esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Synthesized Graphene-Based Photonics and Optoelectronics Devices
## 3 Application of Directly Grown Graphene
### Photodetection  
A technique of explicit growth of graphene coatings on the surface of silicon nanoholes (SiNHs) through plasma enhanced chemical vapor deposition (PECVD) was projected to accomplish transfer-free conformal graphene/SiNH photo detectors for the first time [108]. The conformal graphene/SiNH structures were found to have an exceptional light absorption pursuance with absorptions risen up to 90%, and also consist high power absorption densities, that results in more photo carrier procreations per unit volume in the SiNHs than planar silicon. It was found that the photocurrent of the conformal graphene/SiNH photo detector is likely to be 34 \(\upmu\)A, which is definitely much higher than the other fellows: planar graphene/Si (17 \(\upmu\)A) and SiNHs (10 \(\upmu\)A) as shown in Figure 9a. Consequently, the photo response speeds of three kinds of photo detectors, as shown in Figure 9b, were evaluated with respect to the light modulation frequencies at 1, 100, and 2500 Hz, and the results for the conformal graphene/SiNH photo detector were found to be satisfactory. For all switching frequencies, the photo detectors can switch swiftly between \"on\" and \"off\" states. Hence, the conformal graphene/SiNH photo detectors are quicker than the other two detectors because the carriers originated in SiNHs can infuse into the nearby conformal graphene more promptly. These consequences emerge from the lower reflections, higher absorption densities, and shorter carrier transport paths in conformal graphene/SiNH detectors.
## 2 Materials and Methods
### Postprocessing and CAD Integration  
The refined mesh is converted into a format that can be imported into CAD software. FreeCAD was selected because of a non-proprietary license and extensive documentation. The results are converted into a Python script that uses FreeCAD's API [33] to create parts consisting of cylinders.  
Additionally, the export script creates spheres at each node to ensure a proper connection between cylinders, as shown in Figure 8. The radius of each sphere is 110% of the biggest radius of the element connected to the node. The extra margin was added to avoid numeric errors reported by CAD software when fusing elements with a similar radius to produce the final mesh. Note that those spheres are only added in the final step and are not part of the'main' geometry. Hence, to simplify the calculation of the Fitness Function, it was decided to ignore those extra parts during the refinement step, despite the fact that they contribute to the overall volume and mass of the model.  
An alternative output is the implementation of producing a set of IGES (Initial Graphics Exchange Specification) format files, which provides a vendor-neutral way of importing object information into CAD systems.
## 2 Related Work
### The Composite Distributions  
Let \(Y\) be a random variable that only takes non-negative real numbers. Let \(f_{Y}(y)\) be the probability density function (pdf) of \(Y\). The formal definition of a composite pdf \(f_{Y}(y)\) is given as follows [15]:  
\[f_{Y}(y;\alpha_{1},\alpha_{2},\theta)=\begin{cases}cf_{1}(y;\alpha_{1},\theta )&0\leq y<\theta\\ cf_{2}(y;\alpha_{2},\theta)&y\geq\theta,\end{cases} \tag{1}\]  
where \(c\) stands for the normalizing constant, \(\theta\) is the parameter that denotes the location of probability density change, \(f_{1}\) is the pdf of \(Y\) when \(Y\) is between 0 and \(\theta\), and \(\alpha_{1}\) represents the parameters of \(f_{1}\); \(f_{2}\) is the pdf of \(Y\) when \(Y\) is greater than \(\theta\), and \(\alpha_{2}\) represents the parameters of \(f_{2}\). It is assumed that both \(f_{1}\) and \(f_{2}\) are smooth functions on their supports. \(f_{1}\) is generally called the head pdf of \(f_{Y}\), while \(f_{2}\) is referred to as the tail pdf of \(f_{Y}\).  
