esa-sceva/satcom-chunk-collection huggingface dataset:
# Passwordless Authentication Using a Combination of Cryptography, Steganography, and Biometrics
## 4 Results
### Methodology and Results Evaluation  
To evaluate the performance and validity of SA, a series of experiments were conducted by the authors. As this research served dual purposes, being submitted as a university research work and for publication in this journal, the available time constraints limited the involvement of external testers. Consequently, the authors manually tested the system's functionality by performing authentication, account creation, and account recovery operations on the developed mobile application.  
The testing process involved carrying out each operation six times, recording the duration and success rate for each iteration. The recorded times were then averaged to provide a representative measure of the system's performance for the respective operations.  
The average duration obtained from the experiments is presented in Figure 9. The labels \"T1\" to \"T6\" represent the individual iterations, with the following breakdown:  
* T1--909 ms: The duration for a successful authentication request.
* T1--1762.9 ms: The duration for a successful account recovery.
* T1--3477.84 ms: The duration for a successful account creation.
* T2 to T6: Additional iterations for authentication, account recovery, and account creation, respectively.  
It is important to note that the recorded times encompass the entire process for each operation, including any user interactions, such as entering challenge codes, providing biometric authentication, and uploading images for account recovery. The authors acknowledge the potential variability introduced by manual testing and the limited number of iterations conducted due to time constraints.  
While the results presented in this paper serve as a proof-of-concept and demonstrate the feasibility of SA, further rigorous testing with a larger sample size and diverse user groups is needed to validate the system's performance and usability in real-world scenarios.  
This section provides an explanation of the methodology employed for testing SA, clarifying the number of experiments conducted, the involvement of the authors as testers, and the process of recording and averaging the results.  
Additionally, the section acknowledges the limitations of the testing approach, recognizing the potential variability introduced by manual testing and the need for further rigorous testing with a larger sample size to validate the system's performance and usability comprehensively.
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
###### Abstract  
Due to their superior structural and mechanical properties, materials with negative Poisson's ratio are of increasing interest to research scholars, especially in fuel-efficient vehicles. In this work, a new concave I-shaped honeycomb structure is established by integrating the re-entrant hexagon and the I-shaped beam structure, and its negative Poisson's ratio characteristics and energy absorption properties are investigated. The effect of structural parameters on the energy absorption characteristics is analyzed using the finite element model. The results show that both the specific energy absorption and peak impact force decrease with the increase in cellular length and vertical short cellular height, and increase with the increase in horizontal short cellular length and cellular thickness. To obtain a smaller peak impact force and larger specific energy absorption with smaller mass, the four cell sizes were optimized by using Latin hypercube sampling, Gaussian radial basis function, and non-dominated sorting genetic algorithm II (NSGA-II). Compared with the original design, the SEA increased by 44.175%, and the PCF increased by 25.857%. Meanwhile, the mass decreased by 31.140%. Hence, the optimal structure has better crashworthiness.  
honeycomb structure; negative Poisson's ratio; crashworthiness; multi-objective optimization; finite element +
Footnote †: journal: Applied Sciences
# A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces
## 4 Application of Image Processing Techniques to Underwater Crack Detection in Dams  
Underwater crack image processing refers to the full extraction of underwater crack information through image preprocessing, image recognition, and image segmentation [30]. With the development of image processing technology, digital image detection has become the dominant method for surface crack detection in underwater dams due to its efficiency,low cost, high spatial resolution, and non-contact nature. Currently, the rapid development of underwater robotics has made underwater crack image acquisition more convenient, which has driven the development of digital image detection. The underwater crack detection method for a dam based on underwater robots combined with digital image processing is illustrated in Figure 5. However, the amount of underwater detection image data is huge, and identifying cracks by manual observation alone is time-consuming and inaccurate. Therefore, it is necessary to develop a method suitable for the automatic identification and extraction of underwater crack images on dam surfaces.
