esa-sceva/satcom-chunk-collection huggingface dataset:
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 1 Introduction
### Purpose and Significance
When the diameters of hollow cylindrical components are much larger than their thicknesses, the dispersion characteristics of Lamb waves in hollow cylindrical components can be approximated by studying the characteristics of Lamb waves in plates with equal thicknesses [26; 27; 28]. [PERSON] et al. [29] found that when the diameter to thickness ratio of hollow cylindrical components is less than 0.05, the dispersion curves of the hollow cylindrical components were similar to those of components with the corresponding plate thicknesses. In this study, the diameters and thicknesses of the thin-walled tubes were 35 mm and 0.8-1.2 mm, respectively. Selecting the Lamb-wave dispersion characteristics of plates with equal thicknesses to approximate the propagation characteristics of Lamb waves in thin-walled tubes could not only simplify the model but also facilitate the application of practical engineering practice.
# Automatic Thread Defect Examination System
## 1 Introduction
Thus, a buffer with springs is utilized to overcome instances where the TH is not accurately arranged with the axis of the ID. In our examination system, a camera is further utilized to automatically estimate the TH size. Based on the TH size measurements, the examination system automatically sets the torque threshold that is optimal for the size of the examined TH. In this way, the examination accuracy can be improved irrespective of TH size. The contributions of the present paper are summarized as follows. To the best of our knowledge, the proposed examination method is novel in following aspects:  
1. We address a simple hardware design generating the ID's axial motion from the motor's rotational motion;
2. A buffer with springs is utilized to overcome instances where the TH is not accurately arranged with the ID's axis;
3. Utilizing a camera, the size of the TH is estimated automatically. Then, the TH is examined using the torque threshold that is optimal for the examined TH size. In this way, holes of various sizes can be examined both adaptively and optimally. Note that above three aspects do not hold for commercial examination systems from companies such as BEHCO [16] or Newwistacorp [13]. Furthermore, these companies do not make public the detailed structure of their products. To the best of our knowledge, our paper is novel in examining THs of various sizes by adaptively setting the torque threshold which is optimal for the examined TH. In the present paper, we verify the performance of the proposed examination system through experiments. The rest of the article is organized as follows: Section 2 presents the examination process used by the proposed system; Section 3 addresses the experiments used to verify the proposed examination system; finally, our conclusions are presented in Section 4.
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Depth-Based Layering MAC (DL MAC)
#### 4.2.2. _Underwater Acoustic Multi-Channel MAC (UAMC-MAC)_  
In [93] researchers introduced the underwater acoustic multi-channel MAC (UAMC-MAC) protocol, which is based on the simultaneous usage of multiple channels, that is allowing communicating nodes to send data at the same time. This protocol combines CDMA with a handshake mechanism to accommodate a single hop's lengthy propagation delay factor and low throughput ratio between neighbors [94].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & **Author/Year** & **Topology** & **Energy-Efficiency** & **Synchronization** \\ \hline ERMAC & ([PERSON]/2008) & Centralized & Very High & Yes \\ ST-MAC & Hsu/2009 & Centralized & High & No \\ \hline GC-MAC & Alfouzan/2019 & Distributed & Low & No \\ DL-MAC & Alfouzan/2019 & Distributed & Low & No \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of TDMA-based energy-efficient MAC protocols for UWSNs.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 3 Methods
### Scheme for Encoding and Decoding Sensitive Objects  
In this work, we encrypt sensitive data in transit and at rest. We use a designed encoding and decoding algorithm to protect data transferred and stored on servers in a cloud. When transferring data, we do not apply any cryptographic protocol like TLS/SSL designed to secure communication over a network. Instead of using the cryptographic protocol, a logger initiates a connection to the VMS server. The designed encoding and decoding algorithm consists of two phases, handshake and encoding:  
1. (_Handshake_ ) The logger sends its digital certificate with a public key, part of a key pair in PKI, to the server. The server verifies the certificate to ensure that it is valid and issued by a trusted Certificate Authority (CA). If the certificate is valid, the logger sends the logger a filter, which plays a role as a shared secret key.
2. (_Encoding_) All subsequent communication between the client and server is encrypted using the shared secret key. This encryption ensures that any transmitted data are unreadable to anyone who might intercept them.  
In the proposed sensitive-data safe-transmission scheme, the object detection model detects an object in an image, and when a sensitive object is identified, the sensitive object is encoded to prevent exposure, and the proposed transmission scheme is applied. To ensure the safe delivery of sensitive objects, the PKI public key used for signatures is configured as a filter, and XOR is performed to strengthen the data-hiding function.  
