esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application of Train Axle Loads Identification Method
### Train Speed Analysis for Urban Rail Transit  
The train speeds on the simply supported composite beam bridge of Shanghai Metro Line 3 were estimated using signal cross-correlation functions, as detailed in Section 4.1. Taking the 2 nd train (reverse direction) and the 12 th train (forward direction) on April 27 th as examples, after data pre-processing, the strain time history curves and cross-correlation functions of corresponding measurement points on the quarter and mid-span sections of the bridge are illustrated in Figure 9. The cross-correlation function on the horizontal axis ranges symmetrically about zero time, encompassing both positive and negative values, indicating the time difference between the two signals. The time difference \(t_{d}\) corresponding to the maximum cross-correlation value represents the time taken for the train to travel between the quarter and mid-span sections of the bridge. From Figure 10a, it is observed that the time difference \(t_{d}\) is 3.46 s when the cross-correlation function of the strain time history signal reaches its maximum value. Therefore, the average speed of the 2 nd train crossing the bridge is \(v_{t}=L/(4t_{d})=11.68\) km/h. Similarly, according to Figure 10b, the time difference \(t_{d}\) is 1.05 s, and the corresponding cross-correlation function reaches its maximum value. Thus, the average speed of the 12 th train crossing the bridge is \(v_{t}=L/(4t_{d})=38.48\) km/h.  
For other detected trains, the cross-correlation function method was also employed to analyze their average travel speeds. The resulting histogram depicting the travel speed distribution of a total of 68 trains (36 in the forward direction and 32 in the reverse direction) is presented in Figure 10. The speed distribution of trains on Shanghai Metro Line 3 shows a certain degree of dispersion. Both forward and reverse train speeds follow a logarithmic normal distribution, with \(R^{2}\) values of 0.95 for both directions. Specifically, the speed range for forward trains is 8.9 to 82.5 km/h (Figure 10a), whereas reverse trains exhibit speeds ranging from 10.8 to 55 km/h (Figure 10b). The dispersion of speeds among forward trains is relatively large, with a variance of 341.4, likely due to acceleration as trains depart from nearby stations. In contrast, reverse trains show comparably less variance at 136.5, reflecting braking and deceleration as they approach the station. The statistical mean speed for forward trains is 29.2 km/h, while for reverse trains, it is 22.5 km/h. This difference is  
Figure 9: Cross-correlation functions of strain time history of corresponding measurement points on the quarter and mid-span sections of the simply supported composite beam bridge: (**a**) the 2 nd train (reverse direction); (**b**) the 12 th train (forward direction).  
attributed to the braking deceleration stage of reverse trains near the station. Overall, the speed distributions observed for both forward and reverse trains on Shanghai Metro Line 3 are deemed reasonable.
## 6 Conclusions  
In this work, components C and D were used in simulated lightning strike tests. Ultrasonic C-scans and stereomicroscopy were used to evaluate the damage sustained by the specimens during the lightning strike tests. Moreover, the electrothermal coupling theory was adopted to model the different effects of the arc heat and Joule heat. The following conclusions are drawn.  
(1) The damage to the laminate was concentrated around the fasteners. The conduction current flowed through the fastener to all layers and caused damage in each layer. With increasing distance from the fastener, the current density and the arc heat decayed.  
(2) Component D, which had a high current amplitude and a short duration, led to serious surface and internal damage in the CFRP laminate and little damage to the fastener. The damage was mainly caused by the Joule heat generated by the conduction current. Component C, which had a low current amplitude and a long duration, ablated and gasified the fastener and caused less damage to the CFRP laminate. In this process, the arc heating produced by the arc played a leading role.  
(3) The temperature profiles in the conduction current + heat flux model were analogous to the thermal decomposition damage and ablation damage profiles from the C-scan. Therefore, the conduction current + heat flux model is reasonable in FEA of CFRPs with fasteners subjected to lightning strikes. The simulation results show an obvious anisotropy in Component D but not in Component C, because component C has sufficient time for the Joule heat to spread in all directions, whereas component D lacks sufficient time.  
This work evaluated the damage in CFRP laminates with fasteners subjected to lightning current components C and D and found that the damage under different lightning current components presented unique characteristics. Due to the variety of lightning strikes in nature, the damage induced by other lightning current components and multi-components needs further study on the basis of the research results of the single lightning current component.  
