esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Acoustic Mapping in Automotive Wind Tunnel: Algorithm and Problem Analysis on Simulated Data
## 1 Introduction
The DAMAS capability of returning correct source locations and strengths was assessed in relation to frequency, source separation distance, and array design. In order to achieve high spatial resolution in all directions, four planar arrays surrounding the object in the wind tunnel test section were exploited in [11]. Acoustic data were processed with different mapping techniques. [PERSON] et al. [12] compared the performances and the computation time of different acoustic imaging methods applied to data achieved with one planar array and with two arrays placed on orthogonal planes. [PERSON] et al. exploited Compressed Sensing techniques to get volumetric maps [13]. [PERSON] et al. described how to obtain accurate high-resolution maps with inverse methods using a single planar array [14]. In that paper, different approaches were compared such as Covariance Matrix Fitting (CMF) [15] with different solvers and an Equivalent Source Method (ESM) based on Iteratively Re-weighted Least Squares (IRLS) and Bayesian Regularization (BR) [16,17]. Due to its relatively low computation demand, ESM has been chosen for challenging applications such as an aircraft model in a wind tunnel [18]. First, the method was tested on simulated data to evaluate its ability in terms of localization and dynamics, and then the results on experimental data were shown. The aim of this paper is to identify the challenges in the application of acoustic mapping techniques to automotive wind tunnel testing and to evaluate the effect of different testing and processing parameters on the resulting noise source map. The experimental aspects studied are the use of a single or dual planar array and the effect of different masking levels and Signal-to-Noise Ratios (SNR). The processing parameters considered are the definition of the ROI and its discretization with potential sources, which heavily affect inverse method performances and computational time. In order to objectively evaluate the quality of the maps obtained, a set of scores has been defined. The paper is organized as follows. Section 2 describes the inverse acoustic problem and the data preprocessing, and Section 3 illustrates the strategies to achieve the volumetric acoustic maps. The virtual experiment synthesized to perform a sensitivity analysis in relation to experimental and numerical factors (e.g., noise and calculation volumetric point grid resolution) is described in Section 4. The results of the sensitivity analysis are reported in Section 5. Section 5 shows the results obtained on a simulated test case which considers all the effects present in a real experiment. Finally, Section 6 draws the conclusions from the parametric study conducted.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Energy Consumption for Direct Transmission from CH to Cloud Server  
Figure 12 shows how much energy is utilized when data is transferred straight from a CH to a cloud server without the use of any between nodes. The idea is that data is sent directly to the cloud server in this case rather than the fog node, which, on average, uses more energy as shown in the figure. This is because it is obvious that without an intermediate relaying node, more energy from the source node is used to send the data to the cloud server.
# Semiautomated Statistical Discontinuity Analyses from Scanline Data of Fractured Rock Masses
## 4 Application Example  
### Geological and Geographical Situation  
Scanline surveys in granodioritic greisses were made to collect discontinuities for rock mass characterization in a tributary valley of the Otztal valley (Austria). Geologically, the fractured granodioritic greisses belong to the Otztal-Stubai basement complex [38]. Eight scanlines with a total of 596 discontinuity measurements were sampled at outcrops at altitudes of about 2150-2450 m.a.s.l. (Figure 3). Given that only scanlines of a selected geological domain were taken, i.e., only those located in the granodiorite greis unit, the numbering of the scanline does not correspond to a consecutive sequence. Scanlines labeled as 2, 5, 7, 8, 11, 12, and 14 are outside the area of interest and therefore neglected. However, a new labeling of the scanlines is not necessary, because the proposed Octave m-files do not require consecutive numbering. The scanlines are oriented in various directions and are located along planar surfaces of the rock walls. Due to inaccessibility and the difficult outcrop situation of the study area, it was a challenge to find favorable and large-enough scanline outcrops. However, locations were selected to enable sufficient scanline lengths ranging from 9 to 28 m. As lower limit, all discontinuities with trace lengths of at least 10 cm intersecting the scanlines were recorded. Parameters according to Section 2.1 were measured and recorded. Termination characteristics of the upper and lower ends of the discontinuity are recorded as (i) termination not visible (labeled as 1), (ii) termination in intact rock (labeled as 2), and termination at another discontinuity (labeled as 3).
