esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimode Guided Wave Detection for Various Composite Damage Types
## 5 Summary, Conclusions, and Future Work  
### Summary  
In this paper, a new inspection technique using multiple pure-mode guided waves was proposed for the detection and separation of various composite damage types. First, the proposed method was applied to a 2-mm quasi-isotropic composite plate with delamination simulated by a Teflon insert and actual impact damage. A pure S0 mode and SH0 mode were successfully generated using an adjustable ABT pair. Then, pure S0 mode and SH0 mode guided waves were conducted to detect and separate the delamination and impact damage. Finally, the same technique was applied to a 3-mm quasi-isotropic composite plate to validate the proposed method. In addition, a long-distance experiment was conducted to validate the feasibility of this technique for damage detection over a long distance. Pure S0 mode and SH0 mode were used to detect the simulated delamination and actual impact damage. The experimental results showed that the method could successfully detect and separate the delamination and impact damage in composite plates.  
The developed technique is intended for the rapid detection of different types of manufacturing flaws and operational damage in composite structures over large areas without doing point-by-point  
Figure 24: Signal comparison between the pristine area, the Teflon insert, and the impact damage using pure SH0 mode at 500 kHz on a 3-mm quasi-isotropic composite plate: (**a**) time-domain signals; (**b**) frequency spectrums.  
through-thickness scanning. For a more detailed evaluation, the method can be followed up using local-area conventional methods (e.g., through-thickness ultrasounds, X-rays, and eddy currents), which are more precise but are slower and labor-intensive. The advantage of the developed method is that it permits a rapid decision for local structural areas of interest that would require a labor-intensive evaluation through conventional inspection methods. In the application, the incident angle must be set to the theoretical tuning angle of the particular material and frequency in order to generate the desired pure-mode guided wave.
## 3 Results  
### Results of Seismic Dilatometer Tests  
Field investigations on test sites included seismic dilatometer tests and drillings with collecting of undisturbed soil samples by a Shelby sampler with an inner diameter of 89 mm. As part of the research work, 16 seismic dilatometer tests were made up to a depth of 9.5-16.0 m below ground level. The tests were carried out in nodes consisting of three to four SDMT, located in the vicinity of the borehole. The exceptions included studies carried out at the Bielany site, where the two drillings were completed with two SDMT. The seismic dilatometer test was performed in accordance with [PERSON]'s guidelines [7; 8; 19]. In order to generate the shear wave, a steel beam with a hammer construction that allows the hammer head to be easily hit against the beam's forehead was used. A hammer with a weight of approx. 15 kg and a beam with dimensions of 0.2 m \(\times\) 0.7 m \(\times\) 0.1 m were used. The instrument used to generate the waves in a ground was pressed by means of the probe's foot (Figure 4).  
SDMT data profiles including the corrected pressure readings \(p_{o}\) and \(p_{1}\), shear wave velocity \(V_{s}\) as well as the index parameters \(I_{D}\), \(K_{D}\), and \(E_{D}\) from dilatometer tests obtained at the Ursynow, Bielany and Stegny sites, respectively, are presented in Figures 5 and 6. The cohesive soils from the Ursynow site showed that the values of the index parameters \(K_{D}\) and \(E_{D}\) decreased with depth. A greater variation of \(K_{D}\) and \(E_{D}\) values was obtained in brown moraine clays of the Warta Glaciation than in grey moraine clays of the Odra Glaciation. In the brown moraine clays of the Warta Glaciation the presence of cementation was detected. The index parameters \(K_{D}\) and \(E_{D}\) were increasing with depth in the moraine and lacustrine deposits of Warta Glaciation from the Bielany site and Pliocene clays from the Stegny site. The mean values, standard deviations and coefficients of variation of these readings and indexes are presented in Table 3. The average coefficients of variation for \(V_{s}\), \(I_{D}\), \(K_{D}\), and \(E_{D}\) were 0.13, 0.43, 0.20, and 0.41, respectively. The dilatometer modulus \(E_{D}\) showed quite a high coefficient of variation. As an independent variable the shear wave velocity \(V_{s}\) showed quite a small coefficient of variation.  
Figure 4: Steel beam with a hammer used to generate shear waves in SDMT.  
Figure 5: Profiles of the corrected pressure readings \(p_{o}\) and \(p_{1}\), and shear wave velocity \(V_{s}\) from seismic dilatometer tests obtained at the Ursynów, Bielany, and Stegny sites.  
Figure 6: Profiles of the index parameters \(I_{D}\), \(K_{D}\), and \(E_{D}\) from seismic dilatometer tests obtained at the Ursynów, Bielany, and Stegny sites.
