esa-sceva/satcom-chunk-collection huggingface dataset:
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 4 Research Methodology
### Action 6: Communication  
The components of the model were implemented in several experimental prototypes and European project use cases (Arrowhead, Mantis, and Productive 4.0). These projects involved industrial partners and research organizations where digitization, automation, SCM, and other challenges related to IoT and lifecycle management were addressed. Some of the experiments addressed in these projects and the model stage they confront are outlined next:  
* Design, deployment, and system management supported by the Arrowhead framework in an industrial context (machine tooling) were implemented in Productive 4.0 [83; 84].
* Research on business needs as well as on functional and quality requirements are considered in the design stage of [85] before developing an architectural solution for proactive maintenance in the Mantis project [86]. This use case was implemented in a company leader in the construction of power transmission components for metal forming machine tools such as clutches, brakes, or cams where CPSs are used to monitor performance.
* In Productive 4.0, the lifecycle model was also validated in the context of workflow management of the manufacturing process using the Arrowhead framework. The results are briefly demonstrated in Section 7.2 and detailed in [82; 87].
* The lifecycle management issues are continually addressed in the current Arrowhead Tools project [88].  
The next section introduces an Industry 4.0-compliant PLM model, which was developed based on the process engineering frameworks and lifecycle management models presented earlier and taking into account the changed scope of the product.
## 3 Results
### Chemiluminescent EIA of NP in Natural Waters
It should be noted that the detection method (colorimetric or chemiluminescent) did not affect the recovery values. However, the ranges of working concentrations were different. As a result, the sensitivity of chemiluminescence-based assay was higher. In the case of chemiluminescent detection, the IC\({}_{10}\) parameter was 9 ng/mL, compared to 55 ng/mL for colorimetry. Summarizing the analytical characteristics of both methods, it turns out that the limit of detection of nonylphenol was 20 ng/mL when performing chemiluminescent EIA. Colorimetric determination is characterized by the LOD value of 98 ng/mL. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Added, NP**} & \multicolumn{5}{c}{**Detected, NP, Chemiluminescence**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Probe 1 (River)**} & \multicolumn{2}{c}{**Probe 2 (Waterfall)**} & \multicolumn{2}{c}{**Probe 3 (Pond)**} \\ \hline
**ng/mL** & **ng/mL** & **\%** & **ng/mL** & **\%** & **ng/mL** & **\%** \\ \hline
**45** & 55 \(\pm\) 8.4 & 122 \(\pm\) 15.2 & 53 \(\pm\) 2.3 & 117.7 \(\pm\) 4.3 & 50 \(\pm\) 5.3 & 111.1 \(\pm\) 10.5 \\ \hline
**135** & 140 \(\pm\) 13.7 & 103.7 \(\pm\) 9.8 & 130 \(\pm\) 7.5 & 96.3 \(\pm\) 5.8 & 148 \(\pm\) 10.4 & 109.6 \(\pm\) 7.0 \\ \hline
**400** & 431 \(\pm\) 49.1 & 107.8 \(\pm\) 11.4 & 411 \(\pm\) 42.3 & 102.8 \(\pm\) 10.3 & 395 \(\pm\) 15.0 & 98.8 \(\pm\) 3.8 \\ \hline
**1200** & 1023 \(\pm\) 53.2 & 85.3 \(\pm\) 5.2 & 954 \(\pm\) 127.8 & 79.5 \(\pm\) 13.4 & 992 \(\pm\) 69.3 & 82.7 \(\pm\) 7.0 \\ \hline \multicolumn{7}{c}{**Detected, NP, Colorimetry**} \\ \hline
**200** & 235 \(\pm\) 3.2 & 117.5 \(\pm\) 1.4 & 184 \(\pm\) 8.1 & 92 \(\pm\) 4.4 & 234 \(\pm\) 7.9 & 117.0 \(\pm\) 3.4 \\ \hline
**600** & 589 \(\pm\) 6.4 & 98.2 \(\pm\) 1.1 & 532 \(\pm\) 16.5 & 88.7 \(\pm\) 3.1 & 519.6 \(\pm\) 16.6 & 86.6 \(\pm\) 3.2 \\ \hline
**800** & 830 \(\pm\) 12.2 & 103.8 \(\pm\) 14.7 & 792 \(\pm\) 13.6 & 99 \(\pm\) 1.8 & 762.4 \(\pm\) 11.4 & 95.3 \(\pm\) 1.5 \\ \hline
**1000** & 1206 \(\pm\) 10.4 & 120.6 \(\pm\) 0.9 & 998 \(\pm\) 19.2 & 99.8 \(\pm\) 2.0 & 832 \(\pm\) 16.2 & 83.2 \(\pm\) 2.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data on NP concentration observed by the developed chemiluminescent EIA in the spiked water samples (10 th measurement, n = 3) and colorimetric EIA. Figure 7: Calibration curve in distilled water. The concentration of NP–STI was 1 \(\mu\)g/mL; antiserum dilution: 1:10,000. All measurements were performed in duplicate. The SD in the 10 th measurement did not exceeded 8%. The works of predecessors showed interest in this issue (Table 5). However, it should be noted that the presented work has no analogs both in terms of the type of analytical signal detection and in studying the influence of data processing. In addition, the high sensitivity of the analysis achieved in the presented work was previously achieved using either high-affinity antibodies or the use of a sample concentration step. Thus, the results demonstrated in this work demonstrate the applicability of the developed test system for the determination of nonylphenol in natural waters.
