esa-sceva/satcom-chunk-collection huggingface dataset:
# Car Bumper Effects in ADAS Sensors at Automotive Radar Frequencies
## 4 Experimental Setup and Procedure
### Measurement Procedure  
Measurements were performed in the range of 76 GHz to 81 GHz with frequency steps of 1 GHz. First, we set one of the desired frequencies in the signal generator. Then, the same frequency was tuned in the frequency analyzer that was configured in the zero-span mode. Thus, the power level of a single frequency is measured in the time domain, allowing for noise reduction when averaging all measurement points. For this purpose, up to 1001 measurement points were taken at each frequency. The resolution bandwidth was set to 1 kHz. This procedure was repeated for each frequency spot.  
The transmitting and receiving frequencies were synchronized by connecting both the signal generator and the spectrum analyzer to the same Rubidium frequency standard. With each frequency change, we provided the system with a warm-up time of several minutes until a stable signal was received. Once the signal was stable, the free-space transmission technique [34; 35; 36; 37] was used to compute the attenuation. First, a reference measurement was taken with the empty sample holder. Then, a bumper sample was mounted in the holder to measure its effect on the propagated signal. Finally, the attenuation induced by the bumper was computed at each frequency as follows:  
\[\text{Attenuation (dB)}=\text{Power}_{\text{free\_space}}\text{(dBm)}-\text{Power}_{ \text{bumper}}\text{ (dBm)} \tag{1}\]  
The variable \(\text{Power}_{\text{free\_space}}\) represents the power received when there is no sample, and \(\text{Power}_{\text{bumper}}\) averages the power received when the transmitted signal passes through the bumper sample. Both quantities averaged 1001 samples taken at each frequency point.
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Left Handed Dipole Antenna  
The left-handed dipole antenna is a new kind of antenna. Its name derives from its left-handed mode of transmission [45; 46]. The design of this antenna is fundamentally built on shunt inductors and capacitors. The capacitor is placed by the line that prompts current of different amplitude on either side. This current does not totally cross out in the far-field, thus, it transmits.
## Materials and Methods
### Application to Spaceborne Data  
To test the applicability of the method to spaceborne data, we used a RapidEye satellite image of June 5 th, 2015 with a spatial resolution of 5 m. The image was atmospherically corrected using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) model, which is known to perform well [61]. This atmospheric correction model requires geometrical, atmospherical, sensor, spectral, ground reflectance, and signal input (see Table 2). The atmospheric profile was set to midlatitude summer.  
In the RapidEye image, areas dominated by _Spartina_ were selected, the ProSail model was applied to the reselected areas to estimate LAI. The spatial driver information was extracted at the center of every inverted RapidEye pixel. All analyses were performed on 5 \(\times\) 5 m pixels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameter Name** & \begin{tabular}{c} **Model** \\ **Abbreviation** \\ \end{tabular} & **Mean Value** & \begin{tabular}{c} **In Situ** \\ **Range** \\ \end{tabular} & **Source** \\ \hline Observer zenith angle & \begin{tabular}{c} \begin{tabular}{c} To (\({}^{\circ}\)) \\ \end{tabular} & N.A. & N.A. \\ Relative azimuth angle \\ \end{tabular} & \
\begin{tabular}{c} N.A. \\ \end{tabular} & N.A. &
\begin{tabular}{c} Always 0 \\ From \\ timestamp \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._Again, linear regressions were used to compare the different spatial drivers with the estimated LAI (see method in Section 2.4). The correlation between the effects of different drivers was tested with Pearson correlation tests. In addition a multiple linear regression was used to relate the estimated LAI with the spatial drivers in a single test to avoid explaining variation several times. This allowed us to establish the relative importance of each spatial driver.
## 4 Study on the Influence of Blast Vibrations on Tunnels
### Variation in Blast-Induced Stress at Different Measurement Points  
When blasting occurs within rock or other solid media, the resulting shockwave generates stress disturbances that propagate after a certain distance from the explosion center. These stress disturbances propagate through the medium in the form of waves, forming what is known as blast stress waves. Different regions at varying distances from the explosion center exhibit blast stress waves with different waveforms. When stress changes linearly, it is referred to as an elastic wave, whereas nonlinear changes signify shock waves and plastic waves [29]. We processed the simulation results to analyze the patterns of stress variation at each measurement point before and after blasting.  
