esa-sceva/satcom-chunk-collection huggingface dataset:
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 4 Characterizing the Fracture Properties of Metallic Glasses
### Variability of Fracture Toughness  
As MG samples can hardly meet the requirement of size and fatigue pre-crack in the test standard, some nonstandard measurements were used for substitution. Some studies used the notch toughness (without fatigue pre-crack) as an approximation to characterize the fracture toughness of MGs [30; 106]. In some studies, the fatigue pre-cracking process was replaced by machining a notch tip with a very small radius [6]. J-integral toughness tests were also used, as the size requirement of J-integral toughness test is smaller than that of standard measurement [22; 52]. In these studies under different loading conditions and different test methods, a high variability in the measured fracture toughness is observed.  
Experiments of notch toughness show that with increasing notch-tip radius, the degree of stress concentration at the notch-tip decreases, and a greater load is needed to form shear bands from the notch-tip. A larger fracture toughness can be measured [107]. In small size samples, a shorter shear band sliding distance is required to release the same stress. The material prefers to form multiple shear bands rather than shear band delamination, and a higher fracture resistance can be achieved [42; 43; 51]. The measured fracture toughness is also sensitive to the temperature [108], loading rate, loading mode [107], heat history [109], residual stress [110], geometry [111] and the distribution of impurities/flaws [112]. For example, the cooling rate directly affects the fracture energy of a Ti-Zr-Cu-Ni-Be MG, which can range from 148.9 to only 0.2 kJ m\({}^{-2}\)[26]. The measured fracture toughness of Vitreloy-1 MG can vary widely from \(\sim\)18 to \(\sim\)130 MPa\(\cdot\)m\({}^{1/2}\) in different reports with different test methods [39; 75; 113].  
To understand the variability of fracture toughness in MGs. Some studies suggest that the properties of MGs are \"intrinsically variable\" [114], as the shear band behaviors of MGs depends on both the material and the loading condition [42; 107; 115]. Some studies suggest that the sample geometry to measure the fracture toughness of BMGs should be much smaller than suggested by the standards for crystalline alloys [111]. According to the discussions of this review, the variability of mechanical properties of MGs actually reflects the sensibility of the shear band behaviors to the external loading condition and the randomness in the shear band motions. We have seen the tendency that new experimental methods, theoretical criteria, or definitions will be proposed to further study these shear band controlled materials, especially for the engineering important metallic glasses.
## 5 Discussion  
During the process of image-to-image translation, it is common to lose detailed information and generate inconsistent features, which can potentially disrupt the performance of subsequent image registration. In this paper, we design DTR-GAN to achieve MRI-CT registration, which can generate consistent grayscale images and transform multimodal image registration into unimodal image registration.  
To alleviate the cycle-consistency loss limitation of the CycleGAN, which would lead to the generation of distorted shapes, we improve the CycleGAN with PatchNCE loss to generate a shape-consistent transformed image, which enables the registration network to better complete alignment tasks. Specifically, the translation network is designed to implement bidirectional MRI-CT transformation, capturing and preserving more comprehensive anatomical information, which leads to more accurate translated images. During the encoding stage, the information obtained decreases layer by layer  
Figure 11: Comparison of our variant methods on the CHAOS dataset. (**A,E**) are warped images, (**B,F**) are deformation fields, (**C,G**) are checkboard grids, and (**D,H**) are overlapping images.  
Figure 10: Comparison of our variant methods on the Learn2 Reg dataset. (**A,E**) are warped images, (**B,F**) are deformation fields, (**C,G**) are checkboard grids, and (**D,H**) are overlapping images.  
as the network layers deepen. Downsampling can result in the loss of low-level feature information that is extracted from the previous layers. To capture global and local features of the image in the translation network, we introduce the MFF module between the downsampling and upsampling layers in the translation network DT-GAN. By aggregating deep-level and top-level information, it leverages the quality of the translated image and improves the subsequent registration performance. In addition, we design a mixed similarity loss to calculate the similarity between warped and target images, which enhances the appearance of alignment in DTR-GAN.  
One of the challenges in abdominal registration is the deformation at organ boundaries, which can be impacted by a variety of causes, such as large organ deformation, the filling and activity of adjacent organs, and respiratory movement. However, there are still some limitations in our approach. Our method is primarily designed for 2D registration and lacks the spatial structure information of the image. Hence, we will extend our network to 3D in the future. In addition, we will consider the decomposition of the target deformation field into several simpler ones in the next work, because it is hard to generate the accurate deformation field when the displacement between the images is large. Moreover, considering our proposed model has high computational complexity, we will focus on lightweight networks.