## 4 Data Analysis and Evaluation by AI
A squat is a surface defect caused by metal fatigue. To automatically detect cracks, their location, and the crack's boundary, ref. [96] uses a bi-layer data-driven framework. Ref. [97] deals with the inspection of sleepers and rail fastenings with the help of line-scan cameras. The focus is on the detection of good, broken, or missing fasteners (object detection) and the segmentation of chips and crumbling concrete ties and other material classes. Another major area that needs to be examined for defects is the catenary wire and the catenary support devices. Catenary systems are an important part of the electrified railroad system. This area is becoming increasingly complex and variable. Ref. [98] is concerned with the detection of defective fasteners of the cantilever joints on the catenary support devices. They use a three-stage architecture to automatically assess the three cantilever joints and the six fasteners and judge the missing elements using images. DCNNs are used and the results show promising accuracy in the detection of defects. A similar topic is dealt with in [99]. Here, AI is used to determine the status of split pins in catenary support devices (Figure 13). A distinction is made between three different categories: normal, severely loose, and missing. The proposed system provides effective and fast results but still shows potential for latent loose cases. Figure 12: (**Left**) back projection of object recognition from the images into the point cloud. (**Right**) segmentation from the image data. The aim of [100] is to recognize several catenary components simultaneously using AI. In images six categories of catenary components such as the messenger cable base, the flat cantilever, the positioning support, the insulator, the 42 type sleeve double ear, and the 55 type sleeve double ear with rotating double ear are mainly to be recognized. Different algorithms such as linear local tangent space alignment (LLTSA) and kernel function extreme learning machine (KELM) are used to detect components based on new optimized features. Ref. [101] presents a solution for the detection of defects on the insulator surface. The images were captured by an inspection train on a high-speed line. A deep material classifier (DMC) and a deep denoising autoencoder (DDAE) are integrated into a deep multitask neural network (DMNN). The advantage is that no defective samples are required for training to enable simultaneous segmentation and defect detection. The experimental results show that surface defects are successfully and effectively detected.
# Quantification of Energy-Related Parameters for Near-Fault Pulse-Like Seismic Ground Motions
###### Abstract  
An energy-based approach facilitates the explicit consideration of the damage associated with both maximum displacements and cumulative plastic deformations under earthquakes. For structural systems that can undergo pulse-like seismic ground motions close to causative faults, an energy-based approach is deemed especially appropriate with respect to well-established force- or displacement-based strategies. In such a case, in fact, most of the damage is attributable to the dominant pulse-like component, which usually occurs into the velocity time history of the seismic ground motion, thus implying high energy levels imparted to a structural system. In order to enable the implementation of an energy-based approach in the analysis and design of structures under near-fault pulse-like seismic ground motions, this study presents a comprehensive numerical investigation about the influence of seismological parameters and hysteretic behavior on the spectra of the following energy-related parameters: inelastic absolute and relative input energy; input energy reduction factor; hysteretic energy dissipation demand; hysteretic energy reduction factor; dimensionless cumulative plastic deformation ratio. Closed-form approximations are proposed for these spectra, and the numerical values of the corresponding parameters have been also calibrated (with reference to both mean and standard deviation values) as functions of earthquake magnitude, type of hysteretic behavior (i.e., non-degrading or degrading) and ductility level. The outcomes of this study are meant to support the derivation of design spectra for the energy-based seismic design of structures under near-fault pulse-like seismic ground motions.  
energy-based seismic design; hysteresis; pulse-like seismic ground motion +
Footnote †: journal: Applied Sciences
## 2 Related Work
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Characteristics** & **Advantage** & **Limitation** \\ \hline \multirow{3}{*}{CS} & Replace the spatial component of the LRMS image with a high-resolution PAN image. & Small computational burden; Significantly enhance the spatial details. & More susceptible to spectral distortion. \\ \hline \multirow{3}{*}{MRA} & The high-frequency information of the PAN image is injected into the upsampled MS image. & Reduces the risk of spectral distortion. & The fusion effect is sensitive to the detailed injection process. \\ \hline \multirow{3}{*}{VO} & Transform the image fusion problem into an optimization problem. & Achieve a balance between spatial detail and spectral preservation. & \multirow{3}{*}{Time-consuming.} \\ \cline{1-1} \cline{3-3}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Traditional pansharpening methods. PAN and MS images. As a result, our DCMFN produces pansharpened images with less spectral distortion and loss of detail.
