esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Applications to Cardiovascular Medicine
### Arterial Wall
#### 6.4.1. Aneurysms
Each element on the wall surface was assumed to be part of an ellipsoid sharing the center to the cross-section of the vessel and was assumed to behave as a linearly elastic material. The stiffness was directly calculated element-wise from local balance equations. Their results suggested that diseased tissue was stiffer in the bulging part of the aneurysm and generally stiffer than the adjacent non-aneurystic tissue. [PERSON] et al. further applied this methodology to a cohort of 30 aTAA patients, 12 with bicuspid aortic valves, and the remaining with normal tricuspid valves [281]. Wall stiffness distribution was heterogeneous for each individual, however, regional differences appeared to be marginal within the cohort due to interindividual variability. Notably, this study found no significant differences in stiffness nor its distribution between the bicuspid and tricuspid valve groups, suggesting that no distinction should be made in the surgical management of aneurysms between these groups.
# Sentiment Analysis of Students' Feedback on E-Learning Using a Hybrid Fuzzy Model
## 2 Preliminaries
### Deep Neural Networks
#### 2.1.2 Transformers  
The transformer architecture, introduced in the paper [32] by [PERSON] et al., has had a profound impact on natural language processing (NLP) and various other machine learning tasks. In fact, the authors proposed a self-attention mechanism that allows the model to weigh different parts of the input sequence differently, capturing long-range dependencies more effectively than traditional recurrent or convolutional architectures.  
In the same context, the authors in [33] introduced OpenAI's GPT-2, which is a transformer-based language model that demonstrated the power of generative pre-training. GPT-2 achieved remarkable results in tasks ranging from text completion to text generation.  
The work presented in the paper [34] explored the interpretability of transformer models in the context of biomedical text mining. It investigated how attention mechanisms in transformers can be analyzed to gain insights into the model's decision-making process. As follows, we provide the advantages and the disadvantages of the transformers:  
**Advantages**:  
1. Attention Mechanism: Transformers, with their attention mechanisms, can capture the global dependencies in the input sequence, allowing them to consider the entire context simultaneously [32].
2. Parallelization: Transformers can efficiently parallelize computations during training, leading to faster training times, especially on hardware that supports parallel processing [32].
3. Transfer Learning: Pre-trained transformer models, such as BERT, can be fine-tuned for sentiment analysis tasks. Transfer learning often leads to improved performance, especially when labeled data is limited [32].
4. Effective for Various Sequence Lengths: Transformers can handle input sequences of varying lengths without the need for padding, which is beneficial for sentiment analysis tasks with variable-length texts [32].  
**Disadvantages**:  
1. Computational Resources: Transformers, especially large pre-trained models, can be computationally intensive and may require significant resources, both in terms of memory and processing power [33].
2. Interpretability: Transformers may be seen as less interpretable than LSTMs due to their parallel processing and attention mechanisms, making it challenging to trace the flow of information through the model [33].
# Experimental Flow Performance Investigation of Francis Turbines from Model to Prototype
## 3 Experimental Results and Discussions
### Performance Observation through Hill Chart and Torque
It was found that the turbine exhibited its lowest efficiency of 87.5% when the guide vanes were set at an angle of 3.2 degrees. Performance was measured through specific operational parameters, including a specific speed ratio \(n_{ED}\) of 0.18 and a specific discharge \(q_{ED}\) of 0.15. These tests were conducted within a closed-loop water circuit \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Head** & **Turbine** & \multicolumn{6}{c}{**Working Conditions Loads**} \\ \hline
**m** & **Type** & **100\%** & **90\%** & **80\%** & **70\%** & **60\%** & **50\%** \\ \hline
38.10 & Model (\(\eta\)) & 93.25 & 93.15 & 93.06 & 92.40 & 91.02 & 89.22 \\
238.6 & Prototype (\(\eta\)) & 94.30 & 94.10 & 93.42 & 92.87 & 91.26 & 89.50 \\
38.50 & Model (\(\eta\)) & 93.33 & 93.25 & 93.03 & 92.61 & 91.06 & 89.30 \\
240 & Prototype (\(\eta\)) & 95.03 & 94.93 & 94.54 & 93.86 & 92.27 & 91.10 \\
37.50 & Model (\(\eta\)) & 93.45 & 93.20 & 92.88 & 92.10 & 90.50 & 89.73 \\
254 & Prototype (\(\eta\)) & 94.60 & 93.92 & 93.18 & 92.53 & 91.05 & 89.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prototype efficiency estimation with different working conditions. Figure 5: (**a**) Model turbine performance. (**b**) Estimated prototype turbine performance. operating under a head of 30 m. In the current investigation, the performance of the Francis model turbine was analyzed, revealing a minimum efficiency of 89.3%. These assessments were carried out within a closed-loop water system with a head of 40 m.
