esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Materials
### Model Architecture
#### 3.1.1 Data Acquisition  
This research adopted a web-scraping methodology to collect its initial dataset. The primary goal of this method is to automatically extract the data from a variety of unstructured websites that retrieve relevant content based on the query. Moreover, this study focused on academic publications from the field of computer science, which is our own research area, meaning we have a clear grasp of the publishing community; thus, the Publish or Perish (PoP) tool was used to gather the required data, which encompasses raw metadata from a variety of data sources such as Google Scholar, DBLP, and Microsoft Academic Search [35].  
In addition, the collected publications were further restricted to authors from twelve Saudi Arabian universities, making it a more focused case study. Specifically, King Abdul Aziz University (KAU), King Fahd University of Petroleum and Minerals, King Saud University, Umm Al-Qura University, King Faisal University, Imam Abdulrahman Bin Faisal University (IAU), Islamic University of Madinah, Qassim University, Taibah University, Taif University, Jouf University, and the University of Hail were concentrated [36]. Hence, the authors' names were manually selected from the official university websites in relation to three different academic ranks: professor, associate professor, and assistant professor. Each university had 10 selected authors with 100 or fewer publications each. This narrower focus also allowed for the consideration of the specifics of the authors' publication time ranges. However, while the quality of the similarity result can be enhanced by providing additional data for the author, the data extracted using the PoP tool alone was insufficient and missing the keywords that the scholars had assigned to their publications, which is one of the important features of this study. Hence, we integrated the data from the PoP tool with the keywords and affiliations related to these authors' publications. Figure 4 illustrates the full process of collecting the required data.  
Figure 3: The proposed approach of the academic network.
## 3 Results
### X-ray Diffraction (XRD) Analysis  
Figure 2 presents the XRD structure of the precursor glass sample. Referring to Figure 2, the XRD measurement at 27 \({}^{\circ}\)C showed that the atomic structure in the glass sample was amorphous in nature, where the arrangement of atoms in the precursor glass sample was unstructured and randomly organized. However, the amorphous structure of the precursor glass changed when it was subjected to the heat-treatment process. Figure 2 displays the XRD pattern of the glass and glass-ceramic sample against heat-treatment temperature. When the glass sample was treated at 600 \({}^{\circ}\)C, the arrangement of atoms in the glass structure remained unorganized. This is evidenced by the presence of the amorphous phase through the XRD test. At the earlier stage of the heat-treatment process, the energy supplied to the glass sample was still insufficient to alter the arrangement of the atoms and the structure of the sample [19]. However, the structure change started to take place when the heat-treatment temperature was increased to 700 \({}^{\circ}\)C. Figure 2 shows that crystallization started to occur at the higher temperature of 700 \({}^{\circ}\)C. At 700 \({}^{\circ}\)C, several diffraction peaks started to appear, indicating the formation of zinc silicate crystals. Diffraction indicated the crystalline phase existed inside the glass structure [20]. There were seven major zinc silicate peaks formed at 20 = 22.75\({}^{\circ}\), 25.62\({}^{\circ}\), 31.63\({}^{\circ}\), 34.72\({}^{\circ}\), 38.92\({}^{\circ}\), 49.07\({}^{\circ}\) and 65.87\({}^{\circ}\), which were proven by standard diffraction pattern of zinc silicate and \(\alpha\)-Zn\({}_{2}\)SiO\({}_{4}\) indicated by ICDD cards No. 37-1485 [21]. In terms of the different heat-treatment temperature applied, the increased of heat-treatment temperature caused higher diffusion of ions inside the sample, thus increasing the atomic mobility, which then accelerated the rate of crystal growth [22]. Furthermore, the full width at half maximum (FWHM) of the XRD decreased as the heat-treatment temperature increased. This indicates that the higher the heat-treatment temperature, the better the crystallization phase formed in the glass matrix [23]. The increase of NBOs promotes the replacement of oxide that leads to  
Figure 1: The linear shrinkage percentage and the density of the 55 ZnO–5B\({}_{2}\)O\({}_{3}\)–40 SLS glass and glass-ceramic sample at a various heat-treatment temperatures.  
a greater formation of crystal, thus a higher composition of ZnO leads to a better crystallization of the zinc silicate phase [24].
## 2 Materials and Methods  
### Samples  
Plants from three blueberry cultivars ('Rocio', and two experimental ones, V2 and V3) were grown under two cultivation systems (open field and plastic tunnels) and subjected to two irrigation regimes (100% and 80% crop evapotranspiration).  
