esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Viability and Oxidative Stress  
The cell viability of differentiated SH-SYSY cells was assessed using the MTT assay after 24 h of dose-dependent treatments with stem and SFH extracts from the Orte 1 F4 P10, Orte 2 F7 P2, and Grato 1 genotypes. This approach yielded an EC\({}_{50}\) value of 190.1 ug/mL only for the cells treated with the Orte 1 F4 P10 stem extract. In contrast, no cytotoxicity was observed at the concentrations of treatment considered for the Orte 2 F7 P2 and Grato 1 stem extracts or for the SFH extracts from any of the three genotypes; in these cases, cell viability remained unaffected by exposure to extracts up to 250 ug/mL. No solvent-related effects were observed at the highest concentration tested. The neuroprotective potential of the tested extracts was evaluated by exposing pretreated cells to oxidative stress induced by H\({}_{2}\)O\({}_{2}\) for 1 h. Pre-treatment was carried out 24 h earlier using five two-fold serially diluted, non-cytotoxic concentrations of stem and SFH extracts, ranging from 0.39 to 6.25 ug/mL for stem extracts and from 12.5 to 200 ug/mL for SFH extracts (Table 1). The stem and SFH extracts from the Orte 1 F4 P10, Orte 2 F7 P2, and Grato 1 genotypes were able to increase the viability of cells stressed by H\({}_{2}\)O\({}_{2}\). Specifically, in cells exposed to H\({}_{2}\)O\({}_{2}\), the stem extracts restored cell viability from 29.62% to 49.62% and 34.42% after 24 h of treatment with 6.25 ug/mL of Orte 1 F4 P10 and Orte 2 F7 P2 extracts, respectively. Moreover, the stem extract from Grato 1 increased cell viability from 29.62% to 33.65% after 24 h of treatment with 3.13 ug/mL of extract. Regarding the SFH extracts, those from the Orte 1 F4 P10 and Orte 2 F7 P2 genotypes showed greater activity at a concentration of 100 ug/mL, restoring the cell viability to 50.77% and 45.96%, respectively, while 200 ug/mL of extract was the most active concentration for Grato 1 (46.73% of cell viability) (Table 1). The ability to counteract H\({}_{2}\)O\({}_{2}\) induced intracellular ROS production in differentiated SH-SYSY cells was investigated by pre-treating the cells for 24 h with two concentrations that showed a significant protection: 6.25 ug/mL for the stem extracts and 200 ug/mL for the SFH extracts. The reduction in free radical ROS following treatment was assessed using the DCFH-DA assay. As reported in Figure 1, the Grato 1 SFH extract led to the highest and most significant reduction in intracellular ROS levels, restoring the values detected in the cells not treated with H\({}_{2}\)O\({}_{2}\) (Crtl), followed by Orte 2 F7 P2 stem extract.
# Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
## 3 Analysis of Test Results
### Effect of Normal Stress on Shear Stress  
The shear stress-displacement curves of each group in Figure 4 were measured under the condition of non-lubricated slurry and the analysis of the shear stress-displacement curves of sand samples with different particle sizes under the normal stress of 40 kPa, 60 kPa and 80 kPa. It can be concluded that the peak and stable values of the shear stress between the contact surface of the sand and steel plate increase as the normal stress increases. The reason is that when the normal stress increases, the sand and soil compactness increases, the porosity decreases, the extrusion between the soil particles is more intense, the friction increases, and the shear stress between the contact surfaces increases as a result. With the increase of normal stress, the shear displacement corresponding to the maximum shear stress in the sandy soil increases. The reason is that for the same particle gradation sand sample, when the normal stress increases, the interaction force between the particles increases. During the shear process, the frictional force between the particles increases, and the relative position between the soil particles is difficult to change and cannot reach a relative stable state in a short time [4]. The strain-softening phenomenon occurs in sandy soils under different normal stresses, and the shear stress-reduction value increases with the increase of normal stress, but it has no effect on the trend of the normal stress-displacement curve. The reason may be that normal stress only changes the magnitude of the interaction force between the sand and soil particles, without changing their contact mode and movement, and the shear stress-strain curve only changes the numerical magnitude, without changing the curve direction and trend. When the normal stress is larger, the interaction force between the sandy soil particles is larger, the soil particles transport violently, the  
Figure 3: Shear stress-displacement of sand with different particle sizes. (**a**) No slurry lubrication; (**b**) Slurry lubrication.  
density of soil particles near the contact surface of the sandy soil and steel plate decreases, and the strain softening is more obvious at this time.
