esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Proposed Stream Cipher (Strike)  
### Mathematical Model of Strike Cipher  
In this section, we introduce the detailed structure of our proposed stream cipher (Strike). The mathematical model of Strike is inspired by the lightning strike development model mathematically described in [18]. The model assumes that every single point can be presented in a Cartesian coordinate system \((x,y,z)\). If a plane with \(z=0\) is chosen, the point will directly touch the surface of the ground. The stepped leader head starts at a given point at height \(h_{0}\) and moves downward to the ground, as depicted in Figure 3.  
The starting point of the lightning strike is point T\({}_{0}\) of coordinate \((x_{0},y_{0},h_{0})\), where \(x_{0}\), \(y_{0}\) and \(h_{0}\) are stochastically chosen according to the following equations:  
\[x_{0}=r\cdot a \tag{4}\]  
\[y_{0}=r\cdot b \tag{5}\]  
\[h_{0}=r\cdot c \tag{6}\]  
where \(r\) is a pseudorandom value, \(r\in[0,1]\).  
Choosing the peak value of the lightning current is also carried out stochastically. The lightning current peak values can be observed as an interval such that \(I\in[I_{min},I_{max}]\). This interval is divided into \(m\) classes. Accordingly, the lightning current's peak value is selected from a given class \(i\) as follows:  
\[I=i_{I_{min}}+r\cdot\left(i_{I_{max}}-i_{I_{min}}\right) \tag{7}\]  
where \(i_{I_{min}}\) represents the minimal value and \(i_{max}\) represents the maximum peak value chosen from the \(i\)-th class. Note that both \(i_{I_{min}}\) and \(i_{max}\) are calculated as follows:  
\[i_{I_{min}}=I_{min}+(i-1)\cdot\Delta I \tag{8}\]  
\[i_{I_{max}}=i_{I_{min}}+\Delta I \tag{9}\]  
\[\Delta I=\frac{I_{max}-I_{min}}{N_{c}} \tag{10}\]  
Figure 3: Simulating \(n\) jumps of lightning strike stepped leader head.  
As the stepped leader descends toward the surface, it makes several jumps of different distances in a stochastic path. The strike distance (jump) is calculated in metres as a function of the chosen lightning current peak as follows:  
\[R=A\cdot\left(TP\cdot I^{\cos\left(I_{\mathit{t}_{\mathit{t}_{\mathit{t}_{ \mathit{t}_{\mathit{t}}}}}\right)}}\right) \tag{11}\]  
With reference to Figure 3, the stepped leader head starts at a given point in space, then makes \(j\) jumps downward. The coordinates of the stepped leader head are calculated as follows:  
\[x_{j}=x_{j-1}+R\cdot\sin\sigma_{j-1}\cdot\cos\varphi_{j-1} \tag{12}\]  
\[y_{j}=y_{j-1}+R\cdot\sin\sigma_{j-1}\cdot\sin\varphi_{j-1} \tag{13}\]  
\[h_{j}=h_{j-1}+R\cdot\cos\sigma_{j-1} \tag{14}\]  
where the coordinates of the next position of the stepped leader head are determined stochastically as follows:  
\[\varphi_{j-1}=r\cdot 2\pi \tag{15}\]  
\[\sigma_{j-1}=(r+1)\cdot\frac{\pi}{2} \tag{16}\]  
Equations (12)-(16) present continuous stochastic processes that continue until reaching the earth's surface. In addition, this process is repeated an arbitrary number of times, as new lightning strikes may start at different positions on any given cloud. This process summarises the randomness of the lightning strike phenomena through the undetermined random paths that each strike initiates.
# Federated Few-Shot Learning-Based Machinery Fault Diagnosis in the Industrial Internet of Things
## 3 Preliminary and Problem Definition
### Convolutional Denoising Auto-Encoder  
The auto-encoder (AE) is an unsupervised neural network designed for data reconstruction. It consists of an encoder and a decoder, both structured symmetrically. Given an input \(x\), the auto-encoder aims to learn a mapping function \(h_{w}(x)\approx x\) that enables the middle layer to capture the hidden features of the input data. These hidden features can subsequently be fed into a supervised model. Figure 3 illustrates the denoising auto-encoder (DAE), which introduces noise to the input of AE. By extracting robust features from the noisy data, the DAE reconstructs the original data. In our approach, we employ the Dropout method to incorporate noise into the input. Dropout is a commonly utilized regularization technique during neural network training, randomly setting network nodes to zero with a specific probability. This process can be interpreted as adding noise to the original data. We set the random deactivation rate, denoted as \(\alpha\), to 0.5. \(\hat{x}\) represents the input after adding noise, \(z_{n}\) denotes the hidden feature, and \(y\) corresponds to the reconstructed input.  
