esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation
### MATLAB Simulation

Table 2: (a) Performance results by distance between targets according to padding length: 1/4 error of existing range resolution. (b) Performance results by distance between targets according to padding length: 1/2 error of existing range resolution. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & **(a)** & & & \\ \hline Range Resolution & 0.4 m & 0.5 m & 0.6 m & 0.7 m & 0.8 m & 0.9 m & 1.0 m \\ \hline Number & 3.72 & 3.81 & 4.02 & 3.91 & 3.97 & 3.98 & 3.85 \\ \hline  & & & **(b)** & & & \\ \hline Range Resolution & 0.4 m & 0.5 m & 0.6 m & 0.7 m & 0 samples, 100 each for the case where the size of the target1 is 2 to 6 times larger than the target2 and the size of the target2 is 2 to 6 times larger than the target1. The OS-CFAR algorithm was utilized for target detection. The application of OS-CFAR demonstrated that the proposed Estimated Signal Padding outperforms both Zero Padding and Neural Network Padding. When applying the proposed algorithm, it was observed that targets of various sizes, as well as those of the same size, could be effectively detected in terms of both their positions and quantities. The computational complexity of Neural Network Padding and the proposed Estimated Signal Padding was compared by multiplication and addition. For neural network padding, FFNN (Feedforward Neural Network) and Zoom-FFT were used. It consists of nine hidden layers. The total computational complexity for Neural Network Padding is 720,896 multiplications and 719,872 additions. Estimated Signal Padding finds the position of the largest signal after FFT and generates a waveform. The process of adding the original signal and the generated signal and performing FFT again is repeated. The total computational complexity for Estimated Signal Padding is 10,240 multiplications and 22,517 additions. As the number of targets to detect increases, the computational complexity increases proportionally. Since the maximum number of targets is four, the maximum complexity is 22,528 multiplications and 47,101 additions. It can be observed that the proposed algorithm has a lower computational complexity compared to the neural network.
# On Finding and Enumerating Maximal and Maximum \(k\)-Partite Cliques in \(k\)-Partite Graphs
## 2 The MMCE Algorithm
### Multipartite Graphs  
We begin by considering the \(k=2\) case. Enumerating bicliques in bipartite graphs has been widely studied. In data mining, for example, the need is often to enumerate closed frequent item sets in transactional data [14]. Faster methods have more recently been devised using a classical graph-theoretical approach [15], with various applications in computational biology [16].  
For arbitrary \(k\), however, much less seems to have been done. The only known direct method [4] is highly inefficient. It enumerates maximal bicliques for each pair of partite sets. All biclique subsets must then be examined to determine if they can be extended to \(k\)-partite cliques whenever \(k>2\). As its designers observe, such a brute-force approach scales poorly. A related approach [2] centers on the problem of categorical data clustering, enumerating all subspace clusters. Such a cluster is akin to a \(k\)-partite clique, but without the requirement that all partite sets be covered. Superficially similar but even more distantly related algorithms include a branch-and-bound strategy [1], improved with the use of bitsets [17]. These methods find only \(k\)-partite cliques that contain a single vertex from each partite set. They are thus unsuitable as a basis for enumerating maximal \(k\)-partite cliques.  
The following observation is generalized from the well-known relationship between maximal bicliques in bipartite graphs and maximal cliques in general graphs [18].  
**Observation 1**.: _If \(G\) is a \(k\)-partite graph, and if a simple graph \(G^{\prime}\) is built from \(G\) by adding all intrapartite edges, then \(C\) is a maximal \(k\)-partite clique in \(G\) if \(C\) is a maximal clique in \(G^{\prime}\) with at least one vertex in each partite set._
## 5 Methodology
### Features Selection
#### 5.5.2 Attribute Evaluators  
From the feature selection methods offered by WEKA, we will choose the two most popularly used:  
* Wrapper methods. The _WrapperSubsetEval_ routine implemented in WEKA will allow us to evaluate some approaches via multivariate techniques. For univariate ones, we need to instead use the _ClassifierAttributeEval_ procedure. We will execute the following predictors:
* Linear Regression: This offers fast computation, fixing the coefficients for each feature.
* Random Forest [68]: As stated earlier, this is a tree-based algorithm well-known for classification purposes.
* Instance-Based K-nearest neighbor algorithm (IBk) [69]: A K-nearest neighbors classifier, this algorithm allows for selecting an appropriate value of K based on cross-validation but is also able to carry out distance weighting.
