esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 1 Introduction
We will see that the hidden community detection algorithm begins its function by using some existing community detection algorithm as its base algorithm in the network, then weakens the structure of the network to detect the hidden communities. The iterative process continues until no further hidden structures are detected. Through experiments on real-world networks, we observed that the existence of non-redundant or inessential communities having less importance in some cases but some importance in other scenarios, are rarely detected by many other community detection algorithms. A hidden community structure can be contemplated as a special type of overlapping community structure. However, existing overlapping community detection methods mainly focus on communities in which a considerable portion of the members are not \"hidden\"; that is, they could also belong to other weaker communities, but this  
Figure 1: An example of hidden community structure. The solid black lines show the relationship among the nodes of dominant communities and the colored dotted lines represent the weaker communities. community is clearly the strongest for these members. We believe the intuition and observations obtained on the scheme of hidden community structure will provide valuable guidance for future investigations in the domain of community detection. The main contributions of this paper are as follows:  
* Introduction of the concept of hidden community structure in multi-layer networks. * Introduction of the hidden community detection method by using an overlapping community detection method as a base algorithm and demonstrating the structure weakening method of the layers of communities. * Application and validation of the proposed method on real-world datasets. * The most captivating conclusion of the hidden community detection method is that it is one of the works to delineate the presence of hidden/weaker communities in multiple layer networks of several domains. Also, we compare the proposed method with some other multi-layer community detection algorithms. The structure of the rest of the paper is as follows: in Section 2, we discuss some details about the related work from the proposed work in this paper; in Section 3, we discuss the preliminaries and definitions related to our work; in Section 4, we introduce the hidden community detection method in detail and talk about the steps of its operation; in Sections 5 and 6, we discuss the experiments; and evaluate the results of HCD method with several real-world datasets; later in Section 7, we compare our method with some multi-layer and other hidden community detection approaches developed so far, in terms of some predefined attributes; lastly, we end this paper with the conclusion.
## 2 Related Work
Their work focused on mapping the existing literature to determine the most commonly used design elements and learning theories, on identifying the domain structure of the learning content, and on defining the foundations that render the virtual reality-based education successful. Additionally, the study revealed 18 potential application domains for virtual reality in education, which further highlights the significant influence that it can have. [PERSON] et al. [51] focused on educators' motivation for integrating virtual reality in education, explored relevant applications, and examined the associated issues and problems reported in the literature. Based on their findings, most studies put an emphasis on increasing students' intrinsic motivation and the design elements of the interventions were limited. The study also went over the existing drawbacks and issues regarding the adoption of virtual reality in education and suggested techniques and actions to potentially address them. [PERSON] and [PERSON] [52] explored the use of virtual reality and gamification in education by analyzing 112 documents until August 2022. The study addressed 15 research questions, followed a mixed-methods approach, and evaluated the quality of the studies using the Mixed Methods Appraisal Tool (MMAT). The study discussed the combination of virtual reality with gamification and revealed the main advantages and disadvantages associated with their integration in educational settings. Through its content analysis, the study presented the state of the art regarding gamification and virtual reality in education. Based on the aforementioned, it is evident that extended reality technologies, learning analytics, and educational data mining can positively influence the educational process. However, the studies conducted have not focused on the role and use of learning analytics and educational data mining within augmented reality and virtual reality environments. Additionally, most of the studies that have been carried out do not involve the analysis of recently published documents. Given the potential that their combination can bring about in the educational domain, this study seeks to bridge this research gap by focusing on how educational data mining and learning analytics can be combined within extended reality environments and how this combination can influence teaching and learning practices by providing a thorough representation of the existing state of the art, identifying the main benefits that can be yielded, and suggesting future research directions.
# Opportunities and Challenges of Smartglass-Assisted Interactive Telementoring
## 2 Related Work
### Medical and Surgical Telementoring  
Telementoring is most beneficial when local and remote users have different degrees of knowledge, experience and expertise. Promising application domains for telementoring (satisfying the conditions for remoteness and mentoring as shown in Table 1) are medical, surgical, and healthcare domains in which there exist a sufficient knowledge and experience gap between two groups of people (i.e., also known as mentors and mentees). Medical or often surgical telementoring is a well-examined application area that exploits these conditions to enhance or improve the overall performance of the involved users, as shown in Table 1.  
