esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Source and Experimental Methodology
### Experimental Comparison Models
#### 2.4.3 Support Vector Regression Model  
Support vector regression (SVR) model is one of the most widely used models in machine learning. It was proposed by former Soviet Union scientists [PERSON] and [PERSON] in 1963 and 1995 respectively [18]. For a sample data (x,y), general regression models are usually based on the direct difference between the model's predicted value f(x) and the true label value y to calculate the loss. Only when the predicted value f(x) is completely equal to the y value, the regression model's loss function is 0. There is a big difference between the SVR model and the general regression model, which is SVR allows a maximum deviation of \(\epsilon\) between the predicted value f(x) and y. Only when the direct deviation between f(x) and y is greater than \(\epsilon\), SVR will calculate the error between the two. It is equivalent to taking f(x) as the center and establishing an interval band with a width of \(2\epsilon\). When the predicted value of the sample falls within the interval band, the prediction is considered accurate [19]. SVR tries to find the optimal hyperplane to minimize the deviation from all sample points to the optimal hyperplane. Seeking the optimal hyperplane is equivalent to finding the maximum interval. In this experiment, the support vector regression model used the radial basis function as the kernel function, the regularization constant C was set to 5 and gamma was set to 0.02.
## 1 Introduction and State-of-the-Art
### Relevance of the Research
For instance, the User Datagram Protocol (UDP) [15; 16] is confined to overseeing the integrity of data within a single datagram and does not preclude the chance of losing an entire packet, duplicating packets, or disturbing the order of received data packets. On the other hand,  
Figure 1: Structural representation of the research objective. the TCP guarantees dependable and uninterrupted data transmission, preventing data loss or disruption of the sequential order, and can redistribute data by breaking down large data portions into smaller fragments and, conversely, consolidating fragments into a single packet. This circumstance, as well as the fact that over 90% of the world's data traffic is controlled via TCP [17; 18], became a decisive factor influencing the authors' choice.
###### Abstract  
Hybrid bioactive composite resins combine the benefits of glass ionomer cements with composite resins. Its self-adhesiveness is achieved through functional polyacrylic acids and carboxylic groups, hybridizing the smear layer and establishing ionic interactions between calcium and dentin. These materials are defined as having good aesthetics, moisture tolerance, durability, simplicity in technique and handling and are able to maintain a low microfiltration rate while releasing calcium, phosphate and fluorine. The aim of the present study was to evaluate microleakage in restorations using Surefill One(tm) bioactive resin. The null hypothesis is that this type of resin does not obtain a lower microleakage rate when compared to other materials. An in vitro study was carried out using thirty-six premolars and molars extracted for orthodontic reasons. Identical preparations were thus performed in all of them (Class V with 4 mm meso-distal, 3 mm occluso-gingival and 3 mm in depth) and divided into different experimental groups: one positive control, one negative control and two tests with bioactive composite resin and conventional composite resin (Surefill One(tm) and Spectra(tm) ST HV, respectively). Through quantitative techniques using nuclear medicine, it was possible to evaluate microleakage through the use of a radioactive isotope, technetium. Radioactivity emitted by the specimens was detected by a gamma camera. The different groups were compared using the Kruskal-Wallis test and the Games-Howell test for multiple comparisons. The results of the experimental study point to statistically significant differences between the test groups (\(p=0.002\)) with increased microleakage in the bioactive composite resin group. Based on the present microleakage study, it was possible to conclude that the bioactive composite resin (Surefill One(tm)) does not reduce the microleakage rate when compared to a conventional nanohybrid composite resin (Spectra(tm) ST HV). However, Surefill One(tm) can be used in temporary restorations, primary teeth and in cases of tissue remineralization, thus avoiding more invasive procedures.  
