esa-sceva/satcom-chunk-collection huggingface dataset:
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 2 Protoplanetary Disc Dynamics in Alpha Centauri
### Binary Setup  
We initialised the binary orbital parameters using the most recent and accurate orbital solution for Alpha Cen based on highly precise astrometric measurements [1]. We set the following values for the semimajor, eccentricity, inclination, argument of periastron, and longitude of the ascending node: \(a_{\rm b}=23.3\) au (corresponding to 17.493 arcsec), \(e_{\rm b}=0.51947\), \(\omega_{\rm b}=231.519\) deg, and \(\Omega_{\rm b}=205.073\) deg, respectively. The true anomaly \(f_{\rm b}\) was initially set equal to 180 deg to begin with the stars at the apoastron. The binary was modelled using sink particles [16] with both sink radii equal to 0.1 au. The masses of the primary (A) and the secondary (B) were set to \(M_{\rm A}=1.0788\,M_{\odot}\) and \(M_{\rm B}=0.9092\,M_{\odot}\), respectively, which gives a mass ratio of \(q=M_{\rm B}/\,M_{\rm A}\approx 0.84\). The binary orbital parameters are able to evolve during the simulation as the stars accrete gaseous particle from the discs. However, given that the binary angular momentum largely dominates the total angular momentum budget of the system, the binary orbital parameters remain practically unchanged.
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
## 3 Research Methodology
### Generating Virtual Dark Spot Image Dataset
Looking at the image dataset, the data in the \"pass\" category represent images of cells in good condition, while the data in the \"non-pass\" category represent images of cells with defects. Among the dataset categories, the \"Initial Image\" category comprises 950 images representing the initial dark spots formed in the OLED cells. The \"10,000 (H) Image\" category consists of 1050 images depicting dark spots that formed after 10,000 h. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Category** & **Initial Image** & **10,000(H) Image** & **Total Image** \\ \hline \multirow{2}{*}{Training Dataset} & Pass & 300 & 400 & 700 \\  & Non-pass & 300 & 300 & 600 \\ \multirow{2}{*}{Validation Dataset} & Pass & 200 & 200 & 400 \\  & Non-pass & 100 & 100 & 200 \\ \multirow{2}{*}{Test Dataset} & Pass & 25 & 25 & 50 \\  & Non-pass & 25 & 25 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of images in the dataset used for VGG-16. Figure 1: Comparison between real and simulated datasets. (**a**) Real image of dark spot. (**b**) Simulated image of dark spot. (**c**) Image of dark spot expanded 100 times. To determine the optimal deep learning algorithm for dark spot detection, various algorithms were selected and utilized as the foundation for developing a defect detection model. For this purpose, 1300 images from the \"Training Dataset\" were used for model training, and 600 images from the \"Validation Dataset\" were utilized to perform model validation. The remaining 50 images from the \"Test Dataset\" were reserved for evaluating the performance of the defect detection model in identifying actual dark spots.
###### Abstract  
Research on longitudinal discrepancies in local ionospheric variability, especially in equatorial and low-latitude regions, is a focal point of interest for the space weather modeling community. The ionosphere over these regions is influenced by complex electrodynamics, wind, and temperature dynamics that can seriously impact dynamic technological systems such as satellite tracking and positioning, satellite radio communication, and navigation control systems. Here, we researched the longitudinal variability in the ionospheric total electron content (TEC) by analyzing observed global positioning system (GPS)-derived TEC values along with those extracted from the most reliable global ionospheric maps (GIMs) and the International Reference Ionosphere (IRI-2016) model at selected stations in the vicinity of the magnetic equator along the American, African, and Asian longitude sectors. The period of study covered the descending (2016-2017) and deep solar minimum (2018-2019) years in the 24 th solar cycle. Apart from the decreasing trend of the TEC from the descending to deep solar minimum period irrespective of season and longitude sector, the results showed a relatively higher magnitude of TEC in the African longitude than the other two longitude sectors. Despite evident overestimation and underestimations of TEC in both models, GIM predictions generally looked better in terms of observed variation patterns, especially in the African longitude. The study also highlights the seasonal and semiannual effects of longitudinal variations in TEC, manifesting in local time offsets and some peculiar anomalies, which seemed to be different from previously reported results, especially during the solar minimum years at the three longitude sectors. The insignificant effects of longitudinal variations on the equinoctial asymmetry are attributed to the diverse electron density distribution and ionospheric morphology at the three longitude sectors that will prompt further investigations in the future. The outcomes from this study may augment the past efforts of scientists to understand the seasonal effects of the longitudinal variations in TEC, thereby complementing the improvements of ionospheric representations in global ionosphere models and maps.  
