esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Modified Electrode Types
#### 1.1.3 Carbon Paste Glassy Carbon Electrodes [33]  
A carbon paste electrode is a type of electrochemical electrode that is composed of a mixture of graphite or other carbon materials and a liquid binder, such as oil or water. The paste is then packed onto the surface of a metal or conductive substrate, which serves as the current collector for the electrode. The carbon paste serves as the working electrode in electrochemical experiments, and the electrode can be modified with various chemical additives to give it specific properties or functions.  
Carbon paste electrodes are often used in electroanalytical chemistry, where they are used to measure the concentration of various species in solution. One advantage of carbon paste electrodes is that they are relatively easy to prepare and can be easily modified to suit a particular application. However, they can be prone to fouling and instability, which can limit their performance.  
In addition, glassy carbon (GCE) is widely used in the detection of various analytes. This is due to the following significant advantages:  
1. CGE offers an attractive electrochemical reactivity, negligible porosity, and good mechanical rigidity;
2. It has a low background current, wide potential window, and chemical inertness, and it is low cost and suitable for various sensing and detection applications. Among the carbon family, glassy carbon is the most popular electrode material that offers attractive electrochemical reactivity, negligible porosity, and good mechanical rigidity. Based on these advantages, glassy carbon microparticles were first introduced by [PERSON] et al. [22] as electrode materials to fabricate glassy carbon paste electrodes.  
Meanwhile, electrochemistry is a relatively clean chemical system that is easy to control and can be performed in aprotic and aqueous solutions, allowing the evaluation of the behavior of free radicals generated in biological systems. Thus, voltammetry is an extremely sensitive electrochemical technique for measuring trace amounts of pharmaceually active compounds either in dosage forms or in biological samples using glassy carbon or carbon paste electrodes, selectively detecting these pharmaceutically active components. Since pharmaceutical products are biologically active chemicals with functional groups that can undergo redox processes, their redox activity can be detected by cyclic voltammetry and other voltammetric techniques [33; 43]. An example can be given dealing with the application of such electrodes for trace iron(III) determination in compounds of pharmaceutical significance [43].
## 4 Discussion
The introduction of a new sequencing system, called MiSeq Illumina, has the advantage of reducing the procedural error and increasing the ability to detect more bacterial species. It has been shown that, at healthy peri-implant sites, there is a predominance of bacteria belonging to the class Actinomycettia and bacterial species such as _Veillonella dispar_, _Rothia dentocariosa_ and _Streptococcus sanguinis_, while in the presence of peri-implantitis, the microbiota is characterized by the quantitative increase of bacteria belonging to the classes Bacteroid, Spirochaetes, Synergtistis (species _Synergetics_ spp. _HOT-360_), Clostridia (species _Clostridiales_ spp. _HOT-093_ and _Catorella morphi_), Detaproteobacteria, of periodonopathogenic bacteria belonging to the \"red complex\" and finally of bacterial species such as _Porphyomonas_ spp. _HOT-395_, _Porphyomonas nigrescens_, _Porphyomonas oris_, _Treponema molphin_, _Dialister invisus_, _Eubacterium sophenum_, _Eililator alocis_, _Freitbacterium fastidiosum_, _Mitsuokella_ spp. _HOT 131_, _Chloroflexi_ spp., _Tenericutes_ spp. and _Freitbacterium HMT 360_[26]. Based on the studies in the literature, through the introduction of NGS and the MiSeq Illumina system, the authors agreed that the peri-implant and periodontal microbiota presents quantitative and qualitative differences: in particular, the peri-implant microbiota presents less bacterial diversification than the periodontal microbiota, regardless of health or disease status, becoming more complex as it moves from peri-implant mucositis to peri-implantitis [35]. The microbial diversity detected between the peri-implant and periodontal microbiota should not be related to quantitative and qualitative changes in individual bacterial species, but rather to bacterial populations [36]. Results of the single study performed to date, based on the assessment of bacterial messenger RNAs (mRNAs), suggest that the intrinsic characteristics of the microorganisms composing the microbiota in sites with peri-implantitis and periodontitis, which favor the expression of bacterial pathogenicity, are similar to each other [27]. However, the capacity for inter-bacterial interaction appears to be more sophisticated at sites with peri-implantitis, with the presence of some significantly associated bacterial species [27]. Summary table of the main clinical trial and systematic review included in this work is shown in Table S1 (Supplementary Materials) and a complete list of bacteria typically presents in healthy peri-implant sites, with peri-implantitis, and with periodontitis are shown in Figure 2, also highlighting bacterial species in common.
