esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Analysis
### Comparison with Other Heuristics Algorithms' Performance
#### 5.2.2 Experimental Results Analysis for the Heuristics Algorithms  
Due to the randomness of the results, each heuristics algorithm proposed in the experimental is run 30 times. Three instances are proposed in the research. Instance 1: Five carrier aircrafts (Nos. 2, 3, 9, 10, 12), \(n=5\). Instance 2: Nine carrier aircrafts (Nos. 2, 3, 4, 5, 8, 9, 10, 11, 12), \(n=9\). Instance 3: 13 carrier aircrafts (Nos. 1-13), \(n=13\). The objects are the makespan (M), average results (A), and _variance_, which are calculated with different wave carrier aircrafts. The final convergence gen (CG) can be also obtained. The results of the three instances are shown in Table 3.  
In the case of the smaller scale for the aircraft first wave attack (Instance 1), the four algorithms can achieve the same optimal results. When the scale increases, the difference among the four algorithms' performance increases. The advantages of DPGA are highlighted. The evolutionary convergence curves of the four algorithms for 13 carrier aircrafts and nine carrier aircrafts can be achieved in Figure 6.  
From a comparative analysis of the results from Table 2 and Figure 6, it can be seen that the proposed DPGA algorithm not only has strong global searching ability, it also has a more stable and faster convergence speed to global optimal solutions. The local search strategies integrated in the Memetic algorithm can improve the diversity of the population and increase the search performance of the optimal results. When the number of carrier aircrafts increases, the results showed that the global optimal scheduling can be obtained faster and better by adopting the bidirectional schedule strategy in the DPGA algorithm by adaptively adjusting the crossover and mutation parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(n\) & **Algorithm** & \(M\) (min) & \(A\) (min) & _Variance_ & _CG_ \\ \hline \multirow{4}{*}{5} & GA & 43 & 44.74 & 2.3739 & 6 \\  & DE & 43 & 44.68 & 2.4215 & 7 \\  & Memetic & 43 & 44.22 & 2.1045 & 6 \\  & DPGA & 43 & 43.74 & 1.3673 & 5 \\ \hline \multirow{4}{*}{9} & GA & 56 & 61.60 & 4.3494 & 61 \\  & DE & 56 & 61.35 & 4.2587 & 66 \\  & Memetic & 55 & 60.44 & 4.1078 & 35 \\  & DPGA & 54 & 59.33 & 3.825 & 21 \\ \hline \multirow{4}{*}{13} & GA & 69 & 73.44 & 5.182 & 67 \\  & DE & 69 & 73.56 & 5.0236 & 72 \\ \cline{1-1}  & Memetic & 68 & 72.98 & 4.9045 & 52 \\ \cline{1-1}  & DPGA & 64 & 68.28 & 4.0213 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the algorithm performance. DE: different evaluation algorithm; GA: genetic algorithm.
## 3 Improved Multiobjective Particle Swarm Optimization Algorithm
### Algorithm Improvements
#### 3.2.3 Maintenance Strategy of External Archive  
The non-inferior solution obtained by MOPSO algorithm is stored in the external archive. Due to the consideration of storage capacity and computational efficiency, the archive cannot be infinite. In order to avoid the maximum size of external archive, it is necessary to delete the most crowded individuals in the external archive. Most optimization algorithms use the crowding distance in NSGA-II to indicate the crowding degree [24].  
Although NSGA-II can maintain the external archive to a certain extent, it also has the defect of reducing the diversity of non-dominated solutions. Let's illustrate with an example. If there is a given set of non-dominated solutions as shown in Figure 1a, two of these solutions are redundant. Since solution C and solution D are the two solutions with the minimal crowding distance in the set of solutions, according to the processing method of NSGA-II, these two solutions need to be deleted at the same time. The result is shown in Figure 1b. We can intuitively see from the figure that the solution obtained by this method has poor distribution. In view of the above problems, this paper adopts a dynamic maintenance strategy to maintain external archive. The way to deal with crowding distance in dynamic maintenance strategy is to delete only the solution with the smallest crowding distance in the external archive every time, then identify those individuals whose order has been changed in the solution set and recalculate their crowding distance values. Repeat this process several times to ensure that the number of solutions in the external archive does not exceed the maximum threshold. Based on the dynamic maintenance strategy, solution C is deleted first, then calculate the crowding distance between B and D, and then solution E is deleted instead of solution D. The maintenance result is shown in Figure 1c. Comparing Figure 1c with Figure 1b, it is obvious that the solution set generated by dynamic maintenance strategy is more evenly distributed.
