esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Neural Network Model  
The backpropagation (BP) neural network model integrates principles from mathematics, neuroscience, and computer science to emulate aspects of human brain functionality [38]. It establishes nonlinear mappings between input and output layers, enabling machine learning and the approximation of complex continuous functions [39]. For the coal quality geophysical logging prediction model, the BP network comprises three core layers: input, hidden, and output. The hidden layer, positioned between input and output layers, processes data without relying on predefined functional relationships to describe input-output mappings (Figure 2). Input variables (X: x\({}_{1}\), x\({}_{2}\),..., x\({}_{n}\)) include lateral resistivity (LLD), natural gamma (GR), density (DEN), apparent resistivity (\(\rho_{\rm{s}}\)), short-spaced gamma-gamma (CGS), and acoustic transit time (AC). Output variables (Y: y\({}_{1}\), y\({}_{2}\),..., y\({}_{l}\)) predict coal quality parameters: moisture (M\({}_{\rm{ad}}\)), ash yield (A\({}_{\rm{d}}\)), volatile matter (V\({}_{\rm{d}}\)af), fixed carbon (FC), total sulfur (S\({}_{\rm{t,d}}\)), and calorific value (Q\({}_{\rm{gr,ad}}\)).  
The hidden layer (S) mediates information transfer to the output layer (Y). The neural network's output is mathematically expressed as [40]  
\[Y\ =\ g[f(Xh_{1})h_{2}] \tag{3}\]  
The functions g and f represent activation functions within the BP neural network model. To enhance the algorithm's capacity for nonlinear learning, tanh and sigmoid are typically selected for g and f, respectively [40].  
A two-sample independent \(t\)-test evaluates whether the difference between the mean measured and predicted values of coal quality parameters is statistically significant relative to their populations. The \(t\)-test statistic is calculated as [40]  
\[t=\frac{Y_{1}-Y_{2}}{\sqrt{\frac{(n_{1}-1)S_{1}^{2}+(n_{2}-1)S_{2}^{2}}{n_{1}+ n_{2}-2}}\Big{(}\frac{1}{n_{1}}+\frac{1}{n_{2}}\Big{)}} \tag{4}\]  
In the formula, Y\({}_{1}\) and Y\({}_{2}\), S\({}_{1}\)\({}^{2}\) and S\({}_{2}\)\({}^{2}\), n\({}_{1}\) and n\({}_{2}\) are the average values, variances, and sample sizes of measured and predicted coal quality parameters, respectively.
# Formation and Failure Mechanism of the Xinfangzi Landslide in Chongqing City (China)
## 3 Deformation History of the Xinfangzi Landslide  
According to the on-site investigations, the deformation history of the Xinfangzi landslide can be divided into three stages.  
_The first stage of deformation_. A large number of cracked rocks identified as landslide residues are distributed in the study area, with a large thickness. These collapsed Quaternary deposits are located on the top of the fractured rock and the cracked slot formed by the ancient landslide. This indicates that the landslide has experienced many large-scale sliding events since the Holocene, and the Xinfangzi landslide occurred on the basis of the reactivation of the ancient landslide.  
_The second stage of deformation_. During the flood season in 1988, large-scale overall slip occurred in the study area. The left side of the landslide slide about 1.5-3.5 m downward (the same position as the new Xinfangzi landslide; Figure 2). A sliding mass of about 5000 m\({}^{3}\) was pushed out at the shear outlet of the landslide, damaging five residential houses on the slope and blocking the G210 national road.  
_The third stage of deformation_. After several days of continuous heavy rainfall (Figure 3), the trailing edge of the landslide began slow creep deformation on 28 June 2020. At about 2:00 a.m. on 1 July 2020, the landslide slipped as a whole. A sliding mass of about 2000 m\({}^{3}\) at the front edge blocked the G210 national road. At the same time, the loosening and deformation of the right boundary of the landslide damaged one house.  
Figure 3: Cumulative rainfall prior to the occurrence of the Xinfangzi landslide.
