esa-sceva/satcom-chunk-collection huggingface dataset:
# Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies
## 4 Numerical Results  
Now we examine the absorptive and dispersive effects of fog on the propagation of an ultra-wideband 'chirp' transmitted at millimeter and sub-millimeter wavelengths and received by the FMCW detector. Note that both the attenuation coefficient \(\alpha\left(f\right)\) and the wave number \(\beta\left(f\right)\) play a role in the wave propagation due to their non-uniform frequency response. The resulting detection leads to a deviation in the distance measurements. The MPM model also allows considerations of limited visibility due to fog and haze [16]. Fog and cloud conditions are characterized via the water droplet concentration W (in (g/m\({}^{3}\)). Figure 1 shows the effect of different values of droplet concentration on the attenuation and phase of a millimeter and THz wave.  
The intermediate frequency (IF) obtained at the output of the detector is given in Equation (10). It is proportional to the distance to the target, as can be seen in Equation (12). In the following, we consider a wideband FMCW millimeter wave radar transmitting a chirp with \(\Delta f~{}=~{}10\) GHz and \(T_{\text{sweep}}~{}=~{}1\) ms. A study of the resulting intermediate frequency is carried out for different frequency regimes centered at 60 GHz, 94 GHz, 120 GHz, and 325 GHz. For a given frequency sweep, the normalized detected IF frequency \(f_{IF}\left(t\right)\) is shown in the graphs of Figure 3. The normalization is done by dividing the resulting frequency by \(f_{m}\), which is the IF expected frequency if the wave would have propagated in a vacuum.  
Figure 3: Variation of the instantaneous frequency \(f_{IF}\left(t\right)/f_{m}-1\) obtained for different frequencies and fog conditions. (**a**) 60 GHz; (**b**) 94 GHz; (**c**) 325 GHz.  
Inspection of the graphs in Figure 3 reveals that \(f_{IF}\left(t\right)>f_{m}\) during the whole sweep time. This means that there is an up-shift in the detected intermediate frequency; the resulting IF is higher than \(f_{m}\) which is expected when the chirped signal is propagating in a vacuum (see Equation (11)).  
Its average value is explained by the constant component \(N_{0}\) in the atmospheric refractivity (see Equation (15)), causing an increase in the overall path delay. The frequency-dependent dispersive response \(N\prime(f)\) is the reason for the temporal variations in the intermediate frequency at the receiver output.
## 4 Energy Management Systems
### Optimization-Based Energy Management Systems
#### 4.2.1 Dynamic Programming  
Dynamic programming (DP) represents a widely recognized global optimization technique that operates on the principles outlined by [PERSON] [88]. At its core, DP aims to break down a complex multi-stage decision-making process into a sequence of individual single-stage decision processes. This entails dividing the problem into multiple sub-problems that can be solved sequentially. The recursive approach involves solving these subproblems from the final state back to the initial state. However, one notable challenge associated with DP is the issue of the dimensionality of state variables, which must remain within scope [89]. An excessively large dimension can significantly impede the calculation and analysis processes.  
Addressing this challenge can be accomplished through three main approaches. Firstly, the SOC can be adopted as a single-state variable. Comparative analysis against FLC demonstrated that DP significantly enhances the fuel economy of FCHEV. However, the frequent variations in power load, start-stop operations, and temperature fluctuations in the FCS can have repercussions on FCS durability. Thus, it is prudent to incorporate multi-state quantities when designing an energy management system with DP [90; 91; 92; 93]. The second approach involves the utilization of an optimal matrix development algorithm, aimed at eliminating the iterative and cyclic structures associated with dynamic programming. This optimization technique has yielded substantial enhancements in computational performance [94]. The third strategy involves the implementation of a weighted and improved DP algorithm, as detailed in reference [95].  
Moreover, the multi-state approach introduces a challenge related to interpolation leakage, which can lead to discrepancies between the theoretically predicted hydrogen consumption and the actual observed consumption [96]. [PERSON] et al. [97] devised a solution involving the automated exploration of the optimal decision sequence, guided by real-time SOC data. There was a remarkable reduction in the error margin between the actually observed consumption and the theoretically predicted consumption, with the error falling below 0.5%. [PERSON] et al. [98] introduced a state variable filter. This filter  
Figure 19: EMS based on frequency separation.  
effectively transforms the traditional linear grid into a nonlinear grid, obviating the need for interpolation calculations.
