esa-sceva/satcom-chunk-collection huggingface dataset:
# An Iterative Guidance and Navigation Algorithm for Orbit Rendezvous of Cooperating CubeSats
## 1 Introduction
Small space platforms have light architectures and weight constraints that require innovative navigation solutions, such as vision-based navigation. With this approach, a single [15] camera or multiple [16] cameras are employed on the chaser to measure the angular displacement to the target. A problem arising with the single-camera solution is the unavailability of relative range information [17], which affects the accuracy of the guidance law unless specific maneuvers are realized to improve the range observability [18; 19]. Range becomes observable if the camera is offset with respect to the center of mass of the chaser [20], but not if the camera is placed in the line of sight between the target and the chaser, which is often the case. A thorough analysis of the performance of monocular visual navigation system is presented in [21]. Another possibility arises when the target is cooperative: in this case, direct range measurement becomes available thanks to an on-board intersatellite link (ISL), as in the case of the Milani 6U CubeSat mission [22]. The relative navigation solution is provided in real time by a recursive state estimator of the Kalman filter family of algorithms. Given the nature of the relative navigation equations, nonlinear Kalman filters are needed for this task, such as the extended Kalman filter (EKF) and the unscented filter (UF). The EKF has been a standard in relative navigation problems for a long time [23; 24]. However, EKF's poor performance against highly-nonlinear systems and even divergences have led to the wide adoption of the UF, for navigation problems in either its regular [25] or square-root [26] versions. In fact, the UF provides a more accurate and robust solution to the estimation problem [27] than EKF; therefore UF is chosen for this work. The paper is organized as follows: the mathematical model that represents the relative motion of the spacecraft is described in Section 2; the design of the guidance and navigation algorithms is given in Section 3; the setup of the numerical simulations and the results are presented in Section 4; conclusions and final remarks are given in Section 5.
## 4 Performance Analysis
### Simulation Results for the Misalignment Model  
Figure 2 shows the simulation results using Equation (7) for the MIMO transmissions with the different numbers of transmitters and receivers. To analyze the effect of different  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Wavelength & \(1550\) nm \\ Transmitter power & \(30\) dBm \\ Receiver sensitivity & \(-20\) dBm \\ Tx optics loss & \(-3\) dB \\ \hline Rx optics loss & \(-3\) dB \\ Receiver aperture size & \(0.1\) m \\ Link distance & \(20\) km \\ Distance between transmitters & \(2\) m \\ RMS wind speed & \(21\) m/s \\ Refractive index structure on the ground & \(1\times 10^{-12}\) m\({}^{-2/3}\) \\ Standard deviation of pointing jitter & \(20\)\(\mu\)rad \\ Target outage probability & \(1\times 10^{-5}\) \\ \hline \end{tabular}
\end{table}
Table 1: Vertical FSO link parameters.  
channels, the distance between transmitters was fixed to 2 m. The amount of misalignment was shown as the atmospheric turbulence strength became stronger with the different number of channels. The alignment error increases as the number of transmitters increases. In contrast, when the number of receivers increases, the alignment error decreases by averaging the detected misalignment. Thus, fewer transmitters and more receivers can reduce the increased alignment error due to multiple beam transmissions.  
However, in the bi-directional communication characteristics of vertical FSO based back-haul networks, fewer transmitters and more receivers for one direction mean more transmitters and fewer receivers for the opposite direction. Therefore, the ratio between the transmitters and receivers for ground and UAV terminals becomes significant.
