esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Statistical Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & & \\ \hline \multicolumn{2}{c}{**Type of Filter,**} & \multicolumn{4}{c}{**Stage**} \\ \multicolumn{2}{c}{**Filling**} & \multicolumn{2}{c}{**1**} & **II** & **III** & **IV** \\ \hline \multirow{3}{*}{Submerged, PPA} & Reg. & y = 0.0137e0.540\% & y = -0.1324x2 + & y = 6E + 10x7218 & y = 0.0007x2 - \\  & Reg. & y = 0.0137e0.540\% & y = -0.33778 & y = 6E + 10x7218 & y = 0.0065x1 + 1.8372 \\  & (R) & 0.9988 & 0.9553 & 0.9916 & 0.6644 \\  & Duration (d) & 1–11 & 11–24 & 24–36 & 36–60 \\ \hline \multirow{3}{*}{Submerged, PSC} & Reg. & y = 0.0117e0.458\% & y = -0.16x2 + & y = 5E + 16x10-1096 & y = 0.0013x2 - \\  & (R) & 0.9894 & 0.9909 & 0.9931 & 0.7628 \\  & Duration (d) & 1–13 & 13–27 & 27–36 & 36–60 \\ \hline \multirow{3}{*}{Submerged, CF} & Reg. & y = 0.0115e0.8991\% & y = -0.24x2 + & y = 0.002x2 - \\  & (R) & 0.9991 & 0.9031 & 0.9817 & 0.6522 \\  & Duration (d) & 1–8 & 8–18 & 18–22 & 22–60 \\ \hline \multirow{3}{*}{Dripping, PPA} & Reg. & y = 0.0109e0.6266\% & y = -0.1126x2 + & y = -0.002x2 - \\  & (R) & 0.9953 & 3.5649\% - 16.232 & y = 6E + 09x6-6821 & y = -0.0002x2 - \\  & Duration (d) & 1–11 & 11–19 & 19–31 & 31–45 \\ \hline \multirow{3}{*}{Dripping, PSC} & Reg & y = 0.0151e0.5440\% & y = -0.0698x2 + & y = 0.002x2 + \\  & (R) & 0.9979 & 0.9807 & 0.9913 & 0.981 \\  & Duration (d) & 1–12 & 12–22 & 22–33 & 33–45 \\ \hline \multirow{3}{*}{Submerged, CFst} & Reg. & y = 0.0127e0.6536\% & y = -0.0698x2 + & y = 2E + 12x8-8719 & y = 0.0013x2 - \\  & (R) & 0.9995 & 0.9390 & 0.9914 & 0.9199 \\ \hline \multicolumn{2}{c}{**(b)**} & \multicolumn{4}{c}{**Stage**} \\ \multicolumn{2}{c}{**Type of Filter,**} & \multicolumn{4}{c}{**Stage**} \\ \multicolumn{2}{c}{**Filling**} & \multicolumn{2}{c}{**1**} & **II** & **III** \\ \hline \multirow{3}{*}{Dripping, CF} & Reg. & y = -0.0351x3 + & & \\  & Reg. & 0.469x2 & 0.8346x + & y = 18770x3-3647 & y = 0.001x2 - \\ \cline{1-1}  & & 0.4202 & & 0.067x + 1.4065 \\ \cline{1-1}  & (R) & 0.9998 & 0.9917 & 0.8223 \\ \cline{1-1}  & Duration (d) & 1–9 & 9–21 & 21–45 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (**a**) Nitrite nitrogen concentration analysis in experimental RAS systems using regress equation (Reg.) and correlation coefficients (R): PPA—polpropylene aggregate, PSC—polyethylene screw caps for PET bottles, CF—commercial fittings HXF12 KLL, CFsf—control submerged filter for dripping filters, commercial fittings HXF12 KLL. (**b**) Nitrite nitrogen concentration analysis in experimental RAS systems (dripping, CF) using regress equation (Reg.) and correlation coefficient (R).
