esa-sceva/satcom-chunk-collection huggingface dataset:
# Gridshells in Recent Research--A Systematic Mapping Study
## 3 Gridshells in Recent Research
### Which Types of Gridshells, Which Materials, and Which Parts of the Gridshells Are Investigated?
#### 3.4.3 Gridshell Type  
The gridshell type facet was mapped in the four categories, _bending active, smooth, discrete_ and _not mentioned_, as described in Section 1.1. As seen in Figure 15, the type studied is typically _discrete_ with 79 studies. This is followed by studies on _bending active_ gridshells (66) and _smooth_ (6). There were, however, 176 studies where the type was _not mentioned_ or not possible to determine.  
The _bending active_ gridshells, marked in blue in Figure 12, typically focus on _all parts_ or the _global shape_ regarding the gridshell part. Furthermore, the studies on _bending active_ gridshells are mostly _timber_ or _composite_ regarding material. Examples from the _bending active_ facet are [21; 24; 34]. The _discrete_ gridshells, marked in red in Figure 12, typically focus on _all parts_ or the _node_ regarding the gridshell part. Furthermore, the studies on _discrete_ gridshells are mostly _steel_ regarding material. Examples from the _discrete_ facet are [64; 65; 66]. The _smooth_ gridshells, marked black in Figure 12, are relatively few and placed with no particular trends regarding part or material. Examples from the _smooth_ facet are [23; 67; 68]. Examples of studies where the type was _not mentioned_ are [54; 69; 70].  
Figure 14: Gridshell part facet and the number of articles.  
Figure 15: Gridshell type facet and the number of articles.
## 1 Introduction  
High-intensity focused ultrasound (HIFU) is a technique that utilizes ultrasonic waves to focus and deliver mechanical energy into a small volume inside a material [1]. It is a non-invasive therapeutic technology used in clinics as an alternative or complement to conventional surgery. The mechanical energy delivered into the targeted tissues can stimulate or ablate the tissues. For example, HIFU had non-invasively permeabilized the blood-brain barrier to deliver drugs into brain tissue [2]. More importantly, HIFU can be used to ablate various benign and malignant tumors by applying temperatures above 60 \({}^{\circ}\)C [3; 4; 5; 6].  
In the case of using HIFU therapy, it is important to raise the temperature at the target as quickly as possible to reduce thermal damage to the surrounding tissues. If HIFU is exerted for a long time, nearby non-targeted normal tissue is damaged by the transferred heat energy. For the validation of these HIFU treatment devices, a tissue-mimicking phantom with an inserted thermal probe array has been developed [7; 8; 9]. The phantom should have acoustic properties that are similar to those of soft tissue, such as speed of sound and attenuation coefficient. To minimize ultrasound reflection inside the phantom and make it transparent to ultrasound propagation, thermal probes such as thermocouples [9] and thermochromic material [10] have been used instead of conventional PT-100 probes. The thickness of the thermal probes needs to be much thinner than the wavelength of ultrasound (1.57 mm for 1 MHz ultrasound frequency). Non-metallic thermal probes are also needed to minimize the acoustic impedance mismatch and increase the ultrasound transmission. Since the minimum beam diameter of HIFU deices is about 1 mm [3,11,12], a spatial resolution of 1 mm or less is also required for temperature monitoring systems. For this purpose, polyimide-based thin-film RTD arrays [7,13] have been developed as thermal probes inside the phantom. The thin-film includes a total of 100 RTDs that are arrayed in a 10 \(\times\) 10 layout to measure the temperature distribution inside the phantom caused by HIFU [13].  
The use of HIFU leads to rapid changes in temperature inside the phantom, which necessitates using thermal probes with fast response times. While the thin-film RTD array sensors are expected to have a fast response time due to their small size, there is a lack of quantitative results on their effectiveness. This study aims to characterize and discuss the thermal response time of the thin-film RTD sensor.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 4 Theoretical Analysis and Experimental Results
### Results and Analysis
#### 4.4.4 Influence of the Node Number on Network Lifetime  
Figure 14 shows the impact of the number of nodes on network lifetime. From the graph, it can be observed that when the network adopts DBR, the network lifetime is not sensitive to changes in the number of nodes. This is because as the number of nodes increases, there is an increase in redundant data packets within the network. For networks employing EEDBR and EAVARP, the network lifetime initially increases with an increase in the number of nodes and then levels off. This is due to the fact that as the number of nodes increases, there are more paths from source nodes to sink nodes. However, the presence of redundant data packets or additional control packets results in higher energy consumption. Since APCDBRP exhibits lower energy consumption, it achieves the highest network lifetime.  
