esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The simulation results confirmed that the suggested technique is speedier and more precise than the existing algorithms. The GMPP was achieved in 0.15 s with an efficiency of 99.19%. From the review above, the research gaps for the modified P&O techniques under partial shading include tracking the GMPP accurately, reducing the oscillation near the GMPP, limiting the computation time, increasing the convergence speed, and keeping the algorithm at the same simplicity level as the conventional P&O. Taking into account all of these limitations, an enhanced adaptive P&O algorithm is proposed herein for tracking the GMPP of a PV system under partial shading conditions. The proposed technique has considerable prominent features that are not available for any other classical or soft computing method, as follows: (1) it is simpler than any modified P&O; (2) GMPP achievement is quicker than that by any available method; (3) there are nil oscillations near the MPP; and (4) the GMPP is precisely achieved with the best efficiency. Various patterns involving diversified weather situations were tested by simulation. The performance of the suggested algorithm under partial shading conditions was investigated and compared with that of the standard P&O and IC methods. The comparisons illustrated most of the performance aspects, such as tracking time, efficiency, precision, and presence of oscillations around the MPP under partial shading conditions.
# Numerical Methods Comparison of Shock-Induced Separation on Transonic Axisymmetric Hump
## 3 Results and Discussion
### Grid Resolution and Field Validation
Verification of grid resolution with the density variations at the monitor point shown in (**b**), and the velocity profiles at the location of \(x/c=0\) shown in (**c**). Pressure coefficient and friction coefficient were provided by the experiment and the comparisons are displayed in Figure 7. The error bars of the experiment were plotted in the meanwhile. In Figure 7a, before the peak of negative pressure, all three grid cases fit the experimental data well. At the peak, the negative \(C_{p}\) was much higher in the coarse grid condition, and the results were getting worse after encountering the shock wave; the condition did not become better until \(x/c\) greater than 1.6, at which point the position was far away from reattachment point (refer to Figure 7b) and the flow became relatively steady. Looking at the \(C_{p}\) value in the cases of fine grid and medium grid, it performed well whether at the peak or after shock wave. Figure 7b shows the surface friction coefficient. Overall, the simulation results had certain deviations and fluctuations compared to the experiment, a condition observed in many other references [19; 21]. These proved that the current CFD methods still have some weakness in the prediction of \(C_{f}\). In contrast, the errors produced by the coarse grid were more obvious than the fine grid and medium grid. Compared to the specific data in Table 2, the separation points were very close to the experiment in the three cases; however, the significant deviation about reattachment point appeared in the coarse grid. In this part, different flow filed characteristics were compared in three quantities of grids, and the results approved that the coarse grid could not simulate well the details of the field. The comparison between a fine grid and medium grid indicated that improvement in accuracy was very limited, although the grid quantity increased a lot. In the following analysis, the results of the medium grid are applied to discuss the predictive ability for different methods.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 4 Evaluation and Analysis of NSCC with Representative Parameters
### Anti-Jamming Capability  
Figure 8 shows the numerical result for anti-jamming merit factors of NSCC and legacy signals. Clearly, the NSCC presents superior Dem&AJNB and Dem&AJSM performance among the signals analyzed due to its excellent spectral efficiency and flat power distribution. Note that, the merit factor of NSCC signal is at least 2 dB greater than P code. As for anti-jamming of code tracking performance, however, the NSCC appears inferior to P code resulting from the more high-frequency components of the NSCC signal but slighter power distribution fluctuation.  
Figure 9 shows the anti-jamming merit factors of NSCC modulation within the 20 MHz pre-filtering bandwidth. Overall, the NSCCs with the different chip rates all display great anti-jamming capability for Dem&AJNW, Dem&AJMS and CT&AJMS, although there is a slight numerical difference. This means the three merit factors are not sensitive to the chip rate within the same pre-filtering bandwidth. The Dem&AJNW, Dem&AJMS and CT&AJMS performances of the two baseline signals are approximately 3 dB less than that of NSCCs due to the halving power and number of sub-carriers. The NSCC bimodal presents superior CT&AJNW performance resulting from the large proportion of high-frequency component.  
Figure 8: The comparison of anti-jamming merit factor for 20 MHz pre-filtering bandwidth.  
Figure 9: The comparison of anti-jamming merit factor for NSCCs within the 20 MHz bandwidth.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 1 Introduction
As such, developing an efficient ISL allocation algorithm is a critical task in satellite network planning technology and the main contribution of this paper. The remainder of this paper is structured as follows. Section 2 presents an overview of static and dynamic satellite network topology. Section 3 describes the time slice division strategy, the way static links are combined, and the dynamic link selection algorithm. Section 4 provides a detailed description of the basic methodology of the simulation, the step-by-step process, and the parameters to be set. Section 5 reveals the simulation results using the proposed strategy. Finally, this work is concluded in Section 6.
