esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology: IW-ABC
### Employed Bee Phase  
The employed bee phase is the first iterative procedure of the ABC where food source information is shared by employed bees in the dance area to the onlooker bees. The onlooker bees then select and remember the food sources according to their nectar amount, \(f(\mathbf{x}_{i})\), which is the fitness function of the problem to be optimized.  
In the IW-ABC, a new candidate food source, \(v_{ij}\), is generated using Equation (1).  
\[v_{ij}=\omega x_{ij}+\varphi_{ij}\Big{(}x_{ij}-x_{kj}\Big{)} \tag{1}\]  
where \(\omega\) is the inertia weight. It controls the influences of the current food source in the generation of the new one. Three type of inertia weight are used here: exponentially increasing inertia weight, linearly increasing inertia weight, and linearly decreasing inertia weight. All of these variants are adaptive according to the iteration number. The exponential inertia weight is shown in Equation (2).  
\[\omega=exp\,\left(\frac{t}{t_{max}}-1\right) \tag{2}\]  
where \(t\) is the iteration number, and \(t_{max}\) is the maximum iteration number. On the other hand, the linear inertia weight is calculated using Equation (3).  
\[\omega=\left(\frac{t_{max}-t}{t_{max}}\right)\Big{(}\omega_{init}-\omega_{final }\Big{)}+\omega_{final} \tag{3}\]  
The linear increasing inertia weight is \(\omega_{init}<\omega_{final}\), while the linear decreasing inertia weight is \(\omega_{init}>\omega_{final}\). The rate of the changes for these inertia weights is shown in Figure 2. High inertia encourages exploration and prevents convergence, while small inertia encourages convergence and fine tuning.  
Figure 1: Flowchart of the ABC Algorithm.  
Based on the second part of Equation (1) it can be seen that the new candidate food source is generated around the current solution, \(x_{i}\), and a randomly selected solution, \(x_{k}\), where \(k\
eq i\) and is randomly selected in the range of \(1-SN\). The \(\varphi_{ij}\) is a random number between \(-1\) and \(1\).  
Figure 3 shows the pseudocode for the employed bee phase. For each of the food sources the new candidate food source, \(v_{i}\), is generated using Equation (1) (line 3 in Figure 3). The fitness of the new candidate food source is then evaluated and if it is better than the current food source, \(x_{i}\), it is adopted as the food source of the agent.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 3 Classification of Multi-Objective Evolutionary Algorithms from the View of Search Strategy
### Dominant Relation-Based MOEA Algorithms
#### 3.2.3. Niched Pareto Genetic Algorithm  
Based on the Pareto dominance connection, [PERSON] et al. propounded the NPGA [7]. It randomly selects two individuals \(i\) and \(j\) from the population, randomly selects a comparing set CS (whose size is larger than two and generally smaller than ten), and compares individual \(i\) and individual \(j\) with the individuals in CS, respectively. If one of the individuals is dominated by the CS and the other is not dominated by the CS, the individual that is not dominated by the CS will be chosen to be involved in the next generation of evolution, and if both individuals \(i\) and \(j\) are not dominated by the CS or both are dominated by the CS, then the individual with the greater shared fitness will be selected to participate in the next generation of evolution using a sharing mechanism.  
Fitness sharing is an effective way to achieve population diversity. Let the fitness of individual \(i\) be \(fitness(i)\), the niche count of individual \(i\) be \(m_{i}\), and \(m_{i}\) is calculated as follows in Equation (3):  
\[m_{i}=\sum_{i\in P}S[d(i,j)]. \tag{3}\]  
The \(P\) is the current evolutionary population, \(d(i,j)\) is the distance or similarity between individual \(i\) and individual \(j\), the sharing function is \(S[d]\), and \(S[d]\) is defined as follows in Equation (4):  
Figure 4: An example of the lexicographic optimization process for a bi-objective minimum optimization problem. Firstly, \(f_{1}(x)\) is minimized, while the optimal value obtained is used as a constraint. Secondly, after \(f_{2}(x)\) has been minimized, its optimal value is used as a constraint for cyclic optimization. Finally, Pareto optimal solutions are found. This figure is adopted from [80].  
\[S[d(i,j)]=\left\{\begin{array}{ll}0,&d>\sigma_{share}\\ 1-d/\sigma_{share},&d<\sigma_{share}\end{array}\right.. \tag{4}\]  
The niche radius is \(\sigma_{share}\), usually determined by the user based on the minimum desired spacing between individuals in the optimal solution set.  
