esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 4 Proposed Combination Approach for an IoT Intrusion Detection System
### Stage Two: AIDS Stage  
To improve the identification of unknown attacks, the SIDS stage's output is utilized to train the AIDS stage in recognizing abnormal behaviors. By training with benign samples, one-class SVM is used to teach AIDS how to differentiate activities that do not appearnormal, which are commonly exhibited by malware. With one-class SVM, benign activities are learned, and this kind of classifier can accurately identify normal activities, as normal class training data are abundant. On the other hand, zero-day attacks are uncommon, so few or even none of these attacks may be present in the training datasets. Therefore, in the second stage, normal behavior is recognized, and any activities that are outside the usual behavior are classified as zero-day attacks. One-class classification methods attempt to build models when the malware class is not present, has a poor sample, or is not well defined. Unlike traditional multi-class classification, in one-class classification, normal behavior is effectively defined by the training data, while unknown malware has no examples.
# Sample-Pair Envelope Diamond Autoencoder Ensemble Algorithm for Chronic Disease Recognition
## 4 Experimental Results and Analysis
### Algorithm Comparison
#### 4.4.3 Comparison with Recent Chronic Disease Detection Algorithms  
To further verify the accuracy of SP_DFSaeLA algorithm, its performance is compared to those of state-of-the-art algorithms proposed in recent years for classifying chronic diseases, including those by [PERSON] et al. [54], [PERSON] et al. [55], [PERSON] et al. [56], [PERSON] et al. [57], [PERSON] et al. [21], and [PERSON] et al. [22], [PERSON] et al. [30]. For a more visual representation of the comparative algorithm information, we describe the state-of-the-art (SOTA) studies with their pros and cons in Table 14. Tables 15 and 16 show that all the models perform better in either positive or negative chronic disease prediction; the two cross-validation methods (CV, including k-fold and Holdout) are used for fair comparison. As seen from the table, the SP_DFSaeLA is better than the others in precision or F1_score or both. The cases are similar under fivefold and Hold out CV. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **SAE [49]** & **SSAE [50]** & **SDAE [51]** & **SPSAE [52]** & **SSAE and LASSO [53]** & **ESAE** & **Proposed** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline PD & 64.15 & 66.48 & 66.48 & 66.22 & 65.87 & 60.29 & **75.67** \\ AD & 57.67 & 61.67 & 59.58 & 61.78 & 57.66 & 60.94 & **71.11** \\ Vehicle & 67.30 & 70.00 & 72.00 & 74.76 & 80.06 & 74.89 & **80.77** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Classification accuracy of different deep autoencoder classifiers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Performance** & **Literature** & **Literature** & **Literature** & **Proposed** & **Proposed** \\  & **Indices** & [54] & **[55]** & **[56]** & **[57]** & **Method** & **Method** \\  & & **(5-Fold)** & **(5-Fold)** & **(Holdout)** & **(5-Fold)** & **(5-Fold)** & **(Holdout)** \\ \hline  & Acc (\%) & **88.8** & 87.1 & 80.04 & 75.71 & 84.54 & 83.18 \\  & Sens (\%) & 78.9 & 85.4 & 73.51 & - & **80.99** & 74.07 \\ PID & Spec (\%) & **93.4** & - & - & - & 88.11 & 92.45 \\  & Prec (\%) & 84.2 & 80.6 & 84.93 & - & 88.50 & **90.91** \\  & F1\_score (\%) & - & 82.9 & 78.80 & - & **84.18** & 81.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance comparison of proposed method with state-of-the-art chronic disease detection algorithms.
# Experimental Study on Anisotropic Mechanical Characteristics of Shale under Triaxial Loading
## 2 Experimental Methodology  
### Shale Samples Preparation  
The shale cores used in the experiment were taken from a traffic tunnel in a shale formation in the western Hubei province, China. The large shale blocks, as shown in Figure 1a, were selected at the tunnel construction site and were drilled to obtain shale cores, and then the cores were cut and polished into standard cylindrical shale samples. The diameter and height of the prepared cylindrical shale samples are 50 mm and 100 mm, respectively, and the flatness deviation at both ends of the samples is within 0.02 mm,whereas the deviation along the height direction does not exceed 0.1 mm. At the same time, in order to determine the anisotropic mechanical properties of shale, the angle \(\theta\) between the static loading direction and the normal direction of the bedding planes of the shale samples was defined as the bedding angle. Shale samples with bedding angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 45\({}^{\circ}\), 60\({}^{\circ}\), and 90\({}^{\circ}\) were prepared, and there were 15 samples for each bedding angle, as shown in Figure 1b.  
