esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 4 Our Proposed Mechanism
### Initialization Method Based on Blockchain Cooperative Authentication  
The initialization phase is performed by the domain manager of each domain and its purpose is mainly to generate the necessary parameters needed by the whole system. Elliptic curve cryptography (ECC) is a public key encryption algorithm based on elliptic curves. Its essence is to achieve encryption using the discrete logarithm problem. The main advantage of ECC is to provide faster performance and higher levels of security while using smaller keys. Therefore, we will use the elliptic curve cryptography algorithm to generate our keys. Let us take domain A as an example; the initialization method based on blockchain cooperative authentication is shown below.  
First, \(M_{A}\) picks an elliptic curve \(Ep(a,b)\), where \(p\) is a large prime and \(a,b\in Z_{p}\). Then, \(M_{A}\) picks a base point \(G\) on the elliptic curve. \(M_{A}\) then picks a random number \(r_{A}\longleftarrow Z_{p}\) in \(Z_{p}\) as its private key \(sk_{A}\), which is kept strictly secret by \(M_{A}\), and computes the public key \(pk_{A}=sk_{A}.G\). Next, \(M_{A}\) chooses a hash function \(h_{0}\), which can be \(SHA-1\) or a more secure version. Then, \(M_{A}\) signs the above parameters \(\{p,a,b,G,h_{0}\}\) and publishes them to the blockchain proxy node \(P_{A}\), which then publishes them to the federated chain.  
The joining process for other domains is similar to domain A and will not be repeated here.
# A Survey on Machine-Learning Based Security Design for Cyber-Physical Systems
## 4 ML-Based Cyber-Physical Attack Detection
### Physical System Layer
An anomaly detection module to detect cyber-physical attacks targeting a wide-area damping control system is proposed in [110]. The proposed anomaly detection strategy adopts various supervised learning algorithms, such as the SVM, the decision tree, k-nearest neighbors (KNN) and a neural network, with complete consideration of sensor attacks, controller attacks, and combined attacks. The performance of the anomaly detection module is evaluated in a hardware-in-the-loop testbed environment for two-area, four-machine power systems, resulting in a real-time attack detection with more than 96% accuracy. In [111], an anomaly detection method based on density ratio estimation (DRE) is proposed to detect a stealthy sensor attack targeting an AC microgrid. The proposed ML model with DRE does not require a dataset for abnormal behaviors, unlike other ML-based anomaly detection methods that require massive numbers of attack signals for detection. In simulations, the proposed DRE-based detection method shows a performance that is superior to conventional model-based anomaly detection methods and existing SVM-based detection methods. A covert attack detection method on a smart grid is proposed in [112], which utilizes an SVM model to learn the decision boundary between benign data and a covert attack. To improve the covert attack detection performance and to reduce computational complexity, the authors in [112] propose a genetic algorithm-based feature selection strategy. Compared with a conventional ML technique, such as multi-layer perceptron (MLP), naive Bayesian, and KNN methods, the proposed detection method with a feature selection strategy shows superior performance in terms of its accuracy and F1-score. A classification method for actual faults due to external disturbances and cyber-physical attacks in a large-scale smart grid is proposed in [113], which utilizes an unsupervised dynamic Bayesian network (DBN) model with time-series energy information. A symbolic dynamic filtering technique is adopted to extract features from collected information, which reduces computing resources and discovers interaction relationships between subsystems in large and complex power systems. For a sensor attack, the proposed methods are evaluated in simulations, where the performance of the proposed method achieves an accuracy of 98% and a false-positive detection rate of less than 2%. We briefly review the ML-based attack detection methods against physical system layer attacks. Table 1 summarizes the existing ML-based detection methods.
