esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result Experiment
### Extended Aging Model and Aging Coefficient  
A three-dimensional (3D) aging index model was developed to predict tensile strength and elongation as functions of aging conditions (temperature and time). Figure 7a,b  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & & \\ \hline
**Aging Time** & & **Aging Temperature (\({}^{\circ}\)C)** & \\
**(Hour)** & **25** & **90** & **110** & **130** \\ \hline
0 & 1075 & - & - & - \\
1000 & &  
illustrates this model, where the X-axis represents aging time, the Y-axis indicates aging temperature, and the Z-axis corresponds to tensile strength and elongation, respectively. The blue spheres represent the measured mechanical properties of the aged specimens, while the yellow diamonds indicate the surface fitting results derived from the experimental data. Under all aging conditions, both tensile strength and elongation exhibited exponential decreases with increasing aging time and temperature. This trend was quantified through the aging models presented in Equations (3) and (4), which were developed based on the exponential functional form described in Equation (2). The corresponding model parameters are summarized in Table 7. The goodness-of-fit for the 3D degradation models was evaluated using the coefficient of determination (R\({}^{2}\)), yielding values of 0.845 for tensile strength and 0.834 for elongation, indicating relatively high predictive accuracy.  
Figure 6: Measured results of (**a**) tensile strength and (**b**) elongation as a function of aging time under different thermal aging conditions (90 \({}^{\circ}\)C, 110 \({}^{\circ}\)C, and 130 \({}^{\circ}\)C). Error bars represent 95% confidence intervals, and the dotted lines indicate nonlinear fitting curves.  
Figure 7: 3D exponential model results based on Equation (2), illustrating the degradation trends of (**a**) tensile strength and (**b**) elongation under various thermal aging conditions. The blue spheres represent experimentally measured data points, and the fitted surfaces depict the nonlinear aging behavior.  
A sensitivity analysis of the aging parameters revealed that, for tensile strength, the aging coefficient for temperature was 0.01 and for time was 0.0003, suggesting that temperature had a more significant effect on degradation than time. A similar trend was observed for elongation, with an aging coefficient of 0.01 for temperature and 0.005 for time.  
\[z_{\textit{Tensile strength}}(T,\ t)=-0.46 exp^{(\frac{T_{\textit{temp}}}{15 \times 2}+\frac{t_{\textit{time}}}{250\times 3})}+21.4 \tag{3}\]  
\[z_{\textit{elongation}}(T,\ t)=-12.43 exp^{(\frac{T_{\textit{temp}}}{17\times 7 }+\frac{t_{\textit{time}}}{17\times 7})}+1010.18 \tag{4}\]
## 2 Materials and Methods
### Physical and Chemical Wood Degradation
#### 2.3.2 XRD Analyses  
X-ray diffraction analysis was conducted at the MCX beamline of the Elettra-Sincrotron Trieste Research Centre to evaluate the cellulose crystallinity index (CrI) as a measure of wood degradation [56; 57]. The wood samples were pulverized using an agate mortar and analysed at room temperature using a capillary spinner sample holder. The experimental setup comprised a 4-circle Huber diffractometer with a 3D translation stage, equipped with a high-count rate fast scintillator detector. The detector configuration included two slits with vertical apertures of 200 and 300 \(\upmu\)m, positioned 90 cm from the sample. Diffraction patterns were acquired using a monochromatic beam with the following parameters: energy = 12 keV, acceleration current = 158.84 mA and X-ray wavelength = 1.0332 A. Sample alignment was achieved using a laser interferometer, with measurements conducted at a fixed incident angle of 4 degrees. Data collection was performed in transmission mode across the angular range from 4\({}^{\circ}\) to 70\({}^{\circ}\) to identify cellulose crystallinity index and the range from 4\({}^{\circ}\) to 30\({}^{\circ}\); other interfering compounds were observed in the remaining ranges. Data analysis was performed using the PDF4 software.  
The degradation degree was quantified through cellulose crystallinity measurements using the widely adopted Segal method. The crystallinity index (CrI%) was calculated according to the following equation:  
\[\text{CrI}\%=[(\text{I}_{200}-\text{I}_{\text{am}})/\text{I}_{200}]\times 100\]  
where I\({}_{200}\) represents the intensity of the (200) peak, corresponding to both crystalline and amorphous phases, and I\({}_{\text{am}}\) denotes the intensity minimum between the (200) and (101) peaks, with the (101) peak representing amorphous cellulosic fibres [58; 59].
