esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Experimental Results and Discussion on Algorithms Comparison
Figure 8: Plots of the non-dominated solutions in the objective space of _eil_51_n50 obtained by nine algorithms in 15 independent runs (Pareto front obtained in each run are denoted by each color). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(4.70\times 10^{1}\) & \(2.29\times 10^{2}\) & \(1.34\times 10^{2}\) & \(1.40\times 10^{2}\) & \(5.71\times 10^{1}\) \\ MOPSO & \(2.11\times 10^{2}\) & \(8.59\times 10^{2}\) & \(3.80\times 10^{2}\) & \(4.67\times 10^{2}\) & \(2.23\times 10^{2}\) \\ MOAO & \(2.10\times 10^{2}\) & \(6.60\times 10^{2}\) & \(3.99\times 10^{2}\) & \(4.25\times 10^{2}\) & \(1.33\times 10^{2}\) \\ MOHBA & \(1.67\times 10^{2}\) & \(1.21\times 10^{3}\) & \(4.10\times 10^{2}\) & \(5.20\times 10^{2}\) & \(2.97\times 10^{2}\) \\ MOWOA & \(2.84\times 10^{2}\) & \(1.38\times 10^{3}\) & \(5.10\times 10^{2}\) & \(5.66\times 10^{2}\) & \(2.79\times 10^{2}\) \\ MOGWO & \(1.86\times 10^{2}\) & \(1.43\times 10^{3}\) & \(5.07\times 10^{2}\) & \(5.63\times 10^{2}\) & \(3.57\times 10^{2}\) \\ MOFECO & \(8.55\times 10^{1}\) & \(4.89\times 10^{2}\) & \(1.65\times 10^{2}\) & \(1.89\times 10^{2}\) & \(1.09\times 10^{2}\) \\ LSMOFECO & \(6.91\times 10^{1}\) & \(1.91\times 10^{2}\) & \(7.76\times 10^{1}\) & \(8.51\times 10^{1}\) & \(2.99\times 10^{1}\) \\ MOFECO-MS & \(3.45\times 10^{1}\) & \(1.81\times 10^{2}\) & \(6.11\times 10^{1}\) & \(6.65\times 10^{1}\) & \(3.52\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Statistical results of SP indicator of the eight comparison algorithms for _eil_51_n250. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Best** & **Worst** & **Median** & **Average** & **StdDev** \\ \hline NSGAII & \(1.84\times 10^{10}\) & \(8.05\times 10^{9}\) & \(1.10\times 10^{10}\) & \(1.16\times 10^{10}\) & \(2.88\times 10^{9}\) \\ MOPSO & \(1.26\times 10^{9}\) & \(4.33\times 10^{8}\) & \(8.43\times 10^{8}\) & \(8.80\times 10^{8}\) & \(2.31\times 10^{8}\) \\ MOAO & \(1.20\times 10^{9}\) & \(5.77\times 10^{8}\) & \(9.32\times 10^{8}\) & \(8.86\times 10^{8}\) & \(1.81\times 10^{8}\) \\ MOHBA & \(3.46\times 10^{8}\) & \(1.02\times 10^{8}\) & \(2.37\times 10^{8}\) & \(2.24\times 10^{8}\) & \(7.30\times 10^{7}\) \\ MOWOA & \(1.03\times 10^{9}\) & \(3.67\times 10^{8}\) & \(6.94\times 10^{8}\) & \(7.32\times 10^{8}\) & \(1.76\times 10^{8}\) \\ MOGWO & \(9.00\times 10^{8}\) & \(4.39\times 10^{8}\) & \(7.48\times 10^{8}\) & \(7.19\times 10^{8}\) & \(1.36\times 10^{8}\) \\ MOFECO & \(1.42\times 10^{10}\) & \(6.98\times 10^{9}\) & \(1.09\times 10^{10}\) & \(1.05\times 10^{10}\) & \(1.83\times 10^{9}\) \\ LSMOFFECO & \(3.84\times 10^{10}\) & \(1.96\times 10^{10}\) & \(3.75\times 10^{10}\) & \(3.43\times 10^{10}\) & \(6.37\times 10^{9}\) \\ MOFECO-MS & \(3.90\times 10^{10}\) & \(2.60\times 10^{10}\) & \(3.78\times 10^{10}\) & \(3.70\times 10^{10}\) & \(3.10\times 10^{9}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Statistical results of HV indicator of the eight comparison algorithms for _eil_51_n500.