* Filter Method. On the side of the univariate methods, we will use the _Ranker_ operation according to the below predictors:
* Relief Attribute (Rlf) [70]: Relief feature selection is based on scoring by the identification of feature value differences between the nearest neighbor instance pairs.
* Principal Component Analysis (PCA) [71]: With this technique, a new set of orthogonal coordinate axes is introduced, and, at the same time, the sample data variance is maximized. This leads to the scenario that the other directions, in which the variance is minor, are less important and, hence, can be removed from the dataset. PCA offers a very effective way of transforming the data in a lower dimensionality, while also being able to reveal some simplified patterns that often underlie the data.
###### Abstract  
In this article, a high-gain axisymmetric Minkowski fractal reflectarray is designed and fabricated for Ku-Band satellite internet communications. High gain is achieved here by carefully optimising the number of unit cells, their shape modifier, focal length, feed position and scan angle. The space-filling properties of Minkowski fractals help in miniaturising the fractal. The scan angle of the reflectarray varied by adjusting the fractal scaling factor for each unit cell in the array. The reflectarray is symmetric along the X-axis in its design and configuration. Initially, a Minkowski fractal unit cell is designed using iteration-1 in the simulation software. Then, its design parameters are optimised to achieve high gain, a narrow beam, and beam scan capabilities. The sensitivity of design parameters is examined individually using the array synthesis method to achieve these performance parameters. It helps to establish the maximum range of design and performance parameters for this design. The proposed reflectarray resonates at 12 GHz, achieving a gain of over 20 dB and a narrow beamwidth of less than 15 degrees. Finally, the designed fractal reflectarray is tested in real-time simulation environments using MATLAB R2023b, and its performance is evaluated in an interference scenario involving LEO and MEO satellites, as well as a ground station, under various time conditions. For real-world applicability, it is necessary to identify, analyse, and mitigate the unwanted interference signals that degrade the desired satellite signal. The proposed reflectarray, with its performance characteristics and beam scanning capabilities, is found to be an excellent choice for Ku-band satellite internet communications.  
reflectarray; Minkowski fractal cell; array miniaturisation; fractal scaling factor; axisymmetric design; Ku-band; satellite internet communication; sensitivity analysis +
Footnote †: journal:  
Article
## 3 General and Local Implications of Titanium Alloys
### Generating an Immuno-Inflammatory Response  
Several research papers focused on the encountered immune-inflammatory response generated by the titanium plates and screws used for osteosynthesis. Existent results that suggest an association between titanium alloys and a secondary local osteolysis have gained the attention and became a concern. In relation with the area where the titanium screws and plates became loose, an analysis of the tissue sample revealed an important implication of the macrophages in the surrounding fibrous tissue [84].  
In the study performed by [PERSON] et al. [84] the aim was to investigate the immune-inflammatory response of the soft tissue surrounding the titanium-based alloys used for osteosynthesis and the potential role of the macrophage's infiltration. Their results revealed that an important layer of fibrous connective tissue was formed with a loose outer part that contained blood vessels. Near the blood vessels, an important number of macrophages-like cells that contained fine metal particles were identified. The performed immunohistochemical analysis showed that the tissue samples examined contained CD68, CD11c, CD11a, CD11b, CD8, CD4, CD54, CD62P positive cells. The identified macrophages showed alteration that induced their lysis followed by their death. The results of the research point to the existence of a mild-moderate inflammatory condition. This was the first research that outlined an immune-inflammatory response and a fibrous encapsulation near titanium miniplates.  
[PERSON] et al. [85] in a study performed on mice, reported that in case of the presence of titanium particles that were injected into the knee joint, no remarkable tissue reaction was present, although that human fibroblasts exposed to pure titanium released a certain amount lactate dehydrogenase, inducing cell alterations.  
Several existent reports also suggest that the macrophages that have phagocytized titanium particles can influence the further release of immune mediators that have a direct action upon the bone resorption and the failure of the titanium devices by their secondary loosening [86; 87; 88; 89; 90]. Also, the macrophages release cytokines (b-FGF, IL-1, TGF-\(\beta\)) that stimulate the fibroblasts to produce more collagen, which subsequently contributes to the fibrous encapsulation, as shown in Table 3[91; 92].
