esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Microbial Strains and Cell Preparation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strain and Plasmid** & **Description** & **Source or Reference** \\ \hline \multicolumn{3}{c}{_Vibrio parahaemolyticus_ strains} \\ RIMD2210633 & KP-positive, serotype O3:K6; clinical isolate; Wild-type strain in this study \\ _VP0097_ & RIMD2210633 mutant \#0002, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP0097_, Tmp\({}^{\text{r}}\) & This study \\  & RIMD2210633 mutant \#03058, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP0136_, Tmp\({}^{\text{r}}\) & This study \\  & RIMD2210633 mutant \#0521, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP0140_, Tmp\({}^{\text{r}}\) & This study \\  & RIMD2210633 mutant \#0092, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP0315_, Tmp\({}^{\text{r}}\) & This study \\  & RIMD2210633 mutant \#0078, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP3069_, Tmp\({}^{\text{r}}\) & This study \\  & RIMD2210633 mutant \#0229, & This study \\  & Ez-Tn5 DHFR-1 insertion to _VP3075_, Tmp\({}^{\text{r}}\) & This study \\  & Plasmids & \\ pSA19 CP & Expression vector; Cm\({}^{\text{r}}\) & [PERSON] et al. [21] \\ pVP0097 & pSA19 CP expressing _VP0097_, & This study \\  & controlled by the _tUHA_ promoter; Cm\({}^{\text{r}}\) & This study \\ pVP0136 & pSA19 CP expressing _VP0136_, & This study \\  & controlled by the _tUHA_ promoter; Cm\({}^{\text{r}}\) & This study \\  & pSA19 CP expressing _VP0140_, & This study \\  & controlled by the _tUHA_ promoter; Cm\({}^{\text{r}}\) & This study \\  & pVP0315 & pSA19 CP expressing _VP0315_, & This study \\  & controlled by the _tUHA_ promoter; Cm\({}^{\text{r}}\) & This study \\  & _VP3069_ & pSA19 CP expressing _VP3069_, & This study \\  & controlled by the _tUHA_ promoter; Cm\({}^{\text{r}}\) & This study \\ \hline \hline \end{tabular} Note: KP, Kawasaki phenomenon; Cm\({}^{\text{r}}\), chloramphenicol resistant; Tmp\({}^{\text{r}}\), trimethoprim resistant; _tUHA_, thermostable direct hemolysin A; DHFR-1, dihydrofolate reductase-1.  
\end{table} 
Table 1: Bacterial strains and plasmids used in this study.
## 5 Results and Analysis
### Shadow Fading and Signal Attenuation  
It can be observed that in the case of a mine sub-gallery, the typical pillars of irregular dimension and the random distribution of pillars is a source of reflection and diffraction for propagated signals. Each mode experiences independent and distributed shadow fading, as a single mode comprises a bunch of rays with an identical angle. Therefore, the net field at any location can be obtained by adding up the field of all modes [29], as given below in Equation (44)  
\[E_{rx}(y,z)=E_{o}\sum_{n}C_{n}(z)E_{n}^{x}(y)e^{(-\alpha_{n}+j\beta_{n})z}X_{n} \tag{44}\]  
where \(X_{n}\) is an identically distributed and independent lognormal random variable; the approximate equations for \(\alpha_{n}\) and \(\beta_{n}\) are given as  
\[\alpha_{n}=\frac{1}{b}\Big{(}\frac{n\pi}{2 bk}\Big{)}^{2}Re\frac{1}{\sqrt{k_{h} -1}}\quad;\quad\beta_{n}=\sqrt{k^{2}-\Big{(}\frac{n\pi}{2b}\Big{)}^{2}} \tag{45}\]  
By compiling distance-dependent path loss from Equation (35) and the shadow-fading parameter from Equation (44), a relationship can be analyzed between shadow fading and path loss.  
The shadow-fading parameters vary in different zones of the mine, and this variation shows a lognormal distribution. The cumulative distribution function (CDF) is drawn, and the probability of its deviation is shown in Figure 11. It can be observed that obstructions in the internal congested mine galleries and rough non-uniform structure creates a heavy signal-deteriorating effect for propagation. The attenuation coefficients are measured by considering the attenuation of each mode of every field, as elaborated in Equations (7) and (45).  
Figure 10: Signal-to-noise ratio and bit error rate analysis for LOS, and NLOS links.
