esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 MSCT Topology
### MSCT Creation  
We suppose a graph \(G=(V,E)\) representing the topology of the static sensor network, where \(V\) represents the sensor nodes and \(E\) the link weight between two connected nodes. In theory, the link weight in the sensor network represents the Euclidean distance between two nodes. This distance is not representative in the case of an indoor environment. Therefore, our algorithm has to take into consideration some obstacles for transmission, mainly the walls, and does not consider other kinds of obstacles. Thus, in Equation (4), we presented a formula to compute the weight of connection from the Euclidean distance that considers the path loss. To construct the MST, we used Algorithm 1 based on Kruskal to sort the weight of edges in an increasing order. We then considered each sorted edge for inclusion in the tree if it did not create a cycle. Following this algorithm, we obtain a topology hlwith the minimum cost of communication.  
```
0: Graph \((G,w)\) with \(N\) nodes
0: Minimum Spanning Tree \(T\)
1:\(T\ \leftarrow\ \emptyset\)
2:for all Vertex do
3: Make-set(V)
4:endfor
5: Sort the edges of the graph in increasing order by weight \(W\)
6:for i = 1 to N do
7: Select the next smallest weight edge
8:if No cycle is created then
9:\(T\ \leftarrow\ edge(i)\)
10:endif
11:endfor
12: return \(T\)
```  
**Algorithm 1** Minimum Spanning Tree Construction Algorithm  
Figures 5 and 6 show an illustration of the minimum spanning tree topology construction and how costs are reduced. Let us consider the topology (graph) in Figure 5 with eight nodes (vertices) that can be connected by several edges. The numbers on the edges represent the weight of connection that may be the distance between nodes and is considered as the cost of transmission in our study. Algorithm 1 sorts the weights of edges in an increasing manner and starts adding them to the tree. The edges that generate cycles will be excluded from the tree.  
At the end of this process, the topology obtained is a spanning tree where the total weight is minimal, as illustrated in Figure 6. As the weight represents the transmission cost, mainly related to the energy dissipation, we can suppose that building such a topology may reduce considerably the consumption of energy in the network.
# Study on Dynamic Behavior of Bridge Pier by Impact Load Test Considering Scour
## 5 Conclusions  
In this study, the dynamic response analysis was performed on a shallow foundation used as a railroad pier through non-destructive tests. The full-scale model was conducted by constructing a full-scale model pier, and the effect of scouring was performed through the field pier test. Natural frequencies and phase differences were calculated by measuring the acceleration, and the modal number of piers was analyzed. The results of the study are summarized as follows.  
1. Using the numerical analysis, the eigenvalue and mode number of the pier were derived according to the direction of impact. Based on this, the test method for the piers that can derive the first, second, and third modes was established through the full-scale model pier test and the field pier test.
2. Through the full-scale model pier, the natural frequencies of the first, second, and third modes were derived when the surcharge load on the pier increased. It was determined that the natural frequency of the first mode decreased as the surcharge load increased, and the second and third modes were not significantly affected by the surcharge load.
3. Through the field pier test, scour was simulated on the ground that was adjacent to the side of the pier to measure the natural frequency when the scour occurred. Due to the influence of the scour, the first mode exhibited the largest decrease in the natural frequency, followed by the second and third modes. In the case of the third mode, the amplitude of the acceleration was significantly small on the side that simulated the scour even though the natural frequency change was the smallest. This indicates that the direction of the scour can be determined through the third mode.
4. The results of the full-scale model pier test and the field pier test showed that the mode number that is most affected by the surcharge load and the scour is the first mode. If the stability of a pier is evaluated with the first mode, there are limitations in identifying accurate problems. Therefore, it is reasonable to determine the boundary state of the ground that is adjacent to the pier by using the second mode, which is not affected by the surcharge load.
5. These research results have a limitation for applying to other types of bridge piers and can be applicable to the deteriorated bridge with a shallow foundation and a plate girder. For further study, additional field tests and analysis will be performed and an indicator will be suggested for applying other type foundations, such as pile foundation.  
