esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### B. Regression Model
For the Elastic Net, the best value of the regularization parameter estimate is \(\lambda\) = 0.007, corresponding to \(\alpha\) = 0.1. The mean square error of each model was calculated, and the (\(\lambda\), \(\alpha\)) values with the minimum mean square error were chosen to build the best model. The estimates of these parameters for the four competing regression models with their root mean squares (RMSE) are listed in Table 4.  
The results of the information criteria for the 10-fold CV for the four regression models are listed in Table 5. These results show no apparent differences among the four regressions models; all the accuracy measurement results are close. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\lambda\) & \(\alpha\) & **RMSE** \\ \hline MLR & 0 & 0 & 0.435 \\ Ridge & 0.035 & 1 & 0.434 \\ Lasso & 0.009 & 0 & 0.435 \\ Elastic Net & 0.007 & 0.1 & 0.435 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimates of regularization parameters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE** & **RMSE** \\ \hline MLR & 0.669 & 0.351 & 0.435 \\ Ridge & 0.670 & 0.348 & 0.434 \\ Lasso & 0.670 & 0.349 & 0.435 \\ Elastic Net & 0.670 & 0.348 & 0.435 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the regression models. Figure 9: Ridge and Lasso plots as a function of log (\(\lambda\)). Figure 10 shows the performances of the compared models. The R\({}^{2}\) values of the models indicate that the ANNS outperformed all the other models. The results of the accuracy measurements, i.e., RMSE, MAE, and R\({}^{2}\), are listed in Table 6 for comparing the performances of the six models. The R\({}^{2}\) and MAE values indicate that the ANNS model outperformed all the other fitted models. With regard to RMSE, the Lasso and MLR models have a slight advantage over the other models. Penalized regression methods work very well when the number of explanatory variables is large, whereas ANNS performs best when the sample size is large.
_Article_  
Characterization of Human Dermal Papilla Cells in Alginate Spheres  
[PERSON] 1,2,t, [PERSON] 4,t, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 1,2, [PERSON] 5, [PERSON] 2,3,* and [PERSON] 1,2,3,*()  
Department of Anatomy, Kyungpook National University School of Medicine, 680 Gukchaebosang-ro, Daegu 41944, Korea; [EMAIL_ADDRESS] (N.M.M.); [EMAIL_ADDRESS] (J.M.P.); [EMAIL_ADDRESS] (D.K.); [EMAIL_ADDRESS] (W.H.)  
Department of Biomedical Science, Biomedical Research Institute, Kyungpook National University, Daegu 41940, Korea  
Joint Institute for Regenerative Medicine, Kyungpook National University Hospital, Daegu 41940, Korea  
Department of Microbiology, Kyungpook National University School of Medicine, 680 Gukchaebosang-ro, Daegu 41944, Korea; [EMAIL_ADDRESS]  
Department of Developmental and Cell Biology, University of California, Irvine, CA 92697, USA; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS] (J.O.L.); [EMAIL_ADDRESS] (J.W.O.); Tel.: +82-53-420-6950 (J.O.L.); +82-53-420-4806 (J.W.O.); Fax: +82-53-427-5447 (J.O.L.); +82-53-427-5664 (J.W.O.)  
These authors contributed equally to this work.  
Received: 20 July 2018; Accepted: 3 October 2018; Published: 19 October 2018  
Maintenance of trichogenecity of dermal papilla cells (DPCs) have been a problem during cell therapy for androgenic alopecia, as they lose their regenerative potential in _in vitro_ culture. Various spheroid culture techniques are used to increase and maintain trichogenecity of these cells. However, there are some critical drawbacks in these methods. Applying a hydrocell plate for sphere formation or hanging drop methods by hand would be difficult to control the size and cell density inside it. It would be difficult to commercialize or mass production for clinical therapy. In aim to address and overcome these drawbacks, we have introduced alginate sphere. The alginate sphere of DPCs were prepared by electrospinning at different voltages to control the size of sphere. Then the obtained alginate spheres were evaluated for cellular dynamics and density of DPCs under different conditions. In this study, we found that DPCs do not proliferate in alginate sphere. However, the number of DPCs were maintained and found to be in dormant state. Further, the dormant DPCs in the alginate sphere have upregulated DPC signature genes (SOX2, ALPL, WIF1, Noggin, BMP4 and VCAN) and proliferative capacity. Thus, we speculate that alginate sphere environment maintains the dormancy of DPCs with increased trichogenecity.  