The study was carried out in a commercial plot of approximately 11,000 m\({}^{2}\) (latitude: 37\({}^{\circ}\)10.37\({}^{\prime}\)9\({}^{\prime}\) N; longitude: 6\({}^{\circ}\)29.20\({}^{\prime}\)43\({}^{\prime\prime}\) W) belonging to a partner company located in the Dofana National Park (Almonte, Huelva, SW Spain). The experimental design was a strip-split-plot with four blocks in which the cultivars were randomly distributed (3 cultivars \(\times\) 2 cultivation systems \(\times\) 2 irrigation treatments \(\times\) 4 blocks = 48 per season). The elementary plot (56 \(\times\) 4.26 m with 150 each) included two rows of plants installed on ridges 70 cm high and with a base of 1.6 m. Drip irrigation lines were used with four emitters per plant line: two for water irrigation and two others for integrated drip fertigation. Blueberry plants were managed following a standard nutrient solution for blueberries. The main nutrients applied in fertigation were N (N-NH\({}_{4}\)+N-NO\({}_{3}\)) (70 kg/ha), P\({}_{2}\)O\({}_{5}\) (30 kg/ha), K\({}_{2}\)O (80 kg/ha) and MgO (30 kg/ha). pH was maintained between 5-5.50 using phosphoric acid in the irrigation water. No fungicides and pesticides were necessary during the growing season. The irrigation lines were distributed in the same way, differing only in the drip rate: 2.6 L/h for control treatment and 2.1 L/h for the deficit irrigation treatment. The measurement of water applied at each treatment was performedwith a Iberconta analog and pulse water meters (registered in datalogger and calibrated to circulate 10 L per pulse). The moisture in the soil was measured by installing capacitance probes in the frequency (FDR, frequency domain reflectometry) or time (TDR, time domain reflectometry) domains.  
The berries were collected in the same maturity stage in two consecutive seasons (2011 and 2012). The commercial maturity was defined as homogeneous blue color and a minimum value for SST of 11\({}^{\circ}\) Brix. Thus, samples were collected 6 times during the harvesting period, from April to June. The sampling was performed with four replicates per variety, treatment and cultivation system. Each replicate consisted of 500 g of berries from ten different bushes of three years of age and measuring over a meter in height. After their collection, the samples were transported from the field to the laboratory under refrigerated conditions and stored in the dark at 4 \({}^{\circ}\)C until their analysis (day after harvesting). The blueberries were evaluated for their quality attributes: soluble solids content, titratable acidity, firmness, color, weight and size (diameter). The factors evaluated were variety (V), sampling year (SY), cultivation system (CS) and irrigation regime (IR).
## 2 Materials and Methods
### Analytical Methods  
For electrochemical analysis, a 3 mm-diameter glassy carbon working electrode, a platinum wire counter electrode, and an Ag/AgCl reference electrode (NaCl, 3 M) were used in an electrochemical cell with a working volume of 10 mL. Cyclic voltammetry was performed using a potentiostat (660E; CHI Instruments, Inc.). The working electrode was polished with aluminum-water slurries (0.3 \(\upmu\)m and 1 \(\upmu\)m-diameter), and the electrochemical cell was completely cleaned. Oxygen was removed from the electrolyte by bubbling it with oxygen-free N\({}_{2}\) for 10 min before electrochemical measurements were obtained. The scanning rate applied was 10 mV s\({}^{-1}\) within the range of \(-\)0.55 to 0.7 V. 0.1 mM sodium phosphate buffer (pH 7.0) was used as the electrolyte.  
For liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) analysis, medium samples and standards were unfiltered so as to prevent loss of compounds caused by adsorption. Centrifuged samples were analyzed by a C8 reverse-phase column (Kinetex) with a 2.6 \(\upmu\)m particle size. Eluted compounds were analyzed by a channel electron multiplier (CEM) detector system (API 4000 LC-MS/MS System, AB-SCIEX). Metabolites were monitored by high-performance liquid chromatography (HPLC) using an Accucore C18 column (2.6 \(\upmu\)m particle size, 4.6 \(\times\) 150 mm; Thermo Fisher, Waltham, MA, USA). A fluorescence detector (AB-SCIEX) with an excitation wavelength of 440 nm and an emission wavelength of 525 nm was employed as well.  
