esa-sceva/satcom-chunk-collection huggingface dataset:
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 2 Background
### Centrality Measures
If we let \(M_{PR}=I-\alpha A^{T}D^{-1}\), then we can solve the linear system \(M_{PR}\boldsymbol{c}_{PR}=\boldsymbol{b}_{PR}\) for \(\boldsymbol{c}_{PR}\). Since solving for many linear algebra based centrality measures directly is generally intractable, in practice, we use iterative solvers to solve them [11]. Iterative methods approximate the solution \(\boldsymbol{x}\) in a linear system \(M\boldsymbol{x}=\boldsymbol{b}\), given \(M\) and \(\boldsymbol{b}\). The iterative method we use in this work is the Jacobi method [12] given in Algorithm 1, although the work applies to other iterative techniques. At each iteration \(k\) of the iterative solver, we obtain new approximations \(\boldsymbol{x}^{(k)}\) and \(\boldsymbol{c}^{(k)}\) to the unknown exact solutions \(\mathbf{x}^{*}\) and \(\mathbf{c}^{*}\), respectively. The error at each iteration is denoted as the difference between the exact and approximation, \(\|\mathbf{x}^{*}-\mathbf{x}^{(k)}\|_{2}\) and the residual norm as \(r_{k}=\|\mathbf{b}-M\mathbf{x}^{(k)}\|_{2}\), where \(\|\cdot\|_{2}\) denotes the 2-norm. In practice, as the exact solution is not known, typical stopping criteria for the iterative solver is that the solution has not changed much. With Jacobi iteration, this is equivalent to terminating once the residual norm is below the input convergence threshold. ```
1:procedureJacobi(\(M,\mathbf{b},tol\))
2:\(k=0\)
3:\(\mathbf{x}^{(0)}=\mathbf{0}\)
4:\(\mathbf{r}^{(0)}=\mathbf{b}-M\mathbf{x}^{(0)}\)
5:\(D=diag(M)\)
6:\(R=M-D\)
7:while\(\|\mathbf{r}^{(k)}\|_{2}>tol\)do
8:\(\mathbf{x}^{(k+1)}=D^{-1}(R\mathbf{x}^{(k)}+\mathbf{b})\)
9:\(\mathbf{r}^{(k+1)}=\mathbf{b}-M\mathbf{x}^{(k+1)}\)\(\triangleright\) Next residual
10:\(k+=1\)
11:endwhile
12:return\(\mathbf{x}^{(k+1)}\)
13:endprocedure
```  
**Algorithm 1** Solve \(M\mathbf{x}=\mathbf{b}\) to tolerance \(tol\) using Jacobi algorithm. Given a graph and its adjacency matrix \(A\), we can solve the corresponding linear system for \(\mathbf{c}_{PR}\) (for PageRank) or \(\mathbf{c}_{Katz}\) (for Katz centrality) for the centrality scores on vertices of the graph.
# Sport Performance Analysis with a Focus on Racket Sports: A Review
## 3 Results
### SR1: An Overview of Sport Performance Analysis in General
#### 3.1.2 Tools Used for Data Collection/Automation Analysis
\begin{table}
\begin{tabular}{l l l} \hline
**Tools** & **Sports** & **No. of Articles** \\ \hline Amisco Pro & football, running & 2 \\ \hline Babolat Play & tennis & 1 \\ \hline Digital.Stadium (R) & football & 1 \\ \hline Garmin (TM) Heart Rate Band & basketball & 1 \\ \hline GPS & cycling, horse racing, sailing, running, surfing, tennis, basketball, football (13), handball & 32 \\  & (3), hockey, netball, rugby (6), volleyball & \\ \hline IMSs & cycling & 1 \\ \hline Microsoft Kinect 2 & gymnastics & 1 \\ \hline Pliance (TM) electronic saddle mat, Casio high speed camera with Quintic (TM) biomechanical software & horse racing & 1 \\ \hline Remote, RowX & rowing & 1 \\ \hline RGB camera & skeleton, table tennis & 2 \\ \hline RTLS & skiing & 1 \\ \hline SAGIT & table tennis & 1 \\ \hline Sony Smart Tennis & tennis & 1 \\ \hline VERT & volleyball & 1 \\ \hline Vicon Motion Systems & football & 1 \\ \hline Wimu (TM) & basketball & 1 \\ \hline \end{tabular}
\end{table} 
Table 2: Data collection tools. analysis to the player. The paper by [PERSON] et al. [68] offered two different wearable devices for rowing (Remote and RowX). Both devices used sensors from the boat and a person on the boat. These sensors allow for the evaluation of a boat's movement, individual rower performance, or performance in comparison to other crew members. [PERSON] and [PERSON] [69] used a combined approach to record all trials and show the influence of the rider on the horse by using a wearable device (Pliance (TM) electronic saddle mat) and a Casio high-speed camera with Quintic (TM) biomechanical software. **Summary:** GPS is the most commonly used approach across all 32 projects because it is a simple and cost-effective tool that is easily accessible. It was no surprise that football was the most popular sport using more advanced data collection tools. The popularity of sport has been shown to play a significant role in the income generated by performance analysis tools. It is necessary to conduct technical development across various sports, with a particular emphasis on racket sports. Wearable data collection tools for squash and badminton, for example, must be implemented. One area of concern is the use of wearable devices and the data collected, as data is typically not collected during tournaments because the extra weight affects the stroke.
