esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Rolling Bearing Fault Diagnosis Method Based on MFO-Optimized VMD and DE-OSELM
## 3 MFO Algorithm and Its Improvement
### The Improved MFO Algorithm  
To address the issues related to local optimization, low convergence accuracy, and slow convergence speed of the original optimization algorithm, the MFO is modified by using both a dynamic adaptive weight factor and a crossover operator to improve the performance in terms of global optimization ability, accuracy, and efficiency.  
In the original concept of the MFO algorithm, the moth population is supposed to fly around the flame population in the form of a logarithmic spiral curve, and the flame is not fully utilized, which makes the algorithm fall into local optimal easily. To improve its global optimization ability and convergence speed, the dynamic adaptive weight factor \(\mu\) is introduced into the position updating strategy of moths, as formulated below.  
\[\mu=\frac{1+\cos(\frac{|\pi}{T})}{2} \tag{13}\]  
where; \(l\) represents the current iteration number, and \(T\) represents the maximum iteration number. The updated moth position combined with the adaptive weight factor \(\mu\) is introduced in Equation (14).  
\[S\big{(}M_{i},F_{j}\big{)}=\mu\cdot D_{i}\cdot e^{bt}\cdot\cos(2\pi t)+\mu \cdot F_{j} \tag{14}\]  
The gradual decrease as the iteration \(\mu\) increases from 1 to 0 results in an enlarged search scope during the initial stages. Consequently, the algorithm performance significantly improves concerning search and global optimization abilities, accuracy, and efficiency.  
In order to effectively elevate the algorithm out of local optima, the positions of the front \(m\) flames are disordered by applying the crossover operator from the genetic algorithm. The main idea is to make \(p\) times of cross recombination of each dimension data in the matrix involving front \(m\) flames, and the corresponding dimension data of other flames are combined into a new flame. If the fitness value of the new flame is better than the original flame, the original flame will be replaced. The flame population contains a relatively high diversity and can jump out of local optimum in a certain probability by perturbation of the front \(m\) optimal flames.  
The process for improving the MFO is shown in Figure 1.
# Development of a Wearable Finger Exoskeleton for Rehabilitation
## 4 Experiment Results
### Structural Analysis  
ANSYS software was used for finite element analysis. For this simulation, only the elements of the mechanical structure were contemplated and the CAD design from ANSYS is shown in Figure 9a. The main base of the mechanism was taken as a fixed support and the friction forces between the elements were not included. A force exerted on the bolt that joins the linear actuator with the mechanism that moves to the proximal phalanx was applied. The force exerted on that element was taken with respect to a maximum force exerted by a selected linear actuator (40 N), see Figure 9b. The average size of the mesh elements was \(2.088\times 10^{-4}\) m. The material used to make the 3D print was ABS.  
In the simulation of static forces, only total strain analysis was considered. Using these criteria, the feasibility of the design was evaluated, focusing on total deformation. As can be seen in Figure 10a,b, the deformation magnitudes for each part of the mechanism are defined according to the colors shown in the column on the left, where the pieces in blue are the ones that suffered less deformation; the more deformation there was the more the pieces changed color, and those presented in red were the pieces that suffered more deformation. The maximum deformation obtained was \(1.41073\times 10^{-5}\) m, and is present on the \"pin slide\" element, which is the bolt that joins the linear actuator with the elements that move to the proximal phalanx.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phalange**} & \multirow{2}{*}{**Joints**} & \multicolumn{2}{c}{**Torque (Nm)**} \\ \cline{3-4}  & & **Maximum** & **Minimum** \\ \hline Proximal & MCP & 0.0170 & 0.0068 \\ Media & PIP & \(9.5847\times 10^{-4}\) & \(-7.9009\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Torque for each phalange.  
Figure 10: Results obtained in the “total deformation” simulation, where (**a**) shows the side mechanism view and (**b**) shows the same isometric view.  
Figure 9: (**a**) CAD design in ANSYS exported from SolidWorks. (**b**) Maximum force by a linear actuator.  
As result, a one-DOF prototype mechanism for a finger was developed, where the links, together with the base, were manufactured using ABS plastic by means of a 3D printer, see Figure 11. The bolts were made of aluminum alloy using a lathe, and their dimensions are based on those of the design.