# Antioxidant Activity and Polyphenolic Content of North Macedonian Wines
## 1 Introduction  
Many plants and plant products are natural sources of antioxidants, and wine existing on Earth for more than 6000 years is one of them [1]. Polyphenols are the main antioxidant constituents in grapes and wine and many efforts have been made in order to elucidate their structure and their antioxidant mechanisms. Additionally, a plethora of studies have proven the beneficial effects of the antioxidants in wine, for the prevention of coronary disease and atherosclerosis and a phenomenon known as the French Paradox [2].  
Polyphenols are directly related to the antioxidant activity of wines, apart from their role in the taste, aroma, mouth feel, astringency and colour. The composition of wine regarding phenolic compounds is very diverse, since it depends on the species [3] and cultivar [4] and is also influenced by different environmental factors during grape maturation, their extraction from the grapes during vinification as well as vinification techniques applied, and chemical modification of the phenols due to maturation and contact with the wood barrels [5]. Attention must be given to the specific adjustments in vinification procedures required for different grape varieties, in order to exploit the maximum of the polyphenolic potential of the grapes.  
Interest in the evaluation of the polyphenolic content and antioxidant activity of wines indigenous to the Balkan region has arisen due to limited knowledge of the potential of some local varieties. Since many international wines have spread on a global scale, most of the domestic grape varieties have been replaced with international grape varieties, their cultivation has been markedly reduced, and consequently the production of the wines of those varieties has decreased as well. As a result,some of these varieties have been neglected and their phenolic profile and antioxidant activity has not been clearly established. Additionally, no adequate viinification techniques have been developed to exploit their potential, in order to produce wines with higher quality.  
Therefore, the aim of this study was to reveal the polyphenolic profile and antioxidant activity of local Balkan varieties, cultivated in the Republic of North Macedonia, providing data that could be later used as a basis for future research focused on implementing more adequate techniques for viinification, specifically adapted for the selected varieties. In this context, the polyphenolic content of selected varieties was examined by the total polyphenol content (TPC), total flavonoid content (TFC), total tannin content (TTC) and total anthocyanin content (TAC) methods and the antioxidant activity with the DPPH (2,2-diphenyl-1-picrylhydrazyl), FRAP (ferric-reducing antioxidant power) and CUPRAC (cupric ion-reducing antioxidant capacity) assays.
## 3 Finite Element Simulation Model of Annular Concrete Structure  
The finite element model of the annular concrete structure is established to simulate its performance test and cooling experiment under different stress constraints. The inner diameter, outer diameter and length of the ring concrete structure are 300 mm, 380 mm and 100 mm respectively. The model parameters of concrete structure are shown in Table 1 [22].  
In order to explore the mechanical characteristics of the structure, a thermodynamic coupling model of the annular concrete structure under the double constraints of temperature and pressure was established, as shown in Figure 3. The grid model (solid186) uses a 20-node solid structure unit, the selected mesh size is 5 mm, the number of model grid nodes is 145,200, and the number of cells is 32,000. The model uses the Menetrey-William Concrete (MWC) model to take into account the differences in the performance of concrete during tensile and compressive processes [23]. HSD model is added to consider the strengthening and softening stages of concrete under tensile and compressive action. The model has strong convergence.  
In this model, the pressure load is achieved by applying pressure to the upper contact surface in the vertical direction of the annular concrete structure. Remote displacement  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Elastic modulus, \(E\) (MPa) & 12,000 \\ Poisson’s ratio, \(\
u\) & 0.2 \\ Density, \(\rho\) (kg/m\({}^{3}\)) & 2300 \\ Compressive strength, \(\sigma_{c}\) (MPa) & 19.1 \\ Tensile strength, \(\sigma_{t}\) (MPa) & 1.83 \\ Dilation angle, \(\psi\) (\({}^{\circ}\)) & 35.0 \\ Coefficient of thermal expansion, \(\alpha\) (10\({}^{-6}\) C\({}^{-1}\)) & 11.5 \\ Thermal conductivity, \(\lambda\) (Wm\({}^{-1}\) C\({}^{-1}\)) & 1.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Concrete parameters used in finite element analysis.  
Figure 3: Mesh model overall structure diagram.  
constraints are applied to lower contact surfaces to secure annular concrete structures, preventing rotation or translation degrees of freedom. The first strength theory is adopted as the failure criterion of concrete pipe, taking into account that the material fails when the maximum principal stress exceeds the ultimate tensile strength. At the same time, temperature load is applied to the ring concrete structure, and the cooling strain curve is obtained by step cooling method.  
