esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The mentioned study [38] showed that the raw signal of a distributed fiber optic sensor is quite difficult to use for decomposition or for machine learning, so it still requires pre-processing: for example, by Wavelet and Curvelet filtering or/and correlation algorithms [39; 40; 41; 42]. Primary processing of raw data should be implemented on simple mathematical operations, since the volume of processed data is quite high, and the algorithms themselves are implemented with the use of programmable microcontrollers. Basically, these are simple algorithms, which are various types of integral or differential averaging of a discrete signal in a scanning window [43; 44; 45]. The number of simple mathematical operations at each individual moment when the scanning window is at a certain point in the trace rarely exceeds the number of discrete counts in this window. That is why it was decided to use just such methods for pre-processing data received from a distributed acoustic sensor. Thus, the key task of this study is to test, evaluate, and improve the effectiveness of simple signal processing algorithms acting jointly or separately. Backscattered light traces, obtained using distributed fiber optic acoustic sensor subject to vibration impact and based on phase-sensitive time domain reflectometry, are considered as an input for the techniques under research. As it was mentioned above, it is vital to use simple algorithms here since the volume of data acquired with such instruments is generally quite large. The algorithms taken into account are expected to give a significant increase in the signal-to-noise ratio and, consequently, better distinguish signal and noise. This will allow the use of less expensive components, broader-band lasers, for instance, in the design of such tools. Various simple digital data processing methods, such as moving differential and dynamic window averaging, have already found their way into various fields of science and technology, proving their effectiveness. Distributed acoustic sensor's data processing involves several stages and at each of them, supplementary changes can be made to the algorithm. Individual parameters of data processing methods may be subject to variation. The objective of this work is to determine the optimal signal processing stages at which one or another method can be involved in the calculation process, as well as to determine the basic parameters for their use. Similar results were obtained for various optical fiber samples and instrument configurations (on the order of 10s). The demonstration of the results is carried out on data that provides the most convenient visualization.
## 3 Signal Models for Direct Position Determination
### Frequency-Domain Signal Model  
To determinate the emitter position directly from all observations, it is desirable to separate the propagation delay \(\tau_{n}(\mathbf{p}\,)\) and transmit time \(t_{0}\) from the signal waveform. This is easily achieved using the frequency-domain representation of the problem. Taking the discrete Fourier transform (DFT) of (2) produces [29]  
\[\overline{\mathbf{x}}_{n,k}=\beta_{n}\mathbf{a}_{n}(\mathbf{p})\overline{s}_{k}\cdot\exp \{-\mathrm{j}\omega_{k}(\tau_{n}(\mathbf{p})+t_{0})\}+\overline{\mathbf{e}}_{n,k}\quad (1\leq n\leq N\,;\,1\leq k\leq K)\,, \tag{3}\]  
where  
* \(\omega_{k}=2\pi(k-1)/(KT)\) is the \(k\)th known discrete frequency point,
* \(\overline{s}_{k}\) is the \(k\)th Fourier coefficient of the unknown signal corresponding to frequency \(\omega_{k}\),
* \(\overline{\mathbf{e}}_{n,k}\) is the \(k\)th Fourier coefficient of the random noise corresponding to frequency \(\omega_{k}\).  
It must be emphasized that the unknown and deterministic parameter set in (3) consists of \(\mathbf{p}\,\), \(t_{0}\), \(\beta_{n}\) and \(\overline{s}_{k}\). However, only the location vector \(\mathbf{p}\,\) is of interest for the DPD approach. In addition, because the DFT is an orthogonal linear transformation, the distribution of the random noise vector \(\overline{\mathbf{e}}_{n,k}\) is the same as that of \(\mathbf{e}_{n,k}\), with first- and second-order moments given by.  
\[\left\{\begin{array}{l}\mathrm{E}[\overline{\mathbf{x}}_{n,k}]=\mathbf{O}_{M\times 1 }\,,\\ \mathrm{E}[\overline{\mathbf{x}}_{n,k}\overline{\mathbf{x}}_{n,k}^{\mathrm{T}}]=\mathbf{O}_ {M\times M}\,,\,\mathrm{E}[\overline{\mathbf{e}}_{n,k}\overline{\mathbf{e}}_{n,k}^{ \mathrm{H}}]=\sigma_{e}^{2}\mathbf{I}_{M}\,,\end{array}\right.\quad(1\leq k\,,\, l\leq K\,;\,k\
eq l)\,. \tag{4}\]  
Note that the DPD technique studied below is derived from (3).