The Convolutional Denoising Auto-Encoder (CDAE) is an encoder network that enhances the feature extraction ability of the Denoising Auto-Encoder (DAE) specifically for temporal data. This is achieved by incorporating convolutional layers to create multiple hidden layers. In our approach, we utilize a one-dimensional convolutional neural network based on WDCNN (Wide-Depth Convolutional Neural Network) [18] as the encoder. The first layer employs a 64 \(\times\) 1 wide convolutional kernel to extract features for suppressing high-frequency noise. Subsequently, a 3 \(\times\) 1 narrow convolutional kernel is used in the following layer to extract deep semantic features. For the decoder, we employ deconvolution  
Figure 2: Illustration of few-shot learning.  
and upsampling operations to restore the hidden layer features to the same dimensions as the input. The specific network structure of the CDAE is presented in Table 2.
# Assessment of Coating Properties in Car Body by Ultrasonic Method
## 3 Results
### Correlational Research
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{R_{a}}\) & \(\mathbf{R_{z}}\) & **Reflection Coefficient** & **Force [N]** & **Stress [MPa]** \\ \hline
50.1 & 0.832 & 5.600 & 0.516 & 1527 & 7.60 \\
50.2 & 1.735 & 8.890 & 0.562 & 1056 & 5.25 \\
50.3 & 0.398 & 3.470 & 0.631 & 917 & 4.56 \\
50.4 & 0.819 & 6.955 & 0.579 & 995 & 4.95 \\
50.5 & 0.523 & 3.535 & 0.596 & 955 & 4.75 \\
50.6 & 0.656 & 3.930 & 0.516 & 1225 & 6.10 \\
50.7 & 1.037 & 6.295 & 0.546 & 1096 & 5.45 \\
50.8 & 0.792 & 6.265 & 0.516 & 1366 & 6.80 \\
50.9 & 0.878 & 6.505 & 0.562 & 962 & 4.79 \\
50.10 & 0.756 & 6.025 & 0.613 & 985 & 4.90 \\ Average & 0.842 & 5.747 & 0.564 & 1108.4 & 5.516 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of ultrasonic and mechanical test for samples cured at 50 \({}^{\circ}\)C. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{R_{a}}\) & \(\mathbf{R_{z}}\) & **Reflection Coefficient** & **Force [N]** & **Stress [MPa]** \\ \hline
40.1 & 0.552 & 4.170 & 0.794 & 699 & 3.48 \\
40.2 & 0.750 & 4.815 & 0.750 & 842 & 4.19 \\
40.3 & 0.731 & 5.595 & 0.750 & 783 & 3.90 \\
40.4 & 0.412 & 3.600 & 0.708 & 870 & 4.33 \\
40.5 & 0.469 & 3.360 & 0.772 & 776 & 3.86 \\
40.6 & 0.649 & 5.395 & 0.750 & 703 & 3.50 \\
40.7 & 1.820 & 10.820 & 0.708 & 1081 & 5.38 \\
40.8 & 0.659 & 4.905 & 0.708 & 1057 & 5.26 \\
40.9 & 1.440 & 8.355 & 0.729 & 851 & 4.23 \\
40.10 & 0.981 & 6.280 & 0.266 & 666 & 3.31 \\ Average & 0.846 & 5.730 & 0.693 & 832.8 & 4.144 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of ultrasonic and mechanical test for samples cured at 40 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & \(\mathbf{R_{a}}\) & \(\mathbf{R_{z}}\) & **Reflection Coefficient** & **Force [N]** & **Stress [MPa]** \\ \hline
60.1 & 0.673 & 4.760 & 0.398 & 1472 & 7.32 \\
60.2 & 1.210 & 6.790 & 0.299 & 3262 & 16.23 \\
60.3 & 1.130 & 6.535 & 0.316 & 3025 & 15.05 \\
60.4 & 0.693 & 5.575 & 0.345 & 1802 & 8.97 \\
60.5 & 0.689 & 5.255 & 0.398 & 1241 & 6.18 \\
60.6 & 0.749 & 6.185 & 0.307 & 3509 & 1