Figure 4 shows the simulated stress state of the complete ring concrete structure under the obtained external load. It can be seen that the maximum tensile stress is on the inner wall at the top and bottom, followed by the outer wall at the waist, which is consistent with the physical model.
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
## 2 Materials and Methods
### Feature Selection and Preprocessing
#### 2.2.5 Data Augmentation  
Crop yield prediction through the use of remote sensing data usually faces the challenge of imbalanced target distribution, especially when dealing with agricultural datasets collected from fields [19]. Such imbalances can negatively impact the performance of regression models by biasing predictions towards the more frequently occurring yield ranges. In turn, these biased predictions can result in poor generalization and reduced reliability in practical decision-making. To address this issue, SMOTER was implemented, a data augmentation method specifically designed to handle imbalanced regression problems, where the target variable is continuous. Unlike traditional SMOTE, which is designed for classification [61], SMOTER identifies sparse areas in the feature space, selects nearest neighbors, and creates new instances through interpolation while adjusting target values to maintain realistic distributions [62]. In particular, the synthetic target value, \(y_{i}^{new}\), is calculated through:  
\[y_{i}^{new}=\frac{\frac{y_{i}}{d_{1}}+\frac{y_{i}^{nearest}}{d_{2}}}{\frac{1}{d _{1}}+\frac{1}{d_{2}}}, \tag{15}\]  
where \(y_{i}\) is the target value of the data point \(x_{i}\), while \(x_{i}^{nearest}\) is its nearest neighbor with a target value of \(y_{i}^{nearest}\)[63]. The target value of a newly generated point, \(x_{i}^{new}\), is proportional to its Euclidean distance from its parent points, meaning that its value is more influenced by the parent point it is closer to. Moreover, \(d_{1}\) corresponds to the Euclidean distance between \(x_{i}^{new}\) and \(x_{i}\), while \(d_{2}\) represents the Euclidean distance between \(x_{i}^{new}\), and \(x_{i}^{nearest}\).  
Consequently, the SMOTER algorithm methodically balances synthetic sample generation as a means of avoiding over- or under-sampling, therefore, ensuring that the augmented dataset has a more uniform distribution across the target range. This reduces model bias towards over-represented yield levels and improves prediction accuracy for rare or extreme yield values. SMOTER also incorporates mechanisms to limit the creation of unrealistic synthetic points by restricting interpolation within defined boundaries. Overall, the integration of SMOTER into the preprocessing pipeline ensured that the ML models were better equipped to handle heterogeneity in the yield data as well as minimized the risk of overfitting to dominant target ranges.
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
###### Abstract  
Estimating the minimal methanol required to inhibit gas hydrate formation in pipelines is vital for operational efficiency, cost reduction, and sustainability. This task is complex due to dynamic variables like flow rate, temperature, pressure, gas specific gravity, and water content. Machine learning offers a robust solution by leveraging pipeline condition data to effectively forecast methanol needs. We evaluated four models--extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and k-nearest neighbors (KNN)--to predict the methanol quantities required for hydrate prevention in gas pipelines. Using a dataset of 74,000 samples, with 80% for training and 20% for testing, we enhanced model robustness with 50 Monte Carlo iterations and tenfold cross-validation. The results revealed that XGBoost was the top performer in precision and consistency, surpassing the others. The performance rankings, based on the adjusted R-squared (adj, R\({}^{2}\)), mean absolute error (MAE), and root mean square error (RMSE), were KNN! DT! RF! XGBoost. A feature analysis highlighted flow rate and temperature as dominant influencers of methanol needs. These findings support real-time methanol optimization in pipelines, enhancing safety and efficiency. A correlation analysis showed that flow rate positively boosted methanol demand while temperature reduced it, with pressure, gas specific gravity, and water content exerting milder positive effects.  
m _Article_  
1
# Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
## 3 Methods
### Normalized Least Mean Squares  
If the input signal is relatively high, the LMS algorithm tends to often amplify the noise instead of the effective signal. For these types of signals, the NLMS algorithm has the greatest potential. Step size \(\mu\) is normalized by the energy of the input signal by Equation (4). When the input signal consists of high values, it takes more time to reach the lowest possible error value. On the other hand, when the input signal consists of lower values, it will be faster to reach the lowest possible error value [33; 36].  
