esa-sceva/satcom-chunk-collection huggingface dataset:
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Path Planning
#### 3.2.4 Hazardous Weather  
The proposed approach was further extended to consider scenarios where the weather affects the followed path. The hazardous weather conditions considered in this research are heavy rain and strong wind gusts. These scenarios were embedded in the \(A^{*}\) algorithm to force the UAV to take a detour.  
Figure 12 shows the obtained results and Table 3 summarizes the average number of successful links under hazardous weather. The results show that by incorporating these scenarios into the \(A^{*}\) algorithm, a slight increase of 6.1% was obtained in path length, and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Path Type** & **Distance (m)** & **UAV Elevation** & **No. of Links** & **Links/Distance** \\ \hline Classic & 62,539 & 100 & 721,936 & 11.543773 \\ \hline Blockage map & 76,581 & 100 & 1,011,607 & 13.20963 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average number of successful links per meter of flight.  
Figure 11: Cumulative links between the UAV and points of interest throughout the paths.  
Figure 10: \(A^{*}\) algorithm planned paths. The yellow area denotes the zones within the visibility region, whilst the purple one denotes the blockage regions.  
a proportional increase of 6.9% was obtained in successful links. These results were further confirmed by the cumulative link plot shown in Figure 13.
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 4 Results and Discussion
### The Effect of Delamination Location on Laser Ultrasound  
The effect of delamination location on laser ultrasound was studied by scanning laser source method and the schematic diagram is shown in Figure 9, where the PRD was set to 2 mm. The scanning step was 1 mm and the widths of the delamination were 6 mm and 2 mm. The variations of the displacements of LS-SL, 2S, and 2S\({}_{\text{b}}\) are shown in Figure 10. In  
Figure 8: Comparison of displacement of ultrasonic waves in different modes in good model and a model with delamination: (**a**) LS-SL, (**b**) 2S, (**c**) 2S\({}_{\text{b}}\), and (**d**) Displacement differences of the LS-SL, 2S, and 2S\({}_{\text{b}}\) in the models without and with defects.  
Figure 7: Comparison of laser ultrasonic waves in a model without defect and a model with delamination defect with different PRDs: (**a**) 1 mm, (**b**) 2 mm, (**c**) 3 mm, and (**d**) 4 mm.  
Figure 10a-c, LS-SL, 2S, and 2S\({}_{\text{b}}\) could effectively detect the approximate position and size of the 6 mm wide delamination defect, and the minimum point of 2S was the center of the delamination defect. In Figure 10d,e, LS-SL and 2S could effectively detect the approximate position and size of the 2 mm wide delamination defect, and the minimum point of LS-SL was the center of the delamination defect. In Figure 10f, 2S\({}_{\text{b}}\) was used to detect the 2 mm defect, and the results showed that there were two defects, indicating that the error of using 2S\({}_{\text{b}}\) to detect small defects is larger. The reflected shear wave (2S) from the composite interface is very suitable for the detection of delamination defects in composite interfaces.
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 4 Abstract Conceptual Structure of Organizational Models
### Structural Dimension
#### 4.2.1 Graphs of Roles and Groups  
In the structural dimension, the organizational models present three fundamental concepts: Role, group and role relationships. The instantiation of these three interrelated concepts forms the specification of the internal structure of agent organizations. In essence, structural specifications can be characterized directed graphs where:  
* The nodes correspond
* either to the definition of _groups_ (in AGR, MOISE+ and OPERA) or _teams_ (in STEAMS),
* or to the definition of _roles_ (in all models);
* the edges represent
* either the _decomposition of a group in subgroups_ (or a team in subteams), forming a _group_ (_team_) _hierarchy_,
* or binary _relationships between roles_,
With regard to the _group_ (_team_) _hierarchy_, there are two situations present in the models. On the one hand, we have a unique _root group_ that represents the organization as a whole (the hierarchy is a rooted tree), what occurs in STEAM, MOISE+ and OPERA. On the other hand, there is no explicit _root group_, or even the decomposition of groups in subgroups, what is the case of AGR. Regarding the  
Figure 3: Similar structural specifications written in STEAM, AGR, OPERA, MOISE+ and ISLANDER, for a team of agents part of a simulated soccer game (example taken from [24; 25]). The team is composed of eleven _players_ (one _goalkeeper_, three _backs_, five _mid-filders_ and three _attackers_) and one _coach_, and is divided in three groups: _Defense_, _midfield_ and _attack_. The class diagram (Ecore metamodel) in the center of the figure represents the conceptual pattern identifiable in the various approaches of structural modeling. The dotted arrows detail what concepts are captured by what classes of the metamodel. binary relationships between roles_, in general they are directed and, in the models AGR, MOISE+ and ISLANDER they are subdivided in various kinds with diverse interpretations. Such graphs of roles and groups receive the names of \"organization hierarchy\" in STEAM, \"organizational structure\" in AGR, \"structural specification\" in MOISE+. In ISLANDER, they are part of the definition of a \"dialogic framework\". In OPERA, they form the \"social structure\". In Figure 3, the conceptual pattern identified in the structural dimension is represented by means of an Ecore metamodel. This metamodel is presented in more detail in Table 4.