In real practice, \(f_{Y}(y|\alpha_{1},\alpha_{2},\theta)\) is usually assumed to be continuous and differentiable. Thus, the following conditions are imposed:  
\[\begin{cases}\lim_{y\to\theta^{-}}f_{Y}(y;\alpha_{1},\alpha_{2},\theta)=\lim_ {y\to\theta^{+}}f_{Y}(y;\alpha_{1},\alpha_{2},\theta)\\ \lim_{y\to\theta^{-}}\frac{df_{Y}(y\alpha_{1},\alpha_{2},\theta)}{dy}=\lim_{ y\to\theta^{+}}\frac{df_{Y}(y\alpha_{1},\alpha_{2},\theta)}{dy}.\end{cases}\]  
In real practice, the Pareto distribution and the generalized Pareto distribution (GPD) are commonly used for modeling highly right-skewed data at the right tail due the theoretical properties of such distributions. However, both of these distributions can only fit the data beyond a specific threshold. Hence, they cannot be used to globally fit the data.  
Many composite models with Pareto or GPD tails have been developed by utilizing the idea of composite distributions, including lognormal-GPD [15], lognormal-Pareto [16], exponential-Pareto [17; 18], Weibull-Pareto [19; 20; 21], inverse gamma-Pareto [22], and so on. Moreover, [PERSON] and [PERSON] [23] explored a large number of composite distributions with Pareto and GPD tails with a general framework of computational tools.  
Composite models with a Pareto tail have been widely used in different fields such as actuarial data and reliability data modeling. While these models were frequently used in the literature, it was found that some of the models such as exponential-Pareto [24] and inverse gamma-Pareto [22] do not perform well when fitting real sets with right skewed distributions. Therefore, a new concept named generalized family of exponentiated composite distribution (GEC) was proposed to address the issue [14].  
In the next subsection, we introduce the concept of generalized family of exponentiated composite distributions and the properties of this family.
# Zn Doping Improves the Anticancer Efficacy of SnO2 Nanoparticles
###### Abstract  
Tin dioxide (SnO2) nanoparticles (NPs) can be applied in several ways due to their low cost, high surface-to-volume ratio, facile synthesis, and chemical stability. There is limited research on the biomedical application of SnO2-based nanostructures. This study aimed to investigate the role of Zn doping in relation to the anticancer potential of SnO2 NPs and to enhance the anticancer potential of SnO2 NPs through \(Z\) doping. Pure SnO2 and Zn-doped SnO2 NPs (1% and 5%) were prepared using a modified sol-gel route. XRD, TEM, SEM, EDX, UV-Vis, FTIR, and PL techniques were used to characterize the physicochemical properties of produced NPs. XRD analysis revealed that the crystalline size and phase composition of pure SnO2 increased after the addition of Zn. The spherical shape and homogenous distribution of these NPs were confirmed using TEM and SEM techniques. EDX analysis confirmed the Sn, Zn, and O elements in Zn-SnO2 NPs without impurities. Zn doping decreased the band gap energy of SnO2 NPs. The PL study indicated a reduction in the recombination rate of charges (electrons/holes) in SnO2 NPs after Zn doping. In vitro studies showed that the anticancer efficacy of SnO2 NPs increased with increasing levels of Zn doping in breast cancer MCF-7 cells. Moreover, pure and Zn-doped SnO2 NPs showed good cytocompatibility in HUVECs. This study emphasizes the need for additional investigation into the anticancer properties of Zn-SnO2 nanoparticles in various cancer cell lines and appropriate animal models.  