Data encoding is performed on sensitive objects within videos, such as human figures, using the YOLOv8 [25; 26] object detection model. The encoding and decoding processes are as follows. This paper does not cover the eight integrity check processes:  
1. The logger generates a public-private key pair based on PKI and transmits the public key to the verifier.
2. The logger obtains object information from video data using an object detection model.
3. Based on the object information of identified sensitive objects, the logger applies a filter to perform encoding.
# The Influence of Capacitance and Inductance Changes on Frequency Response of Transformer Windings
## 3 Model of Inductance and Capacitance Changes on FR Curve  
The computer model has been prepared in order to verify the conception that capacitance from other windings and additional inductances (e.g., from the tap changer) could balance out. As a result, the first parallel resonance would remain in its original position, but some changes in the MF range could be expected.  
Figure 2: Example of frequency response amplitude curves of a three-phase transformer, measured in end-to-end open test setup, presented in a logarithmic scale. Three frequency ranges are marked.
## 2 Problem Description
### Array Signal Model
 \tag{3}\]  
Assuming that each array element has no directionality and coupling between the arrays, the final received signal vector of the array at the \(t\)th snapshot is mathematically expressed as:  
\[X(t)=A(\theta)S(t)+N(t) \tag{4}\]  
and the final array signal matrix is described as:  
\[X=\left[X(t_{1}),X(t_{2}),\ldots,X(t_{n})\right] \tag{5}\]  
where \(n\) denotes the number of snapshots. The goal of DOA estimation is to extract the direction angles of the sources from \(X\). Figure 2: Uniform linear array used in this work.
# Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion
###### Abstract
Further, a reasonable assumption can be made that the correct constraints are most likely to occur in regions with a high degree of coincidence between trajectories since both temporal and spatial dimensions are considered. On this basis, the high-dimensional consistency matrix can be decomposed into multiple matrix blocks to efficiently solve the consistency subset, significantly improving the real-time performance  
This paper proposes a fast inter-robot loop closure selection method with spatiotemporal consistency for multi-robot map fusion by integrating the graph topology of loop closures and consistency between constraints. Then, we introduce a clustering method that considers the degree of topology correctness of inter-robot loop closures, partitioning the entire measurement set containing inlier and outlier loop closures into clusters, where the constraints within the cluster are topologically correct. A weight function is defined to find the topologically correct and consistent subset with the maximum cardinality, in a block way, instead of the traditional constraint by constraint. Determining the solution to the weight function can be solved by transforming it into the maximum clique problem from graph theory. The experimental results show that our method can effectively find the proper pairwise internally consistent set for map fusion and has high real-time performance. Although some of the potential true measurements may be discarded, the experimental results demonstrate that the proposed method does not lose any important, meaningful information. The main contributions of our work can be summarized as follows:  
* We present a fast, robust method based on spatiotemporal consistency to find the topologically correct and consistent subset of inter-robot loop closure for map fusion. * Our method decomposes the traditional high-dimensional consistency matrix into sub-matrix blocks corresponding to the overlapping trajectory regions, improving the real-time performance greatly. * Compared to state-of-the-art methods, the results show that our method can achieve competitive performance in accuracy while reducing computation time by 75%. The remaining paper is organized as follows. Section 2 gives a review of the literature. We present the formulation for the single robot pose graph and its extension to the multi-robot pose graph in Section 3. Section 4 presents a complete overview of our method. The experimental results are shown in Section 5. Lastly, Section 6 concludes this paper.
## 3 Mathematical Model of Geolocation and Tracking Scenarios
### State Tracking Estimation  
The state estimation is used as information in the conventional Kalman filter. The nonlinear system process \(\mathbf{x}_{k+1}\) and observation process is specified by  
\[\mathbf{x}_{k}=\mathbf{f}(\mathbf{x}_{k-1})+\mathbf{v}_{k-1}\, \tag{19}\]  
\[\mathbf{y}_{k}=\mathbf{H}_{k}(\mathbf{x}_{k})+\mathbf{\hat{n}}_{k}\, \tag{20}\]  
where \(\mathbf{f}(\cdot)\) is state transition matrix; \(\mathbf{H}_{k}(\mathbf{x}_{k})\) is geolocation measurement matrix; \(\mathbf{v}_{k-1}\) is system noise and \(\mathbf{\hat{n}}_{k}\) geolocation measurement noise, which are statistically independent Gaussian processes of zero mean and known covariance matrices (\(\mathbf{Q}_{k-1}\), \(\mathbf{R}_{k}\)) [9; 35].  
