esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Deacidification Treatment on the Flavor Quality of Zaosu Pear-Kiwifruit Wine
## 3 Results and Discussion
### Chemical Composition of Pear-Kiwifruit Wine
#### 3.2.3. Polyphenol Content of Pear-Kiwifruit Wine after Deacidification  
Seven polyphenols were identified in pear-kiwifruit juice (Figure 3B), including catechin (10.17 ug/mL), chlorogenic acid (8.26 ug/mL), and epicatechin (6.33 ug/mL). After alcoholic fermentation, the concentrations of most polyphenols, including the three above, increased by at least 20%, which can be attributed to the extraction of polyphenols from the fruit pomace during fermentation. \(\beta\)-Glucosidase produced by lactic acid bacteria, such as _O. oeni_, can increase the content of chlorogenic acid by hydrolyzing chlorogenic acid glycosides [27]; _O. oeni_ GF-2 has a strong capacity to produce \(\beta\)-glucosidase [13]. Consequently, the highest concentration of chlorogenic acid (11.06 ug/mL) was observed in MLF wine (Figure 3B), whereas the concentrations of epicatechin, catechin, and phloretin  
Figure 3: Deacidification modulates organic acid and polyphenol accumulation in pear–kiwifruit wine: (**A**) organic acid; (**B**) polyphenol. Mixed juice: pear and kiwifruit juice at the blend ratio of 60:40. AF wine: pear–kiwifruit juice co-inoculated with _Saccharomyces cerevisiae_ ES488 and _Metschnikowia. pulcherrima_ 346. MLF wine: AF wine inoculated with _Oenococcus oeni_ strain GF-2. CD wine: wine chemically deacidified by 1.0 g/L Na\({}_{2}\)CO\({}_{3}\), 1.0 g/L K\({}_{2}\)CO\({}_{3}\), and 5.0 g/L KHC\({}_{4}\)H\({}_{4}\)O\({}_{6}\). Data are the mean \(\pm\) SD of three independent experiments. The same symbols with different letters represent significant differences (_p_ < 0.05).  
decreased by 15.9%, 12.5%, and 26.2%, respectively, after MLF (Supplementary Table S4), in agreement with reports that MLF significantly reduced the content of catechin in red wine, resulting from metabolism by _O. oeni_[28; 29]. However, the content of epicatechin decreased, in contrast to a previous report [29]; a possible reason for decreased epicatechin is that it may be involved in polymerization during MLF.  
The concentrations of gallic and caffe acids decreased during alcoholic fermentation, but increased during MLF (Figure 3B), which may have resulted from a different balance among oxidation, biotransformation induced by _O. oeni_ GF-2, and hydrolysis of higher oligomers. There was no significant difference in protocatechuic acid concentration after MLF, whereas it was undetectable after chemical deacidification. The content of other individual polyphenols decreased by at least 18.6% after chemical deacidification, except for gallic acid, resulting in a lower total polyphenol concentration than that in MLF wine (Figure 3B). Chemical deacidification resulted in low total organic acids, which may decrease the stability and, consequently, the total content of polyphenols. Overall, both chemical and biological deacidification modified the contents of organic acids and polyphenols in pear-kiwifruit wine.
## 4 Discussion
### Durability and Cost  
The average reported socket durability was 2.30 (\(\pm\)1.72) years, which is relatively short, particularly when considering the high expenses associated with socket renovation. In fact, the participants reported a limited ability to afford the economic costs of sockets, with an average rating of 2.00 (\(\pm\)0.94) out of 5. The capacity to afford prosthetic costs was even lower, with an average rating of 1.80 (\(\pm\)0.79) out of 5. When assessing the importance of various aspects, the participants identified cost as a highly significant factor (see Figure 12). There is a clear need to develop cost-effective solutions that can facilitate the necessary fit modifications and adjustments at a lower cost.
## Materials and Methods
### Design Procedure Using the TO Method
#### 2.2.1 Pre-Preparation for TO  
Object Shape Creation for Optimization  
The first step is to create an optimization object. The object can be either an existing component or the maximum allowed building volume, the so-called semi-finished product. In this case, the existing components (design solution after the first iteration) did not meet the strength requirements. Therefore, semi-finished mount bodies were created. A semi-finished product volume is the maximum allowable build volume that a future component can occupy. The design of the semi-finished product is designed to avoid collision with the frame, interior and other components of the passenger car. The designs of the semi-finished mounts and their comparison with the first iterations of the mount design are shown in Figure 3a,b.  
