esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Power Converter Topologies
### Charger Stage (Bidirectional DC-DC Converters)
#### 4.3.2 Isolated Charger Topologies  
The following are the isolated bidirectional DC-DC converter topologies that will be discussed:  
1. ZVS dual active bridge (DAB) converter (Figure 9a);
2. Phase-shift FB converter (Figure 9b);
3. FB LLC resonant converter (Figure 9c);
4. ZVS FB converter with capacitive output filter (Figure 9d);
5. Four interleaved flyback converters (Figure 9e). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & \begin{tabular}{c} **Charger** \\ **Topologies** \\ \end{tabular} & \multicolumn{1}{c}{**Advantages**} & \multicolumn{1}{c}{**Disadvantages**} & \multicolumn{1}{c}{**Applications**} \\ \hline \multirow{8}{*}{[13, 38, 74]} & & \(\bullet\) & For heavy loads, by selecting a ZVS mode, it is obtained a high efficiency & \(\bullet\) & At light conditions the efficiency decrease & \multirow{8}{*}{\(\bullet\)} & \\  & \begin{tabular}{c} ZVS HB Converter \\ with Coupled \\ \end{tabular} & \begin{tabular}{c} For light loads, by selecting a hard-switching mode, it is also obtained a high efficiency \\ \end{tabular} & \(\bullet\) & When the load decrease, the recycled energy increases & & \\ \cline{1-1}  & & & \(\bullet\) & Efficiency is improved by using separate inductors & \(\bullet\) & The number of phases is inversely & \(\bullet\) & High power DC drive \\ \cline{1-1}  & \begin{tabular}{c} Three-Phase \\ Interleaved Buck \\ Converter \\ \end{tabular} & \begin{tabular}{c} Three-Phase \\ Interleaved Buck \\ \end{tabular} & \begin{tabular}{c} The current is shared \\ between the \\ multi-phase modules \\ \end{tabular} & \(\bullet\) & \
\begin{tabular}{c} proportional to the \\ inductor size \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Partial DC charger \\ Applications with isolated \\ front \\ low-frequency transformers \\ \end{tabular} \\ \cline{1-1}  & & & \(\bullet\) & In phase current, it has a high THD & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._Table 5 describes a comparison of the main characteristics of the above-mentioned isolated bidirectional DC-DC converters used in the EV charger stage. Table 5 describes a comparison of isolated bidirectional DC-DC converter topologies.
# Data-Driven Test Scenario Generation for Cooperative Maneuver Planning on Highways
## 2 Materials and Methods
### Concept
Within the generation of the test scenarios, a cooperative global planning algorithm is used that computes the best possible solution of the scenarios within a given discretization. The results of this fully cooperative planner represent the lower limit of achievable costs, whereas the reference driver model without behavior planning ability can be regarded as the upper cost limit. Both algorithms are described in Section 2.4. Economy: From the perspective of economy, the resources required for the execution of a test should not be unnecessarily high. Therefore, we use a scenario-based approach instead of a large-scale traffic simulation that allows only scenarios that are relevant for testing cooperative behavior to be computed. Practicability: A test is practicable if the test method is appropriate for the research purpose. Since the goal of the test scenarios is to assess how a cooperative behavior planner would perform in road traffic, the scenarios contained in the test data set should be as natural and realistic as possible. The easiest way to achieve this is to extract the scenarios from a real traffic data set. This approach ensures that the test scenario generation is not restricted by manual inputs such as defined parameter spaces or a certain behavior model. A further aspect of practicability is that not every scenario that could be solved cooperatively in theory would be solved cooperatively in practice because of the acceptance of the driver of the AV. For example, it cannot be assumed that drivers are willing to accept a high individual disadvantage for a small overall advantage. From which ratio of ego-vehicle-costs to surrounding-vehicle benefit a cooperative action is accepted depends on the degree of cooperativity of the AV. According to [1; 17], this ratio lies between 0 (uncooperative because no extra costs are accepted) and 1 (fully cooperative because every extra cost is accepted for an overall benefit). Since there is no established value regarding to what extent AV behave cooperatively, we assume an intermediate value of 0.5. This means for our scenarios that the drawback of the cooperating vehicle has to pay off twice-once to compensate the extra costs of the cooperating vehicle to an overall cost difference of 0 and the second time to reach the overall benefit in size of the drawback. Scenarios in which the overall cost benefit is smaller than the drawback of the cooperating vehicle are assumed to have no practical relevance and are therefore discarded.
