esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Experimentation
#### 2.2.3. SampEn of HRV  
A Bio-Radio wireless physiological monitor was used to collect ECG data of each participant at a sampling rate of 250 Hz. The HRV signals were obtained based on the QRS waves of the ECG signal extracted by the BioCapture physiological monitoring system. In this study, SampEn was used to assess the complexity of HRV (cardiovascular dynamics), which was calculated as follows [24]:  
Given N data points from a time series \(\{x(n)\}=x(1),x(2),\ldots,x(N)\), the m vectors \(X_{m}(1),X_{m}(2),\ldots,X_{m}(N-m+1)\) were reconstructed to be \(X_{m}(i)=\big{[}x(i),x(i+1),\ldots,\)\(x(i+m-1)\big{]}\), for \((1\leq i\leq N-m+1)\).  
1. The vector distance \(d[X_{m}(i),X_{m}(j)]\) between vector \(X_{m}(i)\) and vector \(X_{m}(j)\) was defined as the maximum absolute difference between their corresponding elements (Equation (3)) \[d[X_{m}(i),X_{m}(j)]=\underset{k=0,1,\ldots,m-1}{max}(|x(i+k)-x(j+k)|)\] (3)
2. Given a similar capacity \(r\) (\(r>0\)), the number of \(j\) satisfying the formula (Equations (4) and (5)) was counted and denoted as \(B_{i}\). \[d[X_{m}(i),X_{m}(j)]\leq r\ (1\leq j\leq N-m,j\
eq i)\] (4) Then, for \(1\leq i\leq N-m\), \[B_{i}^{m}(r)=\frac{B_{i}}{N-m-1}\] (5)
3. \(B_{i}^{m}(r)\) of the average value of \(i\), was represented as \(B^{m}(r)\) (see Equation (6)). \[B^{m}(r)=\frac{\sum_{i=1}^{N-m}B_{i}^{m}(r)}{N-m}\] (6)
4. Another \(m+1\) vectors were constructed with above steps to obtain \(B^{m+1}(r)\). Then, the \(SampEn\) could be estimated as: \[SampEn(m,r,N)=ln\frac{B^{m}(r)}{B^{m+1}(r)}\] (7)
## 2 Algorithms
### Chaos Synchronization
\begin{array}{c}x_{1s}(k+1)=0.99x_{1s}(k)+0.01x_{2s}(k)\\ x_{2s}(k+1)=0.028x_{1s}(k)+0.999x_{2s}(k)+u(k)\\ x_{3s}(k+1)=0.997x_{3s}(k)+0.001x_{1s}(k)x_{2s}(k),\end{array}  \tag{11}\]  
where \(x_{1m}\), \(x_{2m}\), and \(x_{3m}\) are state variables of the master system (10); \(x_{1s}\), \(x_{2s}\), and \(x_{3s}\) are state variables of slave system (11); and \(u\) is the proposed controller. To synchronize both systems, error functions are defined as \(e_{i}=x_{is}-x_{im}\), \(i=1,\ 2,\ 3\). If \(e_{i}\) can converge to zero, it means that the master and slave systems can be synchronized. To achieve synchronization, we used a sliding mode controller to ensure that the system reached the switching function we designed and the switching function in the sliding manifold ensured that \(e_{i}\) could converge to zero, thus achieving synchronization. The switching function for the sliding mode control was selected as follows:  
\[s(k)=e_{2}(k)+ce_{1}(k). \tag{12}\]  
If the system smoothly goes into sliding mode with \(u\), in other words, \(s(k)=0\), then \(e_{2}(k)=-ce_{1}(k)\). Applied to the error equation of the master-slave system, we have  
\[e_{1}(k+1)=0.99e_{1}(k)-0.01 ce_{1}(k). \tag{13}\]  
When the parameter \(c\) is selected to satisfy \(|0.99-0.01c|<1\), \(e_{1}\) will converge to zero. Due to system sliding, \(s(k)=0\), so \(e_{2}\) will also converge to zero. Eventually, \(e_{3}\) becomes \(e_{3}(k+1)=0.997e_{3}(k)\), and will also converge to zero. In this moment, two systems achieve synchronization. In order to let the error function in sliding mode, we used a similar design in [21] for the controller \(u(k)\), given as follows:  
\[u(k)=-f(\mathrm{k})-\alpha s(k), \tag{14}\]  
where \(0<\alpha<1\), and  
\[ \begin{array}{c}f(k)=(0.028-0.01c)e_{1}(k)+(0.01c-0.001)e_{2}(k)-0.001x_{1s }(k)x_{3s}(k)+\\ 0.001x_{1m}(k)x_{3m}(k).\end{array}  \tag{15}\]  
Computing  
\[ \begin{array}{c}\triangle s_{k}=s(k+1)-s(k)=e_{2}(k+1)+ce_{1}(k+1)-e_{2}(k )-ce_{1}(k)\\ =f(\mathrm{k})+u(k).\end{array}  \tag{16}\]  
