esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Cell Culture Conditions  
The cells were cultured in a fibroblast growth basal medium (FBMTM, Lonza, Basel, Switzerland), which was enriched by adding 10% fetal bovine serum (FBS), gentamicin, insulin and basic human fibroblast growth factor (FGMTM SingleQuotsTM; Lonza, Basel, Switzerland). The cells were grown in monolayers in 25 cm\({}^{2}\) culture flasks (Sarstedt, Numbrecht, Germany).  
NHDF cells that were between passages four and eight were used to evaluate the influence of fluoride and an SMF. Their number and viability were assessed using a Countess TM Automated Cell Counter (Invitrogen, Carlsbad, CA, USA) and 0.4% trypan blue.  
For the research, the cells were seeded into culture flasks at a density of 1 \(\times\) 10\({}^{6}\) cells and were then incubated for 24 h. Next, the cells for the experiment were divided into four groups: (1) a control group (C), which was represented by cells that had not been treated with NaF (sodium fluoride) and had not been exposed to an SMF; (2) a group that had been treated sodium fluoride (F), which was represented by cells that had only been treated with NaF at a concentration of 0.3 mmol/I (Sigma-Aldrich, St Louis, MO, USA); (3) a group that had been exposed to an SMF (SMF), which was represented by cells that had only been exposed to an SMF with magnetic flux density of 0.65 T; and (4) group that had been exposed to an F + SMF, which was represented by cells that had been exposed to a combination of NaF and an SMF. After 24 h of being cultured with the tested factors, the cells were washed with phosphate-buffered saline (PBS, Lonza, Basel, Switzerland) and the pellets were collected for further analysis.  
An SMF was generated in a special magnetic chamber that was designed for in vitro research and consisted of a ferromagnetic yoke and a permanent magnet. For chambers that were fully fit to the cell flask, the uniformity of the static magnetic field induction was maintained on the entire surface of the culture [25; 26]. For the unexposed cultures, the control chambers in which steel (0.0 T) was used instead of a permanent magnet were used. The concentration of fluoride ions and magnetic induction used were decided based on recent studies that showed that they had no significant effect on cell viability [23; 27]. In addition, a concentration of NaF at which the magnesium and calcium fluorides did not precipitate, which would have affected the functioning of the cells, was chosen. Moreover, the selected concentration of NaF (0.3 mmol/I) was higher than is usually observed in healthy adult serum [28], which could be clinically significant. The method for exposing the cell cultures to both factors has been previously described [23].
## 2 Materials and Methods  
### GNSS and Jamming Devices Used in the Experiment  
Four double-frequency smartphones were tested along with two Leica GS18 and two Leica GS15 receivers (Figure 1). The basic characteristics for the smartphones are given in Table 1. Additionally, a Leica Viva TS16 SmartPole, i.e., a combination of a GNSS Leica 15 receiver, a 360\({}^{\circ}\) prism and a tachymeter TS16, was used to determine the jammer's position during a walking experiment.  
Table 1 lists some basic characteristics for the smartphones used in the experiment. All of them were multi-constellations, making it possible to receive GPS (L1, L5), GLONASS (G1), Galileo (E1, E5a), and BeiDou (B1) signals. Additionally, the Huawei P40 also received B1I, B1C, and B2a BeiDou signals.  
The jamming actions were performed with three different devices, listed in Table 2, shown by their consecutive numbering from left to right (Figure 1b). In which part of the frequency band each jammer interferes with the assigned signals, while sweeping the observed band, is shown by the signal output power of the jammer as a function of the frequency. The experimental setup for measuring the output power of jammer 2 at port L1 via a cable and a 10-dB coaxial attenuator is shown in Figure 2; port L2 was terminated with a dummy load. After a similar experiment for L2, the output signals recorded at ports L1 and L2 were summed. Jammer 3 does not have a SMA port, so its output power had to be measured with a non-attenuated trapping antenna next to the jammer housing. The results of both jammers are shown in Figure 3. The spectrum of the peak power for the output of jammer 1 was already presented in [6].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Smarthone** & **Launch** & **Android** & **Chipset** \\ \hline Xiaomi Mi8 & 31 May 2018 & Android 8.1 (Oreo) & BCM447/75 \\ Samsung S20 & 11 February 2020 & Android 10 & Snapdragon 865 \\ Huawei P40 & 26 March 2020 & Android 10 & Kirin 990 \\ Xiaomi 11T & 15 September 2021 & Android 11 & Snapdragon 888 \\ \hline \end{tabular}
\end{table}
Table 1: Some basic characteristics of smartphones used in the experiment.  
