esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. Both Tresca and Von Mises failure criteria displayed similar qualitative results, while the quantitative ones were 15% higher for T. Since the Tresca criteria were designed to describe non-homogenous materials' behavior, they are better suited for quantitative results than VM and should therefore be preferably used for FEA simulations.
2. For all three loads, the qualitative stress display was similar for both failure criteria and for all bone loss levels, implying that the deformations and displacements were constant, and manifested in the same areas independently of the load's amount, the only difference being their intensity (stress was doubled for 1.2 N and quadrupled for 2.4 N).
3. The boundary condition assumptions (homogeneity, linear elasticity, isotropy, and perfectly bonded interfaces) seem to be correct for up to 2.4 N of an applied load, and thus correctly used in the FEA of dental tissues.
4. Clinically speaking, in an intact periodontitis, forces up to 2.4 N could be relatively safely applied (with mild risks), whereas, in a reduced periodontium after 4 mm of loss, any more than 1 N should be carefully considered (with high ischemic and resorptive risks).
5. Clinically speaking, 4 mm of bone loss seems to be a reference point in orthodontic treatment, since, from this moment, ischemic and resorptive risks significantly increase, and the benefits must be balanced with risks.  
Conceptualization: R.-A.M.; methodology: R.-A.M.; software: R.-A.M.; validation: R.-A.M. and C.D.O.; formal analysis: R.-A.M.; investigation: R.-A.M.; resources: R.-A.M.; data curation: R.-A.M.; writing--original draft preparation: R.-A.M.; writing--review and editing: R.-A.M., A.G.D. and C.D.O.; visualization, supervision, and project administration: R.-A.M., A.G.D. and C.D.O.; funding acquisition: R.-A.M., A.G.D. and C.D.O. All authors have read and agreed to the published version of the manuscript.  
The authors were the funders of this research project.  
The research protocol was approved by the Ethical Committee of the University of Medicine (158/2 April 2018).  
Informed Consent Statement: Informed oral consent was obtained from all subjects involved in the study.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no conflicts of interest.
## 2 Background on the Identification Problem
### Identification Problem
**K-identification scenario:** In the standard DI or RI problems [32; 49], the receiver aims to identify the occurrence of a _single_ message, that is, the decoder at the receiver selects an arbitrary message from the message set referred to as the goal message, and then, by exploiting a decision rule (decoder), determines reliably whether or not this goal message is _identical_ to the sent message. The identification problem can be _extended_ in the subsequent sense: The receiver chooses a subset of \(K\) messages from the message set, called the goal message set (denoted by \(\mathsf{K}\)) and, unlike the standard DI or RI problems, it checks whether or not the sent message is a _member_ of \(\mathsf{K}\). This problem is called K-identification in the literature [26]. The goal message set selected by the receiver can be any arbitrary subset of the message set of size \(K\), among the total \(\binom{M}{K}\) such subsets. The K-identification framework can be thought of as a _generalization_ of DI or RI problems, in which the receiver's single goal message is replaced with a collection of \(K\) goal messages, where \(K\geq 1\). Therefore, the DKI for the special case where \(K=1\) corresponds to the DI problem studied in [48; 59]. Moreover, the K-identification problem is extended in [26] to _generalized identification with decoding_, where when the receiver identifies that the message belongs to set \(K\), it also identifies the message itself. The K-identification problem, as considered in this paper, is different from a similar scheme called _multiple object identification_[60], where the sender's data contains the information of \(K\) messages and the receiver's objective is to identify whether or not a specific message belongs to set \(K\). Here, it is assumed that the receiver does not know the set of objects selected by the sender.