Keywords:Ionosphere; longitudinal variations; geomagnetic equator; GPS; GIM; IRI-2016
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 1 Introduction
### Explainable Deep Learning-Based NIDS  
The black-box nature of deep learning models has given rise to trust issues regarding how these models are making predictions, resulting in many organizations being reluctant to implement deep learning-based NIDS [11]. This predicament has sparked research in the field of explainable artificial intelligence (XAI) to aim at making insightful explanations of the internal operation of ML and DL models [12].  
The interpretability of AI models can generally be grouped into two major categories: intrinsic interpretability and post hoc interpretability, where intrinsic interpretability is derived by directly incorporating interpretability into the model's structure, which some self-explanatory models such as decision trees, rule-based models, and attention models do. Conversely, the post hoc requires creating a surrogate model to provide explanations from an existing model. [PERSON] [12] et al. further differentiated the AI interpretability into global interpretability and local interpretability, where local interpretability refers to the local explanation of an individual prediction of a model, revealing how some decisions are derived. AI interpretability will aid in unveiling the relationship between specific input and its matching output. Global interpretability involves the inspection of the structure and parameters of a complex model to understand the modalities of the model.  
Global interpretability demystifies the internal mechanism of AI models, thereby increasing their transparency. Given this growing trend of XAI and its applicability in other domains such as computer vision, it is also imperative to apply these technologies into deep learning-based NIDS to demystify them, thus encouraging operational deployment. There are many recommended ways to generate model explanations. This paper used the SHapley Additive exPlanations (SHAP) [8] method. SHAP combines local and global interpretability simultaneously, thus improving the interpretation of IDSs.
## 2 Integrated Predictive Traffic Resilience Framework
### Augmented LSTM-Based Traffic Prediction
The new memory cell state is updated by multiplying the past state \(\mathbf{c}_{t-1}\) by the forget gate \(\mathbf{f}_{t}\) with the addition of the multiplication results from the input gate \(\mathbf{i}_{t}\) with the modulation vector \(\widetilde{c_{t}}\). The \"\(\circ\)\" denotes element-wise multiplication. The output gate \(\mathbf{o}_{t}\) manages the propagation of the latest cell output \(\mathbf{c}_{t}\) to the final state \(\mathbf{h}_{t}\), typically using a sigmoid activation function. In this step, the weight matrix is \(\mathbf{W}_{o}\), and the bias vector is \(\mathbf{b}_{o}\). The final state \(\mathbf{h}_{t}\) also employs tanh as the activation function over the cell state \(\mathbf{c}_{t}\). The prediction horizon or the length of time into the future that an augmented LSTM can accurately forecast depends on various factors, including the specific model architecture, the complexity of the data, and the training methodology. LSTM models, known for their ability to capture long-term dependencies, are particularly effective in handling time series data. In traffic studies, LSTM models have been successfully used to make accurate predictions several time steps or even several days into the future [27; 28]. However, it is important to note that as the prediction horizon increases, the uncertainty and potential errors in the forecasts may also increase. To obtain the final volume predictions, the LSTM approach generates two sets of spatial and temporal predictions, which are then combined using the Kalman-filtering concept, as illustrated in Figure 2. Since the Kalman Filter approach has been extensively studied in the literature, this paper will not delve into its details. For interested readers, refs. [29; 30] provide more comprehensive explanations. LSTM is indeed powerful in capturing sequential dependencies and modeling complex patterns in traffic data. However, it is important to note that LSTM is not specifically designed for directly quantifying probabilities, particularly in the context of the parameters \(\alpha\) and \(\beta\) that would be used in the quantification of traffic resilience. By considering both \(V(t)\) and \(C(t)\), and the parameters \(\alpha\) and \(\beta\), a more holistic assessment of traffic resilience can be achieved. This approach allows for the consideration of the variability in traffic volume and the relationship between traffic volume and available capacity, providing a more accurate and comprehensive measure of the system's ability to handle traffic fluctuations and uncertainties.
