esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimized MLP-CNN Model to Enhance Detecting DDoS Attacks in SDN Environment
###### Abstract  
In the contemporary landscape, Distributed Denial of Service (DDoS) attacks have emerged as an exceedingly pernicious threat, particularly in the context of network management centered around technologies like Software-Defined Networking (SDN). With the increasing intricacy and sophistication of DDoS attacks, the need for effective countermeasures has led to the adoption of Machine Learning (ML) techniques. Nevertheless, despite substantial advancements in this field, challenges persist, adversely affecting the accuracy of ML-based DDoS-detection systems. This article introduces a model designed to detect DDoS attacks. This model leverages a combination of Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) to enhance the performance of ML-based DDoS-detection systems within SDN environments. We propose utilizing the SHapley Additive exPlanations (SHAP) feature-selection technique and employing a Bayesian optimizer for hyperparameter tuning to optimize our model. To further solidify the relevance of our approach within SDN environments, we evaluate our model by using an open-source SDN dataset known as InSDN. Furthermore, we apply our model to the CICDDoS-2019 dataset. Our experimental results highlight a remarkable overall accuracy of 99.95% with CICDDoS-2019 and an impressive 99.98% accuracy with the InSDN dataset. These outcomes underscore the effectiveness of our proposed DDoS-detection model within SDN environments compared to existing techniques.  
SDN; DDoS attacks; deep learning; CNN; MLP; optimization; feature selection Article
# Automatic Generation of Guidance for Indoor Navigation at Metro Stations
## 2 Related Work
### Indoor Navigation in Bus Terminals, Railway Stations, and Airports  
A critical part of navigating users of public transport is the transfer to another vehicle. The process might be straightforward if it requires a short walk between outdoor stops, but it becomes challenging if it takes place inside a building. In that case, it incorporates indoor navigation. Examples of public transport routes provided by popular applications show a tendency to generate turn-by-turn directions. This type of guidance is often impractical indoors because many people have difficulty orienting themselves with reference to the map. Even applications that include positioning components fail to provide valuable real-time directions due to the low accuracy of determining the user's position.  
Research on airport navigation applications [21; 22; 23] concludes that one of the most important assumptions to be taken into account at the initial stage of designing the system is that airports are not navigation destinations but rather transitional spaces. The building is accessed from the outside by selecting the appropriate entrance from a number of, sometimes distant, entrances. Once inside the building, the user performs standard tasks such as baggage check-in, going through security, and finding the departure gate. Similar problems are encountered in pedestrian navigation at railway stations [24; 25]. In such cases, users go to the ticket office and find the correct platform.
# Spaceborne GNSS-R for Sea Ice Classification Using Machine Learning Classifiers
## 3 Theory and Methods
### Spaceborne GNSS-R Features
#### 3.1.1 Surface Reflectivity
The \(\mathcal{R}\) represents the Fresnel reflection coefficient, which can be derived through Equations (4)-(6):  
\[\mathcal{R}=\frac{1}{2}(\mathcal{R}_{VV}-\mathcal{R}_{HH}), \tag{4}\]  
\[\mathcal{R}_{VV}=\frac{e_{i}cos\theta_{i}-\sqrt{e_{i}-sin^{2}\theta_{i}}}{e_{ i}cos\theta_{i}+\sqrt{e_{i}-sin^{2}\theta_{i}}}, \tag{5}\]  
\[\mathcal{R}_{HH}=\frac{cos\theta_{i}-\sqrt{e_{i}-sin^{2}\theta_{i}}}{cos\theta _{i}+\sqrt{e_{i}-sin^{2}\theta_{i}}}, \tag{6}\]  
where \(\theta_{i}\) is the incidence angle over the sea ice, and \(e_{i}\) is the permittivity of sea ice, which is related to sea ice types. In this study, the data with an incidence angle below 45\({}^{\circ}\) are applied. Data marked by the quality flags eclipse or direct signal in DDM [51] have also been removed to deal with reliable data. The TDS-1 surface reflectivity and corresponding incidence angle distribution in Arctic regions in 5 days (from 11 to 15 February 2018) is presented in Figure 3.
# Personalized Scholar Recommendation Based on Multi-Dimensional Features
## 5 Evaluation
### Experimental Results
#### 5.4.2 Impact of Recommendation Model  
Figure 5 shows the comparison results of the error of Mul-RSR and the other recommendation models on the ResearchGate dataset. Figure 5,b are respectively the experimental results of MAE and RMSE. It can be seen that the error values of the Mul-RSR model are both lower than the other models.  
