esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Distribution Network Reliability Evaluation Method
### Reliability Index Calculation  
Based on the system reliability index, this paper evaluates the power supply reliability of a medium and low power level distribution system. The system reliability index is as follows: _SAIFI_ (System Average Interruption Frequency Index), _SAIDI_ (System Average Interruption Duration Index), _ASAI_ (Average Service Availability Index), and _ENS_ (Energy Not Supplied) are specific as follows.  
\[SAIFI=\frac{\sum\limits_{i}\lambda_{i}N_{i}}{\sum\limits_{i}N_{i}} \tag{6}\]  
\[SAIDI=\frac{\sum\limits_{i}N_{i}U_{i}}{\sum\limits_{i}N_{i}} \tag{7}\]  
\[ASAI=\frac{8760\sum\limits_{i}N_{i}-\sum\limits_{i}U_{i}N_{i}}{8760\sum\limits _{i}N_{i}} \tag{8}\]  
\[ENS=\sum L_{i}U_{i} \tag{9}\]  
where \(N_{i}\) is the number of users of load point \(i\), and \(\lambda_{i}\) is the failure rate of load point \(i\). \(U_{i}\) is the annual average outage time of load point \(i\), and \(L_{i}\) is the average load (kW) of load point \(i\).
## 6 Conclusions  
This research offers a significant contribution to the field of naval maintenance by advancing fault diagnosis automation, improving the monitoring of onboard systems and supporting the development of autonomous maritime transport.  
Incorporating ML algorithms like kNN, ANN and CNN into fault diagnosis tasks improves fault classification by comparing new cases with known fault patterns. These techniques effectively classify and identify faults by comparing new data points to known fault patterns, contributing to the overall reliability and safety of the system. Performance evaluation tools, such as the confusion matrix, assess the classification model's accuracy, providing insights into errors and overall model performance.  
The proposed algorithm in this study, designed for multi-class predictions, follows an ML lifecycle involving problem identification, analysis, design, implementation and testing, with iterative development to refine the model. The algorithm demonstrates the potential to improve fault detection, classification and visualization of operational trends, enabling more informed and timely maintenance decisions for complex installations. By combining strategies for efficiency (dimensionality reduction, efficient distance metrics), robustness (cross-validation, noise handling), flexibility (adaptive parameters, weighted kNN), and adaptability (incremental learning, real-time processing), the algorithm can perform effectively across a wide range of general-purpose scenarios. This makes it suitable for diverse real-world applications, from fault detection in industrial systems to dynamic decision-making in online environments.  
Future research should focus on expanding the algorithm's applicability to other naval systems, integrating more sensors and developing tools for training and personnel development to optimize system operation and maintenance management.  
Conceptualization, D.S., E.F. and F.P.; methodology, D.S., E.F. and F.P.; software, F.P.; validation, D.S., E.F., F.P. and B.F.; formal analysis, D.S., E.F. and B.F.; investigation, D.S., E.F. and B.F.; resources, D.S. and F.P.; data curation, E.F. and B.F.; writing--original draft preparation, D.S. and F.P.; writing--review and editing, F.P.; visualization, D.S., E.F., F.P. and B.F.; supervision, E.F. and F.P.; project administration, D.S. and F.P.; funding acquisition, E.F. and B.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National University of Science and Technology Politehnica Bucharest under Grant PubArt. The APC was funded by PubArt.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Formulation of HRESs
### Excess and Deficit Cases of HRESs and Sizing of the Batteries  
When the total energy produced by the PV and WT is greater than \(\xi^{ld}\), then the battery bank is in the SoC at time slot \(t\), which is obtained by Equation (6) [29].  