# Property Analysis of Slag Composite Concrete Using a Kinetic-Thermodynamic Hydration Model
## 2 Hydration-Based Property Evaluation Model of Binary Blends of Cement-Slag  
### Cement Hydration Model  
Our previous studies [20; 21] utilized a kinetic model that mimics the hydration process for Portland cement. The material composition, such as the cement composition and the concrete mixtures, and the curing conditions, such as the curing temperature, are considered in this kinetic model. The model considers the kinetic process of cement hydration as a period of initial dormancy, a process of phase-boundary reaction, and a process of diffusion. The degree of hydration of the linker composition \(\alpha_{i}\) (\(i=1\), 2, 3, and 4 denote the mineral composition of cement, such as C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A, and C\({}_{4}\)AF, respectively) can be calculated based on the numerical integration method in the time domain as \(\alpha_{i}=\int_{0}^{t}\left(\frac{da_{i}}{dt}\right)dt\), where \(\frac{da_{i}}{dt}\) is the rate of cement hydration. The equation for the rate of cement hydration \(\frac{da_{i}}{dt}\) is simplified as follows:  
\[\frac{da_{i}}{dt}=f(B,C,De,kr_{i})*\lambda_{1}*\lambda_{2}, \tag{1}\]  
\[\alpha=\frac{\sum\limits_{i=1}^{i=4}\alpha_{i}m_{i}}{\sum\limits_{i=1}^{i=4}m _{i}}, \tag{2}\]  
where \(B\) and \(C\) denote the rate-determining coefficients in the period of initial dormancy, \(De\) and \(kr_{i}\) are the reaction parameters in diffusion-controlled periods and phase boundary-controlled periods, respectively, and \(\lambda_{1}\) and \(\lambda_{2}\) consider the capillary water withdrawal during the hydration process. The withdrawal of capillary water can lower the rate of hydration. \(\lambda_{1}\) covers the capillary water withdrawal because of the development of microstructure, while \(\lambda_{2}\) covers the capillary water withdrawal because of the consumption of capillary water (\(\lambda_{2}=\frac{W_{cap}}{W_{0}}\), where \(W_{cap}\) and \(W_{0}\) are the mass of capillary water and the mass of mixed water in concrete mixtures, respectively). For \(m_{i}\), \(i=1\), 2, 3, and 4 denote the mass fractions of the mineral composition of cement, such as C\({}_{3}\)S, C\({}_{2}\)S, C\({}_{3}\)A, and C\({}_{4}\)AF, respectively. \(\alpha\) is the reaction degree of Portland cement. For concrete with a normal strength, the withdrawal of capillary water is less obvious, while for high-strength concrete at the late age of hydration, the withdrawal of capillary water is significant. Due to the consideration of capillary water withdrawal, the kinetic model is valid for both concrete with normal strength and concrete with high strength. The degree of hydration of Portland cement can be determined based on the individual hydration degree of the linker component.
## 5 Implementation and Results
### Experiment 1  
For the sake of comparison and to investigate the advantage of using the optimized wrapper selection methods based on bio-inspired algorithms, we conducted the first experiment in which we used the four classical ML classifiers (NB, KNN, SVM, and DT) with the two datasets (PBC and GE) for ASD prediction without using the optimized wrapper selection method.  
Table 5 presents the results of the four classifiers on the two datasets. As we can see from the table, the DT classifier achieved the highest accuracy with the PBC dataset. For the GE dataset, we noticed that the highest accuracy was 86.6% obtained by DT.  
Therefore, we can see that using ML classifiers without any FS methods for the GE dataset did not give an efficient ASD prediction compared to the PBC dataset due to its high dimensionality. It has also been noticed that the accuracy of KNN was relatively low, especially when compared to other classification algorithms for all datasets.
