esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study presents a synthesis framework for thinned wideband antenna arrays by integrating fractal Eisenstein geometry with advanced multi-objective evolutionary optimization techniques. Exploiting the inherent self-similarity properties of the Eisenstein fractal structure, combined with algorithms such as MO-BDE and MO-PSO, the proposed method successfully reduced the number of active elements by up to 37.5%, without compromising performance across the wideband frequency range.  
Two distinct design cases were evaluated. Design A, optimized for a primary operational frequency, achieved an SLL reduction of approximately 12 dB compared to the original structure, demonstrating its suitability for high precision demanding scenarios. In contrast, Design B maintained a consistent SLL reduction of up to \(-3\) dB across the bandwidth, offering well-balanced performance for multiband and frequency-agile systems.  
The findings validate the effectiveness of combining fractal geometries with evolutionary algorithms to achieve efficient array thinning while preserving beamforming and spectral integrity. This work advances the theoretical foundations of fractal-based antenna synthesis and offers practical pathways for implementing energy-efficient, high-performance arrays in next-generation technologies. Future research should explore the integration of these optimized arrays into real-world devices and communication platforms such as MIMO radar systems, which benefit from self-similar geometries, and IoT devices that demand compact, wideband, and interference-resilient antenna solutions.  
Conceptualization, L.A.G.; Methodology, L.A.G. and M.A.P.; Software, L.E.C.; Validation, M.A.P., A.R. and M.A.Z.; Formal analysis, L.E.C., L.A.G. and M.A.P.; Investigation, L.E.C. and L.A.G.; Resources, L.A.G. and A.R.; Data curation, L.E.C. and L.A.G.; Writing--original draft, L.E.C.; Writing--review & editing, L.A.G., M.A.P. and A.R.; Visualization, L.E.C. and L.A.G.; Supervision, L.A.G. and M.A.P.; Project administration, L.A.G.; Funding acquisition, L.A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Consejo Nacional de Ciencia y Tecnologia (CONACYT), through scholarship grant number 833095.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Structural Optimization of MicroMED Dust Analyzer
## 3 Results
### Optimal OB-Model B  
The rough optimal solution was obtained via three TO iterations, whose outcomes are reported in Figure 12. The Model B final design proposal is depicted in Figure 13.  
Figure 11: Overview of MicroMED Model A assembly relative thermal deformations for the cold case (**a**) and displacements at the defined probes (**b**).  
Figure 12: TO iterative cycles results (red colour) for the Model B.  
Figure 10: MicroMED Model A first resonance mode shape (**a**); Von Mises stress distribution [MPa] with 1000 m/s\({}^{2}\) load in Y direction (**b**) and thermoelastic cold case (**c**).  
The optimization process took an overall computational time comparable with the Model A design process. For the Model B, the FEM comprised 32,999 finite elements, split between 29,240 solid tetrahedrons and 3759 shell triangles. The FEAs results are summarized in Table 3, while the most critical loading conditions (i.e., 1000 m/s\({}^{2}\) acceleration along the \(Y\)-axis for the quasi-static analysis and the cold case for thermoelastic stresses) are reported in Figure 14.  
In particular, a first natural frequency of about 607 Hz was estimated, while a maximum Von Mises stress of 87 MPa (i.e., leading to a _MoS_ of 2.86) and 31 MPa (i.e., leading to a _MoS_ of 9.9) was found for the quasi-static and the thermoelastic analyses, respectively. The thermal deformation is reported in Figure 15. Negligible relative displacements were obtained for the model B design (i.e., 761 nm and 752 nm for the cold case and hot case, respectively). The displacements measured at the probes in the three directions with respect to the reference frame XYZ are reported in Appendix B.  
Figure 14: MicroMED Model B first resonance mode shape (**a**); [PERSON] stress distribution [MPa] with 1000 m/s\({}^{2}\) load in Y direction (**b**) and thermoelastic cold case (**c**).  