\[\xi^{str}(t)=\xi^{str}(t-1)\times(1-i^{str})+\left[\left(\xi^{pv} (t)\times\eta_{i}+\xi^{wt}(t)\times\eta_{i}^{2}\right)-\frac{\xi^{ld}(t)}{\eta _{i}}\right]\times\eta^{bat}, \tag{6}\] \[\forall\ \ \left(\xi^{pv}(t)\times\eta_{i}+\xi^{wt}(t)\times\eta_{i}^{2} \right)>\xi^{ld}(t),\]where \(\xi^{str}(t)\) and \(\xi^{str}(t-1)\) depict the amount of stored energy in the battery bank at time slots \(t\) and \(t-1\), respectively. \(t^{str}\) represents the self-discharging state. The term \(\left(\xi^{pv}(t)\times\eta_{i}+\xi^{wt}(t)\times\eta_{i}^{2}\right)\) shows accumulative energy generation by PVs and WTs. \(\eta_{i}\) denotes the efficiency of the inverter, and \(\eta^{bat}\) connotes battery bank charging efficiency.  
In another case, when accumulative energy generation by PVs and WTs is less than \(\xi^{ld}(t)\) at a given time slot \(t\), then the energy stored in the battery bank is utilized to fulfill the user's load. In this situation, the state of the battery bank is changed to discharging. In this paper, the battery bank discharging efficiency is assumed to be one, and the temperature effects are also not considered. The discharging of the battery bank at time slot \(t\) is given as:  
\[\xi^{str}(t)=\xi^{str}(t-1)\times(1-\xi^{dr})-\left[\left(\frac{ \xi^{ld}(t)}{\eta_{i}}-\left(\xi^{pv}(t)\times\eta_{i}+\xi^{wt}(t)\times\eta_ {i}^{2}\right)\right]/\eta_{i},\right. \tag{7}\] \[\left.\forall\quad\left(\xi^{pv}(t)\times\eta_{i}+\xi^{wt}(t) \times\eta_{i}^{2}\right)<\xi^{ld}(t).\right.\]  
The total number of batteries (\(N^{b}\)) in the battery bank depends on the user's load and the HRESs' generation capacity. The size of batteries is dependent on the difference between the maximum and minimum points of a curve; where positive values indicate the generation availability by HRESs and negative values show generation deficiency. Thus, the \(N^{b}\) required for a given system can be derived using the formula [32]:  
\[N^{b}=\left\lceil\frac{max(point)-min(point)}{1.35}\right\rceil, \tag{8}\]  
where \(max(point)\) and \(min(point)\) represent the maximum and minimum energy generation points on the curve, respectively. The value 1.35 shows the nominal capacity of a battery. The ceil is a MATLAB function used to take the upper bound value of a variable.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 3 Evidence on How H. _pylori_ Forms Biofilm  
_The H. pylori_ organism exists in planktonic (solitary) and biofilm aggregates, which shows the adaptation of _H. pylori_ in high-stress environments. _H. pylori_ is a motile bacterium with multiple flagella [9] that matches the organism's planktonic form. However, an unfavorable environment triggers biofilm formation and an aggregate or colony of sessile bacteria. The main features of biofilms are heterogeneity, slow growth, stability, and resistance to eradication [10]. Biofilm production allows for many strains within a single species, numerous species, and even multiple kingdoms within a single structure. Organisms reside within the adhesive structure of exopolysaccharides (EPSs) and, frequently, extracellular DNA (eDNA). This form provides a best-fit place for bacterial survival in stressful environments, including high acidity and the presence of antimicrobial agents. This profile may extend some environments' colonization periods [11].  
_H. pylori_ biofilm development was observed in vivo using human stomach biopsy specimens and imaged using scanning electron microscopy. The study revealed that individuals with peptic ulcer disease who tested positive for urease had a biofilm coverage of 97.3%, whereas _H. pylori_-negative patients had a biofilm coverage of 1.64% [12; 13]. The visualization of biofilm formation in gastric samples could also be evaluated via immunohistochemistry [14].  
Mouse stomachs also clearly exhibit the formation of biofilm [15]. In an ecological context, it has been reported that _H. pylori_ may be present in water distribution networks [16; 17]. Moreover, it can be found in biofilms that form on the inner surfaces of pipeworld in drinking water systems, and it can attach itself to various types of materials [18; 19; 20].  
Meanwhile, in vitro studies reported that biofilms grown on cover glass can demonstrate biofilm formation in 72% of the strains evaluated, while another study showed a biofilm in 93.1% [21].  
