esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation
### Surrounding Vehicle Localization  
#### 4.2.1 Experimental Environment  
In order to obtain relative position estimates of the remote vehicle from the radar and camera and also from V2X communications, driving experiments were conducted under the scenarios where the remote vehicle is driven ahead the host vehicle. For the performance evaluation of the proposed data fusion approach for different relative target locations, the relative distance between the two test vehicles was gradually increased. As shown in Figure 9, the first set of experiments was carried out under a scenario where both test vehicles drove in the same lane, and the second set of experiments was conducted under a scenario where the remote vehicle drove in the adjacent lane. The two driving scenarios, which are referred to as the \"normal driving scenarios\" here, are summarized in Table 6. For these two normal driving scenarios, dynamic driving maneuvers such as lane change maneuvers were not performed. For both driving scenarios, the host vehicle was driven at a speed of about 20 km/h while the remote vehicle was driven at about 25 km/h. The red and blue dashed lines in Figure 9 indicate the FOV of the camera and radar sensors, respectively. The narrower set of dashed lines in blue indicates the long-range radar coverage, whereas the wider set indicates the mid-range radar coverage. The relative positions of the target vehicle obtained from the radar, camera, and V2X communications are plotted in different colors. The red dot indicates the camera measurement while the blue dot indicates the radar measurement. The dot in magenta indicates the relative position obtained based on V2X communication data, and finally black dot denotes the ground truth position. All of these dots represent the estimated distance to the rear center of the remote vehicle from the origin, which is the center of the front bumper of the host vehicle.  
Figure 8: Proving ground at the Korea Automotive Technology Institute.  
Figure 7: Overview of the hardware interface.  
The magenta bounding box is created and rotated based on the width, length, and heading information obtained from V2X communications at a given time step. Finally, the black bounding box represents the ground truth position of the remote vehicle.
## 2 Material and Methods
### Models Design and Evaluation
#### 2.2.4 ANN-PSO Model  
A hybrid ANN-PSO is integration technique in-between ANN and PSO for the architecture of the ANN [33]. The details of ANN, PSO and ANN-PSO can be found in [22; 33; 34]. Herein, the output neuron (\(y\)) of ANN can be expressed as follows [33]:  
\[y=\max\left(0,\sum_{i}w_{i}x_{i}+b\right) \tag{12}\]  
where, \(w,\ x,\textit{ and }b\) are the weight, input neuron, and bias, respectively.  
The mapping between ANN layers can be presented as follows [33]:  
\[h_{i}=\max(0,M_{i}h_{i-1}+b_{i})\ \ \ \ for\ 1\leq i\leq L,\ \textit{and}\ h_{0}=x \tag{13}\]  
\[y=\max(0,Vh_{L}) \tag{14}\]  
where, \(L\) is the layers numbers, matrices \(M\) and \(V\), and vector \(b\) are the learned parameters of ANN.  
In ANN-PSO, the learned parameters of ANN can be estimated using PSO to minimize the classic ANN algorithm errors. Here, the mean square error (MSE) was defined as a fitness cost function into estimating the ANN parameters. The best model was achieved based on a lower value of MSE. The PSO started with generating a random swarm of particles for each parameter of ANN. Next, the swarm was updated based on the position update of particles. The updated circulated up to obtain the optimum position of particles. The position update can be expressed as follows [33]:  
\[v_{i}^{t+1}=wv_{i}^{t}+c_{1}r_{1}\left(p_{best,i}^{t}-X_{i}^{t}\right)+c_{2}r_ {2}\left(g_{best,i}^{t}-X_{i}^{t}\right) \tag{15}\]  
\[X_{i}^{t+1}=X_{i}^{t}+v_{i}^{t+1} \tag{16}\]  
where, \(X\textit{ and }v\) denote the position and velocity of the particle, respectively; \(w\) is the inertia parameter; \(c_{1}\textit{ and }c_{2}\) represent the cognitive and social component parameters, respectively. \(r_{1}\textit{ and }r_{2}\) are randomly assumed in between 0 and 1. \(p_{best,i}^{t}\)_and_\(g_{best,i}^{t}\) are the best positions of the particle at individual and global positions, respectively.  