Figure 13: Model B without (**a**) and with (**b**) the stiffening ribs grid. FEAs features (i.e., lumped masses, rigid links, shells, and constraints) are highlighted. The mesh was applied to the model (**c**).
# Statistical Process Control with Intelligence Based on the Deep Learning Model
## Methodology  
### Simulation Method of Histogram Patterns  
Seven typical HPs of the production process are shown in Figure 2, including normal (NOR) patterns, bimodal (BIM) patterns, left and right island (LI and RI) patterns, left and right skew (LS and RS) patterns and flat top (FT) patterns.  
The Monte Carlo simulation algorithm is recognized as a HP simulation method in the SPC field [48]. In this paper, the method is used to simulate the quality data of each HP.  
If \(y(t)\) is the value of quality data measured at \(t\), then the quality data in the NOR pattern is normal distribution:  
\[y(t)\sim N\left(\mu,\;\sigma^{2}\right) \tag{1}\]  
where \(\mu\) is the mean of quality data under controlled conditions, \(\sigma\) is the standard deviation.  
The quality data of BIM pattern is a combination of two normal distributions, which can be simulated by the following formula [48]:  
\[y(t)\sim aN\left(\mu-b_{1},\;\left(\frac{3\sigma-b_{1}}{3}\right)^{2}\right)+ \left(1-a\right)N\left(\mu+b_{2},\;\left(\frac{3\sigma-b_{2}}{3}\right)^{2}\right) \tag{2}\]  
where, \(a\) and \(\left(1-a\right)\) are the proportion of two normal distributions, and \(b_{1}\) and \(b_{2}\) are the distance between the center of two normal distributions and \(\mu\).  
The LI and RI pattern can be seen as a small normal distribution next to the normal distribution., which can be simulated by the following formula:  
\[y(t)\sim aN\left(\mu,\;\sigma^{2}\right)+\left(1-a\right)N\left(\mu\pm b,\; \left(\frac{3\sigma-b}{3}\right)^{2}\right) \tag{3}\]  
Figure 2: Seven typical histogram patterns.  
where, \(a\) and \((1-a)\) are the proportion of two normal distributions, and \(b\) is the distance between the center of small normal distributions and \(\mu\); \"+\" represents RI pattern, and \"-\" represents LI pattern.  
The quality data of LS and RS pattern can be combined by several normal distributions [48], which can be simulated by the following formula [48]:  
\[y(t)\sim\sum_{i=0}^{m-1}\frac{1}{m}N\Big{(}\mu\pm 3\sigma((\frac{2}{3})^{i}-1), \ ((\frac{2}{3})^{i}\sigma)^{2}\Big{)} \tag{4}\]  
where, \(m\) is the number of normal distributions; \"+\" represents LS pattern, and \"-\" represents RS pattern.  
FT pattern quality data can be formed by mixing uniform distribution with normal distribution, which can be simulated by the following formula:  
\[y(t)\sim aN\Big{(}\mu,\ \sigma^{2}\Big{)}+(1-a)U(\mu-3\sigma,\ \mu+3\sigma) \tag{5}\]  
where, \(a\) and \((1-a)\) are the proportion of normal distribution and uniform distribution respectively.
###### Abstract  
The present paper aims to study the effect of cellulase hydrolysis and high-pressure homogenization on the structural and physicochemical properties of rice bran dietary fiber (RB-DF). Scanning electron microscopy showed that cellulase treatment led to the formation of a porous structure on RB-DF surface. High-pressure homogenization affected the laminated microstructure of RB-DF, leading to the formation of an irregular and loose surface structure. X-ray diffraction demonstrated that joint processing destroyed the amorphous hemicellulose and cellulose regions, and changed the crystallinity of RB-DF, albeit with a minor impact on the crystalline region of cellulose. Fourier transform infrared spectroscopy indicated that combined processing promoted dissociation of some glycosidic bonds in fiber structure, exposing the hydroxyl groups in cellulose, thus improving their ability to bind water molecules. Thermogravimetric analysis showed a significant decrease in the thermal decomposition temperature of RB-DF (\(p<0.05\)) as well as a decrease in thermal stability after combined processing. Cellulae hydrolysis and high-pressure homogenization treatment did not improve their oil holding capacity, but significantly increased water holding capacity, swelling capability, and cation exchange capacity of RB-DF. Thus, enzymatic hydrolysis and high-pressure homogenization treatment can change the structure of RB-DF, exposing a large number of hydrophilic groups and enhancing hydration, obtaining uniform RB-DF particle.  
