esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Efficient Fuzzy Management System for Internet of Things Connected Vehicular Ad Hoc Networks
###### Abstract  
Many algorithms use clustering to improve vehicular ad hoc network performance. The expected points of many of these approaches support multiple rounds of data to the roadside unit and constantly include clustering in every round of single-hop data transmission towards the road side unit; however, the clustering in every round maximizes the number of control messages and there could be the possibility of collision and decreases in network energy. Multi-hop transmission prolongs the cluster head node's lifetime and boosts the network's efficiency. Accordingly, this article proposes a new fuzzy-clustering-based routing algorithm to benefit from multi-hop transmission clustering simultaneously. This research has analyzed the limitation of clustering in each round, different algorithms were used to perform the clustering, and multi-hop routing was used to transfer the data of every cluster to the road side unit. The fuzzy logic was used to choose the head node of each cluster. Three parameters, (1) distance of each node, (2) remaining energy, and (3) number of neighbors of every node, were considered as fuzzy criteria. The results of this research were compared to various other algorithms in relation to parameters like dead node in every round, first node expire, half node expire, last node expire, and the network lifetime. The simulation results show that the proposed approach outperforms other methods. On the other hand, the vehicular ad hoc network (VANET) environment is vulnerable at the time of data transmission. The NS-2 software tool was used to simulate and evaluate the proposed fuzzy logic opportunistic routing's performance results concerning end-to-end delay, packet delivery, and network throughput. We compare to the existing protocols, such as fuzzy Internet of Things (IoT), two fuzzy, and Fuzzy-Based Driver Monitoring System (FDMS). The performance comparison also emphasizes an effective utilization of the resources. Simulations on the highway environment show that the suggested protocol has an improved Quality of Service (QoS) efficiency compared to the above published methods in the literature.  
fuzzy logic; IoT; intelligent system; energy efficiency
## 3 The PML-V2 Model
### The Estimation of ET  
ET includes transpiration from leaf stomata (\(T\)), evaporation from the soil (\(E_{s}\)), and wet leaf surfaces (\(E_{i}\)) ([PERSON] et al., 2016; [PERSON] et al., 2016). \(T\) is estimated using the Penman-Monteith model ([PERSON] and Monte, 1979; [PERSON] and Monte, 1982), shown as the following:  
\[T=\frac{\varepsilon A_{c}+\ (\rho c_{p}/\gamma)D_{a}G_{a}}{\varepsilon+1+G_{a}/G_ {c}} \tag{6}\]  
where \(\varepsilon=\Delta/\gamma\)(-), \(\Delta\) is the slope of the saturated vapor pressure to the air temperature (kPaK\({}^{-1}\)), \(\gamma\) is the psychrometric constant (kPa K\({}^{-1}\)), and \(A\) is the available energy (net radiation \(R_{n}\) minus soil heat flux \(G\), MJ m\({}^{-2}\) d\({}^{-1}\)); \(\rho\) is the density of air (g m\({}^{-3}\)); \(c_{p}\) is the specific heat of air at constant pressure (J g\({}^{-1}\) K\({}^{-1}\)); \(G_{a}\) is the aerodynamic conductance (m s\({}^{-1}\)), and \(G_{c}\) is the canopy conductance (m s\({}^{-1}\)). \(R_{n}\) was estimated as the difference between the incoming and outgoing radiation at the surface, i.e., \(=(1-\alpha)*\)R\({}_{a}+(R_{li}-R_{lo})\), where \(\alpha\) is the albedo, \(R_{s}\) is the incoming shortwave solar radiation, \(R_{li}\) is the incoming longwave radiation, and \(R_{lo}\) is the outgoing longwave radiation.  
