esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Cracks in Arch Dams Using Monitoring Data and Numerical Models
## 4 Discussion and Conclusions
### Estimation of Crack Length and Depth
This relationship cannot be assured if the number of measuring points is reduced. In such cases, the relationship is not met in one out of four of the simulated cases. In summary, using higher precision devices improves crack detection, while having more monitoring points makes crack dimension estimates consistent with the actual crack size. The alternative methodology does not appear to provide benefits that outweigh the increased complexity involved. For this first analysis of the methodology, several simplifications have been made which partially limit the generalizability of the results and the comparison with other methodologies. This research is a basis for necessary further development involving reference cracks that can vary not only in dimensions, but also in crack position. In this way, the methodology is not only generalized but can also be extended to automatically detect the most probable position. Alternatively, it can be useful to compare this straightforward methodology with advanced methods like the ones using machine learning. Conceptualization, M.A.T. ; methodology, M.A.T., A.C., and E.S. ; validation, A.C.; investigation, A.C.; writing--original draft preparation, A.C.; writing--review and editing, M.A.T. and E.S. ; supervision, M.A.T. and E.S. ; project administration, M.A.T. ; funding acquisition, M.A.T. All authors have read and agreed to the published version of the manuscript. This research was funded by the Spanish Ministry of Science and Innovation within the CORCHEA Research Project (grant number PID2020-118820 RB-I00). Not applicable. The raw data supporting the conclusions of this article will be made available by the authors on request. **Acknowledgments:** We are grateful to the members of the research group SERPA--Dam Safety Research and the company ACIS2 in for the support provided. **Conflicts of Interest:** The authors declare that they have no conflicts of interest. The funders had no role in the design of the study or the collection, analysis, or interpretation of data.
## 2 Data Analysis and Preprocessing
### Analysis of Wind Power Characteristics
#### 2.1.2 Analysis of Different Time Scales  
Due to the fact that the sampling waveform in the previous text is only one week of data, there may be some specificity. In Figure 1, the data characteristics in the time dimensions of year, season, month, and week can be observed. After the same standardization process, we drew European standard fluctuation charts for different time periods, as shown in Figure 1b. It can be seen that, when observed on a weekly basis, the wind power shows a climbing phenomenon on the 5 th to 6 th days, while other time periods have very small values with occasional slight fluctuations. When observed on a monthly basis, it can be seen that the frequency of climbing phenomena is relatively low and there is no specific pattern. When observed quarterly and annually, it is found that this characteristic runs throughout the year and is not affected by quarters and months. It can be seen that wind power presents high volatility and randomness throughout the year, and it is difficult to find its regular characteristics. In a short time period, the peak-valley characteristics of the data are obvious, and the wind power is low at most times. When the wind blows, the power increases rapidly, and, after a period of time, it quickly falls back. This characteristic is different from the long-term stability of and short-term regular fluctuation in classical time series. Therefore, it is necessary to mine and analyze the features of this unstable data source.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
###### Abstract  
With the widespread application of infrared technology in military, security, medical, and other fields, the demand for high-definition infrared images has been increasing. However, the complexity of the noise introduced during the imaging process and high acquisition costs limit the scope of research on super-resolution algorithms for infrared images, particularly when compared to the visible light domain. Furthermore, the lack of high-quality infrared image datasets poses challenges in algorithm design and evaluation. To address these challenges, this paper proposes an optimized super-resolution algorithm for infrared images. Firstly, we construct an infrared image super-resolution dataset, which serves as a robust foundation for algorithm design and rigorous evaluation. Secondly, in the degradation process, we introduce a gate mechanism and random shuffle to enrich the degradation space and more comprehensively simulate the real-world degradation of infrared images. We train an RRDNet super-resolution generator integrating the aforementioned degradation model. Additionally, we incorporate spatially correlative loss to leverage spatial-structural information, thereby enhancing detail preservation and reconstruction in the super-resolution algorithm. Through experiments and evaluations, our method achieved considerable performance improvements in the infrared image super-resolution task. Compared to traditional methods, our method was able to better restore the details and clarity of infrared images.  
