esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Testbed-Architectures Used  
The testbed used is a small-scale first generation AMI system, and the experiments are conducted in the lab environment. One data concentrator is used, and 12 single-phase smart meters were chosen from the available ones for the experiments. Figure 5 shows the testbed used for this work. The test bed includes 18 meters including 2 triphase ones. For this experiment, only twelve residential smart meters were used, which communicate with the data concentrator using Power Line Communication technology (PLC). They communicate with the PRIME protocol which is a popular option in some Member States of the European Union for power line communication. The data concentrator software allows the interrogation of the smart meters to request measured values, such as power, current and voltage values at a predefined periodicity.  
In this experiment, we test the ability of the data concentrator to acquire the aforementioned measurement values as a function of the number of smart meters connected to the data concentrator, and with different sampling periods of requests of measured values.  
Figure 5: Testbed used for evaluating the data concentrator-smart meters interaction.  
More specifically, the two parameters changed in the experiments are the number of meters working together (1, 2, 4, 8 and 12) and the periodicity of request for information from the data concentrator to the meters (15, 10, 5, 4, 3, 2 and 1 min). All combinations of these two parameters' values were tested. Hence, a total of 35 experiments was executed. Each experiment was conducted over 180 min to guarantee representative statistics of counts.  
The stress tests have the scope to identify the critical parameters' values where data acquisition would not be 100% successful, namely where the system would miss the acquisition of some of the requested data.
# Effect of Rotational Speed Variation on the Flow Characteristics in the Rotor-Stator System Cavity
## 3 Results and Discussion
### Vortex Structure Characteristics  
The distribution of the vortex core in the cavity of the rotor-stator system is shown in Figure 13. In this study, the \(\lambda\)-2 method was used to extract the vortex structure on the isosurface. The surface contour map of the vortex structure shows the vorticity, which is used to describe the strength of the vortex. The \(r\)-\(z\) plane streamline shows that the position of the main vortex in the streamline corresponds to the vortex structure.  
There was not much difference in the vortex structure in the cavities of the four cases. The increase in the rotating Reynolds number caused the flow in the cavity affected by the entrainment effect to increase. The cross-sectional area of the main vortex gradually decreased, and the return vortex structure near the outlet increased. The most obvious  
Figure 12: Contour maps of Reynolds stress in the r-z plane (**a**) Reynolds normal stress \(R_{\theta\theta}\); (**b**) [PERSON] normal stress \(R_{rr}\); (**c**) [PERSON] normal stress \(R_{zz}\); (**d**) [PERSON] shear stress \(R_{r\theta}\); (**e**) [PERSON] shear stress \(R_{x\theta}\); (**f**) [PERSON] shear stress \(R_{z\theta}\).  
Figure 13: Vortex structure in cavity.  
effect of the rotating speed of the turntable was the vortex in the turbulent boundary layer close to the rotor. When \(Re_{\Phi}\) = 2.12 \(\times\) 10\({}^{6}\), there were fewer vortex cores in the boundary layer, but the strength of the vortex was higher than in the other three cases. As the rotating speed increased, the distribution of vortex cores in the boundary layer gradually became dense, and relatively high-strength vortices were mainly concentrated at the position where the secondary vortex formed. Figure 13 shows that a vortex rope with greater annular strength also appeared at \(r^{*}\) = 0.5-0.7 under different working conditions. Its appearance is similar to the position where the shear strain rate in the rotor boundary layer separates, as shown in Figure 11a. As mentioned earlier, after the separation occurs, the flow in the boundary layer changes the radial flow dominated by the inertial force, and the adhesion of the rotor wall to the fluid makes a greater contribution to the fluid in the boundary layer. Therefore, a vortex rope with a higher strength appears at this location.