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
## 1 Introduction
\right]\downarrow\text{Time} \tag{8}\]  
So, the received OTDM signals \(R_{ij}\) can be represented as  
\[R_{11}=(H_{11}\;X_{1})+(H_{12}\;X_{2})+z_{1}, \tag{9}\]  
\[R_{21}=(H_{21}\;X_{1})+(H_{22}\;X_{2})+z_{2}, \tag{10}\]  
\[R_{12}=(-H_{11}\;X_{2}^{*})+(H_{12}\;X_{1}^{*})+z_{3},\text{ and} \tag{11}\]  
\[R_{22}=(-H_{21}\;X_{2}^{*}\;)+(H_{22}\;X_{1}^{*})+z_{4}, \tag{12}\]  
where \(i\) is the number of receive antennas and \(j\) is the number of transmit antennas, and \(z_{1}\), \(z_{2}\), \(z_{3}\), and \(z_{4}\) are the additive white Gaussian noise (AWGN) with zero mean and variance \(\sigma^{2}\). \(H_{ij}\) are the channel response between the \(i\)th receiving antennas and \(j\)th transmitting antennas. Then, these signals are exposed to the receiver sequence.
## 1 Introduction
Therefore, the parameter combination has a great influence on the property of the component prepared by LSF. In traditional technology, such as the induction melting in vacuum atmosphere, has a long processing cycle and it is difficult to form complex components. Considering the connection application background of the NiTiNb SMA tube joint which has a complex structure, we note that LSF can provide a better method for preparing NiTiNb because of its advantage of rapid prototyping complex components. However, there are few reports on the properties of NiTiNb alloys prepared by LSF. So, the preparation of NiTiNb alloy by LSF has great research value and the work will provide guidance for using LSF to process complex NiTiNb SMA tube joints. Therefore, we selected a variety of parameter combinations to preliminarily explore the microstructure and phase transformation temperature of NiTiNb prepared by LSF, the results show that LSF can play a better role in the preparation of NiTiNb. Through experiments, we have obtained the feasible parameter combination of NiTiNb prepared by LSF, preliminarily obtained the microstructure and phase transformation temperature of NiTiNb prepared by LSF using mixed powder, which provides an experimental basis for the preparation of NiTiNb by LSF. In this work, we explore the feasibility of preparing NiTiNb alloy by LSF using mixed powder, viz., elemental Ni, Ti, and Nb powders were mixed in the ratio of 47:44:9, and then NiTiNb alloys were prepared by seven LSF parameter combinations including laser power \(P\), scanning speed \(v\), layer thickness \(t\), and corresponding energy density. The microstructure, phase transformation temperatures, phase transformation behaviors, and tensile properties of subsequent heat-treated LSF-NiTiNb were reported. According to the research results, some suggestions for preparing NiTiNb by LSF using mixed powder are put forward.
## 2 Discretization Strategy  
Consider the general model for a \(L\)-Path multipath channel (Equations (13.1)-(5) of [15], Equations (3.3)-(3.6) of [16])  
\[h(\tau;t)=\sum_{i=0}^{L-1}\alpha_{i}(t)\exp(-j2\pi f_{c}\tau_{i}(t))\exp\biggl{(} j2\pi\frac{v_{i}\cos\phi_{i}(t)}{c_{0}}f_{c}t\biggr{)}\delta(\tau-\tau_{i}(t)) \tag{1}\]  
in which \(\alpha_{i}(t)\) is the real-valued gain of \(i\)-th path, \(\exp(-j2\pi f_{c}\tau_{i}(t))\) is the phase shift (often, these two terms are considered as one term, called the complex attenuation factor), \(f_{c}\) is the carrier frequency, \(\tau_{i}(t)\) is the propagation delay of \(i\)-th path, \(v_{i}\) is the velocity associated with the \(i\)-th path, \(\phi_{i}(t)\) is the angle of arrival of the wave (for the sake of simplicity, its value is assumed zero throughout this article), and \(c_{0}\) is the speed of light. We consider that the channel remains constant during each symbol period. This implies that the channel can change symbol by symbol, i.e., \(L\) changes symbol by symbol. In this project, we model the path gain in (1) by the exponential function as \(\alpha_{i}(t)=\exp(-\tau_{i}(t)a_{i})\) in which \(a_{i}\) is the attenuation coefficient. It is assumed that \(\tau_{i}\) is a Weibull random variable. The parameter of the Weibull distribution is adjusted such that its behavior closes a semi-exponential function. We assume that each \(\tau_{i}(t)\) is smaller than the symbol duration \(T\). This ensures partial paths overlapping across the successive symbol, and guarantees dense overlapping environment over a block of received symbols. At the receiver side, the following channel model is employed, i.e., all channel hypotheses are considered.  
