esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Real-Time Contrasts Control Chart Using Novelty Detection and Variable Importance
## 3 Proposed Method  
We propose an improved RTC control chart with novelty detection and variable importance. In the case of the original RTC control chart, the reference data defined as normal state was used only as a comparison group for the contrast data. However, in this study, we not only used the reference data as a comparison group, but also took the reference data's maximum value of the variable importance as the control limit of the real-time variable importance. The proposed method consists of Phases I and II, as shown in Figures 2 and 3.  
In Phase I, there are three steps: deciding the variable importance threshold of the real-time contrasts, deciding the decision boundary using novelty detection, and deciding the RTC control limit. We use the reference data to determine the novelty detection's decision boundary, extract the variable importance of the real-time contrast's threshold, and set the control limit. In this paper, SVM is used to perform novelty detection. Especially, the radial basis function (RBF) kernel is known to perform well in novelty detection, so this study used the RBF kernel [15].  
As shown in Figure 4, we have confirmed that the distribution of the contrasts differs from the reference data. Figure 4 shows when an abnormal state is caused by \(X_{2}\). As shown in Figure 4, some data are separated from the normal state distribution. However, it can be seen that there are data that are very close to the distribution of the normal state even though it is in an abnormal state. For this reason, when an abnormal state occurs in the RTC control chart, abnormal and normal data may be mixed in the contrasts. In such a case, the performance of \(ARL_{1}\) may deteriorate.  
Figure 2: Phase I of the proposed method.  
Figure 5 shows how the data is shifted to the variable \(X_{2}\), which differs from the other variables. Unlike in Figure 4, Figure 5 shows that the cause of the abnormality can be clearly detected. For this reason, we set the threshold for the variable importance of the reference data as a criterion for determining whether the proposed algorithm works. However, monitoring statistics will often be falsely replaced with variable importance charts. Therefore, we try to set the threshold and improve the performance by using the variable importance chart of normal data. In Phase II, the information obtained from Phase I is utilized as soon as the variable importance of the contrast exceeds the threshold. We introduce an improved real-time contrast using novelty detection and variable importance and describe the proposed method in Sections 3.1 and 3.2 in more detail.  
Figure 4: Abnormal state data in sliding window.  
Figure 3: Phase II of the proposed method. MEWMA: multivariate exponentially weighted moving average.
# A Trustworthy Framework for Skin Cancer Detection Using a CNN with a Modified Attention Mechanism
## 4 Results and Discussion
### Ablation Study  
As shown in Table 8, various ablation studies were conducted to determine the optimal skin cancer detection model. The proposed model was evaluated using four different settings in an ablation study. In setting (1), Solo ConvNeXtBase was employed, and the worst results were obtained, with an average accuracy of 96.40%. Setting (2) used ConvNeXtBase with a channel attention mechanism; this strategy achieved a higher performance than setting (1). The performances of settings (3) and (4) were better than those of setting (2). In setting (3), the spatial attention mechanism was employed with ConvNeXtBase, which achieved an improved performance compared with settings (1) and (2) and a lower performance compared to setting (4). Setting (4) involved the proposed model coupled with channel and spatial attention mechanisms, and it achieved improved performances throughout the experiment compared to the other settings. Table 8 shows the model effectiveness analysis obtained by integrating different modules with ConvNeXtBase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S: No** & **Optimizer** & **ResNet** & **Inception** & **DenseNet** & **EfficientNet** & **Xception** & **MobileNetV2** \\ \hline
1 & SGD & 65.7 & 64.3 & 89.3 & 91.4 & 90.0 & 89.4 \\
2 & Adamax & 94.3 & 93.6 & 95.7 & 92.2 & 91.2 & 89.7 \\
3 & Nadam & 93.7 & 82.2 & 96.3 & 92.5 & 91.5 & 90.0 \\
4 & Adagrad & 78.5 & 77.5 & 90.6 & 91.0 & 89.7 & 88.8 \\
5 & adadelta & 95.2 & 53.5 & 66.4 & 91.8 & 90.3 & 89.1 \\
6 & RMSprop & 92.5 & 90.8 & 94.7 & 92.7 & 90.7 & 89.3 \\
