esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Method
### The Formulation of the Present Model  
To date, the previous models have been considered with a degree of smoothness \(p\) independent of the location \(x\) in the domain \(\Omega\). The aim of our paper is to observe the effects of the degree of smoothness depending on the location \(x\). Taking into account the spatial location \(x\), the smoothness is considered a dynamic process linked to the following energy function.  
\[E(f,f_{0},\lambda)=\int_{\Omega}\frac{1}{p(x)}\lvert\
abla f\rvert^{p(x)}dx+ \frac{\lambda}{2}\int_{\Omega}\lvert f-f_{0}\rvert^{2}dx \tag{7}\]  
where  
\[p(x)=1+\frac{1}{1+k\lvert\
abla G_{\sigma}\star f_{0}(x)\rvert^{2}},\,k,\, \sigma>0, \tag{8}\]  
\(G_{\sigma}(x)=\sigma^{-1}\exp\Bigl{(}-\lvert x\rvert^{2}/4\sigma^{2}\Bigr{)}\) represents the Gaussian filter function. Models (7)-(8) were suggested and investigated by [PERSON], [PERSON], and [PERSON] in [4] in the special context of \(\text{BV}^{p}(\cdot)\) functions. For more details and properties on \(\text{BV}^{p}(\cdot)\) functions, an interested reader can consult [8].  
The variable exponent function \(p(\cdot)\) belongs in the interval \([1;+\infty[\)  
It represents a regularisation degree. It also improves the quality of the image resulting from the smoothing process. More precisely, this function encodes the information on the regions where the gradient is near zero (the homogeneous regions). Therefore, the model avoids the staircase effect by always keeping the edges. When the \(p(\cdot)\) function is constant (\(p(\cdot)=p\)), this situation is namely called static restoration while the opposite situation is called dynamic restoration.  
In addition, \(\lambda\) is the data fidelity parameter. This parameter specifies the compromise between the two competing terms and determines the amount of noise removed.  
It is not difficult to see that the objective function is strictly convex, and so using standard convex analysis arguments demonstrates that the optimisation problem has a unique solution.  
\(\lvert\
abla f\rvert\) represents the local variation of the function \(f\) over the domain \(\Omega\). Let us note that, in Model (7), the parameters \(p(\cdot)\) and \(\lambda\) strike a balance between the regularisation and approximation energies.
# A Framework for Real-Time 3D Freeform Manipulation of Facial Video
## 1 Introduction
[14] projected a sparse 3D face model on a 2D face image before and after deformation and computed the affine transformation using the corresponding triangles of the projected 2D meshes. This approach allowed natural and efficient manipulation without any visible distortions in real time with the help of a GPU. Since the image warping based manipulation can suffer from arbitrary rotation of head, they demonstrated it as augmented reality mirror, an appropriate application that does not require large head rotations. Similarly, [PERSON] et al. [15] showed fine manipulation results, but the proposed method could not be applied in live video. Moreover, as deformation was achieved by adjusting the weights of predefined attributes mapped to a bilinear face model, the deformable range was highly dependent on the bases of the face model. [PERSON] et al. [19] implemented facial animation for a digital avatar that could be instantly generated even from a single image and rendered to reflect lighting conditions as well as expressive details (e.g., wrinkles and inside of mouth). However, deformation was confined to the range of human facial expression--in other words, it was difficult to manipulate faces into arbitrary shapes that were funny or exaggerated faces. Our framework can overcome these limitations as well. In the proposed framework, a 3D face model is registered on a face region in a video frame by detecting facial landmarks [20] and then fitted to a 3D morphable model (3 DMM) [21]. At the same time, the 3D model is deformed by a user's input based on the as-rigid-as-possible (ARAP) method [22]. Finally, the video frame is mapped to the deformed model as a texture, and the model is rendered on the video frame. By taking account of the face's 3D geometry, the proposed framework supports natural 3D manipulation of a face without background distortion (Figure 1b) and enables visualization of the deformed faces from different viewpoints across video frames. As faces can be interactively manipulated by the user's input, this allows freeform manipulation without any range or shape limitations. Building on our previous work [23], this study presents a complete framework with real-time performance based on parallelization and acceleration schemes, which is much sought after for practical applications. This study also details performance evaluation and comparison with existing applications.