# Path Planning Based on Obstacle-Dependent Gaussian Model Predictive Control for Autonomous Driving
## 2 Related Work
### Model Predictive Control  
This section describes the MPC function, as described in [29; 30]. The MPC algorithm finds the optimal control to minimize the cost function that satisfies the constraints consisting of the predicted horizon and system model [29; 30]. Therefore, as depicted in Figure 6, the MPC algorithm is a way to predict status variables or outputs using models of control targets. This method can provide optimized control using appropriate cost functions and constraints.  
The MPC method [29; 30] has the advantage that it can control a set of target points in the constrained system. If the target's steady state is not admissible, [PERSON] et al. [29] induced the system into an acceptable safe state. The rest of this section describes how to implement the MPC algorithm.  
The discrete-time LTI system is given by (5). Moreover, \(x\in\mathbb{R}^{n_{x}}\) denotes the state, and \(u\in\mathbb{R}^{n_{u}}\) denotes the input vector. The variable \(A\) indicates the system matrix, and \(B\) is the input matrix:  
\[x(k+1)=Ax(k)+Bu(k), \tag{5}\]  
where \(A\in\mathbb{R}^{n_{x}\times n_{x}}\) and \(B\in\mathbb{R}^{n_{x}\times n_{u}}\). The constraints are expressed in (6):  
\[z=(x^{\mathrm{T}},u^{\mathrm{T}})^{\mathrm{T}}\in Z\stackrel{{ \Delta}}{{=}}\{z\in\mathbb{R}^{n_{x}+n_{u}}:A_{z}z\leq b_{z}\}, \tag{6}\]  
where Z is a nonempty compact convex polyhedron set containing the origin in its interior, and \(A_{z}\in\mathbb{R}^{n_{z}\times(n_{x}+n_{u})},\ b_{z}\in\mathbb{R}^{n_{z}},\ n_{z}\) denotes the number of constraints. The inputs of  
Figure 5: Result of obstacle-dependent Gaussian (ODG) potential field (PF).  
Figure 6: Generalized model predictive control architecture.  
the system in (5) and the subspace of the steady states have a linear representation in the following form:  
\[z_{ss}=M_{\theta}\theta, \tag{7}\]  
where \(z_{ss}^{\rm T}=(x_{ss}^{\rm T},u_{ss}^{\rm T})\) is the stack of the steady-state solution of (5), and \(x_{ss}\) is obtained by applying the control action \(u_{ss}\) to the system in (5). In addition, \(\theta\in\mathbb{R}^{n_{\theta}}\) is the parameter vector, and \(M_{\theta}\) is a matrix of suitable dimensions. In accordance with the works in [29; 30], the objective is to find a control of the form \(u=f_{N}(x(k),\hat{x})\) such that it minimizes the cost function subject to the system constraints. Equation (5) is controlled to the target state while fulfilling the constraints in (6). Furthermore, with \(N\) being the prediction horizon, the function map \(u=F_{N}(x(k),\hat{x})\) can be calculated by solving the following optimization problems parameterized in \(x\) and \(\hat{x}\). The optimization problem of (8) can be solved by expressing it as a quadratic programming problem [29; 30].  
