esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Pairwise Constraint Clustering  
The concept of pairwise constraints has been widely implemented in many clustering algorithms such as k-means [33] and complete link [34] clustering to impose must-link and cannot-link constraints on pairs of nodes during the clustering as illustrated in Figure 2. The must-link constraint forces pair nodes \(n_{a}\) and \(n_{b}\) to be placed in the same cluster, while the cannot-link constraint disallows pair nodes \(n_{a}\) and \(n_{b}\) to be placed in the same cluster [17]. This significantly influences the outcome of the clustering, since pair nodes with common links usually belong to the same cluster, while those without common links belong to different clusters.  
Therefore, the pairwise constraint concept can also be implemented in a spectrum-aware clustering algorithm, which can be explained by comparing it with the groupwise constraint method employed in [17,18] for spectrum-aware clustering. Unlike conventional WSN clustering schemes, spectrum-aware clustering schemes require each node to sense the spectrum band and detect spectrum holes that can be used for data communication. In addition, each clusterhead must have at least one common available channel with its member nodes since each member node transmits its data directly to the clusterhead without intermediary nodes. To highlight the difference between pairwise and groupwise constraints, we consider a clusterhead \(CH_{j}\) and three member nodes \(MN_{1}\), \(MN_{2}\), \(MN_{3}\) that operate on a set of licensed channels \(S_{ch}=\{Ch_{\rm z}|z=1,2,3,...,n_{\rm z}\}\) as shown in Figure 3. The numbers beside them represent the vacant licensed channels detected by the respective member node; a dotted line between them indicates a cannot-link constraint, while a solid line indicates a must link constraint as illustrated in Figure 2. The figure shows that member node \(MN_{1}\) shares channel \(Ch_{2}\) with \(MN_{2}\) and \(CH_{j}\), member node \(MN_{2}\) shares channel \(Ch_{2}\) and channel \(Ch_{3}\) with \(CH_{j}\) in addition to member node \(MN_{1}\), while member node \(MN_{3}\) shares only channel \(Ch_{1}\) with \(MN_{1}\). Based on this scenario, the pairwise constraint imposes a must-link constraint on member nodes \(MN_{1}\) and \(MN_{2}\) to form a cluster with clusterhead \(CH_{j}\) because they all share a vacant channel (\(Ch_{2}\)). It also imposes a cannot-link constraint on \(CH_{j}\) and \(MN_{3}\) so that the member nodes cannot form a cluster since member node \(MN_{3}\) has no common vacant channel with \(CH_{j}\) even though it shares a vacant channel with member node \(MN_{1}\). On the other hand, the groupwise constraint imposes cannot-links on all the four nodes, including \(CH_{j}\), disallowing them from forming a cluster, because the nodes share no common vacant channel. While a pairwise constraint requires at least one common vacant channel for pair nodes of clusterhead and member node to form a  
Figure 2: Illustration of must-link and cannot-link constraints.  
cluster, a groupwise constraint requires at least one common vacant channel for all nodes in a group to a form cluster.
## 3 Results and Analysis  
The fuzzy AGC system used a 32-bit microprocessor system equipped with a 12-bit successive-approximation ADC. The AGC test system corresponds to the structure shown in Figure 4. Figure 4 shows the AGC system implemented in the microprocessor. Other examples of the implementation of fuzzy drivers in microprocessor systems have been presented in [27; 28].  
To verify the operation of the proposed AGC system, the analog experimental signal (\(V_{\alpha\
u}\)) shown in Figure 6 (channel 2) was given at the input.  
The output signal (\(V_{\alpha 12}\)) of the AGC circuit receives 3.98 V, which is close to the desired value of 4 V (Figure 6, channel 1). The voltage drop shown in Figure 6 (\(Cs\)) is a result of switching between controllers working on different ranges (see Figure 4, marked as: l, s, h). Analysis of the signals shown in Figure 6 reveals that the AGC systems implemented on the microprocessor allow for a measuring range of 4 V. To verify the dynamic operation of the AGC system, a square signal was provided at the input (Figure 6, channel 2). The output (\(V_{\alpha 12}\)) of the DAC (Figure 6, channel 1) is shifted in phase to the given signal (\(V_{\alpha\
u}\)), due to the processing time required by the AGC algorithm. This should be considered in the second measuring circuit (voltage channel) of the electricity meter.  