super-resolution; infrared image; degradation; spatially correlative loss Article
# An Ensemble Approach for Speaker Identification from Audio Files in Noisy Environments
## 3 Proposed Framework
### Learning  
In this phase, features are learned from the preprocessed data, represented as MFCC features. An RNN is employed due to its ability to process sequential data, which, in this case, consist of audio features. The RNN autoencoder compresses MFCCs and spectrograms from a higher-dimensional space into a lower-dimensional space. To accelerate model convergence, the decoder's expected output from the previous step (i.e., the previous epoch) is fed back as input into the decoder RNN. Figure 2 shows an RNN in its simplest form. The equations for a recurrent neural network (RNN) are given below to show how it maps an input \(x\) into an output \(y\):  
\[h(t) =f_{H}(W_{IH}\,x(t)+W_{HH}\,h(t-1))\] \[y(t) =f_{O}(W_{HO}\,h(t))\]  
where \(W_{IH}\), \(W_{HH}\), and \(W_{HO}\) are weight matrices (\(I\) for input, \(O\) for output, and \(H\) for hidden layers).
## 3 A Partial Multiplicative Dimensional Reduction-Based Reliability Estimation Method
### Numerical Demonstration and Validation
#### 3.2.2 A Cantilever Beam  
As shown in Figure 4, given a cantilever beam whose length is \(l\), width of cross section is \(b\), and height is \(h\), the right end of the cantilever beam is subjected to two forces, \(P_{x}\) and \(P_{y}\). \(l\), \(b\), and \(h\) are random variables, while \(P_{x}\) and \(P_{y}\) are interval variables (as shown in Table 2).  
Considering that the structural failure occurs when the maximum stress at the fixed end of the cantilever beam reaches the yield strength \(S=370\) MPa, the performance function becomes  
\[G=S-(\frac{6P_{x}l}{b^{2}h})-(\frac{6P_{y}l}{bh^{2}}) \tag{23}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Parameter 1** & **Parameter 2** & **Variables Type** \\ \hline \(b\)(mm) & 100 (mean) & 15 (standard deviation) & Normal variable \\ \(h\)(mm) & 200 (mean) & 20 (standard deviation) & Normal variable \\ \(l\)(mm) & 1000 (mean) & 100 (standard deviation) & Normal variable \\ \(P_{x}\)(N) & 50,000 (midpoint) & 3000 (radius) & Interval variable \\ \(P_{y}\)(N) & 25,000 (midpoint) & 2000 (radius) & Interval variable \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptions of the input variables in Section 3.2.2.  
Figure 4: Configuration of the cantilever beam.  
According to the proposed method, the following approximation is obtained:  
\[\begin{array}{c}M\approx M^{\prime}=[370\times 10^{6}-(\frac{3\times 10^{8} \times l}{p_{\text{A}}})-(\frac{15\times 10^{8}\times l}{bh^{2}})]\times\\ (370\times 10^{6}-3\times 10^{3}\times P_{x}-1.5\times 10^{3}\times P_{y})\div(1.825 \times 10^{8})\end{array} \tag{24}\]  
After computation, it is obtained that \(\eta=15.2>1\) and \(\beta_{\mathbf{X}}=1.93\), indicating that the structure is unsafe.  
In order to test the proposed method, 100 sample values of the approximated values obtained from Equation (24) and the corresponding values obtained by the original performance function are presented and compared in Figure 5, and the close match between them can validate the proposed method.  
To further investigate the accuracy of the proposed method, the errors (differences between the values obtained from the proposed method and the corresponding target values) obtained from \(10^{7}\) samples are presented in Figure 6, from which it can be seen that the method is unbiased and sufficiently accurate.  
Figure 5: Comparison between the original function and the approximation obtained by the proposed method.