## 3 Results and Discussion
### Electrochemical Impedance Spectroscopy Analysis
Fly ash reacts with the CH covering its surface and eventually precipitates pozzolanic products, which strengthens the connection between the fly ash particles and cement hydration products, densifies the overall structure, and leads to the increasing pore solution resistance. With the increase in the sodium sulphate concentration, sulphate ions penetrate the matrix, increasing the amount of strong electrolyte in the porous solution and enhancing the conductivity of the porous solution. Considering this, the resistance \(R_{\text{s}}\) of the porous solution decreases. On the other hand, the corrosion induced by the sodium sulphate also generates ettrinigite (with a low solubility), which is easily precipitated and stuck to the pore surface. With the increasing high-sulphur fly ash content, some Al\({}^{3+}\) and AlO\({}_{4}\)[5]- will be bound to C-S-H and other gel structures; however, some Al\({}^{3+}\) dissociates in the pore solution and cement. Hydration products bind to new hydrated aluminates, and the intrusive SO\({}_{4}\)[2]- reacts therewith to form AFt, resulting in an increase in the difficulty of charge transfer. However, some Al\({}^{3+}\) ions remain free in the porous solution and combine with cement hydration products to form new hydrated aluminates. The intrusive SO\({}_{4}\)[2]- reacts therewith to form AFt, which results in the greater hindrance of charge transfer (this is consistent with the performance evinced by the Nyquist plots). Figure 10: Effect of sodium sulphate concentration on components.
# Combined Effect of Ferrous Ion and Biochar on Cadmium and Arsenic Accumulation in Rice
## 2 Materials and Methods
### Chemical Analyses  
Soil pH was measured with a glass electrode using a soil to carbon dioxide-free water ratio of 1:2.5 (w/v, UB-10, Denver Instrument, Arvada, CA, USA). For determination of soil total organic carbon (TOC), 0.3 g soil was added to 50 mL glass tubes, and the tubes were placed in a paraffin oil bath for 5 min at 170 \({}^{\circ}\)C with 5 mL of K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\)-H\({}_{2}\)SO\({}_{4}\) solution, and the mixture was titrated with ferrous sulfate solution after cooling. The soil cation exchange capacity (CEC), available N, available P, and available K were measured using the BaCl\({}_{2}\)-MgSO\({}_{4}\) exchange, NaOH hydrolyzed-H\({}_{3}\)BO\({}_{3}\) absorption, sodium bicarbonate extraction Mo-Sb anti-spectrophotometry, and ammonium acetate extraction flame photometry method, respectively [27]. Soil samples of 0.2000 g were digested in a ternary mixture of HF, HNO\({}_{3}\), and HClO\({}_{4}\) (4/5/2, v/v) acid in microwave digester (Mars 6, CEM, BSN, USA) at 220 \({}^{\circ}\)C for 30 min until a clear solution about 1-2 mL was obtained. Then, the solution was transferred into a volumetric flask, and adjusted to 50 mL with 3% (v/v) HNO\({}_{3}\) for later analysis. Plant samples of 0.5000 g were digested with HNO\({}_{3}\)/HClO\({}_{4}\) (8/2, v/v) in a microwave digester. Available Cd was extracted by diethylene triamine pentaacetic acid-triethanolamine (DTPA) according to ISO 14870 [28], and available As were extracted by 0.5 M NaHCO\({}_{3}\)[29]. All of the extracts were diluted with Milli-Q water and filtered using 0.45 \(\upmu\)m nylon filter membranes and subsequently analyzed for Cd and As concentrations using inductively coupled plasma mass spectrometry (ICP-MS, iCAP Q, Thermo Scientific, Waltham, MA, USA). The analysis methods for As and Cd in rice tissues have been described in detail in previous studies [30; 31]. The resulting solutions were diluted to 50 mL in 3% HNO\({}_{3}\) in volumetric flasks, filtered, and stored at 4 \({}^{\circ}\)C prior to analysis.  