\[\begin{array}{l}\min_{u_{i},\theta}J_{N}(u,\theta,x,\hat{x})\\ \mbox{\
# Genus, Species, and Subspecies Classification of _Salmonella_ Isolates by Proteomics
###### Abstract  
Identification of bacteria by mass spectrometry offers the potential of a high-throughput non-targeted method to determine the presence of _Salmonella_. While MALDI-TOF mass spectrometry can identify _Salmonella_ at the genus and species level, few studies have reported subtyping beyond the species level due to the diversity and complexity of _Salmonella_ that includes more than 2600 servovars. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) approaches enable profiling of a greater number of proteins over a larger dynamic range and offer the potential to detect small differences between closely related isolates. We evaluate the discriminatory power of bottom-up LC-MS/MS with a collection of nineteen isolates that differ at the genus, species, subspecies, or strain level. Isolates were classified by matching the sequence of identified peptides to reference proteomes translated from genomes with known taxonomic ranks. The degree of proteomic similarity between the tested isolates and reference strains correlated with how closely they were related. All tested _Salmonella_ isolates were easily distinguished from their close relatives, _E. coli_ and _Shigella_, and readily grouped by species and subspecies. Additionally, each _Salmonella_ isolate most closely matched to its correct serovar. This approach presents a simple and effective proteomic approach to identification of _Salmonella_ genus, species, and subspecies.  
Salmonella; subtyping; proteomics; mass spectrometry +
Footnote †: journal: Applied Sciences
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 4 Cs-Vdh
### Design Issues
Based on the boolean vector, a node can quickly check if its sensed information can be combined with that of a received data packet. This process is done simply by checking that the cells with a '1' in its own boolean matrix are set to '0' in the received boolean matrix. Otherwise, the combination cannot be made. When a node combines data, it updates the cells of the resulting boolean vector accordingly. For instance, let us assume that we have defined a \(2\times 2\) RoI. That nodeA has added the information of the cells (1,1) and (1,2) represented as the following equation describes. \[Booleanvector= \begin{pmatrix}1&1\\ 0&0\end{pmatrix} \Rightarrow \begin{pmatrix}1&1&0&0\end{pmatrix} . \tag{5}\]  
If nodeB intends to insert the information of the cell (1,1), just by checking that it is marked as true, it knows that the information has already been added, and directly forwards the message to the next hop. After identifying the main obstacles that our proposed solution must overcome, as well as a specific mechanism that can help us in overcoming them. In the next subsections, we shed some light on the detailed operation of our proposed solution.
# The Role of Transliterated Words in Linking Bilingual News Articles in an Archive
## 3 Similarity Measure Based on Transliteration Words  
### Transliteration  
\"Transliteration is a process of using the text of one script in another script or the process of converting text from one language to another\". Transliteration replaces words from a source language with the target language's spelling equivalents or approximate phonetics. In linguistics, the process through which a word or set of words of a language is adapted for use in another language's script is referred to as borrowing, and the word(s) are also known as loanwords [31]. Transliteration utilizing a phrase or word in a language with a distinct writing system [32] becomes more difficult if a language has a distinct sound and writing scripts [33].  
Transliteration is not a translation in linguistics. In language translation, the written and spoken sense of the text or words in the target language is transferred from a source language. In contrast, in transliteration, the meaning of the words or text does not change or render, but only the source characters or letters change into a corresponding target language.  
English Transliteration in Urdu Scripts  
Most of the spoken languages acquire several words from other languages using different character sets. Similarly, native speakers of Urdu frequently use several words from other languages, especially from the English language. The English-based origin words are used with different characters and identical pronunciations, despite having alternative words in the Urdu language. As a considerable proportion of English transliteration words are used in Urdu, the effect of these words in Urdu news articles must be estimated for the link, especially for calculating similarity among news articles in the DNSA. Table 1 shows examples of transliteration words from English in the Urdu scripts.  
Here are two examples of Urdu language sentences with underlined transliteration words: has \"budget\" and \"pension\" as transliteration words, and has \"all-time\", \"test\", and \"team\" as English transliteration words used in Urdu language scripts.  
A sample of six hundred (600) Urdu news articles collected from different sources was analyzed to specify the use of English transliteration words in Urdu news articles using the DNSE. The stopwords were removed from the news articles during preprocessing, and the corpus contained a total of 117,393 tokens. The estimation was analyzed against a collection of 2705 English transliteration words. Table 2 summarizes the percentages of total tokens, Urdu words, English words, and Unique tokens in the corpus. Figure 1 shows that 9.5% are English transliteration words, 19.5% are other words (for example, symbols, digits, etc.), and 71% of the words in the Urdu news articles are Urdu origin words in the sample corpus.
