esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Test of Size Recommendation Function  
In Figure 16, an evaluation was performed concerning the accuracy of the recommendation for each automatically extracted size and actual size. As the results from the size recommendation are based on the sizes entered during the setting process, the accuracy can be largely influenced by the inputs. '0' means that the automatic extraction has recommended an accurate size whereas '1' ('\(-\)1') means that it has recommended one size larger (lower). The recommended size will be more accurate if one's own measurements have been entered instead of automatically extracted sizes.  
Figure 15: Data deviations between automatically extracted sizes and actual sizes.  
Additionally, a selection can be made according to the customers' preferences. It is interesting to observe that the automated recommendation works better for the casual wear, and this is because their size differences often remain within four sizes. That is, the size recommendation is useful when one wishes to purchase casual wear based on his/her height and weight only but for the tight dresses, it is recommended to use the actual measurements for input.
## 3 Dual-Band Doherty Power Amplifier
### Linearization of Dual-Band DPA  
Finally, power amplifiers in general often need to be linearized, especially when they operates at the nonlinear region to avoid interfering with the next adjacent channel. This can be achieved, for example, by using digital pre-distortion (DPD) which is widely renowned in its ability to improve the linearity performance of the designed dual-band due to its reconfigurable capability, accuracy, and its implementation in the digital domain. Using DPD will improve the signal linearity; however, the system complexity will be increased, as there has to be an additional circuit added to the system [63]. However, there is a deterioration in the performance of concurrent dual-band power amplifiers since it is not easy for the DPD to correct the signal at two different frequency bands at the same time. This is obvious because the nonlinearity behavior of the power amplifier is different from one frequency band to another. A possible solution is to use a new technique called two-dimensional digital predistortion (2D-DPD), which, though more complex than ordinary DPD, can deal will dual-band PA [30]. At the same time, the crest factor also leads to performance degradation in dual-band PA. A two-dimensional crest factor reduction was studied and used in [30] to improve the linearity. Table 2 shows a comparison of performance achieved in dual-band DPA in addition to the signal test type that was used in each work; it can be noticed that most of the works were done using GaN HEMT.
## 4 Time Domain Spectral Finite Element Method (TDSFEM)
### Wave Propagation in 2D Elements
Solution accuracy has been examined in terms of model size, computation time, and memory storage for Legendre spectral finite elements and for quadratic serendipity elements calculated in a commercial finite-element code. Linear systems for both model types have been solved with the same sparse-system direct solver. At their best, Legendre spectral finite elements provide many orders of magnitude more accuracy than the quadratic elements for a fixed measure (e.g., computation time). At their worst, Legendre spectral finite elements provide the same accuracy as the quadratic elements for a given measure. The Legendre spectral finite elements have been insensitive to shear locking and were shown to be more robust in the thin-plate limit than their low-order counterparts. As a type of composite material may be considered as media with solid and fluid layers, which is commonly used for marine seismic exploration. The data analysis of wave propagation in such media has been performed in [142]. In this paper, the authors have compared the accuracy of grid  
Figure 3: A node distribution in the plate element proposed by [139]. dispersion criteria and numerical examples based on methods like: the classical finite-difference method (FDM) based on the second-order displacement formulation of the elastic wave equation (DFDM), the staggered-grid finite difference method (SGFDM), the velocity-stress FDM with a standard grid (VSFDM) and the time domain spectral finite element method (TDSFEM). All of the considered methods yield limited accuracy for the surface waves because the proper boundary conditions are not incorporated into the numerical scheme. Overall, it has been demonstrated both by analytic treatment and numerical experiments, which a first-order velocity stress formulation can, in general, be used in dealing with fluid-solid interfaces without using staggered grids necessarily. Functionally graded (FG) composite materials have been modelled by [143] where a cylinder subjected to an impulsive loading starting elastic wave propagation has been studied in detail. The displacement and stress distributions and the time histories of displacement and stress components have been obtained for various grading patterns. The results have shown that the compositional gradient exponent \(n\) does not have a distinct effect on the displacement or stress profiles but has an obvious influence on the wave travelling speed as well as the displacement and stress levels. The proposed method can be used to other types of axisymmetric FG structures like thick truncated cones and hollow cylinders, and also various loading and boundary conditions can be considered to these problems.