Using in situ stress values of 1 MPa and 5 MPa, respectively, we extracted the maximum values of stress in the X, Y, and Z directions and the effective stress direction for measurement points A to G. We then employed Origin to plot the curves showing stress variations with changes in the blast center. For specific results, please refer to Figure 10.  
According to Figure 10, during the propagation of stress waves, their intensity gradually decreases with increasing distance. In the same medium, under a 5 MPa in situ stress condition, the numerical value of the stress waves is much higher than that under a 1 MPa in situ stress condition, indicating a significant influence of the in situ stress on the stress wave intensity. During the propagation of stress waves, they are influenced by medium absorption and scattering, resulting in a gradual decrease in intensity. When the distance from the explosion center is within the range of 10 to 25 m, the variation in stress waves is extremely intense, with their intensity decreasing rapidly. However, beyond a distance of 25 m, the rate of decrease in stress wave intensity slows down. The attenuation rate of stress waves is affected when they pass through different types of media or encounter obstacles. The presence of more free surfaces at measurement points A and C alters the propagation path of the stress waves generated by the explosion; thus, the numerical values of stress waves may increase or decrease, rather than exhibiting a single decay trend.
## 4 Discussion  
The innovations in digital technologies have encouraged the research on the simulation of a 3D virtual patient [42]. This progress was evident in this systematic review: 90% of the selected papershave been published in the last decade and 52% within the last five years. Concerning data integration, three methods have been described in literature: voxel-based, point-based and surface-based with or without fiducial markers. Marker-based registration protocols use easily identifiable reference markers that can be clearly identified on anatomical structures [38]. Surface-based registration is an automatic procedure that involves the selection of the surfaces to be matched then, iterative closest point (ICP) algorithm matches the 3D objects to minimize the distance between the selected areas. Two studies compared surface-related and point-related registration: both concluded that the surface-related one has a lower error, as the identification of landmarks is biased from the human perception [32]. Point-based registration is a direct and semi-automatic procedure based on the manual selection of at least three corresponding landmarks on two 3D objects; then, the \"least squares\" moves the 3D objects to find the best fit for the point pairs. [PERSON] et al. showed that the accuracy does not increase when increasing the number of reference landmarks, and the minimum registration error is when seven corresponding points are considered [38]. Based on the results of that study, a surface-based registration is more accurate than the point-based one; however, a point-based registration is a good option when streaking artifacts impede to identify homologous areas on the CBCT. Voxel-based registration requires to overlap voxels belonging to different CBCT files. Even if this procedure has the lowest registration error declared, it has the inconvenience of necessitating a double radiographic examination, which is associated with an increase of radiation exposure [25]. To keep the radiation dose as low as reasonably achievable (ALARA principles), a 13-cm FOV CBCT scan can be performed with good registration results [36].
# Precise Orbit Solution for Swarm Using Space-Borne GPS Data and Optimized Pseudo-Stochastic Pulses
## 1 Introduction
Orbit quality is evaluated by GPS phase residuals and comparison with PSOs released by ESA [10]. This article consists of the following sections. Section 2 introduces three main approaches for Low Earth Orbiter (LEO) precise orbit solutions. Force models, GPS observations and estimated parameters for Swarm Orbit Determination (OD) are introduced in detail. Section 3 presents the data processing strategy. Section 4 introduces the relevant experiments. Conclusions are showed in Section 5.
# MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems
## 7 Preprocessing Methodology
### Dimensionality Reduction  
To address the high dimensionality of the data set, principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) were employed. These methods were chosen for their complementary strengths and widespread use in the literature. PCA is a well-established, computationally efficient method for linear dimensionality reduction. It is particularly effective for capturing global structures in high-dimensional data by identifying orthogonal directions of maximum variance [49; 50]. Mathematically, PCA identifies orthogonal directions (principal components) by solving the eigenvalue decomposition of the covariance matrix of the data, thereby ensuring that the majority of the information is retained in a reduced set of dimensions. In the context of condition monitoring and fault diagnosis, PCA has proven invaluable for reducing noise and highlighting dominant patterns in the data [51]. On the other hand, t-SNE excels at preserving local structures and revealing clusters in high-dimensional data, making it highly suitable for visualizing complex relationships. t-SNE works by minimizing the Kullback-Leibler divergence between the high- and low-dimensional distributions of pairwise similarities. This approach is particularly effective for uncovering clusters, patterns, and relationships in complex data sets, making it well suited for this study [52; 53]. Unlike PCA, t-SNE is capable of capturing nonlinear relationships, which are often critical for understanding fault patterns in mechanical systems [54]. By employing both PCA and t-SNE separately on the same data set, the unique strengths of each method are leveraged. PCA is utilized for efficient dimensionality reduction and noise reduction, enabling the identification of dominant patterns and global structures in the data. In contrast, t-SNE is applied for detailed visualization and cluster analysis, as it excels at preserving local structures and revealing nonlinear relationships. This dual approach allows for a comprehensive exploration of the data set from different perspectives, enhancing the ability to detect and diagnose faults robustly.
