esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The visible and infrared wavelengths are the most frequently used electromagnetic (EM) waves in the frequency spectrum; able to penetrate the atmosphere and reach Earth's surface. These wavelengths have attracted much attention in solar energy harvesting; thermography; and infrared imaging applications for the detection of electrical failures; faults; or thermal leakage hot spots and inspection of tapped live energized components. This paper presents a numerical analysis of a compact cubic cross-shaped four-layer metamaterial absorber (MA) structure by using a simple metal-dielectric-metal-dielectric configuration for wideband visible and infrared applications. The proposed MA achieved above 80% absorption in both visible and near-infrared regions of the spectrum from 350 to 1250 nm wavelength with an overall unit cell size of 0.57A \(\times\) 0.57A \(\times\) 0.59A. The SiO\({}_{2}\) based anti-reflection coating of sandwiched tungsten facilitates to achieve the wide high absorption bandwidth. The perceptible novelty of the proposed metamaterial is to achieve an average absorptivity of 95.3% for both visible and infrared wavelengths with a maximum absorptivity of 98% from 400 nm to 900 nm. Furthermore, the proposed structure provides polarization insensitivity with a higher oblique incidence angle tolerance up to 45\({}^{\circ}\).  
metamaterial absorber; anti-reflection coating; optical window; infrared window
## 3 Experimental Setup  
### Dataset Description  
In this section, we briefly discuss the five publicly available benchmark datasets used in our experiments. These datasets are the Ninapro DB4 [29], Ninapro DB5 [29], BioPatRec DB1 [30], BioPatRec DB2 [30], and BioPatRec DB3 [30] datasets. sEMG sensors were placed at various muscle locations in the upper limbs during their respective measurement processes. The datasets encompass hand activities, broadly categorized into gestures, wrist movements, object grasping, and hand motions. Detailed descriptions of the datasets are provided in the references listed below.  
The Ninapro DB5 dataset comprises muscle activity signals collected through two Thalmic Myo armbands, which contain 16 active single-differential wireless electrodes. The sampling frequency was 200 Hz. The dataset includes recordings of 10 participantsperforming 53 gesture movements, as well as a rest condition, with each movement repeated six times. The 53 gestures are categorized into the following three groups: A, B, and C. Group A primarily documents fundamental finger movements, while Group B encompasses finger flexion and extension, along with wrist gestures. Group C focuses on grasping actions involving everyday objects. Furthermore, analysis was conducted using the Ninarop DB4 dataset, which utilized the same experimental design but was collected using 12 sensors with a sampling frequency of 2000 Hz. For training, we used trials numbered 1, 3, 4, and 6 of all 10 subjects, and for testing, we used trials numbered 2 and 5.  
The BioPatRec DB1, DB2, and DB3 databases are subsets of the BioPatRec toolbox. BioPatRec DB1 and BioPatRec DB3 capture 10 hand movements using 4 EMG electrodes, while BioPatRec DB2 records 26 hand movements employing 8 EMG electrodes. These datasets comprise EMG signals collected from 20, 17, and 8 subjects, respectively. Each gesture repetition was performed thrice, with a 3 s relaxation period between repetitions. The sampling rate was set to 2000 Hz. The duration of muscle contraction is determined by a contraction time percentage, with a default value of 0.7. Electrodes were spaced at an inter-electrode distance of 2 cm and evenly distributed around the most proximal third of the forearm. For the training phase, data from trials 1 and 3 of all subjects were utilized, while for testing, data from trials 2 were employed. More details are described in Table 1.
# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
## 2 Materials and Methods
### Dataset  
The scenario selection process presented in Section 2.2 requires naturalistic traffic data as the input source. For the scope of this paper, the highD dataset [29] that was generated by a video drone capturing vehicle trajectories within a 420 m long segment of highways was used. It contains recordings from both driving directions of six different locations in Germany with an overall recording time of 147 h, of which the upper driving direction was used for the scenario extraction process. The videos were captured between 8:00 AM and 5:00 PM during sunny and windless weather indicating dry road conditions and unrestricted visibility. The included roadway models cover two- and three-lane motorways as well as three-lane sections with entrance lanes.
