esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Recently has been proposed that a combination between high power and high frequency of diode laser 980 nm (using really high peak power combined with a low average power, in order to reduce thermal effects) and hydrogen peroxide 10 volume 3%, or modified hydrogen peroxide 10 volumes 3% [16; 17; 18; 19; 20; 21; 22; 23]. This treatment modality was named \"photodynamic therapy without dye\" (OHLLT; oxygen high level laser therapy) in the protocol proposed by [PERSON] in 1999 [24]. OHLLT technology is a therapy based on the combination of a penetrating laser with a modified and stabilized H\({}_{2}\)O\({}_{2}\) 10 vol. 3% solution. Several in vitro studies showed bactericidal activity of laser irradiation combined with hydrogen peroxide on numerous bacterial species. A comparative study on the effects of laser alone and combined with H\({}_{2}\)O\({}_{2}\) 10 vol. 3% showed these results [21; 22; 23]: (1) laser used alone produces poor results in the elimination of bacterial species involved in periodontal disease, (2) H\({}_{2}\)O\({}_{2}\) used alone produces little effects in micro-organism elimination, and (3) laser combined with hydrogen peroxide shows an antibacterial action that is much more effective on most of the microorganisms involved in periodontal disease. Laser energy activates the modified H2O2 solution, releasing free radicals and singlet oxygen that has antibacterial activity on Gram-positive and Gram-negative periodontal pathogens. The photochemical effect of this photodynamic therapy consists of activation of a photosensitizer (in this case hydrogen peroxide 10 vol. 3%), with a monochromatic beam--a laser beam characterized by a single wavelength (980 nm diode). The interaction between this photosensitizer and the laser produces photochemical reactions in which the energy acceptor is oxygen. The stabilized hydrogen peroxide contains oxygen, and its presence allows the reactions of photoactivation and production of singlet oxygen. The singlet oxygen is an oxygen-free radical that determines bacterial cells death (destruction of bacterial membrane, degradation of lysosomal membrane, alteration of mitochondrial function, and denaturation of DNA molecules) [17; 19; 21; 22; 23]. The aim of our study is to investigate, retrospectively, if a meticulous, laser-assisted, maintenance protocol could reduce the risk of peri-implantitis onset, including with the use of rough dental implant surfaces.
## 3 Methodology
### Feature Selection (FS)
#### 3.2.5 Principal Component Analysis (PCA)  
PCA is a widely used technique to perform dimensionality reduction for reducing the number of features considerably in massive data sets while preserving much of the information from the original data set. PCA is often applied as the first step before moving to another technique, such as multiple regression, cluster analysis, and/or discriminant analysis. The data are initially centered in PCA by subtracting the mean (in practice, the mean is estimated as the average value of the vector in a sample). The vector is then turned into a new array, maybe of a smaller dimension, where the development work members are uncorrelated, using a linear transformation. The differences of the data projections on the new coordinate axes are increased, with the first axis corresponding to the greatest variance. The second axis represents the greatest variation opposite to the first axis.  
In mathematical terms, an orthonormal basis vector is generated using PCA, which maximizes the scatter of objects. Considering \(X=[x_{1},x_{2},\ldots,x_{N}]\) represents the set of vectors, where \(N\) is the number of objects. The next step in PCA is to normalize the \(X\) to become a unit norm. Then, the average is subtracted to produce a new set of vectors \(Y=[y_{1},y_{2},\ldots,y_{N}]\). The next step is to compute the covariance matrix of \(Y\) as:  
\[\sum_{Y}=\frac{1}{N}YY^{T} \tag{9}\]  
After that, the eigenvalue and the eigenvector are computed as in the following equation:  
\[\sum_{Y}\Phi=\Phi\Lambda \tag{10}\]  
where \(\Phi\) and \(\Lambda\) represent the eigenvector and eigenvalue, respectively. Then, the eigenvalue is sorted and select the most relevant eigenvectors \(\Phi_{r}\) are selected which correspond to the best eigenvalues and reduce the data as in the following:  
\[\mathit{Tar}=\Phi_{r}^{T}Y \tag{11}\]  
where \(T\) represents the reduced dataset.