In the _HitRate_ experiment, we respectively set the top five and top ten scholars in the label matrix as the target recommendation set of the model, i.e., True = 5 and True = 10. For each situation, we carry out Top-\(N\) recommendation. The experimental results are shown in Table 3. These models are compared in the respective scenarios of recommending Top-5 and Top-10 similar scholars for all the designated scholars. No matter if True = 5 or True = 10, the _HitRate_ of Mul-RSR is higher than the other approaches.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Accuracy** & **Recall** & **F1** \\ \hline With WIKI corpus & 66.28\% & 65.27\% & 65.85\% \\ Without WIKI corpus & 65.52\% & 62.32\% & 64.14\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: WIKI test results.  
Figure 4: Sentiment analysis results of different models.  
Figure 5: Error of each recommendation model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**True = _N_** & **Model** & **Top-5** & **Top-10** & **Top-15** & **Top-20** & **Top-25** & **Top-30** \\ \hline \multirow{8}{*}{True = 5} & CB & 11.41 & 17.38 & 22.55 & 27.03 & 31.32 & 34.76 \\ \cline{2-7}  & DT & 16.28 & 19.42 & 23.31 & 29.13 & 33.77 & 36.49 \\ \cline{2-7}  & CNN & 29.96 & 34.81 & 40.52 & 44.50 & 47.88 & 50.39 \\ \cline{2-7}  & RBM & 30.74 & 35.15 & 40.09 & 45.54 & 49.61 & 52.81 \\ \cline{2-7}  & LSTM & 30.41 & 38.53 & 43.29 & 46.86 & 50.39 & 53.03 \\ \cline{2-7}  & MLP & 30.84 & 39.39 & 44.26 & 47.62 & 51.30 & 53.90 \\ \cline{2-7}  & LSTM-AT & 35.00 & 44.01 & 48.63 & 52.38 & 55.12 & 58.01 \\ \cline{2-7}  & **Mul-RSR** & **35.21** & **44.73** & **49.21** & **53.82** & **56.28** & **59.31** \\ \hline \multirow{8}{*}{True = 10} & CB & 9.89 & 15.50 & 20.69 & 25.07 & 29.18 & 32.90 \\ \cline{2-7}  & DT & 13.06 & 15.96 & 19.49 & 26.42 & 29.74 & 33.19 \\ \cline{2-7}  & CNN & 21.34 & 27.27 & 33.01 & 35.02 & 41.08 & 42.47 \\ \cline{2-7}  & RBM & 22.39 & 28.88 & 34.76 & 38.53 & 41.81 & 44.59 \\ \cline{2-7}  & LSTM & 23.88 & 30.12 & 35.75 & 40.07 & 42.49 & 45.33 \\ \cline{2-7}  & MLP & 24.03 & 32.25 & 37.23 & 41.13 & 44.66 & 47.62 \\ \cline{2-7}  & LSTM-AT & 25.37 & 34.11 & 39.05 & 42.60 & 45.89 & 49.00 \\ \cline{2-7}  & **Mul-RSR** & **25.89** & **35.67** & **40.52** & **45.37** & **48.40** & **51.00** \\ \hline \hline \end{tabular}
\end{table}
Table 3: _HitRate_ (%) result of recommendation model.
## 4 Numerical Simulation of the Displacement Field  
According to the previous analysis, the leakage of the retaining structure will change the seepage field of the foundation pit. In order to study the influence of leakage on the displacement field of the foundation pit, further modeling and analysis were carried out.  
The finite element software GTS Midas (Developed by MIDAS IT) was used to establish the numerical calculation model of soil-envelope interaction of the foundation pit considering leakage and not considering the action. The excavation depth of the foundation pit was taken to be 29 m and the pit size was 21 m \(\times\) 250 m. Four models with different leakage sizes (without leakage point, leakage point width 0.01 m, leakage point width 0.005 m, leakage point width 0.001 m) were established. For the concrete material, a linear elastic model was used, and a Mohr-Coulomb model was used for each layer of soil. The influence of the soil layers on the deformation of the foundation pit was analyzed using solid unit action mainly from two aspects: the horizontal displacement of the enclosure structure and the ground settlement around the foundation pit. The finite element model diagram is shown in Figure 17.  