rice bran; enzymatic hydrolysis; cellulase; high-pressure homogenization Article  
Structural and Physicochemical Characteristics of Rice Bran Dietary Fiber by Cellulae and High-Pressure Homogenization
# Predicting Output Power for Nearshore Wave Energy Harvesting
## 6 Conclusions  
Renewable energy is considered as an option to reduce the use of natural resources and to mitigate climate change. Despite high power density and low environmental impact, wave energy has not been as widely explored as solar and wind. A number of devices and mechanisms has been proposed to harvest wave energy, but the development of effective and efficient energy converting methods remains a challenge.  
In this study, we proposed an approach to predict the output power of a nearshore wave energy converter (WEC) based on the movement of the floating buoy and using machine learning algorithms. Since the output power from wave energy harvesting stations varies greatly with the location of installation and other non-human factors, it is important to design an effective prediction model to find optimal locations. The WEC device used in this study is comprised of an energy absorbing unit and a power generating unit. To monitor the movement of the buoy, we developed a data collection module that incorporates a microcontroller, a gyro-accelerometer sensor and a wireless transceiver. The data for the experiments were collected from a wave tank simulator and an actual wave energy harvesting plant. We thoroughly analyzed the collected dataset by applying several machine learning techniques. We proposed models for prediction based on SVM, RF and ANN. Experimental results indicated that the combined model, which is a classifier followed by regressor, worked accurately enough to be utilized to predict the output power from the movement of a buoy. The developed model is useful for finding the optimal locations for wave harvesting plants and to guide the design of the buoy to maximize output power.  
This work was supported by the 2016 research fund of Myongji University.  
[PERSON] carried out the experiments. [PERSON] and [PERSON] wrote the paper under the supervision of [PERSON]. [PERSON] finalized the paper.  
The authors declare no conflict of interest.  
Figure 11: Comparison of predictors based on: (**a**) MSE; (**b**) MAE; (**c**) R\({}^{2}\).
# Wiener Filter and Deep Neural Networks: A Well-Balanced Pair for Speech Enhancement
## 2 Speech Enhancement
### SNR Estimation  
Classical speech enhancement algorithms are commonly described in terms of the a priori and a posteriori SNR [1; 5; 6; 24]. The a priori SNR is defined in terms of the PSD of the clean speech and the noise signal:  
\[\xi_{k,l}=\frac{P_{x}(k,l)}{P_{d}(k,l)} \tag{7}\]  
where \(P_{x}(k,l)=E[|X(k,l)|^{2}]\) is the clean speech PSD, \(P_{d}(k,l)=E[|D(k,l)|^{2}]\) is the noise signal PSD, both in frequency bin \(k\). The a posteriori SNR depends on the noise signal PSD and the noisy spectral power \(P_{y}(k,l)=|Y(k,l)|^{2}\):  
\[\gamma_{k,l}=\frac{P_{y}(k,l)}{P_{d}(k,l)} \tag{8}\]  
As we can see, with an estimate of the PSD of the noise, the a posteriori SNR can directly be obtained using the noisy spectral power \(|Y(k,l)|^{2}\). Many statistical algorithms have been proposed to estimate the noise spectrum. For instance, there are histogram-based approaches [27], minimum statistics [28], minima controlled recursive averaging (MCRA) [24], etc. However, in general, they lose accuracy when handling realistic non-stationary noises. Additionally, they could distort the speech signal or generate annoying artifacts. The a priori SNR also needs an estimate of the PSD of the clean signal, which is another challenging point of these approaches.
