esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Expected Cost Per Time Unit  
To determine the expected cost per unit time, the expected global cost (sum of the four costs in the previous sections) has to be divided by the expected life of the component, which can be achieved by adding up three different expected lives: (1) \(\overline{V_{Ff}}\), life until the functional failure occurs; (2) \(\overline{V_{Fp}}\), life until a potential failure is detected; and (3) \(\overline{V_{nF}}\), life until the moment \(T_{p}\) when no failure has occurred.  
The expected life until the functional failure \(\overline{V_{Ff}}\) is given by adding up the products of the various UTW average lives with the correspondent probabilities (9).  
\[\overline{V_{Ff}}=\sum_{n=1}^{n\ast}\{F.\big{[}(P_{n}-H)|H\big{]}-F\big{[}(M_{ n-1}-H)|H]\}.\frac{(P_{n}-M_{n-1})}{2} \tag{9}\]The expected life until detection of a potential failure \(\overline{V_{Fp}}\) can be found by adding up the products of the various STW average lives with the corresponding probabilities (\(1\leq n\leq n^{\star}\) in the case of alternate STW and UTW and \(n^{\star}+1\leq n\leq N\) when only STW are left).  
\[\begin{split}\overline{V_{Fp}}=\sum_{n=1}^{n\star}\{F[(M_{n}-H) |H]-F[(P_{n}-H)|H]\}.\frac{(M_{n}-P_{n})}{2}+\\ \sum_{n+1}^{N}\{F[(M_{n}-H)|H]-F[(M_{n-1}-H)|H]\}.\frac{(M_{n}-M_ {n-1})}{2}\end{split} \tag{10}\]  
Life until the moment \(T_{p}\) if no failure has occurred, \(\overline{V_{nF}}\), is given by the product of \(T_{p}\) and the conditional reliability until moment \(T_{p}\).  
\[\overline{V_{nF}}=\big{\{}1-F\big{[}\big{(}T_{p}-H\big{)}|H\big{]}\big{\}}.T_{p} \tag{11}\]  
Finally, the expected life of the component \(\overline{V}\), which might undergo a failure (noticed in time to avoiding functional failure) or a functional failure, or on the contrary survive until moment \(T_{p}\) can now be obtained by adding up those three expected lives (12).  
\[\overline{V}=\overline{V_{Ff}}+\overline{V_{Fp}}+\overline{V_{nF}} \tag{12}\]  
The expected cost per unit time \(\overline{C_{h}}\) is finally given by a uniform series of payments (13).  
\[\overline{C_{h}}=\Big{(}\overline{C_{f}}+\overline{C_{rp}}+\overline{C_{i}}+ \overline{C_{d}}\Big{)}.\frac{j.(1+j)^{\overline{V}}}{(1+j)^{\overline{V}}-1} \tag{13}\]
###### Abstract  
Maize (_Zea mays_ L) is the most significant grain crop in South Africa. Despite its importance, the cereal is ravaged by several foliar fungal pathogens, which reduce maize quality and quantity at harvest. Hence, this study investigates the fungi associated with foliar diseases of maize in Moelwane, North-West Province, South Africa. The fungi were isolated, characterized and subjected to in vitro nanoparticle control. Samples of diseased maize leaves were aseptically collected from two maize-growing farms. Fungi associated with the samples were isolated and characterized using standard procedures. Bi\({}_{2}\)S\({}_{3}\) (metal-containing) and gC\({}_{3}\)N\({}_{4}\) (non-metallic carbon-based) nanoparticles were synthesized and used to challenge the pathogens using standard procedures. Foliar fungal pathogens isolated from the diseased maize leaves in this study were characterized as _Bipolaris zicola, Phoma herbarium, Epicoccum nigrum, Alternaria alternata_ and _Fusarium brachyglobsum. Phoma herbarium \(>\) A. alternata_ \(>\) B. _zeicola \(>\) F. brachyglobosum \(>\) E. nigrum_ was the order of percentage fungal inhibition by the nanoparticles. Bi\({}_{2}\)S\({}_{3}\) was more effective against the pathogens at lower concentrations and gC\({}_{3}\)N\({}_{4}\) at higher concentration levels. The two nanoparticle types evaluated in vitro shows potential for managing the foliar fungal pathogens, and this needs to be further validated in field studies.  