The composition and microstructure of shale are the internal factors that determine its mechanical behavior and anisotropic characteristics [3]. By using X-ray diffractometer (XRD) to analyze the mineral composition of the shale samples, it can be found that the shale composition is mainly divided into clay minerals and other minerals. The proportions of each component were as follows: quartz 41.9%, illite 28.4%, albite 11.6%, chlorite 8.8%, kaolinite 8.6%, and hematite 0.7%. The XRD results indicated that the mineral composition of the shale is mainly composed of quartz and illite, which account for about 70% of the total composition.  
Meanwhile, the remaining shale fragments after sample preparation were selected, and the microstructure of shale samples was observed by scanning electron microscope (SEM). The scanning results are shown in Figure 2, and it can be found that the layered structure of shale was significant. In Figure 2a, the electron microscope was scanning in the direction of the bedding planes, and the organic matter (inside the red solid circle) mixed on the bedding planes can be seen. The flaky bedding structure can also be clearly identified. In Figure 2b, the electron microscope was scanning in the direction perpendicular to the bedding planes, and it can be seen that the microcracks (inside the red dotted ellipse) between bedding planes are completely developed; most of the pores are between 5 \(\upmu\)m and 10 \(\upmu\)m, and impurities were filled with them.  
Figure 1: Schematic diagram of prepared shale samples: (**a**) Large shale blocks; (**b**) Shale samples with different bedding angles (the orange lines mark the direction of the bedding planes of different shale samples).  
Figure 2: Microstructure of shale observed by SEM: (**a**) Direction of the bedding planes; (**b**) Direction perpendicular to the bedding planes.
# Smooth Design of 3D Self-Supporting Topologies Using Additive Manufacturing Filter and SEMDOT
## 3 Numerical Experiments
### Different Critical Overhang Angles  
When [PERSON]'s AM filter is integrated into traditional element-based algorithms such as SIMP, the critical overhang angle is fixed at 45\({}^{\circ}\) because of the defined element layout (see Figure 1). As intermediate elements can be cut during the formation of smooth boundaries in SEMDOT, the possibility of exploring different critical overhang angles is therefore provided by the combination of [PERSON]'s AM filter and SEMDOT. To obtain self-supporting topologies with different critical overhang angles, the weighting factor of \(w_{s}=1/\tan\alpha\) is introduced to the locations of elements \(X_{1}\), \(X_{2}\), \(X_{4}\), and \(X_{5}\) in the  
Figure 7: Convergence process and optimized topology with a fine mesh for a target volume fraction of 0.15: (**a**) convergence process, (**b**) Optimized topology.  
supporting region (refer to Figure 1) where \(\alpha\) is the critical overhang angle. Equation (7) is therefore rewritten as  
\[\Xi_{(i,j,k)}=\max(w_{s}\xi_{(i-1,j,k-1)},w_{s}\xi_{(i,j-1,k-1)},\xi_{(i,j,k-1)},w_{s}\xi_{(i,j+1,k-1)},w_{s}\xi_{(i+1,j,k-1)}) \tag{19}\]  
The related equations are accordingly changed.  
Following Section 3.2, a simply supported deep beam with a finite element mesh of 30 \(\times\) 20 \(\times\) 20 is used to explore self-supporting topologies with different critical overhang angles (i.e., 30\({}^{\circ}\), 45\({}^{\circ}\) and 60\({}^{\circ}\)). The objective function value is multiplied by a scale factor of 100.  
Figure 8a shows that the reference design case converges at 326.4 J after 83 iterations, and self-supporting design cases with different critical overhang angles of 30\({}^{\circ}\), 45\({}^{\circ}\), and 60\({}^{\circ}\) converge at 328.4 J after 133 iterations, 328.7 J after 109 iterations and 332.2 J after 261 iterations, respectively. Generally, a higher critical overhang angle will cause worse performance as it provides less design freedom. Based on the results presented in Figure 8a, the critical overhang angle of 45\({}^{\circ}\) requires the least number of iterations to converge among self-supporting design cases. The optimized reference topology is shown in Figure 8b, and optimized topologies with different critical overhang angles are shown in Figure 8c-e.  
It should be acknowledged that the mentioned strategy of exploring different critical overhang angles is not suitable for all test cases. For some specific cases, numerical issues may occur during the optimization process, resulting in improper self-supporting designs.