The glassware used for Cd and As analyses in the experiment was soaked with 10% HNO\({}_{3}\) before. To ensure the quality, 20 samples including blank samples, duplicate samples, and reference material were digested. For the whole procedure, soil and plants were digested using documented standard materials for quality control (GBW10044 (grain) and GBW07454 (soil)).
# Occlusion-Robust Facial Expression Recognition Based on Multi-Angle Feature Extraction
## 4 Experiment
### Comparison with SOTA  
This section presents a comparative analysis of the performance of MAFE with other methods on the Occlusion-FERPlus and Occlusion-RAFDB datasets.  
Summary of Occlusion-RAFDB: Table 1 presents the performance of the methods proposed by researchers in the field of FER, working on the Occlusion-RAFDB dataset for the five-year period from 2020 to 2024. MAFE demonstrated the highest accuracy, with an accuracy rate of 89.42%.  
Summary of Occlusion-FERPlus: Table 1 presents the performance of the methodologies proposed by researchers in the field of FER, based on the Occlusion-FERPlus dataset over the five-year period from 2020 to 2024. The results demonstrate that the MAFE approach exhibits superior accuracy, with an accuracy rate of 86.94%.  
The superior performance of MAFE on the Occlusion-RAFDB and Occlusion-FERPlus datasets validates its effectiveness in handling occlusion scenarios. Through its unique feature fusion strategy and optimized loss function, MAFE is able to significantly improve accuracy under occlusion and complex conditions.  
Furthermore, MAFE achieved good performance when working on the original RAF-DB and FERPlus datasets.  
Summary of RAF-DB: Table 2 lists the performance of the methods proposed by researchers in the field of FER, working on the RAF-DB dataset for the five years from 2020 to 2024. MAFE performed well in terms of accuracy at 92.11%, which is 2.51% and 2.57% higher than the previous Latent-OFER and MPA method performance.  
Summary of FERPlus: Table 2 lists the performance of the methods proposed by researchers in the field of FER on the FERPlus dataset for the five years from 2020 to 2024, and MAFE performs well in terms of accuracy with 90.15%, which is 0.73% and 1.02% higher than the previous SCAN-CCI and MPA performance, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Method** & **Accuracy (\%)** \\ \hline \multirow{8}{*}{Occlusion-RAFDB} & ResNet18 [15] & 80.19 \\  & RAN [15] & 82.72 \\  & EfficientFace [37] & 83.24 \\  & MA-Net [38] & 83.65 \\  & VTF [39] & 83.95 \\  & MPCSAN [40] & 86.26 \\  & SCAN-CCI [41] & 85.03 \\  & Latent-OFER [18] & 84.20 \\  & Facial Chirality [42] & 88.16 \\  & MPA [43] & 87.47 \\  & MAFE & 89.42 \\ \hline \multirow{8}{*}{Occlusion-FERPlus} & ResNet18 [15] & 73.33 \\  & RAN [15] & 83.63 \\ \cline{1-1}  & VTF [39] & 84.79 \\ \cline{1-1}  & SCAN-CCI [41] & 86.12 \\ \cline{1-1}  & MPCSAN [40] & 86.12 \\ \cline{1-1}  & OADN [44] & 84.57 \\ \cline{1-1}  & MPA [43] & 86.28 \\ \cline{1-1}  & MAFE & 86.94 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with other methods for handling occlusion datasets.  
The results show that MAFE consistently outperforms previous methods on both datasets, making it a more robust model for FER.
