esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis and Design of the Proposed System
### Design Requirements  
The design requirements of the system were divided into four categories: basic functionalities, hydraulic clutch (i.e., the virtual object), documentation, and shared experiences. Each of them can be seen as a different module of the application.  
The following are the requirements for the basic functionalities of the system:  
* User interaction with the system through the gestures detected by the HoloLens smart glasses (gaze and tap).
* The cursor that indicates the position the user is looking at (gaze) at any given moment.  
* The panel through which the user can interact with the part to scale it, move it, start animations, or read additional information about it.
* Panel tracking of the user, so that when the user changes her/his location or rotates his/her head, the panel is positioned in front of the user. A button should also be included on the panel to enable or disable this functionality.
* A modular internationalization system for real-time translation of the application, allowing more languages to be easily added.
* Sounds and sound effects that help the user recognize and reaffirm different actions, such as a click or the start and end of the animations.  
Below are the requirements for the 3D model of the hydraulic clutch:  
* Creation of animations that illustrate the assembly steps contained in the part assembly manual.
* Creation of an assembly-disassembly animation that makes all the parts of the clutch visible.
* Scaling of the 3D model so that it can be made bigger and smaller.
* Movement of the 3D model. During this process, the surrounding surfaces are mapped by the HoloLens to avoid possible collisions and intersections of the part with real elements.  
The following are the requirements related to the documentation of each of the virtual parts:  
* The parts that have associated documentation should be highlighted to facilitate their detection and interaction by the user.
* There should be an independent panel that shows the documentation available for each part.
* There should be an independent panel that shows the manufacturing order and the hydraulic clutch assembly manual.  
Finally, the requirements of the shared experience module are the following:  
* The AR environment should be synchronized among multiple HoloLens devices.
* Sharing of the hydraulic clutch, taking into account its location and rotation, as well as its real-time status, so that all users share their interactions with the part (animations, scaling, movement) with the rest of the used HoloLens glasses.
# Analysis and Design of Integrated Blocks for a 6.25 GHz Spacefibre PLL
## 4 Simulation's Results  
### PFD/CP Characterization  
In Figure 17, the average CP current is represented as function of the phase difference between the inputs for different technology corners (Figure 17a) and different temperatures (Figure 17b). As can be seen from the figure, the PFD/CP is dead zone-free thanks to the presence of the RESET state in the PFD.
# Multi-Sensor Precipitation Estimation from Space: Data Sources, Methods and Validation
## 2 Satellites and Sensors
Each member contributes PMW measurements to the mission for generating uniformly processed global precipitation products. The GPM mission has played a crucial role in the advancement of MPE [75]. By utilizing updated sensors, more recent periods can expect higher-resolution and better-quality precipitation datasets.
## 2 Related Work
### SIN Dynamic Topology Model
#### 2.2.1 Multi-Attribute Node  
In the section of the SIN concept, it was mentioned that the function of SIN is to provide integrated services such as reconnaissance, navigation, and communication, and information acquisition and transmission, etc. It can process space information in real-time, and implement communication broadcasting, surveillance, information detection, and navigation. It also can accomplish positioning, missile warning, and battlefield situational awareness, including weather, hydrology, terrain, and so on.  
Therefore, from a functional view, we can classify the nodes of SIN according to their node attributes. For example, we can divide the highest-level into reconnaissance nodes, communication nodes, navigation nodes, etc. We can divide them according to their different division of labor, such as acquisition nodes, space information processing nodes, control nodes, etc. We can also divide them based on their implementation functions, such as broadcast nodes, investigation monitoring nodes, intelligence detection nodes, navigation and positioning nodes, missile warning nodes, and battlefield situational awareness nodes, including weather, hydrology, terrain, etc. There are different kinds of nodes according to the concept and functions of SIN given above (Table 2). In the actual operation, we can expand and improve the table contexts according to specific requirements. If we represent the table with a matrix, \(x\) and \(y\) represent rows and columns, and the mathematical representation is:  
\[V_{xy}\in V_{XY}=\left[\begin{array}{ccc}v_{11}&\cdots&v_{n1}\\ \vdots&\ddots&\vdots\\ v_{m1}&\cdots&v_{mn}\end{array}\right] \tag{1}\]  
Figure 1: Composition and architecture of SIN.  
where, \(x=1,2,\ldots,m\), \(y=1,2,\ldots,n\), and \(V\) represents a node, and \(V_{xy}\) represents an SIN node which has a certain attribute. Because a certain node (corresponding equipment entity or information system) that composes SIN may have multi-functions, it has multiple attributes. Defining a multi-attribute node is conducted as follows, and the mathematical representation is:  
\[V=<ID_{v},\;Attr_{ij}> \tag{2}\]  
where, \(ID_{v}\) is the number of nodes, \(Attr_{ij}\) is the attribute of the node, and \(Attr_{ij}=V_{xy}\in V_{XY}\).
