esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Effect of Process Parameters on Curing Depth and Width  
Since each P\(\upmu\)SL process parameter has critical effect on the printing quality, i.e., curing depth and width, evaluation of the effect of each parameter is necessary. Through multiple simulations using the computational model we developed, we studied the impact of each parameter on printing quality of AM parts. Table 4 lists the range of values for each parameter we used for this study. Note that the largest oxygen concentration used in our simulation was 21% as shown in the bold in Table 4 because it is the actual oxygen concentration in the air. Therefore, the corresponding normalized parameter value for this data point is 2.1.  
First, reference curing depth and width were determined from the results produced with the set of parameters in column B. Then, simulations were performed while one parameter was varied with all other parameters being kept constant. Resulting curing depth and width were normalized by the reference value to evaluate the effect of the parameter studied.  
Figure 7a shows the effect of the parameters on curing depth. Curing depth increases as (PI) increases because PI increases reactivity of the resin. When (PA) concentration increases, the cure depth reduces because light penetration depth decreases with PA. Cure depth is relatively insensitive to environmental (O). When light intensity and exposure time increase, cure depth increases because light energy is product of light intensity and exposure time. It is observed that (PA) influences cure depth the most, which is also found from results in Figure 6 where \(D_{p}\) and \(C_{d}\) both decrease as (PA) increases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Values**} \\ \cline{2-6}  & **A: 0.5** & **B: 1** & **C: 1.5** & **D: 2** & **E: 2.5** \\ \hline
[PI] \% & 1 & 2 & 3 & 4 & 5 \\
[PA] \% & 0.05 & 0.1 & 0.15 & 0.2 & 0.25 \\ \(I_{0}\) (mW/cm\({}^{2}\)) & 10 & 20 & 30 & 40 & 50 \\
[O] \% & 5 & 10 & 15 & 20 & 21 (**E: 2.1**) \\ Time (s) & 1 & 2 & 3 & 4 & 5 \\ \hline \end{tabular}
\end{table}
Table 4: Printing parameter evaluation using normalized value concept.  
Figure 6: Comparison of curing depth between experiment and simulation. \(x\)-axis represents energy dosage in log scale and \(y\)-axis is curing depth in linear scale.  
Similar analysis was performed to study the effect of the parameters on curing width. As shown in Figure 7b, it is interesting that environmental oxygen concentration has the highest effect on curing width. As environmental oxygen concentration increases, oxygen inhibition becomes more prominent at the surface of the resin, which results in decrease in curing width. As expected, when (PI), light intensity, and exposure time increase, curing width increases due to increased light energy or reactivity of resin. In contrast to curing depth case, (PA) has least effect on curing width because its role is primarily to control the penetration of light in depth direction.
# Mechanical Properties of Green Synthesized Graphene Nano-Composite Samples
###### Abstract  
Graphene quantum dots are zero-dimensional nanoparticles that are used widely in advanced composite materials such as filtration membranes, adsorbent materials, optical devices, biomedical applications (especially biosensors), flame retardancy, and automotive, aerospace, agricultural and environmental applications. In this article, the mechanical properties (flexural strength, flexural strain and elastic modulus) of polymer-based nanocomposites will be investigated. The main novelty of the current work is the green synthesis of graphene quantum dots which were extracted from lemon juice. XRD and FTIR tests have been conducted to determine the composition of the prepared powder. The polyester resin and graphene quantum dots were mixed with different weight percentages (0.25%, 0.5% and 1% wt. graphene) and processed to fabricate nanocomposite samples. The mechanical properties of the prepared samples were measured according to the ASTM D790-17 standard testing method. The experimental results show that the strength increased from 80 MPa to about 112 MPa (40% increase in strength) by adding 0.25% wt. graphene quantum dots. The flexural modulus decreased from 2.70 GPa to 2.06 GPa by adding 1% wt. graphene content (23% decrease). The flexural strain increased considerably (up to 14.2%) by adding 1% wt. graphene quantum dots. Consequently, the ductility of the nanocomposites increased by adding green synthesized graphene quantum dots. The fracture behavior changed from brittle fracture mode to ductile fracture mode by adding the graphene quantum dots. Additionally, a flame retardancy test has been carried out by implementing the UL-94 test. The fabricated nanocomposites showed fire retardancy due to char barrier formation on the surface of the nanocomposites.  