[PERSON] organized the paperwork, made a test plan, performed the impact load test; [PERSON] and [PERSON] helped the data analysis; [PERSON] performs numerical analysis; [PERSON] supported making a test plan; all authors contributed to the writing of the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from R&D Program (PK2002A4) of the Korea Railroad Research Institute, Republic of Korea.  
The authors declare no conflict of interest.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 4 Design of the TeamUp Network Based on LoRaWAN
### Gateway  
Gateways are intermediary elements that are usually located close to the central node or even incorporated into it. Their function is to receive the packets coming from the motes and relay them to the network server located at the central node. Three variants of the gateway have been implemented. The first is a one-channel gateway on a NodeMCUv03, which does not offer the best performance due to the use of a single reception channel but may be interesting for future developments. The second case is a gateway over a PyCom Fipy as a hardware gateway, which is an interesting implementation due to its good performance, the mobility it offers and lower consumption than the third case. Finally, the most complete alternative is installed directly at the central node using IMST Gateway Lite. It uses eight channels for reception. In addition, it has a greater computational capacity, and this allows it to serve as a central node.
# Modeling and Multi-Objective Optimization Design of High-Speed on/off Valve System
## 6 Conclusions  
This paper proposes a multi-objective optimization approach that integrates neural networks with the AFSA algorithm to achieve high-fidelity modeling and an efficient optimization design of high-speed on/off valves. Starting from simplifying mathematical modeling, using neural networks to replace traditional modeling not only saves computational resources but also ensures the accuracy of modeling. By establishing a BPNN model to capture the nonlinear relationship between structural parameters and performance, the need for approximating the valve's open and close processes using functions is eliminated. Following training and testing, the BPNN model demonstrates low RMSE and high R\({}^{2}\) values. Employing this BPNN model in conjunction with the AFSA algorithm yields a Pareto optimal solution set comprising 30 solutions, allowing for the selection of optimal design parameters. To evaluate the response speed of the high-speed on/off valve, a prototype was manufactured and a dedicated experimental platform was constructed, revealing an opening time and closing time of 3 ms for the designed structure. Comparing the experimental results with the predicted outputs of the trained backpropagation neural network further attests to the network's precise predictions, thereby reinforcing the viability of the proposed system modeling approach. The method proposed in this article is not only of great significance for the structural optimization design of high-speed on/off valves but also has more experimental data training. It will have a wider range of applications in the fault detection of various types of valves in hydraulic systems and the field of digital twins.  
Conceptualization, Y.M. and D.W.; Methodology, Y.M.; Writing--original draft preparation, Y.M.; Writing--review and editing, Y.M.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by China University of Mining and Technology (Beijing) Doctoral Talents Cultivation Fund for Top Innovative Talents, grant number BBJ2024062.  
Not applicable.  
The data will be available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.  
Figure 16: Flow-pressure drop curve.
## 3 Results and Discussion
### The Multi-Region Micro-Structure for Pristine Structures Using UIGWs  
To verify the accuracy of the propagation of UGWs when using the multi-region formulation, a comparison with the homogenized solution was conducted. The homogenized solution was obtained considering only \"one\" grain (i.e., the micro-structure is a plate with the same external boundaries as the multi-region one). Each grain was characterized by its unique crystallographic orientation and shape. In this comparison, four different numbers of grains were considered, namely 15, 30, 50, and 100. To accurately simulate a 3-cycle toneburst with a center frequency of 1 [MHz], a total of 300 Laplace parameters were employed. The time step used in the simulation was 10 [ns], while the time window for the entire simulation was set at 35 [\(\upmu\)s]. The material under investigation was aluminum, and its material properties were defined as follows: a Young's modulus of \(E~{}=~{}70\) [GPa], a Poisson's ratio of \(\
u=0.35\), and a density of \(\rho=2700~{}[\frac{\mathrm{kg}}{\mathrm{m}^{3}}]\). The boundary conditions are illustrated in Figure 12a. The results obtained for the averaged Y traction at the bottom edge of the micro-structure, which consider the different numbers of grains, are presented in Figure 12b. As expected, it was observed that, as the number of grains increased, the solution approached closer to the homogenized one.  