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Access Point**} & \multirow{2}{*}{\(\delta_{Q1}\)} & \multirow{2}{*}{\(\delta_{Q2}\)} & \multirow{2}{*}{\(\delta_{Q3}\)} & \multirow{2}{*}{\(\delta_{Q4}\)} & \multirow{2}{*}{\(\delta_{Q5}\)} & **Evaluation Level** \\ \hline
4 & \(-0.581\) & \(-0.248\) & \(-0.181\) & \(-0.152\) & \(0.183\) & 4 \\ \hline
8 & \(-0.462\) & \(-0.129\) & \(-0.062\) & \(-0.033\) & \(0.302\) & 4 \\ \hline
13 & \(-0.258\) & \(0.075\) & \(0.142\) & \(0.171\) & \(0.506\) & 2 \\ \hline
25 & \(-0.05\) & \(0.283\) & \(0.35\) & \(0.379\) & \(0.714\) & 1 \\ \hline
31 & \(0\) & \(0.333\) & \(0.4\) & \(0.429\) & \(0.794\) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table A4: Proximity of comprehensive efficiency of each decision unit and standard unit. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**DMU**} & \multicolumn{2}{c}{**Input Indicator**} & \multicolumn{2}{c}{**Output Indicators**} \\ \cline{2-4}  & **X1** & **X2** & **X3** & **Q** \\ \hline DMU1 & \(0.275\%\) & \(0.875\%\) & \(0.5\%\) & \(1\) \\ \hline DMU2 & \(0.825\%\) & \(2.625\%\) & \(1.5\%\) & \(2\) \\ \hline DMU3 & \(1.375\%\) & \(4.375\%\) & \(2.5\%\) & \(3\) \\ \hline DMU4 & \(1.825\%\) & \(6.125\%\) & \(3.5\%\) & \(4\) \\ \hline DMU5 & \(16.1\%\) & \(18.5\%\) & \(4.5\%\) & \(5\) \\ \hline \hline \end{tabular}
\end{table} 
Table A2: Hierarchical decision-making units. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Access Point**} & \multicolumn{2}{c}{**Power Quality Index**} \\ \cline{2-4}  & **X1** & **X2** & **X3** \\ \hline
4 & \(3.340\%\) & \(2.075\%\) & \(0.118\%\) \\ \hline
8 & \(4.008\%\) & \(4.753\%\) & \(0.198\%\) \\ \hline
13 & \(4.359\%\) & \(5.191\%\) & \(0.215\%\) \\ \hline
25 & \(5.200\%\) & \(2.054\%\) & \(0.208\%\) \\ \hline
31 & \(5.391\%\) & \(2.258\%\) & \(0.247\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table A3: Data of various indicators of power quality.
## 5 Evaluation and Results  
In this section, the operational performance and decision accuracy of the proposed ABAC-based blockchain framework are evaluated within a permissioned mining governance context. The evaluation is conducted by simulating 1500 access requests involving 50 distinct users and 30 tokenized mining licenses, executed on a locally hosted Hyperledger Fabric network. The assessment encompasses key performance metrics, including latency and throughput, as well as access decision distributions. A comparative analysis between ABAC and RBAC models is also presented. Additionally, visualizations and log data are used to support the auditability and practical applicability of the framework under dynamic, attribute-based policy conditions.
# How Human Mobility Models Can Help to Deal with COVID-19
## 5 Evaluation of the Scenarios
### Spatial Distribution  
Now we analyse the pedestrians' movement in the different scenarios using density maps. For creating these density maps we used the previously generated mobility traces, considering a resolution of 1 m\({}^{2}\) and 1 s. That is, the studied area is divided into cells of 1 m\({}^{2}\), and for each cell, we obtained the accumulated number of seconds of all individuals that have been stayed in this cell. For example, a density value of 30 represents that individuals have stayed 30 s, which can be the result of a lot of different temporal and spatial combinations (for example, 2 individuals staying 15 s each one, or 30 individuals staying 1 s). This way, high-density cells represent places where more people stay.  
Figure 4 shows the density maps for the synthetic scenarios. The results for RWP and SWIM are very similar, showing a completely random distribution of the density. For the RWPa scenario (Figure 4c), we can clearly see the effect of the attraction points, which are the areas with higher density and also the movement between these attraction points. Furthermore, the areas far away from these attraction points have not even been walked.  
Figure 3: Number of individuals along time. (**a**) Plaza scenario; (**b**) Station scenario.  
The density maps generated from the PedSim generated scenarios are shown in Figure 5. In the Plaza scenario (Figure 5a) we can see that the areas with more density correspond to the defined points of interest. We can also distinguish the main paths used for the pedestrians to enter and exit the plaza. For the Station scenario (Figure 5b), we can see that the higher densities are in the platforms, where the pedestrians stay and wait for the train. We can also observe the paths for entering and leaving the station, with some higher densities at the turnstiles where people wait to pass. Note that the turnstiles areas are at both sides of the figure, around the points (10,24) and (142,24).  