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 3 Methodology
### Optimization Algorithms
#### 3.3.1 Particle Swarm Optimization
NFE can be defined as  
\[NFE(t)=n_{pop}+n_{pop}\times n_{it}=n_{pop}(1+n_{it}). \tag{22}\]  
Algorithm 1 presents the pseudo-code of the PSO algorithm, and Table 3 lists the general parameters in the simulation scenarios. Since Scenarios II and III environmental scales are the same, the general simulation parameters are considered to be the same. ```
1:Input:\(n_{pop}\): swarm size; \(n_{var}\): no. of variables; \(n_{it}\): maximum no. of iterations; \(w\): inertia weight; \(c_{1},\ c_{2}\): acceleration coefficients
2:Output: Best solution
3:for\(i=1:n_{pop}\)do initialize the parameters)
4: Randomly generate \(n\) initial positions \(X^{i}(i=1,2,\ldots,n)\) of \(n_{pop}\) particles
5: Set \(n\) initial velocities \(V^{i}(i=1,2,\ldots,N)\) of \(n_{pop}\) particles to 0
6: Calculate the cost value of each particle
7: Set \(P_{best}\) and \(g_{best}\) in the swarm
8:endfor
9:for\(i=1:n_{it}\)do
10:for\(i=1:n_{pop}\)do
11: Update \(V^{i}\) of the \(i^{th}\) particle using Equation (15)
12: Update velocity bounds using Equation (20)
13: Update \(X^{i}\) of the \(i^{th}\) particle using Equation (16)
14: Apply velocity bounds
15: Calculate the cost value of the new particle \(X^{i}\)
16:if\(X^{i}\) is superior to \(P_{best,i}\)
17: Set \(X^{i}\) to be \(P_{best,i}\)
18:if\(X^{i}\) is superior to \(g_{best}\)
19: Set \(X^{i}\) to be \(g_{best}\)
20:endif
21:endif
22:endfor
23: Check the feasibility of the solution by checking Equations (9)-(11) = 0
24: Update best cost ever found, \(g_{best}\)
25: Update the inertia weight using Equation (19)
26:endfor
```  
**Algorithm 1**Particle swarm optimization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Scenario I** & **Scenario II** & **Scenario III** \\ \hline Reliable zone radius (m) & 500 & 500 & 500 \\ No coverage zone (m) & 50 & 50 & 50 \\ \(x_{min}/x_{max}\) & \(-500/2200\) & \(-6000/6000\) & \(-6000/6000\) \\ \(y_{min}/y_{max}\) & \(-500/2200\) & \(-7000/5000\) & \(-7000/5000\) \\ \(z_{min}/z_{max}\) & 65/85 & 65/85 & 65/85 \\ \(\alpha\) in Equation (3) & 0.8 & 0.8 & 0.8 \\ \(\beta,\ \gamma,\ \delta\) in Equation (3) & \(10^{5}\) & \(10^{5}\) & \(10^{5}\) \\ \(n_{it}\) & 150 & 150 & 150 \\ \(n_{pop}\) & 50 & 50 & 50 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: General and PSO simulation parameters.