**Keywords:** alginate sphere; dermal papilla cell; trichogenecity; dormancy
# Effects of Caprolactam Wastewater on Algal Growth and Nutrients Removal by _Arthrospira platensis_
## 2 Materials and Methods
### Analytical Methods  
The N (NO\({}_{3}\)\({}^{-}\)-N) and P (PO\({}_{4}\)\({}^{3-}\)-P) concentrations in the medium were analyzed using ion chromatography [31]. Organic matter (CPL, CHN, CHL, and CHO) in the WCP was determined by gas chromatography, as described by [11].  
Cell growth was estimated using the biomass DW. The DW was measured by filtering 5 mL of algal suspension on 0.45 um pre-weighed membranes and dried at 105 \({}^{\circ}\)C. The inhibition ratio (\(I\)) was calculated as follows:  
\[I\ (\%)=(DW_{Control}-DW_{Treatment})/DW_{Control}\times 100\% \tag{1}\]  
Chl-a and Car were determined using the method described by [32]. The Chl-a and Car contents were calculated according to the following equations:  
\[\text{Chl-a (mg/L)}=16.72\times\text{OD}_{665.2}-9.16\times\text{OD}_{652.4} \tag{2}\]  
\[\text{Car (mg/L)}=(1000\times\text{OD}_{470}-1.63\times\text{Chl-a})/221 \tag{3}\]  
The photosynthetic status of _A. platensis_ was measured using a PAM 2500 fluorometer [33]. Phycobiliproteins were extracted with phosphate buffer, and absorbance was measured at 620 nm and 652 nm, respectively. The concentrations of PC and alophyocyanin (APC) were calculated according to the equation reported by [34].  
\[\text{PC (mg/mL)}=\frac{\text{A}_{620}-0.474\times\text{A}_{652}}{5.34} \tag{4}\]  
Figure 1: Scheme of the circular photobioreactor.  
\[\text{APC }(\text{mg/mL})=\frac{\text{A}_{652}-0.208\times\text{A}_{620}}{5.09} \tag{5}\]  
Total phycobiliproteins (T-PC) = PC + APC  
The total nitrogen (TN) contents in the algal biomass were determined using a Soli TOC cube analyzer (Elementar, Germany). The total carbohydrate and lipid contents were determined using the method described by [35]. To measure the crude protein content, an empirical formula described by [36] was adopted, as follows:  
\[\text{Crude protein }(\%)=6.25\times\text{TN}(\%) \tag{6}\]  
where TN (%) is the mass fraction of N in the biomass.
# An Open-Source R Package for Building and Using US Environmentally-Extended Input-Output Models
## 2 Materials & Methods  
The complete model building process in _useeior v1.0.0_ requires the following six successful steps and returns a _model_ object as the primary output:  
1. Initialize model;
2. Load IO data;
3. Load and build satellite tables;
4. Load and build indicators;
5. Load demand vectors;
6. Construct EEIO matrices.  
For hybrid models, hybridization processes such as aggregation and/or disaggregation of sectors are incorporated in step 2 and 3.  
