esa-sceva/satcom-chunk-collection huggingface dataset:
# Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions
## 3 Methods
### Learning with Data Augmentation
That is, the augmentation was applied to transform a normal skin image into burn injury images with a certain severity level. Thus, we built four CycleGAN models for four different burn types. With these CycleGAN models, we evaluated their augmentation capability and utility for limited datasets. A small set of annotated burn images in the target domain with a large set of unannotated, normal skin images in the source domain can be used as a bootstrap to expand the limited datasets and improve the performance of burn severity classifiers learned on the expanded datasets. This approach could be promising when it is difficult to build a sufficiently large dataset of paired images (in our case, paired images of burn injuries and respective normal skins for different burn types) for data augmentation and subsequent model training. Among several style-based GANs, StyleGAN exploits the benefits of progressive GANs [47] that involve initial learning on low resolution images via a simple neural network and incrementally increase the number of layers in the network for better quality and higher resolution. StyleGAN is now considered to represent a state-of-the-art GAN. Figure 5 depicts the building blocks of StyleGAN used for our burn image synthesis with different burn types. In the StyleGAN architecture, the generator network is specifically extended to include a mapping function that maps a latent vector (**z**) to the intermediate latent space (**w**), while the discriminator architecture is not modified. This structural extension enables control of the style at every level in the generator (synthesis network), combined with injected noise (\(N\)) at every level.
## 1 Introduction
### Theoretical Foundation of Our Approach  
As a summary, the three main theoretical aspects taken as reference framework for the development of this study, are presented below.  
Firstly, organizational culture is conceived as a factor determining human behavior within organizations. Furthermore, as revealed in the analysis of catastrophic events in high-risk industries, it is necessary to consider that organizational culture has an impact on the management of safety.  
Secondly, this study takes the differentiation perspective of [PERSON] as reference framework, thus considering that the working environment is comprised of groups with differentiated subcultures, in line with the views of [PERSON] [48] (p. 26):  
'Cultures are neither homogeneous not fully integrated. On the one hand, because disagreement and even conflict will always arise. On the other hand, because people within a culture adopt its core with mixed intensity.'  
At this point, it is important to clarify that we agree with the following definition of organizational subculture [49] (p. 38):  
'A subset of an organization's members who interact regularly with one another, identify themselves as a distinct group within the organization, share a set of problems commonly defined to be the problems of all, and routinely take action on the basis of collective understandings unique to the group.'  
From this differentiation perspective, we think it is relevant to assess the complementarity of organizational subcultures to prevent the existence of \"blind safety spots\" [40].  
Thirdly and last, this study embraces the approach of [PERSON] [45] on the relevance of organizational structure and its organizational components, which in our opinion, could be contributors to the creation of subcultures.  
Based on these theoretical perspectives, our research is focused on the following:  
1. Describing the organizational culture of the Spanish nuclear industry.
2. Identifying features of different subcultures within the Spanish nuclear industry. To do so, the influence of some demographic variables on organizational culture is analyzed. The following variables have been studied: * Sector (working at nuclear power plants or at nuclear public companies); * Location (working at the facility or at the headquarters); * Contractual relationships (own staff or contractors); * Organizational components identified by [PERSON] [45] (strategic apex, middle line, operating core, technostructure and support staff).