# Case Study on Secondary Building Materials
## 3 Results
### Sorting of C&D Waste  
The applied treatment process in the pilot plant [23] for the crushed sand (material CD-1, see above) produced two separate material streams via density separation. The largest fraction, with 89 w%, was the heavy product fraction with reduced impurity contents. The light fraction, in turn, exhibited impurity contents that increased accordingly and comprised 11 w%. The key results of the applied process regarding the analysis of the heavy fraction were a 56% reduction in PAHs and a 29% reduction in sulfates within the solid product. A significant improvement was also achieved regarding the eluate concentrations of sulfate, which was reduced by 31% (from 1895 to 1311 mg L\({}^{-1}\)). However, the limit value for quality class RC-1 is 1000 mg L\({}^{-1}\). Leaching of PAH was slightly reduced but, with 8.4 \(\upmu\)g L\({}^{-1}\), is still above the limit value for RC-2 of 8 \(\upmu\)g L\({}^{-1}\). However, the scattering of the leaching values is 3.7 \(\upmu\)g L\({}^{-1}\).  
The sorting of material CD-2 with the mobile pilot scale jigging machine Tripe A [24] separated a light fraction with a high content of organic material (loss on ignition at 600 \({}^{\circ}\)C 12.1%). The sieving curve of the heavy fraction is displayed in Figure 2.  
The grain size fraction 16-32 mm of material CD-2 was sorted manually in six different categories (bricks, mortar, stones, glass, metals, and ceramics), see Figure 3.  
The findings from the examination of material CD-1 and CD-2 outline the main obstacle with C&D waste recycling. The fine fraction from crushing often contains elevated concentrations of contaminants that cannot be easily separated even by sophisticated treatment processes such as jigging. Therefore, the fine fraction of C&D waste recycling is often not taken into account for concrete production [9].  
Figure 3: Photos of the individual material groups and screen fractions (from top left to bottom right: >16 m unsorted, >16 mm bricks (25.8%), >16 mm mortar (50.6%), >16 mm stones (14.2%), >16 mm glass and metals (0.4%), and sieving fractions <0.63, <2 and <4 mm together). Material group “ceramics” (9%) not illustrated. (Photographs by [PERSON]).  
Figure 2: Sieving curve of the heavy fraction of material CD-2.  
According to guidelines from the German Committee for Reinforced Concrete (DAF-Stb [27]), two possible types of recycled aggregates are defined: In type 1, more than 90% of the material must originate from concrete or from natural stones. However, this value is lowered to 70% in type 2, thus allowing a 30% content of brick material. C&D waste can hardly be separated to yield recycled aggregate type 1 when materials from concrete structures are mixed with those from brick buildings. However, the 30% limit for brick material in type 2, would be maintained.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 2 Antenna Design and Performance
### Analysis of Human Body Effect
In free space, the mutual coupling is lower than \(-25.02\) dB within the target band, but when there was a phantom, it became lower than \(-21.02\) dB when \(g=5\) mm. The important point is that even though the phantom is present around the proposed antenna, it still has a low mutual coupling characteristic of lower than \(-20\) dB. Figure 15b shows the simulated radiation patterns in the \(xz\) plane for various \(g\) values. As the phantom approaches closer to the antenna, the radiation pattern in the direction of the phantom (\(+x\) direction; \(\theta=90^{\circ}\)) is deteriorated compared to the case without the phantom. However, the radiation performance of the other parts remains almost the same. Therefore,  
Figure 14: Simulation of the power density: (**a**) simulation setup, and (**b**) power density simulation of the proposed \(1\times 8\) array antenna. it is considered that the radiation performance of the magnetic-type antenna is less sensitive to the presence of the human body compared to electric-type antennas. \[Z_{1}Z_{2}\ =Z^{2}/4 \tag{2}\]
# Surface Roughness Analysis in the Hard Milling of JIS SKD61 Alloy Steel
## 4 Results and Discussion
### Analysis of Variance for Response Surface  
Analysis of variance (ANOVA) is a statistical analysis which is commonly applied to evaluate the data of experiments. In this research, the analysis of direct and interactive effect of the input factors (\(V,f,a\), and \(H\)) on the surface roughness is conducted through ANOVA. The regression equation and investigated factors are called to be statistically significant if the \(p\)-value in ANOVA Table is less than 0.05. Besides, the percentage contribution (\(PC\)) of terms in the estimated model on the total variation is also considered to evaluate the influence degree of the controllable factors on the model.  
