esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Impact of PCV  
#### 4.3.1 Estimation of PCV Maps  
Precise phase center modeling of the LEO receiver antenna is of importance in the performance of LEO satellite missions. Taking the electrical phase center as a single  
Figure 8: RMS of orbital differences for GRCC and CS01 with/without PWU corrections.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Spacecraft**} & \multirow{2}{*}{**Solutions**} & \multirow{2}{*}{**Residual**} & \multicolumn{6}{c}{**Overlap Comparison (cm)**} & \multicolumn{6}{c}{**JPL Comparison (cm)**} \\ \cline{3-10}  & & **(mm)** & **R** & **T** & **N** & **3D** & **R** & **T** & **N** & **3D** \\ \hline \multirow{3}{*}{GRCC} & without PWU & 7.93 & 0.75 & 1.56 & 1.25 & 2.15 & 2.07 & 2.08 & 1.15 & 3.17 \\  & with PWU & 7.81 & 0.73 & 1.52 & 1.24 & 2.11 & 1.89 & 1.91 & 1.23 & 2.98 \\ \multirow{3}{*}{CS01} & without PWU & 9.37 & 0.72 & 1.44 & 1.01 & 1.93 & / & / & / & / \\  & with PWU & 9.31 & 0.71 & 1.39 & 0.98 & 1.88 & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: The RMS of residual and orbital comparison with/without PWU for GRCC and CS01.  
Figure 9: The difference in clock offset between solutions with and without PWU corrections.  
point is no longer possible in high-precision applications when the signal wave front fluctuates because of the antenna manufacturing characteristics. Although PCV values may be provided in advance, there will be a difference between the in-flight and ground calibration values because of the special space environment, satellite mass change and other reasons [23]. First, to avoid the influence of GPS satellites' PCO and PCV, we introduce precise values from the antenna information exchange file (igs14.atx) provided by IGS [35]. After the PCO of LEO antennas is fixed, we use post-fit phase residuals of GPS ionosphere-free phase measurements (DOY 273-293, 2018) by iterations until convergence, namely the residual approach [24, 25], to estimate the PCV maps and analyze its impact.  
The PCV maps (\(5^{\circ}\times 5^{\circ}\)) of GRCC and CS01 in the Antenna Reference Frame (ARF) system obtained by the residual approach are displayed in Figure 10. The azimuth of \(0^{\circ}\) (X-axis) nominally points in the flight direction, but it should be noted that the X-axis of GRCC is opposite in our testing period. For GRCC, the overall scale is limited to \(-6\) mm to \(+10\) mm, with extreme values of \(-5.56\) mm and \(+8.81\) mm, respectively. The PCV maps of CS01 use the same scale of \(-10\) to \(10\) mm, but with extreme values of \(-10.27\) mm and \(26.32\) mm. The overall deeper colors of the CS01 PCV maps compared to the GRCC mean that the PCV values of CS01 are larger than GRCC. In addition, the PCV map of CS01 has a stronger elevation-dependent pattern than GRCC.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
###### Abstract  
Motivated by big data applications in the Internet of Things (IoT), abundant information arrives at the fusion center (FC) waiting to be processed. It is of great significance to ensure data freshness and fidelity simultaneously. We consider a wireless sensor network (WSN) where several sensor nodes observe one metric and then transmit the observations to the FC using a selection combining (SC) scheme. We adopt the age of information (AoI) and minimum mean square error (MMSE) metrics to measure the data freshness and fidelity, respectively. Explicit expressions of average AoI and MMSE are derived. After that, we jointly optimize the two metrics by adjusting the number of sensor nodes. A closed-form sub-optimal number of sensor nodes is proposed to achieve the best freshness and fidelity tradeoff with negligible errors. Numerical results show that using the proposed node number designs can effectively improve the freshness and fidelity of the transmitted data.  