Define \(fitness(i)/m_{i}\) as the shared fitness, where \(m_{i}\) is essentially the degree of niche aggregation of individual \(i\). Individuals within the same niche reduce each other's shared fitness. The higher the degree of aggregation of an individual, the more its shared fitness relative to its fitness is reduced. As shown in Figure 5, candidate solutions \(A\) and \(B\) are both non-dominated, but \(A\) has a greater degree of aggregation than \(B\), and therefore \(A\) has a smaller shared fitness than \(B\). The purpose of using shared fitness is to spread the evolutionary population over distinct areas throughout the entire search space.  
The main advantage of the NPGA is that it operates more efficiently and achieves a better Pareto optimality boundary. The disadvantage is the difficulty in selecting and adjusting the radius of the niche [81].
# Analyzing the SAR in Human Head Tissues under Different Exposure Scenarios
## 2 Materials and Methods  
The ICNIRP releases the guidelines for EMF exposure and states the limits for an electric field (E), magnetic field (H), or power density (S). Recommended reference levels within the frequency range of 400 MHz-2000 MHz are \(1.375\,\sqrt{f}\) (f is the frequency in MHz) [1]. For the frequencies of 900 MHz and 1800 MHz, the limits are 41.25 V/m and 58.34 V/m, respectively, for E.  
In the present guidelines, SAR is also a basic restriction which is defined as the amount of electromagnetic energy that biological tissue absorbs, and is calculated using (1):  
\[SAR=\frac{\sigma|E|^{2}}{\rho} \tag{1}\]  
where \(\sigma\) and \(\rho\) are the conductivity and the mass density of the tissue, respectively, and \(E\) is the root mean square of the internal electric field. Specifically, SAR 10g presents the power absorbed over a 10g cubical mass while whole-body average SAR is the amount of power absorbed over the entire body. SAR limits recommended by ICNIRP for the whole body, local head/torso, and local limb are given in Table 2[1].
## 3 Results  
Fourteen healthy controls (seven women and seven men with a mean age of 35.76 \(\pm\) 4.49 years (mean \(\pm\) SD), fourteen volunteers with neck pain (eight women and six men with a mean age of 33.07 \(\pm\) 6.07 years), and fourteen subjects with TTH (eleven women and three men with a mean age of 32.71 \(\pm\) 2.89 years) were evaluated. The total sample measurements were carried out on forty-two subjects--25 women and 17 men--with a mean age of 33.33 \(\pm\) 4.59 years (Table 1).  
The ICC values showed that the test-retest reliability of the CVA-CVapp obtained a high value with excellent reliability; these results are shown in all the subgroups and in the whole sample (Table 2).  
Table 3 showed the inter-rater reliability, and the results of the ICC showed excellent reliability for all the measurements.  
The concurrent validity calculated by the Pearson's correlation coefficient showed a strong correlation between the assessment using the Kinovea web tool and the CVA-CVapp (Table 4). The Bland-Altman plot is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Healthy Control** & **Neck Pain** & **Tension Type Headache** \\  & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** \\ \hline Weight (Kg) & 69.43 (10.24) & 67.43 (13.96) & 64.50 (11.26) \\ \hline Height (cm) & 172.57 (10.04) & 170.29 (9.14) & 168.14 (9.46) \\ \hline VAS (pain) (cm) & 0.07 (0.26) & 2.647 (1.08) & 1.53 (1.56) \\ \hline VAS (headache) (cm) & 0.17 (0.27) & 1.82 (1.19) & 2.11 (1.65) \\ \hline CVA\({}^{\circ}\) & 54.65 (7.01) & 57.98 (6.55) & 51.01 (7.22) \\ \hline NDI & 2.00 (2.01) & 8.93 (3.07) & 3.43 (3.08) \\ \hline HIT 6 & 40.43 (2.38) & 48.85 (7.64) & 56.14 (7.99) \\ \hline CVA, Cranivertebral angle. & & & \\ \end{tabular}
\end{table}
Table 1: Descriptive analysis of the demographic data.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Assessment** &
\begin{tabular}{c} **CVA-CVapp** \\ **Mean (SD)** \\ \end{tabular} & **ICC (95\% CI)** & **SEM** & **MDC** \\ \hline Healthy & 1 & 54.65 (7.00) & 0.91 (0.74–0.97) & 2.10 & 5.82 \\ control & 2 & 54.68 (7.08) & & & \\ \hline Neck pain & 1 & 57.67 (5.72) & 0.92 (0.76–0.97) & 1.61 & 4.46 \\ \hline Tension type & 1 & 51.55 (5.53) & 0.92 (0.77–0.97) & 1.56 & 4.32 \\ headache & 2 & 52.19 (6.92) & & & \\ \hline Whole & 1 & 54.63 (6.48) & 0.92 (0.86–0.95) & 1.83 & 5.07 \\ sample & 2 & 54.77 (6.71) & & & \\ \hline \end{tabular}
\end{table}
Table 2: Test-retest reliability for subgroups and the whole sample for the CVA-CVapp.