Every iteration of the NLMS algorithm requires four different steps. In the first step, the FIR filter output value is calculated by Equation (1). Then, the estimated error value is calculated by Equation (2). In the third step, the convergence constant \(\mu\) is calculated by Equation (4). In the last step, the values of the FIR filter weights are updated with respect to the next iteration by Equation (3) [33; 36].  
\[\mu(n)=\frac{1}{\mathbf{x}^{\mathrm{T}}(n)\mathbf{x}(n)}. \tag{4}\]
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 2 Related Work
### Learning-Based ABR Algorithms  
To overcome the shortcomings of existing model-based ABR algorithms, several learning-based ABR algorithms such as RL-based approaches have been proposed in recent years. RL is an appropriate learning method for achieving optimal QoE because it aims to maximize the expected cumulative reward for a given environment [15].  
An RL-based ABR algorithm should be designed in consideration of the following issues: First, the network state fluctuates over time and has a large deviation depending on the client's environment. A high bitrate must be maintained even when the amount of remaining buffer is reduced to achieve optimal QoE in a link with a high and stable average bandwidth. In the opposite case, the client should consider the remaining buffer amount as a priority when determining the quality of a video segment to minimize playback interruption. Second, adaptive streaming must balance multiple goals, such as maximizing average quality, minimizing playback interruption, and minimizing quality fluctuations. Streaming by requesting only high-quality segments increases the average quality but increases the risk of playback interruption. Also, changing the quality of a segment whenever the bandwidth of the network fluctuates leads to frequent quality fluctuations, which is a cause of QoE degradation. Finally, the factors that affect QoE can vary greatly from user to user [16; 17]. High QoE cannot be achieved when users who value maximum quality as the most important element of video streaming use an algorithm that aims to minimize playback interruption. Therefore, it is necessary to optimize QoE for various network conditions.  
An RL-based ABR algorithm helps the agent experience various network conditions through learning. The goal of RL is to learn a policy suitable for the agent through trial and error, using the feedback from the actions as experience. After finding the optimal policy, the agent can dynamically adapt to various environmental conditions to maximize the predicted cumulative reward. In order to apply RL to the DASH system, ABR algorithmsapplying the deep-Q learning (DQN) method and the actor-critic algorithm have been proposed [18; 19]. However, the existing RL-based ABR algorithms have a disadvantage in that their performance is degraded when they are not used in the targeted network environment [20]. To address this shortcoming, we propose an online learning-based HTTP adaptive streaming technique that can update the learned neural network based on the network environment of a specific user.
# Implementing and Evaluating a Font Recommendation System Through Emotion-Based Content-Font Mapping
## 5 Usability Evaluation of the Font Auto-Recommendation System
### Application of the Implementation Results  
In this paper, beyond a simple web-based recommendation system, we developed an additional service utilizing a font recommendation model to suggest fonts based on blog posts. The blog post font recommendation system analyzes the text and images within user-created blog posts to recommend suitable fonts for the content. Sentiment analysis of blog posts is performed in two different cases. First, an analysis of whether the images posted on the blog contain facial expressions is conducted. Images that are categorized by facial expressions are utilized to extract emotion keywords using the Deepface API.  
In cases where facial expressions are not present, general images undergo image analysis using the Google Cloud Vision API [22]. This analysis recognizes objects, text, colors, object locations, and more within the images, with the results of the image analysis being derived as descriptive text.  
Subsequently, the derived text undergoes sentiment analysis based on the IBM Watson Natural Language Understanding service. This service analyzes the text to identify the emotional tone of the given text, allowing the determination of the text's positive or negative emotions. As emotional analysis for Korean text is not supported, translation to English is performed before sentiment analysis.  
The results of facial expression and image analysis, as well as text sentiment analysis, are integrated to determine the overall sentiment of the blog posts. Through such analysis, a more accurate understanding of the emotional aspects of the content within the posts  
Figure 4: Web-based font recommendation system with expression and sentence input options, showcasing matching emotions in images to the top five recommended fonts, each linked to its webpage.  
is achieved, providing richer emotional information through the combination of images and text.  
An example of font recommendation results is as follows: Figure 5 displays a blog post containing both text and images. In this example blog post, there are no images depicting human emotions. In such cases, we generate descriptions for the pictures and analyze the emotions within these descriptions to recommend fonts. Additionally, as blog posts primarily consist of textual content, we analyze the content of the main body to suggest fonts.  
As a result, the system provides font recommendations aligned with the emotional analysis of both images and text, empowering users to make their preferred selections. In Figure 5a, fonts corresponding to the emotion \"sadness\" were recommended based on the analysis. Similarly, Figure 5b demonstrates the outcomes of blog post analysis conducted in a parallel fashion, resulting in font suggestions. Here, fonts associated with the emotion 'anger' were recommended, drawing from a comprehensive analysis of both image and text content.