Figure 1: (a) Leica GS18 receiver and two smartphones setup; (b) jammers used in the experiment.
# Spectral Domain-Based Data-Embedding Mechanisms for Display-to-Camera Communication
## 3 Performance Evaluation and Results  
In this section, we describe the results of simulations and real-world experiments of the proposed model, and performance evaluations in terms of BER, PSNR, and ADR. The real-world experiment was performed in an indoor environment considering a wireless optical channel between the display and camera. The parameters used in the real-world experiments are shown in Table 1. The frame synchronization problem was alleviated by setting the capture rate of the camera to twice the transmitting rate of the display. To  
Figure 5: (**a**) The captured image, and (**b**) reconstructed image.  
observe the feasibility of the proposed model with varying input data size, we set the length of data vector to 250 and 500 bits. Embedding was performed in such a way that all bits were successfully encoded in the preferred position of spectral domain image, signifying a 100% embedding rate. Figure 6 shows actual experimental environments under various lighting conditions. Figure 6a-c shows experiments performed under normal lighting conditions with \(D\) = 15 cm, \(AOC\) = 90\({}^{\circ}\); \(D\) = 20 cm, \(AOC\) = 90\({}^{\circ}\); and \(D\) = 15 cm, \(AOC\) = 30\({}^{\circ}\), respectively. Similarly, Figure 6d shows an experimental environment in the presence of ambient lighting with \(D\) and \(AOC\) set to 20 cm and 90\({}^{\circ}\), respectively. For the input image, a 256 \(\times\) 256 grayscale Lenna image was used to embed the data. The data bits were encoded using the three different embedding mechanisms described in Section 2.2. For M1, \(\alpha\) was set in the range [10, 50], and for M2 and M3, the \(\alpha\) values were in the range [0.1, 0.3]. For error correction, we used the \(\frac{1}{2}\) convolution code in the experiment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Binary data bits & 250, 500 \\ Image & Lenna grayscale (256 \(\times\) 256) \\ Display unit & Samsung monitor, 59.8 cm (1920 \(\times\) 1080), transmit rate of 60 Hz \\ Receiver camera & One-plus 5T, 20 Megapixel, (1920 \(\times\) 1080), capture rate of 120 fps \\ Test environment & Indoor \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental parameters.  
Figure 6: Experiments conducted under various lighting conditions. (**a**) Normal lighting, (**b**) normal lighting with a varying distance, (**c**) normal lighting with a varying angle, and (**d**) ambient lighting.
# Predictive Power Management for Wind Powered Wireless Sensor Node
## 3 System Model
### Hardware Model  
Recently, scientific explorations show that wind energy could become a potential source to harvest for powering small autonomous sensors [25; 26]. Such EH-sensor does not need harsh working conditions and provide a new sight for EH-WSN in the forest. Accordingly, we conduct the research and design of a prototype wind-powered wireless sensor node, with its block diagram shown below in Figure 1.  
Figure 1: Block diagram of the proposed Energy Harvesting (EH)-node.  
The wind-powered sensor node consists of three main blocks: (i) energy harvester incorporating the wind turbine coupled to an electrical generator; (ii) power management unit, which contains power conditioning circuit and energy storage; (iii) wireless sensor node itself. As shown above, a small Wind Turbine Generator (WTG) harvests the wind energy into electrical energy, a Boost converter continuously tracks its optimal working point and transfers the energy onto a DC Bus (here only one set of harvester is mounted), and then a storage device (supercapacitor) would be charged gradually. Simultaneously this supercapacitor could power the followed TPC-enabled wireless sensor node through a Buck-Boost converter. Note all the drive signals of DC-DC converters are controlled by the wireless node. A detailed description of each block is analyzed as follows.