## 2 Materials and Methods
### Method
#### 2.4.4 Semantic Decoder
Subsequently, the weighted feature maps are fused through convolution operation \(\mathcal{F}_{i}(\cdot)\) to generate the attention feature map \(S\). The calculation process is shown in Equation (12). \[S=\mathcal{F}\Bigg{(}\sum_{i=1}^{N}\big{(}\widehat{SA_{i}}\cdot\widetilde{U _{i}}\big{)}\Bigg{)} \tag{12}\]  
Finally, the input feature map \(X\) is element-wise-multiplied with the attention feature map \(S\) to obtain the output feature map \(Y\). The calculation process is shown in Equation (13). \[Y=X\cdot S \tag{13}\]  
Based on this design, the LSK (Large-Scale Kernel) module can adaptively adjust the receptive field range according to the target's scale, enabling the network to more precisely extract contextual information needed for mangrove targets of different scales. This adaptive mechanism not only effectively addresses the problem of insufficient contextual information but also reduces interference from redundant background information, thereby improving the accuracy of feature extraction. Through the above analysis, it can be seen that the semantic decoder proposed in this paper constructs an efficient multi-scale feature fusion and enhancement framework through the collaborative work of two core modules: CPFM and MSKAM. CPFM achieves a seamless fusion of multi-level features based on a cascaded feature pyramid structure and effectively captures global contextual information through the PPM module. MSKAM innovatively integrates the ECA channel attention mechanism and LSK Large Selective Kernel module, achieving precise channel selection and adaptive receptive field adjustment at different levels of the feature pyramid. This multi-level feature processing mechanism fully aligns with the characteristics of mangrove remote sensing images, effectively addressing core challenges in semantic segmentation such as multi-scale feature fusion, key information enhancement, contextual information acquisition, and computational efficiency optimization, significantly improving the performance of mangrove remote sensing image semantic segmentation.
# Hate Speech Detection by Using Rationales for Judging Sarcasm
## 3 Methods
### Dataset Selection  
Among the various available hate speech datasets, HateXplain was selected for this research due to its unique features. HateXplain provides annotated rationales for each labeled instance, which is critical for developing models that not only predict hate speech but also offer insights into the reasons behind these classifications. This transparency is vital for the interpretability of models in sensitive applications like content moderation. Additionally, HateXplain's focus on both hate speech and offensive language, annotated in a real-world context, aligns well with the objectives of this study to handle nuanced expressions like sarcasm effectively.
## 2 Methodology
### Extraction of Sizing Criteria
#### 2.6.2 Frequency-Domain Features  
As the computation of each state or output variable's FFT has already been developed in [16; 31], a special focus is placed on the spectrum magnitude, derived from the FFT. The result of the FFT is a frequency vector composed of complex numbers, from which the spectrum magnitude is derived, as indicated by expression (3).  
\[\forall\mathrm{k}\in[0,\mathrm{M}-1],|\hat{\mathrm{x}}(\mathrm{k})|=\sqrt{ \mathrm{Re}(\hat{\mathrm{x}}(\mathrm{k}))^{2}+\mathrm{Im}(\hat{\mathrm{x}}( \mathrm{k}))^{2}} \tag{3}\]  
Here:  
* \(x\) is a component of \(\mathrm{X}\), and \(\hat{x}\) its FFT;
* \(M\) is the number of points in the frequency spectrum.  
Each element of the FFT vector corresponds to a frequency. The first element represents the average value (its frequency being zero), the second corresponds to the fundamental frequency \(\mathrm{f}_{\mathrm{found}}\), while the remaining elements pertain to the harmonics. Subsequently, the requisite elements for optimization are extracted.  
The derivatives of the FFT module are computed using the analytical formula provided in expression (4).  
\[\begin{array}{c}\forall\mathrm{i}\in\{1,\ldots,\mathrm{nI}\},\forall\mathrm{ k}\in[0,\mathrm{M}-1],\\ \frac{\partial|\hat{\mathrm{x}}|}{\partial\mathrm{I}_{\mathrm{i}}}(\mathrm{k}) =\frac{\mathrm{Re}\left(\frac{\mathrm{a}\hat{\mathrm{x}}}{\hat{\mathrm{x}}}( \mathrm{k})\right)\cdot\mathrm{Re}(\hat{\mathrm{x}}(\mathrm{k}))+\mathrm{Im} \left(\frac{\mathrm{a}\hat{\mathrm{x}}}{\hat{\mathrm{x}}}(\mathrm{k})\right) \cdot\mathrm{Im}(\hat{\mathrm{x}}(\mathrm{k}))}{\sqrt{\mathrm{Re}(\hat{\mathrm{ x}}(\mathrm{k}))^{2}+\mathrm{Im}(\hat{\mathrm{x}}(\mathrm{k}))^{2}}}\end{array} \tag{4}\]  
Here:  
* \(I_{\mathrm{i}}\) is a component of \(\mathrm{I}\).  
Figure 7: Dynamic simulation procedure (adapted from [17]).  