Defining the Functional Elements of the Object for Optimization  
Functional elements are those parts of the optimization object volume that are to be preserved after TO. The individual functional elements have been created as separate _Bodies_. The body shape of the functional elements has a significant impact on the quality of the _Mesh_ and therefore on the progress of the individual simulations that are part of the TO. Therefore, the shapes of the _Bodies_ of the functional elements were designed to follow, roughly, the contour of the shape of the semi-finished product body.  
In the case of the front mount, the following functional elements of the object were retained for optimization (Figure 4a):  
* Through-holes and recesses for screws (marked in yellow);
* Holes for silentblocks (marked in orange).  
In the case of the rear mount, the following functional elements of the object were retained for optimization (Figure 4b):  
* Through-holes for screws (marked in yellow);
* Thread holes for attaching the rear arm (marked in orange).  
Figure 3: Object for optimization: (**a**) front semi-finished mount and design solution after the first iteration, (**b**) rear semi-finished mount and its design solution after the first iteration.  
Creating the _Body_ of the Object for Optimization  
By linking the _Body_ of the semi-finished product with the functional element _Bodies_, the _Body_ of the optimization object was created for use in the next steps of the optimization process. The _Partition Design Space_ function was used to create the _Body_ of the optimization object.  
Creating and Assigning a Material Profile to an Optimization Object  
In order for a material profile to be usable for TO, three parameters need to be defined:  
* Density--\(\rho\);
* Young's modulus--\(E\);
* Poisson ratio--\(\mu\).  
For the structural design of the electric motor mounts by the TO method, the AlSi10 Mg aluminium alloy was selected, which is suitable for production by DMLS or SLM additive manufacturing technologies. Its parameters are:  
* Density: \(\rho\) = 2650 kg/m\({}^{3}\);
* Young's modulus: \(E\) = 66 GPa;
* Poisson ratio: \(\mu\) = 0.325;
* yield stress: \(Re\) = 260 MPa.
# Collapse Analysis of a Two-Span Reinforced Concrete Bridge Model
## 2 Description of Bridge Model  
The prototype bridge structure is long and of great quality. In order to study its seismic response, the bridge must be scaled. Considering the test conditions, the geometric similarity ratio of 1:3 was finally determined. For comparison with the performance of the prototype bridge, the materials of the scaled model were the same as the prototype materials, with similar ratios of gravity, stress, and strain and an elastic modulus equal to 1, as shown in Table 1. Based on the shaking table test results of the two-span continuous bridge model, the collapse resistance of this structure and its influencing factors were further explored.  
The scaled model was a two-span continuous girder bridge with a main girder length of 14.2 m (see Figure 1a). The cross section was two T-beams, and the bearing was an  
ordinary plate rubber bearing. The width of the T-beams was 128 cm and the height of the girder was 45 cm, as shown in Figure 1b. The substructure comprised two-column reinforced concrete piers with diameters of 30 cm. The detailed geometric arrangement and reinforcement design of the pier are shown in Figures 1c and 2. The concrete strength title of the beam and pier was C30 and the compressive strength was 32 MPa. The reinforcement was HPB235 grade steel. The yield strength and the tensile strength of \(\Phi\)10 bar were 351 MPa and 558 MPa, respectively. The yield strength and the tensile strength of \(\Phi\)6.5 bar were 350 MPa and 531 MPa, respectively. Figure 3 shows the picture of the bridge model.  
The shaking table test of the structure was carried out to obtain the seismic response characteristics for medium and small earthquakes in the engineering structure laboratory of the Civil Engineering Test Center of Fuzhou University. The seismic simulation shaking table array system consists of a 4 m \(\times\) 4 m middle table and two 2.5 m \(\times\) 2.5 m side tables. The test bridge models included the conventional system of ordinary rubber bearings and the isolation system replaced by high damping rubber (HDR) bearings. Except for the different bearings, the other components were completely consistent. More details are illustrated in the paper on experimental research [28]. Unfortunately, limited to the conditions of the test equipment, the bridge model was not failed in the end. The collapse mode was only studied by numerical analysis.  
