esa-sceva/satcom-chunk-collection huggingface dataset:
# Genetic Multi-Objective Optimization of Sensor Placement for SHM of Composite Structures
###### Abstract  
Increasingly often, due to the high sensitivity level of diagnostic systems, they are also sensitive to the occurrence of a significant number of false alarms. In particular, in structural health monitoring (SHM), the problem of optimal sensor placement (OSP) is appearing due to the need to reach a balance between performance and cost of the diagnostic system. The applied approach of considering nondominated solutions allows for adaption of the system parameters to the user's expectations, treating this optimization problem as multi-objective. For this purpose, the NSGA-II algorithm was selected for the determination of an optimal set of parameters in the OSP problem for the detection of delamination in composite structures. The objectives comprise minimization of type-I and type-II errors, and number of sensors to be placed. The advantage of the proposed approach is that it is based on experimental data from the healthy structure, whereas all cases with a presence of delamination were acquired from numerical experiments. This makes it possible to develop a customized SHM system for the arbitrary location of damage.  
optimal sensor placement; structural health monitoring; multi-objective optimization; delamination; NSGA-II algorithm; numerical experiment; type-I and type-II errors Article
###### Abstract  
Macrophyte are of fundamental importance to the functioning of lake ecosystems. They provide structure, habitat, and a food source and are a required component in monitoring programs of lake ecological quality. The key aim of this study is to document the variation in spatial extent and density of macrophytes seasonally between 2015 and 2020 of the Sirmione Peninsula (Lake Garda, Italy), using Sentinel-2 imagery. In addition to this, our results were compared to previous data from imaging spectrometry; individual parameters affecting macrophtye communities were tested, and the possible effect of the COVID-19 lockdown on macrophtye colonization was evaluated. Satellite images allowed the mapping of the spatiotemporal dynamics of submerged rooted macrophytes in order to support monitoring of the shallow water ecosystem under study. Substantial changes were found in both spatial extent and density over the period from 2015 to 2020, particularly in 2019 when there was almost a complete absence of dense macrophytes. Variables found to influence the amount of macrophytes included transparency, chlorophyll-a, water level, winter wave height, and grazing by herbivores. A separate analysis focusing on areas associated with boat transit found a recovery in macrophtye coverage during the period of COVID-19 lockdown. The outcome of the study highlights a decline in the density of the macrophytes and a shift towards deeper areas compared to the situation in 1997. The area examined is part of an internationally important site containing the highest abundance and diversity of overwintering water birds in Italy. Exploiting satellite data at high frequency provided an insight to understand the dynamic changes and interactions with herbivorous birds, environmental factors, and anthropogenic pressures, revealing a delicately balanced and threatened ecosystem.  
multispectral sensor; satellite images; change detection; macrophtye coverage; macrophtye density; Sirmione Peninsula +
Footnote †: journal: Applicid  
Article Evaluation of Macrophyte Community Dynamics (2015-2020) in Southern Lake Garda (Italy)
# Detection of Students' Problems in Distance Education Using Topic Modeling and Machine Learning
## 4 Results
### Model Evaluation  
The evaluation is performed on 70% for training and 30% for testing. To evaluate the performance of the model, we used the popular statistical measures: accuracy, precision, recall, and the harmonic average score (F1). F1 is a weighted average between precision and recall, where the best score is 1 and the worst is 0 [33]. Since our classification model is a multi-class classifier, we used the confusion matrix with multiple classes to measure the performance of the model [34]. An example of a confusion matrix focusing on the social issues (SI) class is shown in Table 5, where TP, FP, TN, and FN represent the number of true positives, false positives, true negatives, and false negatives, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Issues** & **Tweets Count** & **Percentage** \\ \hline
**Education issues** & **23,976** & **11.56\%** \\ Professor-student interaction & 8082 & 3.90\% \\ Lack of focus & 11,933 & 5.75\% \\ Student-student interaction & 3961 & 1.91\% \\ \hline
**Social issues** & **17,701** & **8.53\%** \\ Lack of time & 7847 & 3.78\% \\ Lack of social interaction & 9854 & 4.75\% \\ \hline
**Technology issues** & **13,912** & **6.70\%** \\ Poor internet connectivity & 7881 & 3.80\% \\ Lack of tech equipment & 6031 & 2.90\% \\ \hline
**Health issues** & **11,148** & **5.39\%** \\ Overuse of digital & 1864 & 0.90\% \\ technologies & 9320 & 4.49\% \\ \hline
**Attitudes and ethical issues** & **9964** & **4.80\%** \\ Lack of motivation & 5258 & 2.53\% \\ Cheating & 4706 & 2.27\% \\ \hline
**Others** & **130,751** & **63.01\%** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The counts and percentages of general and sub issues.  