###### Abstract  
The purpose of the study was to investigate the effect of seven weeks inter-repetition rest vs. traditional strength training on lower body strength, rate of force development (RFD), and vastus lateralis (VL) muscle architecture. Sixteen male participants were assigned into two groups: the inter-repetition rest (IRRG) and the traditional (TG) group. Both groups performed the leg press exercise with four sets of six maximum repetitions (RM) for two training sessions per week. IRRG added a 20 s inter-repetition rest period between single repetitions. Before and after the training period, 1-RM in leg press, isometric leg press RFD, and peak force (PF), VL muscle architecture, vastus intermediius (VI) thickness, and quadriceps' cross sectional area (CSA) with ultrasonography, were measured. Two way ANOVA for repeated measures was used for statistics. One-RM strength increased similarly for both groups (\(p<0.05\)), while percentage increases in RFD were greater for IRRG compared to TG (\(p<0.05\)). Isometric PF was increased similarly for both groups (\(p<0.05\)). VL and VI thickness as well as CSA of the quadriceps increased similarly in both groups, while VL fascicle length increased more following IRRG compared to TG (IRRG: 4.8 \(\pm\) 6.1% vs. TG: \(-3.9\)\(\pm\) 5.4%, \(p=0.001\)). These results suggest that 20 s inter-repetition rest during strength training may effectively increase lower body explosive strength and muscle fascicle length without compromising muscle hypertrophy.  
Keywords:resistance training; fascicle length; lower body explosiveness; skeletal muscle hypertrophy +
Footnote †: journal: Applied Sciences
## 2 Basic Theory and Calculation Method
### An Improved Method  
Equation (9) shows that the distribution of stratum displacement caused by shield construction (stratum loss) along the y-axis driving satisfies the law of \(\frac{1}{2}\times\left[1+\frac{y}{(x^{2}+y^{2}+H^{2})\frac{1}{2}}\right]\). Therefore, based on this law, the [PERSON]'s solution is extended to the three-dimensional. It is noticed that the coordinate system adopted in this paper is different from that used in the [PERSON]'s solution, so the distribution law should be revised, and its expression is as in Equation (10):  
\[\varepsilon_{y}=\frac{1}{2}\times\left[1-\frac{y}{(x^{2}+y^{2}+H^{2})\frac{1} {2}}\right]\text{.} \tag{10}\]  
The plane stratum loss parameter \(\varepsilon_{x,z}\) is extended to three-dimensional parameter \(\varepsilon_{x,z,y}\):  
\[\varepsilon_{x,y,x}=\varepsilon_{y}\times\varepsilon_{x,z}=\left[1-\frac{y}{ (x^{2}+y^{2}+H^{2})\frac{1}{2}}\right]\times\frac{4 gR+g^{2}}{8R^{2}}\exp\left\{ -\left|\frac{1.38x^{2}}{(H+R)^{2}}+\frac{0.69z^{2}}{H^{2}}\right|\right\} \tag{11}\]  
\(\varepsilon_{x,z}\) is replaced by \(\varepsilon_{x,z,y}\) in Equation (7) and Equation (12) is the three-dimensional analytical solution of stratum displacement caused by stratum loss. When \(y=0\), \(z=0\), the curves of settlement trough calculated by Equations (7), (8), and (12) are the same, and Equation (8) is the particular solution about the surface settlement value of Equation (12) at the tunnel face.  
