esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Algorithms for Detection of Social Interactions
## 2 Mathematical Definition of Modularity in Networks  
The main aims in community detection are to uncover the inherent community structure of a network, that is to say, those groups of nodes sharing common and similar properties. This means, then, to detect groups (_communities_ or _modules_) internally strongly connected, and externally weakly connected. Being able to detect community structures is a key and relevant task in many areas (biology, computer science, engineering, economics, and politics) because they allow for uncovering and understanding important information about the function of the network itself and how its elements affect and interact with each other. For instance, in World Wide Web networks, the communities can identify those pages dealing with topics which are related; in biological networks, instead, they correspond to proteins having the same specific function; in social sciences, they can identify circles of friends or people who have the same hobby, or those people who live in the same neighborhood.  
In order to evaluate the quality of the uncovered groups in a network, _modularity_ is certainly the most used quality index [4]. It is based on the idea that a random graph is not expected to have a community structure; therefore, the possible existence of communities can be revealed by the difference of density between vertices of the graph and vertices of a random graph with the same size and degree distribution. Formally, it can be defined as follows: given an undirected graph \(G=(V,E)\), with the set of vertices (\(|V|=N\)), and \(E\) the set of edges (\(|E|=M\)), the modularity of a community is defined as:  
\[Q=\frac{1}{2M}\left[\sum_{i=1}^{N}\sum_{j=1}^{N}\left(A_{ij}-\frac{d_{i}d_{j}}{2M }\right)\delta(i,j)\right], \tag{1}\]  
where \(A_{ij}\) is the adjacency matrix of \(G\), \(d_{i}\) and \(d_{j}\) are the degrees of nodes \(i\) and \(j\) respectively, and \(\delta(i,j)=1\) if \(i,j\) belong to the same community, 0 otherwise.  
As asserted by [PERSON] et al. in [PERSON] et al. (2015), the modularity value for unweighted and undirected graphs lies in the range \([-0.5,1]\). Then, a low \(Q\) value, i.e., close to the lower bound, reflects a bad clustering, and implies the absence of real communities, whilst good groups are identified by a high modularity value that implies the presence of highly cohesive communities. For a trivial clustering, with a single cluster, the modularity value is 0. However, the modularity has the tendency to produce large communities and, therefore, fails to detect communities which are comparatively small with respect to the network ([PERSON] et al., 2015).
## 4 Numerical Simulation Results Analysis
### Impact of Excavation at Individual Stages on the Retaining Wall
#### 4.1.2 Lateral Deformation of the Diaphragm Wall  
In this case, three inclinometer casings in the wall (the configuration points are shown in Figure 20) were set up to measure the wall displacement in each excavation stage. Figures 21-23 show the numerical analysis results of the lateral displacements of the diaphragm wall at different excavation stages. In addition, the comparisons between the wall deformation curves and the standard warning value (30 mm) after the completion of the pedestrian crossing are also presented in Figures 21-23. The maximum lateral displacement of the wall obtained in the analysis mode of monitoring point 1 was 4.82 mm, and its depth was at GL \(-\)8.78 m, which was 25 mm different from the warning value. The maximum lateral displacement of the wall at monitoring point 2 in the analysis mode was 27.95 mm, and its depth was at GL \(-\)6.75 m, which was only 2 mm away from the warning value. The maximum lateral displacement of the wall obtained in the analysis mode of monitoring point 3 was 5.42 mm, and its depth was at GL \(-\)8.42 m, which was 24 mm different from the warning value. It can be seen from Figures 21-23 that the lateral displacement of monitoring point 2 was much larger than the values of monitoring point 1 and monitoring point 3. It was assumed that the main reason for this was that part of the lateral support in the launching shaft was removed for the convenience of personnel operations and the space requirements for equipment placement before the construction of the pipe roof. In this area, only the supports of the first stage and the foundation plate were left to resist the lateral soil pressure outside the diaphragm wall, which in turn affected the lateral displacement of the diaphragm wall.  
Figure 20: Configuration diagram of the diaphragm wall deformation monitoring points.  
Figure 21: Lateral displacement curves for the diaphragm wall at monitoring point 1: (**a**) numerical analysis results in different excavation stages; (**b**) comparison of the lateral displacement and warning value.  
Figure 22: Lateral displacement curves for the diaphragm wall at monitoring point 2: (**a**) numerical analysis results in different excavation stages; (**b**) comparison of the lateral displacement and warning value.
