esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Improved Penzien Model
### Parameter Determination of the Improved Penzien Model
#### 2.3.1 Stiffness and Damping of Soil-Pile Interaction  
[PERSON] thought that [PERSON]'s hypothesis was correct by analyzing the half-space theory, so the average displacements of the pile planes at different depths under the horizontal unit force can be solved according to the Mindlin formula, and then the reciprocal is the horizontal spring stiffness of the interaction between soil and pile in each soil layer [51]. The calculation formula for \(k_{hi}\) is as follows:  
\[\begin{array}{l}k_{hi}=\frac{8\pi E_{i}}{3}\Bigg{\{}\frac{2}{3r^{2}}\Bigg{[} \frac{r^{2}h_{i}-2r^{2}z_{i}+h_{i}z_{i}^{2}+z_{i}^{3}}{\big{[}r^{2}+(h_{i}+z_{ i})^{2}\big{]}^{\frac{1}{2}}}-\frac{z_{i}^{3}-2r^{2}z_{i}}{\big{(}r^{2}+z_{i}^{ 2}\big{)}^{\frac{1}{2}}}\Bigg{]}+\text{arsinh}\,\frac{h_{i}-z_{i}}{r}+\text{arsinh }\,\frac{h_{i}+z_{i}}{r}\\ \\ -\frac{2}{3}\Bigg{[}\frac{z_{i}-h_{i}}{\big{[}r^{2}+(h_{i}-z_{i})^{2}\big{]}^{ \frac{1}{2}}}-\frac{z_{i}}{\big{(}r^{2}+z_{i}^{2}\big{)}^{\frac{1}{2}}}\Bigg{]} +\ \Bigg{[}\frac{r^{2}z_{i}+h_{i}z_{i}^{2}+z_{i}^{3}}{\big{[}r^{2}+(h_{i}+z_{i})^ {2}\big{]}^{\frac{1}{2}}}-\frac{r^{2}z_{i}+z_{i}^{3}}{\big{(}r^{2}+z_{i}^{2} \big{)}^{\frac{1}{2}}}\Bigg{]}\Bigg{\}}^{-1}\\ \\ r=\sqrt[4]{\frac{64I_{u}}{\pi}}\end{array} \tag{12}\]  
where \(E_{i}\), \(h_{i}\), and \(z_{i}\) are the elastic modulus, the soil thickness, and the vertical distance from the soil center to the soil surface of the soil layer \(i\), respectively. \(r\) denotes the equivalent inertia radius of the equivalent single pile. \(I_{u}\) is the sum of the inertia moments of all the single piles.  
Figure 3: Flowchart for the calculation process of soil dynamic nonlinearity.  
The method proposed by [PERSON] et al. [69] is used to determine the damping coefficient, and the viscous damper is used to simulate the radiation damping of the wave energy dissipating to the semi-infinite site soil. The method is used to simulate the horizontal damping of the interaction between the soil and pile, and the damping \(c_{hi}\) of the interaction between the soil and pile in soil layer \(i\) is calculated by Equations (14) and (15).  
\[c_{hi}=2 rh_{1}\rho_{1}\big{(}v_{p1}+v_{s1}\big{)} \tag{14}\]  
\[c_{hi}=2r\big{[}h_{i}\rho_{i}(v_{pi}+v_{si})+h_{i+1}\rho_{i+1}\big{(}v_{pi+1}+v _{si+1}\big{)}\big{]} \tag{15}\]  
\[v_{pi}=\sqrt{(\lambda_{i}+2G_{i})/\rho_{i}} \tag{16}\]  
\[v_{si}=\sqrt{G_{i}/\rho_{i}} \tag{17}\]  
\[\lambda_{i}=\mu_{i}E_{i}/[(1+\mu_{i})(1-2\mu_{i})] \tag{18}\]  
where \(v_{pi}\), \(v_{si}\), and \(\mu\) represent the longitudinal wave velocity, shear wave velocity, and Poisson's ratio of the soil layer \(I\), respectively. \(G_{i}\) and \(\rho_{i}\) are the shear moduli and the density of soil, respectively, and \(\lambda_{i}\) is the Lame coefficient.