To simplify the model building process, the six steps are integrated into a wrapper function, _buildModel_, that builds a USEEIO model in one line of code, and provides logging of the build process using the _logging_ package [21]:  
model \(<\) buildModel(modelname)  
Before building a model, it is recommended to check if the model is available in _useeior_ using this function:  
seeAvailableModels() _useeior v1.0.0_ comes with nine built-in national models (Table 1), and their configuration files are found in the 'inst/extdata/modelspecs/' folder.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline
**Model Name** & **Description** & **Number of Sector** & \
\begin{tabular}{c} **Number of** \\ **Impact Category** \\ \end{tabular} & **IO Data Year** &
\begin{tabular}{c} **Environmental** \\ **Data Years** \\ \end{tabular} \\ \hline USEEIOv2.0 & A detail level commodity model with full life cycle inventory & 405 & 23 & 2012 & 2010-2017 \\ USEEIOv2.0-411 & A detail level commodity model with waste sector disaggregation and full life cycle inventory & 411 (404 + 7) & 23 & 2012 & 2010-2017 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Built-in models in _useeior_ v1.0.0. All models are single-region USEEIO models, with the 50 states of the US modeled as one region. IO data used in all models are before BEA’s redefinition and in producer price.  
Model configurations are stored in relevant.yml (interchangeable with.yaml) files. YAML is a simple text-based format used to store configuration data across the USEEIO tool ecosystem [11]. _useior_ uses the _configr_ package [22] to parse YAML files.
# Privacy Preserving Face Recognition in Cloud Robotics: A Comparative Study
## 6 Conclusions  
We studied several approaches for robot secure face recognition in the cloud environment. We used different algorithms to encrypt a set of images. The encrypted versions were used for training and testing. We trained the robot with various deep learning algorithms. We adopted some tests to measure safety, time complexity, and recognition accuracy. Both cloud and robot environments were considered. The findings showed that some algorithms were well suited for the security criterion. Others were better placed for recognition accuracy. The findings also revealed that, compared to a large range of algorithms, the genetic algorithm is a good candidate for better security and better recognition accuracy. Our study also revealed that the recognition accuracy provided by AES, RSA, and RCP is not reasonably high, although these methods are more secure. The percentages of improvement from PCA to EffNet were 16.2%, 6.16%, 20.4%, 22.1%, 9.56%, 279.88%, 392.1%, 366.32%, and 480% for plain images and encrypted images using AES, Bit slice, Chacos, GA, DNA, Hill, RSA, and RCP encryption algorithms, respectively. By comparing cloud and robot environments, we concluded that the recognition was faster for the cloud. It is advisable to run the same algorithm on the cloud to improve the robot's recognition speed. Conceptualization, C.K. and O.C. ; methodology, C.K. and O.C. ; writing--original draft preparation, C.K. ; writing--review and editing, C.K. and O.C. and A.H.; visualization, A.H.; supervision, H.H. ; project administration, O.C. ; funding acquisition, A.Z. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**CNN Models**} & \multicolumn{2}{c}{**Without**} & \multirow{2}{*}{**AES**} & \multirow{2}{*}{**Bit Slice**} & \multirow{2}{*}{**Chacos**} & \multirow{2}{*}{**DNA**} & \multirow{2}{*}{**GA**} & \multirow{2}{*}{**Hill**} & \multirow{2}{*}{**RSA**} & \multirow{2}{*}{**RCP**} \\  & & & & & & & & & & \\ \hline LeNet & 0.559 & 0.749 & 0.98 & 0.67 & 0.793 & 0.612 & 0.876 & 0.628 & 0.753 \\ Alexnet & 0.85 & 0.945 & 0.98 & 0.774 & 0.569 & 0.575 & 0.629 & 0.92 & 0.627 \\ VGG16 Net & 0.671 & 0.671 & 0.765 & 0.796 & 0.656 & 0.812 & 0.640 & 0.796 & 0.656 \\ GoogLeNet & 0.687 & 0.753 & 0.781 & 0.656 & 0.671 & 0.562 & 0.968 & 0.812 & 0.578 \\ ResNet & 0.538 & 0.765 & 0.640 & 0.968 & 0.906 & 0.906 & 0.671 & 0.875 & 0.906 \\ DenseNet & 0.527 & 0.875 & 0.671 & 0.781 & 0.890 & 1.000 & 0.875 & 0.781 & 0.781 \\ MobileFaceNet & 0.765 & 0.828 & 0.796 & 0.828 & 0.718 & 0.906 & 0.781 & 0.781 & 0.687 \\ ShuffleNet & 0.907 & 0.622 & 0.965 & 0.675 & 0.598 & 0.626 & 0.808 & 0.737 & 0.676 \\ EffNet & 0.915 & 0.793 & 0.775 & 0.959 & 0.643 & 0.879 & 0.877 & 0.69 & 0.784 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Testing time (Sec) of CNN models on robots.