## 3 Results and Discussion
### Choice of Sustainable DB Process  
In terms of its environmental friendliness and impact on human health, DB under flood lubrication is not a sustainable process. However, this DB configuration is a conventional process, and the results achieved using this approach can serve as a reference in terms of the specimens' SI and fatigue limit. All three dry DB processes satisfy the criteria for sustainable finishing processes as they are eco-friendly, do not have harmful impacts on human health, and do not involve costs for the purchase, recycling, and purification of LCL. From an economic perspective, the cheapest configuration is dry DB without cooling. The two sustainable processes A and N provide essentially the same fatigue limit as the conventional flooding DB process. Therefore, in applications where high fatigue strength is required, DB with cold air cooling should be chosen, as it provides a more favorable cost/quality ratio while also being more sustainable than the conventional LCL process. Overall, based on the obtained SI results and the known correlations between SI and tribological behavior under boundary and mixed lubrication conditions [46; 57; 59; 61], dry DB without cooling is the most appropriate sustainable finishing process when increased wear resistance is required.
## 2 KGs Extraction  
This illustration details the annual distribution of research papers across various subfields of KG extraction from 2022 to 2024. This highlights that while some areas--such as relation extraction and multimodal/domain-specific extraction--maintain steady, high levels of scholarly activity, other subsections, like graph-based methods and intelligent reasoning, experience significant surges in certain years.  
**Steady or Growing Areas.** Multimodal and Domain-Specific Extraction (Section 2.2.2) and Relation Extraction (Section 2.1.2) appear to maintain consistently high interest. This sustained attention could be driven by the increasing need to handle diverse data formats--such as images, videos, and specialized domain texts--and the growing importance of accurate entity-to-entity linking in complex applications.  
**Peaks and Surges in Specific Years.** Topics like Graph-based Methods (Section 2.2.1) and Intelligent Reasoning (Section 2.3.1) show marked surges in certain years. These spikes may be attributed to breakthroughs in neural architectures (e.g., attention-based GNNs) or new reasoning paradigms that have temporarily shifted the research focus. Additionally, improvements in computational hardware and libraries can accelerate development in these areas, causing sudden increases in publication volume.  
**Fluctuations and Declines.** Some subsections experienced sporadic peaks followed by rapid declines, suggesting that research efforts in those areas either reached a saturation point or were eclipsed by more innovative methods. Shifting priorities--such as the rise of end-to-end extraction pipelines or more advanced reasoning techniques--can lead to a decrease in traditional rule-based or less flexible approaches.  
**Emerging Use Cases and Practical Constraints.** Real-world applications requiring scalable, domain-specific extraction solutions often motivate the research community to refine existing methods and explore novel frameworks. For instance, heightened interest in industry adoption might sustain certain topics for longer, whereas academic research might pivot quickly if a method shows diminishing returns or new challenges arise.  
Overall, the trends in KG extraction highlight how some techniques remain stable focal points, whereas others gain or lose prominence in response to new methodological breakthroughs, changing computational capabilities, and domain-driven requirements.
## 2 Basic Theory of HSDTPEs
### Load Boundary Condition  
A distributed force \(\mathbf{f}_{0}=\left[\begin{array}{cccc}0&0&f_{z}&(x,y)\end{array}\right]^{T}\) along the transverse direction \(z\) is applied at element \(e_{d}\), as illustrated in Figure 5. By using Equation (5), the external force potential energy of element \(e_{d}\) is written as:  
\[\Pi^{f}=-(\mathbf{a}^{e_{d}})^{T}\iint\limits_{\Delta e_{d}}(\mathbf{N}^{e_{d }})^{T}\mathbf{f}_{0}\;dx\;dy \tag{31}\]  
where \(\mathbf{N}^{e_{d}}\) is the shape function of element \(e_{d}\) calculated by Equation (5), and \(\mathbf{a}^{e_{d}}\) is the DOFs of element \(e_{d}\).  
Figure 4: Fixed displacement boundary condition.  
Figure 5: Distributed transverse force.  