## 4 Shield Tunnel Load-Bearing Performance Analysis by Full-Scale Test  
### Loading Scheme of Test  
Considering the operability of the full-scale test, three rings of the shield tunnel are selected as the research object. The external loads applied on the shield tunnel are zoned for design, and 24 loading points are arranged to simulate the soil pressure and the ground surface loading on the shield tunnel, as shown in Figure 19. In the test, 24 loading points are symmetrically divided into three groups: SP1 is the vertical soil pressure at the top of the tunnel and the foundation reaction at the bottom of the tunnel; SP2 is the lateral pressure on the shield tunnel; and SP3 is the transitional pressure. SP2 is calculated by the vertical pressure SP1 and the lateral pressure coefficient \(\lambda\). It should be noted that the lateral pressure coefficient \(\lambda\) represents the ratio of horizontal pressure to vertical pressure and is set as 0.7 in the test. SP3 is calculated by taking half of the sum of SP1 and SP2. The soil layer around the tunnel is shown in Figure 10. Therefore, the loads in the test for the tunnel buried depth of 20 m are as follows: SP1 = 356 kN, SP2 = 249 kN, and SP3 = 303 kN.  
Figure 17: Convergence deformation curves of the tunnel under different loading ratios: (**a**) horizontal deformation value and (**b**) vertical deformation value.  
Figure 18: Tunnel convergence deformation normalization diagram under different loading ratios: (**a**) horizontal variation value and (**b**) vertical variation value.  
To simulate the longitudinal constraint effect between the tunnel rings during operation, six uniformly distributed vertical loading points are installed in the test, and the vertical force of each loading point is 25 t. The vertical loading device includes a vertical jack, tensioning threaded rod and loading beam, as shown in Figure 20.  
The loading process of the test involves the following steps: firstly, applying vertical loading force F1-F6; then gradually loading P1 from zero to the design load while keeping P2 = 0.7 \(\times\) P1 and P3 = (P1 + P2) simultaneously; finally, gradually unloading P2 to the tunnel failure state while keeping P1 unchanged and P3 = (P1 + P2).
# Cognitive Radio Network Technology for IoT-Enabled Devices +
## 3 Cognitive Radio Network Based on IoT  
The amount of connected hardware has increased because of the IoT's many different connectivity methods. This category includes a wide variety of electronic equipment, some examples of which are smartphones, smart home appliances, and other forms of smart equipment. These high-tech gadgets can communicate with one another, collect information, process it, and take appropriate action. This identifies the internet of things at the center of both present and future economics. In addition, the exponential growth of internet-of-things devices has raised competition for a limited spectrum. In terms of spectrum availability, cognitive radio looks like an achievable solution that might support both existing and emerging internet-of-things devices. This constitutes an extensive understanding of how cognitive radio and the internet of things can work together. The Internet of Things (IoT) is able to be combined with cognitive radio technology, and the resulting system is referred to as the Cognitive Radio Internet of Things (CRIoT). CRIoT will be used in many applications, including time-sensitive ones like technology and intelligent transportation. The combined technologies must satisfy many network parameters, including channel assignment delay, end-to-end latency, dependability, energy efficiency, and high throughput. Adapting to expanding connectivity requires several communication standards and technologies. CR applications for Bluetooth, ZigBee, and Wi-Fi interior smart environments are among the many IoT applications and services. IoT networks must accomplish various tasks. In the Primary User's presence, interference-free channels will be collected, and providing any IoT operating channel changes with continuous PU monitoring. For many Internets of things gadgets, licensed spectrum access is regulated to reduce interference with core users [12].