## 4 Stepwise Process Enclosed in Proposed Research
### Second Stage: Trust-Based Model for Secure Data Transmission  
The two-fish algorithm is a cipher algorithm with a block size of 128 bits and can admit a variable length of key [33]. This cipher algorithm is composed of a bijective F function and is a sixteen-round network. The F-function performed three functions: pseudo-Hadamard transform (PHT), distance separable matrices (MDS), and key-dependent S-boxes. These three F-functions are briefly discussed in the following steps.  
* Initially, the input data consisting of 128 bits are segmented into four parts, each containing 32 bits, based on the little-endian convention.
* Out of the four parts, two parts of the bits are correct, and the rest of the bit is left.
* Then, these four input parts are XOR with four keys, and this process is referred to as whitening. The expression for describing whitening is given by Equation (13). \[\mathrm{C}_{0}=\mathrm{P}\,\oplus\,\mathrm{K}_{i}\quad,\,\mathrm{where}\,\, \mathrm{K}_{\mathrm{i}}=0,1,2,3\ldots\] (13)
* In Equation (13), \(\mathrm{C}_{0}\) represents the converted bit, \(\mathrm{P}\) signifies the input bit, and \(\mathrm{K}_{\mathrm{i}}\) is used for encryption.
* The resultant bit obtained by performing the XOR operation is rotated either left or right by one bit, and again, it is swapped to produce a new result. This rotation in the initial argument is based on the second argument. This process is repeated 16 times; therefore, it is termed a two-fish round.
* **S-box:** This S-box is a substitution operation that is based on a table. Normally, four types of S-boxes are used in the two-fish algorithm. These four S-boxes are merged with the MDS to generate the h-function. Using this S-box, the input and output sizes can be randomly altered. Rather, the S-box contains an 8-bit permutation with an XOR with two sub-keys.
* **MDS matrix:** The MDS matrix is termed as a building block to the cipher because it guarantees a certain degree of diffusion. This MDS matrix is coupled with an S-box and acts as the main diffusion mechanism. If one of the input elements is changed, then every output element must be altered.
* **Pseudo-Hadamard Transform (PHT):** This PHT is a mixing operation that is necessary for software. This is a simple reverse diffusion mechanism. The expression used to represent this transform is given by Equation (14). \[\mathrm{A}^{\prime}=\mathrm{A}+\mathrm{B}\] (14)Finally, after performing this transform, the final encrypted data, which is referred to as ciphertext, will be obtained. Based on this two-fish algorithm, the aggregated data in the sink are encrypted. The encrypted data are sent to the server via a gateway for monitoring.  
Based on this proposed architecture, energy-efficient and secure data transmission in smart building applications can be achieved. An experimental investigation is carried out using MATLAB software to evaluate the functionality of the proposed framework, and the results gathered through implementation are briefly discussed in the following section.
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
## 3 Results and Discussion  
### Frequency Response of the Open-Loop and Closed-Loop System  
The transfer functions of the open-loop and closed-loop systems, shown in Equations (18) and (34), are used to determine the frequency responses in Figure 6. The figure compares the frequency responses for both systems and demonstrates that increasing \(K_{p}\) results in a reduced amplitude response in the closed-loop system compared to the open-loop system. This indicates that the proportional ride-blending controller effectively reduces the vibration of the vehicle's sprung mass.
# Development of a Hardware-in-the-Loop Platform for a Teleoperation Flexibility Robotic System
## 3 Methodology  
### Position Mapping Method  
The main blocks of the teleoperation system are a local site and a remote site, as depicted in Figure 7. The haptic device and cable robot represent the local and remote sites, respectively, and the shapes and sizes of their workspaces are different. Therefore, we use the position mapping method to determine the positions of the cable robot and haptic device.  