## 3 Results and Discussion
### Evaluation of Sensory Acceptance  
Sensory evaluation was carried out on whole pieces of pouting fish (Table 4). According to the above-mentioned microbial and chemical determinations, the high quality of the initial fish (E score in all analyzed descriptors) was observed, and this decreased progressively as the chilling time increased. However, the fish specimens were still acceptable on day 6. On the contrary, the fish were found to be of objectional quality on day 9. According to the evaluation carried out by the panelists, eye appearance (concave and milky aspect) and external odor (i.e., putrid odor development) were found to be the limiting descriptors.  
Previous research accounted for the decrease in sensory acceptance of pouting fish specimens during storage under refrigeration conditions. Thus, [PERSON] et al. [23] showed that refrigerated (2 \({}^{\circ}\)C) pouting was unacceptable after 6-7 days of storage. Notably, the first sign of deterioration was the development of a slightly off odor after 3-4 days of storage. An average decrease in the general aspect, texture and taste scores was observed after a 6 month storage period at \(-20\)\({}^{\circ}\)C [19].  
A progressive sensory quality decrease has also been detected in related lean fish species during chilled storage. In general, a longer shelf life has been found for relatively larger species, while relatively small-sized species have shown shorter shelf lives. Thus, chilled wild turbid (_S. maximus_) was found to be acceptable at day 15 [51], while farmed turbot (_P. maximus_) was still acceptable after a 19 day storage period [30]. European hake (_M. merlucius_) [58] and ray (_R. clavata_) [44] showed relatively short shelf lives (5 and 3 days, respectively) during chilled storage. Regarding underutilized lean species, Bombay duck (_H. nehereus_) [46] and lobster krill (_Munida_ sp.) [11] showed shelf lives of 10 and 6 d, respectively, during refrigerated storage.
## 3 Massive-MIMO Sparse Uplink Channel Estimation Using a First-Order Statistics Based Approach
### Proposed SiT Based Massive-MIMO Sparse Uplink Channel Estimation Techniques
#### 3.2.2 SiT Based Gradient Pursuit (SiT-GP)  
The GP [37] is a greedy algorithm for the reconstruction of sparse signals. It utilizes the steepest descent methodology to compute the step-size for each iteration, i.e., the sparse solution vector is updated at each iteration with a directional update computed on the basis of gradient or conjugate gradient. The only additional computational cost compared to MP is that of the evaluation of the step size. The SiT-GP algorithm is given below:  
1. Initialize the residual vector \(\mathbf{r}^{0}=\mathbf{\hat{d}}_{n_{r}n_{t}}\), the estimate of the channel coefficients vector \(\mathbf{\hat{h}}_{n_{r}n_{t}}^{0}=\mathbf{0}\), and \(\Gamma^{0}=\emptyset\);
2. for \(i=1;i=i+1\) until stopping criteria is met, do 1. \(\mathbf{g}^{i}=\mathbf{C}_{n_{t}}^{H}\mathbf{r}^{i-1}\) ; 2. \(m^{i}=\arg_{m}\max\ |\ \mathbf{g}_{m}^{i}\ |\) ; 3. \(\Gamma^{i}=\Gamma^{i-1}\cup m^{i}\) ; 4. Compute the update direction \(\mathbf{u}_{\Gamma^{i}}=\mathbf{C}_{n_{t}\Gamma^{i}}^{H}(\mathbf{\hat{d}}_{n _{r}n_{t}}-\mathbf{C}_{n_{t}\Gamma^{i}}\mathbf{\hat{h}}_{n_{r}n_{t}\Gamma^{i} }^{i-1})\) ; 5. \(\mathbf{v}^{i}=\mathbf{C}_{n_{t}\Gamma^{i}}\mathbf{u}_{\Gamma^{i}}\) ; 6. \(\alpha^{i}=\langle\mathbf{r}^{i},\mathbf{v}^{i}\rangle/\|\mathbf{v}^{i}\|_{2}^ {2}\) ; 7. \(\mathbf{\hat{h}}_{n_{r}n_{t}\Gamma^{i}}^{i}:=\mathbf{\hat{h}}_{n_{r}n_{t} \Gamma^{i}}^{i-1}+\alpha^{i}\mathbf{u}_{\Gamma^{i}}\) ; 8. \(\mathbf{r}^{i}=\mathbf{r}^{i-1}-\alpha^{i}\mathbf{v}^{i}\); 9. Output \(\mathbf{r}^{i},\mathbf{\hat{h}}_{n_{r}n_{t}}^{i}\).  