The biofilm's morphology and cell interaction can be visible on an electron micrograph. In SEM images, at the initial stage of the biofilm, _H. pylori_ is rod-shaped but rarely has flagella. At a further stage, the coccoid-shaped bacteria pile up and are covered with polysaccharides [22]. In addition to scanning electron microscopy (SEM), biofilms can be observed using confocal laser scanning microscopy (CLSM). Using CLSM makes it possible to visualize biofilms on biotic surfaces, such as AGS cells. Biofilms can be stained with fluorescence, and a biofilm can be seen on the entire surface of the AGS cells [23]. Biofilms are, thus, important for the survival of _H. pylori_ in many environments. Several steps or phases were reported to form a complex biofilm because it is not as simple as toxin excretion but requires the interaction of a single bacterium with the surface and other bacteria [24].
## 3 Results
### Temporal Relationship between \(\mathrm{GPP_{VI}}\) and \(\mathrm{GPP_{EC}}\)
The results at the YNC site were similar to the results at the RIG site. The \(\mathrm{GPP_{CIr}}\) correlated closely with \(\mathrm{GPP_{EC}}\) (average \(R^{2}\) = 0.69), but other red-edge-related \(\mathrm{GPP_{VI}}\) corresponded poorly to \(\mathrm{GPP_{EC}}\). The \(\mathrm{GPP_{EVI}}\) and \(\mathrm{GPP_{NIRv}}\) showed \(R^{2}\) values of 0.61 and 0.66 with \(\mathrm{GPP_{EC}}\), respectively. Figure 2: Cloud cover fractions of Sentinel-2 images of the coordinate tile at each site. The \(y\)-axis is the month tag in (YYYYMM; Y = year, M = month) since the launch of Sentinel-2A, and the \(x\)-axis is the day of that month. The color bar represents the percentage of clouds in the image. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**RIG (N = 17)**} & \multicolumn{6}{c}{**YNC (N = 54)**} & \multicolumn{6}{c}{**YNC (N = 109)**} \\ \cline{2-13}  & \(\mathbf{R^{2}}\) & **RMSE** & **RMSE** & **a** & **b** & \(\mathbf{R^{2}}\) & **RMSE** & **rRMSE** & **a** & **b** & \(\mathbf{R^{2}}\) & **RMSE** & **rRMSE** & **a** & **b** \\ \hline Clr & 0.87 & 1.02 & 0.37 & 0.32 & 0.14 & 0.69 & 0.63 & 0.46 & 0.31 & \(-\)0.05 & **0.89** & **0.34** & **0.33** & 0.31 & \(-\)0.29 \\ Clg & 0.84 & 1.13 & 0.42 & 0.04 & 0.37 & 0.48 & 0.80 & 0.59 & 0.03 & 0.32 & 0.73 & 0.53 & 0.51 & 0.03 & 0.00 \\ MTCI & 0.54 & 2.05 & 0.76 & 0.32 & \(-\)12.03 & 0.08 & 1.10 & 0.81 & 0.02 & 1.04 & 0.12 & 0.95 & 0.93 & 0.11 & 1.80 \\ NDARE1 & 0.51 & 2.08 & 0.79 & 0.27 & \(-\)0.79 & 0.23 & 0.99 & 0.73 & 0.10 & 0.03 & 0.37 & 0.80 & 0.78 & 0.12 & \(-\)0.60 \\ NDARE2 & 0.55 & 1.99 & 0.76 & 0.23 & \(-\)0.73 & 0.25 & 0.98 & 0.72 & 0.10 & \(-\)0.01 & 0.41 & 0.78 & 0.76 & 0.11 & \(-\)0.66 \\ EVI & 0.77 & 1.36 & 0.52 & 1.60 & \(-\)0.87 & 0.61 & 0.69 & 0.51 & 0.97 & \(-\)0.32 & 0.76 & 0.50 & 0.49 & 0.99 & \(-\)0.53 \\ NIRv & 0.86 & 1.07 & 0.39 & 2.77 & \(-\)0.25 & 0.66 & 0.64 & 0.47 & 2.30 & \(-\)0.13 & 0.83 & 0.42 & 0.41 & 2.32 & \(-\)0.34 \\ NDVI & 0.66 & 1.67 & 0.65 & 1.31 & \(-\)1.47 & 0.49 & 0.80 & 0.59 & 0.49 & \(-\)0.10 & 0.76 & 0.50 & 0.49 & 0.52 & \(-\)0.48 \\ MODI72 AH & 0.66 & 1.76 & 0.30 & - & - & **0.81** & 0.91 & 0.17 & - & - & 0.85 & 0.93 & 0.18 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The