A comparison of the results for AGC systems based on different structures is given in Table 1. The results were obtained by a computer simulation in Matlab/Simulink software for three types of AGC structures, based on a PGA amplifier, a PGA connected to a VGA, and the fuzzy controller.  
The NOT_P symbol in Table 1 indicates that measurement is not possible. This problem occurs with a cascaded connection of two amplifiers (PGA and VGA) and is related to the permitted input range of the VGA. The data in Table 1 prove the effectiveness of AGC systems based on a fuzzy controller. The proposed structure does not have an analog multiplier, which is associated with greater dynamics in relation to other AGCs. An important advantage of the AGC fuzzy system is its ability to measure each signal thanks to a switchable range.  
The workbench of AGC circuit with fuzzy logic were presented in Appendix A in Figure 11.
## 3 Analysis of Lightning Strike Effects on Patch-Repaired Composite
### Ablation Damage Results of Patch-Repaired Laminate
#### 3.1.2 Lightning Strike Damage to Patch-Repaired Laminate  
When applying a 5 kA peak current to the repaired laminate, Figure 8a shows that within the first ply, ablation damage is observed along the 45\({}^{\circ}\) fiber direction, which is confined to the boundary of the circular patch. This is attributed to the misalignment of  
Figure 7: Patch repair assembly diagram for composite laminates. (**a**) First layer; (**b**) second layer.  
fibers at the joint face between the patch and the original laminate, effectively causing fiber breakage at the junction. The misalignment limits current transfer to the laminate, containing damage within the patched area. Also, a notable temperature rise above 175 \({}^{\circ}\)C is observed at the laminate's surface. This may be ascribed to the presence of minute gaps at the junction (unlike newly manufactured laminates, repaired sections cannot achieve the perfect, seamless fit of the original material, resulting in minute gaps) which diminish the thermal conductivity and increase the thermal resistance. An increase in thermal resistance within the damaged region may lead to a redistribution of heat around the area, thereby influencing the overall temperature field of the whole ply. However, ablation damage extends beyond the patch area to the second ply, with two additional damage regions noted along the 45\({}^{\circ}\) fiber direction, as shown in Figure 8b. Damage within the patch area shows no correlation with other laminate areas, with its cause analyzable from two perspectives: firstly, damage within the patch area is due to the dielectric breakdown of the upper plies, allowing the current to continue penetrating along the thickness direction of the laminate; secondly, damage within the laminate area is caused by intense heat being conveyed to the second ply via thermal conduction. The area of damage measured is 6.55 cm\({}^{2}\), which represents a 33.67% increase from the original structure's damage area subjected to the same lightning strike current (4.91\(\sim\)6.55 cm\({}^{2}\)). This damage increase is primarily attributed to the new damage areas on the second ply, which are discontinuous from the damage in the patched area and may be challenging to identify during actual repair processes. Consequently, there exists a potential risk in the laminate patch repair, necessitating optimization of current repair methodologies to mitigate adverse effects at the joint face.