If \(0<\alpha<1\), (16) will be transposed to \(s(k+1)=(1-\alpha)s(k)\), and \(s(k)\) will converge to zero. It is obvious that the parameter \(\alpha\) is relative to the convergence speed of the switching function \(s(k)\). To test the synchronization control design, the initial condition and parameters were selected as \(x_{1m}=0.1\), \(x_{2m}=-0.1\), \(x_{3m}=0.4\), \(x_{1s}=0.2\), \(x_{2s}=-0.5\), \(x_{3s}=0.2\), \(c=49\), and \(\alpha=0.5\). The result is presented in Figure 3, showing that \(e_{1}\) and \(e_{2}\) converge quickly, and \(e_{3}\) needs some time to converge.
# Temporal Variations of Sediment Provenance in a Karst Watershed, China
###### Abstract  
The environmental quality of the sediments in karst areas is a common concern, and it is of great significance to analyze the sources of the sediments. This study investigates the sources and its temporal variations of catchment sediments in a typical small karst watershed area. Toxic metal concentrations in the catchment area were monitored via three geochemical baseline projects in China. The sediment identification fingerprint tool (SIFT) was used to establish a geochemical model for tracing the main source contributions and its temporal variations of catchment sediments over the past 28 years (1992-2019). The catchment sediments in the small karst catchment area were mainly sourced from the background lithologies, among which limestone contributed the most, followed by dolomite, sand-shale, and mudstone; however, the anthropogenic lead-zinc tailings contributed the least. The contributions and temporal variations of each source were closely related to the lithology, topography, and landform, as well as the change in land-use and vegetation cover and the degree of rocky desertification. Moreover, the implementation of vegetation restoration and control of rocky desertification decreased the contributions of the upstream geological bodies, and the toxic metal content of the catchment sediment decreased accordingly. This study is of great significance for environmental governance in karst areas.  
Karst; catchment sediment geochemical tracing; toxic metal pollution; temporal variations
# Standalone GPS L1 C/A Receiver for Lunar Missions
## 2 The _WeakHEO_ Receiver Architecture
### GNSS Signal Characteristics
#### 2.3.2 Signal Power and Dynamics  
In this section we report some significant simulation results from our previous studies [13; 24], and [26] used to define the main receiver characteristics. Figure 4 shows the received power levels of all the GPS L1 C/A signals as a function of the time, during the full considered trajectory. As shown in Figure 5, the fourth strongest GPS L1 C/A signal (at least four ranges are required to compute the 3D position) at receiver antenna reaches approximately a minimum power level of \(-168.5\) dBm. For this reason, a very high sensitivity receiver architecture is required. Moreover, the carrier frequency is affected by Doppler shifts and Doppler rates (see Figures 6 and 7 respectively) of up to 20 kHz and up to 4 Hz/s respectively at the Moon altitude (where the signals are weaker) and of up to 60 kHz and up to 65 Hz/s at the beginning of the MTO (where the signals are stronger), requiring a certain robustness against high dynamics for the receiver.  
The identified minimum power level of \(-168.5\) dBm of the fourth strongest signal during the full considered MTO can be seen as a power sensitivity value required for the receiver to acquire and track at least four signals (and then provide the navigation solution) with a certain probability. Then, assuming a receiver antenna gain of \(10\,dBi\), we have targeted a sensitivity of at least \(-159\) dBm, slightly higher than the minimum required.  
According to [27], it is possible to calculate the carrier-to-noise density ratio C/N\({}_{0}\) (in dB-Hz) from the received power \(P_{r}\) (in dBm) by using the following formula, valid for a front-end noise figure of 2 dB and an effective antenna temperature of 130 K:  
\[C/N_{0}=P_{r}+174 \tag{2}\]  
A received signal level of \(-159\) dBm then corresponds to a C/N\({}_{0}\) of 15 dB-Hz.  