Similarly, a distributed resultant moment \(\mathbf{M}_{0}=\left[\begin{array}{cc}M_{s}&M_{sn}&0\end{array}\right]^{\mathrm{T}}\) is applied to the edge of element \(e_{m}\), as illustrated in Figure 6. By using Equation (5), the external force potential energy of element \(e_{m}\) is written as:  
\[\Pi^{m}=-(\mathbf{a}^{e_{m}})^{\mathrm{T}}\int_{-l/2}^{l/2}\left(\mathbf{\lambda }^{m}\mathbf{\tilde{N}}^{e_{m}}\right)^{\mathrm{T}}\mathbf{M}_{0}\;ds \tag{32}\]  
where \(\mathbf{a}^{e_{m}}\) is the DOFs of element \(e_{m}\), and \(\mathbf{\tilde{N}}^{e_{m}}\) is the shape function of element \(e_{m}\) corresponding to the moment, where:  
\[\mathbf{\tilde{N}}^{e_{m}}=\left[\begin{array}{cccc}\mathbf{0}&\mathbf{0}& \mathbf{P}^{2}&\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{P}^{2}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{0}&\mathbf{0}&0\end{array}\right] \tag{33}\]  
and the transformation matrix  
\[\mathbf{\lambda}^{m}=\left[\begin{array}{cccc}\cos\beta&\sin\beta&0\\ -\sin\beta&\cos\beta&0\\ 0&0&1\end{array}\right] \tag{34}\]
## 4 Experiment
### Comparative Experiment
LSTM-MoT and CNN(10 runs) + LSTM(10 runs) utilize the document-scale features of the essay to extract deep semantic information, which has significantly improved the effect compared to the manually extracted features; however, the individual document-scale DNN structure does not extract the semantic features of longer texts well. Furthermore, the whole essay is split into words, with the relevance between the words in the same sentence and the relevance between different sentences of the essay being lost. The training of multiple individual networks using integrated learning has slightly improved the effect but the scoring performance is not good. (2) Incorporating the SkipFlow mechanism into the LSTM to model the semantic relevance between hidden layers leads to a slight improvement in performance. However, it can only rely on word-level co-occurrence patterns rather than capturing the overall meaning and coherence of the essay. Concretely, in comparison to the conventional LSTM, the performance improvement is only significant for the P1 and P8 subsets, whereas the performance enhancement is comparatively similar for the remaining subsets. The CNN-LSTM-ATT model uses sentence-scale features as the basis for scoring, which is better than document-scale scoring; however, the performance improvement of the model is limited by scoring only at the sentence semantic scale. Furthermore, its accuracy in scoring largely depends on the sentence vectorization method. (3) Combining manually extracted features and deep semantic features extracted by DNN models can effectively improve the scoring performance of essays. The TSLF model with character features improves by 2.7% over LSTM-MoT. The experimental results show that the hand-crafted features and deep learning features are complementary and are effective complements to the semantic features; additionally, fusing multi-scale features can effectively improve the performance of essay scoring. However, it relies too much on document-level LSTM feature extraction capabilities and does not use additional methods to extract other scales features. 4. The Tran-BERT-MS-ML-R model achieves a QWK coefficient of 79.1% on the ASAP dataset, indicating the effectiveness and superiority of the pre-trained model in the AES task. This also indicated that scoring based on features of different scales can effectively improve the performance but would cause more computational load. Moreover, our proposed MSSF method outperforms the Tran-BERT-MS-ML-R model and achieves the highest performance scores on four subsets because MSSF extracts document-scale global features and sentence-scale local features from deep semantic features, proposes the similarity relevance between the essay and prompt from the topic scale, and extracts linguistic features from shallow information for scoring. Thus, the proposed model in this paper exhibits the best overall performance when compared to the other models and without excessive computational load.
###### Abstract  
The complexity of quality function deployment (QFD) matrices often hinders efficient decision-making in product design, leading to missed opportunities and extended development times. This study explores the integration of principal component analysis (PCA) with analytic hierarchy process-QFD (AHP-QFD) to address these challenges. PCA, a machine learning technique, was applied to QFD matrices from product design research to reduce complexity and enhance prioritization efficiency. The integrated method was tested with a product design team across various industries, including logistics, healthcare, and consumer electronics. The analysis demonstrated that PCA effectively reduced matrix complexity, optimizing feature prioritization. In the logistics sector, PCA explained 99.2% of the variance with the first five components, while in consumer electronics, it accounted for 86.9% with the first four components. However, PCA showed limitations in the healthcare sector due to evenly distributed variance among components. Expert feedback highlighted the practical benefits of the integrated approach: 75% of logistics experts and 62.5% of consumer electronics experts found the method clearer. For speed, 100% of logistics and 87.5% of consumer electronics experts preferred the method for quicker evaluations. For accuracy, 75% of logistics and 62.5% of consumer electronics experts deemed the method more accurate. Overall, the PCA-AHP-QFD method simplifies decision-making processes and reduces development time, particularly in industries where feature prioritization is crucial. These findings underscore the potential of the integrated approach to enhance product development efficiency and feature prioritization, with suitability varying based on industry characteristics.  