# A Vehicle Conflict Risk Identification Method Based on an Improved Intelligent Driver Model
###### Abstract  
With accelerating urbanization, vehicle conflict risk identification has become a critical research focus for improving road traffic safety. To address the discrepancies between microscopic traffic simulation outputs and real-world traffic flow characteristics caused by stochastic factors, this study proposes a vehicle conflict risk identification framework based on an enhanced intelligent driver model (IDM). Through VISSIM secondary development and scenario calibration, a simulation environment was constructed to replicate real-world road test conditions. Multi-vehicle trajectory data were employed to calibrate the IDM parameters. The conventional IDM was further improved by integrating driver-state variables and braking dynamics, while the inverse time to collision (ITTC) was adopted as the primary metric for collision risk assessment. Three risk levels were defined: potential collision (PC), general collision (GC), and serious collision (SC). The experimental results demonstrated strong alignment between the enhanced model and VISSIM simulations in vehicle speed and headway calibration, achieving classification accuracy rates exceeding 92.71%. The ITTC thresholds (0.25/s and 0.48/s) effectively differentiated between risk levels. This research provides theoretical and technical foundations for dynamic vehicle conflict risk identification and offers actionable insights for safety-critical decision making in intelligent driving systems.  
vehicle conflict risk; improved intelligent driver model; inverted time to collision; traffic safety Article
## 4 Bringing CRKG into the Field: Securing Practical IoT Communications
### Short-Range IoT Communication Solutions
#### 4.1.2 Ultra-Wideband  
Ultra-wideband technique uses a very low energy to achieve short-range, high-bandwidth communications. In [52], two wireless sensor nodes measure the reciprocal CIR using ultra-wideband signals with 500 MHz bandwidth in the 4 GHz band. Messages are transmitted with impulse radio ultra-wideband, which supports short pulses in the time domain, with a typical duration of less than 3 ns. The received preamble sequence is correlated against the expected preamble sequence to estimate the CIR. [PERSON] and [PERSON] transmit messages under line-of-sight conditions in a laboratory and in the adjacent hallway. After reception of a packet, they estimate their CIR. [PERSON] and [PERSON] are stationary and 5 m apart. The receivers are placed 1 m above ground to ensure optimal transmission quality. The line-of-sight path is interrupted when people move along causing random changes of the CIR. [PERSON] and [PERSON] transmit messages every 370 ms, generating six CIRs per second. One single measurement consists of 200 data points. Measurements were performed for six days, generating more than 20 GB of data. Depending on the signal processing for bit extraction and key agreement, secret key rates in the order of up to 12  
Figure 6: Example channel measurements of IEEE 802.15.4 transceivers between 2.4 and 2.483 GHz in office environment: RSSI over frequency of three reciprocal links.  
bits per channel use are reported in [52]. Using Results-Based-Adaption (RBA) in order to adapt the quantization decision so that its outcome is as close as possible to the uniform distribution, reasonable relative frequencies of the resulting 16-bit sequences is illustrated in Figure 7.
## 2 Definition of System Requirements and Assessment of Fitting Communication Technologies
### Security  
The PEEV-WS messages have to be secured to avoid the risk of fraudulent use. Security of vehicular communication itself is a complex and evolving topic, hence, it is difficult to cover it comprehensively within the scope of this paper. Considering [25], we adjust a communication message length of the proposed system to account for the use of future authentication and integrity check mechanisms. Therefore, the length of the communication message in the simulations was set to 300 B.
## 4 Application
### Parameter and Tolerance-Cost Optimization  
To find the optimal robust parameter and tolerance design, the optimization problems from Equations (1)-(3) are solved in MATLAB R2019b using advanced optimization and sampling techniques. The utilized optimization process is thoroughly described in [70; 71]. Additional information needed for the optimization of the concepts, such as design parameters and tolerances, is summarized in Table A1 in Appendix A.  
Although there exist different approaches and measures for robust parameter optimization, the quality loss function is exemplarily used in this case study to define the objectives illustrating the influence of early robust design: \(QL_{k}=\kappa_{k}\cdot\left(\sigma_{k}^{2}+(\mu_{k}-m_{k})^{2}\right)\) for each \(k\) KC, where \(\kappa_{k}=\kappa=1\) corresponds to the loss constant, \(\sigma_{k}^{2}\) to the variance, \(\mu_{k}\) to the mean and \(m_{k}\) to the target value [36]. In tolerance-cost optimization, the quality assurance is measured by the non-conformance rate representing the relative fraction of assemblies that do not meet the requirements. A non-parametric, empirical estimation technique is used to realistically consider the dependencies between the KCs in tolerance-cost optimization according to [71] forming the inequality conditions of Equations (2) and (3). With respect to computational efficiency, instead of ordinary Monte Carlo Sampling, the Latin Hypercube Sampling is used for tolerance analysis to evaluate the influence of uncertainty on the system behavior in terms of the quality loss and non-conformance rate. A sample size of 10,000 and 100,000 is set for the robust parameters respectively tolerance-cost optimization, as a compromise between validity and computational effort. The individual costs are defined by \(C_{i}=1/t_{i}\) forming the total costs \(C_{\text{sum}}\) as the objective according to Equation (1), where \(t_{i}\) are the individual tolerances, and the costs are given here in dollars for clarity.  