## 1 Introduction
The network aims to improve the accuracy of the sentiment analysis by fusing the syntactic dependencies and rich semantic information. Referring to similar research works, we found that although there have been some studies that incorporated syntactic structure and semantic information into graph neural networks, they tend to overlook the importance of effectively fusing the aspect-related syntactic structure and semantic information in complex situations. Specifically, our model has two modules: the syntactic attention module and the semantic enhancement module. First, we reshape the common dependency tree to construct a tree that regards the aspect terms as its roots, then discard unnecessary relations and apply a GAT to study dependencies more relevant to various aspects. Subsequently, the GCN is applied to fetch the semantic information, and the attention matrix produced by the self-attention acts as the adjacency matrix to deeply extract the semantic information in the text. The two features are then effectively fused  
Figure 1: Aspect-level sentiment classification (ALSC) and sentiment classification (SC). The top sentence is the ALSC task and below it is the SC task. for sentiment classification through multi-head cross-attention. To validate the model's performance, we experimented on public datasets and the results performed well. Our main contributions are shown below:  
* An aspect-level sentiment classification fusion network with enhanced syntax and semantics combined with graph neural networks is proposed, which integrates syntactic structure and semantic information. * The model integrates the semantic GCN and relational GAT to associate dependencies with semantic features through multi-head cross-attention, to strengthen sentence representations. * Experiments are conducted on three public datasets. From the results, our proposed model is effective.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 5 The Methodology
### Validation  
The final step of this study involved validating the processed flood extent maps generated in previous stages. The accuracy of the SAR-RST-FLOOD approach was assessed using various methods and a range of reference datasets, including reliable flood information from CEMS.  
We assessed the accuracy of the flood maps by comparing them with the same Sentinel-1 imagery taken on the same date using another approach, such as the automatic threshold method provided by the CEMS. In cases where we were unable to find a Sentinel 1 image for the same date, we assessed the accuracy of the flood maps by comparing them with different types of images, including ESRI World Imagery, COSMO-SkyMed, Radarsat-2, and other satellite images, and then with the provided ground truth information regarding floods in the TIF files containing flood extents. Through these rigorous validation processes, we aimed to ensure the reliability and precision of the flood mapping results obtained using the SAR-RST-FLOOD approach.  
The agreement between the two mapping approaches was assessed using a confusion matrix that served as a concise summary of the classification model predictions, indicating the numbers of correct and incorrect predictions. By analyzing the values in the confusion matrix, various metrics were calculated to assess the agreement between the two mapping approaches ([18], such as the user's (UA), producer's (PA) and overall accuracy (OA), as well as the Kappa index (K), as described in the following.  
\[\text{UA}_{\text{f}}=\frac{A}{A+B} \tag{2}\]  
\[\text{UA}_{\text{nf}}=\frac{D}{C+D} \tag{3}\]  
\[\text{PA}_{\text{f}}=\frac{A}{A+C} \tag{4}\]  
\[\text{PA}_{\text{nf}}=\frac{D}{B+D} \tag{5}\]  
\[\text{OA}=\frac{A+D}{A+B+C+D} \tag{6}\]  
\[\text{K}=\frac{\frac{A+D}{A+B+C+D}-\frac{(A+B)(A+C)+(C+D)(B+D)}{(A+B+C+D)^{2} }}{1-\frac{(A+B)(A+C)+(C+D)(B+D)}{(A+B+C+D)^{2}}} \tag{7}\]  
where \(A\) is the number of pixels considered flooded by both the RST and the reference map, \(B\) and \(C\) are the \"False\" negative or positive RST detection (considering the reference map as the true), and \(D\) represents the pixels that were not recognized as flooded by both methods. In Equations (2) and (4), \(f\) is for \"flooded\", whereas in Equations (3) and (5), \(nf\) is for \"not flooded\".
