esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Formulation  
The traffic assignment problem consists of estimating traffic flows on a transportation network. In order to solve this problem, we need three elements: the travel demand (summarised in an Origin-Destination matrix), the transportation supply (modelled with a supply model) and the user path choice behaviour (modelled with a path choice model). Several models can be used for solving the problem depending on the transportation system (road network, mass-transit network, multimodal transportation network, etc.), the assumptions on the travel demand (fixed or variable), the approach to studying the interactions between demand and supply (user equilibrium or dynamic approach), the dependence or otherwise of link costs on flows (uncongested or congested), the assumption on the route choice model (deterministic or stochastic), and so forth. An in-depth analysis of traffic assignment models can be found elsewhere (see, for instance, Reference [24]). Even if the proposed algorithm can also be used to solve other assignment problems, in this paper, we consider the congested road network, fixed demand, user equilibrium approach and stochastic route choice model.  
On a road network, the link flow vector, \(f\), once the transport demand has been fixed, depends on link costs, and hence, on the link cost vector, \(c\):  
\[f=\varphi(c) \tag{1}\]with:  
\[\varphi(c)=A\ P(A^{\rm T}c)\ d\]  
where \(A\) is the link-path incidence matrix, whose components, \(a_{lp}\), are equal to 1 if link \(l\) belongs to path \(p\) and 0 otherwise; \(P\) is the path choice probability matrix, with a column for each \(od\) pair and a row for each path \(p\); the generic element, \(P_{p,od}\), of this matrix represents the probability that a user will use path \(p\) from origin \(o\) to destination \(d\) (if path \(p\) does not connect the \(od\) pair, \(P_{p,od}\) is equal to 0); \(d\) is the demand vector, whose components are the demand values \(d_{od}\) for each \(od\) pair.  
The generic cost, \(c_{l}\), of link \(l\) can be assumed dependent only on the flow on the same link, \(f_{l}\).  
Formally, we can write:  
\[c=\chi(f) \tag{2}\]  
Substituting (2) in (1), we obtain the following:  
\[f=\varphi(\chi(f)) \tag{3}\]  
which relates flows, \(f\), and costs, \(c\). Equation (3) is a fixed-point problem; the solution is represented by the equilibrium traffic flows, \(f^{*}\), which is congruent with the corresponding link costs, \(c=\chi(f^{*})\).  
Formally, we can write:  
\[f^{*}=\varphi(\chi(f^{*}))\qquad\quad\text{or}\qquad\quad f^{*}=A\ P(A^{\rm T }\chi(f^{*}))\ d \tag{4}\]  
In this case, it may be stated, under some assumptions, that the solution to the fixed-point problem (4) exists and is unique [5] and that the MSA algorithm converges to it [4].
## Appendix A  
Computations of the radiated field by the array were carried out in the ACS \((\hat{x}_{a},\hat{y}_{a},\hat{z}_{a})\). However, the final field must be expressed in the NFCS \((\hat{x}_{nf},\hat{y}_{nf},\hat{z}_{nf})\). In general, the rotation of the NFCS with regard to the ACS can be expressed by three angles, \(\theta\), \(\varphi\), and \(\psi\), as described in [41]. The definition of \(\theta\) and \(\varphi\) in spherical coordinates is the usual, while \(\psi\) defines a rotation around axis \(\hat{z}_{2}\), as shown in Figure 11.  