## 3 Synchronization of Laser
The synchronization to the electron bunch is realized by resetting the count of RF with the 60-Hz timing signal reflecting the timing of the electron bunch. The TDU can delay the output signals of 5712 MHz, 238 MHz, 960 Hz, and 60 Hz simultaneously by setting the parameters of the 5712-MHz RF phase, 238-MHz RF phase, and 238-MHz count delays calculated from the input delay value. The delay value can be set in the range from \(-2\) to +990 microsecond (positive: the XFEL pulses arrives after the optical laser pulses, negative: opposite) with sub-picosecond accuracy.
###### Abstract  
The advent of 5G heralds unprecedented connectivity with high throughput and low latency for network users. Software-defined networking (SDN) plays a significant role in fulfilling these requirements. However, it poses substantial security challenges due to its inherent centralized management strategy. Moreover, SDN confronts limitations in handling malicious traffic under 5G's extensive data flow. To deal with these issues, this paper presents a novel intrusion detection system (IDS) designed for 5G SDN networks, leveraging the advanced capabilities of binarized deep spiking capsule fire hawk neural networks (BSHNN) and blockchain technology, which operates across multiple layers. Initially, the lightweight encryption algorithm (LEA) is used at the data acquisition layer to authenticate mobile users via trusted third parties. Followed by optimal switch selection using the mud-ring algorithm in the switch layer, and the data flow rules are secured by employing blockchain technology incorporating searchable encryption algorithms within the blockchain plane. The domain controller layer utilizes binarized deep spiking capsule fire hawk neural network (BSHNN) for real-time data packet classification, while the smart controller layer uses enhanced adapting hidden attribute-weighted naive bayes (EAWNB) to identify suspicious packets during data transmission. The experimental results show that the proposed technique outperforms the state-of-the-art approaches in terms of accuracy (98.02%), precision (96.40%), detection rate (96.41%), authentication time (16.2 s), throughput, delay, and packet loss ratio.  
5G SDN; intrusion detection system; binarized deep spiking capsule fire hawk neural network; blockchain; cybersecurity +
Footnote †: journal:  
_Article_  
An Intrusion Detection System for 5G SDN Network Utilizing Binarized Deep Spiking Capsule Fire Hawk Neural Networks and Blockchain Technology.
## 3 Results
### Space Charge Distribution  
The space charge distribution of LDPE under a 10 kV/mm and a 40 kV/mm DC electric field within 30 min at room temperature is shown in Figures 12 and 13. It can be seen from Figure 12a that the accumulation of the homocharge is observed near the cathode and the anode in the LDPE when the semiconductive layer is not added to with LSC. Among them, the heterocharge is derived from the ionization of the crosslinked byproducts and the ionization of the impurities, and the homocharge is derived from the injection of the electrodes. It can be seen from Figures 12c and 13c that there is almost no accumulation of the homo charge at the cathode. However when the content of LSC in the semiconductive layer exceeds 1%, as the LSC content increases, the space charge injection in the LDPE increases; that is, the inhibition effect of the semiconductive layer is weakened, which may be related to the agglomeration of the LSC. It can be inferred that semiconductive materials with an LSC content of 1% can suppress the injection of space charge. Due to the scattering effect at the interface between the nanoparticles and the polymer, the mean free path of electrons is increased and the migration rate of electrons is reduced.  
Figure 11: The total amount of charge in the LDPE when the composite with different LSC content acts as a semiconductive layer.  
Figure 12: _Cont._  
When the composite without LSC is used as the semiconductive layer, the maximum charge density near the cathode and anode is 11.46 and 9.37 C/m\({}^{3}\) respectively under a 10 kV/mm DC electric field. After doping by LSC with 1 wt%, the interface charge near the cathode and the anode is reduced to 6.53 and 7.76 C/m\({}^{3}\). The maximum charge density near the two electrodes is 22.15 and 21.36 C/m\({}^{3}\) under a 40 kV/mm DC electric field. When the semiconductive layer is doped with 1 wt% LSC, the interface charge reduced to 12.77 and 20.89 C/m\({}^{3}\), respectively. When the charge is injected from the metal electrode to the insulating layer, it passes through the semiconductive layer, and the charge receives the Coulomb effect of the LSC particles in the semiconducting shielding layer, so that part of the charge cannot be injected into the insulating layer through the semiconductive layer, thus reducing the charge injection in the insulating layer.  