## 2 Design of Broadband CP Antenna
### Parameter Analysis
#### 2.2.1 Width of the Middle Stub (g\({}_{5}\))  
It is clear from previous discussions that the rectangular stub loaded to the antenna improves return loss and bandwidth. The width of the rectangular stub g\({}_{5}\) is varied with different values to get the optimized value, as seen in Figure 3. At the optimal value of g\({}_{5}\) = 1 mm, the antenna operates at wideband and offers a \(<\)\(-\)20 dB return loss. With the  
Figure 2: Antenna design steps (**a**) geometrical configuration (**b**) return loss.  
increase in value fixed on 1.5 mm, the wide band is compromised as well as the return loss. At this value, the antenna offers 26-29 GHz band with a \(<\)\(-\)17 dB return loss. On the other side, if the return loss is decreased to 0.5 mm, the wideband split into two small bands and operates at 25.25-25.8 GHz and 28.7-30.2 GHz with a return loss of \(<\)\(-\)15 dB.
# Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions
## 5 Joint Optimization Scheme for Communication Rate
### Optimization of Relay Drone Transmission Power  
To study the optimization problem of relay drone transmission power \(P^{R}(k)\), it is necessary to fix the power allocation factor \(\lambda^{(c-1)}(k)\) of the cluster drones in the \((c-1)\)th round of iterations within time slot \(k\), the 3D trajectory \(Q^{(c-1)}(k)\), and the modulation method \(U^{(c)}(k)\) of the relay drones that was just updated in the \((c)\)th round. Consequently, the original optimization problem \(C_{1}\) can be simplified as follows:  
\[\begin{array}{c}C_{4}:\max\limits_{\{P^{R}\}}:R_{S}^{C,P^{R}}\\ (21),\ (22),\ (32),\ (33),\ (35)\end{array} \tag{51}\]  
From Equation (34), the computational relationship between \(BER_{\{2,4,16,64\}}\) and the transmission power \(P\) is as follows:  
\[P=\left\{\begin{array}{c}\left(erfc^{-1}(BER)\right)^{2}n_{0}B_{n}^{Ruav}/2H _{i}^{w}\ \text{U}=\{2\}\\ \left(erfc^{-1}(BER/4)\right)^{2}(U-1)n_{0}B_{n}^{Ruav}/3H_{i}^{w}\ \text{U}=\{4,16,64\}\end{array}\right. \tag{52}\]  
Then, constraint (35) can be transformed into:  
\[P^{R,(c)}(k)\geq\dfrac{n_{0}B_{n}^{Ruav}(b_{1}f_{1}+3b_{2}f_{2}+15b_{3}f_{2}+ 63b_{4}f_{2})^{(c-1)}(4\pi\|Q(k)\|_{2}^{(c-1)})^{2}}{G_{t}G_{r}(\lambda^{G})^{ 2}} \tag{53}\]  
Then the above optimization problem \(C_{4}\) becomes the following:  
\[\begin{array}{c}C_{5}:\max\limits_{\{P^{R}\}}:R_{S}^{C,P^{R}}\\ (21),\ (22),\ (32),\ (33),\ (53)\end{array} \tag{54}\]The above optimization problem \(C_{5}\) is convex and can be solved by directly applying the CVX toolkit to obtain a \(P^{R,(c)}(k)\) global suboptimal solution.  
Proof: Let the expression of the objective function, \(c_{1}^{pr}=\lambda_{i}^{(c-1)}(k)P^{F}(k)H_{i}^{w,(c-1)}(k)\)\(H_{2}^{w,(c-1)}(k)\), \(c_{2}^{pr}=I_{3}^{t}H^{w,(c-1)}(k)\), \(c_{3}^{pr}=\lambda_{i}^{(c-1)}(k)P^{F}(k)n_{0}B_{n}^{[PERSON]}H_{i}^{w,(c-1)}(k)+ \frac{\widetilde{I}_{3}^{p}}{3}n_{0}B_{n}^{[PERSON]}\), \(R_{S}^{C,P^{R}}\) be:  
\[f(P^{R,(c)}(k))=\frac{c_{1}^{pr}P^{R,(c)}(k)}{c_{2}^{pr}P^{R,(c)}(k)+c_{3}^{pr}} \tag{55}\]  
Taking the second order derivative of \(f(P^{R,(c)}(k))\) results in the following:  
\[f^{\prime\prime}(P^{R,(c)}(k))=\frac{-2c_{1}^{pr}c_{2}^{pr}c_{3}^{pr}}{(c_{2}^{ pr}P^{R,(c)}(k)+c_{3}^{pr})^{3}} \tag{56}\]  
From the constraint restrictions and physical significance of each variable, \(c_{1}^{pr}\geq 0\), \(c_{2}^{pr}\geq 0\), \(c_{3}^{pr}\geq 0\), \(P^{R,(c)}(k)\geq 0\), the second order derivative \(f^{\prime\prime}(P^{R,(c)}(k))\leq 0\), and the objective function is concave. Also, the restriction constraints satisfy the convex set property and hence the optimization problem \(C_{5}\) is convex, as evidenced.