\[\begin{split} h(\tau;t)&=\sum_{i=0}^{N-1}PE_{i}\exp \left(-iT_{N}\sum_{i^{{}^{\prime}}=1}^{N^{{}^{\prime}}}AE_{i^{{}^{\prime}}}i^ {{}^{\prime}}a_{N^{{}^{\prime}}}^{{}^{\prime}}\right)\exp(-j2\pi f_{c}iT_{N} )\\ &\quad\times\exp\left(j2\pi\left(\sum_{i^{{}^{\prime\prime}}=1}^{ N^{{}^{\prime\prime}}}\frac{VE_{i^{{}^{\prime\prime}}}i^{{}^{\prime\prime}}V_{N^{{}^{ \prime\prime}}}}{c_{0}}\right)f_{c}t\right)\delta(\tau-iT_{N})\end{split} \tag{2}\]  
where \(T_{N}=\frac{T}{N}\) is the delay resolution (\(T\) is the symbol duration), \(a_{N^{{}^{\prime}}}^{{}^{\prime}}=\frac{1}{N^{{}^{\prime}}}\) is the attenuation resolution, and \(V_{N^{{}^{\prime\prime}}}=\frac{V_{N}}{N^{{}^{\prime\prime}}}\) is the velocity resolution (in which \(V_{m}\) is the maximum possible velocity). \(PE\), \(AE\), and \(VE\) are Bernoulli random variables representing path, attenuation, and velocity existences, respectively. Their prior probabilities are set to 0.5. If \(N\), \(N^{{}^{\prime}}\), and \(N^{{}^{\prime\prime}}\) go to infinity and we have exactly the knowledge of \(PE\), \(AE\), and \(VE\), the error modeling goes to zero. It is important to highlight that the low-pass equivalent equations mentioned above primarily represent our discretization strategy. Our simulations are rooted in our front-end approach.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.6 Image Stabilization  
Camcorders use accelerometers for image stabilization, where the information about acceleration is used to alter the image to remove the blur caused by sudden movements; both references [39; 40] present such applications of image stabilization using accelerometers and different stabilization algorithms.
## 4 Numerical Simulation
### Sound Transmission Level Analysis  
The STL properties of the corrugated composite panels was numerically evaluated by using the commercial FE software COMSOL Multiphysics(r). Different from the test processes, both the reverberation and anechoic rooms were not built in the model. Instead, an ideal diffuse field was directly added on the source side of the composite panels, while an ideal anechoic termination was added on the receiver side. The model of the tested composite panel was consistent with that in Figure 4, with the same material properties obtained from Tables 1 and 2. By solving the vibroacoustic problem, the incident and transmitted sound intensity can be calculated by [45; 46]:  
\[I_{x,in(tr)}=\frac{1}{2}\text{Re}\Big{(}p_{in(tr)}\text{v}_{x,in(tr)}^{*}\Big{)} \tag{5}\]  
where \(p_{in}\) and \(\text{v}_{x,in}\) are, respectively, the sound pressure and velocity of the incident wave on the source side of the panel, \(p_{tr}\) and \(\text{v}_{x,tr}\) are, respectively, the sound pressure and velocity of the transmitted wave on the receiver side, \(x\) is the normal direction of the panel, \({}^{*}\) represents the sign of the conjugate, and Re is the real part of a complex number. The STL of the panel is then written as:  
\[STL_{sim}=L_{li}-L_{lt} \tag{6}\]  
The simulation was performed in the frequency range of 100 to 7000 Hz under the assumption of fixed-constrained boundaries.
## 3 Results
### Sea Mammal Vocalization and Anthropogenic Noise  
The Observer/AMARC4 hydrophone installed on the Seaglider(r) was able to record cetacean vocalizations, anthropogenic operations, and weather events across the LoVe continental shelf break and results from the spring records (March-April 2018) were recently published [11]. In short, delphinids, fin whales _Balanoptera physalus_, humpback whales _Megaptera novaeangliae_, and sperm whales _Physeter macrocephalus_ were identified during the deployment. During the first weeks (March), humpback whale calls were dominant on both the shelf and shelf edge, coinciding with the species' well-known migration patterns toward breeding grounds [66; 67]. From mid-April, during the spring phytoplankton bloom,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Month** & **Temperature (°C)** & **Salinity (ppt)** & **O\({}_{2}\) Saturation (\%)** \\ \hline April & 4.5 +/ – 0.7 & 33.6 +/ – 0.2 & 101.2 +/ – 2.1 \\ \hline May & 8.6 +/ – 0.4 & 33.5 +/ – 0.0 & 110.0 +/ – 1.6 \\ \hline July & 14.1 +/ – 0.1 & 34.5 +/ – 0.0 & 104.7 +/ – 0.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average and standard deviation of monthly temperature, salinity, and oxygen saturation measured from the Sailbuoy glider, from April to July 2018.  