## 4 Experimental Verification
### Overall Performance of the Proposed System  
A statistical classification system, namely a confusion matrix, was used to evaluate the overall performance of the proposed system. The confusion matrix comprised the following elements: true positive (TP) rate, representing the number of healthy floors correctly predicted as healthy; true negative (TN) rate, representing the number damaged floors correctly predicted as damaged; false positive (FP) rate, representing the number of damaged floors incorrectly predicted as healthy; false negative (FN) rate, representing the number of healthy floors incorrectly predicted as damaged. The HI results were classified according to these elements. The following indices were calculated for performance assessment:  
\[\text{Accuracy }=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\;\text{Precision }=\frac{\text{TP}}{\text{TP }+\text{ FP}}\;\text{Recall }=\frac{\text{TP}}{\text{TP }+\text{ FN}} \tag{14}\]  
The classification results are listed in Table 19. A high accuracy of 89.8% was obtained, implying that the predictions were consistent with the practical value. A precision of 95.5% was achieved, signifying that a high percentage of healthy floors were identified as healthy. Furthermore, a recall of 90% was observed, indicating high efficiency to detect the actual health of a structure.
## 4 Mathematical Modelling of the Vibration Energy Recovery System  
The vibration simulation of the beam was conducted using the Siemens NX 2206 software with the Pre/Post Nastran application. The simulated object is an aluminium beam with dimensions depicted in Figure 2. Two MFC M8514-P1 films (manufactured and offered by Smart Material Corp.) were attached to its surface, and 8514 means that the active area surface is 85 by 14 mm. The MFC was glued 25 mm from the edge of the beam attachment (from the clamps). Based on the dimensions provided by the manufacturer, an area on the beam's surface corresponding to the active area of the film was determined. Knowing the values of deformations in this area, assuming a rigid attachment of the films, further calculations can be made based on the equations of the piezoelectric phenomenon. Figure 2: Dimensions of the beam (1), MFC film (2), and the active surface area of the MFC film (3) and the tested sample. The beam material chosen was Aluminium 6061. Damping coefficients were determined separately before simulating each of the investigated cases. Similarly, the beam mounting conditions were handled because the surface area and type of fixation varied depending on the version of the measured test setup. The results were obtained as Strain Elemental-Nodal. For calculation purposes, nodes located at the ends of the active surface were symmetrically selected, and calculations were performed based on the arithmetic mean of the obtained readings. Using known principles regarding the piezoelectric phenomenon and electrical circuit theory, it is possible to transform the simulation results, which are stresses or strains, into electrical quantities such as voltage drop. The setup used during the study with the National Instruments data acquisition module NI cDAQ-9191 CompactDAQ Wi-Fi Chassis device is an RC circuit [32; 33], where the voltage drop across a 95 k\(\Omega\) resistor is measured. The capacitor and voltage source are represented by the MFC film. By applying constitutive equations and the equation describing the RC circuit, we obtain the following [33]:  
\[U_{P}(t)=U_{C}(t)+U_{R}(t), \tag{6}\]  
where  
\(U_{P}\) [V]--voltage generated by the piezoelectric transducer;  
\(U_{C}\) [V]--voltage generated across the capacitor plates;  
\(U_{R}\) [V]--voltage drop across the resistor. and  
\[U_{P}(t)=U_{C}(t)+R_{Z}C_{P}\frac{\partial U_{C}(T)}{\partial t}, \tag{7}\]  
where  
\(R_{Z}\) [\(\Omega\)]--resistor resistance;  
\(C_{P}\) [F]--capacitor capacitance. Based on Equation (5), it is possible to determine \(U_{P}\) by knowing the value of the electric charge generated by the transducer and its capacitance. By transforming the constitutive equations, one obtains the following:  
\[D_{3}(t)=\frac{d_{31}}{s_{11}^{E}}S_{1}(t)+s_{33}^{T}\Bigg{(}1-\frac{d_{31}^{2 }}{s_{31}^{E}s_{33}^{T}}\Bigg{)}E_{3}, \tag{8}\]  
where \(S_{1}\) represents the time-varying strain. The electrode surface of the piezoelectric transducer is not continuous; however, it can be calculated by knowing the transducer's active surface area multiplied by the volumetric fraction of copper fibers \(V_{E}\) = 0.190 [33; 34].