The soil evaporation (\(E_{s}\)) is estimated using the PT equation, shown as follows:  
\[E_{s}=\frac{f\epsilon A_{s}}{\epsilon+1} \tag{7}\]  
where the fraction coefficient \(f\) varies from 0 to 1 when the soil surface is changing from completely dry to wet. In PML-V2, \(f\) is determined by the relative magnitude between the accumulated precipitation and soil equilibrium evaporation in the previous 32 days [29]. In this study, we added a parameter \(\alpha_{s}\) to account for the saturation property of the rice paddy field because a previous study showed that, on average, evaporation of wetland in this region is 1.26 times the equilibrium evaporation [61]:  
\[E_{s}=\alpha_{s}\frac{f\epsilon A_{s}}{\epsilon+1} \tag{8}\]  
The evaporation from wet leaf surfaces (ET\({}_{i}\)) is expressed as  
\[E_{i}=f_{v}P\text{ for }P\text{ }<\text{ }P_{wet} \tag{9}\]  
\[E_{i}=f_{v}P_{wet}+f_{ER}(P-P_{wet}) \tag{10}\]  
with  
\[P_{wet}=-\ln(1-F_{0})\frac{S_{l}LAI}{f_{v}F_{0}} \tag{11}\]  
where \(P_{wet}\) is the reference threshold rainfall amount if the canopy is wet (mm d\({}^{-1}\)), \(f_{ER}\) is the ratio of average evaporation rate over average precipitation intensity storms (unitless), \(f_{ER}=f_{V}*F_{0}\). \(f_{V}\) is the fractional area covered by intercepting leaves (unitless), and \(P\) is the daily precipitation (mm d\({}^{-1}\)). \(S_{l}\) and \(F_{0}\) are two free parameters for \(E_{i}\). \(S_{l}\) is the specific canopy rainfall storage capacity per unit leaf area, and \(F_{0}\) is the specific ratio of average evaporation rate over average rainfall intensity during storms. For more details on the estimation of \(E_{i}\), please refer to the supplementary material of [PERSON] et al. [29].
## 4 Case Study
### Impact of the Network Partitioning in the Centralized VOP

Table 6: Voltage performance index for the partitioning in four VCZs and seven VCZs provided by solving problem (3). \begin{table}
\begin{tabular}{c c c} \hline \hline
**VCZ** & **DER Nodes** & **Pilot Node** \\ \hline
1 & 3 & 3 \\
2 & 9, 17 & 8 \\
3 & 52, 160, 105, 63 & 67 \\
4 & 112 & 112 \\
5 & 18, 25 & 25 \\
6 & 40, 51, 38 & 42 \\
7 & 77, 87 & 76 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PNs and DERs nodes in the 7-VCZ partition. For the sake of completeness, as in the previous subsection, with reference only to _Case 3_, the histograms of the distribution of the network node voltages for the partitioning in four VCZs and in seven VCZs are reported in Figures 7 and 8, respectively. Figure 7 shows a bell-shaped histogram with the peak centered in the same voltage interval as in Figure 4. Figure 8 does not show a markedly bell shape histogram but shows an almost uniformly distributed shape histogram with a slight peak in the middle of the voltage range [0.995, 1] p.u. Drawing some conclusions, it can be stated that the linear optimizations based on the partition in four VCZs and in seven VCZs are quite equivalent in terms of VPI. Since the \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{\(V\) (p.u)} & \multicolumn{2}{c}{\(\sigma\) (p.u)} & \multicolumn{2}{c}{\(V_{M}\) (p.u)} & \multicolumn{2}{c}{\(V_{m}\) (p.u)} \\  & **4 VCZs** & **7 VCZs** & **4 VCZs** & **7 VCZs** & **4 VCZs** & **7 VCZs** & **4 VCZs** & **7 VCZs** \\ \hline
1 & 0.9954 & 0.9967 & 0.0043 & 0.0034 & 1.0036 & 1.0038 & 0.9881 & 0.9893 \\
2 & 0.9963 & 0.9960 & 0.0025 & 0.0037 & 1.0025 & 1.0034 & 0.9925 & 0.9886 \\
3 & 0.9978 & 0.9959 & 0.0022 & 0.0036 & 1.0034 & 1.0037 & 0.9924 & 0.9892 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical parameters of the distribution of the nodal voltage amplitudes for the partitioning in four VCZs and seven VCZs provided by solving problem (3). Figure 8: The histogram of the distribution of the nodal voltage amplitudes for the partitioning in seven VCZs in _Case 3_. Figure 7: The histogram of the distribution of the nodal voltage amplitudes for the partitioning in four VCZs in _Case 3_. practical implementation would require a smaller number of measurements of the nodal voltages of the PNs in the partition in four VCZs with respect to the partition in seven VCZs and a reduced data exchange among VCZs, the partition in four VCZs is preferable. Furthermore, the results in terms of standard deviation show that the VOP polarizes the voltage profiles around the reference value, guaranteeing in practice that all the nodal voltages should not exceed the normal operating bounds.