The adoption of directional gradient pursuit in the GP algorithm results in fast approximations to OMP but with the same computational complexity as that of MP algorithm.
# Removal of Chromium(VI) by Chitosan Beads Modified with Sodium Dodecyl Sulfate (SDS)
## 4 Conclusions  
In this study, chitosan was chemically modified by sodium dodecyl sulfate (SDS) to enhance its adsorption capacity for the removal of Cr(VI). The SDS-chitosan beads were characterized by SEM-EDS, FT-IR, and XPS. The effect of several operating parameters, such as the loading amounts of SDS, solution pH, contact time, adsorbent dose, temperature, and initial Cr(VI) concentration, on the adsorption performance was examined in a batch system. The experimental data were found to be fit best using a Langmuir isotherm and pseudo-first-order kinetic models. The performance of the adsorption process was favorable at pH 4-5 and higher temperatures. The results showed that the maximum adsorption capacity and partition coefficient (PC) of Cr(VI) on SDS modified chitosan beads were 3.23 mg\(\cdot\)g\({}^{-1}\) and 9.5 mg\(\cdot\)g\({}^{-1}\).mM\({}^{-1}\), respectively. In conclusion, the SDS-chitosan beads synthesized in this work can be effectively utilized for removing Cr(VI) ions.  
Experiment and writing, X.D.; experiment and data evaluation, C.K.; measurements of instrument, H.Z.; measurements of instrument and data evaluation, N.M.; supervision and writing, N.K. All authors have read and agreed to the published version of the manuscript.  
The present study was partially supported by The Uchida Energy Science Promotion Foundation. This research was also supported by a fund for the promotion of Niigata University KAAB Projects from the Ministry of Education, Culture, Sports, Science and Technology, Japan.  
The authors are grateful to [PERSON] of the Industrial Research Institute of Niigata Prefecture for the measurement of XPS and useful advice. The authors also thank [PERSON] of the Office for Environment and Safety, and [PERSON], [PERSON] and [PERSON] of the Facility of Engineering in Niigata University for permitting the use of ICP-MS, FT-IR, and SEM-EDS.  
The authors declare no conflicts of interest.
# Phase Shift Design in RIS Empowered Wireless Networks: From Optimization to AI-Based Methods
###### Abstract  
Reconfigurable intelligent surfaces (RISs) offer the potential to customize the radio propagation environment for wireless networks. To fully exploit the advantages of RISs in wireless systems, the phases of the reflecting elements must be jointly designed with conventional communication resources, such as beamformers, the transmit power, and computation time. However, due to the unique constraints on the phase shifts and the massive numbers of reflecting units and users in large-scale networks, the resulting optimization problems are challenging to solve. This paper provides a review of the current optimization methods and artificial-intelligence-based methods for handling the constraints imposed by RISs and compares them in terms of the solution quality and computational complexity. Future challenges in phase-shift optimization involving RISs are also described, and potential solutions are discussed.  
