esa-sceva/satcom-chunk-collection huggingface dataset:
# Antimicrobials in Dentistry
## 2 Antimicrobials Used in Dentistry
### Quaternary Salts  
Quaternary ammonium compounds (QACs) form a group of cationic antimicrobials that possess a positively charged head with a hydrophobic tail, which is usually a chain of alkyl groups, and the number of units governs the hydrophobicity of the molecule [40]. Although the mechanism of the antibacterial activity of these molecules is not clearly understood, it is broadly associated with the adsorption of QACs on the bacterial cell wall that then penetrates it, leading to the disruption of the cell structure, eventually causing disorganisation of the cells [41]. Since monomers with quaternary ammonium salts are not dependent on the release of an active agent to impart antibacterial activity, these systems are considerably more efficacious than systems that rely on the release of an active agent. Thus, employing QACs in dental materials to inhibit microbial activity led to polymerisable quaternary ammonium methacrylates, as integrating methacrylates into these compounds seemed to improve their efficiency, and this efficacy was directly related to the alkyl chain length of the quaternary ammonium groups [40]. 12-methacryloyloxydodecylpyridinium bromide [42], an analogue of cetylpyridinium chloride, methacrylacryl ethyl dimethyl ammonium chloride, and poly(2-methyloxazoline) [43] are some examples of monomeric methacrylates with pendant quaternary ammonium salt species that have been incorporated in dental composite/adhesive systems to impart intrinsic antibacterial properties in dental composite restorations. Furthermore, improved antimicrobial performance has been reported by combining two quaternary ammonium groups with one polymerisable group [44], and by crosslinking the antimicrobial monomers through two polymerisable groups [45]. QACs have since been incorporated into various dental materials, including composite resins [37; 38; 46; 47], adhesive systems [39; 40; 41; 48; 49; 40], acrylic resins [50], and bone cements [51], that have demonstrated effective antimicrobial properties. Among these tested compounds, the polymerisable QAC, 12-methacryloyloxydodecylpyridinium bromide (MDPB), well known for its potent antimicrobial activity, has been incorporated into a commercially available adhesive system. Although MDPB has demonstrated a good ability to inhibit MMP activity [52] as well as the preservation of bond strengths for up to one year [53], some studies report overall lower bond strengths compared to other adhesive systems [54; 55].
# Biosynthesized Zinc Oxide Nanoparticles Disrupt Established Biofilms of Pathogenic Bacteria
## 2 Materials and Methods
### Characterization of ZnO-NPs  
The initial characterization of ZnO-NPs was performed by recording its absorbance. The suspension of ZnO-NPs was made in water (double-distilled) by 30 min sonication. The UV-Vis absorbance of ZnO-NPs was recorded using spectrophotometer (Shimadzu, UV-1800, Kyoto City, Japan). The blank was set using water (double-distilled), and the absorption was recorded from 300 nm to 630 nm.  
The crystal nature of ZnO-NPs was analyzed using X-ray diffraction (XRD). The diffraction pattern of the X-ray was taken using Cu-K\(\alpha\) radiation, and nickel monochromator was used as a source of light. ZnO-NPs size was calculated using the Debye-Scherrer equation. All prominent peaks were used for the calculation of the particle size of ZnO-NPs.  
The chemical characterization of ZnO-NPs was carried out using Fourier-transform infrared spectroscopy (FTIR) to explore the vibrational and rotational property of the nanoparticle. The FTIR was recorded using spectrometer (Spectrum Two, Perkin Elmer Life, Waltham, MA, USA), at 1 cm\({}^{-1}\) resolution in transmittance mode. The FTIR spectrum was taken from 4000 to 400 cm\({}^{-1}\)[18].  
Transmission electron microscopy was performed to analyze the size and shape of the green synthesized ZnO-NPs. TEM analysis was performed on a JOEL-2100 transmission electron microscope (Japan). The TEM grid was prepared by placing the aqueous suspension of ZnO-NPs on the TEM grid. The grid was air-dried overnight at room temperature before imaging. TEM images were taken at 200 kV and 50,000X magnification [19]. The particle size distribution analysis was carried out by calculating all nanoparticles in a frame and plotting their abundance against the size range. The morphology of ZnO-NPs was further analyzed by SEM analysis.