Summing up, the results for the synthetic scenarios are very artificial, hardly representing any real scenario. Maybe the RWPa generated scenario could resemble some possible scenario, such as a shop. That is the main reason for using a pedestrian simulator, which generates a realistic scenario.
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 2 Related Work
### Bio-Inspired Based Schemes  
The adaptation of nature-inspired solutions in science and engineering has brought great communication benefits. Swarm intelligence based algorithms such as ant colony optimization [36], artificial bee colony [37], firefly algorithm [38] have the capabilities of handling complex problems with simple rules or methods. In WSNs, swarm intelligence based routing protocols utilizes the forging behavior of small insects for solving complex routing problems [39]. In ad-hoc networks, exploitation of self-organizing capabilities of natural species provides scalability, robustness and effectiveness [40]. The last few years also witnessed the scope of bio-inspired approaches in vehicular ad-hoc networks due to their close communication similarity with the natural species. Bio-inspired vehicular ad-hoc networks routing protocols provide better scalability, robustness, low computational complexity and adaptability as compared to traditional vehicular ad-hoc networks routing protocols [41]. Similarly, in the context of the internet of things, bio-inspired routing techniques are nominated as a suitable choice for handling the routing challenge of frequent changing topology and network object diversity [42]. Firefly algorithm low complexity among other metaheuristic algorithms has attracted much attention in the last decade [43]. The minimum computation cost of the firefly algorithm in terms of time has led to its adaptation in various applications, including digital image compression [44], antenna design [45], traveling salesman problem [46], security [47; 48; 49] and multi-modal optimization [50].
## 3 3D Foot Reconstruction Method
### Fundamentals of Camera Imaging and Camera Calibration  
The camera imaging model is determined by the relationship between each point in the photo and the corresponding point in real space. Since the size of the actual object space is converted to the size of the camera image, camera calibration is required, and the lens imaging process will cause some image distortion, which is called camera distortion. Camera calibration is needed to correct the distortion. The camera calibration is the process of determining the external and internal parameters of the camera. The objects known to the three-dimensional position calibration and their images in the camera image plane (it is usually in the form of a known 3D coordinates calibration point and the image pixel coordinates of these points in the camera image) can be defined. The internal and external parameters of the camera involved in the transformation from a world coordinate system to image pixel coordinate system are solved by the optimization method.  
In the imaging process of the camera, the image pixel coordinate system, the camera coordinate system and the world coordinate system are involved. The hypothesis is that the point of the 3D world coordinate system is \(P=[X,Y,Z,1]^{T}\), and the two-dimensional plane camera coordinate system \(m=(u,v,1)^{T}\). According to [PERSON]'s Camera Calibration Method [19; 20], we assumed that \(Z\) = 0 of the checkerboard plane; the homography matrix \(H\) of the image plane would be solved by the coordinates of point P and point m, and the internal parameters and external parameters of the camera would be obtained. The basic principle is as follows:  
\[s\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=K[\begin{array}{ccc}r_{1}&r_{2}&r_{3}&t\end{array}] \left[\begin{array}{c}X\\ Y\\ 0\\ 1\end{array}\right]=K\left[\begin{array}{c}X\\ Y\\ 1\end{array}\right] \tag{1}\]  
\[H=[h_{1}h_{2}h_{3}]=\lambda K[r_{1}r_{2}t], \tag{2}\]  
Figure 2: Overall experimental process.  
where \(s\) is the scale factor, \(K\) is the camera's internal parameters, \(r_{1}\), \(r_{2}\) are the vectors in the rotation matrix R, and T is the translation vector. According to the nature of the rotation matrix, that is, \(r_{1}^{T}r_{2}=0\) and \(\|r_{1}\|=\|r_{2}\|=1\), the following two basic constraints on the internal parameter matrix can be obtained for each image:  
\[h_{1}^{T}k^{-T}k^{-1}h_{2}=0 \tag{3}\]  
\[h_{1}^{T}k^{-T}k^{-1}h_{1}=h_{2}^{T}k^{-T}k^{-1}h_{2}. \tag{4}\]  
Since the camera has 5 unknown parameters, when the number of captured images is greater than or equal to 3, the \(K\) can be solved to obtain the internal parameter matrix and can be obtained from:  
\[\lambda=\frac{1}{s}=\frac{1}{\|A^{-1}h_{1}\|}=\frac{1}{\|A^{-1}h_{2}\|} \tag{5}\]  
\[r_{1}=\frac{1}{\lambda}K^{-1}h_{1} \tag{6}\]  
\[r_{2}=\frac{1}{\lambda}K^{-1}h_{2} \tag{7}\]  
\[r_{3}=r_{1}\times r_{2} \tag{8}\]  
\[t=\lambda K^{-1}h_{3}. \tag{9}\]  
After obtaining \(K\), \(R\), \(t\), the spatial point X is calculated by triangulation.