Surface views of the bare and chitosan-hydrogen-coated carbon cloth electrodes were obtained using the SUPRA 55 VP (Carl Zeiss) at an accelerating voltage of 2 kV after sputter-coating of a thin platinum layer. Bacterial attachment on the electrodes were fluorescently stained with the SYTO9 (Molecular Probes, Eugene, OR, USA) following to manufacturer's instruction. After the incubation step, SYTO9 dye were mixed in the anolyte and samples were soaked again in fresh anolyte for 10 min to remove excess dye. Bacteria were examined with upright fluorescence microscope equipped a 5\(\times\) objective lens (Axio Imager.A2, Carl Zeiss, Germany).
## 2 ANC Method Based on VMD and GBDT
### Construction of GBDT for ANC  
As shown in Figure 3, different GBDT regressors were constructed and regressors corresponded with a single type of noise. GBDT is an iterative decision tree algorithm.  
Figure 2: Feature extraction based on VMD, PCA and k-means++.  
Each individual regressor is realized from a decision tree. The mathematical equation of GBDT can be written as:  
\[F_{x}(x)=F_{m-1}(x)+\operatorname*{argmin}_{h}L(y_{i},\ F_{m-1}(x_{i})+h(x_{i})) \tag{8}\]  
where \(F\) represents the decision regression tree function, \(L\) represents the Lagrange multiplier and \(h\) represents a weak regressor. The core idea of GBDT is to optimize the weak regressor step by step according to Equation (8) with input and output data. In this paper, the input and output data of Equation (8) are raw noise data and secondary output data (see Figure 1), respectively. Then, the error signal, \(e(n)\), is sent into the loss function to obtain update the GBDT adaptive controller.  
The noise dataset used in this paper is NOISEX-92 [25], which contains 15 different types of noise, such as white noise, pink noise, interior noise, mechanical noise and factory noise. K-means++ cluster algorithm will map different noise into different regional data sets. As shown in Figure 3, for 10 kinds of noise data in NOISEX-92, k-means++ divides it into nine regions and the noise type in each region is not unique. According to the clustering results, 10 different types of noise are classified into 9 types. For this problem, the processing idea is to construct several GBDT regressors without considering whether the noise types in the divided regions are consistent.  
The performance of the GBDT (Gradient Boosting Decision Tree) regressor is mainly affected by the loss function, number of estimators and maximum depth of the individual regression estimators. For different noise data sets, the optimal GBDT structure parameters are different. To simplify the GBDT structure optimization, this paper assumes that GBDTs with different noise types share the same structure. On the basis of the study in Section 4.2, the optimal GBDT structure parameters for NOISEX-92 noise data set are shown in Table 1.
## 4 Results
The FCN-DK with both RSI and SVI achieved the highest detection results, identifying 2283  
Figure 8: Slum classification outputs from four model configurations: (**a**) FCN (RSI only), (**b**) FCN-DK (RSI only), (**c**) FCN (RSI + SVI), and (**d**) FCN-DK (RSI + SVI). Figure 9: Comparative performance of FCN and FCN-DK models across slum categories (light and medium) and field survey results. light slum and 40 medium slum areas with a remarkable 99.74% recall, closely aligning with field survey results, which documented 435 light slum and 27 medium slum areas with 100% recall. This progression underscores the efficacy of the FCN-DK architecture in conjunction with both RSI and SVI, demonstrating its capacity to achieve the most precise automated slum detection and efficacious processing of larger geographical areas. However, while the integration of SVI and RSI enhances the precision of slum segmentation, it encounters significant challenges in slum classification due to limitations inherent in the dataset. The 2020 Slum Area Map inadequately delineated severity categories, and although the FCN-DK outperformed in classification accuracy, the FCN showed comparable results for light slum categories. The FCN with RSI demonstrated the poorest performance, due to its reliance on a solitary, restricted data source (RSI) and a rudimentary model architecture, which collectively proved inadequate for the accurate and comprehensive detection of slum areas. The incorporation of more sophisticated features (e.g., SVI) and the utilization of enhanced architectures (e.g., FCN-DK) has been demonstrated to result in a substantial enhancement in detection performance, as evidenced by the elevated recall rates exhibited by the alternative models. Nonetheless, the survey results presented in Figure 9 unequivocally demonstrate that irrespective of the efficacy of the FCN-DK model in identifying slums, the optimal approach for doing so is through the implementation of a field survey method. As the field survey method requires greater investment in human resources and time to classify the slum-ness of a larger area, it naturally generates less data than the other methods.