## 3 Results
### Influence of Slag and Graphene on the Electrical Resistivity of the Asphalt Mixtures  
According to other studies [27; 28; 29; 30], the electrical resistivity of the mixture slightly decreases with the additive content, until a critical value, called percolation threshold, where resistivity sharply decreases. The effect of adding slag or graphene on the electrical resistivity of the mixture is shown in Figure 12. In the case of slag addition, the percolation threshold is reached approximately between 6% and 9% of slag, when the electrical resistivity passes from \(1.5\times 10^{8}\)\(\Omega\)-m to \(1.7\times 10^{6}\)\(\Omega\)-m, corresponding to a reduction of 99%. Nevertheless, in the case of graphene, higher contents should be added to reach the percolation threshold. Comparing these results with the heating models (Figure 11), the contribution of the conductivity to the microwave heating can be analyzed. If the conductivity influenced the microwave heating, the sudden reduction of electrical resistivity (percolation threshold) would have led to a drastic increase in the heating rates. However, observing the heating curves of slag mixtures (Figure 11), the increase of the heating rate (\({}^{\circ}\)C/ kWh/kg) with the slag content is  
Figure 11: Effect of slag (a) and graphene (b) on the microwave heating consumption.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Additive** & **Content (\%)** &
\begin{tabular}{c} **Linear Regression** \\ **Equation** \\ \end{tabular} & \(\mathbf{R^{2}}\) & \({}^{\circ}\)**C/ kWh/kg** & \({}^{\circ}\)**C/s** \\ \hline - & 0 & y = 1160x + 24.57 & 0.998 & 1160 & 0.232 \\ \hline Slag (\% & 3 & y = 1634x + 25.48 & 0.993 & 1634 & 0.327 \\ o/aggregates) & 6 & y = 1850x + 25.98 & 0.987 & 1850 & 0.370 \\  & 9 & y = 2089x + 25.89 & 0.992 & 2089 & 0.418 \\ \hline Graphene & 1 & y = 1638x + 25.60 & 0.996 & 1638 & 0.328 \\ (\%o/binder) & 2 & y = 2301x + 25.58 & 0.998 & 2301 & 0.460 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Output of the linear regression models.  
almost linear. This result can be referred to the fact that at microwaves frequencies, ranging from 300 MHz to 300 GHz, the conductivity contribution to heating is very low, and the heat is produced mostly by dipolar polarization rather than by the current created and the resulting Joule's effect. In this sense, the dielectric and thermal properties of the mixtures should be analyzed in future researches, in order to better understand the microwave heating phenomenon of asphalt mixtures and optimize the heating process.
# Ultrasound Localization Microscopy in Liquid Metal Flows
## 1 Introduction
### State of the Art  
Previous experimental studies have achieved breakthroughs by means of flow measurements using ultrasound methods. The potential of ultrasound for the flow mapping of liquid metals was first demonstrated by [PERSON] and [PERSON] [15]. The intrinsic oxides and impurities in the material act as scattering particles for the ultrasound [16]. Flow measurements in turbulent liquid metal convection are generally a challenge and so far, few experiments have been performed using the pulsed wave Doppler method (ultrasound Doppler velocimetry, UDV). It is a robust instrumentation, in which the estimated Doppler frequency shift is related to the flow velocity and the duration of flight to the position [17]. In our previous works, UDV was exploited for two-dimensional (2D) flow mapping [18] and for two velocity components (2D2C) using a combination of two ultrasound line arrays [19; 20]. This method provided a frame rate of 10 Hz via the line scanning of the ultrasound transducers with time-division multiplexing and a spatial resolution of 5 mm.  
