esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Stability Analysis  
#### 3.3.1 Thermogravimetric Analysis  
The flame retardant effect of the material has been studied above, while the flame retardant effect of the material is often related to its pyrolysis process. In order to study the thermal stability of the material and the effect of the additive on the thermal stability of the material, the samples were heated from 20 \({}^{\circ}\)C to 800 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) by a differential thermogravimetric analyzer. The TG curves of EP, F5, F5N5, and F5N10 are included in Figure 4a, and the corresponding DTG curves are shown in Figure 4b. Figure 4c are the TG curves of EP, F7, F7N7, and the corresponding DTG curves are shown in Figure 4d. Figure 4e shows the TG curves of EP, F10, F10N5, and Figure 4f shows the corresponding DTG curves. Six partial enlarged views of vague regions are shown in every figure of Figure 4, which clearly show that there are some differences.  
Figure 4: Transition temperature (TG) and DTG curves of CF/EP and CNTs/CF/EP composites. (**a**) TG and (**b**) DTG curves of EP F5 F5N5 F5N10, (**c**) TG, and (**d**) DTG curves of EP F7 F7N7, (**e**) TG and (**f**) DTG curves of EP F10 F10N5.  
The figures show that the thermogravimetric curves of the material also decrease stepwise as temperature increases. From Figure 4a, the TG curves of the four samples are almost coincident. It can be seen from Table 3 that the initial decomposition temperature of F5N5 and F5N10 are less than that of EP and F5, but it increases with the increase of CNTs. From Figure 4b, it can be seen that the temperature of the first peak of the three-phase composite is not changed, but the peak area is relatively small, and decreases with the increase of the CNTs, indicating that mass loss decreases. This shows that the addition of CNTs inhibits the pyrolysis of EP composites. It can be observed from Figure 4c, the mass loss rate of F7N7 is slower than EP and F7 at 20-250 \({}^{\circ}\)C. From Table 3, it can be seen that the initial decomposition temperature of F7N7 is higher than that of the other two materials. This shows that the addition of CNTs improved the initial decomposition temperature of the material. At 250-350 \({}^{\circ}\)C, the trend of all the curves in Figure 4c is nearly coincident, indicating that the maximum mass loss rate is almost the same, which coincides with the temperature of the DTG peak shown in Figure 4d. The first peak areas of F7N7 and F7 are almost similar but are smaller than that of EP. As can be seen from Figure 4e, the mass loss rate of EP is smaller than that of F10 and F10N5 at 20-250 \({}^{\circ}\)C. The initial decomposition temperatures of F10 and F10N5 from Table 3 are lower than that of EP. From Figure 4f, it can be seen that the area of the first peak of F10 is smaller than others indicating the mass loss is less. It is understood from Table 3 that the temperature increased at this time. This shows that the addition of CNTs and CF under this proportion synergistically inhibits the pyrolysis of EP composites.
# Enhanced Teaching-Learning-Based Optimization Algorithm for the Mobile Robot Path Planning Problem
## 5 Experimental Studies
### Presentation of best routes  
The best routes of information of different algorithms for all instances are detailed in Figures 6-9 in order to vividly state the algorithm performance. In this regard, the best robot path for a candidate algorithm for one instance was described by virtue of a single environment map. Meanwhile, objective values versus the number of iterations are illustrated in each figure by means of convergence curves, which represent the best results among 50 independent trials of each algorithm for the corresponding instance. The following comments can be drawn based on these results.  
* For instance 20 \(\times\) 20, it can be seen from Figure 6 that ETLBO yielded the best solution performance of the three test algorithms, with the best route length of 151.92 (Figure 6d). ACO(G) performed second to ETLBO, with the best route length of 154.85 (Figure 6c). ACO(L) showed the weakest performance in the best route length, with an objective value of 156.07 (Figure 6b);
* For the 30 \(\times\) 30 instance, ETLBO obtained more accurate solutions than ACO(L) and ACO(G) in terms of the best route length metric. The objective value of the proposed ETLBO amounted to 225.56 (see Figure 7d), which is superior to 232.63 (given by ACO(L) in Figure 7b) and 231.42 (provided by ACO(G) in Figure 7c);
* Similar conclusions can be drawn from Figures 8 and 9, in which ETLBO yielded the best solution performance. The corresponding objective values for instances 40 \(\times\) 40 and 50 \(\times\) 50 were 291.27 (see Figure 8d) and 378.70 (see Figure 9d), respectively. ACO(G) performed second to ETLBO, with metric values of 291.27 (see Figure 8c) and 378.70 (see Figure 9c). ACO(L) ranked at the bottom, with an objective value of 302.99 (see Figure 8b) and 400.42 (see Figure 9b).