## 3 Results and Discussion
### Possible Adsorption Mechanism between the CPs and MPs  
[PERSON] et al. (2016) highlighted the significant influence of the water matrix on the adsorption behavior of pharmaceuticals whose structures contained functional groups similar to those in the structure of CPs (hydroxyl and chloride), depending on the physicochemical characteristics of the compound. They implied that the different influences of water constituents on the adsorption behavior of organic pollutants depended on the physicochemical properties of the compounds, which is in accordance with the results obtained here with the adsorption of CPs, 2,4-DCP, 2,4,6-TCP and PCP on the selected MPs [58].  
The obtained results presented in Figure 4 imply a significant influence of the pH value on the adsorption affinity and adsorption capacity of the CPs on the MPs. The highest adsorption capacity of the selected CPs was obtained at pH 7. In order to obtain a better understanding of the possible adsorption mechanisms of CPs, the behavior of the active functional groups and the physico-chemical properties of the selected CPs and the MPs at the selected experimental pH levels were determined. The possible interactions between the MPs and the selected CPs are shown in Figure 5.  
Based on the values of the \(\mathrm{pK_{a}}\) shown in Table S2, it can be seen that, at acidic and neutral pHs, the selected CPs were in their molecular form, while the surface of the MPs were positively charged. Taking into account that the \(\mathrm{pH_{PZC}}\) values were in the range of 4.04-8.71, it can be inferred that electrostatic interactions between sorbent and sorbate at this pH range did not occur. Furthermore, at pH 10, the selected MPs were in their ionic forms, as were the CPs 2,4-DCP, 2,4,6-TCP and PCP, resulting in a low adsorption affinity with the selected MPs due to electrostatic repulsion. Therefore, other types of interactions are responsible for the adsorption of CPs on MPs at pH 4 and pH 7, such as \(\pi\)-\(\pi\) interactions, hydrogen bonding, halogen bonding and van der Waals interactions. It is possible that the  
Figure 5: Possible adsorption mechanism between the CPs and MPs.  
higher adsorption affinity at pH 7 in comparison with pH 4 occurred due to competition between H\({}^{+}\) ions and the free electron pairs of phenol functional groups in the molecules of the selected CPs [67].  
In general, the obtained results for the adsorption isotherms for the selected CPs on MPs imply that the dominant adsorption mechanism is chemisorption. Additionally, the obtained results also indicate the possible development of non-specific van der Waals forces and hydrophobic and \(\pi\)-\(\pi\) interactions, which has also been pointed out by other authors. The results also indicate that the adsorption mechanisms of organic compounds on MPs can be significantly influenced by the physico-chemical properties of the organic compounds and MPs, as well as the water matrix's chemical properties, pH value and salinity [11; 53; 60; 68].
# Variance-Triggered Two-Step GPS Acquisition
## 3 Variance-Triggered Two-Step Acquisition  
The technique proposed in this paper, denominated Variance-Triggered Two-Steps (VTTS) acquisition, was first drafted and evaluated in [12; 13] with the objective of reducing acquisition time approach without compromising its modest hardware requirements of the conventional serial.  
The VTTS acquisition is composed of a Coarse Search (CS) step and a Fine Search (FS) step. In the CS step, the goal is to identify a promising search region in a space with low-frequency resolution by incrementally updating the correlation values of each cell in the search space, sample by sample. The incremental update causes a gradual increase in the correlation values of all cells. By looking for larger variations of these values, it is possible to detect the growing peaks prematurely and then identify a small neighbourhood around it as a promising region, according to the magnitude of these variations. Section 3.1 presents the details about the CS step.  
The FS step is used to validate the hypothesis of the signal's frequency and phase being within the region identified by the CS step. The search in this step is performed using the full resolution.  
Figure 2: Conventional Acquisition matrix.  
The higher accuracy achieved within this region allows for the validation of the hypothesis at lower computational cost than seeking the same accuracy for the whole search space. True peaks will raise distinguishing themselves from the other cells in the region whereas false peaks will fade down. If the peak value is validated by the FS step, the search procedure ends with an acquired satellite. Otherwise, the CS step restarts from where it was interrupted. Section 3.2 presents the details about the FS step.  