In this study, the PSO was used to estimate the best parameters of ANN, number of hidden layers and neurons in each layer. Based on our trails, the best architecture of the ANN was achieved with 3 layers and 20 neurons, respectively, for estimating S of SCC; whereas, for CS, the 3 layers with 10 neurons were found the best performance. The \(w,\ c_{1}\textit{ and }c_{2}\) were selected to be \(\frac{1+r\textit{and}}{2}\), 1.0, and 2.0, respectively.
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 3 Network Model
### Transmission Strategy  
Two transmission modes exist in our network model: helping transmission mode and _ad hoc_ transmission mode, as shown in Figure 3. In _ad hoc_ transmission mode, packets are delivered from source nodes to destination nodes in a multi-hop fashion, which are relayed by _ad hoc_ nodes. In helping transmission mode, packets are first delivered from their source nodes to the backbone network which consists of helping nodes, and then they are transmitted from the helping nodes to their destinations.  
In mobile networks, the transmission delay can not be ensured, hence a \(D\)-delay transmission strategy is considered in this paper. Specifically, a packet will be transmitted by _ad hoc_ transmission mode if it can reach its destination node within \(D\) time slots. Otherwise, the packet will be transmitted by helping transmission mode.  
We divide the wireless channel into different sub-channels so that the packet can be carried with different frequency bands. We assume that _ad hoc_ nodes have a bandwidth of \(W\). We assign \(W\) into \(W_{1}\), \(W_{2}\) and \(W_{3}\), which denote the intra-cell, uplink and downlink sub-channels, respectively. Hence we have \(\sum\limits_{i=1}^{3}W_{i}=W\). Since the amount of traffic carried over uplink sub-channels is the same with that carried over downlink sub-channels, we have \(W_{2}=W_{3}\). Moreover, we assume that the helping nodes have much higher bandwidth than the normal nodes, and the two types of nodes carry packets with different frequency bands, which implies that there is no interference between them.  
Figure 3: Two Transmission Modes in Networks.
# Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
## 3 Method
### SAR Image-Processing Algorithm for Flood Extent Extraction
Moreover, the speckle noise inherent to all the coherent imaging devices causes statistical fluctuations in the backscattering levels of single pixels. All of these factors often become critical during flood events so the knowledge of land cover at 1:5000 scale, available from the Regione Basilicata geoportal ([[http://rsdi.regione.basilicata.it/](http://rsdi.regione.basilicata.it/)]([http://rsdi.regione.basilicata.it/](http://rsdi.regione.basilicata.it/))) and detailed at finer scale through an orthophoto visual interpretation, was utilized by an operator to understand, for example, if it was necessary to look for dark spots in the image (bare soil) or to some increase in intensity with respect to an image taken before the flood event (e.g., over forested areas). Although the proposedprocedure aims to make radar interpretation as automatic as possible, a certain degree of supervision by an expert operator was considered to be essential to reduce uncertainties arising in SAR flood mapping. For example, the presence of permanent tree vegetation, characterized by low penetration of electromagnetic waves through leaves and branches, makes it difficult to detect the presence of water below the canopy. Indeed, taking into account that (i) mapping flooded vegetation can be still considered a critical issue for operational inundation mapping using SAR data [36] and (ii) being aware that the aim of the proposed research is to contribute to scientific knowledge in the field of operational methods and models for real-time flood damage assessment, the area inside the river channel and the pinewoods that is closer to the river mouth were excluded from the calibration. Indeed, these parts of the study area are characterized by the presence of very dense and high vegetation without involving vulnerable elements so floods cannot produce direct damage in these zones.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 4 Self-Drilling Anchor Grouting Slope Simulation Analysis
### Slope Model Optimization
#### 4.2.1 Numerical Model and Calculation Parameters
The maximum settlement is concentrated in the slope top region, with a gradient reduction from the slope top to the toe. Specifically, the maximum vertical displacement of the original slope was 12.77 cm (along the negative z-axis), which decreased to 8.69 cm after excavation, a reduction of 31.9%. Using the FLAC3D strength reduction method, the stability factor of the original slope was calculated as 1.023, which increased to 1.086 after excavation, a 6.15% increase. These data confirm that through slope angle adjustment and removal of surface soil at the front edge, the sliding thrust of the potential sliding mass was effectively reduced, while the stress distribution of the slope body was optimized. This engineering measure improved the overall stability safety margin of the slope by 0.063, creating a favorable support environment for the subsequent construction of the self-drilling anchor support system.
