esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Frequency Shift and Code Phase Estimation Theory
### Code Phase Estimation  
Taking GPS as an example, the first PRN chip and the first bit of the 50 bits per second (bps) data stream are sent out from every satellite at the midnight of Saturday in GPS time, so the code phase can be calculated according to the signal sending time of GPS, and expressed as follows:  
\[\tau\ =\ \text{mod}\ (1023\ \times\ t_{\text{SV}}\times\ 1000,1023) \tag{12}\]  
where \(t_{\text{SV}}\) is the signal sending time of GPS, which can be calculated using the signal receiving time \(t_{\text{R}}\), the signal transmitting time \(D_{t}\) and the clock error \(t_{c}\). Therefore the signal sending time estimation \(\hat{t}_{\text{SV}}\) is described as:  
\[\hat{t}_{\text{SV}}\ =\ t_{\text{R}}-D_{t}+t_{c} \tag{13}\]  
The code phase estimation of the received signal \(\hat{\tau}\) is:  
\[\hat{\tau}\ =\ \text{mod}[1023\ \times\ (t_{\text{R}}-D_{t}+t_{c})\ \times\ 1000,1023] \tag{14}\]  
where \(t_{c}\) is provided by GNSS ephemeris, \(D_{t}=\boldsymbol{E}\cdot(\boldsymbol{P}_{r}-\boldsymbol{P}_{s})/c\), \(\boldsymbol{P}_{r}\) and \(\boldsymbol{P}_{s}\) represent the carrier position and the satellite position. The code phase estimation can be calculated by INS position which error is dependent on the INS accuracy.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 3 State-of-the-Art  
Research on chaotic image encryption is going on in three aspects; chaos, image, and encryption. These aspects are shown in Figure 2. The state-of-the-art in each of the mentioned aspects is reviewed below.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Survey** & **Year** & **Chaotic** & **Ecosystem** & **Roadmap** \\ \hline
[22] & 2022 & Yes & No & Yes \\ \hline
[23] & 2021 & Yes & No & No \\ \hline
[24] & 2019 & Yes & No & No \\ \hline
[25] & 2021 & Yes & No & Yes \\ \hline
[26] & 2021 & Yes & No & No \\ \hline
[27] & 2017 & Yes & No & No \\ \hline
[28] & 2021 & No & No & No \\ \hline
[29] & 2021 & No & No & No \\ \hline
[30] & 2021 & No & No & No \\ \hline
[34] & 2020 & No & No & No \\ \hline
[31] & 2020 & No & No & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: Aspects of Research on Chaotic Image Encryption.  
As seen in Figure 2, the chaos aspect is about chaos domains, sources, and dimensions. The image aspect studies 2D versus 3D images, gray-scale versus color images, and image transforms. Moreover, the encryption aspect is related to symmetric versus asymmetric encryption, block ciphers versus stream ciphers, and cryptographic primitives. The research works reviewed in this section are categorized according to these aspects.
# End-of-Life Impact on the Cradle-to-Grave LCA of Light-Duty Commercial Vehicles in Europe
## 3 Results
### Insight into the Avoided Burden Scenario  
Figure 7 shows the environmental impacts of EoL for different environmental impact categories other than GWP. The results are reported for each vehicle configuration, and the avoided burden scenario is assumed as the EoL scenario.  
Considering the abiotic depletion category for each vehicle configuration, the benefits outweigh the burdens. In conventional vehicles, the main contributor to the share of avoided impacts (blue bars) is the avoided production of lead (about 60% of the total share of avoided impacts for DIE-ICEV and about 45% for CNG-ICEV). Moreover, the avoided impacts related to aluminum production account for about 30% of the total avoided impacts for DIE-ICEV and about 40% for CNG-ICEV. For conventional vehicles, a significant share  
Figure 6: Avoided impacts of the two EoL scenarios studied for the three vehicle configurations.  
of the avoided impacts is due to reused spare parts (orange bars). Spare parts are mainly composed of metal components, for which aluminum and steel production emissions are avoided (as reported in Table S2, it was assumed that the 36% in weight of the metal components is reused).  