Because of these reasons, a method of evaluating the condition of these structures using a combination of non-destructive methods that could be used in situ was sought [4; 5]. This article follows up on the study [5] which documents the preparatory phase of experimental measurement. It describes the material batch A2, which consists of rectangular thin slabs with dimensions of 700 \(\times\) 700 \(\times\) 60 mm and made of SFRC (steel fiber reinforced concrete). The mixture and mechanical properties of the testing specimens were also published in [5]. Six specimens (A2_1 to A2_6) were produced in this batch. Three specimens (A2_1 to A2_3) were taken to the military training area and loaded with a blast, and specimens A2_3 to A2_6 were tested in a laboratory. This article describes a field experiment of blast load of test specimens made of SFRC. The SFRC is a material with a high-energy absorption capacity and high toughness due to its high ductility. Understanding of the behavior of SFRC for blast loads is crucial for finding a method suitable for evaluating the condition of fortification structures subjected to a blast load. The dispersed reinforcement in the form of fibers significantly affects the working diagram of the material in compression but especially in tension. As a result, the SFRC differs qualitatively from plain concrete, as it achieves higher strengths and it is characterized by higher toughness. Thanks to these material properties, the SFRC is very resistant to shocks, and therefore is used in dynamically loaded structures. The toughness and associated properties of SFRC are based on the fracture process of the composite material during loading, during which the fibers are pulled out and torn at the same time as the number and width of cracks in the brittle matrix increase. The even distribution of the fibers leads to the effect of their spatial action in the structure of the composite and to the stiffening of its entire structure [6; 7].
## 2 Materials and Methods  
### LB Model for Fluid Flow  
The LB-CA model was originally developed for simulating the filtration of a single fiber, as described in the previous publication [38; 40]. In this work, the \(D3Q15\) lattice model was used to simulate 3D flows. The state of each grid was denoted by the fluid particle distribution function \(f_{i}(x,t)\), and the lattice Bhatnagar-Gross-Krook propagation scheme can be calculated from the classic Boltzmann equations. The discrete LB equation is described as:  
\[f_{i}\left(x+\mathbf{c}_{i}\cdot\Delta t,t+\Delta t\right)-f_{i}\left(x,t \right)=\left[f_{i}^{eq}\left(x,t\right)-f_{i}\left(x,t\right)\right]/\tau \tag{1}\]  
where \(f_{i}^{eq}(x,t)\) represents the equilibrium distribution function, \(\mathbf{c}_{i}\) is the lattice velocities in the \(D3Q15\) model, \(\Delta t\) represents the time step, and \(\tau\) is the dimensionless relaxation time. By decomposing the above discrete LB equation, the fictitious fluid particles on regular lattices experience two sequential sub-steps:  
\[\text{Collision}:\ f_{i}(x,t)^{\prime}=f_{i}(x,t)+\left[f_{i}^{eq}(x,t)-f_{i} (x,t)\right]/\tau \tag{2}\]  
\[\text{Streaming}:\ f_{i}(x+\mathbf{c}_{i}\cdot\Delta t,t+\Delta t)=f_{i}(x,t) ^{\prime} \tag{3}\]  
where \(f_{i}(x,t)^{\prime}\) represents the post-collision distribution function. Note that the collision step is an absolutely local behavior and thus conducive to parallel computing. The equilibrium distribution function is given by:  
\[f_{i}^{eq}=\rho\alpha_{i}[1+\frac{\mathbf{c}_{i}\cdot\mathbf{u}}{c_{s}^{2}}+ \frac{1}{2}(\frac{\mathbf{c}_{i}\cdot\mathbf{u}}{c_{s}^{2}})^{2}-\frac{ \mathbf{u}}{2c_{s}^{2}}] \tag{4}\]  
where \(\mathbf{u}\) is the macroscopic inlet fluid velocity, \(\rho\) is the fluid density, \(\alpha_{i}\) is a weight coefficient, and \(c_{s}\) is the local speed of sound. The macroscopic density \(\rho\) and momentum fluxes \(\rho\mathbf{u}\) are calculated as [43]:  
\[\rho=\sum\
olimits_{i=0}^{Q-1}f_{i}:\ \rho\mathbf{u}=\sum\
olimits_{i=0}^{Q-1}f_{ i}\mathbf{c}_{i} \tag{5}\]
# A General Framework for Visualization of Sound Collections in Musical Interfaces
## 3 Proposed Framework
### Feature Extraction
#### 3.3.1 Position Features  
The proposed framework allows visualising collections of sounds in two-dimensional maps. This is based on dimensionality reduction of high-dimensional feature spaces. Different sets of features may be used for top-down or bottom-up interfaces:  
* **Feature learning**: automatically learnt features are by definition adaptive. They may be interpretable, but their aim is to capture the main dimensions of the space spanned by the data. We use the system proposed by [25] based on Principal Components Analysis (PCA) and spherical k-means. These features have been applied to audio in different works [26; 27]. The underlying feature is the magnitude spectrum processed by a Mel filterbank, so the algorithm can be seen as an adaptive version of MFCCs.