7 & Adam & 96.6 & 95.9 & 96.3 & 92.3 & 91.1 & 90.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance of different models when evaluating various optimizers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Dataset** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline [PERSON] [43] & HAM10000 & 89.00 & 83.00 & 83.00 & 83.10 \\ [PERSON] [44] & KCGMH, HAM10000 & 75.18 & — & — & 85.80 \\ [PERSON] [45] & ISIC 2018 & 86.20 & 79.60 & — & 89.90 \\ [PERSON] [46] & HAM10000 & 88.00 & 76.00 & 82.00 & 90.00 \\ [PERSON] [47] & HAM10000 & 88.00 & 88.00 & — & 93.20 \\ [PERSON] [48] & King Fahd Hospital Dataset & 92.22 & 84.20 & 88.03 & 95.80 \\ [PERSON] et al. [39] & HAM10000 & 96.00 & 96.50 & 97.00 & 96.14 \\
**DCAN-Net** & HAM10000 & **97.00** & **97.57** & **97.10** & **97.57** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed model comparisons with state-of-the-art models.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
## 3 Fractal Reflectarray Antennas
### Review of Existing Fractal Reflectarray Elements
#### 3.1.1 Variable-Size Fractal Patches  
The first numerical analysis of a variable-dimension fractal reflectarray was performed in [43], in which some first-iteration fractal elements based on the Koch triangle, Koch square, and Minkowski shapes (Figure 7a) were proposed and studied. That analysis showed that all configurations had low losses (Figure 7c) but limited phase ranges (Figure 7b). According to the authors, the Minkowski element offers the widest phase range for a \(\pm 20\%\) patch size variation around its resonance, despite a higher sensitivity to manufacturing errors (Figure 7b). However, the phase ranges provided by the configurations in Figure 7 may not be practical for a reflectarray design. To enhance this, one approach is to raise the percentage of patch variation, thereby reducing the benefits of fractal miniaturization.  
Figure 7: Simulated fractal unit cells @\(f\) = 11 GHz [33]: (**a**) variable-size fractal shapes, (**b**) reflection phase versus patch size variation percentage \(n\%\) for different shapes; (**c**) reflection coefficient versus \(n\%\) computed for Minkowski and square patches.  
Additional examples of variable-sized fractal reflectarrays can be found in [44]. The goal of this work was to obtain a phase range exceeding 360 degrees, while also reducing the slope of the reflection-phase curve as a function of the element dimension. To this end, a novel fractal patch was proposed by introducing two vertical slots, symmetrically positioned with respect to the center of a square microstrip patch (Figure 8a). The dimensions of the slices, including their lengths, widths, and separations, are fixed independently, while ensuring that the overall structure fits within the square with sides of length L\({}_{1}\)[44]. The sliced square patch exhibits multiband behavior, resulting in a wide phase variation range at 10 GHz (Figure 8b). In [44], various parametric analyses were performed, involving cell size, substrate thickness, slot positions, and widths, in order to reduce the slope of the phase curve, which is critical for both the reflectarray bandwidth and the unit cell fabrication tolerances. Figure 8c demonstrates a good extension of the phase range and smaller curve slopes. However, this is achieved at the cost of an excessively large unit cell size (i.e., 0.7\(\lambda\)\(\times\) 0.7\(\lambda\) at 10 GHz), which prevents one from working with a small array lattice.
## 3 Simulation Studies
### Frequency Domain Performance Evaluation  
The simulated acceleration transmissibilities of the active seat suspension with respect to the sprung mass acceleration using the proposed control strategies (A1-A5) are compared with those of a PID active seat suspension and the passive seat suspension as shown in Figure 3. Results are presented over the frequency range (0.5-18 Hz). It can be seen that for all the proposed control strategies the active seat suspension provides better vibration attenuation compared with both the passive and the PID active seat suspension systems, although this performance deteriorates at very low frequencies, less than 1 Hz. In general, the controller A3, which utilises feedforward preview information signals together with the feedback seat acceleration signal, delivers the best vibration attenuation over the whole frequency range. The next best performance is achieved by the simplest controller A2, which uses only preview information from the vehicle suspension. Although controller A5 provides the best vibration attenuation performance over the human body sensitive frequency range (4-8 Hz) compared with other strategies, its performance reduces outside this frequency range. All proposed control strategies significantly outperform the tuned passive and PID active control systems. Controller A4, which employs both preview information and the feedback seat and sprung mass acceleration signals, performs very similarly to the controller A3.