Keywords:fungal characterization; DNA; nanoparticles; bismuth-based material; non-metallic material; plant disease management; South Africa +
Footnote †: journal: _Applied Sciences_
# Biophotonics in Dentistry
## 3 Applications in Dentistry
### Photobiomodulation (PBM) and Ozone Treatment  
Another clinical application of biophotonics in dentistry is the use of photobiomodulation (PBM) and ozone treatment. For instance, one interesting study investigated management of periodontal disease with adjunctive therapy with ozone and photobiomodulation. In this study, both ozone and PBM appeared to be effective adjuvant treatments to scaling and root planning (SRP), obtaining a slightly better outcome for the latter in the long term, with significant differences at T5 and T6 for probing pocket depth (PPD) [162].
## 2 Test Device and Scheme Design
### Sample Preparation  
The test samples were taken from the 2461 working face of Baijiao colliery, Sichuan Coal Group, Yibin, China, and the B2 coal seam was mined with an average thickness of 2.0 m, the buried depth of the coal seam was 476.3-582.5 m, and the permeability coefficient was \(2.7\times 10^{-3}\)-\(3.0\times 10^{-2}\) m\({}^{2}\)/(MPa\({}^{2}\)-d), the density was 1485.8 g/cm\({}^{3}\), the gas pressure was 1.57-3.2 MPa, and the industrial index of coal seam was moisture 0.35%, ash 27.01%, volatile matter 9.76%, and fixed carbon 63.77%. The preparation methods of coal samples were as follows: selecting coal blocks larger than 200 mm in size from the fresh coal wall, packaging the lump coal with plastic wrap and transporting it back to the laboratory, and drilling a standard sample of \(50\times 100\) mm from the lump coal, polishing both ends of coal samples with a cutting machine smoothly and parallel. The two ends of the coal sample were smoothed and paralleled by a cutter, and the non-parallelism and the non-perpendicularity were less than 0.02%. The prepared raw coal samples are shown in Figure 2.  
Figure 1: Thermal-fluid-solid coupling triaxial servo seepage device.  
Figure 2: Standard raw coal sample.  
In order to prevent the evaporation of water in the coal sample, the standard samples processed before the test were packaged in plastic wrap, and each sample was treated with different water-bearing states (d\({}_{1}\)# coal sample drying treatment, d\({}_{2}\)# and d\({}_{3}\)# coal samples unsaturated treatment, d\({}_{4}\)# coal sample saturated treatment). The dry coal samples were put in a 100 \({}^{\circ}\)C oven for 24 h to constant weight; d\({}_{2\#}\) and d\({}_{3\#}\) coal sample were unsaturated coal samples, and the water content was 0.15% and 0.32% for 12 h and 24 h, respectively. The d\({}_{4}\) # coal sample was treated with water for more than 48 h and was regarded as a saturated coal sample with a water content of 0.65%.
## 5 Results and Discussion
### Effect of the Population Size on SDSGA Performance  
This section discusses how the proposed SDSGA performs when different percentages \(P\) of the total population size were chosen as the number of parents, ranging from 10% to 90% with a 10% increment, when applied to the benchmark test functions, as well as how the considered ML models, namely the random forest and the CNN performed when trained on the MNIST and UCI datasets. Following Figure 4, the total number of best results recorded across the entire benchmark functions by the SDSGA was 13 at both \(P=20\%\) and 30% parent sizes.  
The least performance was at \(P=90\%\), which may be attributed to the fact that almost the entire population was considered as parents, thus effectively reducing the chance for exploitation through recombination. Furthermore, the fact that the 20% and 30% parent sizes yielded the same number of the best outcomes shows that there were no statistical differences between the two parent sizes. As a result, selecting any of the two parent sizes will result in the same performance. Summarily, Figure 4 simply indicates that the SDSGA achieves the best performance in 13 of the benchmark functions when the parent size was set to 20% and 30% of the total population.  
A similar study was performed on some UCI datasets, and it was discovered that a parent size of 20 and 30% yielded the best results when applied to different datasets, as shown in Figure 5. Nonetheless, we see a minor improvement in performance by the proposed SDSGA when a parent size of 20% was chosen and implemented on the LETTER and ADULT datasets as opposed to 30%. In the instance of the LETTER dataset, there was a modest improvement in accuracy from around 96.5% for a parent size of 30% to about 96.52% for the size of 20%, and, similarly, about 85.88% was recorded for the parent size of 20% compared to 85.86% recorded for the 30% parent size (see Figure 5). We should point out that the little change may not be statistically significant. As a result, the parent size of either 20 or 30% can be selected for evaluating the SDSGA.