## 4 Enhancement of Brillouin Stokes Signal (I): Induction of Stimulated Scattering
### Experimental Results  
Figure 3 shows the measured BGS without averaging when the pump power and the probe power were 23 dBm and 22 dBm, respectively. The power was normalized so that the peak power was 1.0. Although the PFGI-POF length was only 1 m and the polarization state was scrambled, the BGS was observed with much higher S/N ratio than that previously reported [30]. The BFS was 2.86 GHz, which is slightly higher than the previously reported value of 2.83 GHz [27]. This discrepancy seems to originate from the difference in temperature and the time constant of the LI-A which is not short enough. The 3-dB bandwidth of the BGS measured in this experiment was about 160 MHz, but further research is needed on the bandwidth because it is also dependent on the time constant of the LI-A (Note that, when the time constant was shorter than 10 ms, the BGS was distorted).  
Figure 4 shows the dependence of the BGS on probe power when the pump power was fixed at 23 dBm. The probe power was reduced from 22 dBm down to 6 dBm, and averaging was conducted 30 times for the observable readout when the Stokes power was very low. As the probe power decreased, the Stokes power also decreased, which proves that this BGS is caused by the interaction between the pump light and the probe light, _i.e._, SBS.  
Figure 4: Dependence of BGS on probe power in PFGI-POF.  
Figure 3: BGS in PFGI-POF observed without averaging.  
The dependence of the BGS on temperature was also measured as shown in Figure 5(a). The pump power and the probe power were 23 dBm and 22 dBm, respectively, and averaging was conducted 30 times. The temperature was set to 20, 40, and 60 \(\lx@math@degree\)C. With the increasing temperature, the BGS shifted toward lower frequency. The temperature dependence of the BFS is shown in Figure 5(b). The slope of \(-\)4.05 MHz/K is in good agreement with the previous report [30], which confirms that the BGS in a PFGI-POF observed with the pump-probe technique can be applied to high-accuracy temperature sensing.
## 3 Analysis of the Dual-Peak Resonance Restructured by Thermal Strains
### Analysis of the Temperature Sensitivity Difference  
The effect of the coating thickness on the temperature sensitivity difference is shown in Figure 11. We can conclude the followings: (1) When \(h_{2}\) increases to a certain value, they all tend to be constant values. (2) For the same \(h_{2}\), \(\Delta K_{T}\), \(\Delta K^{\prime}{}_{T}\), and \(\Delta K^{\prime\prime}{}_{T}\) decrease with the increasing of \(h_{1}\). (3) \(\Delta K^{\prime}{}_{T}\) is the largest among them. (4) There is a threshold of \(h_{2}\) between \(\Delta K_{T}\) and \(\Delta K^{\prime\prime}{}_{T}\) for every \(h_{1}\leq 8\)\(\upmu\)m and \(\Delta K_{T}>\Delta K^{\prime\prime}{}_{T}\) when \(h_{2}\) is smaller than this threshold. For example, for \(h_{1}=2\)\(\upmu\)m, the threshold of \(h_{2}\) is 200 \(\upmu\)m.  
Figure 12 shows the effects of the coating's thermal expansion coefficient on \(\Delta K_{T}\), \(\Delta K^{\prime}{}_{T}\), and \(\Delta K^{\prime\prime}{}_{T}\). Under the certain coating thickness (e.g., \(h_{2}\) = 200 \(\upmu\)m, \(h_{1}\) = 5 \(\upmu\)m) and Young's modulus, \(\Delta K_{T}\) and \(\Delta K^{\prime}{}_{T}\) increase with the increase of \(\alpha_{3}\) when \(\alpha_{2}\) remains unchanged. Whereas, \(\Delta K_{T}\) and \(\Delta K^{\prime}{}_{T}\) decrease slightly with the increase of \(\alpha_{2}\) when \(\alpha_{3}\) remains unchanged. \(\Delta K^{\prime\prime}{}_{T}\) increases with the increasing of \(\alpha_{2}\).