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Optimization of the Fuzzy Logic Controller Based on the Genetic Algorithm  
The FLC design, the shape of the MFs, and the fuzzy rules are built entirely on heuristic definitions. Such the FLC design is complex, time-consuming, and its performances could be still poor. This section presents an optimization process defining the optimal parameters of the MFs for the FLC.  
Currently, many researchers are using different heuristic algorithms that are not just bio-inspired but also based on physical laws [14], or using method cooperation in optimization process [33]. The computational methods inspired by biology are being used increasingly to deal with complex problems in robotics, engineering, computer science, etc. Among them, bio-inspired optimization is a term which covers a wide variety of optimization approaches. These methods are based on the principles of biological systems, such as bee colony optimization, bat algorithms, ant colony optimization, etc. Some other classes of optimization techniques are simulated annealing, neural network methods, and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\Delta\theta\) & **BN** & **N** & **Z** & **P** & **BP** \\ \hline
**VC** & VBL & BL & VSL & SL & VSL \\
**C** & VBL & BL & SL & SL & VSL \\
**M** & VBL & BL & MBL & SL & VSL \\
**F** & VBL & BL & BL & SL & VSL \\
**VF** & VBL & BL & VBL & SL & VSL \\ \hline \hline \end{tabular}
\end{table}
Table 4: The rules of the left wheel angular velocity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\Delta\theta\) & **BN** & **N** & **Z** & **P** & **BP** \\ \hline
**VC** & VSR & SR & VSR & BR & VBR \\
**C** & VSR & SR & SR & BR & VBR \\
**M** & VSR & SR & MBR & BR & VBR \\
**F** & VSR & SR & BR & BR & VBR \\
**VF** & VSR & SR & VBR & BR & VBR \\ \hline \hline \end{tabular}
\end{table}
Table 3: The rules of the right wheel angular velocity.  
Figure 7: **(a)** The membership functions of the distance error; **(b)** The membership functions of the angle error; **(c)** The membership functions of the right wheel angular velocity; **(d)** The membership functions of the left wheel angular velocity.  
evolutionary algorithms including the GA [34]. As a method for the FLC optimization, the GA has been selected.  
A typical GA cycle is shown in Figure 8. The GA is based on following operations: selection, genetic operations (crossover, mutation), and replacement [20; 32; 34; 35].
## 3 Results
### Intra-Annual Sky Status
SSD/DCS = sunshine duration/duration of clear skies; DIS = duration of intermediate skies; DOS = duration of overcast skies. All regression fits are at the 95% confidence level (\(\alpha\)). Figure 6: _Cont._Table 3 presents the regression polynomials of the fitted curves to the monthly mean duration of sky conditions as function of the month; all of them are sixth-order polynomials. It is worth observing the high R\({}^{2}\) values (accuracy of the estimated regression curves) and the decreasing SEE values (dispersion of the monthly data around the regression curves) from clear to overcast skies. The latter outcome is due to the following reason: the k\({}_{\text{du}}\) and k\({}_{\text{dl}}\) limits mentioned in Section 2.2 (step 5) are not clear-cut ones, meaning that there is a slight difference in the fitted curves. Figure 6: Intra-annual variation in the monthly mean (**a**) DCS, (**b**) DIS, and (**c**) DOS events from the TMYs of the 43 sites. The black lines represent the mean values, and the green ones the best-fitted curves to the mean lines. The \(\pm 1\)o band is also indicated by the red/blue curves. The expressions for the regression curves are shown in Table 3.  