Figure 11: A graph of 500 grains: the # of grains and the cumulative # of grains vs. the mean grain area.  
Figure 12: Pristine condition. (**a**) Boundary conditions. (**b**) Averaged Y traction at the bottom edge in pristine condition.
## 2 Materials and Methods  
### Samples and Blends  
The blends used in this study were prepared by adding commercial cricket (_Acheta domesticus_) powder (CKP) to commercial chickpea (_Cicer arietinum_) (CPF) and flaxseed al (_Linum usitatissimum_) flours (FxF). Batches (n = 3) of commercial CKP (approx. 1 kg each) were obtained as cricket protein powder (Hoppa Foods Pty Limited, Wollongong, NSW, Australia). Commercial CPF (n = 3) (desi type) and FxF (n = 3) (brown seeds) (approx. 1 kg each) were procured from local supermarkets in the Brisbane area (Queensland, Australia). A subsample from each batch was used to create binary mixtures using the following combinations: 100:0 % (pure flour no addition of other flour samples), 95:5 % \(w/w\), 90:10 % \(w/w\), 85:15 % \(w/w\), 80:20 % \(w/w\), 75:25 % \(w/w\), 70:30 % \(w/w\), 65:35 % \(w/w\), 60:40 % \(w/w\), and 50:50 % \(w/w\). The blending protocol was the same for the different mixtures developed. For example, the 100 % CPF and 0 % CKP mixtures were made using 5 g \(w/w\) CPF and 0 g \(w/w\) CKP, and so forth. In this study, only the high levels of adulteration between the insect powder and plant flours were considered during the analysis. In addition, samples were analysed for crude protein (CP), crude fat (CF) and total fibre (TF) content using proximate analysis. The proximate analyses were carried out by the analytical services unit of the School of Agriculture and Food Sustainability, The University of Queensland (St. Lucia Campus, Brisbane, QLD, Australia). The association of official analytical collaboration (AOAC) methods 960.39 and 992.15 were used to determine crude fat (CF) and crude protein (CP). The proximate composition obtained was as follows: CKP had 60% CP, 17% CF, and 23% TF, CPF had 21% CP and 8% CF, and FxF had 18% and 42% CP and CF, respectively.
## 5 Conclusions  
In this paper, a quantitative analysis method based on the polynomial chaotic expansion (PCE) model was proposed to solve the dynamic uncertainty problem of the gear system while considering the error randomness. The sensitivity indices of gear manufacturing errors were analyzed based on the Sobol\({}^{\prime}\) method and the PCE model. The main conclusions are as follows:  
1. Based on tooth contact analysis (TCA), loaded tooth contact analysis (LTCA), and the system dynamic model, a PCE model with the peak-peak value of the dynamic meshing force as the statistical index of the dynamic response was established. When the polynomial expansion ended at the third order, and the number of sample points was four times the number of polynomial coefficients, the PCE model could not only meet the computational accuracy requirements, it also had high computational efficiency.
2. The accuracy of the PCE model was compared and verified by using the Monte Carlo method. The results showed that the relative error of the mean of the system response was less than 1%, and the relative error of the standard deviation was less than 2%. When the two methods achieved the same accuracy, the PCE method only called the original system model 140 times, which took 0.4 h. The Monte Carlo method called 1000 times, which took 6 h. The PCE method had a higher efficiency and could provide an efficient and accurate new idea for the quantification of dynamic uncertainty and the reliability design of gear systems.
3. Based on the PCE model, the Sobol\({}^{\prime}\) index of each error was quickly solved. The influence degree of each error on the dynamic performance of gear systems and the change in the interaction effect between errors were compared and analyzed under different precisions. It provides a reference for the machining precision control in the actual machining and application of batch gears.  