## 3 Results and Discussion
### Water Absorption by Capillarity and Electrical Resistivity  
The grams of water absorbed by capillarity are proportional to the square root of time [45]. The graph in Figure 11 shows this relationship, averaging the results obtained in the three samples tested per mixture.  
After 10 min, the water absorbed per unit area by the two mixtures is virtually identical. The greater slope of the initial section indicates that water absorption by capillarity is faster in the first few minutes [45]. Only later does it become less important, and a reduction in the slope curves can be observed. This is particularly evident in R2 CEBs, indicating their lower water absorption capacity. For a more immediate comparison, a single value can be obtained by considering the angular coefficient of the straight line passing between the capillarity coefficients at 10 min and 5 h [45]. This number, denoted as \(C_{b}\), along with the dry bulk density (\(\gamma_{d}\)) and the electrical resistivity (\(\rho\)) measured at the end of the capillarity test on saturated blocks, is reported in Table 9.  
Results confirm that finer-grained blocks absorb more water than coarser-grained ones. However, the results of the latter are more dispersed (Figure 12a). The results also show some correspondence with the electrical resistivity (Figure 12b).  
Figure 11: Evolution of the water absorbed per unit of surface area with the square root of time.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**R1 CEBs**} & \multicolumn{3}{c}{**R2 CEBs**} \\ \cline{2-7}  & \(Y_{d}\) & \(C_{b}\) & \(\rho\) & \(Y_{d}\) & \(C_{b}\) & \(\rho\) \\  & [kg/m\({}^{3}\)] & [g/cm\({}^{2}\)\(\sqrt{\text{min}}\)] & [k\(\Omega\)cm] & [kg/m\({}^{3}\)] & [g/cm\({}^{2}\)\(\sqrt{\text{min}}\)] & [k\(\Omega\)cm] \\ \hline \multirow{3}{*}{28 days} & 1716.65 \(\pm\) 11.80 & 0.191 \(\pm\) 0.018 & 1.06 \(\pm\) 0.04 & 1847.66 \(\pm\) 20.37 & 0.094 \(\pm\) 0.037 & 1.44 \(\pm\) 0.08 \\  & (0.69\%) & (9.16\%) & (3.65\%) & (1.10\%) & (39.48\%) & (5.81\%) \\ \cline{1-1}  & 1718.14 \(\pm\) 9.21 & 0.200 \(\pm\) 0.012 & 1.27 \(\pm\) 0.06 & 1830.52 \(\pm\) 17.73 & 0.147 \(\pm\) 0.037 & 1.57 \(\pm\) 0.15 \\ \cline{1-1}  & (0.54\%) & (6.04\%) & (4.56\%) & (0.97\%) & (25.40\%) & (9.75\%) \\ \hline \hline \end{tabular}  
* This table shows the average data with its standard deviation and, in brackets, the coefficient of variation.  
\end{table}
Table 9: Water absorption by capillarity test results 1.  
In porous media such as soil, the electrical resistivity can depend on factors such as the nature of the constituents, porous structure, degree of saturation, salt concentration and temperature [56]. In general, higher resistivity values indicate a lower electrical conductivity, which can be an indication of denser and more compacted material. In this study, besides the porous structure, the other listed variables can be neglected as they were kept constant. Therefore, the results obtained can be attributed to the effect of particle size.
## 3 Solving Method
### Kruskal Algorithm  
When connecting each station using a tree topology, the fundamental task is to address the minimum spanning tree problem. In the design of the minimum spanning tree, commonly used algorithms include the Prim algorithm and Kruskal algorithm, whose kernel is based on the greedy principle. By comparing the two algorithms, it was found that the Kruskal algorithm is always superior to the Prim algorithm in solving time, so we chose to use the Kruskal algorithm as the tree topology network design algorithm. The Kruskal algorithm is a spanning tree algorithm based on weighted edges. In the process of solving, the smallest weighted edge is successively selected and added to the tree set. When the added edge generates a cyclic topology, the weighted edge is removed, until all edges have been selected [27].  
The solving process of the Kruskal algorithm is shown in Figure 11. All nodes are divided into the join node set \(U\) and the unjoined scatter node set \(V\). At the beginning, \(V=\{v_{1},v_{2},v_{3},v_{4},v_{5}\}\) and \(U=\{\phi\}\). Firstly, the nodes that form the shortest edge are selected and added to the join node set, that is \(U=\{v_{4},v_{5}\}\) and \(V=\{v_{1},v_{2},v_{3}\}\). Then, in the same way, edges \(v_{1},v_{2},v_{1},v_{3},v_{4},v_{2}\), and \(v_{4},v_{3}\) are selected to the join node set. When a cyclic typology is created, the edge that produced it is removed and the solution is complete. Finally, \(V=\{\phi\}\) and \(U=\{v_{1},v_{2},v_{3},v_{4},v_{5}\}\).
