esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 PSO Techniques for Adaptive Equalization
### Performance Analysis
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**f(x)** & \multicolumn{2}{c}{**fi**} & \multicolumn{3}{c}{**f2**} \\ \hline
**Value** & **Mean** & **Iterations** & **Comp** & **Mean** & **Iterations** & **Com** \\ \hline PSO & x & x & x & 150.12 (98\%) & 500 & 100\% \\ PSO-D & 1.266e\(-\)53 & 500 & 100\% & 0 & 273 & 100\% \\ PSO-DE & 1.46e\(-\)19 & 446 & 70.29\% & 0 & 291 & 66.94\% \\ \hline DMS & x & x & x & 164.86 (62\%) & 500 & 100\% \\ DMS-D & 1.616e\(-\)54 & 500 & 100\% & 0.00\% & 269 & 100\% \\ DMS-DE & 9.58e\(-\)20 & 420 & 69.90\% & 0\% & 304 & 66.49\% \\ \hline CL & 6.546e\(-\)44 & 500 & 60\% & 115\% & 500 & 60\% \\ CL-D & 52 & 500 & 60\% & 0.00\% & 275 & 60\% \\ CEDE & 1.42e\(-\)18 & 310 & 39.19\% & 0\% & 259 & 37.59\% \\ \hline HP & 0.16 & 500 & 80\% & 6905\% & 500 & 80\% \\ HP-D & 6.566e\(-\)106 & 500 & 80\% & 0.00\% & 92 & 80\% \\ HP-DE2 & 9.9e\(-\)15 & 3.98\% & 1.52e\(-\)228 & 228 & 12.78\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The 60D particles’ convergence comparison. \begin{table}
\begin{tabular}{c c c c c} \hline
**No.of Dimensions** & **PSO** & **DPSO** & **CPSO** & **HPSO** \\ \hline
10 & \(-\)185.37 & \(-\)156.31 & \(-\)300 & \(-\)50.10100664 \\ \hline
20 & \(-\)81.951 & \(-\)60.194 & \(-\)296.087 & \(-\)33.131 \\ \hline
30 & \(-\)48.781 & \(-\)32.039 & \(-\)281.737 & \(-\)30.1019 \\ \hline
40 & \(-\)31.22 & \(-\)19.417 & \(-\)45.652 & \(-\)27.6767 \\ \hline
50 & \(-\)18.537 & \(-\)7.767 & \(-\)3.9130 & \(-\)26.4646 \\ \hline
60 & \(-\)18.537 & \(-\)3.8835 & \(-\)1.396 & \(-\)26.2623 \\ \hline
70 & \(-\)13.659 & \(-\)1.9418 & \(-\)2.6628 & \(-\)24.8481 \\ \hline
80 & \(-\)7.8049 & \(-\)3.8835 & \(-\)3.9138 & \(-\)24.2424 \\ \hline
90 & \(-\)6.8293 & 0 & \(-\)5.2173 & \(-\)24.040 \\ \hline
100 & \(-\)4.878 & 0 & 0 & \(-\)22.4242 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Mean function value of different PSO algorithms. Comparing the techniques, it is evident that, under the given SNR conditions, the PSO-CCF, PSO-VCF, and HPSO methods consistently outperformed the basic LMS method, showcasing their efficacy in optimizing the adaptive filtering process for a linear channel. Among these three advanced methods, HPSO tended to yield the lowest MSE, suggesting its potential to provide the best approximation to the desired signal under different SNR scenarios. These findings establish HPSO as a promising optimization approach that can provide significant benefits in terms of convergence speed and solution quality, making it an attractive choice for numerous real-world optimization problems. The experiment was conducted for a linear time-invariant (LTI) system and nonlinear digital channel model having a sphere and cubic function model. The LTI system showed a linear mapping between the input and output signals, while time invariance indicated that the system will produce the same output signals if an input is used now or T seconds later, except for the time delay. The results showed that HPSO had better performance as compared to all other techniques that exist in the literature.
# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 4 Results and Discussion
### The Effect of Hyperparameter in the HDBSCAN
#### 4.3.1. \(Min\_Cluster\_Size\)  
First, we examined how \(min\_cluster\_size\) affected HDBSCAN's topic-clustering behavior. This parameter controls the minimum number of samples required for establishing new topics within the dataset. The results are presented in Table 7.  
For the InfoLogger dataset, a progressive increase in \(min\_cluster\_size\) decreased the model's precision while maintaining high recall values. The optimal configuration was achieved at 100, yielding the highest F1-score (0.810) and minimal error rates. Conversely, the BGL dataset exhibited different behavior, reaching peak performance at 400 with exceptional F1-score (0.944) as illustrated in Figure 7. However, a further increase to 500 led to a slight degradation in all performance metrics. These findings emphasize the delicate fine-tuning in parameter selection. While larger \(min\_cluster\_size\) values generally improved precision and reduced false positives, excessive values sometimes led to cluster overgeneralization, compromising the model's ability to maintain high recall. This analysis established dataset-specific optimal values, with InfoLogger and BGL performing best at 100 and 400, respectively.
## 2 Design of Detector Parameters
### XS Anode  
The XS anode is a metal and ceramic cross-strip pattern structure with 32 + 32 strips for the top and bottom layers, respectively. For planar anodes, the amount of charge received on each anode is mainly related to its exposed area and charge distribution. For an XS anode with a double-layer structure, the top-layer anode will shield the bottom anode to some extent. The difference of charge amount between top and bottom layers will affect the signal-to-noise ratio in different spatial directions and the selection of amplifiers.  
A detector composed of an XS anode and a resistive layer in the electric field satisfies [PERSON]'s equations. It is assumed that there is a weak current density caused by the electric field. Therefore, the magnetic flux density B is negligibly small. Then the charge of the anode can be achieved by the [PERSON]'s law [15]:  
\[\
abla\cdot(-\varepsilon\
abla V)=\rho_{v} \tag{2}\]  
where \(\varepsilon\) is the dielectric constant, \(V\) is potential, \(\rho_{v}\) is free charge density. To reveal the relationship between the induced charge collected by the anode and its structure, the resistive layer and XS anode model are established by COMSOL, as shown in Figure 4. An electron cloud is generated on the resistive layer according to Equation (1), and the amount of induced charge on anode strips is integrated. The charge ratio of top and bottom anodes \(R_{c}\) as a function of insulation thickness for different exposed area ratio \(R_{a}\) is shown in Figure 5.  
Figure 4: XS anode simulation model established by COMSOL.  
Figure 3: Variation of induced charge cloud radius with different substrate thickness.  
It can be seen that for the same exposed area, the charge ratio increases linearly with the increase in insulation thickness. For the anode of \(R_{a}=1\), the charge ratio \(R_{c}\) is greater than 1. This is because the bottom strip is farther away from the resistive layer and the electric shielding effect of the top strip. For the same insulation thickness, the charge ratio \(R_{c}\) decreases with the increase of the exposed area ratio \(R_{a}\). The initial broadening and central falling point of the electron cloud on the resistive layer also affect the charge ratio, so \(R_{c}\) is an average value. Based on the above rules, the anode parameters could be designed and the charge amplifier with appropriate magnification could be selected.
# Analysis of Distributed Measurement Method for Array Antenna Position
## 3 Structural Design and Method
### Measuring Method  
The measurement device of the array antenna phase center includes the FBG sensor system, main POS, sub-POS, phased array antenna and distributed POS computer system (DPCS). The FBG sensor system consists of several FBG sensor arrays evenly arranged on the wing surface, and the main POS is arranged in the middle of the wing model. Two sub POS are arranged symmetrically at both ends of the wing model. The phased array antennas are uniformly and symmetrically arranged under the wing model. A DPCS navigation computer is used to calculate the pose information of main POS and sub POS.  