operative dentistry; bioactive resin; composite resins; microleakage; radioisotopes; conservative dentistry +
Footnote †: journal: Applied Sci. 2022, 12, 11827. [[https://doi.org/10.3390/app122211827](https://doi.org/10.3390/app122211827)]([https://doi.org/10.3390/app122211827](https://doi.org/10.3390/app122211827))
## 4 Experiment and Results
The robot started patrolling from the same location in each experiment. The patrol time comparison of each experiment is shown in Table 1. The average time of patrolling with/without inspection points was about 206/205.75 sec, respectively. Though the average time of patrolling with the inspection point was larger than the other one, however, the minimum elapsed time of the former can be reduced to only 194 sec. On the other hand, the maximum elapsed time of the latter was 260 sec, which was larger than any elapsed time of the experiment with inspection points placed. This is because the proposed system runs the sing-in procedure while inspection points are placed in the patrol environment, and the sing-in process took about 5 to 8 sec per inspection point. In addition, without placing inspection points, the robot can only used the first method and the built-in obstacle avoidance function to find the patrol path. While the robot was moving to corners of a room and could not find a way out, this caused time-wasting for backing to patrolling. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inspection Points** & **Starting Time** & **Stopping Time** & **Elapsed** & **Average** \\
**Placed** & **(hh:mm:ss)** & **(hh:mm:ss)** & **Time (s)** & **Time (s)** \\ \hline \multirow{4}{*}{Yes} & 10:53:55 & 10:57:17 & 202 & \multirow{4}{*}{206} \\  & 15:22:02 & 15:25:48 & 226 & \\ \cline{1-1}  & 17:40:33 & 17:43:55 & 202 & \\ \cline{1-1}  & 18:11:19 & 18:14:33 & **194** & \\ \hline \multirow{4}{*}{No} & 13:41:57 & 13:44:40 & 163 & \multirow{4}{*}{205.75} \\  & 16:38:24 & 16:41:56 & 212 & \\ \cline{1-1}  & 16:54:04 & 16:57:12 & 188 & \\ \cline{1-1}  & 09:24:43 & 09:29:03 & **260** & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The patrol time comparison of each experiment. Figure 12: Depth information recorded in the form of polar space. Several other experiments were conducted to verify the efficacy of the intruder detection function. Figure 13 presents recorded images of the intruder in one of the experiments. While the robot was executing its patrol task, an intruder suddenly ran into the FOV of the SVS (at 10 s), whereupon the SVS detected the skeleton of the intruder (at 13 s). The robot simultaneously ceased its patrol task and initiated the intruder tracking task. From 19 s to 43 s, the intruder was pacing around, during which time the robot rotated to keep the intruder within the center region of the image plane. As the intruder was walking backward (from 43 s to 49 s), the robot followed in order to maintain the depth value of tracking target \(d_{h}\) between the near-threshold value \(d_{kn}\) and far threshold value \(d_{kf}\). In this experiment, the robot proved highly effective in using the proposed algorithm to detect, identify, and track the intruder. Finally, the recorded images can be queried from a database by the graphic user interface (GUI) built in the user client.
## 3 Results and Discussion
### Analyses of Results
#### 3.3.2 Comparative Analysis of Performance between W-SPRG and P-SPRG  
Both samples A and B of W-SPRG met the KS quality standard in all performance tests. Accordingly, to compare the performance between W-SPRG (sample A) and P-SPRG, it was identified that the performance of W-SPRG (sample A) was more improved than P-SPRG in viscosity (205%), solid content (3.6%), underwater loss resistance (161.7%), wet surface adhesion (165.5%), and chemical resistance safety (138.5%), as shown in Table 9 and Figure 14. In particular, the performance improvement was much higher in wet surface adhesion improvement viscosity, underwater loss resistance, and wet surface adhesion, which showed a great viscosity improvement by the mixture of waste oil and waste rubber. The viscosity improvement of W-SPRG means that it can have better cohesiveness than P-SPRG that is not lost in the water in an underwater environment that is in direct contact with water and can also maintain a stable waterproof layer without any bumps on a wet surface.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Evaluation Item**} & \multicolumn{2}{c}{**Performance**} & **Improvement Level (\%)** \\  & & **W-SPRG** & **P-SPRG** & \(\frac{\textbf{W}-\textbf{P}}{\textbf{P}}\times\textbf{100}\) \\ \hline
1 & Viscosity & 6,410,733 & 2,100,000 & 205 \(\uparrow\) \\ \hline
2 & Solid content & 99.5 & 96.0 & 3.6 \(\uparrow\) \\ \hline
3 & \
\begin{tabular}{c} Water \\ permeability \\ resistance \\ \end{tabular} & No & No & & \\  & &
\begin{tabular}{c} Leakage \\ \end{tabular} & Leakage & 0 \\ \hline
4 &
\begin{tabular}{c} Wet surface \\ adhesion \\ \end{tabular} & 300 & 113 & 165.5 \(\uparrow\) \\ \hline
5 & \
\begin{tabular}{c} Structural \\ behavior \\ responsiveness \\ \end{tabular} & No & No & & \\  & &
\begin{tabular}{c} Leakage \\ \end{tabular} & Leakage & 0 \\ \hline
6 &
\begin{tabular}{c} Underwater \\ loss resistance \\ \end{tabular} & 0.037 & \(-\)0.060 & 161.7 \(\uparrow\) \\ \hline
7 &
\begin{tabular}{c} Chemical \\ resistance safety \\ \end{tabular} & 0.020 & \(-\)0.052 & 138.5 \(\uparrow\) \\ \hline
8 & \
\begin{tabular}{c} Temperature \\ safety \\ \end{tabular} & No & No & & \\  & &
\begin{tabular}{c} Leakage \\ \end{tabular} & Leakage & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparative analysis of performance improvement of W-SPRG compared to P-SPRG.  
