esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fundamentals of Probabilistic Shaping
### Probabilistic Amplitude Shaping (PAS) Scheme  
The data are modulated to the two polarizations of the optical carrier in the polarization multiplexing coherent optical transmitter, and the in-phase and quadrature components of the optical carrier are modulated separately in each polarization; so, the constellation of two-dimensional square \(m^{2}\)-QAM signal can be expressed as the Cartesian product of the one-dimensional m-ASK signal. Use the variable X to represent the constellation point of the m-ASK signal, and use x to represent the specific value. Take x = \(\pm 1,\pm 3,\ldots,\pm(\mathrm{m}-1)\); when the gray mapping is utilized, each constellation point can be stated as the product of magnitude and sign and is independent--that is, X = A * S, where A = 1, 3,..., (m -1) and S = 1 or -1. Since each constellation point is symmetrical about the origin, the probabilities of the symmetrical constellation points are equal, \(P_{X}(x)=P_{X}(-x)\), \(P_{S}(1)=P_{S}(-1)=1/2\), the probability shaping of the two-dimensional square \(m^{2}\)-QAM signal can be converted into the probability shaping of the one-dimensional m-ASK signal, which can be further converted into probabilistic shaping of the positive amplitude of the m-ASK signal, hence the name probabilistic amplitude shaping.  
Figure 1a shows the principle diagram of the probability amplitude shaping using a constant composition distribution matcher and low-density parity check code. Taking the 64 QAM modulation format signal as an example, the I channel or the Q channel is an 8 ASK signal; the generating process of the bit sequence corresponding to the PS-64 QAM signal is shown in Figure 1b.  
A sequence of binary data of length \(U_{1}\) at the input is fed into CCDM to generate a sequence of amplitudes of length \(V_{1}\), the amplitudes are picked from \(\{1,3,5,7\}\), and the number of occurrences of each amplitude are equal to the target probability. The rate of CCDM is \(R_{DM}=U_{1}/V_{1}\). Then, apply the gray map on the \(V_{1}\) amplitude sequences, where the mapping rule is \(\{1,3,5,7\}\Leftrightarrow\{10,11,01,00\}\), the mapped binary bit sequence is \(B_{L}\), and the length is \(m*V_{1}\) (\(m\) = 2). Next, the binary information sequences of length \(U_{2}\) at the input and \(B_{L}\) are concatenated and encoded together in LDPC to create check bits \(B_{P}\) of length \(n-k=V_{1}-U_{2}\), the encoding rate determined by (3). Finally, \(U_{2}\) and \(B_{p}\) are utilized as the sign bit together and \(B_{L}\) is used as the amplitude bit, which are, respectively, assigned to different positions of the codeword by the interleaver. The interleaved bitstream can be used as data information for the I channel or the Q channel to modulate the QAM signal. At the receiving end, the input binary bit sequence can be recovered by performing the reverse process. In the following sections, the mutual information and bit error rate performance of PS-QAM signals will be analyzed based on this probabilistic amplitude shaping scheme.  
\[R_{c}=k/n=(m*V_{1}+U_{2})/(V_{1}-U_{2}+k)=(m*V_{1}+U_{2})/(m+1)*V_{1}, \tag{3}\]
## 8 Discussion
### Challenges
#### 8.1.3 3D Coverage  
By designing a 3D communication infrastructure that includes terrestrial and aerial radio access points as well as mobile edge hosts, cloud functions can be provided on demand.  
This system is far more cost-effective than the existing approach, which relies on a fixed infrastructure, when requests fluctuate greatly over place and time, as they do with periodic events that draw large crowds or in remote areas, such as in the case of disasters. The idea is to manage a variety of aerial platforms, such as unmanned aerial vehicles, high-altitude (approximately 20 km) platform stations, and constellations of Very-Low-Earth-Orbit satellites flying at a few hundred kilometres in space, in order to provide cloud functionalities while maintaining controllable delay constraints.
## 4 Experimental Results  
### Experiment Settings  
A set of 13 well-known benchmark functions, listed in Table 1, was used in this experiment [33,38]. The number of dimensions (_i.e._, \(n\) in Table 1) of the search space is set to 30. Functions \(f_{1}\)-\(f_{5}\) are unimodal, and functions \(f_{6}\)-\(f_{13}\) are multimodal. Notably, the function \(u\) in functions \(f_{12}\) and \(f_{13}\) is defined as follows.  
\[u(x_{i},a,k,m)=\begin{cases}k(x_{i}-a)^{m},&x_{i}>a;\\ 0,&-a\leq x_{i}\leq a;\\ k(-x_{i}-a)^{m},&x_{i}<-a.\end{cases}\]  
This performance study contains two experiments. In experiment 1, PSO and three ICA variants (Perturbed ICA [35], ICAAI and ICACI) were tested. For the three ICA methods, the number of countries, the number of imperialists, \(\beta\) in Equations (4) and (8), and \(\xi\) in Equation (5) are set to 88, 8, 4 and 0.02, respectively. For ICACI, the crossover ratio \(v\) is set to 0.8. For both ICAAI and ICACI, \(\rho\) in line 12 of Figure 2 is set to 1. For PSO, the number of particles, inertia weight \(w\), and two acceleration coefficients \(c_{1}\) and \(c_{2}\) are set to 88, 0.7, 2 and 2, respectively.  
Experiment 2 studied the effect of competition frequency on both ICAAI and ICACI. All experimental settings are the same as those in experiment 1 except that the value of \(\rho\) varied from 0 to 1 in step of 0.1. Notably, \(\rho=0\) means that no competition occurs among empires.  