The spacing between two adjacent FBG sensor arrays in the FBG sensor array is determined according to the size of the wing model. In the experiment, each FBG sensor has 15 nodes, and the central wavelength range of each node is from 1529 mm to 1584 mm for gradient distribution. The bandwidth of the surface-attached FBGs is 0.232 nm, the length of gate area is 10 mm, and the reflectivity is 92.06%. The frequency of SM130 interrogator is 1 kHz, and the resolution is less than 1 pm. There will be severe jitter in the flight of the aircraft. When the main POS and the sub POS are installed, they need to be equipped with damping devices to ensure the stability of the IMU systems and reduce the error caused by the vibration. The output frequency of the main system inertial device is 200 Hz. The initial speed of the carrier is 0.001 m/s in all three directions. The layout of the main POS and sub POS is shown in Figure 7.  
After the installation shown in Figure 8, the RPS datum point measurement system is used to obtain the boom value when the wing model naturally sags, which is used to determine the reference coordinate system at the initial stage. The displacement of each node of the aircraft wing model can be obtained by the wavelength value obtained by the FBG sensor, and then the displacement value provides a higher precision reference for POS technology, which is conducive to improving the accuracy of measuring the phase center. Thus, in the process of aircraft flying, the wing shakes and deforms and FBG sensors measure the wavelength change. The dynamic change curve of the wing model is obtained by fitting, which provides a reference coordinate system for the sub-POS. The sub-POS and the main POS work to obtain the spatial position and attitude information of the IMU systems in flight.  
Figure 6: Installation layout.  
Through the solution, the spatial coordinates of each phased array antenna in each node of the wing model are obtained, that is, the phase center of the phased array antenna is obtained.
# DataMatrix Code Recognition Method Based on Coarse Positioning of Images
## 4 Binarization Method Based on Grayscale Change Trend  
After determining the sampling grid, the grid can be binarized by calculating the proportion of black and white pixels in the grid, calculating the proportion of black and white pixels on the cross-shaped symbol in the center of the grid, and scanning the center pixel of the grid. However, these methods have high requirements for the quality of DM code images. When the background noise or image distortion of DM code images is severe, large errors are likely to occur. This section proposes a binarization method based on the gray change trend. The gray value of unit pixels after iterative division of the grid is calculated to obtain the gray change trend from the periphery to the center of the grid. The LOF algorithm is used to detect outliers in data. After correcting the detected outliers, the local weighted linear regression algorithm is used to predict the gray value of the grid based on this gray change trend and binarize it to convert the DM code image into a data matrix.
# Simulation of Fidelity in Entanglement-Based Networks with Repeater Chains
## 6 Case Study: Analysis of a Repeater Chain
### Multi-Hop with Four Memory Positions  
In this scenario, QRs consists in four memory positions with labels from 0 to 3, differing from the example architecture in Figure 4 with just two memory positions. By convention, even (odd) position numbers are employed to pair with the forward (backward) node. The motivation behind this structure is to have a defined architecture for the memory positions, rather than a dynamic arrangement of the pairings, which is used to assess the fidelities of these pairs. In this three-node portion of the network, only the middle node should perform Bell measurements on the states, as any subsequent attempt to obtain the fidelity of already measured qubits fails. This redundancy is avoided by classically communicating the adequate positions to both forward and backward nodes. Another change we implemented is the improvement of the error model because the default values were fundamentally flawed. Merging the depolarization probability with the attenuation of the fiber, although it may make sense from a computational standpoint, has no physical value, as the depolarization is orders of magnitude lower than the attenuation [39]. Instead, we use the polarization mode dispersion time \(\tau_{\text{TMD}}(L)\), which is the main source of the error of light pulses travelling in fiber, as a normally distributed variable with a standard deviation of 100% to account for our depolarization probability. This statistical model accounts for the photon variability of processes to create single photons and weak pulses that could represent the flying qubits. In such a model, we want to start to see depolarization effects when \(\tau_{\text{TMD}}\geq\tau_{\text{coh}}=1.6\) ps. These results are thoroughly discussed in [38]. For this, we employ the following formula  
\[p_{\text{depo1}}=\begin{cases}0&\tau_{\text{PMD}}<\tau_{\text{coh}}\\ 1&\tau_{\text{PMD}}\geq\tau_{\text{coh}}\end{cases}. \tag{9}\]  
Lastly, for this physical configuration only, we changed the flow of the program to be able to compare two types of quantum architectures as in [25], where the quantum protocols were interchanged: performing purification first on each link and then performing entanglement swapping on the nodes (PS), and first performing entanglement swapping on all the nodes and then applying purification in the end nodes (SP). This differentiation will be used to check if the processes are interchangeable in terms of output fidelity. With these improvements and the corresponding modifications, we can now properly simulate a network of \(N\) nodes, with two memory positions each and routing capabilities, based on fidelity.
