esa-sceva/satcom-chunk-collection huggingface dataset:
# An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing
## 2 Related Work
### Accessibility Indicators  
The accessibility indicator represents the ease at which food can be accessed between a residential address and the food store [30; 31], with some early studies basing this measure on the distance between them as a straight line or Euclidean distance. The use of straight-line distances alone is insufficient to assess food accessibility because of different road network conditions and travel modes. The road network-based travel time evaluation method optimizes the accuracy of food accessibility based on different travel patterns [30].  
Given the rate of private car ownership in the city and the convenience of public transportation, travel by private car [12; 32] and by public transportation [33] are the two most common modes. Other studies have identified actual travel impedances (e.g., one-way streets, speed limits, restricted turns, and traffic volumes) [34; 35]. There are both spatial and temporal dimensions to restrictions to food access [36; 37]; [PERSON] and [PERSON] [31] argued that travel time should be considered when analyzing the accessibility of healthy foods. Travel time related to the road network does not adequately consider the time spent by those using public transport during transport connection delays. [PERSON], [PERSON] [38] measured the accessibility of healthy foods by comparing the length of travel at different time levels. [PERSON] analyzed the spatial and temporal constraints on the accessibility of healthy foods [30]. [PERSON] compared the weekday and weekend access to healthy foods by children [39]. Although these efforts have contributed to the understanding of healthy food accessibility, modeling large urban road networks is a monumental task, which complicates the process of assessing urban food accessibility.
## 2 Methodology
### Numerical Procedures
#### 2.1.3 Numerical solution  
ANSYS FLUENT 2020R1 was used to solve the numerical model; as described in Section 3, the laser heat source is defined using a user-defined function (UDF) containing all the laser parameters, including laser power, laser spot diameter, and sintering speed. The numerical procedure followed in this study is described by the flow chart shown in Figure 4. The computational domain (the mesh) is shown in Figure 5, where the ANSYS Mechanical Meshing Tool was used. Very fine discretization was used for the powder layer and slightly coarse was used for the unscanned powder and the baseplate. To confirm this, the results obtained from the model due to poor mesh quality were avoided. Three different levels of mesh discretization (A, B, and C) were considered in this study. The element type used in mesh discretization is hexahedral element type. The number of elements for mesh A, B, and C are 280,576, 1,000,576, and 1,540,576, respectively. The mesh density analysis was summarized in Table 2. The maximum temperature values were 2376.3 K and 2379.4 K for the last two meshes (levels) with a difference of 0.13%, which is considered low. Therefore, any discretization after level C would be valid and give accurate results, and therefore, the third level mesh size (2 \(\upmu\)m discretization) was used in this study. Additionally, it is worth mentioning that the time step size used in this study was 0.00001 s, while the number of iterations per time step was 20 iterations/time step. These values were selected and tested to obtain more accurate results for the temperature and sintering contours. Moreover, all measurements were performed when the laser beam temperature became stable.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Baseplate** & **Powder Layer** \\ \hline Length (mm) & 4 & 3 \\ Width (mm) & 2.5 & 1.5 \\ Thickness (mm) & 1 & 0.022 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model dimensions.  
Figure 4: Flowchart of the model solution procedure.
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 3 Design and Results
### Metalens Design
In addition, the high refractive index region obtained with the metalens can be used to change the EM wavefronts from spherical to approximately planar ones, resulting in a more confined radiation pattern and higher directivity. This is explained by considering that the phase velocity of the wavefront in the central region is smaller than that of the wavefront edges due to the higher refractive index metalens. The approximated equivalent circuit reported in Figure 4b models the proposed unit cell behavior. By exploiting the Advanced Design System (ADS) [34] and making use of the tuning module, the following set of values, \(L=0.22\) nH, \(C_{1}=1.6\) pF, \(C_{2}=0.3\) pF, \(C_{3}=1.2\) pF, \(C_{4}=0.55\) pF, and \(R=2.625\)\(\Omega\), have been identified, leading to a modulus of the scattering parameter \(|S_{11}|\) in good agreement with that of Figure 7a. \begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **Description** & **Value (mm)** \\ \hline \(L_{x}\) & L-element length along x-axis & 1.40 \\ \(L_{y}\) & L-element length along y-axis & 2.00 \\ \(w\) & Metal strip width & 0.50 \\ \(g_{x}\) & Distance between two adjacent metal elements along x-axis & 0.40 \\ \(g_{y}\) & Distance between two adjacent metal elements along y-axis & 0.25 \\ \(dx\) & Lattice length along x-axis & 6.00 \\ \(dy\) & Lattice length along y-axis & 6.60 \\ \(t\) & Substrate thickness & 0.762 \\ \(t_{c}\) & Metal strip thickness & 0.035 \\ \hline \end{tabular}
\end{table} 
Table 2: Geometrical parameters of the L-shaped unit cell.