may be an \"intrusion\" of DSC events registered as DIS, \"intrusion\" of DIS occurrences into the DCS and DOS zones, and \"intrusion\" of DOS events into the DIS band. It is apparent that the DCS data may be \"disturbed\" from the intrusion of some DIS events. To the contrary, the DIS data are more insensitive to an intrusion of some DCS or DOS cases because the sky, in this case, is partially covered by clouds. Finally, the DOS data do not respond to an intrusion of DIS events because the sky is fully covered in this case. The same explanation applies to the SEE values of Table 1. More about this \"intrusion\" rationale is given in Section 5.
## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Crypto Combination** & **Target Scope** \\ \hline [PERSON] et al. [37] & PQC, QKD & Key Exchange \\ \hline [PERSON] et al. [38] & QKD, Digital Signature & Key Exchange \\ \hline [PERSON] et al. [39] & PQC, Digital Signature & Blockchain-based Security \\ \hline [PERSON] et al. [40] & PQC, QKD & Key Exchange \\ \hline [PERSON] et al. [41] & PQC, QKD & Key Exchange \\ \hline [PERSON] et al. [42] & QKD, AES & File Transmission \\ \hline
**Our Study** & PQC, QKD, AES & File Encryption \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent cryptographic approaches integrating quantum and classical techniques. outperforms conventional AES by up to 8.11\(\times\) in encryption speed. This highlights that strong quantum-resilient security can be achieved without sacrificing performance, ensuring efficient and secure processing across both classical and quantum computing environments. Our contributions are as follows:  
* We design a scalable encryption scheme for large-scale file protection, which enhances key security by integrating Kyber-based post-quantum key encapsulation with BB84-based quantum key distribution. * We design and implement an offset-aware partitioning distributed encryption framework that performs subset-level AES encryption in parallel across multiple slave nodes, while decoupling key generation and transmission through centralized master node coordination. * We demonstrate that our scheme achieves up to 8.11\(\times\) faster encryption performance compared to conventional AES, while maintaining stable CPU utilization under real-world workloads.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 5 Measurement Results and Discussion
### Power Measurements and Path Loss Modeling  
#### 5.1.1 Narrowband at 868 MHz  
For the narrowband measurements in the NLOS-1 zone, a fit is made, and a slope of \(n=1.97\) for the CI model and \(\beta=1.94\) for the FI model is obtained, while for the measurements in the NLOS-2 zone, the slope is \(n=3.03\) for the CI model and \(\beta=7.26\) for the FI model. The measured path loss and the models are shown in Figure 7, together with the dispersion and cumulative distribution function (CDF) of the models for both NLOS zones. The parameters of the path loss models are summarized in Table 3. Analyzing the slopes, we observe that for the NLOS-1 zone, the slope value is in agreement with the result of the work [16] performed in a similar environment and at the same frequency. Meanwhile, in the NLOS-2 zone, the value of the slope increases considerably, which is due to the fact that the signal loses a large part of its energy in diffraction at point R1, corresponding to the wedge between corridors.  
Figure 7: Narrowband path loss models at 868 MHz and their dispersion.
## 3 Results and Discussion
### Analysis Outputs
#### 3.2.2 Steam Quality Indicators  
Live Steam Pressure  
The steam pressure to the high-pressure steam turbine decreases from 235 to 182 bar (see Figure 8). Due its lower LHV and higher moisture content, the combustion of biomass generates relatively more flue gas than the combustion of coal. This results in a lower adiabatic flame temperature and therefore in a less radiant heat transfer. As a consequence, reaching the nominal steam temperature cannot be achieved without decreasing the nominal steam pressure and steam flow rate. Considering the provided fuel thermal input, the control system of the power plant is programmed to first reach the nominal steam temperature considering the steam pressure and flow rates as secondary parameters.  