## 5 Simulation Results and Discussion
### Case 5: Minimization of Total Generation Cost with Valve Point Effect and Carbon Tax
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Parameters**}} & \multicolumn{2}{c}{**Min. Value**} & \multicolumn{2}{c}{**Max. Value**} & \multirow{2}{*}{**PSO**} & \multicolumn{2}{c}{**GWO**} & \multicolumn{2}{c}{**HPSO-GWO**} & \multicolumn{2}{c}{**SHADE-SF**} & \multicolumn{2}{c}{**BMO [46]**} & \multicolumn{1}{c}{**GOA [46]**} \\ \hline \multirow{5}{*}{Generator} & V\({}_{\text{5(p,u)}}\) & 0.95 & 1.10 & 1.100 & 1.072 & 1.071 & 1.035 & 1.043 & 1.071 \\  & V8(p,u) & 0.95 & 1.10 & 1.100 & 1.097 & 1.088 & 1.100 & 1.047 & 1.095 \\  & V11(p,u) & 0.95 & 1.10 & 1.100 & 1.098 & 1.100 & 1.098 & 1.100 & 1.002 \\  & V13(p,u) & 0.95 & 1.10 & 1.100 & 1.096 & 1.095 & 1.051 & 1.060 & 1.052 \\ \hline \multirow{5}{*}{Generator} & Q\({}_{\text{TG1(MVA)}}\) & \(-20\) & 150 & 12.477 & \(-14.266\) & 12.264 & \(-3.215\) & \(-1.849\) & 41.855 \\  & Q\({}_{\text{TG2(MVA)}}\) & \(-20\) & 60 & \(-20.000\) & 20.714 & \(-20.000\) & 10.738 & 12.406 & \(-20.000\) \\ \cline{1-1}  & Q\({}_{\text{WT5(MVA)}}\) & \(-30\) & 35 & 35.000 & 23.310 & 35.000 & 22.232 & 22.918 & 35.000 \\ \cline{1-1}  & Q\({}_{\text{WT5(MVA)}}\) & \(-15\) & 40 & 40.000 & 40.000 & 40.000 & 40.000 & 35.686 & 40.000 \\ \cline{1-1}  & Q\({}_{\text{WT11(MVA)}}\) & \(-25\) & 30 & 19.789 & 18.089 & 19.916 & 30.000 & 28.506 & \(-0.900\) \\ \cline{1-1}  & Q\({}_{\text{SPV13(MVA)}}\) & \(-20\) & 25 & 24.819 & 23.340 & 24.979 & 15.950 & 17.094 & 21.178 \\ \hline \multirow{5}{*}{Objective} & Gen. cost & \multirow{5}{*}{\(\text{(\$/$h$)}\)} & \multirow{5}{*}{-} & \multirow{5}{*}{-} & \multirow{5}{*}{810.071} & \multirow{5}{*}{810.154} & \multirow{5}{*}{**809.277**} & \multirow{5}{*}{810.404} & \multirow{5}{*}{810.798} & \multirow{5}{*}{822.307} \\  & & & & & & & & & \\ \cline{1-1}  & P\({}_{\text{loss (MW)}}\) & - & & 5.128 & **5.029** & 5.132 & 5.281 & - & - \\ \cline{1-1}  & Emission & \multirow{2}{*}{(\(\$/$h$)}\) & & \multirow{2}{*}{-} & \multirow{2}{*}{0.898} & \multirow{2}{*}{**0.854**} & \multirow{2}{*}{0.914} & \multirow{2}{*}{0.871} & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\ \cline{1-1}  & & & & & & & & & \\ \cline{1-1}  & Carbon tax & - & & 17.96 & **17.08** & 18.28 & 17.42 & - & - \\ \cline{1-1} \cline{2-11}  & V.D (p.u.) & - & - & 1.033 & 1.097 & 1.027 & **0.462** & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
Figure 25: OPF convergence curves comparison (Case 5). Figure 28: Generators reactive powers comparison (Case 5). Figure 27: Generators real power comparison (Case 5).