Figure 1: Dimensional details of bridge specimen: (**a**) section view of main girder, (**b**) section view of column, (**c**) pier (unit: cm).
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 5 Conclusions  
The present work has addressed metallurgical changes in the welding of clad pipes, and several conclusions may be drawn:  
* Complex metallurgical phenomena present in the welding of clad pipes due to multi-material systems, especially concerning the weld metal. Clad pipes represent a solid economic alternative to pipes made of stainless steel which is vital to the oil and gas industry.
* Welding process significantly alters the base metal near the weld metal, i.e., the heat-affected zone. Therefore, in complex clad pipes, utilizing expensive materials must be considered in detail.
* Solidification parameters have very high importance on solidification behavior and resulting mechanical properties. Moreover, they can be efficiently estimated by numerical simulation at any point in welded joints.
* The subsea clad pipe network requires an emergency method for pipe repair to be developed. Therefore, the current manuscript represents an important contribution to the knowledge on microstructural changes and diffusion that may occur in welding, and how these may influence the pipeline integrity.
* During repair under hyperbaric conditions, the use of multiple filler wires is very complicated for clad pipes. Therefore, the suitability of filler wire for multi-material systems, both for carbon and nickel-based/stainless steel materials, must be carefully investigated for single filler wire applications.  
[PERSON] wrote the manuscript and performed numerical simulations. [PERSON] and O.M.A. supervised the further improvement of the manuscript.  
The authors wish to thank the Research Council of Norway for funding through the Petromaks 2 Programme, Contract No. 234110/E30. The financial support from Statoil Petroleum AS, Gassco AS, Technip Norge AS, EDF Induction AS, and Pohang Iron and Steel Company Posco is also acknowledged.  
The authors declare no conflict of interest.
# Novel Methodology for Condition Monitoring of
## 2 Theoretical Considerations  
### Infrared Imaging  
Infrared imaging is a technique used to record infrared radiation, which permits the estimation of a body's surface temperature. Any object at a temperature above absolute zero (0 K or \(-\)273.15 \({}^{\circ}\)C) emits energy electromagnetic radiation depending on its temperature [32]. An infrared imaging camera absorbs the infrared radiation emitted by a body through a noncontact method and, using Stefan-Boltzmann law, the body's temperature is obtained [33].  
Additionally, the consideration of infrared cameras allows us to obtain either digitized intensity values or temperature values, and depending on the application, one or both values are used. In this sense, it should be mentioned that although several research studies exist that consider these values for carrying out condition monitoring and fault identification in rotating machinery, most of the reported works have not based the fault assessment on the estimation of the thermal matrix.
## 2 Literature Review
The SVM classifier displayed the most encouraging results (with an F1 score of \(0.827\pm 0.010\)) in accurately detecting abusive content. User comments from publicly viewable Facebook posts made by athletes, officials, and celebrities were analyzed in a study by [PERSON] et al. [40]. The researchers distinguished between Bengali-only comments and those written in English or a mix of English and other languages. Their research showed that 14,051 initial comments in total, or approximately 31.9% of them, were directed at male victims. However, a significant number of the 29,950 comments, or 68.1% of the total, were directed at female victims. The study also highlighted how comments were distributed according to the different types of victims. A total of 9375 comments were directed at individuals who are social influencers. Among these, 5.98% (equivalent to 2633 comments) were aimed at politicians, while 4.68% (or 2061 comments) were focused on athletes. Additionally, 6.78% (about 2981 comments) of the comments were centered around singers, and the majority, which is 61.25% (totaling 26,951 comments), were directed at actors. For the classification of hate speech in the Bengali language, [PERSON] et al. [41] used neural networks, including LSTM (long short-term memory) and BiLSTM (bidirectional LSTM). They used word embeddings that had already been trained using well-known algorithms such as FastText, Word2 Vec, and Glove. The largest dataset of its kind to date, the extensive Bengali dataset they introduced for the research includes 30,000 user comments. The researchers thoroughly compared different deep learning models and word embedding combinations. The outcomes were encouraging as all of the deep learning models performed well in the classification of hate speech. However, the support vector machine (SVM) outperformed the others with an accuracy of 0.875. [PERSON] et. al. [42] used large amounts of data gathered from Facebook and YouTube to identify abusive comments. To produce the best results, they used a variety of machine learning algorithms, such as multinomial naive Bayes (MNB), multilayer perceptron (MLP), support vector machines (SVM), decision tree, random forest, and SVM with stochastic gradient descent-based optimization (SGD), ridge classifier, and k-nearest neighbors (kNN). They used a Bengali stemmer for preprocessing and random undersampling of the dominant class before processing the dataset. The outcomes demonstrated that, when applied to the entire dataset, SVM had the highest accuracy of 0.88. In their study, [PERSON] et al. [43] used transformer-based deep neural network models, like BERT [44] and ELECTRA [45], to categorize abusive comments on Facebook. For testing and training, they used a dataset with 44,001 Facebook comments. The test accuracy for their models, which was 0.85 for the BERT classifier and 0.8492 for the ELECTRA classifier, showed that they were successful in identifying offensive content on the social media platform.
