esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Damage Morphology
#### 3.3.2 Low-Velocity Impact  
Figure 6 demonstrates that composites with varying fiber hybrid ratios exhibited distinct damage morphologies on both front and back surfaces post-impact, including matrix cracking and delamination, with severity correlating to carbon/glass fiber content ratios. It can be seen that the damage decreased first and then increased with the increase in the hybrid ratio. The impacted surface exhibits a hemispherical crater morphology. Proximal to the S-1 impact locus, complete fiber fracture with cruciform failure patterns is observed, accompanied by extensive perforation zones near the punch contact area. The front panel underwent complete structural failure, with impact energy dissipation occurring through sequential absorption by the face sheet-core-back sheet sandwich configuration. Residual energy transfer induced discernible convex deformation on the rear surface. The cavity near the impact point of the punch of the impacted face of S-2 is reduced, while there is no cavity near the impact point of the punch of the impacted face of S-3. The damage of the impacted face of S-2 and S-3 is significantly improved compared with that of S-1. The damage degree of S-4 is more serious than that of S-3. Due to the greater brittleness of carbon fiber, the uniform fracture of the fibers on the impacted surface has a very obvious cross-shaped damage. The impact damage morphology of samples with different hybrid ratios can be seen. S-3 has the smallest damage degree and uniform damage surface distribution, indicating that the promiscuous ratio can better absorb the impact energy. This improvement can be attributed to the use of carbon fibers with higher mechanical strength as the surface layer. These fibers absorb significant energy and constrain impact damage to the core layer of glass fibers during the impact process. Furthermore, the glass fiber core layer plays a critical role in stress transfer and bridging after the carbon fibers fracture. Consequently, the hybrid fiber structure enhances the impact resistance of the composites.  
Figure 5: The macroscopic damage morphology of samples in different hybrid ratios (from left to right: S-1, S-2, S-3, and S-4).
## 5 Parameter Analysis  
Because the minimum shear connection ratio of the SC composite beam under cyclic loading was the focus of this paper, in this section, the influences of the shear span versus depth ratio, stiffness of the tie-bar, and number of tie-bars on the minimum shear connection ratio were investigated. The parameter analysis was based on 175 developed finite element models of SC composite beams: every five developed finite element models could determine one minimum shear connection ratio.
## 5 Experimental Design Analysis of Results
### Testing-Attack Detection Phase
Considering all this, it is also important to have intelligent models to possess some level of explainability of the decision being made, for example, through linguistic and human-understandable approaches like Fuzzy Logic rules in addition to numerical ANN weights on IoT nodes. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**ID**} & \multicolumn{3}{c}{**1 st Epoch**} & \multicolumn{3}{c}{**16 th Epoch**} \\ \cline{2-7}  & **Target** & **Output** & **Time, \(\upmu\)s** & **Target** & **Output** & **Time, \(\upmu\)s** \\ \hline
1 & 0 & 0.39667 & 1196 & 0 & 0.03863 & 1388 \\
2 & 0 & 0.39667 & 1896 & 0 & 0.03863 & 1932 \\
3 & 1 & 0.70681 & 1752 & 1 & 0.97040 & 1836 \\
4 & 0 & 0.27704 & 1176 & 0 & 0.02999 & 1176 \\
5 & 0 & 0.32847 & 1668 & 0 & 0.02803 & 1668 \\
6 & 1 & 0.75069 & 1792 & 1 & 0.98339 & 1856 \\
7 & 1 & 0.70681 & 1752 & 1 & 0.97040 & 1836 \\
8 & 1 & 0.70512 & 1756 & 1 & 0.96993 & 1828 \\
9 & 1 & 0.70606 & 1752 & 1 & 0.97020 & 1820 \\
10 & 1 & 0.70662 & 1740 & 1 & 0.97035 & 1836 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example of predicted ANN output (“Output”), normal/attack class (“Target”) and used time (“Time”) for the classification of first 10 network packets.