## 3 Results and Discussion
### Effect of Process Parameters
#### 3.1.3 Effect of Extraction Temperature  
Figure 3 showed the total extraction yield of glycyrrhizic acid from _Glycyrrhizic__unalensis_ under different extraction temperatures and extraction time. The extraction temperatures had markedly effected on the total glycyrrhizic acid yield. As can be seen from Figure 3, with an increase in the extraction temperatures from 20 \({}^{\circ}\)C to 25 \({}^{\circ}\)C the glycyrrhizic acid yield was rapidly increased from 133.6 mg/g to nearly 160.0 mg/g. However, a further increase in the extraction temperature to 60 \({}^{\circ}\)C only increased by 13.5 mg/g. This result showed a low efficiency of extraction temperatures between 25 \({}^{\circ}\)C and 60 \({}^{\circ}\)C, which not only did not significantly increase the glycyrrhizic acid yield but was also power-consuming. As expected, after the extraction temperature of 60 \({}^{\circ}\)C, the glycyrrhizic acid yield rapidly increased as the extraction temperature increased once again. The possible reason for this is that the rising of high extraction temperature can accelerate the swelling and softening of the samples, which is favorable for increasing the solubility of extracted elements and decreasing the viscosity of the solvent [37]. As a result, the mass transfer of glycyrrhizic acid from sample particles was improved when the extraction temperature was increased to a high level. These results indicated that the high extraction temperature in an appropriate range could facilitate the extraction of raw materials under ultrasound irradiation. By considering the convenience and cost of the operation, the recommended temperature was 25 \({}^{\circ}\)C.  
Figure 2: Evolution of extraction yields of glycyrrhizic acid with different ultrasonic frequencies at an extraction temperature of 25 \({}^{\circ}\)C and ultrasonic power of 125 W.  
Based on the present results, when other extraction conditions were constant, the three process parameters--i.e., ultrasonic power, frequency, and extraction temperature--played an important role in the extraction of glycyrrhizic acid from _Glycyrrhizic uralensis_ in most cases of ultrasound treatment. Therefore, it was necessary to study the extraction model for the extraction of glycyrrhizic acid under the three key process parameters.
## 2 Proposed Topology and Principle of Operation
### Proposed Inverter Topology  
Figure 6(a) presents the proposed topology, which includes five power switches (\(S_{1}\), \(S_{2}\)... \(S_{5}\)), three diodes (\(D_{1}\), \(D_{2}\) and \(D_{3}\)), one inductor (\(L_{b}\)) in the buck-boost configuration, and one flying capacitor (\(C_{FC}\)), in addition to the input DC link capacitor (\(C_{in}\)) and output LCL filter (\(L_{f}\), \(L_{g}\), \(C_{f}\)).  
The modulation technique shown in Figure 6(b) illustrates that in this topology, only one switch carries the load current during each power cycle; \(S_{1}\) creates the positive power cycle (active state) whilst \(S_{2}\) creates the negative power cycle (active state). The switches \(S_{4}\) with \(D_{2}\) and \(S_{3}\) with \(D_{3}\) work in a complementary manner with \(S_{1}\) and \(S_{2}\), respectively, to achieve the zero voltage level (non-active states) in the output and allow the output inductor \(L_{f}\) to achieve current freewheeling. Power switch \(S_{5}\) works continuously to supply the power from the input side to the flying capacitor, utilizing the inductor \(L_{b}\) assisted by the diode \(D_{1}\) in buck-boost mode. In order to reduce the passive component sizes, MOSFET switches are adopted to achieve higher frequency. Furthermore, SiC MOSFETs can be used because of their low switching losses and fast recovery of the body diode compared with conventional Si MOSFETs. Figure 6(b) illustrates the unipolar SPWM modulation technique used in the proposed inverter. Two sinusoidal signals are generated by 180\({}^{\circ}\) phase shift between each other as a reference waves (generated from the grid voltage in grid-tied inverters) and compared with triangle signal to generate required pulses. In the unipolar SPWM scheme \(+V_{dc}\), 0 and \(-V_{dc}\) voltage levels generated in the inverter output consequently it reduces the overall losses, required output filtration and EMI [28].