## 3 Tuning the Voltage FPR
### Applying of the Tuning Procedure  
This section illustrates the tuning procedure that was previously detailed. It will be considered as \(\omega_{0}\) set at 100\(\pi\) rad/s (50 Hz). The inner control loop is assumed to have a bandwidth of 3000 Hz. Thus, the different steps are detailed in the following Sections 3.1 and 3.2.  
**Step 1**. For robustness purposes, it is recommended a phase margin, \(\phi_{m}\), value of 60\({}^{\circ}\). This \(\phi_{m}\) will be set at \(\omega_{c}\) = 500 Hz (this fact allows to avoid cross-coupling between inner and outer control loops).  
**Step 2**. Table 3 shows the obtained set of values for \(\alpha\in[0.5,1.5]\) applying (12) and (13).  
**Step 3**. This step allows for comparing the different ideal open-loop Bode diagrams to make a decision (no approximation used for the term \(s^{\alpha}\)).  
In Figure 5, it can be seen how the \(\phi_{m}\) is 60\({}^{\circ}\) at 500 Hz \(\forall\alpha\). This is also reflected in the close loop Bode diagram in Figure 6. Due to this tuning procedure, the \(\alpha\) exponent is relegated to be selected for a better gain or phase delay tracking enhancement. It should be noted that when this criterion is applied \(\alpha\) values below 1 offer an overall better tracking response. The opposite conclusion was obtained compared with Figure 3. In Figure 3, the gains are kept equal between the compared controllers. Thus, stability margins are delegated to this decision.  
**Step 4**. Following [PERSON]'s approximation described in [16], it is required to set the corresponding Charef's approximation parameters. For clarity and ease of comparison with PR and PRHC, only \(\mathrm{s}^{0.75}\) and \(\mathrm{s}^{1.5}\) are approximated. Thus, for both \(\alpha\) cases, the Charef's  
Figure 5: Open\(-\)loop bode diagrams for ideal FPR controller, based on Table 3.  
Figure 6: Close\(-\)loop bode diagrams for ideal FPR controller, based on Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\alpha\) & \(k_{p}\) & \(k_{i}\) & \(\alpha\) & \(k_{p}\) & \(k_{i}\) \\ \hline
0.5 & 0.4507 & 215.71 & 1.25 & 0.2172 & 0.3057 \\
0.75 & 0.3540 & 20.273 & 1.5 & 0.1204 & 0.0490 \\
1 & 0.2856 & 2.2999 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Controller gains values, obtained from (12) and (13), considering \(\phi_{m}=60^{\circ}\) and \(\omega_{c}=500\) Hz.  
parameter \(p_{T}\) is set to 1 (filter coefficient). The other Charef's parameters are set to \(y=3.5\) dB (maximum deviation between ideal and approximated curve) and \(n=2\) (approximation order) to approximate \(s^{0.75}\)t, and \(y=4\) dB and \(n=3\) to approximate \(s^{1.5}\).  
**Step 5 and 6**. The detailed analysis of the robustness and stability is addressed in Section 4.
## 2 MIMO Antenna Design Approaches  
MIMO antennas have received much attention in the modern wireless communication system as it can use multi-paths to transmit or receive data, and hence increase the range and output performance [15]. It is noted that the significant isolation between components of the same MIMO system is necessary so that elements of the MIMO antenna can work independently to transmit or receive signals simultaneously without deteriorating the antenna parameters.  
In order to ensure the quality of a MIMO antenna, in addition to S-parameters and radiation characteristics, certain diversity parameters are used. The MIMO antennas must satisfy the predefined values of the diversity parameters for practical applications. Therefore, some basic diversity parameters for MIMO antennas are mentioned in this section.
# Geophysical Monitoring of Leachate Injection in Pretreated Waste Landfill
## 3 Results  
### Geophysical Characterization  
The measurements revealed a highly conductive medium, with an electrical current in the range of 1000-2000 mA and very low voltage values, down to a few mV depending on the geometric arrangement of the single measurement. Data quality was estimated by the data standard deviation, achieving values in the range of 0.1-5%. The quality analysis of the data set was performed to exterminate from the data set values with a standard deviation higher than 5%. Only a few data were discharged for each section.  
