esa-sceva/satcom-chunk-collection huggingface dataset:
# Preparation and Modification of Biochar Materials and their Application in Soil Remediation
## 2 Preparation and Modification of Biochar
### Preparation of Biochar
#### 2.1.1 Pyrolysis  
Pyrolysis, also known as the thermal decomposition under oxygen-free conditions, is the most common method for preparing biochar. In general, pyrolysis involves the heating of organic materials to temperatures greater than 400 \({}^{\circ}\)C under inert atmospheres by electric heating or high-temperature medium. There are many parameters influence physicochemical properties of biochar, such as raw material, reaction temperature, heating rate, residence time, and reaction atmosphere.
## 4 Experimental Results and Analysis
### Traffic Parameter Extraction Results
#### 4.1.1 Link Flow Characteristics  
Link flow serves as a fundamental indicator reflecting road utilization intensity. By calculating the number of vehicles passing through each link during different time periods, the flow distribution characteristics of the road network were obtained.  
According to the flow box plot analysis in Figure 6, the flow distribution in the road network exhibits distinct hierarchical characteristics. Major arterials, particularly the north-south arterial formed by intersections 1-8 and the east-west arterial formed by intersections 1, 9, and 17, carry the predominant traffic flow in the network. These links demonstrate significantly higher average flows compared to other segments, with relatively stable flow variations (indicated by smaller box sizes).  
Secondary links exhibit notably lower flows with varying degrees of volatility. Some secondary links maintain stable but low flows, likely serving localized traffic demands, while others display higher flow fluctuations, potentially influenced by specific time periods or special events. This differentiated flow distribution provides a basis for differential signal timing design: major links should receive longer green times and better coordination control, while secondary links can adopt shorter cycles to minimize delays.  
Temporal stability of flows is reflected through the dispersion of data across different time periods. Most links exhibit relatively small flow standard deviations, indicating good regularity in weekday morning peak traffic demands. This regularity constitutes an important prerequisite for implementing fixed-time signal control.
# Training Autoencoders Using Relative Entropy Constraints
## 4 Experiments
### Analysis of Factors Affecting Classification Performance
#### 4.2.2 Number of Hidden Nodes  
The learning ability of a neural network is closely related to the number of hidden nodes. The greater the number of hidden nodes, the stronger the learning ability of the network. The sample dimension of the MNIST dataset is \(784\), so the number of hidden nodes is taken on a grid \(\{150,250,350,450,550,650,750\}\). The sample dimension of the CIFAR-10 dataset is \(1024\) (the color images in the CIFAR-10 dataset are converted to grayscale images), so the number of hidden nodes is taken on a grid \(\{350,450,550,650,750,850,950\}\). In the REAN autoencoder network, the search range for both the regularization parameter of \(\lambda\) of the autoencoder and the regularization \(\tau\) of the Softmax classifier is grid \(\{10^{-5},10^{-4},\cdots,10^{2}\}\), the specific parameter values are determined by cross-validation. The experimental results are shown in Figure 3.  
It can be seen from Figure 3 that when the number of hidden nodes is small, the classification performance of the REAN autoencoder network increases with the increase in the number of hidden nodes. This conclusion holds for different average hidden layer activation value constraints. This is because as the number of hidden nodes increases, the autoencoder trained by REAE can extract more effective features. Thus, the classification performance of the REAN autoencoder network is improved. After the number of hidden nodes increases to a threshold, if the number of hidden nodes continues to increase, the improvement of the classification performance of the REAN autoencoder network will slow down or even decrease.  
Figure 2: The relationship between the average activation value of the hidden layer outputs and classification performance in the REAN autoencoder network.  
Figure 2(a) shows that, on the MNIST dataset, when the number of hidden nodes exceeds 650 and the average activation value of the hidden layer is constrained to \(\rho=0.1\), the classification performance improvement of the REAN autoencoder network becomes very low. When the average activation value of the hidden layer is constrained by \(\rho=0.25\) and \(\rho=0.5\), the classification performance of the REAN autoencoder network is not improved but decreased. The best classification effect on the MNIST dataset is obtained when the average activation value of the hidden layer is constrained to \(\rho=0.1\) and the number of hidden nodes is set to 750. Its best classification accuracy is 97.4%. Figure 2(b) shows that, on the CIFAR-10 data set, when the average hidden layer activation value constraint \(\rho\) is equal to 0.025, 0.1 and 0.5, respectively, the best classification effect of the REAN autoencoder network is obtained when the number of hidden nodes is 750, 850 and 550, respectively. The best classification effect on the CIFAR-10 dataset is obtained when the average activation value of the hidden layer is constrained to \(\rho=0.5\) and the number of hidden nodes is set to 550. Its best classification accuracy is 39.2%.