Figure 6: Three examples of echograms of volume backscattering strength (S\({}_{\rm V}\)) at 333 kHz collected by WBT Mini on the Sailbuoy in the upper 100 m and integrated over a 24 h period. The echograms display differences in zooplankton aggregation during the three periods and regions of the 2018 deployment. **(A)** April, deep and thin registrations in Vestfjord, **(B)** June, persistent dense layer in the upper 10–30 m, along the western coast of Vesteralen, **(C)** August, wide vertical distribution with indications of diel vertical migration, along the shelf break.  
sperm whales and delphinids were mostly recorded. Sperm whales were mostly detected in more open waters and within a 400 m underwater canyon, coinciding with their preferred foraging habitat [11]. This analysis showed seasonal and spatial variability in marine mammal detections.  
Manual and automatic detection of anthropogenic noise identified sounds that could be attributed to distinct human activities in the area. The most common anthropogenic noises detected were vessel propellers (maritime traffic), seismic surveys, and offshore operations. A preliminary analysis of the entire survey seems to confirm that ship traffic is the most encountered type of anthropogenic sound. However, while ship traffic represents the most frequently recorded source of anthropogenic disturbance, seismic shooting activities were also detected, with some activities overlapping in both time and frequency with marine mammal vocalizations (Figure 7).
## 2 TBM Selection Decision-Making Method
### Determination Method of Evaluation Index Weight
#### 2.3.2 Expert Survey Scoring and Scoring Correction Method  
The index weight of the AHP method is determined by experts. When scoring is conducted by experts, their subjectivity tends to reduce the reliability of scoring. Therefore, it is necessary to adopt some means to revise the scores of multiple experts so as to obtain more reliable scores. In order to improve the reliability of expert scoring, the modified weighting method was used to modify the scoring.  
The method is as follows: First, select a number of experts to score. The scoring rule is the nine-scale method (see Table 1) [25]. The score of each expert is expressed by matrix \(A_{n}\).  
After that, the revised weighting method is adopted to reduce the subjectivity of experts' scoring. The specific steps are as follows:  
1. [label=Step 1]
2. Assign an initial weight of 1 to all expert scores of a single index;
3. [label=Step 2]
4. Calculate the initial weighted average value with Equation (1): \[\overline{X}^{(0)}=\frac{\sum\limits_{i=1}^{m}P_{i}^{(0)}X_{i}^{(0)}}{\sum \limits_{i=1}^{m}P_{i}^{(0)}}\] (1)where \(\overline{X}\) is the score of the \(i\)th expert, \(P_{\text{i}}^{(0)}\) is the initial weight of the \(i\)th expert, and \(X_{i}^{(0)}\) is the initial weighted score of the \(i\)th expert. \(m\) is the number of experts;  
Step 3. Take \(\overline{X}\) as the reference value and calculate the first correction weight with Equation (2):  
\[\mu_{i}^{(1)}=\frac{X_{\text{max}}^{(0)}-X_{\text{min}}^{(0)}}{\overline{X}^{ (0)}}-\sqrt{\frac{2\left|X_{i}^{(0)}-\overline{X}^{(0)}\right|}{\overline{X}^{ (0)}}} \tag{2}\]  
where \(\mu_{i}^{(1)}\) is the correction weight of the \(i\)th expert in the first iteration, \(X_{\text{max}}^{(0)}\) is the maximum value of all expert scores, and \(X_{\text{min}}^{(0)}\) is the minimum value of all expert scores.  
Step 4. Use Equation (3) to calculate the modified weight.  
\[P_{\text{i}}^{(1)}=P_{\text{i}}^{(0)}+\mu_{i}^{(0)} \tag{3}\]  
Then, iterate to \(\overline{X}\) (two decimal places are reserved) according to this method, and \(\overline{X}\) is the final score. After the above method is adopted for all indexes, the matrix is constructed to obtain the revised expert scoring matrix \(C_{n}\).
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 4 Classifiers
### Svm  
SVM [1] is a non-probabilistic classifier formally defined by a separating hyperplane. The given training data are labeled (supervised learning), and the algorithm draws an ideal hyperplane, which has the maximum distance from the support vectors. In two-dimensional space, this hyperplane is a line dividing a plane into two classes. The tuning parameters of the SVM classifier are the epsilon (\(\epsilon\)), regularization, and kernel parameters.