The unit vectors of the rotated coordinate system are defined as [41]:  
\[\hat{x}_{nf} =\hat{\theta}\cos(\varphi-\psi)-\hat{\varphi}\sin(\varphi-\psi), \tag{11a}\] \[\hat{y}_{nf} =\hat{\theta}\sin(\varphi-\psi)+\hat{\varphi}\cos(\varphi-\psi),\] (11b) \[\hat{z}_{nf} =\hat{r}, \tag{11c}\]  
where  
\[\hat{\theta} =\hat{x}_{a}\cos\theta\cos\varphi+\hat{y}_{a}\cos\theta\sin \varphi-\hat{z}_{a}\sin\theta, \tag{12a}\] \[\hat{\varphi} =-\hat{x}_{a}\sin\varphi+\hat{y}_{a}\cos\varphi,\] (12b) \[\hat{r} =\hat{x}_{a}\sin\theta\cos\varphi+\hat{y}_{a}\sin\theta\sin \varphi+\hat{z}_{a}\cos\theta. \tag{12c}\]  
Substituting (12) into (11) yields:  
\[\hat{x}_{nf} =\hat{x}_{a}[\cos\theta\cos\varphi\cos(\varphi-\psi)+\sin\varphi \sin(\varphi-\psi)]+\] \[\hat{y}_{a}[\cos\theta\sin\varphi\cos(\varphi-\psi)-\cos\varphi \sin(\varphi-\psi)]- \tag{13a}\] \[\hat{z}_{a}\sin\theta\cos(\varphi-\psi),\] \[\hat{y}_{nf} =\hat{x}_{a}[\cos\theta\cos\varphi\sin(\varphi-\psi)-\sin\varphi \cos(\varphi-\psi)]+\] \[\hat{y}_{a}[\cos\theta\sin\varphi\cos(\varphi-\psi)-\cos\varphi \sin(\varphi-\psi)]-\] (13b) \[\hat{z}_{a}\sin\theta\sin(\varphi-\psi),\] \[\hat{z}_{nf} =\hat{x}_{a}\sin\theta\cos\varphi+\hat{y}_{a}\sin\theta\sin\varphi +\hat{z}_{a}\cos\theta. \tag{13c}\]  
Then, if \(\vec{r}_{a}=(x,y,z)_{\text{ACS}}\) is a point in the ACS, and \(\vec{r}_{nf}=(x,y,z)_{\text{NFCS}}\) is the same point in the NFCS, the matrix of change of coordinates from the ACS to the NFCS is:  
\[\vec{r}_{nf}=T\vec{r}_{a}, \tag{14}\]with:  
\[T=\left(\begin{array}{cccc}\hat{x}_{nf}\cdot\hat{x}_{a}&\hat{x}_{nf}\cdot\hat{y} _{a}&\hat{x}_{nf}\cdot\hat{z}_{a}\\ \hat{y}_{nf}\cdot\hat{x}_{a}&\hat{y}_{nf}\cdot\hat{y}_{a}&\hat{y}_{nf}\cdot\hat{ z}_{a}\\ \hat{z}_{nf}\cdot\hat{x}_{a}&\hat{z}_{nf}\cdot\hat{y}_{a}&\hat{z}_{nf}\cdot\hat{ z}_{a}\\ \end{array}\right). \tag{10}\]  
Conversely, to perform a change of coordinates from the NFCS to the ACS we can calculate:  
\[\vec{r}_{a}=T^{T}\vec{r}_{nf}, \tag{11}\]  
where \(T^{T}\) is the transpose of \(T\), which is an orthogonal matrix, i.e., \(T^{T}=T^{-1}\).
## 3 Data and Model Configuration
### Model Configuration  
The configuration of each module of our model is as follows: The CNN module consists of three convolution layers whose convolution kernel sizes are, respectively (1, 1, 1), and the output dimensions of each convolution layer are, respectively, (512, 512, 512). The output of the segment-level statistical pooling layer is projected linearly to obtain a D-dimensional phoneme embedding, D = 64. The transformer module in the global information extraction branch has eight headers, \(d_{model}=512\), \(d_{ff}=2048\). The local information extraction branch adopts the cgMLP model except that the size of the deep convolution kernel in the model is set to 3, and the other parameters are consistent with the original paper [33]. The latter linear module contains a total of three linear projections whose output dimensions are 512, 512, and C, where C represents the number of categories of the language. The B-transformer model is trained on 17 epochs on the LRE17 dataset. The learning rate of the model warmup from 0 to \(10^{-4}\) for the first three epochs, and then decays to 0 by cosine annealing. During the training, we set the batch size to 128 and used the Adam optimizer to optimize the model. The indicators that measure the performance of the model are equal error rate (EER) and average cost (Cavg), and the lower their values, the better the model performance. The training of the model was implemented on a single NVIDIA GeForce RTX 3090 GPU.
## 2 RF Features
### Angle of Arrival  
The Angle-Of-Arrival (AOA) method for localisation has not been incorporated as much as its counterparts. The use of directional antennas, multi-element arrays for MIMO, and mmWave, especially for 5G cellular networks, has attracted more attention. Similar to TOA, AOA also suffers from LOS blockage. Other multi-path components from NLOS can lead to erroneous and misleading information about the direction of arrival. Thus, AOA is usually fused with other data for localisation purposes.
