esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Distribution Pattern and Influencing Factors of Homestays in Chongqing, China
## 4 Discussion  
### Complexity and Spatial Heterogeneity of Influencing Factors of Homestays Distributions  
With the accelerating process of urbanization and the continuous promotion of tourism in Chongqing, the homestay industry is rapidly expanding. In general, the distribution of homestays showed a 'one-pole multi-core' development pattern around the main urban area, which fully confirmed the decisive role of location in the tourism and accommodation industry, and the spatial behavior law of tourists also played an important role. The vast majority of homestays were distributed in cities and towns and surrounding areas, which were more affected by economic factors. The highly concentrated comprehensive resources in the urban center were the core driving force for the development and concentration of urban homestays. They had stronger comprehensive service capacities, comprehensive advantages in transportation, scenic spots, commerce and other aspects, showing diversified functions. Urban homestays pay more attention to urban humanities and culture and modern accommodation services; consumer audiences also tend to be younger. In vast rural areas, homestays are scattered and less in number, which are mainly built by relying on local scenic-spot resources or government policies. Scenic spots in districts and counties, such as Wulong fairy mountain and Shizhu Huangshui National Forest Park, have a good brand effect. The development and improvement of scenic spots produces a large quantity of rigid demands for tourists and accommodation, which has become the core driving force of rural homestay agglomeration. Rural homestays pay more attention to the ecological environment and original ecological experience, but they show a unity of function, which fails to promote the development of the homestay industry to scale and agglomeration. The consumer audience also tends to travel with families with elderly individuals and children.  
In addition, through the OLS quantitative simulation, it was observed that tourism resource endowment, economic development, service industry development, traffic location, consumption demand, and social development conditions had a significant impact on the layout of homestays. The test results of the geographically weighted regression model show that the regression coefficients of various variables have a significant spatial heterogeneity, indicating that there are significant differences in the action intensities of specific factors on different types of regions. For example, economic development plays the strongest role in promoting the development of homestays in the central urban area, while tourism resource endowment affects the regions outside the central urban area more, especially the new area of the main city. This not only verifies the complexity of the spatial differentiation law of homestays, but also increases the difficulty of analyzing its driving mechanisms.
## 2 Designing the \(4\times 1\) Microstrip Square Patch Array Antenna
### Effects from the Length of the Metallic Plates when \(\theta\) = 70\({}^{\circ}\)  
Next, the effects on performance from length \(l_{\rm m}\) of the metallic plates were investigated, as shown in Figure 8. In this case, the length of the metallic plates was varied from 5 mm to 50 mm in increments of 5 mm when the tilting angle was fixed at \(\theta\) = 70\({}^{\circ}\) for maximum gain enhancement. Figure 8b,c shows the input reflection coefficients, and the frequencies and magnitudes at the minimum of the input reflection coefficients. The minimum frequency of the input reflection coefficients increased from 23.894 GHz to 24.156 GHz as \(l_{\rm m}\) increased from 5 mm to 15 mm. When \(l_{\rm m}\) increased from 20 mm to 50 mm, the minimum frequency decreased to within the 24.054 GHz to 24.094 GHz range. The magnitude at the minimum frequency ranged from \(-\)21.96 dB to \(-\)26.08 dB except for \(l_{\rm m}\) = 20 mm. For \(l_{\rm m}\) = 20 mm, the magnitude increased to \(-\)19.6 dB.  
Figure 7: _Cont_.  
Figure 8d,e shows the realized gain characteristics in the +\(z\)-axis direction, and the frequencies and values of maximum gain. The maximum gain frequency ranged from 23.9 GHz to 24.1 GHz, whereas the maximum gain value increased gradually from 12.33 dBi to 18.29 dBi. Figure 8h shows the 3D radiation patterns for \(l_{\text{m}}=50\) mm. As mentioned earlier, the maximum detection range can be doubled by increasing antenna gain by four times (6 dB) according to the radar equation. In this work, a 7 dBi gain enhancement was selected to double the maximum detection range in consideration of additional losses and errors occurring during fabrication. Therefore, a metallic plate length of \(l_{\text{m}}=50\) mm was chosen for gain enhancement of more than 7 dBi, compared to the 4 \(\times\) 1 patch array antenna without the metallic plates where maximum gain was 11.1 dBi.  
Figure 8: _Cont_.  