Additive manufacturing (AM) has emerged as one of the most enabling new manufacturing technique; the topic has been extensively researched worldwide for almost two decades. The unique capabilities and potential of various AM techniques have led to almost homogeneous worldwide research efforts irrespective of international boundaries; such efforts have aimed at developing a thorough and critical understanding to harness the capabilities of AM that may translate to industrial practice. The motive behind these extensive research activities was to:  
1. Optimize the use of materials to reduce wastage [1].
2. Optimize the use of manpower to enhance efficiency [1].
3. Optimize the use of resources to limit production time [2].  
Both global governments and the private sector have invested billions of dollars to develop AM techniques to realize the goal of enabling sustainability as well as a profitable manufacturing route. All three categories of materials (metals/alloys, polymers, and ceramics) have been researched and practically all applications, whether in engineering or related to biomedical fields, have been equally targeted.  
To further this cause, a Special Issue was launched in the MDPI journal 'Applied Sciences', which sought original contributions to develop further understanding of this fascinating area of manufacturing. A total of nine articles were accepted after a rigorous peer review process and subsequently published. Overall, the papers addressed:  
1. AM process control/optimization including aspects of online monitoring [1, 3, 4]
2. Comparison studies with existing manufacturing methods to validate the acceptability of AM [5]
3. Product design and development [6]
4. Properties improvement using AM techniques [7]  
Many AM techniques have been developed over last two decades; the work done thus far has enabled current researchers to understand both the scientific and technical capabilities and the limitations of these techniques. Accordingly, researchers have been clear in their selection of AM techniques, choices which have been primarily governed by material and end applications.  
The articles collected in the present Special Issue indicate an emphasis on:  
1. Metal-based materials including stainless steels, magnesium alloys, and nickel-based alloys [4, 5, 7].
2. Polymer-based materials [8, 9].  
The industrial sectors which are likely to benefit from the studies presented in this Special Issue include but not limited to the following sectors:  
1. Construction
2. Transportation, including automobile and aerospace sectors
3. Nuclear* Biomedical
* Manufacturing  
Articles presented in this issue are expected to be of considerable interest to students and researchers working in a wide spectrum of engineering and biomedical fields as well as for a number of existing and new applications.  
Finally, I would like to thank all the authors for their excellent contributions to this Issue, to the reviewers for making useful comments to improve the quality of each article, and to the Applied Sciences editorial staff for processing and publishing these articles at their earliest convenience.
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 4 Experiments and Results
### Performance Analysis according to the Length of Surface  
The length of the _Surface_ is the most important factor determining the performance of the proposed fingerprinting method. As its length increases, the performance of the proposed method becomes more robust against signal noise and distortion, and the ambiguity can be reduced while performing a comparison with the RSS pattern. On the other hand, the following problems may occur as the length of the _Surface_ increases:  
* Drift error of walking distance from PDR The walking distance is calculated by accumulating the step lengths from the PDR, and the _Surface_ is constructed based on this distance. Drift error of the walking distance induces a localization error when comparing the measured _Surface_ with the reference RSS pattern.
* Sensitivity degradation in fixing reference trajectory at crossroad As the length of _Surface_ increases, the proportion of the RSS pattern to the common reference trajectory is higher than that of the RSS pattern after a crossroad. As a result, the similarity of RSS patterns between reference trajectories increases and the ratio of correlation coefficient becomes smaller. This serves as a delay in fixing the reference trajectory, which may lead to a problem of fixing the previous reference trajectory beyond the next crossroad.  
We selected the optimal length by comparing the localization errors for different lengths of _Surface_. In order to correctly analyze the accuracy according to the length of the _Surface_, localization was performed only for the reference path in Figure 8b while the pedestrian moved as shown in Figure 7a. As shown in Figure 15, accuracy improves as the length of the _Surface_ increases up to 72 m.  
Figure 14: Test scenarios: (**a**) Open space into narrow alley; (**b**) Narrow alley to alley.  
