esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Bringing electricity to areas of difficult terrain is a complicated task, so it is convenient to generate power using local natural resources, such as wind, through a small horizontal-axis wind turbine (S-HAWT). However, at the rotor height of these wind turbines, the wind is often turbulent due to obstacles such as trees and buildings. For a turbine to function properly in these conditions, the action of the wind force on the rotor must be smoothed out by controlling the pitch angle. A commercial derivative-integral-proportional (PID)-type pitch controller works well when system dynamics are stable, but not when there are disturbances in the system. This paper proposes a hierarchical fuzzy logic controller (HFLC) to solve the nonlinear system effects produced by atypical winds. The methodology includes a statistical analysis of wind variability at the installation site, which determines the functions of belonging and its hierarchy. In addition, installing an anemometer in front of the turbine allows an advanced positioning of the blades in the presence of wind gusts. The algorithm was implemented in an S-HAWT, and a comparison was made to quantify the performance difference between the proposed control strategy and a conventional PID controller.  
fuzzy logic controller; pitch control; renewable energy; small wind turbine; wind power generation +
Footnote †: journal: Applied Sciences
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Magnetic Ferric Oxide Nanomaterials
developed a new type of nanocomposite (GO@Fe\({}_{3}\)O\({}_{4}\)@benzothazole-carboxylate) to modify GCE--using magnetic graphene oxide (GO@Fe\({}_{3}\)O\({}_{4}\)) and introducing benzothiazole-2-carbaldehyde (2-CBT) for the first time, and simultaneously detecting trace Pb\({}^{2+}\) and Cd\({}^{2+}\) in aqueous solution by SWASV [133]. Under the best conditions, the LOD of Pb\({}^{2+}\) and Cd\({}^{2+}\) were 0.03 ng/mL and 0.02 ng/mL, respectively. Figure 13: The modification process of Co(II)-IIP-Fe\({}_{3}\)O\({}_{4}\) electrode. Reprinted with permission from Ref. [61]. Copyright 2017, Elsevier. This sensor was a kind of high selectivity and sensitivity sensor, which can be used for the determination of Pb\({}^{2+}\) and Cd\({}^{2+}\) in different water samples. In 2020, they grafted a novel IIP on Fe\({}_{3}\)O\({}_{4}\) NPs, namely 2-(2-aminophen)-1H benzimidazole as a novel ligand for the synthesis of IIP for the establishment of Fe\({}_{3}\)O\({}_{4}\)-IIP-GCE for determination of Pb\({}^{2+}\) content in complex samples [134]. The constructed electrochemical platform exhibited linearity over a wide linear concentration range (0.1-80 ng/mL), low LOD (0.05 ng/mL), high selectivity, convenient use, and low cost. With the advantages of fast response speed and high sensitivity, it can be used for the determination of Pb\({}^{2+}\) in natural water and fruit juice. [PERSON] et al. prepared the Fe\({}_{3}\)O\({}_{4}\)/C/Cu(II)/IIPs/GCE for efficient detection of Cu\({}^{2+}\)[135]. First, the surface of carbon spheres was modified with Fe\({}_{3}\)O\({}_{4}\), and then carbon-based ion-imprinted materials were synthesized by IIT. The carbon imprinted material can improve the conductivity, and the magnetic properties of the Fe\({}_{3}\)O\({}_{4}\) matrix enabled the material to be selectively identified and quickly restored. Therefore, Fe\({}_{3}\)O\({}_{4}\)/C/Cu(II)/IIPs/GCE can detect Cu\({}^{2+}\) rapidly and selectively. In this study, the electrochemical performance of Cu(II)/IIPs/GCE was evaluated by DPV. The LOD was 5.99 \(\times\) 10\({}^{-6}\) M (S/N = 3), which can meet the detection requirements of Cu\({}^{2+}\) in water. This method provided a new idea for the detection of metal ions in water. It can also be used for rapid selective removal and recovery of Cu\({}^{2+}\) in water.