## 2 Related Work
### Traditional DST Approaches
[36] designed six models of MaxEnt where they parted one model for method, one for the requested slot, and four for joint goals. The models for the joint goals were applied on four informable slots (price range, food, area, name), separately. For each informable slot-value pair in SLU in \(k\)-th turn, the MaxEnt for the corresponding slot was employed to see whether the slot value in the user goal is right or not. The input of the model consists of 160 features extracted from the feature functions of joint goals and the model passed the confidence score as output. The MaxEnt model for the method was implemented to determine the user interaction with the system. The input comprises 97 features extracted from feature functions of the method and the output contains five confidence scores. The model for the requested slots determined the true or false requests of a user in the SLU. The input is comprised of 10 features extracted from feature functions of requested slots and a true confidence score is the output. Poor generalization regarding morphological or lexical variations in the training data, presents a constant key challenge in the DST domain. This can be counteracted using various techniques [47] such as delexicalization features. The systems using delexicalization pose exact matching of words for slot-value pairs generation with the construction of semantic dictionaries to identify alternative mentions of ontology [12; 31]. For example, \"_I am looking for a cheaper restaurant in the downtown area._\" where the words cheaper and downtown rephrase into Cheap and Centre, respectively. This type of word-rephrasing occurs with the adoption of a defined semantic dictionary with rephrasing for ontology val  
Figure 3: A typical Bayesian network for DST. ues Food = Cheaper: (affordable, low cost, inexpensive, low budget, cheaper, cheaply,...), Area = Centre: (central, center, downtown, midtown, center of the city,...). The delexicalized features for RNN word-based tracking can be used to share training data across disparate domains and improve performance in each individual domain [32]. Unfortunately, all the aforementioned models are typically heavily engineered with rules, and generative and discriminative approaches such as delexicalization, Bayesian networks, maximum entropy, and SVM. Thus, the extraction of features relevant to each state and the lack of learning the semantic information is challenging for these approaches. These models require separate training for each slot value. Additionally, the word embedding techniques are mostly dense-based, that cannot extract words features in a contextual way. Therefore, downstream modules are prone to morphological and lexical errors. Furthermore, the parameters grow with the ontology size and are proportional to the number of slots [21]. To overcome these issues, the model requires extra techniques to reduce the issues that increase the complexity of the model architecture.
## 3 Results
### Partial Least Squares--Path Modelling  
The results of the PLS-PM measurement model (outer model) provided evidence for the reliability and validity of all the constructs examined in the present study, as indicated by the loadings, \(\rho\) values, AVE values, and first and second eigenvalues (Table 2). The results from the structural (inner) model showed that: (a) Satisfaction of social support and family support for competence negatively predicted BPN frustration for competence (\(\beta=-0.41\) and \(-0.21\), respectively) and relatedness (\(\beta=-0.21\) and \(-0.41\)) significantly; (b) family support for competence positively predicted BPN satisfaction for competence (\(\beta=0.21\)), relatedness (\(\beta=0.22\)) and competence (\(\beta=0.32\)) significantly and negatively predicted BPN frustration for autonomy (\(\beta=-0.31\)) significantly; (c) family support for autonomy positively predicted BPN satisfaction for autonomy (\(\beta=0.27\)) and relatedness (\(\beta=0.24\)) significantly; (d) BPN frustration for relatedness and competence positively predicted depressive feelings (\(\beta=0.26\) and \(0.29\) respectively) significantly and negatively predicted purpose in life (\(\beta=0.22\) and \(0.36\)) significantly; and (e) BPN satisfaction for competence positively predicted purpose in life (\(\beta=0.19\)) significantly (Table 3).
## 3 Results
### Performance Comparison with Transformer
#### 3.4.1 Baseline Models  
In the experiment, we employed the Encoder structure of the Transformer as the standard feature extractor and used the Concat and Mean methods for feature fusion and representation, which are the same as the CAC model. Regarding parameter settings, we utilized a Transformer structure consisting of 6 stacked Encoders, with each Encoder having 8 attention heads (h = 8). To ensure consistency in the experiment, we adjusted the computation of position embeddings in the Transformer to use learnable position vectors, similar to the CAC model. The word embedding dimension and input sequence length were set to 768 and 512, respectively. We also employed the same data preprocessing methods and dictionary as the CAC model. The number of training epochs was set to 100, and the learning rate was set to 1e-3. Other parameter settings remained consistent with the CAC model.