## 4 Results and Discussion  
This section tested the proposed method on intensity-heterogeneous underwater images captured from underwater videos downloaded from the NATURE FOOTAGE website and Fish Dataset. Moreover, the method was compared with some state-of-the-art contour extraction methods in terms of its efficiency and accuracy. All contour extraction results were produced on the same computer to ensure fairness. The computer was configured as Intel(R) Core(TM) i7-8650U CPU @ 2.11 GHz, 16.00 GB memory, Windows 10 system, and x64 processor. MatlabR2017a was the software platform. We used the same parameters \(\eta^{2}=6,\sigma=2,\varepsilon=1,\lambda_{1}=3,\lambda_{2}=1\) and time-step \(\Delta t=0.1\). The initial level set function was defined by:  
\[\phi(x,t=0)=\left\{\begin{array}{ll}-c_{0},&x\in\text{in}(C)\\ 0,&x\in C\\ c_{0},&x\in\text{out}(C)\end{array}\right., \tag{22}\]  
where \(c_{0}>0\) is a constant (in our experiments, \(c_{0}=1\)) and \(\text{in}(C)\) and \(\text{out}(C)\) represent the region inside and outside of the contour \(C\), respectively.  
Moreover, the parameter \(\omega_{1}\) is a constant, which controls the influence of the saliency image fitting energy and original image fitting energy. When the missing information of the saliency target contour is severe, \(\omega_{1}\) should be relatively larger; otherwise, \(\omega_{1}\) should be a small value. Moreover, \(\omega\) should be smaller when the intensity inhomogeneity of the saliency image is severe. This is because the local intensity fitting can better segment the target in the intensity inhomogeneity region, and the results of contour extraction rely on the local intensity fitting. Otherwise, \(\omega\) should be larger to suppress the noise interference. In the experiment, we need to choose appropriate values for \(\omega\) and \(\omega_{1}\) according to the degree of inhomogeneity and the degree of saliency detection deviation. In the experiment of this paper, the value of \(\omega\) was from 0.5 to 0.9, and the value of \(\omega_{1}\) was from 0.1 to 0.8.
## 3 Method
### Model
#### 3.2.4 CRF Layer  
We add the CRF model to solve the dependency problem between labels by adding many mandatory constraints between the output sequence labels. For instance, \(I-Drug\) can only be the middle part of the drug entity instead of the beginning of the entity, and the label after it cannot be \(I-Reason\), \(O\) is used to indicate that the character does not belong to any entity, etc. For input sentence \(X=\{x_{1},x_{2},...,x_{n}\}\), the probability matrix is denoted by \(P\), and the fractional matrix is the output of BiLSTM. \(P\) is a matrix of \(n\times k\), where \(k\) denotes the total number of the labels and \(P_{i,j}\) denotes the probability that the \(i\)-th word in the sentence is labeled as the \(j\)-th tag. For output sequence \(y=\{y_{1},y_{2},...,y_{n}\}\), the score can be calculated from Equation (2):  
\[s(X,y)=\sum_{i=0}^{n}A_{y_{i},y_{i+1}}+\sum_{i=1}^{n}P_{i,y_{i}} \tag{2}\]  
where \(A\) denotes the label transfer matrix and \(A_{y_{i},y_{i+1}}\) denotes probability of label \(i\) transfer to label \(i+1\). A is a \(1\times 2\) matrix with start flag \(y_{0}\) and end flag \(y_{n+1}\) added to the label set. The probability of \(y\) is calculated by softmax for all possible tags in the sentence, as shown in Equation (3):  
\[p(y|X)=\frac{e^{s(X,y)}}{\sum_{\beta\in Y_{X}}e^{s(X,\beta)}} \tag{3}\]  
In the training process, maximizing the log probability of correctly labeled sequences is used as the optimization objective which is calculated in Equation (4):  
\[\log(p(y|X))=s(X|y)-log(\sum_{\beta\in Y_{X}}e^{s(X,\beta)}) \tag{4}\]  
where \(Y_{X}\) denotes all possible label sequences for a given sequence \(X\) (including labeled sequences that do not conform to the BIO format). The final output sequence obtained uses the Vibit algorithm to calculate the label sequence with the largest score following Equation (5):  
\[y^{\star}=\arg\max_{\vec{y}\in Y_{X}}s(X,\hat{y}) \tag{5}\]  
Finally, a dynamic optimization algorithm is used in the prediction phase to solve for the optimal sequence of labels \(y^{\star}\).  