* The convergence curves illustrated in four figures (Figures 6a, 7a, 8a and 9a) indicate that the proposed ETLBO not only had a faster convergence in solving all test instances when compared to the state-of-art ACO(L) and ACO(G) but the final convergence accuracy of the proposed approach was better than that of the compared algorithms.  
Figure 6: Best solution information of instance 20 \(\times\) 20.  
Figure 8: Best solution information of instance 40 \(\times\) 40.  
Figure 7: Best solution information of instance 30 \(\times\) 30.  
In summary, among 50 independent trials, the proposed ETLBO exceeded other compared algorithms in terms of the best route length metric.
## 4 Discussion  
Air route alternation in abnormal or emergency situations may affect operators' cognition and performance, and subsequently bring risks of human errors and accidents. Under such circumstances, it is also necessary to examine whether the interaction displays designed based on the routine environment can provide effective support for an operator's cognition and performance in an abnormal situation. In the present study, 24 participants were involved to perform simulated flight attitude control tasks, with the two conditions of air-route-as-planned and air-route-altered and three simulated display designs (center-layout with a low-salience design for Interface 1, center-layout with a medium-salience design for Interface 2, and off-center-layout with a high-salience design for Interface 3). Data of the subjective scales, behavioral performances and eye movements of all the partic  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**AoIs**} & \multicolumn{4}{c}{**Air Route Alternation**} & \multicolumn{4}{c}{**Display Design**} & \multicolumn{4}{c}{**Interaction**} \\ \cline{2-11}  & \multicolumn{2}{c}{**As**} & & & & & & & & & \\  & **Planned** & **Altered** & \(p\)**-Value** & **ES** & **Interface 1** & **Interface 2** & **Interface 3** & \(p\)**-Value** & **ES** & \(p\)**-Value** \\ \hline Fixation rate (*) & & & & & & & & & & \\ Overall & (0.180 & 0.339 & \textless\)0.001 ** & 0.893 & 0.246 (0.031) & 0.260 (0.027) & 0.273 (0.030) & 0.371 & 0.073 & 0.736 \\ Altitude & (0.168) & (0.307) & \textless\)0.001 ** & 0.904 & 0.231 (0.025) & 0.210 (0.023) & 0.271 (0.031) & 0.014 * & 0.279 & 0.360 \\ Speed & (0.160) & (0.297 & \textless\)0.001 ** & 0.883 & 0.207 (0.031) & 0.216 (0.028) & 0.217 (0.037) & 0.258 & 0.099 & 0.306 \\  & (0.021) & (0.031) & & & & & & & & \\ Course & (0.152) & (0.251) & & & & & & & & \\ (0.017) & (0.031) & & & & & & & & & \\ Average pupil diameter (mm) & & & & & & & & & & & \\  & 40.300 & 40.529 & \textless\)0.001 ** & 0.593 & 40.432 (0.108) & 40.502 (0.112) & 40.309(0.113) & \textless\)0.001 ** & 0.424 & 0.830 \\  & (0.109) & (0.113) & & & & & & & & & \\ \hline \hline \end{tabular} Note: * Significant difference at the 0.05 level; ** Significant difference at the 0.001 level. Partial n\({}^{2}\) was used to calculate the effect size (ES) for the ANOVA results.  
\end{table}
Table 3: ANOVA results for the eye movement measures (mean (SD)).  
ipants were collected to evaluate the effects of air route alternation and display design on an operator's SA, performance and mental workload.
## 3 Post-Processing Methodologies  
The focus of this paper lays on both the time-domain and frequency-domain pixel-wise analysis approaches. Further, a distinction was made between processing techniques that evaluated either a single time (or frequency) bin, or an integration of multiple time (or frequency) bins. In this study, the former was indicated by '_single-bin evaluation_', while the latter was indicated by 'integrated-bin evaluation'. Besides the single-bin and integrated-bin evaluation techniques that are discussed hereafter, there is a wide range of processing techniques that perform their evaluation by considering multiple time (or frequency) bins [3; 26; 28; 30; 31; 34].  
