esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a New Rubber Buckling-Restrained Brace System for Structures
## 1 Introduction
Despite the advantages of elastomeric isolators, extensive research has also been conducted on friction-based damping systems. [PERSON], [PERSON], and [PERSON] (2024) investigated the effects of suspension friction on ride comfort, revealing that increased friction exacerbates body vibration and discomfort on smooth roads [71]. [PERSON], [PERSON], and [PERSON] (2024) proposed a novel shock absorber design for deep drilling with Polycrystalline Diamond Compact (PDC) bits, addressing challenges such as stick-slip and whirling vibrations and demonstrating its effectiveness in mitigating these issues [72]. [PERSON] and [PERSON] (2023) developed a dry friction shell shock absorber with an elastic filler, demonstrating its efficacy in structural damping under cyclic loading, with potential applications in industries such as mining and construction [73]. Building on the development of friction-based damping systems, advancements in smart controllers for vibration management have also emerged as a significant area of innovation. These controllers integrate with traditional damping mechanisms to enhance their adaptability and efficiency in mitigating structural vibrations under varying conditions. [PERSON] et al. (2024) tested a smart controller for monitoring drill string vibrations during deep well drilling, demonstrating its ability to assess vibration impacts and optimize drilling modes. Its low power consumption and memory efficiency render it suitable for long-term field applications [74]. [PERSON] and [PERSON] (2023) introduced a Semi-Active Adaptive Vibration Control System (SABFD) for bridges, integrating fluid dampers with fuzzy-logic Programmable Logic Controllers (PLCs) for real-time vibration control. This system effectively reduced displacement under traffic loads [75]. Additionally, they developed an Adjustable Bypass Fluid Damper (ABFD) for bridges, which adjusts damping through flow control valves based on displacement. A case study demonstrated a 35% reduction in peak displacement, highlighting the effectiveness of this system [76]. Although the aforementioned vibration dissipation systems demonstrate considerable effectiveness, their practical application in structures is often limited by significant challenges. These systems typically involve heavy mass and weight, and their functionality is heavily reliant on structural movement, which can induce deformation in the primary structural members. Therefore, a high-performance lightweight system without such a dependency on the core element deformation for structural retrofitting and maintenance should be developed. The main idea of this study is to propose a Rubber Buckling-Restrained Brace (RBRB) to increase the load-bearing capacity by increasing the absorbed energy. This paper provides the design procedure for RBRB. The device performance was also assessed experimentally with a dynamic actuator. The macro numerical simulation was developed in order to evaluate the seismic performance of an assumed building as a bare frame furnished by BRB and RBRB.
# Advancing Real-Estate Forecasting: A Novel Approach Using Kolmogorov-Arnold Networks
## 1 Introduction
### The Specific Case of the House Price Prediction Problem  
Like the broader real-estate market, the house real-estate domain is subject to various factors that influence its growth and performance. House prices are one of the most important economic indicators. Changes in prices have substantial consequences for households, policymakers, investors, etc. Understanding the dynamics of house prices is important for assessing the health of the housing market and crucial for evaluating its state [4]. Also, knowing how house prices work helps investors predict trends, forecast future changes, and make successful decisions about buying and selling property.  
Searching for the factors that have an impact on the prices of houses has been a topic of interest for many years. Some factors and trends that influence the price of a house are location, size, condition, the use of technology, etc. [5].
## 5 Post-Evaluation Analysis
### Query-by-Example Spoken Term Detection  
Post-evaluation analyses for the QBe STD task rely on INL and OOL query comparison, INV and OOV query comparison and single-word and multi-word query comparison.