## Abbreviations  
* 3 GPP & Third Generation Partnership Project
* 5G PPP & 5G Infrastructure Public-Private Partnership
* 5 GNOW & 5 th Generation Non-Orthogonal Waveforms for Asynchronous Signaling
* 5 GIC & 5G Innovation Centre5 GCMSIG & 5G Channel Model & AMPS & Advanced Mobile Phone System & AR & Augmented Reality & ARIB & Association of Radio Industries and Businesses AI & Artificial Intelligence & ATIS & Alliance for Telecommunications Industry Solutions & ASA & Authorized Spectrum Access & ASA & Azimuth Angle Spread of Arrival ASD & Azimuth Angle Spread of Departure BDMA & Beam Division Multiple Access BER & Bit Error Rate & BS & Base Station CA & Carrier Aggregation CBSM & Correlation-Based Stochastic Models & CCs & Carrier Components CCSA & China Communications Standards Association CDMA & Code Division Multiple Access CDL & Clustered Delay Line CI & Close-In CIR & Channel Impulse Response CPU & Central Processing Unit CGQI & Channel Quality Index CSI & Channel State Information D2D & Device-to-Device D-rays Deterministic rays DL & Downlink DSS & Dynamic Spectrum Sharing DC & Dual Connectivity DoF & Degree of Freedom ETRI & Electronics and Telecommunication Research Institute EDGE & Enhanced Data Rates for GSM Evolution EE & Energy Efficiency EM & Electromagnetic eMTC & Enhanced Machine-Type Communication eMBB & Enhanced Mobile Broadband ETSI & European Telecommunication Standards Institute EMPHATIC & Enhanced Multicarrier Technology for Professional Ad Hoc and Cell-Based Communications E-UTRAN & Evolved Universal Terrestrial Radio Access Network EVDO & Evolution Data Optimized FBMC & Filter Bank Multi-Carrier under Frequency Selective Channels FDMA & Frequency Division Multiple Access FEC & Forward Error Corrections FDD & Frequency Division Duplex FIR & Finite Impulse Response Filter FRI & Frequency Range 1 FR2 & Frequency Range 2 F-rays Flashing Rays GAA & General Authorized Access GBSM & Geometry-Based Stochastic Models GSA & Global Mobile Suppliers Association GSM & Global System for Mobile Communication GPRS & General Packet Radio Services GO & Geometric Optics GTD & Geometric Theory of Diffraction H2H & Human-to-Human Communications HSDPA & High-Speed Downlink Packet Access HSPA & High-Speed Packet Access HSUPA & High-Speed Uplink Packet Access IAB & Integrated Access Backhaul
## 2 Modeling of Subsystem  
### Reliability Models  
The CNC grinding machine can be divided into different subsystems according to their structure and function, and the distribution types of each subsystem are not same. The fault rate curves of mechanical and electrical products or components are similar to the \"bathtub curve\", and usually only the first and second paragraphs of the bathtub curve are studied, which are the early failure period and occasional failure period. In statistics, there are many distribution types that are close to the bathtub curve, such as the exponential distribution, Weibull distribution, normal distribution, lognormal distribution, gamma distribution, and so on, as shown in Figure 1. The probability density functions of the above-mentioned five distribution types are shown in Equations (1) to (5). In this paper, five distribution models are used to fit for each subsystem separately, and the optimum model for each subsystem is selected.  
\[f(t)=\lambda e^{-\lambda t} \tag{1}\]  
\[f(t)=\frac{\beta}{\eta}\left(\frac{t-\gamma}{\eta}\right)^{\beta-1}\exp\left( -\left(\frac{t-\gamma}{\eta}\right)^{\beta}\right) \tag{2}\]  
\[f(t)=\frac{1}{\sigma\sqrt{2\pi}}\exp\left(-\frac{\left(t-\mu\right)^{2}}{2 \sigma^{2}}\right) \tag{3}\]  
\[f(t)=\frac{1}{\sigma_{l}t\sqrt{2\pi}}\exp\left(-\frac{\left(\ln t-\mu_{l} \right)^{2}}{2\sigma_{l}^{2}}\right) \tag{4}\]  
\[f(t)=\frac{1}{\mu^{2}\Gamma(a)}t^{a-1}e^{-\frac{t}{b}} \tag{5}\]
## 3 Results and Discussion
### Plackett-Burman Design (PBD)
#### 3.1.2 Contour and Surface Plots  
The 2D and the 3D plots for the response surface versus the significant factorial interactions are shown in Figure 3. The left panel is a sample 2D plot that shows the factorial interactions, where pH and DC are represented on the X- and the Y-axes, while the %R is denoted by the contour lines. As shown on the graph and as per the associated legend, the darkest green zone signifies a zone with the maximum response (greater than 90% removal of TY). The right panel of Figure 3 displays a similar liaison; however, it is shown in the 3D arrangement where the measured %R is represented on the Z-axis. As shown, the maximum %R (exceeding 75%) would be attained by employing a DC between 50-70 ppm and a pH value of 4-7.  