Over the last decade, ultrasound scanning methods that are based on phased array plane wave imaging have evolved. Due to digital beamforming, phased array plane wave imaging enables 2D scans to be produced with image acquisition frequencies in the kilohertz range [21; 22; 23]. In a previous work, this method was combined with ultrasound image velocimetry (UIV) [24; 25; 26] and introduced for liquid metal flow mapping [27]. The spatial and temporal resolution was increased slightly compared to UDV. Moreover, one requisite for RBC experiments is to obtain a 2D2C velocity map using only a single access, which is met by the UIV method. The ultrasound localization microscopy (ULM) method has the potential to further increase the spatial resolution [28; 29; 30; 31]. Single tracing particles are identified and tracked and then, assuming that there is an isolated particle, the spatial resolution is defined by how accurately the center of mass of that particle can be estimated [32]. As a result, the spatial resolution can be enhanced beyond the diffraction limit of ultrasound waves [33]. For high-resolution blood flow imaging, contrast agents are injected into the blood flow, which can be distinguished from other reverberating tissues by their harmonic echo frequencies [34]. When using the ULM approach, this method is called super-resolution ultrasound as the flow profiles can be measured, even in small vessels [35; 36; 37] and geometries [38].  
For liquid metal imaging, however, contrast agents that overcome the high surface tension and that have a similar density to the liquid metal are not readily available. Therefore, the imaging has to rely on intrinsic scatterers only. A homogeneous distribution of these scattering particles throughout the entire volume is difficult to achieve due to sedimentation processes [16; 39]. As a result, ULM was yet not been applied to liquid metal model experiments.
### Contributions  
In this manuscript, we make the following contributions:  
* Implement a modern TWR system on COTS hardware that simultaneously enables network communications, distributed coherence, and relative positioning using a single, narrowband waveform.
* Implement and validate efficient, real-time, oversampled ToA estimation techniques on this COTS processing hardware.
* Provide detailed descriptions of the processing architecture and firmware implementation on the ZCU102 evaluation hardware.
* Demonstrate high-precision, over-the-air ranging capabilities (\(<\)2 cm cabled, \(<\)10 cm airborne) using moderately low bandwidth (10 MHz) in experimental flight tests.
# Reliability Analysis of Multi-Process Machining Based on Information Entropy
## 4 The Reliability Analysis of the Machining Process
### Establishment and Simulation Verification of the Single-Chain Model
#### 4.3.2 Simulation Verification of the Single-Chain Model  
The Monte Carlo simulation is based on probability theory and mathematical statistics and conducts reliability analysis through random simulation or statistical experiments [29]. We take the part shown in Figure 6 as an example and use the Monte Carlo simulation analysis method to verify the correctness of the single-chain model. It can be seen from Figure 6 that the angle between the left slope and the bottom surface of the part is \(60^{\circ}\), and the bottom surface is the process benchmark for machining the bevel. Ideally, the bottom surface is absolutely horizontal, but due to the machining accuracy of the milling machine and other accidental factors, there is an inevitable machining error on the bottom surface where process benchmark A is located. Assume that the bottom surface corresponds to a vertex \(V_{B}\) and the corresponding vertex of the bevel is \(V_{C}\). The PQRVs of \(V_{B}\) and \(V_{C}\) are \(B\) and \(C\) respectively, and the IPQRV of \(V_{c}\) is \(C_{ilt}\). At the same time, according to the characteristics of the machining process: \(C=B-C_{ilt}\)[2]. The specific parameters are shown in Table 1. We use the Scipy library [30] to randomly generate machining quality sample points that conform to the distributions of \(B\) and \(C_{int}\). Then, calculate the sample points of \(C\).  
Figure 5: The composition of the vertex entropy.  