## 2 Proposed Techniques
### Adaptive Neuro-Fuzzy Inference system
\[O_{2,i}=w_{i}=\mu_{A_{i}}(x_{1})\times\mu_{B_{i}}(x_{2})\ for\ i=1,2\] (5)
* 3 Is known as 'normalization'. The firing strength of each rule is normalized via computing the ration of each rule's firing strength to the total of each rules. In Equation (6), \(\overline{w}_{i}\) is defined as the firing strength of each rule, as illustrated below: \[O_{3,i}=\overline{w}_{i}f_{i}=\frac{w_{i}}{\sum w_{i}}=\frac{w_{i}}{w_{1}+w_{2 }},\ for\ i=1,2\] (6)
* 4 Is recognized as 'defuzzification'. This layer tries to compute the output of the previous layer, based on its node function; each node function is adaptive in accordance with the node function, as given by Equation (7). \[O_{4,i}=\overline{w}_{i}\times f_{i}=\overline{w}_{i}(p_{i}x_{i}+q_{i}x_{i}+r _{i}),\ for\ i=1,2\] (7) where \(\overline{w}_{i}\) is the output of the third layer and the parameters \((p_{i},q_{i},r_{i})\) are set parameters, which are being assumed by the conditions of the determined parameter. The parameters in the fuzzy inference layer are considered as consequent parameters. * 5 Is called 'aggregation'. This layer is utilized to compute the total of the outputs of all of the rules to produce the overall ANFIS output, whose equation is represented as follows: \[O_{5,i}=\sum\limits_{i}\overline{w}_{i}f_{i}=\frac{\sum\limits_{i}w_{i}f_{i} }{\sum\limits_{i}w_{i}}=f_{out}\] (8)  
The aforementioned architecture is employed to adjust ANFIS model for SOH estimation, as discussed in the next section.
# Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods
## 2 Methods of MCDM
### TOPSIS Method  
To apply this method, the following steps need to be taken [3]:  
Step 1: Using step 1 of the MARCOS method.  
Step 2: Determining the normalized values \(k_{ij}\) by using the following equation:  
\[k_{ij}=\frac{x_{ij}}{\sqrt{\sum_{i=1}^{m}x_{ij}^{2}}} \tag{12}\]  
Step 3: Identifying the weighted normalized decision matrix by:  
\[l_{ij}=w_{j}\times k_{ij} \tag{13}\]  
Step 4: Finding the best alternative \(A^{+}\) and the worst alternative \(A^{-}\) by the following equations:  
\[A^{+}=\left\{l_{1}^{+},l_{2}^{+},\ \ldots,\ l_{j}^{+},\ \ldots,\ l_{n}^{+}\right\} \tag{14}\]  
\[A^{-}=\left\{l_{1}^{-},\ l_{2}^{-},\ \ldots,\ l_{j}^{-},\ldots,\ l_{n}^{-}\right\} \tag{15}\]  
where \(l_{j}^{+}\) and \(l_{j}^{-}\) are the best and worst values of the \(j\) criterion (\(j=1,\,2,\,...,\,n\)).  
Step 5: Determining the values of better options \(D_{i}^{+}\) and worse options \(D_{i}^{-}\) by:  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{n}\left(l_{ij}-l_{j}^{+}\right)^{2}}\ i=1,\ 2,\ \ldots,\ m \tag{16}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{n}\left(l_{ij}-l_{j}^{-}\right)^{2}}\ i=1,\ 2,\ \ldots,\ m \tag{17}\]  
Step 6: Calculating values \(R_{i}\) of each alternative by:  
\[R_{i}=\frac{D_{i}^{-}}{D_{i}^{-}+D_{i}^{+}}\ i=1,\ 2,\ \ldots,\ m;\ 0\leq R_{i}\leq 1 \tag{18}\]  
Step 7: Ranking the order of alternatives by maximizing the value of \(R\).