Optimum conditions for maximizing each response (individually) were obtained using the Minitab response optimizer. Data obtained show that maximum %R was achieved with a desirability of \(d=1.0000\) and using a factorial combination of pH = 4, AD = 500 mg/50 mL, DC = 100 ppm, and ST = 5 min. This high value of desirability function (d) indicates that the proposed factorial combination was in favor of maximum %R. The increase in %R with the increase of AD and DC implies that adsorption sites were not getting saturated, even with higher dye concentrations. Maximum \(q_{\rm e}\), however, was obtained using a combination of pH = 4, AD = 100 mg/50 mL, DC = 100 ppm, and ST = 5 min with a desirability of d = 1.0000. This can be explained since \(q_{\rm e}\) could be maximized using the lowest AD and the highest DC, as shown in Equation (2).
# Combined Attenuation Zones of Combined Layered Periodic Foundations
## 3 Results and Discussion  
### Attenuation Zones of Traditional LPFs with Identical Unit Cells  
According to the findings of previous studies, the starting frequency and bandwidth of attenuation zones decrease with the increase in initial stress [18]. Thus, in engineering, the initial stress needs to be controlled in a reasonable range. In the cases discussed later, the initial stress due to the self-weight of a superstructure is taken as \(\sigma_{zz}^{0}=0.4G_{R}\), where \(G_{R}\) is the shear modulus of rubber material. Here, the filling ratio of the rubber layer \(f_{r}\) is defined as the ratio of the thickness of the rubble layer to that of the unit cell.  
By using the computational methods in Section 2, the AZs and FRFs of traditional LPFs with I-type and II-type unit cells with \(f_{r}=50\%\) can be obtained, as shown in Figure 4, where the thicknesses of unit cells are taken as \(a=0.5\) m and \(b=1\) m. It can be easily found from Figure 4 that the frequency ranges where the values of FRF are negative match well with the corresponding AZs. Moreover, by comparing the AZs in Figure 4a,b, it can be seen that the AZs of the traditional LPF with thicker unit cells are lower and narrower, which demonstrates that it is difficult to achieve both comparatively low and wide AZs of traditional LPFs with identical unit cells by tuning the thickness of unit cells.  
Figure 5 shows the AZs of the traditional LPFs with I-type and II-type unit cells changing with the filling ratio of rubber material, which is confined from 10 to 90%. It can be found from Figure 5 that the AZs of traditional LPFs with I-type and II-type unit cells are getting narrower and the gaps between adjacent AZs wider as the filling ratio of rubber material increases to 90%. This is because that the dynamic property of traditional LPF is approaching that of homogeneous rubber material when the filling ratio of rubber material is sufficiently large and approaching 100%. It should be noted that similar cases can also be found as the filling ratio of rubber material approaches 0.  
Figure 4: AZs and FRFs of traditional layered periodic foundations with (**a**) I-type and (**b**) II-type unit cells.
## 4 Results
### End-to-End Results  
With promising results from both the DL-OFDM and LDPC-GNN models, we then completed an end-to-end evaluation of our overall deep learning receiver architecture, by feeding the output of the DL-OFDM into the LDPC-GNN. We refer to this end-to-end model as DL-WholeRx for simplicity. This test was completed on both Rician Fading and  
Figure 5: CNN models—combined 3 GPP LOS results.  
Figure 6: A 16—QAM Rician LDPC comparison.  
3 GPP LOS channel models shown in the previous sections, with our obtained results shown in Figures 7 and 8.  
The LDPC-GNN was retrained on the LLR output from the DL-OFDM model, as the amplitude for a given LLR was not guaranteed to fall in the same range as a traditional receiver. The DL-OFDM model did not need to be retrained, as the input signal from the FFT was the same.  