# Connection-Aware Digital Twin for Mobile Adhoc Networks in the 5G Era
## 4 Experimental Results
### Analysis of the Results  
The assessment of the results is organized into three main subsections: QoS analysis, computational complexity cost analysis, and routing of offline nodes. First, the QoS results compare the performance of the actual network with the calculated outputs of the DT. Second, the resource consumption of the implementation is addressed, by analyzing energy consumption and CPU usage. Finally, an assessment of the routing of information in partially online topologies is conducted, aiming to evaluate the performance of these transmissions.
## 2 Test and Result Analysis
### Test Results Analysis  
#### 2.3.1 Shock Wave Overpressure  
The blast wave overpressure can be determined using the CONWEP formula. The equation for this calculation is as follows [30]:  
\[p=\begin{cases}p_{r}\cos^{2}\theta+p_{i}(1+\cos^{2}\theta-2\cos\theta)&\cos \theta>0\\ p_{i}&\cos\theta\leq 0\end{cases} \tag{1}\]  
where \(p\) denotes the blast load pressure, \(p_{r}\) refers to the reflected overpressure, and \(p_{i}\) signifies the incident overpressure. Additionally, the angle of incidence is represented by \(\theta\).  
As shown in Figure 7, taking the effective measurement point PC1 at the top plate of box chamber 1 of specimens G, PCG, and PCGR as an example, the experimentally measured shock wave overpressure values were compared with those calculated using the CONWEP empirical formula. The error rates were 6.5%, 7.32%, and 6.30%, respectively. The peak overpressure values and the variation trends at the same measurement points  
Figure 4: In situ arrangement of the explosion test.  
Figure 5: Measuring point of rebar strain.  
Figure 6: Placement of measurement for overpressure, displacement, and acceleration.  
for specimens G, PCG, and PCGR showed good agreement, confirming the validity of the applied blast loading in the experiment.
# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
## 6 Suggestions for Future Work  
1. Developing a dynamic user representation model: Our study focused on designing a model that incorporated user differences but did not consider the dynamic nature of user preferences over time. Future research could explore the development of dynamic user representation models to capture changing user preferences.
2. Incorporating contextual information: Although our study took into account user differences, it did not consider contextual information that may influence user preferences. Future research could explore the integration of contextual information such as location, time, and device usage to improve the accuracy of cold-start recommendations.
3. Enhancing model scalability: Our study demonstrated the effectiveness of incorporating clusters of users into a cold-start recommendation model but did not address the issue of scalability. Future research could focus on developing scalable models to handle large datasets and large numbers of users.
* 4. Evaluating long-term effects: Our study focused primarily on the short-term effects of incorporating user cluster information into cold-start recommendation models. Future research could assess the long-term effects of these models on user engagement, satisfaction, and retention. This might require conducting longitudinal research or long-term tracking of user behaviours while observing the correlation between cluster dynamics and recommendation effectiveness in stages. Short-term assessment should focus on click-through rate and cold-start retention rate. Mid-term monitoring should cover interest drift. Long-term analysis should examine user lifetime value and churn rate to verify the adaptability and sustainability of the recommendations.  
Conceptualization, H.W. and W.W.; Data curation, Y.D.; Writing--original draft, H.W., Y.D. and W.W.; Writing--review & editing, Y.D.; Funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 61772249, 61702241), the Basic Research Projects of the Liaoning Provincial Department of Education (Grant No. IJKZ0362).  
Not applicable.  
Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
###### Abstract
AACT, acetyl-CoA acetyltransferase; DMAPP, dimethylly pyrophosphate; DXS, 1-deoxy-d-xylulose 5-phosphate synthase; DXR, 1-deoxy-d-xylulose 5-phosphate reductase; FPP, famesyl pyrophosphate; FPDS, FPP synthase; G3P, glyceraldehyde 3-phosphate; GGPP, geranylgeranyl pyrophosphate; GGPS, GGPP synthase; GPP, geranyl pyrophosphate; GPPS, GPP synthase; HDS, 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase; HMBPP, (E)-4-hydroxy-3-methylbut-2-en-1-yl diphosphate; HMGR, hydroxymethylglutaryl-CoA reductase; HMGS, hydroxymethylglutaryl-CoA synthase; IPP, isopentenyl pyrophosphate; MCT, 2-C-methyl-d-erythritol 4-phosphate cytidylyltransferase; MVPP, mevalonate 5-pyrophosphate; PMK, phosphomevalonate kinase; TPS, terpene synthase. gibberellic acid. Another angiosperm-specific TPS-g encodes monoterpene synthase without the R(R)X\({}_{8}\)W motif. TPS-h is only observed in _Selaginella moellendorffii_[1; 9; 10; 11]. In addition, TPS harbors conserved structural features such as DDxxD, NSE/DTE, and R(R)X\({}_{8}\)W motifs [1]. To date, TPS gene families have been identified at the genome-wide level in various plant species, including _Arabidopsis thaliana_[12], _Camellia sinensis_[13], _Daucus carota_[14], _Eucalyptus globulus_ and _E. grandis_[15], _Malus domestica_[16], _Solanum Lyopersicum_[17], _Selaginella moellendorffii_[18], and _Vitis viinifera_[19]. Orchids form one of the largest families of flowering plants, and their metabolic profile contains various terpen [7]. Only a few _TPS_ genes have been identified thus far in orchids. _PbTPS5_ and _PbTPS10_ might be involved in monoterpene biosynthesis in _Phalanopsis bellina_[20]. _FhTPS1_ catalyzes the formation of linalool, while _FhTPS4_, _FhTPS6_, and _FhTPS7_ are bifunctional enzymes that can simultaneously recognize FPP and GPP as substrates [21]. However, no comprehensive study about _TPS_ genes in _Dendrobium officinal_ exists. _D. officinal_ is an endangered orchid native to South and Southeast Asia, and is used for medicinal purposes in Chinese culture [22]. Moreover, _D. officinal_ is a unique orchid because it grows on rocks, trees, or even cliffs. In order to adapt to harsh growth conditions, terpene compounds are synthesized [6; 22; 23; 24; 25]. Therefore, it is necessary to characterize the _TPS_ gene family and study the roles of TPS in _D. officinal_. These findings will provide a valuable reference about the terpene biosynthetic pathway in orchids.
## 4 Results
### Student Engagement Trends  
The analysis of student engagement utilized learning analytics to monitor their time of interactions with AI technology as well as traditional feedback methods during each week. The 20-week engagement trends can be observed in Figure 13.  
Students who received AI-based feedback demonstrated increasing engagement during the semester according to the data, which reached its peak during Week 12 until it stabilized. The control group participants demonstrated a gradual reduction of engagement following Week 8 because they lost interest in typical feedback systems. Results from regression analysis show that AI-driven interactions maintain conceptual growth because of their direct positive relationship (\(R^{2}=0.76\), \(p<0.001\)) that enhances student participation and motivation levels.  
The visual display verifies that students obtaining multiple weekly interactions with the AI system achieved better learning results. Students demonstrated a 30% average learning improvement from AI feedback when they interacted with it more than 15 times per week while students who interacted with AI less than five times per week showed an average increase of 10%. AI administrative feedback works best for students who consistently interact with it.
## 3 Results and Discussions  
### Tensile Test Results  
The mechanical properties, ultimate tensile strength, yield strength, Young's modulus, and elongation at break of the specimens are presented in Table 3. The average and the standard deviation (Std_dev) are used to compare the mechanical properties.  