## 4 Numerical Simulation Analysis
### The Seismic Response under Different Ground Motions
#### 4.1.1 Acceleration Responses  
The acceleration amplification factors under 0.20 g are shown in Table 3, in which the acceleration amplification factor is defined as the ratio of the peak acceleration of the layer over the peak acceleration of input motion.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Direction** & **Acceleration Input Value** \\ \hline M1 & Horizontal & 0.2 g0.17 g (X:Y) \\ M2 & Vertical & 0.13 g (Z) \\ M3 & Coupling of horizontal and vertical & 0.2 g0.17 g0.13 g (X:Y:Z) \\ \hline \end{tabular}
\end{table}
Table 2: The conditions of peak ground acceleration (PGA).  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multirow{2}{*}{**Section**} & \multicolumn{3}{c}{**EL-Centro Motion**} & \multicolumn{3}{c}{**Lanzhou Motion**} \\ \cline{3-6}  & & **M1** & **M2** & **M3** & **M1** & **M2** & **M3** \\ \hline \multirow{3}{*}{**Roof**} & X-direction & 0.671 & / & 0.683 & 0.683 & / & 0.714 \\  & Y-direction & 0.468 & / & 0.480 & 0.489 & / & 0.502 \\  & Z-direction & / & 1.476 & 1.526 & / & 1.108 & 1.163 \\ \hline \multirow{3}{*}{**Floor 3**} & X-direction & 0.677 & / & 0.695 & 0.699 & / & 0.726 \\  & Y-direction & 0.573 & / & 0.582 & 0.503 & / & 0.520 \\  & Z-direction & / & 1.476 & 1.525 & / & 1.108 & 1.162 \\ \hline \multirow{3}{*}{**Floor 2**} & X-direction & 0.679 & / & 0.719 & 0.722 & / & 0.742 \\  & Y-direction & 0.595 & / & 0.623 & 0.536 & / & 0.565 \\  & Z-direction & / & 1.475 & 1.523 & / & 1.016 & 1.159 \\ \hline \multirow{3}{*}{**Floor 1**} & X-direction & 0.708 & / & 0.721 & 0.746 & / & 0.760 \\  & Y-direction & 0.681 & / & 0.692 & 0.568 & / & 0.590 \\ \cline{1-1}  & Z-direction & / & 1.475 & 1.522 & / & 1.105 & 1.158 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Acceleration amplification factors under 0.20 g.  
Figure 15: Time–history curves of the midpoint’s acceleration of soil.  
The following can be inferred from Table 3:  
1. The acceleration amplification factors in the X-direction are larger than those in the Y-direction in M1 and M3, which means that the seismic response of the underground substation in the X-direction is more severe than that in the Y-direction. The seismic response including acceleration responses, displacement responses and internal force responses of 0.17 g PAG in the X-direction and 0.2 g PAG in the Y-direction would be reduced but the response rule is uniform.
2. The acceleration amplification factors in the Z-direction of M3 are larger than those in M2. The reason is that the seismic response is magnified under multi-direction earthquake motions compared with that under the one-direction earthquake input motion and the horizontal earthquake motions have a reverse effect on the seismic response in the Z-direction.
3. The peak vertical and horizontal acceleration amplification factors are 1.526 and 0.760, respectively, which means that the oblique incidence of input motion has a slight influence on the horizontal seismic response, but has a significant impact on the vertical seismic response.
# A Filtering Switch Made by an Improved Coupled Microstrip Line
## 5 Conclusions  
The performance of the filtering switch made with the PCB fabrication technique was compared with that of the filtering switch discussed earlier, and the information is summarized in Table 1. In comparison with the performance of the filtering switches reported in [2, 10, 11, 12], the suggested filtering switch in this study exhibits strong selectivity in the on state, as well as a high degree of in-band insertion loss. In the off state, it possesses good out-of-band rejection capabilities. Furthermore, this filtering switch has a low in-band insertion loss. In this paper, we proposed a filtering switch based mainly on coupled microstrip lines, and we experimentally verified its theoretical overall performance. The proposed filtering switch has good frequency selectivity, low insertion loss in the on state, and good overall rejection performance in the off state. Its operating frequency also satisfies the specific 5G frequency band, which means that it has good application prospects for future 5G communication systems.  