###### Abstract  
In this paper, we propose a novel approach to coordinate-based acoustic emission (AE) source localization to address the challenges of limited and imbalanced datasets from fiber-optic AE sensors used for structural health monitoring (SHM). We have developed a hybrid deep learning model combining four generative adversarial network (GAN) variants for data augmentation with an adapted inception neural network for regression-based prediction. The experimental setup features a single fiber-optic AE sensor based on a tightly coiled fiber-optic Fabry-Perot interferometer formed by two identical fiber Bragg gratings. AE signals were generated using the Hsu-Nielsen pencil lead break test on a grid-marked thin aluminum plate with 35 distinct locations, simulating real-world structural monitoring conditions in bounded isotropic plate-like structures. It is demonstrated that the single-sensor configuration can achieve precise localization, avoiding the need for a multiple sensor array. The GAN-based signal augmentation expanded the dataset from 900 to 4500 samples, with the Wasserstein distance between the original and synthetic datasets decreasing by 83% after 2000 training epochs, demonstrating the high fidelity of the synthetic data. Among the GAN variants, the standard GAN architecture proved the most effective, outperforming other variants in this specific application. The hybrid model exhibits superior performance compared to non-augmented deep learning approaches, with the median error distribution comparisons revealing a significant 50% reduction in prediction errors, accompanied by substantially improved consistency across various AE source locations. Overall, this developed hybrid approach offers a promising solution for enhancing AE-based SHM in complex infrastructures, improving damage detection accuracy and reliability for more efficient predictive maintenance strategies.  
a _Applied_  
_sciences_  
13 August 2024  
Revised: 12 September 2024  
Accepted: 25 September 2024  
Published: 30 September 2024  
**Copyright**\(\copyright\) 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# The Role of Elasticity in the Vortex Formation in Polymeric Flow around a Sharp Bend
## 3 Results
### Polymer Flow
#### 3.2.1 Inertial Vortices
Figure 7: Downstream inertial vortices, Newtonian vs. polymer flow (\(\beta=0.1\)) for Re =100: (**a**) Newtonian flow; (**b**) polymer flow, (Wi = 0.5, \(\text{El}=0.005\)); (**c**) polymer flow, (Wi = 1.0, \(\text{El}=0.01\)); (**d**) polymer flow, (Wi = 1.5, \(\text{El}=0.015\)). From Figure 9 it could be inferred that adding polymers to the solution does not affect the size of the inertial vortices. Figure 10 presents the same data as Figure 9 but as a function of the elasticity number. At every Weissenberg number, the predictions clearly indicate that increasing elasticity leads to a rapid decrease in vortex size. This figure also highlights that it is the Reynolds number, indeed the inertial effects, that is predicted to drive the size of the inertial vortex, with the Weissenberg number playing a secondary role. This figure also seems to indicate that there is a maximum size for the inertial vortex at any given Reynolds number. This maximum vortex size is predicted for the cases where the inertial and elastic forces are balanced: Weissenberg equal to 1.0.
## 1 Introduction
Another advantage is that through control of parameters such as the total number of iterative runs, these kinds of metaheuristics can avoid the computational intractability faced by exact approaches [7]. Examples of these kinds of metaheuristics include Ant Colony Optimization [8; 9; 10; 11; 12; 13; 14; 15], Particle Swarm Optimization (PSO) [7; 16; 17], and genetic algorithm (GA). In [18], it was shown that GAs have been widely used to deal with seaside container terminal operational problems. Proposed by [PERSON] and [PERSON] [19], the Shuffled Frog-Leaping Algorithm (SFLA) is an evolutionary population-based metaheuristic. It employs a swarm of frogs to mimic memetic evolution with the aim of finding the position (solution) with the greatest amount of food. During evolution, these frogs exchange information that can affect the behavior of other frogs. As genes can only be passed from parents to offspring, memetic spread is considered to be faster, as memes can propagate between any individuals [20]. In fact, the SFLA includes the advantages possessed by evolution-based memetic algorithms (MA) and social behavior-based PSO algorithms [20]. The SFLA and its variants are currently attracting increasing attention and are increasingly being used to deal with different problems. However, to our best knowledge, they have never been used to deal with the BAP. The main purpose of this research is to develop an improved SFLA (ISFLA) to deal with the DCBAP, as this new kind of metaheuristic has never been used for this purpose. The ISFLA is employed in a two-stage procedure. In the first stage, the ISFLA is used to generate alternative ship placement sequences as inputs to the second stage. In the second stage, a specific heuristic is used to place ships one by one into a berth plan and resolve overlaps of ships in order to generate a feasible solution. The ISFLA includes some novel features, such as self-adaptive jump, direct-jump prevention, push jump, and neighborhood jump, enabling smart leaps for frogs in order to better search the solution space for a possible solution. We conducted experiments investigating the effectiveness of the ISFLAin comparison to FCFS and SFLA, which were employed in the first stage. The results showed that the ISFLA outperformed the other two in terms of solution quality. The rest of this research is organized as follows: Section 2 presents a literature review on the CBAP and basic SFLA; Section 3 presents a formulation of the Mixed Integer Linear Programming (MILP) model for the DCBAP; Section 4 develops the ISFLA for solving the DCBAP; Section 5 includes numerical examples; Finally, Section 6 presents the conclusion and future research directions.