The inversion procedure was interrupted after less than five iterations for each transect, as the RMS lowered the 5%, which agrees with the uncertainties of data.  
The results of the preliminary characterization are reported in Figure 3. For the sake of simplicity, the example of L1, L3 and L6 is herein reported, the distribution of the other sections being very similar to the presented ones.  
Particularly, the sections pointed out that the electrical response of waste appears quite homogeneous, with resistivity values lower than 10 \(\Omega\cdot\)m. This is well depicted in the resistivity image of L3 and L6.  
The resistivity distribution of the section of Line 1 depicts higher resistivity values in the uppermost part of the image; particularly, lenses of high-resistive waste material in the range from 20-50 \(\Omega\cdot\)m are reported, up to a depth of 2 m from the ground. The lower part of the section is characterized by a resistivity ranging between 1 \(\Omega\cdot\)m and 5 \(\Omega\cdot\)m. The bottom part of the image shows a slight increase in the resistivity values, up to 10 \(\Omega\cdot\)m.  
The uppermost layer is slightly more resistive, with local heterogeneities that exhibit local electrical resistivity values in a range higher than 20 \(\Omega\cdot\)m. This is visible in the vertical section of line L3, where the resistivity contrast between the upper part and the lower one is enhanced. In such a case, the resistivity of the waste in the depth range of 2-6 m is lower than 5 \(\Omega\cdot\)m, according to the increase of waste moisture in such zone. This agrees with the resistivity distribution of the section of line L6 where a spot of low resistivity is pointed out at the coordinates 55-70 m, that is, the coordinate where line L3 crosses the L6 one (see Figure 1).
## 3 Convolutional Neural Networks
### Proposed Structures
#### 3.1.2 Shape of Convolution and Pool Patches  
Each of the EEG electrodes experiences the presence of the P300 wave in different magnitudes, and there are certain regions that are more likely to show it. This has been reported by different studies [14; 25]. However, in most studies that attempt to classify EEG data, the two-dimensional position of the channels along the scalp of the user is mapped, generating a one-dimensional array that positions channels from different regions of the brain next to one another.  
Given that applying either of the kernels in a squared-shaped fashion like that demonstrated in Figure 2 will result in feature maps that mix data from both the space and time domains, it is advised [14] that patches be constructed such that they only consider information of one dimension and one channel at a time. In this study, we considered three different convolution and pool patch sizes, including one pool patch that considers data from two adjacent channels simultaneously in the one-dimensional array. These patch sizes were chosen as a result of preliminary tests, in which a wide number of options was analyzed using data from one subject. The different CNN structures that were tested for this study consist of combinations of the selected number of layers and sizes of convolution and pool patches, which are summarized in Table 1. For a given number of convolution layers, the nine possible combinations of convolution and pool patches are considered. For the CNN with two layers, the same combination of convolution and pool patches is used in each layer. For the pooling operation, max pooling is applied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Convolution Layers** & **1** & **2** \\ \hline Size of convolution patch & \(2\times 1\) & \(4\times 1\) & \(5\times 1\) \\ Size of pool patch & \(2\times 1\) & \(3\times 1\) & \(3\times 2\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed and tested number of convolution layers, size of convolution patches and size of pool patches.  
For easier identification within the text, we will use brackets to refer to the patches listed above, e.g., pool patch \([3\times 2]\). The convolution layers will only be referred to as layers in the following sections. In this study, 64 feature maps of the same size are generated after the convolution.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
###### Abstract  
The urban heat island (UHI) is one of the essential phenomena of the modern urban climate. In recent years, urbanization in China has gradually accelerated, and the heat island effect has also intensified as the urban impervious surface area and the number of buildings is increasing. Urban building density (UBD) is one of the main factors affecting UHI, but there is little discussion on the relationship between the two. This paper takes Hefei as the research area, combines UBD data estimated by Luojia1-01 night-time light (NTL) imagery as the research object with land surface temperature (LST) data obtained from Landsat images, and carries out spatial correlation analysis on \(0.5\times 0.5\) km to \(2\times 2\) km resolution for them, so as to explore the relationship between UBD and UHI. The results show the following: (1) Luojia1-01 data have a good ability to estimate UBD and have fewer errors when compared with the actual UBD data; (2) At the four spatial scales, UBD and LST present a significant positive correlation that increases with the enlargement of the spatial scale; and (3) Moreover, the fitting effect of the Geographically Weighted Regression (GWR) model is better than that of the ordinary least squares (OLS) regression model.  