# Energy Consumption Characteristics for Design Parameters of Permanent Magnet-Based Al Billet Heater
## 4 Heating Characteristic Effects on Design Parameters
### Effects of Magnet Arrangement Clearance
As shown in the distribution of Case05, if the clearance between the permanent magnets became too small, the amount of magnetic flux density that entered the interior of the billet increased, and the value of the torque due to the electromagnetic force increased. As shown in Figure 16b, the power loss increased rapidly and the heating efficiency of Case05 was lower than those of Case01-Case04. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \begin{tabular}{c} **Inner Radius** \\ **[mm]** \\ \end{tabular} & \begin{tabular}{c} **Outer Radius** \\ **[mm]** \\ \end{tabular} & \begin{tabular}{c} **Theta** \\ **[mm]** \\ \end{tabular} & \
\begin{tabular}{c} **Clearance** \\ **[Degree]** \\ \end{tabular} &
\begin{tabular}{c} **Angle** \\ **[Degree]** \\ \end{tabular} \\ \hline Case01 & 30.5 & 52.84 & 10 & 20 \\ \hline Case02 & 30.5 & 46.60 & 15 & 15 \\ \hline Case03 & 30.5 & 43.14 & 20 & 10 \\ \hline Case04 & 30.5 & 40.93 & 25 & 5 \\ \hline Case05 & 30.5 & 39.94 & 28 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Model composition for verification of magnet arrangement clearance. Figure 15: Design parameters for analyzing the effects of magnet arrangement clearance. Figure 16: Analysis results according to magnet arrangement clearance: (**a**) electromagnetic field analysis result; (**b**) torque, heating time, and energy consumption result graph. In this study, the heating effects of changes in the inner/outer radius and the angle of the permanent magnets were analyzed to determine the optimal conditions for the location and arrangement of permanent magnets that maintain a constant volume. As a result of the electromagnetic-thermal analysis according to changes in the design parameters of the normal and tangential directions, the optimal model, consisting of 30.5 mm, 40.93 mm, and 25\({}^{\circ}\) for the inner/outer radii and angle, respectively, of permanent magnets, showed the best heating characteristics.
## 2 Materials and Methods  
### Preparation of Ti-6 Al-4V Samples  
Ti-6 Al-4V (Ti-64) alloy sheets with dimensions of 14 mm \(\times\) 40 mm \(\times\) 1 mm were used as the study materials. To enhance the cell adhesion properties, the surface morphology was modified through various corrosion conditions. The samples were meticulously prepared to ensure consistent surface quality. Initially, the samples were ground sequentially with SiC papers from 220 to 1200 grit to achieve a smooth surface. To obtain a pristine surface free from residual contamination, careful polishing was conducted using 0.3 um alumina powder suspended in acetone under ultrasonic agitation for approximately 10 min. To prevent any contamination from the alumina powder and human oils, the polished samples were placed in beakers containing acetone and subjected to further ultrasonic cleaning for 10 min. Subsequently, the samples were thoroughly rinsed with deionized water and completely dried using cold air. For mechanical treatments, zirconia beads with a diameter of 0.25 mm were used as a medium for surface shot peening. The shot peening parameters were carefully selected to ensure uniform and controlled effects. During the shot peening process, a nozzle was positioned 100 mm from the sample surface. Shot peening was performed at pressures of 2, 1, and 0.5 kg/cm\({}^{2}\) for durations of 5, 10, and 20 s, respectively. For sandblasting, zirconia sand with grit sizes of 80 (particle size < 186 \(\upmu\)m) and 120 (particle size < 125 \(\upmu\)m) was used at an incident angle of 45\({}^{\circ}\). Multiple chemical corrosion techniques were employed to modify the surface characteristics of the samples. The primary corrosive medium was HF solution, prepared at concentrations of 3 M and 12 M [71]. To ensure reagent stability, all solutions were prepared under subambient conditions at 5 \({}^{\circ}\)C or \(-\)5 \({}^{\circ}\)C using a recirculating chiller (WB-10, Paymo Technology Ltd., Taipei, Taiwan). For the electrochemical corrosion treatment, polished Ti-64 samples were positioned as an anode in contact with the positive terminal of a pulsed potentiostat. A platinum electrode connected to the negative terminal of the potentiostat was used as the cathode, positioned approximately 20 mm from the sample. Stirring was facilitated with a magnetic stirrer to ensure an even distribution of ions within the corrosive medium. To promote uniform corrosion and prevent polarization effects, a positive-to-negative potential alternation approach was employed. Two sets of current density conditions were used: \(\pm\)30 mA/cm\({}^{2}\) and \(\pm\)10 mA/cm\({}^{2}\).