## 2 Simulation Methodology  
### Coupled Pore Fluid Diffusion and Stress Analysis  
Fracture propagation behavior of hydraulic fracturing is a fluid-solid coupling problem, concerned with the deformation of the solid phase, fluid flow in the porous media, fracturing fluid flow in the fracture, and fracturing fluid leak-off. In this paper, coupled pore fluid diffusion and stress analysis were used to simulate the propagation of a hydrofracture in rock. The rock was regarded as a multiphase material, and an effective stress principle was adopted to describe its behavior. When the pores in the porous medium are filled with single-phase liquid medium, the effective stress of the porous medium is defined as follow:  
\[\overline{\sigma}^{*}=\sigma+\chi u_{w}\mathbf{I} \tag{1}\]  
where \(\overline{\sigma}^{*}\) is effective stress, \(\sigma\) is total stress, and \(\chi\) is a factor that depends on saturation and surface tension of the liquid-solid system [37]; it is 1.0 when the medium is fully saturated. \(u_{w}\) is the fluid pressure of the porous medium, and \(\mathbf{I}\) is the second-order tensor. The rock stress equilibrium equation can be expressed by the principle of virtual work as follow:  
\[\int_{V}\sigma\cdot\delta\varepsilon dV-\int_{S}\mathbf{t}\cdot\delta\mathbf{v}dS-\int _{V}\mathbf{f}\cdot\delta\mathbf{v}dV=0 \tag{2}\]  
where \(\delta\mathbf{v}\) is the virtual velocity field, \(\delta\varepsilon\) is the virtual deformation, \(\sigma\) is the Cauchy stress, \(\mathbf{t}\) is the surface traction of per unit area, and \(\mathbf{f}\) is the body force of per unit volume. We considered the presence of liquid fluids in porous medium and assumed that the fluid was relatively incompressible. The porous medium was modeled by attaching finite element mesh to solid phase, and a continuity equation was used to calculate the mass of liquid in unit volume of porous media, which was written in a variational form as a basis for finite element approximation. The total mass of the liquid in volume (occupies space \(V\) with surface \(S\)) can be written as:  
\[\int_{V}\rho_{w}[dV_{w}+dV_{t}]=\int_{V}\rho_{w}(n_{w}+n_{t})dV \tag{3}\]  
Figure 1: Schematic of the intersection between a hydraulic fracture and a natural fracture using pore pressure cohesive elements. Inspired by [32]. where \(\rho_{w}\) is the mass density of the liquid, \(V_{w}\) is the volume of liquid that passes through at any time, and \(V_{t}\) is the volume of liquid trapped at any time. The time rate of change in this mass of liquid can be calculated as:  
\[\frac{d}{dt}\bigg{(}\int_{V}\rho_{w}(n_{w}+n_{t})dV\bigg{)}=\int_{V}\frac{1}{J} \frac{d}{dt}(J\rho_{w}(n_{w}+n_{t}))dV \tag{4}\]  
where \(J\) is the volume change in the material. The mass of the liquid passing through the surface into the volume in unit time is:  
\[-\int_{S}\rho_{w}n_{w}\mathbf{n}\mathbf{\cdot}\mathbf{v}_{w}dS \tag{5}\]  
where \(\mathbf{v}_{w}\) is the average velocity of the liquid relative to the solid phase (seepage velocity), and \(\mathbf{n}\) is the outward normal of \(S\).
## 3 Results and Discussion
### Rheological and Colorimetric Results
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **SM** & **SR** \\ \hline Moisture, \% & 45.8 \(\pm\) 0.77 \({}^{\text{a}}\) & 49.1 \(\pm\) 0.74 \({}^{\text{b}}\) \\ Fat, \% & 22.66 \(\pm\) 0.96 & 20.06 \(\pm\) 0.82 \\ Protein, \% & 25.56 \(\pm\) 0.60 & 24.16 \(\pm\) 0.53 \\ Salt, \% & 4.54 \(\pm\) 0.07 & 4.75 \(\pm\) 0.15 \\ TBAR\({}_{\text{s}}\), mg/Kg & 0.067 \(\pm\) 0.009 \({}^{\text{A}}\) & 0.049 \(\pm\) 0.003 \({}^{\text{B}}\) \\ FFA\({}_{\text{s}}\), \% oleic acid & 0.946 \(\pm\) 0.04 & 0.797 \(\pm\) 0.11 \\ \hline \hline \end{tabular} \(\text{TBAR}_{\text{s}}\), thiobarbituric acid reactive substances; FFA\({}_{\text{s}}\), free fatty acids. The \({}^{\text{A,B,D}}\) mean values differ significantly for \(p<0.05\) (lowercase) or \(p<0.01\) (capital); \(n=3\).  