Conceptualization, J.C. and Z.F.; data curation, J.C.; formal analysis, J.C., X.B., and J.L.; funding acquisition, Z.F.; investigation, J.C., X.B., and J.L.; methodology, J.C.; project administration, Z.F.; resources, Z.F.; supervision, Z.F.; validation, J.C.; visualization, J.C., X.B., and J.L.; writing--original draft, J.C.; writing--review and editing, Z.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number 2017 YFC1501506.  
The authors declare no conflict of interest.
# Design of Software-Defined Radio-Based Adaptable Packet Communication System for Small Satellites
## 4 Tests and Results  
### Transmitted and Received Signals  
With the help of the \"QT Sink\" block in GNU Radio, the signals within the system can be obtained at different stages and in different scenarios. Figure 11 shows the original signals transmitted from the satellite transceiver. When these transmitted signals are directly fed to the ground station transceiver it outputs the signals shown in Figure 12.  
Figure 10: Adding white noise to the signal.  
The plots resulting from different noise levels being added to the transmitted signals are shown in Figure 13 and the received signals are in Figure 14 below.  
Figure 11: (**a–c**) The transmitted signals of 2400 bps, 4800 bps and 9600 bps, respectively. No noise added.  
Figure 12: (**a–c**) The received signals of 2400 bps, 4800 bps and 9600 bps, respectively. The transmission channels are noiseless.  
Figure 13: _Cont._  
Figure 13: (**a–d**) Comparison of the signals transmitted in the 2400 bps GMSK mode with different SNR values.(**a**) SNR = \(-3\), (**b**) SNR = 5, (**c**) SNR = 9, (**d**) SNR = 14. (**e–h**) Comparison of the signals transmitted in the 4800 bps GMSK mode with different SNR values. (**e**) SNR = \(-3\), (**f**) SNR = 5, (**g**) SNR = 9, (**h**) SNR = 14. (**i–l**) Comparison of the signals transmitted in the 9600 bps GMSK mode with different SNR values. (**i**) SNR = \(-3\), (**j**) SNR = 5, (**k**) SNR = 9,(**l**) SNR = 14.  
Figure 14: (**a–d**) Comparison of signals received in the 2400 bps GMSK mode with different SNR values. (**a**) SNR \(=-3\), (**b**) SNR \(=5\), (**c**) SNR \(=9\), (**d**) SNR \(=14\). (**e–h**) Comparison of the signals received in the 4800 bps GMSK mode with different SNR values. (**e**) SNR \(=-3\), (**f**) SNR \(=5\), (**g**) SNR \(=9\), (**h**) SNR \(=14\). (**i–l**) Comparison of the signals received in the 9600 bps GMSK mode with different SNR values. (**i**) SNR \(=-3\), (**j**) SNR \(=5\), (**k**) SNR \(=9\), (**l**) SNR \(=14\).
## 2 Theoretical Background of the Uncertainty Theory
### Uncertainty Theory: Measure, Variable, and Distribution
#### 2.1.1 Uncertainty Measures  
Uncertainty measures have been applied in a variety of SCM contexts, such as the quantification of uncertainty in lead time, demand forecasting, and supplier reliability.  
Recent research has shown that these techniques can be used to develop more flexible supply chain strategies by incorporating uncertainty into inventory, purchasing, and logistics planning [6; 16]. An uncertainty measure is a set function \(\mathcal{M}\) on some sample space that assigns numbers from 0 to 1 and satisfies the axioms of normality, duality, sub-additivity, and product measure, providing a precise analysis regarding the possibility of something uncertain.  