## 3 Optimization Problem
### Objective Function  
Partially constrained layer damping composite plates are widely used to suppress vibration, especially in civil and mechanical engineering. The damping loss factor is one of the most important parameters of composite plates and varies with the position of the constrained damping layer. The optimization goal of this study is to obtain the optimal location for the patches (constrained damping layer) of different sizes to ensure the maximum damping loss factor of the composite plate with partial constraining damping. This optimization problem is described by the previously derived model. It can be seen that the position coordinates (\(a\), \(b\), \(c\), \(d\)) of the patch are defined as the design variables shown in Figure 1. The length and width of the patch are \(P_{L}\) and \(P_{W}\), respectively. In this study, the size of the patch is a constant. Thus, the optimization objective function can be formulated as:  
\[\begin{array}{rl}\max:&f(x,y)=\eta_{i},i=1,\;2,\;3\ldots\\ \text{s.t.}\;:&0\leq x\leq L-P_{L}\\ &0\leq y\leq W-P_{W}\end{array} \tag{25}\]  
where \(\eta_{i}\) represents the loss factors and subscript \(i\) is the order of the loss factors. Optimization variables \(x\) and \(y\) correspond to \(a\) and \(c\), respectively.
# Influence of Drift on Robot Repeatability and Its Compensation
## 2 Methodology
According to the datasheet [27] precision of this sensor is \(\pm\)0.49 \(\upmu\)m. The Keyence LJ-X8080 is a laser profiler sensor head that detects 3200 points in a single line. For distance measurement, a middle point value is used. The value is obtained with a precision of \(\pm\)0.5 \(\upmu\)m. When the robot is in a detection range, the measurement is triggered by a control application. The measured values from a single measurement cycle are divided into six chunks; the first two (approaching the target and stabilization) and the last (departure) are excluded from processing. The result is an average from the remaining data. A value of repeatability is read from the sensors with a rate of 100 ms, and thus after the processing, the result is an average of approximately 25 values. The data provided by the laser profilers might be affected by a random error in the form of peaks if the laser beam angle is not set correctly. To get a valid data from the laser profiler, it is necessary to set an appropriate laser beam angle that depends on a detected surface. Based on research [28], the angle to the surface was set to \(-18^{\circ}\). These settings guarantee results of the finest quality. This combination of sensors is used for its high level of precision and its availability. Any other suitable high accurate sensor such as laser tracker or Digital Image Correlaiton (DIC) cameras could have been used instead. All the sensors are connected via a communication network to a custom. NET C# application that runs on Intel NUC PC. The application collects, controls, and transmits the temperature and the position deviation. In addition, it stores the data in a file for later offline analysis and, at the same time, sends the data to the robot controller for online processing utilized for a positioning error compensation (described later in this article).
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 4 Result and Discussion
### Comparative Analysis
#### 4.4.2 Segmentation of Liver Tumor  
The tumor is separated from the segmented liver in our method. If no liver is in any slice of CT volume, then that slice will not account for the further procedure in tumor segmentation. The comparison of the proposed method with state-of-art techniques is highlighted in Table 5. [PERSON] et al. [13] utilized FCN to segment liver and tumors in CT and MRI abdomen scans (CFCNs). The spiralled FCN produced VOE values of 0.823. The model proposed by [PERSON] et al. [37] operate in a 2.5D context, in which the deep learning model based on the DCNN technique yielded a DSC of 0.67. [PERSON] et al. [38] suggested an automated technique for liver tumor delineation in CT volumes based on adaptive fuzzy C-means (FCM) and graph cuts, which resulted in a DSC of 0.83. [PERSON] et al. [39] used a multi-channel convolutionary network (MC-FCN) to separate liver tumors from a CT scan and achieved a VOE of 15.6. [PERSON] et al. [24] implemented 3D hybrid residual attention-aware (RA-UNet) and obtained DSC score of 0.59 for liver tumor segmentation. [PERSON] et al. [25] introduced a modified U-Net to segment the liver CT scan and obtained DSC scores of 0.74 for liver tumor segmentation. Our method demonstrated high extension and generalization abilities in another tumor segmentation dataset and achieved competitive performance in the liver tumor challenge. The ResU-Net and recurrent neural block performed better segmentation of the hepatic and liver tumour. The U-Net network collects more data with recurrent neural blocks, allowing for a better liver CT scan segment than other state-of-the-art studies. The proposed model has great potential for application to other modalities of medical images. Additionally, it may assist surgeons in finding treatments for novel tumors.