esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Performance Evaluation Metrics  
Intending to analyze a model's results for different categories and the overall performance, precision (P) is utilized to evaluate the veracity of the model predicting positive samples, recall (R) is employed to evaluate a model's power to identify all positive samples, whereas the F1 score thinks about the overall performance of precision and recall. The average precision (AP) characterizes the area under the PR curve, representing the average of the model's predicting precision over different classes, and the mean average precision (mAP) is the average of the average accuracy over all classes. These metrics enable a thorough evaluation of an object-detecting system's performance in predicting both positive and negative classes and provide an overall insight into the effect of the model. Specifically, the expressions for precision and recall are:  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Configuration** \\ \hline CPU & Intel(R) Core(TM) i7-12700K \\ Operating systems & Win 10 \\ GPU & NVIDIA GeForce RTX 3060 \\ GPU memory size & 12G \\ Deep learning framework & Pytorch 1.12.0 \\ Programming language & Python 3.7 \\ Dependent package & CUDA 11.7 + CUDNN8.3.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Framework for the evaluation of the experimental platform.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Initialization** \\ \hline Image size & 640 \\ Learning rate & 0.01 \\ Momentum & 0.9 \\ Weight decay & 0.005 \\ Batch size & 16 \\ Training epoch & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter settings for model training.  
\[P=\frac{TP}{TP+FP} \tag{15}\]  
\[R=\frac{TP}{TP+FN} \tag{16}\]  
where TP (true positive) stands for the quantity of true positive examples, FP (false positive) reflects the quantity of negative examples that are misjudged as positive examples, and FN (false negative) presents the quantity of positive examples that are misjudged as negative examples.  
The F1 score is a comprehensive evaluation index, which is the harmonic mean between [recision and recall, and is suitable for the comparison of different model performances. Its equation is expressed as follows:  
\[F1=\frac{2*P*R}{P+R} \tag{17}\]  
In object detection tasks, the average accuracy is evaluated by quantifying the area beneath the precision-recall (PR) curve. For each category, the values of the precision and recall under that category are calculated, and then the average accuracy is calculated based on the PR curve. The average accuracy mean provides a comprehensive performance assessment of the model over the entire dataset, taking into account multiple categories of prediction accuracy. The AP and mAP expressions are shown in Equations (18) and (19).  
\[AP=\int_{0}^{1}P(R)dR \tag{18}\]  
\[mAP=\frac{1}{N}\sum_{i=1}^{N}AP(i) \tag{19}\]
## 3 Results and Discussion
### Parametric Investigation of UV-A/TiO\({}_{2}\) Process
#### 3.2.2 The Effect of Solution pH  
As mentioned above, under certain pH conditions, different charges can be induced, leading to electrostatic interactions between the nanoparticles and the contaminants. These interactions are directly influenced by pH variations, which emphasizes the importance of investigating its effects on the overall efficiency of the process [49]. The type of TiO\({}_{2}\) that was employed has a point of zero charge (pH\({}_{\mathrm{PZC}}\)) of approximately 6.7 [32; 41], while SDX has a pKa of 6.17 [42]. Therefore, using concentrated H\({}_{2}\)SO\({}_{4}\) and NaOH buffers, the pH of the solution was adjusted to approximately 3 and 8, while the pH of the solution without the addition of reagents was 5.2. SDX was completely degraded in all three cases, indicating the robustness of the heterogeneous photocatalytic process across a range of pH conditions. However, significant differences were observed in terms of mineralization efficiency. No difference was observed between the experiments at pH 3 and 5.2, with mineralization reaching 78 \(\pm\) 3% and 77 \(\pm\) 2%, respectively. Conversely, under alkaline conditions (pH 8), TOC removal was limited at 31%. Based on the PZC of the nanoparticles and the pKa of the SDX molecule, electrostatic attraction should have been approximately  
\begin{table}
\begin{tabular}{c c c c} \hline
**Experiment No.** & **UV-A Light Dose (W/L)** & **TOC Removal (\%)** & **EEO (kWh/m\({}^{3}\))** \\ \hline R 1 & 2.13 & 54 \(\pm\) 3 & 30.8 \\ R 2 & 4.26 & 77 \(\pm\) 2 & 32.2 \\ R 3 & 6.39 & 85 \(\pm\) 4 & 35.7 \\ R 4 & 8.52 & 87 \(\pm\) 2 & 49.9 \\ \hline \end{tabular}
\end{table}
Table 1: UV-A radiant power per unit volume and experimental results, after 60 min.  
the same under all of the tested pH conditions. Thus, the potential lack of adsorption could not explain this change of mineralization efficiency. This indicates that under alkalic conditions, the transformation products that are generated exhibit significant robustness to further degradation, or exhibit significant alterations of pKa values compared to the parent compound.