Sn2 nanoparticles; Zn doping; modified sol-gel process; characterization anticancer activity; biocompatibility +
Footnote †: journal:  
Article
## 3 ANFIS with an Incremental Tree Structure Based on the CFCM Clustering Method
### ANFIS with an Incremental Tree Structure  
For applications, several fuzzy tree structures are available. The input values are integrated into multiple stages in the incremental tree system used in this analysis to optimize the output values at various steps. The previous diagram, for example shows a three-level incremental fuzzy tree with a \(FIS_{i}^{n}\) fuzzy inference method, where \(i\) represents the FIS index of the \(n\)th level. At each step, there is only one fuzzy inference method in an incremental fuzzy tree; \(i=1\), that is. The \(j\)th input of the ith FIS at level \(n\) is indicated in the previous figure by input \(x_{ij}^{n}\), where the kth output of the ith FIS at level n is indicated by the \(x_{ik}^{n}\) input. \(n=3\), \(j=1\) or \(2\) and \(k=1\) in the figure. Each FIS has a complete \(m^{2}\) rule set if each input has \(m\) MFs. The total number of laws, then, is \(nm^{2}=3\times 3^{2}=27\). The monolithic (\(n=1\)) FIS is seen in Figure 4 with four inputs (\(j=1,\ 2,\ 3,\ 4\)) and three MFs (\(m=3\)).  
Therefore, with the number of input sets, the cumulative number of rules in the incremental fuzzy tree is linear. Based on the contribution to the final output value, input selection at various levels of the incremental fuzzy tree uses input rank. Generally, the input value that contributes the most is used at the lowest level, and at the highest level, the input value that contributes the lea  
Figure 4: Tree structure in incremental form.  
that the input value of the low-rank depends upon the input value of the high-rank. In the incremental fuzzy tree, irrespective of other important inputs, each input value usually contributes to some degree to the inference method. In this paper, to prevent over-generation of fuzzy rules due to large-scale databases and to generate meaningful rules, we propose a CFCM-ANFIS with an incremental tree structure rather than a single type of CFCM-ANFIS. As seen in Figure 5, this allows one to rank inputs using existing data to create the fuzzy tree.  
Figure 5: Design of three ANFISs with an incremental tree structure: (**a**) ANFIS structure based on CFCM clustering and (**b**) ANFIS structure based on incremental-tree-structured CFCM clustering.
# Spatiotemporal Contextual 3D Semantic Segmentation for Intelligent Outdoor Mining
## 4 Discussion and Insights
### Implications for Practical Applications  
The findings of this study have important implications for practical applications in outdoor mining and autonomous driving. In mining scenarios, the ability to accurately segment terrain boundaries and objects such as mining trucks and material piles is crucial for operational safety and efficiency. The proposed method, which combines spatiotemporal feature aggregation and long-term dependency modeling, offers a promising solution to improve semantic segmentation in such challenging environments. Moreover, the demonstrated computational efficiency of our approach makes it suitable for real-time deployment, even in environments with limited computational resources.  
However, while the proposed method shows significant improvements over existing models, there are still challenges to address. For instance, the performance of the model can be affected by noise and occlusions in the point cloud data, which are common in real-world mining scenarios. Future work could explore methods for enhancing the robustness of the model to such disturbances, perhaps by incorporating noise reduction techniques or domain adaptation strategies.
## 2 Slope Excavation Energy Discontinuity Criterion
### Establishment of Destabilization Criteria for Slope Excavation  
Dissipation theory suggests that rock damage during slope excavation is the result of energy dissipation. Using the FLAC3D (Version 6.0) finite difference software, after excavating the slope, the energy of the slope can be calculated. This can be achieved either by writing a Fish script to convert using extra or by writing a Fish script to monitor the energy of the model elements [40].  
To assess the stability discontinuity of the surrounding rock during the slope excavation, the energy after \(k\) steps of excavation can be defined as follows:  
\[E_{\text{k}}(\text{a}^{\text{s}})=\sum_{\text{i}=1}^{n}E_{\text{k}}^{\text{i} }(\text{a}^{\text{s}}) \tag{13}\]  
In the formula, \(E_{\text{k}}^{\text{i}}(\text{a}^{\text{s}})\) is the change in energy dissipation density of the unit body after the \(a^{\text{s}}\) excavation.  
\[V_{(\text{i})}=a_{0}+a_{1}t+a_{2}t^{2}+a_{3}t^{3}+a_{4}t^{4} \tag{14}\]  
Figure 1: Equilibrium surfaces and control planes.  