\end{table} 
Table 3: Chemical composition and lipolysis and oxidation indexes of Salame Napoli made with different starter cultures (SM, Euroferment Medium, and SR, Euroferment Rapid) [45]. 1999 [50], the higher value in hardness could represent a useful indicator for identifying problematic texture developments. According to [PERSON] et al., 2010 [51], hardness and chewability are sensory characteristics of salamis that are crucial in determining consumer satisfaction. Based on our knowledge, there are limited studies about Salame Napoli's rheological characteristics. However, [PERSON] et al., 2021 [52] demonstrated that Salame Napoli is a product with high chewability and cohesiveness, but it lacks hardness compared to other Italian dry-cured salami. In this regard, SM seems to have a texture profile more aligned with the preferences of consumers as well as typical characteristics of \"Salame Napoli\" than SR.
## 3 Results of Analysis of Ground Subsidence Risk Levels Using Machine Learning
### XGBoost (eXtreme Gradient Boosting)  
XGBoost (XGB) is a typical algorithm of a boosting technique where a result is derived by learning a single model sequentially, and the result of the previous model affects the next result. XGB is a tree-based algorithm used in solving regression and classification problems. It is effective in preventing overfitting due to its different regularization penalties. In addition, it has the advantage of being able to process big data in a short period of time, so it has been actively used in various fields [22; 23].  
The calculation equation for the decision-making of XGBoost is presented in (2), where \(\hat{y_{i}}\) refers to the i-th sample's prediction value and \(f_{k}\) refers to the prediction value where the k-th tree's sigmoid function is applied. The output is derived by summing all prediction values. The prediction value can be calculated using (3).  
\[\hat{y_{i}}=\sum_{k=1}^{K}f_{i}(x_{i}) \tag{2}\]  
\[\hat{y_{i}}=\frac{1}{1+e^{-f(x_{i})}} \tag{3}\]  
The error is calculated using the difference between the prediction and real values in the tree, and the weight is calculated to reduce the error as presented in (4). \(\hat{y_{i}}^{(t-1)}\) refers to the prediction value of the previous model, \(h_{t}(x_{i})\) refers to the tree trained by the current model, and \(\eta\) refers to the learning rate, which is the percentage of reflections from the prior model. The model's error is reduced by iterating this method [24; 25].  
\[\hat{y_{i}^{(t)}}=\hat{y_{i}^{(t-1)}}+\eta h_{t}(x_{i}) \tag{4}\]
## 3 Experimental Results
### Behavior at Starting  
Practical results found in Figure 11 interpret the safe starting of the BLDC motor under irradiance level 1000 W/m\({}^{2}\) and 300 W/m\({}^{2}\). Initially, the duty cycle is kept at 0.5 to run the motor. The sufficient motor speed is obtained by controlling the starting current, which runs the motor-pump system successfully. Figure 11 portrays the successful action of BLDC-PV pump at the start by limiting starting current, which reveals the progression with safe and soft start. The obtained results prove the more relevant performance conducted for the EMI reduction and soft starting for the experimental test conducted in [28].  
Figure 11: BLDC driven Luo converter employed PV pumping under soft starting (**a**) 1000 W/m\({}^{2}\); (**b**) 400 W/m\({}^{2}\)  
Figure 10: BLDC-driven Luo converter (**a**) increased solar irradiance (**b**) decreased solar irradiance.  