However, when a GNSS simulator is used, the effective antenna temperature is the room temperature, which increases the noise. In that case, the carrier-to-noise ratio can be obtained as  
\[C/N_{0}=P_{r}+172 \tag{3}\]  
A received signal level of \(-157\) dBm then corresponds to a C/N\({}_{0}\) of 15 dB-Hz for testing with the GNSS simulator.  
Figure 4: Received power levels of the GPS L1 C/A signals as a function of the time, during the full considered trajectory, by assuming a 0 dBi receiver antenna gain.  
Figure 5: First, second, third and fourth highest received power levels of the GPS L1 C/A signals as a function of the time, during the full considered trajectory, by assuming a 0 dBi receiver antenna gain.  
Figure 6: Doppler shift of received GPS L1 C/A signals across the MTO.
# A Method for Managing ADS-B Data Based on a 4D Airspace-Temporal Grid (GeoSOT-AS)
## 6 Experiments and Results
### GeoSOT-AS Shape Change Law Analysis Test
The organization method and extraction of the actual corresponding air charts were reasonable, complete, and in line with the actual ADS-B data storage and use requirements.
## 6 Conclusions
\begin{tabular}{c c c c c} \hline
**Sampling Point** & **Longitude** & **Latitude** & **Altitude (m)** & **Soil** \\ \hline Konchoch & 39.042167 & 68.332750 & 2361 & Luvisol \\ Kanchoch mine adit & 39.039528 & 68.337889 & 2457 & Luvisol \\ Iskandandardaya 1 & 39.101361 & 68.403583 & 2015 & Regosol \\ Iskandandardaya 2 & 39.134028 & 68.453528 & 1820 & Regosol \\ Iskandardarya 3 & 39.186556 & 68.534944 & 1658 & Regosol \\ (estuary of river) & 39.196389 & 68.536028 & 1644 & Regosol \\ Alovdin Lake 1 & 39.237642 & 68.254278 & 2780 & Regosol \\ Alovdin Lake 2 & 39.237472 & 68.261083 & 2780 & Regosol \\ Sarvoda 1 & 39.250194 & 68.268889 & 2620 & Regosol \\ Sarvoda 2 & 39.262944 & 68.310917 & 2488 & Cambisol \\ Sarvoda 3 & 39.246278 & 68.355750 & 2358 & Cambisol \\ Sarvoda 4 & 39.252111 & 68.405083 & 2166 & Gleysol \\ Sarvoda 5 & 39.226611 & 68.441028 & 1958 & Gleisol \\ Sarvoda 6 & 39.223194 & 68.484611 & 1808 & Gleisol \\ Sarvoda 7--estuary of river & 39.219889 & 68.527639 & 1660 & Regosol \\ Fondarya 2 & 39.225167 & 68.530250 & 1626 & Regosol \\ Pete 1 (right tributary to & 39.248639 & 68.561778 & 1895 & Gleisol \\ Fondarya) & 39.249361 & 68.549889 & 1710 & Gleisol \\ Fondarya 3 & 39.258806 & 68.544000 & 1589 & Regosol \\ Kumargi Bolo 1 & 39.276861 & 68.573056 & 2029 & Regosol \\ Kumargi Bolo 2 & 39.275444 & 68.547583 & 1690 & Regosol \\ Chore 1--upper of river & 39.313639 & 68.479694 & 2148 & Regosol \\ Chore 2--adit & 39.308944 & 68.506194 & 1863 & Regosol \\ Chore 3--adit & 39.308639 & 68.514056 & 1753 & Regosol \\ Chore 4--adit & 39.308222 & 68.528694 & 1577 & Regosol \\ Chore 5--estuary of river & 39.306083 & 68.532917 & 1580 & Regosol \\ Fondarya 4 & 39.305528 & 68.533861 & 1580 & Cambisol \\ Fondarya 5 & 39.344750 & 68.554333 & 1466 & Cambisol \\ Kumargi poyon 1 & 39.325944 & 68.628389 & 3629 & Gleysol \\ Kumargi poyon 2 & 39.363167 & 68.602694 & 1782 & Gleysol \\ Kumargi poyon 3 & 39.363967 & 68.561083 & 1434 & Gleysol \\ Fondarya 6 & 39.380528 & 68.549556 & 1389 & Cambisol \\ Zarafshon 1 & 39.452667 & 70.426100 & 2732 & Regosol \\ Zarafshon 2 (Dihisor) & 39.426194 & 70.307583 & 2612 & Regosol \\ Zarafshon 3 (Dihisor) & 39.44472 & 70.189750 & 2517 & Regosol \\ Zarafshon 4 (Paldorak) & 39.457583 & 70.076194 & 2423 & Regosol \\ Zarafshon 5 (Paldorak) & 39.426417 & 69.963444 & 2330 & Regosol \\ Zarafshon 6 (Sabak) & 39.447639 & 69.841194 & 2272 & Regosol \\ Zarafshon 7 & 39.453167 & 69.723861 & 2267 & Regosol \\ Morvut- tributary to & & & & \\ Zarafshon & 39.449194 & 69.676306 & 2242 & Regosol \\ Zarafshon 8 & 39.440358 & 69.601324 & 2158 & Regosol \\ Madrushkat- tributary to & 39.446944 & 69.563472 & 2123 & Regosol \\ to Zarafshon & 39.446944 & 69.563472 & 2123 & Regosol \\ \hline \end{tabular}
## 4 Conclusions  
Combining in-situ infrared spectroscopic studies and first-principles DFT calculations, we have shown that the dissociation reaction of water molecules within MOF-74 can be controlled by co-adsorption of alcohols, just as it is hindered by inert gases. Bulky alcohol molecules such as IPA prevent water clustering, thus reducing water dissociation rates more effectively than inert gases at comparable external pressures. On the other hand, small alcohol molecules with a low boiling point, such as methanol, effectively accelerate water dissociation, without sterically hindering the process. In the confined environment of MOF-74, several water molecules can coexist within each pore, forming nano-structures, which generally lowers the dissociation barriers and stabilizes the dissociated state at low pressures. These results offer a potential approach to controlling water dissociation inside MOF-74, which is important for gas storage and separation applications. Since water is present in most biological, catalytic, and daily life applications, the understanding derived here provides new insight into the possibility of controlling water dissociation rates, which in turn affords a novel means for tuning chemical reactions in general.  
The following are available online at www.mdpi.com/2076-3417/8/2/270/s1. Sample preparation and activation methods; Powder X-ray diffraction pattern of Zn-MOF-74 sample; isotherm measurement of N\({}_{2}\) adsorption into Zn-MOF-74; IR spectra of pure methanol (MeOH) and deuterated methanol (MeOD) interaction with Zn-MOF-74; Water dissociation reaction pathway in the presence of one and four water molecules; IR spectra of isopropanol (IPA) and D\({}_{2}\)O interaction with Zn-MOF-74. This material is available free of charge at [[http://pubs.acs.org](http://pubs.acs.org)]([http://pubs.acs.org](http://pubs.acs.org)).  
This work was entirely supported by the Department of Energy Grant No. DE-FG02-08 ER46491. [PERSON] also acknowledges generous support from the Simons Foundation through Grant No. 391888, which endowed his sabbatical leave at MIT.  
[PERSON] and [PERSON] synthesized and characterized Zn-MOF-74 used in this work; [PERSON]-[PERSON], [PERSON] and [PERSON] conceived, designed and perform the experimental part of the paper; [PERSON], [PERSON] and [PERSON] performed the theoretical analysis. All authors contributed to the writing, and analysis of this paper.  
The authors declare no conflict of interest.
# Distance Assessment by Object Detection--For Visually Impaired Assistive Mechatronic System
## 4 Results
### Experimental Results  
#### 4.2.1 Results Using Low-Cost Cameras  
The best results from the experimental evaluation of the distance to objects using sign detection with low-cost cameras are presented in Table 1. The signs are shown in Figure 14.  
Figure 12: Curved contour test object located on the west direction of the plane, at a distance of 1.00 m.  