## 6 Energy Efficiency  
Compared to conventional short-range wireless communication protocols (i.e., Zigbee and Bluetooth), WBAN is varies more in energy consumption. As WBANs involve the human body, in order to make them wearable, it is often necessary to limit the size of the hardware (small sizes of 1 to 3 cm or even less), which makes it difficult to expand the battery power. Additionally, frequent battery changes are extremely inconvenient for patients, especially for the implanted devices, which can only be replaced by surgery. The hardware size and battery power limitations of a WBAN directly affect its usability and the user's satisfaction. Therefore, energy efficiency is very important for a well-designed WBAN, and the optimization of energy consumption has become one of the current focuses of WBANs in many studies. Some studies that worked on energy harvesting included solar, vibration and thermal energy to improve energy efficiency [136; 137; 138; 139]. Others worked on extending the battery capacity by using PHY or MAC protocols with low power consumption; some studies on the ultra-low-power IC have provided a new window for these problems and achieved good results [140; 141]. [PERSON] et al. [142] designed an ultra-low power baseband transceiver IC with 0.18 \(\upmu\)m CMOS technology. [PERSON] et al. [143] proposed a low-power, area-efficient baseband processor for WBAN transmitters based on the IEEE 802.15.6, which supported the specifications of the narrowband (NB) PHY by optimizing the DPSK modulator and clock gating technology. [PERSON] et al. [144] created a hardware scheme applicable to IEEE 802.15.6, which was implemented based on FPGA and supported NB PHY, including baseband processing, radio frequency and digital-to-analog conversion. Finally, the throughput, working frequency and energy consumption of this scheme were verified. [PERSON] et al. [145] proposed a baseband architecture supporting the UWB digital baseband PHY in the IEEE 802.15.6 standard, and the FPGA experiment proved that this scheme has a low packet error rate and bit error rate. [PERSON] et al. [146] put forward a complete NB PHY transceiver implemented in FPGA, which consists of baseband transceiver modules. An ASIC of baseband processing module in NB has been performed to solve the energy consumption of WBANs [147]. This ASIC conforms to the IEEE 802.15.6 standard. Compared to other publicized IC design schemes, this proposed ASIC showed advantages in power consumption, throughput, small area and energy efficiency. The detailed results are demonstrated in Table 13.  
Designing a suitable IC for WBANs requires not only optimizing the power consumption. This is also one of the key conditions to enable WBAN to be applied on a large scale. However, many of the technologies in IC for WBAN are still in the early stage and there is no common chip for WBAN yet. In the future, the proposed ASIC can be improved by considering the cooperation between the PHY and MAC layers and the security issues.
# Quality of Service and Congestion Control in Software-Defined Networking Using Policy-Based Routing
## 4 Proposed Methodology
### Proposed Policy-Based Routing  
A key factor in ensuring QoS is the use of differentiated services, which can be effectively implemented through policy-based routing (PBR). SDN, with its inherent flexibility, makes PBR implementation more straightforward, practical, and efficient by utilizing flow-based protocols rather than traditional packet/frame-based protocols.  
OSPF, a widely used routing protocol, determines routes based on a configured cost associated with each link, typically related to bandwidth. However, OSPF does not dynamically account for current network conditions, such as link congestion. Cisco routers, for example, calculate the OSPF cost as follows:  
\[\text{Link Cost}=\frac{\text{Reference bandwidth}}{\text{Link bandwidth}} \tag{1}\]While this approach is efficient, it does not address real-time link congestion, which can negatively impact network performance.  
In this work, we develop a PBR module within the SDN controller and integrate it into OSPF to mitigate link congestion and ensure QoS for traffic profiles specified by the network administrator, referred to as QoS flows. This approach allows administrators to select specific QoS flows for enhanced performance. The PBR module continuously monitors the queuing delay after a QoS flow is initiated. If the queuing delay exceeds a predefined threshold, the PBR module identifies an alternative low-delay path (detailed in Section 4.1.2) and reroutes the QoS flow along that path.  
Our focus is on achieving QoS for real-time multimedia communication that uses the Real-Time Transport Protocol (RTP) for data transmission. The results, discussed in Section 6, show that integrating the PBR module with OSPF effectively ensures QoS and reduces network congestion. Figure 1 illustrates the operation of an SDN network, with shaded areas highlighting the proposed policy-based routing module.