## 2 Related Works
Its focal point is the step before ML and DL training with specific attention to data quality and similarity to the original data that are scarce and difficult to acquire in the domain of cybersecurity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Data** & **Result** & \begin{tabular}{c} **Multiple GAN** \\ **Architectures** \\ \end{tabular} & **ML Classifiers** & **GAN Layers** \\ \hline \multirow{4}{*}{[PERSON] et al. [15]} & \multirow{4}{*}{IoT botnet attacks} & GANs can preserve continuous and discrete data structures, alleviating data imbalance & Yes & Yes & N/A \\ \cline{1-1} \cline{3-5}  & & \begin{tabular}{c} Benign class data \\ augmentation using \\ GANs helped ML \\ solutions achieve an \\ accuracy of around 91\% \\ \end{tabular} & No & No & 5 \\ \hline \multirow{4}{*}{[PERSON] et al. [17]} & \multirow{4}{*}{Botnet attacks} & Improved rate detection when ML classifiers were trained with a balanced synthetically generated dataset & No & Yes & 3 \\ \cline{1-1} \cline{3-5}  & & \begin{tabular}{c} Improved detection rate \\ both for ML classifiers and ELLA algorithm \\ when trained with a balanced synthetically generated dataset \\ \end{tabular} & No & No & N/A \\ \hline \multirow{4}{*}{[PERSON] et al. [18]} & \multirow{4}{*}{Botnet attacks} & Improved detection rate both for ML classifiers and ELLA algorithm when trained with a balanced synthetically generated dataset & No & Yes & N/A \\ \cline{1-1} \cline{3-5}  & & \
\begin{tabular}{c} SGAN outperformed \\ other ML methods both \\ in accuracy and in \\ computational efficiency \\ \end{tabular} & No & Yes & N/A \\ \hline \multirow{4}{*}{[PERSON] and [PERSON] [21]} & \multirow{4}{*}{Botnet attacks} & Tackle imbalanced dataset and enhance performance SFBAM by achieving F1-score of 96\% & No & Yes & 10G/4D \\ \cline{1-1} \cline{3-5}  & &
\begin{tabular}{c} Tackle imbalanced \\ dataset problem to \\ enhance ML classifier problem \\ \end{tabular} & Yes & Yes & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._As can be inferred from the related works presented in this section, the need for data augmentation is of utmost importance in the cybersecurity domain. Thus, this study is mainly focused on data generation for dataset augmentation concerning botnet attacks. Through the methodology presented in the next section and the results provided in Section 4, different GAN models are evaluated using quality metrics in order to explore the generated data quality.
## 6 Conclusions  
The strain behavior of a buried long pipeline under an impact load was obtained through comparison among theoretical results (THE), experimental results, and FEA results. The following conclusions can be drawn:  
1. For the buried pipeline under an impact load, a generally good agreement was achieved among the FEA results, the EXP results, and THE results in terms of the peak impact force (\(F_{\rm max}\)) on soil and the peak strain (\(\varepsilon_{\rm max}\)) on pipelines, which indicates that the mathematical formula based on the mechanical model of a buried pipeline and the finite element models are accurate for the prediction of pipeline response under the impact load.
2. The peak longitudinal strain (\(\varepsilon_{\rm L}\)) was much larger than the peak transversal strain (\(\varepsilon_{\rm T}\)); thus, longitudinal bending was the main source of deformation for specimens under the impact load.
3. Under the same impact height (\(H\)) of the drop hammer, the diameter (\(D\)) had an obvious influence on \(\varepsilon_{\rm max}\), while the wall thickness (\(T\)) had a subtle influence on \(\varepsilon_{\rm max}\). Furthermore, \(\varepsilon_{\rm max}\) decreased with the increase of \(T\), while it increased with the increase of \(D\) and \(H\), which is similar to the distribution of the high-strain area of the pipeline in the experimental results and FEA results. This means that a small diameter and a thick wall thickness are beneficial for the pipeline to resist the impact load.
