esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Solutions in Blockchain-Enabled Internet of Vehicles
## 6 Privacy-Preserving Blockchain-Based Solutions
### Advertising  
The evolution of vehicular system contributed to the development of various relevant services such as entertainment, advertising and online shopping. Advertisement dissemination in VANETs is a service which is considered very popular for the effective promotion of products and is discussed in the following privacy-preserving solution.  
[PERSON] et al. [62] proposed a blockchain-based advertisement dissemination framework. In this proposed scheme, a register authority is responsible for users' identification with a single certification and key generation, while vehicles' private key is generated by two parts, one part by their-selves and another by RSUs. After their registration, advertisers promote their ads to one or more RSUs which fulfil their requirements and provide rewards depending on the number of vehicles receiving the ad. When a vehicle receives an advertisement, it returns a response with a signature to the sender or forward it to nearby vehicles with economic motivation. A smart contract is used to ensure the fairness and honesty of the advertisement dissemination and to ensure the predefined reward. Vehicles generate anonymous credentials with zero-knowledge proof of knowledge techniques to prove the validity of the dissemination without revealing any private information. Receivers of ads send a transaction to the permissioned blockchain, proving that they have broadcast the advertisement in order to receive the predefined reward by the smart contract. For that purpose, a secure wallet is used by users to manage coins which act as rewards and also to connect to blockchain. RSUs sustain the blockchain and verify the validation of blocks. In addition, RSUs communicate with vehicles and broadcast advertisements.  
The privacy of advertisers is not considered as they want to communicate their products and services. In contrast, vehicles' privacy is protected, as they use anonymous credentials signed by blinded signature based on zero-knowledge proof of knowledge (ZKPoK) which protect them from malicious users who might want to learn their real identity. Even RSUs and Register authority cannot identify their real identity. In addition, vehicles can change their blockchain address whenever they want, so their position and direction cannot be traced by the information stored in the blockchain by different ads. The authors used VANETsim platform, Parity permissioned Ethereum blockchain and the programming languages Java and Solidity to simulate their scheme. In addition, Proof-of-Authority (PoA) consensus protocol was adopted. The experimental results and security analysis have shown that this model can work properly and efficiently.
## 2 Instrumentation Methods
### Jet Nozzle Instrumentation  
In most cases, the jet nozzle is instrumented to determine the engine thrust or the outlet nozzle velocity. On the ground, on the test bench, at sea level conditions, the thrust (\(F\)) [9] is defined as the sum between the product of gas flow (\(G_{f}\)) and the outlet velocity (\(C_{5}\)) and the product of the outlet cross-section (\(A_{5}\)) and the differential pressure (\(p_{5.t}-p_{0}\)).  
\[F=G_{f}\text{-}C_{5}+C_{5}\text{-}(p_{5.5}-p_{0}) \tag{4}\]  
For thrust calculation, it is necessary to know the gas flow (\(G_{f}\)) and to determine the outlet velocity (\(C_{5}\)). The gas flow (\(G_{f}\)) is determined by measuring the fuel flow (\(F_{f}\)) and the air mass flow (\(M_{af}\)). A simple instrumentation method, as presented in Figure 4, to determine the outlet velocity (\(C_{5}\)) is indicated to measure the outlet total temperature (\(T_{5.t}\)) and is necessary to measure the static and total outlet pressures (\(p_{5.s}\), \(p_{5.t}\)).  
The accuracy of the measurement is given by the number of instrumentation parameters. In our case, due to the limited capacity of the ports that can be installed on the TV2-117A engine target, only one point was measured, which leads to a lower precision or a higher measurement error in the targeted section. In other cases, where the engine allows a higher number of instrumentation probes to be mounted, it is recommended to use as many points on the circumference of the section as possible, in order to introduce a smaller error.  
Figure 3: Turbine instrumentation method scheme, model (**a**); turbine instrumentation method scheme, model (**b**).