Figure 16: Numerical analysis and comparison of the amount of groundwater level change in the actual project.  
In this paper, we mainly studied the deformation law of the perimeter during the precipitation excavation of the foundation pit. The thick soil layer used is cohesive soil. The Mohr-Coulomb model fits better with the deformation of the perimeter settlement and the simulation values are more appropriate. Moreover, the parameters required by the Mohr-Coulomb model are easier to obtain. Therefore, the Mohr-Coulomb model was used in this paper.
## 2 Preliminaries
### Linear Control System  
A closed-loop, cascading position control system was selected as the reference control system. In this system, the speed in the inner loop and the position in the outer loop are adjusted. This mode of adjustment is the natural one. The internal speed control loop must respond faster. The position control loop must have an asymptotic aperiodic behavior. It is considered that the process has a high mechanical time constant \(J/k_{f}\) and a low time constant \(T_{T\omega}\) of the sensor, and its model is approximated with a transfer function:  
\[\omega_{m}(s)\approx\frac{1}{Js(T_{T\omega}s+1)}M(s) \tag{4}\]  
Taking into account this model, the speed controller is dimensioned with the symmetric criterion in [PERSON]'s variant [36], which recommends a PI speed controller:  
\[H_{R\omega}(s)=K_{R\omega}\left(1+\frac{1}{T_{R\omega}s}\right) \tag{5}\]  
where  
\[K_{R\omega}=\frac{J}{2T_{T\omega}}=1.875 \tag{6}\] \[T_{R\omega}=4.T_{T\omega}=0.48\]  
This design ensures a speed overshot \(\sigma_{1\omega}\%\) = 43.5%.  
The block diagram of the speed control system is shown in Figure 1.  
Where the speed \(\omega\) is denoted with \(w\), \(w^{\star}\) is the speed reference, \(e_{w}\) is the speed error, \(M\) is the mechanical torque, and \(w_{m}\) is the measured speed. The control system is analyzed with a unitary step signal applied at its reference input \(w^{\star}=1\) rad/s.  
The response at the step signal applied at the input of the speed control system is presented in Appendix A.  
Since the controlled process has a purely integrative character, a position regulator of the proportional type was chosen with gain coefficient \(K_{R0}=0.8\). The block diagram of the position linear control system is shown in Figure 2.  
The position \(\theta\) is denoted with \(p\), \(\theta^{*}\) is the position reference, \(\theta_{m}\) is the measured position, and \(e_{\theta}\) is the position error.  
The frequency characteristics of the position open loop are presented in Appendix A. The position controller is designed to ensure a high phase reserve of \(80^{\circ}\). The response at the step signal applied at the input of the speed control system is presented in Appendix A.  
The linear position control system, based on error feedback, assures good efficiency criteria: a short response time of position for a high moment of inertia; zero error in the steady state; and an asymptotic, aperiodic behavior of position at the step input signal.
## 3 Notations and Problem Definition  
Here we first introduce some relevant definitions of DHINs and then give the formal descriptions and problem descriptions for DHINs.  
**Definition 1**.: _Heterogeneous information network. To simplify the problem, a heterogeneous undirected network \(G=(V,E)\) is given, where \(V\) is the set of nodes, \(E\) is the set of edges, and \(G\) contains maps of node types, the function \(\varnothing:V\rightarrow\mathcal{A}\) and an edge type mapping function \(\varphi:E\rightarrow\mathcal{R}\), satisfy \(|\mathcal{A}|+|\mathcal{R}|>2\). In addition, the node attribute is defined as the initial feature matrix \(X\in\mathbb{R}^{|V|\times D}\)._  
**Definition 2**.: _Dynamic heterogeneous information network. A heterogeneous information network including time attributes \(GT=(V,E;T)\), \(T\) represents the time attribute set. For each connecting \(u,v\) nodes, \(e=(u,v,t)\), with timestamp \(t\in T\)._  
**Definition 3**.: _Meta-path [45]. Let \(V_{t}\) be the set of target class nodes. The meta-path between nodes \(v_{t1}\) and \(v_{tm}\) is defined as \(v_{t1}\overset{R_{t}}{\rightarrow}v_{t2}\overset{R_{2}}{\rightarrow}\ldots \overset{R_{n-1}}{\rightarrow}v_{tn}\), the meta-path can reflect the closeness between the target type nodes._  
**Definition 4**.: _Meta-path context. Define the meta-path set as \(\rho=\left\{\rho_{1},\rho_{2},\cdots\rho_{p}\right\}\), where \(\rho_{i}\) represents the \(i\)-th meta-path. Based on the predefined meta-path, the corresponding adjacency matrix set \(A^{\rho}=\left\{A^{\rho_{1}},A^{\rho_{2}},\cdots A^{\rho_{p}}\right\}\) can be obtained, where \(A^{\rho_{i}}\in\mathbb{R}^{|V_{t}|\times|V_{t}|}\)._  
**Problem Description.