## 4 Experimental Results and Discussion  
### Experimental Setup  
A microgrid platform is built in the laboratory environment to verify the proposed method, as shown in Figures 4 and 5. The microgrid platform is designed in a double layer structure with a flexible topology that contains flexible line connection, distributed generations like PV array and wind turbine, line impedance simulators, including resistance, and inductive and capacitive loads (100 kW RLC Load). To be specific, the distributed generations consist of centralized inverters for the photovoltaic roof, microinverters for the photovoltaic roof, a photovoltaic simulator, a diesel generator simulator, and a wind turbine simulator. Moreover, the load of the microgrid platform consists of lighting systems, an induction motor, charging piles for electric vehicles, and a 100 kW programmable PLC load. The microgrid platform is designed to operate at 400 V three phases with 50 Hz. Two sub-microgrid layers are coupled with bidirectional converters, which allow for the electrical energy to be transmitted in bidirectional flows. These sub-microgrid layers are connected to the bulk power system by deploying the three phases AC bus. Moreover, the microgrid platform is able to simulate different line faults to recur by utilizing the line impedance simulator under the logical program control.  
Figure 3: The diagram of the “one-versus-rest” (OVR) multiclass strategy.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 2 5G Massive MIMO at Sub-6 GHz
### 5G Smartphone mMIMO Array Antenna Techniques
#### 2.2.2 12-port 5G Massive MIMO  
A 12-port LTE antenna array (42/43/46) for 5G high-capacity networks up to 6 GHz, 3400-3600 MHz, 3600 MHz-3800 MHz, and 5150 MHz-5925 MHz was used. The three different antenna element types that make up a MIMO smartphone application are: an inverted -shaped antenna, a shorter inverted L-shaped open slot antenna, and a longer inverted open slot L-shaped antenna. Figure 11 illustrates eight antenna elements for MIMO in LTE 42/43 band and six antenna elements for MIMO in LTE 46 band (12). According to the actual measurement results, the LTE frequency band (42/43/46) had a reflection coefficient that was better than \(-\)6 dB, an isolation of <\(-\)12 dB, and an overall efficiency of >40%. Additionally, the suggested antenna array displayed good MIMO performance in LTE bands 42/43 and 46, with envelope correlation coefficients of 0.15 and ergodic channel capacity of over 34 b/s/Hz and 26.5 b/s/Hz, respectively [59].  
Figure 10: 18-element antenna system of the slot antenna with decoupling open-ended slots [58].
## 3 Formal Definition and Security Model
### Security Model  
This considers an extreme case: the adversary \(\mathcal{A}\) compromising the \(n-1\) participants and leaving _only one_ honest user, denoted \(\mathit{signer}_{1}\). The \(\mathit{signer}_{1}\) user is controlled by the challenger \(\mathcal{C}\). When the game starts, \(\mathcal{C}\) gives \(\mathcal{A}\) the honest identity of \(\mathit{signer}_{1}\) and allows \(\mathcal{A}\) to compromise the other signers' private keys. It also assume that a secure channel between the signers is not guaranteed. All of the communication among the signers can be eavesdropped upon. \(\mathcal{C}\) provides \(\mathcal{A}\) a hash oracle, a key extraction oracle and a multi-sign oracle. \(\mathcal{A}\)'s final target is to successfully forge a multi-signature.  