## 3 eDSA = Efficient Dual Sink Algorithm for Event-Driven WSNs by using SS and MS  
### Optimized Barrier Coverage Design  
Although maintaining full sensing coverage guarantees an immediate response to intruding targets, sometimes it is not favorable due to its high energy consumption. We have investigated a new and more efficient approach for deploying sensor nodes in a large scale network area. To monitor an area, a WSN should achieve a certain level of detection performance. Due to the considerably high cost in a given monitoring area, better detection capacity and communication coverage is critical to sequential deployment of sensors. In this paper, a new sensor deployment was developed (see Figure 1) to improve barrier coverage.  
**Theorem 1**.: Let A denote the area and _f_(A) denote barrier coverage, namely the fraction of the area that is in the sensing area of one or more sensors where sensors can provide a valid sensing measurement and \(\Gamma\) is the cartographic representation of area. Then:  
\[\Gamma_{f(\beta)}\gg\ \Gamma_{f(\alpha)}\ \text{in}\ \text{G}=\text{(V, E )}\ \text{where}\ \text{E}\
eq\emptyset \tag{1}\]  
**Proof**: In the literature, the majority of researchers prefer grid-based sequential sensor deployment [see Figure 1(a)]. Instinctively, we see that \(\Gamma_{\text{f(g)}}\) is more efficient than \(\Gamma_{\text{f(\alpha)}}\).The computations are as follows:  
\[\Gamma_{f(\beta)} = (2r)^{2}\text{-}4(\frac{nr^{2}}{4})=(4\text{-}\pi)r^{2}\,\approx 0.86r^{2} \tag{2}\] \[\Gamma_{f(\alpha)} = (\sqrt{3}\cdot\frac{\pi}{2})\ r^{2}\,\approx 0.1512r^{2} \tag{3}\]  
Since the calculation work is easy, we have skipped the computation procedure and go directly to the result. The unit difference is obviously given by approximately \(0.71r^{2}\). Although the difference is indistinctive when the value of \(r\) is small enough, for monitoring applications, accuracy is a vital consideration. The smaller the value of \(\Gamma_{f(\cdot)}\) is, the higher possibility that a moving object will not be detected, therefore Figure 1(b) has better detection capacity than Figure 1(a).  
**Theorem 2**. In a hierarchical network architecture, let \(\text{H}_{\text{v}}\) be a threshold hop distance and \(\text{p}_{\text{v}}^{\text{up}}\), \(\text{p}_{\text{v}}^{\text{same}}\) and \(\text{p}_{\text{v}}^{\text{lower}}\) denotes the possible existence of CHs at the upper, same and lower layer respectively. The proposed Triangle-based is more suitable for our monitoring network in term of higher density of hop distance neighborhood.  
Figure 1: Detection capacity-based sensor deployment.  