# Mobility-Assisted Digital Twin Network Optimization over Industrial Internet of Things
## 5 Performance Evaluation  
### Experiments for D2D Link Connection  
In this section, we evaluate the performance of the proposed system. The number of mobile nodes ranges from 1 to 5, and the number of relay nodes is 5. The mobile node and the relay node are set up in a single manufacturing cell measuring 50 m by 50 m. Each mobile node initially selects a random position and stays in that position for 10 s,then moves to another random position. The maximum distance between devices that establish direct D2D links \(\delta_{max}\) is 30 m. All mobile nodes have a transmission power of 23 dBm. The carrier bandwidth is set to 180 kHz. The D2D path loss exponent \(\alpha\) and the D2D path loss factor for a distance in meters are set to 4 and 0.0173, respectively. In addition, the noise power spectrum density is set to \(-174\) dBm/Hz. The performance of the proposed system is evaluated under a dynamic environment based on the parameters shown in Table 2.  
We compare our algorithm with two schemes: random and distance [32]. Random means that the core network randomly selects the relay node. Distance considers the shortest distance between the mobile node and the relay node.
## 4 Results
### Reduction of Torsion Effect for the Structures
#### 4.2.3 Structures with Vertical Geometric Irregularity or Setback  
In this case, the positions of CM and CS are not modified. The concept is similar to case shown in Figure 15 in Section 4.2.2, with the help of modal analysis, and with the results shown in Table 15.  
The 1 st mode of vibration is shown in Figure 17. In the same manner, the 2 nd mode of vibration is shown in Figure 18.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multirow{2}{*}{
\begin{tabular}{c} **Period** \\ **(s)** \\ \end{tabular} } & \multirow{2}{*}{**UX**} & \multirow{2}{*}{**UY**} & \multirow{2}{*}{**RZ**} & \multirow{2}{*}{**Sum UX**} & \multirow{2}{*}{**Sum UY**} & \multirow{2}{*}{**Sum RZ**} \\ \hline
1 & 0.519 & 0.6176 & 0 & 0.00004 & 0.6176 & 0 & 0.00004 \\
2 & 0.512 & 0 & 0.6154 & 0 & 0.6176 & 0.6154 & 0.00004 \\
3 & 0.356 & 0.0001 & 0 & 0.5316 & 0.6176 & 0.6154 & 0.5317 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Ratios of the modal participating mass.  
Figure 17: The 1 st mode shape for \(T_{1}\) = 0.519 s: (**a**) plane view of the 1 st floor, and (**b**) plane view of the 10 th floor.  
near the stairs (Figure 14). It may be observed again that the torsion is not characteristic for the first mode or for the second mode of vibration after reduction of the overall torsion.  
In the case of the multi-storey building with cores (Figure 15) which are torsion sensitive, we may remark that, initially, torsion takes place in the first mode of vibration because the modal participating mass is equal to 0.7815 in rotation with respect to the vertical axis Oz that is parallel to the elevation of the building (Table 13 and Figure 15a). Reduction of the torsion leads to the adding and resizing of the perimeter walls located near the corners of the building. This changing improves the performance of the multi-storey building because the torsion occurs in the third mode of vibration (Table 14 and Figure 16a). It may be observed that after the reducing of the torsion, the modal participating mass is just equal to 0.0008 in rotation with respect to the vertical axis Oz in the first mode of vibration (Table 14).  
The results presented in Table 15 and Figure 19 show that the procedure for the reduction of the overall torsion is also validated for the structures with vertical geometric irregularity. The torsion occurs neither in the first mode nor in the second mode of vibration (Table 15).
## 3 Results
### Microchip Implementation  
To verify the accurate operation of the digital controller, a transmitter was developed using an FPGA (CMOD A7, Xilinx, San Jose, CA, USA) for data transmission. The digital controller processes signals received from the FPGA and generates various pulse patterns. The CHR pulse length was calculated with respect to the received data. The different shapes of the biphasic pulse based on CATH and ANO are shown in Figure 10. In this figure, the biphasic pulse is generated on the specific datum \"11111110\", which generates CHR and \(\overline{\text{CHR}}\) pulses for 8.70 ms. For every specific datum, a varied pulse width is generated. The different amplitude and shape of the biphasic pulse are generated based on these data. The input 8-bit data contained in a 32-bit data packet enabled our system to generate 128 different widths and shapes of biphasic pulses. Varying shapes and amplitudes based on incoming light give our system full control over the current.  