## 6 Earth Rotation Parameters Evaluation
### Formal Error  
The formal errors are usually used as indicators of precision and observability for the estimated parameter [3]. The formula for calculating formal errors is presented as follows:  
\[Q_{xx}=\sqrt{\sigma_{0}^{2}N^{-1}} \tag{4}\]  
where \(\sigma_{0}^{2}\) is the a posteriori variance factor of unit weight and N is the normal equation matrix. We use the formal error as an indication of internal accuracy.  
Figure 11 displays the time series of formal errors in ERP estimation from the 3-day arc BDS-3 B1I/B3I solutions, B1C/B2a solutions, and GPS solutions. The figure illustrates that GPS solutions exhibit the minimal formal errors for PM, its rates, and LOD. The formal errors of the B1C/B2a-based solutions closely align with those of the GPS solutions and are smaller than the B1I/B3I-based solutions. Table 10 summarized the mean formal errors in ERP estimation using BDS-3 satellites from the B1I/B3I solutions and B1C/B2a solutions. The table reveals that the mean formal errors from the 3-day arc solutions are approximately 2.2 times smaller for PM, 4 times smaller for PM rates, and 2.5 times smaller for LOD than the 1-day arc solutions. In particular, the mean formal errors in ERP estimation from the 3-day arc B1C/B2a solutions are approximately 10 \(\upmu\)as for PM, 12 \(\upmu\)as/d for PM rates, and 0.7 \(\upmu\)s/d for LOD. The B1C/B2a-based solutions contribute to a reduction in the mean formal errors by about 17% for PM, 22% for LOD, and 21% for PM rates when compared with B1I/B3I-based solutions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Solution** & **Xp [**\(\upmu\)as**] & **Yp [**\(\upmu\)as**] & **LOD [**\(\upmu\)s/d**] & **Xrt [**\(\upmu\)as/d**] & **Yrt [**\(\upmu\)as/d**] \\ \hline \multicolumn{6}{c}{1-day arc} \\ \hline B1I/B3I & 25 & 26 & 2.1 & 52 & 65 \\ B1C/B2a & 18 & 22 & 1.8 & 42 & 55 \\ GPS & 18 & 20 & 1.3 & 32 & 44 \\ \hline \multicolumn{6}{c}{3-day arc} \\ \hline B1I/B3I & 11 & 12 & 0.9 & 14 & 15 \\ B1C/B2a & 9 & 10 & 0.7 & 11 & 12 \\ GPS & 9 & 10 & 0.7 & 10 & 11 \\ \hline \end{tabular}
\end{table}
Table 10: Mean formal error in ERP estimates from BDS-3 B1I/B3I solutions and B1C/B2a solutions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**RMS [cm]** & \multicolumn{2}{c}{**MEO ALL**} & \multicolumn{2}{c}{**MEO CAST**} & \multicolumn{2}{c}{**MEO SECM**} \\ \cline{2-7}  & **1-Day** & **3-Day** & **1-Day** & **3-Day** & **1-Day** & **3-Day** \\ \hline B1I/B3I & 4.78 & 4.75 & 5.11 & 5.19 & 3.94 & 3.56 \\ B1C/B2a & 4.84 & 4.69 & 5.14 & 5.09 & 4.06 & 3.61 \\ COM & - & 4.70 & - & 4.97 & - & 4.03 \\ \hline \end{tabular}
\end{table}
Table 9: The RMS of 3-day arc orbit SLR residuals for BDS-3 MEO of all satellites from B1I/B3I solutions, B1C/B2a solutions, and CODE MGEX products without consideration of C22, C43, and C44.