## 1 Introduction
Especially recently, screen-based PA and AVGs have been suggested for enjoyable PA [13; 23]. Exergames and AVGs are particularly noted for being more enjoyable than classic exercises [16; 25]. However, the level of enjoyment may differ due to the technological infrastructure and the differences in the screenplay and music of AVGs [23; 26]. As enjoyment increases during playing AVGs, the desire to participate in screen-based PA may increase instead of sedentary activities and passive video games [23]. At this point, it is considered that determining the enjoyment level of AVG alternatives is also essential for screen-based PA participation. In exercise science, the rating of perceived exertion (RPE; Borg scale) scale is frequently used to evaluate exercise intensities and perception of effort during aerobic and resistance training [27]. Especially for these exercises, it has been shown that RPE is a reliable measurement of perceptual effort in terms of reflecting physiological variables. However, whether the RPE reflects the physiological effort in technology-based games still requires investigation [28]. During playing exergame, similar RPE scores have been elicited despite higher physiological responses [29]. A recent study reported that RPE may be lower than the actual exertion (measured via heart rate (HR)) during exergames [28]. This difference can be attributed to the type of exergames. This situation needs to be examined. The possibility of a lower RPE may be a positive side of the AVGs to prolong the duration of exercise [26]. It is crucial to determine RPE for AVG selection. Although previous studies [25; 30] have presented some evidence of physiological responses of AVGs, there is still a need to categorize the AVGs based on the PA intensity and to increase the options of games at a target intensity. Additionally, there are findings that show that the RPE and enjoyment may differ depending on the AVGs' structural differences [13; 25]. For these reasons, the current study focused on RPE and enjoyment. Hence, the purposes of the current study were to (i) investigate and compare the effects of five different types of active video games (AVGs) on physiological and perceptual variables, (ii) evaluate and categorize the AVGs' PA intensity according to The American Collegeof Sports Medicine (ACSM) classification, and (iii) investigate the relationship between perceptual variables. The physiological variables were limited to oxygen consumption (VO2), carbon dioxide production (VCO2), respiratory exchange ratio (RER), energy expenditure (EE), MET, and HR, whereas the perceptual variables were limited to RPE and enjoyment level.
## 1 Introduction
These cements exhibit favorable physical and biological properties, addressing some of the limitations of previous materials and bringing hydraulic calcium silicate cements closer to the ideal standard [27]. Comprising ceramic compounds, these cements demonstrate relevant biocompatibility due to their resemblance to the biological process of hydroxyapatite formation. They also have the potential to stimulate a regenerative and osteoinductive response by absorbing substances during the bone healing process [27; 28]. Bioactive compounds, especially calcium phosphate, interact with tissues and promote tissue regeneration [19]. Their composition includes tricalcium and dicalcium silicates, calcium phosphates, calcium hydroxide and zirconium oxide [28]. The interaction of these materials with dentin, particularly the infiltration of mineral content from hydraulic calcium silicate cements through the intertubular dentin, creates a surface with mineral infiltration [29]. Therefore, in the field of endodontics, hydraulic calcium silicate cements have been extensively used for repairing perforations and as endodontic cements, their success being owed to several properties such as their high pH, low cytotoxicity, low shrinkage, chemical stability non-resorption capacity and easy manipulation [28]. TotalFill(r) BC RRM Fast Set Putty(tm) (FKG, La Chaux-de-Fonds, Switzerland) is one of the latest calcium silicate-based cements introduced to the market [29]. This hydraulic calcium silicate material comprises components such as calcium disilicate, calcium trisilicate and calcium hydroxide, among other components. TotalFill(r) BC RRM Fast Set Putty(tm) is premixed, ready to use and has the ability to absorb moisture during setting [11]. Despite having a setting time of approximately four hours [11], it has demonstrated a high biocompatibility, strength, adaptation to dentin walls and it does not cause dental discoloration [29]. It is available in various forms, including paste, cements or putty [11]. The aim of this ex vivo study was to evaluate and compare the marginal adaptation of TotalFill(r) BC RRM Fast Set Putty(tm) (FKG, La Chaux-de-Fonds, Switzerland) with White ProRoot MTA(r) (Dentsply Maillefer, Ballaigues, Switzerland) in a model of immature teeth using scanning electron microscopy analysis. The null hypothesis is that there are no statistically significant differences in marginal adaptation between these two calcium silicate-based cements when used in apical plugs.