Figure 14: The influence of the node number on network lifetime.  
Figure 13: The influence of the node number on the total energy consumption.
## 2 Background and Numerical Procedures
### The Finite Element Models  
#### 2.4.1 The SENB and SENT Specimens  
The SENB and SENT have been selected as fracture mechanics specimens herein to study the ductile fracture behavior of pipeline steels. Figure 2 shows the configurations of the edge-cracked specimens, where W (W = 12.7 mm) is the specimen width, \(a\) is the crack depth, and 2H is the weld width. The span (S) of the SENB is molded as four times of specimen width (\(S\)/W = 4) and 2L/W = 10 for SENT. The weld fracture specimen is modeled as a bi-material (veld metal and base metal), and the weld zone remains unchanged (2H \(\times\) W= 6 mm \(\times\) 12.7 mm) in all of the numerical analyses. Only a half specimen is modeled due to symmetry. Finite element calculations are performed while using two-dimensional (2D) plane strain models with four-node elements (ABAQUS: CPE4). Figure 2c shows the global mesh arrangement for all models. The region, 3.0 mm above the symmetrical interface, was uniformly meshed (0.1 mm \(\times\) 0.1 mm) and it was used for the simulation of the ductile fracture process. Regarding to the ductile fracture on the basis of complete Gurson model, the element is assumed to damage fail by a specific judgement that the void volume fraction reaches a certain value f\({}_{\text{F}}\) according to the relation f\({}_{\text{F}}\) = 0.2 + 2f\({}_{0}\) by UMAT in ABAQUS. The deformed elements appear along the damage layer in front of the crack tip. More details regarding the ductile fracture simulation model were presented in [38].  
Figure 1: The true stress-strain curve of weld material with different strength mismatch ratios.  
Figure 2: Schematic plots of the specimens, (**a**) single edge notched bending (SENB); (**b**) single edge notched tension (SENT); and, (**c**) finite element mesh.
## 3 Methods
### Linear SVM Classification  
SVM identifies a hyperplane or decision boundary that separates instances (in this case, channels) with a maximum margin of the support vectors to separate instances into classes (in this case, clusters) [15; 16]. The hyperplane for 12 subjects is specified by weights, which are used to construct a new dataset.  
The vector \(\overrightarrow{\mathrm{x}}\) represents a data point in the n-dimensional space. Here, x is the vector of 31 channel cluster ids. The values of x can be integers 0 to 5, corresponding to six clusters. A \(12*31\) matrix for 12 subjects and 31 channels are obtained. The main task of the linear SVM is finding the linear model [11]  
\[\mathrm{f(\overrightarrow{\mathrm{x}})}=\begin{cases}1&\quad\mathrm{if}\ \ \mathrm{w}\ \overrightarrow{\mathrm{x}}+\mathrm{b}\geq 1\\ -1&\quad\mathrm{if}\ \ \mathrm{w}\ \overrightarrow{\mathrm{x}}+\mathrm{b}\leq-1 \end{cases}\]  
that is defined by the best hyperplane:  
\[\overrightarrow{\mathrm{w}\mathrm{x}}+\mathrm{b}=0.\]  
\(\overrightarrow{\mathrm{w}}\) and b need to be determined. They are equivalent to the objective of maximizing the margin:  
\[\mathrm{Margin}=\frac{2}{\left|\overrightarrow{\mathrm{w}}\right|}\]So, the main factor that affects the classification is vector \(\overset{\rightarrow}{\mathrm{w}}\) which is the weight in the linear model. An assessment of this vector \(\overset{\rightarrow}{\mathrm{w}}\) can help to show how the data points are classified and how the classification is determined. The weights, \(\overset{\rightarrow}{\mathrm{w}}\), indicate the importance of each channel for separating the channels into 6 groups for a given subject [3].  
The k-mean SVM boosting method was defined by [PERSON] et al. in Ref. [17]. Then, it was applied to predict cancer by [PERSON] et al. in Ref. [18].  