Figure 5: The distribution of general and sub issues.  
The precision, recall, and F1 with respect to one specific category or sub-category are shown in Equations (3)-(5). Form the respective of all classes, micro-averaged and macro-averaged measures are used. The latter gives equal weight to all categories, whereas the former considers the different distributions of the categories. Equations (6)-(11) show precision, recall, and F1 for micro-average and macro-average [33], where \(\mu\) and \(M\) indices represent micro- and macro-averaging.  
\[Recall(R)=\frac{TP}{TP+FN} \tag{3}\]  
\[Precision(P)=\frac{TP}{TP+FP} \tag{4}\]  
\[F1=\frac{2*P*R}{P+R} \tag{5}\]  
\[Recall_{\mu}=\frac{\sum_{i=1}^{l}TP_{i}}{\sum_{i=1}^{l}(TP_{i}+FN_{i})} \tag{6}\]  
\[Precision_{\mu}=\frac{\sum_{i=1}^{l}TP_{i}}{\sum_{i=1}^{l}(TP_{i}+FP_{i})} \tag{7}\]  
\[F1_{\mu}=\frac{2*Precision_{\mu}*Recall_{\mu}}{Precision_{\mu}+Recall_{\mu}} \tag{8}\]  
\[Recall_{M}=\frac{\sum_{i=1}^{l}\frac{TP_{i}}{TP_{i}+FN_{i}}}{l} \tag{9}\]  
\[Precision_{M}=\frac{\sum_{i=1}^{l}\frac{TP_{i}}{TP_{i}+FP_{i}}}{l} \tag{10}\]  
\[F1_{M}=\frac{2*Precision_{M}*Recall_{M}}{Precision_{M}+Recall_{M}} \tag{11}\]
## 7 Design Applications  
Some new algorithms as well as novel computational approaches have already been developed by scientist and practicing engineers to improve and optimize the design process across the board. However, owing to complexity, computational time, and interfacing obstacles, many of these modelling techniques have not been mobilized within the mainstream engineering design firms. Hence, with a carefully-designed framework, it is possible to combine these state-of-the-art technologies (i.e., finite zoning, advance meshing, novel solver techniques, and so on) to reduce the required design time/cost and accelerate the use of efficient virtual tools.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Measure**} & \multicolumn{3}{c}{**Group**} \\ \cline{3-5}  & & **MT1** & **MT2** & **Control** \\ \hline WBC (\(10^{3}\)/\(\mu\)L) & T0 & 8.00 \(\pm\) 2.36 & 7.63 \(\pm\) 2.33 & 8.26 \(\pm\) 1.87 \\  & T1 & 7.72 \(\pm\) 3.75 & 6.79 \(\pm\) 1.68 & 7.94 \(\pm\) 2.25 \\  & T2 & 8.14 \(\pm\) 4.98 & 6.48 \(\pm\) 1.43 & 7.97 \(\pm\) 1.80 \\ \hline RBC (\(10^{6}\)/\(\mu\)L) & T0 & 4.24 \(\pm\) 0.41 & 4.18 \(\pm\) 0.50 & 4.55 \(\pm\) 0.40 \\  & T1 & 4.13 \(\pm\) 0.51 & 4.21 \(\pm\) 0.65 & 4.51 \(\pm\) 0.49 \\  & T2 & 4.16 \(\pm\) 0.41 & 4.15 \(\pm\) 0.45 & 4.55 \(\pm\) 0.38 \\ \hline HGB (g/dL) & T0 & 13.49 \(\pm\) 1.28 & 12.80 \(\pm\) 1.31 & 13.71 \(\pm\) 1.21 \\  & T1 & 12.99 \(\pm\) 1.59 & 12.85 \(\pm\) 1.54 & 13.65 \(\pm\) 1.43 \\  & T2 & 13.11 \(\pm\) 1.30 & 12.85 \(\pm\) 1.16 & 13.73 \(\pm\) 1.18 \\ \hline HCT (\%) & T0 & 38.90 \(\pm\) 3.17 & 37.03 \(\pm\) 3.50 & 40.49 \(\pm\) 3.65 \\  & T1 & 37.73 \(\pm\) 4.34 & 37.23 \(\pm\) 4.56 & 40.16 \(\pm\) 4.00 \\  & T2 & 38.07 \(\pm\) 3.34 & 36.97 \(\pm\) 3.45 & 38.57 \(\pm\) 7.74 \\ \hline MCV (fL) & T0 & 89.02 \(\pm\) 12.21 & 89.55 \(\pm\) 4.63 & 89.06 \(\pm\) 4.10 \\  & T1 & 91.86 \(\pm\) 2.82 & 89.01 \(\pm\) 4.19 & 88.86 \(\pm\) 3.93 \\  & T2 & 91.40 \(\pm\) 3.63 & 88.69 \(\pm\) 3.49 & 85.71 \(\pm\) 13.03 \\ \hline MCH (pg) & T0 & 31.63 \(\pm\) 1.45 & 31.01 \(\pm\) 2.23 & 30.32 \(\pm\) 1.80 \\  & T1 & 31.60 \(\pm\) 1.31 & 30.82 \(\pm\) 2.43 & 30.36 \(\pm\) 1.85 \\  & T2 & 31.37 \(\pm\) 1.43 & 30.91 \(\pm\) 1.70 & 30.80 \(\pm\) 2.88 \\ \hline MCHC (g/dL) & T0 & 34.52 \(\pm\) 1.12 & 34.55 \(\pm\) 1.65 & 34.06 \(\pm\) 0.98 \\  & T1 & 34.40 \(\pm\) 0.99 & 34.56 \(\pm\) 1.68 & 33.26 \(\pm\) 2.64 \\  & T2 & 34.28 \(\pm\) 1.23 & 34.69 \(\pm\) 1.35 & 33.21 \(\pm\) 2.50 \\ \hline RDW (\%) & T0 & 13.50 \(\pm\) 1.70 & 13.70 \(\pm\) 1.59 & 13.21 \(\pm\) 1.06 \\  & T1 & 13.60 \(\pm\) 1.65 & 13.77 \(\pm\) 1.55 & 13.21 \(\pm\) 0.74 \\  & T2 & 14.56 \(\pm\) 4.85 & 13.76 \(\pm\) 1.86 & 13.28 \(\pm\) 0.77 \\ \hline PLT (\(10^{3}\)/\(\mu\)L) & T0 & 277.10 \(\pm\) 147.34 & 256.87 \(\pm\) 78.97 & 315.57 \(\pm\) 81.42 \\  & T1 & 267.90 \(\pm\) 105.47 & 248.33 \(\pm\) 80.48 & 279.36 \(\pm\) 46.22 \\  & T2 & 253.80 \(\pm\) 104.81 & 239.73 \(\pm\) 80.05 & 291.71 \(\pm\) 76.34 \\ \hline MPV (fL) & T0 & 10.90 \(\pm\) 1.22 & 10.73 \(\pm\) 0.64 & 10.31 \(\pm\) 0.73 \\  & T1 & 10.88 \(\pm\) 1.08 & 10.51 \(\pm\) 0.74 & 10.28 \(\pm\) 0.88 \\  & T2 & 10.84 \(\pm\) 1.08 & 10.50 \(\pm\) 0.60 & 10.44 \(\pm\) 0.92 \\ \hline CREA (mg/dL) & T0 & 1.00 \(\pm\) 0.27 & 1.07 \(\pm\) 0.38 & 0.93 \(\pm\) 0.31 \\  & T1 & 0.95 \(\pm\) 0.23 & 1.11 \(\pm\) 0.38 & 0.99 \(\pm\) 0.30 \\  & T2 & 0.95 \(\pm\) 0.22 & 1.11 \(\pm\) 0.50 & 1.00 \(\pm\) 0.22 \\ \hline UREA (mg/dL) & T0 & 32.84 \(\pm\) 9.42 & 37.16 \(\pm\) 15.54 & 47.12 \(\pm\) 19.36 \\  & T1 & 32.94 \(\pm\) 12.24 & 44.08 \(\pm\) 24.43 & 46.63 \(\pm\) 21.20 \\  & T2 & 33.27 \(\pm\) 11.70 & 40.31 \(\pm\) 18.56 & 37.02 \(\pm\) 19.43 \\ \hline ALT (U/L) & T0 & 27.87 \(\pm\) 20.31 & 21.71 \(\pm\) 4.28 & 52.56 \(\pm\) 43.17 \\  & T1
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## Abbreviations  
The following abbreviations are used in this article:  
\begin{tabular}{l l}