For instance, in the context of supply chains, uncertainty quantifies the likelihood of late deliveries, demand fluctuations, etc. An uncertainty measure \(\mathcal{M}\) on a sigma-algebra \(\mathcal{F}\) over the set \(T\) is defined mathematically by the following equation:  
* **Normality** : \(\mathcal{M}(T)=1\)
* **Duality**: \(\mathcal{M}(A)+\mathcal{M}(A^{c})=1\) for any \(A\subseteq T\)
* **Sub-additivity**: \(\mathcal{M}(\bigcup_{i=1}^{\infty}A_{i})\leq\sum_{i=1}^{\infty}\mathcal{M}(A_{ i})\)
* **Product Measure**: \(\mathcal{M}(A\cap B)=\mathcal{M}(A)\cdot\mathcal{M}(B)\) for independent events \(A\) and \(B\)
## 2 An Overview of Recent Advances in Design Optimization of Electromagnetic Devices  
Design optimization of electromagnetic devices has been an active research topic for many years. Many design optimization methods have been developed through extensive research work worldwide. To compare the performance of different methods, some benchmark works have been developed in International Compumag Society (ICS), like TEAM problems [6; 11; 12; 16; 17; 18]. Some papers reviewed popular design optimization methods of several types of electromagnetic devices, such as electrical machines [4; 5; 6; 13; 14], and antennas [7]. This section presents an overview of the recent advances in design optimization of electromagnetic devices, including multi-objective, multidisciplinary, multilevel, topology, and robust optimization methods. Section 2.1 starts with the deterministic design optimization (without any consideration of uncertainties).
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 2 First Order Second Moment (FOSM)  
The limit state function of slopes may be established as follows:  
\[Z=\mathrm{g}(X_{1},X_{1},\ldots,X_{n})=F(X_{1},X_{1},\ldots,X_{n})-1 \tag{1}\]  
where \(n\) is the number of random variables; \(X_{i}\) (\(i=1,\,2,\,\ldots,\,n\)) are the random variables in the slope reliability analysis; \(\mathrm{g}(X_{1},X_{2},X_{3},\ldots,X_{n})\) is the limit state function; \(Z>0\) indicatesthat the slope is stable, Z \(<\) 0 indicates that it has failed, and Z = 0 means that the boundary is hovering between stable and unstable. \(F(X_{1}\), \(X_{2}\), \(X_{3}\),..., \(X_{n})\) is the safety factor of slope.  
The mean and variance of the limit state function using the first-order approximation can be calculated using  
\[\mu_{z}=\mathrm{g}(\mu_{X1},\mu_{X2},\ldots,\mu_{Xn}) \tag{2}\]  
\[\sigma_{z}^{2}=\sum_{i=1}^{n}\sum_{j=1}^{n}\frac{\partial\mathrm{g}}{\partial X _{i}}\frac{\partial\mathrm{g}}{\partial X_{j}}cov\big{(}X_{i},X_{j}\big{)} \tag{3}\]  
where \(cov(X_{i}\), \(X_{j})\) is the covariance of \(X_{i}\) and \(X_{j}\), \(\mu_{z}\) is the mean value of the limit state function Z, and \(\sigma^{2}z\) is the variance of Z (\(\sigma_{z}\) is the standard deviation of Z). Consequently, the reliability index in slope safety analysis was evaluated as follows based on FOSM:  
\[\beta=\frac{\mu_{z}}{\sigma_{z}} \tag{4}\]  
where \(\beta\) is the reliability index in slope stability analysis. The key step is to calculate the statistical moments of the limit state function, i.e., the mean value \(\mu_{z}\) and the standard deviation \(\sigma_{z}\). In the calculation of \(\mu_{z}\) and \(\sigma_{z}\), the values of the safety factor of slope and the corresponding first-order partial derivatives are needed according to Equations (2) and (3). However, it is difficult to obtain it in the practical slope engineering. Based on the reliability index, the probability of failure can be evaluated from  
\[p_{f}\approx 1-\Phi(\beta) \tag{5}\]  
where \(\Phi\) is the cumulative distribution function of the standard normal variable.  
For most stability analysis methods for slopes, the factor of safety will be calculated using a numerical method and cannot be represented as an explicit function of the random variables. It is difficult to calculate the partial derivatives of the limit state function in slope reliability analysis. However, the partial derivatives are indispensable for the FOSM method in reliability analysis. To overcome the above difficulty, MDRM, which avoids determining the limit state function and their partial derivatives, was adopted to determine the moment of the slope safety factor. Once the first and second moment of the limit state function was determined, FOSM could be utilized to evaluate the slope reliability based on Equation (4) using MDRM.