Conceptualization, X.F. and X.C.; methodology, X.F., X.C., W.X. and L.F.; software, X.C., W.X. and L.F.; validation, X.F. and X.C.; formal analysis, X.F., X.C., W.X., L.F., L.Y. and H.Y.; investigation, X.F., X.C., W.X. and L.F.; writing--original draft preparation, X.F., X.C., W.X. and L.F.; writing--review and editing, X.F., X.C., W.X., L.F., L.Y. and H.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 62001170, China; Guangxi Science and Technology Planning Project (AD22080042); the Guangdong Basic and Applied Basic Research Funding Regional Joint Fund for Youth Project under Grant 2019A1515110417; the China Postdoctoral Science Foundation under Grant 2022M/712513; and the Joint Foundation of Key Laboratory of Shanghai Jiao Tong University Xidian University, Ministry of Education and Shaanxi Key Laboratory of Deep Space Exploration Intelligent Information Technology under Grant No. 2021 SYS04; as well as the Innovation Project of Guangxi Graduate Education (YCSW2023492, YCSW2023477). The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RF & radio frequency \\ TZ & transmission zero \\ CLS & coupled-line stub-load resonator \\ CF & center frequency \\ RL & return loss \\ IL & insertion loss \\ \end{tabular}
## 1 Introduction  
Recently, nanoporous structures have attracted considerable attention because of their unique applications in physics and chemistry, as well as potential technological applications, such as sensing, catalysis, DNA translocations, and templates for nanostructure self-assembly [1; 2; 3; 4; 5; 6; 7]. Nanoporous materials with large internal surface areas have been more extensively employed as host materials in nanotechnology compared with other nanomaterials [8]. For example, the pore size and porosity of nanoporous structures can be controlled such that these structures show a novel sink effect for capturing molecules [2]. Interference lithography (IL) utilizes the interaction between two or more coherent light sources to form periodic structures at nanoscale. This technique is used to obtain nanostructure materials on the order of the wavelength of light for optical applications, such as photonic crystals (PhC) [9; 10; 11; 12; 13; 14; 15; 16; 17]; however, it has limited usage because of its simple repeated array of patterns. To expand the usage of it, we studied two-photon polymerization. Two-photon lithography is a fabrication technique which uses the two-photon absorption process. Two-photon absorption is a third-order nonlinear optical absorption process in which two photons are absorbed to reach a real excited state [18]. As the name suggests, the resins are polymerized by simultaneously absorbing two photons, usually in the infrared region. It became widely exploited for the development of new concepts [19; 20], such as optical computers [21], two-photon fluorescent microscopy [22], two-photon laser scanning fluorescence microscopy [23] and vascular grafts [24]. The two-photon lithography (2 PL) is basically the same as direct laser writing, but with a key difference. Because the two-photon absorption is corresponding to the square of the intensity, the resin polymerizing is only in the ellipsoidal focus, and by moving the sample stage to the x, y, z direction, three-dimensional structures can be fabricated. However, the absorption of the two photons is much less compared to one photon excitation, which necessitates the high intensity needed for 2 PL. Because of this, sometimes certain wavelengths with energy equal to half the energy of a single photon absorption band are favored [25]. In this study, we used IL to produce regular repeated PhC structures and subsequently used 2 PL to create controlled defects on the PhC structures. This approach can overcome the limitations of the PhC structures, which have only periodic structures [26], and facilitate their use for various applications.
## 4 Conclusions  
When planning a feasible robotic task, it is crucial to ensure that the required joint capabilities are below the maximum capabilities available. This can be challenging, especially in the case of more complex robotic tasks, such as robotic surface machining. To calculate the maximum kinematic end-effector capabilities of each waypoint on the predefined robotic path, an approach called the Decomposed Twist Feasibility (DTF) method was used in this study, which can provide precise, dimensionally homogeneous and physically consistent information about the maximum robot tool speed considering maximum joint velocities while fulfilling the surface machining requirements.  
The optimal placement of the workpiece can be determined based on the optimization criterion presented in this study. The optimization criterion is based on the idea of evaluating the robot's motion performance capabilities at the most critical waypoint on the robot's path to determine the best worst-case performance along the entire path. The experiment was shown and verified on the 6-axis collaborative robot UR5e for robotic machining purposes. The optimization results confirm the effectiveness of the described method, as we achieved the lowest values of maximum joint velocities for all the machining paths in the case of optimal workpiece placement. Therefore, it can be concluded that maximizing the achievable linear velocity along the machining path, calculated based on the DTF method, is linked directly to the measured maximum joint velocities along the machining path. By optimizing the workpiece placement based on the proposed method, we can ensure a feasible robotic machining task, or perform the machining task at a higher linear speed. Since the DTF method is designed to analyze only pure kinematic properties, the dynamic capabilities according to the specified task parameters should also be taken into account in order to identify a feasible workpiece placement.  
The presented work was carried out with significant contributions of both authors. S.S.: conceptualization, derivation of the proposed method, software, validation, analysis, and writing--original draft preparation; A.H.: methodology, derivation of the proposed method, formal analysis, writing--review and editing, and supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovenian Research Agency (ARRS) under a Grant for the Research Program P2-0028, and in part by a project of the Republic of Slovenia and the European Union from the European Regional Development Fund, ROBOTOOL-1, Grant number OP20.03540.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.