According to the experimental data in Table 4, the ANOVA for response surface is produced using the Minitab 16 software and their results are further represented in Table 5. Study of these tables indicates that the developed model is found to be statistically significant and is suitable for a Taguchi experiment because of the model's estimated regression coefficient being (\(p<0.0001\)) less than 0.05 [25]. Moreover, the model terms with a \(p\)-value of less than 0.05 reveal that they significantly affect the surface roughness in the investigative range. In such cases, (\(V\)), (\(f\)), (\(a\)), (\(H\)), (\(f^{2}\)), (\(V\times a\)) and (\(f\times H\)) are the statistically significant model terms to be used. Among these terms, the feed rate (\(f\)) is the most influential parameter on the \(Ra\) which explained 65.33% contribution of total variance. The next effect factor is the depth of cut (\(a\)) with a 12.68% contribution. The cutting speed (\(V\)), the material hardness (\(H\)), and product of the factors such as (\(f^{2}\)), (\(V\times a\)), and (\(f\times H\)) have little influence on the \(Ra\). Their contributions were 2.97%, 2.66%, 4.88%, 1.39% and 5.24%, respectively. The terms with \(p\)-value of more than 0.1 (e.g., \(V^{2}\), \(a^{2}\), \(H^{2}\), \(V\times f\), \(V\times H\), \(f\times a\), and \(a\times H\)) are not significant, but are not removed from the model to avoid a significant effect on the model's predictive result [26].  
Furthermore, in statistics, \(R\)-squared (\(R^{2}\)) is called the coefficient of determination for regression analysis that is used to explain the goodness-of-fit of the model to the experimental data. According to ANOVA, the \(R^{2}\) and adjusted \(R^{2}\) value of \(Ra\) that are revealed in Table 5 are 0.9645 and 0.9232,respectively. These values are very close to 1. It can be seen that 96.45% of the total variance is justified by the model [26].
## 3 Results and Discussion
### Total Phenolic Content and Antioxidant Activity

Table 4: Total phenolic content (TPC) and antioxidant activity. Ferric Reducing Antioxidant Power (FRAP), and Trolox Equivalent Antioxidant Capacity with 2,2-diphenyl-l-picrylhydrazyl (DPPH), and 2,2\({}^{\prime}\)-azinobis-(3-ethylbenzothazoline-6-sulfonic acid (ABTS\({}^{+}\)) of extracts of different techniques. other methods [27], in contrast to the trends noted for extraction yields. This decrease can be attributed to the thermal decomposition of some antioxidants at the temperatures used in the process. Several studies reported that thermal processing conditions might result in the loss of natural antioxidants because heat may accelerate oxidation and other degradation reactions [66; 67; 27]. In this work, a different behavior was observed; for thyme, in fact, Soxhlet at 6 h was the best extract in terms of antioxidant activity. This indicates that for this aromatic plant, hot solvent systems under reflux state are more efficient for the recovery of antioxidant components. [PERSON] et al. [68] reported that among different extraction techniques (i.e., reflux, maceration, ultrasound, heating plate), extraction made under reflux using ethanol/water (70:30, _v_/_v_) offered the highest polyphenol levels in _Pterodon emarginatus_ Vogel seeds. This was attributed to the effective extraction under reflux conditions, leading to a higher release of some bound phenolics and with an increase of antioxidant activity [69]. To highlight the comparison of the different extraction techniques, for each plant matrix, the best performing extracts in TPC and antioxidant activity assays are collected in Table 5.  
Table 5 clearly shows that all the extraction techniques are very efficient methods for the recovery of bioactive compounds from plant matrices, particularly to produce extracts retaining good antioxidant capacity. It is worth to notice, however, that more than half of the cases are carried out using unconventional techniques, (N2h/N6h and UAE), thus reducing solvent and energy consumption.