# Architectural Trends in Collaborative Computing: Approaches in the Internet of Everything Era
## 5 Discussions
On the other hand, the SDEC [46] and the 5G-Edge autonomous car architecture [48] incorporate more sophisticated orchestration. SDEC leverages software-defined resources (containers and VMs) for dynamic resource allocation, enabling better service flexibility and resource efficiency [46]. In contrast, the 5G-Edge autonomous car architecture relies on real-time inference, integrating AI-based decision modules for autonomous processes, as seen in 5G autonomous systems, where distributed Edge nodes support autonomous vehicles, facilitating seamless data handover and processing during mobility scenarios [48]. The two layered architectures differ in focus and approach. [PERSON] et al. [52] emphasizes a comprehensive Fog Computing component stack with multiple layers, covering aspects like security, resource management, and pre/post-processing, highlighting the need for efficient data handling and privacy across diverse applications (e.g., IoT, autonomous systems). In contrast, [PERSON] and [PERSON] [51] adopts a streamlined, hierarchical model with three main tiers (embedded system and sensors, network Edge, and Virtual Clusters), prioritizing orchestration, software-defined services, and virtualization. This approach emphasizes scalability, dynamic resource allocation, and efficient management of Edge devices, catering to Cloud-native environments and flexible service deployment. Regarding practical implications for governments, we recommend focusing on clear communication and efficient coordination to provide insights into public infrastructure projects like Smart Cities, where real-time data from IoT devices can be harnessed to improve urban planning, traffic management, and emergency services. Blockchain-based systems could further enhance trust and transparency in these systems by enabling secure, traceable communications among diverse stakeholders. For enterprises, especially in intelligent manufacturing and Edge Computing, adopting architectures prioritizing efficiency and network optimization will be crucial for maintaining competitiveness. The cooperation component in collaborative systems emphasizes the importance of engaging all stakeholders--from developers to end users--to create more robust and adaptive innovation environments. One notable gap in the literature in future research is the relatively limited attention given to cooperation, particularly in collaborative AI and human-machine interaction. Future studies could explore how these systems might foster more active user participation in decision-making, creating decentralized, user-driven ecosystems. Additionally, there is an opportunity to examine cooperative frameworks that integrate autonomous decision-making algorithms with human oversight in IoT systems, ensuring accountability and transparency. On the technological side, future work could also investigate how Edge and Fog Computing architectures can further improve coordination in environments with resource-constrained devices by optimizing task delegation and load balancing.