# Time-Varying Reliability Evaluation of Concrete Based on Carbonation Depth
## 5 Assessment of the Mean Value and Variance of Carbonation Depth  
In previous studies, the carbonation depth was regarded as a function of the square root of time [36]. However, most models using this function were developed on the basis of accelerated experiments, where the carbonation depth and climatic factors were not regarded as variables, and the methodologies were very complex. To realistically capture the irregularity of carbonation depth, the mean values and variance of the data were analyzed in details. According to the distribution fittings from Figure 8, the mean values and variances of the data were obtained, as presented in Table 3.  
From Table 3, it was observed that the mean values and the variance increased with the increase of service time. Figure 9 presents the time-varying model for calculating the mean value of carbonation depth (\(\mu\)), which is expressed as:  
\[\mu(t)=0.992\times\exp(0.063\times t) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Age (Year)** & **Mean Value (mm)** & **Variance** \\ \hline
37 & 10.81 & 116.84 \\
39 & 13.61 & 185.38 \\
41 & 12.44 & 154.57 \\
43 & 13.05 & 170.43 \\
45 & 16.21 & 262.82 \\
47 & 19.07 & 363.84 \\
49 & 22.93 & 525.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Service age, mean value, and variance.  
Figure 8: Exponential distribution fitting with the acceptable R-square: (**a**) \(t=37\); (**b**) \(t=39\); (**c**) \(t=41\); (**d**) \(t=43\); (**e**) \(t=45\); (**f**) \(t=47\); (**g**) \(t=49\). Note: t is the service age of the buildings in years.  
On the basis of Figure 9, if the concrete cover is assumed to be 20 mm, it can be predicted that after about 47.6 years (concrete failure time), the carbonation depth of the concrete will probably reach the concrete cover. Therefore, this concrete is suggested to be retrofitted. However, only the mean value was considered in this model, leading to inaccurate prediction of the concrete failure time. To consider both mean value and variance, a reliability method and guaranteed rate were applied in this paper.
# Resilience Assessment of Beijing Subway Lines under Extreme Precipitation Weather
## 4 Case Study
### Analysis of the Results  
In the first case illustrated above (22 August 2022), all five key indicators have high resilience levels, while the entire Changping Line also has a high resilience level. The results indicate that these five key sub-systems for the Changping Line tend to have lower failure probabilities on this day. Only \"escalators, moving walks and barrier-free\" access has a relatively higher chance to fail compared with the other four key sub-systems. Moreover, the whole Changping Line is predicted to have a high resilience level on this day. Notice  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Line**} & \multirow{2}{*}{**Key Indicator**} & **Resilience Score** & **Weight** & **Resilience Level** \\ \hline \multirow{2}{*}{**Changing Line**} & Escalators, moving walks, and barrier-free access & 98.96 & 1/9 & High \\ \hline \multirow{2}{*}{**Changing Line**} & Low-voltage power distribution system & 100 & 2/9 & High \\ \hline \multirow{2}{*}{**Changing Line**} & Traction power supply system & 100 & 2/9 & High \\ \hline \multirow{2}{*}{**Changing Line**} & Switch equipment & 100 & 2/9 & High \\ \hline \multirow{2}{*}{**Changing Line**} & Vehicle connection system & 100 & 2/9 & High \\ \hline \multirow{2}{*}{**Changing Line**} & Weighted average & 99.88 & & High \\ \hline \hline \end{tabular}
\end{table}
Table 9: Resilience assessment of the Changping Line under a new rainstorm event (22 August 2022).  
that the true resilience scores of the five key indicators are all 100 on this day, which helps to validate our resilience evaluation approach.  
In the second case (28 July 2022), our results suggest that \"escalators, moving walks and barrier-free\" access tends to have a relatively higher chance to fail compared with the first case. The other four key indicators are again predicted to be unaffected by this extreme rainfall event. Similar to the first case, all five key indicators, as well as the whole Changping Line, are predicted to have a high resilience level on this day. Moreover, the true resilience score of this day is 80.97 for \"escalators, moving walks and barrier-free\" access, and they are all 100 for the other four key indicators, which agree well with our resilience evaluation results.  