[PERSON] et al. stated that telementoring \"permits an expert surgeon, who remains in their own hospital, to instruct a novice in a remote location on how to perform a new operation or use a new surgical technology [29].\" [PERSON] and [PERSON] explained that \"surgical telementoring involves the use of information technology to provide real-time guidance and technical assistance in performing surgical procedures from an expert physician in a different geographical location. Similar to traditional mentoring, it plays a dual role of educating and providing care at the same time [30].\" [PERSON] et al. defined that \"these remote work practices entail a local worker performing a physical task supported by a remote expert who monitors the work and gives instructions when needed--sometimes referred to as telementoring [31].\" From the aforementioned views and previous medical and surgical telementoring studies, two types of telementoring can be identified. The first type is sharing the view of a mentor to teach mentees where mentees can learn from the mentor's direct and real-time practice via learning by examples or demonstration strategies. The second type is sharing the view of a mentee, so mentors can appropriately give advice and deliver feedback in real-time for training and instruction purposes. For example, guidelines can be provided to surgeons to train them to perform new operations [29; 32]. Despite the differences in who takes the initiative in telementoring, both types of telementoring can benefit from high-definition videoconferencing, wearable technology, robotic telementoring platforms, and augmented reality [30]. For a comprehensive review on surgical, medical, and healthcare telementoring studies, readers are referred to [10; 11].
## 3 Results and Discussion  
### Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV)  
Figure 2A shows the EIS measurement results, including the impedance and phase, depending on the frequency. The average impedance of a microelectrode ranged from 3.7 k\(\Omega\) to 1.6 M\(\Omega\) and the frequency ranged from 10 kHz to 0.1 Hz, with an average impedance of 13.9 k\(\Omega\) at 1 kHz. The standard deviation of the impedance of each channel showed 1.43 k\(\Omega\), and its coefficient of variation was 10.3 percent. The average phase angle showed an increasing trend from 25\({}^{\circ}\) to 67\({}^{\circ}\). Each channel of the array showed similar tendencies, indicating a good reliability within the microelectrode array. We also performed the equivalent circuit modeling of the electrode, and the extracted parameters from the equivalent circuit model (inset of Figure 2A) are as follows: solution resistance, Rs = 724.5 \(\Omega\); constant phase element, \(Z_{CPE}=\left(1.985^{-7}\alpha^{n}\right)^{-1}\times e^{-\frac{n}{2}ni}\) (\(n\) = 0.67812); polarization resistance, \(\text{Rp}=2.167\text{\,M}\Omega\). These results indicate that the fabricated microelectrode array has a capacitive charge-carrying mechanism that is consistent with previous reports using gold electrodes [15]. Finally, CV was measured using the potentiostat/galvanostat at a scan rate of 1000 mV/s.  
As shown in Figure 2B, the peak current ranged from \(-6.81\times 10^{-6}\) to \(5.22\times 10^{-6}\) A. This result shows a similar trend to that of other conventional large-sized gold-based electrodes [9; 20], indicating that the fabricated 8-channel microelectrode array might be appropriate for sEMG recording.
## 2 Method
### Building Feature Set Extraction from SAR Image  
PolSAR images have abundant polarization information and texture information for building feature extraction. Therefore, this part introduces the PauliRGB features used to describe the polarization information and the G\({}^{0}\) statistical texture parameters used to describe the polarization statistical texture information, which form the feature set extracted by the building.  