## 5 Discussion
### The Variable Importance of Input Parameters
The California bearing ratio (CBR) is a measure of the soil's load-bearing capacity and is widely used in geotechnical engineering to assess the suitability of soils for construction. The CBR test involves measuring the resistance of a soil sample to penetration by a standard plunger, under controlled conditions of moisture and compaction. The stiffness and load-bearing capacity of a soil depend on various factors, including its texture, structure, moisture content, density, and compaction effort. The liquid limit of soil is an important input parameter for predicting CBR, because it reflects the soil's ability to resist deformation and support loads. A soil with a high liquid limit is more plastic and less stable, which results in lower CBR values. In contrast, a soil with a low liquid limit is more rigid and stable, leading to higher CBR values. The number of compaction blows is another critical input parameter for predicting CBR, because it represents the compaction effort applied to the soil during construction. The more compaction blows applied, the higher the soil density and stiffness, which result in higher CBR values. Conversely, inadequate compaction effort leads to low soil density and stiffness, resulting in lower CBR values. Regarding the maximum dry density, it is an indicator of the soil's compactability and weight. However, it does not directly relate to the soil's stiffness and load-bearing capacity, which are the primary factors affecting CBR. Therefore, while it may affect CBR values to some extent, its influence is relatively weak compared to other parameters, such as liquid limit and compaction effort.
## 3 Results
### Comparison of the Results Obtained in the Pre-Test Phase  
The following tables, Tables 6 and 7, present the mean values of the natural frequencies and damping ratios corresponding to individual modes estimated in the previously mentioned stages of the pre-test phase. It can be concluded that, in the case of the analysed honeycomb infill pattern specimen, 11 modes were detected in the selected 0-800 Hz frequency span. Since the specimen is geometrically symmetric, in the case of the realization of multi-reference measurement, information about the repeated modes and their corresponding frequencies or damping ratios was obtained. It should also be mentioned that not all of the above-mentioned modes of the specimen were found in the stages when the structure response measurements were performed at reference degree of freedom locations 50 and 51, respectively (IV\({}^{\text{C}}\) to IV\(  
Moreover, the tables contain information about the mean value of the relative difference (MVRD) calculated concerning the modal parameters obtained in the Iref stage using:  
\[\text{MVRD}=\frac{1}{n}{\sum_{i=1}^{n}}\bigg{|}\frac{q^{\text{i}}-q^{\text{ref}} }{q^{\text{ref}}}\bigg{|}100\%, \tag{4}\]  
where \(n\) corresponds to the number of modes found, \(q^{\text{i}}\) represents the value of the compared modal parameter (either natural frequency or damping ratio) estimated in the ith stage of the pre-test, and \(q^{\text{ref}}\) is the value of the compared modal parameter obtained in the Iref stage of the pre-test. As some modes were not found in the IV stage, the \(n\) number used in Equation (4) for the calculation of the MVRD has various values (\(n=11\), \(n=9\), or \(n=8\) depending on the number of modes found).  
When comparing the values of the estimated natural frequencies, it can be concluded that the results obtained in all stages of the pre-test phase differed minimally (mean value of relative difference varies from 0.08% up to 0.65%). The maximum MVRD (0.65%) was obtained in the IIA stage, where the number of excitation points was reduced.  
The maximum MVRDs (16.46% and even 19.77%) in the estimated damping ratios were obtained in the III stage of the pre-test. The larger values of analysis lines, set in this stage, caused narrower and sharper peaks to appear in the CMIF plots, leading to the underestimation of the damping ratios. In other cases, the MVRDs were in a range from 2.27% up to 5.95%, which is acceptable for the low values of damping ratios obtained.