algorithm results for the 16-day average daytime GPP estimated at grassland (GRA) sites, with the determination coefficient (\(R^{2}\)), RMSE (unit in gC\(-\)m\({}^{-2}\)-day\({}^{-1}\)), and rRMSE presented. We adopted the equation GPP\({}_{\rm EC}\) = a \(\times\) V1\(\times\) PAR + b here, where the VI is the rebuilt time series, and PAR is the photosynthetic active radiation. The number of samples at each site is presented in parentheses after the site name. The first two series of results are based on rebuilt VIs in a 16-day standard interval. The series YNC (\(N=109\)) is based on rebuilt VIs in eight-day standard intervals. The numbers in bold represent the highest \(R^{2}\), lowest RMSE, and lowest rRMSE values.
## 3 Results and Discussions  
### Quasi-Static Compression Result  
By conducting quasi-static compression experiments, the stress-strain curves of aluminum foam under quasi-static compression were obtained, as shown in Figure 7. The \"failure point\" is defined as the point at which the material begins to exhibit significant deformation or fracture. This point corresponds to the location on the stress-strain curve where the curve starts to deviate from its initial linear behavior, indicating the onset of plastic deformation or the beginning of the plateau region. In this plateau region, the cells of the material start to buckle and collapse. For aluminum foam, this point is particularly important because it signifies the transition from elastic deformation to plastic deformation.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**A/MPa** & **B/MPa** & **C** & \(n\) & \(m\) & \(\dot{\varepsilon}\)/s\({}^{-1}\) & **T/K** & **T\({}_{m}\)/K** \\ \hline
320 & 452 & 0.0021 & 0.59 & 1.31 & \(5\times 10^{-4}\) & 298 & 883 \\ \hline \(D_{1}\) & \(D_{2}\) & \(D_{3}\) & \(D_{4}\) & \(D_{5}\) & \(\rho\)/kg\(\cdot\)m\({}^{-3}\) & _E/GPa_ & \(v\) \\
0.096 & 0.049 & 3.342 & 0.0132 & 1.067 & 2700 & 68 & 0.32 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for closed-cell foam aluminum pure aluminum matrix.  
Figure 6: Voronoi diagram generation principle. (The blue dashed line indicates the core connection line and the red solid line indicates the Voronoi edge).  
As shown in Figures 7 and 8, in quasi-static compression, the aluminum foam has obvious 3-stage characteristics: elastic section, stress plateau section and compaction section. When the compressive stress reaches the vicinity of the yield stress, due to the sudden collapse and buckling of the pore wall, the stress-strain relationship curve has some fluctuations in the yielding process, and with the further compaction of the aluminum foam, this fluctuation due to the sudden collapse is obviously reduced, and the curve is smoother. From the change of yield point stress of aluminum foam under different strain rates, it can be seen that there is not much influence on the yield point stress during compression of aluminum foam between each strain rate, and the phenomenon of yield point stress elevation is not obvious, and the platform section is basically the same, which indicates that there is almost no influence on the platform stress of aluminum foam under low strain rate.