The SVM model is applied to each subject based on the k-means cluster labels of the EEG time series. Then, the weights (\(\overset{\rightarrow}{\mathrm{w}}\)) can be aggregated across subjects to construct a subject by the weight dataset to test the most important channels for distinguishing between subjects with PTSD and the healthy control subjects (Figure 1).
## 2 Collision Grid Map Scheme and Convolutional Neural Network  
### Collision Grid Map Scheme  
There is an invariant of the permutation problem, which is not efficiently learned in an occurring matter of a different order, as 3D point cloud data is applied to a Convolutional Neural Network. The proposed method [25] modelled voxel of the binary occupancy grid, the density grid, and the hit grid. Then the network is learned by voxels to solve an invariant of the permutation problem. However, the Collision Grid Map scheme with a two-channel is proposed to predict collision.  
Equation (1) shows measurement data \  
The Posterior of the Collision Grid Map is obtained as shown in Equation (2). In order to calculate the posterior efficiently, log odds notation can be defined, as shown in Equation (3).  
\[p(m|\,\mathbf{s})=\prod_{i}p(m_{i}|\,\mathbf{s}) \tag{2}\]  
\[l(m_{i}|\,\mathbf{s})=\log(\frac{p(m_{i}|\,\mathbf{s})}{1-p(m_{i}|\,\mathbf{s} )}) \tag{3}\]  
Equation (4) is shown as applying log odds notation to Equation (2). Log odds notation is calculated using present point cloud data and previous point cloud data. When an initial grid map is free state, the initial Collision Grid Map can be omitted.  
\[l(m_{i}|\,s_{1:n})=l(m_{i}|\,s_{n})+l(m_{i}|\,s_{1:n-1})-l(m_{0}) \tag{4}\]  
Equations (5) and (6) show \(l(m_{i}|\,s_{n})\) that is modeled to predict the collision situation.  
\[l(m_{1,\,i}|\,s_{n})=\mathrm{G}(x_{n},y_{n}) \tag{5}\]  
\[l(m_{2,\,i}|\,s_{n})=\frac{\sum_{2:\,n}}{m_{1,\,i}} \tag{6}\]  
The first channel of the Collision Grid Map uses the discretization function \(\mathbf{G}\) to grid the x and y data in the point cloud. When discretization data corresponds to the grid cell, grid cell values are added up in counts one by one such as in Equation (4). The second channel is consisted of the average of depth z data. The value of the cell in the first channel is constructed from x-y data based on the probability method. The value of the cell in the second channel is shown as a safe distance between the end effector and the cow.  
The example of generating the first channel in the Collision Grid Map and Collision Grid Map is shown in Figure 1. When the Collision Grid Map is composed of a two-channel, an invariant of the permutation problem that occurs to apply point cloud to a Convolutional Neural Network is solved and raw point cloud data size becomes reduced. By modeling the Collision Grid Map, networks are able to learn not only distribution of the x-y plane data but also collision safety distance that is depth z data.
# The Noise-Reduction Characteristics of Microstructure of Dragonfly Wing Leading Vein
## 1 Introduction
From the above analysis, it can be seen that under certain conditions, the leading-edge sawtooth structure can effectively reduce the leading edge noise caused by the interaction of incident turbulence and the leading edge of the wing, as well as the wing self-noise caused by the pressure fluctuation and vortices on the upper surface of the wing. In the course of studying the flight noise of flapping wing air vehicles, the low-noise flight characteristics of dragonfly wings are found. Through observation, it is found that the leading edge of dragonfly wings has unique microstructure distribution, and similar microstructure have been proven to have the effect of reducing the leading-edge noise. Based on this, this paper mainly studies the morphology of dragonfly wing leading edge and its noise-reduction mechanism and establishes a bionic model. The geometric characteristics of the optimized model are simulated and analyzed to prepare the microstructural samples, and the experimental tests are carried out to reduce the noise of the aircraft. The following parts of this paper are arranged as follows: The second part is mainly about observing the microstructure of dragonfly wings and building bionic models. In the third part, the computational fluid dynamics (CFD) simulation analysis of microstructure is carried out, and the microstructural model is optimized. In the fourth part, the microstructure samples are processed and tested. Finally, the noise-reduction characteristics of the dragonfly wing microstructure are summarized and analyzed.