## 4 Discussion  
For the first time, this pilot study investigated a new SEA 4 Encias@seawater-based mouthwash, compared to the most commonly used mouthwash, such as Chlorhexidine. As this is the first clinical trial of SEA 4, it is impossible to compare the results with any other published research. However, the results provided clear evidence that mouthwash with seawater reduced gingivitis and plaque formation more effectively than 0.2% chlorhexidine digluconate and a placebo saline solution throughout the study period.  
[PERSON] et al. found that the most effective mouthwashes in plaque reduction are those containing Chlorhexidine (CHX) and essential oils (E.O.) [6; 8; 23; 24; 25].  
Figure 4: Gingival Index frequency. Seawater was effective in gingival index reduction compared with Chlorhexidine 0.20% and saline.  
Figure 3: Plaque index frequency. Sea4 Encias produced a more significant reduction in plaque at four weeks than the other two mouth rinses.  
[PERSON] et al. compared the intraoral distribution of 0.2% chlorhexidine without alcohol (CHX-Alc) and 0.1% chlorhexidine with alcohol (CHX + Alc) at different rinse times (10 s, 20 s, 30 s) and described that the shortest rinse-time will assist in monitoring and continuity of the patient's compliance [26]. Another short-term study by [PERSON] and [PERSON] found no statistically significant differences between two commercially available CHX types of mouthwash (0.2% and 0.12%) in plaque inhibition with a 30-second rinse time, reporting side effects of spots and taste disturbances, limiting their potential for long-term use [25].  
Some oral antiseptics, containing ocenidine or polyhexamethylene biguanide, will be considered potential alternatives to chlorhexidine-based preparations to control oral microorganisms (Fusobacterium nucleatum, Streptococcus mutans, Streptococcus sanguinis and Candida albicans) [27; 28; 29].  
It has been clinically proven that three common therapeutic agents found in mouthwashes produce essential benefits for the control of gingivitis and plaque, such as Chlorhexidine, CPC, and essential oils. The SEA 4 Encias(r)mouthwash includes CPC in its formulation, demonstrating that in vitro and clinical studies are useful both, in biofilms and in the reduction of the common gingival inflammation and plaque index.  
The use of seawater mouthwashes will be the best alternative to the use of Chlorhexidine, with the same essential anti-bacterial activity, reducing the side effects of dental staining and taste disturbances.
## 5 Conclusions  
In this study, we identified and quantified 35 phenolic compounds in 14 different balsamic vinegar samples by the optimized HPLC method coupled with MS/MS and MS. The main phenolic compounds in balsamic vinegas are gallic acid, protocatechuic acid, caffeic acid, and \(p\)-comamic acid. Significant differences in the total content of phenolic compounds were found between the types of balsamic vinegar. The HS-SPME-GC-MS method was used for identification and the HS-SPME-GC-FID method was used for the semi-quantitative analysis of 13 volatile phenolic compounds in balsamic vinegar samples. By comparing the results of the analysis achieved by both developed methods, i.e., GC-MS and HPLC-MS/MS, the comprehensive characterization of the samples was achieved, which can be used for the evaluation of the category of balsamic vinegas. Furthermore, several compounds (i.e., 4-hydroxybenzaldehyde, syringealdehyde, 4-hydroxy-3-methoxycinnamaldehyde, and syringe acid) were found to be potential candidates for markers of the quality of balsamic vinegar samples with respect to the content of the phenolic compounds. The profiles of phenolic compounds determined by gas and liquid chromatography could also be used to assess the authenticity of balsamic vinegar samples or to check for adulteration of these products, but this would first require the analysis of a larger pool of balsamic vinegas, in particular premium quality vinegas.  
Table S1: Selection of extraction method--results of the analysis of sample No. 5 using static headspace extraction, Table S2: Selection of extraction method--results of the analysis of sample No. 5 using HS-SPME, Table S3: Central composite design used to optimize the extraction conditions of the HS-SPME method along with the corresponding observed responses, Table S4: Semi-quantitative content of phenolic compounds obtained by GC-FID analysis, Table S5: Optimization of MS/MS conditions for LC/MS/MS analysis of phenolic compounds, Table S6: The content of phenolic compounds in different samples of balsamic vinegas obtained by HPLC analysis, Table S7: Characteristics of calibration curves (slope and its relative standard deviation, RSD), linear ranges, coefficient of determination \(\mathbb{R}^{2}\), and limits of quantification, LOQ.  