In our proposed NER model, the input sentences are first pre-trained with BERT-WWM to generate a corresponding pre-trained word embedding from the sentences. Afterwards, this word embedding will be sent to the Iterated Dilated CNN to extract multiple local feature information through inflated convolution to obtain the local feature vector of the sentences. To further extract the global features of the whole sentence, BiLSTM is used to obtain the global features of the sentence from the sentence local feature vector combined with the context, and then obtain the classification probability of each word. Finally, it is plugged into the CRF layer containing the sequence transfer probabilities, and constraints are added to the final prediction labels to ensure the correctness of the final annotation results. The input sentence is processed as described in Figure 5 in the proposed BIBC model.
## 4 Simulation and Results
### The High-Frequency and High-Amplitude External Disturbances  
The additional external disturbances, which have high frequency and high amplitude, are supplemented in this section. In particular, we add \(10 sin(10t)\) to \(d_{3}(x,t)\), \(10 cos(10t)\) to \(d_{1}(x,t)\), and a random number with frequency 0.1 and amplitude 10 to \(d_{2}(x,t)\).  
By analyzing Figure 9, after adding the internal disturbance at 10s, the state based on the SMC with sign switching function can no longer follow the predetermined trajectory. Then, according to Figure 9a-c, although the method with exponential switching function can follow the predetermined trajectory, its following error and chattering are more serious than that of the method in this paper, especially in the case of adding random disturbances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Switching Function of the** & **Errors in 10 s (\(\times 10^{-3}\))** & **Errors after 10 s (\(\times 10^{-3}\))** & **Adjust Time in 10 s (s)** \\ \hline \multirow{3}{*}{sign switching function} & 1.5 & 2 & 0.015 \\  & 2 & 3 & 0.03 \\  & 2.5 & 3 & 0.03 \\ \hline \multirow{3}{*}{exponential switching function} & 1.2 & 5 & 0.016 \\  & 1 & 0.8 & 0.04 \\  & 2 & 3 & 0.06 \\ \hline \multirow{3}{*}{fuzzy switching function} & 0.08 & 0.1 & 0.15 \\  & 0.05 & 0.05 & 0.3 \\ \cline{1-1}  & 0.02 & 0.03 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The tracking errors of \(x_{i}-x_{id}\) based on SMC with different switching functions.  
Figure 8: Dynamic switching of different switching functions.  
Finally, by comparison, the methods with exponential switching function and with fuzzy switching function can effectively track in the three preset cases, but the method with sign switching function will lose effect for large amplitude interference. In particular, when the added disturbance is an elementary function and has deformation, the method with exponential switching function can also track the predetermined trajectory. The chattering is weak, but when the disturbance is a random value, the chattering is increased, while the method in this paper can not only track the predetermined trajectory but also has the weakest chattering and tracking error in the face of any disturbance. Therefore, this paper well verifies that the proposed SMC with fuzzy switching function can improve the chattering problem of synovial control.
## 3 Power-Law Distributions  
A power-law probability distribution function (PDF) is a very well-known technique [12] which can be defined as follows:  
\[\text{PDF}(\times)=\text{CX}^{-\alpha}\]  
where C is a normalizing constant.  
Figure 4: Two very different US research universities, yet, the two schools’ royalty incomes are similar and can be modeled as power-law distributions. (**a**) Event number vs. royalty income; (**b**) income vs. frequency.  
* When \(\alpha>3\), the distribution has a finite mean and finite variance, similar to working with Gaussian-like distributions.
* When \(2<\alpha\leq 3\), the distribution has a finite mean but undefined variance.
* When \(\alpha\leq 2\), both the mean and variance are undefined. The implications in this region are unique.  