## 5 Methods for Angular Dispersion Detection  
### Measurement of Propagation Direction Angular Dispersion  
The characterization of the angular dispersion of ultrashort laser pulses is inevitable when complete spatiotemporal compression of the pulses is necessary. When the angular dispersion is not compensated carefully, it can lead to an unexpected decrement in the peak intensity. At the same time, spectral components may also be separated spatially, thereby inducing a so-called spatial chirp, which involves an asymmetry of the spectral content in the beam profile. A direct consequence of this spatial chirp is the tilt of the phase front, which raises some further unwanted increase in the pulse duration. Elimination of angular dispersion (_i.e._, the divergence of the spectral components) does not necessarily remove the spatial chirp, since the spectral components may still propagate along distinct parallel axes, even if they travel in the same direction.  
A simple and direct method to measure of propagation direction angular direction is based on spectrally and spatially recorded intensity distribution of the focused spot of the beam. The layout of the technique is shown in Figure 13.  
An achromatic lens or spherical mirror is used to focus the beam on the slit of the spectrograph. In case of angular dispersion, spectral components have different propagation directions, therefore, in the focal plane, also a different spatial position. This position \(y\) is related to relative angle of the propagation direction of an actual spectral component, whereby the component's frequency needs to be determined. This can be done with a spectrograph, which provides a spectrally resolved intensity distribution on its detector and eventually leads to a tilted and elongated spot on the image. The angle of the tilt corresponds directly to the propagation direction angular dispersion.  
Figure 13: Schematic diagram for measuring the propagation direction angular dispersion.  
Unfortunately, this method can only detect the angular dispersion along the cross-section of the beam, which is defined by the slit of the spectrograph. In most cases, it is highly desired to ensure that the beam is free of angular dispersion in both horizontal and vertical direction. It can be executed only when the beam or the spectrograph is rotated by 90\({}^{\circ}\). For this purpose, it is most convenient to use a mechanical beam rotator, which is practically a twistable periscope, but does not alternate the direction of the beam when it is passing through.  
Since this method is limited to measure along one spatial dimension only, at least two subsequent measurements required for complete beam profile characterization. This leads to a lengthy and iterative alignment of the stretcher-compressor stages of the _CPA_ system. Concerning the precision, it is typically sensitive for the pulse bandwidth and the focal length of the focusing element (_i.e._, the sharpness of the elongated spots). During alignment of compressor systems, an accuracy of 0.1 \(\upmu\)rad/nm was demonstrated [103].
# Development of a New Vertical Dynamic Model of a Rail Vehicle for the Analysis of Ride Comfort
###### Abstract  
The rail vehicle industry wants to produce vehicles with higher speeds, to maintain and increase its market share. However, when the speed of the vehicle increases, it may have an undesirable effect on ride comfort, in terms of ride dynamics. Recent developments towards lighter and faster vehicles make the problem of ride comfort at higher speeds increasingly important. Focusing on the behavior of flexible rather than rigid body behavior should not be neglected when designing long and light car bodies. There are several approaches to incorporate body flexibility in multibody simulations and they have some superiorities and weaknesses. In this study, an efficient and accurate vertical dynamic model for the ride comfort analysis is developed and implemented in a commercial object-oriented modeling (OOM) software Dymola (2015 FD01) which uses the open-source code Modelica. This model includes car body flexibility with the assembling of a rigid body approach. The developed model is compared to a three-dimensional vehicle model in the commercial Vampire software (Pro V5.50) at different velocities. For the vertical ride comfort analysis, the ISO 2631-1 standard was used for both the developed model and the three-dimensional model. The results are presented as acceleration history and \(a^{\textit{arms}}\)--weighted r.m.s (root mean square) of accelerations--as required by the standard. The developed model has shown its feasibility in terms of its efficiency and accuracy for the vertical ride comfort analysis. The accuracy of the model is evidenced by the fact that the car body vibration level at high speeds shows minor differences compared to the results of the Vampire, which is a validated commercial software in the area of rail vehicle dynamics. The approach involving the assembly of rigid bodies is applied for the first time for high-speed trains in dynamical modelling, with flexible car bodies for ride comfort analysis. Furthermore, it can be used for parametrical studies focusing on ride comfort, thereby offering a quite beneficial framework for addressing the challenges of ride comfort analysis in high-speed rail vehicles. Improvements for and analyses of other aspects are also possible, since the optimization and other useful libraries are readily available in Dymola/Modelica.  