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
###### Abstract  
Infrared detection, known for its robust anti-interference capabilities, performs well in all weather conditions and various environments. Its applications include precision guidance, surveillance, and early warning systems. However, detecting infrared dim and small targets presents challenges, such as weak target features, blurred targets with small area percentages, missed detections, and false alarms. To address the issue of insufficient target feature information, this paper proposes a high-precision method for detecting dim and small infrared targets based on the YOLOv7 network model, which integrates both local and non-local bidirectional features. Additionally, a local feature extraction branch is introduced to enhance target information by applying local magnification at the feature extraction layer allowing for the capture of more detailed features. To address the challenge of target and background blending, we propose a strategy involving multi-scale fusion of the local branch and global feature extraction. Additionally, the use of a \(1\times 1\) convolution structure and concat operation reduces model computation. Compared to the baseline, our method shows a 2.9% improvement in mAP\({}_{50}\) on a real infrared dataset, with the detection rate reaching 93.84%. These experimental results underscore the effectiveness of our method in extracting relevant features while suppressing background interference in infrared dim and small target detection (IDSTD), making it more robust.  
infrared dim and small target; local-non-local feature fusion; vision system; target detection +
Footnote †: journal: Applied sciences
## 3 Materials and Methods
### Experimental Settings
#### 3.3.2 Fusion Methods  
A combination of classifiers \(\mathrm{C}_{1},\ldots,\mathrm{C}_{\mathrm{m}}\) can be defined as:  
\[C(x)=\mathcal{F}(C_{1}(x),\ldots C_{m}(x)) \tag{2}\]  
Accordingly, the fusion method \(\mathcal{F}\) systematically combines the predictions of the used base classifiers \(\mathrm{C}_{1},\ldots,\mathrm{C}_{\mathrm{m}}\) processing an input object \(x\). Two approaches to obtain a fusion method \(\mathcal{F}\) are used.  
Fixed combination rules combine the class prediction output of base classifiers in a predefined way by utilizing simple arithmetic operations (sum, product) or basic set operations (minimum, maximum).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & \multicolumn{3}{c}{**Hyperparameter**} \\ \hline  & \multicolumn{3}{c}{Feature: Text} \\ \hline SVM & \(nu\in\{0.1,0.2,\ldots,0.9\}\) & \(n-gram\in\{(1,1),\ldots,(3,3)\}\) \\ kNN & \(k\in\{1,5,10,15,\ldots,50\}\) & \(norm\in\{l2,\,l1,none\}\) \\ LogReg & \(c\in\{10^{-6},10^{-5},\ldots,10^{5}\}\) & \(smooth\_idf\in\{true,\,false\}\) \\ ANN & \(\alpha\in\{10^{-4},10^{-3},\ldots,10^{2}\}\) & \(sublin\_tf\in\{true,\,false\}\) \\  & \(hl\in\left\{\begin{array}{l}(50,-),(100,-),(200,-),\\ (50,12),(100,12),(200,12)\end{array}\right\}\) & \\ \hline  & \multicolumn{3}{c}{Feature: Taxonomy} \\ \hline SVM & \(nu\in\{0.1,0.2,\ldots,0.9\}\) & \(n\_prototype\in\{10,20,50,100,200\}\) \\ kNN & \(k\in\{1,5,10,15,\ldots,50\}\) & \(prototype\_selection\in\{10,20,50,100,200\}\) \\ LogReg & \(c\in\{10^{-6},10^{-5},\ldots,10^{5}\}\) & \(\left\{\begin{array}{l}\text{classifier random},\\ \text{classifier spanning}\end{array}\right\}\) \\ ANN & \(\alpha\in\{10^{-4},10^{-3},\ldots,10^{2}\}\) & \(\left\{\begin{array}{l}\text{classifier random},\\ \text{classifier spanning}\end{array}\right\}\) \\  & \(h\prime\in\left\{\begin{array}{l}(50,-),(100,-),(200,-),\\ (50,12),(100,12),(200,12)\end{array}\right\}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grid of hyperparameters to optimize the performance of the base classifiers acting on different textual and taxonomic features in 10-fold cross-validation.  
Considering the classification output for input object \(x\) and class \(\omega_{i}\) of base classifier \(C_{j}\) as \(c_{j,i}(x)\) the sum rule is given by:  
\[\mu_{i}(x)=\frac{1}{n}\sum_{j=1}^{n}c_{j,i}(x) \tag{3}\]  
The product, minimum, and maximum fixed combination rules are constructed likewise [19].  