# A Computation Offloading Strategy in LEO Constellation Edge Cloud Network
## 3 System Model
### Network Model  
Different from the fixed topology of the terrestrial mobile network and wired network, the LEO constellation network has a dynamic time-varying discrete topology as illustrated in Figure 4. The dynamic network topology is considered a periodically repeating series of \(K\) topology snapshots [12] separated by every inter-satellite link switching. Each of the snapshots is modeled as a graph \(G(k)=(V,E(k))\), where \(V\) is the set of satellite nodes, and \(E(k)\) is the set of inter-satellite links in the \(k\) snapshot.  
In the LEO constellation edge cloud network, the computing data of the users may reach the satellite providing computing offloading service through inter-satellite multi-hop path after being scheduled by the orchestrator. The cost of multi-hop path cannot be ignored because of the delay and energy consumption of each hop link. We assume that the ground terminal under each satellite transmits calculation data to the destination satellite using the shortest path. When the topology snapshot changes, the shortest path may also  
Figure 4: The LEO constellation network discrete topology.  
change. So the Dijkstra algorithm runs to calculate the shortest path from each node to other nodes in each topology snapshot. The shortest path contains the next hop to other nodes and the number of hops to go through. When the satellite-ground link changes, for the continuous offloading tasks, such as video codec, the migration overhead caused by shortest path tree switching should be considered. However, it has little influence on the second-level real-time response tasks. The offloading task model studied in this paper is the second-level response task.
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 2 Related Work  
### Multibox Text Detector  
The multibox text detector extracts feature maps using convolutional layers. The detector draws multiple default bounding boxes on feature maps of different resolutions. After the convolution process, the targets in the original image will decrease in size. Since the default box has a fixed shape, the target will be captured at the right size. SSD is a representative of multibox text detectors. SSD has several advantages, including: (1) SSD discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per a feature map location. (2) During the prediction procedure, the network generates scores for the presence of each object category in each default box and adjusts the box to better match the object shape. (3) Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes [20]. Figure 1 shows the SSD network architecture.  
[PERSON] et al. [24] proposed an improved SSD-based network named single shot text detector (SSTD). The inception structure and semantic segmentation are integrated in the SSD. The inception structure shows better performance for extraction convolution features. A new module named the text attention module is used to fuse semantic segmentation information and convolution features. The whole semantic segmentation fusing process has three steps: (1) The feature maps are enlarged and scored during semantic segmentation using a deconvolution process. (2) In order to fuse with the original feature map, the semantic segmentation results need to be reduced to smaller sizes through convolution processing. (3) Finally, the text attention module combines semantic segmentation information and convolution features. In this process, the feature map will be enlarged first and then reduced,introducing noise. This problem is caused by the desire to combine semantic segmentation within the multibox. Thus, this method to integrate semantic segmentation has limited effect.