Figure 2 schematically presents the recorded temporal evolution for a pixel located at damage-free material (sound pixel) and for a pixel located at a defect (defected pixel). Only the cooling regime after the flash excitation was considered for further evaluation [18]. In the following sections, a brief overview of the considered analysis techniques was provided.
# High-Throughput Line Buffer Microarchitecture for Arbitrary Sized Streaming Image Processing
## 2 Background
### Demand on Arbitrary Sized Image Processing  
In many real-world image processing scenarios, the size of the image is unpredictable before the system run-time. To demonstrate, Figure 2 presents two example cases.  
The first case is the Region of Interest (ROI) processing. As Figure 2a shows, the ROI is selected from the entire view of the image for analysis. This mechanism exists in most image applications that effectively reduce the computation workload. However, the ROI is defined by the end-user--hence, the size of ROI is unpredictable during the hardware design time. Furthermore, multiple ROIs may exist on the same view, such that the hardware is required to accommodate images of different sizes in processing one frame.  
The second case presents how the arbitrary size image processing is also demanded in cloud computing. As Figure 2b shows, the users at the edge side upload images to the cloud for the  
Figure 1: A streaming architecture example for image processing with a 2D pattern. The architecture has three components: buffer, operator, and interconnections.  
computation-intensive applications (such as inference of deep learning, etc.). The cloud server sends the workload to the FPGA accelerator to reduce CPU processing time.  
In both cases, the streaming architecture on FPGA is required to process arbitrary sized images. Furthermore, the working mode of hardware should be quickly switched for seamlessly processing the images. The conventional FPGA reconfiguration costs seconds to half a minute, which greatly reduces the system efficiency. Thus, we investigate a streaming architecture that can be rapidly programmed to process images in an arbitrary size.
# Monitoring-System Development for a Bottom-Set Gillnet through Time-Domain Dynamic Simulations
## 5 Global Performance of the Bottom-Set Gillnet
### Effects of Wave Excitations  
Figures 8 and 9 present the time histories of the lateral and vertical responses at the middle location (Y = 0.0 m, Z = \(-\)23.0 m) in different wave conditions and their envelopes for the significant wave height (\(H_{\mathrm{s}}\)) of 5.0 m. The wave and current directions were in the X direction, i.e., perpendicular to the net plane. In this section, the rope length was 100 m and the current velocity was set as 0.6 m/s. Figures 8a and 9a show that the net dynamics were small in typical operating conditions, i.e., \(H_{\mathrm{s}}\) was less than 2.0 m. However, in the fishery-prohibition condition (\(H_{\mathrm{s}}\) = 5.0 m), significant fluctuations were noticed, and their amplitudes increased more than the linear increase. As shown in Figures 8 and 9, the lateral motions were usually more significant in the negative direction than in the positive direction (direction of wave and current) because mooring tensions could more effectively restrict the positive-direction responses. The maximum lateral response was observed in the location between mooring lines because the highly flexible net assembly was free to move there. The vertical responses were, on the other hand, mostly in the positive direction, since the bottom of the net almost touched the seabed. They had the largest values at the positions of mooring lines due to the effects of wood bars and rope tensions. The mooring lines hardly restricted the vertical motions because the mooring lines were almost lateral, i.e., perpendicular to the net plane. The vertical responses between mooring lines were significantly coupled with lateral concaved responses since the net was flexible.  
Figure 8: Time histories of the lateral motions of the net at the top-middle location (Y = 0 m, Z = \(-\)23 m) under different wave conditions (**a**) and motion envelopes along the net length at \(H_{\mathrm{s}}\) = 5.0 m and \(T_{p}\) = 10.0 s (**b**) (current velocity = 0.6 m/s).  
Figure 9: \(Cont.\)Figure 10 shows the time histories of the mooring tension at the mooring line located in the upper center under different wave conditions and the corresponding distribution of mooring tension along the length for an \(H_{s}\) of 5.0 m, considered to be the fishery-prohibition condition. Again, the fluctuations in mooring tensions were small when \(H_{s}\) was less than 2.0 m, whereas mooring tensions were significantly amplified for \(H_{s}\) = 5.0 m. The high mooring tension under this fishery-prohibition condition was mainly caused by the sudden increase of tension by snap loading after the mooring line was slack. In this slack-taut repeating scenario, not only can mooring lines be broken, but also, anchors can be dragged out in harsh waves and strong currents. Progressive failures of mooring lines can happen after one mooring line is broken.