**Remark:** The FFT can also be implemented similarly to average or RMS values. Here, the authors aim to illustrate the post-processing extraction of a sizing criteria computation.  
Alternative methods for computing the FFT are detailed in [32], yet the authors sought a rapid and straightforward approach to computing the frequency-domain features.  
In this section, we have meticulously detailed the methodology. We shall now proceed to elucidate its implementation within an open-source software framework.
# Effect of Fines Content on the Compression Behavior of Calcareous Sand
## 5 Discussion  
With the increasing expansion of land reclamation projects, the issue of the fine-grained aggregation layers in calcareous sandy soil affecting the foundation's bearing performance has gained prominence. In this study, the compressibility differences between calcareous fine sand with different fine particle contents and clean sand were studied in detail.  
The comprehensive analysis revealed that at a medium-density state (\(D_{\rm r}\) = 50%), the axial deformation trend displays a distinct \"\(\cap\)\" shape with an increase in fines content, depicted in Figure 5. Similarly, the coefficient of volumetric compressibility (\(m_{\rm p}\)) of calcareous sand samples exhibits an initial increase followed by a decrease, shown in Figure 6. It can be concluded that when the fines content is between 15% and 20%, the compression behavior of calcareous sand-fines mixture is in a transitional state. From a microscopic perspective, as fine particles are continuously added, the load-bearing soil structure shifts from being primarily composed of original calcareous sand particles to being dominated by fine particles, as demonstrated in Figure 9. This study suggests that the fines content  
Figure 9: Interparticle contact state classification for calcareous clean sand and silty sand.  
threshold (\(f_{\text{c-th}}\)) is approximately 15-20%, which indicates the compressibility changes in powdery calcareous sand. Furthermore, variations in the initial relative density influence the value of \(f_{\text{c-th}}\) timing, with a higher density leading to an earlier threshold appearance and lower density delaying it. In addition, a comparison of particle breakage was carried out. It is worth noting that the particle relative breakage of calcareous sand increases at the initial stage of the addition of fine particles.  
Previous research on the compression behavior of calcareous sand mostly concentrated on coarse particle components [5; 26], and limitations in the selection of fines content for studying the compressibility of calcareous silty sand persist [25]. In this study, the compression behavior of calcareous sand-fines mixtures across the 0-40% fines content range can be supplemented in more detail. The results can offer valuable insights for addressing calcareous soil foundation bearing capacity challenges caused by the fine-grained aggregation layer during land reclamation projects. Furthermore, notable differences exist in the shear mechanical properties between calcareous sand and quartz sand [6; 35]. Subsequent research directions could investigate the impact of fines content on the shear mechanical properties of calcareous sand-fines mixtures, especially focusing on the correlation between the fines content threshold reflecting shear changes and the initial state of calcareous sand.
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology  
### Parametric Based Train Dynamic Model  
To facilitate understanding, in this subsection, we briefly introduce a classical single-point train dynamic model. Other types of dynamic models (such as multi-point models) can be considered as optimized or expanded versions of this model. By ignoring the in-train force and assuming continuous control rates, the model can be formulated as [44]  
\[M\psi(t)=a_{v}^{a}F(t)-a_{v}^{b}B(t)-w(v)-g(x) \tag{1}\]  
\[w(v)=M(c_{0}+c_{1}v+c_{2}v^{2}) \tag{2}\]\[\dot{x}(t)=v \tag{3}\]  
where \(x\), \(v\) and \(t\) denote the train displacement, velocity and time, respectively. \(M=\sum m_{i}\) represents the total mass of the train, where \(m_{i}\) is the mass of the \(i\)th carriage. \(F(t)\) is the train traction force while \(B(t)\) denotes the train braking force. \(\alpha_{v}^{a}\) and \(\alpha_{v}^{b}\) represent the relative accelerating and braking coefficients, respectively. Given a certain train position \(x,g(x)\) describes the impacts of the gradient resistances and curve resistances. \(w(v)=M(c_{0}+c_{1}v+c_{2}v^{2})\) is the Davis formula, which expresses the relationship between the train speed and the aerodynamic drag, where \(c_{0}\), \(c_{1}\) and \(c_{2}\) indicate the resistance parameters.  