# Polarization Imaging Method for Underwater Low-Visibility Metal Target Using Focus Dividing Plane
## 1 Introduction
To improve the detection capability of underwater metal targets, in this paper, an underwater polarization imaging experimental platform is built, and a method of underwater low-visibility metal target polarization imaging with a split-focus plane is proposed. Polarization imaging experiments were carried out on targets made of different materials and metals in a low-visibility environment. The image quality evaluation system was constructed, and the experimental results were analyzed quantitatively. Through polarization imaging technology, it can effectively detect metal targets in low-visibility underwater environments. It has certain reference and application values for underwater target detection.
# Isolation and Characterization of a Novel Cyanophage Encoding Multiple Auxiliary Metabolic Genes
## 2 Materials and Methods
### Transmission Electron Microscopic Analysis  
The phage morphology was observed by transmission electron microscopy (TEM). Briefly, the purified and desalted phages were added dropwise onto 200-mesh carbon coated coppers for 10-30 min in the dark. After being negatively stained with 1% phosphotungstic acid and air-dried for 30 min, samples were imaged using a JEM-2100 transmission electron microscope (JEOL, Tokyo, Japan) at 80 kV accelerating voltage. Images were collected using a CCD image transmission system (Gatan, Pleasanton, CA, USA). Capsid size and tail length of phages were determined by measuring at least five phage specimens.
# Shear Performance for Prestressed Concrete Hollow Core Slabs
## 4 Experimental Results and Discussion
### Shear Strength  
To evaluate the shear strength of HCS in relation to cross-sectional height and presence of topping concrete, the experimental results were compared to the analytical results based on KCI 2017 [15] and ACI 318-19 [16], as shown in Table 3. In the experiment, shear strength was calculated using the hydraulic load cell of the universal testing machine (UTM). For the analytical results based on KCI 2017 and ACI 318-19, shear strength was the smaller of the two values among flexural-shear strength (\(V_{ci}\)) and web-shear strength (\(V_{cw}\)). As recommended by PCI [18], in calculating shear strength, only the web width of HCS was considered for HCS. For more precise analysis of shear strength of HCS with topping concrete, the actual width of topping concrete was measured, and the results are shown in Table 3. The influence of topping concrete was calculated by applying the transformed section method with consideration of the elastic modulus of concrete.  
For S series, the shear strength of S265 averaged 308.1 kN in the experiment, and 212.7 kN in the analysis. The average ratio of analytical to experimental shear strength was 1.45. S320 and S400 had average experimental shear strengths of 434.1 kN and 403.4 kN, and analytical shear strengths of 257.6 kN and 352.3 kN, respectively. The average ratios of analytical to experimental shear strength were 1.69 and 1.14, respectively. As for CS series, CS265 had an average experimental shear strength of 481.8 kN and an analytical shear strength of 260.3 kN, resulting in an average ratio of 1.85. CS320 and CS400 had average experimental shear strengths of 697.9 kN and 715.9 kN, and analytical shear strengths of 320.5 kN and 413.2 kN, respectively. The average ratios of analytical to experimental shear strength were 2.18 and 1.73, respectively.  
Comparing shear strength values to analytical results based on KCI 2017 and ACI 318-19, as shown in Table 3, the average ratio of analytical to experimental shear strength was 1.14-1.69 for S series, and 1.73-2.18 for CS series. That is, shear strength conditions were satisfied regardless of cross-sectional height or presence of topping concrete. In addition, the analytical results based on KCI 2017 and ACI 318-19 had a tendency to provide predictions on the safe side, and CS series had a ratio 29-52% higher than that of the S series. As such, further research is needed to develop prediction equations that allow more rational calculation of shear strength of HCS members with topping concrete.