## 1 Introduction
### Literature Review
#### 1.2.2 Circuity in the Efficiency of Transportation Networks  
The investigation of circuits plays a crucial theoretical role in the disciplines of transport planning, network design, and urban planning. When the circuitry is analyzed in relation to travel time, the initial focus on the spatial arrangement of the road network evolves into a broader assessment of transport efficiency. This encompasses the effectiveness of various modes of travel within their respective networks.  
The existing literature on the effectiveness of various modes of transportation, in terms of circuity, primarily falls into the following two categories: (1) investigations into efficiency across different transportation modes and (2) examinations of its correlation with travel distance patterns. From varying transportation modes' perspectives, distinctions exist in their individual transportation efficiencies. Public transportation commonly adheres to predetermined routes [20], leading to increased circuity. When opting for travel by car, as opposed to public transportation, the vehicle typically prioritizes selecting the quickest route over the shortest route. When faced with traffic congestion or complex road conditions in urban areas, private vehicles frequently opt to take detours [32]. From the standpoint of travel distance, the efficiency of automobile travel is inversely related to Euclidean distance, with an intercept [33]. In terms of bicycle travel, a study conducted in Ohio (United States) indicates that there is a positive correlation between the degree of detour and the distance traveled [34].
## 2 Materials and Methods
### Statistical Information Fusion Function  
Let k be a node that fuse information where \(k=1,2,\ldots,K\) The set that consists of all child nodes is denoted by \(N_{k}\) with size \(e_{k}\). The received information from child nodes are given as \(r_{i}\) where \(r_{i},ieN_{k}\). The information fusion at node k can be expressed using Equation (3) as  
\[\hat{\theta}_{k}=f(s_{k},r_{1},r_{2},\ldots,r_{i};\ldots,r_{e_{k}}) \tag{3}\]  
where \(f(.)\) is the information fusion function that combines the received information. In smart grid environments, three major types of errors: transmission errors, quantization errors, and measurement errors usually exist in the received information. Statistical estimation can be applied to the information fusion functions and exploit the power of statistics of error sources to obtain accurate information fusion results.
## 2 Design and Methods
### Structural Analysis Using ANSYS Finite Element Analysis
#### 2.2.2 FEA Model Validation  
FEA model validation is summarized in Table 2. Mesh analysis, a pivotal facet of FEA, profoundly influences the precision and reliability of its outcomes. FEA relies on discretizing complex structures into finite elements interconnected at nodes. The models use a mixture of tetrahedral and hexahedron quadratic elements throughout the analysis. Mesh quality, measured through aspects such as aspect ratio, Jacobian ratio across Gauss points, and overall element quality, is paramount for accurate stress and strain visualization. For the aspect ratio, an ideal element will have a value of 1 in all these aspects, but it cannot always be maintained. In general, the aspect ratios are maintained in between 1 to 5 for a good-quality mesh in FEA [22]. The Jacobian ratio measures the deviation of an element's shape and a value close to or equal to 1.0 is desired [23]. The element quality (EQ) describes area or volume over sum of square of edge lengths. EQ measures how far elements are from being perfect [24].  
The mesh quality, be it fine or coarse, balances computational resources and precision. Proper meshing ensures solution convergence and enables stress and strain visualization. It helps identify stress concentrations and accounts for local effects. Mesh analysis is vital for model validation, aligning FEA simulations with real-world observations, and thus enhancing scientific rigor and reliability in FEA applications.  
Static structural modulus in ANSYS Workbench provides a mesh convergence tool. This tool uses the calculated stresses across all the node points to find the areas of highest stress. The software then reduces the meshing in the areas around these elements of high stress in an iterative process until the stresses have converged to a required percentage. Figure 3 shows an example of one of the convergence studies. The nodes were increased from 71,749 to 428,258 over three iterations with the last iteration resulting in a 3.3% change from the previous one.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Aspect** & **Average Jacobian** & **Average Element** \\  & **Ratio** & **Ratio (Gauss Points)** & **Quality** \\ \hline Concept 1: Rotational Lift & 3.6 & 0.949 & 0.785 \\ Concept 2: Scissor Lift & 2.3 & 0.96 & 0.733 \\ Concept 3: Double Pivot Lift & 1.1 & 0.950 & 0.787 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of FEA model validation.  
Figure 3: FEA model validation with a mesh convergence analysis.
