esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs
###### Abstract  
Safety application systems in Vehicular Ad-hoc Networks (VANETs) require the dissemination of contextual information about the scale of neighbouring vehicles; therefore, ensuring security and privacy is of utmost importance. Vulnerabilities in the messages and the system's infrastructure introduce the potential for attacks that lessen safety and weaken passengers' privacy. The purpose of short-lived anonymous identities, called \"pseudo-identities\", is to divide the trip into unlinkable short passages. Researchers have proposed changing pseudo-identities more frequently inside a pre-defined area, called a cryptographic mix-zone (CMIX) to ensure enhanced protection. According to ETSI ITS technical report recommendations, the researchers must consider the low-density scenarios to achieve unlinkability in CMIX. Recently, [PERSON] et al. proposed a Chaff-based CMIX scheme that sends fake messages under the consideration of low-density conditions to enhance vehicles' privacy and confuse attackers. To accomplish full unlinkability, in this paper, we first show the following security and privacy vulnerabilities in the [PERSON] et al. scheme: Linkability attacks outside the CMIX may occur due to deterministic data sharing during the authentication phase (e.g., duplicate certificates for each communication). Adversaries may inject fake certificates, which breaks Cuckoo Filters' (CFs) updates authenticity, and the injection may be deniable. CMIX symmetric key leakage outside the coverage may occur. We propose a VPKI-based protocol to mitigate these issues. First, we use a modified version of [PERSON] et al.'s scheme to provide mutual authentication without revealing the real identity. To this end, the messages of a vehicle are signed with a different pseudo-identity \"certificate\". Furthermore, the density is increased via the sending of fake messages in low traffic periods to provide unlinkability outside the mix-zone. Second, unlike [PERSON] et al.'s scheme, we use the Adaptive Cuckoo Filter (ACF) instead of CF to overcome the false positives' effect on the whole filter. Moreover, to prevent any alteration of the ACFs, only _RSUs_ distribute the updates, and they sign the new fingerprints. Third, the mutual authentication prevents any leakage from the mix zones' symmetric keys by generating a fresh one for each communication through a Diffie-Hellman key exchange.  
authentication; privacy; security; non-repudiation; pseudonym; unlinkability; vehicular ad-hoc networks +
Footnote †: journal: _Electronics_
## 2 Materials and Methods
### Magnetoinductive Directional Filters
#### 2.3.1 Frequency Response  
To illustrate the above, Figure 3a shows the frequency dependence of magnitude S-parameters for different terminations, with \(L=1\)\(\mu H\), \(\kappa=0.6\), and \(\kappa_{c}=0.3\) (so that \(\mu=1/2\)). Results for lossless and lossy elements with magnetoinductive terminations are obtained using Equation (14), and results for lossy elements with narrowband resistive terminations using numerical solution of circuit equations.  
In each case, frequencies near \(\omega_{0}\) are diverted into port 4, while the remaining power is sent to port 2. Port 4 is therefore bandpass, while port 2 is a bandstop. The MI passband is as estimated from previous arguments. The isolation of port 3 is unaffected by loss; however, power emerging from port 4 is reduced and port 2 rejection diminished. Since losses are inversely proportional to \(\kappa Q\) and \(\kappa_{c}Q\), high coupling coefficients and Q-factors are required to minimise loss. The effect of a narrowband resistive termination is to reduce the bandwidth. However, this can be partly restored by using a broadband termination.
## 3 System Design and Implementation
### Preamble  
An ad hoc network with flat configuration typically bypasses the need of an external router or an access point and provides a direct path to the endpoints in a network. When we consider emergency situations in far-flung areas, traditional networks become very expensive and are difficult to deploy. In contrast to this, medical or rescue teams may utilize IEEE802.11 radio adapters that are available in their laptops or Personal Digital Assistants (PDAs) as soon as they arrive at the scene.  
An ad hoc network may be utilized in so many ways--for example, if there is a fire in a big forest, it would be much easier for the team of fire-fighters to deploy an ad hoc network, instead of building an entire infrastructure in such an area. To a certain limited extent, it has been established that nodes in an ad hoc network are usually not powerful enough and contingent on the transmission range [21]. However, when we combine these transmission ranges together, we can form a sustainable network based on a larger geographical area. The communication between nodes within the network can then be achieved through a routing protocol. Consequently, the nodes would create a transmission-cloud to exchange information among themselves, through some sort of structured routing algorithm.
