esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### QA/QC Results  
Data from two Niton(tm) XL3t hand-held x-ray fluorescence GOLDD+ analyzers, manufactured in 2011 and 2015, were used to determine false negative and positive rates. Because the 2011 XRF had higher detection limits for Hg, soil samples were collected if any Hg was detected with that XRF. At the completion of field activities, it was determined that the average detection limits of the 2011 and 2015 XRF were 14.82 mg/kg and 7.26 mg/kg, respectively. Table 3 summarizes false-negative (Type II error) rate findings by depth. The 0-2 cm depth had a false negative rate of 8.33%, whereas the2-10 cm depth had a 0% rate. Combined depths (excluding litter) had a false-negative rate of 4.08%.  
Table 4 summarizes the number of false positives (Type I error) with different criteria (in-field criterion, US RSL, and Kyrgyz standard for Hg in soil) for collecting a physical sample. Using the Kyrgyz standard results in the highest rate of false positives for all three depth categories. The US RSL had the lowest rate of false positives across all three depths, matching the in-field criterion percentage for 0-2 cm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **X-Axis** & **Y-Axis** & **N** & \(p\)**-Value** & **R\({}^{2}\)** & **Intercept** & **Conversion Factor** & **Intercept \(p\)-Value** & **Equation** \\ \hline Hg & In Situ & Ex Situ-Bulk & 99 & \(\begin{array}{c}7.08\times \\ 10^{-5}\end{array}\) & 0.15 & 8.8 & 0.3 & \(\begin{array}{c}6.10\times \\ 10^{-9}\end{array}\) & y = 0.3x + 8.8 \\ \hline Hg & Ex Situ-Bulk & Ex Situ-Sieved & 134 & \(<\)0.0001 \({}^{1}\) & 0.89 & 0.0 & 1.1 & NA & y = 1.1x \\ \hline As & Ex Situ-Bulk & Ex Situ-Sieved & 133 & \(<\)0.0001 & 0.96 & \(-\)10.2 & 1.4 & 0.00154 & \(\begin{array}{c}y=1.4x-\\ 10.2\end{array}\) \\ \hline As & In Situ & Ex Situ-Bulk & 100 & \(<\)0.0001 \({}^{1}\) & 0.89 & 0.0 & 1.0 & NA & y = 1x \\ \hline Sb & In Situ & Ex Situ-Bulk & 99 & 4.91E-12 & 0.39 & 140 & 0.2 & \(\begin{array}{c}4.67\times \\ 10^{-5}\end{array}\) & y = 0.2x + 140 \\ \hline Sb & Ex Situ-Bulk & Ex Situ-Sieved & 133 & \(<\)0.0001 & 0.96 & \(-\)24 & 1.4 & \(\begin{array}{c}7.19\times \\ 10^{-4}\end{array}\) & y = 1.4x \(-\) 24 \\ \hline \hline \end{tabular}  
\({}^{1}\)\(p\)-value of intercept was insignificant.  
\end{table}
Table 2: Summary regression results comparing XRF soil fractions. Conversion Factor is the slope of the regression line.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Depth (cm)** & **Total Samples** & **Count** & **Percent (\%)** & **Notes** \\ \hline
0–2 & 12 & 1 & 8.33 & NA \\ \hline
2–10 & 24 & 0 & 0 & NA \\ \hline Combo & 49 & 2 & 4.08 & Excludes litter \\ \hline \end{tabular}
\end{table}
Table 3: False negative rate by depth. The combo depth is all depths excluding litter.
# Analysis of the New Thomson Scattering Diagnostic System on WEST [PERSON]
## 3 Results
### Hydraulic Study  
CFD simulations allow for the checking of the hydraulic design. The mass flow rate in each component depends on the incident plasma heat flux, and the aim is to ensure that the pressure drop of the total diagnostic is lower than 5.6 bar. Since the evaluated pressure drop was too high, the hydraulic design, especially of the bracket, had to be changed.  
The pressure drop and velocity field into the bracket are given in Figure 16.  
In the first design, the pressure drop was above the prescribed limit (1.1 bar) at the velocity required to cool this component (2 m/s). Figure 16b shows how the abrupt change of hydraulic section increased turbulence phenomena. The design optimization shown after reduced the pressure drop from 1.1 to 0.5 bar.  
The curve of the pressure drop, depending on the velocity for the most important components, is plotted in Figure 17.  