While the power plant conversion from coal to biomass results in a steam pressure decrease, the related uncertainty decreases from 14 to 8 bar (see Figure 8) due to a higher sensitivity of this output on the composition of the fuel in the case of coal (especially on carbon content); see Section 3.3.1, Table 5 and Section 3.3.2, Table 6. The higher sensitivity to the LHV of biomass compared to coal does not compensate this effect.  
Figure 7: CHP efficiency. Coal: mean = 41.5%, standard deviation = 1.4%. Biomass: mean = 65.3%, standard deviation = 1.5%.  
Live Steam Flow Rate  
The same effects as for live steam pressure can be observed for live steam flow rate. The steam flow to the high-pressure steam turbine decreases from 542 to 410 kg/s and the uncertainty from 53 to 22 kg/s (see Figure 9), also due to a higher sensitivity of this output on the composition of the fuel in the case of coal, combined with a higher impact of evaporator steam outlet temperature; see Section 3.3.1, Table 5 and Section 3.3.2, Table 6.
## 2 Problem Description and Modeling
### Optimization Objectives
#### 2.5.3 Energy Consumption  
The energy consumption of an SEC network includes three parts: communication, task processing, and standby energy consumption.  
Firstly, the communication energy consumption can be expressed as:  
\[Energy_{1}=\sum_{m_{p}\in\mathbf{W}}\sum_{m_{i}\in pre_{p}}g_{c}T_{i,p}^{trans} \tag{10}\]  
where \(g_{c}\) is the transmission power (W/s) of the ISLs.  
Let \(g_{w}\) represent the energy consumption coefficient of the edge service's chip architecture, and recall that the computing capacity of server \(u_{k}\) is \(f_{k}\) Gcycles/s. Then the task processing energy consumption is  
\[Energy_{2}=\sum_{m_{p}\in\mathbf{W}}g_{w}f_{\text{Tr}(p)}^{2}\sum_{m_{i}\in pre _{p}}d_{i,p}e_{p} \tag{11}\]  
Since edge servers in standby mode still consume energy, and \(g_{s}\) is the standby power (W/s) for an edge server, we have  
\[Energy_{3}=g_{s}\left(nMakespan-\sum_{m_{p}\in\mathbf{W}}T_{p}^{com}\right) \tag{12}\]  
Finally, the total energy consumption can be expressed as  
\[Energy=Energy_{1}+Energy_{2}+Energy_{3} \tag{13}\]
## 2 Materials and Methods
### Statistical Analysis  
Data analysis was performed using SPSS 2000 and STATGRAPHIC 2000 to determine correlations, regression relationships, and statistical constants. Variance analysis and spatial distribution assessments were conducted using satellite images and data from meteorological stations. The least significant difference (LSD) test at \(p<0.05\) was applied to evaluate significant differences among the different average values.