# Novel Method for Bridge Structural Full-Field Displacement Monitoring and Damage Identification
## 2 Full-Field Displacement Monitoring of a Structure Based on Natural Texture Feature Monitoring
### Calibration of Test Beam's Image Monitoring Resolution  
The yellow calibration line in the web member, as shown in Figure 17, corresponds to a series of pixel matrix arrangements in the image. The actual physical size of each pixel was the monitoring resolution, and Figure 17 shows the calibration model. Yellow calibration lines were drawn in the middle of each vertical bar. The exact length of each calibration line was measured, and the number of pixels represented by the corresponding calibration line was counted. Subsequently, the monitoring resolution was calculated as \(R=L/n\) (\(mm/pixel\)), and the results are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vertical Bar No.** & **No. of Pixels** & **Calibration Line Length/mm** & **Calibration Value mm/px** & **Average Value** \\ \hline
1–2 & 1986 & 367.17 & 0.1849 & \\
3–4 & 1999 & 359.21 & 0.1797 & \\
5–6 & 1993 & 360.10 & 0.1807 & \\
7–8 & 1991 & 357.30 & 0.1795 & \\
9–10 & 1998 & 356.32 & 0.1783 & \\
11–12 & 1994 & 312.39 & 0.1567 & \\
13–14 & 1989 & 355.91 & 0.1789 & \\
15–16 & 2654 & 453.94 & 0.1710 & 0.1771 mm/px \\
17–18 & 1982 & 356.83 & 0.1800 & \\
19–20 & 1993 & 321.44 & 0.1613 & \\
21–22 & 1988 & 357.77 & 0.1800 & \\
23–24 & 1987 & 356.75 & 0.1795 & \\
25–26 & 1994 & 361.37 & 0.1812 & \\
27–28 & 1989 & 363.10 & 0.1826 & \\
29–30 & 1983 & 359.47 & 0.1813 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resolution calibration.  
Figure 16: Distribution of feature points on test beam’s surface.  
Figure 17: Calibration of test beam’s image monitoring resolution.  
The monitoring resolution of the bridge image, which converts the pixel scale of the bridge image to a deformation monitoring scale, was obtained by calibrating the pixel size. Subsequently, the deformation value of the structure was obtained by changing the pixel position.
# A Review of Hybrid VLC/RF Networks: Features, Applications, and Future Directions
## 4 VLC/RF Hybrid Network Applications
### Technological Applications  
VLC/RF hybrid technology could be a key tool to achieve smart homes. The data speed and security of VLC networks in conjunction with the ubiquity of RF coverage could be used to interconnect all electronic equipment within smart homes. In this way, any command could be executed on any electronic equipment in the house from a mobile terminal or simply by voice, even if far away from the house. The use of a solar panel on the roof of an IoT smart house represents a way to store energy and transmit information at the same time through the VLC network, which could then be transferred to the interior of the house through the RF network. The authors of [161] investigated the applications of hybrid VLC/RF networks in a three-dimensional indoor IoT system, taking into account temperature sensors, indoor air quality sensors, indoor sensors, and humidity sensors.  
IoT applications associated with factories and production have attracted the attention of researchers in recent years [103; 161; 162]. The goal is to use the performance of VLC/RF networks to interconnect production equipment and automation technologies to enable the generation and sharing of information between all equipment. This can enable big data generation to identify certain production patterns and even predict inefficiencies and future events.  
Vehicular and traffic control applications on the road are another promising options for the development of automotive technology. Due to the random nature of car traffic on the road, it is necessary to implement a hybrid technology that allows communication between cars and organizes their passage. Several authors have investigated this topic and proposed different vehicle control applications using hybrid VLC/RF networks [163; 164; 165]. In [163], the authors proposed an outdoor cognitive network with electric vehicles using mixed VLC and RF channels to establish interference-free communication between vehicles. On the other hand, in [164] the authors presented a hybrid VLC/RF cooperative system in vehicle-to-vehicle communication networks and analyzed the performance of the hybrid  
Figure 10: VLC/RF network applications.  
system in terms of outage probability and bit error rate. The authors of [165] investigated resource management in hybrid VLC/RF systems for wireless communication between vehicles and infrastructures.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 2 HVDC Transmission System Model  
HVDC transmission systems consist of at least two converters (i.e., rectifiers and inverters) and overhead lines that link converters. For the sake of diminishing harmonics generated by AC/DC hybrid systems, AC filters are installed on the AC side of DC terminals. In this section, we review DC transmission systems and corresponding DC power flow equations for our UC formulation.