The results for the four groups of specimens are presented in Table 3. Certain observations were made, especially for the Tx and Ty groups of specimens. First, they exhibited similar mechanical properties. In addition, they exhibited a high yield strength compared to Tz and T45. Finally, the Tz group of specimens shows weak mechanical properties compared to Tx, Ty, and T45. Several authors have shown that the orientation of SLM components affects their anisotropy, microstructural heterogeneity, and mechanical properties [59; 60]. Figure 9 shows the representative load-displacement curves for four different groups of specimens (Tx, Ty, Tz, and T45). Figure 10 shows two deformation stages considered in accordance with ASTM E8. The elastic limit is defined based on the stress-strain data. The first is elastic deformation, where the deformation is linear and reversible, and the stresses are less than or equal to the elastic limit. This phase considers the consolidation zone and the constriction zone, so the strain of the specimen before and after failure is considered. The average value of the proportion between the elastic limit and the ultimate limit for all specimens is 130 MPa to 184 MPa. The specimens in the Tx and Ty groups are  
Figure 8: Flowchart of the training mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Orientation**} & \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{\begin{tabular}{c} **Yield** \\ **Strength** \\ **MPa** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Ultimate** \\ **Tensile** \\ **Strength** \\ **MPa** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Young’s** \\ **Modulus E** \\ **GPa** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Elongation** \\ **at Break \%** \\ \end{tabular} } \\  & & & & & \\ \hline Tx & Mean & 135.1 & 212.8 & 66.3 & 12.2 \\  & Std\_dev & 2.4 & 3.9 & 3.5 & 1.1 \\ Ty & Mean & 138.8 & 215.4 & 68.4 & 12.7 \\  & Std\_dev & 2.7 & 2.2 & 3 & 4.6 \\ Tz & Mean & 123.4 & 208.7 & 63.5 & 7.6 \\  & Std\_dev & 2.3 & 3.4 & 2.7 & 0.5 \\ T45\({}^{\circ}\) & Mean & 125.2 & 212.8 & 64.3 & 9.6 \\  & Std\_dev & 2.4 & 2.5 & 1.3 & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tensile test results of all the specimens.  
quite similar, unlike those in the Tz and T45 groups. Acoustic emission signals are measured during both the elastic and plastic phases. A time-frequency analysis is performed, and several CNN models are built to classify the signals of the different damage stages.
# A Distributed Multicast QoS Routing Construction Approach in Information-Centric Networking
## 4 Simulation
### Message Processing Overhead  
We analyzed the impact of delay constraints on message processing overhead, and the results are illustrated in Figure 13. Our algorithm, Jia's Algo, and PIM-SM demonstrate overall stable trends, with mean values of 7.8, 34.0, and 4.9, respectively.  
The message processing overhead of our algorithm is slightly higher than that of PIM-SM, this can be attributed to the need for path replanning when planned links fail to satisfy constraints, along with additional overhead from lookup operations in the resolution system. The message processing overhead of our algorithm is significantly lower than that of Jia's Algo and QMRP under strict constraints. This is because the search process in our algorithm does not involve message flooding.  
Figure 12: Comparison of Success Ratio and Group Size. (**a**) QoS Requirement is 50 ms; (**b**) QoS Requirement is 100 ms.  
Figure 13: Comparison of Message Processing Overhead and QoS Requirement with a Fixed Group Size of 20 Members.  
While the performance of Jia's Algo remains relatively smooth, its average processing overhead is significantly higher than that of other algorithms. This is attributed to the algorithm's necessity to search for feasible solutions across the entire multicast tree for each new join request.  
Subsequently, we analyzed the trends in message processing overhead for various methods as the group size varied, with delay constraints set to 50 ms and 100 ms, as illustrated in Figure 14a,b.  
With the relaxation of delay constraints to 100 ms, the overhead curves for all algorithms, except for Jia's Algo algorithm, closely align. Compared to the scenario with strict constraints, the differences in overhead among the algorithms can be attributed to the additional cost incurred in searching for more optimal paths.  
Jia's Algo exhibits substantial overhead under both latency constraints due to its consistent need for a tree search, and the associated flooding in the tree incurs significant overhead.  
We observed that under diverse QoS requirements, as the group size increases, the processing overhead of our algorithm does not display linear growth but approaches a horizontal straight line. This indicates that our algorithm demonstrates good scalability in this context.  
Under strict constraints, QMRP is more likely to trigger the search mechanism, resulting in many messages. Therefore, the significant difference in message processing overhead for QMRP under these two different constraints is evident. The message processing overhead of [PERSON]'s algorithm increases significantly with the increase in group size. As the group size increases, the size of the multicast tree also expands, leading to a proportional increase in the required number of detection nodes for [PERSON]'s Algorithm.
## 4 Results
### Sliding Window Tuning  
The sliding window technique can be tuned in two ways--either by maximizing speed, or maximizing accuracy. By using more window sizes, the amount of passes through the image rises, especially with smaller window sizes. With this approach, objects of more sizes can be detected, increasing accuracy. By using less window sizes, the amount of passes through the image falls, trading accuracy for speed. Similar observations can be noticed by modifying the stride value, lowering the stride translates into more window  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Degrees of** & **Sum of** & **Mean** & \multirow{2}{*}{**F-Stat**} & \multirow{2}{*}{**\(p\)-Value**} \\  & **Freedom** & **Squares** & **Square** & & \\ \hline
**Between groups** & 13 & 68,823.4871 & 5294.1144 & 143,244.7783 & 0 \\
**Within groups** & 406 & 15.0052 & 0.037 & & \\
**Total** & 419 & 68,838.4922 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the ANOVA test of analyzed approaches for the masked face classifier  
Figure 17: Comparison of the filtering methods. (**a**) Proposed filtering; (**b**) Filtering from Listing 5; (**c**) Filtering from Listing 6.  
positions, increasing accuracy, and lowering speed, while increasing the stride translates into less window positions, decreasing accuracy and increasing speed. The sliding window parameters were chosen to achieve enough detection coverage while keeping the time relatively low.