In this model, let \(\mathtt{S}=\{S_{1},S_{2},\cdots,S_{m}\}\) denote the set of train positions in a certain operation area. The location data can be obtained by locating devices, i.e., balises, which are extensively deployed in the European Train Control System and the Chinese Train Control System [45]. With low time delay, a balise transmits the position data to the passing train. The velocity of the train can be similarly captured by track-side and on-board devices. Given the fixed parameters of the dynamic model (\(\alpha_{v}^{a}\), \(\alpha_{v}^{b}\), \(c_{0}\), \(c_{1}\), \(c_{2}\) and \(g(\cdot)\)), the inputs and the outputs of the model are shown in Figure 1. As can be seen in Figure 1, when a certain train passes \(S_{m}\), the inputs of the dynamic model are \(x_{m}\), \(v_{m}\), \(F_{m}\) and \(B_{m}\). Using Equations (1) and (2), the model output \(v_{m+1}\) can be calculated.  
Since some of the model parameters are difficult to acquire in real-world scenarios, their values are arbitrarily assigned, resulting in a poor performance of this parametric approach. Furthermore, the velocity of the train varies in a wide range during the operation process. By adjusting the control commands, the operation process can be divided into four phases: train acceleration, train coasting, train cruising and train braking. At different operation phases, the parameters and disturbance factors have different influences of the model outputs. Thus, Equations (1) and (2) may change into various complicated multivariate nonlinear functions, which increases the difficulty of system modeling.
# A 7.5-9 GHz GaAs Two-Channel Multi-Function Chip
## 4 Conclusions  
In this letter, a high-performance 7.5-9 GHz two-channel multi-function chip integrated with a serial-to-parallel converter is presented. A design of the absorptive SPST switch is deployed to optimize the return loss and control channel array calibration. In the 8 dB and 16 dB attenuation bits, a switched-path-type topology is adopted in order to obtain a good flatness of attenuation characteristic and a low additive phase shift. The control pads are reduced from 54 to 9 by integrating the serial-to-parallel converter. The measurement result shows that the two channels of MFC have <13 dB insertion loss at reference state, and >19 dB return loss at all states. An RMS attenuation error of <0.45 dB and an RMS parasitic phase error of >2.4\({}^{\circ}\) are obtained for the attenuator. An RMS phase error of <2\({}^{\circ}\) and an RMS parasitic amplitude error of <0.2 dB are achieved for the phase shifter. Good return loss, high attenuation, and phase shift accuracy are achieved in this two-channel multi-function chip.  
Methodology, S.Z. ([PERSON]); software, J.Z.; validation, H.Y.; data curation, S.Z. ([PERSON]); writing--original draft preparation, S.Z. ([PERSON]); writing--review & editing, Z.W.; supervision, S.Z. ([PERSON]); project administration, J.W.; funding acquisition, Z.W. This research was funded by the National Natural Science Foundation of China grant number 61604128 and the Fundamental Research Funds for the Central Universities grant number 2017 QN81002. The authors would like to thank the Institute of Aerospace Electronics Engineering of Zhejiang University for providing research platform and technical support. The authors declare no conflict of interest.
# Efficient Time-Series Clustering through Sparse Gaussian Modeling
## 3 Gaussian Process Regression for Time Series
### Kernel Functions for Time Series  
Kernel functions play a central role in Gaussian Process Regression because they incorporate the information (and our assumptions) about the smoothness and the degree of correlation between data points in the time series that we aim to approximate. The kernel function \(k:\mathbb{R}\times\mathbb{R}\to\mathbb{R}\) maps any two points in time \(t\) and \(t^{\prime}\) to a real number that quantifies the expected similarity between the observed data points \(x_{t}\) and \(x_{t^{\prime}}\). In any finite dimensions \(n\), the covariance matrix \(K\) of the corresponding \(n\)-variate Gaussian is computed by evaluating the kernel function \(k(t,t^{\prime})\) over all \(n^{2}\) pairs of points \(t\) and \(t^{\prime}\) in time. The kernel function should ensure that the resulting covariance matrix is always positive semidefinite.  