## 2 Materials and Methods
### Stimuli  
The speech stimuli used in this experiment consisted of four excerpts taken from texts written in the Croatian language. Each excerpt was read by a professionally trained speaker employed at a prominent radio or television station in Croatia. Two male and two female speakers were engaged to read these texts. The length of all four recordings was set to 30 to 40 s, so that the excerpts they contain could form a meaningful whole, instead of being cut abruptly. The level of these recordings was left as is, i.e., the difference between the equivalent sound pressure level of the loudest and the softest sample was around 6 dB, both unweighted and A-weighted. No normalization of any kind was undertaken to equalize the level or loudness of all samples to test if this starting difference will play a role and have an influence on the results of the experiment.
## 3 Pathogenesis of AD
### The Amyloid Hypothesis  
[PERSON] and [PERSON] introduced the amyloid hypothesis in 1991 [4; 53]. In this respect, the amyloid \(\beta\)-peptide, which is prevalent in the brain plaques that define Alzheimer's disease (AD), was decoded from the meningeal blood vessels of AD patients and those with Down syndrome about 20 years ago. The same peptide was shown to be the main component of senile plaques in the brain tissue of an AD patient. These results marked the beginning of the current phase of research into this prevalent, and possibly lethal, neurodegenerative disease [4; 53; 54]. Amyloid \(\beta\) peptide is a transmembrane protein generated via the amyloidogenic pathway by hydrolysis of the A\(\beta\) precursor protein (APP). According to published research, APP generates C-terminal fragments via three routes when hydrolyzed by \(\alpha\)-, \(\beta\)-, \(\gamma\)-, and \(\eta\)-secretase [55]. Under normal conditions, the first non-amyloidogenic pathological route results in products that have neurotrophic and neuroprotective properties for nerve cells, such as the soluble ectodomain of APP-\(\alpha\) (sAPP\(\alpha\)), the C-terminal fragment (CTF)-\(\alpha\), and other smaller fragments, via the participation of \(\alpha\)- and \(\gamma\)-secretase. On the other hand, the amyloidogenic pathological pathway is the second route where cleavage of APP by \(\beta\)-secretase results in the formation of CTF-\(\beta\), whereas cleavage by \(\gamma\)-secretase leads to various lengths of A\(\beta\) peptides including A\(\beta\)42 [56; 57]. In addition, \(\eta\)-secretase regulates the third pathway, which is the alternative processing route under physiological conditions. In this hypothesis, the situation is analyzed as a sequence of anomalies in the development and secretion of the amyloid precursor protein (APP), where the difference between the development and clearance of amyloid \(\beta\) is the starting event and the most important factor responsible for the additional irregularities reported in AD [58]. In this regard, amyloid \(\beta\) is a highly susceptible peptide to proteolytic degradation [59]. A\(\beta\) contains 37-43 amino acids, the most common of which are isoforms 1-40 and 1-42 [60; 61]. The amyloid peptide isoform 1-42 is the most hydrophobic and has the highest toxicity [1; 62]. Due to its physical characteristics, the \(\beta\)-plated sheet is often configured, showing a strong tendency to accumulate and form the amyloid plaque nucleus [58]. It is, therefore, the vital ingredient of neuritic amyloid plaques [2].