The simulation result is shown in Figure 7, and the result shows the relationship between the actual vertex entropy of \(V_{\rm C}\) and the vertex entropy calculated by the single chain model of \(V_{\rm C}\) (under different subinterval lengths and different simulation times). It can be seen from Figure 7 that when the length of the subinterval is comparatively large, there is an error between the actual vertex entropy and the vertex entropy calculated by the single-chain model, and it is approximately a constant. With the increase in the simulation time and the decrease in the subinterval length, the actual vertex entropy and the vertex entropy calculated by the single-chain model gradually converge, which verifies the correctness of the single-chain model. Reproducing Figure 7 may produce slightly different simulation results, which
## 2 Materials and Methods
### Average Photon Energy  
Originally proposed by [PERSON] et al. [16] to qualitatively assess the shape of the spectral irradiance, APE is the average energy of the photons over a specific spectrum distribution. It may be expressed as follows:  
\[\text{APE}=\frac{\int_{a}^{b}E(\lambda)\text{d}\lambda}{\int_{a}^{b}\Phi_{ \text{ph}}(\lambda)\text{d}\lambda} \tag{1}\]  
where \(E(\lambda)\) in W\(\cdot\)m\({}^{-2}\cdot\)nm\({}^{-1}\) is the spectral irradiance, \(\Phi_{\text{ph}}(\lambda)\) in m\({}^{-2}\cdot\)nm\({}^{-1}\cdot\)s\({}^{-1}\) is the spectral photon flux density, and \(a\) in nm and \(b\) in nm are the lower and upper limits, respectively, of the considered waveband of the solar spectrum. Solar spectra rich in photons with shorter wavelengths yield higher values of APE, whereas solar spectra rich in photons with longer wavelengths yield lower values of APE.  
An equivalent definition was proposed later [35]:  
\[\text{APE}=hc(kq\lambda_{\text{eff}})^{-1}, \tag{2}\]  
where  
\[\lambda_{\text{eff}}=\frac{\int_{a}^{b}\lambda E(\lambda)\text{d}\lambda}{ \int_{a}^{b}E(\lambda)\text{d}\lambda} \tag{3}\]  
and where \(\lambda_{\text{eff}}\) is the \"effective wavelength\" in nm, \(h\) is the Planck constant, \(c\) is the speed of light in vacuum, \(q\) is the electronic charge, and \(k\) is a constant to reconcile units, equal here to \(1\times 10^{-9}\) for \(\lambda_{\text{eff}}\) expressed in nm. By using Equation (2), possible variations in APE are intuitively linked to varying incident irradiance spectral balances. Thus, blue-biased spectra are conducive to shorter effective wavelengths, which in turn lead to higher values of APE. Conversely, red-biased spectra--conducive to larger values of \(\lambda_{\text{eff}}\)--lead to lower values of APE.  
Obviously, the value of APE also depends on the considered spectral range. In this sense, the 350-1050 nm waveband (typically sensed by the silicon-based detectors used in many commercial spectroradiometers) has been used to calculate APE in many previous studies [16; 19; 32; 34; 40; 41; 42; 43; 44]. For that specific waveband, the values of APE for the AM1.5G and AM1.5D reference spectra equal 1.878 and 1.846 eV, respectively. The corresponding effective wavelengths are 662.6 and 671.7 nm, respectively. The slight red shift in the DNI spectrum compared to the GTI spectrum results from the latter's inclusion of diffuse irradiance, which is rich in blue wavelengths.