## 1 Introduction
### Related Works
However, the final expression for path loss is obtained through data fitting, so the expression cannot reflect the functional relationship between the parameters of the proposed system and path loss. Similarly, in [37], a study by [PERSON] et al. also used Zemax(r)'s non-sequential ray tracing tool for channel modeling, and Mie scattering was considered to simulate the weather conditions of clear weather, rain, and fog. Furthermore, the study derived the path loss exponent as a function of distance and different weather conditions. In [38], [PERSON] et al. used an NS3.25 simulator for realistic channel modeling of VLC-based V2V connection, simulated the system for two modulation methods, open-keyed and variable pulse code modulation, and investigated the proposed system. Most of the above studies consider only LOS links, while, in [39], a model of a V2V-VLC system that considers the effect of atmospheric turbulence (AT) and shadowing was proposed. Under this model, [PERSON] et al. considered both LOS and NLOS links and proposed a novel Rx architecture using the diversity-multiplexing tradeoff (DMT). In [40], [PERSON] et al. took the Internet of Vehicles as the starting point and used the Lambertian radiation model for channel modeling to analyze the reliability performance of ultrareliable and low-latency communications (URLLC) in outdoor V2V-VLC systems. A comparison of the proposed V2I-VLC model in this paper with the existing research results is shown in Table 1.
# Life-Cycle Oriented Risk Assessment Using a Monte Carlo Simulation
## 5 Discussion
### Managerial Implications  
The application described here is a common EOT problem. There are three main themes for the managerial implications, centering around: (i) change management; (ii) risk management, and (iii) process integration.  
Process change management can take place in production for many reasons, leading to changes to the BOM that are not discovered until a warranty claim is later made by a customer. PLM systems today offer the promise of improved control of the BOM yet lack the agility to adapt to new situations (i.e., changes in material supplies or manufacturing processes). QC or development is often left with the challenge of allowing a change without understanding if the changes are material or not. The advent of Industry 4.0 techniques (e.g., statistical modelling) and data and information flows along the lifecycle provides the opportunity to understand the risks of proposed changes.  
In many firms, risk is managed in isolation, whereas risk is in effect systematic, with the cause being individual decisions made without understanding impacts to the whole system. The implication is that firms should move from a semi-quantitative approach to risk to one where risks are quantified along the lifecycle, using data and statistical simulation to overcome the limited data problem. In the longer-term further quantification and validation of simulations could be made based on the data and information flows.  
The first two implications confirm that a need exists for process integration along the lifecycle. Industry 4.0 allows the integration of product development, production, and sales processes as well as the integration of subprocesses. In doing so risk management moves from a \"gut feel\" input to a quantitative approach based on simulations, and this should support further innovation within the manufacturing process. In particular it should be used to support 'cost-out' (or modified re-buy due to change in supplier or obsolescence) where minor modifications to the BOM are made, rather than create a risk-averse approach to change.
## 2 Materials and Methods  
### Materials  
We purified 4-vinylbenzyl chloride (VBC, 90%, Sigma Aldrich, St. Louis, MO, USA), styrene (STY, Sigma Aldrich) and divinylbenzene (DVB, 80%, composed of 80% of divinylbenzene and 20% of ethylvinylbenzene, Sigma Aldrich) by passing them through a layer of aluminium oxide (Fischer Chemical, Guangzhou, China) to remove the inhibitors. Sorbitan monooleate (Span 80, Sigma Aldrich), \(\alpha\),\(\alpha\)-azo-bisisobutyronitrile (AlBN, Fluka, Buchs, Switzerland), acetone (Sigma Aldrich), methanol (Sigma Aldrich), 1,2-dichloroethane (DCE, 99,5%, Sigma Aldrich), nitric acid (HNO\({}_{3}\), 63%, [PERSON], Sabadell, Spain), calcium chloride hexahydrate (CaCl\({}_{2}\)-6H\({}_{2}\)O, Sigma Aldrich) were used as received.  