**Definition 2**.: _Considering the games between \(\mathcal{A}\) and \(\mathcal{C}\)._  
* _Setup:_ \(\mathcal{C}\) _executes the algorithm to generate the master public keys_ \(mpk\) _and sends_ \(mpk\) _to_ \(\mathcal{A}\)_._
* _Query:_ \(\mathcal{A}\) _is allowed to query to_ \(\mathcal{C}\) _in an adaptive way._
* _Extraction-query (_\(mpk\)_,_ \(1D\)_)._ \(\mathcal{C}\) _executes_ _Extra to obtain_ \(sk_{1D}\) _and sends to_ \(\mathcal{A}\) _when_ \(\mathcal{A}\) _asks for the private key of_ \(\mathit{signer}_{1D}\)_._
* _Multi-signature query (_\(mpk\)_,_ \(m\)_,_ \(1 DSet\)_)._ \(\mathcal{C}\) _obtains a multi-signature_ \(m\sigma\) _and sends to_ \(\mathcal{A}\) _when_ \(\mathcal{A}\) _asks for the multi-signature_ \(m\sigma\) _on_ \(m\) _and_ \(1 DSet\)_._
* _Hash-query._ \(\mathcal{C}\) _chooses the returned values by itself and sends to_ \(\mathcal{A}\) _when_ \(\mathcal{A}\) _asks._
* _Forgery._ \(\mathcal{A}\) _makes a multi-signature as a forgery, that is,_ \(m\sigma^{*}\) _on_ \(m^{*}\) _for_ \(1 DSet^{*}\)_, which contains at least one uncompromised user's identity; meanwhile,_ \(\mathcal{A}\) _never sends_ \((mpk,1 DSet^{*},m^{*})\) _to the multi-signature query._  
**Definition 3** (Attack Goals).: _The advantage \(Adv^{\mathbf{IBMS}}_{\mathcal{A}}\) in breaking the \(\mathbf{KG}(k)\) problems is defined as_  
\[Adv^{\mathbf{IBMS}}_{\mathcal{A}}(k)=\Pr\left[x^{\mathcal{J}^{\ell}}\equiv y \pmod{N}\left|\begin{array}{c}(N,p,q)\leftarrow\mathbf{KG}(k)\\ y\leftarrow\mathbb{Z}_{N}^{*}\\ x\leftarrow\mathcal{A}(N,\ell,y)\end{array}\right.\right]\]  
**Definition 4** (Unforgeability).: _An adversary \(\mathcal{A}\)\((t,q_{H},q_{E},q_{S},n,\epsilon)\) breaks the scheme if \(\mathcal{A}\) executes for a time of \(t\) at most, and makes at most \(q_{H}\) hash queries, \(q_{E}\) extraction queries, and \(q_{S}\) multi-signature queries with \(n\) participants, and \(Adv_{\mathcal{A}}\) is at least \(\epsilon\). An IBMS scheme \((t,q_{E},q_{S},q_{H},n,\epsilon)\) has unforgeability if there exists no attacker \(\mathcal{A}\)\((t,q_{H},q_{E},q_{S},n,\epsilon)\) that breaks it._
## 2 Materials and Methods
### Microwave Dielectric Spectroscopy  
Dielectric measurements were carried out in the frequency range from 500 MHz to 40 GHz using a microwave vector network analyzer N5234B PNA-L (Keysight, Santa Rosa, CA, USA), together with a flexible cable and slim-form probe (Keysight N1501A Dielectric Probe Kit). The calibration of the system was performed with the aid of three references: air, a Keysight standard short circuit, and pure water at 25 \({}^{\circ}\)C. Calibration was maintained by Ecal mode. A special stand for the slim-form probe was designed and combined with a sample cell holder for liquids (total volume 7.8 mL). The holder was enveloped by a thermal jacket and attached to a Julabo CF 41 (Julabo, Seelbach, Germany) oil-based heat circulatory system. The cell was held at 25 \({}^{\circ}\)C by the circulator-thermostat, with temperature fluctuations less than 0.1 \({}^{\circ}\)C. The whole measuring system was placed in an air-conditioned room maintained at 25 \(\pm\) 1 \({}^{\circ}\)C. Each sample was measured at least six times. The real and imaginary parts \(\varepsilon^{\prime}(\omega)\) and \(\varepsilon^{\prime\prime}(\omega)\) were evaluated using the Keysight N1500A Materials Measurement Software with an accuracy of \(\Delta\varepsilon^{\prime}/\varepsilon^{\prime}=0.05\), \(\Delta\varepsilon^{\prime\prime}/\varepsilon^{\prime\prime}=0.05\).