In general, the electromagnetic scattering of a radar target can approximate a linear process. After determining the scattering space coordinate system and its corresponding polarization basis, there is a linear transformation relationship of the polarization components between the radar emission wave and the target scattering echo. The scattering  
Figure 1: Flow chart of building extraction based on CNN and superpixel in the PolSAR image.  
process of the target can be represented by a \(2\times 2\) complex matrix, which is the Sinclair S matrix, as follows [27]:  
\[S=\begin{bmatrix}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{bmatrix} \tag{1}\]  
where \(S_{HH}\) and \(S_{VV}\) represent the co-polar scattering components, and \(S_{HV}\) and \(S_{VH}\) represent the cross-polar scattering components. In the monostatic case, the two cross-polar components are equal, i.e., \(S_{HV}=S_{VH}\).  
In the actual analysis of PolSAR data, the polarization covariance matrix C is introduced to describe the dynamically changing environment and objectives. Covariance matrix C can be written as:  
\[C_{3}=\frac{1}{2}\begin{bmatrix}\langle|S_{HH}|^{2}\rangle&\sqrt{2}\langle S_ {HH}S_{HV}^{*}\rangle&\langle S_{HH}S_{VV}^{*}\rangle\\ \sqrt{2}\langle S_{HV}S_{HH}^{*}\rangle&2\langle|S_{HV}|^{2}\rangle&\sqrt{2} \langle S_{HV}S_{VV}^{*}\rangle\\ \langle S_{VV}S_{HH}^{*}\rangle&\sqrt{2}\langle S_{VV}S_{HV}^{*}\rangle& \langle|S_{VV}|^{2}\rangle\end{bmatrix} \tag{2}\]
## 2 Theoretical Background
### Present Study
#### 2.3.2 How Does Similarity Depend on the Image to Be Categorized?  
Our image types aimed to capture relevant distinctions from the psychological literature on scene viewing. For each image type, we used two separate but similar categories to make our task sufficiently difficult. Our first image type, which we will refer to as _objects_ (i.e., lighthouse, windmill), only required the identification of a single diagnostic object to infer the category. This object was embedded in a natural scene context, which certainly facilitates categorization but is not strictly necessary. Our second image type comprised two _indoor scenes_ (i.e., office, dining room). Here, the category can be inferred from several diagnostic objects and their relations, whereas global properties are similar across categories. To this end, we selected two categories that include chairs and tables, which typically are the most salient objects in indoor scenes [21]. Finally, our third image type was _landscapes_ (i.e., desert, wheat field), with the scene category mainly depending on global properties but not on diagnostic objects or their relations.  
We hypothesized that human-CNN similarity would be largest for objects as they provide a single key feature. Furthermore, we expected medium similarity for indoor scenes due to a strong guidance of eye movements by semantic relations between objects. Finally, we expected the lowest similarity for landscapes, assuming eye movements to be widely distributed across the image. However, as indicated above, we were most interested in the interaction between task and image type.
# Design and Shear Analysis of an Angled Morphing Wing Skin Module
## 3 Methods  
### Experiment  
We performed shearing experiments of the angled specimens on an Instron universal testing machine (type 5982). A planar four-bar linkage, as shown in Figure 3a, was designed to mount the specimen with bolts, which was then attached to the testing machine through a crosshead chuck. To eliminate dynamic effects, the loading rate was 10 mm/min. Considering the Mullins effect, i.e., the stress softening phenomenon after prestressing [32], each specimen was sheared three times before the formal experiment. All specimens were loaded until wrinkling occurred. As shown in Figure 3b, the precise deformed configuration was obtained by a 3D scanner (Artec Space Spider, Artec 3D, Luxembourg) with a scanning accuracy of 0.05 mm at a machine temperature of 37 \({}^{\circ}\)C. The configuration was scanned and recorded every 2\({}^{\circ}\). Moreover, to quantify the deformation process of the skin, the strain field of the specimen was captured by a digital image correlation (DIC) system CSI Vic-3D9M at a frame time range of 500 ms, as seen in Figure 3c, and speckles with a density of about 1.4/mm\({}^{2}\) were sprinkled on the surface of specimens. The deformation processes of specimen M90 and specimen M50 are shown in Figures 4a and 4b, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Length \(L\) & 140 mm \\ Width \(W\) & 140 mm \\ Thickness \(H\) & 6 mm \\ Distance \(l\) & 4.5 mm \\ Interval of CFRP rods \(l_{1}\) & 7 mm \\ Interval of FKRP fibers \(l_{2}\) & 4 mm \\ Diameter of CFRP rods \(d_{1}\) & 1.5 mm \\ Diameter of FKRP fibers \(d_{2}\) & 0.05 mm (400 D) \\ Initial angle \(\theta_{0}\) & \(50^{\circ},60^{\circ},70^{\circ},80^{\circ},90^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design parameters of the specimens.  