4. The \(\varepsilon_{\rm max}\) decreased rapidly with the increase of the buried depth (\(d\)) of the pipeline under the same \(D\) and \(T\), which indicates that the pipelines cannot be buried shallowly. According to the extension of THE and FEA results, a buried depth of the pipeline from 0.5 m to 0.6 m was more reasonable for the pipeline designed in this paper.  
Figure 21: The influence of burial depth (\(d\)) on strain distribution: (**a**) \(d=0.3\) m; (**b**) \(d=0.6\) m; (**c**) \(d=0.9\) m.
## 5 Discussion  
The important findings in this study are: (1) the optical comparator designed for a \"00 00 00 00\" code, which consists of a successive four QPSK symbols, yielded an optical symbol whose constellation was located in conformity with a Hamming distance based on the \"00 00 00 00\" code. (2) Three additional comparators were designed, whose code words were \"11 00 11 00\", \"11 10 10 00\", and \"00 10 01 00\", and same results were obtained. Therefore, it is concluded that the proposed comparator can be designed for an arbitrary 8-bit code, and it offers the comparison result based on the designed code.  
Additionally, the author will discuss the feasibility from a point of a practical view: (i) high-order modulation, (ii) code length, and (iii) nonlinear effects. According to the operating principles, the comparator can be utilized for rotationally-symmetric modulations, such as 8-PSK and 16-PSK, since all constellations in the modulations are allowed to exchange each position by phase shift. Similarly, if it is assumed that the additional DLIs are further cascaded, the length of code word can be expanded to an arbitrary bit length. Furthermore, by using an optical compensator for nonlinear effects, it is considered that the comparator can minimize the effect of nonlinear phase noise. Considering the points mentioned above, it is concluded that the proposed comparator can provide highly extensible operation for actual communication systems.  
This research was supported by The Asahi Glass Foundation and The Telecommunications Advancement Foundation.  
The author would like to thank [PERSON] of Tokyo Institute of Technology for assistance with the experimental installation and helpful discussions.  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} OOK & On-Off Keying \\ PSK & Phase-Shift Keying \\ QPSK & Quadrature Phase-Shift Keying \\ DLI & Delay Line Interferometer \\ TLD & Tunable Laser Diode \\ PPG & Pulse Pattern Generator \\ LN-MZI & Lithium-Niobate Mach-Zehnder Modulator \\ PRBS & Pseudo Random Binary Sequence \\ RZ & Rreturn-to-Zero \\ FSR & Free Spectrum Range \\ BPD & Balanced Photo Detector \\ FWHM & Full Width at Half Maximum \\ ASE & Amplified Spontaneous Emission \\ \end{tabular}
# Power Battery Scheduling Optimization Based on Double DQN Algorithm with Constraints
## 5 Experimental Evaluation and Discussion
### Adaptive Capability of the Algorithm  
One of the significant challenges in the development of battery management systems is the adaptability of the controller to changes in battery cycling behavior. The proposed deep RL method demonstrates the advantages of the automatic interactive learning of optimal strategies [19]. Specifically, it can adapt to changing input data. We analyze the adaptability of RL by comparing the cycle life between various scheduling algorithms and the proposed PCA\(-\)DDQN under different battery datasets. The proposed method in this paper gradually converges with continuous iterations of network parameters. The results of the number of cycles in the adaptive testing study are depicted in Figure 12. The change in the battery chemistry affects the electrochemical modeling.  
We download the dataset mentioned in [PERSON]'s article [24] from the Zendo data platform, which contains three commercial 18,650 batteries with different chemical compositions, namely NCA batteries, NCM batteries, and NCM+NCA batteries. The batteries are cycled in the temperature\(-\)controlled chamber at different charging current rates. The selected temperatures are 25 \({}^{\circ}\)C, 35 \({}^{\circ}\)C, and 45 \({}^{\circ}\)C, and the current rates range from 0.25 C to 4 C. Data satisfying the temperature constraints proposed in this paper are selected to verify the generalizability of the proposed method for evaluation.  