* **Mel Frequency Cepstral Coefficients**: MFCCs, computed as the cepstrum of the output of a Mel filterbank, are a very established representation of the audio spectrum. Their main characteristic is the ability to remove pitch by using less coefficients than Mel bands, so they are particularly useful as a timbre descriptor, but they can also be used as a compact description of the spectrum by using the full range. MFCCs are hard to interpret, with the exception of the first coefficient which represents the energy of the signal. Thus, they can be seen as a midpoint between feature learning and perceptually inspired features.
* **Chroma features**: chroma features are histograms of the energy in the audio spectrum where the bins are defined by notes in a musical scale. As such, they are particularly useful for describing the harmonic aspects of tonal and polyphonic music signals. A number of variants are available in the literature; our implementation is based on the algorithm by [PERSON] [28].
* **Spectral shape measures**: spectral shape measures are a set of scalar descriptors that measure the distribution of energy in the spectrum. They were popularized by the MPEG-7 standard [29]. Our implementation includes some of the most established ones: spectral centroid, spread, skewness, kurtosis, rolloff, flatness and crest. As a set, they can be comparable to MFCCs [30], but individually some of them have clear perceptual interpretations. Thus, we use the whole set as a _position feature_, and the most interpretable ones as _icon feature_ (see below).
## 3 Cooperative Communication Algorithms
### Algorithm Design  
In the model, the sending and receiving radius of all nodes \((0,R_{\max}]\) is the same. \(R_{\max}\) is the maximum transmission range, and each node knows its own location and the location of Sink node. Network topology can be dynamically changed (resulted from nodes periodically dormant, link or node failure). The symbolic description in the model is shown in Table 2.  
Graphs \(G=(V,E)\), \(V=\{v_{1},v_{2},\ldots,v_{N}\}\) and \(N=|V|\) are used to represent the number of nodes by multi-hop WSN nodes. When \(|uv|\leq R\), \(|uv|\) refers to the linear distance between node \(\mu\) and \(v\). Next, the KCN algorithm is described in two steps. A. Selecting Cooperative Nodes  
Given the reference node \(m\), then \(C_{m}=\left\{C_{m}^{1},C_{m}^{2},\ldots,C_{m}^{k-1}\right\}\) represents \(k-1\) cooperative nodes of \(m\), and \(R_{KCN}\) is selected as the overlap of the following regions by the next hop cooperative node. The overlapping area of the circle of nodes \(C_{m}^{1},C_{m}^{2},\ldots,C_{m}^{k-1}\), with radius \(R\). 1. The circle between \(m\) and \(f_{m}\), with radius R/2. 2. Taking Sink as the center of the circle, with radius \(r(m)\), \(|mt|-R<r(m)<|mt|\). \(V_{kcn}\) refers to the set of KCNs, \(V_{kcn}=\left\{m,C_{m}^{1},C_{m}^{2},\ldots,C_{m}^{k-1}\right\}\). For consistency, \(m\) can be regarded as \(C_{m}^{0}\). Figure 3: f and k effect on end-to-end reliability. In KCN, the selection of reference and cooperative nodes is based on location and residual energy, as shown in Figure 4. When the PROB package of the upstream node is received by node \(m\), the ideal location for the next hop is included. The operation of node \(m\) is as follows:  
(a) In the search domain of cooperative nodes, \(k\) cooperative nodes are selected according to distance and energy. The four cooperative nodes in Figure 4 are 1-4, and the formula based on distance and energy is as follows:  