## 1 Introduction
[20] systematically investigated the effects of spray characteristics (e.g., mass flux, inlet pressure, and nozzle tip to wall) and plate thicknesses on heat transfer. [PERSON] et al. [21] found that higher spray impingement velocity contributes to the heat transfer between the droplets and the wall. [PERSON] et al. [1] used infrared thermography to investigate the heat transfer characteristics of spray/wall interaction during spray impingement under typical diesel exhaust flow conditions and derived the effect of the exhaust flow conditions on the heat transfer characteristics. [PERSON] et al. [22] investigated the influence of injection rate, injection height, and crossflow on wall temperature evolution with infrared thermography. The results show that at higher jet densities, a higher injection rate promotes the cooling rate of the wall and the diffusion of the low temperature area. A lower injection height results in droplet breakup and promotes evaporation. Crossflow leads to an asymmetric distribution of wall temperature around the impinging center. Since the study of the heat transfer of spray/wall interaction is very challenging, it is increasingly important to investigate its characteristics via computational fluid dynamics (CFD). Well-established mathematical models have been built to investigate the heat transfer characteristics of spray impingement in SCR systems. [PERSON] et al. [23] included spray/wall interaction in his numerical model of UWS injection to calculate the formation of a wall film. An improved surface tension model was developed to improve the prediction accuracy of the liquid film distribution in the work of [PERSON] et al. [24]. [PERSON] et al. [25] proposed a two-zone spray model for the injection process of UWS. The proposed model can calculate the distribution of liquid film more accurately and, thus, improve the prediction accuracy of ammonia distribution uniformity. In this work, a numerical model of the spray process for the SCR system is proposed and implemented in the commercial CFD software FLUENT to investigate the heat transfer characteristics of the spray/wall impingement under the typical low load exhaust flow conditions of a diesel engine. A spray model developed in our previous study is applied to characterize the inhomogeneous mass distribution in the spray cone. The Stochastic Kuhnke Model is applied to account for spray/wall interaction properties by using the Weber number and critical transition temperature. The calculated heat transfer characteristics of spray/wall interaction are verified with the experimental results in ref. [1]. In addition, the effects of different spray/wall interaction models and initial wall temperatures on the wall film formation are simulated and discussed.
# How Useful Are Strain Rates for Estimating the Long-Term Spatial Distribution of Earthquakes?
###### Abstract  
Strain rates have been included in multiplicative hybrid modelling of the long-term spatial distribution of earthquakes in New Zealand (NZ) since 2017. Previous modelling has shown a strain rate model to be the most informative input to explain earthquake locations over a fitting period from 1987 to 2006 and a testing period from 2012 to 2015. In the present study, three different shear strain rate models have been included separately as covariates in NZ multiplicative hybrid models, along with other covariates based on known fault locations, their associated slip rates, and proximity to the plate interface. Although the strain rate models differ in their details, there are similarities in their contributions to the performance of hybrid models in terms of information gain per earthquake (IGFE). The inclusion of each strain rate model improves the performance of hybrid models during the previously adopted fitting and testing periods. However, the hybrid models, including strain rates, perform poorly in a reverse testing period from 1951 to 1986. Molchan error diagrams show that the correlations of the strain rate models with earthquake locations are lower over the reverse testing period than from 1987 onwards. Smoothed scatter plots of the strain rate covariates associated with target earthquakes versus time confirm the relatively low correlations before 1987. Moreover, these analyses show that other covariates of the multiplicative models, such as proximity to the plate interface and proximity to mapped faults, were better correlated with earthquake locations prior to 1987. These results suggest that strain rate models based on only a few decades of available geodetic data from a limited network of GNSS stations may not be good indicators of where earthquakes occur over a long time frame.  
e 25 May 2022  
Accepted: 2 July 2022  
Published: 5 July 2022  
**Published:** **Notic**: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# Comprehensive Analysis of Advanced Techniques and Vital Tools for Detecting Malware Intrusion
## 7 Malware Detection Tools
### Wireshark  
It is an indisputable fact that Wireshark is ubiquitous in the analysis of networks, no matter the context. This software is compatible with a wide array of operating systems, including Windows, OS X, Linux, and UNIX, and is equipped with a comprehensive range of features. There is a regular reliance upon it by many people, which includes those in the fields of networking and security, software development, and education. It is accessible to the public without charge and distributed according to version 2 of the GNU General Public License. This example of innovative technology was engineered and implemented by protocol specialists from around the world.  