To simplify calculations, real variable functions and complex variable functions are established, and the Laplace transform is used. Let \(t=x-A\), \(A=\frac{a_{3}}{4a_{4}}\); the resulting relationship is  
\[V_{(x)}{=}\;\mathrm{b}_{0}+b_{1}\mathrm{x}+b_{2}\mathrm{x}^{2}+b_{4}\mathrm{x}^ {4} \tag{15}\]  
The relationship between \(a_{i}\) and \(b_{i}\) is given by  
\[\begin{array}{l}\mathrm{b}_{0}=A^{4}a_{4}-A^{3}a_{3}+A^{2}a_{2}-Aa_{1}+a_{0} \\ b_{1}=-4A^{3}a_{4}+3A^{2}a_{3}-2 Aa_{2}+a_{1}\\ b_{2}=6A^{2}a_{4}-3 Aa_{3}+a_{2}\\ b_{4}=a_{4}\end{array} \tag{16}\]  
By dividing both sides by \(\mathrm{b}_{4}\), the standard function form of the discontinuity model can be obtained:  
\[V_{(x)}=x^{4}{+}\mathrm{ux}^{2}+vx+c \tag{17}\]  
In the equation, \(c\) is a constant, so it can be simplified to  
\[V_{(x)}=x^{4}{+}\mathrm{ux}^{2}+vx \tag{18}\]  
where \(u\) and \(v\) are defined as  
\[\mathrm{u}=\frac{a_{2}}{a_{4}}-\frac{3a_{3}^{2}}{8a_{4}^{2}}\mathrm{v}=\frac{a _{1}}{a_{4}}-\frac{a_{2}a_{3}}{2a_{4}^{2}}+\frac{a_{3}^{3}}{8a_{4}^{3}} \tag{19}\]  
According to the critical instability judgment criterion of the discontinuity theory, by differentiating \(V_{(x)}\) with respect to the relevant variable, and setting \(V_{(x)}^{\prime}=0\), \(V_{(x)}^{\prime\prime}=0\), the following can be obtained:  
\[\mathrm{8u}^{3}+2\mathrm{\tau}\mathrm{v}^{2}=0 \tag{20}\]  
At this point, using the aforementioned discontinuity theory for instability judgment, the dissipative energy discontinuity in the slope excavation process can be assessed through \(\Delta=8u^{3}+27v^{2}\). When \(\Delta>0\), the system is in a stable state; when \(\Delta<0\), the system is in an unstable state; and when \(\Delta=0\), the system is in a critical state. The slope will become unstable when the dissipative energy reaches the discontinuity condition.
## 2 Materials and Methods
### Experimental Set-Up  
The experimental set-up shown in Figure 1 was developed to evaluate the response of the modified GI-MMF with BG to SRI and temperature variations. The sensor element was interrogated using a broadband light source (Opto-Link C-Band ASE) and an optical spectrum analyzer (Ando AQ6317B). In order to receive the reflection spectrum from the GI-MMF BG, a MMF 1 \(\times\) 2 (all4 fiber) coupler was applied. Since the output of the broadband light source is fiber coupled (SM, 9/125 \(\upmu\)m core/cladding diameter), it was connected to a 2000 m OM4 GI-MMF coil where at the beginning of the coil the OM4 fiber was periodically bent with a bending radius of 5 mm and 30 turns in order to obtain equal excitation of the modes propagating in the GI-MMF (equilibrium mode distribution). A fiber length of 2000 m and a bend radius of 5 mm were chosen in order to prevent modal interferences as well as to obtain a relatively high fiber bending and enabling modal cross-talk without breaking the fiber.  