###### Abstract  
Pure TiO\({}_{2}\) and barium (0.5 wt%) doped TiO\({}_{2}\) (Ba/TiO\({}_{2}\)) nanostructures have been synthesized via facile microwave irradiation method. The pure anatase phase of synthesized photoactive material was confirmed by X-ray diffraction. Ba doping in the TiO\({}_{2}\) host structure influenced the optical band gap as confirmed by UV-visible spectroscopy. The optical band gap increased from 3.21 eV for the TiO\({}_{2}\) to 3.26 eV for Ba/TiO\({}_{2}\). Morphological analysis of synthesized TiO\({}_{2}\) and Ba/TiO\({}_{2}\) was conducted using scanning electron microscopy. Energy dispersive X-ray spectroscopy confirmed the formation of Ba/TiO\({}_{2}\) and no impurities were observed. Electrochemical impedance spectroscopy showed that the charge transfer resistance increased for Ba/TiO\({}_{2}\), which reduced dark current creation in a dye-sensitized solar cell. The highest power conversion efficiency (3.24%) was achieved for Ba/TiO\({}_{2}\) photoanode compared to 2.1% for a pure TiO\({}_{2}\) photoanode-based device.  
photoanode; dye-sensitized solar cell; nanoparticles; microwave synthesis +
Footnote †: journal: Applied Sciences  
Academic Editor: [PERSON]
## 4 Experimental Results
### Other Scenarios  
The above described are two sample scenarios. However, multiple input videos were evaluated as a part of the solution, see Figure 13. Videos were taken using the UAVs at the premises of the Technical University of Kosice; another source of the videos was [26]. The extracted data from the videos are shown in Table 7. Confidence score reaches 99.10%. The ROI took up to 20% of the total area. The direction, both in the front and the rear view, was tested and detected correctly. All vehicles were counted, and accuracy reached 100%. This data confirmed the solution to be usable in favorable weather conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**View** & **Object Detection** & **Performance** & **Area of Analysis** & **Count of Bounding** & **Total Time [in s]** \\  & **Model** & **[in FPS]** & **[in \%]** & **Boxes** & **Total Time [in s]** \\ \hline Front & YOLOv4 & 25.32 & 7.55 & 675 & 271 \\ Rear & YOLOv4 & 26.64 & 11.24 & 261 & 219 \\ \hline \hline \end{tabular}
\end{table}
Table 5: General information of Sample scenario #2.  
Figure 13: Set of testing scenarios. (**a**) Front view of Sample scenario #3; (**b**) front view of Sample scenario #4; (**c**) front view of Sample scenario #5; (**d**) front view of Sample scenario #6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**View** & **Object Detection** & **Performance** & **Area of Analysis** & **Average Confidence** & **Accuracy [in \%]** \\  & **Model** & **[in FPS]** & **[in \%]** & **Score** & **Accuracy [in \%]** \\ \hline Front & YOLOv4 & 98.36 & to back & 18 & 19 & 95 \\ Rear & YOLOv4 & 99.38 & to front & 10 & 10 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Specific information of Sample scenario #2.
## 9 Conclusions  
Polygon-based lattice structures are typically designed and manufactured using struts or beams. The mechanical properties of lattice parts are different for varying designs and configurations. The planar in-plane loaded circular hollow structure ranks highest in terms of the transverse stiffness compared to those built using hexagonal, triangular, and square cells. Polygon lattice structures are predominantly used in applications requiring a high absorption of strain energy. This is associated with their capability in absorbing high strain energies for loading in the planar out-of-plane directions. The higher values of stiffness of lattice structures in the planar in-plane directions create possibilities of using them this way for crashworthiness using materials with lower values of stiffness. The analytical models adopted for describing the load-bearing capability of polygon lattice parts loaded in the planar in-plane directions are observed to be limited to general mathematical representations of strain energies. Additionally, the analytical models are lacking with regard to accounting for the effect of unit cell connectivity or the way in which the polygon lattice architecture functions. For loading polygon hollow parts in the planar out-of-plane direction, analytical models in the literature are observed to be limited to the hexagonal shape only. This creates space to develop models based on other types of polygon structures. Lattice structures built using polygon shapes are highly adaptable and tailored for different applications in the aerospace, medical, and automotive industries. Lattice structure design and optimisation is expected to be more dynamic, as a result of technological advances such as AM and the rising need for robust, lightweight, reusable, and effective structures in different engineering industries.  