**3 GPP** & Third Generation Partnership Project \\
**5G** & Fifth Generation Mobile Network \\
**ACF** & Auto Correlation Function \\
**A/D** & Analog-to-Digital \\
**AoA** & Angle of Arrival \\
**AoD** & Angle of Departure \\
**AP** & Access Point \\
**BEB** & Back-End Block \\
**BLE** & Bluetooth Low Energy \\
**BS** & Base Station \\
**BP** & Back Propagation \\
**CAV** & Connected Autonomous Vehicle \\
**CLP** & Collaborative Localization Protocol \\
**CRLB** & Cramer-Rao Lower Bound \\
**CSI** & Channel State Information \\
**CSI-RS** & Channel State Information Reference Signal \\
**DoA** & Direction of Arrival \\
**DoF** & Degrees of Freedom \\
**dBm** & Decibel Milliwatts \\
**DL** & Down-Link \\
**ESPRIT** & Estimation of Signal Parameters by Rotational Invariance Technique \\
**EKF** & Extended Kalman Filter \\
**FEB** & Front-End Block \\
**FFT** & Fast Fourier Transform \\
**FIM** & Fisher Information Matrix \\
**GA** & Genetic Algorithm \\
**GGMR** & Growing Gaussian Mixture Regression \\
**GNSS** & Global Navigation Satellite Systems \\
**gNB** & gNodeB \\
**IDW** & Inverse Distance Weighted \\
**IMU** & Inertial Measurement Unit \\
**IoT** & Internet of Things \\
**LS** & Least Squares \\
**LiDAR** & Light Detection And Ranging \\
**LoS** & Line-of-Sight \\
**LoRa** & Long-Range Protocol \\
**LTE** & Long-Term Evolution \\
**MAP** & Maximum A Posteriori \\
**MCS** & Modulation Carrier Scheme \\
**MEC** & Mobile Edge Computing \\ \end{tabular}
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
## 4 Results and Evaluation
### Qualitative Analysis  
A few metrics have been proposed to compare and evaluate edge bundling layouts. For example, [PERSON] et al. (2015) calculated the amount of ink saved to measure the clutter reduction but did not address the graph readability. Another metric is the bundling stress proposed by [PERSON] et al. (2018). This metric aims to measure the difference between edges' compatibilities and their distance in the bundling layout based on Kruskal's stress. Although this metric seems promising for our scenario, we could not determine a reliable way to compute the edges' compatibility. Therefore, we qualitatively compared our technique against others since, in general, there is no accepted way to measure the quality of a bundling ([PERSON] et al., 2015). We chose one method from each group of techniques presented in Section 2 for the comparisons. From the group of image-based techniques, we select CUBu ([PERSON] et al., 2017). The techniques FDEB ([PERSON] et al., 2017) and MINGLE ([PERSON] et al., 2017) were selected to represent the force-based and the geometry-based techniques, respectively. We generated CUBu layouts using the authors' source code, while for MINGLE ([[https://github.com/philogb/mingle](https://github.com/philogb/mingle)]([https://github.com/philogb/mingle](https://github.com/philogb/mingle))) and FDEB ([[https://github.com/upphiminn/d3.ForceBundle](https://github.com/upphiminn/d3.ForceBundle)]([https://github.com/upphiminn/d3.ForceBundle](https://github.com/upphiminn/d3.ForceBundle))) we use open source libraries.  