# 3D Behavior Recognition Based on Multi-Modal Deep Space-Time Learning
## 3 Multi-Modal 3D Space-Time Convolutional Framework  
### Space-Time Feature Learning of Depth Sequences  
The proposed 3D deep convolutional neural network is designed based on the previous work [40]. It contains two 3D convolution layers, each of which is followed by a 3D max pooling layer. The input to the network is a cube obtained by simply preprocessing the original depth sequence and normalizing the input to the same size as the training data set. Since the length of the action video is not the same in different data sets, it is difficult to normalize all the videos into the same size while still maintaining sufficient information in the video. Therefore, the sizes of cubes that are fed into the network from different data sets are different. Let's take the MSR-Action3D dataset as an example. Firstly, after a simple pre-processing of the original depth sequence, a normalized depth cube (motion video) with dimension \(32\times 32\times 38\) (height \(\times\) width \(\times\) time) is fed into a 3D deep convolutional neural network. The input cube is processed by the first 3D convolutional layer (C1) and the 3D maximum pooling layer (M1), followed by the second 3D convolutional layer (C2) and the 3D maximum pooling layer (M2). Finally, a fully connected layer and a soft-max layer are applied for classification. The kernel sizes of the convolutional layers C1 and C2 are \(5\times 5\times 7\) and \(5\times 5\times 5\), respectively. In the subsequent 3D maximum pooling layer of the 3D convolutional layer, \(2\times 2\times 2\) downsampling is used in the M1 and M2 layers in order to reduce the space and time dimensions. The numbers of feature maps in C1 and C2 are 16 and 32, respectively. For the UTD-MHAD data set, the cube size of the input layer is \(32\times 32\times 28\). Both of the convolution kernels in C1 and C2 are \(5\times 5\times 5\). Unless otherwise specified, all other parameters in this paper are the same as those used in the MSR-Action3D dataset.
## 3 Results and Discussion
### Sensory Analysis
It is possible to appreciate that the statistical significance was mainly associated with low FP values. This data confirmed the results obtained in the VOCs analysis; in fact, aroma, flavor, and other sensorial characteristics are closely related to the presence of specific volatile analytes, as well as carbohydrates, lipids, organic acids, and chlorogenic acids. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **SAMPLES** & **Aroma** & **Flavor** & **Aftertaste** & **Acidity** & **Body** & **Balance** & **Overall** \\ \hline PB 1 & & 6.5 & 7 & 7 & 7.5 & 7.25 & 7.5 & 7.75 \\ PB 2 & & 7.5 & 7 & 7.5 & 7.75 & 7.75 & 8 & 8 \\ PB 3 & Pure Brew & _7.75_ & 7.75 & 7.75 & 7.75 & 8 & 8 & 8 \\ PB 4 & & \(7\) & 7.5 & 7.5 & 7.75 & 7.5 & 7.5 & 7.5 \\ PB 5 & & _7.5_ & 7.75 & 7.5 & 7.5 & 7.75 & 7.75 & 7.5 \\ PB 6 & & _7.75_ & 7.25 & 7.5 & 7.5 & 7.5 & 7.75 & 7.5 \\  & Mean Values & 7.33* & 7.38* & 7.46* & 7.63 & 7.63* & 7.71* & 7.75* \\ \hline V60 1 & & 6.75 & 7 & 7 & 6.75 & 7.25 & 6.75 & 7 \\ V60 2 & & 7 & 7.75 & 7.5 & 7 & 7.5 & 7.5 \\ V60 3 & V60 & & 7 & 7.75 & 8 & 7.75 & 7.75 & 8 \\ V60 4 & V60 & & 6.5 & 7.75 & 7.5 & 7.5 & 7.5 & 7.75 \\ V60 5 & & 7 & 7.25 & 7.5 & 7.5 & 7.5 & 7.5 & 7 \\ V60 6 & & 6.75 & 7.5 & 7.5 & 7.25 & 7 & 7.75 & 7.75 \\  & Mean Values & 6.83* & 7.50* & 7.54* & 7.29 & 7.29* & 7.50* & 7.50* \\ \hline AP 1 & & 7.25 & 7.5 & 7.25 & 7 & 7.25 & 7.25 & 7.25 \\ AP 2 & & 7.25 & 7.5 & 7.25 & 7.25 & 7.5 & 7.25 & 7.5 \\ AP 3 & & 7.25 & 7.75 & 7.5 & 7.75 & 7.75 & 7.75 & 7.5 \\ AP 4 & AeroPress & 7 & 7.25 & 7.5 & 7.5 & 7.5 & 7.5 & 7.5 \\ AP 5 & & 7.5 & 7.25 & 7.25 & 7.5 & 7.75 & 7.5 & 7.25 \\ AP 6 & & 7.25 & 7.5 & 7.5 & 7.25 & 7.25 & 7.25 & 7.25 \\  & Mean Values & 7.25* & 7.46* & 7.38* & 7.38 & 7.50* & 7.42* & 7.46* \\ \hline FP 1 & & 6 & 6.5 & 6.5 & 7 & 7.25 & 7 & 6.75 \\ FP 2 & & 6.5 & 7 & 6.75 & 7.25 & 7.25 & 7 & 6.75 \\ FP 3 & & 6.75 & 7 & 7.25 & 7.75 & 7.5 & 6.75 \\ FP 4 & & 7 & 6.75 & 7 & 7.25 & 7 & 7.25 & 7 \\ FP 5 & & 6.25 & 6.75 & 6.75 & 7.5 & 7 & 7 & 7 \\ FP 6 & & 6.5 & 6.5 & 6.5 & 7.5 & 7.25 & 7 & 6.75 \\  & Mean Values & 6.50* & 6.75* & 6.79* & 7.38 & 7.21* & 7.13* & 6.83* \\ \hline  & \(p\).value & 0.0009 & 0.0004 & 0.0003 & 0.1962 & 0.0209 & 0.0082 & 0.5666 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sensorial analysis results. Statistically significant differences between groups of samples are reported with different letters on mean values.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Joint Compression and Encryption for the Worst Case Images  
The proposed scheme was evaluated with complete white and black images. As the complete white and black images have no information and in the trnsform domain appear only as a dot. Therefore, wavelet coefficients only have few information in the form of only four values. However, if these values passed to SPIHT encoding, it generates almost the same number of ones and zeros as for the other images. The rest of the procedure have no difference for worst case and other images.
# Side Channel Leakages Against Financial IC Card of the Republic of Korea +
Footnote †: journal: Accepted: 7 November 2018; Published: 15 November 2018  
[PERSON]  
1  
[PERSON]  
1  
[PERSON]  
protocol below (Figure 1), which is called the financial IC card protocol in this paper, is carried out when withdrawing cash from an automated teller machine (ATM) in the domestic market.  
Here, Data Encryption Algorithm (\(DEA\)) indicates block cipher SEED [21]; \(UP_{U}\) indicates user password; \(C=DEA_{K}(P)\) means that the encryption function \(DEA\) takes a key \(K\) and plaintext \(P\) as input and produces a ciphertext \(C\), and all inputs and outputs in the encryption function consist of 128 bits except for \(UP_{U}\). However, \(UP_{U}\) is encoded to 128 bits using a padding scheme [20]. Moreover, the subscripts \(U\), \(C\), and \(T\) for all variables indicate user, IC chip, and ATM, respectively.  
1. A valid user inserts an IC card into an ATM in the baking system of the Republic of Korea.
2. A valid user enters the user password and amount of desired cash through the ATM interface.
3. The ATM generates a random number, \(R_{T}\), which is composed of 128 bits.
4. The ATM transfers \(R_{T}\) and the user password to the IC card.
5. The IC chip generates a random value, \(R_{C}\), which consists of 128 bits, for the financial IC card protocol.
6. In the IC chip, \(R_{C}\) is encrypted to \(TK_{C}\) under a fixed key, \(MK_{C}\), stored in the secure memory.
7. In the same way, \(R_{T}\) is encrypted to \(R_{T}\) under non-fixed key \(TK_{C}\) generated from (5-1).
8. Finally, \(UP_{U}\) is encrypted to \(CT_{C}\) under non-fixed key \(SK_{C}\) generated from (5-2).
9. The IC chip passes \(R_{C}\) and \(CT_{C}\) to the ATM.
10. The bank server receives the \(R_{T},R_{C}\), and \(CT_{C}\) from the ATM.  
Figure 1: Financial IC (Integrated circuit) card protocol. ATM: automated teller machine.  