Figure 7: Effects from tilting angle \(\theta\) of the 4 \(\times\) 1 array antenna appended with two metallic plates when \(l_{\text{m}}=10\) mm: (**a**) the geometry, (**b**) S\({}_{11}\), (**c**) the frequencies and magnitudes at the minimum of S\({}_{11}\), (**d**) realized gain, (**e**) the frequencies and values of maximum gain, (**f**) radiation patterns on the y–z plane for maximum gain frequency, (**g**) radiation patterns on the z–x plane for maximum gain frequency, (**h**) 3D radiation pattern at \(\theta\) = 30\({}^{\circ}\), and (**i**) 3D radiation pattern at \(\theta\) = 70\({}^{\circ}\).  
Figure 8: Effects from length \(l_{\rm m}\) of the two metallic plates on the 4 \(\times\) 1 microstrip square patch array antenna when \(\theta\) = 70\({}^{\circ}\): (**a**) the geometry for \(l_{\rm m}\) = 50 mm, (**b**) S\({}_{11}\), (**c**) the frequencies and magnitudes at the minimum of S\({}_{11}\), (**d**) realized gain, (**e**) the frequencies and values of maximum gain, (**f**) radiation patterns on the y-z plane at maximum gain frequency, (**g**) radiation pattern at z–x planes at maximum gain frequency, and (**h**) the 3D radiation pattern for \(l_{\rm m}\) = 50 mm.
## 2 Materials and Methods
### Simulation  
We use the COMSOL Ray Optics [37] package to simulate the experimental setup shown in Figure 1. The full code used can be found on Github [38]. In these calculations, we consider light propagating in the \(x\)-direction through a region of water that measures \(100\times 100\) cm. Because the speed of the bubbles in the experiment is negligible compared to the speed of light, we ignore the bubbles' motion. We perform simulations for light that is initially vertically polarized and for light with an arbitrary initial state of polarization. In both cases, we consider five initial values for the DOP and run 100 simulations for each DOP. In every simulation, we consider 300 rays that are evenly spaced from \(x=35\) cm to \(x=65\) cm. To ensure that none of the rays can make it through the region of water without hitting bubbles, we create two layers of bubbles spanning the height of domain. The radius of each bubble is determined by sampling a uniform distribution between 0.1 and 1 cm. The \(x\) positions of the bubbles in the first layer are chosen by sampling a truncated normal distribution between 10 and 49 cm, while \(x\) positions of the bubbles in the second layer are selected from a truncated normal distribution between 51 and 90 cm. The gap between the layers is chosen to avoid issues with overlapping the bubbles in COMSOL. In the simulations, we consider only the rays that reach \(x=100\) cm to be detected to mimic the experimental setup. The COMSOL Ray Optics package keeps track of the Stokes parameters of each ray throughout the simulation, so calculating the initial and final DOP for a given simulation can be done by calculating the average \(s_{1}\), \(s_{2}\), \(s_{3}\), and \(s_{0}\) of the detected rays at the beginning and end of the simulation and using Equation (6). In Appendix B it is shown that the intensity distributions from the simulations agree with the experimental measurements.  
As mentioned in Section 1, we also used the COMSOL Ray Optics software to model light passing through atmospheric turbulence. The details of these calculations and the corresponding results can be found in Appendix A.
## 4 Numerical Examples
### Numerical Example 3  
The governing equation of numerical example 3 is represented by Equation (1). For this example, two cases with different initial hydraulic heads are considered. Figure 8 illustrates the schematic of finite confined coastal aquifers. In Case A, the initial groundwater level is considered to be constant, \(h_{s}=\text{constant}\), as indicated in Figure 8a. In Case B, the initial head is considered to be \(h_{s}=ax+h_{msl}\), where \(h_{s}\) denotes the head of the seaside, \(a\) denotes the gradient of the hydraulic head in the seaside, and \(h_{msl}\) denotes the averaged mean sea level. As shown in Figure 8b, the groundwater head may linearly increase as the distance from coastline increases.  
Figure 6: Result comparison of example 2.  
Figure 7: Absolute error at final time.
# A Robust UWSN Handover Prediction System Using Ensemble Learning
## 5 Experimental Results  
### Setup  
Table 1 lists the values of the parameters used in the simulation. The parameter values were selected according to [31; 32]. We conducted two types of experiments. In the first, we supposed that the sensor node had a Global Positioning System (GPS) unit. Thus, the distance between the sensor node and each buoy in the simulation was known, as the buoys were static to their location. We called this type of experiment Exp-I. In Exp-I, the current location of the sensor node was used as a feature in the prediction model in addition to the other features, which were the signal strength, ID of the current cell, and IDs of the neighboring cells. We called the second type of experiment Exp-II. In Exp-II, we supposed that the location of the sensor node was unknown, and that the sensor node was not equipped with a GPS unit. Thus, the signal strength played a vital role in the handover prediction.  