# iRoute--An Adaptive Route Planning Solution for Commercial Vehicle Fleets
## 1 Introduction
The lack of customized options makes it difficult for companies to meet their unique business needs, and the lack of integration with other systems prevents the optimization process from generating routes in real time. The iRoute solution is a software platform that offers planning, optimization, and route monitoring services for picking up and distributing packages within logistics fleets regardless of their main field of activity. Our solution offers other criteria in selecting routes in addition to the traditional ones, such as routes with the minimum distance or duration to reach all locations. The main innovation is the implementation of an adaptative algorithm based on historical routes for commercial vehicle fleets. Instead of finding the best route, it tries to adapt the calculated optimized routes with historical data from previously executed routes in such a way that the newly adapted optimized routes fit better to the customer's business, local conditions, or driving styles of the customer's employees. We compared our adaptive algorithm with other commercial or open-source solutions and in all cases we obtained promising results, with improvements between 15% and 20%. This article is organized as follows. Section 2 details an overview of related work and the background, and Section 3 presents the project's implementation and components. Information about the algorithm and functions used is presented in Section 4, with details about tests and results written in Section 5. Finally, in Section 6 we present the conclusions and future work.
# Fuzzy MCDM Methodology for Analysis of Fibre Laser Cutting Process
## 3 Results and Discussion
### Ranking of Alternatives and Analysis of Results
As can be observed in Figure 4, relatively small changes in the assist gas pressure (0.1 bar) and focus position (0.5 mm), but significant considering cutting speed (1.8 m/min), result in a huge difference in the overall cutting performance. As discussed by [PERSON] et al. [49], in laser reactive cutting, the change in the cutting speed has multiple effects affecting the melt temperature, viscosity and mass flow rates, as well as gas dynamics, cut front geometry and laser absorptivity. Stabilization of the melt film dynamics, decrease in striation wavelength and surface roughness with an increase in the cutting speed have also been previously reported [50]. A more detailed analysis of the obtained results indicated that all cutting conditions in which small cutting speed was used (A1, A2, A9, A11), as well as the combination of high cutting speed and low laser beam focusing (A3), are the least preferred laser cutting conditions that produce second quality group surface roughness (\(R_{z}\)) according to the ISO 9013 standard [33]. All other conditions result in the first quality group with \(R_{z}\leq 11.8\) um.
## 3 IBSA_Net Model Construction and Module Composition
### IBSA_Net Overall Structure  
The entire IBSA_Net forms the main structure by superimposing IBSA_block blocks three times, and the UP_convolution is used for channel up-convolution between IBSA_block blocks. The channel up-convolution uses a convolution kernel size of \(1\times 1\), and the number of output channels is twice the number of input channels. The final output uses global average pooling to extract the information and add the fully connected layer output. The following Figure 7 shows the structure of the entire IBSA_Net network Meanwhile, Table 2 shows the output dimensions of each module in IBSA_Net.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & \
\begin{tabular}{c} **Kernel Size (for Convolutional** \\ **and Pooling Layers Only)** \\ \end{tabular} &
\begin{tabular}{c} **Number of Steps (for Convolutional** \\ **and Pooling Layers Only)** \\ \end{tabular} & **Output Dimension** \\ \hline Input & - & - & (b, 3, 256, 256) \\ Conv2d & \(5\times 5\) & 1 & (b, 128, 252, 252) \\ BatchNorm & - & - & (b, 128, 252, 252) \\ IBSA_block1 & - & - & (b, 128, 126, 126) \\ UP_conv1 & \(1\times 1\) & 1 & (b, 256, 126, 126) \\ IBSA_block2 & - & - & (b, 256, 63, 63) \\ UP_conv2 & \(1\times 1\) & 1 & (b, 512, 63, 63) \\ IBSA_block3 & - & - & (b, 512, 32, 32) \\ UP_conv3 & \(1\times 1\) & 1 & (b, 1024, 32, 32) \\ Avgpool & 1 & 1 & (b, 1024, 1, 1) \\ fc & - & - & (1024, class\_num) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output dimensions of the different layers of IBSA_Net.  
Figure 7: IBSA_Net network structure.