## 2 Principle
### Phase-Shift and Coded Patterns  
Phase-shift methods have been widely used for optical measurement because of their measurement accuracy, spatial resolution, and data density [8]. The three-step phase-shift method requires the fewest number of patterns among various phase-shift methods, thus it is desirable for high-speed applications. Three-step phase-shift patterns can be described as:  
\[\left\{\begin{array}{l}I_{1}(x,y)=A(x,y)+B(x,y)\cos[\phi(x,y)-2\pi/3]\\ I_{2}(x,y)=A(x,y)+B(x,y)\cos[\phi(x,y)]\\ I_{3}(x,y)=A(x,y)+B(x,y)\cos[\phi(x,y)+2\pi/3]\end{array}\right. \tag{2}\]  
where \(A(x,y)\) denotes the average intensity, \(B(x,y)\) denotes the intensity modulation, and \(\phi(x,y)\) denotes the phase to be solved for. Figure 2a-c shows three phase-shift patterns generated using the above equations, and same rows of the three patterns are shown in Figure 3a. Solving the above equations, the three variables can be calculated as:  
\[\left\{\begin{array}{l}A(x,y)=(I_{1}+I_{2}+I_{3})/3\\ B(x,y)=[(I_{1}-I_{3})^{2}/3+(2I_{2}-I_{1}-I_{3})^{2}/9]^{1/2}\\ \phi(x,y)=\tan^{-1}[\sqrt{3}(I_{1}-I_{3})/(2I_{2}-I_{1}-I_{3})]\end{array}\right. \tag{3}\]  
Because of the arctangent operation, the wrapped phase \(\phi(x,y)\) is limited in range of \([-\pi,\pi]\). Thus, phase unwrapping should be carried out to recover the absolute phase. If the fringe order \(k(x,y)\) is determined, the absolute phase \(\Phi(x,y)\) can be calculated as:  
\[\Phi(x,y)=\phi(x,y)+2\pi*k(x,y) \tag{4}\]  
To determine the fringe order, we designed one coded pattern. Figure 2d shows the coded pattern, and one row of this pattern is shown in Figure 3b. The coded pattern can be described as:  
\[I_{M}(x,y)=A(x,y)+B(x,y)*M(x,y)=A(x,y)+B(x,y)*[2*\mathrm{mod}(\lceil x/P\rceil,N)/N-1] \tag{5}\]  
where \(P\) represents the fringe period, the truncated integer \(k=\lceil x/P\rceil\) represents the fringe order, and the remainder \(C=\mathrm{mod}(k,N)\) represents the code word; note that it is a periodic function with a period of \(N\). Once these four patterns are captured, the coded coefficient \(M(x,y)\) ranging from \(-1\) to \(1\) can be calculated as:  
\[M(x,y)=\cos^{-1}[(I_{m}-A)/B] \tag{6}\]  
Then the code word \(C(x,y)\) can be computed as:  
Figure 1: Fringe projection system.  