# Effective Scheme for Inductive Wireless Power Coil Design Using Scan-and-Zoom Optimization
## 4 Performance Comparison and Verification
### Design Examples and Performance Comparisons  
For a design example, a single-layer circular coil in Figure 7 is selected and a case where the coil size and circuit parameter values are symmetrical is considered. In this case, the coil shape parameter only considers two coil shape parameters: the outer coil radius \(R_{out}\) and the inner coil radius \(R_{in}\), therefore the problem is simplified. Among the compensation circuits in IPT, a series-series compensation circuit is selected. For the symmetrical series-series compensation circuits as shown in Figure 8, the PTE equation at the resonance point can be obtained as (12), which is used for the calculation of the objective function [18].  
\[\eta=\frac{k^{2}Q^{2}r_{d}}{\left(1+r_{d}\right)^{2}+k^{2}Q^{2}(1+r_{d})} \tag{12}\]  
where \(k\) is the coupling coefficient, \(Q\) is the quality factor of the coil and \(r_{d}\) is the ratio between of equivalent load resistor and coil parasitic resistance. Coil modeling in ANSYS Maxwell is generated as a single-turn coil as shown in Figure 9, and the coil size and ferrite core size are adjusted within the design constraints. In addition, when using a single-layer circular coil, wire diameter \(d_{w}\) is assumed to be equal to the coil thickness \(t_{w}\). The coil is made of about 3.4 mm diameter Litz wire with 500 strands of 0.10 mm thickness. Therefore, \(t_{w}\) is around 3.4 mm. The Litz wire information is updated in ANSYS Maxwell by the number of conductors in the coil cross-section.  
\[N=\frac{R_{out}-R_{in}}{t_{w}} \tag{13}\]  
Figure 8: Simplified circuit diagram for the symmetric series–series compensation circuit.  
Figure 7: Single-layer circular coil (**a**) Top view (**b**) Side view.  
For the IPT system design requirements, the output power \(P_{out}\) is set to 100 W, the output voltage \(V_{out}\) is 50 V, and the operating frequency \(f_{0}\) is 100 kHz. Through \(P_{out}\) and \(V_{out}\), the load resistance \(R_{L}\) is set to 25 \(\Omega\). For the target performance, the target \(PTE\)\(\eta_{t}\) is set to 95% or higher. This information is summarized in Table 1. The design constraints are set in Table 2 and the optimization problem is defined as an efficiency maximizing problem as shown in (14).  
\[\begin{array}{ll}maximize&\eta(R_{out},R_{in})\\ subject&to&10\text{ mm }\leq R_{in}<R_{out}\leq 200\text{ mm}\end{array} \tag{14}\]  
To examine the performance of the algorithm, the conventional FEA-based coil design method and the proposed coil design method are applied and compared together.
## 3 Results
### Calculation Results and Analysis of Land Use Carbon Emissions
#### 3.1.2 Spatial and Temporal Change Analysis of Land Use Carbon  
There are significant differences in land use carbon emissions among various counties in study region for the years 2010, 2015, and 2020. As depicted in Figure 2, from 2010 to 2015, the carbon emissions in Quyang County, Wanquan District, Xuanhua District, Tang County, and Huai'an County increased significantly, while the carbon emissions in Shangyi County and Kangbao County showed a downward trend. From 2015 to 2020, the total carbon emissions in Kuancheng Manchu Autonomous County, Shangyi County, and Fuping County increased, while those in Yixian County, Tang County, Wangdu County, Wanquan District, and Huai'an County decreased. The urbanization development in the study area from 2010 to 2015 resulted in a growth in the total carbon emissions. However, from 2015 to 2020, the optimization of the energy structure in this region brought about a reduction in the total carbon emissions.  
The carbon emissions of land use in the study area show obvious spatial heterogeneity, which reflects the differences in ecological environment, land use types, and human activities in this area. As can be seen from Figure 3, the areas with low carbon emissions are primarily distributed in Chengde County, Fengning Manchu Autonomous County, Kuancheng Manchu Autonomous County, and Longhua County. These areas have rich forest resources and high forest coverage rate, which absorb and fix a large amount of carbon dioxide, thus reducing the carbon emission capacity in the region. The land use in these areas is relatively traditional, with equal emphasis on agriculture and forestry, and the ecological environment is relatively good, which is conducive to promoting low-carbon development.  