## 5 Conclusions  
This study provided insights into the beneficial effects of using selected phytochemicals as feed additives that show the inhibitory effects of NE-causing organisms and coccidiosis-causing _E. tenella_ parasites. In conclusion, our studies showed that phytogenic extracts that include GRE, GTEC, GTED, and OPEC reduced C. perfringens proliferation. OPEC, GRE, GTED, and GTEC were the ranked order in reducing the proliferation of C. perfringens. The optimal dose for GRE, GTEC, and GTD was 5 mg/mL, with OPEC at 1.25 mg/mL for CP19 and CP240. Our study showed that the efficacy of GRE, GTEC, GTD, and OPEC was comparable to BMD, albeit at a higher concentration. Based on their anti-Clostridial and anti-coccidial effects, the results of these studies clearly support using well-studied, selected phytochemicals in mitigating disease responses for agricultural animals such as poultry. With the risk associated with the use of antibiotics in the prevention and treatment of NE, the phytogenic extracts that we described in this paper have the potential to serve as alternatives to antibiotic uses in broiler chicken production. However, further studies are required to understand the mechanisms by which these phytogenic extracts mitigate the impact of these disease-causing toxins and gut-damaging parasites.  
Authors contributed equally to this work. Study concept and design: Y.O.F., M.K., H.S.L., Y.L. and H.W.K.; acquisition of laboratory data: M.M.K. and Y.L.; data analysis and interpretation: M.M.K., Y.L., Y.O.F., H.S.L. and A.O.A.; drafting of the manuscript and/or critical revision for important intellectual content: A.O.A., P.C.O. and Y.O.F. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the USDA/NIFA SAS award 2020-69012-31823; accession number 12023193, and partly by the National Institute for Food and Agriculture of the United States Department of Agriculture, Project No. NC.X-329-5-20-120-1, in the Agricultural Research Program, North Carolina Agricultural and Technical State University.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors thank members of the Poultry Ethnomedicine Research Laboratory at North Carolina A&T State University (Greensboro, NC) for their technical support. We also thank the reviewers for the time and effort that they invested in the review of our manuscript.  
The authors declare no conflicts of interest.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 6 SDN Simulators and Emulators  
To analyze the network performance, instead of implementing a large experimental testbed, there are two commonly used methods; these are called simulation and emulation. The simulation method provides an application environment where we can test our implemented software program without real deployment. The emulation method utilizes a software program to perform executions with real devices by interacting with them as when required. To analyze network performance by simulation is inexpensive, flexible, controllable, and scalable compared to an emulator. In addition, the simulators allow researchers to analyze and test network behaviors as per defined workload. In SDN, with the development of OpenFlow protocol, the simulation tools have extended support to additional network components for the testing and experimentation of OpenFlow-based network applications. Moreover, network emulators based on software switches have also been developed to test and analyze network applications, for example, Open vSwitch (OvS) [161], ofsoffswitch13 [162], Indigo Virtual Switch (IVS) [163], and so on. The Mininet SDN emulator [164] offers a rapid prototyping workflow and virtualization functionalities along with command line interface (CLI) and API on one physical machine that allows network developers to configure, manage, and test their networks. It helps to create a network topology for a network scenario that consists of virtual hosts, switches, links, and controller platforms. It supports research and development, learning, prototyping, testing, debugging, and any other task related to network experimentation on a computer. In the basic implementation of Mininet, the performance fidelity is not included. In Mininet-HiFi [165], these improvements are implemented. It also has a cluster edition prototype [166], and other releases include Maxinet [167] and Mininet-CE [168], which fixes the limitations of large-scale implementation of SDN emulations. Finally, two experimental frameworks for SDN data centers are also developed; these are datacenter in a box [169] and SDDC [170]. The distributed OF testbed (DOT) [171] is a highly scalable emulator that provides an emulated network across a cluster of computers that guarantees computation and network resources to switches, hosts, and links.