## 4 Experimental Results  
### Dataset Production  
To verify the effectiveness of the method in this paper, the roads in the Yangjiang area of the Jiulongpo District, Chongqing, were selected as the observation objects for simulation experiments. The monitoring data of several roads in the traffic road network are collected to form the monitoring dataset for traffic situation awareness and the monitoring dataset for the traffic trend prediction, respectively.
## 4 Applying the Resource Supply Model to a Smart Grid Environment  
The smart grid is an emerging new technology, which makes use of modern computation and communications technology to improve the efficiency and reliability of power transmission and power distribution in an electric grid. In general, the electric grid is basically made up by the electricity power transmission system (TS), the electricity distribution system (DS) and the central demand management system [20]. The origin of the term \"smart grid\" appears in the article \"Reliability demands will drive automation investments\" [21]. The smart grid transfers electricity from electric stations (suppliers) to personal electricity users and industrial consumers using modernized informatics and communication technology to save energy and increase sustainability and efficiency. In [22], the electric grid is stated as arguably the world's largest engineered system, which is vital to significantly improve human life and is the motor of the economy and the major driver of progress.  
In this paper, we apply DRSM to the smart grid as a case study. Specifically, DRSM is applied to manage the resource, _i.e._, electricity in this case, and provides an optimization-based decision making mechanism for the smart grid electricity transmission and distribution processes. Figure 3 illustrates the whole structure of the scenario. The system consists of three major components:  
(1) DRSM is shown at the left bottom of Figure 3. DRSM will make smart decisions to distribute electric power and charge the price of power through available data.  
Figure 2: Applying the dynamic resource supply model (DRSM) into a cyber-physical system.  
(2) A charging request of plug-in hybrid electric vehicles (CR-PHEV) through a web service is shown in the left block of Figure 3. This part deals with communication between the PHEV and DRSM through the Android system in order to request for the electric power pricing.  
(3) Electricity consumption data collection and allocation (ECDCA) through a ZigBee network is shown in the right block of Figure 3. ECDCA deals with communication between normal electric devices and DRSM in the smart grid. Electricity consumed by devices and consumption data are sent to DRSM. The decisions made by DRSM are sent back to the electric devices.
## 3 Results and Discussion  
### dCl-BisGMA Properties  
The chemical structure of the monomer under investigation, dCl-BisGMA, was reported elsewhere [2]. The substitution generally occurs through a catalytic Appel reaction with an \(\mathrm{S_{N}2}\) mechanism, resulting in an inverted configuration [15; 22; 23] (Figure 1). [PERSON] et al. [2] reported some of the monomer physicochemical properties, including viscosity and biocompatibility. Viscosity was found to be 126-fold lower than that of BisGMA at 22.1 \({}^{\circ}\)C (i.e., 909.9 and 7.2 Pa-s for BisGMA and dCl-BisGMA, respectively). The dCl-BisGMA biocompatibility was also evaluated within resin mixtures of TEGDMA/BisGMA against immortalized human bone marrow stromal cells, using dCl-BisGMA-free resin as a reference. In this study, dCl-BisGMA was incorporated in experimental dental composites. Then, the composite physicochemical properties, including complex viscosity, DC, \(\mathrm{W_{SP}}\), \(\mathrm{W_{SL}}\), were investigated.
# Adaptive Visual Saliency Feature Enhancement of CBCT for Image-Guided Radiotherapy
## 4 Support for CBCT and CT Registration in IGRT  
In this section, the registration results between the source CBCT and CT images and between the CBCT images enhanced using different enhanced algorithms and CT images are compared and analyzed. In this experiment, all the registration results are obtained by the same registration model [39]. In addition to visual analysis, we also give the SSIM values before and after the registration of these images to objectively measure the registration performance.  
As shown in Figure 12, Figure 12a is the unregistered source CBCT and CT images. The SSIM value of the source images is 0.5532, and it can be seen from Figure 12a that the imaging position and size of the source CBCT and CT images are different. Figure 11b is the registration result by the source CBCT and CT, and the SSIM value is 0.7184 if the CBCT image is used directly for the registration task. In the enlarged image, the edge regions are not exactly aligned in Figure 12b. Except for the CBF enhancement method, the registration accuracy was improved after CBCT images were enhanced by other methods. For example, in Figure 12c, the SSIM value is 0.7486 by registering the MSR-enhanced CBCT and CT images, which shows improved accuracy of non-enhanced CBCT and CT registration. In other words, the enhancement of CBCT images is helpful to improve the registration accuracy of CBCT and CT, which is very critical and meaningful for IGRT.  