By contrast, the areas with high values of carbon emissions are mainly concentrated in Tang County, Shunping County, Quyang County, and Wangdu County. The urbanization rate in these areas is relatively high. With the acceleration of urban construction and industrialization, the land use structure has changed significantly. Urbanization has led to a large amount of infrastructure construction and population concentration, resulting in increased energy consumption and higher carbon emissions in construction, transportation, and industry. Moreover, during the urbanization process, some cultivated land and green  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **2010** & **2015** & **2020** \\ \hline land use carbon emissions (10\({}^{4}\) t) & 1144.62 & 1512.26 & 1497.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total carbon emissions from land use.
## 2 Problem Description  
### The Basic Principle of \(\Delta\)Dor  
\(\Delta\)DOR is one of the commonly used VLBI technology systems, the fundamental principle of which is illustrated in Figure 1. Two distant stations, denoted as Station-1 and Station-2 in Figure 1, establish an interferometry baseline, with the distance between them referred to as the baseline length. The two stations simultaneously observe the spacecraft or the reference radio source alternately during a \(\Delta\)DOR measurement. The radio source measurement serves to eliminate the system's linear error, thereby facilitating the acquisition of precise interferometry time delay measurements for the spacecraft.  
Usually, the quasar position is defined in the celestial reference system, while the station or baseline coordinates are established in the terrestrial reference system. Consequently, during data processing, it is necessary to transform these coordinates from the terrestrial to the celestial reference system, where the Earth Orientation Parameters (EOP) including polar motions, the difference between Universal Time-1 and Coordinated Universal Time (UT1-UTC), nutation, and precession will be used. It is evident that the precision of the EOP also has an effect on the accuracy of the \(\Delta\)DOR measurements.
## 2 Materials and Methods
### Precipitation Products
#### 2.3.4 Comorbh  
In the CMORPH technique, the half-hour precipitation estimates from PMW sensors are propagated using motion vectors derived from geostationary satellite IR data. Moreover, the shape and intensity of the precipitation features are modified (morphed) during the gap between PMW sensor overpass by exploiting time-weighted linear interpolation [42]. The CMORPH version evaluated is generated by satellite data sets only [43]. In this study, the 3-hourly CMORPH data (corresponding to the eight synoptic times in a UTC day, 00:00-21:00 UTC) were aggregated into daily data (00:00 UTC) to compare with the other SPPs. The new version of CMORPH v1.0 includes a precipitation product, which is bias-corrected (satellite-only estimates, calibrated against rain gauge analysis for removing bias) and a product that is \"gauge-satellite blended\" (a merging of CMORPH bias-corrected and gauge observations). This study considers the satellite-based version of CMORPH only, which has not yet been evaluated over Malaysia.
## 4 Results
### Modeling Results
#### 4.2.4 Selection of Best Local Regression Models  
The best regression model for each cluster has been selected considering the MSE obtained by all the created models. There are 18 error values for each cluster (15 ANN, two polynomial regression, and LS-SVR). Tables 5 and 6 show the lower MAE and MSE obtained for each cluster; it must be remarked that the MSE is the usual error used to compare the predicted error for regression algorithms.  