# Evaluation of the SOA Formation in the Reaction of Furfural with Atmospheric Oxidants
## 1 Introduction  
Furfural (2-furanaldehyde) is known to be present in the atmosphere as a reaction product of alkylfurans with different tropospheric oxidants [1] and as a biomass combustion product [2]. The main sink for furfural in the troposphere is the reaction with the hydroxyl radical (OH) during daytime [3] and with the nitrate radical (NO\({}_{3}\)) at night [4]. This compound could also be an important ozone generator in the troposphere due to its high estimated Maximum Incremental Reactivity (MIR) values (22.4 gO3/g COV) [3]. The reactivity of furfural with chlorine and bromine atoms has previously been studied by [PERSON] et al. [5] and [PERSON] et al. [6], respectively. Furthermore, the photochemistry of furfural has been studied by [PERSON] and [PERSON] [7] and [PERSON] et al. [8]. Furfural can react with tropospheric oxidants by hydrogen abstraction from the carbonyl group, by electrophilic addition to the double bond of the aromatic ring or by a combination of these two mechanisms. Recent theoretical studies carried out by [PERSON] et al. [9] and [PERSON] and [PERSON] [10] suggested that furfural reacts with OH and NO\({}_{3}\) radicals mainly by C5 addition (\(\sim\)59%). For C2 addition, calculations suggest a \(\sim\)40% contribution. Hydrogen abstraction seems to make a minor contribution only (\(\sim\)0.05% for nitrateradicals and 2.8% for OH) [9; 10]. To date, experimental studies on furfural reaction products have not been reported in the literature.  
Aerosols play an important role in numerous environmental processes. Aerosols can absorb and scatter solar and Earth radiation, influence the generation of clouds and heterogeneous reactions in the atmosphere and thus affect the abundance and distribution of trace gases in it [11; 12]. As a result of these interactions, aerosols influence the Earth's radiation balance, thus playing an important role in climate. It is believed that certain types of organic volatile or semivolatile compounds like cycloalkenes, aromatic hydrocarbons and terpen favor the generation of Secondary Organic Aerosol (SOA). Moreover, interest in studying heterogeneous reactions involving carbonyl compounds has increased in recent years because of their high SOA-forming potential [13]. Therefore, it is believed that furfural could be a potential aerosol-former because it has two main features for generating SOA: a cyclic aromatic structure and a carbonyl group.  
A product study of the gas-phase reactions of furfural with chlorine atoms and hydroxyl and nitrate radicals has been carried out in this work in order to determine the main reaction pathways. SOA formation for the reaction of furfural with Cl, OH and O\({}_{3}\) was also investigated using a smog chamber. Data were analyzed using a Fast Mobility Particle Sizer Spectrometer (FMPS) for SOA experiments and a Fourier Transform Infrared spectrophotometer (FTIR) and a gas chromatograph-mass spectrometer with a time of flight detector (GC-TOFMS) for the product study in gas-phase.
## 4 Results and Discussion
### Verification and Validation
This shows that CST, with fewer parameters, consumed less time than PARSEC with 11 basic parameters. Moreover, a comparison of the optimization results between CST (n = 6) and CST (n = 8) was also performed. It was concluded that the optimized results were obviously better than those of the baseline airfoil, but the CST parametric method with eight design parameters was better than that with six parametric variables. As a result, eight design variables were used for optimization purposes, and the airfoil shape that corresponded to the optimal objective values was the final shape of the optimized airfoil. Figure 12: Pressure distribution curve over the NREL S809 airfoil. Figure 13: The convergence graph with the iterations for the NREL S809 airfoil via (**a**) the CST method and (**b**) the PARSEC method.
