esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Methods
#### 2.6.4 Extreme Gradient Boosting (XGBoost)  
XGBoost is a sophisticated ensemble learning algorithm based on classification or regression trees [79]. First, this algorithm generates several subsequent decision trees utilizing the prediction errors or residuals from the preceding tree instead of averaging independent trees; consequently, it focuses on samples with a higher level of uncertainty. The decision trees generated in previous steps are combined to arrive at the final output [79]. XGBoost aims to minimize computational complexity while optimizing computer resources [79]. In contrast to the above algorithms, XGBoost contains several tunable parameters that add complexity. XGBoost has a few parameters in common with other tree-based algorithms, but it also requires hyperparameters to limit the risk of overfitting, reduce prediction variability, and increase accuracy [76]. XGBoost's key advantages are flexibility and speed. Due to its outstanding performance in a growing several of Kaggle contests, XGBoost has established itself as a unique inclusive algorithm [79]. However, XGBoost has only been used in a few research thus far to map geological hazard susceptibility. The target value (\(O_{t}\)) of the algorithm after \(t\) iterations is calculated using Equations (8)-(10) [79]:  
\[O_{t}\cong-\frac{1}{2}\sum_{r=1}^{T}\frac{G_{r}^{2}}{H_{r}+\sigma}+\gamma T \tag{7}\]  
where \(\sigma\) and \(\gamma\) are penalty factors, \(G_{r}\) and \(H_{r}\) are calculated as follows, \(T\) is the number of leaf nodes, and \(l\) denotes the loss caused by differences between the predicted and true values.  
\[G_{r}=\sum_{i\in I_{r}}\delta_{y_{i,l-1}}l(y_{i},y_{i,l-1}) \tag{8}\]  
\[H_{r}=\sum_{i\in I_{r}}\delta^{2}y_{i,l-1}l(y_{i},y_{i,l-1}) \tag{9}\]  
where \(y_{i}\) is an actual factor, \(y_{i,l-1}\) is the value after \(t\) times calculations.
## 2 Energy Management and Review of Surveys
### Forecasting Algorithms and Energy Management Strategies  
In Ref. [9], focusing on accurately forecasting power generation and load-to-energy management in MGs, forecasting algorithms for the power supply side and load demand were addressed. The forecasting techniques in two categories of hybrid and single models were discussed. In hybrid models, the combination of artificial neural network (ANN) with wavelet, Fuzzy logic, support vector machines (SVMs), and genetic algorithm (GA) were detailed. The single models were classified into artificial intelligence (AI) (including SVM, Fuzzy logic, and ANN) and parametric techniques (including statistic methods and the Kalman filter).  
[PERSON] et al. [12] discussed the forecasting techniques for EM of MGs in terms of different parameters (including load, price, and weather). They categorized them into different types based on the required foresting period, including very short-term (from multi seconds to 30 min, used for the dynamic control of RES to the load requirements); short-term (from 30 min to 6 h, used for energy scheduling of different sources and storage devices); medium-term (from 6 h to one day, used for market pricing); long-term (from one day to one week, used for maintenance and load dispatch). In another classification,the forecasting models were categorized based on the used model, linear (including time series; dynamic programming (state space and ARMA)) and non-linear (SVM; Markov chain; stochastic; Fuzzy neural; ANN (supervised, unsupervised, and reinforced)).
# Changes in Pupil Size According to the Color of Cosmetic Packaging: Using Eye-Tracking Techniques
###### Abstract  
This study examines the relationship between cosmetic packaging color and consumer attention by analyzing changes in pupil size using eye-tracking technology. A controlled experiment with 25 participants (mean age: 24.7 \(\pm\) 3 years, 14 males and 11 females) was conducted to investigate the impact of eight packaging colors (black, white, blue, yellow, orange, turquoise, pink, and sky blue) on pupil dilation during gaze fixation and movement. Pupil size data were analyzed using SAS 9.4, with T-tests used to determine significant differences across colors. The results revealed that pink packaging elicited significantly larger pupil sizes during fixation, indicating heightened attention, while black, white, blue, and orange led to smaller pupil sizes when fixated, suggesting greater focus on the surrounding environment rather than the packaging. In contrast, yellow and turquoise exhibited no significant differences in pupil size during fixation and movement. Additionally, the study highlights that gaze fixation is a more meaningful indicator of attention than gaze movement, as fixation reflects focused interest in specific stimuli. The findings suggest that pink packaging is most effective in attracting consumer attention, while black, white, blue, and orange are better suited for enhancing focus on the surrounding environment. These insights emphasize the growing importance of packaging design in influencing consumer behavior, particularly through color selection. This study contributes to marketing practices by providing empirical evidence for the visual impact of packaging colors, offering valuable guidance for cosmetic industry practitioners. Future research should expand sample sizes and explore additional packaging attributes, such as shape and material, to derive more comprehensive insights.  