## 2 Data and Methods
### Application of GIS Technologies for Climatic Atlas Compilation
#### 2.4.1 Main Data-Layers
The choice of this map projection was made based on the geographical features of the studied area, located in high latitudes. Standard map projections, such as Mercator (ESPG: 4326), distort spatial information in proportion with the increase in latitude. Accordingly, the conic Lambert projection was chosen to compile the atlas maps. Configuration of data files included defining the color palette for groups of data, as well as selecting the color scale for their clear representation. A single scale was selected for the main data categories (temperature, wind speed, precipitation), as well as for auxiliary data (soil temperature and moisture content, air humidity, snow cover thickness) with the values of average changes in the indicators. Numerical scales were selected for the values of average rates of changes (the first derivative), preserving a unified color palette for the entire category of data. Isolines with the same interval for the data group of average values and rates of change were constructed for the majority of data groups. For the air temperature values, isolines were selected with a 1 \({}^{\circ}\)C interval. For wind speed it was 2 m/s, and for precipitation data--0.2 inch. At this stage the prepared raster file along with isolines represent a tool for the comparative analysis, both within and between the data groups. As a platform for this analysis, various geoinformation software packages can be used that have the necessary  
Figure 5: Geographical location of the studied area. Thickened lines show the border of the AZRF (northwards from 60\({}^{\circ}\)N), as well as the western and eastern borders of the studied area (30–85\({}^{\circ}\) E). functionality (e.g., ArcGIS, QGIS, etc.). In our case, the comparative analysis was carried out using ESRI ArcGIS (ArcMap) software in Figure 6[38,39]. The final preparation of data included their compilation into finished maps and exporting into the atlas layout. At this step, we added the coordinate grid along with the  
Figure 6: Comparison of spatial data: **(a)** within data groups (information on the average change in air temperature between the periods 1980–1999 and 2000–2021, comparison between the months January and July); **(b)** between data groups (comparison of average air temperature and precipitation for the period 1980–2021 for January). marginal information, including the legend, color scale, numerical and linear scale. For all groups of data, GIS projects in the MXD (map exchange document) format were created. Each of them contains all the cartographic materials related to a particular group of data for quick access and editing in Figure 7. Subsequently, the maps were exported into PDF for further compilation of the atlas.
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Evaluating the Design Scheme of the Air Outlets and Fresh-Air Intakes
#### 4.3.2 Cases with Different Wind Speeds
As the target area is approximately 730 m \(\times\) 740 m in size with a height of 36 m for the tallest building, a computational domain of 1680 m \(\times\) 1680 m \(\times\) 235 m was constructed, which is in agreement with the AIJ [38] best practice guideline. The whole computational domain was divided into five parts: (1) the subdomain under the height of 55 m, (2) the subdomain between the heights of 55 m and 75 m, (3) the subdomain between the heights of 75 m and 115 m, (4) the subdomain between the heights of 115 m and 155 m, and (5) the subdomain above the height of 155 m. The grid size of the five subdomains is 1 m \(\times\) 1m \(\times\) 1 m, 2 m \(\times\) 2 m \(\times\) 2 m, 4 m \(\times\) 4 m \(\times\) 4 m, 8 m \(\times\) 8 m, and 16 m \(\times\) 16 m \(\times\) 16 m, respectively. The total number of cells was \(1.643\times 10^{8}\). According to the FDS user guide [33], the \"PERIODIC\" boundary condition was adopted for the inlet, outlet, and lateral boundaries, while the \"GROUND\" and \"OPEN\" boundary conditions were used for the ground and top boundary, respectively. Figure 20: Buildings surrounding the Wuhan Huoshenshan Hospital and corresponding monitoring points in the FDS model.