The accuracy decreases as the length increases more than 72 m. Therefore, we selected 72 m as the maximum length of the Surface in this study. In addition, a reference trajectory was set to be combined with up to three links considering the length of the Surface and that of the shortest link. This limitation also has the effect of bounding the increase of computation complexity in the comparison between measured Surface and reference patterns.
# A Sequence-Based Hyper-Heuristic for Traveling Thieves
## 2 Fundamentals
### Optimization Problem
#### 2.1.2 Traveling Salesperson Problem  
A traveling salesperson problem (TSP) is given by a set of cities that must be visited through the shortest path, thus reducing the cost of a tour across the cities. It is essential to highlight that a TSP instance requires each city to be visited only once. The only exception is the departing city, which also represents the final city of the solution. This implies that solutions to a TSP provide a closed loop around the cities (a tour), i.e., a Hamiltonian cycle. Keeping this in mind, the problem can be formulated as follows.  
Let us define a weighted complete graph \(\mathfrak{G}=(\mathcal{V},\mathcal{E})\), with \(\mathcal{V}\) representing the set of nodes such that \(\#\mathcal{V}\) equals the number of cities \(T\), while \(\mathcal{E}\) is the set of edges between each pair of nodes. (Recall that \(\#\) stands for the cardinality of a set). Therefore, each city is related to \(T-1\) edges, and their values represent the cost of moving from the current city to the other ones. Thus, all movement costs related to distances can be associated with a square matrix of order \(T\), \(\mathbf{D}\in\mathbb{R}^{T\times T}_{+}\), where any element \(d_{ij}\in\mathbf{D}\) provides the cost of moving from city \(i\) to city \(j\). To fulfil the Hamiltonian cycle property, \(\text{diag}(\mathbf{D})=\vec{0}\) ensures that the salesperson returns to the first city in the end since \(\text{diag}(\,)\) is the well-known diagonal function and \(\vec{0}\) is a vector of \(t\) elements equal to zero. In this way, we can define a binary variable \(y_{ij}\in\mathbb{Z}_{2}\), which comprises the information on whether the salesperson travels between these two cities. The symmetric binary matrix \(\mathbf{Y}\in\mathbb{Z}^{T\times T}_{2}\) contains all the possible paths \(y_{ij}\) from one city to another. Since each travel represents a cost, and taking advantage of basic linear algebra, the traveling salesperson problem can be summarized as:  
\[\begin{array}{l}\min_{\mathbf{Y}}\ \{\text{tr}(\mathbf{1}(\mathbf{D}\odot\mathbf{Y}))\},\\ \text{s.t.}\ \vec{1}\mathbf{Y}=\vec{1},\\ \vec{Y}=\vec{1},\end{array} \tag{2}\]where **1** is a \(T\times T\) square matrix with elements equal to 1, and \(\overline{1}\) is a vector from this matrix, e.g., \(\overline{1}=\text{diag}(\textbf{1})\). Moreover, \(\text{tr}(\,)\) is the well-known trace matrix function, and \(\odot\) is the so-called Hadamard-Schur product. Consider that the constraints restrict the possible movements of the salesperson. In this way, it visits a single city after visiting the \(i\)th city and comes from a single city when arriving at the \(j\)th city.
## 1 Introduction  
### Conventional Wireless Sensor Networks  
Communications in wireless sensor networks (WSNs) are event driven. Whenever an event triggers wireless sensor (WS) nodes generate bursty traffic. In a dense network environment, wireless sensor nodes deployed in the same area might try to access a channel whenever an event occurs. Recently, many sensitive and critical activities are being monitored and observed increasingly using WSNs. Several heterogeneous WSNs can exist, which causes a long waiting time for the delay sensitive data. Wireless sensors are normally deployed in inaccessible terrain. Therefore, the self-organizing ability and lifetime of the WS nodes are very important.  
WSNs consist of hundreds of WS nodes deployed throughout the sensor field and the distance between two neighboring WS nodes is generally limited to few meters. A sink node or base station is responsible for collecting the data from the WS nodes in single or multiple-hop manner. The sink node then sends the collected data to the users via a gateway, often using the internet or any other communication channel. Figure 1 shows the scenario of conventional WSNs.  