Wireshark is a software program employed to visually display packet traffic on networks in real-time, rendering it comprehensible to the human user. It monitors and interprets digital information for comprehension by humans. Utilizing Wireshark's filtering and colour-coding abilities, users can analyze individual frames of network data. This is an invaluable asset for any dedicated network or system engineer, as it provides unparalleled data analysis of network information. If experiencing difficulty in resolving a network issue, one can employ the use of this gratuitous application that permits users to observe network activity in real-time. Wireshark is a useful tool for resolving common problems such as degraded connection reliability, slow data transfer speeds, and malicious network activity. Utilizing these resources, it is possible to meticulously evaluate network data, thus enabling researchers to target the fundamental cause of the problem. Management employs it to discern data exfiltration or hacking attempts against any firm, as well as to pinpoint damaged network hardware that is disseminating messages [22; 71; 72; 73].
# The Ultimate Strength of Fully Transparent Pressure-Resistant Spherical Cabins
## 3 Ultimate Strength of PMMA Spherical Cabin
### Spherical Cabin with a Single Access Opening
#### 3.2.3 Effect of Hatch Stiffness  
In the previous section, it was found that increasing the thickness can improve the ultimate strength of the PMMA spherical cabin with a single access opening. When the spherical cabin constitutes a PMMA shell and a metal hatch cover, to investigate whether increasing the thickness of the hatch cover can improve the ultimate strength of the pressure-resistant shell, a comparison is made by changing the thickness of the metal hatch cover in the range of 9.6 mm, 14.4 mm, and 19.2 mm to vary the stiffness of the hatch cover. The thickness is changed by using different safety factors, of which 14.4 mm is considering the safety factor of 1.5 and 19.2 mm is for the safety factor of 2.0. The finite element calculation results are shown in Table 4.  
Based on the calculation results, increasing the thickness of the hatch cover within a certain range can improve the overall strength of the spherical cabin. However, blindly increasing the thickness does not necessarily enhance the structural strength. A metal hatch cover that is excessively thick may actually weaken the structure since the entrance wall's strong restrictions will deteriorate before the PMMA shell fails.  
Figure 11: Critical pressure changes the tendency at different \(t/R\) of the spherical cabin with a single access opening.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(t/R\) & & & **0.04** & \\ \hline Initial geometrical imperfection & 0.1\% & 0.2\% & 0.3\% & 0.4\% & 0.5\% \\ Numerical results of buckling pressure (MPa) & 3.2 & 3.18 & 3.15 & 3.13 & 3.11 \\ Failure Mode & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Effect of initial geometrical imperfection on ultimate strength of the PMMA spherical cabin with a single access opening.
# Deployment Strategy of Shore-Based Cooperative Units for the Internet of Inland Vessels
## 3 Deployment Strategy of Shore-Based Collaborative Units
### Model Solving Based on the Genetic Algorithm  
In light of the multi-objective optimization problem proposed in the previous section, the Pareto optimal solution set was obtained by the multi-objective genetic algorithm. Single optimal solutions were screened to obtain the final planning plan of the base station. The steps are as follows.  
(1) Genetic coding. The solution space of the above-mentioned base station planning should be a \(K\times M\)-order matrix, which corresponds to the configuration selection of each type of base station. Turn the solution space into \(1\times M\)\(K+1\) binary strings, and the value in each solution space string represents a certain type of base station. Select its \(1^{st\_K^{th}}\) type of base-station configurations, and \(0\) means that this type of base station is not selected. Then, \(K+1\) binary strings undergo binary coding.  
(2) Target evaluation. Individual binary encoding is decoded to obtain a subset of corresponding base-station selections. Then, calculate the indicators corresponding to the subset. If the constraints are not met, the negative correlation factor of the individual is set to reduce the probability that the individual passes on to the next generation.  
(3) Congestion calculation. Part of the solution is resolved in the area with the dense solution set to ensure the diversity of the population (Equation (15)).  
\[I_{i}=\sum_{j=1}^{m}\frac{\left|f_{j}^{i+1}-f_{j}^{i-1}\right|}{\left|f_{j}^{1 }-f_{j}^{m}\right|} \tag{15}\]  
where \(f_{j}^{i+1}\) and \(f_{j}^{i-1}\) represent the value of the \(j^{th}\) objective functions of the \(i+1^{th}\) and \(i-1^{th}\) points, respectively.  