## 4 Materials and Methods
### Aroma Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Compound** & **LOD** & **LOQ** & \(m/z\) & **Compound** & **LOD** & **LOQ** & \(m/z\) \\ \hline Acids & \(\upmu\)g/L & \(\upmu\)g/L & & Esters & \(\upmu\)g/L & \(\upmu\)g/L & \\ Acetic acid & 30.0 & 100.0 & 60 & Ethyl hexanoate & 1.2 & 4.0 & 115–144 \\ Octanoic acid & 0.9 & 3.0 & 101–115 & Ethyl 4-hydroxybutanoate & 1.2 & 4.0 & 132 \\ Nonanoic acid & 0.6 & 2.0 & 129–158 & Diethyl succinate & 50.7 & 169.0 & 129–174 \\ Decanoic acid & 1.2 & 4.0 & 129–172 & Ethyl octanoate & 9.6 & 32.0 & 127–172 \\ Lauric acid & 0.3 & 1.0 & 157–200 & Ethyl hydrogen succinate & 8.7 & 29.0 & 128–146 \\ Myristic acid & 0.3 & 1.0 & 185–228 & Phenethyl acetate & 2.7 & 9.0 & 104–164 \\ Palmitic acid & 0.3 & 1.0 & 213–256 & Ethyl decanoate & 2.7 & 9.0 & 155–200 \\ Alcohols & & & & Ethyl vanillate & 0.6 & 2.0 & 151–196 \\ Isoamyl alcohol & 96.0 & 320.0 & 70–88 & Ethyl laurate & 0.9 & 30.0 & 183–228 \\
1-butanol & 33.6 & 112.0 & 56–74 & Hexyl salicate & 0.3 & 1.0 & 120–222 \\
2,3-butanediol & 27.6 & 92.0 & 45–90 & Ethyl myristate & 0.6 & 2.0 & 88–256 \\
1-hexanol & 1.8 & 6.0 & 84–102 & Disobutyl phthalate & 3.3 & 11.0 & 149–278 \\ Methionol & 0.6 & 2.0 & 88–106 & Ethyl pentadecanoate & 1.8 & 6.0 & 225–270 \\ Benzyl alcohol & 0.3 & 1.0 & 79–108 & Methyl palmitate & 1.5 & 5.0 & 239–270 \\
1-octanol & 0.3 & 1.0 & 112–130 & Dibutyl phthalate & 1.2 & 4.0 & 149–278 \\
2-phenylethylethanol & 87.3 & 291.0 & 91–122 & Ethyl palmitate & 5.4 & 18.0 & 239–284 \\ Dodecanol & 0.3 & 1.0 & 168–186 & Ethyl linoleicate & 0.3 & 1.0 & 263–308 \\ Carbonyl compounds & & & & Ethyl oleate & 0.6 & 2.0 & 264–310 \\
4-propylbenzaldehyde & 0.3 & 1.0 & 119–148 & Ethyl stearate & 1.5 & 5.0 & 267–312 \\ Geranyl acetone & 0.3 & 1.0 & 176–194 & Volatile phenols & & & \\ Lily aldehyde & 0.3 & 1.0 & 189–204 & 4-ethylphenol & 1.8 & 6.0 & 107–122 \\ Hexyl cinnamaldehyde & 0.3 & 1.0 & 129–216 & 4-ethylylguaiacol & 0.9 & 3.0 & 137–152 \\ Terpenes & & & & & & 2,4-Di-T-butylphenol & 54.3 & 181.0 & 191–206 \\ \(\alpha\)-terpindence & 0.3 & 1.0 & 139–154 & & & & \\ Nerol & 0.3 & 1.0 & 139–154 & & & & \\ \(\beta\)-citronellol & 0.6 & 2.0 & 138–156 & & & & \\ \(\beta\)-damascenone & 0.3 & 1.0 & 175–190 & & & & \\ Phenanthrene & 0.3 & 1.0 & 178 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The limit of detection (LOD), limit of quantitation (LOQ) and mass to charge ratio (\(m/z\)) of volatile compounds identified in Cabernet Sauvignon wine, RO and NF retentates.