## 3 Imaging Algorithm Based on Modified Chirp Fourier Transform
### Coarse Rotation Ratio Estimation Based on DCFT  
In Section 3.1, we showed that if \(K_{\Omega\omega}\) is known, azimuth compression can be achieved with only one MCFT. However, the angular velocity and angular acceleration of the non-cooperative target are unknown. Therefore, the rotation ratio \(K_{\Omega\omega}\) needs to be estimated. As demonstrated in the Section 2 analysis, the rotation ratio is also the ratio of the chirp rate \(k_{i}\) of the sub-echo signal to the central frequency \(f_{i}\). Therefore, the rotation ratio \(K_{\Omega\omega}\) can be estimated indirectly by estimating the chirp rate and the central frequency of the sub-echo LFM signal. Not directly estimating the target's rotational parameters greatly reduces the difficulty of estimating the parameters. In Section 3.1, we showed that CFT is a linear transformation, which does not produce cross-terms for multi-component LFM signals and exhibits a strong anti-noise performance. Therefore, we can use CFT to estimate \(K_{\Omega\omega}\).  
However, the direct discrete chirp Fourier transform (DCFT) has two stringent conditions: the discrete signal parameters must be integers and the signal length must be prime, otherwise the performance will decline sharply [31]. These two requirements are hard to satisfy in practice. Therefore, the revised DCFT is usually used [32], which is defined as:  
\[F(k,l)=\frac{1}{\sqrt{N}}\sum_{n=0}^{N-1}s(n)W_{N}^{kn+\frac{1}{2N}ln^{2}} \tag{24}\]  
where \(l\) is the digital chirp rate. When calculating \(F(k,l)\), we can use the fast Fourier transform: each time, \(l\) is fixed and the discrete Fourier transform of \(x(n)W_{N}^{(l/N)n^{2}}\) is calculated, so that each row of \(X(k,l)\) can be obtained in turn.  
For example, the parameters of a three-component LFM signal are shown in Table 1, where the signal form and parameter interpretation here are shown in Equation (9). The signal is transformed by DCFT, and the result is shown in Figure 3. From the MCDFT estimation result, the estimation accuracy is high and consistent with the set parameters.  
The central frequency and chirp rate of the LFM signal can be obtained by DCFT transformation, and the rotational parameters \(\widetilde{K}_{\Omega\omega}\), of the LFM signal can then be obtained. In practice, as there are many scattering points in the same range unit, a spectrum superposition of the DCFT spectrum may be produced, and estimation error easily occurs in peak detection based on the maximum amplitude criterion. In order to reduce the error between the estimated value \(\widetilde{K}_{\Omega\omega}\) and the true value \(K_{\Omega\omega}\), we can estimate the parameters of the sub-echoes of several scattering points in several different range cells and average the estimated values \(\widetilde{K}_{\Omega\omega}\).
## 2 Test and Result Analysis
### Test Results Analysis
#### 2.3.2 Damage Characteristics  
As depicted in Figure 8, under a single detonation of 3 kg of TNT, an elliptical perforation appeared on the top plate of chamber 2 of G, with a size of 41.50 cm (longitudinal direction) \(\times\) 45.50 cm (transverse direction). In addition, a large area of concrete spalling occurred on the bottom surface, with exposed steel bars and no obvious damage in other areas. As depicted in Figure 9, PCG only showed a slight local indentation with a size of 20 cm \(\times\) 30.50 cm, and only a small amount of bubbles and signs of carbonization appeared on the surface of the polyurea, with no other damage detected. The polyurea coating remains intact and effectively wraps around the box girder. Comparing the damage of sample G and PCG, it can be seen that the polyurea is capable of absorbing a portion of the explosion energy, resulting in superior blast resistance for PCG compared to G. The damage characteristics of PCGR under secondary detonation are depicted in Figure 10. After the second blast with a charge of 5 kg, a nearly circular perforation measuring 52.90 cm \(\times\) 58.90 cm was observed on the top plate of the mid-chamber. In addition, obvious cracks were found near the supports of compartments 1 and 3, although no obvious peeling or damage was found on the top and baseplates of other compartments. The comparison of damage characteristics between specimens PCG and PCGR shows that for polyurea-coated reinforced concrete beams with box sections exposed to repeated explosions, the material near the explosion center deteriorates after the initial explosion, leading to the formation of defects. These defects led to a rapid increase in damage during the subsequent explosion, greatly reducing the structural resistance of the box section to the explosion effect.  