AHP-QFD; machine learning; product design; decision-making; PCA Article
# Microbial Peptides: Key Players in Reducing Gluten Immunogenicity Through Peptide Degradation
Thus, it has been described that heating or other thermal treatments change the gliadin structure, as occurs in bread preparation [22]. Other studies have simulated in vitro digestion after bread baking or thermal processing, to quantify gluten peptides remaining by mass spectrometry, although more studies are necessary to improve biological relevance [23]. Among these strategies to reduce the immunogenicity of gluten peptides, microbial peptidases have emerged as promising and specific tools for the degradation of immunogenic gluten peptides. These strategies are based on the fact that CD4+ T-cells are specifically activated by some epitopes, which are recognized by HLA-DQ2 or 8 receptors present on antigen-presenting cells in individuals with a genetic predisposition to celiac disease [24]. These peptides are known as immunogenic peptides, with 9 to 33 amino acid residues, the most studied being the immunodominant 33-mer peptide from a2-gliadin [25]. These microbial enzymes, produced by various microorganisms, are capable of hydrolyzing peptide bonds within immunogenic epitopes, thereby reducing their ability to trigger adverse immune responses. Gluten-derived peptides are rich in proline and glutamine, making their degradation challenging for broad-spectrum peptidases [26]. In nature, the hydrolysis of proline-rich proteins is primarily carried out by a specialized class of enzymes known as proline-specific peptidases (PSPs), which exhibit unique specificity that makes them ideal for neutralizing the immunogenic potential of these peptides. PSPs play a crucial role in the fine regulation of various metabolic processes, cellular differentiation and maturation,the processing of biologically active peptides and proteins, and immune responses [27]. Their medical relevance has been extensively studied in diseases such as diabetes, cancer, Alzheimer's disease, Parkinson's disease, hypertension, and various neuropsychiatric disorders [28]. Since gluten immunogenic peptides contain both proline and glutamine, it is essential to consider peptidases with specificity for both amino acids. The combination of PSPs with peptidases targeting glutamine represents a potential strategy for partially or completely eliminating the immunogenicity of these peptides. In this regard, oral enzyme therapy with gluten-degrading peptidases is emerging as a promising therapeutic approach [29; 30]  
This review addresses the role of microbial peptidases in gluten degradation, highlighting their mechanisms of action, effectiveness in reducing immunogenicity, and potential applications in therapeutic strategies and the food industry. Additionally, advances in the development of enzymatic therapies, applications in food processing, and challenges associated with optimizing these enzymes for safe and efficient gluten detoxification will be discussed. Exploring the potential of microbial peptidases represents an innovative approach that could facilitate new strategies to mitigate the effects of gluten-related disorders and improve the quality of life of affected individuals.
## 4 Finite Element Analyses
### Numerical Results
Figure 8a reports the average percentage difference between analytical and numerical models with different values of the shear modulus of the rubber (\(E_{G_{e}}\)), while Figure 8b illustrates the differences between models with variable primary and secondary shape factors (\(E_{S_{1,2}}\)). The minimum percentage difference between analytical and numerical results is obtained when using the pressure approach for the effective compressive modulus and Equation (7) for the buckling load. At \(\gamma_{H}=50\%\), this combination returns differences of the order of 2.59% and 6.91% as averages on \(G_{e}\) and \(S_{1,2}\), respectively; at \(\gamma_{H}=100\%\), \(\gamma_{H}=150\%\) and \(\gamma_{H}=200\%\), these differences become 11.0%, 16.1% and 18.6%, as averages on \(G_{e}\), and 10%, 7.41% and 17.8% as averages on \(S_{1,2}\). Figure 8: Percentage difference between analytical and numerical models on the \(\psi\) function at four different levels of shear strain and: (a) shear modulus of the rubber; (b) primary and secondary shape factors. The analytical-numerical accuracy reduces when using the pressure approach for \(E_{c}\) and Equation (7) for \(F_{v,cr}\) as the percentage differences between numerical and analytical results are:  
* \(\gamma_{H}=50\%\): 36.3% (average on \(G_{c}\)) and 30.2% (average on \(S_{1,2}\));
* \(\gamma_{H}=100\%\): 45.9% (average on \(G_{e}\)) and 28.3% (average on \(S_{1,2}\));
* \(\gamma_{H}=150\%\): 57.0% (average on \(G_{e}\)) and 47.1% (average on \(S_{1,2}\));
* \(\gamma_{H}=200\%\): 56.7% (average on \(G_{e}\)) and 48.4% (average on \(S_{1,2}\)). These reductions depend on the assumption at the basis of this equation, as the elastomer is considered incompressible and the reinforcement flexible. In these conditions, the vertical bearing capacity of the device is minimally influenced by the geometric and mechanical parameters compared to FREIs with soft compound and flexible reinforcements. However, as Equation (7) returns greater reductions of the analytical trends of \(\psi(\delta_{H})\) compared to the numerical results, this equation can be used in a design phase as first estimate of the vertical stiffness reduction due to horizontal deformation of the U-FREI.