Conceptualisation, M.I.C., T.C.D., M.M. and A.M.; methodology, M.I.C.; validation, M.I.C.; formal analysis, M.I.C.; investigation, M.I.C.; writing--original draft preparation, M.I.C.; writing--review and editing, M.I.C., T.C.D., M.M. and A.M.; supervision, T.C.D., M.M. and A.M.; funding acquisition, T.C.D. and M.M. All authors have read and agreed to the published version of the manuscript.  
This work is based on research supported by the South African Research Chairs Initiative of the Department of Science and Technology, the National Research Foundation of South Africa (Grant No. 97994), and the Collaborative Program in Additive Manufacturing (Contract No. CSIR-NLC-CPAM-21-MOACUT-03).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Analytic Hierarchy Process and Initial Conditions
### Initial Conditions  
According to the FLUENT calculation results, the wind speed component in three directions of the monitoring point position could be obtained: downwind speed \(u\), crosswind speed \(v\), and vertical wind speed \(w\), and the directions are consistent with the Cartesian coordinate system in the calculation domain. Wind attack angle \(\alpha\) and wind azimuth angle \(\beta\) can be calculated according to Equations (5) and (6). Second, nondimensional wind speed factor \(\overline{u}\), wind attack angle factor \(\overline{\alpha}\), and wind azimuth angle factor \(\overline{\beta}\) are defined and calculated by Equations (7)-(9), respectively.  
\[a=actan\frac{w}{\sqrt{u^{2}+v^{2}}} \tag{5}\]  
\[\beta=actan\frac{v}{u} \tag{6}\]  
\[\overline{u}=\frac{u}{U_{ref}} \tag{7}\]  
\[\overline{\alpha}=\frac{\alpha}{\alpha_{ref}} \tag{8}\]  
\[\overline{\beta}=\frac{\beta}{\beta_{ref}} \tag{9}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Scale Value** & **Description** \\ \hline
1 & Two factors are of equal importance. \\
3 & When comparing two factors, one is slightly more important than the other. \\
5 & When comparing two factors, one is obviously more important than the other. \\
7 & When comparing two factors, one is strongly more important than the other. \\
9 & When comparing two factors, one is greatly more important than the other. \\
2,4,6,8 & Median of two adjacent judgments above. \\ Inverse & If scale value is \(a_{ij}\) when comparing \(i\) to \(j\), then scale value is \(a_{ji}=1/a_{ij}\) when comparing \(j\) to \(i\). \\ \hline \hline \end{tabular}
\end{table}
Table 2: Matrix to judge scale meaning.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \(n\) & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** \\ \hline RI & 0 & 0 & 0.52 & 0.89 & 1.12 & 1.26 & 1.36 & 1.41 & 1.46 & 1.49 & 1.52 & 1.54 & 1.56 & 1.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Random consistency index \((RI)\) value.  
where \(U_{ref}\) is wind speed at a certain height; \(\alpha_{ref}\) is reference wind attack angle at a certain height; and \(\beta_{ref}\) is reference wind azimuth angle at a certain height.
## 2 Results and Discussion
### Nephroprotective Effect of APTM  
#### 2.4.1 Effects of APTM on Urine Volume and Water Consumption  
As shown in Figure 3, by comparing to animals in the control group (NCG), GM provoked a considerable rise in water consumption (\(p<0.01\)) and urinary volume (\(p<0.001\)). However, the treatment with the APTM extract at 200 and 400 mg/kg substantially recovered consumption of water (\(p<0.05\)) and urine output (\(p<0.05\) and \(p<0.01\), respectively) in GM-intoxicated rats as compared to GGM (80 mg/kg). A possible explanation to this is GM accumulation in the renal tissue, which results in the loss of proximal tubular cells and the inability of Henle's loop to reabsorb water, which results in dehydration [10; 17]. A rise in osmolarity brought on by the depletion of the rats' bodily fluid or an effort to replenish lost bodily fluid volume following GM treatment may also be to blame for the increase in water intake. As a result, osmoreceptor cells are stimulated, activating the thirst center in the hypothalamus and promoting increased water consumption [10].