pupil size; fixation; saccade; color; cosmetic packaging; eye-tracking; T-test
## 2 Numerical Simulation of LCO
### Computational Domain and Mesh Arrangement  
The computational domain and the corresponding boundary conditions are shown in Figure 1, where the along-wind domain size is set as \(-5B\leq Lx\leq 15B\) and the cross-wind domain size \(-5B\leq Ly\leq 5B\) (\(B=\) bridge deck width). The computational domain size was sufficiently large to avoid the flow obstacle effect. The velocity inlet condition was set to be the inflow boundary, and the turbulence intensity of the oncoming flow was set to be 0.5%. The constant pressure outlet condition was set to be the outflow boundary. The non-slip condition was used for the bridge deck boundary, while the slip condition was used at the top and bottom boundaries. The hybrid mesh was used to discretize the computational domain to balance the computational efficiency and accuracy. The structured quadrangular grid was generated around the bridge deck section to capture the details of the separation flow, while the unstructured triangular grid was used for the remaining area. In the computational domain, the mesh of the rigid region followed the bridge deck motion exactly while the mesh in the dynamic region deformed based on the spring-based smoothing method at each time step. To accurately simulate the flow in the bridge deck boundary layer, the distance of the first grid layer to the deck surface was set to be 0.00001B. Accordingly, the nondimensional wall coordinate \(y^{+}\) was smaller than unity. The mesh cell number used in the following CFD cases was around 75,000.
###### Abstract  
Theoretical analysis and numerical simulation are used to study the influence of different parameters of variable diameter borehole pressure relief technology on the surrounding rock and support. A strain-softening model was established to analyze the intrinsic connection between the parameters of variable diameter boreholes and the evolution of surrounding rock stress, deformation law, and support strength. The results show that: (1) With the increase in shallow borehole diameter, it is easy to cause anchor de-anchoring phenomenon. (2) After the deep borehole diameter is more than 250 mm, it transfers the peak of the shallow vertical stress to the deep surrounding rock (about 16 m away from the coal wall). (3) If the position of the variable borehole aperture is set between the anchorage zone and the stress peak of the roadway, the stress transfer effect is better, and the influence and effective binding force on the surrounding rock is smaller. (4) When the spacing is 1.0 m\(\sim\)2.0 m, the vertical stress starts to transfer to the deep surrounding rock, the deformation of the surrounding rock is smaller, and the reduction in the effective binding force of the anchors is smaller. The result can provide a reference for similar production conditions.  
variable diameter; pressure relief borehole; strain softening; effective binding forces Article
# An Efficient Explainability of Deep Models on Medical Images
## 1 Introduction  
Recently the emergence of artificial intelligence and the advances of the digital revolution have considerably transformed various sectors, including healthcare. In the medical field, the use of deep learning, in particular convolutional neural networks (CNNs), has opened up new perspectives in the classification of medical images, such as chest X-rays. These technological advances have resulted in impressive classification performances, rivaling even those of human experts. However, one of the major challenges associated with the use of these deep learning models is their opacity and lack of explainability. The decisionsmade by these models often remain difficult to understand, which limits their adoption in critical fields such as medicine.  
The need to understand and explain the decisions made by deep learning models in the medical field has given rise to a new field of research called \"Explainability of Artificial Intelligence\" (XAI). The main aim of XAI is to provide understandable and interpretable explanations of the results obtained by deep learning models, enabling doctors and healthcare professionals to understand the reasons behind these decisions. The central problem of this research is therefore: How can the results obtained by deep learning models applied to the classification of chest X-rays be explained in a clear and comprehensible way? How can the decisions made by these models be made transparent and justifiable to physicians? To address this issue, particular attention is paid to visual explainability methods. These methods aim to identify and highlight regions of interest in an image that have been decisive for the classification performed by deep learning models. By providing a clear visualization of the influential areas, these methods enable physicians to understand the underlying reasons for the model's decision and verify its validity.  
The main objective of this research is to explore visual explainability methods in the context of chest X-ray classification using deep learning models. Other sub-objectives include the following:  
* Classify chest X-ray images using deep learning models, specifically convolutional neural networks (CNNs).
* Apply deep learning methods to two different datasets and analyze the results obtained.
* Compare the performance of the different explainability methods used.
* Highlight the importance of explainability in the medical field.  