The experimental results indicate that, in Figure 12, the traditional heuristic scheduling strategy has good and bad cycle life counts in batteries with different chemical compositions. The heuristic scheduling algorithm has no significant advantage in the cycling process of more complex chemical composition batteries. The traditional heuristic scheduling strategy and PCA\(-\)DDQN have little difference in SOC in the first 100 cycles because the battery cells are still in a fresh state, and the chemical reaction is not apparent at this time. After a certain number of cycles, the battery quickly reaches the parameters limit due to aging, and the heuristic scheduling algorithm fluctuates significantly with the inherent physical  
Figure 11: (**a**) SOC variation curve under different algorithm comparison; (**b**) variation curve of \(\Delta\)SOC under different algorithm comparison.  
degradation of the battery model, while PCA\(-\)DDQN can update the action sequence of the selected agent from the reward (punishment) and performs better than the heuristic scheduling strategy. Thus, PCA\(-\)DDQN shows a clear advantage in achieving adaptive scheduling optimization with different battery types.
## 3 Results and Discussion
### Evaluation of Direct Indexes
#### 3.3.1 Analysis of Form Indexes  
The measured form indexes, ratio of equivalent ellipse axis \(E\) and rectangularity \(R\), of the selected 9 kinds of FA are shown in Table 5. It can be seen that compared to stone chips and machine-made sand, the rectangularity \(R\) of natural sand (FA1) is closer to \(\pi/4\). This means that the natural sand is rounder than stone chips and machine-made sand. However, no significant differences were observed in terms of the ratio of equivalent ellipse axis \(E\) among the three kinds of FAs. The one-way ANOVA analysis was also conducted to analyze the effect of FA type on ratio of equivalent ellipse axis \(E\) and rectangularity \(R\). The calculated \(F\) value for ratio of equivalent ellipse axis \(E\) and rectangularity \(R\) were shown in Table 6. It shows that the calculated \(F\) value for ratio of equivalent ellipse axis \(E\) is greater while that of the rectangularity \(R\) is smaller than the significance level \(F_{0.05}=5.59\). This means that the shape index ratio of equivalent ellipse axis \(E\) can effectively distinguish the geometric characteristic difference of different FAs but rectangularity \(R\) fails under the significance level of 95%.
## 3 Numerical Results
### Probabilistic Analysis of the Reference Case
#### 3.3.2 Variation of the Two Qols  
In the SS-based reliability analyses, a relatively large sample size (i.e., 5000) is considered for each subset, which allows building a distribution of the concerned QoI using the 5000 deterministic results of the first subset as its samples are generated over the entire input space.  
As an illustration, Figure 6 presents the probability density function (PDF) of both QoIs at three positions in the support elements. For the volumetric deformation in the outer flexible liner, the distribution moves to higher values and tends to be more spread out from  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Parameter** & **Symbol** & **Unit** & **Distribution** & **Mean** & **CoV** \\ \hline Vertical stress & \(P_{0}\) & MPa & Lognormal & 6.8 & 15\% \\ Anisotropic coefficient & \(\lambda\) & / & Lognormal & 0.66 & 15\% \\ Viscosity of Kelvin & \(\eta_{K}\) & GPa.s & Lognormal & \(2.07\times 10^{7}\) & 15\% \\ Viscosity of Maxwell & \(\eta_{M}\) & GPa.s & Lognormal & \(4.14\times 10^{9}\) & 15\% \\ Shear modulus of Kelvin & \(G_{K}\) & GPa & Lognormal & 0.35 & 15\% \\ Shear modulus of Maxwell & \(G_{M}\) & GPa & Lognormal & 3.45 & 15\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Uncertainty modelling of the Burger and initial-stress parameters.  