# Execution Model to Reduce the Interference of Shared Memory in ARINC 653 Compliant Multicore RTOS
## 3 Execution Models for Multicore RTOS
### Implementation of Execution Models  
The goal of this work is to reduce interference without altering the source code of existing systems. Commodity OSs, such as VxWorks 653, allow limited source code modification. Moreover, the recertification of modified avionics software can be costly. Therefore, we execution models without modifying the source code.
# An Additively Manufactured Fe-3 Si Stator for a High-Performance Electrical Motor
## 3 Results and Discussion
### Mechanical Properties  
The tensile measurements on the SLM-printed Fe-3 Si were conducted on an ORNL SS3 standard-sized dog-bone (Figure 4a) cutout of a 25.4 mm (1-in.) cube using identical printed conditions, as shown in Figure 4b, with average gauge dimensions of 7.62 \(\times\) 0.889 \(\times\) 1.65 mm\({}^{3}\) [0.3 \(\times\) 0.035 \(\times\) 0.065 in\({}^{3}\)]. The mechanical properties were determined at ambient conditions while using a servohydraulic testing machine (MTS Model 810, Eden Prairie, MN, USA) equipped with hydraulically actuated grips and a clip-on extensometer with a gauge length of 25 mm. All of the tensile tests were carried out at a constant crosshead displacement rate of 5 mm min\({}^{-1}\). The printed Fe-3 Si bulk exhibits a mean tensile strength of 555.57 (\(\pm\)37.35) MPa, which is significantly higher than the minimum required tensile strength for a conventional Fe-3 Si alloy of 400 MPa [2]. The tensile strength of 400 MPa was also reported for Fe-3 Si (French electrical steel FeV 330-35 HA) with an ultimate yield stress similar to these results [22]. The tensile strength of the low-Si-content steel with suitable additives and tempering could be as much as three times greater [28]. The yield strength of around 630 MPa is comparable to the yield strength of around 675 MPa reported for a brittle Fe-5 Si AM alloy [2]. The [PERSON]'s modulus of printed Fe-3 Si was measured to be 27.15 (\(\pm\)1.63) GPa which is in general one order lower in magnitude. The major cause of this low modulus could be related to anisotropic microstructure in printed laminates as discussed in microstructure characterization. The additional cause might be associated with the imperfections, inclusions, and inhomogeneities. The individual yield strength, ultimate tensile strength, and [PERSON]'s modulus of the samples are presented in Table 2.  
Figure 3: (**a**) SEM microstructure along the build direction used for elemental characterization in EDX, (**b**) corresponding EDX spectra, (**c**) C mapping, (**d**) Si mapping, (**e**) Fe mapping, and (**f**) Ir mapping used to avoid the charging problem.
## 4 Theoretical Analysis of Roof Influence on Underlying Coal and Rock Strata After Coal Seam Mining  
### Analysis of the Mechanical Model  
#### 4.1.1 Basic Assumptions  
After the No. 2-1 coal seam is mined, the direct roof caves in immediately, and the main roof above the goaf is in a suspended state. The suspended main roof is in a clamped state between the overlying and underlying strata. The stiffness of the overlying and underlying strata of the main roof is less than that of the main roof. Therefore, assuming that the main roof is clamped by the elastic strata above and below, an elastic foundation boundary model can be constructed, and the following assumptions are made:  
(1) Since the length of the suspended area of the main roof along the working face direction is greater than 13 times the thickness of the main roof, it can be assumed to be a beam along the working face direction with a width of 1 m.  
(2) The overlying and underlying strata of the main roof are elastic foundations, and the main roof is an elastic foundation beam. The overlying and underlying strata satisfy the Winkler elastic foundation assumption.  
(3) The interface between the main roof and the elastic foundation has coordinated deformation and no shear dislocation.