Figure 13: Comparison of performance improvement by sample of W-SPRG compared to KS quality standard.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 8 Assessing State-of-the-Art Defensive Measures
### SSI_Anti-Spoofing_  
SSL_Anti-spoofing [74] is a novel approach for identifying spoofing attacks and deep-fakes in automatic speaker verification. The proposed method leverages Wav2 vec 2.0, a self-supervised representation learning model, to extract trustworthy speaker embeddings. Additionally, the method employs data augmentation techniques to enhance the model's ability to accurately detect spoofing attacks and deepfakes. The model yielded a state-of-the-art (SOTA) EER (Equal Error Rate) of 0.82% on the ASVspoof 2021 LA track.  
**Procedure.** To verify the effectiveness of the model and ensure that it produces results similar to those listed in the SSL_Anti-spoofing paper, we first utilised the provided trained model and dataset and ran the code to generate results. Subsequently, we trained the model on the RFP dataset and then evaluated the trained model on the RFP evaluation subset.  
**Result.** The EER for the SSL model trained on the RFP dataset was 50.16% while the EER for the SSL model as trained on the ASVspoof 2021 listed in the SSL_Anti_Spoofing paper was 0.82%. Although the model achieved state-of-the-art (SOTA) when using entirely fake audio, it was unable to detect partially fake speech. We believe that the model can be improved to detect both entirely and partially faked speech.
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
## 3 Results  
### Reduction Effacies of PLE  
#### 3.1.1 Effects of Contact Time and Rate Kinetics  
The reduction of Cr(VI) by PLE over varying contact times is presented in Figure 1a. A sharp increase in Cr(VI) reduction efficiency was observed with increasing contact time and more than 90% reduction efficiency was discerned in the first 20 min. Thereafter, the reduction efficiency reached 97% in 60 min, with no further increase in reduction efficiency by increasing contact time. Hence, 60 min contact time was optimized for all other experiments. Pseudo first-order rate kinetics for PLE mediated Cr(VI) reduction was determined. In(C/C\({}_{0}\)) was plotted against time and the rate constant was determined from the linear part using regression analysis (Figure 1b). A high correlation coefficient (\(R^{2}\) = 0.957) signifies that the reduction of Cr(VI) by PLE follows pseudo-first order kinetics. The rate constant was obtained from the regression equation and was found to be 0.0835 min\({}^{-1}\).  
Figure 1: (**a**) Effects of contact time, [Cr(VI)]\({}_{0}\) = 10 mg:L\({}^{-1}\), Cr(VI) volume = 25 mL, contact time = 0–80 min, pH = 2, plant leaves extract (PLE) quantity = 1.5 mL; (**b**) Pseudo first order rate kinetics of Cr(VI) reduction.