## 3 Results and Discussion
### Overall Liking and Perceived Sensory Intensity Scores
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Sample**} & **Overall** & **Bitterness** & **Sourness** & **CoffeeID** & **Bitterness** & **Sourness** & **CoffeeID** \\  & **Liking** & **Liking** & **Liking** & **Intensity** & **Intensity** & **Intensity** \\ \hline BlendingCB & 4.3 bed & 4.5 d & 4.5 d & 4.9 cde & 4.9 d & 5.7 b & 5.6 bed \\ ColombiaCB & 3.6 efg & 3.5 f & 3.5 f & 4.4 def & 4.6 e & 6.6 a & 5.2 ef \\ FolgersCB & 4.1 dde & 4.5 cd & 4.5 cd & 4.8 dde & 5.8 bc & 3.1 c & 5.5 cde \\ KenyaCB & 3.5 fgh & 3.4 f & 3.4 f & 4.4 f & 4.4 f & 6.8 a & 5.4 d \\ BlendingCM & 4.6 bed & 4.5 dce & 4.5 dce & 5.0 cde & 4.9 d & 5.8 b & 5.2 ef \\ ColombiaCM & 3.5 gh & 3.5 f & 3.5 f & 4.9 c & 4.9 d & 6.9 a & 5.4 def \\ FolgersCM & 3.2 gh & 4.0 e & 4.0 e & 4.4 e & 6.8 a & 5.4 bc & 6.0 b \\ KenyaCM & 3.1 h & 3.1 f & 3.1 f & 4.1 f & 5.0 d & 6.9 a & 5.8 bc \\ BabinskiRTD & 5.5 a & 5.3 ab & 5.3 ab & 5.8 a & 3.9 f & 5.2 c & 5.2 ef \\ BaristaRTD(A) & 5.5 a & 5.8 a & 5.6 a & 5.7 ab & 4.2 ef & 3.3 f & 5.2 ef \\ BaristaRTD(B) & 5.8 a & 5.8 a & 5.7 a & 5.7 ab & 4.1 ef & 3.4 f & 5.0 f \\ FrenchCafeRTD & 4.7 bc & 5.0 bc & 5.0 bc & 5.2 bc & 4.1 f & 4.7 d & 5.5 cde \\ StarbucksCS & 4.1 def & 4.6 cd & 4.6 cd & 5.0 c & 6.3 b & 5.0 cd & 6.0 ab \\ TwosomeplaceCS & 4.8 b & 5.2 b & 5.2 b & 5.1 c & 5.7 c & 4.1 e & 6.4 a \\ \hline \hline \(p\)-value & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 & \textless{}0.0001 \\ Least significant & \multirow{2}{*}{0.52} & \multirow{2}{*}{0.47} & \multirow{2}{*}{0.44} & \multirow{2}{*}{0.49} & \multirow{2}{*}{0.47} & \multirow{2}{*}{0.46} & \multirow{2}{*}{0.41} \\ difference (LSD) & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis of variance of consumers’ liking and perceived intensity for coffee samples.
# Set-Based Group Search Optimizer for Stochastic Many-Objective Optimal Power Flow
## 2 Modelling of Stochastic Many-Objective Optimal Power Flow Problem
### Objectives
#### 2.2.3 Voltage Deviation  
The measurement of bus voltage deviation is a significant parameter in assessing the safety and power quality of power grid operations. It pertains to the disparity between the voltage of individual load buses and the reference voltage amplitude. Consequently, the cumulative voltage deviation across all buses is regarded as a key objective.  
\[F_{3}(\mathbf{V})=\sum_{j}^{N_{\text{B}}}\lvert V_{i}-V_{\text{ref}}\rvert \tag{7}\]  
where the \(V_{\text{ref}}\) is the referenced voltage magnitude.
# Machine Learning for Bankruptcy Prediction in the American Stock Market: Dataset and Benchmarks
## 4 Machine-Learning Models
### Support Vector Machine  
Support Vector Machine is one of the oldest ML algorithms and aims to identify the decision boundaries as the maximum-margin hyperplane separating two classes. The hyperplane equation is given by Equation (1).  
\[f(x)=\omega^{T}\cdot x+b \tag{1}\]  
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline  & **Variable Name** & **Description** \\ \hline
**X6** & Net Income & The overall profitability of a company after all expenses and costs have been deducted from total revenue. \\ \hline
**X7** & Total Receivables & The balance of money due to a firm for goods or services delivered or used but not yet paid for by customers. \\ \hline
**X8** & Market value & The price of an asset in a marketplace. In our dataset it refers to the market capitalization since companies are publicly traded in the stock market \\ \hline
**X9** & Net sales & The sum of a company’s gross sales minus its returns, allowances, and discounts. \\ \hline
**X10** & Total assets & All the assets, or items of value, a business owns \\ \hline
**X11** & Total Long term debt & A company’s loans and other liabilities that will not become due within one year of the balance sheet date \\ \hline
**X12** & EBIT & Earnings before interest and taxes \\ \hline
**X13** & Gross Profit & The profit a business makes after subtracting all the costs that are related to manufacturing and selling its products or services \\ \hline
**X14** & Total Current Liabilities & It is the sum of accounts payable, accrued liabilities and taxes such as Bonds payable at the end of the year, salaries and commissions remaining \\ \hline
**X15** & Retained Earnings & The amount of profit a company has left over after paying all its direct costs, indirect costs, income taxes and its dividends to shareholders \\ \hline
**X16** & Total Revenue & The amount of income that a business has made from all sales before subtracting expenses. It may include interest and dividends from investments \\ \hline
**X17** & Total Liabilities & The combined debts and obligations that the company owes to outside parties \\ \hline
**X18** & Total Operating Expenses & The expense a business incurs through its normal business operations \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cont.  