artificial intelligence; numerical optimization; resource allocation; reconfigurable intelligent surfaces 23 May 2022  
Accepted: 4 July 2022  
Published: 11 July 2022  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 1 Introduction
In this work, to optimize the final thickness distribution, the weight of the formed part, and to improve the formability of the material, a cold working technique is used which uses an initial plate characterized by a variable thickness profile. The sheet, clamped on a die in the peripheral region by a blank holder, is subjected to the action of a punch that stretches it inside the die. The proposed process can produce an enlargement of the traditional classification of processes that make use of tailored blanks. Therefore, alongside the traditional classification [21] that distinguishes tailored blanks in tailor welded blanks, patchwork blanks, tailor rolled blanks and tailor heat treated blanks, it is possible to add tailored blanks based on removing material. In [22], the reliability of the results due to a numerical simulation on industrial sheet metal forming cases was evaluated. The authors in [23] tested the use of numerical modeling based on the finite element method on a process for superplastic deformation of the sheet with excellent results and the forming limit diagram was analyzed in [24]. The finite element method is widely used in many fields [25; 26]. The proposed paper presents a numerical model based on the finite element method to simulate an axisymmetric stretching of a metal sheet. The sheet is characterized by a variable initial thickness. The resulting strain state was compared with that of a sheet with a constant initial thickness. It was observed that starting from a sheet with variable initial thickness, a lighter final product is obtained, whose final distribution of thicknesses is more uniform than that of the product obtained from a classic stretching process that requires a sheet with constant initial thickness. Encouraging results from an experimental activity carried out on an AA6060 aluminum alloy sheet, whose trend of initial thicknesses was prepared by removing material from a commercial sheet with a constant thickness, allow to note the good agreement with what was theoretically highlighted. The proposed stretchingprocess overcomes the state of the art since it involves processing times shorter than ISF and avoids defects on the surface of the sheet, such as MPF. Finally, it is easy to modify linearly the thickness of the blank using machining. The present work is organized as follows: in Section 2, the numerical model is presented. In Section 3, the experimental tests used to verify the developed numerical model are discussed. In Section 4, the numerical and experimental results are compared. Finally, the conclusions are presented.
# A Feature Construction Method That Combines Particle Swarm Optimization and Grammatical Evolution
## 2 The Proposed Method
### Fitness Calculation  
Each chromosome in grammatical evolution produces a series of artificial features, which are nonlinear functions of existing features. However, an evaluation and a distinction should be made between those sets of features which will yield more in the learning process and those which will yield less. This is accomplished by assessing the appropriateness of these features. In order to be able to compute the fitness of each group of features, the original training set should be reduced using the artificial features that have been produced, and the following steps should be executed for any given chromosome \(p\):  
1. **Denote as \(\mathrm{TO}=\left\{(\overrightarrow{x}_{1}^{\prime},y_{1}),(\overrightarrow{x }_{2}^{\prime},y_{2}),\ldots,(\overrightarrow{x}_{M}^{\prime},y_{M})\right\}\)** the original training set.
2. **Set**\(V=0\) for the penalty factor.
3. **Compute** the mapping function \(g(\overrightarrow{x},p)\) as suggested in Section 2.2.
4. **Set**\(\mathrm{TF}\)=\(\emptyset\) for the modified training set.
5. **For**\(i=1,\ldots,M\), carry out the following steps. 1. **Set**\(\widetilde{x}_{i}=g\big{(}\overrightarrow{x}_{i}^{\prime},p\big{)}\). 2. **Set**\(\mathrm{TF}=\mathrm{TF}\cup(\widetilde{x}_{i},y_{i})\). 3. **If**\(\|\widetilde{x}_{i}\|>L_{\mbox{max}}\), **then**\(V=V+1\), where \(L_{\mbox{max}}\) a predefined positive. value.
6. **End For**.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Expression** & **Chromosome** & **Operation** \\ \hline \(<\)expr\(>\) & 10, 12, 20, 8, 11 & 10 \(\mod 3=1\) \\ \hline \(<\)func\(>\)(\(<\)expr\(>\)) & 12, 20, 8, 11 & 12 \(\mod 4=0\) \\ \hline \(\sin\)(\(<\)expr\(>\)) & 20, 8, 11 & 20 \(\mod 3=2\) \\ \hline \(\sin\)(\(<\)terminal\(>\)) & 8, 11 & 8 \(\mod 2=0\) \\ \hline \(\sin\)(\(<\)xlist\(>\)) & 11 & 11 \(\mod 3=2\) \\ \hline \(\sin\)(x3) & & & \\ \hline \end{tabular}
\end{table}
Table 1: Steps to produce a valid expression from the BNF grammar.  