## 5 Conclusions  
Turning the problem of food processing by-products to products rich in natural polyphenols was recently spotlighted. Therefore, for the first time, we converted date seed polyphenol-rich extracts to healthcare pills produced under large-scale conditions. These are inexpensive sources of polyphenols that have many biological effects, namely anti-obesity activity. After tabulating, the anti-obesity effect of DSPPs was elucidated in 3T3-L1 cells, and their antioxidative polyphenols after digestion were also quantified. The total polyphenols in date seeds were found to be 51.167 g/kg, where epicatechin was a key polyphenol. Results also showed that DSPPs dose-dependently delayed the proliferation of adipocytes in 3T3-L1 cells. DSPPs, especially at a dose of 100 ug/mL, decreased the viable cells quantity, basal lipolysis of adipocytes, and lipid accumulation-related genes. Epicatechin, the key polyphenol of DSPPs, also effectively interacted with both lipase and GPDH enzymes. The pills also showed a high content of polyphenols with antioxidant capacities after digestion, showing the high release of those components after digestion and the importance of the tabulating technique to maintain those active components. In conclusion, the current findings suggested that DSPPs may have anti-obesity effects that could be commercially industrialized as a healthcare tablet.  
Conceptualization, methodology, software, formal analysis, R.S.; investigation, O.M.M.; resources, I.K. and X.Z.; data curation, A.A.Z.; writing--original draft preparation, R.S., O.M.M. and I.K.; writing--review and editing, R.S., X.Z. and I.K.; visualization, A.A.Z.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
We would like to thank the School of Food and Biological Engineering, Jiangsu University, earmarked fund for China Agriculture Research System, Natural Science Foundation of Jiangsu Province (BK20220111), and China Postdoctoral Science Foundation (2020M683372) for their support.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors would like to thank School of Food and Biological Engineering, Jiangsu University; Department of Biochemistry, Faculty of Agriculture, Benha University; Shalli Company; and Tadawina Company for their support.  
The authors declare no conflict of interest.
## 2 Distributed Optical Fibre Sensors
### Brillouin-Based Distributed Sensing
The light at the lower frequency is then amplified due to the energy transfer from the higher frequency wave. A pump pulse light and a continuous wave probe, injected at the two ends of the fibre, are required to generate stimulated Brillouin scattering. The resulting amplification of the probe light--detected at the input of the fibre--is again temperature and strain dependent. The target of detection for this type of Brillouin-based DOFSs is the time evolution of the gain resulting from the interaction of the two counterpropagating signals, and the technique is called Brillouin optical time domain analysis (BOTDA) [68; 80]. A basic BOTDA setup is represented in Figure 6. It is worth noting that all of the commercial implementations of Brillouin-based DOFSs are either BOTDR- or BOTDA-based schemes. In particular, the BOTDR-based one--requiring the access from only one end of the fibre--is the only viable solution in many applications where single-ended deployment is the only possible choice. On the contrary, when both ends of the fibre are accessible, the BOTDA-based technique generally shows better performance. Figure 5: A typical configuration for a Brillouin optical time-domain reflectometer (BOTDR) system with coherent detection scheme (CW: continuous wave). After [78]. A very interesting feature of the BOTDA scheme is that it can measure a very large number of sensing points over long distances. Indeed, with proper implementation, it can work over a distance of 100 km or beyond; e.g., by employing Raman amplification [81; 82; 83] or pulse coding techniques [74; 84; 85; 86; 87; 88]. Intrinsic spatial resolution over that range is limited to approximately 1 m, due to the reduced acoustic wave response time [89]. Correspondingly, several tens of thousands of sensing points can be measured by the BOTDA schemes. Many optical techniques were also introduced over the years to ameliorate the spatial resolution [90; 91; 92; 93; 94]. To our knowledge, a spatial resolution of a few millimetres [95; 94] over a range of a few kilometres represents one of the best results achieved so far. To further increase the number of sensing points over a longer range, other techniques based on stimulated Brillouin scattering were proposed over the years; e.g., Brillouin optical coherence domain reflectometry and Brillouin optical coherence domain analysis, dynamic Bragg gratings, and Brillouin optical frequency domain analysis. The most promising technique at the moment is represented by Brillouin optical correlation domain analysis, which enables measurements over a range of 10 km and beyond, with resolution better than 1 cm (i.e., with more than 1 million sensing points) [96; 97]. As in previous sections, we report the specifications of some commercial interrogators in Table 3.  