**_Temporal information-based embedding of heterogeneous information networks, under the guidance of temporal information, will learn low-dimensional representations \(H\in\mathbb{R}^{|V|\times d}\) of structural information and attribute information in \(G\), where \(d\ll D\)._
## 6 Simulation and Analysis
### Analysis of Simulation Results
#### 6.2.5 DG out of Operation during Faults Test  
Taking feeder B\({}_{7}\)B\({}_{8}\) as the protected section, different types (AB-phase-to-phase, ABg-phase-phase-to-ground, and ABC-3-phase) of metallic multi-phase faults are set at different locations (5%, 50%, and 95% from B\({}_{7}\)) of B\({}_{7}\)B\({}_{8}\). Assuming that DG\({}_{4}\) is out of operation when faults occur, the performance of the proposed protection is tested. The simulation results are shown in Table 8. It can be seen that when the DG\({}_{4}\) is out of operation during faults, the amplitude ratio of PSCFC on both sides of feeder B\({}_{7}\)B\({}_{8}\) is far greater than 1.5. The phase angle compensation coefficient compensates for the phase of the PSCFC on the SBDG side. The compensated ACDP can trip very sensitively, with higher sensitivity than TCDP.
## 2 Related Work
The results show that the sensor node radio draws 15% and 21% less power compared to full-power-no-feedback, respectively, for both environments. [PERSON] _et al._[11] propose mechanisms for transmission power control based on channel prediction up to 2 s in the the future. The power control methods are based on large datasets taken from ten human subjects performing every-day activities. Channel sounders with Chipcon CC2500 radios were used for collecting the dataset. The authors affirm that the dynamic transmit power control using this predictor can save between 8%-22% of the energy compared with a constant transmit power of \(-10\) dBm. [PERSON] _et al._[12] proposed a dynamic power control mechanism, named dynamic postural position inference (DPPI), that performs adaptive body posture inference for optimal power assignments. They assume that the average RSSI values can be modeled approximately as a linear function of the transmission power. The performance of this mechanism was evaluated with the Mica2 Mote using radio chip CC100. With one of the proposed PC schemes, DPDI, they can save 43%-50% of the energy for different testing persons, compared to using the maximum transmit power [13]. However, in [14], the authors ensure that the DPDI mechanism predicts the transmission power incorrectly for cases where the link state varies frequently. Our reactive and predictive approaches for the transmission power control are similar to the work research of [PERSON] [12] from the point of view that both rely on the detection of the body position for making a decision about the optimal transmission power level; however, the proposed mechanism for posture detection is very different. For DPDI, the postural position is inferred based on the RSSI measurements at the receiver during runtime, which can fail for cases where the link state varies frequently. In our approaches, the postural position detection is done throughout the deployed accelerometers, which are more accurate. Moreover, every expected position has been completely characterized in terms of signal reception. The idea behind our proposal is to take advantage of the hardware being used already in applications, such as fall detection, abnormal movement detection and posture and human activity detection, but also to provide optimization policies that optimally adjust the power transmission level. On the other hand, from a detailed review of the literature, we can claim that, to the best of our knowledge, our predictive approach is the first in including the real influence of anthropometric and body composition parameters in the variability of RSSI over on-body channels (both factors place a big impact on the development of optimization policies, as will be shown later). Additionally, the development of our optimization policies has required the acquisition of the largest and most exhaustive collection of experimental data using human subjects in an indoor environment that has been reported so far by researchers.
## 2 Background
### Maximal Overlap Discrete Wavelet Transform  
Multi-resolution analysis (MRA) is a signal processing technique used to analyze signals at multiple scales and resolution. The basic idea is convert the signal \(x(t)\) into the number of successive approximations, each of which is the smoother version of \(x(t)\) corresponding to a different resolution. MODWT is MRA that uses low- and high-pass filters to split the frequency spectrum of the input signal into scaling and wavelet coefficients. The traditional way of constructing the MODWT employs circular convolution directly in the time domain. However, the technique presented here performs the circular convolution within the Fourier domain, potentially improving processing efficiency and treating periodic signals [26].  