**Train** an RF \(\mathcal{C}(x)\) with \(H\) processing NODES on TF and obtain the following error:  
\[f_{p}=\sum_{j=1}^{M}\bigl{(}C(\widetilde{x}_{i})-y_{j}\bigr{)}^{2} \tag{1}\]  
**Compute** the final fitness value:  
\[f_{p}=f_{p}\times\Bigl{(}1+\lambda V^{2}\Bigr{)} \tag{2}\]  
where \(\lambda>0\).
# Predicting the Success of Internet Social Welfare Crowfunding Based on Text Information
## 4 Predictor Analysis
As it is shown in Table 8, tolerance of both explanatory variables and control variables is larger than 0.1 while variance inflation factor (VIF) is smaller than 10. Independent variables show no multicollinearity. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **X\({}_{4}\)** & **X\({}_{5}\)** & **X\({}_{6}\)** & **C\({}_{01}\)** & **C\({}_{02}\)** \\ \hline X\({}_{1}\) & 1 & & & & & & \\ X\({}_{2}\) & 0.060 \({}^{**}\) & \(1\) & & & & & \\ X\({}_{3}\) & 0.095 \({}^{***}\) & 0.065 \({}^{**}\) & \(1\) & & & & \\ X\({}_{4}\) & 0.0330 & \(-0.0200\) & 0.0140 & \(1\) & & & \\ X\({}_{5}\) & \(-0.051\)\({}^{*}\) & 0.0100 & \(-0.053\)\({}^{*}\) & \(-0.457\)\({}^{***}\) & \(1\) & & \\ X\({}_{6}\) & \(-0.063\)\({}^{**}\) & \(-0.00500\) & 0.058 \({}^{**}\) & \(-0.0390\) & 0.139 \({}^{***}\) & \(1\) & \\ C\({}_{01}\) & 0.059 \({}^{**}\) & \(-0.00600\) & 0.0210 & 0.166 \({}^{***}\) & \(-0.144\)\({}^{***}\) & \(-0.092\)\({}^{***}\) & \(1\) & \\ C\({}_{02}\) & \(-0.0440\) & \(-0.0240\) & \(-0.0410\) & 0.0190 & 0.069 \({}^{**}\) & 0.088 \({}^{***}\) & \(-0.313\)\({}^{***}\) & \(1\) \\ Y & 0.196 \({}^{***}\) & 0.076 \({}^{**}\) & \(-0.139\)\({}^{***}\) & 0.069 \({}^{**}\) & \(-0.048\)\({}^{*}\) & \(-0.066\)\({}^{**}\) & 0.0190 & \(-0.0210\) \\ \hline \hline \end{tabular}  
\({}^{***}\)\(p<0.001\); \({}^{**}\)\(p<0.01\); \({}^{*}\)\(p<0.05\).  
\end{table} 
Table 6: Pearson correlations of variables. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Model 1** & **Model 2** & **Model 3** & **Model 4** \\ X\({}_{1}\) & 0.2041 \({}^{***}\) & 0.2019 \({}^{***}\) & \({}^{***}\) & \({}^{***}\) \\  & (0.0277) & (0.0277) & \({}^{***}\) & \({}^{***}\) \\ X\({}_{2}\) & 0.0713 \({}^{***}\) & 0.0711 \({}^{***}\) & \({}^{***}\) & \({}^{***}\) \\The adjusted R-squared indices are small, and at least one coefficient of the models is significantly different from 0. There are three possible reasons leading to such a phenomenon. One is that certain important variables might not be included in the model, the second is that there might be a large amount of random data interference in the original dataset [66], and the third is that the model failed to fit the actual data distribution well [67]. Such a relationship between independent and the dependent variables may also be very important, even though it may not explain a large amount of variation in the response [66]. This study aims to build methods to predict the success of Internet social welfare crowdfunding projects based on text information. Through text information extraction, we identified the factors that significantly contribute to project success predictions. The non-ideal, R-squared indices reflected that the multiple linear regression model does not perform well regarding the prediction issue discussed in this study. Therefore, we need to continue looking for prediction models with excellent performance with the help of machine learning.