# A Review of Thermal Error Modeling Methods for Machine Tools
## 2 Thermal Error Modeling Method for Machine Tools
### Least Square Method (LS)  
The least square method is used to seek the best function matching of data by minimizing the sum of squares of errors. Its general form is shown in Equation (1).  
\[y=a_{1}f_{1}(x)+\cdots+a_{k}f_{k}(x) \tag{1}\]  
By searching for the reasonable parameters \(a_{1}\)... \(a_{k}\), the sum of squares of the error between calculated value \(y\) and measured value \(x\) is minimized [18]. The fitting modeling based on the LS is an earlier way to build the thermal error model of machine tools. Because of its simple principle, it is easier to applied for compensation in practical.  
With the use of Chebyshev polynomial-based orthogonal least squares regression, the thermal drift error curve of the spindle was fitted [19] in 2015. The obtained curve was in good agreement with the measurement error curve. In detail, the maximum modeling residuals in the \(X\), \(Y\), and \(Z\) directions were 0.8-1 \(\upmu\)m, 0.6-0.9 \(\upmu\)m, and 1.2-1 \(\upmu\)m, respectively. In 2016, [PERSON] et al. [20] determined the best position of temperature sensor by judging the covariance characteristic value of the least square. After thermal error compensation, the maximum approximate error of the tool center point was reduced up to 25%. In 2020, [PERSON] and his colleagues [21] used the least square method to establish a thermal error model of the machine tool spindle in the axial and radial direction which was used for compensation afterwards. The experiment showed that the contour accuracy of the work piece was increased by 78.4% after compensation.
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Trajectory Tracking Procedure of the Controlled USV
#### 3.4.2 Power Allocation Design for Actuators
 \tag{67}\]Rearranging the first equation of (68), the following result can be obtained:  
\[\mathbf{f_{e}}=\frac{1}{2}\mathbf{W}^{-1}\mathbf{T}^{T}\boldsymbol{\lambda} \tag{69}\]  
Substituting (69) into the second equation of (68), Lagrangian multiplier \(\boldsymbol{\lambda}\) can be solved as follows:  
\[\boldsymbol{\lambda}=2(\mathbf{T}\mathbf{W}^{-1}\mathbf{T}^{T})^{-1}\tau_{q/2} \tag{70}\]  
Substituting (70) into (69), and we get  
\[\mathbf{f_{e}}=\mathbf{G}\tau_{q/2} \tag{71}\]  
where \(\mathbf{G}=\mathbf{W}^{-1}\mathbf{T}^{T}(\mathbf{T}\mathbf{W}^{-1}\mathbf{T} ^{T})^{-1}\)  
Transforming \(\mathbf{f_{e}}\) from (71) to obtain the output of the actuator, it yields  
\[\alpha_{i}=\tan^{-1}\biggl{(}\frac{F_{yi}}{F_{xi}}\biggr{)} \tag{72}\]  
\[f_{i}=\sqrt{{F_{xi}}^{2}+{F_{yi}}^{2}} \tag{73}\]  
where \(\alpha_{i}\) is the deflection angle of the used actuators,\(f_{i}\) is the output of thrust, and \(i=1\), \(2\), \(\ldots\), \(n\) is  
In all simulation scenarios, modelling uncertainties are considered due to changes of the controlled USV's mass, and 5-10% variations are added in inertia matrix \(\mathbf{M}_{\eta}\) and Coriolis matrix \(\mathbf{C}_{\eta}\). According to the practical situation, ocean disturbances such as wind, wave, and current are considered in the simulation process as well. Based on these setting, for the controlled USV, collision avoidance performance of this proposed collision avoidance control system will be verified by using: 1. A crossing situation for the controlled USV with randomly incoming ships and 2. a case with several fixed obstacles. Besides, triggered levels for the collision risk is \(\mu_{CRI}\) = 0.3 and the residual time is \(\mu_{CA}\) = 0.7.