A kernel function \(k\) is _stationary_, if for any two points in time \(t\) and \(t^{\prime}\) and any translation \(u\), it holds that \(k(t,t^{\prime})=k(t+u,t^{\prime}+u)\). Specifically, the covariance \(k(t,t^{\prime})\) of any two points in time \(t\) and \(t^{\prime}\) only depends on \(t-t^{\prime}\) and is invariant under translation. If the covariance \(k(t,t^{\prime})\) of any two points in time \(t\) and \(t^{\prime}\) only depends on their distance \(|t-t^{\prime}|\), we say that the kernel function is _isotropic_. In the following, we discuss two widely used stationary (and isotropic) kernel functions, the _Radical Basis Function_ (RBF) kernel and the _Matern_ kernel (see also ([14], Chapter 4)).
## 4 Discussion
EOs and their constituents are recognized as antioxidant agents, playing a key role in preventing chronic intestinal oxidative damage and chronic low-grade inflammation, two of the main risk factors in CRC. Similar conclusions have also been put forward in the treatment of melanoma, where, again, the antioxidant activity of EOs is identified as a determining factor in reducing melanogenesis. Overproduction of melanin induces hyperpigmentation and promotes the development of melanoma. Other studies indicate that EOs can reduce angiogenesis, lymphangiogenesis and tumor metastasis proliferation [117]. With regard to the mechanisms of action of EOs and their individual compounds, although it is rather difficult to understandwhich molecule may exert anticancer activity, comforting results were found in the case of the EO of _Origanum vulgarte_[118], _Rosmarinus officinalis_[119], _Lippia microphylla_[120] and the _Mentha_ and _Thymus_ genus [121; 122]. On the basis of these findings and the data reported in this study, further research in the antitumoral field would be appropriate for both alpha-phellandrene and EOs particularly rich in this molecule. Alpha-phellandrene and oils rich in this molecule have proven to be potential biopesticides, larvicides and insect repellents. Furthermore, considering the research conducted against food-borne pathogens, it can be stated that alpha-phellandrene could have promising applications in the food and agro-industrial supply chains, ranging from biological treatment of crops to pre- and post-harvest treatments. The attention of researchers to these new applications of EOs is well documented [123; 124; 125]. Furthermore, the larvicidal efficacy of alpha-phellandrene opens up new research perspectives in the control of tropical disease vectors and human and animal health. These areas of research are relatively new for EOs and their components, which are traditionally marketed in the cosmetics and perfume sectors, but they represent innovative applications already supported by a growing body of scientific literature [126; 127; 128; 129; 130]  
Finally, alpha-phellandrene deserves further investigation in relation to its antinociceptive properties and toxicological profile, which would provide a fundamental basis for further pharmaceutical developments.
## Materials and Methods
### Soft Computing Models Overview
#### 2.2.6 Tuning Procedure of the ANFIS Parameters  
Previous studies confirmed that the standalone ANFIS is limited in solving complex problems due to trapping in local optimum results [95]. Besides, it requires a time-consuming process to tune the parameters of membership functions and fuzzy logic rules [96]. The combination of classical ANFIS with meta-heuristic optimization techniques inspired by nature can provide fast convergence speed while the trapping in of local optimum results can be tackled. The classical ANFIS includes two major sections as antecedent and consequent parts. Tuning the antecedent (\(a_{i}\) and \(b_{i}\) in Equation (4)) and consequent (\(p_{i}\), \(q_{i}\) and \(r_{i}\) in Equation (7)) parameters is essential to obtain reliable solutions. The ANFIS uses gradient-based techniques to tune those parameters. The major disadvantage of these techniques is trapping in local optimum solutions. This study aims to tune ANFIS parameters (antecedent and consequent parameters) by several meta-heuristic optimization algorithms (e.g., PSO, GA, DE and ACO) according to following steps:  
1. Select the training data.
2. Provide a primary structure for ANFIS.  
* Determine the initial values of antecedent and consequent parameters.
* In iterative computation, tune the ANFIS parameters using the meta-heuristic techniques.
* In each iteration, assess the value of the objective function (i.e., root mean square error (RMSE)).
* Save the best parameter set of the fuzzy model and terminate the tuning process when the stopping criterion is satisfied; otherwise, restart step iv.