Figure 12: Space charge distribution of LDPE when the composite with different LSC contents acts as a semiconductive layer under 10 kV/mm DC electric field.  
Figure 13: Space charge distribution of LDPE when the composite with different LSC contents acts as a semiconductive layer under 40 kV/mm DC electric field.
# Laboratory Test of a Vehicle Active Noise-Control System Based on an Adaptive Step Size Algorithm
## 2 Methods
### FxLMS Algorithm with Adaptive Step Size  
When there is a large amplitude transient increase in noise, the FxLMS algorithm will make the amplitude of the filter weight coefficient change too much, due to the sudden increase in the reference signal, which will lead to the instability of the system. If a small step factor is deliberately used to prevent such instability, the convergence of the adaptive process will be particularly slow. To solve the above problems, it is necessary to adjust the step factor so that the adaptive algorithm can adapt to the sudden increase in noise signal. Therefore, this paper proposes an adaptive step size algorithm based on a reference signal to adjust step size parameters, and the variable step size factor can be adjusted as follows:  
\[u(n)=a\Big{(}c\|\mathbf{X}(n)\|^{2}\Big{)}^{2}e^{-2c\|\mathbf{X}(n)\|^{2}} \tag{8}\]  
where \(\|\mathbf{X}(n)\|^{2}\) is the square norm of \(\mathbf{X}(n)\), and the values of parameters \(a\) and \(c\) have a significant influence on the control of step size. The influence of the different values of \(a\) and \(c\) on the step size \(u(n)\) is shown in Figure 3:  
Obviously, \(a\) and \(c\) play a crucial role in the adjustment of step size \(u(n)\), and the different values of \(a\) and \(c\) will lead to different noise reduction effects on step size. When the adaptive step-size FxLMS algorithm is used, the filter weight coefficient is updated as follows:  
\[\mathbf{W}(n+1)=\mathbf{W}(n)+u(n)e(n)\mathbf{X}^{\prime}(n). \tag{9}\]
## 3 Results and Analysis  
### Description of Kiwifruit Sweetness Data  
An analysis of variance (ANOVA) was conducted to assess the kiwifruit sweetness levels across five sampling areas. The results indicated no significant differences among the five sampling areas. The statistical characteristics of the samples for each period was presented in Table 2. As the picking date progressed, the kiwifruit ripened gradually, with its sweetness value increasing from 7.56% in the early stage to 15.73% in the late stage of harvest. The average sweetness value for the entire harvest period was 10.58%. The coefficient of variation of sweetness at the early stage of harvest (T1) was relatively large at 20.64%, gradually decreasing as the reproductive period advanced.
## 5 Results and Analysis  
### Experimental Results  
A total of 273 images from the test set were randomly selected for the detection of floating seedlings, missing seedlings, and qualified seedlings. Simultaneously, the improved YOLOv8n model was compared with the original model using experimental data. Three scenarios were selected for seedling morphological detection: normal seedlings, continuous missing seedlings, and floating seedlings with missing seedlings, and exposed soil in paddy fields, as shown in Figure 12.  
Figure 12: Comparison of seedling quality and floating seedling detection before and after the improvement of YOLOv8n.  