For ozone layer depletion and cumulative energy demand, the results are particularly different between the conventional and BEV configurations. When considering ozone layer depletion, it can be seen that although conventional vehicles show a benefit for the EoL stage, the BEV configuration presents high burdens due to Li-ion battery recycling. This outcome was obtained in [45; 56] also, where the life cycle assessment of the battery  
Figure 7: EoL stage environmental impacts under the avoided burden scenario for different impact categories: ozone layer depletion; abiotic depletion; photochemical oxidation; acidification; eutrophication; cumulative energy demand.  
showed that the benefits do not compensate for the recycling burdens for the ozone layer depletion category. When considering cumulative energy demand, it can be seen that the BEV configuration requires more energy in the EoL stage with respect to conventional vehicles due to Li-ion battery recycling.  
On the contrary, the EoL stage of the BEV configuration is characterized by significantly greater benefits than the other vehicle configurations for photochemical oxidation, acidification, and eutrophication. This is due to the impacts avoided by recycling the Li-ion batteries and CNG tanks.
# UAV Detection and Tracking in Urban Environments Using Passive Sensors: A Survey
## 2 UAV Detection and Identification  
In this section, we will conduct a comprehensive analysis and comparison of RF-based, acoustic-based, and vision-based methods for the detection and identification of UAVs in urban IoT environments. We will explore these methods from various perspectives, including traditional methods, deep learning methods, and the available public and semi-public datasets. Additionally, we will discuss the specific challenges associated with identifying UAV intrusions in IoT environments and review recent research focused on leveraging edge devices for UAV detection. Table 2 provides a performance comparison of various UAV detection methods, with the data obtained from literature research. The vision method based on deep learning demonstrates good accuracy even when experiments are conducted on public datasets that are not specifically optimized for UAVs. However, recent studies in the literature have shown that after fine-tuning, optimization, and training on specialized UAV datasets, the accuracy rate can exceed 90%, indicating a high level of competitiveness.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Category** & **Method** & **Accuracy** \\ \hline \multirow{8}{*}{Traditional RF-based Detection} & Fractal dimension (FD) [29] & 100\% \\  & axially integrated bispectra (AIB) [30] & 98\% \\  & square integrated bispectra (SIB) [31] & 96\% \\  & Signal Spectrum (SFS) [32] & 97.85\% \\  & Wavelet energy entropy (WEE) [32] & 93.75\% \\  & Power spectral entropy (PSE) [32] & 83.85\% \\ \hline \multirow{8}{*}{RF-based Detection Using Deep Learning} & [PERSON] et al. [33] & 99\% \\  & [PERSON] et al. [34] & 100\% \\ \cline{1-1}  & [PERSON] et al. [35] & 98\% \\ \cline{1-1}  & [PERSON] et al. [36] & 90.2\% \\ \cline{1-1}  & [PERSON] et al. [37] & 98.57\% \\ \hline Traditional Acoustic-based Detection & Mel Frequency Cepstrum Coefficient (MFCC) & 97\% \\ \hline \multirow{4}{*}{Acoustic-based Detection Using Deep Learning} & [PERSON] et al. [27] & 96.38\% \\  & [PERSON] et al. [38] & 99\% \\ \cline{1-1}  & [PERSON] et al. [39] & 98.78\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of different UAV detection methods.
# Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things
## 6 Result and Discussions
### Simulation Results
#### 6.2.5 Actual and Reconstructed Sensor Data  
Figure 9 depicts the actual data for the reconstructed sensor data. It is observed that the actual sensor data and reconstructed sensor data in EDADA-RPL is almost similar when compared to both the values. It is noted that the sensor IDs are represented from 1 to 120. Here, the sensor readings are nearly 30. The sensor values are varying, due to various reasons such as noise, the distance between the source and the destination, etc. So, the sensor values are falling up and down, but it maintains the consistency value by using compressed sensing theory. Hence, the EDADA-RPL recovers the original data from the source node without any losses.