## 2 Quantum Model and Configurations
The coupling matrix exponential can be easily calculated following an approach similar to that used in the classical \(\mathcal{PT}\)-symmetric dimer [1],  
\[\hat{U}(\zeta) = e^{i\hat{H}\zeta}, \tag{9}\] \[= \hat{1}\cos\left(\Omega\zeta\right)+\hat{H}\;\zeta\;\text{sinc} \left(\Omega\zeta\right),\qquad\Omega\in\mathbb{C}.\]  
Here, the symbol \(\hat{1}\) stands for the two by two identity matrix, we have used the cardinal sine function \(\text{sinc}(x)=\sin(x)/x\), and the complex dispersion relation is given by the following expression,  
\[\Omega=\sqrt{1+n^{2}}. \tag{10}\]Note that this analytic propagator can describe any non-Hermitian dimer and, in the special case of purely imaginary auxiliary refractive index, \(n=i\gamma\), we recover the propagator for the standard classical \(\mathcal{PT}\)-symmetric dimer [18],  
\[\hat{U}(\zeta)=\left\{ \begin{array}{ll}\hat{1}\cos\left(\Omega\zeta\right)+i \ \hat{H}\ \zeta\ \text{sinc}\left(\Omega\zeta\right)&\gamma<1,\\ \hat{1}+i\ \zeta\ \hat{H},&\gamma=1,\\ \hat{1}\cosh\left(\left|\Omega\right|\zeta\right)+i\ \hat{H}\ \zeta\ \text{sinhc}\left(\left|\Omega\right|\zeta\right),& \gamma>1.\end{array} \right. \tag{11}\]  
Experimentally, the effective \(\mathcal{PT}\)-symmetric dimer can be reached via different configurations: balanced gain-loss dimer, \(n_{1}=n_{R}-in_{I}\) and \(n_{2}=n_{R}+in_{I}\), in that order, such that \(n_{0}=n_{R}/g\) and \(\gamma=-n_{I}/g\); gain-gain dimer, \(n_{1}=n_{R}-in_{I,1}\) and \(n_{2}=n_{R}-in_{I,2}\) such that \(n_{0}=\left[2n_{R}-i\left(n_{I,1}+n_{I,2}\right)\right]/\left(2g\right)\) and \(\gamma=-(n_{I,1}-n_{I,2})/\left(2g\right)\); passive-gain dimer, \(n_{1}=n_{R}\) and \(n_{2}=n_{R}-in_{I}\), in that order, such that \(n_{0}=\left(2n_{R}-in_{I}\right)/\left(2g\right)\) and \(\gamma=n_{I}/\left(2g\right)\); passive-loss dimer, \(n_{1}=n_{R}\) and \(n_{2}=n_{R}+in_{I}\), in that order, such that \(n_{0}=\left(2n_{R}+in_{I}\right)/\left(2g\right)\) and \(\gamma=-n_{I}/\left(2g\right)\); and loss-loss dimer, \(n_{1}=n_{R}+in_{I,1}\) and \(n_{2}=n_{R}+in_{I,2}\) such that \(n_{0}=\left[2n_{R}+i(n_{I,1}+n_{I,2})\right]/\left(2g\right)\) and \(\gamma=\left(n_{I,1}-n_{I,2}\right)/\left(2g\right)\); for all of these configurations, we have assumed \(n_{R}\), \(n_{I}\), \(n_{I,j}>0\). The formal solution presented in this section allows us to explore the propagation properties of all of these experimentally feasible configurations. Table 1 shows the values of these parameters for the different configurations delivering an effective \(\mathcal{PT}\)-symmetric dimer; and we have added the imaginary part of the effective bias refractive index, \(\beta=\left(n_{I,1}+n_{I,2}\right)/\left(2g\right)=-\Im(n_{0})\), as it will be useful in the following sections.