# Removal of Cochineal Dye Color through Atmospheric Pressure Plasma Discharge Jet
## 1 Introduction
The presence of reactive nitrogen species creates an acid environment that promotes the bleaching process through oxidative color removal mechanisms [18; 28; 29; 30; 31]. According to [PERSON] et al. [32], pigments exhibit coloration due to the absorption of light in the visible spectrum (400-700 nm) as a result of the presence of at least one chromophore group and a structure with alternating double and single bonds in their chemical composition. In the case of cochineal dye, the acid medium generated by reactive species causes oxidative color removal through the rupture of the anthraquinone chromophore group bond and auxochromes of carminic acid (-OH, -CH\({}_{3}\)) [33; 31; 32]. This results in the anthraquinone compound losing its ability to absorb radiation in the visible range, leading to the loss of its coloration. In recent years, there has been an increase in the number of research studies focusing on the removal and discoloration of pigments and industrial dyes using non-thermal plasma discharge. [PERSON] et al. [34], reported on the degradation of cochineal red using birmseite thin films that were treated with moist air plasma. [PERSON] et al. [35] compared the degradation of indigo carminic in water using different methods, such as non-thermal plasma (NTP), ozone (O\({}_{3}\)), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). NTP was found to induce physicochemical changes in water that facilitated degradation. [PERSON] [36] documented the properties of helium (He) plasma at atmospheric pressure and its effect on methylene blue (MB) dye. The study showed that contact between the plasma and the MB solution generated reactive species such as OH, He*, and O, which led to the degradation of the dye. Given this background, the main objective of this study was to evaluate the effect of the reactive species produced by atmospheric pressure plasma discharge (APPJ) on the degradation of the natural dye extracted from cochineal.
## 4 Experimental Part  
_Synthesis of Lactate Derivatives_  
The lactate derivatives (\(\pm\))-**1b**, (\(\pm\))-**2b**, (\(-\))-**2b** and (\(\pm\))-**3b** were synthesized in a 50 mL round-bottomed flask by adding 0.595 mmol of lactic acid (equal to 63 mg of solution of 85% lactic acid in water) to 0.595 mmol triethylamine in 2 mL of acetonitrile or water. The mixture was reacted for 30 min at 60 \({}^{\circ}\)C. After this time, the benzyl halide (0.595 mmol) was added and the mixture was reacted for 3.5 h at 60 \({}^{\circ}\)C. After this time, the solvent was evaporated under vacuum. The crude mixture was extracted three times with ethyl acetate and purified by silica gel column chromatography using ethyl acetate/hexane (4:6) as the eleuents to give the following products:  
**Benzyl 2-hydroxypropanoate-**-(\(\pm\))-**1b**. The product is a colorless oil (77 mg, 72% yield).  
\({}^{1}\)H-NMR (CDCl\({}_{3}\), 500 MHz) \(\delta\) 7.37-7.33 (m, 5 H), 5.20 (s, 2 H), 4.31 (q, J = 5.0 Hz, 1 H), 2.89 (broad s, 1 H), 1.43 (d, J = 5.0 Hz, 3 H).  
\({}^{13}\)C-NMR (CDCl\({}_{3}\), 125 MHz) \(\delta\) 175.4, 135.2, 128.6, 128.5, 128.2, 67.2, 66.8, 20.4.  
HRMS (ESI-TOF), \(m/z\): calcd for [C\({}_{10}\)H\({}_{12}\)O\({}_{3}\) + Na]\({}^{+}\) 203.0679, found [M + Na]\({}^{+}\) 203.0670, FT-IR (KBr): \(\
u\) 3449.61 (m), 1739.77 (m), 1455.05 (m), 747.58 (m), 698.96 (m) cm\({}^{-1}\).  
**2-nitrobenzyl 2-hydroxypropanoate-(\(\pm\))-2b**. The product is a yellow oil (120 mg, 89% yield).  
\({}^{1}\)H-NMR (CDCl\({}_{3}\), 500 MHz) \(\delta\) 8.15-8.10 (m, 1 H), 7.71-7.63 (m, 1 H), 7.58-7.51 (m, 2 H), 5.64 (d, J = 15 Hz, 1 H), 5.57 (d, J = 15 Hz, 1 H), 4.39 (q, J = 7.0 Hz, 1 H), 1.48 (d, J = 7.0 Hz, 3 H).  
\({}^{13}\)C-NMR (CDCl\({}_{3}\), 125 MHz) \(\delta\) 175.1, 133.9, 133.8, 131.2, 129.2, 129.1, 125.2, 66.8, 64.0, 20.4.  