Preparation of 4-vinylbenzyl Chloride/Divinylbenzene/Styrene Poly(High Internal Phase Emulsions) (PolyHIPEs)  
An internal phase consisting of a 1.76% aqueous solution of CaCl\({}_{2}\)-6H\({}_{2}\)O was added dropwise under constant stirring to a continuous phase, consisting of 4-vinylbenzyl chloride (VBC), styrene (STY) and divinylbenzene (DVB) monomers (at different molar ratios, see Table 1), surfactant Span 80 (20 vol% with regards to monomers) and initiator AIBN (1 wt.% with regards to monomers). The emulsion was stirred for a further 60 min after the addition of the internal phase at 250 rpm and then poured into polypropylene moulds and transferred to the oven for 24 h at 60 \({}^{\circ}\)C to complete the polymerisation. After polymerisation, the monoliths were purified by Soxhlet extraction (24 h in deionized water and 24 h in acetone) and then air-dried. The polyHIPEs are labelled according to their DVB content (A 2 mol.%, B 5 mol.% and C 10 mol.%), and the molar percentage of VBC in the monomer mixture. Detailed information on the monomer mixture composition is given in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Continuous Phase**} & \multicolumn{2}{c}{**VAP *} & **m (Span 80)** \\ \cline{2-6}  & **m (STY) [g]** & **m (DVB) [g]** & **m (VBC) [g]** & **[mL]** & **[g]** \\ \hline A93 & 0.181 & 0.095 & 4.747 & 18.6 & 1.264 \\ A73 & 0.899 & 0.086 & 3.724 & 17.
## 3 Data Collection
For the bus mode, a transit Route 8 is chosen for its wide coverage throughout the city while Route C9 is chosen because it travels through the central business district (CBD) which is notorious for extremely high-rise buildings that may cause serious multi-path errors with GPS signals. For the tram mode, the Dubai Tram route is chosen, which is the only tram route available in Dubai. For the truck mode, highway E311, also known as a \"truck road\" is chosen which has intentionally been built in parallel with a major highway, E11, in order to reduce the load on E11. ([PERSON], New highway connects Abu Dhabi to Dubai, Gulf News, [[http://gulfnews.com/news/uae/transport/new-highway-connects-abu-dhabi-to-dubai-1.1937213](http://gulfnews.com/news/uae/transport/new-highway-connects-abu-dhabi-to-dubai-1.1937213)]([http://gulfnews.com/news/uae/transport/new-highway-connects-abu-dhabi-to-dubai-1.1937213](http://gulfnews.com/news/uae/transport/new-highway-connects-abu-dhabi-to-dubai-1.1937213))) For the walk and bike modes, the data are collected from Jumeira Beach due to the relatively higher density of the population using the two modes in the city. Due to the extremely hot weather, major metro stations are connected with indoor pathways that are elevated roughly 20 m above the ground to major shopping malls that often stretch over a few kilometers which introduces a new indoor-walk mode in Dubai. This enables collecting data from an indoor walk mode based on sufficiently-long indoor pathways that can provide consistent streams of raw data to the mode detection ANNs in this study. Dubai is located by the Persian Gulf and its waterways play a major role as a part of the overall transportation infrastructure. Ferries (Route F1 and F2) and \"Abra\" boats (B1 and B2) are chosen for the data collection. Abra boats refer to small motorized water taxis that operate in the Dubai Creek area. Figure 2: Data collection area in Dubai in various modes of transportation.