For the mirror, the curve shows how the pressure drop value is still low when incrementing the velocity. This component will have the highest mass flow rate because of its plasma heat flux proximity. In addition, this relationship shows that friction losses are not negligible in front of regular pressure losses.  
These equations will be essential during diaphragm characterization for flow balancing. These diaphragms will be designed using the formulas included in [11].  
Figure 16: CFD results for an inlet velocity of 2 m/s. (**a**) Pressure field of the first design. (**b**) Velocity streamline of the first design. (**c**) Pressure field of the new design. (**d**) Velocity streamline of the new design.
# Neural Network-Based Reference Block Quality Enhancement for Motion Compensation Prediction
## 3 Proposed Method
### Training Strategy  
Specifically, given a collection of \(n\) training samples, \((\mathbf{x}_{i},\mathbf{y}_{i})\), \(i\in\{1,2,\ldots,n\}\), the mean squared error (MSE) was used to minimize the loss function. The loss function is formulated as  
\[L(\Theta)=\frac{1}{n}\sum_{i=1}^{n}||F(\mathbf{x}_{i}|\Theta)-\mathbf{y}_{i}| |^{2}, \tag{6}\]  
where \(\Theta\) is the parameters of the network. The proposed RBENN was trained using PyTorch 1.8.0 on an NVIDIA GeForce GTX 2080 Ti GPU. The loss function was minimized by using a first-order gradient based optimization method called Adam [30]. A batch-mode learning method was adopted with a batch size of 64. The momentum of Adam optimization was set to 0.9 and the momentum 2 was set to 0.999. The base learning rate was initially set to decay 0.1 times per 60 epochs from 0.0001. The training lasted 240 epochs.
## 4 Mechanical Properties of Geopolymer Concrete
### Efficiency of Alkaline Solution and Ambient Condition on GPC
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Author** & **Aim** & **Results** \\ \hline [PERSON] et al. [43] & The efficacy of NaOH on the improvement of GPC compressive strength & At 7 and 28 days, the maximum compressive strength was increased while increasing the NaOH molarity from 8M to 16M, as well as the curing time from 24 to 48 h; however, no significant change was seen from 48 to 72 h curing time. \\ \hline [PERSON] et al. [44] & The efficiency of alkaline solution on compressive strength of GPC & GPC concrete with the compressive strength of 30 MPa could occur by adopting an alkaline solution to fly ash ratio of 0.50, which was the highest amount achieved. \\ \hline [PERSON] et al. [45] & The effect of alkaline solution on the mechanical properties of GPC and its behavior. & The highest value of mechanical properties such as compressive, tensile, flexural strength at 28 days was at a 50\% cement replacement ratio. Compressive strength with material content of 350 kg/m\({}^{3}\) and alkaline solution ratio of 0.55 was the highest value. \\ \hline [PERSON] et al. [46] & The effect of alkaline solution, binder content and curing time & Workability of fresh concrete was attributed to water/solids ratio in the mixture. The highest value of compressive strength was recorded for the mixture with binder content of 400 kg/m\({}^{3}\), SS/SH ratio of 2.5 and 7-day curing. \\ \hline [PERSON] et al. [47] & The effect of water addition, plasticizer and alkaline solution constitution & The increment of additional water up to 30 kg/m\({}^{3}\) enhanced the workability of fly ash up to 200\%. GPC workability was increased up to 115\%. A slight increase in water absorption and porosity and decrease in compressive strength and elasticity modulus were observed while increasing alkaline solution content. The properties of GPC were improved by increasing NaOH molarity up to 16 M. The optimum ratio of NaOH /Na\({}_{2}\)SiO\({}_{3}\) was 0.4. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of liquid additives (water-alkaline solution plasticizer) on GPC. Figure 6: Effect of (**a**) SS/SH ratio; (**b**) recycled concrete aggregate (RCA) replacement on the compressive strength [49].
## 5 Conclusions  
This paper has performed an analysis of the life-cycle costs and life-cycle assessment of the Xikema No. 1 high-speed railway tunnel in China. In terms of the LCC analysis, the initial capital costs (i.e., tunnel and rail construction), and operation and maintenance costs have been separately considered. NPV values, with the different discounted rates of the project, have also been estimated. The results show that the discounted rate has a great influence on NPV values. In the LCA analysis, the CO\({}_{2}\) emissions and energy consumption during the life of the overall design has been accounted for. The LCA analysis shows that the materials used in the construction process (i.e., tunnel and rail construction) contribute over 97% of the life cycle's CO\({}_{2}\) emissions, and the CO\({}_{2}\) emissions caused by the maintenance process are relatively small compared to that of the construction process. In addition, the maintenance process consumes about 55.2% of the life-cycle energy. The energy consumption of the tunnel construction process is around 44.3%, and the portion from the rail construction can be ignored.  