polymer-based nanocomposites; graphene; green synthesize; nanoparticles; flexural properties +
Footnote †: journal: Applied Sciences
# VITA-D: A Radionic Web Tool for Predicting Vitamin D Deficiency Levels
## 3 Materials and Methods
### Private and Public Data Collection
#### 3.3.4 Encoding of 0 and 1 Values  
Upon completing the dataset integration process, which combined sources from UTPL and FigShare while incorporating previous steps related to data cleaning, normalization, and the removal of inconsistent values, the next step involved defining the variables for the classifier selected for analysis. Among the identified variables, vitamin D levels exhibited significant variation, encompassing cases indicative of deficiency as well as sufficiency. This variability led to the selection of an appropriate baseline variable for classification.  
Vitamin D levels were categorized based on established guidelines for molecular concentrations, where values below 15 ng/mL were classified as vitamin D deficiency, while values above this threshold were considered sufficient. Exploratory data analysis revealed that a substantial proportion of the recorded vitamin D levels clustered around 20 ng/mL (Figure 3). This distribution warranted a reassessment of the classification threshold to achieve a more balanced division between categories, aligning with recommendations found in the specialized literature.  
Figure 3: Vitamin D molecule levels, Penner E, MPH, vitamin D: 15 ng/mL; [[https://www.elo.health/biomarkers/vitamin-d-overview/15/](https://www.elo.health/biomarkers/vitamin-d-overview/15/)]([https://www.elo.health/biomarkers/vitamin-d-overview/15/](https://www.elo.health/biomarkers/vitamin-d-overview/15/)). Accessed on 20 May 2024.  
To enhance the classification scheme, the threshold was adjusted to 18 ng/mL. Under this revised criterion, vitamin D deficiency was defined as levels below 18 ng/mL, while sufficiency was assigned to levels equal to or greater than 18 ng/mL.  
Figure 4 presents the distribution of vitamin D levels among the analyzed patients. A clear clustering of data around the 20 ng/mL threshold is observed in the graph. To facilitate visualization, a red color scheme represents vitamin D deficiency values (less than 18 ng/mL), while green is used for sufficiency values (greater than or equal to 18 ng/mL). The ranges are visually highlighted with colored boxes and vertical lines corresponding to the defined thresholds.  
Each patient was assessed for vitamin D levels based on the standard cut-off point of 18 ng/mL. A binary classification (0 or 1) was assigned to a newly added column named vitamin D Deficiency, according to predefined criteria. Levels below 18 ng/mL were assigned a value of 1, indicating vitamin D deficiency, whereas levels equal to or above 18 ng/mL were assigned a value of 0, indicating no deficiency.  
This classification approach refined the scope of the original variable, enabling a binary distinction (yes/no) solely for the diagnosis of vitamin D deficiency. No differentiation was made regarding the degrees of vitamin D sufficiency. A separate column was introduced in the dataset to facilitate specific analyses based on the presence or absence of vitamin D deficiency.
# A Novel Wideband Splitter for a Four-Element Antenna Array
## 5 Conclusions  
A novel wideband power splitter for a four-element antenna array using two RF antiphase segments is proposed. The proposed antenna array power splitter achieved an extremely flat transmission response, an excellent phase accuracy, a relatively high bandwidth, and low insertion loss. The parameters of the proposed power splitter were evaluated through both simulations and measurements. It is important to note that there was a good agreement between the simulated and measured results.  
The properties and simulation results of the proposed splitter were verified with measured under practical conditions in the current 5G system. Measurements of the proposed antenna array splitter confirmed the parameters achieved from the simulations performed in a Matlab simulation model. The proposed splitter achieved an insertion loss of less than 0.8 dB, a reflection coefficient of less than \(-\)26.5 dB, and a reflection flatness of \(\pm\)6.5 dB, which makes it superior to other state-of-the-art solutions.  