# Predicting C/N0 as a Key Parameter for Network RTK Integrity Prediction in Urban Environments
## 5 Prediction Results
### Predicted Measured Difference  
Figure 14 illustrates the histogram of the differences between predicted and measured C/N\({}_{0}\) for the four different visibility status. The differences for LOS and MP are, in most cases, far below 10 dB-Hz, and a mean offset of 1.78 dB-Hz and 3.21 dB-Hz is found, respectively, cf. Table 3. Taking into account that (i) the actual antenna gain pattern is only approximately known and the side lobes are not considered and that (ii) some trees and details may still be missing in the model, the general C/N\({}_{0}\) prediction fits well the real data of the one commercial receiver it was compared with. Considering the typical C/N\({}_{0}\) scatter of approximately 1 db-Hz at higher elevations, the obtained mean offsets are within this order of magnitude.  
For NLOS situations, the simulation is less performant; this is also true for \"blocked\" cases. Here, the behavior of the other receivers has to be studied. In addition, the C/N\({}_{0}\) for the multi-reflection case which is partially contained in both cases must be taken into account.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Visibility Type** & **LOS** & **MP** & **NLOS** & **BLKD** \\ \hline Mean of Difference [dB-Hz] & 1.78 & 3.21 & 11.90 & 8.74 \\ \hline \end{tabular}
\end{table}
Table 3: The mean value of the difference between predicted and real C/N\({}_{0}\) in Round 5 for four different visibility status. The modified gain pattern is used for these calculations.  
Figure 12: The observed and predicted C/N\({}_{0}\) in Round 5 for GPS constellation: (**a**) PRN 3, (**b**) PRN 4, (**c**) PRN 17. The lower bar (at 0) shows the solution status (cf. Figure 2a) and the upper bar (at 10) indicates the visibility status (cf. Figure 5). The way-points A to K in subfigures a, b and c, are as defined in Figure 1a. (**d**) Shows the sky plot of the GPS satellites during the round.  
Figure 13: The observed and predicted C/N\({}_{0}\) in Round 5 for GPS constellation. (**a**) PRN 19, (**b**) PRN 22, (**c**) PRN 32. The lower bar (at 0) shows the solution status (cf. Figure 2a) and the upper bar (at 10) indicates the visibility status (cf. Figure 5). The way-points A to K in subfigures a, b and c, are as defined in Figure 1a. (**d**) Shows the sky plot of the GPS satellites during the round.
## 3 Results
### Nitriding of a Single Micro-Nozzle  
AISI316 micro-nozzle specimen in Figure 2a was also plasma nitrided at 673 K for 7.2 ks. Figure 4a shows the nitrided micro-nozzle. The spot for EDX was controlled to move from the edge of the outlet toward the step of the nozzle body along the line-A in Figure 4a. Figure 4b depicts the nitrogen solute distribution along this line-A, from the top of the outlet down to the nozzle body. Both the nozzle outlet and the nozzle body surfaces are uniformly nitrided to have high nitrogen contents, except for the vicinity of a step between two regions. On the surface of this step, the electron beam in EDX significantly scattered to lower the measured intensity. This average intensity in Figure 4b corresponds to the nitrogen content by \(8.6\) mass%. This nitrogen content is equivalent to the measured content of \(9\) mass% for the nitrided AISI316 plate in Figure 3a.  
Figure 4: Plasma nitrided AISI316 micro-nozzle at 673 K for 7.2 ks. (**a**) Outlook of nitrided micro-nozzle, and, (**b**) nitrogen mapping measured from the top of the outlet down to the nozzle body.  