wireless sensor network; age of information; sensor node deployment Article
# Investigation of Vehicle Stability with Consideration of
## 3 Investigation of the Factors Affecting the Vehicle Stability
### Road Description
The spatial record of the road microprofile was used to determine the characteristics of the transverse road and to accurately model vehicle movement. Data from the 3 m wide road lane microprofile were recorded at nine points in the transverse, longitudinal direction, with a 1 m interval between the data. The description of the road span microprofiles was facilitated by the fact that the microprofile records generated by the DYNATEST 5051 RPS profilometer were densified in the spatial (3D) mode in the ruts to reproduce the microprofile specifics of the tire-road contact area more accurately. The spatial microprofile record analysis showed waviness (Figure 5) that was characterized by perpendicularity, or a slight tilt relative to the axis, and relatively low transverse waves. The analysis of the spatial road surface roughness fragment (Figure 6) suggested that the roughness was a random set of unevennesses, having a certain prevailing shape of unevennesses. \begin{table}
\begin{tabular}{c c c} \hline
**Road Surface Type** & **Dispersion \(\sigma_{q}\), mm** & **Characteristic Wavelength, m** \\ \hline Even asphalt pavement & 3.492 & 50–52 \\ Uneven asphalt pavement & 12.437 & 75–77 \\ Gravel road & 13.667 & 45–47 \\ Roughness I (relatively even) & 0.276 & 0.04–0.1 \\ Roughness II (medium) & 0.695 & – \\ Roughness II (very rough) & 1.667 & 0.06-0.14 \\ \hline \end{tabular}
\end{table} 
Table 1: Microprofile and roughness characteristics of the investigated road surfaces. Figure 4: Correlation functions of road surfaces of different roughness: 1 = even, 2 = medium, 3 = very rough. In the case of spatial modeling, it is necessary to assess the nature of the road surface in the transverse plane relative to the vehicle movement. The spatial road microprofile record was used as the baseline in the calculation application and accounted for the nature of the variation of the microprofile in the longitudinal and transverse directions. These data provided accurate information on the specifics of the road surface in the transverse directions (ruts, camber, banks). A microprofile of a smaller interval was reproduced according to more detailed records. Road surface roughness was also assessed during modeling. The calculation application includes the option to assess random damages to the road surface, e.g., cracks present in the road surface.
## 3 Results and Discussion
### Photovoltaic Properties and Surface Morphologies of Blend Films
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**M1:PC\({}_{71}\)BM (\(\mathbf{x}\)/\(\mathbf{x}\) ratio in Chloroform)** & \(\mathbf{J_{\kappa}}\) **(m/cm\({}^{2}\))** & \(\mathbf{V_{\infty}}\) **(V)** & **FF (\%)** & **PCE (\%)** & \(\mathbf{R_{s}}\) **(\(\Omega\)**-cm\({}^{2}\))** & \(\mathbf{R_{sh}}\) **(\(\Omega\)**-cm\({}^{2}\))** \\ \hline
70:30 & 1.34 & 0.90 & 30 & 0.36 & 940 & \(4.75\times 10^{6}\) \\
60:40 & 1.55 & 0.87 & 37 & 0.50 & 430 & \(3.23\times 10^{6}\) \\
50:50 & 1.53 & 0.87 & 43 & 0.57 & 240 & \(1.84\times 10^{6}\) \\
40:60 & 1.83 & 0.86 & 57 & 0.89 & 70 & \(0.14\times 10^{6}\) \\
30:70 & 1.68 & 0.84 & 49 & 0.69 & 160 & \(0.89\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of device parameters for SM-BHJ (small molecule -bulk heterojunction) solar cells based on **M1**:PC\({}_{71}\)BM active layers cast at different blend ratios from chloroform. All values are averaged from five devices with two electrodes on top of each device and a standard deviation within \(\pm\)4%. Figure 2: **(a)** Current–voltage (J-V) characteristics of BHJ solar cells based on **M1**:PC\({}_{71}\)BM active layers with different blend ratios cast from chloroform and **(b)** J-V curves in the dark based on the different blending ratio between **M1** and PC\({}_{71}\)BM. Figure 4: Surface topographic AFM images (2 \(\upmu\)m \(\times\) 2 \(\upmu\)m) of **M1**:PC\({}_{71}\)BM blend (40:60) films cast from 2% (\(w/v\)) chlorobenzene solution with (**a**) DIO (0.2%, \(v/v\)) and (**c**) CN (0.5%, \(v/v\)), respectively. (**b,d**) Phase images corresponding to (**a,c**). \begin{table}