\[Q(m)=\sqrt{\left(1-\frac{d}{R}\right)+\left(\frac{e_{res}}{e_{init}}\right)^{2}} \tag{16}\]  
Formula \(d\) is the distance from the cooperative node to the ideal position \(f_{m}\) of the next hop; \(R\) is the transmission radius of the node; \(e_{res}\) is the residual energy; \(e_{init}\) is the initial energy. \(K\) nodes are sorted into array by node \(m\) in order of \(Q\) value from big to small. (b) The location and residual energy of nodes 1-4 are notified to other cooperative nodes \(C_{m}^{1}\) and \(C_{m}^{2}\) of this hop. (c) The location and residual energy of nodes 1-4 are notified to nodes 1-4. (d) The cooperative node with the largest \(Q(m)\) value is selected. Its corresponding ideal location for the next hop is \(f_{m+1}\). The location is put into the PROB message and sent. The unselected nodes enter a dormant state to save energy consumption. Before the next cooperative node selection, they wake up to participate in the selection.
# Modeling Wicking Fabric Inhibition Effect on Frost Heave
## Coupled THM Modeling
### Coupled Parameters  
#### 2.2.1 Thermal Properties  
The thermal conductivity of frozen and unfrozen soils can be expressed in a general way shown in the following equation [26]:  
\[\lambda=\Big{(}\lambda_{sat}-\lambda_{dry}\Big{)}\lambda_{r}+\lambda_{dry} \tag{4}\]The thermal conductivity of saturated soil and normalized thermal conductivity can be calculated by Equations (5) and (6) [26].  
\[\lambda_{sat}\left\{\begin{array}{cc}\lambda_{s}^{1-n}\lambda_{i}^{n-\theta_{u} }\lambda_{u}^{\theta_{u}}&T<T_{f}\\ \lambda_{s}^{1-n}\lambda_{u}^{\theta_{u}}&T\geq T_{f}\end{array}\right. \tag{5}\]  
\[\lambda_{dry}=\chi\times 10^{-\eta n} \tag{6}\]  
\[\lambda_{r}=\frac{K_{0}S}{1+(K_{0}-1)S} \tag{7}\]  
The volumetric heat capacity can be calculated using a weight algorithm as  
\[C_{v}=C_{s}(1-n)+C_{u}\theta_{u}+C_{i}\theta_{i} \tag{8}\]  
To reduce the non-linearity of the governing equation, the concept of apparent heat capacity can be used to merge the heat capacity with the second term of Equation (1) on the right-hand side, which means the enthalpy change is due to the phase change [27; 28].  
\[C_{u}=C_{v}-L_{f}\rho_{i}\frac{d\theta_{i}}{dT}=C_{v}+\frac{L_{f}{}^{2}\rho_{i }}{gT}\frac{d\theta}{dh} \tag{9}\]  
By using the generalized Clapeyron equation, the apparent volumetric heat capacity (\(C_{a}\)) can be redefined by the hydraulic capacity (\(C_{H}\)) according to [PERSON]'s research [29]. The hydraulic capacity is defined as the derivative of the water content concerning the pressure head, which can be expressed as  
\[C_{H}=\frac{d\theta}{dh}=\left\{\begin{array}{cc}\frac{\alpha_{pg}\left(n_{ pg}-1\right)\left(\alpha_{pg}h\right)^{n_{pg}-1}}{\left(\left(\alpha_{cg}h \right)^{n_{pg}}+1\right)^{\frac{2n_{pg}-1}{n_{pg}}}}&h<h_{s}\\ 0&h\geq h_{s}\end{array}\right. \tag{10}\]  
where \(\alpha_{pg}\) and \(n_{pg}\) are the van Genuchten-Mualem fitting parameters. Therefore, Equation (9) can be rewritten as  
\[C_{a}=C_{v}+\frac{L_{f}{}^{2}\rho_{i}}{gT}C_{H}. \tag{11}\]  
When ice generates, unfrozen water can also exist in unsaturated soil. At this time, the soil water potential remains in equilibrium with the vapor pressure over pure ice [30]. Based on the thermodynamic relationship and the Clausius-Clapeyron equation, [PERSON] et al. [31] proposed an equation to describe the relationship between the soil nitric potential and final freezing temperature in the freezing process. When \(T\geq T_{f}\), the soil is unfrozen; when \(T<T_{f}\), the soil is under the freezing condition.  