**Author Contributions:** Conceptualization, P.C. and P.B.; methodology, T.B.; formal analysis, M.K.; investigation, M.K.; resources, P.B.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, P.C., T.B. and P.B.; supervision, P.C.; project administration, P.C.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Czech Science Foundation, grant number 18-14893S.  
Not applicable.  
**Data Availability Statement:** The data presented in this study are available in Supplementary Materials.  
The authors declare no conflict of interest.  
Samples of the balsamic vinegars are available in limited amounts upon request from the authors.
# Reduction of Dust on Solar Panels through Unipolar Electrostatic Traveling Wave
## 1 Introduction
The lunar environment may hinder NASA exploration missions [30] due to the dust particles on the Moon (average 72 \(\upmu\)m), which can adhere to the surfaces [33]. A method for protecting spacseuits from lunar dust has been developed using carbon nanotube technology [39]. Moon dust accumulated on the surface of solar panels can be cleaned by electrostatic methods and the cleaning devices can be charged with the help of solar UV irradiation, wind and cosmic rays [38]. Moreover, moon dust removal should be able to be performed more easily in a low gravity environment [26; 36]. Electrostatic systems are also being developed as sampling systems to perform reliable and autonomous sampling of regolith from asteroids [36]. Efficiency of dust removal for a wide variety of particle size distributions varies with the geometric parameters of the electrode [40]. Tests conducted to determine the effect of different factors on the dust removal performance, including electrode shape, electrode thickness, electrode area, insulating film [33] provide information on electrode design to optimize the efficiency of photo-voltaic (PV) panels used in future lunar exploration. The main parameters were [37]:  
* Electrical power requirements and dust accumulation rates
* Frequency of the operation of the screens
* Efficiency of EDS and Dust Removal Efficiency
* Placement of EDS system on the solar panels to reduce power loss in PV panel and increase optical transmission efficiency. Studies on PV panel cleaning focus primarily on the design and performance of cleaning robots. However, there are few on the electrostatic cleaning method [41] although it is believed that electrostatic cleaning method can make a significant contribution. In our previous studies, pollution data was obtained using analytical and experimental methods to determine content and impact on energy loss in PV panels [6]. Dust pollution is seen to have seasonal variation, which will cause varying losses in SPP and cleaning costs [42]. This study proposes a design for the electrodes of an electrostatic cleaning system that will reduce the contamination caused by atmospheric transported particulates on a solar panel. The electronic controller is examined in Section 2. The electric field and design of the electrode array and PCB are analyzed using numerical analysis with the Ansys Maxwell simulation software in Section 3. The performance of the proposed electrode array is compared with a traditional parallel electrode array. Then, a case study of the cleaning of 1 MW SPP in Sanhurfa province to compare costs of cleaning by a water system and the electrostatic method in Section 4. The results of the study are included in Section 5.
## 3 Results
### Effect of the Input Dataset Size on the UAP Performance  
Finally, we investigated the effect of the size of the input dataset used to generate a UAP on UAP performance. As a representative example, in terms of such performance, we focused on the skin lesion image dataset and evaluated the performance of nontargeted UAPs with \(p=2\). The UAPs were generated using an input dataset consisting of \(N\) images randomly selected from the test images in the medical image dataset. Here, we considered \(N=100\), \(N=500\), and \(N=1000\). The value of \(R_{f}\) was computed using the input and validation datasets. The validation dataset consisted of 2015 images randomly selected from the test images, excluding the images in the input dataset.  