To elaborate, the wavelet and scaling filter coefficients at a particular level, '\(j\)', are computed by first performing the signal's discrete Fourier transform (DFT) and the wavelet or scaling filter's DFT at the '\(j\)th' level. The DFT is a mathematical approach commonly used for frequency analysis of time-domain signals to transform a function of time into frequency. The product of these DFTs is then computed, which essentially captures the multiplication of their spectral components. This procedure relates to the time domain convolution of the original signals. Finally, the inverse DFT is done on this product which converts the information back into the time domain, producing the wavelet and scaling filter coefficients for the '\(j\)th' level [27].  
This method efficiently computes the circular convolution in the Fourier domain before bringing it back into the time domain. The benefit of this approach is its computing efficiency and precision, which is especially useful for big datasets and when the signal or system of interest is intrinsically periodic or well-represented in the frequency domain.  
Let \(H\) and \(G\) are \(j\)th level MODWT wavelet and scaling filters defined by orthogonal wavelets, respectively. If \(N\) represents the size of the sample which is an integer multiple of \(2^{j}\), then the DFT of the higher order wavelet filter is defined by  
\[\frac{1}{N}\sum_{k=0}^{N-1}H_{j,k}e^{2\pi k/N},\hskip 28.452756 ptk=0,\ldots.\ldots,\ N-1 \tag{7}\]  
\[H_{j,k}=H_{2j-1k\text{mod}N}\prod_{m=0}^{j-2}G_{2^{m}k\text{mod}N}\]  
where \(k\) is the width of the filter while \(H_{j,k}\) is the \(j\)th level wavelet filter of the higher order. In addition, \(k\) mod \(N\) is \(k\) modulo \(N\).  
where  
\[G_{j,k}=\prod_{m=0}^{j-2}G_{2^{m}k\text{mod}N}\]  
Figure 5 represents the MODWT of a Lamb wave signal. The original signal on the top is decomposed up to five decomposition levels with symlet wavelets. It captures both high and low frequency details and components, providing a comprehensive detailed of the signal.
## 2 Conditions for SHG
### Results  
Figure 2a shows the fundamental mode wavelengths that satisfy the SHG conservation laws for a fixed waveguide thickness (\(e=280\) nm) and different waveguide widths and fundamental mode azimuthal numbers. It is interesting to note that many geometries are available to generate a SH using a pump wavelength longer than \(2.2\)\(\upmu\)m, which is the limit to prevent two-photon absorption (TPA) [30]. Note that we use dashed curves in the figure to underline that the SHG conditions are not continuous, since the microrings have separate resonances--i.e., the azimuthal number is an integer. We can see that along one curve, the radius increases with \(m_{f}\), which is consistent with the fact that more wavelengths have to fit on one round-trip. Moreover, the curves are steeper for small radii and saturate for large radii. This can be easily understood, since when the radius becomes large, the ring resembles more a straight waveguide where the angular momentum conservation is replaced by the linear momentum conservation which does not depend on the radius. Besides, the three curves show that the fundamental wavelength is longer for a given \(m_{f}\) when the width increases. This is consistent with the fact that when \(w\) is larger, the effective index increases and thus the wavelength lengthens to keep the azimuthal number constant.  
Figure 1: (**a**) System and notation used for the SHG study. (**b**) Cross section of the microring showing the materials and thicknesses. (**c**) Mode profiles of a TE mode with radial number \(p=1\) and TM mode with radial number \(p=3\).  
Figure 2b is a complement of (a) showing the thickness's influence. This time the width is fixed to \(w=800\) nm and the waveguide thickness is varied. We can observe that the variations of the radius and of the wavelength are not monotonous with the thickness. For small thicknesses, \(e<400\) nm, the thicker the microring, the larger the wavelength. However, for \(e>400\) nm, the wavelength decreases with the thickness. This evolution will be thoroughly described and explained in the fourth section. We can note that, unlike the previous evolution of \(\lambda_{f}\) with \(R_{in}\) at fixed \(w\) and \(e\), this time the evolution shown by the curves is continuous: for any thickness \(e\), at a given \(w\), an internal radius \(R_{in}\) and a pump wavelength \(\lambda_{f}\) exist that fulfill the SHG condition.