As shown in Figure 11, due to the similarity in features between qualified seedlings and floating seedling states, the original YOLOv8n model is prone to false positives and false negatives when encountering complex scenes resembling exposed soil. Additionally, it may produce confused bounding box predictions when detecting indistinct targets, with lower confidence scores. In contrast, the improved YOLOv8n model demonstrates precise and rapid identification of seedling targets during detection, with slightly enhanced confidence scores. The utilization of the enhanced model along with the missing seedling detection algorithm accurately assesses the planting status of seedlings, as depicted in Figure 13.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Error Detection Point  
For a BPSK signal, (4) shows that the signal can be represented as a cosine waveform. However, the signal amplitude around the symbol instant decreases gradually due to the forming filter. Nevertheless, since the symbol period is usually much larger than the carrier period for medium wave, the amplitude of the filter can be approximated as constant when considering only the waveform of the first carrier period after the symbol instant. At this point, the passband waveform can be approximated as a cosine function.  
Observing the baseband waveform in Figure 2, we find that its shape is similar to that of a cosine waveform. Therefore, the three points used for error detection in the CB-Gardner are chosen with a phase difference of \(\pi/2\), where the phase of the first detection point belongs to \([0,\frac{\pi}{2},\pi,\frac{3\pi}{2}]\), and all detection points are located after the symbol instant. Specifically, the phase values of the three error-detection points \((  
information. Considering the variation of the signal length due to Doppler, the number of sampling points corresponding to \(\triangle t_{y_{1},n}\) is  
\[\begin{array}{ll}\triangle p&=\triangle t_{y_{1},n}F_{\mathrm{s}}/\lambda_{n-1 }\\ &=\left\{\frac{\pi}{2}-[(n-1)w_{c}T]\mathrm{mod}\left(\frac{\pi}{2}\right) \right\}/w_{c}*F_{\mathrm{s}}/\lambda_{n-1}\\ &=1/\lambda_{n-1}*\left\{\frac{\pi}{2}-[(n-1)w_{c}T]\mathrm{mod}\left(\frac{ \pi}{2}\right)\right\}/(2\pi)*\frac{F_{\mathrm{s}}}{f_{\mathrm{s}}}\\ &=1/\lambda_{n-1}*spc*\left\{\frac{\pi}{2}-[(n-1)w_{c}T]\mathrm{mod}\left( \frac{\pi}{2}\right)\right\}/(2\pi)\end{array} \tag{9}\]  
where \(spc=F_{\mathrm{s}}/f_{c}\) denotes the average number of samples per carrier cycle. Therefore, the positions of the error-detection points \((y_{1},y_{2},y_{3})\) are  
\[\left\{\begin{array}{ll}p_{y_{1},n}=P_{n}+1/\lambda_{n-1}*spc*\left\{\frac{ \pi}{2}-[(n-1)w_{c}T]\mathrm{mod}\left(\frac{\pi}{2}\right)\right\}/(2\pi)\\ p_{y_{2},n}=p_{y_{1},n}+1/\lambda_{n-1}*spc/4\\ p_{y_{3},n}=p_{y_{1},n}+1/\lambda_{n-1}*spc/2\end{array}\right. \tag{10}\]  
Once we get the positions of all three error detection points, we can obtain their corresponding values using the interpolation method, which will be described later.
## 4 Results and Discussion
### Effect of H/C, O/C, ER, and T on CGE  
Cold gasification efficiency (CGE) is a measurement of gasification performance [79] Several factors affect CGE, including biomass type, reactor type, particle size, temperature, and reactor type [80] Equation (54) was developed to evaluate the effect of N/C, O/C, H/C, ER, and T on CGE. Equation (54) indicates that the concentrations of oxygen, hydrogen, and carbon have a significant impact on biomass CGE. Figure 9 shows the different O/C and H/C ratios on CGE (%).  