Figure 13: Dynamic simulation of robot arm motion. (**a**) Home position of robot arm; (**b**) position near target.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Images Set**} & \multicolumn{2}{c}{**Real Distance**} & \multicolumn{2}{c}{**Left Camera**} & \multicolumn{2}{c}{**Right Camera**} \\ \cline{2-7}  & **(mm)** & **Calculated** & **Error (\%)** & **Calculated** & **Error (\%)** \\ \hline \multirow{4}{*}{SET-8} & 150 & 165.88 & 10.59 & 200.81 & 33.87 \\  & 200 & 205.44 & 2.72 & 210.13 & 5.07 \\  & 250 & 263.25 & 5.3 & 290.21 & 16.08 \\  & 300 & 318.65 & 6.22 & 333.25 & 11.08 \\ \hline \multirow{4}{*}{SET-11} & 150 & 164.47 & 9.64 & 163.82 & 9.21 \\  & 200 & 205.43 & 2.71 & 212.83 & 6.41 \\  & 250 & 257.07 & 2.82 & 254.10 & 1.64 \\  & 300 & 314.70 & 4.90 & 314.27 & 4.75 \\ \hline \multirow{4}{*}{SET-12} & 150 & 179.36 & 19.57 & 179.17 & 19.44 \\  & 200 & 224.02 & 12.01 & 265.52 & 32.76 \\ \cline{1-1}  & 250 & 288.06 & 15.22 & 291.65 & 16.66 \\ \cline{1-1}  & 300 & 363.16 & 21.05 & 324.33 & 8.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental distance evaluation based on low-cost cameras’ vision.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**GoogLeNet**} & \multirow{2}{*}{**ResNet-50**} & \multirow{2}{*}{**Fisheye**} & \multirow{2}{*}{**Telephoto**} & \multirow{2}{*}{**Distance**} & \multirow{2}{*}{**Average**} & \multirow{2}{*}{**Error**} & \multirow{2}{*}{**Frame**} & \multirow{2}{*}{**Accuracy**} \\
**Database** & & & & & & **(mm)** & **(\%)** & **(fps)** & **(\%)** \\ \hline \multicolumn{2}{c}{} & x & x & 500 & 509 & 1.80 & 13.34 & 60.00 \\ \hline \multicolumn{2}{c}{} & x & x & 500 & 493 & 1.40 & 17.48 & 50.00 \\ \hline \multicolumn{2}{c}{} & x & x & 1000 & 1011 & 1.10 & 14.16 & 8.33 \\ \hline \multicolumn{2}{c}{} & x & x & 1000 & 985 & 1.50 & 19.20 & 8.33 \\ \hline \multicolumn{2}{c}{} & x & x & 1500 & 1562 & 4.13 & UO & 0.00 \\ \hline \multicolumn{2}{c}{} & x & x & 1500 & 1526 & 1.73 & 18.98 & 11.67 \\ \hline \multicolumn{2}{c}{} & x & x & 1500 & 1440 & 4.00 & UO & 0.00 \\ \hline \multicolumn{2}{c}{} & x & x & 1500 & 1557 & 3.80 & UO & 0.00 \\ \hline \multicolumn{2}{c}{} & x & x & 1000 & 966 & 3.40 & UO & 0.00 \\ \hline \multicolumn{2}{c}{} & x & x & 1000 & 1042 & 4.20 & UO & 0.00 \\ \hline \multicolumn{2}{c}{} & x & x & 500 & 491 & 1.80 & 13.51 & 36.46 \\ \hline \multicolumn{2}{c}{} & x & x & 500 & 495 & 1.00 & 17.85 & 8.33 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test object recognition experimental research results. (UO = unrecognized object).  
Figure 14: Reference images used for Table 2. **(a)** SET-8; **(b)** SET-11; **(c)** SET-12.
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results  
The Secure Water Treatment (SWAT) dataset [42; 43] is the most popular ICPS dataset, and has been widely used to evaluate the performance of anomaly-based detection works. However, this dataset lacks precise timestamps to calculate the time interval and execution time. Power System datasets [23; 44], the Electra dataset [45] and the Water Storage Tank dataset [44; 46] also lack detailed information to train the proposed model.  
In this work, the proposed model was evaluated on a testbed, as shown in Figure 5. This testbed emulated an ICPS subsystem of a coal mining factory using Open-PLC project [47; 48]. It consisted of one master device with IP [**.**204.73] and two slave devices [**.**41.10,520,0] and [**.**12.85,520,0]. A laptop installed with Kali was used to launch cyberattacks. Our model ran on another computer and was fed the mirrored traffic. The operation pattern that was run in this testbed is shown in Table 2 and is the same as the ICPS subsystem of the coal mine factory. Each operation cycle consisted of fifteen operations that operated two slave devices with five unique operation types. This ICPS subsystem comprised one master device, two slave devices, and several application servers (e.g., FTP and HTTP servers). A total of 170 h of industrial traffic were collected at the network switch from the factory to analyse the operation pattern, and we only observed the master device operating the slave devices in this collected traffic.