where \(\omega\) is the normal vector and \(b\) the bias. The objective function of SVM can be expressed as:  
\[\left\{\begin{array}{c}\text{min}\Big{\{}\frac{1}{2}\|\omega\|^{2}\Big{\}}\\ \text{s.t.}\ \left\{\begin{array}{l}y_{i}-\omega^{T}x_{i}-b\leqslant\varepsilon\\ \omega^{T}x_{i}+b-y\leqslant\varepsilon\end{array}\right.\end{array}\right. \tag{2}\]  
where \(\varepsilon\) is the deviation between \(f(x)\) and the target \(y_{i}\).
## 4 In Situ Testing
### Dynamic Characteristics of In Situ Testing  
A comprehensive summary of the collected results from the entire in situ test is presented below in Table 2. The evaluation of the results for the remaining three bridges follows the methodology outlined above.  
A comparison of the measured dynamic characteristics clearly shows the pronounced similarity in the eigenfrequencies of structurally identical bridge pairs despite variations in soil stiffness. The robust eigenfrequencies emphasise the prevalence of structural stiffness as the dominant factor influencing the natural frequency of portal frame bridges, superseding the effects of the (S)SSI. Analogous results are supported by numerical investigations presented in [12].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bridge** & **L [m]** & \(f_{1}\) [Hz] & \(f_{2}\) [Hz] & \(\zeta_{1}\) [\%] & \(V_{s}\) [m/s] & **Limitation** \\  & & **Ambient** & **Ambient** & **Mean** & & \(a_{0}\) \\ \hline
12,842 & 17 & 10.1 & 14.7 & 6.4 & 358 & \(0.90<a_{0,G}\) \\
12,836 & 17 & 10.1 & 14.9 & 5.9 & 485 & \(0.66<a_{0,G}\) \\
12,829 & 9.5 & 21.8 & 25.7 & 10.9 & 246 & \(2.85>a_{0,G}\) \\
13,871 & 9.5 & 21.3 & 25.6 & 11 & 600 & \(1.08>a_{0,G}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the studied bridges and their dynamic characteristics.  
Figure 11: Analysis of modal damping on the basis of the integrated decay curves in a quiet environment with soft impacts. (**a**) Time domain, (**b**) CWT, (**c**) frequency spectrum.  
In addition, the results are compared with the data collection of the \"Railway bridge dynamics DIBRST: concrete frame bridges--Expert Exchange\" [52] in Figure 12. The measured parameters fit well with the data collection.  
It is evident that the natural frequencies show minimal deviation from the analytical natural frequency of the frame \(f_{R}\). This frequency is directly related to the stiffness of the superstructure and consequently influences the normative deflection criterion. The tendency of the bridge to have a natural frequency higher than \(f_{R}\) is due to the stiffening effects induced by the backfill and abutment walls, which are not included in the frame formula. A wide range of damping ratios is observed, with the range appearing more extensive for shorter bridges. However, no general principle can be derived for longer spans due to the limited data set. The variability in damping ratios is not surprising, as the (S)SSI, rather than the structure itself, determines the resulting damping characteristics.
## 3 Vibration Localization Characteristics and Their Influence Factors
### Mode Localization
#### 3.1.2 Modal Shapes Under Different Coupling Strengths  
According to the parameters of the E2 example in Table 1, the masses of the resonant units are assigned, and the mode shapes of the CMS are calculated for coupling coefficients \(R=0\), \(R=0.1\), and \(R=0.2\). The results are shown in Figure 4. It can be seen that when there is no coupling between the units, the mode shapes of all orders are manifested as prominent vibration of a single resonant unit, which produces a strong mode localization. As the coupling strength increases, the mode localization diminishes, and the mode shapes of each order gradually transition to multiple resonant units vibrating simultaneously.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Example** & **Range (g)** & **Interval (g)** & **Mistuning Strength \(\varepsilon\) (\%)** \\ \hline E1 & 16.1\(\sim\)17.9 & 0.2 & 3.6 \\ E2 & 15.2\(\sim\)18.8 & 0.4 & 7.2 \\ E3 & 14.3\(\sim\)19.7 & 0.6 & 10.9 \\ E4 & 13.4\(\sim\)20.6 & 0.8 & 14.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Different mistuning strengths of the resonant units.  