## 3 Theoretical Analysis of Proposed Smart Home Control System
### Design of VLC LED Lightbulb
The BLE module [34] loads the VLC ID code stored in the embedded flash memory to the RAM and sends out pulse-width modulation (PWM) waveforms through the serial peripheral interface (SPI). Furthermore, the VLC ID code can be changed wirelessly through Bluetooth using a programming application [34]. To meet the requirement of output power driving capability, a CMOS inverter-based driver is inserted between the I/O of the BLE module and the power MOSFET. The power MOSFET acts as a switch controlling the on/off states of the LEDs under a switching speed of 8-16 kHz, and it is chosen to sustain a drain-to-source voltage of up to 72 V during the LED off state. Figure 4 shows the simulated results of the designed high-power VLC lightbulb, showing the AC input voltage; LED supply input voltage; modulation signal, which is the stored VLC signal in the BLE module; and LED driving current. The zoomed-in waveforms reveal that the simulated supply voltage for the LEDs is around 74 V and the average value of the driving current is 130 mA, and these values are consistent with the design targets. It can also be noticed that the waveform of the driving current changes in correspondence with the modulation signal. Figure 3: Schematic of high-power LED lightbulb with VLC functionality.
## 1 Introduction
Secondly, the NBS contact detection algorithm and potential contact force are used to deal with the contact problems caused by crack closure, sliding, and block collision. In recent years, the FDEM has been widely used in tunnel excavation [13; 14], laboratory test-scale simulation [15; 16], etc. For example, [PERSON] et al. [17] used a 3D-FDEM to perform a full-scale three-dimensional analysis of the SHPB test for granite materials. The softening behavior of the sample after crushing can be reproduced, and the simulation results are consistent with the laboratory observations. [PERSON] et al. [18] combined the FDEM and GPU parallel technology to simulate a full-scale SHPB test and observed the dynamic fracturing process and stress response phenomena of a Brazilian disk marble specimen. [PERSON] et al. [19] used the FDEM to simulate the complete 3D fracture process in routine laboratory tests, including Brazilian splitting and uniaxial and biaxial compression tests. The numerical results are in good agreement with the experiments, which shows that the FDEM can simulate the rock fracture well. Later, [PERSON] [20] proposed a new potential function, which not only retained all the characteristics of the original potential function in the FDEM but also reduced the dependence on the mesh. [PERSON] developed a series of 2D/3D hydro-mechanical coupling models [21; 22; 23; 24; 25; 26; 27; 28], contact heat transfer models [29; 30; 31], thermo-mechanical coupling models [32; 33; 34; 35; 36], hydrothermal coupling models [37], and moisture diffusion-fracture coupling models [38; 39; 40], which make it possible to solve complex problems such as hydraulic fracturing, thermal cracking, shrinkage cracking, etc. Later, these models were integrated and combined with GPU parallel technology to develop the MultiFracS software by [PERSON] [36]. From the above discussion, it can be seen that the FDEM has been used in various fields [41], and its related functional modules have been continuously improved. The functions of the FDEM have completed the transition from pure mechanical fracture calculation to multiphysics fracture calculation, and from small-scale 2D fracture calculation to larger-scale 3D fracture calculation. In view of the unique advantages of the FDEM, we use the FDEM-based software MultiFracS to establish a numerical model for tunnel blasting excavation, considering near-field fracture and breakage. This model is used to study the effects of a millisecond delay and different blasting excavation construction technologies, including the blasting vibration velocity and the degree of fracture of the surrounding rock around the blast hole.
# Peptide Mediated Antimicrobial Dental Adhesive System
In a recent study, nisin, an antimicrobial peptide, was used to make a cured nisin-incorporated dental adhesive that exhibited a significant inhibitory effect on the growth of S. mutans.The inhibitory effect was strengthened as the nisin concentration increased however, there were no significant differences in the agar diffusion test for the cured nisin-incorporated adhesives when compared to the control group [58; 59]. \(\varepsilon\)-Polyllysine (\(\varepsilon\)-pL) is already recognized for its safe use as a commercial food preservative due to its natural antimicrobial properties [60; 61]. As a small homopolymer, \(\varepsilon\)-pL, was approved by the FDA as a food additive, noting its antimicrobial activity and low toxicity to human cells [62]. [PERSON] and colleagues recently explored polylysine addition to the mixture of monoclonal phosphate monohydrate and tristrontium phosphate into a dental composite system and demonstrated increased apatite layer thickness [63]. Herein, we propose a novel, bio-enabled dental adhesive system with multiple antibacterial mechanisms and innate biocompatibility to address the critical weakness of bacteria-induced degradation at the a/d interface. We explore the effects of adding \(\varepsilon\)-pL to our dental adhesive formulation and incorporating antimicrobial peptide to the adhesive system as part of our approach to provide multiple antibacterial mechanisms. First, we developed an amine-functionalized dental adhesive formulation, building upon our previous work which addressed the challenges of the hydration difference between the dental adhesive and the demineralized dentin [64; 65; 66]. Next, we added \(\varepsilon\)-pL to the adhesive formulation coupled with antimicrobial peptides as an additional mechanism to combat bacterial colonization at the a/d interface. To achieve this goal, we engineered derivatives of an established antimicrobial peptide, GH12, with known activity against _S. mutans_[67], GH12 has been shown to reduce the cariogenic virulence factors of _S. mutans_, an activity that makes GH12 a candidate for dental caries prevention [68]. In addition, GH12 was successfully employed in a rodent model in a pioneer study aimed to analyze the efficacy of an antimicrobial peptide on the onset and development of dental caries [69]. Promising sequence derivatives were selected for synthesis through computational structure generation verified via analysis of GH12 and modified peptide folding using circular dichroism in the presence of a kosmotropic agent. To ensure effective activity, the peptide was designed to allow for amine coupling to the enhanced dental adhesive formulation through the addition of a spacer sequence. Our results demonstrate that the addition of both \(\varepsilon\)-pL and the engineered antimicrobial peptide components provide critical bacterial inhibition for the adhesive formulations.