As shown in Figure 3, \(R_{f}\) increases with \(N\). In addition, \(R_{f}\) for the validation dataset was lower than that for the input dataset; however, the difference in \(R_{f}\) between the validation dataset and input dataset decreased with an increase in \(N\). These tendencies were observed regardless of the model architecture. The results indicate that UAPs achieve high performance and generalization, resulting in a misclassification of the DNN model with an increasing in \(N\). However, the UAPs showed a relatively high performance despite a small \(N\). When \(N=100\), \(R_{f}\) for the validation dataset was >50% for the Inception V3 and VGG models, although it was ~30% for the ResNet50 model. Moreover, \(R_{f}\) for the validation dataset was >60%, regardless of the model architecture, when \(N=500\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset Target Class/** & \multicolumn{2}{c}{**Skin Lesion**} & \multicolumn{2}{c}{**OCT**} & \multicolumn{2}{c}{**Chest X-ray**} \\
**Architecture** & **Control** & **Case** & **Control** & **Case** & **Control** & **Case** \\ \hline Inception V3 & 63.8 (64.8) & 60.9 (10.4) & 27.3 (27.2) & 92.2 (25.2) & 67.1 (54.4) & 91.8 (45.9) \\ ResNet50 & 76.0 (66.6) & 81.2 (10.6) & 28.6 (28.3) & 93.5 (24.8) & 53.8 (57.6) & 97.6 (42.4) \\ VGG16 & 80.0 (72.4) & 79.0 (7.7) & 25.1 (26.5) & 97.9 (24.6) & 97.4 (51.5) & 97.1 (48.5) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Target attack success rates \(R_{s}\) (%) of UAPs for targeted attacks against the DNN models for medical image datasets. Values in brackets indicate random controls (\(R_{s}\) of random UAP).
## 3 Experiments and Results
### Experiments on the MHGSA System
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mode** & **Model** & \
\begin{tabular}{c} **Avg. Acc.** \\ **(S.D.)** \\ \end{tabular} & **Best Acc.** &
\begin{tabular}{c} **Avg. Time** \\ **(ms/Video)** \\ \end{tabular} & **Params** \\ \hline \multirow{4}{*}{Multi-Modal} & ResNet-50-MM & 69.1 (\(\pm\)0.4)\% & 69.7\% & 61.4 & 52M \\  & Eff.Net-b1-MM & 71.3 (\(\pm\)0.5)\% & 71.9\% & 74.3 & 17M \\  & MHSA * & 75.9 (\(\pm\)0.5)\% & 76.5\% & 91.1 & 30M \\  & MHGSA * & 77.3 (\(\pm\)0.3)\% & 77.6\% & 149.3 & 34M \\ \hline \hline \end{tabular}  
* Model using multiple frames on a visual path.  
\end{table} 
Table 3: Experiment on VGGSound-DR in multimodal mode. For inference time, the introduction of multi-branching structure and graph learning implies more computational consumption, so MHGSA-VO, MHGSA-AO, and MHGSA take about 46.1 ms, 74.7 ms, and 149.3 ms, respectively, in recognizing the disaster category of a single video clip, which is higher than the baseline model. However, it could be negligible in comparison to the length of the video clip (10 s). The inference time may vary with the size of the number of frame samples \(K\). In addition, as mentioned previously, the training process of MHGSA involves multiple stages. The feature extractors of the corresponding modalities need to be trained in advance to obtain a reasonable representation of the multimodal data. This may imply a more tedious training process when more modalities are introduced. A potential challenge is to introduce textual information, including plain text and text that appears in visual and audio, to obtain more comprehensive information for disaster event recognition. In addition, graph learning approaches may help to introduce modal information with multiple views and features.
## 2 Materials and Methods  
### Description of the Study Area  
The Tebessa-Morsott plain is located in the northeastern part of Algeria, 650 kilometers southeast of the capital city, Algiers. It occupies a part of the large Medjerda watershed--classified as basin number 12 according to the orohydrographic delimitation proposed by the National Water Resources Agency (ANRH)--and the entire Ksob wadi sub-basin. It corresponds to a large, closed depression extending along the NW-SE direction [33] and covering a surface of 600 km\({}^{2}\)[34]. It lies between longitudes of 7\({}^{\circ}\)64\({}^{\prime}\) E and 8\({}^{\circ}\)18\({}^{\prime}\) E and between latitudes of 35\({}^{\circ}\)22\({}^{\prime}\) N and 35\({}^{\circ}\)46\({}^{\prime}\) N (Figure 1).  