For the evaluation, we artificially generate graphs combining distinct distance relationships among data objects (vertices) and edges distributions to cover different scenarios and better control the experiments. We combine three sets of vertices with two different sets of edges distributions. Dataset I has 360 data objects in 3 groups with equal numbers of objects. Objects of two groups have some overlap, while objects of the third are well-separated. Dataset II has 380 data objects separated in 4 groups with varying sizes, containing 50, 30, 180, and 120 objects. These 4 groups are separated, but the borders between them are not well-defined. Finally, Dataset III has five well-separated groups with 80 data objects in each. Scatterplots of these datasets (they are 2D points) are presented in the first row of Figure 5. We define two different edges distributions for each data set, Distribution A and Distribution B, representing different patterns of edges connecting vertices of different groups. Figure 5 presents the resulting 6 graphs using adjacency matrix visualizations. All graphs have 1600 edges.  
Figure 4: Boxplots summarizing the results obtained by the proposed H-Tree swap strategy. Compared to the opposite swap, the results are considerably better, showing its efficacy on improving the distance preservation of the original H-Tree technique.
## 4 Conclusions
The results obtained in this paper were compared with those in the existing literature using both single and multiple DG integration. Based on the comparative analysis, the results obtained from this study show a significant loss reduction of 70.1% when a single DG is integrated while an 80.1% loss reduction is obtained when multiple DGs are integrated. Thus, the method presented in this paper offers superior voltage profile enhancement and power loss minimization using the integration of DGs. The time taken for a solution to be obtained when considering single DG and multiple DG installations was 11.28 s and 19.40 s, respectively. As part of future work, a comprehensive sensitivity analysis should be carried out to investigate how different network configurations affect reductions in power losses and voltage deviations. Also, future studies should consider incorporating more diverse case studies to demonstrate the effectiveness of the proposed methodology across different types of practical and real-time distribution networks. The mathematical formulations and programming in this work were developed within the framework of the doctorate of [PERSON]. He is supervised by [PERSON] and co-supervised by [PERSON] The written manuscript was extensively discussed with the supervisors. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. All data used for this study are reported within the manuscript. The authors declare no conflicts of interest.
## 4 Numerical Simulation  
### Establishment of the SHPB Finite Element Model  
Considering the limitations of the test, it can only obtain the macroscopic mechanical properties of the test and cannot further study the stress propagation law of the specimen under impact load. That is, the finite element software ANSYS/LS-DYNA is used to carry out SHPB numerical simulation, and the simulation results and experimental results are compared and analyzed, so as to achieve the purpose of mutual verification. The model is mainly composed of four parts: the concrete specimen, incident bar, transmission bar, and bullet. The eight-node hexahedron Solid164 volume element is selected. The bullet, incident bar, and transmission bar were made of linear elastic steel material with a length of 2 m, a radius of 0.025 m, a modulus of elasticity of 211 GPa, and a density of 7850 kg/m\({}^{3}\). The incident bar, transmission bar, and specimen were all meshed hexahedrally using the mapping method, the contact of the specimen with the compression bar was encrypted with an 8-quantile mesh, and the specimen was meshed with a 30-quantile mesh. The spindle-shaped bullet was divided into hexahedral meshes using the sweeping method, with eight equal encryptions on the two end faces and the middle circular portion. The contact between the spindle-shaped bullet and the incident bar was defined as automatic end-face contact, and the contact between the specimen and the incident and transmission bars was defined as erosional end-face contact. A penalty function algorithm was adopted as the contact algorithm to effectively control and reduce the hourglass effect. Numerical  
Figure 16: Impact compression damage process of S1 PP0.2 HyFRC (impact loading from the right side).  
simulation of the uniaxial dynamic impact compression and splitting tests of C40 concrete and S1 PP0.2 HyFRC were performed (see Figure 17).
