esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
Research on the cognitive assessment of cognitively normal individuals revealed that demographic parameters, particularly age, have a substantial impact on the RAVLT assessment of memory [50]. When normal aging influences were excluded from the measured RAVLT scores, there was a small improvement. This observation was linked to the impact of AD pathology on MRI and RAVLT, dominating the effects of normal aging [47]. MCI was negatively associated with two subgroups of RAVLT (immediate response and forgetting) in our logistic regression model for fully adjusted RAVLT. The final model revealed no significant association between the learning subgroup and the perception of forgetting test. Our analyses also agree with those previously highlighting the role of education level and socio-economic status in maintaining cognitive function and protecting against AD diagnosis. The community-based longitudinal study, Kungsholmen Project, highlighted the influence of education on dementia incidence using data of demographic variables such as sex, age, and educational level [28]. Participants of this study were assigned to three categories of schooling < 8 years of school, 8-10 years, and the university for those with 11 years of education. Data analysis for this study suggests that participants with a low level of education are at high risk of developing dementia or AD, especially women. In parallel with our analyses, many studies have demonstrated that longitudinal brain changes (as measured by MRI) are linked to changes in cognitive capacities, particularly episodic memory tasks [9; 10; 11; 12; 13]. Amyloid deposition is considered a major biomarker of the histopathological classification of AD [51]. In neurodegenerative disease, amyloid is projected as the lead of detrimental events that progress to dementia and AD [52]. Neuroplasticity is a well-known cognitive decline that evolves when the brain fails to compensate for accumulating insults. Healthy normal aging and the earliest stages of AD may be related to neuroplasticity [31; 45]. Various studies outside and inside the United States highlighted the marital status difference as a crucial lead in cognitive impairment and dementia. Studies conducted outside the United States declared that Alzheimer's was substantially more likely to affect single men and women than married people, and widowed [53; 54; 55; 56]. In contrast, widowhood is considered a risk factor for being diagnosed with Alzheimer's among American old residents [57]; however, men were more vulnerable than women [33; 34; 35]. Furthermore, a recent study involving an old population of 65 or more years old confirmed these previous studies and found marital status as a protective aspect for cognitive impairment, but those who were previously married (widowed and divorced) were at high risk of cognitive impairment [58]. The results cited previously are in line with our findings, but as was expected in many studies, there are some limitations, which compel further investigation. Our current models should be validated by considering more variables in future research.
## 2 Related Work
### Federated Learning
In addition to the study on communication overhead and communication security, there are many other efforts on federated learning, such as the application of federated learning, other security issues, and optimal design in federated learning. In terms of the application of federated learning, research has been conducted on the specific architecture of federated learning [31], the application of federated learning to the field of mobile recommendation [32], and the combination of deep learning and edge computing to intelligently utilize the collaboration among devices and edge nodes to exchange the learning parameters [33]. In view of the problems that the client is autonomous in federated learning and can easily deviate from the prescribed process of the model, some studies have proposed the generation of low-dimensional surrogates of model weight vectors, which are then used to detect anomalous clients at the server side [34]. Previous studies did not take the heterogeneity of data and privacy constraints into consideration. Reference [35] proposed a heterogeneous federated learning approach to analyze human behavior and recognize human emotions based on EEG technology, and train machine learning models over heterogeneous data, while preserving the data privacy of each party. Regarding studies on the optimization of the aggregate parameter server, it is believed that the centralized parameter updates are easily affected by server failure; therefore, a block-chained federated learning architecture has been proposed, where mobile devices' local learning model updates are exchanged and verified by leveraging the blockchain [36].
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Fabrication Process  
Photolithography is usually used to fabricate microbots. The 2 PP method of nanoprinting was first introduced in 1998 [151]. Many times the 2 PP is called by different names such as two-photon-absorbed photopolymerization [151], two-photon induced polymerization [152], two-photon lithography [153], sometimes 3D laser lithography or DLW [154]. In the 2 PP fabrication process, a tightly focused near-infrared laser beam is projected to the photoresist. The motion and trajectory of the beam are controlled and the photoresist gets exposed to the focused laser beam accordingly. The smallest focus point of the laser is called a voxel or volumetric pixel. This process builds the input structure inside the photoresist layer by layer, which can essentially create a 3D structure from these 2D layers. Figure 7 describes the DLW process and advanced fabrication techniques. After directly writing the structure, the photoresist needs to be developed. To develop the printed 3D structure, propylene glycol monomethyl ether acetate (PGMEA) and isopropyl alcohol (IPA) are usually used. A brief comparison between the different fabrication techniques is presented in Table 2.  
