esa-sceva/satcom-chunk-collection huggingface dataset:
# A Regression-Based Technique for Capacity Estimation of Lithium-Ion Batteries
## 3 Capacity Estimation
### Proposed Methods to Estimate \(Q\)  
Here we define a parameter that shows voltage variation proportional to capacity, similar to the last part:  
\[S_{v_{b,k}}^{Q}=\frac{Q}{v_{b,k}}\;\frac{dv_{b,k}}{dQ}=\frac{Q}{v_{b,k}}\; \frac{d}{dQ}\left(\mathit{occ}\;(\mathit{soc}_{k})+Mh_{k}-\sum_{i}R_{i}i_{R_{ i,k}}-i_{b,k}R_{e}\right) \tag{10}\]  
The derivative of components in parenthesis was computed as the following equation:  
\[\frac{dOCV}{dQ}=\frac{\partial{OCV(SOC_{k})}}{\partial{SOC_{k}}}\;\frac{dSOC _{k}}{dQ} \tag{11}\]  
In most battery cells, \(\frac{\partial{OCV(SOC_{k})}}{\partial{SOC_{k}}}\) is very small. The equation which was derived from SOC relationships can be rewritten as the following equation:  
\[SOC[k+1]=SOC[k]-\frac{\eta[k]\Delta t}{Q}i_{b}[k] \tag{12}\]  
In which \(SOC[k]\) is state of charge, \(\eta[k]\) is columbic efficiency, which shows the consumer will not receive the whole charge stored in the battery due to some unwanted side reactions. In addition, a tiny part of it is being lost. Usually, this parameter is very near to one. \(\Delta t\) is sampling time for discretization, \(i_{b}[k]\) is battery current, and \(Q\) is the total capacity of the battery.  
It can be derived from the following equation that the sensitivity of voltage to capacity through hysteresis term is minor and zero through other terms. So total capacity is not observable [28].  
\[\begin{array}{cl}\frac{dSOC[k]}{dQ}&=\frac{dSOC[k-1]}{dQ}-\eta[k-1]i_{b}[k- 1]\Delta t\frac{d\frac{1}{Q}}{dQ}\\ &=\frac{dSOC[k-1]}{dQ}+\frac{\eta[k-1]i_{b}[k-1]\Delta t}{Q^{2}}\end{array} \tag{13}\]  
It can be seen from the above equation that \(\frac{dSOC[k]}{dQ}\) increases while the direction of \(i_{b}[k]\) does not change in a considerable period. This is because of \(\Delta t\) (by order of \(1/3600\) or less).  
In this part, a procedure is described to analyze some possible methods of estimating \(Q\) and their disadvantages. Finally, an approach is proposed to have an accurate, robust estimate of \(Q\).  
Considering Equation (13), we have:  
\[SOC[k_{n}]=SOC[k_{1}]-\frac{\Delta t}{Q}\sum_{k_{1}}^{k_{n-1}}\eta[k]i_{b}[k] \tag{14}\]  
The above equation could be rewritten as:  
\[-\Delta t\sum_{k_{1}}^{k_{n-1}}\eta[k]i_{b}[k]=Q(SOC[k_{n}]-SOC[k_{1}]) \tag{15}\]  
By defining \(Y=-\Delta t\sum_{k_{1}}^{k_{n-1}}\eta[k]i_{b}[k]\) and \(X=SOC[k_{n}]-SOC[k_{1}]\), it could be written in a linear format: \(Y=QX\).
# Reliability Model Based Dynamic Multi-Level Trust Analysis
## 6 Experiment
### Dynamic Changes Evaluation  
In this section, we designed experiments to verify the rationality of the dynamic algorithm. An example of a four-levels trust modeling approach is demonstrated, including uncertainty level. The parameters are set as: \(\alpha_{1}=0.15\), \(\alpha_{2}=0\), \(\delta=2\), \(k=3\). Furthermore, \(\beta_{i}=3.5\). According to Equations (5) and (6), the changes of uncertainty opinion under different ratings are showed as Figure 12. Here the x axis represents rating times. The y axis is the corresponding trust.  
A completely uncertainty view can become full trust after 20 times of ratings on Level 2, and turn to full 1-level trust with 10 times ratings on Level 1. However, for distur  
Figure 11: Evaluation time.  
Figure 12: The dynamic changes of uncertainty: (**a**) dynamic changes of uncertainty under ratings in Level 2; (**b**) dynamic changes of uncertainty under ratings in Level 1; (**c**) dynamic changes of uncertainty under ratings in Level 0.  