## 1 Introduction
However, the double-difference single-frequency occultation processing results used in the follow-up calculations cannot be compared with the double-frequency occultation processing results because the Turbooge receiver did not receive the dual-frequency signal. The single-frequency occultation processing method has the advantage of simple instrumentation, but it is not clear about the accuracy of the Beidou-based single-frequency occultation processing. At present, the latest GNSS radio occultation technology uses single-difference or non-difference technology for data processing [23; 24], which requires algorithm verification and accuracy analysis of the single-frequency occultation processing method based on BDS observations under the current differential technology. This paper introduces a method of using B\({}_{1}\) single-frequency carrier phase and pseudorange observations to calculate the relative ionospheric total electron content (TEC). It then reconstructs the algorithm of B\({}_{3}\) carrier-phase observations, which is described in detail in Section 2. The sources of the data are described in Section 3. Then, Section 4 reconstructs the second frequency B\({}_{3}^{*}\) of occultation data observed by the FY3E satellite, compares the consistency of the B\({}_{3}^{*}\) with the actual B\({}_{3}\), and ends up with inverting the atmospheric refractive index product. In Section 4.1, the reITEC change rate results are compared, and in Section 4.2, the correctness of the single-frequency occultation processing method is verified by comparing the excess phase Doppler. In Section 4.3, this paper compares the BDS single-frequency occultation refractive index product with the ERA5 reanalysis field data to evaluate the accuracy of the single-frequency occultation product.
# Exploring and Visualizing Research Progress and Emerging Trends of Event Prediction: A Survey
## 4 Results and Discussion
### Author Co-Citation Network Analysis
This observation suggests that an increasing number of scholars have directed their attention towards this field in recent years. We further conducted a cluster analysis to detect author clusters exhibiting comparable co-citation patterns in the event prediction field. Those authors with similar academic influence and cooperative relationships are grouped in one cluster. As presented in Figure 6, there exist five significant clusters in the network, which are rendered in different colors. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Betweenness Centrality** & **Degree Centrality** & **Closeness Centrality** & **Year** \\ \hline [PERSON] & 0.12 & 33 & 0.83 & 2013 \\ [PERSON] & 0.11 & 27 & 0.76 & 2012 \\ [PERSON] & 0.10 & 26 & 0.65 & 2012 \\ [PERSON] & 0.09 & 12 & 0.47 & 2012 \\ [PERSON] & 0.09 & 45 & 0.71 & 2012 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The top five authors sorted by the betweenness centrality. Figure 5: The author’s co-citation network for the years 2012–2022. The modularity indicated by the Q value equals 0.5069 which is over 0.3, reflecting that the modularization of the author co-citation network is significant. The authors in the same cluster are homogeneous, which can be distinguished from other clusters since the average S value equals 0.786 which is larger than 0.7, proving the clustering outcomes are both reasonable and desirable. Table 6 presents the cluster details, arranged in descending order based on the cluster size, which refers to the number of authors within each cluster. The table also includes the S value, mean year, and labels generated by the LLR method for each cluster, aiding in the interpretation of the clustering outcomes. Notably, all S values surpass 0.7, indicating the meaningful grouping of authors with similar research interests into distinct clusters. For example, cluster #0 appearing around the year 2013 includes 37 authors, who were mainly engaged in predicting and validating weather-related events, e.g., extreme sea level forecasting and performance assessment by regions. Other clusters concerning rainfall events and debris flow (cluster #1), ground motion events (cluster #3), and eruption and earthquake (cluster #4) occur in similar years to cluster #0. In the most recent years, deep learning technologies such as convolutional neural network has rapidly emerged in the event prediction field, which aligns with the research trends of leveraging artificial intelligence (AI) for academic research as well as industrial applications.
## 3 Application Results
### Discussion
It is therefore not surprising that much attention has been devoted to the techniques of hydrological forecasting, development, and improvement of hydrological models, as well as the use of hydrological models in practice [67; 68; 69; 70]. The challenges and barriers for high-accuracy forecasting of flow's quantity are the nonlinearity and uncertainty hidden in the streamflow. An approach with high forecasting precision and efficiency would be qualified in the real application.