## 5 Test Results  
To verify the performance of the LEO augmented acquisition and tracking structure, we use the GPS signal software simulator to generate the GPS IF signal with the augmentation of LEO. A software receiver [27] is used to acquire and track the signal. The input information of the signal simulator includes an LEO-assisted position error, frequency error, GPS satellite trajectory, start time and duration, ephemeris, almanac, and receiver's oscillator model. The output is a digital intermediate frequency signal. The satellite constellation uses the real GPS constellation on 21 December 2014. The sampling frequency is set to be 5 MHz, and the intermediate frequency is 0 Hz. The white noise coefficient \(h_{0}\) and random walk noise coefficient \(h_{-2}\) of the receiver's oscillator are set to \(2\times 10^{-19}\) and \(2\times 10^{-20}\), respectively. The sensitivity performance of the proposed LEO-assisted GNSS signal acquisition and tracking is tested experimentally.  
Figure 12: Phase jitter using the 2 nd-order FLL assisted 3 rd-order Phase-locked loop (PLL).  
Figure 13: FLL frequency jitter caused by thermal noise.
## 4 Strain Results and Analysis
### Strain Analysis
#### 4.2.3 Influence of Waveform Conversion  
When \(R\) = 3.75 m, because the blast wave was located in the waveform conversion zone of the longitudinal wave to surface wave, the characteristics of the transformation of the PCS and PTS of each test point were very obvious. As shown in Figure 8, the PCS and PTS of 21H were selected for comparison. As the scaled distance increased, the difference between the two became gradually smaller until the amplitude was approximately equal. As shown in Figure 9, the PCSs were compared for 31H and 31Z. As the scaled distance increased, the relative magnitudes changed, from the PHS being greater than the PAS to the opposite scenario. This was the result of the evolution of the blast waveform.  
Figure 8: The PTS and PCS of 21H (\(R\) = 3.75 m).  
Figure 7: PTS of 23Z and 33Z (\(R\) = 3.75 m).  
Figure 9: The PCS of 31H and 31Z (\(R\) = 3.75 m).  
When \(R=2.7\) m, the scaled distance was in the range of 4-8, and the blast wave was still dominated by the compression wave. Fitting the PCS data of 33H, as shown in Figure 10, the PCS attenuation index was 3.137 at \(R=2.7\) m, which was much smaller than the attenuation index of 6.8761 at \(R=3.75\) m. The composite attenuation index was 3.0699. This shows the influence of different blast wave zones; load attenuation was faster in the waveform conversion zone.
# Barriers and Chemistry in a Bottle: Mechanisms in Today's Oxygen Barriers for Tomorrow's Materials
## 2 Background  
### Oxidative Degradation Mechanisms  
In order to increase the shelf life by eliminating microbial contaminants, bottled products with high contamination risks undergo either aseptic filling, hot filling or pasteurization [11; 15; 16]. The recent breakthroughs in producing thermally resistant jars have opened up more possibilities for PET to compete with glass in the hot-filled food area. The new technologies have increased the thermal stability and new designs have reduced the containers deformation during the filling process [17; 18]. Low-acidity products such as sauces, pickled vegetables and fruit preparations are prone to microbial contamination without a high temperature decontamination step. These hot-filled products have now been made available for packaging in PET. However, removing microbial threats by either thermal or chemical treatments does not exclude the occurrence of chemical reactions with food constituents that might occur during the shelf life of the product. The oxidation sensitivity of specific food components leads to a high necessity for oxygen removal. To indicate the importance of preserving foodstuffs under anaerobic conditions, a summary has been made of the major oxidative degradations for some common food and beverage components.
# Improving Passive Time Reversal Underwater Acoustic Communications Using Subarray Processing
## 2 System Model  
The transmitted signal is represented in baseband form as the following equation:  
\[u(t)=\sum_{n}d(n)g(t-nT), \tag{1}\]  
where \(d(n)\) are the M-ary phase shift keying (MPSK) modulated data symbols transmitted every T seconds, and \(g(t)\) is the transmitter pulse shape filter. This signal is modulated on the center frequency \(f_{c}\) and transmitted over the underwater acoustic channel. The channel impulse response for a time-varying multipath underwater acoustic channel can be described by the following equation:  
\[h(\tau,t)=\sum A_{p}(t)\delta(t-\tau_{p}(t)), \tag{2}\]  
where \(A_{p}(t)\) is the amplitude of path \(p\) and \(\tau_{p}(t)\) is the path delay. On the receiver side, the Doppler is usually compensated by resampling of the incoming signal [22]. Let \(y_{k}(t)\) be the received baseband signal at the \(k\)-th hydrophone after resampling processing as follows:  
\[y_{k}(t)=\sum_{n}d(n)h_{k}(\tau,t-nT)e^{i\theta_{k}(t)}+w_{k}(t),k=1,\...\ M, \tag{3}\]  
where \(h_{k}(\tau,t)\) is the overall channel response of the \(k\)-th channel, including physical channel and transceiver filters; \(w_{k}(t)\) is the additive white Gaussian noise (AWGN); and \(\theta_{k}\) is the phase rotation caused by symbol timing offset and Doppler shift.