As depicted in Figure 7, the coordinate system and workspace of the haptic device are different from those of the cable robot. To control the remote site, the position of the haptic device is calculated using the position mapping method to determine the position of the cable robot, as follows [29]:  
\[\bf P_{robot}\text{=}KP_{haptic}\text{+}P_{w} \tag{10}\]  
where \(\bf P_{robot}\in\Re^{3\times 1}\) is the position vector of the cable robot with respect to the \(X^{\prime}Y^{\prime}Z^{\prime}\) axes, \(\bf P_{haptic}\) is the position vector of the haptic device with respect to the \(XYZ\) axes, \(\bf K\in\Re^{3\times 3}\) is  
Figure 6: Haptic model: (**a**) original coordinate system of the device. The description of axis system: (**b**) one leg; (**c**) base.  
The formula for computing the elastic force is as follows:  
\[\textbf{F=K}_{\text{s}}\textbf{AX}+\textbf{Cv} \tag{11}\]  
where \(\textbf{F}=\begin{bmatrix}\text{f}_{1}&\text{f}_{2}&\text{f}_{3}\end{bmatrix}^{T}\) is the force vector; \(\textbf{K}_{\text{s}}\in\Re^{3\times 3}\) is the spring factor; and \(\textbf{AX}\in\Re^{3\times 1}\) is the linear displacement of the spring. In this work, \(\textbf{AX}\) also denotes position displacement; the constant \(\textbf{C}\in\Re^{3\times 3}\) is the damping coefficient; and \(\textbf{v}\in\Re^{3\times 1}\) is the speed vector of the spring.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 4 Experiments
### Experimental Dataset and Setting  
To increase the diversity of the training samples, flipping and rotation techniques are introduced into a large database. For example, we rotate the images by \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), or \(270^{\circ}\) to generate different directions. Then, we randomly flip between the horizontal or vertical images. Additionally, there are four different scales, including \(\times 0.9\), \(\times 0.8\), \(\times 0.7\), and \(\times 0.6\) for the training images to enlarge the multiscale samples. After data augmentation, the dataset made up of 600,000 images from the ImageNet dataset [37] was divided into a validation set and training set, with a ratio of 8:2. For testing, the benchmark datasets, including Set5 [35], Set14 [38], BSD100 [36], and Urban100 [39] are adopted, respectively.  
Most of the experiments are performed between the LR and HR images at a scale factor of \(\times 2\), \(\times 3\), and \(\times 4\). For a fair comparison, the averaged peak signal-to-noise ratio (PSNR), the structural similarity index (SSIM), and the information fidelity criterion (IFC) are calculated for quantitative evaluation. In addition, the SR images for the methods, which include SRCNN, VDSR, DRRN, and SRGAN, are obtained from [PERSON] et al., and are available online.  
Our network is conducted on a workstation with an Intel i9 12900K CPU, a Linux operating system, and two GeForce RTX3080 Ti GPUs.
## 2 Background Theories
### Long Short-Term Memory (LSTM)  
Deep learning is a type of machine learning that simulates the process of the human brain in terms of data and pattern formation for making decisions. The number of architectures and algorithms used in deep learning is wide and various [34]. Countless deep learning architectures such as Recurrent Neural Network (RNN) have been applied to NLP [35]. RNN is a variant of the Artificial Neural Network (ANN) which is designed to handle tasks with sequence data. The idea of RNNs is to make use of the output from the previous state as an input to the next state. This allows the model to recognize the pattern of the input sequence. RNN has the benefit of using past data to predict future events. As a result, everything that has occurred in the past will have an influence on the future. However, RNN is ineffective for very long-term dependencies. This is due to the exponentially decreasing gradients and the decay of information for long-term dependencies. This problem is called the vanishing gradient problem.  
LSTM proposed by [36] is an improved version of RNN, avoiding the encountering of problems. LSTM is specifically modeled to manage tasks involving long-term dependencies information because it has a capacity to forget irrelevant information or store information for longer periods of time with memory cell support. The LSTM has a chain-like structure consisting of several subunits joined together. The unit of the LSTM architecture is a block memory with memory cells. These memory cells have three structures to control  
Figure 1: A flowchart of sifting processes.  
information flow: forget gate layer, input gate layer and output gate layer. The forget gate layer determines what information from the previous cell is fed onto the current cell. The input gate layer determines the relevant information to update the cell state. The output gate layer determines the output value for the next hidden state based on the input and memory of the block [37].  