# Application of Quantum Dots for Photocatalytic Hydrogen Evolution Reaction
## 2 Quantum Dots-Based Photocatalyst for Hydrogen Evolution
### Semiconductor-Quantum-Dot-Based Hybrid Photocatalyst  
Similar to a majority of conventional semiconductor photocatalysts, the photocatalytic performance of single-semiconductor QDs alone is typically limited. One of the most important reasons for this is that the short electron-hole diffusion distance in small-sized QDs easily leads to the recombination of the photogenerated charge carriers before their migration to the surface active sites. Other causes may include a low light-harvesting capability, and the slow kinetics on the QDs' surface. To address these issues, researchers have realized that utilizing semiconductor QDs alone is insufficient for achieving efficient photocatalytic H\({}_{2}\) evolution. Therefore, a hybrid photocatalyst consisting of semiconductor QDs and other chemical materials has been developed and prepared, which exhibits some unique characteristics, such as good solar light utilization and the rapid migration of photogenerated charge carriers; thereby, an improved photocatalytic H\({}_{2}\) generation efficiency can be obtained from the hybrid photocatalyst.
## 4 Numerical Simulation Analysis of the Hole-by-Hole blasting and Three-Hole Simultaneous Blasting
### Simulation Calculation and Analysis
#### 4.4.4 Characteristics of Displacement Changes at Typical Locations  
In this section, the Ls-PrePost (4.3) post-processing software was applied to determine and record the maximum displacement at the ten typical measurement points selected in the model. The results are presented in Table 6.  
Furthermore, the data shown in Table 5 was processed using the Origin professional data processing software. On this basis, Figure 19 presents the histogram of the maximum displacement at each measurement point as drawn when the hole-by-hole blasting and three-hole simultaneous blasting methods were adopted.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multicolumn{3}{c}{**In Front of the Middle Part of the Two Blast Holes**} & \multicolumn{3}{c}{**Directly ahead of the Blast Hole**} \\ \cline{2-10}  & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** & **I** & **J** \\ \hline Hole-by-hole blasting & 3.88 & 5.02 & 6.47 & 9.49 & 5.05 & 7.28 & 8.66 & 15.8 & 21.2 & 9.01 \\ \hline Three-hole simultaneous blasting & 5.06 & 10.7 & 18.3 & 24.1 & 10.8 & 5.94 & 10.6 & 17.8 & 31.9 & 10.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The maximum displacement at typical locations of the model (unit: cm).  
Figure 18: Histogram of maximum effective stress at the measurement points of the model.  
According to the results presented in Figure 19, the displacement at the measurement points D, H, and I was relatively significant when the hole-by-hole blasting method was used, reaching 9.48 cm, 15.8 cm, and 21.2 cm, respectively. On the other hand, when implementing the three-hole simultaneous blasting method, the displacement at the measurement points C, D, and I was found to be relatively significant, attaining 18.3 cm, 24.1 cm, and 31.9 cm, respectively. Overall, the displacement at each measurement point was much more significant when the three-hole simultaneous blasting method was applied compared to the case when the hole-by-hole blasting method was adopted.
## 4 Analysis of Results
### Analysis of Interview Feedback
#### 4.3.2 Interviewees' Feedback
* C/M BIM-I (1): For Question 6, the interviewee recommended that for mobile/cloud BIM technology to be adopted, mobile/cloud BIM should be user-friendly. The interviewee felt that for people, since a new technology is harder to use, most times it was easier for the users to revert back to the old system. * C/M BIM-I (2): For Question 6, the interviewee recommended that campaign initiatives and training of project teams can help with the adoption of mobile/cloud BIM technology. The interviewee reiterated that mobile/cloud BIM as a new technology in the construction industry requires some good campaign initiatives. The interviewee claimed that the project teams' awareness of it will make them appreciate its benefits. * C/M BIM-I (3): In Question 6, the interviewee recommended parties to agree and adopt uniform file formats as the key to the adoption of mobile/cloud BIM technology in construction. The interviewee also encouraged collaboration earlier in the construction process. The interviewee explained that will mean using procurement systems that promote early contractor involvement in the project. Training and educating project teams on mobile/cloud BIM technology use was highly recommended. Moreover, for project teams to appreciate the benefits, it is critical that the knowledge was teams understand the to they need to know how to use it well. * C/M BIM-I (4): For Question 6, the interviewee recommended the standardisation of file formats for information exchange and that project teams be educated from top to bottom. The interviewee reiterated the need to use much simpler systems, as too complex ones discourage teams from using them. As is the case with some new technology, when users stumble across a problem, it may be easier to revert back to old ways. * C/M BIM-I (5): In Question 6, the interviewee recommended that for the adoption of mobile/cloud BIM technology, the key thing was having the right training and awareness of its benefits. The interviewee underscored the importance of investing more in time and people, remarking that it is not about technology, but people. Quote: \"Embed the right attitude into the company culture from top to bottom\". The interviewee emphasised that the drive should be with the contractor to use mobile/cloud BIM technology. Exhibiting those benefits to the client will help them appreciate the better quality and the cost savings brought about by the use of mobile/cloud BIM technology. The literature agrees that people are more critical than the technology being adopted or implemented [10; 37; 38].