Future work will be focused on the modification of the antenna splitter structure for other frequency bands. Moreover, further optimization of the proposed antenna splitter will be performed to improve the design and achieve better characteristics in terms of reduced power loss, improved impedance matching, and improved symmetry of individual segments. Moreover, the minimization of the physical dimensions while maintaining the presented parameters and minimization of the parasitic elements in the structure can represent further areas of future work.  
Conceptualization, B.A. and J.M.; methodology, B.A.; validation, B.A., J.M. and P.B.; formal analysis, B.A.; investigation, B.A.; resources, P.B.; data curation, J.M.; writing--original draft preparation, B.A. and J.M.; writing--review and editing, B.A., J.M. and P.B.; visualization, B.A. and J.M.; supervision, P.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovak VEGA grant agency, Project No. 1/0588/22 \"Research of a location-aware system for the achievement of QoE in 5G and B5G networks\".  
Not applicable.  
**Data Availability Statement:** Authors will provide data upon request.  
The authors declare no conflicts of interest.
## 3 Scenario and System Model
### Estimation of Channel Impulse Response  
In order to better capture the characteristics of the natural channel environment, the two strongest clusters \(\zeta\in\{1,2\}\) are spread to three different sub-clusters with fixed delay offsets. The \(M\) rays within a cluster are mapped to sub-clusters \(\xi\in\{1,2,3\}\), and are divided into three groups \(R_{\zeta}\), each with a power \(R_{\zeta}/M\) and delay offset \(\tau_{\zeta,\xi}-\tau_{\zeta}\) for a NLOS channel. Then, the CIR from \(s\) to \(u\) in the NLOS case can be expressed as  
\[H_{u,s}^{NLoS}(\tau,t)=\sum_{\xi=1}^{2}\sum_{\zeta=1}^{3}\sum_{m\in R_{\xi}} H_{u,s,\xi,m}^{NLoS}(t)\delta(\tau-\tau_{\zeta,\xi})+\sum_{\xi=3}^{Z}H_{u,s, \xi}^{NLoS}(t)\delta(\tau-\tau_{\zeta}), \tag{3}\]  
where \(H_{u,s,\xi}^{NLoS}(t)\) represents the channel coefficient of cluster \(\zeta\) for the antenna pair \(u,s\), which is formulated as \(H_{u,s,\xi}^{NLoS}(t)=\sum_{m=1}^{M}H_{u,s,\xi,m}^{NLoS}(t)\). In the LOS case, by adding the LOS channel coefficient to the CIR in the NLOS case and adjusting the scaling factor \(\gamma\), we obtain the CIR, as follows:  
\[H_{u,s}^{LoS}(\tau,t)=\sqrt{\frac{1}{\gamma+1}}H_{u,s}^{NLoS}(\tau,t)+\sqrt{ \frac{\gamma}{\gamma+1}}H_{u,s,1}^{LoS}(t)\delta(\tau-\tau_{1}), \tag{4}\]  
where \(H_{u,s,1}^{LoS}(t)\) represents the LOS channel coefficient with the strongest power transmission when cluster \(\zeta=1\), and \(\tau_{1}\) symbolizes the minimum delay of arrival. Note that positioning mainly depends on the CIR in the NLOS case because a heavy NLOS environment exists in the scenario.