## 3 Data and Methods
### Calculation of Three-Dimensional Deformation Field  
The least-squares variance component estimation (LS-VCE) method [58; 59] was utilized to calculate the high-precision 3D field (Figure 4) by combining the InSAR and GNSS velocity fields. First, the GNSS horizontal deformation rate field is interpolated based on the InSAR grid points. Then, by combining the ascending and descending InSAR data with the east-west and north-south GNSS deformation components, the 3D deformation components are calculated. Specific calculation formulas can be found in the work by [PERSON] et al. [60].
# A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks
## 2 Antenna Design Analysis  
The schematic diagram of the designed ultrawideband antenna is presented in Figure 1. The structure of the UWB antenna involves the jug-shaped printed monopole with handle at the right side of the radiator, sized at Ls \(\times\)Ws \(\times\) hs. The printed monopole is fed by a coplanar waveguide (CPW) feedline of length \"Lf\" and width \"wf\". The width of the CPW feedline is kept at 3 mm to attain 50 \(\Omega\) input impedance. The antenna is designed on a low-cost FR-4 substrate having relative
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 2 System Description
### Fractional Order Systems  
Fractional calculus is a branch of mathematics that deals with the differentiation and integration of arbitrary order, which can be real or even complex. Here the derivative and  
Figure 1: Buck converter and waveforms.  
integral terms have a fractional order instead of integer order. Here, an integro-differential operator is defined, which combines differentiation and integration. This operator is defined as \({}_{c}D_{t}^{\alpha}\), where \(\alpha\) is the non-integer order, \(c\) and \(t\) are the limits of the operation. Many definitions of fractional calculus have been given, but the Riemann-Liouville (RL) and Caputo definitions are most commonly used [40; 41]. Basics and definitions of fractional order systems can be found in [42].  
Riemann-Liouville Definition  
\[{}_{c}D_{t}^{\alpha}\ (f(t))=D^{k}j^{k-\alpha}\ (f(t))=\frac{1}{\Gamma(k- \alpha)}\bigg{(}\frac{d}{dt}\bigg{)}^{k}\int_{c}^{t}\frac{f(u)}{(t-u)^{\alpha- k+1}}du \tag{3}\]  
Caputo's Definition  
\[{}_{c}D_{t}^{\alpha}\ (f(t))=\frac{1}{\Gamma(k-\alpha)}\int_{c}^{t}\frac{j^{k}(u) }{(t-u)^{\alpha-k+1}}du \tag{4}\]  
In these equations, \(k\) is an integer and \(\alpha\) is a non-integer value between \(k-1\) and \(k\).
## 3 Method Implementation and Application
### Detail Design  
The last design phase (detail design) requires developing the layout structure with standardized, commercial, or in any case easily achievable components. For the layouts of Figure 15, the design of the beams was carried out iteratively, imposing for each one a circular hollow section and the degree of slimmess \(\lambda\leq 15\) (according to the regulations) and verifying the structure with a Finite Elements Analysis (FEA) under the four loading conditions imposed by the regulations (LC#1, LC #2, LC #3, LC #4). The FEA was carried out by Altair OptiStruct(r)2018 software considering one-dimensional elements connected to each other in a rigid way, simulating threaded or welded couplings. Even if the standard imposes the degree of slimmess of the beams, in the structural calculation a non-linear algorithm was imposed to take into account possible instability phenomena for peak loads according to [PERSON]. Figure 15 shows the stress values and displacements for the final configuration under the four load hypotheses. The maximum stress values (obtained for LC#2) are about one order of magnitude lower than the limit values for AISI 304.  
The final pylons were engineered by assembling standard lattice components with bolted and welded connections. For the sail-shaped pylon, with the dual aim of protecting the welds and improving the aesthetic aspect, according to the results of the analyzed state-of-the-art, the reticular structures of the main mast and the sail were covered by sheet metal. Figure 16 shows the render of the lattice structure, with the final dimensioning of the beams of the elements of the mast, the sail, and the support for conductors.  
Figure 14: Initial reticular structure. (**a**) Eagle; (**b**) Sail.  
Figure 16: Render of the pylons final structures. (**a**) Eagle; (**b**) Sail.  