In [8; 13; 14], the information regarding the theory regarding power-law distribution is presented. Several computational programs contain specific functions that can be used for calculating the power-law distribution when a large amount of data is studied, such as [15].  
Interestingly, most VC return-on-investment [16], AUTM income, and the two universities' income data presented above follow a power-law curve with an \(\alpha\) range of 0.5 to 2.0. The lower the \(\alpha\), the higher the chances of an extreme or \"Black Swan\" event [17].  
Understanding the implications of power-law statistics requires a different thought process than more common Gaussian statistics [18]. The primary difference between a Gaussian and power-law driven statistic is the tail. So-called tail events are much more likely in a power-law versus a Gaussian-driven process. A Gaussian-driven process is much more familiar and widely analyzed via averages and standard deviations. A power-law-driven process is much less familiar to most, not as intuitive, and, in some cases, cannot even be characterized via averages and standard deviations. However, power-laws are common in business, nature, and economics [8]. For example, earthquakes can be modeled with power-law distributions. Many little earthquakes have minimal impact, while a few big ones have a significant impact--just as with TTO deals, where there are many small ones and a few big shakers/Black Swan events/unicorns.  
From a financial modeling perspective, one important point is that this is only concerned with high-income events. Events that generate a minimal income are of little value since they contribute a small fraction of the overall return. A non-power-law distribution might better model them.
### Motivation and Main Contributions
The boat speed was kept constant (approximately 10 knots) as long as weather conditions were allowed. Speed information was monitored using the GPS device of the boat. In the first scenario (M1), the weather was rainy, and the sea was relatively rough compared to the other scenarios. In the second scenario (M2), measurements were taken when the weather was cloudy, there was no rain, and the sea was rough. The third (M3) and fourth (M4) scenarios were carried out in a clearer and calmer sea environment than scenarios M1 and M2. Scenarios M3 and M4 were performed consecutively on the same day. The difference between these two scenarios is that the Tx antenna heights were different. In M3 scenario, the Tx antenna is positioned on the shore at 5.8 m, as in scenarios M1 and M2, while in the M4 scenario, the Tx is placed in the 3 rd floor window of a building at 13.3 m. The comparative data for all scenarios, including weather, sea and wind conditions, as well as some parameter values of the measurements, are listed in Table 3.  
In addition, the route followed by the Rx boat in the sea for all scenarios and an example of the coastal image taken from the boat during the measurement are shown in Figure 2. The red and blue lines in Figure 2 depict outbound and return routes, respectively. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline Standard & IEEE 802.11p \\ Frequency Band & 5.6 GHz \\ Data Rates & 3–27 Mbps \\ Bandwidth & 10 MHz \\ Max. Tx Power & 23 dBm \\ Antenna Gains & 5 dBi \\ GNSS & 2.5 m Accuracy \\ Antenna type & Omnidirectional \\ Receiver Sensitivity & \(-\)99 dBm @ 3 Mbps \\ \hline \end{tabular}
\end{table} 
Table 2: Experimental setup specifications [22]. \begin{table}
\begin{tabular}{c c c c} \hline
**M1** & **M2** & **M3** & **M4** \\ \hline Weather state & Light rain shower & Mostly cloudy & Fair & Partly cloudy \\ Weather temp. & 6.5–7 °C & 8.6–10 °C & 10 °C & 7 °C \\ Sea water temp. & 10.3 °C & 9 °C & 9.1 °C & 9.1 °C \\ Wind speed & 7 knots & 6 knots & 6 knots & 6 knots \\ Sea state & 3 & 3 & 2 & 2 \\ \(h_{r}\) & 3.5 m & 3.5 m & 3.5 m & 3.5 m \\ \(h_{best}\) & 3.61 m & 3.43 m & 3.55 m & 3.47 m \\ \(h_{t}\) & 5.8 m & 5.8 m & 5.8 m & 13.3 m \\ \(\beta_{0}\) & 0.025 & 0.01 & 0.001 & 0.001 \\ \(\sigma_{h}\) & 0.24 m & 0.24 m & 0.1 m & 0.1 m \\ \hline \end{tabular} \(\beta_{0}\): Root Mean Square (RMS) surface slope, \(\sigma_{h}\): RMS surface height (m).  