Furthermore, LSTM is appropriate for time series prediction because it can learn and remember long-term memory topics such as market movement [38]. Advanced versions of LSTM can be used for various applications such as energy consumption [39], gas field production [40], chatbot messages classification [41] and rice export price prediction [42].
## 1 Introduction
Generally, those back-up units are electrically driven chillers and fossil-fuel boilers, for the cooling and heating loads, respectively [18]. Nevertheless, fossil-fuel boilers are exergetically inefficient, especially when the temperature of the delivered heat is low, as in 4 GDH. On the other hand, electrically driven reversible heat pumps are a versatile technology, capable of providing both heating and cooling, alternately [19; 20; 21]. Furthermore, among other factors, the performance of heat pumps is highly dependent on the supply temperature; therefore, this technology is especially suitable for low-temperature heating applications. Moreover, unlike fossil-fuel boilers, electric heat pumps represent a hub between the electric and thermal networks, thus enhancing system flexibility and integration of RES. In view of all this, this paper aims to investigate the integration of reversible electric heat pumps in trigeneration systems, as single back-up units to replace traditional fossil-fuel boilers and non-reversible electric chillers. An energy supply system for a district heating and cooling microgrid integrating RES is considered, consisting of an internal combustion engine, a single-effect absorption chiller, a reversible electric heat pump, photovoltaic panels, and a wind turbine. The thermal network serves office buildings equipped with fan coil terminals, thus requiring low-temperature heat. Energy demand data from a case study located in Pisa, Italy, are considered. The integrated optimal sizing and hourly operation of the proposed system are evaluated from the economic point of view. A two-level optimization algorithm is developed: the optimal operation problem is solved by exhaustive search, while a genetic algorithm is applied to the sizing problem. Moreover, a comparison with the conventional trigenerationsystem (i.e., with fossil-fuel boiler and non-reversible electric chiller) is also performed in terms of economic and environmental perspectives.
## 3 Results and Discussion
### Color Parameters, Browning and Whitening Index  
Color is one of the principal quality factors that highly influence food acceptance [64]. It was observed that processed fruits are more acceptable by consumers if their colors are close to unprocessed fruits [65]. Results of color parameters measured for fresh and dried apple chips are presented in Table 2. The degree of freshness of the fruit is described by the lightness index (\(L^{*}\)) [6]. The \(L^{*}\) values of dried samples are higher than the \(L^{*}\) values of fresh samples, which means that the dried fruits are lighter in color than the fresh ones, possibly due to the lemon salt solution used in the preparation of the samples [66]. According to [PERSON] et al. [6], the yellow color of the apple fruit is given by the carotenoids, while the presence of anthocyanins leads to the red color of the fruit. All fresh apple samples had _a*_ values situated in the negative region more toward green, while _a*_ values measured for dried apple samples were situated in the positive region which means more toward red, in accord with [23]. The yellownes (_b*_) of fresh apple samples was between 18.36 \(\pm\) 0.45 and 27.30 \(\pm\) 1.64. The _b*_ values increased for S1, S2 and F2 and decreased for G1 and F2 dried apple samples compared with the _b*_ values of fresh apple samples.
# A New Method for Calculating Prediction Parameters of Surface Deformation in the Mining Area
## 3 Methodology
### Analysis of Influencing Factors of PIM in Huainan Mining Area
#### 3.2.1 Subsidence Coefficient  
Many studies have proved that the subsidence coefficient can be affected by the mining height, the treatment method of the goat and the nature of the overlying strata. Based on the surface subsidence coefficients, mining depths and loose layer thickness of some working faces in the Huainan mining area, the regression formula between subsidence coefficient and bedrock and mining depth ratio (\(H_{0}-h_{\text{s}}\))/\(H_{0}\) can be obtained through regression analysis, As shown in Equation (1) and Figure 3,  
\[q=-1.0305(H_{0}-h_{\text{s}})/H_{0}+1.3489 \tag{1}\]  
Goodness of fit \(R^{2}=0.7744\), showing that the subsidence coefficient is closely related to the bedrock and mining depth and decreases with the increase in the proportion of bedrock in the mining depth.