## 3 Results and Discussion
### Alterations of Phytochemical Compositions of Green and Black Tea after the Decaffeination
#### 3.1.1 Non-Volatile Components
Thus, the decaffeination technology may significantly reduce the bitterness, astringency, and umami of GT and slightly reduce the bitterness and astringency of BT. This speculation will be verified in subsequent sensory experiments. Next, the phytochemical compositions in tea samples were analyzed in detail. As is shown in Figure 2B, the theaflavin (TF) and theaflavin-3-gallate (TF-3G) were not significantly different between the BT and DeCAF-BT, but DeCAF-BT contained higher levels of theaflavin 3'-O-gallate (TF-3'-G) and theaflavin 3,3'-digallate (TFDG) than that of BT. After the decaffeination, the levels of GA, gallocatechin (GC), and epicatechin (EC) in the DeCAF-GT were not significantly changed, but the levels of epigallocatechin (EGC), epigallocatechin gallate (GCG), and epicatechin gallate (ECG) were reduced by 66.2, 90.9, 88.6, and 96.0% in the DeCAF-GT, respectively. For the BT, all catechins (except for ECG) and GA changed insignificantly. The supercritical CO\({}_{2}\) extraction method removed 87.2% catechins in the GT but only 39.3% catechins in the BT. The SCD method also reduced most amino acids (asparatic acid, glutamic acid, serine, theanine, cysteine, and tryptophan) of the GT, and only levels of asparagine, phenylalanine, lysine, and methionine were not significantly changed. For the BT, asparagine, serine, theanine, cysteine, phenylalanine, lysine, and tryptophan were significantly reduced by the decaffeination process. Importantly, theanine has been regarded as an important unamitastte contributor in tea, and it was almost removed in the DeCAF-GT and DeCAF-BT, respectively. However, arginine levels increased to 0.656 and 1.06% in the DeCAF-GT and DeCAF-BT, respectively. Collectively, the SCD method removed 26.9% of amino acids in the GT but increased 18.5% of amino acids in the BT. Organic acids are important flavor materials of sourness in tea. In the GT, acetic acid and succinic acid were significantly increased, and the decaffeination process significantly decreased malic acid, tannic acid, citric acid, and succinic acid. In the BT, only oxalic acid, tartaric acid, and tannic acid increased significantly after the decaffeination process, and other organic acids did not change significantly. Collectively, the SCD method removed 32.6% organic acids in the GT but increased 29.2% organic acids in the BT. Figure 1: Contents of (**A**) total water-soluble solid (TWSS), (**B**) total phenolics, (**C**) total free amino acids, (**D**) caffeine of the green tea (GT), decaffeinated green tea (DeCAF-GT), black tea (BT), and decaffeinated black tea (DeCAF-BT). Different letters indicate significant differences (\(p<0.05\)). Figure 2: _Cont._
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 1 Introduction
The designated method not only needs to improve the efficiency of protocol syntax information extraction, but also should maintain high versatility and accuracy to ensure that it is applicable to a wider range of protocols [22; 23]. To address these challenges, we propose a new automated network protocol syntax information extraction method, the main idea of the method is to extract detailed network protocol syntax information by parsing the protocol's Wireshark dissector file. The reason for proposing this approach is based on:  
* Wireshark is an open-source protocol analysis tool that supports a wide variety of protocols. From mainstream Internet protocols and emerging Internet of Things (IoT) protocols in IT (Information Technology) fields to Industrial Network protocols in OT (Operation Technology) fields, Wireshark offers wide coverage across various communication domains. * Wireshark plug-ins, in this paper, mainly refer to protocol dissector files written in C language under certain writing specifications. These files usually contain detailed parsing logic of related protocols, based on this parsing logic we can acquire abundant syntax information including field information, data format, and the possible values of a certain field, etc. * We propose a network protocol syntax extraction method based on Wireshark network protocol dissector file. The method includes single-layer protocol field information extraction, multi-layer protocol hierarchical relationship extraction, packet type acquisition based on basic path collection and field dependency judgment. It has high efficiency and can extract more comprehensive syntax information. Besides Byte-level field information, the method can extract Bit-level information for some fields. * We designed a network protocol syntax extraction system based on the method. The system consists of a pre-processing module and a main parsing module. The system can extract the syntax information of a specified protocol together with its associated protocols by only one process. Our code is open source at GitHub platform [[https://github.com/QingJiuYS/Syntax_extraction](https://github.com/QingJiuYS/Syntax_extraction)]([https://github.com/QingJiuYS/Syntax_extraction](https://github.com/QingJiuYS/Syntax_extraction)), accessed on 10 March 2024. The rest of this paper is structured as follows: Section 2 introduces and provides insights into related work. Section 3 elucidates the core components of the proposed method. Section 4 presents a comprehensive exposition of the method's intricate design and systematic implementation. Section 5 presents the experimental results and comparisons with other methods, and Section 6 encapsulates the paper with a succinct conclusion.