# Decomposition of Multicomponent Micro-Doppler Signals Based on HHT-AMD
## 1 Introduction
The existence of end point effect limits the practical application of the HHT algorithm [21]. Besides, if there are close-frequency components in the signal, the HHT method cannot perform the characteristic frequency extraction effectively. Therefore, some supplementary processes are under investigation [22]. To solve the problems mentioned above, a new signal decomposition approach named Hilbert-Huang transform and analytical mode decomposition (HHT-AMD) for decomposing multicomponent micro-Doppler signals is put forward here. In this method, the potential components of micro-Doppler signals are obtained by the HHT firstly. If there is a frequency aliasing phenomenon, then AMD is applied to decompose the extracted signal into mono-component signals until all the signal components are obtained. Simulation studies and real dataset experiments show that the method can exactly decompose micro-Doppler signals with close-frequency components. The remainder of this paper is arranged as follows. In Section 2, the micro-Doppler signal model is introduced. In Section 3, the concepts of HHT and AMD and the signal decomposition method based on HHT-AMD for multicomponent micro-Doppler signals is proposed. Typical simulation and experiment results are analyzed to verify the validity of HHT-AMD in Sections 4 and 5, respectively. Finally, concluding remarks are drawn in Section 6.
# Voltage Tunable Spoof Surface Plasmon Polariton Waveguide Loaded with Ferroelectric Resonators
## 3 Results and Discussion  
### Crystal Structure and Dielectric Properties of BCZT Ferroelectric  
Figure 1 presents the X-ray diffraction (XRD) patterns of the BCZT ferroelectric resonator, sintered at 1250 \({}^{\circ}\)C. The indexed diffraction peaks indicate that the ferroelectric resonator displays a single phase without any impurity. The inset of Figure 1 depicts the cross section grain morphology of the as-sintered BCZT ferroelectric resonator. The well-densified grain structure and the even distributed grain size illustrate an optimized sintering condition at 1250 \({}^{\circ}\)C. Also, the BCZT ferroelectric resonator became crystallized in the trigonal-tetragonal morphotropic phase boundary (MPB) region at room temperature, with the small full width at half-maximum (FWHM), which suggests that the BCZT ferroelectric resonator has excellent crystallinity and will be beneficial for the large permittivity as well as tunability of the subsequent ferroelectric resonator.  
In order to construct a tunable microwave device, the dielectric properties as well as the turnability of the BCZT resonator should be declared firstly. Therefore, we begin to investigate the dielectric permittivity as well as the unloaded quality factor of the BCZT ferroelectric ceramic at the 0.1 GHz\(\sim\)1 GHz frequency range; the results are exhibited in Figure 2.  
Figure 1: XRD pattern and corresponding SEM image (the inset) of the BCZT ferroelectric resonator sintered at the optimized temperature of 1250 \({}^{\circ}\)C.  
As can be viewed, the BCZT ceramic has a stable permittivity in a wide frequency range from 0.1 GHz to 1 GHz, and the permittivity at the 1 GHz frequency band is around 190. The other important issue for ferroelectric devices is the loss tangent of the BCZT dielectric resonator in GHz band ranges from 4.0 \(\times\) 10\({}^{-4}\) to 6.3 \(\times\) 10\({}^{-4}\). The voltage tunability of the BCZT ferroelectric can be altered according to the data reported by [PERSON] et al. [56; 57], where a 75% capacitance tuning is obtained in the Pt/BCZT/Pt heterostructure system under the biased voltage of 20 V. Our previous work on the dielectric tunability of the BCZT heterostructure system also shows a larger than 60% capacitance tunability [55]. The large permittivity as well as the high tunability of the BCZT ferroelectric resonator will be beneficial for the following design of the large tunability in the ferroelectric compound resonator.
# Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
## 3 Optimization Algorithms
### Algorithm's Implementation  
Our angle-based method (Section 2) was optimized with each of the algorithms using the parameters in Table 2 following the variables described in the three previous sections. We used \(p=11\) angles to parametrize the splitter. For the optimization, we executed the algorithms \(k=100\) times and each step works with \(n=40\) exploratory designs. The parameter \(r\) is described in Section 3.1 and the parameters \(\omega,c_{i}\) in Section 3.2.  
Let us impose the non-optimized geometry limits of our 3 dB Y-junction: a 2000 nm long and 1500 nm wide rectangular multi-mode interferometer (when \(\alpha_{i}=0\) and all \(W_{i}\) are the same). Thus, using \(p=11\) the feature size of each subsegment is well above 120 nm, which is the limit of fabrication of many UV-lithography processes even by multi-project wafer standards [43].