## 2 Materials and Methods
### Data Sources  
The nighttime light data used in this paper include the DMSP/OLS and NPP-VIIRS datasets. The former was downloaded from [[https://www.ncei.noaa.gov/products/dmsp-operational-linescan-system](https://www.ncei.noaa.gov/products/dmsp-operational-linescan-system)]([https://www.ncei.noaa.gov/products/dmsp-operational-linescan-system](https://www.ncei.noaa.gov/products/dmsp-operational-linescan-system)) (accessed on 6 August 2021) and the latter from https://eogdata. mines.edu/products/vnl/ (accessed on 6 August 2021), respectively. The nighttime light data were preprocessed to obtain an annual value of a 1 km resolution. The two datasets were integrated to obtain long-term datasets [64; 65; 66]. Population data and built-up land data were downloaded from the Global Human Settlement Layer (GHSL) with a resolution of 1 km. The built-up land was residential and non-residential. We regard the non-residential land as public built-up land in this paper. The enhanced vegetation index (EVI) was downloaded from the Global Monthly MODIS data (MOD13A3) with a resolution of 1 km and processed as the annual value of the study area. The Land Surface Temperature dataset was downloaded from MODIS with a resolution of 1 km (MOD11A2), including an average of 8 days of daytime and nighttime data. The temperatures in June, July, and August and December, January, and February were preprocessed to obtain the summer and winter mean temperatures, respectively. The administrative division data for the study area were downloaded from the Resource and Environment Science and Data Center (RESD) ([[https://www.resdc.cn/](https://www.resdc.cn/)]([https://www.resdc.cn/](https://www.resdc.cn/))) (accessed on 26 August 2021) and the Ministry of Land, Infrastructure, Transport and Tourism (MLIT) ([[https://nlftp.mlit.go.jp/ksj/](https://nlftp.mlit.go.jp/ksj/)]([https://nlftp.mlit.go.jp/ksj/](https://nlftp.mlit.go.jp/ksj/))) (accessed on 2 September 2021) Administrative divisions for the study areas were focused on 2021.P.
# An Overview of Quantum Machine Learning Research in China
## 4 Innovation Aspect Analysis
### Algorithm/Model Design  
Innovation in model design refers to either the proposal of originally innovative QML algorithms or the development of new frameworks or structural components for a certain QML algorithm model. These new algorithms/models can outperform the conventional ones in terms of computation accuracy and/or efficiency. Some relevant research cases are shown below.  
In an original innovation work, [PERSON] et al. first proposed a QPLS regression algorithm as a quantum version of classical PLS algorithm. By investigating time and space complexity, they have theoretically demonstrated that the QPLS can realize exponential speed-ups over classical PLS on the independent variable dimension, the dependent variable dimension, and the number of variables [41].  
[PERSON] et al. proposed a computationally resource-efficient QCNN model, which employed amplitude encoding and quantum alternating operator ansatz to construct the quantum convolutional layer. The complexity of either the forward or the backward propagation process in such a quantum convolutional layer is much lower than that of the convolutional layer in a classical CNN model, and hence the computational resources required for the convolutional layer can be significantly reduced [51].  
In the QSAM model proposed in the aforementioned study [38], the three important model components, namely, query, key, and value, were obtained by a method based on amplitude phase decomposition measurement. This new framework can lead to fewer parameters in the model while achieving high performance on different datasets. Upon two natural language processing datasets, Yelp and IMDb, this new QSAM model can acquire accuracy values of 91.03% and 88.35%, surpassing both classical self-attention neural network and quantum self-attention neural network models. Upon the MNIST dataset, the new QSAM model can acquire a classification accuracy of 82.22%, higher than that of a QCNN model.  