This paper is organized as follows. Section 2 describes the related work. Section 3 defines the proposed model. Section 4 presents some results to evaluate the proposed approach, and Section 5 is devoted to discussion. Finally, the last section concludes this paper and highlights some improvements.
## 3 Modeling Methods
### McKibben Model  
It was the product of extensive research carried out by the Saskatchewan Research Council (SRC) (Saskatoon, SK, Canada) on WAF. The experiments were conducted using flowloops comprised of 25, 100, and 260 mm steel pipes. The thicknesses of the wall-fouling layers were quantified using a double-pipe heat exchanger and a hot-film probe. The ranges of oil viscosities and input water fractions were 0.62-91.6 Pa-s and 30-50%. It was demonstrated that the model could consider the most significant factors, such as inertia, gravity, water fraction, the additional shear caused by wall-fouling, and viscosity ratio. The model's inputs are pipe diameter, average velocity, densities, viscosities, and water fraction. One of the key factors that was addressed by [PERSON] et al. [7] was the contribution of the wall-fouling layer to the effective hydrodynamic roughness of the WAF regime. The strong performance of the SRC model has been recognized by other investigators, such as [PERSON] et al. [4] and [PERSON] et al. [6]. Both of these independent groups of researchers demonstrated the superiority of the [PERSON] model over other analytical models in predicting the WAF pressure losses. Even though it is more accurate, its predictions still involve up to \(\pm\)100% error. Probably, the most significant limitation of the model is the ambiguous and labor-intensive trial and error procedure used to optimize its performance. It includes a multivariate power-law function for friction factor (\(f\)) with five coefficients, the values of which were established without any rigorous statistical analysis. The model is concisely presented with Equation (1), while a detailed description of the model is available in [PERSON] [22].  
\[\frac{\Delta P}{L}(WAF)=f\frac{\rho_{w}V^{2}}{2D}=30\left(\frac{V}{\sqrt{gD}} \right)^{-0.5}\left(\frac{0.079}{R\epsilon_{w}^{0.25}}\right)^{1.3}\left( \frac{16}{Re_{o}}\right)^{0.32}\left(C_{w}\right)^{-1.2}\left(\frac{\rho_{w}V ^{2}}{D}\right) \tag{1}\]  
where, \(f\): equivalent friction factor; \(Re\): Reynolds number; \(Re_{w}\): water equivalent Reynolds number (\(Re_{w}=\frac{DV\rho_{w}}{\mu_{w}}\)); \(Re_{o}\): oil equivalent Reynolds number (\(Re_{o}=\frac{DV\rho_{w}}{\mu_{o}}\)); \(\Delta P/L\): pressure gradient (Pa/m); \(\rho\): density (kg/m\({}^{3}\)); \(V\): average velocity (m/s); \(D\): pipe's internal diameter (m); \(g\): gravitational force (m/s\({}^{2}\)); \(C_{w}\): water fraction (-); \(\mu\): dynamic viscosity (Pa.s); \(w\): water; \(o\): oil.
## 4 Experimental Results and Discussion  
We conducted experiments on the WMT 2014 English-German and English-French corpora. The World Machine Translation dataset contains data collected from news translation tasks launched by the World Machine Translation Conference [5], and it has been continuously updated until last year. In addition to the news corpus, public information from the United Nations is also included. The WMT 2014 English-German (EG) and English-French (EF) corpus was used in the original Transformer paper [4], and was also used as the basis for the evaluation of the model performance in this paper. The BLEU score [23] was used as the evaluation standard.  
At the same time, we also trained the BERT-fused NMT model. BERT was pre-trained using a considerable number of corpora, including BooksCorpus [24] and the English version of Wikipedia. The contents in the BooksCorpus are divided into 16 categories, and the majority of entries belong to the romance, fantasy, science fiction, and teen categories. The contents of English Wikipedia include culture, art, biographies, geography and so on [25], as shown in Figure 6.  
Figure 5: Construction of the X-Transformer model.  
The experimental results are separated into two aspects:  
* First, the comparison of the performance between the X-Transformer and related models. In addition to comparing our model with the baseline Transformer, we also compared it with the results of adding the pre-trained model BERT, so that readers have a clearer understanding the performance of the X-Transformer.
* Second, the learning situation of the self-attention mechanism in the encoder is presented, which is displayed by a visual heat map. Through the visualized heat maps of the Transformer and X-Transformer, we can understand whether the mechanism pays attention to the correct tokens and the attention relationship between tokens. These also reveal the comprehension of the models.  
The computer specification used in the experiments of this paper was Intel core i7-9700, with 32 GB RAM, and the GPU was Nvidia Titan RTX. The experimental operating system was Ubuntu 18.04 LTS, and the software used was CUDA 10.1, cuDNN v7.6.5.