## 4 Results and Discussion
### Activity Concentrations of the Radioisotopes
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample ID**} & \multicolumn{3}{c}{**Activity Concentration [Bq/kg]**} \\ \cline{2-4}  & **U-238** & **Th-232** & **K-40** \\ \hline S1 & 53.40 \(\pm\) 5.60 & 39.73 \(\pm\) 4.10 & 911.76 \(\pm\) 58.10 \\ S2 & 29.70 \(\pm\) 3.05 & 34.75 \(\pm\) 4.11 & 769.02 \(\pm\) 50.65 \\ S3 & 28.59 \(\pm\) 2.97 & 28.60 \(\pm\) 2.97 & 852.35 \(\pm\) 56.54 \\ S4 & 45.65 \(\pm\) 5.04 & 42.55 \(\pm\) 5.02 & 689.23 \(\pm\) 49.77 \\ S5 & 19.07 \(\pm\) 2.00 & 21.97 \(\pm\) 2.31 & 805.07 \(\pm\) 51.04 \\ S6 & 29.89 \(\pm\) 3.25 & 32.13 \(\pm\) 3.50 & 759.38 \(\pm\) 54.16 \\ S7 & 22.94 \(\pm\) 2.56 & 24.33 \(\pm\) 2.66 & 827.67 \(\pm\) 58.52 \\ S8 & 16.89 \(\pm\) 1.58 & 23.24 \(\pm\) 1.32 & 883.75 \(\pm\) 42.42 \\ S9 & 31.31 \(\pm\) 3.06 & 30.84 \(\pm\) 1.73 & 828.44 \(\pm\) 40.37 \\ S10 & 27.80 \(\pm\) 2.74 & 39.06 \(\pm\) 3.83 & 857.86 \(\pm\) 45.29 \\ S11 & 34.96 \(\pm\) 3.43 & 42.20 \(\pm\) 4.29 & 790.19 \(\pm\) 42.63 \\ S12 & 36.48 \(\pm\) 3.15 & 33.32 \(\pm\) 3.44 & 930.04 \(\pm\) 50.00 \\ S13 & 56.23 \(\pm\) 5.05 & 52.91 \(\pm\) 5.08 & 871.26 \(\pm\) 46.46 \\ S14 & 72.00 \(\pm\) 6.48 & 52.91 \(\pm\) 4.17 & 868.98 \(\pm\) 10.71 \\ S15 & 65.00 \(\pm\) 5.22 & 70.00 \(\pm\) 6.33 & 950.00 \(\pm\) 52.30 \\ S16 & 27.88 \(\pm\) 2.19 & 22.78 \(\pm\) 1.15 & 814.38 \(\pm\) 39.00 \\ S17 & 34.09 \(\pm\) 3.12 & 26.29 \(\pm\) 2.59 & 901.43 \(\pm\) 47.87 \\ S18 & 43.04 \(\pm\) 4.27 & 35.44 \(\pm\) 3.85 & 857.46 \(\pm\) 47.57 \\ S19 & 50.23 \(\pm\) 4.84 & 57.99 \(\pm\) 5.79 & 1037.73 \(\pm\) 55.91 \\ S20 & 49.36 \(\pm\) 4.14 & 53.47 \(\pm\) 2.55 & 1008.23 \(\pm\) 48.79 \\ Min. & 16.89 \(\pm\) 1.58 & 21.97 \(\pm\) 2.31 & 689.23 \(\pm\) 49.77 \\ Max. & 72 \(\pm\) 6.48 & 70 \(\pm\) 6.33 & 1037.73 \(\pm\) 55.91 \\ AV \(\pm\) SE & 38.72 \(\pm\) 3.38 & 38.23 \(\pm\) 2.99 & 860.71 \(\pm\) 18.59 \\ GAV in regular soil [21] & 35 & 30 & 400 \\ GAV in building materials [22] & 50 & 50 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The activity concentration values of radioisotopes (with \(\pm\) uncertainties) for EISibai-Abu EITiyur granite samples. Figure 5: Normalization of radioisotope values (\({}^{226}\)Ra, \({}^{232}\)Th, and \({}^{40}\)K) from previous literature to their corresponding values in the current work for comparison purposes. The dashed line represents the present work normalized values.
# Three-Dimensional Wide-Band Electromagnetic Forward Modelling Using Potential Technique
## 3 Results and Discussion  
### Model 1  
In verifying the accuracy of our finite volume algorithm, we synthesize an electromagnetic (EM) investigation for a homogeneous 1-D model. The background domain has a resistivity value of 1 \(\Omega\) m, and a 15 km-thick layer of resistivity 100 \(\Omega\) m is embedded in the background half-space (as indicated in Figure 2). A period range of 1-1000 s and \(E^{x}\) polarization are considered for the EM modelling. The extent of the mesh is 40 km in both horizontal axes while 60 km in the vertical direction. Their number of cells used is 15, 15 and 84 in the x, y, z direction, respectively. The solid dash green lines indicate the analytical (A) solution while the red circle denotes the finite volume (FV) approach.  
For this model, we compared our finite volume algorithm with the analytical solution [39]. From the plot in Figure 3a,b, it shows that the apparent resistivity and phase from our finite volume scheme are in good agreement with the analytic solution. It is also observed from Figure 3c,d that the relative error trend from the numerical solution rises at the transition zone between 2 to 200 s as a result of penetrating the conductive basement region.