This analysis can provide important practical implications. The LCC analysis shows that the construction stage contributes the most to the overall cost; therefore, more cost-effective construction measures can be taken to reduce the life-cycle costs of a railway tunnel. Due to the fact that the production of concrete and steel always emits a great deal of CO\({}_{2}\), more environmentally friendly materials could be applied to minimise their environmental impact.  
Conceptualization, S.K. and S.Y.; Data curation, S.Y.; Formal analysis, S.Y.; Investigation, S.Y.; Methodology, S.Y.; Project administration, S.K.; Resources, S.K.; Supervision, S.K.; Validation, S.Y.; Visualization, S.Y.; Writing--original draft, S.Y.; Writing--review and editing, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission for the financial sponsorship of the H2020-MSCA-RISE Project No. 691135 \"RISEN: Rail Infrastructure Systems Engineering Network\", which enables a global research network that tackles the grand challenge in railway infrastructure resilience and advanced sensing. The APC is sponsored by the MDPI's Invited Paper Initiative.  
The authors are sincerely grateful to European Commission for the financial sponsorship of the H2020-MSCA-RISE Project No. 691135 \"RISEN: Rail Infrastructure Systems Engineering Network,\" which enables a global research network that tackles the grand challenge of railway infrastructure resilience and advanced sensing in extreme environments (www.risen2 rail.eu) [43].  
The authors declare no conflict of interest.  
Figure 3: CO\({}_{2}\) emissions and energy consumption: (**a**) CO\({}_{2}\) emissions (ton) and (**b**) energy consumption (\(\times 10^{6}\) M).
## 4 Discussion
Paper 1 examined S2 data using the same testing procedures as those employed in this paper; those results and those presented here found that LaSRC provided SR retrieval with greater accuracy than Sen2 Cor. CMAC retrieved SR that was remarkably consistent with LaSRC for low-to-moderate Atm-I, but with greater accuracy at higher levels of Atm-I. These results confirm that CMAC is application-ready for optical satellites, including numerous members of smallsat flocks.
# Application of Machine Learning Ensemble Methods to ASTRI Mini-Array Cherenkov Event Reconstruction
## 5 Tests on New Methods for Gamma/Hadron Separation
### Quality Factor  
The quality (Q) factor is a metric used to evaluate the performance of a classification method in distinguishing between gamma (signal) and hadron (background) events. It is defined as  
\[Q=\frac{\epsilon_{\gamma}}{\sqrt{\epsilon_{bgg}}}, \tag{4}\]  
where \(\epsilon_{\gamma}\) is the \(\gamma\) acceptance rate while \(\epsilon_{bgg}\) represents the hadron acceptance rate. The \(\gamma\) acceptance rate is defined as the correctly classified \(\gamma\) events out of the total number of \(\gamma\) events. The hadron acceptance rate is defined as the ratio of proton events which behave like \(\gamma\) events after the classification (background contamination). We also define the hadron rejection rate as the number of hadron events which have been correctly classified out of the total number of hadron events:  
\[1-\epsilon_{h}=\epsilon_{bgg} \tag{5}\]  
The higher the Q factor, the better the discrimination between gamma and hadron events, and therefore, the better the performance of the method.
## 2 Materials and Methods
### Impregnation Procedure  
The vacuum impregnation processes were carried out in an IS-PP apparatus (INTER-SONIC, Olsztyn, Poland), the schematic of which is presented in Figure 1.  
A water solution of analytically pure ascorbic acid (0.5% \(w/w\)), citric acid (0.5% \(w/w\)) and sucrose (8% \(w/w\)) purchased from Chempur (Piekary Slaskie, Poland) was used for impregnation. The effective concentration of the solution was approximately 9%, which was similar to the soluble solids content of the raw material, measured with a PAL-\(\alpha\) digital refractometer (ATAGO, Saitama, Japan). The literature shows that the application of an isotonic solution to the tissue does not induce swelling or shrinkage of the tissue and mass exchange occurs by replacing gasses and fluids in the pores of the tissue with the soaking solution [18].  