The detection of a peak is performed using the variance of the two largest normalized peaks. During the incremental accumulation of the correlation values, a set with the largest peaks values is kept. After each complete iteration in the coarse search space, the highest and the lowest of these peaks are normalized and their variance is calculated. This variance is compared to a threshold of \(\lambda_{CS}\) in the CS step. In the FS step, the variance is compared to a different threshold, \(\lambda_{FS}\). Both threshold values and the number of peaks kept are design parameters of the receiver. Since the variance values compared to these thresholds are calculated with normalized peak values, this threshold is less sensitive to the noise present in the signal, as indicated by the results in this paper. The details about the detection of the peaks are given in Section 3.3.
## 4 Methods
### Preprocessing
#### 4.1.5 Vegetation Correction
As previous studies showed, the range of backscatter changes decreases with increased vegetation. Indicating a decreasing sensitivity of the backscattering signal to changes in soil moisture with growing vegetation, this relationship can be described by an empirical function [77; 78; 79]. As the vegetation influence on the backscattering ratios can vary quite significantly, depending on the crop type and the vegetation parameters (e.g., plant height, plant geometry/structure, biomass, vegetation water content) [42; 80], we choose a linear regression on pixel-basis to remove this trend by:  
\[\left(\frac{\sigma_{0}^{F2}}{\sigma_{0}^{F1}}\right)^{detr.}=\left(\frac{ \sigma_{0}^{F2}}{\sigma_{0}^{F1}}\right)-m\Big{(}\sigma_{0}^{F1}-\sigma_{ mean}^{F1}\Big{)} \tag{12}\]  
with \(m\) and \(\sigma_{mean}\) being the regression slope and mean backscattering value. Applying a pixel-based linear regression, no information about the specific crop type is used. Nonetheless, it is assumed that all crops affect the corresponding backscatter ratios during the transition from bare soil to fully vegetated conditions, and this is the dominating factor for a systematic change in the backscatter ratios during the season, as vegetation sensitive C-band is used. By resampling pixels to a coarser resolution of 200 m, each pixel furthermore contains different plant types and their individually mixed vegetation-induced attenuation behavior. The linear regression is only applied to stations with distinct bare soil and vegetation conditions during the growing season, with a yearly backscattering range greater than 0.10, which was empirically chosen for the study area. Figure 5: Range and mean of co-polarized backscattering signals and CRNP soil moisture measurements from each of four agricultural stations (**A**) and meadow stations (**B**) for the year 2020. As shown, the backscattering values range between 0.025 and 0.200 at the agricultural stations, while ranging between 0.025 and 0.120 at the meadow stations. Furthermore, the lowest backscattering values at the crop dominated stations can be observed within the shooting-phase of the crops within the months March to May, while being significantly biased against the observed soil moisture, which has its lowest values in August.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model  
In this section, the system and network model used for the design of MROR is explained. The section also presents the developed mobile routing channel availability model that captures the fluctuating nature of the spectrum opportunities in the mobility scenario. After developing the equations that characterize the spatio-temporal spectrum opportunities, the stay time for the OFF and ON states of the PU activity with respect to the mobile nodes is derived. Mobility-enhanced spectrum sensing considerations were also taken into consideration during the formulations.
## 4 Applications of CDs in Cancer Theranosis
### Cancer Therapy via Drug Delivery, PTT, PDT, PTT+PDT, siRNA Delivery, SDT, and Gas Therapy
#### 4.2.6 SDT  
The SDT is a non-invasive therapeutic modality that utilizes low-intensity ultrasound (US) and sonosensitizers to generate cytotoxic ROS to induce tumor cell apoptosis [64; 190; 191; 192]. The ultrasound can penetrate deeper into tissues compared to light, making it suitable for the treatment of deeper tumors. [PERSON] et al. synthesized arginylglycisparastic acid (RGD)-tagged carbon dots (RB-CDs@RGD) by synthesizing RB-CDs using glycine and rose Bengal (RB) as precursors in a hydrothermal method and then conjugating RGD with RB-CDs via NHS/EDC coupling reaction [192]. The therapeutic efficacy of RB-CDs@RGD was assessed in C6 tumor-bearing nude mice. No significant weight changes between experimental and control groups were observed for 14-day treatments, indicating that RB-CDs@RGD and its combination with ultrasound did not induce toxicity. Notably, tumor growth was significantly inhibited for the RB-CDs@RGD+US  
Figure 14: (**a**) Cancer volume growth curves over time (***_p_ < 0.001) and (**b**) photographs of HeLa cancer-bearing mice and excised cancers on the 15 th day after treatment [153]. (**c**) Cancer volume growth curves of A549/ADM cancer-bearing subcutaneous xenograft model mice over time (***_p_ < 0.0001) and (**d**) photographs of excised cancers on the 21 th day after treatment [188]. (**e**) Relative cancer volume changes over time of the MDA-MB-231 cancer-bearing mice (***_p_ < 0.01) [185]. (**f**) Cancer volume changes over time of A375 cancer-bearing mice (The arrows on the axis represent the days of drug administration) [183]. _p_-values were calculated by Student’s _t_-test.  
group, whereas the control (i.e., saline) and RB-CDs@RGD groups exhibited significant tumor growth (Figure 15a,b), demonstrating the promising anti-tumor efficiency of the SDT using CDs.