\begin{tabular}{c c c c c} \hline
**M1:PC\({}_{71}\)BM (40:60, \(w/w\) Ratio in Chlorobenzene)** & \(J_{\text{sc}}\) (**mA/cm\({}^{2}\))** & \(V_{\text{oc}}\) (V)** & **FF (\%)** & **PCE (\%)** \\ \hline No additives & 2.35 & 0.86 & 61 & 1.23 \\ \hline \multirow{4}{*}{DIO (\%, \(v/v\))} & 0.8 & 2.09 & 0.54 & 34 & 0.38 \\  & 0.4 & 3.00 & 0.78 & 42 & 0.98 \\  & 0.2 & 3.03 & 0.81 & 56 & 1.36 \\  & 0.1 & 2.73 & 0.72 & 40 & 0.77 \\ \hline \multirow{4}{*}{CN (\%, \(v/v\))} & 1.0 & 3.0 & 0.76 & 52 & 1.17 \\  & 0.5 & 3.53 & 0.85 & 62 & 1.86 \\ \cline{1-1}  & 0.3 & 3.39 & 0.90 & 59 & 1.80 \\ \hline \end{tabular}
\end{table} 
Table 2: Summary of device parameters of SM-BHJ solar cells based on **M1**:PC\({}_{71}\)BM active layers (40:60 in chlorobenzene) incorporating different amounts of DIO and CN additives. All values are averaged from five devices with two electrodes on top of each device and a standard deviation within \(\pm\)4%. Figure 3: J-V characteristics of **M1**:PC\({}_{71}\)BM (40:60) solar cells cast from chlorobenzene incorporating different amounts of solvents additives: (**a**) 1,8-diiodooctane (DIO) and (**b**) chloronaphthalene (CN).
# Pilot Sequence Allocation Schemes in Massive MIMO Systems Using Heuristic Approaches
## 1 Introduction  
The increasing number of multimedia services and online platforms are reflected as a growth in the demand for connectivity and throughput worldwide. According to Cisco [1], in 2018 there were 3.9 billion Internet users globally, and it estimates 5.3 billion in 2023, representing 66% of the global population by that year according to [2]. Part of the global data traffic, 54%, will originate from mobile devices which will be responsible for 160 Exabytes (EB) per month of data traffic in 2025, a 321% growth when compared to the 38 EB per month in 2019 [3].  
To support this demand, the wireless communications standard must evolve and increase the total available throughput and the supported number of connected devices, while keeping the average network latency to a minimum. These characteristics will even make new services possible, such as autonomous cars [4].  
Massive MIMO is a technology incorporated in 5G networks which corresponds to the transmission of data through several antennas. [PERSON] describes the use of this technology for any multi-user MIMO system with more than 16 antennas [5]. The proposed communications scheme relied on low complexity transceivers in the mobile devices and time division duplexing (TDD) to provide a reliable communication channel.  
Within a Massive MIMO system, an arbitrary array of bits, known as a pilot sequence, is designated to each user and used in the uplink training process to simplify the signal detection routines in mobile terminals. However, the use of non-orthogonal sequences causes interference among users, which is known as the pilot contamination problem.  
To guarantee orthogonality, the size of the pilot sequence must increase with respect to the number of users in the system. Hence, it is easy to verify that pilot sequences must be reused in multi-cell environments and high-density urban areas to avoid long periods of uplink training since as the number of orthogonal sequences increases the size of these sequences also increases.  
This work proposes to solve the pilot sequence allocation problem in multi-cell scenarios in a centralized fashion with the maximization of the network spectral efficiency as a goal. The optimization problem is modeled into two different, but equivalent, ways: a binary optimization problem and an integer optimization problem. Meanwhile, to solve these optimization problems, we used different versions of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) metaheuristics.