Each impregnation process was carried out using 3 kg of solution. The weight ratio of raw material to solution was approximately 1.7.5. The temperature of the solution was controlled during the process and stabilized at about 25 \({}^{\circ}\)C. For each experimental variant, the impregnation process was carried out in duplicate.  
The study examined the effects of cultivar and vacuum pressure on impregnation efficiency. Carrot slices were submerged in the impregnation solution, and a pre-set vacuum pressure was applied in the chamber and held constant for 30 min (vacuum phase) before gradual aeration, which lasted about 10 min in all cases. After reaching the atmospheric pressure in the chamber, the 30 min relaxation phase began. During this phase, the samples were kept in solution under atmospheric pressure. After impregnation, the samples were dried and divided into two portions: one for color and texture analysis, and the other frozen at \(-50\)\({}^{\circ}\)C for subsequent chemical analysis. All tests were completed within 14 days of processing. Figure 2 presents a schematic of the experimental procedure.  
Figure 1: Vacuum impregnation equipment—schema [17]. Numbers 1 and 4—valves; 2—PT100 temperature probe; 3—vacuum gauge; 5—lid; 6—vacuum chamber; 7—water jacket; 8—vacuum pump; 9—ultrasonic transducers.
# Regulated Power Supply with High Power Factor for Hyperspectral Imaging Applications
## 3 Proposed Circuit Configuration
### Proposed Implementation  
The proposed power supply is based on a full-bridge switched mode capable of supplying 500 VA. As shown in Figure 7, the proposed system consists of an automatic PF and THD corrector integrated with the bridge rectifier of the primary voltage and the filtering of the rectified voltage. It follows a double feedback strategy in a boost converter, which samples the input current to the bridge and subsequently regenerates it using the \(LC\) filter of the boost converter. The boost converter is kept at a ratio of 1 (_input_) to 1.3 (_output_) to avoid using an inductance with very low resistive losses. This would require increasing the switching frequency to reduce the size of the inductance, losing the energy storage capacity of \(L\), and worsening the final performance of the system, which aims to keep PF close to unity.  
The next stage is a high-frequency transformer switched through a full bridge with low-loss MOSFETs. The duty cycle of these MOSFETs is not critical, as the focus is on ensuring that the dead time between switches is sufficient to avoid the pulses generated by the short circuits due to the simultaneous switching of MOSFETs with opposite switching periods. When the two MOSFETs that were conducting stop, and the other two start, this causes short circuits (in the range of nanoseconds) that result in very high current peaks at very high frequencies that are difficult to filter.  
The output of this transformer is filtered by means of a rectifier bridge based on Schottky diodes since the system works at high frequencies and an output filter based on a combination of several high-frequency electrolytic capacitors in parallel.  
The regulated voltage is applied to two synchronous variable voltage step-down converters with double feedback (output voltage current given by the MOSFETs, Supplementary  
\begin{table}
\begin{tabular}{c c} \hline
**Harmonic Order (\(n\))** &
\begin{tabular}{c} **Maximum Permissible Harmonic Current** \\ **(\% of Input Current at Fundamental Frequency)** \\ \end{tabular} \\ \hline
2 & 2 \\
3 & 30\(\cdot\lambda^{2}\) \\
5 & 10 \\
7 & 7 \\
9 & 5 \\ \(11\leq n\leq 39\) & 3 \\ \hline \end{tabular}
\end{table}
Table 1: IEC 61000-3-2 current harmonic limits for Class C Equipment. \(\lambda\) is the circuit PF.  
Figure 7: Power supply with two Buck DC/DC converters.  
Figure S1). This allows us to have two outputs of 150 W each, with the voltage varying between 7 and 21 V independently.  
A PCB was built to implement all the components, and it was completely shielded to avoid the emission of electromagnetic interference and achieve the electromagnetic compatibility imposed by the current regulations. Two voltage step-down converters with a controlled output voltage between 7 and 21 V and 150 W of maximum power each were connected to the output of the aforementioned source (Supplementary Figure S1). All this equipment, in turn, was shielded inside an aluminum box to which were added, among other items, output voltage and current displays.
# Wearable Plasma Pads for Biomedical Applications
## 4 Conclusions  
A wearable hybrid-DBD plasma pad with a wearable power supply was developed. Unlike earlier plasma jets or DBD devices, this wearable plasma pad offers a variety of applications and convenience in terms of treatment area and time.  