Figure 15: Results of Finite Elements Analysis (FEA) for the final configuration of the eagle (**a**) and sail (**b**) models under one of the load conditions (LC#2).
# Blind Image Separation Method Based on Cascade Generative Adversarial Networks
## 2 Materials and Methods
### Loss Function  
In the training process, we use a generated image and a real image, respectively, to train the GAN generators' and discriminators' anti-loss. In addition, in order to improve the performance of the loss function, the \(L_{1}\) is also used to participate in training [11; 21].  
Given an observation image \(\mathbf{X}\), a random interference vector \(\mathbf{z}\) and an objective image \(\mathbf{Y}\), GAN learns the mapping from \(\mathbf{X}\) and \(\mathbf{z}\) to \(\mathbf{Y}\), that is, \(\ G:\{\mathbf{X},\mathbf{z}\}\to\mathbf{Y}\). The process of the UGAN and PAGAN can be expressed as follows:  
\[L_{\text{GAN}}(G,D)=\mathbb{E}_{\mathbf{X},\mathbf{Y}}[\log D(\mathbf{X},\mathbf{Y})+\mathbb{ E}_{\mathbf{X},\mathbf{Y}}[\log(1-D(\mathbf{X},G(\mathbf{X},\mathbf{z})))]], \tag{1}\]  
where \(G\) (the generator) attempts to minimize this objective to generate an image that is more consistent with the true distribution, and \(D\) (the discriminator) maximizes the objective to improve its discriminability. The processing of the \(G\) and the \(D\) with the objective can be expressed as follows:  
\[G^{*}=\underset{G}{\text{argminmax}}L_{\text{GAN}}(G,D). \tag{2}\]  
Existing methods prove that it is effective to combine the GAN objective with a traditional loss method, such as \(L_{1}\) distance [21]. The discriminator only models the high-frequency structures of the image and, on the contrary, the \(L_{1}\) loss measures the low-frequency structures. The generator is tasked not only with tricking the discriminator but also with generating content near the ground truth output in an \(L_{1}\) sense, that is:  
\[L_{1}(G)=\mathbb{E}_{\mathbf{X},\mathbf{Y},\mathbf{z}}[\parallel\mathbf{Y}-G(\mathbf{X},\mathbf{z}) \parallel_{1}]. \tag{3}\]  
The final objective is:  
\[G^{*}=\underset{G}{\text{argminmax}}L_{\text{GAN}}(G,D)+\lambda L_{1}(G), \tag{4}\]  
Figure 3: PAGAN structure.  
where \(\lambda\) is the weight coefficient of the \(L_{1}\) loss.
# Real-Time Pre-Identification and Cascaded Detection for Tiny Faces
## 3 Proposed Method
### Proposed Pre-Identification Mechanism  
#### 3.2.1 Pedestrian Detector  
The bounding boxes of the pedestrians are detected by a deep CNN [32] in order to narrow the detection range of the face detector, as seen in Figure 1. Firstly, the image size is resized to \(300\,\times\,300\). Afterwards, the features are extracted by VGG-16 and then fed to six additional convolutional layers for detecting the targets. All default boxes are predicted by combining many feature maps with different scales and ratios, multiple default boxes are set to cover various sizes and shapes of targets. Finally, non-maximum suppression (NMS) is used to obtain the final bounding box which has the highest score in the set of bounding boxes of the target.  
Figure 1: The whole framework.  
Convolutional layers, except VGG-16 in the pedestrian detector, predict bounding boxes and offsets. There are different receptive fields in different levels of feature maps. The scale of the default boxes is computed as:  
\[\mathrm{s_{k}=s_{min}+\frac{s_{max}-s_{min}}{m-1}(k-1),\ k\ e\ [1,\ m],} \tag{1}\]  
where \(s_{min}\) is 0.2 and \(s_{max}\) is 0.9, the length \(\mathrm{l_{k}}\) and the width \(\mathrm{w_{k}}\) of the bounding box are calculated based on \(\mathrm{s_{k}}\); m is the number of default boxes, \(W\) is the width of the input and \(L\) is the length of the input, \(r\) is the ratio of the length and the width in each feature map. The detailed calculation formula is shown as:  
\[\left\{\begin{array}{c}w_{k}=W\times s_{k}\times\sqrt{r}\\ \mathrm{l_{k}}=L\times s_{k}/\sqrt{r}\end{array}\right.. \tag{2}\]