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
## 7 Conclusions  
This paper provides insight into the directional auditory location in Ambisonics by investigating the MAA for three different ambisonic decoders: the SAD, EPAD, and AllRAD. It describes all considered decoders and discusses the differences in the sound field reproduction when applied to the non-\(t\)-design of the loudspeaker layout. The perceptual evaluation of the MAA reveals different results for each of the decoders, suggesting that the method of sound field reproduction influences the MAA. In particular, the EPAD, which prioritises equal energy distribution, produced the lowest mean values of the MAA. This may be motivated by the more uniform reproduction of amplitude and energy across the sound field compared to the SAD and AllRAD. However, the localisation perception may also be influenced by the familiarity with ambisonic systems and with audio reproduction systems in general. The effect size is too small in this particular study; to better understand potential trends, particularly regarding individual differences related to experience with spatial or stereophonic playback, which show the potential to contribute to the results, future studies should include a larger number of participants. The white noise stimulus was band-limited to 20 Hz-20 kHz, which broadly encompasses the frequency ranges relevant to ITD and ILD-based localisation cues. However, the precise relationship between the spectral content of the stimulus, auditory spatial mechanisms, and the frequency response characteristics of the energy vector decoder remains a subject for further investigation and systematic analysis. In the future, the insights regarding the MAA can be useful in research on the human perception of sound in virtual and extended reality environments, where sound reproduction is most often ambisonic-based. The MAA can also provide guidelines for sound design and music production capable of reproduction that meets the creator's intention in terms of sound-source localisation.  
**Author Contributions:** Conceptualisation, K.S. and P.M.; methodology, K.S.; software, P.M.; validation, P.M., M.P. and J.W.; formal analysis, K.S.; investigation, K.S.; resources, K.S.; data curation, K.S.; writing--original draft preparation, K.S.; writing--review and editing, K.P.; visualisation, K.S.; supervision, P.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 7 Impact on User Due to Smartphone Antenna (SAR Analysis)
### Effect of SAR on Human Tissue  
SAR provides information about the EM absorption by a human body during the transmission and reception of radio frequency signals, which is a grave issue for smartphone systems, and the value of this should be as low as possible. The simulated values of SAR for the proposed MIMO smartphone design in the locality of human tissue model at 3.5 GHz and 5.1 GHz are investigated in Figure 21. The obtained SAR value is lower than the SAR limit in the USA (1.6 W/kg).  
Figure 18: ECC of 4-element mobile MIMO antenna by bending along (**a**) X-axis, (**b**) Y-axis.  
Figure 19: Antenna and human tissue model: (**a**) Layered view; (**b**) Layered view of human tissue; (**c**) Properties of human tissue model.  
Figure 20: SAR at (**a**) 3.5 GHz, (**b**) 5.1 GHz.
# Propagation Characteristics of Modulated EHF Signal in the Wake Region of Plasma Sheath
## 2 Characteristics of EHF Wave in Plasma Sheath
### Analysis Method
\[\theta_{\rm i}=\beta-\gamma \tag{1}\]  
Then, based on the SMM [13; 20; 35], the plasma plate between point A and point B is divided into n homogeneous thin layers (see red parallel lines in Figure 1a). The propagation characteristic parameters of waves in the whole plasma medium, such as transmission, reflection, and absorption, can be obtained by a global scattering matrix, which is expressed as:  
\[s_{g}=s_{1}s_{2}s_{3}\ldots s_{n-2}s_{n-1}s_{n}=\prod_{m=1}^{n}s_{m} \tag{2}\]  
where s\({}_{m}\) is the scattering matrix of the \(m\)-th sublayer, which can be derived from the wave number \(k(m)\) at the m-th sublayer. Then, using the interface condition of the bottom boundary electromagnetic field in Figure 1, we can obtain:  
\[s_{g}\left[ \begin{array}{c}R\\ 1\end{array} \right]=\frac{1}{2k_{x}^{(n)}}\left[ \begin{array}{c}a_{1}e^{b_{ 1}d_{n+1}}\\ b_{1}e^{-i_{1}d_{n+1}}\end{array} \right]\cdot T \tag{3}\]  
where \(a_{1}=k_{\rm x}^{(n)}+k_{\rm x}^{(n+1)}\), \(b_{1}=k_{\rm x}^{(n)}-k_{\rm x}^{(n+1)}\), \(R\) and \(T\) are the global reflection coefficient and global transmission coefficient, respectively. The superscript \(n+1\) denotes the region III. Finally, the signal transmission rate can be expressed as:  
\[T^{\prime}=T\cdot\sin\alpha \tag{4}\]  
Note that \(T^{{}^{\prime}}\) is a complex number, and that the amplitude and the phase of \(T^{{}^{\prime}}\) represent the transmission coefficient and the phase shift, respectively.