Figure 6: Multiple particle trapping using a spatial light modulator (SLM) (**i**) (**A**) Two birefringent vaterite crystals rotated individually in a holographic optical tweezers image sequence (**B**) change in polarization state causes a change in particle orientation [145]. Copyright 2008 Optical Society of America. (**ii**) (**a**) greyscale depiction of the phase pattern directed on the SLM to generate a 4 by 4 optical trap and (**b**) efficient capture of 16 particles (2 \(\upmu\)m in diameter). (**iii**) dynamic rotation of eight polystyrene beads (2 \(\upmu\)m in diameter) trapped in phase-contrast optical traps. Outer six particles revolve 1/8 of a full rotation clockwise, whereas the inner two particles rotate nearly one full rotation counterclockwise [149]. Copyright 2002 Optical Society of America. In accordance with the BOAI terms.
# Study on Dynamic Mechanical Properties and Failure Pattern of Thin-Layered Schist
###### Abstract  
This paper studies the effect of schistosity on the dynamic mechanical properties and failure pattern of thin-layered schist. Wudang Group schist with thin layers is selected as the research object, and the influence of the dynamic mechanical properties and failure pattern of schist under different angles and spacing is studied by combining an SHPB test and numerical simulation. The results indicate that under dynamic loading, the stress-strain curve demonstrates elastic compression, plastic deformation, and strain softening stages. Moreover, it is observed that the dynamic critical failure strength of schist exhibits typical \"U\"-type strength anisotropy. Specimens with a schistosity angle of 0\({}^{\circ}\) or 90\({}^{\circ}\) exhibit higher dynamic compressive strength under dynamic loading, with axial splitting and schistity splitting as predominant failure modes. Conversely, when the schistosity angles are 30\({}^{\circ}\), 45\({}^{\circ}\) or 60\({}^{\circ}\), there is a noticeable decrease in compressive strength accompanied predominantly by shear failure along with local compressive shear failure. We additionally noted that as the spacing between schist decreases from 22 mm to 7 mm, there is a gradual reduction in dynamic compressive strength by approximately 20.3%.  
Keywords:thin-layered schist; schistosity; dynamic mechanical properties; SHPB +
Footnote †: journal: Applied Sciences
# Research on Improved OEE Measurement Method Based on the Multiproduct Production System
## 5 Case Study
### Implementation Process  
#### 5.2.1 Calculating the Overall Production Effectiveness in All Statistical Times  
A work shift is considered as a statistical time (\(ST\)) (Enterprise Q works three shifts and 8 h per shift). The steps in Section 4.1. are used to calculate the overall production effectiveness in 93 statistical times. Product \(x_{266}\) is randomly selected to be an example to understand the computational process.  
1. The theoretical bottleneck speeds (\(y_{i}\)) of each product are measured by Equation (12). There are six manufacturing procedures in the process of manufacturing \(x_{266}\) and the designed production speeds in the procedures are 75 pieces/min, 61 pieces/min, 84 pieces/min, 53 pieces/min, 73 pieces/min, and 72 pieces/min respectively. \[y_{x_{266}}\ =\ min(75,61,84,53,73,72)\ =\ 53\ \text{pieces/min}\]
2. The actual bottleneck speeds (\(x_{i}\)) are measured. In the actual process of manufacturing \(x_{266}\), the speeds in these manufacturing procedures are 64 pieces/min, 41 pieces/min, 66 pieces/min, 44 pieces/min, 58 pieces/min, and 69 pieces/min. Therefore, according to Equation (13): \[x_{x_{266}}\ =\ 41\ \text{pieces/min}\]
3. The theoretical load times are calculated. The planned working time of \(x_{266}\) in a work shift is 480 min, and the planned downtime including cleaning time, maintenance time, adjustment time, and rest time is 55 min. According to Equation (14): \[z_{x_{266}}\ =\ 480\ -\ 55\ =\ 425\ \text{min}\]
4. The actual load time of \(x_{266}\) is measured by Equation (15). The unplanned downtime such as equipment failure time, supply delay time, and unplanned adjustment time, in total is 62 min, thus, \[t_{x_{266}}\ =\ 425\ -\ 62\ =\ 363\ \text{min}\]
5. The production capacity of \(x_{266}\) is calculated. The number of defective products in the statistical time is 63, and the figure can be calculated by Equation (16): \[PC_{x_{266}}\ =\ \frac{41\ \times\ 363\ -\ 63}{53\ \times\ 425}\ =\ 0.65\]