These resilience assessment results support the emergency management of the Changping Line during rainstorm events.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 7 Open Challenges and Future Directions
### Dynamic 3D Drones Path Planning  
Currently, we assume that thanks to the upmost layer nodes discovery and location, drones are able to compute anchor points and the best traveling path visiting all these anchor points. We also assume that at each visit, nodes inform the drone about change in upmost layer representative allowing it to recompute its path. The path computing is realized in 2 steps: first compute the anchor points and then draw a (Close Enough) traveling salesman problem trajectory. This does not consider the drone autonomy nor a drastic change in representative positions and computes a 2D path. But drone coverage depends on the drone altitude and speed; the drone presents a highly flexible 3-D mobility and the higher the altitude, the larger the coverage but the higher the energy consumption [30]. And this could change the anchor points determination since by flying at a higher altitude, the drone will cover more nodes at a time but will consume more energy. An open problem is thus to determine the best  
Figure 13: Example of clique multi-membership.  
energy-efficient and minimum-time 3D path to travel the area as fast as possible while still remaining in range of each survivor long enough to assure full servicing. This path should jointly investigate the drone trajectory and the location of the anchor nodes that could dynamically be adapted with drone altitude, while still integrating the pitstop duration at each anchor point, which has a mandatory minimum duration and should be proportional to the number of nodes to serve at this position [31]. A similar work that already provides a good basis with the optimization of several criteria in the trajectory determination is [32; 35]. These latter jointly optimize the UAV's flying altitude, antenna beamwidth, UAV's location, and ground terminals' allocated bandwidth. We thus intend to rely on this work to extend it and couple it to our dynamic multi-tier architecture.
# Graphene and Iron Reinforced Polymer Composite Electromagnetic Shielding Applications: A Review
## 5 Polymer-Based Composites  
Metals have extensively been utilized for EMI shielding, however, due to easy corrosion and difficult processing, their use has been limited [96; 97]. Therefore, researchers turned their interest towards an alternative material i.e., polymer-based composites, which are more promising as they are lightweight, having a low cost, more processability and give better performance as compared to metal-based composites [98; 99]. Polymer-conductive nanoparticle composite has unique porous morphology which has shown better results as electromagnetic waves absorber. Air insertion in the material permits high electromagnetic waves access which expands interactions with various air-filled pores and high conductive cell walls. As a result, effective electromagnetic wave dissipation occurred within a lightweight structure. It has a massive influence on final shielding properties however the relationship between final morphology and foaming conditions differs and not certainly predictable [63]. Polymer-based composite materials provide exceptional benefits in comparison to metals, such as low density, enhanced flexibility, and ease in processing. However, with limited mechanical properties like inadequate electrical conductivity, lower temperature resistance, it is difficult to utilize polymer for shielding applications, especially under extreme temperature [100]. Hence, such a combination should be created which can overcome the deficiencies of polymer against electromagnetic wave shielding in extreme conditions.
# Instability Study of Magnetic Journal Bearing under S-CO\({}_{2}\) Condition
## 2 Magnetic Bearing Analysis for Instability Study
### Magnetic Bearing Lubrication Model Development
Spatially uniform thermal properties were substituted into Equation (1). Then, the pressure distribution could be obtained by iterative calculation by updating the thermal properties based on pressure distribution in the previous step. For the defined nodes shown in Figure 4b, the governing equation is simplified in matrix form. At first, the variables are bound as k = \(\frac{\rho h^{3}}{k_{X}\mu}\) (left hand side), and c = \(\rho h\), d = \(\frac{\rho hu}{2}\) (right hand side). From this, governing Equation (1) becomes Equation (4). \[\frac{\partial}{\partial X}\left(k\frac{\partial p}{\partial X}\right)= \frac{\partial c}{\partial t}+\frac{\partial d}{\partial X}, \tag{4}\]To form a matrix, the variables are denoted as vectors, such as in Equations (5)-(8). \[\mathrm{P(i)}=P_{i}, \tag{5}\]  
\[\mathrm{K(i)}=\frac{\rho_{i}h_{i}^{3}}{k_{X}\mu}, \tag{6}\]  
\[\mathrm{C(i)}=\rho_{i}h_{i},\ \mathrm{C_{-}(i)}=\rho_{i-1}h_{i-1}, \tag{7}\]  
Figure 3:0  