## 3 Results and Discussion
### 24-hour GPS Daily Displacement Caused by MOT  
In Section 3.1, we give the magnitude of stations' displacement caused by MOT. Through reprocessing the GPS data by using the method outlined in Section 2.2, we obtain the MOT effect on the 24-hour daily GPS solutions. Figure 4 shows an example of the weekly GPS time series for station ALIC (Northern Territory, Australia) and REYK (Reykjavik, Iceland) due to MOT effects.  
Figure 4: GPS coordinate time series for station ALIC (**top**) and REYK (**bottom**) due to MOT effects.  
We observe that the magnitude of MOT on the 24-hour GPS solution is slightly larger than that of the MOT loading time series. The peak-to-peak daily displacement caused by MOT for inland station ALIC reaches 5 mm for the N, E component, and 15 mm for the U component (Figure 4, top panels). With respect to the island station REYK, the magnitude is even more than twice as large for both the horizontal and vertical components (see Figure 4, bottom panels).  
Figure 5 gives the spatial distribution of the STD and maximum displacement of the MOT induced GPS time series. We observe that MOT exhibits quite a different spatial pattern on the 24-hour GPS solution than that on the pure loading time series. In general, there is no strong correlation between station's MOT induced 3-D displacement and its distance away from the sea.  
It influences a lot on many coastal/island stations, for example, station ASC1 (Ascension Island), BAHR (Manama, Bahrain), _etc._ and also some inland station, such as WTZR (Wettzell, Germany), MDO1 (Texas, USA), _etc._ for both the horizontal and vertical components, among which the STD and the absolute maximum of the GPS height reaches more than 5 mm and 20 mm respectively, while the magnitude for the N and E component is slightly smaller. However, for the rest stations, especially  
Figure 5: Spatial distribution of the STD (**left**) and the maximum displacement (**right**) of the MOT induced GPS coordinate time series. From top to bottom are the U, E, and N components. Units are in mm. The black and white dots in the figure indicate that the maximum displacement for the station is smaller than the minimum value and larger than the maximum value on the scale.  
those located in North America and Eurasia plates, MOT only has a small impact on the GPS time series, with most of the 3-D STD and absolute maximum displacement smaller than 1 mm and 5 mm. This may due to the interaction between MOT and the other GPS error sources at different location, since our improved GAMIT estimates the MOT displacement together with other effects epoch by epoch, and then solve stations' daily position through least square methods.
## 5 Experimental Evaluation
### Impact of the Tasks Number  
Previous studies have proved that the task arrival rate has a large impact on the system state due to the random generation of tasks. In this paper, we use data from real scenarios for data streams, so unlike traditional schemes that use task arrival rates to judge the system state, this paper uses different time slots to verify the impact of the number of tasks on the system cost, average task delay, and task discard rate. Specifically, we set the time slots in the dataset to T = 100, 200, 500, 1000, and compare the performance of DQN, double DQN, dueling DQN, and OPO under different time slots. The experimental results are shown in Figure 9. The OPO algorithm has similar trends in various performance metrics as the conventional scheme in a short period. However, as the running time of the system increases (i.e., the number of tasks increases), OPO reduces at least 6.25% of the average latency, 25.6% of the offloading cost, and 31.7% of the task drop rate compared to other algorithms in terms of cost, average latency, and task dropped rate.