# Room-Temperature Formation of Hard BC\({}_{\rm x}\) Films by Low Power Magnetron Sputtering
## 1 Introduction
As for C-rich boron carbide, it is known that the replacement of B atoms by C atoms in the films occurred [36]. The coatings with low boron content exhibit a high degree of graphitization and their mechanical behaviour is close to that of elastic soft films [37]. However, they can protect the substrate materials against oxidation [41] and exhibit low friction coefficient and high wear resistance [42]. Depending on the composition of such films, they demonstrate the variety of obtained band gap energies (E\({}_{\rm g}\)) [43]. Considering that, along with tunable E\({}_{\rm g}\), these materials need to match other requirements such as chemical, thermal, and mechanical stability, they can be considered as a possible candidate material for photovoltaic applications. The nonequilibrium conditions of plasma processes can produce amorphous boron carbide films with various carbon fractions (a-B\({}_{1-x}\)C\({}_{x}\)) [32]. This study aims to explore the effects of experimental growth conditions on the structural, optical, and mechanical properties of BC\({}_{\rm x}\) films prepared by magnetron sputtering processes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of** & **Deposition** & **Precursor/** & **Temperature**, \({}^{\circ}\)**C** & **Chemical** & **Ref.** \\
**Boron Carbide** & **Method** & **Source** & **Temperature**, \({}^{\circ}\)**C** & **Composition** & **Ref.** \\ \hline B-rich & Calculation & – & – & B\({}_{13}\)C\({}_{2}\) & [33] \\ B-rich & Laser CVD & B(OCH\({}_{3}\))\({}_{3}\) & – & B\({}_{8}\)C & [15] \\ B-rich & Hot pressing & B\({}_{4}\)C, B & 1900–2200 & B\({}_{4.2}\)C, B\({}_{5.6}\)C B\({}_{7.6}\)C & [34] \\ B-rich & Hot pressing & B\({}_{4}\)C, B & 1900–2200 & B\({}_{4.6}\)C, B\({}_{6.5}\)C, B\({}_{7.8}\)C, B\({}_{10.2}\)C & [35] \\ B-, C-rich & CVD & B(C\({}_{2}\)H\({}_{5}\))\({}_{3}\) & 700–1200 & B\({}_{4.5}\)C, B\({}_{19.5}\)C, BC\({}_{11.5}\) & [14] \\ B-, C-rich & PLD & Sintered BC & – & B\({}_{29.4}\)C & [25] \\ B-, C-rich & CVD & BC\({}_{3}\), CCl\({}_{4}\) & 1000–1275 & B\({}_{0.99.6}\)C\({}_{0.65}\)C\({}_{0.40.35}\) & [39] \\ C-rich & Calculation & – & – & B\({}_{0.67.08}\)C\({}_{0.33.02}\) & [38] \\ C-rich & CVD & B(CH\({}_{3}\))\({}_{3}\) & 700–1100 & B\({}_{0.29.075}\)C\({}_{0.71.025}\) & [36] \\ C-rich & CVD & BCJ\({}_{3}\), C\({}_{3}\)H\({}_{6}\) & 900 & BC\({}_{19}\), BC\({}_{9}\), BC\({}_{5}\), B\({}_{3}\)C\({}_{7}\), BC & [37] \\ C-rich & Calculation & BCJ\({}_{3}\), CH\({}_{4}\) & 950–1000 & – & [40] \\ C-rich & MS & B, C & 200 & B\({}_{0.81}\)C\({}_{0.19}\), B\({}_{0.44}\)C\({}_{0.56}\), & [23] \\  & & & & B\({}_{0.24}\)C\({}_{0.76}\) & [23] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Selected literature data on the formation of boron- and carbon-rich boron carbide.