## 4 Implementation and Simulation Results  
The algorithms previously studied are applied to a PV system composed of 4 photovoltaic modules. They are connected in a combination of serial and parallel cells and the  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PSO** & **GWO** \\ \hline Inertia weight (\(w\)) & 0.1 & – \\ Personal Learning Coefficient \(c1\) & 1.7 & – \\ Global Learning Coefficient \(c2\) & 1.5 & – \\ Constant \(a\) & – & Self adaption \\ Coefficient \(r_{1}\) and \(r_{2}\) & – & Random numbers in [0,1] \\ Number of iterations \(N\) & 100 & 100 \\ Number of particles \(P\) & 10 & 10 \\ Sampling time & 0.0001s & 0.0001s \\ Initial duty cycle & 0.4 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter of the PSO and GWO MPPT algorithms.  
characteristics are given in Table 2. Considering the size of the PV modules, this system could be implemented in electric vehicle application or even an isolated grid for feeding some isolating area using a solar energy. Some recharge stations can also benefit from this solution and the proposed method can improve energy yield.  
The simulation bloc can be illustrated as it is Figure 8, all the PV cells are connected to the DC bus, which can give information about the global voltage and current, in relation to the solar radiation factor. Then the MPPT algorithm can extract the maximum of power using the PSO or the GWO algorithm. A DC converter is used for having information about the outputted power delivered to the load.  
Indeed, to compare the efficiency of MPPT PSO and GWO we considered four simulation tests so that a wide variety of conditions are modelled, Table 4, gives the maximum power, voltage and current parameters for each shading condition. During the first test, Figure 9a, the irradiation was kept constant and uniform for the four modules (1000 W/m\({}^{2}\)). The second test is characterized by partial shading on two of the four modules of around 40%, represented in Figure 9b. It corresponds to the following lighting distribution on the four modules (600 W/m\({}^{2}\), 600 W/m\({}^{2}\), 1000 W/m\({}^{2}\), 1000 W/m\({}^{2}\)). The third test (in Figure 9c) resembles the second one, but the partial shading on the two modules is varied in the order of 50% on the first and 20% for the second (500 W/m\({}^{2}\), 800 W/m\({}^{2}\), 1000 W/m\({}^{2}\), 1000 W/m\({}^{2}\)). Three modules operate under partial shade during the fourth Test, in Figure 9d. This corresponds to the following lighting distribution (200 W/m\({}^{2}\), 300 W/m\({}^{2}\), 700 W/m\({}^{2}\), 1000 W/m\({}^{2}\))  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(P_{mpp}\) **(W)** & \(V_{mpp}\) **(V)** & \(I_{mpp}\) **(A)** \\ \hline
**Uniform irradiation** & 996 & 120 & 8.3 \\ \hline
**First case of Partial shading** & 646.3 & 126.2 & 5.12 \\ \hline
**Second case of Partial shading** & 637.9 & 93.04 & 6.85 \\ \hline
**Third case of Partial shading** & 363.4 & 60.74 & 5.98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PV characteristics study for a different type of shading.  
Figure 8: Overall system design in the simulation phase.
## 4 Discussion about the Influence of GTR in the Electrical Measurements  
The value of the dielectric constant is very much linked to the polarization of orientation. When the polarization of orientation increases, the dielectric constant also increases, and the polarization of orientation is due to the presence of permanent dipoles in a molecule [30]. It is necessary to understand  
Figure 9: (**a**) Real part of the permittivity (\(\epsilon^{\prime}\)), (**b**) imaginary part of the permittivity (\(\epsilon^{\prime\prime}\)), for different polymers/GTR concentrations and particle size <200 μm, frequency fixed at 50 Hz and 30 \({}^{\circ}\)C.  
that the presence of GTR (and more especially of carbon black, the main component of the GTR) creates changes in the structure of the polymer. But the most important conclusion of this study is that when the carbon atoms are placed between the chains of the polymer, they indirectly decrease the chain-chain distance and therefore the movement of free volume. Therefore, it can be deduced that the structure changes created by the presence of GTR in the polymer can also create changes (increases) in the dipole moments and therefore in the polarization and the dielectric constant that has been measured. The study of the dielectric properties of polymers reinforced with GTR has shown that the presence of a tire out of use in thermoplastics increases the values of the real permittivity (or dielectric constant), the imaginary permittivity, the conductivity, and delta tangent. It has also been possible to see that the presence of GTR is not the only factor influencing the different dielectric properties. In each case, evolution is created by changes in polarization (in particular by polarization of orientation), through changes in measured properties. The presence of a tire out of use causes the evolution of the polarization, changing the structure and dipole moments of the polymer, especially thanks to the carbon black it contains. The electrical properties of an insulator have to be adopted for special uses, which means, in general, that the dielectric constant has to be small. It is noted that the measured values of the dielectric constant with the samples used vary between 2.2186 (HDPE only, 30 \({}^{\circ}\)C) and 7.66 (HDPE with 50% GTR, 30 \({}^{\circ}\)C) at a frequency of 50 Hz. (50 Hz is the frequency that interests us because it is the frequency of electric current distributed by the grid in Europe.) Regarding dielectric constant, mixtures of polymers and pneumatics can be used as insulation, for cases of PA at high concentrations of GTR cannot be considered as an insulator (K = 14.5).