## 2 Single Point Positioning Technology
After compensating for satellite clock bias, ionospheric errors, tropospheric errors, and multi-paths from Equation (2), the corrected pseudo-ranges can be written as:  
\[\rho_{c}^{G,E,R}=r^{G,E,R}+c\delta t_{G,E,R}+\varepsilon_{\rho,G,E,R} \tag{3}\]  
where \(\rho_{c}\) is the corrected pseudo-range; \(r\) is the true range from the satellite to the user:  
\[r^{G,E,R}=\sqrt{\left(x_{G,E,R}-x_{u}\right)^{2}+\left(y_{G,E,R}-y_{u}\right)^ {2}+\left(z_{G,E,R}-z_{u}\right)^{2}} \tag{4}\]  
Given the Doppler effect, the pseudo-range rate \(\dot{\rho}\) can be computed as [31]:  
\[\dot{\rho}=-\frac{Dc}{f} \tag{5}\]  
where \(D\) is the Doppler effect; \(c\) is the speed of light and \(f\) is the satellite transmitted frequency. The velocity can be estimated from the pseudo-range rate, starting by differentiating Equation (3) to obtain:  
\[\dot{\rho}_{c}^{G,E,R}=\dot{r}^{G,E,R}+c\delta\dot{t}_{G,E,R}+\varepsilon_{ \dot{\rho},G,E,R} \tag{6}\]  
where \(\delta\dot{t}\) is the user's clock drift (sec/sec); \(\varepsilon_{\dot{\rho}}\) is the error in observation (meters/sec); and \(\dot{r}\) is the true range rate expressed as:  
\[\dot{r}^{G,E,R}=1_{x}\left(v_{x,G,E,R}-v_{x,u}\right)+1_{y}\left(v_{y,G,E,R}-v_ {y,u}\right)+1_{z}\left(v_{z,G,E,R}-v_{z,u}\right) \tag{7}\]  
where \(\left[v_{x,G,E,R},v_{y,G,E,R},v_{z,G,E,R}\right]\) is the satellite's velocity; \(\left[v_{x,u},v_{y,u},v_{z,u}\right]\) is the true user's velocity; and \(\left[1_{x},1_{y},1_{z}\right]\) is the true line of sight unit vector from the satellite to the user:  
\[\left[1_{x},1_{y},1_{z}\right]=\frac{\left[\left(x_{G,E,R}-x_{u}\right), \left(y_{G,E,R}-y_{u}\right),\left(z_{G,E,R}-z_{u}\right)\right]}{\sqrt{ \left(x_{G,E,R}-x_{u}\right)^{2}+\left(y_{G,E,R}-y_{u}\right)^{2}+\left(z_{G,E,R}-z_{u}\right)^{2}}} \tag{8}\]  
Thus, the combined equations for the corrected pseudo-ranges (Equation (3)) and corrected pseudo- range rates (Equation (6)) can be as expressed as follows:  
\[\rho_{c}^{G,E,R}=r^{G,E,R}+c\delta t_{G,E,R}+\varepsilon_{\rho,G,E,R}\quad \text{and}\quad\rho_{c}^{G,E,R}=r^{G,E,R}+c\delta t_{G,E,R}+\varepsilon_{\dot{ \rho},G,E,R} \tag{9}\]To simultaneously estimate the user position and velocity, the state vector is defined from Equations (7)-(9) as follows:  
\[X=\left[ \begin{array}{cccccccccccc}x_{u}&v_{x,u}&y_{u}&v_{y,u}&z_{u}&v_{z,u}&c \delta t_{G}&c\delta t_{G}&c\delta t_{G}&c\delta t_{E}&c\delta t_{E}&c\delta t_{R} &c\delta t_{R}\end{array} \right]^{T} \tag{10}\]  
This vector will be used in robust extended Kalman filter algorithm detailed in Section 3.
## 4 Results and Discussion
### Flow Patterns
#### Single bluff body flow  
When \(L/D=1.1\), the shear layers of the upstream cylinder attach to the front surface of the downstream cylinders and pass through the gap between the cylinders, creating an extremely unstable shear flow on the gap-side surface of the downstream cylinders. The free-flow-side of the downstream cylinder has vortex shedding symmetry occurring simultaneously, with the vortex pairs taking a form similar to the typical Karman vortex street. This is unfamiliar with the single bluff body flow at low Reynolds numbers (50-300) [21]. At low Reynolds numbers, the shear layers of the upstream cylinder enclose the downstream cylinders, and when \(L/D\) increases, a small amount of fluid will pass through the gap and blend into the shear layers on the free-flow-side of the downstream cylinders and come off together. The critical \(L/D\) is 1.4 for low Reynolds numbers [21; 51].  