# Quantification of Energy-Related Parameters for Near-Fault Pulse-Like Seismic Ground Motions
## 3 Energy-Related Parameters  
The energy-related parameters of interest for the present study were selected with reference to the energy-based design methodology illustrated in [29]. Such an energy-oriented approach in seismic design relies on the energy balance equation in terms of absolute \(E_{Hu}\) or relative input energy \(E_{Ir}\), which reads as follows:  
\[E_{{{\it{la}}}}=E_{{{\it{ka}}}}+E_{\xi}+E_{s}+E_{H},\ E_{Ir}=E_{kr}+E_{\xi}+E_{s}+E _{H}, \tag{1}\]  
where \(E_{{{\it{ka}}}}\) and \(E_{kr}\) are the kinetic energies representing the difference between the absolute and relative input energy, respectively, \(E_{\xi}\) is the damping energy, \(E_{s}\) is the elastic strain energy and \(E_{H}\) is the hysteretic energy. Based on Equation (1) the following parameters are therefore considered in the present study:  
* inelastic absolute \(E_{{{\it{la}}}\mu}\) and relative \(E_{{{\it{lr}}}\mu}\) input energy for the given ductility demand \(\mu\);
* input energy reduction factors, \(R_{{{\it{E}}}{{\it{a}}}}=E_{{{\it{L}}}\mu}\)/\(E_{{{\it{la}}}\mu}\) and \(R_{{{\it{Er}}}}=E_{{{\it{lr}}}}\)/\(E_{{{\it{lr}}}\mu}\) (i.e., the ratio between the elastic input energy and the inelastic value corresponding to a given ductility demand \(\mu\));
* the hysteretic energy dissipation demand \(E_{{{\it{H}}}}\);
* the hysteretic energy reduction factors, \(R_{{{\it{H}}}{{\it{a}}}}=(E_{{{\it{H}}}}\)/\(E_{{{\it{la}}}})_{\mu}\) and \(R_{{{\it{H}}}{{\it{r}}}}=(E_{{{\it{H}}}}\)/\(E_{{{\it{lr}}}})_{\mu}\) (i.e., the ratio between the hysteretic energy dissipation demand and the inelastic input energy value corresponding to a given ductility demand \(\mu\));
* the parameter \(\eta\) that quantifies the plasticity level in terms of dimensionless cumulative plastic deformation ratio, i.e., \(\eta=E_{H}/(R_{y}\delta_{y})\), where \(R_{y}\) and \(\delta_{y}\) are the elastic limit strength and displacement, respectively.  
The influence of seismological parameters and hysteretic behavior on the spectra of these energy-related parameters is initially examined. Next, closed-form approximations are proposed for these spectra, and the numerical values of the corresponding parameters are also calibrated (with reference to both mean and standard deviation values). Overall, the objectives of this study are twofold. On one hand, it is meant to investigate the role of seismological parameters and hysteretic behavior on the seismic demand from an energy-based perspective. On the other hand, it is also intended to support the formulation of energy-based design spectra.
# Optimization and Experimental Study of Penetration Depth of Pocket Penetrometer
## 3 Test Results and Analysis
### Relationship Between Modeled Soil and In Situ Soil Results  
In summary, the values of water content, shear strength, and penetration resistance of red clay 2 in the field can be obtained, and at the same time, the empirical equations of water content and penetration resistance in the indoor test of red clay 2, as shown in Figure 16, are grasped, as well as the empirical equations of tip resistance and shear strength of red clay 2, as shown in Figure 14.  
Figure 15: (**a**) Statistics of in situ penetration test results. (**b**) Statistics of in situ penetration test results.  
The water content of red clay ()\({}_{2}\) is 33%; according to the empirical formula, the penetration resistance should be 61 N, while the measured penetration resistance value in the field is 75-84 N. Using the empirical formula of ultimate resistance and shear strength, it is concluded that the difference in shear strength between remolded soil and in situ soil is 15.06-25.26 kPa. Usually, the difference in shear strength between remolded soil and in-situ soil is mainly due to the soil's structure and the alteration of its physical properties. The original soil forms a stable particle arrangement and stress state under natural conditions, while the structure of the remolded soil is easily damaged after disturbance, resulting in changes in the pore ratio, density, and other characteristics, which in turn affect the shear strength. Even with the same water content, pristine soils tend to have higher strengths due to their natural structure and cementation, while reshaped soils tend to have lower shear strengths due to particle rearrangement and altered frictional properties.  
The measured shear strength of red clay ()\({}_{2}\) was 92.28 kPa, and the range of shear strength based on field end resistances was 84.2-94.4 kPa. Although the empirical formula is based on the test of the remolded soil, and the penetration resistance of the remolded soil is not the same as that of the in situ soil at the same water content state, the agreement between the penetration resistance and the shear strength was not greatly affected. Therefore, the empirical formula can be established by simple and quick indoor tests and used to evaluate the shear strength of soil layers in the field.