The flexible thin-film plasma pad was fabricated with a polyimide film as a dielectric layer in a DBD, where two electrodes were formulated on each surface. Protection from electrical hazard was ensured because the high-voltage electrode was covered with an insulation layer, and the plasma was generated on a ground electrode that was covered with medical gauze facing the skin. A distinctive feature of the proposed plasma pad was the containment of the plasma in the gauze space, even when the pad was attached to human skin. During operation at approximately 1 kV, stability and safety were ensured, as electrical and thermal damage to the skin and aerial ozone or thermal heat could be readily reduced through power modulation.  
From in vitro and in vivo biocompatibility tests, the potential of the plasma pad for biomedical applications was confirmed, with no cytotoxicity for normal cells and no irritation of mouse skin. Skin wound healing with hair growth was also observed after in vivo plasma treatment. Moreover, the activity of the plasma pad against two bacteria present in normal or damaged skin, including MRSA, was demonstrated.  
Future research will be conducted to confirm the actual treatment effect of plasma in the form of a patch, bandage, socks, and cap.  
This work was supported in part by the Kwangwoon University under a Research Grant in 2017 and by a grant provided from the National Research Foundation of Korea (NRF-2017R1A2B4012590 and NRF-2015M3A9E2066855). [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments and wrote the manuscript; [PERSON] analyzed the data.  
The authors declare no conflict of interest.  
Figure 7: Antibacterial activity of the plasma pad. Viable bacterial cells were counted after 24 h post-plasma treatment. Differences of \(P<0.05\) were considered statistically significant (\(n=3\), \({}^{*}P<0.05\), \({}^{**}P<0.005\) compared to the control).
## 3 System-Level SPICE Modeling of EMP Coupling on Wireless RF Circuits  
### SPICE Model for the Coupling Response on the RF Channel with a Pyramid Horn Antenna  
To establish the SPICE model of coupling response on the simplified linear channel with a pyramid horn antenna (Figure 6a) constructed in Section 2, the equivalent circuits of which can be built based on the Thevenin and [PERSON] theorem (Figure 6b) [11; 15]. From the point view of the antenna port into the antenna system, the Thevenin SPICE model of horn antenna response can be established according to the antenna impedance as the source inner resistance and the open circuit (OC) voltage as the excitation source, and the Norton SPICE model requires antenna admittance and short circuit (SC) current. These input parameters used for the SPICE models can be derived from the ways of computation, simulation or tests. In this paper, the equivalent circuits of antenna input impedance and admittance used for the SPICE models will be modeled by the VF method based on the simulated data of impedance, admittance or S parameters according to the commercial EM software.  
From the antenna coaxial port into the equipment, the circuits of the simplified RF channel are the BPF and loads. Due to the micro-strip structure of BPF, it is hard to directly convert to the spice model. Hence, we will build its SPICE model with the VF method based on the measured admittance data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**P-Norms**} & **Mathematical** & \multirow{2}{*}{_f_(**t**)} & **Physical** & \multirow{2}{*}{**Application**} \\  & **Expression** & & **Meaning** & & \\ \hline \multirow{2}{*}{2} & \multirow{2}{*}{\(\left\{\int_{0}^{\infty}\left|f(\mathrm{t})\right|^{2}dt\right\}^{1/2}\)} & Electric field, & \multirow{2}{*}{\begin{tabular}{c} Root of energy \\ voltage, current \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Device is burnout due \\ to overheat \\ \end{tabular} } \\  & & electric field, & & \\ \(\infty\) & \(\left|f(\mathrm{t})\right|_{\max}\) & \begin{tabular}{c} magnetic field, \\ voltage, current \\ \end{tabular} & \begin{tabular}{c} Peak value \\ (absolute) \\ \end{tabular} & \begin{tabular}{c} Digital circuit flip, \\ switch or dielectric \\ breakdown etc. \\ \end{tabular} \\  & & & & \\ \(\left|\frac{\partial f(\mathrm{t})}{\partial\mathrm{t}}\right|_{\max}\) & \begin{tabular}{c} Electric field, \\ magnetic field, \\ voltage, current \\ \end{tabular} & \
\begin{tabular}{c} Local time rate \\ of change of the \\ waveform \\ \end{tabular} &
\begin{tabular}{c} Determining the \\ flashover of dielectric \\ materials and the \\ fliring of surge \\ protection devices \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: P-norms applied in this paper.