# Time-Evolving Graph Convolutional Recurrent Network for Traffic Prediction
## 4 Experiments and Discussion
### Prediction Performance  
The prediction results of our proposed method TEGCRN and the baseline methods on METR-LA and PEMS-BAY are shown in Tables 3 and 4, respectively. We report the results for 15 min (3 steps)-, 30 min (6 steps)-, 60 min (12 steps)-ahead predictions to show the performance in the short term, middle term and long term.  
As shown in Tables 3 and 4, TEGCRN achieves higher performance on both datasets. It almost outperforms these baseline methods in all the time steps except for the long-term prediction on METR-LA. We can also see that the performances of GCN-based methods significantly outperform traditional series models such as ARIMA and FC-LSTM. This emphasizes the importance of introducing spatial information into traffic prediction. Incontrast to the models using only predefined distance-based graphs such as DCRNN and STGCN, Graph-WaveNet and MTGNN achieve better performance. This is because more inter-node dependency can be learned by the inner adaptive adjacency graph from a global view. Moreover, the inception layers in MTGNN may capture more temporal patterns in different time scales, which helps MTGNN surpass Graph-WaveNet in long-term prediction. By using time-evolving graph convolutions and integrating them with recurrent structure, our proposed model TEGCRN can capture more implicit dynamics of inter-node dependency, so it can achieve better performance compared with the models with static graphs. Since the recurrent structure in RNN usually suffers from accumulative errors when the number of output time steps increases, TEGCRN may be more sensitive to missing values in input data than the CNN-based models. However, the proportion of missing values of METR-LA is much higher than that of PEMS-BAY. This may lead to the small performance margin between TEGCRN and MTGNN when making long-term predictions on METR-LA. To sum up, our proposed model TEGCRN shows its superiority over multiple competitive traffic-predicting baseline models especially in short-term prediction.
###### Abstract
This opens up new perspectives for the development of personalized training and relaxation programmes, as well as for the optimization of game environments. The research into the impact of VR and 3D gaming on stress is in its infancy, but initial results show that stress has an impact on the cardiovascular system. One of the modern methods for the analysis of complex dynamic systems, such as the cardiovascular system, is fractal and multifractal analysis, which provides additional information about the nonlinear and chaotic characteristics of ECG signals [28]. Heart rate is not regulated linearly; instead, it has complex dynamics that change depending on the state of the organism and the influence of external factors (stress) that can cause physical or mental strain [29; 30]. Fractal analysis is used to detect self-similar structures in the heart rate signal, allowing the recognition of specific patterns of behaviour at different time scales. It reveals how small changes over a single time interval in HRV can have a similar structure to larger intervals, which is an indicator of a healthy and well-functioning system [31; 32]. Multifractal analysis extends the capabilities of fractal analysis by allowing the detection of more complex and diverse structural features of the signal. It measures multiple fractal dimensions that describe the different levels of variability in HRV [33]. This methodology is particularly useful in identifying more detailed and complex dynamics that would go unnoticed using traditional linear analysis. The use of fractal and multifractal analysis methods in the study of stress during virtual realities is a tool for detecting changes in the autonomic nervous system and, more precisely, measuring the body's response to stressful situations, such as are often created in virtual reality games [34; 35; 36]. This paper is an extension of the work originally presented in [37]. The aim of this paper is to study the influence of mental stress generated by a VR game on the HRV. To reach this aim, the following tasks have been defined:  
* Presentation of the process of creating a VR game, which includes 3D modelling (polygon modelling, Bezier curves, texture, and material), 3D animation (spline and linear interpolators, translational and rotational motion, and morphing) and the mathematical apparatus (linear algebra, trigonometry, and geometry) used;
* Study of the stress reactions generated during the game by the participants by applying the following methods of fractal and multifractal analysis: detrended fluctuation analysis (DFA) and multifractal detrended fluctuation analysis (MFDFA);
* Statistical analysis of the studied parameters before and during the game by applying the \(t\)-test to assess the significance of the observed differences.