rail vehicle; dynamic model; high speed; ride comfort; object-oriented modeling +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# A New Approach to Detect Hand-Drawn Dashed Lines in Engineering Sketches
## 4 Non-Solid Line Clustering
### Input Data  
The input to our algorithm is a sketch represented as a set of strokes. These strokes are hand-drawn lines obtained by sampling scribbled lines by way of a set of consecutive nodes that are automatically sampled by the device (mouse, pen, etc.) between the pen-down and pen-up movements. The result is a set of ordered points, which are connected by segments to approach the original scribble.  
Along with the sketch, we input the endpoints of segments that best fit their strokes. This information was calculated in advance ([21,22]). For this purpose, we employ a regression fit, a method that is slower than others but less susceptible to sketch inaccuracies, which can be critical for short strokes, which are commonly found in dashed lines. The fitted segment is determined by the regression line encapsulating the stroke and delimited by the points on the regression line closest to the tips of the stroke (refer to Figure 5 for an illustration). The implementation of the typical line-of-best-fit algorithm considers the need to swap coordinates for lines with a slope greater than 1, preventing the well-known issue of regression failure for vertical lines. For each stroke, the fitting algorithm calculates  
* Coordinates of the initial tip of the fitting segment (<TipBegin (x, y)>);
* Coordinates of the final tip of the fitting segment (<TipEnd (x, y)>);
* Length of the segment (<Length>);
* Orientation (angle in radians).
## 3 Experimental Results and Analysis
### Experiments on Maximum Adhesion Coefficient Estimation
#### 3.4.1 Acquisition of Standard Rail Surface Curves  
The relationship between the wheel-rail adhesion coefficient and creep-slip velocity is expressed in Equation (8). Based on previous experimental studies, four different rail surface conditions are selected as the standard rail surfaces for this study [24]. The four standard rail surfaces correspond to dry, wet, snowy, and oily rail surface conditions in that order. The adhesion characteristic parameters (\(a\), \(b\), \(c\), \(d\) values) for each rail surface are provided in Table 7, while the corresponding adhesion characteristic curves are illustrated in Figure 11.
## 4 Control Application of Mechatronic Devices: Adaptive Controller
### Study of the Stability  
The stability of the system is directly related to the position of the roots of the characteristic equation of the transfer function of the system. In this sense, Routh-Hurwitz is a useful method to calculate the stability of the system [22].  
The characteristic equation of the feedback system is obtained as follows:  
\[s^{2}+\frac{B+A\cdot K_{p}}{1+A\cdot K_{d}}s+\frac{A\cdot K_{i}}{1+A\cdot K_{ d}}. \tag{6}\]  
The method indicates that there are no roots with positive real parts if all the terms in the first column of the Routh-Hurwitz table (see Table 1) are positive.  
Applying the method, we can obtain the following expressions:  
\[K_{p}>-\frac{B}{A}, \tag{7}\]  
\begin{table}
\begin{tabular}{c|c} \(s^{2}\) & \(1\) & \(\frac{A\cdot K_{i}}{1+A\cdot K_{d}}\) \\ \(s^{1}\) & \(\frac{B+A\cdot K_{p}}{1+A\cdot K_{d}}\) & \(0\) \\ \(s^{0}\) & \(\frac{A\cdot K_{i}}{1+A\cdot K_{d}}\) & \\ \end{tabular}
\end{table}
Table 1: Routh–Hurwitz criterion table to study the stability.  
\[K_{i}>0, \tag{8}\]  
\[K_{d}>-\frac{1}{A}. \tag{9}\]  
These equations provide a method by which to determine, based on the values of the plant, the possible values of the gains that safeguard system stability in a closed loop.  
Thanks to this, the stability of the system is ensured at all time, and the controller's gains are calculated by the laws of adaptation.