Table 5 portrays laboratory-adopted BLDC specification for a motion-controlled PV pump. Figure 12 interprets the existing global nature of the PV system under divergent sun radiation, which is demonstrated by the dark line. The operation begins with open-circuit voltage (VOPENCkt state) and reaches a global power point with variable solar irradiance. With application of hybrid ANFIS-FPA MPPT, steady GPP is attained over a complete day. The performance of the MPPT controllers for two algorithms ANFIS-FPA and FPA are tested with stepped irradiance input. Figure 13a illustrates that the proposed ANFIS-FPA imparts accurate and precise PV system outcomes with zero variation around GPP with fluctuating sun insolation. However, the FPA employed algorithm provides inconsistent and more oscillation nearby GPP that equates to the ANFIS-FPA algorithm described using Figure 13b. Under these situations, ANFIS-FPA has high tracked PV power with proportionately less GPP time. Practical results demonstrate that ANFIS-FPA algorithm contributes rapid and insignificant swinging differentiated with FPA MPPT illustrated in Figure 13a,b. Figure 14 demonstrates the behavior of numerous MPPT Viz. FPA, PSO, FLC and P and O control under standard test conditions. Under standard test conditions, ANFIS-FPA has better PV tracking efficiency compared to ANFIS-PSO, FLC and P and O methods, as illustrated with Figure 14. A hybrid ANFIS-FPA algorithm has global power point trajectory with the most tracked power and has zero oscillation throughout, equated with different controllers. The PV tracked trajectories are also examined under fluctuating weather situations (Figure 15). Under dynamic weather conditions, the PV tracking trajectory is found to be more accurate compared to conventional algorithms and has a zero GPP oscillation around this point, which is explained by Figure 15. Practical results reveal that ANFIS-FPA-optimized MPPT provides optimal tuning with high performance index.  
Figure 14: Behavior of numerous MPPT control under standard test conditions.  
Figure 13: Behavior of MPPT under stepped irradiance (**a**) Hybrid ANFIS-FPA; (**b**) FPA.  
Figure 15: PV tracked trajectories examined under fluctuating weather situations.
# Research on the Fatigue Performance of Composite Girders with CSW and ST Truss
## 2 Fatigue Performance Test Design
### Arrangement of Monitoring Points  
The stress at the weld toe was not suitable to be directly viewed as the hot spot stress, because the intersecting weld is an irregular spatial curve, and the initial internal imperfections will affect the stress. At present, the hot spot stress is defined in two ways, i.e., as the maximum principal stress [25], or by extrapolating structural stress, which is parallel to the weld [26]. According to the ultimate bearing capacity test of a composite girder with CSW and ST truss [11], longitudinal tensile stress failure occurs at the intersecting weld, and the cracking path is perpendicular to the direction of tensile stress. Hence, it was deemed appropriate to use the second method to define the hot spot stress for the composite girders with CSW and ST truss. According to the stipulation for the interpolation interval of hot spot stress [26], gradient strain gauges adopted in the test (Figure 3) were customized with a substrate of a size of 4 mm \(\times\) 8 mm. Three unidirectional strain gauges whose space and resistance were 2 mm and 120 \(\Omega\), respectively, were arranged on the substrate.  
Considering the fatigue characteristics of the composite girders above, the gradient strain gauges were arranged at the intersecting weld in the pure bending section of the model, which was close to both sides. As the mid-span section is a stress-carrying characteristic section of this composite girder, strain monitoring points were arranged within two corrugation wavelengths off the mid-span section. Specifically, they were placed at point S within the range of monitoring wavelength, as shown in Figure 3. The strain monitoring points were denoted as MNK-F, where M represented the component of the composite girders, and the CSW and the chord were denoted as G and C, respectively. N stood for both sides of the CSW or chord, and the left- and right-hand side were denoted as Z and Y, respectively. K was the serial number (1-8) of the gradient strain gauge along the axis of the girder, and F was the serial number (1-3) of the unidirectional strain gauge arranged on the substrate, where the unidirectional strain gauge closest to the weld was numbered as 1. Eight strain measurement sections were arranged on the test model. To obtain the changing rule of the stiffness of the composite girder with the increase of the loading cycles, displacement meters were placed on the chord at \(L/4\), \(L/2\), and 3 \(L/4\) sections, as shown in Figure 3. The displacement monitoring points were denoted as Dm-n, where D represented the displacement monitoring point, m stood for both sides of the chord, and the left- and right-hand side were denoted as Z and Y, respectively. n was the serial number (1-3) of the sections along the axis of the girder. The displacement monitoring points arranged on the chords were denoted as DZ1-DZ3 and DY1-DY3, respectively.