## 3 Results
### Assessment of Process Efficiency: Linking Physico-Chemical Indicators with Process Performance
#### 3.1.3 Comparative Turbidity Removal  
Figure 4 presents the turbidity decrease results after the ECZ and ECZ-MAG processes. The highest turbidity removal efficiency for the 20 min processes was achieved by the ECZ-MAG process at a mixing speed of 350 rpm, with a removal rate of 98.59%. For the 30 min processes, the highest turbidity removal was also observed in the ECZ-MAG process at 350 rpm, reaching 83.25%.  
However, turbidity values for the 10 min processes--ECZ at 250 rpm, ECZ at 350 rpm, and ECZ-MAG at 350 rpm--were higher than the initial value, resulting in negative removal  
Figure 4: Turbidity reduction after ECZ and ECZ-MAG processes at different mixing speeds of 250 and 350 rpm for (**a**) 10 min, (**b**) 20 min, and (**c**) 30 min. Note: turbidity removal is expressed as percentages of mean turbidity values; standard deviation from the average value for turbidity is in the range \(\pm\)(0.14–39.19) NTU.  
efficiencies of \(-289.27\%\), \(-19.4\%\), and \(-150.53\%\), respectively. This clearly indicates that the combination of mixing speed at a shorter process duration and the presence of the magnetic field in the ECZ process with the addition of synthetic zeolite (with particle size \(<40\)\(\upmu\)m) forms a stable suspension, increasing temporarily the final turbidity values. The enhanced turbidity removal observed in the 20 and 30 min magnet-assisted ECZ-MAG processes suggests that the magnetic field promotes better aggregation and floc formation over longer treatment times. This improvement is likely due to enhanced micro-mixing and destabilization of colloidal particles, which allows more effective floc formation. [PERSON] et al. (2006) [33] demonstrated that the application of a magnetic field in combination with EC and Fe electrodes enhances the turbidity removal at the longer contact time of 200 min. They achieved turbidity removal of up to 81.25%, compared to 75.16% in the absence of magnetic assistance. At a shorter contact time, turbidity oscillates, even reaching negative values, which is consistent with our previous finding for the ECZ-MAG process with Fe electrodes [29].
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### Self-Optimizing Traffic Steering for mmWave HetNet
Then, \(R_{k}^{ij}\) can be modeled as  
\[R_{k}^{ij}=B^{i}\sum_{u\in\mathcal{U}^{i}}y_{ku}^{ij}\log_{2}\left(1+\frac{p_{ k}^{ij}g_{ku}^{ij}}{\sum_{\
u\
eq k}y_{ku}^{ij}p_{k^{\prime}u}^{ij}g_{ku}^{ij}+ \sigma^{2}}\right), \tag{1}\]  
where \(g_{ku}^{ij}\) indicates the channel gain from SBS/AP \(j\in\mathcal{J}^{i}\) to user \(k\) at channel \(u\) and \(\sigma^{2}\) is the variance of additive white Gaussian noise (AWGN). Furthermore, \(\mathcal{U}^{i}\) and \(B^{i}\) denote the channel set available for network \(i\) and the corresponding channel bandwidth, respectively. Based on the above definitions, the proposed joint inter-network and intra-network traffic steering can be mathematically formulated as the following sum rate maximization problem:  
\[\mathcal{P}_{0}:~{}\max_{\mathbf{x},\mathbf{y},\mathbf{P}}~{} \sum_{i\in\mathcal{I}}\sum_{j\in\mathcal{J}^{i}}\sum_{k\in\mathcal{K}}x_{k}^{ ij}R_{k}^{ij}\] (2) s.t. \[\sum_{i\in\mathcal{I}}\sum_{j\in\mathcal{J}^{i}}x_{k}^{ij}\geq 1,~{} \forall k  
\[\sum_{i\in\mathcal{I}}\sum_{j\in\mathcal{J}^{i}}R^{ij}_{k}\geq R^{k}_{\text{min}}, \ \forall k\in\mathcal{K}, \tag{6}\]  