Figure 6: A typical configuration for a Brillouin optical time domain analysis (BOTDA) interrogator (SBS: stimulated Brillouin scattering). After [78].
# Development of Intelligent Core Network for Tactile Internet and Future Smart Systems
###### Abstract  
One of the main design aspects of the Tactile Internet system is the 1 ms end-to-end latency, which is considered as being the main challenge with the system realization. Forced by recent development and capabilities of the fifth generation (5G) cellular system, the Tactile Internet will become a real. One way to overcome the 1 ms latency is to employ a centralized controller in the core of the network with a global knowledge of the system, together with the concept of network function virtualization (NFV). This is the idea behind the software defined networking (SDN). This paper introduces a Tactile Internet system structure, which employs SDN in the core of the cellular network and mobile edge computing (MEC) in multi-levels. The work is mainly concerned with the structure of the core network. The system is simulated over a reliable environment and introduces a round trip latency of orders of 1 ms. This can be interpreted by the reduction of intermediate nodes that are involved in the communication process.  
T 31 October 2017; Accepted: 11 December 2017; Published: 2 January 2018
## 3 Results
### Highly Contributing Features  
The comparison for the resistant and non-resistant groups of samples presented a total of 86 unique genera. Subsequently, 28 were separated by the ML algorithm from these genera as the most important features (32.6%) ranking in an interval of statistically calculated Benjamin-Hochberg \(p\)-value from 0.002 to 0.049 between the groups. The most significant differentiation between the resistant and non-resistant groups were observed in the following genera: _Ruminococcus_, _Oscillospiraceae-UCG-002, _Eubacterium elgens_ group, _Barnesiella_, _Bacteroides_, _Oscillospiraceae_ group, _Desulfovibrio_, _Oscillospiraceae-UCG-005, _Clostridium sensu stricto 1_, _Lachnoclostridium_, and _Lachnospiraceae FCS020_ group (0.002, 0.003, 0.005, 0.007, 0.009, 0.010, 0.012, 0.014, 0.016, 0.017, and 0.019 respectively).  
According to the models' predictability and statistical analysis, we extracted the most significant genera acting as potential key biomarkers and factors for modulating the therapy resistance. Our findings are complementary to the other microbiome related studies published in the literature.  
However, besides the practice of analyzing the particular feature significances, we went a step further decomposing the algorithm's prediction path, extracting the aggregated feature contributions. This novel approach's main aim was to explore what genera are mostly seen together and how they are jointly contributing to the resistance class. According to the stochastic nature of the algorithm, the aggregated contribution analysis can be done multiple times, considering all generated models following the same performance metrics as the referent one. For the purpose of our study, we proceed with the analysis using the selected Python-based best performant classifier from the second phase.  
The benefit of the proposed aggregate analysis supports the thesis that resistance is not due to the presence of only a specific pathogenic genus in the patient microbiome, but several bacterial genera that live in symbiosis.  
Figure 4: Median abundances for the most significant genera in resistant and non-resistant groups.