# A Fine Fast Acquisition Scheme for a Communication and Navigation Fusion System
## 5 Simulation Results and Analysis
### Performance Analysis
#### 5.2.2 Acquisition Time Analysis
TDCC method uses compressed correlator to reduce the search space size by about 50%, which leads to shorter acquisition time than DD/MAX and MAX/TC methods. The proposed SSR method uses CPPI to reduce the search space size by about 99% and uses ATI to reduce the search space number by about 81%. DD/MAX and MAX/TC methods focus on improving the detection performance so that the MAT can also be reduced. The proposed DD-MAX/TC-CACFAR method can effectively improve the detection performance compared with the \begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Signals Received** & **Number of Adjacent FSBSs Obtained** \\ \hline
4 & 10 \\
5 & 11 \\
6 & 12 \\
7 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of adjacent FSBSs. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline PRN code number & 12 \\ Cell number in one block & 70 \\ Block number & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: New search space size. other three methods because of the utilization of double dwell coarse-to-fine technique and CACFAR technique. The high \(\text{P}_{\text{fa}}\) of DD/MAX and MAX/TC methods in Figure 14b is the key factor leading to long acquisition time in Figure 17b when SNR is less than about \(-35\) dB. The lower \(\text{P}_{\text{d}}\) of DD/MAX and MAX/TC methods leads to high acquisition time compared with the other two methods when SNR is more than about \(-35\) dB. Figure 18 shows the influence of CIT and NCT on acquisition time. As a matter of fact, the influence of CIT and NCT is nearly the same as the influence of SNR. Acquisition time decreases as the CIT or NCT increases. On the other hand, the frequency error loss and square loss of CIT and NCT becomes bigger with larger CIT and NCT. The larger CIT and NCT require more data, which indicates more time. Therefore, time taken for H0-SS, which the increase of SNR has no effect on, increases as the CIT and NCT increase. Figure 16: (**a**) Acquisition time versus SNR (\(\text{P}_{\text{fa}}=0.001\)); (**b**) acquisition time versus multiple parameter \(\text{T}\) (\(\text{SNR}=-36\) dB). Figure 17: (**a**) Acquisition time versus SNR (\(\text{P}_{\text{fa}}=0.001\)) (noise power remains constant); (**b**) acquisition time versus versus SNR (signal power remains constant).
## 3 Materials and Methods
### Proposed Approach
#### 3.2.2 Model Learning Stage  
According to the time-series regression conceptualization of the problem, a long short-term memory (LSTM) model [34] is proposed in the model selection step. Indeed, LSTM models have proven to be capable of dealing with time-series regression problems in several real SC applications [30, 35, 36, 37]. Regarding the training strategy, a local training strategy is proposed with the root mean squared error (_RMSE_) as the loss function to find the optimal internal parameters of the LSTM model. Although the LSTM model can be trained globally on multiple components, and this option reduces the number of models that need to be maintained, it requires as input a unique feature vector for all components. Therefore, considering the possibility that different features can affect different components, it is proposed to perform training individually for each component. Furthermore, the approach of adopting one model separately for each component has been widely applied in the automotive sector when developing predictive approaches for estimating demand risks [20, 38]. In ref [20], training multiple local models instead of one global model that provides forecasts for all the components led to better results for regression problems. The _RMSE_ was adopted as the loss function as it is widely implemented for solving regression problems and because its formulation helps in avoiding large prediction errors.  
Lastly, a grid search procedure executed individually for each trained model is proposed to find the best value for the number of layers and the learning rate in the hyperparameter tuning step. Although other hyperparameters (i.e., those parameters that cannot be directly learned from data) define the structure of an LSTM model, only the values of these hyperparameters are optimized as they are identified as the most relevant in the tuning process of LSTM models [39]. Lastly, an early stopping mechanism is adopted to identify the best value of the number of epochs. The number of epochs can be regarded as the number of consecutive iterations for which the model is trained to find the best value of the internal parameters that minimizes the adopted loss function. Under this procedure, a high number of epochs can be initially adopted as input, but the training of the model can be automatically stopped early if, for \(\sigma\) consecutive epochs, no improvements in the _RMSE_ computed on the validation dataset are found. Adopting an automated strategy to properly train each model is fundamental for dealing with the typically high number of components supplied in the automotive sector.