## 3 Results
### Questionnaire Results
#### 3.2.2 Participant Feedback on the R2S Education Platform
Feedback is primarily seen as instructive (58.3% \"Agree\"), while the adequacy of questions and time investment received mixed responses, with 43.4% and 29.3% neutral, respectively. The difficulty of online traffic education is not a significant concern, with 33.5% \"Rather Disagree.\" However, the lack of badges as incentives leaves 49.6% neutral, pointing to potential improvements in engagement and motivation. While clarity and usability are key strengths, enhancements in engagement and incentives could further improve user satisfaction. Figure 4: Student satisfaction with the R2S education platform. Qualitative Feedback for Improvement  
The following areas for improvement of online traffic education were produced from the analysis of the open-ended responses to the question, \"In what ways do you think online traffic education can be Improved?\" As such, the participants frequently mentioned technical difficulties such as slow loading of the webpage, unresponsiveness of features like the 360 image, car/bike ride and compatibility with school computers. To address this issue, it was suggested that the platform speed, the quality of the simulation, and the usability of the platform on different devices be improved. Also, participants stressed that content should be more comprehensible and activities more engaging; some participants suggested including more practical tasks, like test drives and better demonstrations. Some stated they would like traffic education to be introduced at an earlier year in school and that the visuals used should be more appealing and more 'gaming' features. These studies underscore that usability issues should be solved and interactivity improved to optimize the effectiveness of innovative online traffic education R2S. It is recommended that subsequent versions of the platform consider the areas highlighted by the students as essential to meet the expectations and learning outcomes.
###### Abstract  
Reconnaissance studies performed after destructive earthquakes have shown that seismic performance of existing buildings, especially constructed on weak soils, is significantly low. This situation implies the negative effects of soil-structure interaction on the seismic performance of buildings. In order to investigate these effects, 40 existing buildings from Turkey were selected and nonlinear models were constructed by considering fixed-base and stiff, moderate and soft soil conditions. Buildings designed before and after Turkish Earthquake code of 1998 were grouped as old and new buildings, respectively. Different soil conditions classified according to shear wave velocities were reflected by using substructure method. Inelastic deformation demands were obtained by using nonlinear time history analysis and 20 real acceleration records selected from major earthquakes were used. The results have shown that soil-structure interaction, especially in soft soil cases, significantly affects the seismic response of old buildings. The most significant increase in drift demands occurred in first stories and the results corresponding to fixed-base, stiff and moderate cases are closer to each other with respect to soft soil cases. Distribution of results has indicated that effect of soil-structure interaction on the seismic performance of new buildings is limited with respect to old buildings.  
Keywords:soil-structure interaction; nonlinear analysis; direct time history analysis; existing buildings; seismic performance +
Footnote †: journal: Applied Sciences
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 3 Model Inputs
### Loads and Boundary Conditions  
The impact loading on the road surface during landing is determined by both the mass and the impact velocity of the aircraft. In this paper, the load of the aircraft is represented by the application of a point mass on the surface of the main landing gear. The maximum landing weight of a Boeing 737-800 is 663.80 kN, the landing gear configuration adopts single-axle two-wheel structure, the conversion factor of the main landing gear is 95%, and the energy absorption efficiency of the buffer system is 70%. The calculated load applied to each landing gear is 9.65 t, and the point mass applied to each landing gear is 9650 kg. In addition, the global standard Earth gravity should also be applied, with the direction vertical to the pavement.  
In order to reduce the model calculation, this paper selected a local model of the pavement. Therefore, except for the top of the road surface, the remaining faces should be set displacement limits; that is, the displacement degrees of freedom of the pavement along their respective directions are set to 0. At the same time, the wave should be scattered outward after the impact of the landing gear on the pavement, instead of reflecting back after reaching the pavement boundary.  
Each surface should be set up with non-reflecting boundary conditions to simulate an infinite area. The dynamic response will be scattered from all around the model, which is consistent with the real situation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Structure** & **Asphalt Mixture Surface** & **Cement-Stabilized Base** & **Soil Foundation** \\ \hline Thickness(m) & 0.24 & 0.5 & 9.26 \\ Elastic modulus (MPa) & 1400 & 1500 & 80 \\ Poisson’s ratio & 0.35 & 0.25 & 0.35 \\ Density(kg/m\({}^{3}\)) & 2700 & 2000 & 1800 \\ \hline \end{tabular}
\end{table}
Table 2: Structural parameters of airport pavement.