## 3 Results and Discussion
### Amplitude Demodulation Analysis
#### 3.3.3 With Two Modulating Frequencies  
The results presented in this section are the ones obtained with the excitations called \"Exc-1-2 kHz-2\", \"Exc-10-11 kHz-2\", and \"Exc-20-21 kHz-2\" in Table 1. With the modulating frequencies of \(f_{1}\) = 22.1 Hz and \(f_{2}\) = 1.4 Hz and the pseudorandom excitation, after an amplitude demodulation analysis, the frequencies expected to be found are the rotating frequency (\(f_{\rm f}\)) for those cases under rotation, as in the previous case commented in Section 3.3.1, and the modulating frequencies \(f_{1}\) = 22.1 Hz and \(f_{2}\) = 1.4 Hz.  
The results obtained for the analysis of the two modulating frequencies and the sensors of the rotating frame are shown in Figure 11. The conclusions here are almost the same than in the case of one modulating frequency: the AE sensor in the shaft (ESH) detects the excitation and not the response of the structure, the accelerometer of the disk (ADISK) detects both modulating frequencies but better in the range of 10-11 kHz, and the accelerometer of the shaft (ASH) is affected by vibrations induced by the motor when water is considered, and it detects the modulating frequencies better at 20-21 kHz. The main difference between one and two modulating frequencies is that near the \(f_{1}\) modulating frequency appear two other frequencies which correspond to \(f_{1}-f_{2}\) and \(f_{1}+f_{2}\), respectively. This has to be considered when analyzing complex systems with more than one modulating frequency, as in the case of a hydraulic turbine.  
The results for the stationary sensors are plotted in Figure 12. Again, the same conclusions than in the case of one modulating frequency can be extracted with this results, but with the difference that the\(f_{2}\) modulating frequency can also be observed as a difference between \(f_{1}\) and the nearby peaks.  
Figure 10: Amplitude demodulation for the sensors on the stationary frame and the excitation with one modulating frequency. Rotating case (\(f_{\rm f}\) = 4.1 Hz).  
The microphone, as well as the AE sensor located in the upper cover, does not obtain the modulating frequencies \(f_{1}\) and \(f_{2}\), but it detects vibrations induced by the motor. This means that the transmission of the acoustic waves through the air is stronger for the motor vibrations than for the disk vibrations.  
The accelerometer located in the upper cover (ATT) again detects similar information than the one installed in the disk (ADISK) when there is water, which confirms than the transmission disk-cover is due mainly to the water. This accelerometer senses the modulating frequencies better in the 10-11 kHz band, which confirms that every kind of accelerometer and every position in the test rig has its own best high-frequency band to detect low-frequency phenomena by means of amplitude demodulation techniques.  
Figure 11: Amplitude demodulation for the sensors on the rotating frame and the excitation with two modulating frequencies. Rotating case (\(f_{\mathrm{f}}\) = 4.1 Hz).
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 2 Empirical Distribution Functions  
The authors of [19] use the concepts of design spaces and model families. A _model family_ is a collection of related neural network models that share some set of high-level architectural design principles. These can range from very high level principles, such as convolutional neural networks versus vision transformer families, to very specific principles, such as which specific layers to use and the relation between their depth, width and input resolution in EfficientNets.  
A _design space_ is a concrete set of architectures that can be instantiated from a model family. A design space consists of two components: a parameterization of a model family, such that specifying a set of model hyperparameters fully defines a network instantiation, and a set of allowable values for each hyperparameter.  
To make a robust analysis of a model family over a wide range of network hyperparameters, [PERSON] et al. [20] introduced a new methodology--comparing model families using empirical distribution estimates. Instead of comparing handpicked variants of a network, they sample from a _design space_ that parameterizes a network family over a wide range of network parameters and train the variants to collect accuracy metrics, which can be compared using empirical distribution functions (EDF). The normalized error EDF of a design space with \(n\) models with errors \(e_{i}\) is given by:  
\[\hat{F}(e)=\frac{1}{n}\sum_{i=1}^{n}w_{i}\mathbf{1}[e_{i}<e] \tag{1}\]  
\(\hat{F}(e)\) gives the fraction of models with error less than \(e\), with normalizing factor \(w\) to control for model complexity.  
While this approach enables comparison of entire model families and has been shown to be a valuable tool to create better design spaces [20], the metric it uses does not capture differences in accuracy/complexity trade-offs between different model families. This makes EDFs a useful tool when designing model architectures around a certain predefined complexity target.