## 5 Gaussian Mixture Modeling
### LDA-Based Visualization  
In Figure 8, we present a visual description of the snippets space colored according to the eight components detected via Gaussian Mixture Modeling. The figure is an instance of the method Linear Discriminant Analysis (LDA) [46], used to project the CPAP use dataset onto a 3-dimensional space. LDA, similar to Principal Component Analysis, finds the component axes that maximize the variance of the data at the same time that it sets axes to maximize the separation between classes. The plot illustrates the set of series as a whole with non-evident gaps nor low-density regions, but for the outliers. The use of GMM allowed the partitioning of the space in a way that density-oriented hard clustering methods failed.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Comp** & **\% Elements** &
\begin{tabular}{c} **avg** \\ **(h/Night)** \\ \end{tabular} & **std** & **std*2** & **Energy** & **avg/std** & **Status** \\ \hline
1 & 6.85\% & 3.69 & 1.64 & 3.29 & 110.61 & 2.24 & Struggling \\
2 & 7.30\% & 6.87 & 3.02 & 6.05 & 206.17 & 2.27 & Unstable \\
3 & 8.29\% & 4.85 & 1.69 & 3.37 & 145.63 & 2.88 & Pre-adaptation \\
4 & 17.51\% & 6.21 & 1.96 & 3.93 & 186.20 & 3.16 & Adapted \\
5 & 13.76\% & 5.56 & 1.47 & 2.94 & 166.82 & 3.79 &
\begin{tabular}{c} Highly \\ adapted \\ \end{tabular} \\
6 & 13.15\% & 7.90 & 1.75 & 3.50 & 236.90 & 4.52 & Good \\
7 & 20.68\% & 7.07 & 1.05 & 2.10 & 212.10 & 6.72 & Ideal \\
8 & 12.46\% & 8.55 & 1.14 & 2.27 & 256.54 & 7.52 & Plain \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the properties of each of the eight components, including percentage of the snippets in each component, average h/night along with standard deviation and standard deviation*2, energy (summation of the average signal). The order of the components is given by ratio average*1/standard_deviation; that is, the higher in the rank the more hours of use and more regularity (lower standard deviation).  
Figure 8: Plot 3D using technique Linear Discriminant Analysis applied over the 30-days summarization snippets.
## 2 Materials and Methods
### Evaluation Parameters
#### 2.3.2 Positioning Accuracy  
The positioning accuracy was determined by counting the positioning errors and adding them. For example, if the test participant turned the rotary control in Task 1 and reached a value of 14 instead of 12, then returned to 11, and then to the required value of 12, three errors were noted.
## Materials and Methods
### Data Analysis  
The spectra were preliminarily processed before proceeding with the analysis. Subtraction of background spectrum, acquired in a free-cell zone of the slide was performed. A baseline and excess noise corrections were performed on the whole data set by means of a numerical procedure based on wavelet algorithms (\"MATLAB Wavelet Toolbox\", MathWorks Inc., Natick, MA, USA) [35; 36; 37]. The FTIR signal was represented using the sum of elementary functions (wavelets) at different frequency scales in a hierarchical representation known as the Discrete Wavelet Transform (DWT). Starting from the decomposed parts, the signal can be reconstructed by an inverted process, knows as Inverse Discrete Wavelet Transform (IDWT); removing the last approximation component from the reconstruction process, to eliminate the smoother part of the signal from the spectra. Similarly, by removing the fast frequency components, it is possible to eliminate non-correlated noise signals. In our case, bi-orthogonal wavelets based on the B-spline function were employed for a ten-level decomposition of the signal. The signal was recalculated from detail components up to the nine levels. Average spectra with standard deviation were obtained for all sample types. All the spectra were normalized to have a total area equal to one. The spectra were analyzed by using convoluted Lorentzian shaped vibrational modes. After a manual selection for the starting conditions of the procedure, a best-fit routine from the Origin software (Version 9.0, OriginLab Corporation, Northampton, MA, USA) was used to determine the optimized intensity, position, and width of the peaks. The \(\chi^{2}\) parameter was used for estimating the convolution procedure performance (see [32]). The deconvolution procedure was mainly used for monitoring the changes in the peak position due to X-ray exposure. The Amide I band was further examined since changes in protein configuration can induce meaningful variations in its characteristics [38; 39; 40]. To investigate protein secondary structure, Gauss-cross-Lorentzian shaped components were used for deconvoluting the Amide I band. The inflection points were localized in the second-derivative spectra, using the Savitsky-Golay algorithm (five-data point window) (\"GRAMS/AI\", Thermo Fischer Scientific, Waltham, MA, USA). The area of each absorption band was considered to be proportional to the relative amount of the structure in infrared spectra [38; 41; 42]. Further quantitative information about the changes occurring in the infrared spectra related to different experimental conditions was obtained by evaluating the ratios between the absorbance of selected peaks. The list of these ratios, related to protein content, rearrangement, and phosphorylation; DNA content and modification; and lipid content and saturation, is reported in Table 1. The ratio values obtained from the spectra acquired from different samples (different dose, different fixation time) were compared using an un-paired t-test with a 0.05% significance level.