Also, in the field of natural language processing, the authors of the work [53] mentioned above designed a novel quantum depth-wise convolution structure in their QCNN model. The new QCNN model can integrate word-level and sentence-level features while reducing the number of parameters in the model and the computational complexity. On the RP dataset, this QCNN model achieved an accuracy of 96.77%, significantly higher than that of a normal QSANN model.  
[PERSON] et al. proposed a novel hybrid quantum classical framework called variational shadow quantum learning (VSQL). This framework used variational shadow quantum circuits to extract classical features in a convolution way and then utilized a fully connected neural network to complete classification tasks. This method can greatly reduce the number of parameters to facilitate the training process of quantum circuits. For a classification testing on the MNIST dataset, a single-layer classical neural network obtained an accuracy of 86.36% with nearly 8000 parameters, while the VSQL model achieved an accuracy of 87.39% with only about 900 parameters [85].
# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 1 Introduction
To the best of our knowledge, this study is the first to employ a multi-objective SOS algorithm to find the best possible unsupervised feature combination that maximizes clustering performance while decreasing the total number of selected features for a given set of data. To show the suggested method's robustness and dependability, it is evaluated using popular datasets from benchmark datasets. The results obtained are compared with the current approaches for both datasets, and the contribution related to the solution quality is given. The results of the study demonstrated that the proposed method performed better in terms of its capacity to provide acceptable outcomes, which included both an improvement in clustering performance and a reduction in the number of selected features. The robustness of this method is demonstrated by the better results it yields for both datasets. This work also examines and applies many SOS algorithm variants. The findings of these algorithms are compared with one another, and their benefits and drawbacks are identified. The rest of the paper is organized as follows: Section 2 presents a review of related works, covering the background of the SOS algorithm, global-search unsupervised feature-selection algorithms based on SOS methods, and the clustering algorithm utilizing SOS algorithms. Section 3 outlines the proposed methods for this study. Sections 4 and 5 detail the experimental settings and results, respectively. Finally, the conclusion of the work is provided in Section 6.
## 4 Case Studies
### A Numerical Case Study on a Synthetic Dataset of Cylinders
#### 4.2.3 Reference MD Space Based on Healthy and Defective Samples  
A two-dimensional MD space is constructed using the reference MD space based on both healthy and defective samples. Similar to Section 4.1.3, the constructed MD space is divided into four regions in terms of reliability by two linear thresholds.  
The two overlapping areas covered by the two thresholds are A1 and A2 as healthy area and defective area, respectively. Therefore, the identified points located within the areas A1 and A2 are considered highly reliable. As can be seen from the classification results presented in Figure 13, almost all the samples in the areas A1 and A2 are correctly identified. Thus, enhancing the classification accuracy in the less reliable areas of A3 and A4 by considering the use of a nonlinear boundary is of great significance.  
Combining the obtained two-dimensional SMDs (SMD-R1 and SMD-R2) in an RF-based classifier further increases the classification accuracies to 99.63% and 99.16% for the training and testing datasets, respectively. The classification results of training and testing samples are presented in Figure 14. It is worth noting that the entire two-dimensional MD space is clearly divided into two areas based on a nonlinear boundary--Green (healthy) and Blue (defective)--instead of the four areas in Figure 13.  
To obtain an average classification result for the three different scenarios, 20 independent runs for each were performed. The mean values and standard deviations for training accuracy, testing accuracy, and the number of screened features are summarized in Table 4 below. Interestingly, only an average value of two features is filtered for SMD-R2, implying the significant impact induced by the change of reference MD space. A slight decline of accuracy can be found using \(\mathbf{IMCS^{BPSO}_{PSO}}\) (SMD-R1+SMD-R2) compared to \(\mathbf{IMCS^{BPSO}_{PSO}}\) (SMD-R1); this is due to the weak identifiability of the two areas of A3 and A4. Thus, the introduction of a two-dimensional nonlinear boundary enables us to maximize classification performance. A mean value of 99.02% \(\pm\) 0.20 on the testing samples was obtained using \(\mathbf{IMCS^{BPSO}_{RF}}\) (SMD-R1+SMD-R2).  