6. All \(PC_{l}\)s are integrated together by Equation (17). Actually, there are 43 kinds of products in the product production system during this statistical time. The \(MPSE\) is: \[
## 2 Case Study: Muyubao Landslide  
### Geological Conditions  
The Muyubao landslide occurred in Shazhenxi Town, Zigui County of Hubei Province on the bank of the Yangtze River and 56 km away from the Three Gorges Dam. The Muyubao landslide is delimited laterally by N-S-oriented trenches, while the upper portion is straight and smooth. It is of a chair-like shape, with smooth topography in the middle and lower parts and steeper terrain in the upper part (Figure 1). The elevation of the Muyubao landslide ranges from 120 m.a.s.l to 425 m.a.s.l, with an average slope angle of about \(20^{\circ}\). It has an average thickness of 50 m and an estimated volume of 9 \(\mathrm{Hm}^{3}\). The entire landslide involves an area of \(180\times 10^{4}\)\(\mathrm{m}^{2}\), with a maximum longitudinal dimension of 1200 m and an average width of 1500 m (Figure 2).  
Figure 1: **(a)** The location of the Three Gorges Reservoir area (TGRA), **(b)** location of the Muyubao landslide, and **(c)** geomorphological delimitation of the Muyubao landslide.  
Figure 2: Topographical map of the Muyubao landslide.  
The Muyubao landslide developed within a flysch formation dipping in the same direction and roughly parallel to the slope. The stratigraphy is made by the typical succession of quartz sandstone and sand-mudstone layers of the Jurassic Xiangxi formation (Figure 3). The sliding body is mainly composed of two parts. The surface layer is made up by loose deposits composed of alluvial sub-clay, gravel layers with clay, colluvial and eluvial sub-clay and collapsed block stones. The lower part of the sliding body is composed of highly disrupted layered Quartz sandstone, which is relatively intact in the western upper portion and weathered and fractured in the upper and lower portions.  
The upper portion of the sliding body has a linear detachment surface in the profile map, with an inclination of about 25\({}^{\circ}\) and a thickness of 60-90 m. The front part is an uplift platform formed by shear sliding, with the thickness of 80-120 m, and the inclination angle of the rock layer is about 27\({}^{\circ}\) (Figure 3). The thickness values here refer to the central part of the landslide, as illustrated in the profile of Figure 3, which may decrease towards the boundaries. The landslide is still active, as it has experienced several reactivation events in the recent past [19; 20]. This makes it a priority case for monitoring, since, in case the movement would develop towards sudden failure, it will endanger the lives and property of 140 households (500 people) in the landslide area and threaten the safety of the road and shipping of the Yangtze River.
## 4 Practical Efficiency  
In practice rotor designs suffer from the accumulation of minor losses resulting from:  
* Tip losses
* Wake effects
* Drive train efficiency losses
* Blade shape simplification losses  
Therefore, the maximum theoretical efficiency has yet to be achieved [9]. Over the centuries many types of design have emerged, and some of the more distinguishable are listed in Table 2. The earliest designs, Persian windmills, utilised drag by means of sails made from wood and cloth. These Persian windmills were principally similar to their modern counterpart the Savonius rotor (No. 1) which can be seen in use today in ventilation cows and rotating advertising signs. Similar in principle is the cup type differential drag rotor (No. 2), utilised today by anemometers for calculating airspeed due to their ease of calibration and multidirectional operation. The American farm windmill (No. 3) is an early example of a high torque lift driven rotor with a high degree of solidity, still in use today for water pumping applications. The Dutch windmill (No. 4) is another example of an early lift type device utilised for grinding corn which has now disappeared from mainstream use, yet a small number still survive as tourist attractions. The Darrieus VAWT (No. 5) is a modern aerodynamic aerofoil blade design which despite extensive research and development has so far been unable to compete with the modern HAWT design, although recent developments [2,3] could see a resurgence of this rotor type. Due to its efficiency and ease of control, the aerofoil three bladder HAWT (No. 6) has become the wind turbine industry benchmark, with a fully established international supply chain securing its dominance for the foreseeable future.