# Computationally Efficient Channel Estimation in 5G Massive Multiple-Input Multiple-output Systems
## 3 Low Complexity Signal Detection  
### Neumann Series Expansion  
In a massive MIMO system, the MMSE signal detection algorithm involves a high-dimensional matrix inversion, \(W^{-1}\), with a computational complexity of \(O\big{(}K^{3}\big{)}\). In order to reduce the computational complexity of \(W^{-1}\), the authors in [11] proposed using Neumann series expansion to approximate matrix inversion results. When \(W\) approximates the invertible matrix \(X\) and satisfies  
\[\lim_{n\rightarrow\infty}\left(I-WX\right)^{n}=0 \tag{8}\]  
then, the Neumann series can be expressed as  
\[W^{-1}=\Sigma_{n=0}^{\infty}\Big{(}X^{-1}(X-W)\Big{)}^{n}X \tag{9}\]  
The decomposition matrix is \(W=D+E\), where \(D\) is the diagonal matrix of \(W\), and \(E\) is the hollow matrix corresponding to \(W\). Since the number of antennas equipped at the base station was much larger than the number of single-antenna users \((N\gg K)\), matrix \(W\) has a diagonal dominant characteristic [3]; that is, \(W\approx D\). Substituting \(D\) for \(X\) in Equation (9) gives:  
\[W^{-1}=\Sigma_{n=0}^{\infty}\Big{(}-D^{-1}E\Big{)}^{n}D^{-1} \tag{10}\]when \(\lim_{n\to\infty}\left(-D^{-1}E\right)^{n}=0\), the progression of Equation (10) converges. If we only expand the first \(i\) term of the Neumann series, we can get:  
\[W_{i}^{-1}=\Sigma_{n=0}^{i-1}\left(-D^{-1}E\right)^{n}D^{-1} \tag{11}\]  
when the value of \(i\) is small, the Neumann series expansion can approximate \(W^{-1}\) with lower complexity. For example, when \(i\ =\ 2\), \(W_{2}^{-1}=D^{-1}-D^{-1}ED^{-1}\), which is computationally complex, and the complexity is \(O\left(K^{2}\right)\).
## 6 Results
### Clinical Results  
Mean values of CAL and PD, recorded after SRP + ALADENT therapy, highlighted a higher reduction than those obtained after SRP treatment (Table 3, Figures 3-5). Before treatment, CAL and PD mean values of case sites were equal to 4.65 and 5.14 mm respectively, while after treatment, a mean value of 2.42 and 2.87 mm was recorded for the two indicators. Control sites presented, at time 0, a CAL of 4.76 mm and a PD equal to 5.24 mm, after 6 months, PD and CAL mean values were 4.73 mm and 4.08 mm respectively. With regards to BOP (Figure 6), the majority of the cases and controls sites (70%, 28 sites out of 40) presented, before treatment, several isolated bleeding points or a single line of blood (Score 2), while Score 1 (single discreet bleeding point) and Score 3 (interdental triangle fills with blood shortly after probing) were found in 10% and 20% of the selected sites, respectively. After SRP plus ALADENT treatment, 90% of the sites showed no bleeding (Score 0), compared to the SRP treated sites, that mostly reached Score 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Media** & **Standard Deviation** & **Standard Error Media** & **Sig (2-Code)** \\ \hline \hline Couple 1 & TBL-0-R—TBL-1-R & 119,458.5 & 107,599.2581 & 24,059.9255 & 0.001 \\ Couple 2 & TBL-0-L—TBL-1-L & 49,798.8 & 25,907.0034 & 5792.9821 & 0.001 \\ \hline \end{tabular}
\end{table}
Table 2: Total bacterial loading (TBL) reduction in the sites treated with SRP (left-L) compared to the site treated with SRP plus ALADENT (right-R).  
Figure 2: Total bacteria loading (TBL) before (time 0) and two weeks after treatment (time 0 + 15 days) in right (**R**) (SRP) and left (**L**) (SRP plus ALADENT) sites.  
Figure 4: CAL and PD mean values (mm) of the selected sites before treatment with SRP alone and SRP plus ALADENT.  
Figure 5: CAL and PD mean values (mm) of the selected sites after treatment with SRP alone and SRP plus ALADENT.  
Figure 3: Examples of pocket depth reduction before and after treatment with Aladent.
# Optimal Design of Small-Aperture Optical Terminals for Free-Space Links
## 2 Materials and Methods
### Numerical Simulation  
We model the turbulent atmosphere with the AOtools Python package (Version 1.0.7) [24] using the equivalent layer method [25], where the volume of atmosphere over a FSO link is reduced to a series of thin phase screens, with Fresnel beam propagation between them. We use seven equivalent layers to model every turbulent link and run 200 independent trials for each link length/turbulence strength combination. The spacing of the layers yields a maximum error of 15% compared with analytical solutions for scintillation of an unbounded plane wave [17] under the strongest turbulence conditions and longest distances simulated.  