## 6 Dependability for MCAs in 6G
### Dependability Analysis of 6G MCAs  
Based on the information from the previous section, MCAs are expected to be extremely dependable. This is not only due to their stringent network requirements, but also due to their use cases. Lack of dependability in MCAs could badly harm operations at a production factory or potentially place human lives at risk in telesurgery, for instance. Below, we discuss the dependability of MCAs from the perspectives of the four dimensions.
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 4 Experiments
### Discussion
#### 4.6.3 Impact of the Number of Layers and Layer Dimensions  
In this section, we evaluate the effectiveness of introducing GCN for learning higher-order mashup and service features. We vary the number of layers \(L\) in the GCN from 1 to 3  
Figure 4: Impact of different fusion strategies. (**a**) Recall@K; (**b**) NDCG@K.  
Figure 3: Performance comparison of different variants of MRGCN. (**a**) Recall@K; (**b**) NDCG@K.  
and evaluate the performance. In addition, we evaluate the impact of feature dimension \(d_{l}\) on the performance, which varies in the set \(\{8,16,32,64,128\}\). The results are shown in Figures 5 and 6. From Figure 5, we can see that the performance of our model increases from one layer to two layers because more layers can aggregate information from higher-order neighbors to enrich mashup and service features. When the number of layers is 3, the performance decreases, which indicates that too many layers can introduce noise and cause the overfitting problem. From Figure 6, we can see that the performance initially increases with the growth of the feature dimension, as a small dimension will probably constrain the model capacity. When the dimension is over 64, the performance decreases when we further increase the dimension, while the training time drastically increases. Therefore, a moderate feature dimension set as 64 is sufficient for model learning.
# Enhancing Logistic Regression Using Neural Networks for Classification in Actuarial Learning
## 2 Notation and Overview of Techniques
### Neural Networks
#### 2.7.5 Embedding Layer  
A common technique for the preprocessing of categorical features is to convert them into numeric vectors. The standard choice is to represent a feature \(x\), with values over \(c\) possible categories \(v_{1},\ldots,v_{c}\), by a binary \(c\)-dimensional vector \(x\) with a single nonzero coordinate such that \(x_{i}=1\) iff \(x=v_{i}\). This is the so-called \"one-hot encoding\".  
Neural networks offer an important alternative, namely that categorical features are represented by numerical vectors and these vectors are learnt just like we learn the weights of the individual layers. This is achieved by the so-called embedding layer that maps each feature value to its corresponding embedding, i.e., its vector representation. This is implemented by a simple hash table that keeps the corresponding representation for each feature value and loads it when required. All modern neural network frameworks such as Keras, PyTorch, etc., offer embedding layers.  
One major advantage is that the learnt feature representations capture the intrinsic characteristics of the features. For example, embeddings are very popular in natural language processing where the semantic similarity between words is reflected in the embeddings. A notorious example is that \(emb(king)-emb(queen)\approx emb(man)-emb(woman)\).
## 1 Introduction and Related Works
The aim of this research was to create an algorithm that can operate in an environment without the need of an observer to predict the motion of the agents and operate without any a prior knowledge or knowledge of the actual state of the environment. Without the centralized control element and with the algorithm not being reliant on communication between agents as its design features, it can be expected that the algorithm will be robust in this regard. Not having the motion predictable but rather emergent due to using evaporating pheromone is a very important feature in a mission where deterministic algorithms perform stable and recognizable patterns (border guarding, area scanning, etc.) and is beneficial in the event of a communication failure. Another advantage of the pheromone marks control swarm strategy is the fact that each part of the explored space has a non-zero probability that it will be explored at any time during the exploration. This is an important feature that is different from deterministic algorithms where a cell has zero probability to be visited between two passes in the same exploration episode, [35]. Another advantage of our approach is its scalability and the ability of the algorithm to change swarm size according to environment type and size. This paper is organized as follows. In Section 2, we recap our previous work and studies regarding pheromone-based swarm search technique. In Section 3, we present the definitions and descriptions of the basic constants of the proposed algorithm, and these are described in detail in Section 4. In Section 5, we present the simulation experiments, discussing the obtained results. In Section 6, we conclude the work and present the results in a broader scope of application possibilities and also expand the ideas for future work and improvements of the algorithm.