# Method for Generating Panoramic Textures for 3D Face Reconstruction Based on the 3D Morphable Model
## 3 Panoramic Texture Generation Method  
This study proposes a technique for a panoramic texture generation for a 3D face mesh by producing a face texture dataset and a texture translator network and utilizes a texture translator in a 3 DMM-based face reconstruction method, which verified the effectiveness of the proposed method. A texture translator was used to restore the relevant panoramic face texture. Considering that most face texture datasets contain private information and are difficult to acquire, the face texture dataset used in this study to train the texture translator was created using a low-cost method. Finally, we applied the texture translator as a texture translation module to other 3 DMM-based 3D face reconstruction methods to produce a panoramically textured 3D face model from a single image. Furthermore, we conducted an experiment using the celeb-faces attributes (Celeb-A) [27] dataset, which comprises 202 K in-wild face images of 10 K celebrities, to evaluate the efficacy of the proposed technique.  
As shown in Figure 1, the primary procedure of panoramic texture generation in this study is as follows. We generated a dataset of face textures as the training data for the GAN-based texture translator, which was trained through adversarial learning. During the training phase, the GAN-based texture translator learns how to convert the unwrapped textures into full panoramic textures from the generated dataset. During the inference stage, the trained texture translator was coupled with various 3 DMM-based face reconstruction algorithms. By inputting an image of a face, the 3D reconstruction algorithms inferred the shape of the 3D face mesh. Then, an unwrapped texture map was created on the 3D mesh using UV unwrapping. The combined 3D texture translator generates the corresponding refined panoramic texture. A textured three-dimensional (3D) mesh was the final product.  
This section is structured as follows. Section 3.1 describes how to generate the dataset for training, Section 3.2 illustrates how to train a GAN-based texture translator and Section 3.3 reports on how we combine the texture translator with other 3D reconstruction algorithms and provide corresponding Panoramic face texture.
# Monitoring Students at the University: Design and Application of a Moodle Plugin
## 2 Materials and Methods
### Instruments  
1. _UBUVirtual Platform._ This platform is an LMS developed in the Moodle environment, version 3.7.
2. _\"eOrientation\" Moodle Plugin._ The \"eOrientation\" plugin was developed within an ongoing research project funded by the Junta de Castilla y Leon (Spain). Customized access can be set up with this plugin by the course (subject) modules that are available on each course. It provides a graph for each group, showing the total number of accesses for each user within a specific period previously selected by the teacher. The graph incorporates the average number of accesses to each platform module. Likewise, personalized notifications related to learning process monitoring can be sent to a student or a group of students using the plugin through an email sent to a platform-messaging system. In addition, a table with all or part of the information registered can be exported in different formats (.csv,.xlsx, HTML table,.json,.ods,.pdf). More detailed information on the \"eOrientation\" plugin is presented in the development of objective 1 (see point 6: Patents) in the results section.
3. _Design of the subjects._ The subjects \"Quality Management Methodology\" and \"Early Stimulation\" kept to the same design and applied the same PBL teaching methodology. The subjects contained five thematic units with the following structure: presentation of the unit, complementary information, and satisfaction survey in each unit. Additionally, in both subjects quizzes were used on the platform, providing automatic feedback to the answers given by the student. Likewise, flipped classroom experiences and glossaries were made available to students. The following types of accesses (to different resources) could be set up, depending on the design of activities and resources performed by the teacher on the platform. In this case, the types of accesses were: \"feedback\", \"quizzes\", \"theoretical information\", \"practice\", \"complementary information\", and \"information on PBL\".