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
## 4 Results and Analysis
### Regression Model  
The purpose of regression analysis of drilling force is to evaluate the relationship between drilling force and point angle, helix angle as well as edge radius.  
ANOVA analyzes the drilling forces obtained in Table 5. First, an analysis of variance was performed on all significant and insignificant items. The models of cutting force are influenced by the parameters of point angle, helix angle and edge radius. Therefore, these models are modified and improved by analyzing the items that greatly influence the drilling  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Point Angle/\({}^{\circ}\)** & **Helix Angle/\({}^{\circ}\)** & **Edge Radius/mm** &
\begin{tabular}{c} **Simulated Drilling** \\ **Force/N** \\ \end{tabular} \\ \hline
1 & 95 & 21 & 0.02 & 29.63 \\
2 & 95 & 28 & 0.03 & 39.13 \\
3 & 95 & 35 & 0.04 & 51.32 \\
4 & 110 & 21 & 0.03 & 44.93 \\
5 & 110 & 28 & 0.04 & 47.75 \\
6 & 110 & 35 & 0.02 & 30.14 \\
7 & 125 & 21 & 0.04 & 49.60 \\
8 & 125 & 28 & 0.02 & 33.33 \\
9 & 125 & 35 & 0.03 & 41.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Orthogonal arrays for three process parameters.  
Figure 6: Contrast diagram.  
force. The empirical regression equation for the relationship between drilling force and bit type is shown in (2):  
\[\widehat{Y}=\widehat{\beta}_{0}+\widehat{\beta}_{1}X_{1}+\widehat{\beta}_{2}X_{ 2}+\widehat{\beta}_{3}X_{3} \tag{2}\]  
After the empirical regression equation expression is obtained, the prediction model of the point angle, helix angle and edge radius on drilling force can be fitted by analyzing the multivariate linear regression database function with MATLAB. As seen in Table 6, all regression coefficients are within the confidence interval of regression. The predictive model equation is shown in (3):  
\[F=206.56\cdot\Phi_{0}^{0.1777}\beta^{-0.0183}r^{0.6808} \tag{3}\]  
where, \(\Phi_{0}\) is the point angle, \(\beta\) is the helix angle, \(r\) is the edge radius and \(F\) is the drilling force.  
Table 7 is the statistical scale of the predictive mathematical model, in which \(R^{2}\) is statistics, F statistics and \(P\) value. \(R^{2}=0.9503\) shows that the model can fit 95% of the experimental data, and if the \(P\) value is less than 0.05, the fitting effect is significant.  
The residual of the linear regression model of drilling force is shown in Figure 7. It is found that the confidence intervals of 9 residual values are all around the zero point, and the residual ranges are all near the zero point.
# Durability of Fabric-Reinforced Cementitious Matrix (FRCM) Composites: A Review
###### Abstract  
Strengthening and rehabilitation of masonry and concrete structures by means of externally bonded fabric-reinforced cementitious matrix (FRCM) (also referred to as textile reinforced mortar (TRM)) composites was proposed as an alternative to the use of fiber-reinforced polymer (FRP) composites due to their good mechanical properties and compatibility with the substrate. However, quite limited studies are available in the literature regarding the long-term behavior of FRCM composites with respect to different environmental conditions. This paper presents a thorough review of the available researches on the long-term behavior of FRCM composites. Namely, (i) test set-ups employed to study the FRCM durability, (ii) conditioning environments adopted, and (iii) long-term performance of FRCM and its component materials (mortar and fiber textile) subjected to direct tensile and bond tests, are presented and discussed. Based on the available results, some open issues that need to be covered in future studies are pointed out.  