## 3 Estimating the Hurst Index and Variance of the Phase Differences from Observed Ground Motions
### Modeling A Stochastic Phase Process with Modified Fractional Brownian Motion  
The earthquake magnitude and epicenter distance at a site of interest are needed to generate a phase process based on the modified fBm. As an example, we obtain \(\widehat{H}\) = 0.80 and \(\widehat{\sigma_{0}^{2}}\) = 150 from Equations (18) and (19) with \(M\) = 6.2 and \(\Delta\) = 207 km. With these two parameters and the discrete circular frequency interval, the sample \(d\psi\) can be simulated using the modified fBm algorithm described in Section 2.3. By adding these phase differences, we can obtain a cumulative phase at each discrete frequency; the result obtained is a sample of the fluctuation part of the phase difference,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Nonlinear Regression Equations** & \(\boldsymbol{R^{2}\left(H\right)}\) & \(\boldsymbol{AIC(H)}\) \\ \hline
1 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{4 }M^{2}+\beta_{5}\Delta^{2}+\beta_{6}\) & 0.603 & \(-1110.395\) \\
2 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{4 }M^{2}+\beta_{6}\) & 0.573 & \(-1094.270\) \\
3 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{5 }\Delta^{2}+\beta_{6}\) & 0.550 & \(-1080.937\) \\
4 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{4}M^{2}+\beta_{5}\Delta^{ 2}+\beta_{6}\) & 0.533 & \(-1071.608\) \\
5 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{3}\text{M}\Delta+\beta_{4}M^{2}+\beta_{5 }\Delta^{2}+\beta_{6}\) & 0.527 & \(-1068.614\) \\
6 & \(\widehat{H}=\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{4}M^{2}+\beta_{5 }\Delta^{2}+\beta_{6}\) & 0.535 & \(-1072.811\) \\
7 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{6}\) & 0.422 & \(-1020.627\) \\
8 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{4}M^{2}+\beta_{5}\Delta^{2}+\beta_{6}\) & 0.517 & \(-1065.579\) \\
9 & \(\widehat{H}=\beta_{2}\Delta+\beta_{3}\text{M}\Delta+\beta_{4}M^{2}+\beta_{6}\) & 0.407 & \(-1013.858\) \\
10 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{3}\text{M}\Delta+\beta_{4}M^{2}+\beta_{6}\) & 0.516 & \(-1064.637\) \\
11 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{3}\text{M}\Delta+\beta_{5}\Delta^{2}+\beta_{6}\) & 0.189 & \(-935.748\) \\
12 & \(\widehat{H}=\beta_{1}\text{M}+\beta_{2}\Delta+\beta_{4}M^{2}+\beta_{6}\) & 0.516 & \(-1064.917\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nonlinear regression results.  
Figure 7: **(a)** Linear association between the original and expected Hurst index values and **(b)** Common logarithm of original and expected variance values.  
as shown in Figure 8. A sample of the earthquake motion phases is obtained by adding the linear delay part to the fluctuation part of the phase as expressed by Equation (4). By changing the initial random number for simulating \(d\psi\), we can obtain a dataset of phase spectra.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 3 POD Method and Strategies
### Observation Model  
The GNSS antenna of HY2D satellite only tracks the GPS or BDS signal separately at present, so we selected ionosphere-free combined observation from GPS or BDS to calculate precise orbits. Equation (1) gives ionosphere-free combined observation of GPS and BDS.  