Figure 5: Failure probability of the two liners at different inclined angles with respect to the springline.  
the springline (0\({}^{\circ}\)) to the crown (90\({}^{\circ}\)), represented by the higher mean value as well as the higher variation. Regarding the equivalent stress in the concrete liner, an inverse trend can be stated when the distribution spread out from the crown to the springline. The results can help to explain statistically the \(Pf\) trends observed in Figure 5. Additionally, further exploiting the 5000 samples allows to obtain more statistics' estimates of the QoIs such as the mean, standard deviation, and confidence bounds [34] as shown in Table 3.
## 1 Introduction  
The 6082 Al alloy belongs to the Al-Mg-Si series of alloys, which can be strengthened by heat treatment and it has medium strength. Because of its good physical and chemical properties (e.g., low density, high strength, good weldability, and corrosion resistance), it is widely used in airplanes, other forms of transportation, the chemical industry, architecture, and machinery manufacturing [1; 2; 3; 4].  
With the increasing emphasis on lightweighting automobiles and rail transit in recent years, an increasing number of aluminum alloys have been used in the field of automobiles, rail transit, aerospace, and other fields [5; 6]. In the automotive industry around the world, a wide range of aluminum-alloy models have been optimized [7; 8; 9; 10]. A large number of aluminum alloys are used in structures, such as section beams, end bumpers, bases, sills, side-member frames, and frame bolsters [11; 12; 13], and these structures also have a number of T-joints present. These play a vital role in their entire structure. The preferred welding process for the 6082 Al alloy is normally metal inert gas welding (MIG) due to its comparatively easier applicability and lower cost [14]. Due to the influence of the welding thermal cycle, the mechanical properties of welded joints greatly change. Observationssuggest that, in series 6000 aluminum alloys, the heat-affected zone is the weakest region, even after natural aging happens. This softening effect can lead to the remarkable reduction of proof strength in the heat-affected zone in the range of 30-50%, and this region typically extends 10-30 mm from the center of the weld [15]. It is reported that, during the MIG welding process, an arc is maintained between a continuous solid-wire electrode and the work piece, thereby causing a current flow that generates thermal energy in the partially ionized inert gas. The arc and the melt pool are shielded from atmospheric contamination by an inert or active gas. The typical microstructure of the fusion zone of an MIG joint contains a dendrite structure with wider arm spacing [16]. In previous work, the mechanical properties and fatigue strength of MIG butt welds with and without reinforcement as well as MIG T-filtel welded joints, both performed on 6082-T651 alloy plates, were analysed. It was observed that the friction stir processing significantly increases the fatigue strength of MIG welds. This behavior was mainly attributed to the geometry modification in the weld toe, reduction of weld defects, and grain refinement of the microstructure [17; 18].  
However, the T-joint is prone to incomplete penetration during welding. At present, there is no complete study on the causes of the occurrence of pores when the T-veld joint is not welded and fatigue performance when the weld is not penetrated [19]. Therefore, research on unwelded aluminum-alloy T-veld joints was carried out to conclude that the light weight and high speed of rail transit in high-speed trains are of great significance.
## 3 Materials and Methods
### HPLC-DAD-MS Analysis Of Phenols  
Samples extraction was made according to [29]. Five grams of oil containing a fixed aliquot of IS (syringic acid = 291 mg/L in MeOH) were dissolved in 5 mL of hexane and then extracted with 5 mL of a methanol: water solution (60:40, _v/v_) five times. Afterwards, 10 mL of hexane were added to the methanolic extracted solution, vortexed, and centrifuged for 5 min. at 5000 rpm. The methanol solution was collected and evaporated to dryness under vacuum. The extract was reconstituted with 2.5 mL of HPLC-grade methanol and then filtered through a 0.22 um mPTFE filter before HPLC-DAD-MS analysis.  