Figure 2: Mode shapes of the CTS.
# FSCB-IDS: Feature Selection and Minority Class Balancing for Attacks Detection in VANETs
## 3 Proposed Methodology
### Data Preprocessing  
The most important step in the ML method is the preparation of the data to obtain high performance. Initially, the data needs to be cleaned as it can have problems such as missing values, conversion of categorical data to numerical, and imbalance classes. Moreover, the unnecessary data must be removed, which can affect the performance and results of using the ML method. The datasets must be processed properly in order to create a useful analysis. In our case, our datasets contain many missing values, so we will replace them with the most common values. Additionally, we have to convert the categorical data of our datasets into numerical to better use our model. For this purpose, we used a one-shot encoding. And finally, to address the issue of imbalance class, we use the SMOTE  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Author** & **Year** & **Used Learning Method** & **Accuracy (\%)** & **Attacks Covered** \\ \hline \multirow{8}{*}{[PERSON] and [PERSON] [14]} & \multirow{8}{*}{2021} & Logistic regression & 60.9 & \multirow{8}{*}{Multiple Misbehaviors} \\  & & Naive Bayes & 51.3 & \\  & & K-nearest Neighbor & 97.1 & \\  & & Decision tree & 87.0 & \\  & & XGBoost & 98.0 & \\  & & Support vector machine & 87.0 & \\  & & Random forest AdaBoost & 97.1 & \\ \hline [PERSON] et al. [13] & 2014 & RF-FSR & 99.90 & \\  & & RF-BER & 99.88 & \\ \hline \multirow{8}{*}{[PERSON] et al. [17]} & \multirow{8}{*}{2021} & Random Forest & 96.93 & \multirow{8}{*}{Multiple Misbehaviors} \\  & & Bayesian-Coresets & 82.40 & \\ \cline{1-1}  & & SVM & 85.20 & \\ \cline{1-1}  & & CNN & 95.14 & \\ \hline [PERSON] et al. [40] & 2018 & SVM Neural network & 99.92 & Grey hole Rushing attack \\ \hline [PERSON] et al. [41] & 2016 & SVM & 99.67 & Multiple Misbehaviors \\ \hline [PERSON]. [PERSON] et al. [42] & 2020 & RF & 90 & Multiple Misbehaviors \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: The components detail of our approach.  
technique [16] which improves random oversampling by the supply of synthetic samples of minority classes and fixes the overfitting issue which may result from simple random oversampling since SMOTE generates new data items in place of duplicating the existing data items.  
Steps of oversampling method:  
For every motif X in the minority class A:  
Select one of its K nearest neighbors x1 (also belong to the minority class).  
Create a new motif S on a random point on the line segment linking the motif and the selected neighbor, as shown in Equation (5):  
The following formula is utilized to generate a new example for each \(x_{i})\in A\) (\(i=1,\,2,\) 3... N):  
\[\text{S}=\text{x}+\text{rand}\ (0,\ 1)\ \times\ (\text{x}_{-}\,x_{i}) \tag{5}\]
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 3 Methods
### Image Preprocessing  
An automated preprocessing for all used S-1 images is performed in a Python script using SNAP Toolbox (version 4.0.0) [56] components. It includes the radiometric calibration to sigma naught, a range Doppler terrain correction by using the SRTM digital terrain model [57], the co-registration of the individual data, and speckle filtering. The coregistration of the images is necessary to perform an analysis based on time series data. It is essential that the images match in their position with pixel accuracy, otherwise the backscatter values over time could lead to a distorted picture of the desired classes. The spatial refined Lee filter with a window size of 7 \(\times\) 7 were used to reduce the noise in the SAR data [58; 59]. This filter allows the preservation of the structure in the image by filtering homogeneous surfaces and preserving edges, as well as flood-related temporal characteristics and patterns. These preprocessing steps were carried out for both polarizations VV and VH, resulting in two independent time series. The result is a multi-temporal layer stack for each polarization.