The bank server possesses keys \(MK_{B}\) and \(UP_{B}\), which are identical to \(MK_{C}\) and \(UP_{U}\), respectively. Therefore, by performing a scheme identical to (5-1)\(\sim\)(5-4), it can determine the truth of this protocol.
* The ATM informs the user whether a cash withdrawal is possible or not.  
For a detailed description of this protocol, refer to [20]. In particular, the security features for the financial IC card protocol are clearly stated in Chapter I.8. This document states that there should be resilience to power analysis, timing analysis, electromagnetic leak, fault injection, TEMPEST, etc. This is due to security threats, such as the risk of IC chip duplication. There is currently only one study [22] on the side channel leakage in the financial IC card protocol. Moreover, that study only found a single byte of round key against block cipher SEED without countermeasure, because a countermeasure was not mandatory at the time.
###### Abstract  
Recent studies in transillumination imaging for developing an optical computed tomography device for small animal and human body parts have used deep learning networks to suppress the scattering effect, estimate depth information of light-absorbing structures, and reconstruct three-dimensional images of de-blurred structures. However, they still have limitations, such as knowing the information of the structure in advance, only processing simple structures, limited effectiveness for structures with a depth of about 15 mm, and the need to use separated deep learning networks for de-blurring and estimating information. Furthermore, the current technique cannot handle multiple structures distributed at different depths next to each other in the same image. To overcome the mentioned limitations in transillumination imaging, this study proposed a pixel-by-pixel scanning technique in combination with deep learning networks (Attention Res-UNet for scattering suppression and DenseNet-169 for depth estimation) to estimate the existence of each pixel and the relative structural depth information. The efficacy of the proposed method was evaluated through experiments that involved a complex model within a tissue-equivalent phantom and a mouse, achieving a reconstruction error of 2.18% compared to the dimensions of the ground truth when using the fully convolutional network. Furthermore, we could use the depth matrix obtained from the convolutional neural network (DenseNet-169) to reconstruct the absorbing structures using a binary thresholding method, which produced a reconstruction error of 6.82%. Therefore, only one convolutional neural network (DenseNet-169) must be used for depth estimation and explicit image reconstruction. Therefore, it reduces time and computational resources. With depth information at each pixel, reconstruction of 3D image of the de-blurred structures could be performed even from a single blurred image. These results confirm the feasibility and robustness of the proposed pixel-by-pixel scanning technique to restore the internal structure of the body, including intricate networks such as blood vessels or abnormal tissues.  
pixel-by-pixel; Attention Res-UNet; DenseNet169; reconstructing 3D; de-blurred; depth estimation; transillumination image +
Footnote †: journal: Applied Physics Letters
## 6 Simulation Verification and Analysis  
To validate the effectiveness of the proposed control strategy, a single-machine VSG grid-connected converter simulation model was developed using Matlab/Simulink, as illustrated in Figure 1. This simulation model compared the performance of TVSG control, TDP-VSG control, and the proposed APFBC-VSG control in suppressing power oscillations under given power and grid frequency disturbances. The main simulation parameters are listed in Table 1. The specific operating conditions are as follows: the system's active power command increases from 0 kW to 15 kW at 2 s and the grid frequency increases from 50 Hz to 50.1 Hz at 4 s, and returns to 50 Hz at 6 s.
## 2 Materials and Methods
### Sample Size Calculation  
Assuming an alpha risk of 0.05 and a beta risk of 0.2 in bilateral contrast between mean numbers of weeks of home whitening, 10 subjects were needed in the single-session group (Group 1S) and ten in the two-session group (Group 2S) to detect a difference equal to or greater than 3 weeks. It was assumed that the common standard deviation in the number of weeks would be 2.2, and a drop-out of 10% has been anticipated.