## 3 Model and Methods
### Correlation-Adaptive Partitioning of Feature Embeddings Module
The patch \(x_{m}\) and correlation calculation results \(X_{m}\) correspond (\(x_{1}\rightleftharpoons X_{1}\)). Considering the integrity of the feature embeddings within a sliding window, we regard the patches with a similar correlation score as having a stronger integrity among patches, such that they can be partitioned into one feature embedding. To achieve this, we order the correlation scores in sequence \(X\) in size, from largest to smallest, to generate the sequence \(X^{\prime}\)=[\(X^{\prime}\)1, \(X^{\prime}\)2, \(\ldots,X^{\prime}\)s], \(X^{\prime}\)\({}_{n}\in\mathbb{R}^{1\times s}\). At the same time, we arrange \(s\) corresponding patches in the \(b\times b\) sliding window according to the order in \(X^{\prime}\), and the matrix \(h\in\mathbb{R}^{1\times s\times c}\) is obtained by splicing along the transverse direction. Finally, the \(1\times(s/p)\) sliding window with a step size of \(s/p\) is used to divide \(h\) into \(p\) feature embeddings. The size of each feature embedding is \(F\in\mathbb{R}^{1\times c}\). The implementation process is shown in Figure 3, which illustrates the first sliding window result in \(v_{an}\). For easy understanding and presentation, \(b=2,p=2\) in the figure.
## 2 Mathematical Modeling of the Air Jet Velocity in the Canopy  
### Previous Results  
In a previous paper [8], an approach to obtain the speed decay equation was presented. It was based applying the momentum theorem on the control volume within the canopy. Here, we now applied the momentum theorem, but only under the first two assumptions (assumption (a) and (b)) listed above:  
1. [label=()]
2. Foliage is uniformly distributed in space,
3. Forward motion of the sprayer is ignored.  
Using these two assumptions, we obtained the subsequent non-linear ODE:  
\[\frac{dv_{x}}{dx}+\frac{(2x-x_{0}+r_{0})}{2(x-x_{0})(x+r_{0})}v_{x}+\frac{1}{ 4}\rho_{l}\cdot c_{r}(v_{x})\cdot v_{x}=0, \tag{1}\]  
where (see Figure 1 and [8]) \(v_{x}\) is the maximum air jet velocity at the centerline (m/s); \(x\) is the distance travelled by the air jet from the output (m); \(x_{o}\) is the distance, which is normally negative, between the vertex of the diffusion triangle and the output section (m); \(r_{o}\) is the radius of the edge of the air outlet from the sprayer (vertical view of Figure 1); \(c_{r}(v_{x})\) is an adimensional drag coefficient of the canopy that is dependent on \(v_{x}\); and \(\rho_{l}\) (m\({}^{-1}\)) is coincident with the LAD.  
In the previous paper [8], the result of experimental activity conducted in a wind tunnel was presented in order to find the function \(c_{r}=c_{r}(v_{x})\).  
This function was formally transformed into \(c_{r}=\frac{k(v_{x})}{v_{x}}\), and the result is presented as in Figure 2, where parameter \(k(v_{x})\) vs. \(v_{x}\) appears. For a higher air velocity in the tunnel (\(v_{x}=v_{xm}\geq 3\) m/s), parameter \(k(v_{x})\) is nearly constant and equal to a mean value of 1.1 m/s, with an error \(<\)5%, thus substantially confirming the results found by [17] and the validity of the third assumption (c) \"inverse relationship between the drag coefficient and air velocity, \(c_{r}=\frac{k}{v_{x}}=\frac{11}{v_{x}}\), for \(v_{x}\geq 3\) m/s\". The fact that a velocity limit exists under which the inverse relationship between the drag coefficient and air velocity is not valid is shown in Figure 2, where \(k\) decreases approximately linearly until the null value when air velocity \(v_{x}\) tends to zero, which is also confirmed by [11, 12].  
Therefore, under assumption (c), the previous non-linear ODE (1) becomes linear and non-homogeneous:  
\[\frac{dv_{x}}{dx}+\frac{(2x-x_{0}+r_{0})}{2(x-x_{0})(x+r_{0})}v_{x}+\frac{1}{4 }\rho_{l}\cdot k=0. \tag{2}\]  
Whereas, as previously stated, the non-linear ODE (1), when \(v_{x}<3\) m/s, remains to be solved:  
\[\frac{dv_{x}}{dx}+\frac{(2x-x_{0}+r_{0})}{2(x-x_{0})(x+r_{0})}v_{x}+\frac{1}{4 }\rho_{l}\cdot k(v_{x})=0. \tag{3}\]  
Figure 1: Sprayer during agrochemical applications to the vineyard: vertical view (**right**) and plane view (**left**).  
Figure 2: Relationship between the \(k\) parameter and mean air velocity within the tunnel, \(v_{xm}\).