## 4 Results and Discussion
### Future Research Work  
The present study conducted state detection and RUL prediction of an HSV applied to a water hydraulic manipulator using a data-driven method. The results of the present study can act as a reference for research into the degradation prognosis of HSVs. However, future research work is required to facilitate the full engineering application potential of the HSV. The simulation experiment tests conducted in the present study were in a  
Figure 12: Predicted value and original value.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \(q=1\) & \(q=2\) & \(q=3\) & \(q=4\) & \(q=5\) & \(q=6\) & \(q=7\) & \(q=8\) & \(q=9\) & \(q=10\) \\ \hline \(p=1\) & \(-2.2847\) & \(-2.2846\) & \(-2.2846\) & \(-2.2848\) & \(-2.2849\) & \(-2.2849\) & \(-2.2852\) & \(-2.2851\) & \(-2.2851\) & \(-2.2852\) \\ \(p=2\) & \(-2.2846\) & \(-2.2845\) & \(-2.3009\) & \(-2.3009\) & \(-2.3008\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3005\) & \(-2.3005\) \\ \(p=3\) & \(-2.3008\) & \(-2.301\) & \(-2.3033\) & \(-2.3033\) & \(-2.3032\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3004\) & \(-2.3027\) \\ \(p=4\) & \(-2.301\) & \(-2.3033\) & \(-2.301\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3031\) & \(-2.3029\) & \(-2.3005\) & \(-2.3028\) \\ \(p=5\) & \(-2.3009\) & \(-2.3033\) & \(-2.3007\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3004\) & \(-2.3004\) & \(-2.3002\) & \(-2.3002\) \\ \(p=6\) & \(-2.3008\) & \(-2.3032\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3031\) & \(-2.3029\) & \(-2.3001\) & \(-2.2999\) & \(-2.3025\) \\ \(p=7\) & \(-2.3008\) & \(-2.3008\) & \(-2.3005\) & \(-2.3005\) & \(-2.303\) & \(-2.3029\) & \(-2.3028\) & \(-2.3001\) & \(-2.3003\) & \(-2.3011\) \\ \(p=8\) & \(-2.3007\) & \(-2.3007\) & \(-2.3006\) & \(-2.3006\) & \(-2.3029\) & \(-2.3003\) & \(-2.3002\) & \(-2.3003\) & \(-2.3043\) & \(-2.3022\) \\ \(p=9\) & \(-2.2851\) & \(-2.3006\) & \(-2.3006\) & \(-2.3003\) & \(-2.3003\) & \(-2.3014\) & \(-2.2999\) & \(-2.3042\) & \(-2.3031\) & \(-2.3022\) \\ \(p=10\) & \(-2.3007\) & \(-2.2849\) & \(-2.3005\) & \(-2.3003\) & \(-2.3026\) & \(-2.3011\) & \(-2.3001\) & \(-2.3036\) & \(-2.3029\) & \(-2.3025\) \\ \hline \end{tabular}
\end{table}
Table 3: AIC values corresponding to different \(p\) and \(q\).  
laboratory environment. Since the HSV examined in the present study operates in a nuclear environment, the conditions of the laboratory may not reflect the complexity of the HSV working environment. This may lead to current waveform distortion and increased uncertainty during current signal acquisition and transmission. Therefore, future studies should collect a corresponding current signal under the actual working environment of a water hydraulic manipulator. In addition, reducing valve clogging or other methods can improve the reliability of the valve, which is also the future research direction of this study.
## Materials and Methods
### Measuring System  
In order to verify the correctness of the design process, measurements of the developed metasurfaces were taken. The measuring setup, as well as the fabricated metasurface structures and copper reference plate are presented in Figure 4. The examined structures were situated in an open, compact anechoic chamber equipped with pyramidal absorbers in order to reduce the side reflections and multiple reflections of the background of the object under test (OUT). A monostatic setup was utilized in order to obtain a complex \(s_{11}\) reflection coefficient. Measurements were made using a Schwarzbeck BBHA-9120D-718 double ridged broadband horn antenna, connected to the port of a Rohde&Schwarz (Munich, Germany) ZVB20 vector network analyzer (VNA). The power at the output of VNA's port was set to 0 dBm (1 mW). The lift-off (antenna to OUT) distance was set to be 420  mm. First, the reference measurements without OUT and with a copper-plated plate of the same size as the metasurface structures were performed. Then, the measurements of the MS structures were made, and isolated responses of the metasurfaces were extracted by removing the influence of the interrogation antenna through the de-embedding procedure. A detailed description of this process can be found in the literature [20]. Finally, the total response of the metasurface was obtained by the subtraction de-embedded copper plate response of the MS response. Similar to the case of the simulations, measurements were performed in 2-8 GHz frequency bands.  