## 3 Experimental Test Results and Analysis
### Total Water Absorption (TWA)  
As illustrated in Figure 10, the control mixture shows the lowest TWA of 7.7% at 28 days. Subsequently, this value sharply rises at early ages as the OWA percentage changes. After 72 h of water immersion, the TWAs are 21%, 19%, 23%, and 27% for 5%, 10%, 15%, and 20% of OWA content, respectively. Compared to the reference paste, these numerical values correspond to growth rates of 169, 141, 205, and 246%, with 10% OWA demonstrating the lowest rate among the OWA-based pastes. It is worth noting that all specimens exhibit constant TWA measurements throughout the next three days. This signifies that the pores are filled with water, meaning that they are saturated, and no additional water can be  
Figure 8: Evolution of flexural strength.  
Figure 9: Percentage decrease in flexural strength.  
absorbed [20]. It can be deduced that OWA has a detrimental influence on the water absorption of cement-based materials, as reported in the literature [20]. This high ability of OWA to absorb water can be attributed to two factors, including the porous structure and the lower specific gravity of OWA compared to ordinary cement. In detail, the incorporation of OWA in the cement paste increases the voids that are occupied by water, leading to greater total water absorption [13]. Moreover, the lower specific gravity of OWA entails a less dense matrix, and thereby, the interaction between water and lighter particles existent in OWA becomes more noticeable [13].
# A Parameter-Driven Methodology of Wheel Flat Modeling for Wheel-Rail Impact Dynamics
## 4 Wheel-Track Coupled Dynamics Model Considering Wheel Flat Geometry  
### Wheel-Track Coupled Dynamics Model  
To investigate the dynamic response of the wheel-rail system under varying wheel flat geometries, a coupled dynamic model of the wheel-rail system was developed, illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Wheel Damage** & **Defect Level** & **Defect Parameter (mm)** & **Speed Limit (km/h)** \\ \hline \multirow{3}{*}{Wheel tread abrasion} & I & \(0.25<d<0.5\) & \(v\leq 200\) \\  & II & \(0.5\leq d<1\) & \(v\leq 120\) \\  & III & \(d>1\) & \(v\leq 80\) \\ \hline \hline \end{tabular}  
After machining, each depth of the wheel flat was divided into 10 parallel sections, and the wheel roughness of each section was tested followed by reconstruction. The results of the wheel flat surface reconstruction are presented in Table 5.  
\end{table}
Table 4: Speed limit table for CRH3C EMU due to wheel damage [31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(d\) = 0.2 mm & \(d\) = 0.4 mm & \(d\) = 0.6 mm \\ \hline \multirow{3}{*}{Wheel flat} & \multirow{3}{*}{\
\begin{tabular}{c} \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} \\ \end{tabular} } \\  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \cline{1-1} \cline{3-4}  & & & \\ \hline \hline \end{tabular}  
\end{table}
Table 5: The results of the wheel flat surface reconstruction.  
The vehicle model comprises a car body and two bogies, each equipped with two wheel pairs. A wheel flat is simulated on the right wheel of the first wheel pair. Vertical dynamics are modeled using a Ruzicka approach, incorporating primary and secondary suspension vertical hydraulic dampers and rubber node stiffness at both ends. Vertical dampers are simplified as Maxwell models in a spring-damper series configuration. The dynamic equations of the entire vehicle system are expressed as Equation (22) [17].  
\[[M]\left\{\ddot{X}\right\}+[C]\left\{\dot{X}\right\}+[K]\left\{X\right\}=\left\{ P\right\} \tag{22}\]  
In Equation (22), \([M]\), \([C]\), and \([K]\) denote matrices of mass, damping, and stiffness. \(\left\{\ddot{X}\right\}\), \(\left\{\dot{X}\right\}\), and \(\left\{X\right\}\) represent vectors of acceleration, velocity, and displacement. \(\left\{P\right\}\) is the force vector of the vehicle system.  