HRMS (ESI-TOF), \(m/z\): calcd for [C\({}_{10}\)H\({}_{11}\)NO\({}_{5}\) + Na]\({}^{+}\) 248.0529, found [M + Na]\({}^{+}\) 248.0520.  
FT-IR (KBr): \(\
u\) 3455.83 (s), 2987.95 (m), 1746.44 (s), 1613.06 (m), 1528.51(s), 1347.31(s) cm\({}^{-1}\).  
**2-nitrobenzyl 2-hydroxypropanoate-(\(-\))-2b**. The product is a yellow oil (109 mg, 82% yield). [\(\alpha\)]\({}_{\rm D}\) = -9.06 (c = 0.75, CH\({}_{3}\)CN).  
**2,4-dinitrobenzyl 2-hydroxypropanoate-(\(\pm\))-3b**. The product is a colorless oil (90 mg, 56% yield).  
\({}^{1}\)H-NMR (CDCl\({}_{3}\), 500 MHz) \(\delta\) 8.90 (d, J = 2.4 Hz, 1 H), 8.52 (dd, J = 8.5, J = 2.4 Hz, 1 H), 7.83 (d, J = 8.5 Hz, 1 H), 5.70 (s, 1 H), 5.69 (s, 1 H) 4.40 (q, J = 6.9 Hz, 1 H), 1.51 (d, J = 6.9 Hz, 3 H).  
\({}^{13}\)C-NMR (CDCl\({}_{3}\), 125 MHz) \(\delta\) 174.7, 147.6, 147.4, 138.1, 130.0, 127.9, 120.7, 66.8, 63.2, 20.4. HRMS (ESI-TOF), \(m/z\): calcd for [C\({}_{10}\)H\({}_{10}\)N\({}_{2}\)O\({}_{7}\) + Na]\({}^{+}\) 293.0380, found [M + Na]\({}^{+}\) 293.0386. FT-IR (KBr): \(\
u\) 3440.97 (s), 2986.90 (m), 1747.23 (s), 1606.41 (s), 1536.78 (s), 1347.66 (s) cm\({}^{-1}\).
## 3 Materials and Methods
### Vulnerability and Threat Analysis  
This section systematically analyzes security vulnerabilities in key nodes and protocols within satellite networks using CVE and CVSS data. The findings from this analysis serve as a foundation for threat modeling and attack scenario design [45; 46].
## 3 Experiments  
### Experimental Setup  
A commercialized PAUT device, namely Topaz/MX2, with a 32/128 channel pulse/receiver function was used for data collection. The experiment was performed as illustrated in Figure 14. First, a mock-up curvature shape specimen was fabricated, and a low-profile scanner that could be attached to the FPAPr was fabricated to obtain consistent data. The collected data were temporarily stored at the main inspection device, followed by signal processing using an internal signal analysis software storage and evaluation [54, 55, 56].  
The FPAPr performance was compared with that of the conventional wedge conclusive type PA probe, and the results were verified. A commercial PA probe that could adhere to a curvature-shaped weld zone was selected as the minimum-probe size for a general PA probe specification, and transverse waves of 4 MHz and 5 MHz (similar to the center frequency of the P/E type FPAPr) were applied for the experiment as specified in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Probe Type** & \multicolumn{2}{c}{**Conventional PA(P/E)**} \\ \cline{2-3} **(Mounted Rigid Wedge)** & **4L16** & **51.32** \\ \hline Total elements (ch) & 16 & 32 \\ Nominal center frq.(MHz) & 4 & 5 \\ Rel.BW (\%) & 131 & 91 \\ Pitch (mm) & 0.5 & 1 \\ Aperture (mm) & 8 & 32 \\ Elevation (mm) & 10 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specifications of the commercially available PA probes mounted on rigid solid wedges.  
Figure 14: Schematic diagram of experimental setup.  
Figure 13: FPAPr amplitudes of elements: (**a**) P/E type FPAPr; (**b**) T/R type FPAPr.