Stacking interprets the combination of base classifiers' output as further classification problem creating a meta-classifier. The base classifiers' output is fed into a classifier for objects with known class assignment. In comparison to the fixed combination rules, this requires an additional training phase including hyperparameter tuning (for details see Appendix A Table A2) for the used meta-classifier while providing more flexibility to adapt to the learning data.
# Recent Advances in Optical Detection of Aminoglycosodies
## 3 Colorimetric Assays
### Based on Target-Induced Aggregation of Metal Nanoparticles
The amine group of kanamycin could react with the quinone moieties on the surface of Ag NPs, forming a Schiff base, which caused the aggregation of the Ag NPs and a color change from yellow to reddish pink, accompanying the decrease in the intensity of the SPR band at 415 nm. The LOD for kanamycin was 0.96 uM [59]. Other metal NPs include gallic acid-coated Ag NPs, methyl gallate conjugated Ag NPs, sodium dodecyl sulfate (SDS)-coated Ag NPs, and chlortetracycline-functionalized Ag NPs, chitosan-coated Au NPs, etc. These Ag NPs have been applied to the colorimetric detection of streptomycin [60; 61], gentamicin [62], tobramycin [63], and kanamycin [64; 65; 66].
## 2 Related Work
### Statistical Analysis-Based Detection for Encrypted Malicious Traffic  
Statistical encrypted traffic detection methods avoid the need for signature libraries by characterizing each flow through aggregate metrics such as packet count, byte volume, flow duration, and timing statistics, flagging deviations that indicate potential malicious behavior [2]. By modeling the baselines of normal traffic, they can effectively detect high-volume anomalies such as DDoS through abrupt shifts in traffic patterns [19,20]. Unlike traditional DPI which fails under encryption conditions, their approaches rely on side-channel characteristics rather than payload content and thus remain viable even when traffic is encrypted [4]. In practice, encrypted flows can be clustered based on statistical traits, allowing abnormal flows, bursty behavior, irregular packet intervals, or excessive duration to be isolated via unsupervised methods [24]. As they adapt over time by updating thresholds or statistical models, such detectors require no prior knowledge of specific attacks and can generalize well to novel threats [25]. Their low computational overhead makes them suitable for real-time or resource-constrained settings [26,27]. However, these methods also face limitations. Rapid traffic changes or evolving attack strategies may lead to misclassification, as statistical baselines can lag behind new behaviors. Moreover, statistical features offer limited semantic insights [28] that reveal anomalies but not their root causes. As a result, deeper inspection or manual analysis is often required to interpret alarms [4]. Statistical-based detection offers an efficient and adaptive alternative for encrypted traffic scenarios. While it may sacrifice fine-grained interpretability, it complements traditional intrusion detection approaches and enhances encrypted traffic coverage [10].
## Materials and Methods
### The Proposed Method  
The entire procedure of the proposed method is as follows:  
Step (1): The bi-temporal PolSAR images need to be co-registered and filtered. Image registration is performed to align the images used in the change detection. Speckle filtering is commonly used to suppress speckle noise before the change detection and classification of PolSAR images. The preprocessing is important for change detection. In this study, the refined Lee filter [39] was used to remove the speckle noise.  
Step (2): The CI is obtained by the test statistic using the covariance matrices of the bi-temporal images. In this step, bi-temporal fully PolSAR data are used to generate the CI.  
Step (3): The improved K&I algorithm based on the GGM is used to simulate the distribution of the CI and obtain the optimum threshold for the CI.  
Step (4): The unchanged and changed classes of position \((i,j)\) are determined. If \(d_{ij}<T\), this means that the PolSAR data in the same position at different dates are similar; otherwise, the corresponding pixel positions are different. Finally, the change detection map is obtained.  
The detailed processing flow of the proposed method is shown in Figure 1.