Figure 3: Experimental setup: (**a**) planar four-bar linkage; (**b**) 3D scanner for precise deformed configuration; (**c**) DIC system for strain field of the specimen.
## 2 Materials and Methods
### Chemicals  
High-performance liquid chromatography (HPLC)-grade ethanol was obtained from J.T. Baker Chemicals B.V. (Denventer, The Netherlands). Milli-Q water was produced by a Milli-Q Plus water system (Millipore, Madrid, Spain). Ethylenediamineetetraacetic acid (EDTA) was supplied by Panreac Applichem (Barcelona, Spain). Sigma-Aldrich (Madrid, Spain) supplied the following: sodium chloride ACS reagent grade (purity \(\geq\)99.8%), and the standard compounds ethyl butyrate (\(\geq\)99%), ethyl hexanoate (\(\geq\)99%), ethyl decanoate (\(\geq\)99%), ethyl decanoate (\(\geq\)99%), ethyl crystate (\(\geq\)99%), ethyl myristate (\(\geq\)99%), ethyl palmit (\(\geq\)99%), ethyl stearate (\(\geq\)99%), propyl acetate (\(\geq\)99%), isobutyl acetate (\(\geq\)99%), isoamyl acetate (\(\geq\)99%), hexyl acetate (\(\geq\)99%), phenylethyl acetate (\(\geq\)99%), ethyl isobutyrate (98%), ethyl 2-methylbutyrate (\(\geq\)99%), ethyl isovalerate (\(\geq\)99%), ethyl phenylacetate (98%), ethyl cinnanate (98%), ethyl dihydroincominate (98%), methyl hexanoate (\(\geq\)99%), methyl decanoate (\(\geq\)99%), isoamyl butyrate (98%), ethyl heptanoate (98%), ethyl nonanoate (98%), ethyl propanoate (\(\geq\)99%), isobutyl hexanoate (\(\geq\)99%), ethyl valerate (\(\geq\)99.7%), and acetaldehyde (\(\geq\)99.5%). The deuterated internal standards [\({}^{2}\)H\({}_{3}\)]-ethyl butyrate, [\({}^{2}\)H\({}_{11}\)]-ethyl hexanoate, [\({}^{2}\)H\({}_{15}\)]-ethyl octanoate, [\({}^{2}\)H\({}_{5}\)]-ethyl trans-cinnamate, [\({}^{2}\)H\({}_{23}\)]-ethyl dodecanoate, [\({}^{2}\)H\({}_{27}\)]-ethyl myristate, [\({}^{2}\)H\({}_{31}\)]-ethyl palmitate, and [\({}^{2}\)H\({}_{35}\)]-ethyl stearate were supplied by CDN isotopes (Pointe-Claire, QC, Canada).
## 6 Conclusions  
This study is based on the multi-objective optimization of the rear guide vane of a diagonal flow fan under uncertain aerodynamic performance conditions. It verifies its feasibility and effectiveness through numerical simulations and experiments.  
1. The Pearson correlation analysis in the correlation analysis method is combined with the multi-objective optimization method to optimize the design of the rear guide vane of a small diagonal flow fan under uncertain aerodynamic performance conditions, reducing the influence of other parameters on the primary analysis parameters and showing the net correlation between each parameter. The total pressure is increased by 86 Pa, and the noise is reduced by 2.4 dB in the interval around the operating design point.