# Simulation and Design of Three 5G Antennas
## 3 Design of 5G Microstrip Array Antennas
### Design and Simulation of 4 \(\times\) 4 Antenna Units  
In general, microstrip antenna units have relatively low gain and may not meet the requirements for real-life antenna applications. To achieve higher gain, it is necessary to configure them into a microstrip array antenna using a feeding network. The design of the array antenna involves considerations such as the number of antenna elements, their arrangement, the spacing between array elements (denoted as 'd'), and the design of the feeding network.
# Topology Optimization Based on SA-BESO
## 2 The Joint Topology Optimization Method Based on SA-BESO  
### The Idea of Combination of SA and BESO  
In the process of solving the optimization problem of the SA algorithm [26; 27], the value of the objective function can change within a certain range under certain conditions in the direction of non-optimization. When certain conditions are met, the SA algorithm can converge to the global optimal solution with probability \"1\". The SA algorithm regulates the optimization process in stages according to decreasing temperature. When the temperature is at a particular temperature, the algorithm randomly generates current solutions. If the current solution is better than the original solution, the improved solution is chosen. If the current solution is slightly worse than the original solution, the current solution is still accepted with a certain probability, thus jumping out of the local solution and searching for the global solution. Among them, the probability varies with the temperature and determines the possibility of the algorithm to jump out of the local solution. The BESO algorithm has a wide range of practical applications and is characterized by its conceptual simplicity, high efficiency, and good convergence in solving structural topology optimization problems. At each optimization solution stage, the BESO method deletes a fixed number of units to achieve a specific phase target volume. Since the deleted elements may not be recovered, if the deleted elements are wrong at each stage, the structural compliance will increase abruptly and the algorithm will become stuck in the local optimal solution, which will lead to the failure to obtain the optimal structural material distribution. The combined approach of SA and BESO is able to judge the change in structural compliance obtained at each iteration, thus avoiding the excessive increase in intermediate topological compliance due to the erroneous deletion of elements. At the beginning of the BESO algorithm, the structural material is continuously reduced, which leads to a certain increase in the structural compliance value and a larger probability. As the optimization proceeds, the temperature and the volume of the structure gradually approach the final target, with decreasing probability, until the final optimized structure is obtained.
## 4 Experimental Results  
Before applying the control, a reference acoustic field acting on the selected passenger's area was experimentally evaluated. The excitation level was set to about 80 dB in order to reproduce a realistic noise level as detected by passengers within an actual aircraft cabin. By assuming that the off-line identified plants were accurate enough to represent the behavior of the secondary paths, the real-time levels of noise were measured and compared with and without control.  
Firstly, after recording acoustic signals for the uncontrolled case, the ANC headrest system was separately invoked for the left and right head positions. The control system capabilities for adaptively attenuating the primary acoustic field were experimentally assessed on the two microphones located left and right of the binaural head. The results are shown in Figure 8. The SPLs measured at the left and right microphones were reduced by approximately 13 dB (left) and 9.5 dB (right), respectively, when only one of the two speakers was alternatively activated. After that, when both controllers were activated, a higher noise attenuation of 19.92 dB (left mic) and 17.36 dB (right mic) was achieved. The noise attenuation results are summarized in Table 1.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 6 Conclusions  
A method, based on hoop strain measurement, was proposed to detect pipeline corrosion and leakage simultaneously. With the advantages of high sensitivity, good electrical-magnetic immunity and chemical stability, an FBG strain hoop sensor was developed for the pipeline monitoring. Two model experiments including corrosion and leakage were conducted to verify the method and test the performance of this sensor. The leakage simulation experiment proved that a turning point arose due to the NPW propagate along the pipeline. Meanwhile, pipeline leakage can be detected by  
Figure 14: Photo of the PVC pipeline model.  
Figure 15: Results of the corrosion simulation experiment.  
the FBG strain hoop sensor. According to the corrosion experiment, FBG strain hoop sensors were demonstrated to be sensitive to the hoop strain variations caused by corrosion. It can be concluded from the two experiments that the pipeline leakage and corrosion monitoring method based on hoop strain measurement are practicable. Further investigation will be conducted to study the leakage monitoring of the gas pipeline and improve the sensitivity of the FBG strain hoop sensor.  
This work has been supported by the National Key Research and Development Program of China (Grant No. 2016 YFC0701107), the Fund of the National Natural Science Foundation of China (Grant Nos. 51421064, 51327003, 51678109 and 51608094). These grants are greatly appreciated.  