## 3 Experimental Study
### Model Structure  
The CAPSO BP ANN model was established with the molecular descriptors selected by CAPSO. The CAPSO BP ANN model adopted the three-layer structure composed of the input layer, the hidden layer, and the output layer. The input layer includes five input parameters representing the selected five molecular descriptors. The input parameters are: relative number of N atoms, Randic index (order 3), RNCG relative negative charged (QMNEG/QTMINUS) (Quantum-Chemical PC), RNCS relative negative charged SA (SAMNEG * RNCG) (Zefirov's PC), and maximum net atomic charge. The output layer has one output parameter representing the corresponding pKa value.  
In this paper, the number of hidden layers is estimated with the formula: (2 \(\times\) sqrt(\(m\times n\)) + 1, where \(m\) and \(n\) are the numbers of the nodes of the input and output layers), and then the number of optimal hidden layer neurons is determined by the heuristic method. The model in this paper contains five input nodes and one output node, so the number of hidden layer neurons is estimated to be 5. Then, we assumed that the number of the neurons of the hidden layer was tested from 3 to 15, respectively. Figure 1 shows the comparison diagram of predicted errors and the number of hidden layer neurons.  
As shown in Figure 1, with the increase in the neurons of the hidden layer, the mean square error (MSE) decreases first and then increases. When the number is 7, the training MSE is the lowest and the structure of the prediction model is optimal. The model structure is 5-7-1.
## 7 Conclusions  
In this work, a new platform for wireless sensor networks including its embedded operating system and its routing algorithm was evaluated in terms of route discovery time, packet delivery ratio, End-to-End delay, Throughput, routing load and overhead. A flat and hierarchical algorithm was evaluated in a small-scale network under test bed conditions in a watermelon field. The flat algorithm proved to be superior with regards to route discovery time, End-to-End delay, and routing load and overhead. The hierarchical algorithm proved to be superior regarding Throughput and scalability. In small-scale network applications, we found that the flat algorithm is more suitable because of its simplicity. Results show that LORA_CBF is suitable for both flat and hierarchical algorithms and is suitable for small-scale agricultural use. We conclude that our proposed technological platform with a modified version of the LORA_CBF routing algorithm can also be applied to precision agriculture because it is a wireless location-based routing algorithm that uses cluster-based flooding and monitors residual battery energy.
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Scheduling Policies
#### 3.3.1 Conventional Scheduling Policies
This probability can be derived from the birth-and-death structure of the Markov chain: \[\pi(n)=\left(\prod_{i=1}^{n}\frac{(N-i+1)\lambda}{(i)}\right)\pi(0)\] (14) By substituting the expression for \(\mu(n)\) previously calculated in Equation (2), \(\pi(n)\) becomes: \[\pi(n)=\frac{N!}{(N-n)!}\frac{\rho^{n}}{N_{S}^{n}\prod_{j=1}^{n}\overline{m}( i)}\pi(0)\] (15)where  
\[\rho=\frac{\overline{x_{on}}T_{F}}{t_{off}} \tag{16}\]  
\(\pi\)(0) is obtained via normalization:  
\[\pi(0)=\frac{1}{1+\sum_{n=1}^{N}\left(\prod_{i=1}^{n}\frac{(N-i+1)\lambda}{(i)}\right)} \tag{17}\]  
With these probabilities, the three performance parameters can be expressed as follows:  
* Average number of active users (\(\overline{Q}\)): \[\overline{Q}=\sum_{n=1}^{N}n.\pi(n)\] (18)
* Average resource utilization (\(\overline{U}\)): \[\overline{U}=\sum_{n=1}^{N}\pi(n)\text{min}\left(n\frac{\overline{x}_{on}}{ \overline{m}(n)},1\right)\] (19)
* Average instantaneous throughput of active users during an ON period (\(X\)): To calculate \(X\), the average number of completions (\(\overline{D}\)) per time unit (the number of mobile stations completing their transfers) must first be determined: \[p_{\leq i}(n)=\left(1-\sum_{j=i+1}^{k}p_{j}\right)^{n}\]0 (20) Using Little's Law, the average duration of an active transfer (\(t_{on}\)) is given by \[p_{\leq i}(n)=\left(1-\sum_{j=i+1}^{k}p_{j}\right)^{n}\]1 (21) Finally, the average instantaneous throughput of an active user during an ON period is: \[p_{\leq i}(n)=\left(1-\sum_{j=i+1}^{k}p_{j}\right)^{n}\]2 (22)  
The same expressions for these performance parameters apply to the three scheduling policies, with the only difference being the expression for \(\overline{m}(n)\), which depends on the scheduling algorithm used.