The simulation generated 216,578 different handover events. The collected marine data in the simulation spanned six months (the first six months of 2020). The simulation included 61 buoys and 610 sensor nodes. While the Korea Hydrographic and Oceanographic Agency has only 30 real buoys, the simulation duplicated the marine data of the nonreal buoys from the closest real buoy.  
The simulation framework was developed in the Python programming language. Hence, to ensure results reproducibility, we made the simulation framework and the dataset used in this work available online ([[https://github.com/[PERSON]/UWSN](https://github.com/[PERSON]/UWSN)]([https://github.com/[PERSON]/UWSN](https://github.com/[PERSON]/UWSN)) (accessed on 26 August 2021)). As mentioned above, the signal strength was used as an effective feature in the handover process. The work in [33; 34; 35] considered the computation of the received power with different parameter values. However, they did not consider ocean or seawater environments. Thus, the values of the parameters in this experiment relied on the first equation in [31; 32]. The proposed system is generic and can handle any network configurations.
# Recent Advances on Machine Learning Applications in
## Nomenclature  
**Acronym**: **Full Name**: AKPCA **A**daptive Kernel Principal Component Analysis **ANFIS**: Adaptive Neuro-Fuzzy Inference System **ASA**: Angular Synchronous Averaging **ATWVD**: Automatic Tool Wear Value Detection **BADS**: Bayesian Adaptive Direct Research **BDE**: Binary Differential Evolution **BDT**: Boosted Decision Tree **Bi-RNN**: Bidirectional Recurrent Neural Network **BLSTM**: Bidirectional Long Short-Term MemoryCABLSTM & Convolutional Bi-directional LSTM with an Attention Mechanism CC & Correlation Coefficient CEEMDAN & Complete EEMD with Adaptive Noise CHMM & Continuous Hidden Markov Models CNN-AD & Convolutional Neural Network with Abnormal Detection CRA & Cumulative Recurrent Accuracy CWT & Continuous Wavelet Transform DAE & Deep Autoencoder DBN & Deep Belief Network Deep-FS & Deep Feature Selection DF & Deep Forest DNN & Deep Neural Network DT & Decision Tree DWFs & Derived Wavelet Frames DWT & Discrete Wavelet Transform ECBCA & Extended Convolutive Bounded Component Analysis EEMD & Ensemble Empirical Mode Decomposition EKF & Extended Kalman Filter ELM & Extreme Learning Machine ETR & Extreme Tree Regressor FA & Fractal Analysis FCNN & Fully Connected Neural Network FD & Frequency Domain FDR & Fisher Discriminant Ratio GA & Genetic Algorithm GASF & Gramian Angular Summation Fields GBR & Gradient Boosting Regression GBRT & Gradient Boosting Regression GenSVM & Generalized Support Vector Machine GTB & Gradient Tree Boosting GWO & Gray Wolf Optimization HE & Holder Exponent HEDS & Hilbert Envelope Demodulation Spectra HMM & Hidden Markov Model IBk & Instance-Based k IELM & Improved ELM KBDBN & Knowledge-Based DBN & Kernel Density Estimation KELM & Kernel Extreme Learning Machine K1 & Kernel Interpolation k-NN & k-Nearest Neighbors KPCA-IRBF & Radial Basis Function Based Kernel Principal Component Analysis LR & Linear Regression LSSVM & Least Mean Square SVM LSTM & Long Short-Term Memory MAML & Model-Agnostic Meta-Learning MFCC & Mel-frequency Cepstrum Coefficients MI & Mutual Information mRMR & Minimum Redundancy Maximum Relevance MSST & Multivariate Synchrosqueezing Transform NSE & Normalized Square Error OA & Order Analysis OLAM & Optimal Linear Associative Memory OS-ELM & Online Sequential Extreme Learning Machine PAA & Piecewise Aggregation Approximation PCA & Principal Component Analysis PCC & Pearson Correlation Coefficient
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 6 Carbon-Based Nanomaterials  
Carbon-based nanomaterials can enhance the photocatalytic oxidation of pollutants due to their high adsorption capacity [149]. The ability to absorb pollutants on the surfaces of carbon-based photocatalysts is influenced by the physical and chemical properties of the nanomaterials, such as specific surface area, pore structure, and surface characteristics. Adsorption also depends on the target pollutant; specifically, carbon-based nanomaterials  
Figure 16: (a) UV-vis absorption spectra of undoped TiO\({}_{2}\), N-TiO\({}_{2}\), C-TiO\({}_{2}\), and C-N-TiO\({}_{2}\) calculated at (A) 400 and (B) 500 \({}^{\circ}\)C. (b) Degradation of methylene blue using no catalyst (A), undoped TiO\({}_{2}\) (B), N-TiO\({}_{2}\) (C), C-TiO\({}_{2}\) (D), and C-N-TiO\({}_{2}\) (E) [148].  