## 3 Results and Discussion
### Sensory Analysis
#### 3.3.2 Texture Analysis
day of storage, the M8 bread sample exhibited the highest values for hardness, elasticity, cohesiveness, and chewiness. The S8 sample showed values closest to the control sample. According to [PERSON] et al., 2019 [PERSON] et al. (2019), the bread samples enriched with sea buckthorn pulp showed increased values of springiness, cohesiveness, and resilience, while the values of hardness, gumminess, and chewiness were lower compared to the control sample. The addition of sea buckthorn pulp led to a significant decrease in the hardness of bread, which could be attributed to the denser crumb structure formed during the fermentation process. The cohesiveness of the bread was increased to 38.03% in bread samples containing 5% sea buckthorn pulp compared to the control. The chewiness of the bread obtained with 10% sea buckthorn pulp was similar to that of the control. However, the resilience decreased with the increasing addition of residues, indicating a less elastic gluten structure of bread. The bread sample containing 5% sea buckthorn pulp showed the most significant effect on resilience, with a difference of 65.17% compared to the control. On the contrary, the addition of sea buckthorn biomass to biscuits led to a decrease in their hardness, most likely due to a gradual decrease in gluten content, which hindered the formation of gluten matrices, as well as competition for water between sugar and flour, as mentioned by [PERSON] et al., 2021 [PERSON] et al. (2021). The reduction in hardness of bakery products enriched with sea buckthorn may also be due to the presence of organic acids, such as malic acid, fumaric acid, or citric acid, as well as limited activity of amylases in the dough, along with the content of phenolic compounds [PERSON] et al. (2021). According to a study conducted by [PERSON] et al. (2016) [PERSON] et al. (2016), the addition of sea buckthorn flour in sponge cakes resulted in a decreased occurrence of starch and protein modifications, leading to a slower progression of changes in the structural, mechanical, and sensory characteristics of the fortified samples. Furthermore, according to the research conducted by [PERSON] et al., 2019 [PERSON] et al., 2019 [PERSON] et al. (2019), the hardness of biscuits was impacted by the drying temperature of the powder they contained. The biscuits that contained powder dried at 80 \({}^{\circ}\)C had higher hardness values but lower total work at the same temperature. This was attributed to the formation of more brittle structures, which led to product fracture. [PERSON] et al., 2021 [PERSON] et al. (2021) highlighted that texture analysis parameters, such as hardness and brittleness, should be kept as low as possible in bakery products since they are closely linked to consumers' perception of freshness. These parameters are crucial for evaluating the quality of bakery products.
## 6 Numerical Results
Figure 11 illustrates the success probabilities \(p_{\text{s}}(K+1)\) for RBB and RBCB schemes. From Figure 11, we can see that the success probability under the Rayleigh channel increases with the number of branches. Figure 11 matches Equation (2) very well. Figure 12 compares the probabilities \(q\) that a node that has successfully received the packet becomes a potential helper for RBB and RBCB schemes. From Figure 12, we can see that \(q\) decreases with the number of helpers. Therefore, Figures 11 and 12 show that, although the increase in the number of helpers can raise the success probability for once broadcasting, it decreases the probability of the occurrence of the cooperative broadcasting at the same time. Thus, we should carefully choose the number of helpers. Figures 13 and 14 compare the PDPs of RBCB with K = 1 and K = 2. The changing trends of the curves in Figures 13 and 14 are the same. When the SNR is small, the PDPs for K = 1 are higher than that for K = 2. When the SNR becomes greater, the PDPs for K = 2 become higher than that for K = 1. The reason is as follows. When the SNR is low, the probability of the occurrence of the cooperative broadcasting for K = 2 is much smaller than that with K = 1. Although the success probability for once broadcast for K = 2 is greater than that for K = 1, the PDPs of RBCB are still lower than those of RBB. When the SNR is high, the probability of the occurrence of the cooperative broadcasting for K = 2 is close to that for K = 1. Therefore, the PDPs for K = 2 become greater than those for K = 1. Figure 11: Comparison of success probability. Figure 12: Comparison of \(q\).
# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 1 Introduction
Section 3 presents the implementation details of the proposed moving vehicle detection and tracking method. Section 4 presents the experimental results obtained using the proposed method at various stages. Finally, Section 5 summarizes the methodology.
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 6 Conclusions
\begin{tabular}{l l} \(\mathbf{F}_{D}\) & Drag force \\ \(\mathbf{F}_{g}\) & Gravitational force \\ \(\mathbf{F}_{g,i}\) & Body forces acting on particle \(i\) \\ \(\mathbf{F}_{h,i}\) & Force acting on particle \(i\) by liquid phase \\ \(\mathbf{F}_{M}\) & Magnus lift force \\ \(\mathbf{F}_{nc,k}\) & Non-contact forces between particles \(i\) and \(k\) \\ \(\mathbf{F}_{p}\) & Pressure gradient force \\ \(\mathbf{F}_{sl}\) & Sum of the forces acted on the liquid phase by solid phase \\ \(\mathbf{F}_{S}\) & [PERSON] lift force \\ \(\mathbf{F}_{t}\) & Turbulence force \\ \(\mathbf{g}\) & Gravitational acceleration \\ \(l_{c}\) & Dimensionless pressure drop related to collisions \\ \(l_{l}\) & Dimensionless pressure drop related to liquid \\ \(\mathbf{I}_{i}\) & Inertia moment of particle \(i\) \\ \(l_{s}\) & Dimensionless pressure drop related to solid \\ \(l_{v}\) & Dimensionless total pressure drop of vertical pipe \\ \(L\) & Pipe length \\ \(m^{*}\) & Equivalent mass \\ \(m_{i}^{*}\), _wall_ & Mass of the wall \\ \(m_{uall}\) & Mass of the wall \\ \(\mathbf{M}_{r,ij}\) & Normal friction moment between particles \(i\) and \(j\) \\ \(\mathbf{M}_{t,ij}\) & Tangential friction moment between particles \(i\) and \(j\) \\ \(\mathbf{Q}_{s}\) & Volume flow rate of solid \\ \(Q_{l}\) & Volume flow rate of liquid \\ \(r\) & Distance to the axis of the pipe \\ R & Radius of the pipe \\ R\({}^{*}\) & Equivalent radius \\ \(R_{l}\) & Radius of the particle \(i\) \\ \(R_{l_{i}\ wall}^{*}\) & Equivalent radius of the wall \\ \(R_{null}\) & Radius of the wall \\ \(S_{u}\) & Normal stiffness \\ \(S_{t}\) & Tangential stiffness \\ \(t\) & Simulation time \\ \(t_{total}\) & Total simulation time \\ \(\mathbf{u}_{l}\) & Liquid velocity \\ \(\mathbf{u}_{s}\) & Solid velocity \\ \(\mathbf{v}_{l}\) & Velocity of particle \(i\) \\ \(V_{l}\) & Average liquid velocity \\ \(V_{mix,0}\) & Initial mixture velocity \\ \(v_{t,ij}^{nl}\) & Relative tangential velocity \\ \(v_{n,ij}^{nl}\) & Normal component of the relative velocity; \\ \(V_{s}\) & Average solid velocity \\ \(V_{slip}\) & Average slip velocity \\ \(\beta\) & The coefficient of restitution \\ \(\delta_{t}\) & Tangential overlap \\ \(\delta_{n}\) & Normal overlap \\ \(\Delta P\) & Pressure drop of vertical pipe \\ \(\Delta t_{l}\) & CFD time step \\ \(\Delta t_{R}\) & Rayleigh time \\ \(\Delta t_{s}\) & DEM time step \\ \(\Delta V_{cell}\) & Volume of CFD grid cell \\ \(\