\[T_{f}=T_{m}+\frac{gT_{m}}{L_{f}}h \tag{12}\]  
where \(T_{m}=273.15\) K is the nominal freezing temperature.
# Predicting the External Corrosion Rate of Buried Pipelines Using a Novel Soft Modeling Technique
## 1 Introduction
Compared with other neural network models (e.g., FF and MLP), ELM is faster to train and has a stronger generalization ability [34; 35; 36]. It is more suitable for dealing with corrosion prediction scenarios with limited data and high requirements for prediction accuracy [37]. PSO optimizes ELM to establish an external corrosion prediction model for buried pipelines, verified by case analysis on model robustness and superiority [38]. While gray predictive models can be used with limited data, they ignore corrosion-related factors and only model the corrosion prediction process over time. This results in a relatively low prediction accuracy [39]. Intelligent algorithms are useful in modeling regression between corrosion influencing factors and failure outcomes but have some inherent limitations. For example, BPNN has a complex structure, and SVM is too dependent on parameter settings. Some optimization algorithms are slow to converge and are prone to local optimization. In summary, the development of accurate and robust corrosion prediction models is essential to ensure the safe and efficient operation of pipelines. Different approaches have been proposed to improve the accuracy and generalization ability of the models, but there are still some challenges that need to be resolved. This paper applies nonlinear kernel principal component analysis (KPCA) to reduce the dimensions of the data. It eliminates redundant information between factors and simplifies the modeling process. Additionally, particle swarm optimization (PSO) is utilized to optimize the extreme learning machine (ELM) and establish an external corrosion prediction model for buried pipelines. The robustness and superiority of the model are confirmed through a case study.
## 3 Methodology
### Pattern Extraction Process
The input sentences were assessed by matching with the POS tags listed in Table 5. The sentences matched with one or more patterns were tagged as RS and NRS, otherwise. Finally, all NRS sentences were filtered out, and RS sentences were left for further processing. The POS-based filter reduced domain dependency and increased the accuracy of our proposed system. Here, the most significant POS tags used for the identification are \"Nouns\" and \"Verbs\". The heuristic patterns displayed in Table 4, are also transformed into UMLS-based patterns to achieve further generalization. The UMLS-based patterns, also known as semantic patterns, cover a wide range of recommendation sentences. This process, additionally improves the accuracy of the system by identifying the semantics of words and phrases in a sentence to clarify its contextual meaning. The UMLS is a knowledge source, which contains medical vocabularies, maintained by the US National Library of Medicine [35]. It provides an interface for retrieving biomedical concepts and semantic relations, by integrating a plethora of services, and assisting in biomedical information processing and retrieval. RS mostly contains some of the biomedical phrases, which can help to distinguish RS from NRS. Using this heuristic, first, we identify the UMLS phrases using a tool called MetaMap [36] which can identify the UMLS concepts behind medical text. Using this information, we map phrases of each sentence with its corresponding biomedical concept. We then extract UMLS patterns by analyzing the tagged sentences, identifiers, and their sequence. The example for one of the extracted patterns is shown in Figure 3. A list of UMLS patterns used in our study is shown in Table 7. The matched sentences with one or more of the UMLS patterns are finally tagged as RS, and NRS otherwise. The NRS tagged sentences are then filtered out, and RS sentences are stored for further processing.