Figure 15: Degradation of p-xylene using P25 and USPNT [85].  
exhibit a high affinity for volatile organic compounds (VOCs) due to various interactions, including coordination bonds, hydrogen bonding, and electrostatic interactions [150].  
Additionally, carbon-based nanomaterials can reduce the recombination rate by forming heterojunctions, acting as charge mediators, and enhancing charge mobility. Typically, these nanomaterials are coupled with semiconductors like TiO\({}_{2}\) to improve the photocatalytic oxidation of pollutants by leveraging their high specific surface area and minimizing charge carrier recombination [151]. Structures such as activated carbon, carbon nanotubes, and reduced graphene oxide can absorb a broad spectrum of light, increasing the generation of electron-hole pairs at lower energy levels than a pristine photocatalyst and narrowing the band gap. This results in higher photocatalytic activity and potential applications under visible light.  
Recently, research on carbon-based nanomaterials has surged, particularly focusing on carbon nanotubes and graphene oxides, which are among the most extensively studied. However, other carbon-based adsorbents (OCBAs) have also been investigated for pollutant removal (Figure 17) [152].
## 3 Large-Scale Photonic Switch Architectures  
We review four architectures-Benes, dilated Benes, double layer network, switch and select--that are widely discussed in literature. In the following, we will use the words Stage to refer to a stack of MZIs in the same line, and interconnection to describe the set of waveguides connecting two stages.  
The Benes network [13] is a recursive architecture. To construct \(N\times N-\)Benes, two blocs of \(N/2\times N/2-\)Benes are put in the center, and themselves connected to the input/output stages with the interconnection shown in Figure 2. Although it has the smallest overall number of MZIs and stages amongst the four different architectures studied in this paper, it generates first-order crosstalk because each MZI is crossed by two signals, drastically decreasing the overall performance.  
Figure 1: Unwanted powers are denoted in blue, \(m\) and \(x\) represent, respectively, the factors of the leaked power portions in the MZI (**a**) and (**b**), and waveguide crossing (**c**).  
In order to mitigate the crosstalk in Benes, [PERSON] and [PERSON] have proposed a dilated Benes topology [14]. The recursion of this architecture is similar to the Benes one, but only one signal travels through any MZI (Figure 3), so that the first-order crosstalk can be fully avoided.  
The double-layer network (DLN) was proposed by [PERSON] and [PERSON] [15]. A \(N\times N-\)DLN consists of two layers, each one composed of two \(N/2\times N/2-\)DLN and connected to the input/output stages with the interconnection shown in Figure 4. The number of stages is as low as in Benes, but the total number of MZIs increases rapidly for a large value of \(N\).  
The switch and select (S&S) consists of \(1\times N\) and \(N\times 1\) switch units, shown in Figure 5 [16]. To build a \(N\times N\) structure, \(N\ (1\times N)\) and \(N\ (N\times 1)\) blocks are used and connected via a central interconnection. This topology has the lowest crosstalk caused by MZIs. Nevertheless, the calculation  
Figure 3: (a) \(N\times N-\)Dilated Benes, (b) \(4\times 4-\)Dilated Benes.  
of the number of waveguide crossings exhibits quadratic growth. Table 1 shows the number of MZIs and waveguide crossings we find for the worst path, and for each topology.