## 3 Results
### Uncertainty Results  
In order to assess the reliability of the experimental measurements, an uncertainty analysis was performed considering both Type A (statistical) and Type B (systematic) components. Based on the repeated airflow velocity measurements, the Type A uncertainty was calculated as 0.2193 m/s, while the Type B uncertainty, which accounts for instrumentation errors, fan performance variations, and geometrical positioning effects, was determined as 0.4807 m/s. The combined uncertainty was obtained by the root sum square (RSS) method, yielding a total uncertainty of 0.5284 m/s. Based on a total uncertainty of 0.5284 m/s and a mean measured airflow velocity of 6.1817 m/s, the calculated relative uncertainty was approximately 8.55%. According to the standards commonly accepted in thermal and fluid mechanics experimental studies, a relative uncertainty within the range of 5-10% is considered normal and acceptable. This result indicates that the overall experimental setup maintained an acceptable level of precision, providing a solid basis for the validation of CFD simulations and the comparative evaluation of different heatsink configurations.
# Building Scalable and Secure Multicast Delivery Infrastructure in a Local Area Network
## 4 Core Based Multicast Tree
### Security Analysis
#### 4.2.2 Compromised Key  
Whenever a key of a particular host or a device is compromised (due to a number of various reasons, which is beyond the scope of this paper), the core will generate a new pair of public and private keys for the compromised node. In our proposed model, we are not revoking the identity of the compromised node. Using the new hash polynomial, every end-host will identify the compromised host through its newly generated keys; thus, the old keys are naturally removed from the system.
# Bidirectional Long Short-Term Memory Neural Networks for Linear Sum Assignment Problems
###### Abstract  
Many resource allocation problems can be modeled as a linear sum assignment problem (LSAP) in wireless communications. Deep learning techniques such as the fully-connected neural network and convolutional neural network have been used to solve the LSAP. We herein propose a new deep learning model based on the bidirectional long short-term memory (BDLSTM) structure for the LSAP. In the proposed method, the LSAP is divided into sequential sub-assignment problems, and BDLSTM extracts the features from sequential data. Simulation results indicate that the proposed BDLSTM is more memory efficient and achieves a higher accuracy than conventional techniques.  
Keywords:linear sum assignment problem; deep neural network; bidirectional long short-term memory structure; resource allocation +
Footnote †: journal: Applied Sciences
## 2 Background and Related Work
### Logistic Map  
The logistic map is used to take advantage of chaotic functions' pseudo-random nature. The bifurcation diagram of the function described in Equation (4) is used to determine its chaotic behavior, which is depicted in Figure 4a. This behavior can be seen when the control parameter \(\mu\in[3.8,4]\). The quasi-uniformity of the invariant natural density of the logistic map, as shown in Figure 4b, confirms this.  
\[x_{n+1}=\mu x_{n}(1-x_{n}) \tag{4}\]  
Exploiting the pseudo-random behavior of the logistic map, 80 pseudo-random values are generated starting from an initial condition \(x_{0}\) and a control parameter \(\mu\); these values are then quantified to 0s and 1s based on a threshold \(T\) to construct an 80-bits ling key, which will represent the secret key for the PRESENT block cipher.
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 4 Proposed Method
### AE Based Low Dimensional Extraction of Feature  
In real-world network environments, normal traffic accounts for the majority and abnormal traffic takes up a much smaller share. Detecting attacks that rarely occur among attack traffic is the most important element of a high-performance NIDS. For infrequent attacks--that is, rare attacks--it is difficult to find patterns to extract useful features. Therefore, in this study, AE was used to help extract the low-level features of sparse classes.  
The CICIDS2017 dataset used in this study consists of a total of 77 features. In the process of encoding 77 features with the AE algorithm, only the core feature information of the data is learned in the hidden layer and the remaining information is lost. When the output value of the hidden layer is extracted during the decoding process, it is an approximation of the input value rather than the perfect value. By extracting the weights so that the output value is the same as the input value, we can extract the feature precisely. The experimental method extracts the optimal features by adjusting the number of hidden layers. When the optimal feature is extracted, the time and performance of detection can be improved as there is no need to learn unnecessary features.