Figure 13: (**a**) Classification on the training dataset; (**b**) classification on the testing dataset.
## 3 Results and Discussion
### Comparison with Reference Methodologies  
In this study, our focus was also on comparing the primary advantages and drawbacks of our proposed methodology with the SPME technique, which stands out as the most widely used technology for HS analysis and serves as a suitable benchmark; additionally, the HS-SPME technique is renowned for its outstanding selectivity, sensitivity, ease of handling, and versatility, particularly in the analysis of volatile and semi-volatile compounds by GC-MS. Figure 4 depicts the comparison of the response obtained from the HS-BA\(\upmu\)E(CN1) against HS-SPME(PDMS/DVB) methodologies, followed by GC-MS analysis of three prevalent BVOCs (\(\upalpha\)-pinene, 1,8-cineole, and thymol), all conducted under similar experimental conditions. This comparison aims to provide a visual representation of how the two methodologies perform relative to each other in terms of detecting and quantifying the selected BVOCs. Hence, if the responses obtained in the analysis of BVOCs are consistent between the two methodologies, it can be inferred that both are equivalent, generating similar and proportionate results. However, by observing the figure, it becomes apparent that the HS-SPME(PDMS/DVB) methodology consistently demonstrates an average order of magnitude approximately fifteen times higher than that of the HS-BA\(\upmu\)E(CN1) methodology, as evidenced by the slopes achieved. Apart from the various factors such as interaction and specific affinity, as well as potential synergistic effects between the chosen sorbent phases and the target BVOCs, the differences in response observed for each monoterpene under study may be attributed to the distinct sample introduction approaches by GC-MS for both methodologies, as was discussed in previous work [7]. While the HS-SPME(PDMS/DVB) methodology relies on TD, wherein all the microextracted sample is analyzed, the HS-BA\(\upmu\)E(CN1) methodology is based on LD, where only a small portion (1/100) of the sample is analyzed. This difference influences the response of each methodology. Moreover, assuming that both analytical technologies align with GAC principles or that any inherent differences are negligible, we can assert that the HS-BA\(\upmu\)E methodology is significantly more cost-effective and comprehensive; this last aspect is due to the ease of interfacing with various instrumental systems. From a practical standpoint, the HS-SPME methodology, based on TD, appears to be more sensitive and user-friendly, although more energy-consuming, while the HS-BA\(\upmu\)E methodology, relying on LD, offers the distinct advantage of enabling sample reanalysis, which is often crucial whenever repetition and data confirmation are mandatory. Finally, the BA\(\upmu\)E methodology proves to be more suitable for routine analysis compared to SPME; while the former merely necessitates a straightforward magnetic plate with multiple positions, the latter demands a costly CombiPAL autosampler.
# Energy of Pythagorean Fuzzy Graphs with Applications
###### Abstract  
Pythagorean fuzzy sets (PFSs), an extension of intuitionistic fuzzy sets (IFSs), inherit the duality property of IFSs and have a more powerful ability than IFSs to model the obscurity in practical decision-making problems. In this research study, we compute the energy and Laplacian energy of Pythagorean fuzzy graphs (PFGs) and Pythagorean fuzzy digraphs (PFGs). Moreover, we derive the lower and upper bounds for the energy and Laplacian energy of PFGs. Finally, we present numerical examples, including the design of a satellite communication system and the evaluation of the schemes of reservoir operation to illustrate the applications of our proposed concepts in decision making.  