## 3 Results and Discussion
### Ethanol pH, Total Acidity, Sugars Content and Color of Must and Wine
The color of Aurore must and wine was measured in the CIE L*a*b* system involving three components: L*--brightness, a*--color from green to red, and b*--color from blue to yellow. A color of pomeranate for fermented beverage was an important parameter for consumer acceptance [23; 24]. An important factor in wine-making is that some of the initial color of the must is modified during vinification. The must of Aurore cv. characterized by L* = 34.8, a* = \(-\)0.26 and b* = 1.9. L* did not change significantly (\(p>0.05\)) for different yeast strains or the temperature of fermentation. Fresh wines were slightly darker (L* from 32.1 to 34.0), probably due to the retained presence of yeast. Parameter a* was not affected by the fermentation temperature but it significantly depended on the yeast strain (\(p<0.05\)). In the wine fermented with _S. cerevisiae_ SIHA(r) Cryarone, a* increased to the highest degree that corresponded to a redder shade. Parameter b* that is responsible for yellow color did not change significantly (\(p>0.05\)) in the presence of commercial yeast used in this study. However, significant changes (\(p<0.05\)) were noticed in the fresh wine depending on the temperature of fermentation and the wine from the must fermented at 20 \({}^{\circ}\)C was less yellow (3.0) than at 12 \({}^{\circ}\)C (3.8). A point worth mentioning is that all variations in color data reported here occurred exclusively during the fermentation stage, the color remaining almost unchanged after that (\(p>0.05\)). Among the main problems of white wine production is its browning. The brown color results from an increase in yellow pigments [24; 25]. Despite that, Aurore wine tested in this study did not darken but was still of bright yellow color after five months of storage. The wine color visibly changed its tonality after maturation. These variations could be linked to phenolic compounds, i.e., flavonoids, flavonls, flavan-3-ols and phenolic acid degradation recorded in the initial period of wine elaboration.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method  
A flowchart of the proposed method is shown in Figure 2. As shown in Figure 2, the proposed dehazing method incorporates the following four stages: extraction of color components in airlight, improved hazy color correction based on the gray world hypothesis, depth map setting based on luminance and saturation, and dehazing. Since both color correction and dehazing in this study used the LAB color space, the RGB color space was first converted into the LAB color space to perform the color correction algorithm, and the color-corrected image was used as an input image for dehazing to estimate the depth map for the LAB color space. The estimated depth map restored the L channel through the ASM, and the restored L, a, and b channels were converted to the RGB color space for the final output.  
Figure 1: Histogram of the gray world hypothesis: (**a**) normal hazy image, (**b**) color-cast hazy image, (**c**) restored normal image, and (**d**) restored color-cast image.
## 5 Results
### Reconstructions and Latent Space  
We provide an in-depth analysis of the relations between inferred parameters and corresponding synthesized audio to support our previous claims. First, we selected two samples from the test set and compare the inferred parameters and synthesized audio in Figure 3.  
As we can see, although the \(CNN\) provides a close inference of the parameters, the synthesized approximation completely misses important structural aspects, even in simpler instances as the simple harmonic structure in the first example (a). This confirms our hypothesis that direct inference models are unable to assess the _relative impact_ of parameters on the audio. Indeed, the errors in all parameters are considered equivalently, even though the same error magnitude on two different parameters can lead to dramatic differences in the synthesized audio. Oppositely, even though the parameters inferred by our proposal are quite far from the original preset, the corresponding audio is largely more similar. This indicates that the latent space provides knowledge on the _audio-based neighborhoods_ of the synthesizer. Therefore, this allows to understand the impact of different parameters in a given region of the latent audio space.  
To confirm this hypothesis, we encode two random distant examples from the test set in the latent audio space and perform random sampling around these points to evaluate how local neighborhoods are organized. We also analyze the latent interpolation between those examples. The results are displayed in Figure 4. As we can see, our hypothesis seems to be confirmed by the fact that neighborhoods are highly similar in terms of audio but have a larger variance in terms of parameters. Interestingly, this leads to complex but smooth non-linear dynamics in the parameters interpolation.  
Figure 3: _Reconstruction analysis._ Comparing parameters inference and resulting audio on the test set with 16 (**a**) or 32 (**b**) parameters, and on the _out-of-domain_ (**c**) sets composed either of sounds from other synthesizers (**left**) or vocal imitations (**right**).