## 3 High-Performance LU Decomposition
### Implementation
#### 3.2.2 Integration into DPsim Real-Time Simulator  
The performance of the LU decomposition methods was analysed in real-time simulation experiments. We employed the open-source C++ real-time simulator DPsim [31]. DPsim is implemented based on the C++ library for linear algebra named Eigen [17]. The Eigen library offers support for custom linear solver libraries. By interfacing our implementations with the open-source Eigen library, they shall become reusable by other software projects. For the integration of a new linear solver, the three main steps, as presented in Section 2.2, must be interfaced:  
* The preprocessing step using the analyzePattern routine,
* the factorisation step using the factorize routine and
* the solution step using the _solve_impl routine.  
Eigen's standard implementation for the LU decomposition of sparse matrices is based on the SuperLU package [32] and named _EigenSparse_. To date, EigenSparse has been employed with the real-time simulator DPsim. Here, we extended the Eigen library by two new solver interfaces named _EigenNICSLU_ and _EigenPartialNICSLU_.  
For EigenNICSLU, the preprocessing routine analyzePattern not only determines the permutations but also performs the allocation and initialisation of matrix- and NICSLU-specific data structures. In addition, the key difference between EigenSparse and EigenNICSLU is the refactorisation capability of the latter, which was implemented as an additional, distinct routine named refactorize.  
Furthermore, EigenPartialNICSLU interfaces and implements the factorisation path algorithm. EigenPartialNICSLU uses the same analyzePattern routine as EigenNICSLU. In addition to this, it modifies the factorize routine and adds the refactorize routine. The factorisation path computation is performed after the first factorisation as part of factorize. It receives a list of tuples of numerically varied entries as input. Using these entries, it computes the factorisation path with respect to NICSLU's permutations. It is assumed that the pattern of numerically varying entries stays the same. Therefore, the factorisation path is only computed once. Then, in each subsequent call of refactorize, the partial refactorisation implementation for NICSLU is called, which iterates over the columns in the factorisation path.  
Apart from NICSLU interfaces, there has been a previous implementation of an interface for KLU in Eigen. However, at present, the KLU interface does not provide a refactorisation implementation. The integration of the factorisation path based partial refactorisation into Eigen's KLU interface could be part of further research work, but it is beyond the scope of this paper.
## 2 Modeling Method
### Performance Indicators  
To evaluate the effectiveness of RSM and PSO methods in enhancing the prediction capability of SVR, four models, including SVR, PSO-SVR, RSM-SVR, and hybrid improved SVR, are trained and tested using the data obtained onsite, within which the hybrid improved SVR combines RSM and PSO to construct a prediction model. CV is used to divide the data into train set and test set and evaluate the prediction capability using four statistical comparative metrics, including the coefficient of correlation (\(R^{2}\)), mean absolute error (\(MAE\)), mean absolute percentage error (\(MAPE\)), and root mean square error (\(MSE\)). Generally, the \(R^{2}\) indicates the correlation between variables, while the \(MAE\), \(MAPE\), and \(MSE\) measure the difference between predicted values and actual values in an equal manner. Lower \(MAE\), \(MAPE\), and \(MSE\) and higher \(R^{2}\) correspond to higher predictionaccuracy and better agreement with actual data. These indicators are computed in terms of equations:  
\[R^{2}=\frac{\sum_{i=1}^{N}(\hat{y}_{i}-\bar{y}_{i})}{\sum_{i=1}^{N}( \hat{y}_{i}-\bar{y}_{i})^{2}}, \tag{18}\] \[MAE=\sum_{i=1}^{N}\frac{|\bar{y}_{i}-y_{i}|}{N},\] (19) \[MAPE=\frac{1}{N}\sum_{i=1}^{N}\frac{|\bar{y}_{i}-y_{i}|}{y_{i}},\] (20) \[MSE=\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{N}, \tag{21}\]  
where \(\bar{y}_{i}=\frac{1}{N}\sum_{i=1}^{N}y_{i}\), \(N\) is the amount of test data. Besides that, the PSO-SVR and proposed algorithm are also compared from the change of fitness to evaluate the convergence speed.
# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 3 The Proposed SSGCN Model
### Semantic and Syntactic Layers of Sentiment Support  
In this section, SSWs are used to achieve a preliminary combination of semantics and grammar. We obtained each word's part of speech in a sentence according to the dependency analysis tree, obtaining the semantic information of a sentence from the part of speech perspective. As shown in Figure 4, we conducted the part-of-speech analysis of the aspect words on a widely used dataset, namely, _Laptop_. In its training dataset, we found that among the 2914 pieces of data, 1074 of the aspect words were nouns (NNs), representing 37%. However, the proportion of nouns (NNs) or noun combination phrases (NN-NNs) and plural forms of nouns (NNSs) reached 90%, and the rest were adjectives (JJs) or combinations of adjectives and nouns (JJ-NNs). That is, the aspect terms were mostly noun phrases, nouns, or adjectives plus nouns. Consequently, we proposed three rules for the extraction of emotive support words to determine the emotive support words in specific aspects of sentences from the perspective of grammar, thus acquiring the grammatical information of SSWs in sentences.  
In the syntactic parsing tree, the rules of SSW extraction are as follows:  
1. [label=(0)]
2. Noun type: when the part of speech of the aspect word is a noun or another form of a noun (such as a noun singular plural or a proper noun singular plural), we regarded the adjective or other form of the adjective closest to the aspect word in the sentence (such as the adjective comparative or adjective superlative) as the SSW for this aspect.
3. Combination: when an aspect word is a combination of an adjective or another form of an adjective with a noun or another form of a noun, we considered the adjective or other form of the adjective closest to the aspect word in the sentence as the SSW for that aspect.
4. Constraint rule: when the relative distance between the adverb and sentiment support word found in the noun and combined types do not exceed two words, we set the adverb as the SSW.  
Figure 4: Part of speech statistics for aspect words in the Laptop dataset.  
We then used the sentiment support weight \(z\) to combine the semantic and grammatical information of the SSW. The weight of the sentiment support is calculated using Equation (5).  
\[z=\begin{cases}2,&w_{i}\in SSW\\ 1,&\text{otherwise}\end{cases} \tag{5}\]  
where \(z\) is the sentiment support weight of the word \(w_{i}\), and SSW is denoted as the sentiment support word. After calculating the sentiment support weight, we used \(z\) to weigh the final output representation of the GCN, obtaining the weighted output representation of the GCN \(H^{\prime}l\).  
\[h_{i}^{\prime\prime}=z_{i}h_{i}^{l} \tag{6}\]  
\[H^{\prime}l=\left\{h_{1}^{\prime},h_{2}^{\prime},h_{3}^{\prime\prime},\dots,h _{n}^{\prime}l\right\} \tag{7}\]
## 4 Evaluation and Discussion
### Time Analysis  
#### 4.5.1 Keystream Generation Time Analysis  
This analysis only assesses the time taken to generate a keystream for each secret image and cover image pair. Seven iterations of this analysis are performed, and the average time of the seven yields aggregate results. The parameters for the chaotic function which is used to generate the keystream are \(x_{0}=0.5\) and \(r=3.87\), and will remain constant throughout all iterations. The precision level for generating the keystream is up to 20 decimal places.  