Figure 10: MDD average generation time.  
ratings on Level 0. This corresponds to the situation in our lives: trust builds slowly, but distrust shapes quickly.  
Figure 13 shows the impact of trust and distrust on each other.  
Figure 13a shows the changes of a completely distrustful viewpoint (0, 1, 0, 0) under the two-level ratings. It can be seen that when 20-21 times two-level trust ratings are received, distrust will be wholly eliminated. However, as Figure 13b displays, the same effect requires 23-24 times 1-level evaluations to achieve. This is consistent with our expectation: the higher the level of trust, the greater its impact on distrust. Correspondingly, Figure 13c demonstrates the impact of distrust on trust. When 17 times 0-level ratings are received, a completely trustful viewpoint (0, 0, 0.5, 0.5) becomes absolutely distrustful. It is worth noting that when 8-9 times distrust ratings are received, the top-level trust has been entirely offset. This indicates that distrust has a greater impact on trust. Furthermore, this also conforms to the rules: trust growing slower, declining faster, and it is difficult to eliminate the distrust ratings. Based on the above experiments, we conclude that the MDD-based trust analysis method is an efficient and accurate solution to the multi-level trust evaluation problem.
## 3 Proposed Framework
### Implementation Details: Interaction Dictionary and Algorithmic Configurations
#### 3.2.2 Parameter Configuration for JPDA and GNG  
To ensure reproducibility, we detail here the configuration of the JPDA tracker and the GNG algorithm. These components support interaction-based clustering and continuous ego vehicle motion prediction under partial observability.  
The GNG network dynamically adjusts its number of nodes based on the complexity of the trajectory during training, with an upper bound of 100 nodes to control overfitting and runtime. The NFF predicts the ego vehicle's motion by assuming no external forces influence its trajectory resulting in a constant velocity movement. It is a simplified Kalman filter that allows us to estimate future states under a zero acceleration assumption [7]. JPDA parameters were tuned for stability across multi-object scenes. All parameters were empirically selected using the training dataset and are summarized in Table 3.  
These values were validated during development and align with established practices in LiDAR-based trajectory modeling.
# Wind Speed Spectrum of a Moving Vehicle under Turbulent Crosswinds
## 4 Wind Speed Spectrum of a Moving Vehicle
### Discussion of Frequencies Corresponding to Wind Speed Spectrum Peak  
In Figure 10, the scaling coefficients of frequencies corresponding to the wind speed spectrum peak are shown. Generally, the scaling coefficients are increased. That is, for a moving point, the frequencies corresponding to the wind speed spectrum peak are larger than those of a static point. Scale coefficients \(\beta\) represent the frequency corresponding to the maximum value of the wind speed spectrum of a moving vehicle to the static.  
From Figure 10a, when the \(V_{\text{ratio}}\) is 0.1 and the wind yaw angle is \(105^{\circ}\), \(120^{\circ}\), \(135^{\circ}\), \(160^{\circ}\) or \(175^{\circ}\), the frequencies corresponding to the wind speed spectrum peak of a moving point are smaller than those of a static point. The frequencies show little change as the wind yaw angles increase. When the \(V_{\text{ratio}}\) is 0.5 and the wind yaw angle is \(135^{\circ}\), \(150^{\circ}\), \(165^{\circ}\) or \(175^{\circ}\), the same rule is true. The frequency increases as the wind yaw angle increases. When the \(V_{\text{ratio}}\) value is 1 and the wind yaw angle is \(150^{\circ}\), \(165^{\circ}\) or \(175^{\circ}\), the frequency corresponding to the wind speed spectrum peak of a moving point is smaller than that of a static point. That is, the scaling coefficient is smaller than 1. When the wind yaw angle is \(45^{\circ}\), the frequency corresponding to the wind speed spectrum peak of a moving point is the same as that of a static point. In addition, the frequency increases as the wind yaw angle  
Figure 10: The frequency corresponding to the wind speed spectrum peak.  
increases. When the wind yaw angle is small, the wind speed spectrum peak of the moving point is mainly caused by the longitudinal wind speed spectrum, and the influence of the transverse wind speed spectrum is small. As the wind yaw angle gradually increases, the influence of the transverse wind speed spectrum also gradually increases.  