# Single Image Super-Resolution Method Based on an Improved Adversarial Generation Network
## 3 An Enhanced Generative Adversarial Network for SR
### Perceptual Loss Function
The content loss ensures the \(G(z)\) and \(x\) are closer to perceptual similarity during the training. While having enough capacity to fool the discriminative model, this encourages the adversarial loss to minimize the error \((D(G(z))-1)\) as little as possible. Although each pixel \(x_{i}\) in the images has more or less correlation with neighbors of \(x_{j}\) in the batch \(x_{1}\),..., \(x_{n}\), there are significant differences between the pixels on the edge. According to [36], we adopt the energy-based regularization \(E(z_{i},z_{j})\) to restrict the relationship between the center pixels and the neighboring ones generated by G. Our generative loss function can be formulated as follows:  
\[L_{G}(x,z)=n_{1}\|G(z)-x\|_{F}^{2}+n_{2}\|D(G(z))-1\|_{F}^{2}+n_{3}\sum_{i,j}E \big{(}z_{i},z_{j}\big{)} \tag{13}\]  
Here, \(||\bullet||_{F}\) denotes the Frobenius norm, and the coefficients \(n_{1}=1\), \(n_{2}=0.1\), and \(n_{3}=0.1\) in our experiment. The energy-based regularization \(\sum_{i,j}E(z_{i},z_{j})\) for the calculation is as follows:  
\[\sum_{i,j}E\big{(}z_{i},z_{j}\big{)}=\sum_{i=1}^{16}\sum_{j=1}^{16}G\big{(}z_{i },z_{j}\big{)}\cdot\exp\left(-\frac{\left\|z_{i}-z_{j}\right\|^{2}}{2a^{2}} \right)+\left(1-G\big{(}z_{i},z_{j}\big{)}\right)\cdot\exp\left(-\frac{\left\| 1-\mid z_{i}-z_{j}\right\|^{2}}{2b^{2}}\right) \tag{14}\]  
where \(z_{i},z_{j}\) represents the center pixel and its 16 connected pixels, respectively. \(G(z_{i},z_{j})\) denotes the gradient to reduce the error probability. In the Equation (10), we set a = 1 and b = 1 for simplicity. Note:  
\[ \begin{cases}G\big{(}z_{i},z_{j}\big{)}=0&\text{ if }z_{i}=z_{j}\\ G\big{(}z_{i},z_{j}\big{)}=1&\text{ otherwise}\end{cases}  \tag{15}\]  
Likewise, the discriminator as a classifier can determine whether the images are from the generator or the data distribution, if given training time. Our discriminative loss function not only calculates the MSE in the case of discriminator misclassification, but it also has enough capacity to distinguish between real images and generated images. Therefore, the discriminative loss function effectively avoids mode collapsing and increases the performance accuracy. Therefore, we have the following loss function:  
\[L_{D}(x,G(z))=\frac{1}{2}\|D(x)-1\|^{2}+\frac{1}{2}\|D(G(z))\|^{2} \tag{16}\]  
In summary, our perceptual loss function, including the generative and discriminative parts, is significant for our network, since it refines the high-frequency details and corrects the errors between the real images and the generated images.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 3 The Calculation of Measurement Vector for Heading Estimation
### Heading Estimation by Magnetometer  
The accelerometer, that measure specific force, can be used to obtain roll and pitch, and the heading can be solved by the leveled magnetic field intensity that is obtained by using roll and pitch. Many references discuss the solution of roll and pitch using acceleration, so we mainly discuss the heading calculated by magnetometer in this section. In this paper, the smartphone axis is show in Figure 1.  
Due to the building structure and materials, the magnetic field is more susceptible to contamination by hard iron, soft iron, and scale factors. This causes bias in magnetometer outputs; hence it is necessary to calibrate the magnetometer. The commonly used calibration method is ellipsoid fitting [36,37].  
After the magnetometers are calibrated, the heading can be solved from calibrated magnetic field intensities. Firstly, the magnetic field intensities can be leveled by roll and pitch.  
\[\left[\begin{array}{c}M_{x}\\ M_{y}\\ M_{z}\end{array}\right]=\left[\begin{array}{ccc}\cos\theta&\sin\gamma\sin \theta&\sin\gamma\cos\theta\\ 0&\cos\theta&-\sin\theta\\ -\sin\gamma&\cos\gamma\sin\theta&-\sin\gamma\cos\theta\end{array}\right]^{T} \left[\begin{array}{c}m_{x}\\ m_{y}\\ m_{z}\end{array}\right] \tag{25}\]  
where \(\left(m_{x},m_{y},m_{z}\right)\) and \(\left(M_{x},M_{y},M_{z}\right)\) are the geomagnetic field intensities in the body frame and navigation frame, respectively, and \(\gamma\) and \(\theta\) are the roll and pitch, respectively, calculated from the raw acceleration data.  