Figure 6a,b shows the contours of normalized instantaneous spanwise vorticities and the time-averaged streamlines for single bluff body flow at the middle plane, respectively. The time-averaged streamlines are symmetric relating to the center line of the wake, which indicates that the statistical range used in this paper is acceptable. Figure 6b shows that the recirculation region behind the three cylinders is longish and begins to occur at about one diameter away behind the downstream cylinders. The flow pattern at this \(L/D\) is identical to a flow through a single bluff body, as seen in Figure 6b.
## 5 Discussion
### AICrit vs. DAD  
DAD is a design-based anomaly detection system created manually for SWaT. The invariants used in DAD discussed in [3] are extracted using the design knowledge obtained from the PLC code, operator manuals, design documents, and other vendor-provided manuals. As it requires a deep understanding of SWaT's physical processes, DAD outperformed all other detectors in several cybersecurity exercises reported in [5; 12; 13; 14]. Thus, DAD is considered to be a gold benchmark anomaly detection system for SWaT and the performance of the other detectors was compared against it.  
AICrit uses a design-enhanced data-centric approach based on machine learning algorithms and computational intelligence techniques. As discussed in Section 4, AICrit takes P&ID and operational data as input to model the higher-order and non-linear dependencies among the highly correlated components of a plant. As reported in Table 9, we experienced similar performance for both AICrit and DAD. Particularly, attacks A1 and A10 were not detected by DAD since they did not raise any process anomalies. Similarly, A18 was not detected due to a lack of data from the historian. To summarize, the overall detection rate of AICrit was found to be the same as that of DAD.
## 3 Results
### Thermal Properties  
DSC confirmed that all synthesized photocross-linked polymers are amorphous materials. Only the glass transitions were observed in the thermograms of all the polymers prepared. The values of the glass transition temperatures (T\({}_{\text{g}}\)) were very low and varied from \(-4.5\)\({}^{\circ}\)C to \(-1.6\)\({}^{\circ}\)C (Table 3) even though these polymers were solid materials at room temperature. Such low T\({}_{\text{g}}\) values were determined by the flexible chains of AESO. The lower T\({}_{\text{g}}\) value of the polymers pAESO/VDM1 and pAESO/VDM2 than pAVDM3 could be explained by their lower yield of insoluble fraction. A huge amount of linear and/or branched macromolecules was formed in these polymers probably due to the different activities of the functional group affected the T\({}_{\text{g}}\) value of these copolymers [51]. The cross-linked structure of polymers caused their solid state at room temperature. Such a feature of the natural oil-based cross-linked polymers was observed in earlier studies [30; 52].  
TGA confirmed that the photocross-linked polymers exhibited high thermal stability. TGA curves of the cross-linked polymers are presented in Figure 6. The 10% weight loss temperatures (T\({}_{\text{dec-10\%}}\)) of pAESO and the polymer series pAESO/VDM range from 295 \({}^{\circ}\)C to 356 \({}^{\circ}\)C (Table 3). It was noticed, that the higher the amount of VDM was used, the lower the T\({}_{\text{dec-10\%}}\) value indicated. Lower T\({}_{\text{dec-10\%}}\) values and a two- or even three-step thermal decomposition of the polymers containing VDM fragments could be due to the higher rate of linear and/or branched macromolecules while higher thermal stability is related to the high density of the cross-links [53].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Polymer** & **Yield of Insoluble Fraction 1 (\%)** & **T\({}_{\text{g}}\)2 (\({}^{\circ}\)C)** & **T\({}_{\text{dec-10\%}}\)3 (\({}^{\circ}\)C)** & **E\({}_{\text{c}}\)4 (Pa)** \\ \hline
**pAESO** & 88 & \(-4.5\) & 356 & 0.62 \(\pm\) 0.1 \\
**pAESO/VDM1** & 31 & \(-2.6\) & 295 & 0.19 \(\pm\) 0.03 \\
**pAESO/VDM2** & 48 & \(-2.6\) & 318 & 0.46 \(\pm\) 0.13 \\
**pAESO/VDM3** & 63 & \(-1.6\) & 331 & 0.66 \(\pm\) 0.13 \\ \hline \hline \end{tabular}  
* After Soxhlet extraction with chloroform for 24 h,2 Class transition temperature estimated by DSC; 3 Temperature at the weight loss of 10 \(\%\) obtained from TGA curves; 4 Compressive modulus from the top pressure test.  