Figure 2: New sensor deployment based on higher density of hop distance neighborhood.
## 3 Results and Discussion
### Principal Component Analysis  
The Kaiser-Meyer-Olkin sampling adequacy measure came out with a value of 0.66, and [PERSON]'s sphericity test showed a chi-square value of 710.16; thus, the correlation matrix was factored and fit for PCA.  
According to the Kaiser-Guttman criterion, three principal components were extracted (eigenvalues: 18.25, 2.02, and 1.31), explaining 93.81% of the total variance (79.33%, 8.78%, and 5.71%, respectively) for Caciocavallo cheese, whereas the three principal components extracted for milk (eigenvalues: 18.18, 2.63 and 1.03) explained 92.04% of the total variance (78.64%, 7.68% and 5.68%, respectively). Analysis of the correlation matrix showed that the highest positive correlations were observed among C14:0 lipids (0.982), MUFA-C22:0 (0.976), and MUFA-C18:1 cis9 and PUFA-C15:1 (0.960), whereas the highest negative correlations were observed for MUFA-C14:0 (\(-\)0.988), MUFA-C18:0 (\(-\)0.956), and C22:0 lipids (\(-\)0.949). Figure 2 shows 2D scatterplots on the plane defined by PC1 and PC2 for the Caciocavallo cheese and milk samples.  
Two groups can be clearly distinguished for cheese and two groups for milk: the ECO samples are separated from the CTR samples on the first component, which explains 79.336% and 78.683% of the total variances; the ECO samples are positioned at positive values of PC1 and are characterized by higher values of MUFA, C18:2 cis9 cis12, C22:0 and polyphenols, while the CTR samples are positioned at negative values of PC1 and have higher values of C12:0, C14:0 and lipids.
## 3 Methodology
### YOLO for Structural Components Detection
#### 3.2.2 Loss Function  
In order to keep a balance among coordinate predictions, category predictions and confidence predictions when training YOLO, an improved sum-squared error (SSE) method [45] is applied in this study to optimize the loss function. The specific loss function is shown as follows:  
\[\lambda_{\text{coord}}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{ B}1^{\text{obj}}_{ij}[(x_{i}-\hat{x}_{i})^{2}+\left(y_{i}-\hat{y}_{i})^{2}\right]\] \[+\lambda_{\text{coord}}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0} ^{B}1^{\text{obj}}_{ij}[(\sqrt{w_{i}}-\sqrt{w_{i}})^{2}+(\sqrt{h_{i}}-\sqrt{h _{i}})^{2}]\] \[+\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0}^{B}1^{\text{obj}}_{ ij}(C_{i}-\hat{C}_{i})^{2} \tag{5}\] \[+\lambda_{\text{noobj}}\sum\limits_{i=0}^{S^{2}}\sum\limits_{j=0 }^{B}1^{\text{noobj}}_{ij}(C_{i}-\hat{C}_{i})^{2}\] \[+\sum\limits_{i=0}^{S^{2}}1^{\text{obj}}_{i}\sum\limits_{cc\text {classes}}(p_{i}(c)-\hat{p}_{i}(c))^{2},\]  
where \(\lambda_{\text{coord}}\) denotes the weight of the localization error while \(\lambda_{\text{noobj}}\) is the weight of the classification error when the bounding box contain no objects. \((x,y,w,h)\) reveals pixel coordinate information and size information of the target's bounding box appearing in grid \(i\), and \((\hat{x},\hat{y},\hat{w},\hat{h})\) represents the same information for the actual object in grid \(i\). \(1^{\text{obj}}_{i}\) indicates that there is an object in the \(j^{\text{th}}\) bounding box of the grid \(i\), while \(1^{\text{noobj}}_{ij}\) means no object exists in the same bounding box. B denotes the number of bounding boxes predicted in each grid, and S\({}^{2}\) grid denotes the number of grids partitioned by the input image. \(C_{i}\) is a component class predicted by grid \(i\), and \(\hat{C}_{i}\) is the actual class of the component in the image.  