From (19), the state-space model for a constant velocity of target-tracking can be defined as  
\[\mathbf{x}_{k}=\begin{bmatrix}1&\frac{\sin(\omega_{k-1}\Delta t)}{\omega_{k- 1}}&0&\frac{\cos(\omega_{k-1}\Delta t)-1}{\omega_{k-1}}&0\\ 0&\cos(\omega_{k-1}\Delta t)&0&-\sin(\omega_{k-1}\Delta t)&0\\ 0&\frac{1-\cos(\omega_{k-1}\Delta t)}{\omega_{k-1}}&1&\frac{\sin(\omega_{k-1} \Delta t)}{\omega_{k-1}}&0\\ 0&\sin(\omega_{k-1}\Delta t)&0&\cos(\omega_{k-1}\Delta t)&0\\ 0&0&0&0&1\end{bmatrix}(\mathbf{x}_{k-1})+\mathbf{v}_{k-1}\, \tag{21}\]  
and system covariance matrix is  
\[\begin{cases}\mathrm{E}[\mathbf{v}_{k-1}]=0,\\ \mathrm{E}[\mathbf{v}_{k-1}\mathbf{v}_{k-1}^{\prime}]=\mathbf{Q}_{k-1}= \begin{bmatrix}\frac{\Delta t^{3}}{3}&\frac{\Delta t^{2}}{2}&0&0&0\\ \frac{\Delta t^{2}}{2}&\Delta t&0&0&0\\ 0&0&\frac{\Delta t^{3}}{3}&\frac{\Delta t^{2}}{2}&0\\ 0&0&\frac{\Delta t^{2}}{2}&\Delta t&0\\ 0&0&0&0&1.75\times 10^{-3}\Delta t\end{bmatrix}\, \tag{22}\]  
where \(\mathbf{x}_{k}=[\boldsymbol{u}_{k},\hat{\boldsymbol{u}}_{k},\omega_{k}]^{\prime}\) is the target state that consists of the three-dimensional (3D) position vector (\(\boldsymbol{u}_{k}^{\prime}\)), velocity vector (\(\boldsymbol{u}_{k}^{\prime}\)), and rotation rates (\(\omega_{k}\)) at time instant \(t_{k}\); \(\Delta t\) is the sampling interval [46].
# An Improved Image Filtering Algorithm for Mixed Noise
## 2 The Filtering Algorithm for Mixed Noise with Salt and Pepper + Gaussian Noise
### The Improved Wavelet Threshold Denoising Algorithm
#### 2.2.2 The Improved Wavelet Threshold Function
* Higher-order differentiability of the function When \(\Big{|}W_{j,k}\Big{|}\geq\lambda\), the improved threshold function is highly differentiable, which is beneficial to the subsequent mathematical processing of the function. * The adjustable factors \(\rho\), \(\alpha\), and \(n\) of the threshold function When \(\rho=0\) and \(n\rightarrow\infty\), the improved threshold function becomes the hard threshold function. When \(\rho=0\) and \(\alpha=0\), the improved threshold function becomes the soft threshold function. Because of the adjustable factors, the function can be adjusted between the soft threshold function and the hard threshold function. The reason why the wavelet coefficients at \(\Big{|}W_{j,k}\Big{|}<\lambda\) are retained instead of being set to zero is that it can deal with noise coefficients in high-frequency components more flexibly. To sum up, by analyzing the continuity, asymptotic property, deviation, and adjustable factors of the function, it is concluded that the improved threshold function of the algorithm is continuous and high-order derivable. It solves the deviation problem and has a relatively high flexibility.
## 4 Speech Presence Probability-Based Noise Statistics Estimation  
The eKF-RTF estimator and the beamforming algorithm also require knowledge of the noise spatial correlation matrix (SCM) \(\boldsymbol{\Phi}_{N}(t,f)\). In this work, we follow the multi-channel speech presence probability (SPP) approach described in Reference [4]. This estimation method is based on the recursive updating of the noise SCM in those time-frequency bins where speech is absent. First, two hypotheses, \(H_{x}\) and \(H_{n}\), are considered for speech presence and speech absence, respectively,  
\[H_{x}:\mathbf{Y}(t,f)=\mathbf{X}(t,f)+\mathbf{N}(t,f), \tag{36}\]  
\[H_{n}:\mathbf{Y}(t,f)=\mathbf{N}(t,f). \tag{37}\]Assuming zero-mean random variables, the SCM of the noise will be updated by means of the following recursion,  
\[\widehat{\boldsymbol{\Phi}}_{N}(t,f)=\alpha(t,f)\widehat{\boldsymbol{\Phi}}_{N}(t -1,f)+\left(1-\alpha(t,f)\right)\mathbf{Y}(t,f)\mathbf{Y}^{H}(t,f), \tag{38}\]  
where the forgetting factor is computed as  
\[\alpha(t,f)=\tilde{\alpha}+p_{x}(t,f)\left(1-\tilde{\alpha}\right), \tag{39}\]  
where \(\tilde{\alpha}\in[0,1)\) is an updating constant and  
\[p_{x}(t,f)=P\left(H_{x}|\mathbf{Y}(t,f)\right) \tag{40}\]  
is the _a posteriori_ SPP. The estimation of this probability is detailed in Subsection 4.1.  