Luojia1-01; urban heat island; urban building density; land surface temperature; geographically weighted regression Article
## 3 Results and Discussion
### Evaluation of Models
#### 3.2.2 Comparison between Conventional Measurement Methods and Machine Learning  
Advanced conventional approach for measuring the compressed liquid density of R227 ea requires a series of apparatus connecting to be an entire system (Figure 6) [1]. However, the measurement requires time and a series of complex operations, which constraints its applicability. Additionally, the purchase and installation of the apparatus of conventional methods require too much manpower and resources, which indicates that it can only be used for acquiring extremely precise values. In contrast, machine learning models can make precise predictions based on the trained data set and give robust responses with a large number of trained data. Users can only input the new measured data of temperature and pressure and the precise predicted results can be automatically outputted by an appropriate machine learning model. Once the models are developed, new predicted data can be acquired in  
Figure 5: Predicted values _versus_ actual values in testing processes using machine learning models. (**a**) The SVM for data provided by [PERSON] _et al._[1]; (**b**) the SVM for data provided by [PERSON] _et al._[24]; (**c**) the MLFN-2 for data provided by [PERSON] _et al._[25]; and (**d**) the SVM for data provided by all the three experimental reports [1; 24; 25].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **RMSE in Training** & **RMSE in Testing** \\ \hline SVM for data provided by [PERSON] _et al._[1] & N/A & 0.11 \\ SVM for data provided by [PERSON] _et al._[24] & N/A & 6.09 \\ MLFN-2 for data provided by [PERSON] _et al._[25] & 11.81 & 2.72 \\ SVM for all data [1; 24; 25] & N/A & 15.79 \\ Theoretical calculation for data provided by [PERSON] _et al._[1] & N/A & 196.26 \\ Theoretical calculation for data provided by [PERSON] _et al._[24] & N/A & 372.54 \\ Theoretical calculation for data provided by [PERSON] _et al._[25] & N/A & 158.54 \\ \hline \hline \end{tabular}
\end{table}
Table 5: RMSEs of different models.  
a very quick way, saving time and manpower. More importantly, it only needs a decent computer and no other apparatus is required anymore.
## 3 Materials and Methods
### Type of Tests and Standards on the Hardened Material
#### 3.3.4 Preliminary Electrical and Sensing Tests  
Electrical and sensing tests were executed to explore the percolation and the piezoresistive characteristics of the material, respectively. Each sample, constituted by a cube with two internal copper wires as electrodes, was powered with a square wave voltage input using a RIGOL DG1022 function generator, while the resulting output current was recorded using the two-probe method. Notably, samples were subjected to a 4 V peak-to-peak signal with a frequency of 1 Hz and a duty cycle of 50% as defined in [33]. Current measurements were acquired via a high-resolution multimeter, NI PXIe-4071, accommodated within an NI PXIe-1073 chassis, operating at a 10 Hz sampling rate. The electrical resistance, represented as \(R(t)\), was determined employing [PERSON]'s law:  
\[R(t)=\frac{V}{I(t)}\]  
where \(V\) signifies the positive value of the voltage input signal (+2 V) and \(I\) denotes the current value sampled at 80% of the positive output signal.  
Sensing properties have been evaluated through compressive tests with maximum load of 7 kN. Strains were evaluated utilizing a pair of strain gauges positioned on lateral surfaces of the samples. The strain-sensing proficiency of the tested samples was assessed by computing the gauge factor, denoted as \(GF\), utilizing the following equation:  
\[GF=\frac{\Delta R/R_{0}}{\Delta\varepsilon}\]  
Here, \(\Delta R\) denotes the variation in electrical resistance relative to its baseline value \(R_{0}\) while \(\Delta\varepsilon\) represents the strain (negative in compression).