Figure 8: Layouts of digital controller embedded onto the chip (with an active area of 150 \(\upmu\)m \(\times\) 400 \(\upmu\)m) and the entire chip (with the active area of 5 mm \(\times\) 4 mm). (**a**) Complete fabrication chip picture. (**b**) Digital controller.  
Figure 9: Simulation results were calculated using Xilinx Vivado, representing overlapping current pulses.
## 3 Results and Discussion
### Micro-Morphology  
The Inconel 718 specimens were corroded at a current density of 5 A/cm\({}^{2}\) in 106 g/L NaNO\({}_{3}\) solution and a mixed solution of 106 g/L NaNO\({}_{3}\) and 30 g/L NaOH. Figure 4a shows the micro-morphology of the specimen surface in 106 g/L NaNO\({}_{3}\) solution. It can be seen that the specimen was seriously corroded with a solid black product film on the surface. This result agrees with the high current efficiency shown in Figure 3. According to the EDX result in Figure 4b, the machining products are mainly the metallic oxides and metallic carbides. The serious corrosion on the surface can be attributed to the formation of a porous oxide film in NaNO\({}_{3}\) solution, as reported in the literature [26].  
Figure 4: Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) results for the corroded specimen in 106 g/L NaNO\({}_{3}\) solution. (**a**) SEM image of the specimen surface; (**b**) EDX of machining products.  
Figure 3: Current efficiency of Inconel 718 dissolution in different electrolyte solutions.  
In contrast, the specimen surface in a mixed solution of 106 g/L NaNO\({}_{3}\) and 30 g/L NaOH is well reserved without any corrosion, and the initial topography of the surface is still visible (Figure 5a). Taking the results in Figures 2 and 3 together, a compact passive film can be assumed to have formed on the specimen surface in alkaline solution after two passivation stages. The passive film is indestructible at low current density, and thereby protected the inner materials from corrosion. From the EDX result shown in Figure 5b, the detected metallic element contents are consistent with the main metal compositions of Inconel 718. No significant oxygen or carbon peaks can be detected. This is because the thickness of the passive film was approximately a few nanometers [31], which is much thinner than the information depth of EDX (1-2 \(\upmu\)m) [26].
# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 2 Image DNA Techniques
### Image Processing-Based Approaches  
Image processing-based methods generate higher-dimensional feature vectors compared to bitmap-based methods, relying on image transformations and computations to extract distinctive features. Traditional computer vision algorithms, such as SIFT (Scale-Invariant Feature Transform), are prominent examples [14]. SIFT extracts key points from images, providing scale- and rotation-invariant features. It generates 128-dimensional feature vectors and can produce hundreds of unique vectors per image based on the number of key detected points.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type** & **Image Characteristics Used for DNA** & **Feature Extraction Method** \\ \hline Bitmap & Pixel intensity, color values & Direct pixel mapping \\ \hline Image & & \\ Processing & Edges, texture, frequency components & Filters, histograms, transforms \\ \hline Deep Learning & High-level patterns, learned representations & CNN feature maps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of image DNA features in three approaches.  
Figure 3: The process of extracting Nabla DNA.  
SURF (Speeded-Up Robust Features) [15], an improvement over SIFT, enhances speed and efficiency by using the Hessian matrix for key point detection and reduces feature vector dimensions to 64. This results in a 3 to 10 times faster performance compared to SIFT.  
ORB was developed to circumvent the patent restrictions of SIFT and SURF, offering a fast and memory-efficient algorithm. ORB combines FAST (Features from Accelerated Segment Test) [16] and BRIEF (Binary Robust Independent Elementary Features) [17], further optimizing speed and memory usage.
# A Novel Preprocessing Method for Dynamic Point-Cloud Compression
## 2 Background Theory
### Point-Cloud Compression  
Generally, the acquisition of a large amount of data is necessary, however, the file size then has to be reduced for use in real applications. A single point-cloud is the simplification researched using this method. Simplification is a technology for sampling vertices to reduce the weight of a point-cloud. Currently, commonly used point-cloud simplification methods are the bounding box algorithm [24; 25], the uniform grid method [26], the curvature sampling method [27], the clustering method [28], and the triangular grid method [29], etc., [30; 31; 32; 33].  