Keywords:Pythagorean fuzzy graphs (PFGs); energy; Laplacian energy; satellite communication system; schemes of reservoir operation +
Footnote †: journal: Mathematics
## 2 Materials and Methods
### Characterization of Synthesized AuNPs  
The formation and stability of AuNPs in aqueous solutions were initially characterized by UV-visible (Shimadzu UV-1650, Shimadzu, Kyoto, Japan) spectrophotometer in a wavelength range from 200-800 nm. FTIR spectra were recorded at room temperature on a spectrophotometer (PerkinElmer, Waltham, MA, USA). The AuNPs were dried at 60\({}^{\circ}\) C for 4 h and mixed with KBr to form a round disk suitable for FTIR measurements. The structural characterization and the crystalline nature of AuNPs were determined by X-ray diffractometer (diffractometer with Philips(r) PW 1830 X-ray generator, Philips, Amsterdam, The Netherlands) operating at a voltage of 40 kV and current of 20 mA with Cu Ka radiation (\(\lambda\frac{1}{4}0.1542\) nm) for the freeze-dried AuNPs. The surface morphology of AuNPs was investigated by Scanning Electron Microscopy (SEM), and the samples were prepared by placing a drop over a carbon coated grid and allowing drying before measurement on a Model JFC-1600 (JEOL USA, INC., Peabody, MA, USA). SEM instruments were operated at an accelerated voltage at 20 kV. The morphology of the nanoparticles was analyzed using the images obtained with a JEOL 3010 HRTEM with an accelerating voltage of 100 kV. A thin film of the sample was prepared on a carbon-coated copper grid by dropping a tiny amount of the sample on the grid and drying it under a lamp. A Malvern Zetasizer Nano ZS (Malvern Instruments Ltd., London, UK) Merck 2423 instrument was used to measure the zeta potential.
## 3 Results and Discussion  
### Data Attribute Reduction  
Using fuzzy rough monotone dependence (FRMD) algorithm to do the data attribute reduction process, BOD is taken as the decision attribute, and components in Table 2 are taken as the conditional attributes. The membership function degrees of each conditional attribute to the BOD are shown in Figure 2. Among them, conditional attributes whose membership degrees are equal to 0 have been discarded.  
Figure 3 shows the membership degrees between BOD and the components parameters; the conditional attributes which have zero degree are not shown in Figure 3. Nine attributes are shown in Figure 3, but we can clearly see that the degrees of Xnd and _TSS_ are much closer to zero than others. Therefore, for reducing the complexity of the system, those two attributes were not taken into account in the next simulation. The remaining seven attributes were taken as the inputs of the ELM soft sensor.
# A New Orthogonal Least Squares Identification Method for a Class of Fractional Hammerstein Models
## 3 Identification Algorithm  
The identification problem of sparse vector \(\boldsymbol{\theta}\) can be formulated as  
\[\hat{\boldsymbol{\theta}}=\arg\min\|\boldsymbol{\theta}\|_{0},\text{ s.t. }\|\boldsymbol{Y}-\boldsymbol{\Phi}\boldsymbol{\theta}\|^{2}\leq\varepsilon, \tag{16}\]  
where \(\|\boldsymbol{\theta}\|_{0}\) denotes the \(l_{0}\) norm of the vector \(\boldsymbol{\theta}\), and \(\varepsilon\) is the tolerance.  
The Orthogonal Matching Pursuit (OMP) algorithm [25] is a typical greedy method used to solve this type of optimization problem. However, it requires computing the inverse of the information matrix in each iteration, which leads to a high computational cost, particularly when the matrix dimension is large. Additionally, using this method may result in an ill-conditioned solution. In this paper, we aim to address these problems by employing the Householder transformation strategy.
# The Influence of the Load Term of the Strain Energy Sensitivity in the Shell Shape Optimization
###### Abstract  
The sensitivity method is a type of effective shape optimization method and the strain energy is a frequently used objective function. The change of a surface shape inevitably causes a change in its quality. However, the influence of the load term on strain energy sensitivity has not been considered in many studies. In order to make the influence clear, the formula of the strain energy sensitivity including the load term was first derived. Secondly, the influence of the sensitivity load term on the structural evolution was studied by using different optimization schemes. The results showed that the load term of strain energy sensitivity accounts for a small proportion of the whole strain energy sensitivity, but it still has a cumulative impact on the structural evolution, and the shell thickness has an impact on the role of the load term of the strain energy sensitivity. The load term could influence the rate of increase in the structural weight and had an impact on the shape change to some extent. Therefore, for general construction projects, the strain energy sensitivity should be with the load term if the shell thickness is very thin or a higher performance of the structure is required.  
load term; shape optimization; structural weight; sensitivity; thin shell +
Footnote †: journal: Applied Sciences