## 3 Impacts of COVID-19 on EVs Charging  
### Dataset and Preprocessing  
The adaptive charging network (ACN) dataset containing more than 30,000 charging sessions [28] was used for this study. The dataset contains charging records from two charging spaces in Caltech University campus, California. The Jet Propulsion Laboratory (JPL) charging facility is only accessible to employees and therefore, only the data from Caltech, which is open to the public, was used. The dataset contains logged charging events as well as user-related attributes. The charging events include the arrival time, departure time, and energy consumption. Meanwhile, the user-related data consist of users' energy requests and their estimated departure times. For this study, data containing the charging events from 1 February 2020, until the end of July 2021 were considered. This enables the analysis of trends in charging behavior throughout the pandemic. Since user-related fields were not used in this study, the dataset did not contain any missing values. Overall, a total of 3787 records were obtained from the Caltech site for the specified period of time. The preprocessing steps involved converting the arrival and departure time values to a suitable numeric format. This was achieved by dividing the minute by 60 and adding to the hour, such that 10:17 was converted to 10:28, for example. Moreover, using the datetime method from the _pandas_[29] library in python, the length of charging sessions was obtained by subtracting the arrival time from the departure time.
## 2 Related Work
### On-Body Communication
#### 2.1.2 Electrostatic Coupling Type  
In an early pioneer study, [PERSON] _et al._ proposed the notion of PAN based on electrostatic coupling [1,7]. This type of near-field electrostatic coupling uses the human body to radiate an electric field [1,7,8]. In a previous study [7], an entire human body that emits low-frequency energy is technically defined as \"the human transmitter.\" The remote receiver device measures the signal from the transmitter wirelessly in the form of displacement currents. Such capacitive coupling between devices through the human body has a strong advantage in terms of encumbrance in that it does not require any devices to be physically wired for communication. Taking advantage of the jamming avoidance mechanism used by some species of weakly electric fish [9] in which transmission frequency shifts autonomously such that each fish uses a unique frequency [7], they further proposed the concept of multiple PANs that can operate together even in the same sensing space. An early work extended the concept of electrostatic coupling to a system that localizes the multitouch position [10]. In their system called _DiamondTouch_, target signals are transmitted through capacitive coupling between electrodes and human body parts. For example, signals from the antenna underneath the insulating layer of a display screen are transmitted to the finger via capacitive coupling. At the receiver side, which shares the electrical ground reference with the transmitter, the user is also capacitively coupled to the receiver antenna embedded in the surroundings (e.g., a chair). This coupling completes the proposed capacitively coupled circuit by sending the received signal back to the system.  
As described, the approaches based on electrostatic coupling have advantages in that a human can be a part of the electrical circuit without any physical wires. However, one major drawback of this approach is that its communication quality is dependent on the surroundings, such as metals and human bodies, as pointed out in [6].
## 5 Experimental Settings
### Ablation Study  
To validate the impact of the multi-head self-attention mechanism on prototype network enhancement and the effectiveness of the contrastive-center loss function, a thorough set of ablation experiments was conducted on the FewRel 1.0 dataset. These experiments covered both the 5-way-1-shot and 10-way-1-shot scenarios. The experimental outcomes are presented in Table 5. In these experiments, the authors of this paper designed several experimental variants. Here, 'w/o modification prototype' indicates that only the basic prototype network was used without introducing the multi-head self-attention mechanism to enhance the prototype network. In addition, 'w/o Contrastive-center loss' represents experiments using the loss function opposite to the contrastive-center loss.  
The multi-head self-attention mechanism and the contrastive-center loss function, as evidenced by the data in Table 5, play crucial roles in improving model accuracy. In both task settings, the simultaneous removal of both the multi-head self-attention mechanism and the contrastive-center loss function resulted in a substantial decline in the model's performance. This further validates the effectiveness of SACT. Specifically, substituting the initial prototype network with the prototype network enhanced by the multi-head self-attention mechanism resulted in a reduction in model accuracy by 1.59% and 6.81% in the 5-way-1-shot and 10-way-1-shot scenarios, respectively. The importance of the multi-head self-attention mechanism is clearly demonstrated. Furthermore, replacing the contrastive-center loss function with the cross-entropy (CE) loss function results in a discernible decrease in model accuracy by 1.62% and 6.41%, respectively, under identical conditions. This further substantiates the efficacy of the contrast-center loss function.  