\[\left\{\begin{array}{l} S_{z}=R^{2}\left\{-\frac{z-H}{x^{2}+ \left(z-H\right)^{2}}+(3-4\mu)\frac{z+H}{x^{2}+\left(z+H\right)^{2}}-\frac{2z \left[x^{2}-(z+H)^{2}\right]}{\left[x^{2}+\left(z+H\right)^{2}\right]^{2}} \right\}\times\varepsilon_{x,y,z}\\ S_{x}=-R^{2}x\left\{\frac{1}{x^{2}+\left(z-H\right)^{2}}+ \frac{(3-4\mu)}{x^{2}+\left(z+H\right)^{2}}-\frac{4z(z+H)}{\left[x^{2}+\left( z+H\right)^{2}\right]^{2}}\right\}\times\varepsilon_{x,y,z}\end{array}\right. \tag{12}\]  
For example, let \(H=13.65\) m, \(\mu=0.4\), \(R=2.325\) m, \(g=0.058\) m, then the displacement of soil layers at \(z=0\) m and \(z=10\) m is showed in Figure 4.
## 3 Results and Discussion
### The Effect of pH on the Adsorption of Cimetidine  
The effect of pH on the adsorption of cimetidine onto WRB, GWRB, and HWRB are shown in Figure 6. The results show that the solution pH values affect the adsorption of cimetidine remarkably. For all three bricks, the adsorption capacity of cimetidine increased to a maximum with pH values increased from 4.0 to 6.0 and then decreased with the solution pH values increased further. This result is in agreement with previous investigations on the adsorption of other pharmaceuticals, which showed a similar structure [35]. The effect of solution pH could be attributed to the speciation of cimetidine in aqueous solution and the main surface chemistry of the three bricks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorbents**} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} \\ \cline{2-7}  & \(\mathbf{K_{\text{L}}}\) & \(\boldsymbol{q_{\text{max}}}\) & \(\mathbf{R^{2}}\) & \(\mathbf{K_{\text{f}}}\) & \(\boldsymbol{1/n}\) & \(\mathbf{R^{2}}\) \\ \hline WRB & 0.12 & 172.1 & 0.99 & 34.2 & 0.41 & 0.96 \\ HWRB & 0.20 & 240.3 & 0.99 & 61.8 & 0.36 & 0.87 \\ GWRB & 0.23 & 214.1 & 0.98 & 66.7 & 0.32 & 0.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameters and correlation coefficients of Langmuir and Freundlich adsorption isotherms.  
Figure 5: Adsorption isotherms of cimetidine onto WRB, GWRB, and HWRB.  
As shown in Table 1, the _pKa_ value of cimetidine was about 6.8. When the pH \(<\) 6.8, cimetidine in the solution was positively charged, while cimetidine showed negative charged with the pH \(>\) 6.8. As shown in Figure 7, in the experiment pH values, the electrical property of WRB, GWRB, and HWRB was almost negative. With the pH values increase from 4.0 to 7.0, the percentage of the neutral form of cimetidine increased, which caused the cimetidine more easily complexed with bricks. The adsorption capacity of cimetidine onto bricks increased due to the increasing electrostatic attractions. With the pH values increasing further, the percentage of anionic form of cimetidine increased. Due to the increasing electrostatic repulsion, the adsorption of cimetidine onto three bricks was inhibited. The maximum adsorption capacity of cimetidine onto three bricks was almost at about 6.0, which is close to the _pKa_ values of cimetidine suggesting a significant fraction of the N-H groups. Such adsorption behavior, which the maximum adsorption occurs at the _pKa_ values has also been observed for the adsorption of some other pharmaceuticals onto iron species [32,35]. Furthermore, the adsorption capacity also followed the order GWRB \(>\) HWRB \(>\) WRB when the pH values below 7.0, which is also confirmed in the adsorption kinetics and isotherms.  
Figure 6: The effect of pH on the adsorption of cimetidine onto WRB, GWRB, and HWRB.  
Figure 7: The zeta potential of WRB, GWRB, and HWRB.