For optimization, metaheuristic algorithms are preferred for the sampling-based robust parameter and tolerance-cost optimization since they can handle the stochastic effects of the sampling in the optimization. As a consequence, the Non-dominated Sorting Genetic Algorithm II is used for multi-objective robust parameter optimization and a single-objective Genetic Algorithm is applied for single-objective robust parameter and tolerance cost optimization. All optimization runs were repeated five times to increase the probability of finding the global optimum. In order to ensure comparability between the results of tolerance-cost optimization, the initially defined nominal values from Appendix A were used in tolerance-cost optimization, thus neglecting the results from previous robust parameter optimization. Corresponding to the theoretical description in Figure 4, this enables a separate consideration of the effects on robust parameter optimization and tolerance-cost optimization.
# Two-Phase Deep Learning-Based EDoS Detection System
## 8 Conclusions  
In this study, we propose a novel mechanism to handle EDoS attacks in each flow coming into a cloud system. This approach not only protects cloud infrastructure from paying much more money for various EDoS attacks such as TCP-HTTP flooding, ICMP flooding, and TCP SYN Flooding, but also help cloud service providers improve their service quality. We present a two-phase deep learning-based detector for EDoS detection based on utilizing the advantages of ANN and LSTM algorithms. By using the period detector, i.e., ANN model to detect when an attack occurs, we can take advantage of the LSTM in terms of accuracy, while eliminating the greatest disadvantage of this algorithm, i.e., the long sequence length of the input data. Finally, our mechanism can apply to different network systems, and adapts well because the deep learning-based detectors are replaced periodically by using an updated training database. The evaluation described in Section 6 shows that our proposed mechanism is extremely efficient in both accuracy, detection time, response time, and resource consumption.  
As a future study, we expect to improve the mechanism by using an SDN-based model to enhance the process of mitigating and collecting network features. Moreover, as discussed in Section 7, we will improve the performance of the period detector by considering the two models mentioned in [36,37]. In addition, we plan to compare the proposed scheme to other EDoS defense systems using more evaluation criteria.  
[PERSON]-[PERSON] proposed the idea, conducted the experiments, performed the analysis, and wrote the manuscript. [PERSON] provided the guidance for data analysis and paper writing. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2020R1F1A1076795).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 2 Analytical Model
### Mathematical Model
#### 2.2.3 Control Equations for Gas Film Thickness  
The thickness of the lubricating gas film between the friction parts of the foil seal depends, in addition to the initial minimum gas film thickness, on the deformation of the compliant end faces. The deformation of the seal end face is determined by the material and structural parameters of the flat and wave foils together. In this paper, the deformation of the compliant seal end face is described by the equivalent stiffness model [40]; therefore, the gas film thickness control equation is:  
\[h=h_{0}+\alpha(p-p_{\rm o}) \tag{5}\]  
where \(p_{\rm o}\) is the outlet pressure, Pa; \(h_{0}\) is the minimum gas film thickness, \(\mu\)m; \(\alpha\) is the flexibility coefficient of the compliant end face;  
When the sealing end face is not deformed, it is regarded as a rigid sealing end face; at this time,  
\[\alpha=0 \tag{6}\]  
When only the wave foil is deformed on the compliant sealing end face,  
\[\alpha=\frac{s}{k_{\rm b}} \tag{7}\]  
where \(k_{\rm b}\) is the unit stiffness coefficient of the wave foil, N/m\({}^{2}\); \(s\) is the pitch, mm.  
The unit stiffness coefficient of the wave foil can be calculated by the following equation [20; 41]:  
\[k_{\rm b}=\frac{E_{\rm b}t_{\rm b}^{3}}{2l^{3}\big{(}1-v_{\rm b}^{2}\big{)}} \tag{8}\]  
where \(E_{\rm b}\) is the elastic modulus of wave foil, Pa; \(v_{\rm b}\) is the Poisson's ratio of wave foil; \(t_{\rm b}\) is the thickness of the wave foil, mm; \(l\) is half of the chord length, mm.  
When the compliant seal end face wave foil and flat foil are deformed, the comprehensive stiffness model of equivalent spring is used for calculation, i.e., the flat foil and wave foil are assumed to be linear springs with homogeneous stiffness, respectively and are arranged in parallel to obtain the overall stiffness model [20; 41]:  
\[\alpha=\frac{1}{\frac{k_{\rm b}}{s}+k_{\rm p}} \tag{9}\]  
where \(k_{\rm p}\) is the stiffness coefficient of flat foils, N/m\({}^{3}\).  