# Copernicus Global Land Cover Layers--Collection 2
## 3 The CGLS-LC100 Product and Accuracy Assessment
### Accuracy Assessment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Forest** & **33.1** & 1.9 & 1.2 & 0.6 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.1 & 37.1 & 38.3 & **89.4** & 0.8 \\ \hline
**Shrubs** & 1.5 & **5.8** & 1.4 & 0.3 & 0.0 & 0.2 & 0.0 & 0.0 & 0.1 & 0.0 & 9.3 & _7.9_ & **62.4** & 3.1 \\ \hline
**Herbaceous vegetation** & 1.2 & 2.3 & **13.8** & 0.6 & 0.0 & 0.5 & 0.0 & 0.1 & 0.3 & 1.0 & 20.0 & 19.2 & **69.2** & 1.8 \\ \hline
**Croplands** & 1.1 & 0.4 & 1.6 & **8.2** & 0.1 & 0.0 & 0.0 & 0.1 & 0.1 & 0.0 & 11.6 & 12.6 & **70.2** & 2.1 \\ \hline
**Built-up** & 0.1 & 0.0 & 0.0 & 0.0 & **0.5** & 0.0 & 0.0 & 0.0 & 0.0 & 0.6 & 3.9 & **77.3** & 5.7 \\ \hline
**Bare/sparse vegetation** & 0.0 & 0.3 & 0.8 & 0.0 & 0.0 & **13.5** & 0.0 & 0.0 & 0.0 & 14.7 & 6.5 & **91.5** & 1.8 \\ \hline
**Snow/ice** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & **1.9** & 0.0 & 0.0 & 0.0 & 2.0 & 2.4 & **94.7** & 3.5 \\ \hline
**Permanent Water** & 0.0 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & 0.0 & **2.0** & 0.0 & 0.0 & 2.1 & 4.0 & **94.9** & 2.0 \\ \hline
**Herbaceous Wetland** & 0.1 & 0.1 & 0.3 & 0.0 & 0.0 & 0.0 & 0.0 & 0.1 & **0.5** & 0.1 & 1.1 & 3.7 & **46.5** & 6.3 \\ \hline
**Lichen/moss** & 0.0 & 0.0 & 0.1 & 0.0 & 0.0 & 0.4 & 0.0 & 0.0 & 0.0 & **0.9** & 1.4 & 1.7 & **63.9** & 7.0 \\ \hline
**Total** & 37.3 & 10.9 & 19.2 & 9.7 & 0.7 & 14.7 & 2.0 & 2.3 & 1.2 & 2.2 & **100** & & & \\ \hline
**Sample count** & 38.9 & 9.2 & 19.4 & 10.6 & 3.6 & 7.3 & 2.3 & 4.3 & 2.6 & 1.8 & & **100** & & \\ \hline
**Producer’s accuracy** & **88.9** & **53.4** & **71.8** & **83.9** & **76.1** & **91.7** & **98.9** & **86.8** & **43.7** & **42** & & & **80.2** \\ \hline
**Confidence interval \(\pm\)** & 0.8 & 2.8 & 1.8 & 2.0 & 7.6 & 1.4 & 0.9 & 3.2 & 6.4 & 5.3 & & & 0.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Confusion matrix for the discrete layer at classification level 1 of the CGLS-LC100 Collection 2 product at a global scale. Adapted from [39]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Average** & **Number of Samples** & **Overall Accuracy (\%)** & **Confidence Intervals \(\pm\)** \\ \hline
**Africa** & 3616 & 80.1 & 2.0 \\ \hline
**Asia** & 3071 & 83.3 & 1.5 \\ \hline
**Northern Eurasia** & 2976 & 79.8 & 1.6 \\ \hline
**Europe** & 3120 & 80.4 & 1.6 \\ \hline
**North America** & 2843 & 77.1 & 1.7 \\ \hline
**Oceania \& Australia** & 2951 & 81.9 & 1.9 \\ \hline
**South America** & 3017 & 79.6 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean absolute error (MAE) and root mean square error (RMSE) of the fractional cover layers of the CGLS-LC100 collection 2 product at global scale. Adapted from [39].