where \(\mathcal{I}=\{0,1,2\}\), \(\mathcal{J}^{i}\) denotes the set of SBSs/APs in network \(i\), and \(\mathcal{K}\) indicates the user set. In problem \(\mathcal{P}_{0}\), constraint (3) signifies that the traffic of each user \(k\) should be steered to at least one network. Constraint (4) ensures that user \(k\) can access at least one channel if it is steered to SBS/AP \(j\in\mathcal{J}^{i}\), and constraints (5) and (6) guarantee the maximum transmit power of each SBS/AP \(j\in\mathcal{J}^{i}\) and the minimum data rate of each user \(k\in\mathcal{K}\). The formulated sum rate maximization problem \(\mathcal{P}_{0}\) is a coupled mixed-integer non-linear programming problem (MINLP), which can be jointly solved by semi-distributed optimization algorithms with fractional programming, such as the semi-distributed alternating direction method of multipliers and block coordinate update method (ADMM-BCU) [19; 20].
## 6 Conclusions  
In this paper, we present a statistical analysis method to evaluate the accuracy and precision of GNSS positioning of smartphones, using only pseudorange measurements. The choice was made to extend such methods to any device using pseudorange only since carrier phase measurements are not always available on all devices.  
Two methods were described for the detection and elimination of outliers in the positioning data, namely, the Tukey and the K-means methods, which can improve the positioning accuracy of mobile devices.  
The K-means method can be further investigated in order to improve the positioning performance of smartphones through artificial intelligence; therefore, the authors propose further studies in this research direction.  
The comparative study of DGNSS and SPP methods showed that SPP is more accurate for medium to long observation times, while DGNSS is more efficient in the short term.  
The results obtained could be aimed at applications in GIS for smartphones, allowing, for example, the creation of citizen science experiments (environmental, extended heritage monitoring, etc.), quickly and relatively accurately.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Planimetric Positioning Errors [m]**} \\ \hline
**Mode** & **Point 1** & **Point 2** & **Point 3** & **Point 4** \\ \hline SPP & 0.090 & 4.510 & 0.760 & 3.246 \\ Global Mapper Mobile & 0.400 & 7.830 & 5.260 & 4.200 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Planimetric positioning errors achieved on the area investigated.  
Figure 6: Test area: (**a**) Orthophoto with indication of the vertexes surveyed; (**b**) points surveyed with SPP and Global Mapper Mobile.  
The application conducted using a smartphone app showed that it is possible to improve accuracy if a tool is implemented to analyse and process corrections in real time, such as a CSRS-PPP service. This implementation could significantly improve GNSS positioning in smartphone GIS and identify developments for further applications.  
Conceptualisation, M.P., D.C., G.V. and V.S.A.; methodology, M.P., D.C., G.V. and V.S.A.; software, M.P., D.C., G.V. and V.S.A.; validation, M.P., D.C., G.V. and V.S.A.; formal analysis, M.P., D.C., G.V. and V.S.A.; investigation, M.P., D.C., G.V. and V.S.A.; data curation, M.P., D.C., G.V. and V.S.A.; writing--original draft preparation, M.P., D.C., G.V. and V.S.A.; writing--review and editing, M.P., D.C., G.V. and V.S.A.; visualisation, M.P., D.C., G.V. and V.S.A. All authors contributed equally to the research and writing of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research was carried out in the project: PON \"Ricerca e Innovazione\" 2014-2020 A. L2 \"Mobilita dei Ricercatori\" D.M. n. 407-27/02/2018 AIM \"Attraction and International Mobility\" (AIM1895471-Line 1).  
The authors declare no conflict of interest.