abla\mathbf{P}\) & Static pressure gradient \\ \(\epsilon_{l}\) & Volume fraction of liquid phase \\ \(\lambda_{l}\) & Friction coefficient \\ \(\mu_{l}\) & Dynamic viscosity of liquid \\ \(\mu_{r}\) & The coefficient of rolling friction \\ \(\rho_{l}\) & Density of liquid \\ \end{tabular}
## 9 Discussion and Simulation Results  
Given the pairs \(\lambda(x)\) and \(\rho(x)\) of Tables 1-3, for each of them, an ensemble with a random rate-\(1/2\) LDPC codes has been generated, and their performance simulated using a customized software built on the basis of [22], assuming a memoryless binary input with an additive white Gaussian noise (BI-AWGN) channel.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{d_{l}}\) & **15** & **20** & **30** & **50** \\ \hline \(\lambda_{2}\) & 0.23802 & 0.21991 & 0.19606 & 0.17120 \\ \(\lambda_{3}\) & 0.20997 & 0.23328 & 0.24039 & 0.21053 \\ \(\lambda_{4}\) & 0.03492 & 0.02058 & & 0.00273 \\ \(\lambda_{5}\) & 0.12015 & & & \\ \(\lambda_{6}\) & & 0.08543 & 0.00228 & \\ \(\lambda_{7}\) & 0.01587 & 0.06540 & 0.05516 & 0.00009 \\ \(\lambda_{8}\) & & 0.04767 & 0.16602 & 0.15269 \\ \(\lambda_{9}\) & & 0.01912 & 0.04088 & 0.09227 \\ \(\lambda_{10}\) & & & 0.01064 & 0.02802 \\ \(\lambda_{14}\) & 0.00480 & & & \\ \(\lambda_{15}\) & 0.37627 & & & 0.01206 \\ \(\lambda_{19}\) & & 0.08064 & & \\ \(\lambda_{20}\) & & 0.22798 & & \\ \(\lambda_{28}\) & & & 0.00221 & \\ \(\lambda_{30}\) & & & 0.28636 & 0.07212 \\ \(\lambda_{50}\) & & & 0.25830 \\ \(\rho_{8}\) & 0.98013 & 0.64854 & 0.00749 & \\ \(\rho_{9}\) & 0.01987 & 0.34747 & 0.99101 & 0.33620 \\ \(\rho_{10}\) & & 0.00399 & 0.00150 & 0.08883 \\ \(\rho_{11}\) & & & & 0.57497 \\ \(\lambda^{\prime}(0)\) & 0.23802 & 0.21991 & 0.19606 & 0.17120 \\ \(\rho^{\prime}(1)\) & 7.01987 & 7.35545 & 7.99401 & 9.23877 \\ \(\lambda^{\prime}(0)\rho^{\prime}(1)\) & 1.67087 & 1.61754 & 1.56731 & 1.58168 \\ \(\int_{0}^{1}\lambda\,(x)\) & 0.24945 & 0.24017 & 0.22240 & 0.19699 \\ \(L_{2}\) & 0.47708 & 0.45783 & 0.44078 & 0.43454 \\ \(\sigma^{*}\) & 0.9622 & 0.9649 & 0.9690 & 0.9718 \\ \(\sigma^{*}_{\rm lensen}\) & 0.98692 & 1.01966 & 1.05485 & 1.04429 \\ SNR\({}_{\rm gap}\) & 0.08052 & 0.36399 & 0.65870 & 0.57131 \\ SNR\({}_{\rm gap-spline}\) & 0.07268 & 0.35615 & 0.65086 & 0.56347 \\ \hline \end{tabular}
\end{table}
Table 3: Computed parameters and decoding threshold bound regarding rate-\(1/2\) codes deduced from Table II of [18] with \(d_{l}=15\), 20, 30, and 50.  
Since \(l\) in Equation (33) is upper bounded by Equation (32), the discussion of the results may be conducted separately for the following two conditions:  
1. \(L_{2}n>(1-r)n\) and
2. \(L_{2}n<(1-r)n\).