A call for proposals (CfP) was issued in 2017 by the MPEG (Moving Picture Expert Group) for methods for reducing the weight of dynamic point-cloud data [34]. Based on this CfP response, two different compression techniques were selected for point-cloud compression (PCC) standardization activities: geometry-based PCC (G-PCC) [35] and video-based PCC (V-PCC) [36]. G-PCC is a format that expresses a point-cloud in an octree structure. In the octree structure, the point-cloud is assumed to be represented by the D \(\times\) D \(\times\) D range. It means that the vertices are represented by 0 and 1 and divided into 8 voxels of \(\frac{D}{2}\times\frac{D}{2}\times\frac{D}{2}\) until D becomes 1. Advantageously, since the vertices are expressed in 1-byte units instead of being expressed in coordinates, the compression performance is high. Lossy compression and non-lossy compression are determined according to the range of depth bits. V-PCC is a method of compression using an existing video codec. Compression using 2D video compression methods such as the advanced video coding (AVC)/H.264 [37] or high efficiency video coding (HEVC) [38] projects a point-cloud into a 2D space to generate 2D images.  
The research undertaken in [39] complements the selected compression technique. In addition, performance evaluation is performed with [40]. However, G-PCC is a compression technology for a single point-cloud, and V-PCC requires a process to generate 2D images in addition to subordinate and complicated process using codec.
## 4 Personal Climatization Systems
### Personal Ventilation with Preconditioned Air
According to [PERSON] et al. [135], under-desk-mounted ventilation devices ensure thermal comfort up to an ambient temperature of 28 \({}^{\circ}\)C and a relative humidity of 50% (Table 6-58), whereas air ventilation devices with preconditioned air above the desk revealed to be more effective with respect to thermal efficiency. Personal ventilation improves air quality independent of the number and behavior of individuals inside a room [136]. Occupants with free control over the supply air speed prefer a supply air speed between 1.2 m s\({}^{-1}\) and 1.7 m s\({}^{-1}\) at ambient temperature of 26 \({}^{\circ}\)C, whereas supply air speeds between 1.5 m s\({}^{-1}\) and 1.7 m s\({}^{-1}\) is preferable at ambient temperatures of 29 \({}^{\circ}\)C [133]. Tests with thermal manikins and corresponding simulations conducted by [PERSON] et al. [90] show that a sinusoidal airflow has a higher thermal effectiveness than a constant airflow. In addition, the cooling effect of the sinusoidal airflow is positively correlated with a frequency increase. The optimal oscillation frequency for thermal comfort and air quality is found to be between 0.5 Hz and 1 Hz at average volume flow rates between 3.5 L s\({}^{-1}\) and 7.5 L s\({}^{-1}\). Hence, such a variable air flows can save 16.1% of the cooling energy compared to a constant air flows. Applying sideways air ventilation, [PERSON] et al. [137] state an optimal frequency for thermal comfort between 2 Hz and 4 Hz. Kalmar [138], Kalmar and Kalmar [139] showed that personal face ventilation with horizontally varying origins of air streams also improves thermal comfort of individuals. According to [PERSON] et al. [140], air stratification and the impact of personal underfloor ventilation depend on the supplied volume airflow. Supplementing a personal underfloor ventilation with a table fan improves the ventilation performance from 10.38% to 22.05% according to [PERSON] et al. [141] and further increases energy savings from 8% to 13%, when compared to mixing ventilation. Personal supply of preconditioned fresh air through the ceiling saves 34% of the cooling energy demand of mixing ventilation [142]. However, the efficiency of this kind of ventilation strongly depends on the window type, if used for seats that are in close distance to a window. The cooling power of vertical ventilation rises concurrently with the increase in air velocity and with the decrease of the ambient and supply air temperature [143].
# Road Event Detection and Classification Algorithm Using Vibration and Acceleration Data
## 6 Impact of the Butterworth Filter on Classification Performance
### Algorithmic Complexity Analysis
#### 6.1.5 Summary of Complexity  
* Preprocessing: \(O(n\cdot m)\).
* Feature Extraction: \(O\big{(}\frac{n-w}{w-o}\cdot w\log w\big{)}\).
* Training: \(O(T\cdot m\cdot f\cdot d)\).
* Inference: \(O\big{(}\frac{n-w}{w-o}\cdot T\cdot d\big{)}\).  
This analysis highlights the trade-offs between computational cost and performance in each stage of the pipeline, underscoring the need for optimization to balance accuracy and efficiency.