# User-Perceived Capacity: Theory, Computation, and Achievable Policies
## 3 System Model
### Physical Layer Model  
In the physical layer, we represent the signal transmitted during the \(t\)-th time-slot as \(X[t]\), and the corresponding signal received at the receiver as \(Y[t]\). The channel gain is denoted as \(h[t]\). We express the received signal as follows.  
\[Y[t]=h[t]X[t]+Z[t], \tag{7}\]  
where \(Z[t]\) represents an additive white Gaussian noise process.  
Note that a quantized channel state is typically used to determine modulation and coding schemes' real-world systems due to the limited resources for channel estimation. As such, we employ a set of thresholds for the quantization of the channel gain \(h[y]\), namely, \(h_{0}^{th}=0<h_{1}^{th}<\cdots<h_{L}^{th}=+\infty\). Specifically, we set \(h[t]\) as \(h_{l}=\frac{1}{2}\big{(}h_{l-1}^{th}+h_{l}^{th}\big{)}\) for \(l=1,\ldots,L\), if the actual channel gain falls within the interval of \([h_{l-1}^{th},h_{l}^{th}]\). Consequently, we can model \(h[t]\) as an \(L\)-state block-fading channel, with \(\mathcal{H}=\{h_{1},h_{2},\ldots,h_{L}\}\) representing the feasible region of channel states, characterized by \(0<h_{1}<h_{2}<\cdots<h_{L}<+\infty\). Furthermore, we define the probability of \(h[t]=h_{l}\) as follows.  
\[\eta_{l}=\int_{h_{l-1}^{th}}^{h_{l}^{th}}p(x)dx\quad\text{for }l=1,2,\ldots,L, \tag{8}\]  
where \(p(x)\) is the probability density function of the channel gain that is conducted from the real-world wireless environment. Additionally, \(h[t]\) is independently and identically distributed (\(i.i.d.\)) across time-slots. We assume that the channel state information \(h[t]\) is available at the transceiver and receiver at each timeslot. The assumption of the full knowledge of the channel state is widely adopted in the literature and grounded in the CSI measuring and reporting processes stipulated for 5G NR systems in the 3 GPP standard [65]. The effect of channel estimation error is also investigated by numerical simulations.  
We denote by \(P[t]\) the power consumption in the \(t\)-th time-slot. When the transmission rate \(s[t]=s\) and the channel state \(h[t]=h_{l}\), the power consumption \(P[t]=P(s,l)\). Without the loss of generality, we assume that \(P(s,l)\) is convex with respect to \(s\) and that it is monotonically increasing. Meanwhile, \(S\) is the maximum value of transmission rate \(s[t]\).
# Techno-Economic Analysis of a Solar Thermal Plant for Large-Scale Water Pasteurization
## 3 Plant Model Design
### Solar Field  
For the solar field, we have considered evacuated-tube solar collectors (Sky Pro 18 Advanced CPC, by Kloben Industries) with Compound Parabolic Concentrator (CPC). These collectors show low sensitivity of their efficiency with respect to external temperature variations, since the vacuum between the absorber and the glass envelope allows to reduce convective losses. The outlet temperature of the heat transfer fluid \(T_{sf,out}\) can be computed via an energy balance on the collector, that is  
\[T_{sf,out}=T_{sf,in}+\frac{\Phi_{sf}}{m_{sf}\,c_{p}}, \tag{1}\]  
Figure 2: Overview of the plant layout, which consists of: a solar field for primary solar thermal energy collection; an auxiliary gas burner to overcome the intermittent nature of the solar resource; a central treatment unit for the pasteurization, which also serves as heat storage system; a plate heat exchanger for waste-heat recovery and pre-heating of the water to be treated.  
where \(T_{sf,in}\) is the inlet temperature and \(\dot{m}_{sf}\) the mass-flow rate of the heat transfer fluid. The useful output thermal power of the solar field \(\Phi_{sf}\) is obtained as  
\[\Phi_{sf}=\eta_{o}I_{s}A_{sf}-\Phi_{hl,sf}, \tag{2}\]  
where \(\eta_{o}=0.72\) is the optical efficiency of the collectors taken from the data-sheet provided by the manufacturer [31], \(I_{s}\) is the normal solar irradiance for Torino (Italy) obtained on hourly basis from PVGIS [32; 33] and \(A_{sf}\) is the total aperture area of the collectors. The thermal losses \(\Phi_{hl,sf}\) have been obtained for each value of the incoming irradiance following an iterative procedure, to match the useful power as a function of the mean temperature inside the collector provided in the available data-sheet [31].