HPLC-DAD-ESI/MS studies were performed while using an Alliance 2695 HPLC with a diode-array detector (DAD 2996) and a mass spectrometer detector qDa MS, Waters (Milford, MA, USA). The separation was achieved on a Synergi Polar reverse phase (RP) (250 \(\times\) 4.6 mm, 4 um) analytical column from Phenomenex (Chesire, UK). The mobile phase for HPLC-DAD-ESI/MS analyzes was water with 0.1% formic acid (A) and methanol/isopropanol solution (90:10 _v/v_) with 0.1% formic acid (B) working in the gradient mode at a flow rate of 1 mL min\(-\)1. The solvent composition varied, as follows: 0 min., 30% B; 0-40 min., 60% B; 40-53 min. 95% B; 53.1-60 min. 30% B; then, the column was reconditioned. The column temperature was set at 35 \({}^{\circ}\)C and the injection volume was 10 \(\upmu\)L [29]. Representative chromatograms are reported in Figure S3.  
Phenols were quantified based on calibration curves of standards when available, while others were expressed as equivalents: 3,4-DHPEA-EDA-1 (as OH-tyrosol), 3,4-DHPEA-EDA-2 (as OH-tyrosol), 3,4-DHPEA-EA (as OH-tyrosol), _p_-HPEA-EDA (as tyrosol), _p_-HPEA-EA (as tyrosol), and acetoxypinoresinol (as pinoresinol).
# Controlled Fault Current Interruption Scheme for Improved Fault Prediction Accuracy
## 2 Short-Circuit Current Prediction Algorithm  
### Short-Circuit Current Model  
The short-circuit situation in a high-voltage power system can be analyzed equivalently using a single-phase short-circuit fault model, as shown in Figure 1.  
In the figure, CB is the circuit breaker; \(u(t)\) is the voltage of the ideal voltage source; \(U_{\text{pk}}\) is the peak voltage; \(\omega\) is the angular frequency; \(\alpha\) is the phase angle of the voltage at the moment of short circuit; \(j\omega L_{S}+R_{S}\) is the fault impedance of the power supply side; \(j\omega L_{\text{L}}+R_{\text{L}}\) is the fault impedance of the load side; \(i_{s}(t)\) is the short-circuit current of the supply side; \(i_{\text{L}}(t)\) is the short-circuit current of the load side; and \(i_{\text{F}}(t)\) is the short-circuit current to ground.  
If a short-circuit occurs at the moment \(t=0\), by applying both Kirchhoff's law and the Rasch transform, the time-domain expression for the short-circuit current on the power supply side can be found as follows [19; 20]:  
\[i_{s}(t)=X_{1}\cdot\sin(\omega t+X_{2})+X_{3}\cdot\text{e}^{-\frac{t}{X_{4}}} \tag{1}\]  
Among  
\[\left\{\begin{array}{l}X_{1}=I_{\text{pk}}\\ X_{2}=\alpha-\phi\\ X_{3}=i(0)-I_{\text{pk}}\cdot\sin(\alpha-\phi)\\ X_{4}=\tau\end{array}\right. \tag{2}\]  
Equation (1) can be viewed as a superposition of the periodic component \(I_{\text{pk}}\cdot\sin(\omega t+\alpha-\phi)\) and the non-periodic component \(\left[i(0)-I_{\text{pk}}\cdot\sin(\alpha-\phi)\right]\cdot\text{e}^{-t/\tau}\), where \(i(0)\) is the current value at the initial moment of the fault, \(\tau\) is the time constant of the faulted circuit, and \(\phi\) is the voltage-current phase difference.  
If the effect of the third and fifth harmonics in the grid is considered, the short-circuit current coefficient expression is as follows:  
\[\begin{array}{ll}i(t)&=X_{1}\cdot\sin(\omega t+X_{2})+X_{3}\cdot\text{e}^{- \frac{t}{X_{4}}}\\ &+X_{5}\cdot\sin(3\omega t+X_{6})\\ &+X_{7}\cdot\sin(5\omega t+X_{8})\end{array} \tag{3}\]  
Figure 1: Single-phase short-circuit model.