Figure 9a illustrates that CGE increases with ER and T under constant H/C, O/C, and N/C. Likewise, the CGE would increase with increasing H/C and O/C of the biomass under constant gasification operational parameters as can be seen in Figure 9b. The positive correlation of CGE with ER and T is in line with experimental studies. The CGE of woodchip gasification increased from 45.6% to 61.9% when the gasification temperature was increased from 558 to 612 \({}^{\circ}\)C, respectively [79]. Rice husk gasification at a constant ER of 0.3 yielded CGE of 39.1% at a gasification temperature of 800 \({}^{\circ}\)C. The CGE value increased to 69.5% when the gasification temperature was raised to 1400 K. Similar trend was also shown with biomasses from sugarcane and palm kernel [81] The positive correlation of CGE with gasification temperature has also been shown by other empirical findings [82, 83]. Positive correlation of CGE with ER has also been proven experimentally. [PERSON] et al. [31] used downdraft fixed bed (DFB) and bubbling fluidized bed (BFB) reactors to gasify solid refuse fuel (SRF) with ER values between 0.2 and 0.6. The CGE of the gasification increased from  
Figure 8: Simulation of \(\text{LHV}_{\text{gas}}\) (MJ/Nm\({}^{3}\)) as a function of (**a**) ER and T at H/C = 1.494, O/C = 0.689, N/C = 0.0198; and (**b**) H/C and O/C at N/C = 0.0198, ER = 0.332, T = 1321 K.  
47.76% to 51.44% in DFB reactor and from 61.4% to 67.25% in BFB reactor. Another study showed that a small increment of ER significantly increases CGE. Increasing ER values from 0.23 to 0.26 increased CGE from 58% to 65% [64]. [PERSON] et al. [57] found that CGE reached its highest values at ER of 0.35, i.e., 72.74%--CC-78.37%, and decreased when the ER was beyond 0.35. However, another study found that the CGE reached 79.46% at ER of 0.378 when lignin-rich biomass was gasified.
## 3 EBI Signal Processing and Algorithm Development for HR Estimation
### Preprocessing Using Hankel Matrix-Based Approach  
Typically, breathing and slow-motion artifacts are eliminated by applying a high-pass (or BPF) filter to the input signal in the time domain. Another approach could be fitting a low-order polynomial to the input signal and subtracting the fitted mean line from the original signal.  
In our previous research, we have developed a Hankel matrix-based solution [33] for constructing three cardiac periods and sliding these through the signal, thus allowing online and real-time signal filtering. In the current paper, a simplified version of the Hankel matrix-based filtering approach is used for offline processing, converting the entire signal into the Hankel matrix. A short description of the algorithm is given below:  
1. Estimate averaged cardiac frequency (HR) for the full signal using FFT.
2. Define the size of Hankel matrix columns equal to the averaged cardiac period (in samples).
3. Construct the Hankel matrix for the full signal.
4. For each matrix column: apply \(\text{mean}()\) function to the column and replace all the column values with the estimated mean value (use of the \(\text{mean}()\) function allows to estimate the _mean line_ of the signal, in turn, applying, e.g., \(\text{min}()\) function to each column estimates the _base line_ of the signal and, respectively, use of \(\text{max}()\) function, estimates the _peak line_ of the signal. In the current work, we used the _mean line_ estimation approach).
5. Apply the _hankelization_ procedure to the matrix--this means to change, individually, each anti-diagonal of the matrix to the same value. In our approach, we use anti-diagonal averaging by mean values. For each anti-diagonal, the mean value isestimated, and all elements of the corresponding anti-diagonal are replaced by the found mean value.
* Transform the obtained (_filtered_) Hankel matrix to the resulting filtered baseline signal.
* Finally, subtract the obtained in the previous step the baseline from the input signal. The result of this step is a filtered cardiac signal \(\mathbf{x}_{\text{filt}}(t)\).