There are five parts in Equation (5), the first two parts denote the localization error about the center coordinate and the width and height of the bounding box. The third part calculates the loss of confidence of the bounding box containing objects, while the fourth part obtains the loss without objects. The final part represents the conditional probability error for the class of objects.
###### Abstract  
Most computer vision algorithms for water level measurement rely on a physical water gauge in the image, which can pose challenges when the gauge is partially or fully obscured. To overcome this issue, we propose a novel method that combines semantic segmentation with a virtual water gauge. Initially, we compute the perspective transformation matrix between the pixel coordinate system and the virtual water gauge coordinate system based on the projection relationship. We then use an improved SegFormer-UNet segmentation network to accurately segment the water body and background in the image, and determine the water level line based on their boundaries. Finally, we transform the water level line from the pixel coordinate system to the virtual gauge coordinate system using the perspective transformation matrix to obtain the final water level value. Experimental results show that the improved SegFormer-UNet segmentation network achieves an average pixel accuracy of 99.10% and an Intersection Over Union of 98.34%. Field tests confirm that the proposed method can accurately measure the water level with an error of less than 1 cm, meeting the practical application requirements.  
water level measurement; water level line detection; virtual water gauge; perspective transformation; semantic segmentation +
Footnote †: journal: Applied sciences  
Article  
Application of Deep Learning Techniques in Water Level Measurement: Combining Improved SegFormer-UNet Model with Virtual Water Gauge
# Geo-Proxy-Based Site Classification for Regional Zonation of Seismic Site Effects in South Korea
## 2 Site Classification System with Seismic Response Parameters
### Site Class Based on the Seismic Response Parameters in South Korea
\(V_{S}D_{S}\) indicates the mean \(V_{S}\) to a depth (\(D\)s) shallower than 30 m and is equal to \(V_{S30}\) times \(Cs\) (i.e., \(V_{S}D_{S}\) = \(V_{S30}\)\(\times\)\(Cs\)). Here, \(Cs\) is expressed by 0.214\(D_{S}\)\({}^{0.453}\) (i.e., \(Cs\) = 0.214\(D_{S}\)\({}^{0.453}\)) and synthetically, \(V_{S30}\) = \(V_{S}D_{S}/Cs\) = \(V_{S}D_{S}/(0.214D_{S}\)\({}^{0.453}\)). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Site Class**} & \multicolumn{3}{c}{**Citeria**} & \multicolumn{3}{c}{**Site Coefficients**} \\ \cline{2-10} \multicolumn{1}{c}{**Generic Description**} & **Current** & **This Study** & \(H\) **(m)** & \(V_{S30}\) **(m/s)** & \(V_{S}D_{S}\) **(m/s)** & \(T_{G}\) **(s)** & \(Fa\) & \(Fo\) \\ \hline \multirow{2}{*}{**Rock**} & A & B & \(<\)6 & \(>\)760 & \(>\)760\(Cs\) & \(<\)0.06 & 1.00 & 1.00 \\ \cline{2-10}  & B & & C1 & \(<\)10 & \(>\)620 & \(>\)620\(Cs\) & \(<\)0.10 & 1.28 & 1.04 \\ \hline \multirow{2}{*}{**Westhered Rock and**} & \multirow{2}{*}{C} & \multirow{2}{*}{C} & C & C2 & \(<\)14 & \(>\)520 & \(>\)520\(Cs\) & \(<\)0.14 & 1.45 & 1.09 \\  & & & C3 & \(<\)20 & \(>\)440 & \(>\)440 & \(<\)0.20 & 1.65 & 1.13 \\ \hline \multirow{2}{*}{**Intermediate Stiff Soil**} & \multirow{2}{*}{D} & \multirow{2}{*}{D} & \multirow{2}{*}{D} & D1 & \(<\)38 & \(>\)320 & \(>\)320 Cs & \(<\)0.38 & 2.08 & 1.23 \\  & & & D2 & \(<\)46 & \(>\)280 & \(>\)280C & \(<\)0.46 & 2.26 & 1.29 \\ \multirow{2}{*}{**Deep Stiff Soil**} & \multirow{2}{*}{D} & \multirow{2}{*}{D} & D3 & \(<\)54 & \(>\)240 & \(>\)240 Cs & \(<\)0.54 & 2.48 & 1.36 \\  & & & D4 & \(<\)62 & \(>\)180 & \(>\)180 Cs & \(<\)0.62 & 2.86 & 1.43 \\ \hline \multirow{2}{*}{**Deep Soft Soil**} & E & \multirow{2}{*}{E} & E & \(>\)62 & \(\leq\)180 & \(\leq\)180 Cs & \(\geq\)0.62 & 1.50 & 2.00 \\ \cline{2-10}  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Domestic site classification system applying criteria including the site period (modified after Sun [4]).