## 5 Discussion
### Data Fusion Methods  
Although the data fusion methods were appropriate to generate time-series 100-m data in this study, several limitations are still unavoidable, such as the STARFM algorithm may not perform so excellent in the complex heterogeneous areas and predictions from the ESTARFM may be inaccurate during a long term because the ratio of reflectance linear change is constant might be not suitable under this situation [22; 24]. In addition, the proposed framework would be less effective in the regions with successive clouds because if the base high-spatial-resolution or coarse-spatial-resolution images are heavily influenced by unfavorable weather conditions, the predicted high-spatial-resolution data might not be so accurate and ultimately the crop classification and yield estimation results will be affected. Recently, remote sensing images from multiple satellite sensors is widely used to construct data time-series for agriculture monitoring. For example, [PERSON] et al. used the Formosat-2, Landsat-8, Deimos-1 and SPOT-4 images to construct high temporal resolution data for the maize biomass and yield estimation in the southwest of France [50]. [PERSON] et al. demonstrated the strong potential of time  
Figure 11: The estimated biomass maps of the blended data in mid-May: (**a**) Site 1; and (**b**) Site 2.  
Figure 12: The estimated yield maps of the blended data: (**a**) Site 1; and (**b**) Site 2.  
series data merged from Landsat-5 TM and HJ-1 CCD for crop classification in northern Xinjiang, China [51]. [PERSON] et al. combined images from RapidEye and Landsat satellites to identify crop types in an irrigated agricultural region of the northern Greece [52]. Since this state-of-the-art method also performs very well in numerous applications and studies, it is necessary to compare performance of the two methods in the future studies to find which method is more suitable for the time series data construction.
## 3 Results
### Emotional and Cognitive Factors  
Patients with FMS had higher scores on the HADS anxiety subscale, indicative of the presence of moderate-to-severe anxiety symptoms compared to healthy controls. The independent sample Student's \(t\)-test revealed significant inter-group differences (\(t\) = 3.405; \(p\) < 0.0001, d = 3.25), with a high effect size. Patients with FMS had higher scores on the BDI-II, at levels indicative of a depressive disorder with moderate or severe symptoms. There were statistically significant differences between patients and healthy subjects on the BDI-II (\(t\) = 14.184; \(p\) < 0.0001, d = 28.58), with a high effect size. Patients with FMS showed cognitive characteristics of pain catastrophism, obtaining higher scores on the PCS compared to the controls. The independent sample Student's \(t\)-test revealed significant inter-group differences (\(t\) = 14.207; \(p\) < 0.0001, d = 19.05), with a high effect size. In the three dimensions of pain catastrophizing, statistically significant differences were also obtained between groups; these were helplessness (\(t\) = 11.361; \(p\) < 0.0001, d = 9.05); magnification (\(t\) = 5.712; \(p\) < 0.0001, d = 3.17); and rumination (\(t\) = 10.135; \(p\) < 0.0001, d = 6.82). FMS  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Measures** & **FMS** & **Pain-Free Controls** & \(p\) **Value Independent** \\  & **(\(n\) = 30)** & **(\(n\) = 30)** & **Samples Student’s \(t\)-Test** \\ \hline Age & 51.29 \(\pm\) 7.22 & 49.71 \(\pm\) 8.50 & 0.56 \\ Pain (NPRS) & 6.00 \(\pm\) 2.00 & - & \\ FIQ & 86.88 \(\pm\) 3.28 & - & \\ \hline \hline \end{tabular} Data are presented as means (SD). Patients in the FMS sample presented high clinical pain intensity on the numeric pain rating scale (NPRS) and high levels of impact of the disease, measured with the Fibromyalgia Impact Questionnaire (FIQ). The FIQ and NPRS were administered only to FMS participants.  
\end{table}
Table 1: Descriptive statistics in demographic measures (\(n\) = 34).  
patients showed negative cognitive beliefs regarding fear of movement/reinjury, obtaining higher scores on the TSK-11 compared to the control group (\(t=6.512\); \(p<0.0001\), d = 17.23), with a high effect size. There were statistically significant differences between patients and healthy subjects on the TSK-11. Data are shown in Table 3.