## 3 Master Control System and Motion Control System
### Motion Control System
With the PID parameter adjustment being incorporated, the controller can achieve a combination of fast response, eliminating steady deviation, reducing overshoot, and thus suppressing the vibration of the moving parts to a minimum level. Figure 6: Schematic diagram of the motion control system. The motion controller is the one with a model number of Mini PMAC-PCI produced by Delta Tau company in the USA. It has a RS-232/ RS-422 serial interface, a 33 MHz 5 V PCI bus interface, a 4-channel shaft interface circuit, a clock frequency of +/\(-\)100 PPM, and a PID/notch/feed-forward servo algorithm. The servo motor has a model number of MDMF152P1U produced by Japanese Panasonic with a power of 1.5 kW. The matching servo driver accompanying the servo motor is MEDLT55 SF. The APEX planetary speed reducer (with a model number of AB142-1:10) produced in Taiwan is selected as the retarder with a reduction ratio of 1:10. The MTS-M type incremental magnetic grating displacement transducer (model number: MTS-M1C0528 LM20 SC) and the corresponding reading head produced in Italy are chosen as the distance measuring element with an accuracy of \(\pm\)15 \(\upmu\)m and a resolution of 0.001 mm/bit.
# Honey-Based Medicinal Formulations: A Critical Review
## 1 Introduction  
### The Chemistry of Honey  
Honey is a natural sugary substance produced by a variety of honeybee species from the nectar of flowers or honeydew. Bees first collect nectar from flowering foliage before the collected nectar is treated in an internal pouch known as the crop where numerous enzymes break down sugars. The subsequent solution is disgorged by the bees into honeycombs within their hives. Lastly, liquid evaporation takes place by air currents generated by the fanning of bee wings [1]. The product, known as honey, is an extremely concentrated viscous solution composed of sugars (about 80%), water (approximately 17%) and 'other' constituents (approximately 3%). Fructose and glucose tend to be the key honey sugars, with very minor quantities of other mono-, di-, or oligosaccharides also being present, for example, sucrose, maltose, isomaltose, nigerose, turnanose, and maltulose [1]. Honey is acidic (pH 3-5) in nature due to the breakdown of glucose into gluconic acid by glucose oxidase from the bee crop [1, 2]. The water content in honey is a vital characteristic that influences its physicochemical and organoleptic properties such as color, taste, specific gravity, solubility, viscosity, and crystallization tendency, and it also plays a major role in the potential microbial contamination of honeys [2]. The smaller fraction (about 3%) of 'other' constituents is considered to be very important in influencing not only the organoleptic characteristics of honesy but also the bioactivity profiles of various honey. These 'other' honey constituents, which are often also referred to as its non-sugar constituents,comprise flavonoids (flavonols, flavonols, flavanols, flavanones, anthocyanin, chalcones, and isoflavones), phenolic and other organic acids, carotenoid-like substances, proteins (enzymes), amino acids, minerals (Ca\({}^{2+}\), Cu\({}^{2+}\), Fe\({}^{2+}\), Mg\({}^{2+}\), Mn\({}^{2+}\), P\({}^{3+}\), K\({}^{+}\), Na\({}^{+}\), Zn\({}^{2+}\)), vitamins (specifically vitamin C, vitamin B6, thiamine, niacin, riboflavin, and pantothenic acid), pigments, and several other compounds. For instance, various enzymes, namely, invertase, amylase, catalase, and glucose oxidase, are expected to be found in honey, and proline is the major amino acid, contributing half of the total free amino acids [2]. The composition, color, and taste of honey, which is strongly linked to the presence of these non-sugar constituents, depends on its floral source, geographical origin, climatic factors, the bee species involved in honey production, human processing techniques, and the storage of the honey prior to its use [3].
###### Abstract  
This work presents a mathematical solution to data privacy and integrity issues in Split Learning which uses Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP). It allows calculations to be conducted on encrypted data, keeping the data private, while ZKP ensures the correctness of these calculations without revealing the underlying data. Our proposed system, HavenSL, combines HE and ZKP to provide strong protection against attacks. It uses Discrete Cosine Transform (DCT) to analyze model updates in the frequency domain to detect unusual changes in parameters. HavenSL also has a rollback feature that brings the system back to a verified state if harmful changes are detected. Experiments on CIFAR-10, MNIST, and Fashion-MNIST datasets show that using Homomorphic Encryption and Zero-Knowledge Proofs during training is feasible and accuracy is maintained. This mathematical-based approach shows how crypto-graphic can protect decentralized learning systems. It also proves the practical use of HE and ZKP in secure, privacy-aware collaborative AI.  
homomorphic; encryption; zero-knowledge proofs; mathematical approaches; securing; privacy Article
## 2 Problem Description, the Proposed Approach, and Aircraft Dynamics
### Proposed Solution  
In this work, the idea is to transform the non-minimum phase aircraft longitudinal dynamics into minimum phase by inserting a fractional-order integral controller (FIC) as a pre-compensator. Further, the design and implementation of an optimal LQR controller to control the whole augmented dynamics as shown in Figure 2 is introduced. Furthermore, the adaptiveness of this control system structure is achieved by simultaneously optimizing the Q and R weighting matrices with the pre-compensator parameters of \(K_{I}\) and \(\lambda\) assigned by the particular swarm optimization (PSO) algorithm according to an IAE performance index.  