\[C(x,y)=round[(M+1)*N/2] \tag{7}\]
## 3 Results
### Antibacterial Properties
It was believed that direct contact with surfaces containing copper would also cause changes to the microbial cell wall and cell membrane [45]. Many physical or chemical processes, including hydrophilic contacts, van der Waals forces, electrostatic interactions, and hydrodynamic forces, are widely assumed to be involved in the adherence of bacteria to a solid surface [46]. Our experimental findings are in line with the consensus that less hydrophilic surfaces can reduce bacterial adherence, which decreases the contact-killing effect of the films, but the Cu-dose-dependent manner of the bacteria-killing effect is also evident. The results from the antibacterial studies indicate the excellent antibacterial activity of the most hydrophilic coating obtained at \(-25\) V substrate biasing. On the one hand, important physical phenomena and processes surrounding the solid-liquid interface, such as crystal dissolution and flotation [47], are determined by the coating's hydration and, therefore, hydrophilicity. Hence, more hydrophilic films can release different amounts of copper ions than less hydrophilic. On the other hand, more hydrophilic coatings enable water-based microbiological solutions, including bacterial biofilms, to adhere to the surface and allow direct contact killing to occur. Then, both the effects of ion release and contact killing can contribute to the best inhibition percentage of the bacterial growth of \(-25\) V biased samples. To obtain a combination of the desired mechanical and corrosion behavior, as in \(-50\) V biased coatings together with high antibacterial activity, not only against _S. aureus_ but also against _E. coli_ strain, the copper content in the target can be somewhat increased without  
Figure 10: Representative photos of **(a)**_E. coli_ and **(b)**_S. aureus_ colonies on agar plates after the 24 h incubation period. The percent of bacterial growth inhibition relative to Ti6 Al4V is indicated by the numbers in the right corner. The data are shown as average \(\pm\) SD (n = 3). Significant deviations from the control sample, Ti6 Al4V, are shown by the symbols: \({}^{\star}p<0.001\), \(\#\)\(p<0.05\). compromising the biocompatibility. However, both the coated samples biased at \(-25\) V and \(-50\) V are promising candidates for further research and development in the field of biomedicine.
## 3 Results and Discussion
### Carotenoid and Chlorophyll Pigments Content  
Carotenoid and chlorophyll pigments are essential constituents of oil products as their metabolism is related to the oxidation processes of UFAs [29]. The tested oils were differentiated for both carotenoid and chlorophyll pigment content in oils (Table 1). FO had the highest content of carotenoid pigments with a value of 153.68 mg/kg. The other oils studied were characterised by much lower contents. WCO had a value of 58.16 mg/kg. CO, DO, PO, and TO had similar carotenoid pigment levels, respectively: 23.03, 20.26, 20.66, and 18.18 mg/kg. [PERSON] et al. [30] reported that the content of carotenoid pigments in CO was 10.1 mg/kg oil. The lowest content of carotenoid pigments was determined in BO (5.40 mg/kg). According to the literature, the difference in carotenoid content in plants depends on the cultivation system (in the greenhouse or field system), the degree of seed maturity, or the oil extraction method [31].  
In the present work, DO was characterised by the highest content of chlorophyll pigments, for which a value of 102.20 mg phenoplytin a/kg was determined. Another oil with a high chlorophyll content (93.27 mg phenoplytin a/kg) was PO. CO had a chlorophyll content of 23.43 mg/kg. This value was higher than that obtained by [PERSON] et al. [30] for this type of oil (11.1 mg/kg oil). FO and WCO, which were characterised by high contents of carotenoid pigments, were simultaneously distinguished by low contents of chlorophyll pigments. TO and WCO had 10.74 and 7.52 mg/kg of chlorophyll pigments, respectively. The lowest amount of phenoplytin a was found in BO (2.60 mg phenoplytin a/kg). [PERSON] et al. [31] reported a similar value (2.8 mg/kg oil) for forage oil.