By setting the time step dt to be equal to \(\mathrm{d}x/\mathrm{u}\), the \(\mathrm{C_{-}}\) variable can describe the C vector with a time step. The spatial derivatives are denoted as diagonal matrices \(\frac{\partial^{2}}{\partial X^{2}}=A\) and \(\frac{\partial}{\partial X}=\mathrm{B}\), as shown in Equations (9) and (10). \[\mathrm{A(i,j)}=\left\{ \begin{array}{c}\frac{1}{(\Delta X)^{2}}\ if\ j=i\pm 1 \\ -\frac{2}{(\Delta X)^{2}}\ if\ j=i\\ 0\ for\ else\end{array} \right., \tag{9}\]  
\[\mathrm{B(i,j)}=\left\{ \begin{array}{c}\frac{1}{2\Delta X}\ if\ j=i+1\\ -\frac{1}{2\Delta X}\ if\ j=i-1\\ 0\ for\ else\end{array} \right., \tag{10}\]  
As a result, Equation (4) now becomes Equation (11). \[\mathrm{P}=\mathrm{inv}(\mathrm{diag(BK)B+diag(K)A})*\left(\frac{1}{dt}( \mathrm{C-C_{-}})+\mathrm{BD}\right) \tag{11}\]  
Forces acting on the shaft are now calculated as below from the obtained pressure distribution. \(F_{r}\) is the force exerted on the shaft with the biased direction (\(\theta=0^{\circ}\)), and \(F_{t}\) is the force with a direction of \(\theta=90^{\circ}\)  
\[ \begin{array}{c}F_{r}=\sum\cos(\theta_{i})*p_{i}*\Delta X*l\\ F_{t}=\sum\sin(\theta_{i})*P_{i}*\Delta X*l\end{array}  \tag{12}\]  
To consider fluid's compressibility, linear approximations of the thermal properties [21] are not considered because the thermal properties of S-CO\({}_{2}\) are nonlinear, near the pseudo-critical line. Instead, this model iteratively calculates by updating the thermal properties  
Figure 4: Geometry of the unbalanced shaft and the stator: (**a**) geometry description; (**b**) coordinate system for the finite difference method (FDM). from pressure distribution changes in previous steps until it converges. These processes are described in the flow chart shown in Figure 5. The code was implemented in MATLAB.
### Contributions  
Based on the above-mentioned gaps, our work makes the following contributions:  
1. In this work, in an attempt to keep the originality of the problem in terms of complexity, a time window with a size of 100 samples is used for extracting time-domain and frequency-domain features, while an overlap of 20 samples is used to increase the number of samples and provide further insights into the correlations among time windows. Compared with previous works, the number of samples is massively increased, to about 45,732 samples. Unlike previous works, we make sure that data scatters represent a very complex feature space difficult to separate, as in real-world applications. This is to make sure that the addressed problems are real-like and not easy;
2. A robust data engineering scheme, including feature extraction, a list of denoising algorithms, a set of outlier removals, filtering, smoothing, scaling, and balancing, is introduced in this work to serve against multiple types of data distortion and provides a better and more meaningful representation of the feature space while uncovering hidden patterns.
3. Data imbalance is taken into account in this work by introducing a synthetic minority oversampling technique (SMOTE) for augmenting data with low proportions and preventing loss of information.
4. This work takes advantage of 15 time- and frequency-domain features to improve the classification performance of the learning process by making the new feature space more robust to noise and providing efficient and interpretable representation more flexible and adaptable to the system current conditions;
5. Compared with previous works, all of which used a random training/test data division process when evaluating the learning model, this work uses a three-fold cross-validation process, resulting in the analysis of approximately 195 confusion matrices for the diagnostic process and about 18 confusion matrices for the prognostic process. These numerous results are all analyzed to ensure the certainty of the performance of the evaluation procedure.
6. CSIDTL, a methodology for selecting and aggregating top learners to transfer additional information at different levels of complexity in different cycles, is proposed in this work. The goal is to achieve better performance using the pattern separation ability of the top learners in each round by taking advantage of the pretrained learning weights to initialize the learning model each time;
7. This work adopts adaptive learning rules of the LSTM network, which adapts to data shifting in time-series analysis better than convolutional networks and other deep learning models [6];
8. Unlike previous works, the proposed CSIDTL model is further investigated on another complex endurance test classification problem that includes data of higher complexity and cardinality, where many samples of different classes look similar to each other at certain points of the representation. This provides further insights into using the model for prognostics investigations, providing better information for predictive maintenance (i.e., maintenance planning).
## 4 Performance Evaluation  
In this section, we evaluate the performances of the proposed authentication mechanism in terms of key parameters as communication and computational overheads, average delay and energy consumption introduced by the authentication mechanism.