\[\left\{\begin{array}{l}P_{B}=\rho_{B}-c\cdot\delta t_{B}^{s}+c\cdot\delta t_{ B}^{t}+\varepsilon_{BP}\\ L_{B}=\rho_{B}-c\cdot\delta t_{B}^{s}+c\cdot\delta t_{B}^{t}+N_{B}\cdot \lambda_{B}+\varepsilon_{BL}\\ P_{G}=\rho_{G}-c\cdot\delta t_{G}^{s}+c\cdot\delta t_{G}^{t}+\varepsilon_{GP} \\ L_{G}=\rho_{G}-c\cdot\delta t_{G}^{s}+c\cdot\delta t_{G}^{t}+N_{G}\cdot \lambda_{G}+\varepsilon_{GL}\end{array}\right. \tag{1}\]  
where \(P_{B}\) and \(P_{G}\) are the code observations of BDS and GPS, respectively, while \(L_{B}\) and \(L_{G}\) are the carrier phase observations of BDS and GPS, respectively; \(\rho_{B}\) and \(\rho_{G}\) are geometrical distance from BDS and GPS signal to receiver, respectively, \(\delta t_{B}^{s}\) and \(\delta t_{G}^{s}\) stand for satellite clock errors for BDS and GPS, \(\delta t_{B}^{t}\) and \(\delta t_{G}^{t}\) are clock errors for BDS and GPS receivers, \(N_{B}\) and \(N_{G}\) refer to combined ambiguity parameters of BDS and GPS observations separately, \(\lambda_{B}\) and \(\lambda_{G}\) refer to combined wave length of BDS and GPS observations separately, \(\varepsilon_{BP}\), \(\varepsilon_{BL}\), \(\varepsilon_{GP}\) and \(\varepsilon_{GL}\) stand for different type observation noise.  
Figure 3: Number of GPS satellites observed on 21 July 2021.
# Tool Wear Monitoring for Complex Part Milling Based on Deep Learning
## 2 Method
### Feature Pre-Selection
#### 2.2.1 Cutting Force Features  
Cutting force is the most sensitive quantity reflecting the changes in tool wear. The magnitudes of cutting forces are selected as the features for tool wear monitoring. Cutting force coefficients can also be used as the cutting condition independent features for tool wear monitoring in complex part milling. Thus, the cutting force coefficients are also selected as the features.  
The cutting forces in the milling process are illustrated in Figure 2. The cutting force model can be expressed as  
\[\left\{\begin{array}{l}F_{\text{t}}(\phi)=K_{\text{tc}}bh(\phi)+K_{\text{te} }b\\ F_{\text{r}}(\phi)=K_{\text{rc}}bh(\phi)+K_{\text{rc}}b\\ F_{\text{a}}(\phi)=K_{\text{ac}}bh(\phi)+K_{\text{ae}}b\end{array}\right. \tag{1}\]  
where \(F_{\text{t}}\), \(F_{\text{r}}\) and \(F_{\text{a}}\) are the tangential, radial and axial cutting force, \(K_{\text{tc}}\), \(K_{\text{rc}}\) and \(K_{\text{ac}}\) are the cutting force coefficients contributed by the shearing action in tangential, radial and axial directions, \(K_{\text{te}}\), \(K_{\text{rc}}\) and \(K_{\text{ac}}\) are the edge constants, \(\phi\) is the instantaneous angle of immersion, \(b\) is the edge contact length, \(h\) is uncut chip thickness  
\[h=f_{z}\sin\phi \tag{2}\]  
where \(f_{z}\) is feedrate per tooth. According to Equations (1) and (2), the cutting force has a linear relationship with the uncut chip thickness \(h\). When the spindle speed and feedrate are fixed, the uncut chip thickness \(h\) changes with the sine of the instantaneous angle of immersion \(\phi\). The linear function can be rewritten as  
\[\left\{\begin{array}{l}F_{\text{r}}/b=K_{\text{tc}}f_{z}\cdot\sin\phi+K_{ \text{te}}\\ F_{\text{r}}/b=K_{\text{rc}}f_{z}\cdot\sin\phi+K_{\text{rc}}\\ F_{\text{a}}/b=K_{\text{ac}}f_{z}\cdot\sin\phi+K_{\text{ae}}\end{array}\right. \tag{3}\]  
As per Equation (3), the cutting force coefficients can be calibrated by linear regression of the cutting force value. Let the slope and intercept of the linear function after linear fitting be \(P_{i}\) and \(Q_{i}\), respectively, the cutting force coefficients can be obtained as  
Figure 1: Framework of tool wear monitoring method for complex part milling based on deep learning.  
\[\left\{\begin{array}{c}K_{\text{tc}}=P_{i}/f_{z}\\ K_{\text{ie}}=Q_{i}\end{array}\right. \tag{4}\]  
In complex part milling, the part is usually cut layer by layer in the axial direction. The tangential and radial force are varied significantly compared with the axial cutting force. Hence, in this study the average tangential and radial cutting forces \(F_{\text{t}}\), \(F_{\text{r}}\), and the tangential and radial cutting force coefficients \(K_{\text{tc}}\), \(K_{\text{rc}}\), \(K_{\text{te}}\), \(K_{\text{re}}\) are selected as the input features.