From Figure 10b, when the \(V_{\text{ratio}}\) is 2, 3, 4 or 5, the frequencies corresponding to the wind speed spectrum peak first are firstly increased and then decreased. When the wind yaw angle is 90\({}^{\circ}\), the frequency corresponding to the wind spectrum peak of a moving vehicle is at its maximum. The maximum scaling coefficient \(\beta\) can reach 10 and the minimum coefficient \(\beta\) can reach 6 when the \(V_{\text{ratio}}\) value is 5. When the \(V_{\text{ratio}}\) value is 0.1 or 0.5, the maximum scaling coefficient \(\beta\) is smaller than 2 at different wind yaw angles. That is, when the wind direction is perpendicular to the vehicle's moving direction, the frequencies corresponding to the wind speed spectrum peak of a moving vehicle are at their largest.
## 5 Results
### Experimental Results and Analysis
#### 5.2.3 Discussion on Tracking Accuracy  
Change the distribution of noises, we perform over 100 Monte Carlo simulations for each algorithm. The simulation results are shown in Tables 2-4, where \(\sigma_{\text{G}}\) is the standard deviation of Gaussian noise, \(\sigma_{\text{R}}\) is the scale parameter of Rayleigh noise, and the tracking performance is defined by the root mean square error (RMSE) of the target position, reflecting the deviation between the estimated position and the real state. It should be noted that those results are close to the optimal accuracy of these algorithms. Nevertheless, the process noises and the measurement noises are randomly generated during the tracking process, so the results are still somewhat random even if simulations are repeated over 100 times.  
For the noises with Gaussian distribution, when the standard deviation of measurement noises \(\sigma_{\text{G}}\) is 50 or 100, the RMSE of PF are the largest. However, as \(\sigma_{\text{G}}\) is increased to 150, the RMSE of UKF is over PF. Obviously, UKF is more susceptible to noise than PF. The tracking accuracy of PS-PF and PS-UPF are higher than that of PF and UPF, respectively. This shows that the introduction of partitioned sampling improves the performance of different algorithms to varying degrees, which is related to the limitations of the algorithm and its corresponding system. MMNF combines GRNN with a multiple-model method, while its tracking accuracy is significantly higher than that of traditional  
Figure 8: Statistical histogram of tracking MAE distribution for 100 Monte Carlo runs. II to I8 represents the numerical interval [0, 50), [50, 100), [100, 150), [150, 200), [200, 250), [250, 300), [300, 350), [350, \(\infty\)), respectively.  
Figure 7: The mean absolute error (MAE) distribution of the estimated target position.  
methods such as PF and UKF, and even slightly higher than that of UPF. By introducing partitioned sampling, the tracking accuracy of PS-UPF is significantly improved. For the proposed PS-AUPF, its tracking error is reduced by 6.06%, 6.43% and 6.60%, compared to UPF, when \(\sigma_{\text{G}}\) is 50, 100 and 150, respectively. It performs better than the traditional algorithms and MMNF, and could achieve closer accuracy of PS-UPF with a small extent of improvement. As we can see, the accuracy of the improved algorithm with adaptive sample-size strategy is close to and even higher than that of the general algorithm, but this type of improvement in accuracy is not significant.  
Changing the noise distribution as a Uniform distribution or Rayleigh distribution, we can find that the performance change of the algorithm is similar to that with Gaussian noise. With the increase of noise randomness, the accuracy improvement of partitioned sampling and adaptive strategy becomes more and more significant. Overall, regardless of the noise distribution, the tracking accuracy of PS-AUPF is higher than that of traditional algorithms and MMNF, and it can be well applied to non-linear, non-Gaussian systems.
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 4 Multiple-Physical Analysis  
Multi-physics analysis is a coupled electromagnetic, thermal, and mechanical analysis of the structure using CST [37]. The power-loss distribution of the RFQ cavity is first simulated. Secondly, the temperature map can be achieved by importing the power loss to the steady-state thermal analysis. Thirdly, the structural stresses and deformations of the RFQ cavity are calculated by applying the thermal result. Finally, the resonant frequency shift can be calculated according to the structural deformations. In the multi-physics analysis, the power consumption of the cavity was set to 68 kW, considering a power redundancy of 20% [38]. The waterways of the RFQ cavity were designed as shown in Figure 16, with three channels on each electrode and two channels on each cavity wall. In addition, each PSL is provided with a water channel with an 8 mm diameter for cooling. In the simulation, the water temperature and ambient temperature are set to 20 \({}^{\circ}\)C. The heat transfer coefficient h can be obtained by the Dittus-Boelter correlation [39]:  
\[h=\frac{N_{u}k}{D} \tag{6}\]  
where \(k=0.6\) W/mK is the water heat conductivity coefficient, and \(D\) is the diameter of the cooling channel. \(N_{u}\) is the Nusselt number, which is defined as:  
\[N_{u}=0.023\cdot Re^{0.8}\cdot Pr^{0.4} \tag{7}\]  
\[P_{r}=\frac{C_{p}\mu}{k} \tag{8}\]  
\[R_{e}=\frac{vD\rho}{\mu} \tag{9}\]  
where \(v\) is the water's velocity, \(\mu=0.001\) Ns/m\({}^{2}\) is the viscosity of water, \(C_{p}=4.2\) kJ/kg is the heat capacity of water, \(\rho=998\) kg/m\({}^{3}\) is the density of water, and \(R_{e}\) and \(P_{r}\) are the Reynolds number and the Prandtl number, respectively. Assuming that the velocity of water is 2.3 m/s, according to the Dittus-Boelter correlation, the heat transfer coefficient in the PSLs and cavity are calculated to be 9682 and 9259 W/m\({}^{2}\cdot\)K.  