## 2 Materials and Methods
### Functionalized Processing of Data  
With the continuous upgrading of data observation equipment, an increasing number of data types are being collected, and the data format is becoming more complex. At this time, the conclusions drawn by traditional statistical analysis methods often deviate greatly from the actual situation, and functional data are introduced to solve this problem. Functional data are used to convert the observed discrete data into a continuous curve through the basis function and apply it to the statistical analysis model to obtain results that are closer to the actual conclusion. Compared to traditional methods, it is able to take advantage of additional information such as dynamic changes, smoothness, etc. To functionalize the data, the process is as follows. Step 1: Observe the error of the data. Generally, observations with errors were smoothed and observations without errors were interpolated [37]. The air quality monitoring stations recorded the data with errors; therefore, the smoothing method was more appropriate to fit the data. Step 2: Selection of the basis function. The Fourier basis function is suitable for fitting data with periodicity, and the B-spline basis function for fitting non-periodic data [38]. Given that the periodicity of the air quality data obtained in Fujian Province was not obvious (Figure S1), the B-spline basis function was used to functionalize the data in this study. If a set of basis functions is defined as \(\phi_{m,k}(t)\), the estimate can be calculated as follows [39]:  
\[\hat{x}(t)=\sum_{k=1}^{K}c_{k}\phi_{m,k}(t) \tag{2}\]  
\[\phi_{m,k}(t)=\left\{ \begin{array}{l}1,t_{m}\leq t\leq t_{m+1}\\ 0,\textit{otherwise}\end{array} \right. \tag{3}\]  
\[\phi_{m,k}(t)=\frac{t-t_{m}}{t_{m+k-1}-t_{m}}\phi_{m,k-1}(t)+\frac{t_{m+k}-t} {t_{m+k}-t_{m+1}}\phi_{m+1,k-1}(t) \tag{4}\]  
where \(c_{k}\) is the coefficient matrix, \(t_{m}\) is the time point of the observed data, \(k\) is the order of the B-spline basis function, and \(K\) is the number of basis functions. In this study, fourth-order B-spline basis functions were set to fit the air quality data, so the value of \(k\) was four. Step 3: Determine the smoothing parameters. To balance the relationship between goodness-of-fit and smoothness, the roughness penalty method was used. If \(x(t)\) is assumed to be derivable, then the mean square error with a penalty term can be defined as follows [39]:  
\[PENSE_{\lambda}(x|y)=\sum_{i=1}^{N}\left\{y_{i}-x(t_{i})\right\}^{2}+\lambda \cdot PEN_{2}(x) \tag{5}\]  
where \(N\) is the number of raw data, \(\lambda\) is the smoothing parameter, \(y_{i}\) is the observed data, and the penalty term function \(PEN_{2}(x)\) is \(\int_{T}\left\{D^{2}x(t)\right\}^{2}dt\). This is calculated in terms of the curvature of the function, where \(D^{2}(\cdot)\) is the second-order derivative of the function. The higher the \(PEN_{2}(x)\), the greater the volatility of the function.
# Evaluating Preprocessing Techniques for Unsupervised Mode Detection in Irish Traditional Music
## 3 Methods
### Evaluation Metrics  
In this work, we have computed several metrics to evaluate the quality of the clustering outcomes. These metrics provide insights into how well the clustering algorithms assign data points to their respective modes. Specifically, we calculated purity, which measures the alignment of clusters with the ground truth labels, as well as Normalized Mutual Information (NMI) and the Adjusted Rand Index (ARI), which assess the coherence and separation of the clusters in relation to the true labels.  
* Purity measures how well each cluster is dominated by a single musical mode. A higher purity score indicates that most samples within a cluster belong to the same mode. We report the highest purity score for each mode.
* Normalized Mutual Information (NMI) [55] evaluates the agreement between the clustering results and the ground truth labels, accounting for the entropy of both. Unlike purity, NMI balances cluster homogeneity and completeness, making it robust to variations in cluster size. A higher NMI score indicates a better alignment between the clustering structure and the true mode labels.
* The Adjusted Rand Index (ARI) [56] assesses clustering quality by comparing the similarity between predicted and true labels, adjusting for random chance. The ARI rewards both correct cluster assignments and correct separations while penalizing incorrect merges or splits. Higher ARI values suggest better clustering performance.