Table 13 shows an increase in the keystream generation time when going from embedding rainbow to monarch. This is consistent with the nature of the keystream generation algorithm, as it only takes into account the size of the secret image. The increase is also linear \(O(n)\), as the size of monarch is six times that of rainbow, which can be seen in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cover Image** & **Secret Image** & **Keystream Generation Time (s)** \\ \hline lenna & rainbow & 0.0606 \\ malamute & rainbow & 0.0612 \\ malamute & monarch & 0.3643 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Average keystream generation time.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{4}{c}{**MSE**} \\ \cline{2-5}  & **R** & **G** & **B** & **Aggr** \\ \hline rainbow from lenna & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline rainbow from malamute & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline monarch from malamute & 0.0 & 0.0 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 11: MSE original secret image vs. extracted secret image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multicolumn{4}{c}{**PSNR**} \\ \cline{2-5}  & **R** & **G** & **B** & **Aggr** \\ \hline rainbow from lenna & \(\infty\) & \(\infty\) & \(\infty\) & \(\infty\) \\ \hline rainbow from malamute & \(\infty\) & \(\infty\) & \(\infty\) & \(\infty\) \\ \hline monarch from malamute & \(\infty\) & \(\infty\) & \(\infty\) & \(\infty\) \\ \hline \hline \end{tabular}
\end{table}
Table 12: PSNR original secret image vs. extracted secret image.
# Empirical Perturbation Analysis of Two Adversarial Attacks: Black Box versus White Box
## 4 Adversarial Noise Visualization and Frequency Analysis
### Assessment of the Frequencies Present in the Adversarial Noise  
With the adversarial perturbations \(\mathcal{D}^{\textit{attk}}_{k}(\mathcal{A}^{p}_{q})-\mathcal{A}^{p}_{q}\) having been assessed (Section 4.1) for each RGB channel, we proceed to an analysis of the frequencies present in the adversarial noise per channel. Specifically, the Discrete Fourier Transform (DFT) is used to obtain the 2D magnitude spectra of the adversarial perturbations. We compute two quantities: magn (diff) \(=|DFT(\mathcal{D}^{\textit{attk}}_{k}(\mathcal{A}^{p}_{q})-\mathcal{A}^{p}_{q})|\), and diff (magn) \(=|DFT(\mathcal{D}^{\textit{attk}}_{k}(\mathcal{A}^{p}_{q}))|-|DFT(\mathcal{A}^{ p}_{q})|\). Figure 3 displays a typical example of the general outcome regarding the adversarial noise in the red channel added by the EA or by BIM. For each image, the low frequencies are represented in the center, the high frequencies are represented in the corners, and the vertical bar (on the right) maps the frequency magnitudes to the colors shown in the image.  
A clear difference between the EA and BIM is visible from the magn (diff) visualizations. With the EA, the high magnitudes do not appear to be concentrated in any part of the spectrum (with the exception of occasional high magnitudes in the center), indicating the white noise nature of the added perturbations. Supporting evidence for the white noise nature of the EA comes from the \(2D\) autocorrelation of the noise. Figure 4 shows that the \(2D\) autocorrelation for both attacks have a peak at lag 0, which is expected. It turns out that this is the only peak when one considers the EA, which is no longer the case when considering BIM. Unfortunately, this is difficult to see in Figure 4, since the central peak takes very high values; hence, the other peaks fade away in comparison. With BIM, the magn (diff) visualizations display considerably higher magnitudes for the low frequencies, indicating that BIM primarily uses low-frequency noise to create adversarial images.  
In the case of diff (magn), both the EA and BIM exhibit larger magnitudes at high frequencies than at low frequencies. This can be interpreted as a larger effect of the adversarial noise on the high frequencies than on the low frequencies. Natural images from ImageNet have significantly more low-frequency than high-frequency information [20].  
Figure 4: For \(\textit{attk}=EA\) (**left**) and \(\textit{attk}=BIM\) (**right**), autocorrelation of \(\mathcal{D}^{\textit{attk}}_{6}(\mathcal{A}^{p}_{5})-\mathcal{A}^{4}_{5}\) for the red channel.  
Therefore, even a quasi-uniform noise (such as the EA's) has a proportionally larger effect on the components that are numerically less present than on the more numerous ones.