Then, taking into account the local magnetic declination, the heading can be calculated by:  
\[\psi_{mag}=\arctan\left(\frac{m_{x}\cos\theta-m_{z}\sin\gamma}{m_{x}\sin\gamma \sin\theta+m_{y}\cos\theta+m_{z}\cos\gamma\sin\theta}\right)+D \tag{26}\]  
where \(\psi_{mag}\) is the heading and \(D\) is the magnetic declination. \(\gamma\) and \(\theta\), calculated by accelerations, can be expressed as follows:  
\[\gamma=\arctan\left(\frac{a_{x}}{\sqrt{a_{y}^{2}+a_{z}^{2}}}\right) \tag{27}\]  
\[\theta =-\arctan\left(\frac{a_{y}}{a_{x}*\sin(\gamma)+a_{z}*\cos(\gamma)}\right) \tag{28}\]  
Figure 1: The body coordinate system of smartphone.  
where \(\left(a_{x},a_{y},a_{z}\right)\) are the accelerations in the body frame.
## 4 Results and Discussions
### Comparison of Ordinary LSTM and CFST-LSTM  
Besides the comparison between CFST-LSTM and other commonly seen machine learning/deep learning algorithms, this study also compared the performance of CFST-LSTM and the ordinary LSTM network. To better illustrate the comparison and the advantages of the proposed CFST-LSTM model, this experiment expands the test site form site 11 to all the sites within California State. For each site, we will separate the data into 70% training set and 30% testing set, and then train the models using ordinary LSTM (O-LSTM), full site inputs LSTM (F-LSTM), and CFST-LSTM. The differences between these three models are shown in Equations (17) to (19).  
\[\begin{split}&\mathit{Inputs}_{\mathit{Site\ i}}\overset{O-LSTM}{ \rightarrow}\mathit{Predictions}_{\mathit{Site\ i}}\end{split} \tag{17}\]  
\[\begin{split}&\mathit{Inputs}_{\mathit{All\ Sites}}\overset{F-LSTM}{ \rightarrow}\mathit{Predictions}_{\mathit{Site\ i}}\end{split} \tag{18}\]  
\[\begin{split}&\mathit{Inputs}_{\mathit{All\ Sites}}\overset{CFST-LSTM}{ \rightarrow}\mathit{Predictions}_{\mathit{Site\ i}}\end{split} \tag{19}\]  
The results are all calculated based on the testing sets of different sites, and they are shown in Figure 9. Figure 9 (left) is the R\({}^{2}\) values on different sites. \(R_{O}^{2}\) means the R\({}^{2}\) value calculated using O-LSTM, and is marked using a blue line. \(R_{F}^{2}\) means F-LSTM, and is marked using a green line. \(R_{C}^{2}\) means CFST-LSTM, and is presented using an orange line. It can be seen that overall, CFST-LSTM performs the best, O-LSTM the second, and F-LSTM the third. To show the difference between these three models more clear, we calculated the values of \(R_{O}^{2}-R_{F}^{2}\) and \(R_{O}^{2}-R_{C}^{2}\), and also plotted them on Figure 9 (right). In this way, it can be seen that O-LSTM performs better than F-LSTM in most sites, while it also seldom surpasses CFST-LSTM.  
The average values of these three indicators on all sites are shown in Table 4. It can be seen that CFST-LSTM has the highest average R\({}^{2}\) with a value of 0.9155, and it is 2.88% better than O-LSTM and 6.29% better than F-LSTM. This is reasonable since CFST-LSTM not only considered the influence from nearby stations, but also filtered out less related time series inputs. F-LSTM performs the worst with a value of 0.8613, which is 3.32% lower than O-LSTM. This is also understandable since it contains too much noise when modeling the PM2.5 concentrations.  
Figure 9: Comparison of different LSTM models on different sites.  
Overall, the comparison between these three models helps prove the effectiveness of the proposed CFST-LSTM. This reflects that retaining the stations with higher correlation and dropping those with lower correlation can effectively improve the prediction accuracy of PM2.5 concentrations.
# Tuning Guidelines for an Adaptive-Gain Parabolic Sliding Mode Filter
## 4 Tuning Guidelines of AG-PSMF
### Tuning Guideline for \(N_{\max}\)  
In Section 3.2, it has been shown that the minimum AME appears around \(N_{\max}=50\). Thus, it is suggested that \(N_{\max}\) should be set as around \(N_{\max}=50\). However, in the applications where the computational time is a major concern, it is advisable to set around \(20\leq N_{\max}\leq 30\) by considering the results of Section 3.3. This is because under such values of \(N_{\max}\), AG-PSMF consumes slightly less computational cost while maintaining relatively low level of AME.