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 1 Introduction
Additionally, it defines link quality in terms of delay, encompassing back-off, switching, and queuing delays to estimate link weight, ensuring route stability and reducing link failures. The paper also introduces a robust route selection mechanism to detect malicious nodes or external threats, a clustering strategy, enhancing trust in received messages during data exchange, and a cluster formation mechanism. It outlines route maintenance for managing multiple routes discovered during selection or addressing link failures during data transmission. Simulation results compare the protocol's performance with itself on different packet sizes and others, focusing on metrics like throughput, communication delay, packet delivery ratio and normalized routing load. The rest of this work is structured as follows: Section 2 addresses the present protocols for FANETs in brief. This highlighted the advantages and disadvantages of several current protocols. Section 3 examines the proposed routing protocol's network model. Section 4 goes into detail about the proposed routing protocol. Section 5 route selection and maintenance process for the UAVs to ensure safer connectivity. Section 6 discusses the simulation result. Section 7 finishes the article by discussing the unresolved research questions.
# Women's Singles Tennis Match Analysis and Probability of Winning a Point
## 3 Results
### Probability of Winning a Point Based on Various Combinations of Performance-Influencing Variables  
Figure 2 illustrates the probability that the serving player wins a point on the different court types, considering combinations of the serve and rally variables.  
The probability of winning a point on the first serve varied by surface: 61% on clay, 70% on grass, and 69% on hard court. The chance of winning a point with second service was 55%, 57%, and 57%, respectively, which represented a decrease of 6%, 13%, and 12%. When the point started with a first serve and ended with a short rally, the probability of winning the point was 65%, 75%, and 75% depending on the surface. However, this probability decreased to 58%, 58%, and 56%, respectively, when second serves were played, representing a decrease of 7%, 17%, and 19%. In situations where  
Figure 2: Probability of winning a point on each surface as a function of type of service and rally length.  
Significant differences were detected between the combinations of first service, shortrally, and point ending variables as a function of surface type (\(\chi 2=25.452\); sig. = 0.005). These differences can be explained by the higher number of unforced errors recorded on clay compared to the other two surfaces, and by the lower number of winners playing serve on clay compared to the fast surfaces. No significant differences were found in the rest of the possible combinations of variables according to the type of surface.
## 4 Biosynthesis and Health Implications of Pyrrolizidine Alkaloids
### Biosynthesis of Pyrrolizidine Alkaloids
1,2-unsaturated PAs are considered the most toxic because they can be oxidated in highly reactive pyrroles, which have been related to severe cases of hepatotoxicity, as a consequence of acute toxicity, and carcinogenic and genotoxic effects, as a consequence of chronic toxicity [49]. The necine bases commonly found in plants are the following: retronecine, heliotridine, otonecine, supinidine, tracheanthamidine, and platycerine (Figure 5). The first four types have an 1,2-unsaturated in the pyrrolizidine ring and are therefore considered to exhibit major toxicity whereas the platynecine and tracheanthamidine types have a saturated necine base and are generally regarded as non-toxic. Except for the otonecine type, in which N-oxides cannot be formed, N-oxides of the other types naturally occur and often coexist with basic PA forms in plant materials. On the other hand, the type of esterification between the necic acids and the necine base allows for differentiation among three further types, which are monoesters, open-chained diesters, and cyclic diesters (Figure 5). The esterification often occurs at C-7 in monoesters and at C-7 and C-9 in open-chained and cyclic diesters [48; 56]. Figure 4: Biosynthetic pathway proposed for PA formation. The polyamines putrescine and spermidine are derived from the basic amino acids ornithine and arginine. Subsequently, homosperidine synthase (HSS) exchanges the 1,3-diamonopropane residue of spermidine via putrescine, which releases 1,3-diaminopropane and forms symmetric homospermidine. Oxidation of homospermidine to 4,4’-iminodibutanal initiates cyclization to pyrrolizidine-1-carbaldehyde. Desaturation and hydroxylation via unknown enzymes form retronecine, heliotridine, and otonecine type PAs.