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 4 Results
### Feature Importance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Undirected Network**} \\ \hline  & \multicolumn{10}{c}{RandomForest} \\ \hline  & \multicolumn{10}{c}{Accuracy (\%)} & \multicolumn{10}{c}{AUCROC} & \multicolumn{10}{c}{AUCPR} \\ \hline Days & 1 & 7 & 30 & 1 & 7 & 30 & 1 & 7 & 30 \\ \hline \(G_{MIT}\) & 82.19 & 80.52 & 84.91 & 0.683 & 0.663 & 0.700 & 0.30 & 0.46 & 0.29 \\ \hline \(G_{Email}\) & 76.29 & 87.47 & 88.23 & 0.714 & 0.644 & 0.724 & 0.40 & 0.32 & 0.31 \\ \hline \(G_{UCI}\) & 89.46 & 84.95 & 84.67 & 0.764 & 0.713 & 0.654 & 0.34 & 0.29 & 0.29 \\ \hline \(G_{FF}\) & 85.03 & 84.98 & 85.33 & 0.687 & 0.636 & 0.773 & 0.39 & 0.36 & 0.43 \\ \hline  & \multicolumn{10}{c}{Bagging} \\ \hline \(G_{MIT}\) & 70.69 & 71.71 & 71.77 & 0.611 & 0.614 & 0.671 & 0.33 & 0.44 & 0.31 \\ \hline \(G_{Email}\) & 77.22 & 77.69 & 75.96 & 0.656 & 0.594 & 0.603 & 0.34 & 0.27 & 0.33 \\ \hline \(G_{UCI}\) & 84.47 & 83.81 & 82.99 & 0.630 & 0.619 & 0.632 & 0.29 & 0.31 & 0.28 \\ \hline \(G_{FF}\) & 73.11 & 72.80 & 72.22 & 0.622 & 0.588 & 0.644 & 0.35 & 0.32 & 0.39 \\ \hline  & \multicolumn{10}{c}{Logistic Regression} \\ \hline \(G_{MIT}\) & 73.30 & 72.22 & 72.68 & 0.536 & 0.613 & 0.590 & 0.26 & 0.38 & 0.22 \\ \hline \(G_{Email}\) & 78.23 & 77.91 & 78.13 & 0.654 & 0.637 & 0.563 & 0.36 & 0.30 & 0.25 \\ \hline \(G_{UCI}\) & 85.25 & 84.73 & 84.64 & 0.628 & 0.573 & 0.619 & 0.29 & 0.26 & 0.22 \\ \hline \(G_{FF}\) & 75.44 & 75.22 & 75.03 & 0.664 & 0.618 & 0.579 & 0.40 & 0.35 & 0.27 \\ \hline  & \multicolumn{10}{c}{**Directed Network**} \\ \hline  & \multicolumn{10}{c}{RandomForest} \\ \hline Hours & 6 & 12 & 24 & 6 & 12 & 24 & 6 & 12 & 24 \\ \hline \(G_{Rstart}\) & 87.87 & 87.59 & 87.03 & 0.739 & 0.712 & 0.720 & 0.36 & 0.26 & 0.26 \\ \hline  & \multicolumn{10}{c}{Bagging} \\ \hline \(G_{Rstart}\) & 85.81 & 84.55 & 85.11 & 0.695 & 0.644 & 0.574 & 0.21 & 0.21 & 0.19 \\ \hline  & \multicolumn{10}{c}{Logistic Regression} \\ \hline \(G_{Rstart}\) & 88.11 & 88.13 & 88.01 & 0.735 & 0.712 & 0.622 & 0.32 & 0.26 & 0.23 \\ \hline  & \multicolumn{10}{c}{**Co-Authorship Network (Window Size = 1 Year)**} \\ \hline  & \multicolumn{10}{c}{RandomForest} \\ \hline  & \multicolumn{10}{c}{Accuracy} & AUCROC & \multicolumn{10}{c}{AUCPR} \\ \hline \(G_{th}\) & 77.90 & 0.663 & 0.49 \\ \hline \(G_{ph}\) & 81.49 & 0.722 & 0.18 \\ \hline  & \multicolumn{10}{c}{Bagging} \\ \hline \(G_{th}\) & 81.35 & 0.702 & 0.56 \\ \hline \(G_{ph}\) & 80.95 & 0.711 & 0.32 \\ \hline \(G_{th}\) & 66.45 & 0.593 & 0.43 \\ \hline \(G_{ph}\) & 70.90 & 0.581 & 0.11 \\ \hline \end{tabular}
\end{table} 
Table 3: Classification performances by three classifiers considering the classification datasets of undirected, directed networks and co-authorship networks considering three different window sizes used to sample dynamic networks. Both directed and undirected network datasets used three different sampling window sizes to generate SINs in the dynamic networks, whereas the co-authorship networks used only a yearly sliding window.