2. The multi-objective robustness optimization design under aerodynamic performance uncertainty balances the traditional requirements of performance optimization and robustness design to ensure the robustness of performance while changing the mean value of aerodynamic performance to meet the requirements of fan performance under actual operating conditions. The optimization process is simple with few variables and has engineering application value, which can be extended to the optimal design of similar fluid machinery.
3. The research in this paper provides insight into the characteristics of the flow field inside the rear guide vane of an inclined-flow fan under actual operating conditions. It optimises the blade shape of the rear guide vanes. The airflow velocity between the guide vanes is more uniform, thus significantly reducing flow losses in the fluid domain. The study provides a theoretical reference for improving the resistance to disturbances in the aerodynamic performance of the fan under uncertain conditions and ensuring the efficient, safe and stable operation of other impeller machinery such as the diagonal flow fan.  
**Author Contributions:** Formal analysis, W.J.; Methodology, L.L.; Software, Z.M.; Writing--original draft, B.X.; Writing--review & editing, S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China grant number 51706203 and Natural Science Foundation of Zhejiang Province grant number LY20E090004.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 25: A circumferential noise pointing diagram.
## 5 Experimental Results
### Summary of Experimental Results  
The experimental implementation of the proposed lasted upper gluing system demonstrated the feasibility of integrating 3D vision, robotic path planning, and multi-stage adhesive application within a real production environment. Through a structured three-stage process--comprising initial drying and scanning, treatment agent and adhesive spraying, and final drying and bonding--the system successfully automated a traditionally manual process. Each robotic station performed precise manipulation and spraying based on customized 3D path planning, while synchronization with the outside line ensured seamless assembly preparation.  
Although the final bonding step is currently performed manually to accommodate variability in shoe profiles and ensure alignment accuracy, the system layout has been designed with future robotic integration in mind. Overall, the results validate the system's ability to support flexible and efficient adhesive application for footwear manufacturing.
## 2 Materials and Methods
### Preparation of the Hydrogel  
Using the one-pot radical polymerization approach, the P(BHMP-AM)-Zn2+ hydrogel was synthesized from AM, BHMP, and zinc acetate dihydrate. More specifically, BHMP (100 mg) and zinc acetate dihydrate (100 mg) were dissolved in distilled water (5 mL) and stirred vigorously for 30 min until a homogeneous solution was obtained. In addition, a 10 mg/mL solution of MBA was prepared and added to the reaction system (50 \(\mu\)L) along with AM (150 mg), and stirring was continued for 15 min. After this time, the system was purged with nitrogen for 30 min prior to injection into a mold. The solution was injected into a self-designed mold which consisted of a pair of parallel glasses and a 1 mm thick silicone gasket [41]. This mold was sonicated for 15 min to expel any gas from the solution, and radical polymerization was performed under 50 W UV light for 5 h to obtain the desired P(BHMP-AM)-Zn2+ hydrogel.  
BHMP was prepared in the following manner.  
Iminodiacetic acid (IDA) was added to the potassium hydroxide (KOH) solution, then glycidyl methacrylate (GMA) was added to the solution. After the solution was stirred at 65 \({}^{\circ}\)C for 1 h, the solution changed from a mixture of oil and water to a transparent water phase. The pH was adjusted to 7. Functional monomers were extracted in acetone. The product was verified by FT-IR and 1H-NMR.  
FT-IR (KBr): = 3419 (OH), 3070, 2964, 2875, 1714, 1631, (C=O), 1440 (C=C), 1172 cm\({}^{-1}\). 1H NMR (500 MHz, Deuterium Oxide) 6 6.09 (d, J = 1.3 Hz, 1H), 5.69-5.66 (m, 1H), 4.39-4.33 (m, 1H), 4.20 (d, J = 4.1 Hz, 1H), 4.14 (d, J = 5.3 Hz, 1H), 3.82 (d, J = 6.1 Hz, 5H), 3.45 (dd, J = 13.3, 2.9 Hz, 1H), 3.32 (dd, J = 13.3, 10.4 Hz, 1H), 1.86 (d, J = 1.3 Hz, 3H).