# Dual-Circularly Polarized 60 GHz Beam-Steerable Antenna Array with 8 \(\times\) 8 Butler Matrix
## 2 Design of Antenna Array System
The ground plane of the designed antenna element is on the bottom layer and the square patch and the parasitic stacked patch are positioned on the mid layer and top layer, respectively. The optimized lengths of each square patch and parasitic stacked patch are obtained as 1.513 mm and 1.5 mm, respectively. The simulated reflection coefficients for the various lengths of the parasitic stacked patch are shown in Figure 10a. The characteristic impedance of the antenna is set as 73 \(\Omega\) to integrate with the designed Butler matrix. Each antenna element has two feed lines to generate the circularly polarized radiation pattern. The edges of the parasitic stacked patches are slightly cut to further guarantee that the radiation has a circular polarization. The distance between the two adjacent  
Figure 5: Simulated amplitudes of S-parameters of the crossover. Figure 6: Simulated results of the \(8\times 8\) Butler matrix: (**a**) averaged insertion losses and (**b**) output phase for excitation of port 4.  
elements in the designed array is 2.6 mm, which is about a half-wavelength at 60 GHz. Figure 10b shows the simulated gain pattern of the 1 \(\times\) 4 patch antenna array in the \(xz\)-plane at 60 GHz with the same phase and amplitude excitation. Figure 8: Simulated results of the transition part: (**a**) transmission coefficient with and without series via array and (**b**) line impedance. Figure 7: Structure of the transition part. Figure 9: Geometry of the proposed 1 \(\times\) 4 patch array antenna.
# Spectrum Modeling of Out-of-Band Intermodulation for Dual-Band RF Amplifiers in OFDM Modulation
## 2 Spectrum Model  
### Calculating the Correlation for the Dual-Band OFDM Output Signals  
Based on ([14], Equation (3)), the input dual-band OFDM signal can be expressed as:  
\[s(t)=\tilde{s}_{1}(t)cos(2\pi f_{c_{1}}t)+\tilde{s}_{2}(t)cos(2\pi f_{c_{2}}t) \tag{1}\]  
where \(\tilde{s}_{1}(t)\) is the OFDM signal in one band centered at frequency \(f_{c_{1}}\) with bandwidth \(B_{1}\), and \(\tilde{s}_{2}(t)\) is the OFDM signal in the other band centered at \(f_{c_{2}}\) with \(B_{2}\). \(\tilde{s}_{1}(t)\) and \(\tilde{s}_{2}(t)\) are the baseband representation, which equal to \(\left|\tilde{s}_{1}(t)\right|e^{i\tilde{\theta}_{1}(t)}\) and \(\left|\tilde{s}_{2}(t)\right|e^{i\tilde{\theta}_{2}(t)}\), respectively. The power spectrum density (PSD) of \(\tilde{s}_{1}(t)\) and \(\tilde{s}_{2}(t)\) can be expressed respectively as follows [14]:  
\[P_{\tilde{s}_{1}}(f)=\left\{ \begin{array}{cc}\frac{N_{1}}{2},&\left|f\right| \leq B_{1}\\ 0,&\left|f\right|>B_{1}\end{array} \right.\]  
and  
\[P_{\tilde{s}_{2}}(f)=\left\{ \begin{array}{cc}\frac{N_{2}}{2},&\left|f\right| \leq B_{2}\\ 0,&\left|f\right|>B_{2}\end{array} \right. \tag{2}\]  
where \(N_{1}B_{1}\) and \(N_{2}B_{2}\) are the powers of \(\tilde{s}_{1}(t)\) and \(\tilde{s}_{1}(t)\), respectively. From [14], we know the third-order term of [PERSON] series model generally dominates in the nonlinear effect. Using the third-order Taylor series model of RF amplifiers ([14], Equation (6)) and (1), the output amplified dual-band OFDM signals are described as:  
Figure 1: Output power spectrum of dual-band signals including out-of-band intermodulation.