## 5 Removal of Arsenic
### Rice Straw and Rice Bran as Adsorbents for Removal of Arsenic  
The efficiency of As(V) removal from aqueous solution was investigated using iron-modified biochar derived from rice straw and synthesized using FeCl\({}_{3}\) as iron source [119]. The authors compared the arsenic removal efficiency of raw biochar and iron-modified biochar. The study revealed that the ability of arsenic removal is better for iron-modified biochar with respect to raw biochar at the initial pH of 5.0. [PERSON] et al. [131] carried out an experiment to study the optimal parameters for arsenic removal from water by rice polish using a fixed bed columns system. The investigation was directed to analyze different design parameters like flow rate, bed height, initial concentration and their effect on the adsorption process, and revealed that the As(III) and As(V) uptake capacities are 66.95 and 78.95 \(\upmu\)g/g, respectively (Table 12). Another study was carried out by [PERSON] et al. [132] in order to remove As(V) from aqueous medium by using rice straw-derived biochar produced from slow pyrolysis process at 600 \({}^{\circ}\)C. Batch tests were conducted at room temperature under different conditions, such as initial concentration, contact time, and adsorbent dosages. The study highlighted an adsorption capacity of 25.6 \(\upmu\)g/g for As(V) by rice straw biochar. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Adsorbent** & **Type of Water** & **Type of Arsenic** & **Experimental Conditions** & **Maximum Removal (\%)** & **Adsorption Capacity** & **References** \\ \hline \multirow{4}{*}{**Rice straw** **biochar**} & & & \multicolumn{2}{c}{PDH = 6.5} & \multirow{4}{*}{-} & \multirow{4}{*}{25.6 \(\upmu\)g/g} & \multirow{4}{*}{[132]} \\  & & & Temperature = 298 K & & & \\  & & & reaction time = 120 min & & & \\  & & & Initial concentration = 5 mg/L & & & \\  & & & Surface area = 366 m\({}^{2}\)/g & & & \\  & & & Adsorbert = 0.3 g/L & & & \\  & & & pH = 5 & & & \\ \hline \multirow{4}{*}{**Iron–Manganese oxide incorporated active rice husk silica**} & & & \multicolumn{2}{c}{Temperature = 298 K} & \multirow{4}{*}{-} & \multirow{4}{*}{11.9 mg/g} & \multirow{4}{*}{[127]} \\  & & & Reaction time = 24 h & & & \\ \cline{1-1} \cline{5-5}  & & & Initial concentration = 2–40 mg/L & & & \\ \cline{1-1} \cline{5-5}  & & & Surface area = 366 m\({}^{2}\)/g & & & \\ \cline{1-1} \cline{5-5}  & & & Adsorbent = 0.4 g/L & & & \\ \cline{1-1} \cline{5-5}  & & & pH = 7 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Arsenic adsorption capacities by rice straw, synthesized rice straw, and rice polish (rice bran).
## 2 Materials and Methods
purpose, the relative germicidal effectiveness values, \(RGE_{IESNA,LEDx}\), \(RGE_{DIN,LEDx}\), and \(RGE_{Gates,LEDx}\), of each of the tested LEDs' radiation were calculated:  
\[RGE_{IESNA,LEDx}=\frac{OP_{g,IESNA,LEDx}}{OP_{g,LP-Hg}}=\frac{\int OP_{LEDx}( \lambda)\cdot S_{g,IESNA}(\lambda)d\lambda}{\int OP_{LP-Hg}(\lambda)\cdot S_{g}( \lambda)d\lambda}, \tag{4}\]  
\[RGE_{DIN,LEDx}=\frac{OP_{g,DIN,LEDx}}{OP_{g,LP-Hg}}=\frac{\int OP_{LEDx}( \lambda)\cdot S_{g,DIN}(\lambda)d\lambda}{\int OP_{LP-Hg}(\lambda)\cdot S_{g}( \lambda)d\lambda}, \tag{5}\]  
\[RGE_{Gates,LEDx}=\frac{OP_{g,Gates,LEDx}}{OP_{g,LP-Hg}}=\frac{\int OP_{LEDx}( \lambda)\cdot S_{g,Gates}(\lambda)d\lambda}{\int OP_{LP-Hg}(\lambda)\cdot S_{g}( \lambda)d\lambda}, \tag{6}\]  
where \(x\)--the LED number, \(RGE_{IESNA,LEDx}\), \(RGE_{DIN,LEDx}\), and \(RGE_{Gates,LEDx}\)--the IESNA, DIN, and Gates relative germicidal effectiveness of LED radiation, \(OP_{g,IESNA,LEDx}\), \(OP_{g,DIN,LEDx}\), and \(OP_{g,Gates,LEDx}\)--the IESNA, DIN, and Gates germicidal weighted power of LED radiation (W), \(OP_{g,LP-Hg}\)--the germicidal weighted power of LP-Hg lamp radiation (W), \(OP_{LEDx}\)(\(\lambda\))--the spectral power distribution of LED radiation, \(OP_{LP-Hg}\)(\(\lambda\))--the spectral power distribution of LP-Hg lamp radiation, and \(S_{g}\)(\(\lambda\))--the average relative spectral sensitivity of the microorganism. To estimate the impact of the adopted germicidal curve, the value of \(t_{dis}\) for the SARS-CoV-2 virus was calculated as an example for each of the curves (Figure 1), assuming a constant UVC radiation power density \(E\) of 1 W/m\({}^{2}\) on the exposed surface, as follows:  