# Wideband Vibro-Acoustic Modulation for Crack Detection in Wind Turbine Blades
## 2 VAM Model for Detecting Cracks in WT Blades
### Comparison between Traditional VAM (Active Vibration) and Proposed VAM (Passive Vibration)
#### 2.3.2 Passive VAM
Figure 8:0 DEMON spectra of cases when the damage produced changes in the resonance frequency and Q-factor: (**a**) Case 1—eight-foot section, and SNR is 28 dB (**b**); Case 2—the 52-m WT blade in the band 1900 Hz to 2250 Hz, and SNR is 19.9 dB; (**c**) Case 3—the 52-m WT blade in the band 2500 Hz to 2700 Hz, and SNR is 10.5 dB. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Case 1** & **Case 2** & **Case 3** \\ \hline SNR for wide band & 28 & 19.9 & 10.5 \\ SNR for wide band for \(\beta\) = 0 & \(-\)4 & 14.25 & 7.33 \\ MI for harmonic & \(-\)32.29 & \(-\)43.45 & \(-\)42.477 \\ MI for harmonic for \(\beta\) = 0 & \(-\)32.993 & \(-\)43.68 & \(-\)42.491 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The SNR and MI values derived from the application of the developed model for cases with and without amplitude-dependable attenuation.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 4 Simulations and Experiment
### Simulations
The localization angles are 90\({}^{\circ}\), 151\({}^{\circ}\), 270\({}^{\circ}\), and 1\({}^{\circ}\) in Room A, 91\({}^{\circ}\), 155\({}^{\circ}\), 269\({}^{\circ}\), and 0\({}^{\circ}\) in Room B, respectively, and the SIR improvement is shown in Figures 11-13, where  
Figure 8: SIR improvement with separation under three sources in Room A.  
Figure 10: SIR improvement with modified separation under three sources in Room B.  
Figure 9: SIR improvement with modified separation under three sources in Room A. Finally, the computation time with different number of sound sources was used to evaluate the processing ability with respect to multiple sources separation, and the time of separation is shown in Figure 14.  
From Figure 14, the following conclusions are obtained. 1. For the separation with different number of sources, the computation time of our algorithm is approximately the same. The relationship between the computation time and the usage of ICA is approximately linear. 2. The computation time of our localization is \(\sim\)0.49-0.63 s and is slightly proportional to the source number due to the process of detection of positioning result, whereas the computation time of our separation has little correlation with the source number. 3. Considering the signal length which is 1.1 s in our simulation, the computation time using the ICA increases by approximately 1 s compared with the DSB-based approach. With speech segments of other lengths, the ratio of the running time of the localization algorithm and the DSB to the length of the speech is almost constant. Moreover, when processing the longer speech, the ratio of the FDICA to the length of the speech could decrease slightly under the situation where the sound source and environment are basically unchanged. If the usage ratio of ICA is controlled to \(\sim\)20%-30%, the total computation time of the localization and separation together is less than the length of the processed signal when an ideal separation result is obtained, which means the real-time processing is possible.
# Changes in Soil Chemical Properties Due to Long-Term
## 2 Materials and Methods  
### Field Experimental Design and Soil Sampling  
The study site was located at the Rural Development Administration (RDA), Miryang, South Korea (latitude 36\({}^{\circ}\)36\({}^{\prime}\) N; longitude 128\({}^{\circ}\)45\({}^{\prime}\) E). Long-term fertilization started in 1967 and rice was cultivated as a monocrop [19]. A completely randomized arrangement for the experimental area was done with 10 m \(\times\) 10 m plots replicated three times for each treatment. Different compost fertilization treatments, with and without inorganic NPK additions, were selected for the study. The treatments included: (a) unfertilized control (NF), (b) NPK fertilizers plus compost (NPKCom), and (c) compost (Com). Urea (N), fused superphosphate (P), and potassium chloride (K) (NPK), were added at a rate of 120, 80 and 80 kg ha\({}^{-1}\) from 1967 to 1972. Before the transplantation of rice seedlings, NPK was added as the basal fertilizer, which supplied 50, 100 and 70% of N, P and K, respectively. The rice straw and the cattle manure compost were decomposed for a minimum of 6 months, containing approximately 431, 19.8, 5.2 and 29.1 g kg\({}^{-1}\) of total C, N, P and K, respectively. An equivalent of 10 tons ha\({}^{-1}\) was amended for compost treatments. Soil sampling was done in April 2021 during the rice fallow period before submergence with water and the transplantation of rice seedlings. Five subsample soils were taken at a depth of nearly 20 cm from each of the three replicates belonging to individual treatments. A single composite sample was created for each treatment by pooling the subsamples together. The composite samples were sieved (<2 mm) after the removal of visible rock and root debris. The soils were immediately stored in a sterile polypropylene bag and transported to the laboratory in ice. Soils used for DNA extraction were stored at \(-\)80 \({}^{\circ}\)C, while those for routine soil chemical property analyses were stored at 4 \({}^{\circ}\)C.