Methodology, F.G.; Validation, F.G.; Formal analysis, F.G.; Resources, J.S.; Data curation, F.G.; Writing--original draft, F.G.; Writing--review & editing, F.G.; Visualization, C.L. ([PERSON]); Project administration, F.G.; Methodology, C.L. ([PERSON]); Funding acquisition, C.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Fundamental Research Funds for the Central Universities, CHD (Grant No. 300102252111) and the National Natural Science Foundation of China (Grant No. 52005402).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Material and Method
### Approach of Citrus Tracking and Counting Based on YOLOv7-Tiny and DeepSORT
#### 2.3.1 Citrus Fruit Detection Based on YOLOv7-Tiny  
After investigating and studying the target detection algorithms, we found that the YOLOv7-tiny network used the darknet deep learning framework to achieve end-to-end training of the input image. The YOLO network [20,21] first realizes the input image to 416 \(\times\) 416 and converts it into a 416 \(\times\) 416 \(\times\) 3 tensor, then the backbone network converts the tensor of the input image into a higher dimensional feature map. The neck converts the feature map extracted by the backbone network into a feature map suitable for target detection. The detection head predicts the location and class of the target in the image. Finally, the detection results are post-processed to remove duplicates and sort the results to obtain the final target detection results.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods  
### Structure of the WFCNN Model  
The WFCNN model includes an encoder, a decoder, and a classifier (Figure 5). The encoder is used to extract pixel-by-pixel features, while the decoder is used to fuse the coarse high-level semantic features and fine low-level semantic features. The classifier is used to complete the pixel-by-pixel classification. When training the model, the image patch and the corresponding label file are used as inputs. Once the model is successfully trained, the image to be segmented is used as the input, with the output being pixel-by-pixel label files.
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 5 Algorithm of NGM
### Glucose Estimator  
The function of GE is to estimate BGC by reducing the errors and differences between the independent component concentration vector \(C^{*}\) and the true components concentration vector \(C\). These errors and differences are cased by the weak premise made in ICF, inaccurate measuring and any other unexpected factors. The input and output of GE are \(C^{*}\) and BGC respectively. Because of some unknown factors, the transform process becomes a black-box process that can only be modeled by a machine learning model, such as Random Forest (RF), Adaboost, support vectors machine (SVM), decision tree (DT), etc. Plus, Deep Learning would also seem to be a good choice, but that requires thousands of training data, which is hard to get in our project at present.  
By comparison (see Table 2), we use random forest (RF) to model the transform process in GE in this paper. Random forest is an ensemble learning method for classification and regression by constructing a multitude of decision trees [18]. As other supervised model, RF needs a training data set to adjust parameters, and then RF can be used to estimate BGM from C\({}^{*}\). The estimating value of RF is mean estimating value of the individual trees. The training data set contains a set of C\({}^{*}\) vectors that are transformed from data matrix \(T\) by IFC and a set of reference BGC data acquired by invasive glucose meter.  
There are two reasons why we choose RF to model GE. The first reason is that ICF is based on maximizing entropy of C\({}^{*}\), which has the same optimization goal with RF. Secondly, comparing with other machine learning models in Table 2, its accuracy is proved. Through cross-validation, the number and depth of RF is set to 200 and 2, and the aggregating method is averaging the predictions of all tress in forests. Let Equation (16) represent GE.  
\[BGC=GE(C^{*}) \tag{16}\]  
In summary, the IFC and GE have been built and represented by Equations (15) and (16), which can estimate BGC from absorbance vector \(A\) acquired via the prototype designed in Section 4.
## 3 Graph Neural Networks for Next-Generation Networks
### Backgrounds on Graph Neural Networks  
From now, unless otherwise specified, we denote \(\mathcal{G}\) as a graph and \(\mathcal{G}=(V,E)\), where \(V\) is the set of \(|V|=n\) vertices and \(E\) is the set of edges \((v_{i},v_{j})\) for \(v_{i},v_{j}\in V\) if there exists an edge between them. The set of edges can also be represented under the form of an adjacency matrix \(\mathbf{A}\), which is a binary matrix of size \(n\times n\), \(\mathbf{A}_{ij}\in\{0,1\}\) where 1 (or 0) means the existence (or the nonexistence) of the edge \((v_{i},v_{j})\).  