## 2 Materials and Methods
### Research Methods
#### 2.1.1 Global Spatial Autocorrelation Method  
The global spatial autocorrelation analysis is based on the macro level. Moran's I is used to compare the average production cost of corn in the country with the cost in each province in order to obtain the correlation of the entire study area. The calculation formulas are  
\[I=\frac{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}W_{ij}(x_{i}-\overline{x}) \left(x_{j}-\overline{x}\right)}{S^{2}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^ {n}W_{ij}} \tag{1}\]\[S^{2}=\frac{1}{n}\sum_{i=1}^{n}{(x_{i}-\overline{x})^{2}\overline{x}}=\frac{1}{n} \sum_{i=1}^{n}{x_{i}} \tag{2}\]  
In the formulas, \(n\) is the sum of all research objects. \(x_{i}\) and \(x_{j}\) are the values of the corn production cost at the adjacent paired space points. \(\overline{x}\) is the average of all production costs of corn. \(W_{ij}\) is the matrix element in row \(i\) and column \(j\) of the geographical feature spatial weight matrix. Both rows and columns correspond to the spatial unit. The spatial weight matrix of the geographical feature set in this paper is a (0, 1) adjacency standard matrix. The value range of [PERSON]'s \(I\) is \([-1,1]\). If \(I>0\), there is a positive spatial correlation, and objects with similar attributes gather together. Conversely, if \(I<0\), negative spatial correlation exists, meaning that spatially adjacent units do not have similar properties. Finally, \(I=0\) indicates random distribution with no spatial autocorrelation.  
After calculating the [PERSON]'s \(I\) index, the \(p\) value of the standardized statistic \(Z\) value is generally used to test whether there is spatial autocorrelation in the production cost of the regional unit. The calculation formula is  
\[Z=\frac{I-E(I)}{\sqrt{Var(I)}} \tag{3}\]  
In Formula (3), \(E(I)\) represents the digital expected value, while \(Var(I)\) is the coefficient of variation. In cases where \(Z<1.96\) or \(Z>1.96\), the global spatial autocorrelation between regions is taken to be significant. Moreover, if \(p\) is less than the given significance level (5% in the present article), the global spatial autocorrelation between regions is considered to be significant.
## 3 Simulation Results
### Joint Design System for Downlink Communication
#### 3.2.3 Unveiling the Impact of Mobile User Equipment  
Integrating mobile user equipment (UE) into the framework of joint communication and sensing systems unveils a realm of dynamic interactions. Within this paradigm, both transmit and receive antennas at the base station stand at the ready, numbering \(N_{t}=4\) and \(N_{r}=4\), respectively. These antennas, meticulously arrayed in linear formations with precise half-wavelength spacing, serve as the conduits for communication and sensing endeavors alike.  
Embarking on a journey through this landscape, we encounter the rhythmic dance of communication signals \(s(n)\) and radar signals \(x(n)\), each pulsating with vitality through the medium of the QPSK modulation scheme. Yet, this dynamic canvas is further enriched by the Doppler effect, an ever-present companion sculpted by the classical Clark model. Here, the velocity of the UE, interwoven with the carrier frequency, breathes life into the narrative, shaping the contours of our exploration.  
Beholden to this intricate interplay, Figure 9 unfurls a tapestry of insights. Within its confines, the communication performance, measured by BER, and the sensing prowess, gauged through MSE, converge. Fixed sensing power stands as a stalwart sentinel against the tides of change, while varying communication power offers glimpses into the shifting dynamics of our system.  
Yet, it is the velocity of the UE that casts a spell of transformation, ranging from static to mobile, with velocities spanning 40 m/s, 70 m/s, and 140 m/s according to the specifications of 5G and beyond [19]. Each velocity, a marker of motion, imprints its signature upon the landscape, altering the trajectory of our exploration.  
Figure 8: Communication and sensing performance using different numbers of receive antennas for the realistic channel: (**a**) Communication performance, (**b**): Sensing performance.  
These figures illustrate the fluctuations in communication and sensing performance under the Rayleigh channel conditions applied to mobile UE, with fixed sensing power and varying communication power. An analysis of the graphs reveals a consistent trend in the BER curves, mirroring the velocity variations in the UE. Specifically, higher velocities correspond to elevated BER, indicating increased error rates during faster UE motion. A parallel trend is observed in sensing performance.  
Moreover, the MSE curves exhibit a distinctive pattern, initially rising from \(-20\) dB and subsequently declining within the 0-5 dB range. This behavior is attributed to the escalation of communication power. As communication power increases, detection performance improves, concurrently reducing interference. However, this improvement eventually exceeds the worsening effect caused by the turning points in the curves.  
In essence, these observations underscore the intricate dynamics between communication power, detection performance, and interference mitigation, highlighting the nuanced interplay within the system under study.