## 5 ICT Fluency and the Digital Divide
### Measuring Digital Inequality
\begin{table}
\begin{tabular}{l l l l l l l l l l l} \hline \hline \(N\) = 477 & Gender & Age & Location & ClassYear & Major & PC\_Owners & Educ\_Att & Family\_Size & IncomePerCap & EmplStatus \\ \hline Gender & 1 & 0.003 & \(-\)0.087 & \(-\)0.014 & \(-\)0.084 & \(-\)0.043 & \(-\)0.006 & \(-\)0.194 \({}^{*}\) & \(-\)0.180 \({}^{*}\) & 0.027 \\  & (0.976) & (0.341) & (0.881) & (0.357) & (0.637) & (0.948) & (0.032) & (0.046) & (0.765) \\ Age & 0.003 & 1 & 0.027) & 0.047) & \(-\)0.062 & \(-\)0.058 & 0.027) & 0.062 & \(-\)0.015) & 0.023) \\  & (0.976) & (0.765) & (0.609) & (0.497) & (0.527) & (0.763) & (0.493) & (0.868) & (0.797) \\ Location & \(-\)0.087 & 0.027 & 1 & 0.047) & \(-\)0.092 & \(-\)0.104 & \(-\)0.003 & (0.899) & 0.102) & 0.178 \({}^{*}\) \\  & (0.341) & (0.765) & (0.604) & (0.312) & (0.251) & (0.976) & (0.329) & (0.262) & (0.049) \\ ClassYear & \(-\)0.014 & 0.047 & 1 & 0.008) & \(-\)0.024 & \(-\)0.062 & 0.004) & 0.087) & 0.160) \\  & (0.081) & (0.609) & (0.604) & (0.928) & (0.796) & (0.497) & (0.966) & (0.338) & (0.078) \\ Major & \(-\)0.084 & \(-\)0.062 & \(-\)0.092 & 0.008 & 1 & \(-\)0.233 \({}^{**}\) & \(-\)0.027 & 0.000) & 0.020) & 0.006 \\  & (0.357) & (0.497) & (0.312) & (0.529) & (0.009) & (0.769) & (0.998) & (0.824) & (0.948) \\ PC\_Owners & \(-\)0.043 & \(-\)0.058 & \(-\)0.104 & \(-\)0.024 & \(-\)0.233 \({}^{**}\) & 1 & \(-\)0.013) & \(-\)0.022 & 0.043) & \(-\)0.032 \\  & (0.637) & (0.527) & (0.251) & (0.796) & (0.009) & & (0.887) & (0.811) & (0.635) & (0.724) \\ Educ\_Att & \(-\)0.006 & 0.027 & \(-\)0.003 & \(-\)0.062 & \(-\)0.027) & \(-\)0.013 & 1 & \(-\)0.071) & 0.173) & \(-\)0.128) \\  & (0.948) & (0.763) & (0.976) & (0.497) & (0.769) & (0.887) & & (0.435) & (0.055) & (0.159) \\ Family\_Size & \(-\)0.194 & 0.062 & 0.089) & 0.004 & (0.000) & 0.022) & 0.071) & 1 & \(-\)0.028) & 0.126) \\  & (0.032) & (0.493) & (0.329) & (0.966) & (0.998) & (0.811) & (0.435) & & (0.757) & (0.163) \\ IncomePerCap & \(-\)0.180 \({}^{*}\) & \(-\)0.015 & 0.102) & 0.087) & (0.020) & 0.043) & 0.173) & \(-\)0.028) & 1 & \(-\)0.032) \\  & (0.046) & (0.865) & (0.262) & 0.338) & (0.824) & (0.635) & (0.055) & (0.757) & & (0.729) \\ EmplStatus & 0.027) & 0.023 & 0.178\({}^{*}\) & 0.160) & 0.006 & \(-\)0.032 & \(-\)0.128) & 0.126) & \(-\)0.032 & 1 \\  & (0.765) & (0.797) & (0.049) & (0.078) & (0.948) & (0.724) & (0.159) & (0.163) & (0.729) \\ \hline \hline \end{tabular} Note. Educ\_Att = Educational attainment, Family\_Size = Family Size, IncomePerCap = Income Per Capita, EmplStatus = Employment Status.\({}^{*}\). Correlation is significant at the 0.05 level (2-tailed).\({}^{**}\) Correlation is significant at the 0.01 level (2-tailed).  
\end{table} 
Table 3: Correlation between variables characteristics of Matrix M and W for the 1 st year.