^{3}\)] & \(k_{b}\) & \(8.69\times 10^{9}\) & calculated from Ref. [21] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identified parameters of equivalent spring model of PZT actuator and foil structures.  
Figure 6: Measurement of structural stiffness (\(K_{p}\)) of PZT stacks: (**a**) test rig for recording PZT stack displacement and applied static load, (**b**) measured PZT stack displacement versus static loads for stack #1, and (**c**) estimated PZT stack stiffness for stacks #1, #2, and #3.
## 3 Results and Discussion
### Spherulitic Morphology and Crystal Growth of P3 HBHHx Samples
#### 3.4.2 Nucleation Density  
Figure 16 presents the nucleation densities of neat P3 HBHHx and the nucleated samples at low (50 \({}^{\circ}\)C) and high (100 \({}^{\circ}\)C) isothermal crystallization temperatures. At 100 \({}^{\circ}\)C, spherulites were observed only in the nucleated samples, each exhibiting a nucleation density of approximately 20 nuclei/mm\({}^{2}\). This density was similar for all these samples since similar concentrations of the nucleating agent were incorporated (0.5 or 2 wt%), which remained active even at elevated temperatures.  
At 50 \({}^{\circ}\)C, the nucleation density increased significantly in all samples due to the combined effects of heterogeneous and homogeneous nucleation. For example, PHH_05 BN exhibited a sharp increase in nucleation density from approximately 20 to 850 nuclei/mm\({}^{2}\). This increase is attributed to the earlier activation of heterogeneous nucleation sites introduced by boron nitride, followed by homogeneous nucleation as supercooling progressed. In contrast, PHH_2 Talc exhibited a lower nucleation density under the same conditions, suggesting that the homogeneous nucleation process was slightly hindered by the presence of the added inorganic particles.  
Interestingly, at 50 \({}^{\circ}\)C, neat P3 HBHHx exhibited a high nucleation density of approximately 1440 nuclei/mm\({}^{2}\). This high value is attributed entirely to homogeneous nucleation, which becomes increasingly favorable at low temperatures due to higher degrees of supercooling. However, in the nucleated samples, the early formation of heterogeneously nucleated spherulites likely reduced the available material/space for homogeneous nucleation, resulting in overall lower densities.  
Above 65 \({}^{\circ}\)C, no nuclei were detected in neat P3 HBHHx, confirming that homogeneous nucleation becomes ineffective due to the increased energy barrier for primary nucleation at higher temperatures. In contrast, the nucleated samples still displayed measurable nucleation densities at 100 \({}^{\circ}\)C, demonstrating that the additives significantly lowered the energy barrier required for nucleation. This ability to promote nucleation at elevated temperatures confirms that the nucleating agents provide energetically favorable sites for the formation of stable nuclei.  
Figure 16: Primary nucleation density for crystallization performed from the melt state at isothermal temperatures of 100 \({}^{\circ}\)C (**a**) and 50 \({}^{\circ}\)C (**b**).
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
###### Abstract  
In recent years, modular multilevel converters (MMC) are becoming popular in the distribution and transmission of electrical systems. The multilevel converter suffers from circulating current within the converter that increases the conduction loss of switches and increases the thermal stress on the capacitors and switches' IGBTs. One of the main solutions to control the circulating current is to keep the capacitor voltage balanced in the MMC. In this paper, a new hybrid control algorithm for the cascaded modular multilevel converter is presented. The Harrishawk's optimization (HHO) and Atom search optimization (ASO) are used to optimally design the controller of the hybrid MMC. The proposed structure of modular multilevel inverters allows effective operation, a low level of harmonic distortion in the absence of output voltage filters, a low switching frequency, and excellent flexibility to achieve the requirements of any voltage level. The effectiveness of the proposed controller and the multilevel converter has been verified through testing with the application of the MMC-static synchronous compensator (STATCOM). The stability of the voltage capacitors was monitored with balanced and unbalanced loads on the studied network.  
modular multilevel converter; STATCOM; optimization; harmonics; [PERSON]'s optimization; Atom search optimization +
Footnote †: journal: Applied Physics Letters  
1
# A Comprehensive Real-Time Traffic Map for Geographic Routing in VANETs
## 5 Simulation
### CBR Performance
#### 5.1.4 Overhead  
In Figure 12, we compare the overheads of three routing protocols by varying the vehicular density from 100 to 300 vehicles. It is easy to image that the overhead increases as the networks density increases since more vehicles are involved. In GyrAR, control packets including beacon messages and CDP packets are taken into account. The CDP packets for collecting traffic density on each road are the major cost. RIVER has the least overhead, since a piggyback is used to disseminate traffic information. Hence, only the beacons are considered. In the proposed CBR, we have beacon messages and data messages in the map exchange process. The result shows that the overhead of the CBR is higher than RIVER but lower than GyTAR. Considering the end-to-end delay and delivery ratio of the CBR discussed above, the extra cost should be acceptable. Although RIVER has the least overhead, its performance is worse than the proposed CBR, especially in a high-speed environment.  
Figure 11: Average hop count vs. moving speed: (**a**) 100 vehicles; and (**b**) 300 vehicles.  
Figure 12: Routing Overhead (per route) vs. Number of vehicles.