## 3 The Proposed Routing Mechanism
### CCS-DTN Routing
#### 3.4.1 Intra-Cluster Routing  
According to the clustering algorithm, the contact probability between nodes in the same cluster is greater than the given threshold, so we can adopt the routing scheme which limits the number of copies of a message. Spray and Wait with two copies of a message can be adopted as the intra-cluster routing  
Figure 3: Clustering Process.  
scheme in the proposed solution. Since within the same cluster, nodes have high contact probability with each other, two copies for Spray and Wait are enough to obtain a high delivery rate.
## 2 Parameter Optimization VMD-NLMS Principle
### Principle of the VMD Algorithm
The iteration stops when the following stopping criterion is satisfied:  
\[\sum\limits_{k=1}^{K}\frac{\|u_{k}^{n+1}-u_{k}^{n}\|_{2}^{2}}{\|u_{k}^{n}\|_ {2}^{2}}<\varepsilon \tag{5}\]  
where \(\varepsilon\) is a small positive threshold, which determines the convergence precision. The summary of the VMD algorithm steps is as follows:  
1. Initialization \(\{u_{k}^{1}\}\), \(\{\omega_{k}^{1}\}\), \(\lambda_{k}^{1}\) and set \(n=0\);
2. Update \(u_{k}\), \(\omega_{k}\) using the frequency domain update equations;
3. Update the Lagrange multiplier \(\lambda\);
4. Check the stopping criterion. If satisfied, stop; otherwise, repeat steps (2)-(4).
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 3 The Framework
### Pre-Processing Phase  
This phase involves constructing the framework's KG, including creating nodes for symptoms and diseases and edges linking symptoms to diseases. In our previous studies [4], we demonstrated the KG's construction using Neo4j Graph Database, Version 5 ([[https://neo4j.com/](https://neo4j.com/)]([https://neo4j.com/](https://neo4j.com/)), accessed on 8 October 2024), with raw data from Kaggle [26]. The dataset comprises patient records, each associated with a diagnosed disease and reported symptoms, covering 41 diseases and 130 unique symptoms. While some symptoms are linked to a single disease, others are associated with multiple conditions.  
Additionally, this phase enriches the KG by incorporating hierarchical structures. These structures consist of a root symptom node (a KG symptom) and additional ontology symptom nodes connected via ISA edges [5]. Semantic knowledge from the SYMP ontology [7] was used to create these structures, enhancing the KG's capacity to generate a broader range of recommended questions for medical experts [27].  
The final step in this phase involves identifying clusters of diseases--called _communities_--that share similar symptoms. For this, we applied the Louvain hierarchical clustering algorithm [28] to the KG, a process defined as \"Algorithm 1\" in [4].  
Figure 1 presents a Neo4j screenshot illustrating a subgraph of the enriched KG. The example highlights the cough KG symptom node linked to the GERD disease node via a symptomOf edge. Additionally, cough serves as the root of a hierarchical structure comprising ontology symptom nodes (e.g., dry cough), each connected to its parent node via an ISA edge.
# Pressure-Reducing Design of 3D-Printed Diabetic Shoe Midsole Utilizing Auxetic Lattice Structure
## 3 Results
### Midsole Deformation and Plantar Contact Area  
Table 6 presents the maximal displacement values resulting from the deformation of each midsole across the different plantar regions under various movement states based on the results of the FE simulation. In identical movement states, the maximal displacement of the A60 midsole across the different plantar regions exceeded those of both A75 and N90, indicating that the midsole with an auxetic 60\({}^{\circ}\) lattice structure had the highest degree of deformation and the lowest stiffness.  