\[ \begin{split} t_{dis,IESNA,SARS-Cov-2,LEDx}=\frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{1}{RGE_{IESNA,LEDx}}=\\ \frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{OP_{g,LP-Hg}}{OP_{g,IESNA, LEDx}},\\ t_{dis,DIN,SARS-Cov-2,LEDx}=\frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{1}{RGE_{DIN,LEDx}}=\\ \frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{OP_{g,LP-Hg}}{OP_{g,Gates, LEDx}},\end{split}  \tag{7}\]  
\[ \begin{split} t_{dis,GESNA,SARS-Cov-2,LEDx}=\frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{1}{RGE_{DIN,LEDx}}=\\ \frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{OP_{g,LP-Hg}}{OP_{g,L -Hg}},\end{split}  \tag{8}\]  
\[ \begin{split} t_{dis,Gates,SARS-Cov-2,LEDx}=\frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{1}{RGE_{Gates,LEDx}}=\\ \frac{D_{SARS-Cov-2,LP-Hg}}{E}.\frac{OP_{g,LP-Hg}}{OP_{g,Gates, LEDx}},\end{split}  \tag{9}\]  
where \(x\)--the LED number, \(t_{dis,IESNA,SARS-Cov-2,LEDx}\), \(t_{dis,DIN,SARS-CoV-2,LEDx}\), and \(t_{dis,Gates,SARS-CoV-2,LEDx}\)--the IESNA, DIN, and Gates disinfection time of the SARS-CoV-2 virus with UVC LED radiation (s), \(D_{SARS-Cov-2,LP-Hg}\)--the dose of the LP-Hg lamp radiation required to inactivate the SARS-CoV-2 virus by 90% (J/m\({}^{2}\)), \(E\)--irradiance on the surface, \(OP_{g,LP-Hg}\)--the average germicidal weighted power of LP-Hg lamp (W), and \(OP_{g,IESNA,LEDx}\), and \(OP_{g,DIN,LEDx}\), \(OP_{g,Gates,LEDx}\)--the IESNA, DIN, and Gates germicidal weighted power of each LED (W).
## 3 Results and Discussion  
### Effects of Loading Frequency and Testing Temperature on the Dynamic Modulus  
Asphal mixture can be considered a linear viscoelastic material at low strain level. Its stress-strain relationship under a continuous sinusoidal (i.e., haversine) load is defined by a complex number called the \"complex modulus\". The absolute value of the complex modulus is defined as the dynamic modulus [30]. Research indicated that the dynamic modulus of asphalt mixture has a good correlation  
Figure 4: Simple Performance Tester. Figure 3: Demonstration of samples: (**a**) the original SGC sample and the sample after coring and sawing; (**b**) the SPT samples. with its field rutting resistance [31]. In this study, the dynamic modulus (\(|\)E*\(|\)) and the phase angle (\(\delta\)) were determined by the following equations [32]:  
\[|E^{*}|=\sigma/\varepsilon \tag{1}\]  
\[\delta=\left(T_{i}/T_{p}\right)\cdot 360 \tag{2}\]  
where \(|\)E*\(|\) is the dynamic modulus (MPa); \(\sigma\) is the peak dynamic stress (MPa); \(\varepsilon\) is the peak recoverable axial strain (\(\mu\varepsilon\)); \(\delta\) is the phase angle (\({}^{\circ}\)); \(T_{i}\) is the time lag between a cycle of stress and strain (s); \(T_{p}\) is the time for a stress cycle (s). Generally, the higher the dynamic modulus value is, the more resistant to rutting (permanent deformation) the asphalt mixture will be. The lower the phase angle value, the more elastic the asphalt mixture. Tables 4-6 show the test results of the dynamic modulus and phase angle at different testing temperature, respectively, where G-S represents group of samples; M-P represents the mechanical parameters; S-P represents the statistic parameters; \(\overline{E^{*}}\) is the average value of dynamic modulus; \(\overline{\delta}\) is the average value of phase angle; \(\sigma\) is the standard deviation; \(C_{\mathrm{P}}\) is the coefficient of variation (\(\%\)).