[PERSON] performed the experiments and wrote the paper; [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] made constructive comments on the experiments and paper writing.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### \(2t\)-Periodic Orbits
#### 2.5.1 Unsaturated \(2t\)-Periodic Orbits  
In this type of orbit, the duty cycles are not saturated. They belong to the interval \((0,T)\). We assume that the unsaturated \(2\)-cycle corresponds to \(\big{\{}\mathbf{x}_{0},\mathbf{x}_{1}\big{\}}\). From Equation (26), the following expression is obtained:  
\[\begin{split}\mathbf{x}_{1}((n+1)T)&=\phi_{1}\bigg{(} \frac{d_{n}}{2}\bigg{)}\phi_{2}(T-d_{n})\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)} \mathbf{x}_{0}(nT)\\ &+\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\phi_{2}(T-d_{n})\psi_{1 }\bigg{(}\frac{d_{n}}{2}\bigg{)}\\ &+\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}\psi_{2}(T-d_{n})+\psi_{ 1}\bigg{(}\frac{d_{n}}{2}\bigg{)}.\end{split} \tag{33}\]  
Now,\[\begin{split}\mathbf{x}_{2}((n+2)T)&=\phi_{1}\bigg{(} \frac{\tilde{d}_{n}}{2}\bigg{)}\phi_{2}\big{(}T-\tilde{d}_{n}\big{)}\phi_{1} \bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}\mathbf{x}_{1}((n+1)T)\\ &+\phi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}\phi_{2}\big{(}T -\tilde{d}_{n}\big{)}\psi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}\\ &+\phi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}\psi_{2}\big{(}T -\tilde{d}_{n}\big{)}+\psi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}.\end{split} \tag{34}\]  
As no saturation is assumed in the duty cycles, then \(d_{n}\in(0,T)\) and \(\tilde{d}_{n}\in(0,T)\).  
By considering \(\mathbf{x}_{0}(nT)=\mathbf{x}_{2}((n+2)T)\), the initial conditions for the existence of orbits and unsaturated \(2T\)-periodic orbits are obtained:  
\[\mathbf{x}_{0}(nT)=\big{(}\mathbf{I}_{2}-\mathbf{A}\big{)}^{-1}\cdot(\mathbf{ B}(\mathbf{C}+\mathbf{D})+\mathbf{J}), \tag{35}\]  
where:  
\[\begin{split}\mathbf{A}&=\mathbf{B}\cdot\phi_{1} \bigg{(}\frac{d_{n}}{2}\bigg{)}\phi_{2}\big{(}T-\tilde{d}_{n}\big{)}\phi_{1} \bigg{(}\frac{d_{n}}{2}\bigg{)},\\ \mathbf{B}&=\phi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2} \bigg{)}\phi_{2}\big{(}T-\tilde{d}_{n}\big{)}\phi_{1}\bigg{(}\frac{\tilde{d}_{ n}}{2}\bigg{)},\\ \mathbf{C}&=\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)} \phi_{2}\big{(}T-\tilde{d}_{n}\big{)}\psi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}, \\ \mathbf{D}&=\phi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)} \psi_{2}\big{(}T-\tilde{d}_{n}\big{)}+\psi_{1}\bigg{(}\frac{d_{n}}{2}\bigg{)}, \\ \mathbf{J}&=\phi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2} \bigg{)}\phi_{2}\big{(}T-\tilde{d}_{n}\big{)}\psi_{1}\bigg{(}\frac{\tilde{d}_{ n}}{2}\bigg{)}+\phi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}\psi_{2}\big{(}T- \tilde{d}_{n}\big{)}+\psi_{1}\bigg{(}\frac{\tilde{d}_{n}}{2}\bigg{)}.\end{split} \tag{36}\]  
Numerically, the matrix \(\mathbf{I}_{2}-\mathbf{A}\) is invertible, as the spectral radius of the matrix \(\mathbf{A}\) is less than \(1\). This shows that unsaturated \(2T\)-periodic orbits are found.