# Fractal Contact Mechanics Model for the Rough Surface of a Beveloid Gear with Elliptical Asperities
## 5 Numerical Analysis and Discussion of Results  
### Effect of Fractal Parameters and Eccentricity on Contact Area  
The above analysis shows that the real contact area is closely related to the fractal dimensions \(D\) and the eccentricity \(e\) of the elliptical asperities as well as the contact coefficient \(\lambda\) for a certain material parameter of the rough surface and normal contact load. If we take the material parameters of the equivalent rough surface as shown in Table 1, the fractal dimension \(D_{1}=1.56\), the fractal roughness \(G_{1}=G_{2}=1.0\times 10^{-10}\) m, and the value range of fractal dimension \(D_{2}\) is 1.3-1.8. Figure 9 shows the relationship curves between different fractal dimensions \(D_{2}\) and the real contact area of the rough surface.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Profile 1** & **Profile 2** \\ \hline Fractal dimension \(D\) & 1.3–1.8 & 1.3–1.8 \\ Characteristic scale \(G\) (m) & 1.0 \(\times\) 10\({}^{-10}\) & 1.0 \(\times\) 10\({}^{-10}\) \\ Young’s modulus \(E\) (Pa) & 2.06 \(\times\) 10\({}^{11}\) & \\ Poisson’s ratio \(\
u\) & 0.26 & \\ Plastic yield stress \(\sigma_{y}\) (Pa) & 235 \(\times\) 10\({}^{6}\) & \\ \hline \end{tabular}
\end{table}
Table 1: Calculated parameters for the mechanical properties of rough surfaces.  
As seen in Figure 9, the real contact area grows in proportion to the fractal dimension. This is because the rough surface becomes flatter as the fractal dimension increases, increasing the real contact area.  
As seen in Figure 9, the real contact area reduces as the eccentricity \(e\) increases when the fractal dimensions are determined. This is because the contact area of a single elliptical asperity is less than that of a spherical asperity whenever the eccentricity is considered; so, the real contact area of the rough surface reduces as the eccentricity \(e\) increases. As seen in Figure 9, the real contact area of the rough surface decreases as the contact coefficients grow when the fractal dimensions are given. This is because when the contact coefficients grow, the contact area of the two rough surfaces reduces, resulting in a reduction in the real contact area.  
The relationship between the ratio of the elastic contact area to the real contact area and the fractal dimension \(D\) at different eccentricities \(e\) is shown in Figure 10. As can be demonstrated, although the real contact area decreases as the eccentricity \(e\) grows, the ratio of the elastic contact area to the real contact area almost remains constant. The elastic contact area ratio increases as the fractal dimension \(D\) increases and stabilizes at 1.55.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 5 Experimental Results and Analysis  
We performed WCFC on the New York University (NYU) Depth V2 dataset [31], compared with the state-of-the-art RGBD superpixel methods, with a source code including SLIC, DASP, and VCCS and evaluated the results through boundary recall, undersegmentation error and time performance. Additionally, we used the Linux operating system and C programing language to implement the performance comparison. In the following experiments, the parameters \(N_{c}\), \(N_{s}\), and \(N_{g}\) in Equation (8) were set to 1, 40, and 2000, respectively. In practice, \(N_{c}\) was fixed to 1, and we tuned \(N_{s}\) and \(N_{g}\) to have good evaluation indices using the NYU Depth V2 dataset. Furthermore, the mean filter size applied in normal estimation was set to 15 by 15, because several experiments have shown that the 15 \(\times\) 15 mean filter is suitable for the point cloud in the estimation of normal maps and can ensure that there is no fragmentation in the normal maps produced. Examples of the superpixel segmentations produced by each method appear in Figure 4, and more detailed results compared with other superpixel algorithms are provided in Appendix A, Figures A1-A4. The results show that, in image regions with similar or dull colors, WCFC superpixels adhere to the object boundary more strictly than other methods. Meanwhile, WCFC manages to reduce the impact of depth noise in image regions with brilliant colors.