## 3 Analysis and Discussion
### Illumination System Analysis  
Figure 13 shows the relative irradiance of each field of view and incoherent irradiance. Relative illumination is the ratio of the illuminance at any point on the sensor to the maximum illuminance in the field of view; RI is proportional to cos4 (semi-FOV) [26], which is related to field of view, vignetting, distortion, and transmittance. Vignetting causes the image to have high brightness in the center and darkness around it, commonly known as vignetting, and can also cause color distortion and low illumination. When the half-field angle semi-FOV = 30\({}^{\circ}\), the theoretical RI < 56%, and the RI that can be distinguished by the human eye is about 50%, so the basic requirement of RI is greater than 50% [26]. The RI of each field of view in this design was much higher than 63%, so the system met the requirements and did not produce vignetting.  
After the non-sequential conversion was completed, the light source and illuminance detector were inserted into the illumination optical path, the light source power was set to 1 W, and 10,000 traces were traced. The light rays arriving at the illuminance detector were traced and analyzed, and the fundus illuminance distribution curve of Figure 13 was  
Figure 12: Spot Diagram and distortion map of imaging system. (**a**) Spot Diagram. (**b**) Field Curvature Distortion.  
obtained, and it can be seen that the diameter of the fundus illumination area was greater than 12 mm, which met the illumination area range required by the imaging field of view.  
\[U=1-\frac{P_{center}-P_{85\%}}{P_{max}} \tag{14}\]  
The lighting uniformity of this study reached 86.3%, which met the requirements of use. In addition, according to ISO 15004-2.2 standard [27] ophthalmic optical instrument guidance [28], in the visible light band, in order to avoid thermal damage to the human eye, the irradiance needs to be less than 706 mW/cm\({}^{2}\). The simulation results show that the maximum irradiance in this study was 520 W/cm\({}^{2}\); when considering the attenuation effect of lenses on light, the actual irradiance reaching the fundus will be as low as 130 W/cm\({}^{2}\), so it will not cause thermal damage to the human eye.
## 2 Materials and Methods
### Determination of Saliva CRP Concentration  
The concentration of CRP in the saliva samples was determined by using the Salivary C-Reactive Protein ELISA Kit Generation II (Salimetrics, State College, PA, USA), which is an enzyme-linked immunoassay (ELISA) for the quantitative measurement of human CRP in oral fluid. This is an indirect sandwich ELISA kit wherein a \"sandwich\" is formed when the precoated captured anti-CRP antibody present on the plate binds CRP from the standard (Salimetrics' High and Low CRP Generation II Controls) and tested samples. After each incubation, the unbound components were washed away. Bound anti-CRP antibody enzyme conjugate was then added, and the levels of detected CRP were measured by the reaction of the horseradish peroxidase enzyme to the substrate tetramethylbenzidine. The effect of this reaction is a blue colour in the solutions in the plate wells. Next, a yellow colour was formed after stopping the reaction with an acidic solution, and then the optical density was measured at 450 nm using a microplate reader EonTM microplate Spectrophotometer (BioTek, Winooski, VT, USA). The corresponding concentration of CRP in pg/mL was determined from the standard curve (nonlinear regression curve fit) using the average optical density values of the controls and saliva samples. The functional sensitivity of the Salivary C-Reactive Protein ELISA Kit Generation II is 19.44 pg/mL.  
Figure 2: Implantation in the control group. Visible sutures immediately after the procedure and after 7 days. All patients were carefully examined immediately before surgery, on the first post-operative day, and on the seventh post-operative day and were asked to rate their pain on a 10-degree VAS (Visual Analogue Scale) [38].