# K/Ka-Band Very High Data-Rate Receivers: A Viable Solution for Future Moon Exploration Missions
## 2 Basic Link Budget
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Band** & \(f_{0}\) **(GHz)** & \(\lambda\) **(cm)** & \(R\) **(km)** & **Path Loss (dB)** & \(D_{RX}\) **(cm)** & \(\eta_{a}^{RX}\) **(\%)** & \(G_{a}^{RX}\) **(dBi)** & \(\Theta_{h}^{RX}\) **(dBi)** & \(\Theta_{h}^{RX}\) **(dBi)** \\ \hline K & 22.85 & 1.31 & \(384.4\times 10^{3}\) & 231.3 & 50 & 60 & 39.4 & 1.7\({}^{\circ}\) \\ Ka & 28.65 & 1.05 & \(384.4\times 10^{3}\) & 233.3 & 50 & 60 & 41.3 & 1.4\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Earth-Moon Free-Space Losses in the K/Ka-Bands and Receiver Antenna Gains. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Band** & \(f_{0}\) **(GHz)** & \(\lambda\) **(cm)** & \(P_{TX}\) **(W)** & \(D_{TX}\) **(m)** & \(\eta_{a}^{TX}\) **(\%)** & \(G_{a}^{TX}\)** & \(\Theta_{h}^{TX}\) **(dBi)** & \(\Theta_{h}^{TX}\) **(dBi)** & \(\Theta_{h}^{RX}\) **(dBm)** \\ \hline K & 22.85 & 1.31 & 100 & 10 & 60 & 65.4 & 5.1 & 115.4 & 231.3 & 39.4 & \(-\)76.5 \\  & & & & 3 & 60 & 54.9 & 16.9 & 104.9 & & & \(-\)87.0 \\ Ka & 28.65 & 1.05 & 100 & 10 & 60 & 67.3 & 4.1 & 117.3 & 233.3 & 41.3 & \(-\)74.7 \\  & & & & 3 & 60 & 56.8 & 13.6 & 106.8 & & & \(-\)85.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Transmitter EIRP and Received Power in the K/Ka-Bands versus \(D_{TX}\).
# SATCOM Earth Station Arrays Anti-Jamming Based on MVDR Algorithm
## 4 Simulation Analysis
### Two Beamforming Methods and Single Antenna Gain  
In this section, the conventional beamforming (CBF) and MVDR algorithms are used with four reflector antennas, with a spacing of \(d=\mathrm{c}/(2\times f)\times 42=0.504\) m, a snapshot of 512, an jamming direction of \(\theta_{1}=10^{\circ}\), a signal power flux density of \(-110\) dBW\(/\)m\({}^{2}\), an jamming power flux density of \(-80\) dBW\(/\)m\({}^{2}\), and a noise power of \(-133.38\) dBW.  
Without jamming, the array gain using CBF and MVDR algorithms is the same. The difference between the two algorithms when jamming is present lies in the assignment of weight vectors. However, since the signal direction in this paper is \(0^{\circ}\), the array gain in the signal direction is the same for both algorithms. To facilitate comparison, the maximum gain in the \(0^{\circ}\) direction of the MVDR beamforming diagram is set to be the same as that of the CBF beamforming diagram. From Figures 2 and 3, it can be seen that after four satellite communication earth station reflector antennas are arranged into a uniform linear array, the maximum gain using CBF and MVDR algorithms is about 46.8 dB, which is 12 dB higher than the maximum gain of a single satellite earth station reflector antenna in Figure 4 (34.8 dB). The array gain in the \(10^{\circ}\) direction using CBF algorithm is about 7.6 dB, while using the MVDR algorithm with diagonal loading, a null is formed in the direction of the jamming (the jamming direction corresponding to the normalized gain in the signal direction), and the gain is about \(-15.4\) dB. The gain of a single earth station in the \(10^{\circ}\) direction is 7 dB. Therefore, it can be concluded that when the jamming direction is \(10^{\circ}\), using CBF and MVDR algorithms with diagonal loading can improve the signal gain and suppress the jamming, and the MVDR algorithm has a better jamming suppression effect.  
Figure 2: CBF pattern of antenna array.
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 2 Error Sources in PPP
### Antenna Phase Center Effects  
The distance between a GNSS satellite and a receiver is measured between their respective Antenna Phase Centers (APCs). If the precise satellite orbit data are referred to the satellite Center of Mass (CoM), such as in the IGS SP3 files and some real-time products, the difference between the satellite CoM and APC must be accounted for. On the other hand, the receiver's reference location is often determined at a marker position that can be the Antenna Reference Point (ARP) or another point on the ground below the receiver antenna. Consequently, the difference between the receiver reference point and its APC must be considered.  
APC errors are frequency-dependent and can be classified into Phase Center Offset (PCO) and Phase Center Variation (PCV). Calibrated corrections for these errors are available in the ANTenna Exchange (ANTEX) format file provided by the IGS [54]. The calibration files for the antenna phase center can be accessed through the IGS online archive [55]. Starting from GPS week 2238, 27 November 2022, the IGS adopted the IGS20 reference frame and igs20.atx antenna calibration files [56].