Figure 8: Dimensions of the hole of mid-chamber (G). (**a**) Dimensions of the hole of the top surface (longitudinal direction). (**b**) Dimensions of the hole of the top surface (transverse direction). (**c**) Dimensions of the hole of the bottom surface (transverse direction).  
Figure 7: Comparison of overpressure of explosion shock wave.
## 3 Data and Empirical Results
### Empirical Results
#### 3.2.4. Vine COPAR-Based Granger Causality  
The results of the LR tests is shown in Table 3 under null hypothesis of non-causality. For the manufacturing sector, Granger-bidirectional causality existed between GPP and FDI, and TRADE was Granger-causal of FDI. Consistently with the agriculture sector, Granger-bidirectional causality occurred between FDI and TRADE, and GPP was Granger-causal of TRADE. In addition, for the service sector, Granger-bidirectional causality took places between GPP and FDI as well as GPP and TRADE, while FDI was Granger-causal of TRADE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Null Hypothesis** & **Statistics** & **Manufacturing** & **Agriculture** & **Service** \\ \hline FDI does not & LR test & 13.406 & 0.367 & 22.287 \\ Granger-cause GPP & \(p\)-value & 0.004 *** & 0.947 & \(5.683\times 10^{-5}\)*** \\ \hline Trade does not & LR test & 0.361 & 4.368 & 24.438 \\ Granger-cause GPP & \(p\)-value & 0.948 & 0.224 & \(2.023\times 10^{-5}\)*** \\ \hline GPP does not & LR test & 17.956 & 3.714 & 23.443 \\ Granger-cause FDI & \(p\)-value & 0.000 *** & 0.294 & \(3.265\times 10^{-5}\)*** \\ \hline TRADE does not & LR test & 10.301 & 13.814 & 19.852 \\ Granger-cause FDI & \(p\)-value & 0.016 *** & 0.003 *** & 0.000 \\ \hline GPP does not & LR test & 2.924 & 34.975 & 23.205 \\ Granger-cause TRADE & \(p\)-value & 0.404 & \(1.233\times 10^{-7}\)*** & \(3.660\times 10^{-5}\)*** \\ \hline FDI does not & LR test & 3.550 & 33.741 & 23.930 \\ Granger-cause TRADE & \(p\)-value & 0.314 & \(2.247\times 10^{-7}\)*** & \(2.583\times 10^{-5}\)*** \\ \hline \hline \end{tabular} Note: Significance codes are defined as *** = 0.01, ** = 0.05 and * = 0.1.  
\end{table}
Table 3: Vine COPAR-based Granger causality test results.  
Figure 2: The forecasts of the SEZ’s economic performance for the next five years.
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
###### Abstract  
Based on the latest design requirements proposed by the Beijing On-Line Isotope Separation (BISOL) project, a new Sn\({}^{22+}\)-based, 81.25 MHz CW radio frequency quadrupole (RFQ) with external bunching has been designed. This RFQ can accelerate Sn\({}^{22+}\) to 0.5 MeV/u with an output longitudinal-normalized rms emittance of 0.20 keV/u-ns over a length of 5.6 m. The tolerance and error analysis results indicate that this RFQ can handle a wide range of non-ideal beams while maintaining relatively lower longitudinal emittance growth and higher transmission efficiency. To maintain the beam intensity, the RFQ will simultaneously accelerate three kinds of high-charge-state mixed ions (\({}^{132}\)Sn\({}^{21+}\), \({}^{132}\)Sn\({}^{22+}\) and \({}^{132}\)Sn\({}^{23+}\)), the simulation results given by Impact-T show that the RFQ can achieve high transmission of the mixed beam. Compared with the previous Sn\({}^{21+}\)-based internal bunching RFQ scheme, this RFQ has a shorter length and smaller output emittance, which is beneficial to the designs of subsequent Medium-energy Beam Transport (MEBT) and Drift Tube Linac (DTL). In electromagnetic design, a four-vane structure with 48 tuners and 16 \(\pi\)-mode stabilizers (PSLs) were chosen. The results of the multi-physics analysis show that the maximum temperature rise and the maximum deformation of the cavity are 13.6 K and 40.3 um, respectively. The results simulated with CST Microwave Studio (CST) and HFSS software were consistent.  