\end{table}
Table 3: The yield of the insoluble fraction and the thermal and mechanical characteristics of the cross-linked polymers.  
Figure 6: The thermogravimetric curves of the polymers pAESO and the polymer series pAESO/VDM.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Tested Parameters**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Group of Rats**} \\ \cline{3-6}  & & **GI (control)** & **GII** & **GIII** & **GIV** \\ \hline \multirow{2}{*}{Total LDL (mg/dL)} & week 4 & 109.0 \(\pm\) 20.0 \({}^{\text{A}}\) & 140.5 \(\pm\) 32.0 \({}^{\text{B}}\) & 88.0 \(\pm\) 15.2 \({}^{\text{A}}\) & 78.5 \(\pm\) 20.0 \({}^{\text{A}}\) \\ \cline{2-6}  & week 8 & 182.0 \(\pm\) 58.1 \({}^{\text{A}}\) & 119.5 \(\pm\) 28.8 \({}^{\text{A}}\) & 249.0 \(\pm\) 33.0 \({}^{\text{B}}\) & 303.0 \(\pm\) 54.6 \({}^{\text{B}}\) \\ \hline \multirow{2}{*}{TC (mg/dL)} & week 4 & 341.0 \(\pm\) 20.0 \({}^{\text{AB}}\) & 381.0 \(\pm\) 47.1 \({}^{\text{A}}\) & 295.2 \(\pm\) 73.0 \({}^{\text{B}}\) & 329.8 \(\pm\) 27.9 \({}^{\text{AB}}\) \\ \cline{2-6}  & week 8 & 413.4 \(\pm\) 106.1 \({}^{\text{AB}}\) & 388.4 \(\pm\) 32.3 \({}^{\text{A}}\) & 473.9 \(\pm\) 111.3 \({}^{\text{B}}\) & 500.8 \(\pm\) 50.9 \({}^{\text{B}}\) \\ \hline \multirow{2}{*}{ALT (\(\mu\)kat/L)} & week 4 & 1.66 \(\pm\) 0.1 \({}^{\text{AB}}\) & 1.77 \(\pm\) 0.2 \({}^{\text{A}}\) & 1.73 \(\pm\) 0.1 \({}^{\text{A}}\) & 1.53 \(\pm\) 0.1 \({}^{\text{B}}\) \\ \cline{2-6}  & week 8 & 1.90 \(\pm\) 0.3 \({}^{\text{A}}\) & 1.65 \(\pm\) 0.2 \({}^{\text{AB}}\) & 1.48 \(\pm\) 0.2 \({}^{\text{B}}\) & 1.39 \(\pm\) 0.2 \({}^{\text{B}}\) \\ \hline \multirow{2}{*}{AST (ukat/L)} & week 4 & 3.88 \(\pm\) 0.2 \({}^{\text{A}}\) & 4.36 \(\pm\) 0.5 \({}^{\text{A}}\) & 4.33 \(\pm\) 0.4 \({}^{\text{A}}\) & 4.14 \(\pm\) 0.5 \({}^{\text{A}}\) \\ \cline{2-6}  & week 8 & 4.15 \(\pm\) 0.4 \({}^{\text{A}}\) & 4.02 \(\pm\) 0.9 \({}^{\text{AB}}\) & 4.00 \(\pm\) 0.8 \({}^{\text{AB}}\) & 3.30 \(\pm\) 0.7 \({}^{\text{B}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: LDL and TC (as determined using the Lipoprint® LDL System) and enzyme aminotransferase.