# Rough Set Theory and Soft Computing Methods for Building Explainable and Interpretable AI/ML Models
## 3 Background
### The Importance of Feature Selection in Machine Learning  
Feature selection is a critical preprocessing step in ML, aimed at reducing dimensionality, improving model performance, and enhancing interpretability. High-dimensional datasets, common in applications like genomics and image processing, often contain  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Key Novelty** & **Advantage over Existing Methods** & **Applicability** \\ \hline \multirow{4}{*}{MLReduct} & Exhaustive search with positive region preservation criterion. & Ensures optimality by evaluating all attribute combinations; handles complex attribute dependencies. & Datasets with complex, non-linear attribute relationships. \\ \hline \multirow{4}{*}{MLSpecialReduct} & Dynamic, dependency-driven feature selection with iterative optimization. & Efficiently handles high-dimensional datasets; minimizes redundancy and maximizes relevance. & High-dimensional datasets with many irrelevant or redundant features. \\ \hline \multirow{2}{*}{MLFuzzyRoughSet} & Integration of fuzzy logic for handling uncertainty and imprecision. & Robust to noise and continuous data; introduces efficient membership computation. & Noisy or uncertain datasets, such as healthcare or IoT sensor data. \\ \hline \multirow{4}{*}{Existing RST Methods} & Often rely on heuristic or greedy search strategies; limited scalability. & Struggle with high-dimensional or noisy data; lack dynamic optimization. & Limited to small or well-structured datasets. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of proposed methods with existing RST-based techniques.  
redundant or irrelevant features that can degrade classifier performance and increase computational complexity [24]. Traditional feature selection methods, such as filter-based approaches (e.g., Variance Threshold) and wrapper-based techniques, have limitations in handling noisy, incomplete, or imbalanced data. This has led to the exploration of alternative methods, including RST-based approaches, which excel in identifying minimal feature subsets that preserve the underlying structure of the data [25].
###### Abstract  
Diabetic foot is a prevalent chronic complication of diabetes and increases the risk of lower limb amputation, leading to both an economic and a major societal problem. By detecting the risk of developing diabetic foot sufficiently early, it can be prevented or at least postponed. Using artificial intelligence, delayed diagnosis can be prevented, leading to more intensive preventive treatment of patients. Based on a systematic literature review, we analyzed 14 articles that included the use of artificial intelligence to predict the risk of developing diabetic foot. The articles were highly heterogeneous in terms of data use and showed varying degrees of sensitivity, specificity, and accuracy. The most used machine learning techniques were support vector machine (SVM) (n = 6) and K-Nearest Neighbor (KNN) (n = 5). Future research is recommended on larger samples of participants using different techniques to determine the most effective one.  
artificial intelligence; machine learning; thermography; diabetic foot prediction; diabetes; diabetes care; diabetic foot; literature review +
Footnote †: journal:  
different and increasing amounts of health data, allowing for greater patient autonomy and personalized treatment [16]. Some research has been carried out to diagnose and predict diabetic mellitus and its complications, such as diabetic foot [17]. The automation of healthcare management has led to a transformation in the field by introduction of artificial intelligence-based solutions, due to the ease of mass data collection and powerful computational processing. It has the potential to prevent delayed diagnosis and identify preventive treatments [18]. Clinical practice can use these predictive models to better determine which high-risk people with diabetes should be monitored more closely and treated more intensively [19]. Thermography is also one of the non-invasive methods that can be used to predict risk, as temperature differences in the foot can indicate problems associated with diabetic foot [20].  
The aim of this rapid review is to answer the research question: which AI techniques are most effective in predicting the risk of developing diabetic foot?
# UV Light Application as a Mean for Disinfection Applied in the Dairy Industry
## 5 Application Areas  
When compared to fruit juices, milk and dairy products contain a considerably higher number of bacteria and spoilage microorganisms, making UV treatment more difficult. In brine, sweet, and acid whey, cumulative bacterial count reductions of 7 log CFU/mL were achieved, demonstrating the possible use of UV light technology in whey and brine in dairy production [9]. Some of the major applications of UVC are described below.