## 5 Conclusions  
This study addresses the persistent challenge of bearing fault diagnosis in industrial applications, particularly under conditions of limited and imbalanced datasets. We introduced a novel Multi-Domain Feature Transformer Generative Adversarial Network (MDFT-GAN) that effectively augments data by transforming bearing signals into two-dimensional RGB images across time, frequency, and time-frequency domains. The integration of a Transformer encoder with an efficient channel attention mechanism within the MDFT submodule allows the MDFT-GAN to capture intricate global feature interactions and local dependencies, thereby generating high-quality synthetic samples. Additionally, the proposed enhanced hybrid vision transformer classification model, which combines front-end convolutional layers with residual connections, significantly improves the robustness and accuracy of fault classification.  
Experimental evaluations conducted on the CWRU and Jiangnan University fault datasets demonstrate that the MDFT-GAN method substantially outperforms existing state-of-the-art approaches in terms of both robustness and diagnostic accuracy. These results underscore the efficacy of our approach in mitigating the limitations posed by scarce and unbalanced data, thereby advancing the reliability of bearing fault diagnosis in industrial settings.  
Furthermore, a Grad-CAM-based interpretability framework is incorporated to visualize hierarchical feature activations within the discriminator and classifier, offering intuitive and quantitative insight into the model's decision process and enhancing its transparency in real-world industrial deployment.  
Future work will explore the application of MDFT-GAN to other types of industrial fault diagnosis and investigate how to integrate other domain-specific functions to further improve performance. In addition, more lightweight generative models will continue to be explored without compromising model performance.  
**Author Contributions:** Conceptualization, C.G. and V.V.P.; methodology, C.G.; software, C.G.; validation, C.G. and E.A.K.; formal analysis, P.L. and J.L.; investigation, C.G.; resources, P.L. and J.L.; data curation, C.G. and E.A.K.; writing--original draft preparation, C.G.; writing--review and editing, V.V.P.; visualization, C.G. and E.A.K.; supervision, V.V.P., P.L. and J.L.; project administration, V.V.P.; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was funded by the China Scholarship Council (CSC.202309810002).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used in this study are available from the first author upon reasonable request.  
**Acknowledgments:** The authors thank the anonymous reviewers and editors for their insightful comments and suggestions, which helped to improve the quality of the work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Iterative Learning for K-Approval Votes in Crowdsourcing Systems
## 5 Analysis of Algorithms
### Bound on the Average Error Probability  
In this section, we show the error bound of our algorithm. Theorem 1 claims that the probability of error decays exponentially as the quality of workers \(q\) or the degree of worker nodes \(l\) increases.  
From now on, let \(\hat{l}\equiv l-1\), \(\hat{r}\equiv r-1\), and we use the quality \(q\) as defined in (4). Additionally, \(\sigma_{k}^{2}\) denotes an effective variance in the _sub-Gaussian_ tail of the task message distribution after \(k\) iterations.  
\[\sigma_{k}^{2}\equiv\frac{2q}{\mu^{2}T^{k-1}}+\frac{5}{8}\bigg{(}1+\frac{5}{16 }\bigg{(}\frac{D}{D-1}\bigg{)}^{2}\frac{1}{q^{7}}\bigg{)}\bigg{[}\frac{1-1/T^{k -1}}{1-1/T}\bigg{]}, \tag{5}\]  
where \(T=\frac{32}{25}\Big{(}\frac{D-1}{D-K}\Big{)}^{2}q^{2}\hat{l}\hat{r}\).  