In the track system, the rails are modeled as Timoshenko beams, discretely supported on sleepers by rail pads and fasteners, which are represented as a combination of springs and dampers. Considering the elastic properties of the ballast, springs and dampers are inserted between the sleepers and the subgrade. Additionally, no stochastic excitation is introduced into the track system, except for the wheel flat.  
The main parameters of the vehicle and track model are shown in Appendix A.
# An Aeromagnetic Compensation Algorithm Based on a Temporal Convolutional Network
## 2 Materials and Methods  
### Tolles-Lawson Model  
The Tolles-Lawson model (T-L model) is widely used in aeromagnetic compensation of fixed-wing aircraft. The basic idea is to use the magnetic measurements of the vector magnetometer to calibrate the scalar magnetometer. The main components of aeromagnetic survey data can be expressed as follows:  
\[\textbf{B}_{\text{tmi}}=\textbf{B}_{\text{geo}}+\textbf{B}_{\text{tgt}}+ \textbf{B}_{\text{a}}+\textbf{B}_{\text{tv}} \tag{1}\]  
where \(\textbf{B}_{\text{tmi}}\) represents the total magnetic field acquired by the magnetic sensor; \(\textbf{B}_{\text{geo}}\) represents the geomagnetic field; \(\textbf{B}_{\text{tgt}}\) represents the magnetic anomaly of the target; \(\textbf{B}_{\text{a}}\) represents the magnetic interference caused by the flight platform; and \(\textbf{B}_{\text{tv}}\) represents the diurnal variation in the geomagnetic field. During short-term navigation, the diurnal variation in the geomagnetic field \(\textbf{B}_{\text{tv}}\) can be removed through monitoring stations. When there is no target, it can be assumed that the total magnetic field \(\textbf{B}_{\text{tmi}}\) only includes the geomagnetic field \(\textbf{B}_{\text{geo}}\) and the magnetic interference of the flight platform \(\textbf{B}_{\text{a}}\), which can be expressed as follows:  
\[\textbf{B}_{\text{tmi}}=\textbf{B}_{\text{geo}}+\textbf{B}_{\text{a}} \tag{2}\]  
The above equation can be further derived as follows:  
\[\left|\textbf{B}_{\text{geo}}\right|^{2}=\textbf{B}_{\text{geo}}\cdot \textbf{B}_{\text{geo}}=(\textbf{B}_{\text{tmi}}-\textbf{B}_{\text{a}})\cdot (\textbf{B}_{\text{tmi}}-\textbf{B}_{\text{a}}) \tag{3}\]  
\[\left|\textbf{B}_{\text{geo}}\right|^{2}=\textbf{B}_{\text{tmi}}\cdot \textbf{B}_{\text{tmi}}-2\textbf{B}_{\text{tmi}}\cdot\textbf{B}_{\text{a}}+ \textbf{B}_{\text{a}}\cdot\textbf{B}_{\text{a}} \tag{4}\]  
\[\left|\textbf{B}_{\text{geo}}\right|=\sqrt{\left|\textbf{B}_{\text{tmi}} \right|^{2}-2\textbf{B}_{\text{tmi}}\cdot\textbf{B}_{\text{a}}+\left|\textbf{ B}_{\text{a}}\right|^{2}} \tag{5}\]  
\[\left|\textbf{B}_{\text{geo}}\right|=\left|\textbf{B}_{\text{tmi}}\right| \sqrt{1-2\frac{\textbf{B}_{\text{tmi}}\cdot\textbf{B}_{\text{a}}}{\left| \textbf{B}_{\text{tmi}}\right|^{2}}+\frac{\left|\textbf{B}_{\text{a}}\right|^ {2}}{\left|\textbf{B}_{\text{tmi}}\right|^{2}}} \tag{6}\]  
The magnetic interference of a flight platform, with a capacity of about 100 nT, is much smaller than that of the geomagnetic field, with a capacity of about 50,000 nT, so the \(\left|\textbf{B}_{\text{a}}\right|^{2}/\left|\textbf{B}_{\text{tmi}}\right|^{2}\) term can be ignored.