# Free Vibration Analysis of Curvilinearly Tapered Axially Functionally Graded Material Beams
###### Abstract
Additionally, [PERSON] and [PERSON] (2020) [40] developed a method based on metric minimization, a modal participation factor, and the proper orthogonal decomposition to accurately approximate a Euler-Bernoulli motion equation with spatially varying coefficients with an equivalent constant coefficient model. A literature search indicates that studying the dynamics of tapered AFGM beams involves complexities. While various techniques have been reported, closed-form solutions are limited to specific cross-sections, material variations, and defined boundary conditions. Numerical methods like FEM are proficient but make it challenging to derive general conclusions about system behavior [41]. In this respect, accurate semi-analytical approaches can be a good alternative. Despite extensive research, the vibration characteristics of AFGM beams with arbitrary continuously variable cross-sections are not fully addressed. This study aims to use the differential transform method (DTM) to obtain accurate semi-analytical solutions for the free vibration of curvilinearly tapered AFGM beams under various boundary conditions, determining the effects of variable cross-sections on natural frequencies and mode shapes. This is an innovation of the present contribution.
# Research on Feature Fusion Method Based on Graph Convolutional Networks
## 3 Methods  
In this section, a detailed description of the BGF model is presented, and the detailed architecture of the model is shown in Figure 2.  
Figure 2: BGF model diagram feature. The model begins by constructing a text heterogeneous graph on the corpus, where document nodes are initialized with BERT embeddings. The edge weights between nodes are determined using word frequency–inverse document frequency (TF-IDF) and pointwise mutual information (PMI) [27], which serve as inputs to the GCN. The GCN performs convolutional operations to obtain textual feature representation \(\text{f}_{\text{GCN}}\) in line with the characteristics of the textual graph. This feature representation \(\text{f}_{\text{Bert}}\) containing textual information from BERT pre-training is then fused to enhance the model’s understanding of textual semantics. The GCN obtains textual feature representation \(\text{f}_{\text{GCN}}\), which conforms to the features of the text graph, through convolution operations and merges it with the feature representation \(\text{f}_{\text{Bert}}\) obtained from BERT pre-training to enrich the model’s comprehension of text semantics. Subsequently, the output serves as the final representation of text nodes for text label classification prediction through linear layers and classifiers, yielding the final probabilistic representations of the input text, \(\text{y}_{\text{BAG}}\) and \(\text{y}_{\text{GCN}}\), respectively. These predictions from both parts are then combined.  
In this process, a heterogeneous graph is first constructed using the TF-IDF and PMI values between documents and words. After instantiating the BERT and GCN models based on the number of categories, the document node features are initialized using BERT. Finally, the features from BERT and the GCN are fused. By employing multilayer fusion, feature information from different layers is amalgamated to enhance the model's expressiveness, thereby improving its generalization ability and performance. Moreover, multilayer fusion helps filter and integrate the most useful features for the model task, reducing sensitivity to noise and irrelevant information and thus enhancing the stability and robustness of the model. Additionally, it enhances the model's nonlinear representation, further improving its generalization ability and performance.
## 1 Introduction
Given the advantages of LIBS in elemental measurement, many scholars have carried out the research on the application of LIBS technology in coal quality analysis and have achieved certain results [11; 12; 13; 14]. To improve the accuracy and precision of the LIBS system in coal analysis, great efforts mainly focus on the experimental apparatus setup [15; 16] and calibration model improvement [17; 18] to enhance the spectral signal-to-noise ratio and obtain better analysis results [19]. In this study, 59 commonly used coal samples in China power plants were tested by a lab-made field-portable LIBS instrument and the quantitative analysis of ash content, volatile matter and calorific value was carried out by three chemometrics models [20; 21]: partial least square regression (PLSR) [22; 23], supported vector regression (SVR) [24; 25], and random forest (RF) [14]. Necessary data pretreatment and data partitioning methods were also established. The performances of the three calibration models were evaluated and further discussed.