Figure 15: Dipole field components caused by structural errors. (**a**) Dipole field component caused by vane offset dH, (**b**) Dipole field component caused by vane offset dR.  
The temperature distribution of the cavity is shown in Figure 17a. The maximum temperature rise in the cavity is 13.6 \({}^{\circ}\)C and is located at the output undercut of the cavity. The maximum deformation occurs at the same location with a size of 40.3 \(\upmu\)m. As shown in Figure 17c, the maximum thermal stress is 14.3 MPa, which is lower than the yield strength of oxygen-free copper (60 to 70 Mpa) [40]. Finally, the frequency shift caused by the deformation is \(-22.8\) kHz.
## 2 Composition of the \"Target Situational Awareness\" Test System and Experimental Methods  
### Objectives of the Test System  
The composition of the \"target situational awareness\" test system is shown in Figure 1. The pod flight was mounted on the aircraft to simulate the flight state of the missile after launch, and a typical aircraft was used to simulate the target aircraft tracked by the airborne radar and pod seeker. According to the preset operating procedures and time sequence, while the pod is flying toward the target carried by the aircraft, the airborne radar intercepts and tracks the target aircraft and sends the tracking target information to the pod. Data calculation was conducted by the pod, and the autonomous flight state was simulated based on the designated time sequence as well as the attack process of target interception and tracking by the radar guidance system in the pod.  
Figure 1: Composition of the “target situational awareness” test system.  
The test system aimed at solving the following problems: Upon modification, the shape and structure of the pod should satisfy the flight safety requirements of the carrier in terms of structure and strength; the control of multiple transitions from hanging flight state to simulated attack state should be involved in the tests; power supply and long-time cooling of high-power equipment on the missile should be involved in the transition of multiple attack states; data recording of the carrier, missile, and target aircraft should be conducted during tests; and operational control of the time sequence and work mode of the carrier, missile, target aircraft, and other test equipment should be performed [10; 15; 16; 17; 18; 19].
## 1 Introduction
### Frequency Domain  
#### 2.1.1 Two-Soliton Bound States  
We assume that the complex amplitude of slowly-varying envelope of a single soliton in time domain can be described by \(f(t)\) and the complex amplitude of its optical spectrum can be described by \(F(\
u)\), with \(\
u\) denoting the frequency difference compared to the central optical frequency. It means that Fourier transform of \(f(t)\) corresponds to \(F(\
u)\).  
Consider a two-soliton bound state with pulse separation of \(\Delta T\) and phase difference of \(\theta_{1}\), which reads \(f(t)+f(t-\Delta T)\exp(j\theta_{1})\) in time domain. According to Fourier transform, the spectral amplitude of the bound state is expressed by  
\[F(\
u)+F(\
u)\exp(-j2\pi\
u\Delta T)\exp(j\theta_{1})=F(\
u)\exp[-j(\pi\
u \Delta T-\frac{\theta_{1}}{2})]\cdot 2\cos(\pi\
u\Delta T-\frac{\theta_{1}}{2}) \tag{1}\]  
The spectral intensity is therefore proportional to  
\[|F(\
u)+F(\
u)\exp(-j2\pi\
u\Delta T)\exp(j\theta_{1})|_{2}=2|F(\
u)|_{2}\cdot [1+\cos(2\pi\
u\Delta T-\theta_{1})] \tag{2}\]  
Equation (2) indicates that the bound state manifests itself by a modulated optical spectrum under the envelope of 4 times single-soliton counterpart. The modulation period is \(\Delta\
u=1/\Delta T\) and the modulation depth is 100%. Importantly, the intensity at the centre of the spectrum depends on \(\theta_{1}\), which provides us a rather unique method to characterize the phase difference. As an example, the spectra for \(\pi,0\) and \(\pm\pi/2\) phase differences are shown in Figure 1a-d respectively (the envelope is normalized).  