The simulation parameters are chosen using the procedure detailed in [26]. The pixel scale is set small enough to ensure adequate sampling of the phase screens and of the beam scintillation. The grid size is set to minimise edge effects and ensure accurate Fresnel propagation. We note that after beam propagation, only a smaller central portion of the grid will contain valid results [26].
# Robust Control with Uncertain Disturbances for Vehicle Drift Motions
## 4 Simulation and Discussion
### Motion Mechanism Analysis Result
In non-limit conditions, if the vehicle moves in a circle with a constant velocity and the yaw velocity based on the hypothesis in [32], the radius of the steady-state cornering is described as:  
\[R=\frac{v}{\gamma}=\left(1-\frac{m}{2\left(l_{f}+l_{r}\right)^{2}}\frac{l_{f}C_{ f0}-l_{r}C_{r0}}{C_{f0}C_{r0}}v^{2}\right)\frac{l_{f}+l_{r}}{\delta_{f}} \tag{35}\]  
Figure 6: The simulation result of group (b) in Table 2 with the origin uniform linear: (**a**) the variation curve of the velocity; (**b**) the variation curve of the sideslip angle; (**c**) the variation curve of the yaw rate; (**d**) the variation curve of the steering angle; (**e**) the variation curve of the TYDEX longitudinal slip ratio of the rear tire; (**d**) the variation curve of the radius. where \(C_{f0}\) and \(C_{r0}\) are the cornering stiffnesses of front and rear tires respectively, when slip angles equal zero. According to Equation (35), it suggests that the radius of the steady-state is smaller with the larger steering angle and the smaller velocity in non-limit conditions, which means the radius reaches the maximum value 3.67 m when the velocity is approaching to zero. Based on the comparison between the above analysis and Figure 4d, it suggests that vehicle in drift conditions can turn smaller corners and turn same corners with higher velocities than in non-limit conditions, which means the autonomous vehicle will take the full advantage of kinematic capability and be safer if it can realize drift motions.
## 3 Proposed Neural-Network-Based Decoding Scheme
### Decoding Scheme  
Our proposed decoding scheme can be summarized briefly: we segment each extracted frame into a series of sub-images in order and decode one sub-image in one time step using CAE-LSTM-Net. The proposed decoding scheme of the RGB-LED-based OCC system is shown in Figure 9.  
In this OCC system, each data packet is transmitted three times repeatedly within (1/frame rate) second to ensure that a data packet can be recorded completely at least once in a captured frame. Each packet transmission had a 16-bit header and a payload, as shown in Figure 10. The signal is transmitted by a video recording with 60 frames per second (fps) and decoded offline. In an optical camera, the resolution of each frame in a captured video is 1080 \(\times\) 1920. Firstly, an appropriate column of pixels is selected, and image enhancement is performed in these pixels. Utilizing the averaging-based signal tracing algorithm proposed in [9] and gamma correction, the signal intensity can be normalized, and the nonlinear distortion introduced by hardware can be compensated. These processes also make a large contribution to increasing the gaps between symbols and improving the recognition rate. The 8-CSK modulation is adopted in this work; it means that on-off keying is employed for each channel so that each symbol includes three bits. Before image incision, the reference position should be sought out by the threshold method [34], which is called header location hereinafter. Since the header of each packet transmission only comprises of the two symbols, one is the \"brightness\", and the other is the \"darkness\", both of them are easily observed. As shown in Figure 9a-f, a G channel signal best serves to recognize the header symbol. However, even though in the demodulation of the most distinct symbols, we can find that intense distortion happens in the other two channels, which means that the performance of conventional decoding algorithms with a sampling process will be deteriorated in most other cases as well. It is the reason why conventional algorithms are probably not feasible solutions for higher-rate transmission. After header location and interpolation, the sub-image with a size of 9 pixels in row is cut starting from the reference position. In the second dimension, if \(L\) is the column of selected pixels, then the column matrix of a sub-image is set from \(L-4\) to \(L+5\). A frame is regarded as a basic processing sequence; we input all sub-images to the trained channel-attention-enhanced LSTM sequentially from time step 0 to time step \(t\). The output sequence comprised the output from time step 0 to time step \(t\) in chronological order. Each frame is used to recover only one data packet in timing recovery.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 3 System Model
### Compensation Strategies  
The input-output relation formulated in (6) models an asynchronous GF access scheme, where signals arrive at different time instants. However, by considering more advanced terminals, different levels of synchronization can be achieved. If the terminal is able to detect downlink synchronization signals and extract the system information, the misalignment can be reduced to a high extent. Upon acquiring the system information, the terminal  
Figure 3: Satellite geometry.  