## 2 Mathematical Models
### Mathematical Model of Generation Output Power  
The generation system consists of transmission line impedance and a natural gas generator. The single-equivalent circuit from the generator to the PCC point is shown in Figure 13. In this paper, it is assumed that the line impedance between the generator and the PCC point is mainly inductive, which means the inductance is much greater than the resistance. Therefore, the active and reactive power output from the generator to the PCC point can be expressed as (8), respectively.  
\[\left\{\begin{array}{l}p_{g}=3\frac{U_{m}U_{g}}{\sin\delta}\\ q_{g}=3\frac{(U_{m}-U_{g}\cos\delta)U_{m}}{X}\end{array}\right. \tag{8}\]  
where \(p_{g}\) and \(q_{g}\) stand for the active and reactive power of the generator, \(U_{g}\) is the voltage amplitude of the generator, Um is the voltage amplitude of the grid bus, and \(\delta\) is the power angle of the generator.  
The generator terminal voltage and PCC point voltage are approximately equal to a constant when the system is steady state. Therefore, linearizing this equation at the stabilization point of the system as follows:  
\[\left\{\begin{array}{ll}p_{g}|&U_{m}=U_{m0}\\ &\frac{3U_{m0}U_{\phi 0}}{X}\sin\delta_{0}+\frac{3U_{m0}U_{\phi 0}}{X}\cos\delta_{0}( \delta-\delta_{0})\\ &U_{g}=U_{g0}\\ &\delta=\delta_{0}\\ &q_{g}|&U_{m}=U_{m0}\\ &U_{g}=U_{g0}\\ &\delta=\delta_{0}\end{array}\right.=\frac{3(U_{m0}-U_{g0}\cos\delta_{0})U_{m0} }{X}+\frac{3U_{m0}U_{\phi 0}\sin\delta_{0}}{X}(\delta-\delta_{0}) \tag{9}\]  
Calculating (8) to find its derivative function as follows:  
\[\left\{\begin{array}{ll}\frac{\partial p_{g}}{\partial t}|&U_{m}=U_{m0}\\ &U_{g}=U_{g0}\\ &\delta=\delta_{0}\\ &\frac{\partial q_{g}}{\partial t}|&U_{m}=U_{m0}\\ &U_{g}=U_{g0}\\ &\delta=\delta_{0}\end{array}\right.=\frac{3U_{m0}U_{\phi 0}\cos\delta_{0}}{X}( \omega-\omega_{0}) \tag{10}\]  
Since (10) is linearized at the fixed point of the generator, the equation does not appear to include the control input signal. This does not mean that the output power of the generator during stabilization is a constant value. Because the voltage of the generator varies according to the excitation system. Note that the excitation system is not considered, and the generator output power is considered with bus frequency in this paper. According to (10), the generator power is constant when its speed no longer varies, such that  
\[p_{g},q_{g}\to cons\tan,\frac{\partial p_{g}}{\partial t}\xi\frac{ \partial q_{g}}{\partial t}\to 0\ \mbox{as}\ t\rightarrow\infty \tag{11}\]  
Equation (10) is brought into (7) as a disturbance term. The dynamic equation of the compensation system can be expressed as Equation (12).
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## 3 Design of Vehicle State Estimator Based on IMM-MCCKF
### Vehicle State-Space Model  
Based on Equations (1)-(3), the vehicle's longitudinal, lateral, and yaw dynamics are formulated as recursive discrete state equations, as illustrated below:  
\[\begin{array}{ll}x(k)=f(x(k-1),u(k-1)+v(k-1))\\ z(k)=h(x(k),u(k))+w(k)\end{array} \tag{13}\]  
where \(x(k)\) represents the state vector and \(z(k)\) represents the measurement vector. The system noise \(v(k)\) is characterized by its covariance array \(Q\). The measurement noise \(w(k)\) is defined by its covariance array \(R\).  