## 1 Introduction
If the field survey is conducted with long time intervals, new landslides would not be detected. The development of Synthetic Aperture Radar (SAR) technology has opened new opportunities for identifying potential landslides in alpine forest regions [33]. SAR satellites have various advantages, such as being able to penetrate vegetation, continuously observe the earth surface with all-day and all-weather capability, and collect high-resolution SAR images with a short time interval [34]. With continuous SAR images, Interferometric SAR (InSAR) techniques, including Differential InSAR (D-InSAR) [35; 36], Permanent Scatterer (PS) InSAR [37; 38], and Small Baselines Subset (SBAS) InSAR [39; 40], can be applied to analyze ground deformation and identify potential landslides with high precision, especially in alpine forest regions [41; 42]. For example, [PERSON] et al. [43] identified 1012 active landslides and 64 new potential landslides within an area of 4470 km\({}^{2}\) in the Calabria region, south Italy, using ENVISAT data. [PERSON] et al. [28] performed SBAS-InSAR analysis on 40 scenes of Sentinel-1 SAR data and identified 72 active landslides in Wenchuan, China. The InSAR technique has proved to be a powerful tool for landslide studies [44; 45]. However, previous studies mainly focused on landslide deformation analysis [46; 47], potential landslide detection [48; 49], and improving the InSAR method [50]. Only a few studies have discussed the role of the InSAR technique in understanding the development of potential landslides at the regional scale. In summary, as SAR satellites support continuous observation of an area and can detect ground deformation with high precision, InSAR analyses would contribute to the construction of a complete regional landslide inventory and, therefore, provide an opportunity to improve the understanding of landslide development at the regional scale, especially in alpine forest regions. Accordingly, this study performed SBAS-InSAR analysis and hotspot analysis in an attempt to build a new landslide inventory for Xiaojin county, which is a typical alpine forest region in western Sichuan Province, China. The new landslide inventory was then compared with a landslide inventory built using field survey data to explore the effectiveness of the InSAR technique in understanding landslide development.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 2 Measurement Algorithms
### Common Background on Synchrophasor Measurements
Having available the phase \(a\), \(b\) and \(c\) synchrophasors, the corresponding positive, negative and zero sequence synchrophasors (\(\bar{X}_{+}(t)\), \(\bar{X}_{-}(t)\) and \(\bar{X}_{0}(t)\), respectively) can be obtained as follows by means of the Fortescue transformation (here the unitary formulation is considered):  
\[\left[ \begin{array}{c}\bar{X}_{+}(t)\\ \bar{X}_{-}(t)\\ \bar{X}_{0}(t)\end{array} \right]=\left[ \begin{array}{c}X_{+}(t)e^{i\varphi_{ +}(t)}\\ X_{-}(t)e^{i\varphi_{-}(t)}\\ X_{0}(t)e^{i\varphi_{0}(t)}\end{array} \right]=\frac{1}{\sqrt{3}}\left[ \begin{array} []{ccc}1&\bar{\alpha}&\bar{\alpha}^{2}\\ 1&\bar{\alpha}^{2}&\bar{\alpha}\\ 1&1&1\end{array} \right]\left[ \begin{array}{c}\bar{X}_{a}(t)\\ \bar{X}_{b}(t)\\ \bar{X}_{c}(t)\end{array} \right] \tag{6}\]  
where \(\bar{\alpha}\triangleq e^{i2\pi/3}\). Among the symmetrical components, the positive sequence term has by far the highest magnitude, since three-phase systems are weakly unbalanced during regular operation, in particular as far as the transmission grid is considered; for this reason, many applications rely on a positive sequence representation of the power system. It is worth noting that processing the single-phase signals while using the definition (4) may lead to different frequency measurements for each of the phases; conversely, as from [7], a unique frequency (and ROCOF) value should be provided for each three-phase quantity. On the one hand, this can be obtained through additional processing (e.g., averaging) on the three frequency estimates [27]; on the other hand, computing a shared frequency value reminds that, from a physical point of view, a three-phase quantity is not a mere set of three independent single-phase quantities. In this regard, a combined processing of the three signals would enable a direct estimate of frequency and ROCOF other than the symmetrical components of the synchrophasors, and in particular of the positive sequence term. Moreover, native three-phase PMU algorithms may exploit that, under regular operation, three-phase quantities are weakly unbalanced: this may be beneficial to improve the quality of the estimates while reducing the computational cost.
## 3 Materials and Methods
### Experimental Design  
Calibration of the SPME fiber was conducted by exposing the fiber inside a glass liner to the air with an acetic acid concentration of 617 \(\upmu\)m\({}^{-3}\) at 25 \({}^{\circ}\)C generated by the standard gas generation system. Retraction depth was fixed at 1.7 cm. The inner diameter of the glass liner (a standard GC liner recommended for SPME injections) was measured using a digital microscope (CC-HDMI-CD1, New Haven, CT, USA) and was equal to 0.844 mm. As an adsorptive fiber [35], the SPME fiber required testing of different sampling times to make sure that the fiber did not reach its sorptive capacity. Thus, sampling times of 1, 4, 8, and 12 h were examined to determine the longest sampling time before the sorptive capacity limit of the fiber was reached. All experiments were completed in triplicate. To improve the S/N ratio, quantification of acetic acid was performed using SIM mode at \(m/z\) 60.0. Limits of detection (LOD) and quantification (LOQ) were calculated by estimating concentrations corresponding to signal-to-noise (S/N) ratios 3:1 and 5:1, respectively.