## 3 Bursting Liability Indices
### Distribution Range  
In order to compare the bursting liabilities of coals and non-coal rocks, the distribution ranges of bursting liability indices of coal and non-coal rocks are calculated and shown in Figure 7. The bursting liability data are basically collected from the underground engineering data in China (Figure 2), as shown in Tables S3 and S4 in the Supplementary Material files. The statistical parameters of the rockburst indices of coals and non-coal rocks are shown in Table 2.  
As shown in Figure 7, the distribution ranges of the bursting indicators, such as elastic strain energy index \(W_{ET}\), bursting energy index \(W_{cf}\) and brittleness index \(B\), are 0-26, 0-22 and 0-52 for coals, respectively, and which are 0-26, 0-35 and 0-55 for non-coal rocks, respectively. It is easily concluded that there are few differences for \(W_{ET}\), \(W_{cf}\) and \(B\) between coal and non-coal rocks in the distribution range. However, there are large discrepancies for \(DT\) and \(\sigma_{c}\) between coal and non-coal rocks, as shown in Figures 3a and 7c. The distribution ranges of \(DT\) and \(\sigma_{c}\) are 0-3000 ms and 0-50 MPa for coals, respectively, and 0-4000 ms, and 20-310 MPa for non-coal rocks, respectively. The distribution ranges of \(\sigma_{c}\) and \(DT\) for coals are much smaller than these for non-coal rocks.  
Figure 7: Statistics of bursting liabilities of coals and non-coal rocks: (**a**) elastic strain energy index \(W_{ET}\), (**b**) bursting energy index \(W_{cf}\), (**c**) dynamic fracture duration time \(DT\), (**d**) brittleness index \(B\).
## 7 Conclusions  
In this paper, we have proposed ana priori knowledge-based adaptive clutter cancellation and moving target detection technique for SAR-GMTI applied to the distributed spaceborne SAR systems. A priori knowledge, such as the road network database, is integrated into the adaptive processor to reduce any moving target steering vector mismatch. Two kinds of adaptive detection techniques are exploited for moving target detection. The MHT technique is proposed to detect the moving targets and resolve the moving target steering vector estimation ambiguity for the dense road network cases. For the MHT technique, AMF and ABORT have similar detection and classification performance. SBRAS developed by our research group has been exploited to simulate the multistatic spaceborne SAR transmitted signals and received echoes. Based on the SBRAS, the SAR-GMTI signal processing simulation results have shown that the proposed method can efficiently detect the moving targets with a small number of relocation errors.  
In fact, apart from the road mapping data, other knowledge of the external environment can also be integrated into the proposed adaptive clutter cancellation and moving target detection techniques. They may include the knowledge of clutter statistics of nonhomogeneous terrain and the characteristics of the moving targets. As a remark, although the distributed spaceborne SAR system is employed in the paper, the method can also be modified to other multistatic or multi-aperture (multi-antenna or multi-channel) SAR-GMTI systems. For example, the SAR satellite formations with nonuniform baselines and the spaceborne/airborne multi-channel SAR systems.  
Conceptualization, B.C.; data curation, X.H.; formal analysis, L.C. and J.L.; funding acquisition, B.C.; investigation, L.C. and J.L.; methodology, B.C.; project administration, J.L.; resources, X.H.; software, X.H.; supervision, L.C.; validation, B.C.; visualization, T.C. and Y.L.; writing--original draft, B.C.; writing--review & editing, T.C. and Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China under Grant 61871396.  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank the handling editor and the anonymous reviewers for their valuable comments and suggestions for this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models
### Review of Models
#### 1.2.1 GARCH Model  
The ARCH model proposed by [PERSON] [35] was the first model with the conditional distribution to describe the fat tail characteristics or the volatility clustering properties of time series. However, the ARCH model has computational problems when a large number of parameters are needed for a high order model. To solve these problems, [PERSON] [36] proposed the GARCH model, which is one of the most popular models for forecasting the volatility of time series. Since the GARCH models include the conditional variance terms as well as the squared residual terms, the models can predict the volatility well by using a sum of weighted products of the predicted variance from the past.  