## 6 Evaluation of Antenna Pointing Control Error
### Stability Analysis  
The controller, which consists of PID elements, for the antenna driving control system was designed by using classical control theory. The bode diagram of the open loop transfer function is shown in Figure 19. From the diagram, the influence can be secured. The phase margin is secured to be about 90 degrees. The crossover frequency of 10 Hz is secured.  
\begin{table}
\begin{tabular}{c c c} \hline \hline Attitude (\(\phi,\theta,\psi\)) & [deg.] & 0.5 \\ \(H\) & [m] & 0.2 \\ RF sensor & [deg.] & 0.1, 0.05, 0.01 \\ \(v\) & [m/s] & 0.17 \\ GPS position & [m] & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard deviations of the noises of sensors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(I_{\text{xx}}\) & [kg\(\cdot\)m\({}^{2}\)] & 37.4 \\ \(I_{\text{yy}}\) & [kg\(\cdot\)m\({}^{2}\)] & 25.2 \\ \(I_{\text{zz}}\) & [kg\(\cdot\)m\({}^{2}\)] & 55.7 \\ \(I_{\text{xz}}\) & [kg\(\cdot\)m\({}^{2}\)] & 0.033 \\ \(I_{\text{GY}}\) & [kg\(\cdot\)m\({}^{2}\)] & 0.01 \\ \(I_{\text{GZ}}\) & [kg\(\cdot\)m\({}^{2}\)] & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the fixed-wing UAV and antenna.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\alpha\) & - & 0.15 \\ \(\beta\) & - & 7.5 \\ \(v_{0}\) & [m/s] & 30 \\ \(W_{\text{d}}\) & [m/s] & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Conditions of the simulation.  
Figure 23: Time history of the antenna’s pointing control errors using RF sensor with 0.1 deg. accuracy directions. (**a**) Elevation. (**b**) Azimuth.  
Figure 24: Time history of the antenna’s pointing control errors using RF sensor with 0.05 deg. accuracy directions. (**a**) Elevation. (**b**) Azimuth.  
Figure 25: Time history of the antenna’s pointing control errors using RF sensor with 0.01 deg. accuracy directions. (**a**) Elevation. (**b**) Azimuth.  
The results of the simulation confirmed that two control systems designed independently worked well. In the case of using commonly available sensors with standard deviations of 0.1 or 0.05 degrees for RF sensor, the target antenna pointing accuracy was not satisfied. However, when using a high-accuracy sensor of 0.01 degrees, regardless of attitude deviations, with a roll angle of \(\pm\)1.9 degrees, a pitch angle of 3.0 \(\pm\) 2.4 degrees, and an azimuth angle of \(-\)0.8 \(\pm\)9.2 degrees, the antenna's pointing control errors as standard deviations were 0.10 degrees for the elevation angle and 0.020 degrees for the azimuth angle, respectively.  
The results also confirmed that the design method is valid and the proposed antenna pointing control system meets the required antenna pointing control accuracy in terms of the standard deviation of the elevation and azimuth angle for typical fixed-wing UAVs for wireless relay stations. In addition, we found that the effect of noise from the RF sensor is important, given that the RF sensor requires a standard deviation of 0.01\({}^{\circ}\).