## 3 Mechanical Performance of Fe-SMA  
### Basic Mechanical Properties  
When applied in practice, Fe-SMA materials can be manufactured into various shapes and sizes, such as rods, bars, and strips [70; 71; 72], as shown in Figure 4. As an alloy, the basic properties of Fe-SMA are similar to those of steel. The physical properties of Fe-SMA, steel, and concrete are summarized in Table 3. Their thermal expansion coefficients are numerically close, and the three materials can deform collaboratively when the temperature changes; hence, Fe-SMA has prominent advantages in strengthening steel and concrete structures [73; 74; 75].  
Fe-SMA is a typical elasto-plastic material without a yield platform, as shown in Figure 5. Therefore, the stress of \(\sigma_{y0,2}\), corresponding to its residual strain of 0.2%, is used to describe the yield strength. The mechanical properties of Fe-SMA tested by different researchers are summarized in Table 4. Due to the various factors such as elemental composition, processing technology, and fine precipitates the mechanical characteristics of Fe-SMA studied by scholars are different. The elastic modulus of Fe-SMA is distributed in  
Figure 4: Schematic diagram of Fe-SMA [71; 72]: (**a**) Fe-SMA bars, (**b**) Fe-SMA strips.  
Figure 3: SME and activation performance of Fe-SMA: (**a**) SME, (**b**) activation performance.  
the range of 125-200 GPa, the ultimate strength is in the range of 676-1140 MPa, the yield strength is in the range of 260-600 MPa, and the ultimate strain of Fe-SMA can exceed 50%, demonstrating excellent ductility and deformability.
## 2 Message Replication in OppNets for Sensed Data Collection
### Existing Message Replication Techniques
#### 2.3.1 Single-Copy Replication  
Message replication techniques can be classified into two main categories, namely single-copy and multiple-copy. Single-copy replication allows only one copy of each message in the network.  
Figure 2: Taxonomy of message replication techniques in OppNets.  
Examples are the replication techniques of Direct Transmission [41] and First Contact [42]. The former buffers the message at the source until the destination is directly encountered, while the latter achieves single-copy replication by removing from the buffer messages that have been successfully forwarded to a relay node.
# Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
## 2 Background
### Non-Dominated Sorting Genetic Algorithms (NSGA)  
Multi-objective optimization (MOO) problems involve optimizing different, often competing, objectives _simultaneously_. These problems typically do not have a single best solution. Instead, they are characterized by a range of _non-dominated_ or _Pareto optimal_ solutions. A non-dominated solution is one which cannot be improved in one objective without causing degradation in another. Algorithms targeting MOO problems attempt to produce a set of non-dominated solutions, called a _Pareto front_, which characterizes the trade-offs between objectives in the solution space. In a MOO problem instance, the fitness of a solution might be characterized by a vector \(\mathbf{x}=[x_{0},\ldots,x_{n-1}]\) where each \(x_{i},\ 0\leq i<n\) is an objective value. A solution with vector \(\mathbf{a}=[a_{0},\ldots,a_{n-1}]\) is said to _dominate_ another solution with vector \(\mathbf{b}=[b_{0},\ldots,b_{n-1}]\) if and only if (1) and (2) hold true. The number of other solutions which dominate a given solution is sometimes referred to as its _domination level_ or _rank_ in the set of solutions. A Pareto front is, therefore, a set of solutions which have a rank of 0.  
\[\forall\ i,\ 0\leq i<n,\quad a_{i}\ \textbf{equal or better than}\ b_{i} \tag{1}\]\[\exists\;i,\;0\leq i<n,\quad a_{i}\;\textbf{strictly better than}\;b_{i} \tag{2}\]  
Due to their flexibility, many GAs have been proposed which attempt to target MOOs [14; 25; 26; 27; 28; 29]. One such family of algorithms is called the Non-dominated Sorting Genetic Algorithm (NSGA) [14; 25; 28]. NSGAs operate by performing a _non-dominated sort_ on the population which ranks solutions with a lower domination level as better. Over time, the GA discards dominated solutions while preserving non-dominated ones, progressing toward the Pareto front of the solution space. In this work, we leverage the NSGA-II [14] variant for our quantum compiler. We chose this over the newer NSGA-III [28] because our proposed work targets three objectives, which is within the threshold of NSGA-II, and thus we can avoid the additional processing overhead of NSGA-III without any loss of efficacy.