From Figure 1, we can see that out-of-phase and in-phase bound states have symmetrical spectra with respect to the centre wavelength, while \(\pm\pi/2\) phase-difference bound states have asymmetrical spectra. In detail, out-of-phase bound state has a minimum at the centre, while in-phase bound state has a maximum at the centre. For comparison, \(-\pi/2\) phase-difference bound state has a second maximum peak on the left side (i.e., blue shift) of the first maximum peak, while \(\pi/2\) phase-difference bound state has a second maximum peak on the right side (i.e., red shift) of the first maximum peak. Due to close-to-linear leading and trailing edges of the single-soliton spectrum, the spectral peaks of the \(\pm\pi/2\) phase-difference bound states satisfy the approximate relationship when modulation period is relatively small  
\[I_{k}\approx(I_{k-1}+I_{k+1})/2\ (k=2,3,...), \tag{3}\]  
where \(I_{k}\) denotes the intensity of the \(k\)th maximum peak.
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## 4 Satellite-to-Ground QKD Analysis  
In this section, we simulated a satellite-to-ground QKD experiment using the BB84 decoy-state protocol [28; 39]. We determined the sifted key rate \(R_{sift}\) and quantum bit error rate (QBER) of the designed receivers. Then, we estimated the secret key length by applying data post-processing for signal state QKD.
## 3 Results
### Blast Excavation Pattern and Weight  
The excavation pattern of each bench specimen after blasting is shown in Figure 11. As seen in Figure 11, all bench specimens were severely damaged.  
In the post-blast specimens of BS1-BS3 (0-100 us), there were toes of rock formed in the area between boreholes above the bench floor. Although the superposition and interaction of stress waves occurred with short delays, the rock mass between boreholes was not sufficiently broken in BS1 and BS2, resulting in the significant generation of toe rock above the bench floor. In the first three experiments, BS2 (50 us) produced the largest toe rock. With a further increase in delay time, the range of toe rock remaining above the bench floor for BS4 (150 us) and BS5 (200 us) decreased. The range of toe rock on the bottom floor of BS6, which had the longest delay (250 us), presented an increase; however, it was still smaller than that of BS1 and BS2. The post-blast bench specimens indicate that delayed initiation influences the range of toe rock, and the use of a long delay can reduce the amount of toe rock between boreholes compared to a short delay.  
Figure 11: Post-blast pattern of bench specimens: (**a**) post-blast pattern of BS1; (**b**) post-blast pattern of BS2; (**c**) post-blast pattern of BS3; (**d**) post-blast pattern of BS4; (**e**) post-blast pattern of BS5; (**f**) post-blast pattern of BS6.  
To better compare blast excavation results, the blast fragments produced by each bench specimen were weighed. The blast excavation weights of all bench specimens are shown in Figure 12.  
As shown in Figure 12, BS1 and BS2 obtained similar fragments weights of 61.2 kg and 58.3 kg, respectively. When the inter-hole delay was increased to 100 us, the blast excavation weight of BS3 increased to 85.2 kg, which is 1.4 times the weight of blast excavation for simultaneous detonation. However, a further increase in delay time did not continue to increase the blast excavation weight, which was 55.8 kg for BS4 (inter-hole delay of 150 us). BS5 recorded the second heaviest blast excavation weight of all bench specimens, reaching 65.9 kg. BS6, with the longest inter-hole delay, obtained the minimum blast excavation weight of 47.1 kg. Although the specimen dimensions and the charge weights were the same, there were differences in blast excavation weight at different inter-hole delays, indicating that delay time influences blast excavation weight.
# A Robust Security Architecture for SDN-Based 5G Networks
## Robust Security Architecture of SDN-Based 5G Networks
### Security Architecture
After receiving the request from DPS1, DPS2 will send authentication request \(M_{2}\) to the SGW. Figure 4: Robust security architecture. Figure 3: Illustration of synchronous secrets. Similar to the process in the control channel, SGW will then transmit the combination of authentication request \(REQ\) to SecE and then get the authentication message \(ACK\). SGW will then send \(M_{3}\) to DPS2, and \(M_{3}\) is the authentication message associated with DPS1 and DPS2. Finally, if this request passes the authentication process, DPS2 will send the authentication message to DPS1, building the data channel and then both of the two DPSs will update their own seed. If one of the DPSs doesn't pass this authentication, both of them will do nothing.