The Rician channel results show similar results to those in the DL-OFDM section, with the deep learning receiver performing better across all modulation schemes. An interesting observation for the traditional receiver when utilizing the BP algorithm with 10 iterations is the degradation of 16-QAM BER, as the achieved BER is closer to 64-QAM results than the coded bits BER. This is likely due to the high rate of the LDPC code, combined with the relatively poor channel conditions. Compared to the traditional receiver, we observe  
Figure 8: 3 GPP LOS end\(-\)to\(-\)end comparison.  
Figure 7: Rician end\(-\)to\(-\)end comparison.  
that our deep learning receiver performs better for all modulation schemes, and reports significantly better BER at higher SNR for both 16-QAM and 64-QAM, with 16-QAM BER improving by 5 decades, and 64-QAM improving by 3.5 decades.  
When examining the 3 GPP LOS model results shown in Figure 8, we see a dramatic increase in performance between the traditional and deep learning receiver, as 16-QAM shows a two-decade BER improvement, and 64-QAM shows a three-decade BER improvement. When compared against the coded bits BER in Figure 5, a marginal BER performance increase is shown for our deep learning receiver and similar performance for the traditional receiver.  
Given the maintained convergence of the QPSK model and improvement for 16-QAM and 64-QAM, we can conclude that our architecture manages to significantly outperform traditional receivers in adverse channel conditions. Our work expands on existing research by combining channel estimation, symbol demodulation, and channel decoding into a single deep-learning architecture.
## 5 Simulation Results
### Cyclic Pulse Modulation Method Verification  
Firstly, the feasibility of the proposed RMVSC topology and the effectiveness of the cyclic pulse modulation are verified through simulation. The system parameters listed in Table 1 are applied. The main bridge operates in a 180\({}^{\circ}\) conduction. mode, while the reinjection circuit employs a cyclic pulse modulation with a system trigger angle set 3\({}^{\circ}\). The corresponding simulation results are presented in Figure 9. The Y reinjection voltage and D reinjection voltage are illustrated in Figure 9a,b, respectively. The reinjection voltages exhibit excellent balance with minimal distortion. The new reinjection circuit demonstrates multilevel output capability and validates the cyclic driving pulse. As shown in Figure 9c, the RMVSC produces an AC voltage waveform that closely resembles a sine wave without requiring a filter. As illustrated in Figure 9d, the three-phase AC current exhibits a high degree of smoothness. In Figure 9e, the A-phase voltage spectrum shows a THD of only 2.57%, which is closely aligned with the theoretical value of 2.36%. Figure 9f presents the current spectrum with a total harmonic distortion (THD) of 0.83%, indicating favorable low-harmonic grid characteristics. Additionally, Figure 9f,g demonstrate the ZVS process of the first IGBT in the Y bridge. When the IGBT approaches its turn-on or turn-off moment, the voltage across it drops to zero, enabling ZVS. These results confirm that the proposed RMVSC topology can achieve high-quality voltage waveform output without the need for a filter and at low switching frequency, demonstrating exceptional performance.  
Given a total harmonic distortion (THD) of 0.83% for the current, the harmonic factor can be calculated as follows:  
\[Kd=\frac{1}{\sqrt{1+THD^{2}}}=0.99997 \tag{23}\]  
The relationship between the power factor and the displacement factor is expressed as follows:  
\[\lambda=K_{d}*\cos\varphi \tag{24}\]  
Since the value is very close to 1, the power factor can be approximated as equal to the displacement factor.
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
###### Abstract  
This investigation analyses driving behaviors that lead to accidents on overly sharp mountain road curves in Nanjing Province, China. We collected information through field observations and driving simulations while analyzing key indicators like the mean speed of vehicles and spacing between vehicles. The FP-Growth algorithm was used to identify frequent behavioral patterns and measure their relationship with traffic conflicts. The findings showed that unsafe driver behavior on sharp turns was common, while the combination of \"speeding-tailigating-frequent lane changing\" behavior increased conflict risk by 3.7 times. A predictive LSTM neural network model was developed with driver, vehicle, road, and environmental factors. Testing on 4795 samples achieved 83.7% accuracy in foreseeing conflict risk levels. The model, which distinguishes between safety conditions and three severity levels of potential conflict, can provide the most fundamental level of safety needed. The research provides quantitative tools for improved road safety management aimed at supporting real evidence-based \"safe roads\" approaches.  
prediction model; association rule; long short-term memory neural network; mountain roads; driving behavior 2020 IEEEexample:BSTcontrol