Based on field observations, topographic map data and satellite images (downloaded from [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 12 August 2022) Digital Elevation, SRTM 1 ArcSecond Global; the model domain was processed using ArcGIS 10.8), this region is surrounded by mountains ranging in altitude from 1713 m to 780 m a.s.l (Figure 1). We cited Zitouna (1324 m) and Edyr (1471 m) mountains in the north, Doukane (1713 m) and Arousal (1545 m) mountains in the south, Belkeif (1338 m) mountain in the west and Djebisa (1120 m) mountain in the southeast. In contrast, the plain is characterized by a relatively flat surface with elevations ranging from 950 m to 700 m a.s.l at the south, east and west borders from the center and the north of the study area, forming a slight slope (average 0.25%) that favors runoff towards the depressions and the wadis. This morphology allows the formation of an important alluvial aquifer (Figure 1). The plain is drained by an important permanent wadi named El Ksob wadi, which follows a SE-NW direction, and its two tributaries, El Kebir wadi and Chabro wadi, which cross it for a length of about 60 km [35]. The study area is characterized by a semi-arid climate with wet winter months (November to May) and dry summer months (June to August). The annual average precipitation is about 380 mm, and the annual average temperature is 17 \({}^{\circ}\)C. The evaporation rate is about 650 mm/year. Other climatic parameters, such as wind speed and evaporation, show remarkable seasonal variation. Specifically, between winter (December) and summer (August), the wind speed varies from 3.2 m/s to 1.9 m/s, and the humidity rate varies from 18% to 76%.
## 3 Results and Discussion
### Change in the pH Values in Response to Various Concentrations of MgO/Mg(OH)\({}_{2}\) NPs  
The degradation behavior of various concentrations of MgO/Mg(OH)\({}_{2}\) NPs was studied in 1 mL of DMEM under standard conditions (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)) to understand the degradation behavior of these corrosion products. The change in the pH values was measured from days 1 to 7. As represented in Figure 3, the pH values of the aqueous suspensions of MgO NPs (1 mM = 7.43, 5 mM = 7.60, 10 mM = 7.67, 50 mM = 7.80, and 100 mM = 7.83) were more alkaline when compared to the control group (0 mM = 7.40) for day 1. On day 7, the pH values of the aqueous suspension of MgO NPs were increased (1 mM = 7.93, 5 mM = 8.03, 10 mM = 8.07, 50 mM = 8.57, and 100 mM = 8.73) significantly when compared to the control group (0 mM = 7.40). An increase in the pH values clearly indicated the higher degradation rate of the MgO NPs in DMEM.  
Figure 4 shows that the pH values of the aqueous suspension of Mg(OH)\({}_{2}\) NPs (1 mM = 7.50, 5 mM = 7.70, 10 mM = 7.80, 50 mM = 8.0, and 100 mM = 8.0) were more alkaline when compared to the control group (0 mM = 7.40) for day 1. On day 7, the pH values of the aqueous suspension of Mg(OH)\({}_{2}\) NPs were increased (1 mM = 8.07, 5 mM = 8.30, 10 mM = 8.50, 50 mM = 8.80, and 100 mM = 8.80) significantly compared to the control group of Mg(OH)\({}_{2}\) NPs (0 mM = 7.40). However, we observed no  
Figure 3: Immersion test results through two-way analysis of variance (ANOVA) techniques representing the change in the pH in response to various concentrations of MgO NPs from days 1 to 7 in 1 mL of Dulbecco’s Modified Eagle’s Medium (DMEM). Data are presented as the mean \(\pm\) standard error of the mean (SEM), \(n\) = 3, ***\(p\) value \(<\) 0.001 when compared to control.  
Figure 2: SEM image of Mg(OH)\({}_{2}\) NPs at (**a**) low magnification and (**b**) high magnification.  
influence on the pH values of the control group for both MgO and Mg(OH)\({}_{2}\) NPs (0 mM = 7.40) from days 1 to 7.  
The immersion test result showed that the size and concentrations of the MgO/Mg(OH)\({}_{2}\) NPs regulated their degradation behavior in DMEM. The higher concentrations of NPs (\(>\)50 mM) showed increased degradation behavior when compared to lower concentrations (\(<\)50 mM) and the control groups (no influence on the pH). At higher concentrations, the solution was more alkaline due to the release of an OH\({}^{-}\) ion. The release of increased OH\({}^{-}\) ions at higher concentrations of MgO/Mg(OH)\({}_{2}\) NPs indicates a challenge for future biodegradable Mg-based alloys. The result of this immersion test provides tolerable lower concentrations of MgO/Mg(OH)\({}_{2}\) NPs that might be released from the biodegradable Mg-based alloys to help develop innovative alloys for successful implementation in future in vivo testing.