In order to alter the SRI, different RI liquids (series A and AA) from Cargille Labs (\(n\) = 1.40, \(n\) = 1.42, \(n\) = 1.43, \(n\) = 1.45 and \(n\) = 1.47) and deionized water (\(n\) = 1.33) were applied. Cargille Labs specifies the refractive index of the liquids for an optical wavelength of 589.3 nm and a temperature of 25 \({}^{\circ}\)C. After each RI measurement, the sensor element was cleaned subsequently with acetone, ethanol and deionized water. The temperature response was measured by inserting the fiber sensor into a rectangular aluminum bar (\(100\times 20\times 20\) mm\({}^{3}\)) with an 8 mm diameter and 70 mm long borehole. The temperature of the aluminum bar was controlled using electrical resistors which were used as heating elements that were connected to a temperature controller (Quick-Cool QC-PC-CC-12). The reflection spectrum of each measurement was observed and saved using Labview and the measured sensor response was analyzed using Matlab. The wavelength shifts of the individual Bragg reflection peaks were obtained by determining the \"center of mass\" of each peak. Moreover, the Bragg reflection peaks in the wavelength region from 1549-1558 nm were analyzed and presented since reflection peaks in this wavelength range were consistent for all measurements.  
Figure 2: Measured transmission spectrum at a temperature of 20 \({}^{\circ}\)C of a BG written in OM4 GI-MMF using the phase mask technique. The red dashed line shows the reference intensity level of the applied light source.
## 1 Introduction
However, most existing research has primarily focused on the impact of variations in the internal structure of CSCCBD, such as different sizes or structural configurations of shear connectors. In fact, like OSD [32; 33; 34], the size of corrugated plates (height, thickness, opening width, etc.) also influences the mechanical performance of CSCCBD, posing an intriguing question worthy of exploration. This study presents a novel bi-directional U-ribbed stiffening plate (BUSP)-concrete composite bridge deck system, which combines the advantages of continuous placement of U-ribs as in CSCCBD with the avoidance of welded connections between U-ribs and steel plates, thereby enhancing the lateral stiffness of the bridge deck system, exhibiting superior resistance to eccentric bridge deck loading. Moreover, to address the difficulties in positioning and installing rebars within the PBL shear connector groups (a commonly used type of shear connector in CSCCBD) [1], improvements have been made to the conventional PBL shear connectors in the innovative BUSP-concrete composite bridge deck system. The openings of the new type of PBL shear connectors extend to the bottom of the BUSP, and after concrete pouring, these openings are filled. This approach effectively enhances the longitudinal shear resistance between the concrete and steel structures. In this paper, a parameterized modeling script for ABAQUS was developed based on Python. The script considers five size parameters, including the height, thickness, and opening width of a single U-rib in BUSP, as well as the transverse and longitudinal spacing between U-ribs. Based on an actual bridge in Guizhou Province, China, a structural finite element model of the BUSP-concrete composite bridge deck was established. The study aims to comprehensively investigate the influence of size parameter variations on the mechanical performance of BUSP-concrete composite bridge deck structures. Furthermore, the combined variations of multiple size parameters will be compared to assess the extent of their impact on structural performance. The research findings of this study will provide valuable insights into the design and application of novel BUSP-concrete composite bridge deck structures.
# Antimicrobial Effect of Titanium Hydroxapatite in Denture Base Resin
## 3 Results
### The Antimicrobial Effect of TiHA against Single-Species Bacterial Biofilms  
The antimicrobial effect of TiHA against bacteria forming biofilms was next evaluated (Figure 3). The specimens were soaked in a fresh medium supplemented with 5% (\(v/v\)) of each bacterial culture to allow for biofilm formation. The bacteria that formed biofilms on specimens containing 0% (control), 3%, 6%, or 9% TiHA were quantified after irradiation. No viable bacteria were retrieved from _S. aureus_ biofilms on specimens that had been irradiated for 4 h. Therefore, the antimicrobial effect of TiHA was evaluated only after 2 h of irradiation.  