# A New Method to Verify the Measurement Speed and Accuracy of Frequency Modulated Interferometers
## 4 Conclusions  
In this study, high-speed displacement generation using an electro-optic modulator (EOM) was proposed and validated. A frequency modulated interferometer was established to measure the virtual displacement produced by the EOM. To produce a high-speed displacement, an EOM was phase changed using a high-speed sinusoidal modulation voltage. A 20 kHz sine-wave signal with an amplified amplitude of 60 V was applied to the EOM. A corresponding sinusoidal virtual displacement of \(\sim\)152 nm at 20 kHz was then generated. The interferometer extracted an interference signal that contained the phase change in the EOM. Two LIAs and a Lissajous diagram were adopted for detecting the phase change and calculating the virtual displacement. The measurement showed that the virtual displacement, with an amplitude of 152 nm at 20 kHz and a measurement speed of 6.08 mm/s, was successfully measured by the interferometer. A displacement noise floor \(<\) 100 pm/\(\surd\)Hz above 2 kHz was achieved. This experiment was successful at developing a displacement reference at high speed. For future study, the possibility of using a higher modulation for the LDs and the residual amplitude modulation effect will be investigated, as well as high-speed mechanical displacements when the relevant hardware is available.  
Conceptualization, T.-D.N. and T.-T.V. ([PERSON] ); methodology, T.-T.V. ([PERSON]); software, V.-D.T.; validation, T.-D.N, and V.-D.T.; formal analysis, N.-T.B.; investigation, T.-T.V. ([PERSON] ); resources, N.-T.B.; data curation, V.-D.T.; writing--original draft preparation, T.-T.V. ([PERSON]); writing--review and editing, T.-T.V. ([PERSON] ); visualization, T.-T.V. ([PERSON]); funding acquisition, N.-T.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Hanoi University of Science and Technology, grant number T2020-PC-201.  
Not applicable.  
This study did not report any data.  
This work was supported by the Centennial SIT Action for the 100 th anniversary of Shibaura Institute of Technology to enter the top ten Asian Institute of Technology.  
The authors declare no conflict of interest.
# A Vehicle Velocity Prediction Method with Kinematic Segment Recognition
## 4 Prediction and Analysis  
### Training Setting  
The three types of kinematic fragment samples in Figure 3 were used to train the sub-neural network. In order to accurately evaluate the prediction effect of different methods quantitatively, the root mean square error (_RMSE_), mean absolute error (_MAE_), and coefficient of determination (\(R^{2}\)) were adopted as evaluation indices in this paper, which are commonly expressed as follows:  
\[RMSE=\sqrt{\frac{1}{Q}\sum\limits_{\epsilon=1}^{Q}\left(v_{\text{true}}( \epsilon)-v_{\text{pre}}(\epsilon)\right)^{2}} \tag{35}\]  
\[MAE=\frac{1}{Q}\sum\limits_{m=1}^{Q}\left|v_{\text{true}}(\epsilon)-v_{\text{ pre}}(\epsilon)\right| \tag{36}\]  
\[R^{2}=1-\frac{\sum\limits_{\epsilon=1}^{Q}\left(v_{\text{true}}(\epsilon)-v_{ \text{pre}}(\epsilon)\right)^{2}}{\sum\limits_{\epsilon=1}^{Q}\left(v_{\text{ true}}(\epsilon)-\overline{v}_{\text{true}}(\epsilon)\right)^{2}} \tag{37}\]  
where \(Q\) is the number of test samples; \(v_{\text{true}}\) and \(v_{\text{pre}}\) are the truth velocity and predicted velocity at time \(\epsilon\), respectively. \(\overline{v}_{\text{true}}\) is the average of the true vehicle velocity in the test set. The smaller the values of _RMSE_ and _MAE_, the lower the prediction error, indicating a higher performance of the vehicle velocity prediction model. \(R^{2}\) serves as an indicator to assess the level of model fitting, with a value closer to 1 implying a better fitting effect on the data and increased prediction accuracy.