## 4 Results and Discussion
### Effects of Seepage
It takes more time to achieve a frozen ring around the tunnel profile. Figure 10: Schematic illustrations of the seepage field with the seepage velocity of 0.5 m/d: (**a**) after 5 days; (**b**) after 15 days. Figure 9: Temperature comparison of horizontal monitoring points under different velocities of seepage: (**a**) 0 m/d, (**b**) 0.5 m/d, (**c**) 1 m/d, and (**d**) 1.5 m/d. Four typical freezing durations (i.e., 10 days, 30 days, 50 days and 70 days) are selected. Secondly, the water flow releases more heat to the upstream zone of pipes and inhibits the expansion of the frozen body in the upstream direction, which is why the temperature curves of points \(A\) and \(D\) drop slower in Figure 8. This means that the frozen body cannot form concentrically around the freeze pipes, but expands to downstream faster and larger. Similarly, in the simulation results of [PERSON] et al. [15], an oval-shaped zone toward the downstream side is obtained. In this research, the frozen zone with a freezing temperature lower than \(-3\)\({}^{\circ}\)C is defined as the effective frozen zone. As depicted in Figure 12, the final effective frozen zones after 80 days under different velocities of seepage are made up of two red \(-3\)\({}^{\circ}\)C isotherm curves, and the desired frozen region is marked with a ring for comparison. With the increase in seepage velocity, the effective frozen zone expands to downstream more clearly, and the upstream soils of freeze pipes get more difficult to cool down, and so the lowest temperatures which points \(A\) and \(D\) drop to at the end of the simulation gradually rise when the seepage velocity is faster (see Figure 8). When the seepage velocity increases to 1.5 m/d, a completed desired frozen wall cannot even be achieved, which may lead to serious water leakage accidents in construction. Therefore, when there is a groundwater flow with high seepage velocities, it is suggested that the arrangement of freeze pipes should be optimized in practical engineering and more freeze pipes should be arranged on the upstream face of the tunnel. Figure 11: Influence of seepage flow on the formation of frozen wall between adjacent freeze pipes with different seepage velocities. Four typical freezing durations (i.e., 3 days, 6 days, 9 days and 12 days) are selected. Figure 12: Illustrations of the effective frozen zone after 80 days under different velocities of seepage (i.e., 0 m/d, 0.5 m/d, 1 m/d, and 1.5 m/d).
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 7 Power Budget  
To compute the power provided to a receiver located on the lunar surface, three different scenarios were analyzed, one for each orbit. Every scenario takes into account eight sub-cases, determined by four different angles of transmission and two configurations of the number of satellites in orbit. In fact, in this study we have examined a constellation of three and ten satellites which are evenly distributed around the Moon as explained in Section 5. We chose a constellation of three and ten satellites to compare the difference between a small constellation (lower economic cost) and a large constellation (higher economic cost) in terms of the energy delivered. In two out of the three scenarios the receiver is located at the south pole at a latitude of 89.54\({}^{\circ}\) S (the same as the Shackleton Crater), while the last one considers a receiver placed on the lunar equator. As mentioned, each of these cases was then studied for different angles of the cone of transmission: 50\({}^{\circ}\), 60\({}^{\circ}\), 70\({}^{\circ}\) and 80\({}^{\circ}\). When the satellite enters into the cone shown in Figure 12, the WPT begins. In this study we computed the irradiance that gives a receiver with the Gaussian beam model explained in Section 4. The laser data used for the simulation are as follows:  
* Laser wavelength: \(\lambda=1064\) nm;
* Laser peak power: \(P_{L}=30\) kW;
* Laser electrical efficiency: \(\eta_{L}=0.5\);
* Laser waist radius: \(w_{0}=250\) mm.  
For each position of the satellite the code computes the distance between the satellite and the receiver \(z\). If the satellite is inside the previous transmission cone set, the code computes the irradiance \(I_{r}\) for each cell in the receiver. Therefore, the power received from the receiver \(P_{r}\) at time \(t\) becomes  
\[P_{r_{(t)}}=\sum_{k=1}^{N_{cells}}I_{r_{(t)}}\cdot A_{(k)}\cdot\cos\theta_{(t, k)}\cdot\eta_{cell} \tag{6}\]  
where \(\eta_{cell}\) is the cell efficiency considered equal to 0.689, the subscript \(r\) means received, while the subscript \(k\) indicates the \(k\)-th cell.  
In the graphs to follow, the red lines represent the energy transmitted from the laser to the receiver as the satellite enters the cone of transmission. The dashed black line, on the other hand, represents the average power that that architecture manages to send to the Moon.  
Figure 12: Transmission cone schematization.