# BSM-Data Reuse Model Based on In-Vehicular Computing
## 5 Study Case: In-Vehicle BSM Data Services Platform
### Data Forecasting  
To take our model a step further, we suggest applying a machine learning (ML) method on our database to perceive whether preserved BSMs can help the road traffic volume prediction (TV). Our study applies the Artificial Neural Network (ANN) approach to predict the traffic volume while using past BSMs data. We deal with the following basic steps of the ML: get the data from database, prepare it, choose a model, train it, evaluate it, export it, and make the predictions available for use. Our development environment has relied on TensorFlow as the framework. For the generating part, we made use of Python, with Jupyter Notebooks, and for the prediction serving part, we adopted Java, while using Spring Boot.
## 3 Design Characteristics
### Geometry Selection  
The commercial feed arm and aperture holding structures define the main geometrical parameters in the designed reflectarray antenna. In the geometry defined in Figure 2:  
* The feed is located at position \(X_{f}=138.7\) mm, \(Y_{f}=7.1\) mm (due to feedarm misalignment) and \(Z_{f}=179.7\) mm. This position implies a focal length \(F=227\) mm. It has an angle of rotation \(\theta_{f}=28.78^{\circ}\) with respect to the normal of the aperture.
* The feedarm limits physically the maximum semi-distance from the centre to one edge of the aperture in X axis dimension to \(S_{x}=220\) mm.  
Figure 4: _Skyware_ antenna assembly for characterization in anechoic chamber and its measured radiation pattern and the simulated one with the model created from measurements results.  
Figure 3: 2D mask in UV space to calculate the SLL outside (yellow part) the main beam (blue part) area.  
* The focal length to reflectarray diameter ratio (\(F/D\)) is selected to maximize the aperture efficiency \(\eta_{a}\) in Y axis dimension, while the X axis size (\(D_{x}\)) is increased from the one achieving maximum aperture efficiency in order to decrease the elevation beamwidth. According to the horn radiation pattern and focal length, the aperture efficiency in the normal plane to Y axis is maximum for \(f/D_{y}=0.83\) (\(\eta_{a}=68.6\%\)), which means \(D_{y}=273\) mm. \(D_{x}\) is fixed at about 374 mm, achieving an aperture efficiency of \(\eta_{a}=61.4\%\).
* The inter-element spacing is fixed to be 17 mm, so a reflectarray of \(22\times 16\) elements can be designed.
## 3 Results and Discussions
### Effect of Installation  
The installation of helical piles is accomplished by imparting torque to the shaft. Making substantial shear stresses in the soil by turning the helices causes significant soil disturbance and alteration of the soil's structural characteristics [37,43]. Several studies discussed the disturbance that was caused to soil as piles were driven into the ground. [PERSON] and [PERSON] [44] compared the results of the uplift and compression capacity of a single helix pile and found that the disturbed zone of soil above the helix and close to the installation path increased the compressive capacity by 1.4 to 1.5 times its equivalent uplift capacity. Furthermore, [PERSON] [45] reported that after a certain critical embedment depth ratio, the failure mode under the uplift and compressive loads remains similar. This is due to the fact that the soil bed is made of remolded clay and the strength loss due to installation disruption was negligible.  
The advancement of the pile shaft into the earth and the rotation of the helical plates are responsible for the cause of the soil disturbance, while the disturbance caused due to shaft advancement is expected to be greater than the helical plate penetration. The helix plates disrupt soil by cutting spirally at intervals corresponding to the pitch spacing, shearing, and displacing the soil traversed by the helix vertically and laterally. From the previous laboratory studies on model helical anchors installed in remolded clay beds [9,22,24], it is observed that the strength of remolded clay soil does not change significantly. As a result, the installation effects of remolded clay materials are difficult to differentiate.  
Thus, the soil disturbance caused due to the pile installation and the shearing from helices is not considered in this study, assuming that the loss in strength due to installation is negligible in remolded clay materials. Furthermore, the remolded undrained shear strength is used to figure out capacities and make numerical evaluations.  
Figure 6: (**a**) Schematic view of the experimental setup (Uplift loading); (**b**) Data Acquisition Arrangements (DAQ); (**c**) Completed test installation of Helical pile and LVDT arrangements prior to Loading; and (**d**) Pictorial view of the Experimental Test Setup-Uplift loading.