Figure 3: Effect of DC-bias on the nitrogen content and hardness. (**a**) Variation of the nitrogen solute content, and, (**b**) variation of hardness profile.  
As had been discussed in [12; 13], the inner surface of the outlet channel in the micro-nozzle could be hardened when using the nozzle channel as a hollow cathode. A single micro-channel with the outlet diameter of 0.2 mm was also used as the hollow cathode in the present plasma nitriding to evaluate the hardness across the nozzle thickness.  
Figure 5b depicts the hardness distribution across the thickness of nitrided micro-nozzle. The average hardness becomes more than 1000 HV0.1 at the vicinity of the channel toward the outlet. The surface hardness of the nitrided AISI316 plate by 100 g or 1 N, becomes 1000 to 1100 HV in Figure 3b. This suggests that a single micro-nozzle is nitrided and hardened to be 1000 HV not only on its outer surface but also on its inner surface even at 673 K for 7.2 ks.
# Reversible Data Hiding in Encryppted Images with Extended Parametric Binary Tree Labeling
## 2 Related Work
### Pbtl  
The reversible data hiding in encrypted image using PBTL, proposed by [PERSON] and [PERSON] [26], first selects sampling pixels of an image as the reference pixels. The other pixels are classified into two different groups, the non-embeddable group G1 and the embeddable group G2, and labeled with PBTL. Figure 1 shows a complete binary tree. The principle of PBTL is to label G1 with the code of a leftmost node as the red codes in Figure 1 and label G2 with the other decodable/distinguishable codes. Each code for G2 is then used to label the pixels of a specified prediction error.  
The parameters \(\alpha\) and \(\beta\) are the lengths of codes for G2 and G1 groups, respectively. An example of \(\beta=1\) is shown in Figure 2. The leftmost node with a length of 1-bit is \({}^{\circ}0^{\prime}\), which is applied to label all non-embeddable pixels. The decodable nodes are thus the descended nodes of '1'. The value of \(\alpha\) determines the number of available nodes to label the pixels in G2. When \(\alpha=1\), only the code '1' can be used. As the value of \(\alpha\) increases, the number of available codes also increases as listed in Figure 2. For the case of \(\beta=2\), the code '00' is applied to label G1. The decodable codes are therefore the descended nodes of '01', '10', and '11'. When the chosen value of \(\alpha\) is greater than 2, more codes are available to label G2 as listed in Figure 3. A value of is also applicable. Only the leftmost node should be reserve to make the G2 codes decodable. The number of available codes for G2 is denoted with \(n_{\alpha}\) and can be analyzed to be  
\[n_{\alpha}=\left\{\begin{array}{c}2^{\alpha}-1,\alpha\leq\beta,\\ \left(2^{\beta}-1\right)\times 2^{\alpha-\beta},\alpha>\beta.\end{array}\right. \tag{1}\]  
Figure 1: Binary code distribution based on a complete binary tree.  
Figure 3: An example of flag bit selection when \(\beta=2\) and \(\alpha=1\) to 7.
# A Local Information Perception Enhancement-Based Method for Chinese NER
## 3 Approach  
The overarching model framework of our method is shown in Figure 6. The primary composition of this system consists of four distinct network modules. Initially, the encoder layer is employed to acquire contextual information pertaining to sentences and to represent the semantic information associated with the lexicon. Then, it uses SoftLexicon to fuse the lexicon information and use short-sequence CNN and Bi-LSTM to obtain the sequence's local and global hidden details, respectively. Semantic relations between characters and their adjacent characters' matching words were captured using GAT [26]. Finally, the feature representation after fusing the lexicon is summed with the output of the sequence encoder, and the tags are decoded using the standard CRF model [31].  
Figure 6: The overall proposed NER architecture. After the encoder layer, first, the sequence hidden state is obtained by short-sequence CNN combined with a Bi-LSTM structure after the fusion of the matching word information using the lexicon. Then the relationship between characters and their adjacent characters’ matching words is captured using GAT, and finally, the two outputs are summed to predict the sequence labels using a CRF layer.