The stiffness coefficient of the flat foil can be obtained from the bending stiffness \(D_{\rm p}\) of the flat foil [42].  
\[D_{\rm p}=\frac{E_{\rm p}t_{\rm p}^{3}}{12\Big{(}1-v_{\rm p}^{2}\Big{)}} \tag{10}\]  
where \(D_{\rm p}\) is the bending stiffness of the flat foil, N\(\cdot\)m; \(E_{\rm p}\) is the elastic modulus of flat foil, Pa; \(\
u_{\rm p}\) is the Poisson's ratio of flat foil; \(t_{\rm p}\) is the thickness of the flat foil, mm.
## 5 Evaluation and Discussion
### Security of WSN Data Processing
#### 5.2.1 Privacy Preservation against Eavesdropping
To select the adequate values of \(p\), we trained a data mining model named _HoeffdingTreeRegressor_[66] from the _skmultiflow_[79] library on preliminary data from the Mrakova farm pilot building. The available data were acquired from 1 sensor, with 15 k measurements about indoor air quality assessment taken during a 2-month interval. The model was trained to estimate the VOC (Volatile Organic Compounds) concentration based on other predictors. We trained the model several times by constraining the maximum model size in bytes to [10 k, 25 k, 50 k]. The high value of the coefficient of determination \(0.65<r^{2}<0.8\) indicates the appropriateness of the values selected for the independent variable \(p\), the onion body size. The results of the simulation are displayed in Figure 4, and summary statistics are presented in Table 4. From the collected data, it is possible to notice that at the onion body size of {1 k, 2.5 k, 5 k}, the ORTT measurements do not substantially differ at matching message path length. A probable explanation is the large size of the onion head, which at a message path length of 60 nodes, corresponds to 3127 bytes; therefore, affecting the message transmission more than the onion body at {1 k, 2.5 k}. Moreover, the ORTT is considerably affected by onion body sizes larger than 10 k bytes, and at the onion path length of 60 nodes, the onion body size severely affects the ORTT. Interestingly, from the data in Table 4, the number of interrupted onion messages seems quite irregular. We emphasize that a simulator's parameter affects the number of interrupted onion messages. In the present investigation, we adopt the ORTT as the indicator of the delays introduced by the DQ3P protocol applied to train a data mining model. The presented results show that onion messages are complete their path on average in less than 1 min at onion body sizes \(\leq\) 50 k, even for messages including 60 nodes in the message path. Therefore, delays introduced by DQ3P should be far lower than the cumulative delay introduced by the processing of the data mining model at nodes in the onion message path. Hence, we can conclude that delays introduced by DQ3P applied to train a data mining model of size equal to or smaller to 100 k at onion message path of up to 60 nodes are acceptable.
## 4 Results and Analysis
### Hyperparameter Selection  
Overfitting is a problem that must be addressed with special care when it comes to transfer learning networks that are accomplished by the proposed model. To address the overfitting problem, model training along with validation performance are measured. The results of this monitoring are collectively presented across various optimizers in Figure 11. When comparing the various optimizers, it is important to keep in mind that the value range on the y-axis varies from one to the next. This allows for a more accurate depiction of the variations. In addition to that, the findings achieved from the experiments with various optimizers and learning settings are described in detail.  
Figure 8: Comparative result analysis of TL-MVF with existing benchmarked models over the Fake and Real News dataset.  
Figure 9: Comparative result analysis of the TL-MVF with existing benchmarked models on the PolitiFact dataset.  
The Adam, AdaGrad, and Adadelta optimizers are used in many tests with learning rates ranging from 0.1 to 0.0001. We determined that the learning rates for Adam (0.0001), Adadelta (0.1), and AdaGrad (0.01) are appropriate for hyperparameter selection. When there is no noteworthy performance increase, the training of the model is stopped using an optimal early stopping mechanism after a variable number of epochs (up to 200) for each architecture. After all the optimizers converge, then there is little to no difference in performance, making training time a crucial parameter. The Adam optimizer provided the learning rate is not too high.  
Figure 11: The training and validation (loss and accuracy) of the TL-MVF model for various optimizers. Validation scores–dashed lines; training scores–solid lines.  
Figure 10: Comparative result analysis of the TL-MVF model with existing benchmarked models over the Pymedia dataset.  
best stable performance across all architectures with the fewest necessary epochs; hence, it is the one utilized for all of the findings shown there. There appears to be no clear overfitting given the use of the early stopping mechanism, and the accuracy and loss while validating the model may not ensure that they regularly diminish throughout epochs of training. Keep in mind that the reported findings are the average of five independent experiments, making the likelihood of overfitting even smaller.