The vehicle's state and measurement models, derived from discretizing Equations (1)-(3) with the first-order Euler method, are expressed as follows:\[\left[\begin{array}{c}v_{x}(k)\\ v_{y}(k)\\ r(k)\end{array}\right]=\left[\begin{array}{c}v_{x}(k-1)+[mr(k-1)v_{y}(k-1)+(F_{x 11}(k-1)+F_{x12}(k-1))\cos\delta_{f}(k-1)\\ +F_{x21}(k-1)+F_{x22}(k-1)-(F_{y11}(k-1)+F_{y12}(k-1))\sin\delta_{f}(k-1)]T/m;\\ v_{y}(k-1)+[-mr(k-1)v_{x}(k-1)+(F_{x11}(k-1)+F_{x12}(k-1))\sin\delta_{f}(k-1)\\ +(F_{y11}(k-1)+F_{y12}(k-1))\cos\delta_{f}(k-1)+F_{y21}(k-1)+F_{y22}(k-1)]T/m;\\ r(k-1)+[(F_{x11}(k-1)+F_{x12}(k-1))]f_{y}\sin\delta_{f}(k-1)\\ +(F_{x12}(k-1)-F_{x11}(k-1))\cos\delta_{f}(k-1)t_{w1}/2+\\ (F_{x22}(k-1)-F_{x21}(k-1))t_{w1}/2+(F_{y11}(k-1)+F_{y12}(k-1))l_{f}\cos\delta _{f}(k-1)\\ +(F_{y11}(k-1)-F_{y12}(k-1))\sin\delta_{f}(k-1)t_{w2}/2-(F_{y21}(k-1)+F_{y22}( k-1))l_{r}]T/I_{z}\end{array}\right] \tag{14}\]  
\[\left[\begin{array}{c}a_{x}(k)\\ a_{y}(k)\\ r(k)\end{array}\right]=\left[\begin{array}{c}[(F_{x11}(k)+F_{x12}(k))\cos \delta_{f}(k)+F_{x21}(k)+F_{x22}(k)-(F_{y11}(k)+F_{y12}(k))\sin\delta_{f}(k)]/m \\ [(F_{x11}(k)+F_{x12}(k))\sin\delta_{f}(k)+(F_{y11}(k)+F_{y12}(k))\cos\delta_{f }(k)+F_{y21}(k)+F_{y22}(k)]/m\\ r(k)\end{array}\right] \tag{15}\]
# Granular Flow Impact on Shed Tunnels and the Buffering Effect of Cushion Layers
## 3 Results
### Influence of the Cushion Layer
#### 3.2.2 Particle Size of the Cushion Layer  
To gain a deeper understanding of the buffering effect of cushion layers with different particle sizes, gravels with three different particle sizes, 3 mm\(\sim\)5 mm, 5 mm\(\sim\)10 mm, and 10 mm\(\sim\)20 mm, are used as cushion layer materials in this study. The cushion layer thickness is set at 200 mm.  
Figure 10a shows the time history curves of the frontal normal impact force on the shed tunnel as the particle size of the cushion layer varies. None of the curves exhibit significant peaks. It can be observed that before 1.0 s, there was no significant difference in the impact force among the three cases. However, after that, there is a trend where the impact force decreases as the particle size of the cushion layer becomes smaller. The peak and residual values of the impact force for the fine particles are approximately 79 N and 73 N, respectively. Compared to the coarse particles, they are reduced by approximately 22.5% and 18.0%, respectively. This indicates that reducing the particle size of the cushion layer can further decrease the impact force. Comparing these results with the case without a cushion layer mentioned in Section 3.2.1, using a 200 mm cushion layer with fine particles reduces the impact force by more than half. The buffering effect is significant and can effectively protect the safety of the shed tunnel.  