# Cooperative Crossing Cache Placement in Cache-Enabled Device to Device-Aided Cellular Networks
## 5 Simulation Results
### Impact of Varying Network Parameters  
In Figure 2, we compare the offloading probability with different values of the required sending time \(\tau\). As shown in this figure, with the growth of user density \(\lambda_{u}\), the offloading probability decreases because the increase in density of device transmitters facilitates more simultaneous cache-aided transmission links, leading to increased interference among links. We also observe that when the user density is low, the offloading probability increases as the sending time increases. This result is consistent with our expectations. Given a longer sending time, the users have a greater chance of transmitting the desired content but as the user density increases, the increased number of potential device transmitters leads to increased interference. Thus, the offloading probability decreases. This implies that \(\lambda_{u}\) is the main factor that has an impact on offloading traffic.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Values** \\ \hline D2D distance: \(R\) & 30 m \\ Density of BSs: \(\lambda_{b}\) & \(1/\pi 500^{2}\) \\ Density of users: \(\lambda_{u}\) & \(1/\pi 50^{2}\) \\ Proportion of D2D transmitters: \(\
u\) & 0.5 \\ Sending rate: \(\lambda_{t}\) & 0.6 \\ Transmit power of BS: \(P_{b}\) & 46 dBm \\ Transmit power of user: \(P_{u}\) & 23 dBm \\ Content library size: \(K\) & 50 \\ Cache storage size: \(M\) & 7 \\ Size of content: \(S\) & 20 Mbits \\ Bandwidth: \(W\) & 10 MHz \\ Sending time: \(\tau\) & 15 s \\ Path loss exponent: \(\alpha\) & 4 \\ Zipf distribution parameter: \(\eta\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
Figure 2: Comparison of offloading probability versus user density \(\lambda_{u}\) with different values of sending time \(\tau\).  
In Figure 3, we show the effect of the D2D distance \(R\). We can see that the offloading probability first increases and then decreases with \(R\). This can be attributed to the fact that the increase in D2D distance leads to more device transmitters, generating more interference. On the other hand, when the Zipf parameter \(\eta\) is larger, the offloading probability increases with \(R\) for small values of \(R\) because increasing \(\eta\) makes the popularity distribution even higher and the requesting user can thus find the desired content at a smaller D2D link distance. In addition, the offloading probability further decreases for a high value of \(\eta\) at a large D2D distance, owing to more severe interference. From this figure, we can see this trend is more obvious with the increase in the Zipf parameter \(\eta\).
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 3 Different TO Software Platforms
As mentioned in Section 1, an in-depth comparison was conducted in this paper among three TO commercial software platforms--Solidworks, ANSYS Mechanical, and ABAQUS. Solidworks offers size, parametric shape, and topology optimization in the student edition. Regarding the TO, it uses the traditional compliance TO with the SIMP method when mass, stiffness, and displacement can be used as objective functions. Furthermore, it offers member size, mold (pull direction), and planar symmetry in addition to the standard manufacturing constraints. The optimized models consist of faceted geometries. Stress and displacement plots can be created together with the plot of element density distribution. ANSYS Mechanical supports the same optimization categories as SolidWorks. However, it also contains lattice and non-parametric shape optimization. Concerning the TO, it applies both the SIMP and the level set methods. Compliance, mass, and volume are the three available values that can be used as objective functions. Extrusion, cyclic symmetry, overhang angle, and 3D printing direction are the four additional manufacturing constraints. The optimized models are also faceted geometries here, but the only available plot is the element density distribution. Finally, ABAQUS offers additional topography optimization. Furthermore, it contains a plethora of objective functions and constraints that can be also used in combinations. A faceted geometry represents the optimized designs again, while stress, strain, and displacement estimations are available here. The optimization time, optimized designs, and pre-and post-processing of these three software platforms are compared further in this paper using the case studies presented in Section 4.
# Reinforcement Learning Based Resource Management for Network Slicing
## 6 Performance Evaluation
### QoS Weight  
We investigated the effect of penalty setting on the safety pursuit of the resource trading in Q-learning. In Equation (11), the penalty is a function of the QoS weight \(\omega\). The tenant putting a high value on the QoS satisfaction rate of end-users may set the QoS weight large for the Q-learning process. In this case, the tenant purchases more resources than it needs because it tends to pursue safety against QoS violations.  
Figure 6: QoS satisfaction rate with respect to the trading interval.  
Figure 7: QoS satisfaction rate with respect to the flow ratio.  
Figure 8 shows the total amount of the resource that the tenant trades during the lifetime of the slice. When a high QoS weight was applied, the tenant unnecessarily purchased more resources. This implies that a higher QoS weight can decrease the number of QoS violations but also lead to a lower profit.