fabric-reinforced cementitious matrix (FRCM); textile reinforced concrete (TRC); textile reinforced mortar (TRM); inorganic-matrix; open-mesh textile; long-term behavior
## 5 Case Studies
### Problem Description 1  
The first experimental dataset refers to the shear capacity of RC beams with rectangular cross section without stirrups and was recently adopted by the authors to develop the first data-driven model for the shear strength based on a new robust hybrid methodology [13]. It includes a total of 374 samples. The candidate explanatory variables within the considered database are given by: effective depth \(d\), web width \(b_{w}\), geometrical ratio of the tense longitudinal reinforcement (in percent) \(\rho_{l}\), shear-span to depth ratio \(a/d\), shear compressive strength \(f_{c}\), and yielding stress of longitudinal reinforcement \(f_{yt}\). All forces and lengths are expressed in N and mm, respectively, whereas the experimental values of shear strength are in kN. The compressive strength of concrete \(f_{c}\) ranges from 12.00 to 105.4 MPa, the yielding stress of longitudinal reinforcement \(f_{yl}\) ranges from 283 to 1779 MPa, the longitudinal steel percentage \(\rho_{l}\) ranges from 0.139 to 6.635%, the shear span to depth ratio \(a/d\) ranges from 2.4 to 8.1, the effective depth \(d\) ranges from 110 to 2000 mm, and the web width \(b_{w}\) ranges from 90 to 1000 mm.  
The frequency of the variables occurring within the experimental database is provided in Figure 2. The number of input data corresponding to each specified range of variation shows a quite homogeneous distribution as well as all input values are greater than zero. Nevertheless, the histogram of the shear strength does not reveal a perfect Gaussian distribution, being the dispersion of data non symmetric with respect to the median value [32; 33]. So the observations further from the median value may be considered as leverage points and could affect the accuracy of the regression analyses. As a consequence, a robust regression approach could be appropriate for estimating the parameters of the mathematical models [13; 14; 15; 16].
## 4 GPU Performance Issues
### Memory Access Overhead  
While threads are running, they can access data in different memory areas. Each memory area has its unique function, accessibility, and access speed. Each thread has its local memory. Each thread block has a shared memory accessible by all threads in the block and has the same lifetime as the block. All threads have access to the same global memory. As with any device memory hierarchy, local memory on the GPU is faster than global memory. Thus, executing the packet-processing process using shared memory instead of global memory shortens the packet-processing time [7]. However, shared memory has less memory capacity than global memory. Therefore, the memory space required in the matching process must not exceed the shared memory capacity so that the packet processing can proceed in shared memory.
## 3 Materials and Methods
### Performance Metrics  
Evaluating a model's performance is necessary since it explains how close the model's predicted outputs are to the corresponding expected outputs. The evaluation metrics are used to evaluate a model's performance. However, the evaluation metrics differ with the types of models. The types of models are classification and regression. Regression refers to the problem that involves predicting a numeric value, whereas classification refers to the problem that involves predicting a discrete value. The model of classification problem uses the accuracy metric for evaluation. Unlike the classification problem model, the regression problem uses the error metric for evaluating the model. Our dataset contains numerical values which fall into the regression problem, so we use the error metric to evaluate all used models. The most commonly used error metrics for evaluating a regression model are: Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) [28; 29; 30; 31].  
**RMSE:** RMSE is a widely used error metric for performance calculation process.  
The RMSE can be calculated as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2}} \tag{2}\]  
where, \(y_{i}\) refers to predicted values, \(x_{i}\) refers to actual values, and \(n\) refers to number of samples. A perfect RMSE value is 0, which means all predicted values match the actual values. the smaller the RMSE value, the better the accuracy is [32].  
**MAE:** Unlike the RMSE, the changes in MAE are linear. This is because MAE does not square the error value in it; instead, the scores increase linearly. The MAE can be calculated as follows:  
\[MAE=(\frac{1}{n})\sum_{i=1}^{n}\lvert y_{i}-x_{i}\rvert \tag{3}\]  
where, \(y_{i}\) refers to predicted value, \(x_{i}\) refers to actual value and \(n\) refers to number of samples. Like RMSE, a smaller MAE score indicates better performance of the model.  
In a regression problem, most of the evaluation metric verifies how close the predicted output with the actual value. Therefore, RMSE and MAE are efficient in checking if a model works well or not. Some less popular metrics, such as the R2 score, have not been addressed in previous research works [28; 33]. Therefore, we have evaluated only using the RMSE and MAE metrics.