Current WSNs operate in the ISM band, which is shared by many other successful communication technologies. Research has shown that this coexistence in the ISM band can degrade the performance of the WSNs. The wide deployments, large transmit power, and large coverage range of IEEE 802.11 devices and other proprietary devices can degrade the performance of WSNs significantly when operating in overlapping frequency bands. The coexistence of wireless personal area networks (WPAN) with other wireless devices operating in an unlicensed frequency band is addressed in reference [1].  
Figure 1: Conventional wireless sensor networks.  
WSN devices are not only a victim but are also an interferer sometimes [2]. The coexistence interference can be avoided by the intelligent use of three types of diversity, namely frequency, time and space. Coexistence issues in unlicensed bands have been the subject of extensive research. Some solutions are also suggested in references [3; 4; 5].  
Researchers and industry are working to improve the performance of WSNs in terms of cost, energy consumption, data rate, robustness, networks throughput, QoS and security, _etc._ Considerable hardware and software enhancement has been implemented in recent years to enhance the network performance. A range of logical techniques have been employed to achieve the required network performance, such as power aware MAC, cross-layer design technique, efficient sensing technique, and significant enhancement in hardware design, _etc.,_ but these techniques have their own limitations.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation
### Propagating Regularity of the Coda Wave in Concrete  
#### 3.3.1 Frequency Effect  
Figure 5 depicts the waveforms at transmission signal frequencies of 300 kHz, 150 kHz, and 50 kHz. It was found from the received waveforms of three different frequencies that the attenuation of the first wave amplitude with a frequency of 300 kHz is about 2%. The first wave amplitudes with frequencies  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Center Frequency** & **Size** & **Weight** & **Operating Temperature** & **Frequency** & **Resonance** \\
**(kHz)** & **(mm)** & **(g)** & **Range (\({}^{\circ}\)C)** & **Range (kHz)** & **Frequency (kHz)** \\ \hline
300 & \(8\times 8\) & 2 & \(-\)65–177 & 150–400 & 300 \\
150 & \(23\times 19\) & 27 & \(-\)45–125 & 50–200 & 150 \\
50 & \(23\times 19\) & 33 & \(-\)45–125 & 35–80 & 50 \\ \hline \end{tabular}
\end{table}
Table 1: Performance parameters of sensors.  
Figure 4: Equipment used for the experiment.  
of 50 kHz and 150 kHz also have certain levels attenuation, but they are much smaller than that at 300 kHz. This is because the wavelength of the high-frequency sound wave is shorter than the size of aggregates and defects, resulting in more refracted and scattered wave. So, a large part of the energy is reflected back or scattered around these obstacles. This indicates that a high-frequency wave has an unavoidable defect when used in concrete testing by the direct wave method, due to its rapid attenuation. However, as shown in Figure 4a, coda waves at 300 kHz developed well, manifesting a larger amplitude and longer duration than those at 150 kHz in Figure 4b and 50 kHz in Figure 4c. When a wavelength is close to the size of the obstacles it encounters, multiple scattering occurs, and a coda wave will form. This demonstrates that the higher the frequency is, the better the coda waves develop. As shown in Figure 4c, the coda wave at 50 kHz is basically not developed, so an acoustic wave with this frequency is not suitable for the CWI test.  
Figure 5: Reception waveform of acoustic signals with different frequencies: (**a**) 300 kHz, (**b**) 150 kHz, (**c**) 50 kHz.  
Coda wave spectrums of acoustic signals at 300 kHz and 150 kHz are shown in Figure 6. It was found that the main frequency of the coda wave tends to move towards the low-frequency direction. The spectrum drift of the acoustic signal with a main frequency of 300 kHz is the most obvious. Its coda wave frequency is concentrated in the vicinity of 50 kHz and 130 kHz, while the coda wave frequency of the acoustic signal of 150 kHz is concentrated in the range of 50 kHz to 150 kHz. This is because the coda wave is formed by multiple scattering, and the interaction of the various acoustic interfaces in the concrete causes the wavelength of the acoustic signal to become longer and the frequency to become lower.