## 5 Experiments and Data Analysis  
In experiments, the CSI-RS signal period is 20 ms, and the maximum unambiguous Doppler frequency is 50 Hz. The measurable Doppler frequency shift range is [\(-25\) Hz, 25 Hz]. For a unilateral rotation target, when the signal carrier frequency is 3.55 GHz, the rotation radius is 0.3 m, and the double base angle is 0, the maximum measurable speed is 0.56 rps. The presence of the double base angle makes the maximum measurable speed slightly greater than 0.56 rps. The experiment is conducted in two measurement scenarios.
## 4 Security Analysis
### The Attacking Process after ACD Migration
#### 4.3.2 The Steady-State Probability of Each Node  
Let \(X_{0},X_{1},\ldots,X_{n}\) be random variables, where \(X_{i}\in[0,L]\) denotes the attacking location at the beginning of \(i^{th}\) migration period. At \(X_{0}=0\), the attacker is at the initial position which is the attacking entrance.  
When a Markov chain is irreducible aperiodic, it has only one stationary distribution \(\{\pi_{j},j\in[0,L]\}\)[28]. First, for each pair of nodes in the built Markov chain, a path exists from node \(i\) to node \(j\), so this makes it an irreducible Markov chain. Then, as for periodicity, the  
Figure 6: Markov chain of the stochastic walk.  
Markov chain can return to its state with a period greater than 1. For a positive recurrent state \(i\), its period \(d\) can be calculated as  
\[d=gcd|\{n>0:p(X_{n}=i|X_{0}=i)>0\}, \tag{5}\]  
where \(gcd|\{\cdot\}\) denotes the greatest common divisor of the set elements. The period of this Markov chain is 1. Thus, it is an aperiodic Markov chain.  
Then the steady-state probability \(\pi_{i}\) (\(i=0,1,\ldots,L\)) that the attacker has compromised \(i\) nodes satisfies,  
\[(1-\omega)\mu\pi_{0}=\omega\pi_{1}, \tag{6}\]  
\[\ldots\]  
\[(1-\omega)\mu\pi_{k-1}+\omega\pi_{k+1}=(1-\omega)\mu\pi_{k}+\omega\pi_{k}, \tag{7}\]  
\[\ldots\]  
\[(1-\omega)\mu\pi_{L-1}=\omega\pi_{L}, \tag{8}\]  
where \(k=3,\ldots,L-2\).  
If \(\omega=(1-\omega)\mu\), \(\pi_{i}\) follows a uniform distribution, then  
\[\pi_{k}=\frac{1}{L+1}\text{ for }k\in[0,L]. \tag{9}\]  
Otherwise, \(\omega\
eq(1-\omega)\mu\), according these relations, \(\pi_{i}\) can be given by formulas by tagging \(\pi_{0}\). Then, since the sum of all probabilities is equal to 1, \(\pi_{0}\) can be computed. Through the finite number of computing steps according to the relation between \(\pi_{i}\) and \(\pi_{0}\), we get the values of \(\pi_{k}\). For ease of presentation, we denote that \(D=(1-\omega)\mu\).  
\[\pi_{k}=\frac{\omega-D}{\omega-\left(D/\omega\right)^{L}D}\left(\frac{D}{ \omega}\right)^{k}, \tag{10}\]  
where \(k\in[0,L]\). It can be proved that \(0<\pi_{k}<1\) and \(\omega-(D/\omega)^{L}D\
eq 0\), if \(\omega\
eq D\).  
Hence, the steady-state probability of the target node being compromised can be expressed as  
\[\pi_{L}=\begin{cases}\frac{\omega-(1-\omega)\mu}{\omega-\left[\frac{(1- \omega)\mu}{\omega}\right]^{L}(1-\omega)\mu}\left[\frac{(1-\omega)\mu}{\omega }\right]^{L}&\omega\
eq(1-\omega)\mu,\\ \frac{1}{L+1}&\omega=(1-\omega)\mu.\end{cases} \tag{11}\]