Furthermore, compared to the 5-way-1-shot setting, the decline in model accuracy was more pronounced in the 10-way-1-shot setting, as further observation reveals. This may be  
Figure 12: Comparison of SACT with other models on the FewRel 2.0 dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **5-Way-1-Shot** & **10-Way-1-Shot** \\ \hline SACT & 96.48 & 93.88 \\ w/o modification prototype & 94.89 & 87.07 \\ w/o Contractive-center loss & 94.86 & 87.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The outcomes of the ablation analysis for SACT.  
attributed to the fact that the 10-way-1-shot task setting involves a greater variety of tasks, providing the model with a broader range of task samples. The importance of the multi-head self-attention mechanism and contrastive-center loss function is further validated by this observation. These mechanisms empower the model to adapt and learn different types of tasks, effectively capturing both the commonalities and differences among them.
# Authentication by Keystroke Dynamics: The Influence of Typing Language
## 3 Methodology
### Classification Models
#### 3.2.2 Classifiers  
We implemented four classifiers to evaluate our datasets. As the purpose of this work is to study language as a factor affecting keystroke dynamics, we use four well-known classifiers: AdaBoost, decision tree (DT), random forest (RF), and support vector machine (SVM). These classifiers were carefully selected to present a consolidated view of the dataset. AdaBoost is considered an excellent algorithm that is scalable and can easily deal with missing values while avoiding overfitting. On the other hand, AdaBoost is sensitive to outliers and not interpretable. Decision trees are easy to use and do not require normalisation or scaling of the data. They also provide explainable results, and are insensitive to outliers; however, they are prone to overfitting and usually have worse prediction results compared to other machine learning algorithms. Random forests are also robust to outliers, and have a lower risk of overfitting while maintaining good prediction results. Unfortunately, they are very slow to train and can be sensitive to categorical features. SVMs work well with smaller datasets, such as ours, by defining the boundary between the classes. They are robust to noise and can generalise well, as well as avoiding overfitting. They are, however, expensive to train and their results cannot be interpreted. To choose the best parameters for each classifier, we employed the grid search algorithm to find the best parameters. For each user, we trained two instances of the classifier: one for Arabic, and one for English. The total number of classifiers trained for the password dataset was \(4\times 2\times 50=400\), and for the phrase dataset, a total of \(4\times 2\times 35=280\) classifiers were trained. Each classifier was tested on its corresponding testing data, as well as the test set data from the other language: 800 tests were performed on the password dataset, and 560 tests were performed on the phrase dataset.  
For example, assume we are training an SVM for user-1, who entered password data in English and in Arabic. We train and test as follows:  
1. SVM-Arabic-user-1: Trained on Arabic, tested on Arabic and English.
2. SVM-English-user-1: Trained on English, tested on Arabic and English.  
If the keystroke dynamics of a user are indistinguishable across languages, i.e., a language has no effect on typing patterns, the results from 1 and 2 above should be similar. If, however, a language does affect the keystroke dynamics, then the classifiers should not be able to distinguish the same user typing in a different language. In the next section, we describe the performance measures used to test our dataset, and present the results of the four classifiers selected for the experiments, along with a discussion of those results. In addition, we test the effects of bigram frequencies to classify users.
## 3 Theory and Methods
### Principles of Evaluation of the Model  
To assess the performance of the prediction model for water quality parameters in this paper, and to quantitatively analyze the deviation between the predicted values and the actual values of the obtained water quality parameters, the model evaluation indexes utilized in this paper included the RMSE and MAPE, and the formulas for the mentioned evaluation indicators are presented as the following:  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\frac{{{{\left|{{\widetilde{y}_{i} }-{y_{i}}}\right|}}}}{{{y_{i}}}}}}^{2}} \tag{23}\]  
\[MAPE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\frac{{{{\left|{{\widetilde{y}_{i}}-{y_ {i}}}}\right|}}}{{{y_{i}}}}}\times 100\%} \tag{24}\]  
where \(n\) denotes the number of sample points at which the data were collected, \(y_{i}\) denotes the original actual value of the DO data, and \(\widetilde{y}_{i}\) denotes the predicted value of the DO data.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 2 Principles
### Beam Steering Based on WGAs  
Nearly all the proposed 2D OPAs till now depend on WGAs. Not all the 2D OPAs' far-field patterns are completely formed by phase differences. There are some other steering approaches.