Figure 4: Experimental setup for metasurfaces measurements: (**a**) photo of test stand and (**b**) photo of exemplary metasurfaces and reference conductive plate.
## 3 Results and Discussion
### Tensile Testing
Fractures occurred 22 mm, 21 mm and 21 mm away from the weld centerline for 0%, 30% and 50% EN ratio, respectively. It was found that the welds made by using high currents offer very good strength and elongation. Figure 11: Tensile test specimen dimensions. Figure 14: Stress vs. displacement at Ia of 200 A. The inset shows the fractured surfaces of the tested samples. Figure 12: Stress vs. displacement at Ia of 150 A. The inset shows the fractured surfaces of the tested samples. Figure 13: Stress vs. displacement at Ia of 175 A. The inset shows the fractured surfaces of the tested samples. Figure 16: Stress vs. displacement at Ia of 225 A. The inset shows the fractured surfaces of the tested samples. \begin{table}
\begin{tabular}{c c c c c} \hline Sr. & **Weld** & **Ultimate Tensile** & **Yield Strength** & **Percentage** \\
**No.** & **Description** & **Strength (UTS) (MPa)** & **(YS) (MPa)** & **Elongation (\$) (\%)** \\ \hline
1 & 150 A (0\%) & 360.52 & 116.53 & 4.64 \\
2 & 150 A (30\%) & 370.61 & 123.29 & 4.80 \\
3 & 150 A (50\%) & 375.34 & 119.08 & 5.24 \\
4 & 175 A (0\%) & 399.94 & 116.35 & 6.03 \\
5 & 175 A (30\%) & 402.19 & 117.90 & 6.30 \\
6 & 175 A (50\%) & 406.11 & 110.76 & 6.57 \\
7 & 200 A (0\%) & 424.45 & 134.00 & 6.66 \\
8 & 200 A (30\%) & 457.71 & 124.16 & 9.24 \\
9 & 200 A (50\%) & 458.80 & 98.19 & 8.71 \\
10 & 225 A (0\%) & 472.01 & 318.67 & 13.2 \\
11 & 225 A (30\%) & 474.10 & 320.13 & 11.65 \\
12. & 225 A (50\%) & 469.27 & 324.64 & 13.4 \\
13 & 250 A (0\%) & 477.77 & 322.85 & 11.66 \\
14 & 250 A (30\%) & 473.45 & 322.12 & 12.18 \\
15 & 250 A (50\%) & 476.92 & 324.64 & 12.78 \\ \hline \end{tabular}
\end{table} 
Table 3: Tensile tests results of welded joints. Figure 15: Stress vs. displacement at Ia of 225 A. The inset shows the fractured surfaces of the tested samples.
## 3 Proposed Antenna  
Satellite communications predominantly use circularly polarized signals; hence, the proposed antenna is designed to work circularly polarized. Some beamforming integrated circuits combine vertical and horizontal polarizations to form circularly polarized signals via appropriate superposition [21], while other solutions accept circularly polarized signals [22] from the antenna. Here, we decided to design a dual-band, dual-linearly polarized antenna; an ideal quadrature coupler is used in post-processing to obtain circular polarization. The dual band covers downlink (DL) frequencies from 10.8 to 12.6 GHz and uplink (UL) frequencies from 14 to 14.5 GHz. Since there are no existing compact antenna designs for LEO satellite connectivity in automotive applications to the best knowledge of the author, a comparison to other approaches is not presented.