## 2 Discussion
We established that since sharp La features exist at the Fermi level, a greater degree of theoretical modeling is necessary to  
Figure 1: Eliashberg function \(\alpha^{2}F(\omega)\) for (**a**) LaH\({}_{10}\) and (**b**) LaH\({}_{18}\); the spectral weight at the Fermi level is calculated with different extents of approximation: (i) DFT and (ii) with the full-charge self-consistent formalism (DFT + DMFT + CSC). correctly reflect the superconducting characteristics. In the conducted analysis, for example, the one-shot (DFT + DMFT) and the full-charge self-consistent method (DFT + DMFT + CSC) generate a minor shift of the acute La feature at the Fermi level, which reduces the \(f\) character weight at the Fermi level. According to our results, \(f\) orbitals seem to be particularly significant for the superconducting characteristics of rare-earth hydrates. However, the impact of the quantity of the atoms for hydrogen in hydrate with the same \(f\)-element is unknown. Hence, in addition to LaH\({}_{10}\), we also studied stable LaH\({}_{18}\) structures at 400 GPa with the aim to distinguish any notable tendencies. A comparison between Figure 4a-c shows that the one-shot (DFT + DMFT) and the full-charge self-consistent method (DFT + DMFT + CSC) barely change the 4/-La spectral weight at the Fermi level. This leads to similar results for the superconducting temperature  
Figure 3: (**a**) Density of states produced by DFT calculations. In (**b**,**c**), we show the density of states, obtained within the oneshot DFT + DMFT and within the full-charge self-consistent DFT + DMFT + CSC, respectively. \(tDOS\) and \(fDOS\) signify the density of states corresponding to the lattice and \(f\) impurity Green’s function, respectively. All calculations were carried out in the Fm3m phase of LaH\({}_{10}\) at 400 GPa. Figure 2: The superconducting temperature \(T_{c}\) obtained by the Allen and Dynes formalism for LaH\({}_{10}\) and LaH\({}_{18}\) at 400 GPa. We obtained a theoretical estimation for LaH\({}_{10}\) of \(T_{c}=155.98\) K by DFT and \(T_{c}=184.33\) K by DMFT, and a theoretical estimation for LaH\({}_{18}\) of \(T_{c}=134.69\) K by DFT and \(T_{c}=139.85\) K by DMFT. We have used values of \(U=6\) eV and \(J=0.6\) eV. All these calculations have been carried out using Fm3m-LaH\({}_{10}\) and Fmmm-LaH\({}_{18}\) at 400 GPa. of LaH\({}_{18}\) within DFT, one-shot DFT + DMFT, and DFT + DMFT + CSC approaches. By looking at Figures 3 and 4, it can be seen that the \(f\) orbital DOS modifications within DMFT with respect to DFT are different for different hydrogen content. For LaH\({}_{10}\), DFT significantly overestimates the 4\(f\)-La weight at the Fermi level, while for LaH\({}_{18}\), the 4\(f\)-La weight is low both in DFT and DMFT, leading to a lower \(T_{c}\).