Figure 3: The antimicrobial effect of TiHA against single-species bacterial biofilms. The specimens containing 0% (control), 3%, 6%, or 9% TiHA were used. The specimens, on which biofilms of _S. sanguinis_, _A. naeslundii_, _S. aureus_, or _E. coli_ were formed, were irradiated for 2 h. The bacteria were then plated to determine the CFU values, which were divided by the specimen surface area. Data are presented as the mean \(\pm\) standard deviation for three specimens. The asterisk indicates a significant difference in the CFU values of the control and the TiHA-containing specimens (\(p\) < 0.05).  
Without UVA irradiation, the CFU values of _S. sanguinis_, _A. naslundii_, _S. aureus_, and _E. coli_ biofilms on the specimens were not significantly different from those of each control (data not shown). When _S. sanguinis_, _A. naslundii_, and _S. aureus_ biofilms on the TiHA-containing specimens were irradiated, the CFUs were significantly reduced as the TiHA content of the specimens increased (_p_ < 0.05). By contrast, the CFU values for _E. coli_ biofilms on the TiHA-containing specimens were not significantly different from those of the control, with or without UVA irradiation.  
The Antimicrobial Effect of TiHA against biofilms Formed by Multiple Species of Bacteria from the Human Saliva  
The antimicrobial effect of TiHA against biofilms formed by bacteria from the saliva of human volunteers was also investigated (Figure 4). When the specimens were incubated in BHI containing 25% saliva, the CFU values for bacteria in biofilms formed on the specimens containing 9% TiHA were significantly lower than those formed on the control, for the samples from all volunteers (_p_ < 0.05) (Figure 4a). When the specimens were incubated in 100% saliva, the numbers of CFUs of bacteria from the TiHA-containing specimens were significantly reduced for samples from six out of eight volunteers, compared with the control (_p_ < 0.05) (Figure 4b). These findings demonstrated that TiHA was largely effective against not only single-bacterium species biofilms (Figure 3) but also multispecies of biofilms formed by bacteria from the human saliva (Figure 4).
## 1 Introduction
Both studies show reasonable results that indicate that the WRF model with relatively high-resolution (3 km) can be a better choice in short-term prediction of the solar energy field. The first objective of this study being to obtain better forecasts with very high resolution under complex terrain conditions, we also employ the WRF as a physical model with the horizontal resolution of 0.5 km rather than 3 km as in the references, as mentioned earlier. However, merely using one physical model that certainly has uncertainties due to inaccurate initial/boundary conditions, simplifications in physics, and numerical approximations [21] might not be able to provide an adequate numerical prediction. It is addressed in [PERSON] et al. [20] that combining a model output statistics (MOS) approach with WRF forecasts can improve the forecasting skill of solar radiation to some extent. Many existing studies proved that the Kalman filter is much more efficient than the MOS method in the wind prediction field. Thus, our second objective is to develop a novel hybrid model consisting of the high-resolution (0.5 km) WRF model and the clearness index-based Kalman filter, in order to provide day-ahead solar radiation prediction. Unlike the conventional way of using a Kalman filter [22], we choose the forecasting bias instead of the variable itself as the state variable [23]. More importantly, in our work, the combination of the clearness index and Kalman filter, to the best knowledge of the authors, is proposed to forecast solar radiation for the first time. The clearness index is used to define the different sky conditions (i.e., clear, cloudy and overcast) and then cluster the forecasts series into three groups to conduct Kalman filter separately, to overcome the shortage of a Kalman filter, which is the lack of being able to resolve huge day-to-day changes. The rest of the paper is organized as follows. Section 2 introduces the model and all the methods we used in this study. The specific information on study areas and the dataset are stated in Section 3,and Section 4 presents the results and discussions. The paper ends with concluding remarks in Section 5.