This nested structure comprising the LQR, and the FIC controller is presented in Figure 2. The optimal LQR controller is employed to obtain a closed-loop optimal control law for the augmented system that ensures the state variables converge to zero in a limited time, minimizes the control efforts, and compensates for the actuator faults and external disturbances. On the other hand, a fractional-order pre-compensator, i.e., FIC is added to the control loop to resolve the problems that can be confronted due to the non-minimum phase characteristics. The pre-compensator directly generates control commands for the actuator to deal with the longitudinal aircraft dynamics.  
Figure 1: Open –loop step response of Boeing 747 pitch angle dynamic.  
Figure 2: Proposed LQR-FIC controller scheme.  
Here, the problem is considered as the online single-objective optimization problem exploiting a PSO optimization algorithm. The control design variables of the optimization problem are chosen as weighting matrices (Q and R), and adaptive fractional-order integral pre-compensator parameters (\(K_{I},\lambda\)) to track the desired pitch angle under the conditions of normal flight and actuator losses of effectiveness by minimizing the performance index of IAE. The general form of the online optimization problem is formulated based on the constraints of the design variables vector, as explained in Section 4.2.
# The Power of Machine Learning Methods and PSO in Air Quality Prediction
## 2 Materials and Methods
### Machine Learning Regressors
#### 2.2.1 Support Vector Regression  
Support vector machines are frequently employed in machine learning to solve classification issues. Support vector regression is another way that support vector machines are used to solve regression issues. Instead of categorizing data points, SVR focuses on forecasting continuous outputs, which is how it varies from support vector machines [17]. SVR looks for the hyperplane that matches data points in a constant space. The input variables are mapped to a high-dimensional feature space to determine the hyperplane that minimizes the prediction error and maximizes the distance between the hyperplane and the nearest data points. The SVR model maps the data to a higher dimensional space using a kernel function, which allows it to handle non-linear correlations between input and destination variables. Because of this, it is a helpful tool for regression problems with complicated interactions [19].
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 3 Signal Design Scheme
### Multiplexing Method  
From the previous design, it can be seen that at least two signal components need to be broadcasted, namely, the high-precision ranging component and the high-rate data component, respectively, to achieve high-precision carrier phase measurement and high data rate.  
Assuming that the power of both components is equal, the ranging component and data component can be multiplexed and broadcasted in three ways, as shown in Figure 2. One is to use a phase separation method for broadcasting, that is, I-branch broadcasts the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data Rate**} & \multirow{2}{*}{**Message Mapping**} & \multirow{2}{*}{**Symbol**} & \multicolumn{2}{c}{**Relative**} & \multirow{2}{*}{**Non-Coherent**} & \multirow{2}{*}{**Demodulation**} \\  & & & & **Multiple Access** & & **Demodulation** \\  & & & & **Performance** & & **Threshold (BER = 10\({}^{-6}\))** \\ \hline \multirow{3}{*}{2 kbps/4 ksps} & BPSK & 0.25 ms & \(-\)6 dB & No & 10.5 dB \\  & 16-PSK & 1 ms & 0 dB & No & 18.5 dB \\ \cline{1-1}  & CSK(4,1) & 1 ms & 0 dB & Yes & 8.2 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: High data rate signal design scheme.  
Figure 1: The power spectrum density of GNSS L1 and L5 signals: (**a**) L1 and (**b**) L5.  
ranging component and Q-branch the data component, similar to the GPS L5 signal. The second method is to broadcast in a chip-by-chip time division method, similar to the GPS L2C signal. The third method is to use the code period time division multiplexing method, that is, the multiplexing code shift keying (MCSK) [17].  
Compared to the phase separation method, the time-division method has a higher instantaneous power on the ground and fewer signal components, which can better support the subsequent expansion signal requirements. Compared to chip-by-chip time division, the MCSK method can support a shorter loop update period. The MCSK method can be chosen to achieve the multiplexing of ranging components and data components.