As indicated in Table 7, the areas of plantar contact with the auxetic midsoles (A60 and A75) were significantly larger than that of the non-auxetic one (N90). A60, in particular, showed the largest contact area. In the walking state, the contact areas in the MH, MF, and HF regions for A60 were approximately 2.97%, 1.60%, and 8.33% larger than those for A75 and 4.60%, 12.46%, and 22.49% larger than those for N90, respectively. In the running state, the contact areas in the MH, MF, and HF regions for A60 were, respectively, about 6.76%, 2.14%, and 2.56% larger than those for A75 and 10.95%, 12.09%, and 15.08% larger than those for N90.  
A comparison of the maximal displacement and the plantar contact areas across the various plantar regions also revealed that the higher deformation of the midsoles with auxetic lattice structures correlated with larger plantar contact areas (Figure 8). Therefore, the auxetic midsoles, particularly A60, demonstrated higher plantar conformity compared to the non-auxetic midsole.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Walking**} & \multicolumn{3}{c}{**Running**} \\ \hline  & **A60** & **A75** & **N90** & **A60** & **A75** & **N90** \\ \hline MH & 48.93 & 47.52 & 46.78 & 54.51 & 51.06 & 49.13 \\ MF & 41.97 & 41.31 & 37.32 & 51.64 & 50.56 & 46.07 \\ HF & 49.94 & 46.10 & 40.77 & 50.90 & 49.63 & 44.23 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Contact areas in various plantar regions according to FEA results (cm\({}^{2}\)).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Walking**} & \multicolumn{3}{c}{**Running**} \\ \hline  & **A60** & **A75** & **N90** & **A60** & **A75** & **N90** \\ \hline MH & 2.012 & 1.810 & 1.622 & 4.913 & 3.510 & 3.301 \\ MF & 1.006 & 0.920 & 0.624 & 1.638 & 1.404 & 1.101 \\ HF & 1.509 & 1.380 & 0.811 & 5.731 & 3.008 & 2.801 \\
## 4 Results and Discussion
### Predictive Model Results
#### 4.2.3 Data Analysis  
Data analysis involves summarizing the main characteristics of a dataset, in order to understand what the data reveal, with the feature selection of attributes that influence the outcomes. The result of this step was the creation of the final set of data, which were imported into the model for predictive analysis.  
A heatmap correlation was performed to determine the correlations between the various independent variables and motivation.  
Based on these heat maps, we deduced which aspects had the most positive and negative influences on student status. The most positive features influencing student's motivation to complete their studies were age, parental support, and scholarship. The negative features influencing student's motivation were failures, father's occupation, and mother's occupation (see Figure 8).
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
## 2 Materials and Methods
### Model Setting  
In this study, we used 2000 Lagrangian particles and Bunker-type oil with an API gravity of 14 and a viscosity of 28,000 cP, representing heavy and highly viscous oil, which was the same type of oil used in [34]. These oil characteristics were chosen based on chemical studies on the oil's origin conducted by IEAPM, indicating that the oil had geochemical characteristics very similar to some Venezuelan crudes [19; 32; 44]. The particleswere driven by daily hydrodynamic data provided by the Copernicus Marine Environment Monitoring Service (CMES) [45] with a spatial resolution of approximately 10 km and hourly atmospheric data provided by ERA5 with a spatial resolution of approximately 25 km. The data on the annual average water column temperature and salinity up to 100 m depth are from in situ measurements obtained by the PIRATA moored buoy array [46] at 8\({}^{\circ}\) S, 30\({}^{\circ}\) W.  
The starting coordinates for the oil dispersion simulations were selected based on AIS data emitted by the ships along their routes, choosing the location closest to the area of interest. The modeling parameters are summarized in Table 3. Based on identification of the route closest to the total amount of oil on the coast and the spatio-temporal identification of the first contact, we conducted a probabilistic study of this scenario via 500 simulations with the same initial characteristics and forcing as the selected deterministic simulation. Our aim was to aggregate and identify the uncertainties in each simulation resulting from turbulent and advective movements.
# Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review
## 3 Recent Technical Advancements
### Concurrent Detection of Range and Displacement
One specific application of the technique is for structural health monitoring to wireless detect the infrastructural deformations due to man-made vibrations or natural seismic events, which might not be noticeable through visual inspection.