## 7 Conclusions
Additionally, investigating advanced signal processing techniques, such as deep learning architectures tailored for biomedical signals, may offer novel insights and enhance detection performance. Furthermore, the validation and benchmarking of change detection methods using real-world biomedical data are crucial for assessing their efficacy and generalizability. Collaborative efforts to establish standardized datasets and evaluation metrics would facilitate the comparison and validation of different methods across diverse applications and datasets. Lastly, the translation of research findings into clinical practice and healthcare applications through the development of user-friendly software tools and decision support systems holds promise for improving patient outcomes and advancing the field of biomedical signal processing. By advancing methodological approaches, validating findings with real-world data, and translating research into practical applications, researchers can continue to enhance our ability to detect changes in biomedical signals and ultimately contribute to improving healthcare delivery and patient care. \begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Key contributions:** \\ \hline
**Comprehensive methodological exploration:** & We employed a multifaceted approach, combining classical statistical techniques, machine learning algorithms, and time–frequency analysis to address the challenges of biomedical signal processing. \\ \hline
**Advanced understanding of physiological processes:** & Our study provides valuable insights into the underlying dynamics of biomedical signals, aiding in a deeper understanding of physiological processes. \\ \hline
**Improved clinical decision-making:** & By accurately identifying significant changes in biomedical signals, our research can contribute to more informed clinical decision-making. \\ \hline
**Innovation in healthcare technology:** & Our findings pave the way for the development of new tools and techniques for biomedical signal analysis, potentially leading to advancements in healthcare technology. \\ \hline
**Addressing key challenges:** & We successfully addressed the challenges of high-dimensional data handling and the identification of subtle, non-linear patterns in biomedical signals. \\ \hline
**Robust and accurate change detection:** & Our study demonstrates the effectiveness of the combined methods in achieving robust and accurate change detection. \\ \hline \hline \end{tabular}
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result
### Effectiveness of the Random Pattern Mixing  
This experiment evaluates our random pattern mixing. For comparison, we apply jittering, scaling, permutation, up-sampling, and the SMOTE to the LFP-TCN. We tune the hyper-parameters for each method. For the jittering, we add Gaussian noises with the standard deviation \(\sigma\in\{0.05,0.1,0.15\}\). For the scaling, we set the scaling factor according to Gaussian distribution \(\mathcal{N}(1,\sigma)\), where \(\sigma\in\{0.05,0.1,0.2\}\). For the permutation, we consider the number of segments \(P\in\{5,10,20\}\). For the SMOTE, we interpolate a sample with its \(k\)-nearest neighbor where \(k\in[1,9]\) with a step of 2. Finally, for the random pattern mixing, we adjust the ratio of the number zero in the mask (the ratio of injected random patterns) in \(\{0.05,0.1,0.2,0.3,0.4,0.5\}\).  
Figure 5 illustrates the averaged EERs for different data augmentation methods. As illustrated in Figure 5, the random pattern mixing outperforms other methods with the margins of 0.27% and 0.18% in the HMOG and BBMAS datasets, respectively. In particular, the use of random pattern mixing improves the EER by 1.30% and 0.85% over the model without data augmentation in each dataset.  
Interestingly, the up-sampling improved the EER by 0.98% and 0.32% in each dataset. This is because it prevents the penalties for the impostor class from being heavily biased to a few impostor instances in a batch, which helps balance the genuine and impostor classes.  
Figure 6 depicts the averaged EERs against only unseen impostors. We can see that every existing method improves the EERs in both datasets. This is because some unseen impostors share a part of the feature space with seen impostors.  
Among competing methods, the random pattern mixing is the most robust to unseen impostors, with the EER of 12.33% and 14.58% in each dataset. Note that the difference between the random pattern mixing and the up-sampling is whether the uniform-based random patterns are injected into seen impostor set. Figure 6 shows that mixing random and seen impostor patterns further reduce the EERs from up-sampling by 0.77% and 0.97%  
Figure 5: Averaged EERs (%) for different data augmentation methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Architecture**} & \multicolumn{2}{c}{**Dataset**} & \multirow{2}{*}{**Parameter Size**} \\ \cline{2-3} \cline{5-5}  & **HMOG** & & **BBMAS** \\ \hline MLP & 20.33 & 15.48 & 0.5 M \\ LSTM & 23.55 & 14.09 & 0.6 M \\ CNN & 13.14 & 16.97 & 0.4 M \\ TCN & 12.76 & 14.02 & 0.5 M \\ LFE-TCN & 14.90 & 16.76 & 1.0 M \\ LFP-TCN & **12.25** & **13.70** & 0.5 M \\ \hline \hline \end{tabular}
\end{table}
Table 3: Averaged EERs (%) for different architectures.  
in each dataset. This implies that the random pattern mixing can effectively simulate unseen impostor patterns.