Based on the design patterns of the models, we can classify GNNs into two groups: (i) _Spectral-based GNNs_ and (ii) _Message-Passing-based (or spatial-based) GNNs._
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 8 The Automatic Computer-Assisted Pronunciation Training for Thai Vowel Speech  
The automatic CAPT for Thai vowel speech is shown in Figure 8. The details of this system are as follows:  
1. The user selects a Thai vowel that they want to practice pronouncing, such as /a/.
2. Then the system goes to the /a/ pronunciation practice page. The page contains three types of vowel description: - A video of the 3D /a/ sound pronunciation that shows the movement of the tongue. - The vowel descriptions including details such as vowel duration: short or long, mouth shape: round or not round, and tongue characteristics: low, mid, or high. - A video of a straight-faced person showing the position of the mouth as the vowel is pronounced.
3. When users practice pronunciation, they can press the microphone icon and speak (the speech signal is an analog audio signal).
4. The signal is digitalized and converted into the time-frequency domain during the preprocessing step.
5. Afterward, features are extracted during the extraction step, and their values characterize the phonemes in the acoustic features.
6. Then, the acoustic features are utilized in the classification process. In this process, a deep learning architecture for the recognition of the Thai vowel speech is applied. The class result contains 18 classes (9 short vowels and 9 long vowels like Thai simple vowels grouping). After the classification step, only one class is selected as the output for the vowel classification result.  
7. Finally, a comparison between the vowel selected by the user and the vowel classification result is presented. This procedure compares the vowel chosen by the user for pronunciation practice with the result from the recognition of the Thai vowels. 8. If the vowel selected by the user (e.g., /a/) and the vowel classification result (e.g., /a/) are the same, then the pronunciation result will display a message \"Your pronunciation is correct\". 9. In contrast, if the vowel selected by the user and the vowel classification result are not the same, e.g., the user selects /a/ but the vowel classification result is /o/, the pronunciation result will display a message \"Your pronunciation is incorrect\" and \"You pronounced /o/\". In addition, messages will show a 3D video of the pronunciation and text describing how to pronounce it. The user can go back to the vowel pronunciation practice page to retry by clicking the bottom arrow.  
**Author Contributions:** Conceptualization, N.R. and S.P.; Data curation, N.R.; Formal analysis, N.R. and S.P.; Investigation, N.R. and S.P.; Methodology, N.R. and S.P.; Project administration, S.P.; Resources, N.R.; Software, N.R.; Supervision, S.P.; Validation, N.R. and S.P.; Visualization, N.R.; Writing--original draft, N.R.; Writing--review & editing, S.P. All authors have read and agreed to the published version of the manuscript.  
Figure 8: The automatic computer-assisted pronunciation training for Thai vowel speech.
# A Mobile Anchor Node Assisted RSSI Localization Scheme in Underwater Wireless Sensor Networks
## 3 Detailed Description of the Proposed Localization Scheme
### Determining the Perpendicular Distance from a Node to the Trajectory  
In this section, to determine the perpendicular distance from a node to the trajectory, we design a three-step method, as shown in Figure 6. First, to establish the reference RSSI curve library, we calculate the RSSI value of the broadcast point on n reference trajectories. Then, to accurately compare the similarities between curves, we extract the parts to be compared from the predicted curve and reference curves, and select the feature points on the extracted parts, so as to get the feature point vectors for comparison. Finally, to select the feature point vector with the highest similarity, we compare the similarity between the feature point vectors. The distance corresponding to the selected feature point vector is determined as the perpendicular distance from the node to the trajectory.  
Figure 5: The extension of the location vector.  
Figure 6: The process of determining the perpendicular distance from a node to the trajectory.