## 4 Case Study
### The Benchmark Spectral Clustering Method for Time-Series Clustering  
Unlike the \(K\)-means algorithm, which works directly on data points, spectral clustering (SC) uses the eigenvalues and eigenvectors of a similarity matrix to group similar data points into clusters [32; 33].  
The similarity matrix is a positive semi-definite matrix \(\mathbf{A}\in\mathbb{R}^{n\times n}\), where each entry \(a_{ij}\) represents the affinity between data points \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\). A typical SC algorithm initiates with a graph \(G=(\mathcal{T},\mathbf{A})\), with \(\mathcal{T}\) as the set of vertices and \(a_{ij}\) as the weight of the edge that connects \(\mathbf{y}_{i}\) and \(\mathbf{y}_{j}\). The objective of the SC algorithm is to identify the partition of a graph whose edge weights have low values and are contiguous. The connections between the internal vertices are associated with high-similarity indices, and graph partitioning is performed by assigning large edge weights to each cluster and small edge weights to each cluster. Mathematically, this is the problem of finding eigenvectors of the graph Laplacian from the affinity matrix and then clustering the eigenvectors into clusters.  
The following steps were included in the implementation of the SC algorithm [34]:  
1. A similarity matrix is constructed to quantify the similarity between each pair of data points.
2. The eigenvalues and eigenvectors of the similarity matrix are determined.
3. A threshold value is established for the eigenvalue gap to separate data points into distinct clusters.
4. Each data point is assigned to a cluster corresponding to its eigenvector.  
The SC algorithm has several advantages over the \(K\)-means algorithm, including the ability to handle noisy data and data with nonspherical clusters, as well as the ability to visualize high-dimensional data in a lower-dimensional space. However, it is sensitive to the choice of the distance metric and similarity threshold, and there is no objective method for selecting the best threshold. In addition, it is computationally expensive, particularly for extensive datasets. Recent studies have focused on improving the scalability and robustness of spectral clustering for extremely large-scale datasets [35].
## 2 Materials and Methods  
### Characterizations of the Coal and Resulting Coal Char  
A South African bituminous coal was used to produce coal char particles through pyrolysis experiments performed in an adapted configuration of a drop tube furnace. The coal particles were crushed and sieved to a 36-72 \(\upmu\)m size fraction, with a mean diameter of 50 \(\upmu\)m. [PERSON] et al. pointed out the importance of accounting for the size distribution of coal particles when modeling their combustion in an entrained flow reactor [21]. Accounting for the size distribution of the coal particles was also pointed out in [22].  
The coal char particles were then burnt in a slightly modified configuration of the drop tube furnace.  
The proximate and ultimate analyses of the coal and of the resulting coal char are gathered in Table 1.  
The higher heating value of the coal used in the present study was determined equal to 27.3 MJ.kg\({}^{-1}\). The density of the coal char material was evaluated equal to 296.6 kg.m\({}^{-3}\). The specific surface area of the coal char was measured equal to 131,800 \(\pm\) 1300 m\({}^{2}\).kg\({}^{-1}\) according to the precision equal to 1%, as indicated in the operating manual.  
The proximate analyses show that during the pyrolysis experiments applied to the coal particles, their volatile matter and moisture contents are almost totally removed. Consequently, the ash and fixed carbon percentages increase in the resulting coal char particles. The hydrogen and oxygen percentages are slightly lower and the carbon percentage is higher in the coal char particles than in the coal ones.