From the yellow arrows, it can be seen that our method's results have the highest registration accuracy and achieve the best SSIM value of 0.7729. In the enlarged image, it can be seen that the proposed method outperforms the comparison methods in the registration accuracy of the edge regions. This is because the proposed method enhances the saliency features, which plays a key role in the registration process. In addition, since the proposed method includes the denoising step, the noise points on the edges of image that may affect the registration were filtered out, which also helps to improve the registration accuracy.  
Figure 11: Comparison of the average quantitative metrics of different methods. (**a**) AG. (**b**) SF. (**c**) EI. (**d**) UIQM.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **AG** & **SF** & **EI** & **UIQM** \\ \hline MSR & 4.2137 & 12.2069 & 38.8824 & 1.8911 \\ MSRCR & 3.9836 & 11.6625 & 36.8797 & 1.8193 \\ DCP & 2.4708 & 8.1086 & 21.4251 & 2.2053 \\ CBF & 3.3751 & 9.5269 & 28.0610 & 2.1004 \\ RRM & 6.4005 & 19.8617 & 56.1125 & 2.2311 \\ SMIPC & 4.7916 & 13.7912 & 43.5426 & 1.9177 \\ Proposed & 6.0518 & 15.3863 & 54.7559 & 2.1374 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative metrics of different methods on pair 4.
## 2 Soil Sample and ABAQUS Simulation of Torsional Shear Test  
### Soil Sample Properties  
A total of three soils were simulated in the study. Two soils, namely, a silica and a mixture of weathered soil and pebbles, were tested with the resonant column test; another popular soil, Toyoura sand, was also used in this study with the fundamental properties reported by [PERSON], [PERSON] [11] and [PERSON] [12], as shown in Table 1. The samples prepared in laboratory RCT tests were made with a diameter of 50 mm and height of 105 mm. The silica sand was carefully poured into the cylindrical sampler at a relative density of 80%; the mixture was carefully tamped at an optimum water content, a wopt of 10.5%, defined from compaction test to obtain the highest density, as well as stiffness. The mixed soil included weathered soil and small pebbles with a mixed ratio by weight of 55% and 45%, respectively. Table 1 shows the strength properties of the two soils at similar conditions to the RCT test. The small-strain shear wave velocity, shear modulus degradation (G/Gmax), and damping (D) of the two soils were defined by performing resonant column tests (RCTs) at a confining pressure of 200 kPa, an average value of confining strain when performing RCTs. Figure 1a,b show G/Gmax and the damping curves of the two soils. Figure 1c shows the G/Gmax and D curves of the Toyoura sand, which were reported by [PERSON] and [PERSON] [13].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Properties** & **Silica Sand** & **Mixture** & **Toyoura Sand** \\ \hline Specific gravity, Gs & 2.65 & 2.67 & 2.65 \\ Dry density (t/m3) & 1.54 & 1.82 & 1.62 \\ Young modulus, E (MPa) & 74.26 & 284.86 & 291.52 \\ Poison ratio & 0.243 & 0.237 & 0.227 \\ Cohesion (kN/m2) & 0.1 & 0.1 & 0.1 \\ Friction angle (deg) & 36.6 & 38.5 & 44 \\ Shear wave velocity (m/s) & 139.27 & 250.15 & 270.89 \\ \hline \multicolumn{4}{c}{SNKH model} \\ \hline Initial yield stress, \(\sigma_{o}\) (kPa) & 15.49 & 33.70 & 30.08 \\ C1 (kPa) & 74,257.42 & 284,861.68 & 291,520.28 \\ \(\gamma_{1}\) & 845.96 & 2113.25 & 3230.54 \\ \hline \multicolumn{4}{c}{NKH model (weighting factor w = 1)} \\ \hline \(\sigma_{o}\) (kPa) & 1.56 & 6.17 & 0.36 \\ C1 & 1,007,085.81 & 1,826,119.56 & 3,901,266.00 \\ \(\gamma_{1}\) & 101,045.15 & 79,421.78 & 132,583.39 \\ C2 & 107,118.11 & 371,966.54 & 363,633.29 \\ \(\gamma_{2}\) & 4774.63 & 4700.1 & 5574.23 \\ C3 & 0 & 98,866.01 & 27,380.88 \\ \(\gamma_{3}\) & 1 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Soil properties and parameters of SNKH and NKH models.