Figure 10b illustrates the time history curves of the impact force on the shed tunnel top as the particle size of the cushion layer varies. Compared to the frontal impact force, the differences in the impact force are relatively small. The peak values of the impact force decrease as the particle size of the cushion layer decreases, with values of 128 N, 109 N, and 101 N. The differences in the residual values of the impact force are minimal, but the trend is opposite to that of the peak values. As the particle size of the cushion layer decreases, there is a slow increase in the residual values.  
The research has practical implications for the design and protection measures of shed tunnel structures. According to the results of this section, finer particles can provide a better cushioning effect and protect the shed tunnel structures from the impact of granular flow disasters.  
Figure 10: Impact force time history of the granular flow on the shed tunnel at different particle sizes of cushion layers. (**a**) Normal impact force on the frontal surface; (**b**) normal impact force on the top surface.
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 2 Application of VSL and RM Control in HDV Traffic Flows
### Classical RM Approaches  
As mentioned earlier, RM is used to control the traffic flow entering a motorway using traffic lights at the on-ramps. It was initially modeled as a pre-timed signal controller that was improved to operate on real-time traffic measurement data for traffic-responsive signal control. RM algorithms can be divided into localized single on-ramp controllers and system-wide or coordinated systems with multiple on-ramp controllers [49]. Most of the algorithms used recently employ a feedback control loop approach such as ALINEA. The detectors for RM feedback systems are shown in Figure 2.  
The extensions of ALINEA use their own algorithms derived either for specific circumstances or as a new method for RM [19]. Coordinated RM has the main objective of controlling multiple upstream on-ramps before the on-ramp where the congestion occurs. Algorithms such as METALINE, Stratified Zone Metering (SZM), Heuristic Ramp-Metering Coordination (HERO), and System-Wide Adaptive Ramp Metering (SWARM) set the metering rates for each metered on-ramp. METALINE is the integral coordinated system version of ALINEA [50]. The SZM algorithm uses overlapping zones defined as motorway sections spreading between the two mainline detectors [51]. The commonly used length of an SZM zone ranges from 0.8 km to 4.8 km. The algorithm balances the sum of the upstream mainline flow, the sum of the unmetered on-ramp flow, the sum of the metered ramp flow, and the sum of the metered motorway flow with the sum of the off-ramp flow, the downstream bottleneck capacity, and the free capacity on the mainline [51].  
HERO is an algorithm that uses a master-slave structure to control on-ramp traffic [52]. The master role is assigned to the bottleneck area on downstream on-ramp, while the slaves are assigned to the upstream on-ramps, and their on-ramp storage is used to solve the master on-ramp related congestion. When the algorithm is enabled, both the downstream and upstream metering rates are coordinated to keep the relative queue lengths approximately equal. The minimum queue length on the upstream on-ramp is updated as long as the queue length on the downstream ramp falls below the activation threshold.  