should be able to calculate the delay and the Doppler shift of a given reference point. These values can be used to adopt the time of reference of the satellite. The idea is to compensate time and frequency offsets associated to the reference point in the uplink transmission. Unfortunately, the terminal is unlikely to be located on the same spot that the reference point. Hence, at the receiver, residual time and frequency errors should be expected when terminals are not near the reference point. It becomes evident that the higher the coverage area, the higher will be the residual errors. When strict synchronization cannot be attained, the receiver should be able to find the beginning of the sequence by resorting to the sliding window mechanism [13]. This highlights the necessity of embedding a random access signal within the packet. In a more advantageous situation, when the terminal is aware of the satellite's trajectory and has localization information, the delay and the Doppler shift can be compensated at the transmit side with high accuracy. Then, signals transmitted from different terminals are time-aligned when reaching the satellite. In such a case, (6) can be particularized for \(\tau_{k}=\epsilon_{k}=0\), \(\forall k\). It is worth highlighting that localization information can be obtained even if the GNSS service is not available [34].
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 3 Proposed Method
### Color Compensation  
For images with enhanced illumination, the rate at which pixel values are amplified may vary depending on differences in the mean and variance constants of the RGB channels. This can lead to color distortion or reduced contrast, which reduces the visibility of the resulting footage. The proposed algorithm uses color correction to make the contrast of colors more pronounced after image enhancement. The color correction step compares the pixel values of the RGB channels in the \(x,y\) coordinates of the enhanced image \(S_{p}(x,y)\) to each other, and the channel with the highest value is selected as the correction channel. Color correction applies different correction values to the correction channel and the remaining channels based on the difference between the pixel values of the correction channel and the pixel values of the other two channels. Here, if the pixel value of the RGB channel selected as the calibration channel is \(t(x,y)\) and the pixel values of the other two channels are \(d_{1}(x,y)\) and \(d_{2}(x,y)\), respectively, the color correction values \(\hat{t}_{1}(x,y)\) and \(\hat{t}_{2}(x,y)\) for the two channels are obtained as follows.  
\[\hat{t}_{1}(x,y)=t(x,y)+\alpha(t(x,y)-d_{1}(x,y))(1-t(x,y))d_{1}(x,y) \tag{14}\]  
\[\hat{t}_{2}(x,y)=t(x,y)+\alpha(t(x,y)-d_{2}(x,y))(1-t(x,y))d_{2}(x,y) \tag{15}\]  
Here, \(\alpha\) refers to the color correction weight. The color correction value is calculated proportionally to the difference between \(d_{1}(x,y)\) and \(d_{2}(x,y)\), and the pixel value of the correction channel. In this case, if the pixel values of only one channel of \(d_{1}(x,y)\) and \(d_{2}(x,y)\) are significantly different, the color correction value may show a large value, so we set the weights for the color correction values \(\hat{t}_{1}(x,y)\) and \(\hat{t}_{2}(x,y)\) to minimize color distortion, maintain the naturalness of the image, and emphasize the color contrast. Taking this into account, the combined weights \(\beta_{1}(x,y)\) and \(\beta_{2}(x,y)\) can be calculated as follows.  
\[\beta_{1}(x,y)=\frac{d_{1}(x,y)}{d_{1}(x,y)+d_{2}(x,y)} \tag{16}\]  
\[\beta_{2}(x,y)=\frac{d_{2}(x,y)}{d_{1}(x,y)+d_{2}(x,y)} \tag{17}\]  
The proposed algorithm sets \(t(x,y)\) as the largest pixel value in the RGB channel for color correction, so the smaller \(s_{1}(x,y)\) and \(s_{2}(x,y)\) are calculated, the larger \(\hat{t}_{1}(x,y)\) and \(\hat{t}_{2}(x,y)\) are calculated. The sum of the two color correction values is obtained by applying the sum weight obtained in Equations (16) and (17) to the respective corrections \(\hat{t}_{1}(x,y)\) and \(\hat{t}_{2}(x,y)\). The combined color correction value \(t_{p}(x,y)\) is calculated using the following formula.  