From now on, the time and frequency indices are omitted whenever possible. The noise statistics for the RTF estimator (required by (30)) are directly derived from \(\boldsymbol{\Phi}_{N}\). Assuming a zero-mean, symmetric circular complex Gaussian distribution for \(\mathbf{N}\)[30] and  
\[\boldsymbol{\Phi}_{N}=\begin{bmatrix}\phi_{N_{11}}&\phi_{N_{12}}\\ \phi_{N_{21}}&\phi_{N_{22}}\end{bmatrix}, \tag{41}\]  
with \(\phi_{N_{ij}}=E\left[N_{i}N_{j}^{*}\right]\), the following relations can be demonstrated,  
\[\boldsymbol{\Sigma}_{n_{11}}=\frac{1}{2}\phi_{N_{11}}\mathbf{I}_{2}, \tag{42}\]  
\[\boldsymbol{\Sigma}_{n_{22}}=\frac{1}{2}\phi_{N_{22}}\mathbf{I}_{2}, \tag{43}\]  
\[\boldsymbol{\Sigma}_{n_{12}}=\frac{1}{2}\begin{bmatrix}\phi_{N_{12}}^{T}&- \phi_{N_{12}}^{T}\\ \phi_{N_{12}}^{T}&\phi_{N_{12}}^{T}\end{bmatrix}, \tag{44}\]  
\[\boldsymbol{\Sigma}_{n_{21}}=\boldsymbol{\Sigma}_{n_{12}}^{T}, \tag{45}\]  
where \(\mathbf{I}_{2}\) is the 2-dimensional identity matrix.
## 6 Discussion
### Operational Complexity and Detection Limitations
As we already discussed in our previous work [21], our proposed detection algorithm reduces the detection complexity from \(\mathcal{O}(c! )\) to \(\mathcal{O}(c^{2})\), whereby \(c\) denotes the number of classes. Note that the complexity \(\mathcal{O}(c! )\) corresponds to an exhaustive search, where one has to specifically check all possible class permutations, or at least the half amount of all possible class permutations. Moreover, the sorting complexity, which is generally equal to \(\mathcal{O}(c^{2}\log(c))\), for the rearrangement of the rows and columns of the corresponding PSMs can be neglected. Note that, in general, the number of classes \(c\) for which we try to provide an ordinal structure analysis is usually _low_ and mostly not (significantly) greater than 10. As briefly discussed in Section 3.2, choosing an LSM mapping that is bounded, e.g., reconstitution accuracy which is limited to 1, i.e., 100%, can lead to failures in detecting FS-ordinal structures, even in cases where the data are linearly separably and _obviously_ ordered, as, for instance, is depicted in Figure 4. However, one can overcome this issue by choosing an LSM mapping that can take _arbitrary_ values, as, for instance, the DR measure, which we introduced in Equation (12). Moreover, as discussed in [32], using an accuracy-based LSM mapping might suffer from the _curse of dimensionality_, according to [PERSON]'s theorem [33]. Note that we already discussed the influence of the feature dimension, based on the BVDB, for which the averaged detection time increased from roughly \(44\) ms specific to the DR measure, to approximately \(7.8\) min based on the SVM-Acc mapping. Again, one might overcome this issue by choosing an appropriate LSM mapping, such as the DR measure, which also identified the _correct_ order of the BVDB, nevertheless in less than one second on average.
# A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks
## 3 The Greedy Compact Multicast Routing (GCMR) Scheme
### Preliminaries
* The transmission cost (in Mbytes) is defined as the total cost of transmitting packets through the edges of the MDT to reach a given set of leaf nodes. Concretely, we are counting the number of additional bits required to transmit a single packet to all leaf nodes taking into account a 1500-byte IP packet to be transmitted along a ST as a reference. * The recovery time is defined as the maximum time needed to receive back a multicast transmission at the leaf nodes once a failure occurs in a link or node of the MDT.