The GARCH \((p,q)\) model is defined as the follows.  
\[y_{t}^{2}=w+\sum_{i=1}^{q}\alpha_{i}\varepsilon_{t-i}^{2}+\sum_{i=1}^{p}\beta _{i}y_{t-i}^{2}, \tag{1}\]  
where \(\varepsilon_{t}=y_{t}Z_{t}\), \(\{Z_{t}\}\) is a sequence of independent and identically distributed random variables with zero mean and unit variance, \(\{\varepsilon_{t}\}\) is a sequence of the error terms, the positive parameters \(\alpha_{i}\) and \(\beta_{i}\) satisfy the condition \(\sum_{i=1}^{q}\alpha_{i}+\sum_{i=1}^{p}\beta_{i}<1\) for the stability of the GARCH model. This condition ensures that the conditional variance \(y_{t}\) has nonnegative values and finite expected value. Here, \(w,\alpha_{i}\) and \(\beta_{i}\) are the estimated parameters by using maximum likelihood estimation.
###### Abstract  
In this paper, a novel smart outage management system considering Emergency Demand Response Programs (EDRPs) and Distributed Generations (DGs) denoted as \(SOMS_{DGs}^{EDRPs}\) is proposed. The EDRPs are provided to decrease the cost of load shading in a time of emergency. The objective function of the problem is proposed to minimize the load shading cost, the DG dispatch cost, the demand response cost and the repair dispatch time for crews. The \(SOMS_{DGs}^{EDRPs}\) solves an optimization problem that is formulated as Mixed Integer Linear Programming (MILP) taking into account the grid topology constraints, EDRP constraints, DG constraints and crew constraints. The MILP formulation was demonstrated in the GAMS software and solved with the CPLEX solver. The proposed method was tested on the IEEE 34 bus test system as well as an actual Iranian 66 bus power distribution feeder. The results show that the EDRPs and DGs can be effective in decreasing the outage cost and increasing the served load of the distribution power system in a crisis time.  
outage management system; distribution system; smart grid; Emergency Demand Response Programs (EDRPs); Distributed Generations (DGs) Article
## 3 Biomimetic Covert Whistle Transmitter
As an example, Figure 3a shows the \(H\) matrix for \(\alpha=2\), \(K_{\text{opt}}=2\), \(M_{\text{opt}}=1\), \(K_{\text{tot}}=8\), and the links, and Figure 3b displays the sequential link connections. In Figure 3a, the first and the second columns of \(H\) are mapped to \"0\" or \"1\", respectively, of input bits. No same subgroup set, e.g., (7,2), (6,4), etc., is observed and the subgroup numbers are randomly distributed and appear two times, i.e., \(2^{M_{\text{opt}}}\). Let the \(n\)-th transmitted whistle be \(W_{n}\), and the subgroup index of the whistle be \(X_{n}\). If the input bit stream is 0001 and the first bit starts with the first row, the first bit \"0\" is mapped to the first column of the first row in \(H\). The first column of the first row of \(H\) reads \"7\". One whistle (\(W_{1}\)) in the seventh subgroup (\(X_{7}\)) is randomly selected and transmitted as the first symbol. For the next bit, the \"7\" also indicates the next row number of \(H\). For the second bit \"0\", the first column of the seventh row reads \"8\". One whistle (\(W_{2}\)) is randomly selected from the eighth subgroup (\(X_{8}\)) and transmitted for the second symbol, and \"8\" also indicates the eighth row for the third input bit. Similarly, for the fourth bit \"1\", the subgroup selection is performed in the fifth row which is chosen by the third bit. Since the input bit is 1, the second column of the fifth row is selected and reads \"1\". One whistle (\(W_{4}\)) of the first subgroup (\(X_{1}\)) is randomly picked and the first row is chosen for the fifth input bit. This sequential link result is depicted in Figure 3b. Therefore, the proposed modulation method utilizes all whistles that obtain a large DoM, and provides the large distance among the subgroup sets and the additional link information, which attains the low BER. For the demodulation using the link information, a machine learning based sequential detection is proposed since the machine learning detection outperforms the conventional feature detection methods and the sequential link information also enhances the detection performance. The detailed demodulation scheme is described in the next section.