\[t_{p}(x,y)=\beta_{1}(x,y)\hat{t}_{1}(x,y)+\beta_{2}(x,y)\hat{t}_{2}(x,y) \tag{18}\]
## 7 Simulation Assessment  
Simulations are commonly used in power system research because they can minimize cost and time. MATLAB/Simulink (2019b, The MathWorks, Inc., Natick, MA, USA, 2019) [33] was adopted in this work. It was designed for both modeling the electrical power system and developing the control algorithm. The proposed algorithm can be implemented in readily existing models in the software. A simulation study was performed on an IEEE 34-node radial test feeder to assess the dynamic performances of the proposed separated phase-current control using IBDGs, as shown in Figure 7a. The system parameters are listed in Reference [34]. IBDG1 and IBDG2 were connected at nodes 816 and 860, respectively. During voltage sag, the DC-DC boost converter connecting the photovoltaics (PV) and IBDGs is operated under a power balance strategy to avoid an imbalance between the input power and output power of the IBDGs [20]. The total penetration level of the two IBDGs was 54% of power demand. The output current of the IBDGs was limited to within 2 times of the rated current of the switching device to prevent damage to the inverter [15; 18]. A 0.8-MVA SDG was connected at node 828. A fiberoptic communication was assumed as the means for the main data transmission due to the benefits of high bandwidth capacity and electromagnetic interference (EMI)/radio frequency interference (RFI) immunity. More importantly, optical fibers have already been widely adopted in communication networks [35]. The propagation speed and data rate were 4.978 \(\upmu\)s/km and 10 Mbits/s, respectively. Three single-phase reclosers (Res) installed in the substation and a fuse (F) placed at the beginning of lateral 7 were used to investigate the effects of fault current contribution from the SDG. The benefit of three single-phase reclosers is that the utility still supplies the nonfaulty phase during unsymmetrical faults [3]. The number of customers affected by outages can be reduced. The corresponding time-current curve of the recloser-fuse is shown in Figure 7b. A fuse-saving philosophy is commonly used by allowing circuit breaker relays and recloser controls to trip before the fusible element starts melting, which is indicated by the fast curve below the fuse characteristic in Figure 7b. After the first or second reclosing attempts, the relays turn to the slow curve, coordinated to allow the fuse to melt before the recloser trips to restore service to the rest of the feeder. The simulation assessment is divided into two parts. The impact of delay caused by the PMUs is validated in the first part. The second part evaluates the efficiency of separated phase-current controls.
## 3 Methodology: Proposed SybilSocNet Algorithm
### ML Terminologies and Methodologies Applied in This Study
#### 3.1.4 Random Forest Algorithm  
Random forest, an ensemble supervised method, combines small models to form a larger model for problem solving. Such an algorithm operates in stages: small models tackle subproblems, their solutions contribute to the final prediction through a voting system. The large model is a collection of trees, with each tree handling a subset of features, with the five steps characterizing the random forest algorithm:  
Figure 6: Two and three dimensional hyperplane lines. Colors represent different items categories.  
Dataset preparation: Like prior algorithms, it requires labeled data for training with a dataset consisting of features linked to corresponding classes, e.g., car quality correlates with attributes like make, model, and price.
2. Decision tree: This is the creation and grouping of the data via a central tree consisting of smaller decision trees that predict outcomes via the dataset's distinct portions without all features being related to target outputs to form a diverse set of trees, akin to people uniquely solving problems. Ensemble voting aggregates their solutions to determine the final prediction.
3. Trees' feature selection: Different randomly chosen features are assigned to each tree, to curb overfitting.
4. Bootstrapping: This involves the newly created dataset's data samples, generating variations for each tree to avoid uniformity. Bootstrapping introduces randomness, yielding a diverse array of perspectives on the problem, crucial to preventing overfitting (Figure 7).
5. Output Prediction: The algorithm predicts the output, either class for classification or value for regression. All decision trees contribute predictions, and a voting mechanism selects the majority class for classification or the average output for regression.  
The random forest algorithm offers adjustable parameters, including tree count, bootstrap dataset percentages, and decision tree node count, catering to the application's complexity [65].