\end{table} 
Table 3: Weather and sea conditions of the measurement scenarios. Additionally, in the red boxes labeled Tx, in the first three scenarios, there is a fixed-position Tx vehicle on the shore. In contrast, in the fourth scenario, a Tx device was fixed to a window on the third floor of a building. In all scenarios, the Rx boat was labeled with Rx.
# RSS Indoor Localization Based on a Single Access Point
## 4 Test Environment  
This section analyses realistic multipath Scenarios. The Scenarios refer to a laboratory area of approximately 100 m\({}^{2}\), which approximates a typical residential indoor floor. The 3D environment and the various different wireless network set-ups were simulated in _TruNET wireless_, a 3D polarimetric ray-tracing simulator [37]. The transmitter was a single AP with two dipole antennas, transmitting 20 dBm at 2.4 GHz, which is a typical IEEE 802.11 setup. The aforementioned transmitter was placed in the room most north-east of the laboratory, at a height of 2.2 m, in order to minimize the LOS area as much as possible and allow the multipath and fast fading effects to have a higher impact. The building structure and large furniture were configured using material constitutive parameters obtained from the literature [38], as per Table 1.  
The generated candidate radiomaps include 406 receiver cells at a height of 1.2 m. A series of 11 different Scenarios (numbers 2 to 12) were created by combining different polarization set-ups. The 11 Scenario configuration parameters are shown in Table 2.  
Finally, for reference purposes, a typical Scenario (No. 1) with five APs was created for the same environment. Results were compared with the 11 single AP Scenarios.  
The effects of polarization, antenna orientation and multipath are depicted in the snapshots of Figures 3 and 4. In Figure 5, we present the changes in Power Delay Profile (PDP) for a randomly selected cell. It can be easily observed that the PDP may vary due to the changes in polarization and antenna orientation.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Material** & **EI.Per. (F/m)** & **L. Tangent** \\ \hline
**Concrete** & 3.9 & 0.23 \\
**Wood** & 2 & 0.025 \\
**Brick** & 5.5 & 0.03 \\
**Metal** & 1 & 1,000,000 \\
**Plasterboard** & 3 & 0.067 \\
**Glass** & 4.5 & 0.007 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material constitutive parameters of the test environment.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Scenario ID** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** \\ \hline Antenna 1 Pol & V & H0\({}^{\circ}\) & H45\({}^{\circ}\) & H90\({}^{\circ}\) & H135\({}^{\circ}\) & H180\({}^{\circ}\) & H0\({}^{\circ}\) & H0\({}^{\circ}\) & H0\({}^{\circ}\) & H0\({}^{\circ}\) & H0\({}^{\circ}\) \\ Antenna 2 Pol & V & V & V & V & V & V & H0\({}^{\circ}\) & H45\({}^{\circ}\) & H90\({}^{\circ}\) & H135\({}^{\circ}\) & H180\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antenna configuration per Scenario ID.
# A Connectivity-Based Clustering Scheme for Intelligent Vehicles
## 5 Route Discovery in the Suggested Connectivity-Based Clustering Scheme
### Route Discovery in Intra-Cluster to the Destination  
The route discovery in intra-cluster was adopted from the CEG-RAODV protocol [3]. The author in [3] used CEG-Dijkstra for route discovery, where the source node forwards the route request to \(CH\). Later, the leading node, i.e., \(CH\), calculates all possible routes to the destination. The proposed connectivity-based clustering scheme in this paper also uses CEG-Dijkstra for strong route (i.e., STC) discovery. At the beginning of the route discovery process, CEG-Dijkstra assigns a connectivity value, i.e., \(CV(CH)=1\) and \(CV(d)=\infty\) (The value of CV for destination will change later to the interval [0-1] after its discovery.), to CH and all other vehicles, respectively, as STC routes. Then, using Equation (5), all possible connected routes are calculated from the source to destination. Finally, using (6), a strongly connected route with a higher connectivity value is selected as the STC from source node to destination.