# Optimizing Airfoil Aerodynamic Characteristics by Using Proposed CSA-KJ Method
## 3 Example Analysis and Simulation
### Reliability Verification of CSA-KJ Method  
In order to confirm the validity of the numerical calculation, the NACA4412 airfoil was taken as an example to compare the numerical calculation results by CFD with the experimental data [31]. The lift coefficient of the NACA4412 airfoil at a Reynolds number of \(1.25\times 10^{6}\) in the steady state, is shown in Figure 4. In terms of the lift coefficient, the curve trend obtained by numerical calculation is consistent with the experimental data, and the numerical calculation results are closer to the experimental value; especially if the angle of attack is \(-2^{\circ}\). In general, the results show that the numerical calculation results by CFD are accurate and agree well with the experimental data. The following numerical calculations for the airfoil in this study were performed using the same method mentioned above.  
[PERSON], [PERSON], and [PERSON] [32] obtained the NEW4412 airfoil by optimizing the design of the NACA4412 airfoil, while the CSA-KJ4412 airfoil was obtained through the CSA-KJ optimization algorithm in this work. In order to highlight the lift coefficient of the optimized CSA-KJ airfoil in this study, the curves of lift coefficient (CL) of CSA-KJ4412, NEW4412 and NACA4412 airfoils are plotted in Figure 5. It is not difficult to find that when the Reynolds number is \(1.25\times 10^{6}\) and the attack angle is in the range of \(-4^{\circ}\) to \(4^{\circ}\), the lift coefficients of CSA-KJ4412 and NEW4412 are both higher than those of NACA4412, and all the CL of the CSA-KJ4412 are the largest, indicating that the CSA-KJ method has a better optimization effect on the airfoil.
# Multi-Material Topology Optimization Taking into Account the Position of Material Interfaces in 3D
## 4 Numerical Experiments  
In this section, the effectiveness of the developed method is examined and validated using two numerical examples and a parameter study. The cases used are a cantilever clamped on one side and a bracket.  
Parameters are listed in Table 1 that are used in all the following numerical examples. The modulus of elasticity \(E_{1}\), \(E_{2}\) and the Poisson's ratio \(\
u_{1}\), \(\
u_{2}\) of the materials used. The minimum and maximum normal velocity \(v_{min}\) and \(v_{max}\) already introduced above.  
Furthermore, a normal velocity \(v_{nds}\) is defined in the non-design space, which guarantees that material is always present at the support surfaces.  
The last parameter \(\alpha\) describes the maximum volume reduction in one iteration analogous to [38]. Numerical experiments have shown that an \(\alpha=1\%\) leads to good convergence behavior of the optimization. This value results in a uniform reduction of the volumes, as the optimization is primarily controlled by the sensitivities of the objective function and not by the volume reduction. If the value of \(\alpha\) is too high, the two materials separate and the optimization is aborted. The influence of the remaining parameters (e.g., filter radius) on the convergence behavior was investigated in [15].  
For the selection of \(\beta\), we recommend using the ratio between the two material parameters interface shear strength and normal tensile strength in analogy to the failure criterion of [PERSON] and [PERSON]. The two parameters are the only material parameters that are also used in this failure criterion and can also be determined experimentally. It should be noted that this is a recommendation based on an analogy that has led to good results in our numerical experiments, and not a theoretical derivation.  
For the selection of \(w_{ited}\), we recommend values of 0.05 to 0.3 and starting with a low value at the beginning. Values that are too high cause the optimization to be completely controlled by the ITED and lead to a separation of the two materials. Observing the first 10-20 iterations also provides relatively quick information as to whether the ITED alone is controlling the optimization.  
The interface regularization term is activated in all experiments starting from iteration 110, which guarantees that it does not drive the optimization at the beginning. To determine convergence, a criterion is used which first forms the moving average of the objective function values of the previous five iterations. The percentage change in the moving average of two iterations is then calculated. If this change is less than 0.0001 and the two target volumes are met, the result is considered to be converged.