## 3 Experimental Analysis
### Comparing CNR and Code Multipath Combinations of Different Systems
Though the orbit of GEO satellites is affected by perturbations such as solar radiation, the actual geometry variation between the GEO satellites and the earth is still small. As an example, the elevation of GEO satellite C03 observed at station F713 has a variation of only 3\({}^{\circ}\). To compare the code multipath effects among different GNSS systems, the multipath combinations were estimated from GPS, GLONASS, Galileo, and BDS code observations with Equation (2). To make the comparison fair, only the MEO satellites were selected to represent BDS, since the satellites of all the other three systems are MEO ones. The behaviors of multipath combinations for GEO and IGSO satellites of BDS will be discussed in the following sections. The code multipath combinations of G06, R08, E11, and C14 are shown as Figure 3a-d, respectively. Each satellite represents a GNSS system, and the different rows of each figure represent the multipath combinations for different frequencies. The left panels of each figure show the variations of multipath combinations with respect to elevation, while the right panels show the time series of multipath combination (blue) and elevation angle (red). It can be seen that both the amplitude and dispersion of code multipath combination decrease significantly with the increase of satellite elevation. When the elevation is low, the amplitude of code multipath combination is about 2.0 m and it drops to below 1.0 m when the elevation is over 60\({}^{\circ}\). The code multipath on the first frequency of Galileo satellite E11 has the smallest amplitude and dispersion, for which the multipath magnitude is below 1.0 m, even at low elevation angles. Figure 2: The relationship between CNRs and satellite elevations for GPS, GLONASS, Galileo, and BDS satellites at GNSS station F713. (**a**) G06; (**b**) R01; (**c**) E11; (**d**) C11; (**e**) C08; (**f**) C03. Three frequencies are denoted as X1, X2, and X3 with the colors black, red and blue. The average of code multipath combination varies from about 0.5 m at low elevation to about \(-1.0\) m at high elevation. Compared with Figure 3a-c, a deep trough can be clearly seen in each multipath time series when the satellite has the maximum elevation in the right three panels of Figure 3d. Correspondingly, the left three panels of Figure 3d show that the numerical values of code multipath combination of BDS MEO satellite C14 decrease when the elevation increases, suggesting the existence of elevation-dependent biases. The similar case also holds for the other BDS MEO satellites whose results are not displayed here, and the elevation-dependent trends of B1 and B2 code multipath combinations are usually more significant than that of B3.
## 3 Results and Discussion
### Volatile Composition of Fermented Beverages and Its Relationship with Their Sensory Properties
At the same time, they decreased in the rice beverage, suggesting a completely different role of the process in both matrices. Previous research has reported a decrease in some volatile compounds responsible for off-flavors in plant-based beverages; for example, the levels of hexanal (beany flavor) were reduced with fermentation in peanut beverages [39]. Volatile compounds such as n-hexanal and n-hexanol, originating from the oxidation of plant lipids and potentially responsible for the beany/legume off-flavor of some plant-based beverages [14], were significantly reduced during the rice beverage fermentative process. Finally, confirming the microbiological and previous physicochemical findings, the LLV combination appeared to have a more pronounced influence on the transformation of both dairy and rice matrices. However, it exhibited a variable effect on the volatile compound profiles, either increasing or diminishing their presence. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Attribute** & **Dairy** & **Dairy LLV** & **Dairy CH** & **Rice** & **Rice LLV** & **Rice CH** & _p-Value** \\ \hline Sweet & 3.8 (ab) & 2.1 (c) & 2.4 (bc) & 4.9 (a) & 3.4 (abc) & 4.2 (a) & \textless{}0.0001 \\ Salty & 1.5 & 2.4 & 2.8 & 1.8 & 2.5 & 2.0 & 0.273 \\ Bitter & 1.4 & 2.4 & 2.1 & 1.6 & 2.0 & 2.1 & 0.307 \\ Sour & 2.0 (b) & 4.9 (a) & 4.0 (a) & 1.9 (b) & 4.0 (a) & 2.5 (b) & \textless{}0.0001 \\ Umami & 2.1 (ab) & 3.4 (a) & 3.3 (ab) & 2.1 (ab) & 1.9 (b) & 1.9 (b) & 0.003 \\ Cereal & 1.4 (b) & 2.3 (b) & 1.7 (b) & 5.3 (a) & 4.1 (a) & 3.9 (a) & \textless{}0.0001 \\ Legume & 1.1 (b) & 1.4 (b) & 1.6 (b) & 3.1 (a) & 2.5 (ab) & 3.4 (a) & \textless{}0.0001 \\ Nutty & 1.5 (b) & 1.8 (ab) & 1.5 (b) & 2.7 (a) & 2.4 (a) & 2.5 (a) & 0.043 \\ Earthy & 1.2 (b) & 1.8 (ab) & 1.4 (ab) & 2.0 (ab) & 1.5 (ab) & 2.7 (a) & 0.026 \\ Dairy & 5.4 (a) & 4.6 (a) & 4.8 (a) & 2.4 (b) & 2.7 (b) & 2.2 (b) & \textless{}0.0001 \\ Fruity & 1.4 (b) & 2.6 (ab) & 1.9 (ab) & 2.6 (ab) & 3.0 (a) & 2.6 (ab) & 0.017 \\ Cheese-like & 2.6 (bc) & 4.1 (a) & 3.9 (ab) & 1.6 (c) & 2.4 (c) & 1.5 (c) & \textless{}0.0001 \\ Hay-like & 1.4 & 1.6 & 1.5 & 1.9 & 1.9 & 2.4 & 0.180 \\ Toasted & 1.4 & 1.4 & 1.6 & 2.1 & 1.4 & 2.3 & 0.165 \\ Metallic & 1.4 & 2.4 & 2.4 & 1.8 & 2.9 & 2.8 & 0.114 \\ Soapy & 1.2 & 1.9 & 2.1 & 1.8 & 2.4 & 2.3 & 0.331 \\ Oily & 2.1 & 1.9 & 2.3 & 2.1 & 1.7 & 1.7 & 0.871 \\ Astringent & 1.4 (b) & 3.8 (a) & 2.6 (ab) & 2.2 (ab) & 2.6 (ab) & 2.2 (ab) & 0.008 \\ Graininess & 1.7 (a) & 2.4 (a) & 2.3 (a) & 1.3 (ab) & 1.1 (b) & 1.3 (ab) & 0.020 \\ Watery & 3.9 (ab) & 2.6 (bc) & 1.7 (c) & 4.7 (a) & 5.1 (a) & 4.6 (a) & \textless{}0.0001 \\ Thickness & 2.4 (b) & 4.4 (a) & 5.1 (a) & 1.4 (b) & 1.7 (b) & 1.9 (b) & \textless{}0.0001 \\ Lumpiness & 1.5 (ab) & 2.1 (a) & 2.1 (a) & 1.0 (b) & 1.1 (b) & 1.4 (ab) & 0.031 \\ Aftertaste & 3.5 (ab) & 4.7 (a) & 4.5 (ab) & 3.4 (b) & 3.8 (ab) & 3.6 (ab) & 0.007 \\ \hline \end{tabular}
\end{table} 
Table 3: Results of the RATA test. Different letters within the rows indicate different post hoc groupings by Tukey’s HSD (_p_ < 0.05).
# Design of a SIMO Deep Learning-Based Chaos Shift Keying (Dlcsk) Communication System
## 5 Conclusions  
This paper represents an attempt to develop a new generation of chaos-based communication systems based on DL. We have introduced a trainable DLCSK receiver, which does not need any reference signal transmission or chaotic synchronization. Thus, the DLCSK is more practical, in terms of reliability and compatibility with modern communication infrastructure. A multi-antenna design is presented to achieve a diversity gain. The main objective of the multi-antenna receiver is to improve the classification accuracy of the individual classifiers. Simulation results verify that the SIMO DLCSK system provides an excellent BER performance without channel estimation and complex combining modules at the receiver side. The proposed architecture is an appealing candidate for next generation wireless communications, such as massive MIMO systems and cloud/edge-based communications.  
Figure 11: BER curve of the SIMO DLCSK under Rayleigh fading channel, for \(\beta=50\), in comparison with the LSTM-aided OFDM DCSK [40], for \(K=5\).  
**Author Contributions:** Conceptualization, G.K. and M.H.; methodology, G.K., M.H. and M.M.; software, M.H. and M.M.; validation, M.H. and G.K.; formal analysis, G.K. and M.H.; investigation, G.K. and M.H.; resources, G.K.; data curation, G.K.; writing--original draft preparation, M.M.; writing--review and editing, G.K., M.H.; visualization, M.M.; supervision, G.K. and M.H.; project administration, G.K. and M.H.; funding acquisition, G.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the Tier2 Canada research chair entitled 'Towards a Novel and Intelligent Framework for the Next generations of IoT Networks.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.