## 2 Materials and Methods  
### Virtual Population and Birdcage Coils  
Since the induced fields in the human body are a function of multiple variables like the birdcage geometry, the anatomy of the model and the imaging landmark position [39], 1140 different clinical scanning scenarios were assessed. In particular, four adult anatomical models ([PERSON], [PERSON], [PERSON] and [PERSON]) and two children models ([PERSON] and [PERSON]) from the Virtual Population (ViP version 3.1, IT'S Foundation, Zurich, Switzerland) were used to evaluate a wide range of model variability [40]. Their morphological characteristics are included in Table 1. The tissue properties for both 1.5 T and 3 T were assigned according to the IT'IS Foundation database (version 3.0) [41].  
The models were placed inside 10 different cylindrical RF birdcage body coils from the Birdcage Library (BCLib, IT'S Foundation, Zurich, Switzerland) with different dimensions as shown in Table 2. The resonant frequencies of the coils were set to 64 MHz for the 1.5 T system and 128 MHz for the 3 T system. Clockwise polarization was assumed, implementing a quadrature excitation by feeding the coils with two ports 90\({}^{\circ}\) apart in  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Sex** & **Age** & **Height** & **Weight** & **BMI** & **No. of** \\
**Name** & **[Years]** & **[m]** & **[kg]** & **[kg/m2]** & **Tissues** \\ \hline Duke & M & 34 & 1.77 & 72.4 & 23.1 & 77 \\ Ella & F & 26 & 1.63 & 58.7 & 22.0 & 76 \\ Billie & F & 11 & 1.47 & 35.4 & 16.5 & 75 \\ Louis & M & 14 & 1.69 & 50.4 & 17.7 & 77 \\ Glenn & M & 84 & 1.73 & 65.0 & 21.7 & 84 \\ Fats & M & 37 & 1.82 & 119.6 & 36.2 & 79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Morphological characteristics of the used Virtual Population numerical models.  
azimuth (polar) angle and phase. For each model, 10 different landmarks (LD\({}_{1}\)-LD\({}_{10}\)) were considered, with a 10 cm step from head to feet, where the center of the heart was kept on the craniocaudal axis (z-axis). The first landmark coincided with the RF-coil iso-center 10 cm below the head imaging position, as shown in Figure 1. In Table 3, the evaluated landmarks are matched to usual clinical imaging positions. In order to decrease the computational burden, a Huygens source was used, which was constructed from the field distribution provided by the birdcage coils of the BCLib, and uniform isotropic voxel size was assigned at the human models with a resolution of 2 mm. Full-wave electromagnetic simulations were performed until steady state was reached. Varying the number of the simulated periods from 7 to 15 was tested for convergence of the results; after 8 simulation periods, the deviation in the results was negligible. In particular, the deviation in the maximum electric field was less than 0.05%.
## 5 Uncertainty  
### General Considerations  
The accuracy of locating is based on two factors: accuracy of the system calibration and accuracy of the actual locating procedure. These two factors are completely independent being two different procedures performed at different points of time. System calibration is an operation done only once after system installation. Further on, the realized calibration accuracy is influencing any result of locating as the actual locating accuracy is an additional factor which reduces the overall accuracy.  
Literature dealing with GPS and LPS is often defining its own meaning of the terms \"accuracy\" and \"precision\", e.g., [29]. In these definitions, accuracy is a combination of systematic and random components whereas precision is based on a confidence interval. This has multiple drawbacks, as one cannot individually differentiate between random and systematic components. In addition, the definition of accuracy carries a value of zero when accuracy is infinitely high. In this meaning these values represent deviations of real values.  
The Guide (\"Evaluation of measurement data--guide to the expression of uncertainty in measurement\") [30], which is defined for generic use in measurements and is based on the term \"uncertainty\", which is an interval set up around the measurement result containing the value of the measured quantity. \"High accuracy\", which has only qualitative meaning and does not represent any value, is expressed by an uncertainty with low interval length. The term \"precision\" is not defined in GUM but in most cases the terms \"random component of a measurement\", \"repeatability\" or \"reproducibility\" are a better choice. The GUM definitions are used in this article.