## 2 Literature Review
### The Studies of the Behavioral Adjustment of HDV Drivers Interacting with CAVs
#### 2.2.2 Driving Simulator Experiments  
Driving simulator experiments are capable of making up for the defects of the field tests to some extent. A critical factor, i.e., the design of road alignment and the driving environment, is considered to represent the driving scenario realistically in simulator experiments [40]. For complex road environments, existing driving simulator research has investigated lane-changing accepted gaps on basic and on-ramp/off-ramp sections [59, 60] and the behavioral adaption of HDV drivers driving next to a dedicated lane for CAVs [61, 62]. Furthermore, a driving simulator is capable of exploring the merging decisions at the intersections. For instance, [PERSON] et al. (2019) [63] investigated the potential behavioral differences regarding human-human and human-CAV interactions in a sophisticated merging scenario at the unsignalized intersection through conducting a driving simulator experiment. The study was conducted with 17 subjects using a full-scale fixed-base driving simulator. As indicated by the result, when interacting with CAVs, the drivers can merge more frequently in front of CAVs while displaying a notably higher gap acceptance in merging scenarios at the unsignalized intersection. [PERSON] et al. (2023) [64] conducted a study on the car-following behavior of HDV drivers in mixed-flow traffic using a driving simulator, considering three different CAV control settings (string-stable, string-unstable, and HDV-like). Analysis of post-experiment interview results and eye-tracking data revealed that the majority of HDV drivers preferred the string-stable CAV control setting. However, it was noted that this preference could induce driver distraction.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 5 Testing
### Cryptanalysis  
Cryptanalysis is a technique in which an attacker tries to obtain a key using ciphertext or attempts to decipher the ciphertext. A cryptanalysis algorithm was implemented on the ciphertext to check the efficiency of the proposed algorithm. In cryptanalysis, an attacker makes a key prediction by using some rules. However, key prediction is impossible in this  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ssf** & **Ciphertext Length** & **Key Length** & **Decryption Time (s)** & **Cipher Text Memory** & **Plain Text Memory** \\  & & & & **Allocation** & **Allocation** \\ \hline
1 & 12 & 6 & 96.34 & 13.4 KB & 12.3 KB \\
2 & 16 & 6 & 102.42 & 14.7 KB & 12.8 KB \\
3 & 14 & 6 & 99.12 & 12.5 KB & 11.9 KB \\
4 & 16 & 6 & 103.29 & 18.2 KB & 15.4 KB \\
5 & 10 & 6 & 92.54 & 11.6 KB & 10.9 KB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time and Space Complexity of Decryption Mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Testing** & **Plain Text** & **Plain-Text Length (P)** & **Encoded Text** & **Key** & **Cipher Text** & **Cipher Text Length (C)** & **Length Difference** \\ \hline
1 & N@deem\_953 & 10 & +\#Bm\_1 embi\_1 ( & S7\_45) & O@axix-ROJJB & 12 & 2 \\
2 & WRitten\_by-@Li & 14 & mD\_7/YHi@logy’P\_2 & SuK\_8 & \(>\)?Dp\_-Xi@n\_1@la & 16 & 2 \\
3  
proposed algorithm because the data are stored in different phases using different rules. Unless these rules are applied, the data cannot be decrypted.  
When the ciphertext length and the plain-text length are not equal, the cryptanalysis mechanism cannot be applied. In Table 5, different plain-text lengths are identified, and the length of the ciphertext derived from the plain text is also identified. Plain-text length is represented with \"T\", while ciphertext length is determined by \"C\". After length identification, length equalization is completed, in which plain-text length and ciphertext length are compared. When the lengths \"C\" and \"T\" are not equal, the snatching mechanism cannot be implemented. When security is provided with the help of different roles on the ciphertext, no matter how effectively the attacker develops the algorithm, the data cannot be decrypted, nor can the key identification be possible.
# Autonomous Target Tracking of UAV Using High-Speed Visual Feedback
## 5 Visual Servoing Controller Design
### Coordinate Transformation  
There are three coordinate systems in our system: the object coordinate system \(\mathbf{O}\), the camera coordinate system \(\mathbf{C}\), and the UAV's body coordinate system \(\mathbf{B}\), as shown in Figure 8a. We can obtain \({}^{o}x\) and \({}^{o}R\) directly by PnP.  
Considering the possibility of electromagnetic interference, we estimate the relative attitude by using a vision estimator instead of a magnetometer. The relative yaw angle \({}^{O}\psi\) shown in Figure 8b is calculated by decomposing the relative rotation matrix derived from the PnP algorithm mentioned in the previous section. For the attitude control, the relative yaw angle estimation is used as an observation, and this is integrated with the IMU in the EKF state estimator.