(4) Population update. The previous solution is used as the optimal solution set for the iteration after individuals are sorted by crowding. If the number of iterations is reached, the optimal solution set is output. Otherwise, the latter solution will be cross-mutated to produce a new dominant solution.  
(5) Crossovers and mutations. Random crossovers are performed for the latter \(1-a\%\) solution. Then, variation is performed with the single-point machine (Figure 4).  
Figure 4: Crossover and mutation process.  
(6) Single optimal solution screening. The Pareto solution set should be decoded to convert into corresponding base-station planning. Then, screen the single optimal solutions, and determine the final planning of base stations according to Equation (16).  
\[A^{*}=\text{argmin}\left\{\frac{f_{1}\Big{(}A_{j}^{*}\Big{)}}{f_{1 \text{max}}}+\frac{f_{2}\Big{(}A_{j}^{*}\Big{)}}{f_{2\text{min}}}-\frac{f_{3} \Big{(}A_{j}^{*}\Big{)}}{f_{3\text{min}}}\right\},j=1,2,\ldots,J \tag{16}\]  
where \(J\) is the number of Pareto optimal solution sets; \(f_{1 max}\) is the maximum value of objective function \(f_{1}\) in the Pareto optimal solution sets; \(f_{2 min}\) is the minimum value of objective function \(f_{2}\); \(f_{3 min}\) is the minimum value of objective function \(f_{3}\).
## 4 Experiment Results and Discussion
### Ablation Studies  
To validate the effectiveness of the individual components of ED\({}^{2}\)IF\({}^{2}\)-Net and the loss functions, extensive qualitative and quantitative ablation studies were carried out. All the networks used in the ablation studies were trained and tested on the chair class of ShapeNet. To be specific, the following network options were designed:  
* Option 1: In this option, we keep the original encoder PVT in the network, plus the coarse shape decoder (CSD) and a random sampling strategy, and the loss function \(L_{Coa}\) is applied. It can be seen from Figure 7 that the coarse shape decoder and the random sampling strategy can only reconstruct the coarse shape with few structure details and no surface details. It is consistent with the quantitative results in Table 2.
* Option 2: On the basis of the first option, the network is trained with weighted sampling (WS). It can be found from Figure 7 that WS enables the network to reconstruct more details, especially at small scales. * Option 3: In this option, we still use PVT as the encoder. However, we try to directly initialize a random signed distance value for each query point and iteratively refine it in the deformation decoder (DD). Then, the network is trained only constrained by deformation loss \(L_{Def}\) with WS. It can be observed from Figure 7 that the network without the coarse implicit field reconstructs awful surfaces and topologies. Moreover, quite a few surface artifacts emerge due to the absence of the coarse implicit field near the shape. * Option 4: With this option, CSD together with the DD serve as the decoders and only \(L_{Coa}\) with WS is used for the loss estimation. It can be seen in Figure 7 that such a network creates fewer shape artifacts and distortions, but it still fails to reconstruct a full shape of the structure, which is attributed to the fact that the loss function takes no account of the intermediate implicit fields generated in the iterative deformation. * Option 5: Based on the previous options, \(L_{Coa}\) and \(L_{Def}\) are applied to train the CSD and the DD, respectively. WS is also used here. From Figure 7, it is illustrated that the network with this option is capable of reconstructing more accurate topological structures and producing a smoother shape. * Option 6: In this option, the surface detail decoder in ED\({}^{2}\)IF\({}^{2}\)-Net with WS cancels the prediction of the backward displacement map and the deformed implicit field is only fused with the forward displacement map. The surface detail decoder in this case is represented as SDD_S and the normal case is denoted as SDD_N. It can be noticed from Figure 7 that, without the backward displacement map, the surface details of the results may be incorrectly reconstructed and distortions may occur at the structural level, possibly owing to the lack of the backward displacement map, which prevents fine-tuning.