CW RFQ; beam dynamics; high-charge-state mixed beams; electromagnetic design +
Footnote †: journal:  
Academic Editor: [PERSON]
## 2 Background
### Overview of Recurrent Neural Networks
#### 2.2.3 GRU Model  
Like LSTM, GRU is another improved version of the standard RNN. To solve the vanishing gradient problem of a standard RNN, GRU uses an update gate and reset gate. These are two vectors that decide what information should be passed to the output. The special thing about them is that they can be trained to keep information from long ago without washing it through time or to remove information that is irrelevant to the prediction. To explain the mathematics behind that process, we will examine a single unit of GRU given in Figure 3[36; 37].  
The update gate helps the model to determine how much of the past information (from previous time steps) needs to be passed along to the future. This is very powerful because the model can decide to copy all the information from the past and eliminate the risk of the vanishing gradient problem. It is calculated by Equation (8).  
\[z_{t}=\sigma(W^{(z)}x_{t}+U^{(z)}h_{t-1}+b^{(z)}) \tag{8}\]  
A reset gate is used to decide how much of the past information to forget and is calculated by Equation (9).  
\[r_{t}=\sigma(W^{(r)}x_{t}+U^{(r)}h_{t-1}+b^{(r)}) \tag{9}\]  
The final output of the cell is calculated by the Equation (10).  
\[r_{t}=z_{t}\odot h_{t-1}+(1-z_{t})\odot\tilde{h_{t}}) \tag{10}\]  
where candidate activation vector, \(\tilde{h_{t}}=tanh(Wx_{t}+r_{t}\odot Ul_{t-1}+b^{(h)})\).
## 3 Results and Discussion
### Ethanol-Water Extraction and Optimization
#### 3.3.2 Optimization for Ethanol-Water Extraction  
The ethanol-water extraction experiments had the main objective of identifying those extraction conditions (ethanol concentration, temperature and time) that achieve the maximum content of polyphenols, although it was proven that inulin was also extracted using water-ethanol mixtures as extracting solvents. Then, it was possible to perform a multiple-response optimization, in order to determine the experimental conditions that maximize the extraction of inulin and polyphenols with the highest antioxidant activity in these ethanol-water extracts. In this sense, it has been found that EtOH = 22.4%, T = 81 \({}^{\circ}\)C and t = 217 min give a desirability factor of 0.767 and result in expected values of TPC = 18.71 mg GAE/g (d.b.); Aox = 26.36 mM trolox/g (d.b.) and inulin content = 25.09 mg glu eq/g (d.b.). These values, compared those obtained in the compositional analysis, would represent 90% of the TPC, 38% of the Aox and 28% of the inulin content.
## 4 Resilience-Oriented Emergency Responses Driven by Machine Learning
### Rapid Damage Assessment
Similar detection approaches have also been proposed by [PERSON] and [PERSON] [102], as well as [PERSON] [103], who utilized image segmentation (pixel-wise classification) for post-disaster damage assessment. Meanwhile, [PERSON] et al. [104] proposed a classification approach for detecting damaged buildings immediately after earthquakes using images from social media platforms. Their method leverages transfer learning with a pretrained feature extractor and customizes a classifier using a small manually labelled dataset; additionally, they explored model interpretability techniques to visualize and gain insights into the decision-making process of the \"black-box\" model based on specific features. Figure 3: Illustration of a simple CNN architecture with regard to (binary) damage classification. Most recently, [PERSON] and [PERSON] [105] introduced an end-to-end model that establishes a direct connection between satellite imagery and large-scale damage maps. The primary objective of this model is to facilitate near-real-time decision-making on post-shock emergency responses. To achieve this, the model seamlessly integrates satellite imagery, geographic information systems (GIS), and DL techniques. The case study demonstrated that leveraging GIS enables the automated extraction of sub-images with remarkable accuracy; meanwhile, fine-tuning further enhances the robustness of damage classification, resulting in highly accurate building damage maps at a regional level. It is worth noting that vision transformers (ViTs) have recently emerged as an alternative to CNNs. They have also been adopted for the automated and rapid damage assessment of engineering structures and CISs. For instance, [PERSON] et al. [106] developed a ViT-based model specifically designed for crack detection in concrete and asphalt--the two most commonly used materials in structures and roadways. The ViT was demonstrated to outperform classical CNNs in their study.