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 5 Simulation and Analysis
### Results
#### 5.4.2 SER Performance of the Improved 2 PPSK  
The main system parameters are \(M=2\); \(K=30\); \(N=105\); \(f_{\text{c}}=21\times 10^{6}\) Hz; and a sample rate \(f_{\text{s}}=210\times 10^{6}\) Hz. Corresponding to the set of system parameters, the ratio of the training and testing set is 60,000:10,000, and the structure of stacked DSAE network is 1050-600-300-150-2. There are five layers in the network, with 1050 nodes in the input layer; 600 nodes in the first hidden layer; 300 nodes in the second hidden layer; 150 nodes in the third hidden layer; and 2 nodes in the output layer.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**H1-H2**} & \multicolumn{3}{c}{**SNR (dB)**} \\ \cline{2-4}  & \(-\mathbf{19}\) & \((-\mathbf{18})\)\(-\mathbf{19}\) & \((-\mathbf{3})\)\(-\mathbf{19}\) \\ \hline
200-50 & 0.0050 & 0.0062 & 0.0051 \\
200-100 & 0.0045 & 0.0052 & 0.0047 \\
300-100 & 0.0043 & 0.0062 & 0.0047 \\
300-200 & 0.0052 & 0.0038 & 0.0045 \\
400-200 & 0.0039 & 0.0062 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SER comparison based on two hidden layers in the stacked DSAE network.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**H1-H2**} & \multicolumn{3}{c}{**SNR (dB)**} \\ \cline{2-4}  & \(-\mathbf{19}\) & \((-\mathbf{18})\)\(-\mathbf{19}\) & \((-\mathbf{3})\)\(-\mathbf{19}\) \\ \hline
200-50 & 0.0050 & 0.0062 & 0.0051 \\
200-100 & 0.0045 & 0.0052 & 0.0047 \\
300-100 & 0.0043 & 0.0062 & 0.0047 \\
300-200 & 0.0052 & 0.0038 & 0.0045 \\
400-200 & 0.0039 & 0.0062 & 0.0044 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SER comparison based on three hidden layers in the stacked DSAE network.  
The SER performance of the improved 2 PPSK signaling is shown in Figure 5 where the receiver consists of one SIF followed by the proposed stacked DSAE network. The simulation curves are close to the theoretical curve. The SER simulation values are slightly better than the theoretical values between \(-17\) dB and \(-18\) dB SNR. This is because the approximate processing is introduced in the calculation of theoretical values in Equation (10).  
This seems to indicate that the result violates the Shannon limit on the SNR per bit, assuming an unbounded bandwidth. Furthermore, \(-1.6\) dB SNR with an arbitrarily low error rate is achievable. The result is close to the Shannon limit, with the system parameters are included in the SNR calculation defined in Equation (2). The transmission signal based on the \(-3\) dB noised training data could extract signal features that are closer to reality than the case based on noiseless training data. Thus, the SER performance of the proposed scheme with the case of \(-3\) dB noised training data is slightly better than that with the case of noiseless training data.
# Characterization of Shallow Sedimentary Layers in the Oran Region Using Ambient Vibration Data
## 3 Methodology and Data Processing
### Frequency-Wavenumber (f-k) Analysis  
The f-k analysis is founded on ambient vibration recordings using an array arrangement. It has been described by several authors [37; 38; 39; 40]. By recording data from several sensors simultaneously, this technique allows for the reckoning of surface wave dispersion curves. As in all array techniques, the depth of investigation depends on the dimensions of the array. This method assumes that ambient vibrations are time-stationary in the horizontal level, and only the vertical plane is used to characterize wave propagation properties [38]. The processing is performed in the frequency domain. The seismic wave sources containing the maximum spectral power can be estimated for their slowness and back azimuth using either the Maximum Likelihood Method (MLM), also known as the High Resolution Method (HR), or the Beam Forming Method (BFM), described by [38; 40], respectively. In this study, the Beam Forming method was used to estimate the power spectral density.  
The surface waves consist of Love and Rayleigh waves. As Love waves are horizontally polarized, Rayleigh waves can be analyzed using only the vertical component of the ambient noise recordings. An f-k analysis was performed using the Geopsy package [36]. First, the coordinates of each of the seven sensors were entered into the WARANGPS module (from Geopsy package, release 2.10.1 [36]) to calculate the transfer function of the array and the theoretical wave number limits (Kmin and Kmax). The signals were then loaded into the Geopsy software to apply the BFM. An anti-triggering algorithm has been used to select the windows. The signals have been divided into windows of different lengths determined by frequency (50 periods). The processing required the specification of grid step and grid size parameters. The grid size corresponded to the Kmax value, which is associated with the aliasing limit, while the grid step was set to \(\text{Kmin}/2\), which determined the maximum resolution. In the final step, the dispersion curve was calculated. The same processing steps have been used for all of the fifteen arrays of data.