## 6 Experiments
### Unique Values and Direct Evaluation  
The information provided in Table 15 indicates the number of user trajectories assigned with a risk equal to one for each attack. For most attacks, there is a very high number of directly evaluated risks equal to one. This suggests that directly assessing risk for attacks with features having unique values is effective. The approach seems to work well in scenarios where features contribute to the uniqueness of trajectories.  
Location sequence attacks could have yielded better results. This could be attributed to the need for more uniqueness in a location with a knowledge size of 1, making it challenging to differentiate trajectories based on them. It aligns with the understanding that features with less uniqueness may perform poorly in this direct risk evaluation approach. This happens due to the nature of Wi-Fi data, where location information is not unique enough with a knowledge size of 1, as shown in the previous experiments, to effectively differentiate trajectories due to its dense characteristics.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Feature** & **Attack** & **k** & \multicolumn{2}{c}{**Wi-Fi**} & \multicolumn{3}{c}{**Foursquare**} & \multicolumn{3}{c}{**Breadcrumbs**} \\  & & & **Original** & **Cache** & **Original** & **Cache** & **Original** & **Cache** \\ \hline
**Location** & Visit & 1 & 2314 d 15:56:33 * & 0 d 12:32:48 & 6 d 06:47:34 & 0 d 00:02:25 & 7 d 12:24:40 & 0 d 00:02:27 \\
**Location** & Visit & 2 & 60,395 d 07:15:07 & 11 d 00:45:50 & 51,240 d * 04:08:24 & 0 d 03:11:26 & 69 d 21:21:32 * & 0 d 00:09:44 \\
**Time** & Visit & 1 & 2300 d 11:18:24 * & 5 d 21:52:28 & 5 d 12:12:56 & 0 d 00:02:27 & 7 d 12:24:54 & 0 d 00:02:41 \\
**Time** & Visit & 2 & 66,150 d 12:34:55 * & 12 d 14:52:15 & 39,530 d 04:45:22 * & 0 d 03:15:42 & 69 d 21:33:26 * & 0 d 00:10:36 \\
**Location** & Location Sequence & 1 & 20 d 21:13:00 * & 0 d 13:47:01 & 1 d 13:19:26 & 0 d 00:02:20 & 7 d 12:24:42 & 0 d 00:02:18 \\
**Location** & Location Sequence & 2 & 9793 d 01:38:34 * & 10 d 08:23:16 & 30,023 d 22:55:32 * & 0 d 03:04:53 & 69 d 21:19:14 * & 0 d 00:08:29 \\ \hline \hline \end{tabular}  
d = days, * = estimated value.  
\end{table}
Table 13: Comparing the execution time of the **Original** and **Cache** approaches across different sources.  
In conclusion, the direct risk evaluation approach appears promising for attacks involving unique values, but its effectiveness varies depending on the feature's uniqueness in different datasets. The challenges observed with a location in the Wi-Fi dataset highlight the importance of considering the feature's nature when applying this risk assessment approach.