Table 7 shows the algorithm selected for each cluster. Once the regression technique was chosen, new models were created for each cluster with the selected algorithm using all the available training data; as k-fold was used, not all the data had been used previously. The validation data was applied to the new models to select the best hybrid topology.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Global**} & \multicolumn{8}{c}{**Hybrid Model (Local Models)**} \\ \cline{3-10}  & & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Cl-1 & 0.0283 & 0.0081 & 0.0062 & 0.0204 & 0.0206 & 0.0202 & 0.0205 & 0.0210 & 0.0212 & 0.0206 \\ \hline Cl-2 & & 0.0358 & 0.0200 & 0.0046 & 0.0156 & 0.0057 & 0.0204 & 0.0192 & 0.0146 & 0.0143 \\ \hline Cl-3 & & & 0.0421 & 0.0198 & 0.0044 & 0.0040 & 0.0055 & 0.0150 & 0.0207 & 0.0188 \\ \hline Cl-4 & & & 0.0417 & 0.0341 & 0.0166 & 0.0041 & 0.0054 & 0.0194 & 0.0202 \\ \hline Cl-5 & & & & 0.0407 & 0.0349 & 0.0154 & 0.0035 & 0.0054 & 0.0238 \\ \hline Cl-6 & & & & 0.0403 & 0.0367 & 0.0273 & 0.0035 & 0.0052 \\ \hline Cl-7 & & & & & 0.0403 & 0.0336 & 0.0296 & 0.0035 \\ \hline Cl-8 & & & & & & 0.0476 & 0.0329 & 0.0242 \\ \hline Cl-9 & & & & & & & 0.0474 & 0.0408 \\ \hline Cl-10 & & & & & & & & & 0.0477 \\ \hline \hline \end{tabular}
\end{table}
Table 4: MAE for the least square support vector regression (LS-SVR) regression algorithm.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Global**} & \multicolumn{8}{c}{**Hybrid Model (Local Models)**} \\ \cline{3-10}  & & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Cl-1 & 0.0278 & 0.0080 & 0.0060 & 0.0186 & 0.0199 & 0.0182 & 0.0190 & 0.0197 & 0.0196 & 0.0186 \\ \hline Cl-2 & & 0.0346 & 0.0193 & 0.0044 & 0.0157 & 0.0053 & 0.0204 & 0.0192 & 0.0146 & 0.0143 \\ \hline Cl-3 & & & 0.0403 & 0.0194 & 0.0043 & 0.0037 & 0.0050 & 0.0143 & 0.0200 & 0.0185 \\ \hline Cl-4 & & & & 0.0399 & 0.0326 & 0.0170 & 0.0037 & 0.0051 & 0.0182 & 0.0194 \\ \hline Cl-5 & & & & 0.0398 & 0.0338 & 0.0148 & 0.0034 & 0.0050 & 0.0238 \\ \hline Cl-6 & & & & & 0.0397 & 0.0354 & 0.0269 & 0.0034 & 0.0047 \\ \hline Cl-7 & & & & & & 0.0394 & 0.0337 & 0.0296 & 0.0034 \\ \hline Cl-8 & & & & & & & 0.0448 & 0.0321 & 0.0242 \\ \hline Cl-9 & & & & & & & & 0.0455 & 0.0392 \\ \hline Cl-10 & & & & & & & & & & 0.0463 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mean absolute error (MAE) for each individual hybrid model.
Sensors_**2009**, \(9\), 3695-3712; doi:10.3390/s90503695  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**A Hybrid Stochastic Approach for Self-Location of Wireless Sensors in Indoor Environments**  
**[PERSON] *, [PERSON], [PERSON] and [PERSON]**  
Departamento de Comunicaciones, Universidad Politecnica de Valencia. Camino Vera s/n, 46022, Valencia, Spain; E-Mails: [EMAIL_ADDRESS]; [EMAIL_ADDRESS]; [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-609-549-043; Fax: +34-962-849-313  
_Received: 2 April 2009; in revised form: 4 May 2009 / Accepted: 14 May 2009 / Published: 15 May 2009_  
**Abstract:** Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.  
**Keywords:** indoor location system; positioning system; WLANs
# Landslide Prediction with Model Switching +
## 5 Experiments
### Experimental Datasets  
Historical environmental monitoring datasets from the Shen-Mu station [16] were selected as a case study. Landslides are mainly influenced by rainfall and soil moisture. Using the FS, the occurrence of a landslide is estimated. The monitoring dataset of the Shen-Mu station is shown in Figures 10 and 11. Figures 10 and 11 show the relationships between rainfall and FS, and between soil moisture and FS, respectively. When rainfall and/or soil moisture increase, the FS decreases and the slope becomes unstable.  
In the Shen-Mu datasets [16], data was recorded once per 10 min, and was collected in 2016. The program randomly selected 10 sets of samples, where each set had 1300 samples. Each dataset was further divided into two parts, the training set (75%) and the test set (25%). Note that the original data were used as input; that is, they were not normalized, because we needed to calculate the FS according to the SHALTAB model, as given in Equation (1).  
Firstly, an evaluation of imbalanced-class prediction is described. Then, it is shown how the prediction accuracy is increased due to the proposed MoSLaPS.