The SWARM algorithm incorporates two independent algorithms. The first algorithm, called SWARM1, uses linear regression and Kalman filtering as well as a system-wide apportioning to forecast density, while the second algorithm, called SWARM2, is a local traffic-responsive system that converts measured densities into metering rates using linear conversion [53]. The more restrictive value defines the final metering rate.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 4 Simulation and Experimental Results  
The voltage conversion gain of the double-balanced down-conversion mixer can be described as the ratio of the amplitude of the output to that of the input:  
\[A_{v}=\frac{2}{\pi}g_{m}R_{L}=\frac{2}{\pi}\frac{I_{tail}}{2V_{T}}R_{L} \tag{8}\]
## 3 Numerical Results and Discussion
### Magnetic Field Dependence of the Band Gap Energy  
The difference between ferro- and antiferromagnetic semiconductors can be seen also in the magnetic field dependence of the band gap energy \(E_{g}\). Let us emphasize that the effect of a magnetic field on the energy band gap of an ordinary semiconductor is probably negligible, but for a FMS it is not so. This can be seen from Figures 7 and 8. The results are in full agreement with the conclusions made above and show the importance of accounting for the SPI for the behavior of \(E_{g}\) applying an external magnetic field. We observe that the band structure is altered by the magnetic ordering and can be interpreted as conduction band splitting of spin-up and -down electrons. For \(R>0\), the magnetization increases with increasing the magnetic field \(h\), but due to the different sign of the s-d interaction constant \(I\) in Equation (12), we observe a decrease in \(E_{g}\) with increasing \(h\) for the case \(I>0\) (Figure 7, curve 1, EuO) and an increase for \(I<0\) (Figure 8, curve 1, UTe). For the case of negative SPI \(R\), we obtain the opposite result (see curve 2 in Figures 7 and 8). [PERSON] et al. [39] have reported that in EuTe (\(I>0\)[41]), at low temperatures, the band gap energy \(E_{g}\) decreases with increasing magnetic field \(h\), which is in agreement with our result for EuO and the experimental data of [PERSON] [38] (Figure 7, curve 1). Let us emphasize that in EuTe, the ferromagnetic s-f interaction \(I\) competes with the antiferromagnetic super-exchange \(J\) which is very small, nearly negligible [41]. [PERSON] et al. [37] have reported a blue-shift of the band gap in the antiferromagnetic semiconductor MnTe (\(I<0\)) due to the establishment of the magnetic order, which could be explained with a negative \(R\) value. This could be valid also for UTe (Figure 8, curve 2).  
Figure 5: (Color online) Dependence of the band gap energy \(E_{g}\) on the spin–phonon interaction constant \(R\), \(T\) = 30 K for (1) EuO, \(I\) = 0.2 eV, and (2) UTe, \(I=-0.2\) eV.  
Figure 6: (Color online) Dependence of the band gap energy \(E_{g}\) on the electron–phonon interaction constant \(A\) for \(T\) = 30 K. Inset: The number of conduction electrons in the spin-up (\(n_{\uparrow}\)) and spin-down (\(n_{\downarrow}\)) bands as function of \(A\) for \(T\) = 30 K.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 2 Materials and Methods  
Our approach is based on a U-Net architecture with a ResNet-50 backbone to produce binary ship/no-ship segmentation masks of the input image. U-Net has been used extensively in remote sensing applications, traditionally with a simple downsampling path of consecutive convolution blocks with no downward skip connections.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 5 Results
### Effect of Runtime Parameter Adaptation  
Figure 12 compares GA and GA-F specifically, restricting the plots to only these for clarity. For 2 qubits, both GA and GA-F were able to reach the target error thresholds. We observe that GA-F is able to produce similar median depths and CNOTs for error thresholds of 0.1, 0.001 and 0.001. It performs slightly worse on those metrics for error threshold of \(10^{-8}\). These observations prompt us to take a deeper look using Figure 13a-d, which show the cumulative average number of updates each GA-based compiler is able to sustain throughout its progress. It shows that GA-F generally maintains a higher median value for the average number of updates, indicating it is able to find new solutions, which extend the Pareto-front, faster than the regular GA. The graphs also show the median number of generations required for the proposed techniques to hit the error targets and these are marked with vertical lines. We see that, except for the error threshold of 0.1, GA-F is able to achieve convergence earlier. Figure 13e-h show the progression of the best error achieved over generations. The solid traces plot the median and the shaded areas indicate the region between the maximum and minimum best error values at that generation across trials. We observe that both GA and GA-F progress at similar rates towards convergence in terms of best error for 2 qubits, with GA-F able to achieve slightly faster rates of convergence in some cases.  
The results are more mixed and inconclusive for 3 qubits. Neither configuration has a definitive lead in either the average rate of updates or the progress of achieved best error, as can be seen from Figure 14a-h. Furthermore, since the GAs were not able to achieve the error targets within the given timeout limit, these results are possibly not representative. It is plausible that over a longer number of generations, the GAs would settle into a more stable pattern, perhaps the same as observed for 2 qubit unitaries, but further experimentation is required to confirm.