## 2 Results and Discussion
### FFA Production from Enzymatic Hydrolysis of USCO: Screening the Lipase Sources
In fact, FFA conversion percentages between 64.3 \(\pm\) 2.3% and 73.5 \(\pm\) 0.4% were achieved after a 30-45 min reaction time. In this study, the maximum FFA conversion percentage (73.5 \(\pm\) 0.4%) was achieved after a 45 min reaction time without any water removal strategies (esterification conducted in a closed reactor). A similar FFA conversion percentage (71.8 \(\pm\) 2.2%) was also observed for the reaction system performed in a closed reactor in the presence of molecular sieves at 5% (m\(\cdot\)m\({}^{-1}\)). These reaction systems displayed similar productivity values (9.3 \(\pm\) 0.1 \(\mu\)molFFA\(\cdot\)min\({}^{-1}\cdot\)mg\({}_{\text{prot}}\)\({}^{-1}\)), as shown in Table 1. Interestingly, the esterification reaction conducted in an open reaction (water removal via simple evaporation at atmospheric pressure) exhibited an FFA conversion percentage of 64.3 \(\pm\) 2.3% after only 30 min, which provided the highest productivity value, at 12.4 \(\mu\)molFFA\(\cdot\)min\({}^{-1}\cdot\)mg\({}_{\text{prot}}\)\({}^{-1}\)--almost 16-fold higher than the soluble ET2.0 reaction (see Table 1). These results clearly show better diffusion and starting material molecule access to the active sites of the enzyme adsorbed in its monomeric form in open confor  
Figure 1: Influence of reaction time on the hydrolysis percentage of USCO catalyzed by microbial liposomes with different specificities: CRL. (_Candidia rugosa_), ET2.0 (_Thermomyces languinos_), CALA (_Candidia_ sp. ), ANL (_Aspergillus niger_), RJL (_Rhizopus jaranicus_) and a mixture ET2.0 + CALA. The reactions were conducted in the absence of emulsifier and buffer agents using an oil:water mass ratio of 40%, continuous mechanical stirring (1500 rpm), 40 \({}^{\circ}\)C and initial lipase loading of 50 U per gram of reaction mixture. The units of hydrolytic activity used for the combination of specific liposomes were 17 U of CALA + 33 U of ET2.0 per gram of reaction mixture. mation inside the pores of the PSty-DVB beads [44]. Previous studies have demonstrated improved catalytic performance for ET2.0 immobilized via physical adsorption on several hydrophobic supports, including PSty-DVB beads, to catalyze industrial ester production via esterification reactions [42; 43; 44; 54; 55]. Moreover, esterification reactions conducted in open reactions under moderate experimental conditions (40 \({}^{\circ}\)C and atmospheric pressure) were effective in improving ester production (based on FFA consumption productivity--see Table 1). Therefore, subsequent studies (reusability tests) were performed in closed and open reactors without adding molecular sieves.
# Potential Synergistic Inhibition of _Enterococcus faecalis_ by Essential Oils and Antibiotics
###### Abstract  
Recurrent infections after root canal treatments often involve _Enterococcus faecalis_, a microorganism closely associated with therapy failures due to its biofilm production, survival in nutrient-deprived conditions, and antibiotic tolerance. Essential oils (EOs), which display antimicrobial and antibacterial properties, exhibit inhibitory effects on the growth of many microorganisms including _E. faecalis_. This study assessed the in vitro efficacy of combining 5% antibiotics (kanamycin 2.5 mg/mL, streptomycin 2.5 mg/mL, gentamicin 1.5 mg/mL, and ampicillin 5 mg/mL) with cinnamon (1.25% to 5%) or clove (25% and 50%) EOs in inhibiting the growth of _E. faecalis_, using disk diffusion tests. Disks were treated with EOs-only, antibiotics-only, or EO-antibiotic combinations, placed on BEA agar plates, and incubated for 24 h, and the zones of inhibition were measured. Results showed that EOs (cinnamon and clove) and 5% antibiotics, by themselves, had robust growth inhibition of _E. faecalis_ across all tested concentrations. Moreover, combining 5% aminoglycos (kanamycin 2.5 mg/mL, streptomycin 2.5 mg/mL, and gentamicin 1.5 mg/mL) with 5% cinnamon EO produced significantly enhanced antimicrobial effect than the corresponding 10% antibiotic solution alone. These findings suggest that combining cinnamon EO with aminoglycosotide antibiotics can achieve significant inhibition of _E. faecalis_ at a lower concentration of antibiotics compared to using a higher dose of antibiotics alone. Further in vivo studies should determine the safety, efficacy, and treatment duration, with the potential to reduce antibiotic dosages and associated toxicity while preventing recurrent infections.  
c 2023, 13, 11089. [[https://doi.org/10.3390/app131911089](https://doi.org/10.3390/app131911089)]([https://doi.org/10.3390/app131911089](https://doi.org/10.3390/app131911089))