Theorem 1: _For fixed \(l>1\) and \(r>1\), assume that \(m\) tasks are assigned to \(n\) workers according to a random \((l,r)\)-regular bipartite graph generated according to the pairing model. If the distribution of the reliability satisfies \(\mu\equiv\mathbb{E}[\frac{D}{D-1}(p_{j}-\frac{K}{D})]>0\) and \(T>1\), then for any \(t\in\{e_{i}\}^{m}\), the estimate after \(k\) iterations of the iterative algorithm achieves_  
\[\frac{1}{m}\sum_{i=1}^{m}\mathbb{P}(t_{i}\
eq\hat{t}_{i}^{(k)})\leqslant(D-1)e ^{-lq/(2\sigma_{k}^{2})}+\frac{3 lr}{m}(\hat{l}\hat{r})^{2k-2}. \tag{6}\]  
The second term of the equation is the upper bound of probability that the graph does not have a local tree-like structure and it can be quite small as long as we treat a large number of tasks. Therefore, the dominant factor of the upper bound is the first exponential  
Figure 5: Comparison of quality between negative entropy with offset and \(4^{th}\)-order polynomial approximation.  
term. As shown in (5), \(T=1\) is the crucial condition and we can satisfy \(T>1\) by using a sufficiently large \(l\) or \(r\). Then, with \(T>1\), \(\sigma_{k}^{2}\) converges to a finite limit \(\sigma_{\infty}^{2}\), and we have:  
\[\sigma_{\infty}^{2}=\frac{5}{8}\bigg{(}1+\frac{5}{16}\bigg{(}\frac{D}{D-1} \bigg{)}^{2}\frac{1}{q\hat{r}}\bigg{)}\bigg{[}\frac{T}{T-1}\bigg{]}. \tag{7}\]  
Thus, the bounds of the first term of (6) do not depend on the number of tasks \(m\) or the number of iterations \(k\).
## 4 Discussions
In the present study, the implant geometry had a square thread profile with a large pitch, observed through SEM, and exhibited adequate primary stability for both drilling protocols. These data agree with that of previous studies, which showed that large and self-cutting implant threads have higher primary stability than implants with small-thread designs in regions of poor-density bone [47, 48, 49, 50]. The clinical assessment of bone density usually relies on analyzing tomographic images since histomorphometric and densitometric evaluations, which provide accurate bone-density measurements, cannot be applied routinely [49]. CBCT is used for maxillical imaging because of its advantages over medical computed tomography (CT), such as lower effective radiation doses, lower cost, and simpler image acquisition [29, 30]. Bone density is proportional to the degree of X-ray attenuation, standardized as Hu in CT, and represented as grayscale (voxel values) in CBCT [29]. Although the CBCT software used in the present study refers to the density value as Hu, it is recorded as the voxel value. The voxel values can differ depending on the CBCT equipment used, exposure parameters, the position of the measurement in the field of view [51] (centrally vs. peripherally), and the amount of mass inside and outside the FOV [34, 51, 52]. Although this variability could be considered to be a limitation regarding the use of voxel values to measure bone density in CBCT, previous studies reported that the voxel value could be used for the estimation of bone density [51; 52] with a high degree of agreement in comparison to medical CT analysis [53; 54]. In the present study, the diagnosis of D3 and D4 bone sites was made using the same CBCT equipment to mitigate interequipment variability. A similar approach was previously reported to clinically evaluate the bone density clinically using the \"Hu measurement\" tool [28; 31]. In the present RCT were possible to observe some limitations, and they can be answered with future studies. The OD group had a wider diameter than the standard drilling group. It allowed the creation of the healing chamber with good primary stability. However, other clinical effects suggested by the OD process require further research, such as the spring-back effect presented by [PERSON] and [PERSON], 2017 [15]. Thereby the bone-implant contact and the size of the healing chamber can modify according to the implant design. The correlation between the drilling process and the implant macrogeometry should also be studied in future in vitro and in vivo studies. Therefore, OD is a potential alternative technique to improve the implant bed for implant dentistry. However, new RCTs should be conducted in the future to evaluate the \"healing chamber\" hypothesis combined with osseeddensification, especially with a long-term follow-up to evaluate marginal bone remodeling. Histological analysis should be encouraged to describe bone healing using this combination.