For each setting, 30 runs were conducted, and their average performance was obtained. Each run stops until the maximum number of generations (1000 or 3000 in experiment 1, and 1000 in experiment 2) is reached. For the same run, the same set of initial countries (or particles in PSO) was generated for all four methods.  
Figure 5: Uniform crossover of two countries.
# Influence of Interference Fit and Temperature on High-Speed Permanent Magnet Motor
###### Abstract  
A high-speed permanent magnet motor is the core driving component of a centrifugal air compressor. The power of the centrifugal air compressor is output by the motor. Its safety and reliability are embodied in the stability of the rotor structure, which greatly affects the stability and working efficiency of the centrifugal air compressor. Much research has focused on the material strength, structural characteristics, and fit clearance of a high-speed rotor, whereas few research articles have focused on the influence of interference fit of high-speed and ultra-high-speed permanent magnet motors, and there is also little research on the thermal failure caused by temperature in high-speed motors. In this paper, the influence of the interference fit and temperature of the high-speed permanent magnet motor is studied. Using finite element analysis conducted by Ansys to obtain simulation data, the influencing factors of the strength of the interference are analyzed comprehensively in the centrifugal compressor rotor system. The interference value and rotational speed range are determined via numerical calculation. Under the condition of minimum interference, when the calculated speed reaches 137,628.82 rpm, the structure of the rotor is loose and fails, which is a mechanical failure caused by the relative sliding of the magnet and the sheath. The calculated speed value differs from the simulation result by about 1.2%. The simulation results show that the maximum stress of the structure can be reduced from 1186.1 MPa to 308.42 MPa by adding chamfer to the end covers in interference fit structure. The effects of interference value, rotational speed, temperature, and sheath thickness on the structure are also analyzed. From the perspective of temperature on structural reliability, the failure temperature of the structure decreases when the interference value increases. The lowest failure temperature is 182.3 \({}^{\circ}\)C when the interference value is 70 \(\upmu\)m. After that, the interference value increases and the failure temperature increases. The reason for this is the interaction between radial stress and contact stress. These results are caused by the interaction between interference fit and temperature, which should be paid attention to when the structure of a high-speed permanent magnet motor is designed.  
rotor strength; chamfer; finite element analysis; interference fit; stress; failure temperature +
Footnote †: journal:  
_Applied_  
_sciences_
# A Research Platform for Autonomous Vehicles Technologies Research in the Insurance Sector
## 5 Conclusions and Future Work  
In this article, a new research platform designed jointly by MAPFRE-CESVIMAP, LSI-UC3M and INSIA-Universidad Politecnica was presented. The vehicle has been designed to evaluate autonomous vehicle technologies and their impact in the field of automobile insurance in the coming years. To this end, an ad-hoc architecture designed for research into high-level systems for autonomous vehicles, both for control and perception, has been presented. The advances presented by this platform include perception, communications, human factors and self-awareness technologies, all of which are at the forefront of current advances in autonomous and connected vehicles. In addition to presenting the platform, different use cases have been presented in which the use of this innovative platform has allowed us to propose new results advancing the state of the art. These examples include demonstrations at international conferences such as IROS 2018, advanced perception algorithms and location and tracking algorithms, as well as communications systems, in addition to international competitions, such as the second place achieved in the Dubai World Challenge for Self-Driving Transport 2019. These use cases have demonstrated the viability of the system and the usefulness of advancing and testing autonomous driving algorithms.  
The future work on the development of this platform will seek to demonstrate the suitability of new location algorithms in urban environments, based on vision and LiDAR technologies, as well as the fusion of this information with GNSS systems. On the other hand, we aim to improve the detection systems by means of LiDAR and camera fusion based on modern sensorial fusion technologies. Finally, we will evaluate the capacity of these systems in real driving environments and investigate new techniques for communication with pedestrians.  
Conceptualization, P.M.-P., M.A.d.M., R.E.-M. and F.M.M.; methodology, P.M.-P., M.A.d.M. and F.M.M.; software and validation, M.P., M.A.d.M. and F.M.M.; resources, A.A.-K, D.M., R.E.-M. and F.G.; writing original draft preparation, F.M.M. and M.A.d.M.; writing review and editing, F.M.M. and F.G.; supervision, A.A.-K. and F.G.; funding acquisition, A.A.-K., D.M., R.E.-M. and F.G. All authors have read and agreed to the published version of the manuscript.  
Research was supported by the Spanish Government through the CICYT projects (TRA2016-78886-C3-1-R and RTI2018-096036-B-C21) and the Comunidad de Madrid through SEGVAUTO-4.0-CM (P2018/EMT-4362) and PEAVAUTO-CM-UC3M.  
The authors declare no conflict of interest.  
Figure 15: IROS18 autonomous driving event.
## 5 Simulation Results  
This section presents the results obtained by optimizing the membership functions using the dynamic parameter adaptation with Type-1 and interval Type-2 fuzzy logic. This methodology was applied to the three benchmark problems shown in Section 4, where the objective is to follow a desired trajectory, and 30 experiments were carried out with disturbances and without disturbances with the original algorithm of harmony search (HS), Type-1 fuzzy harmony search algorithm (FHS), and interval Type-2 fuzzy harmony search (FHS2). The noise applied to each plant was at a level of 0.5 using the uniform random number generator. The objective function of the methods is the root mean square error (RMSE) of the trajectory, although other control metrics were also used: MSE (Mean Squared Error), ISE (Integral Squared error), IAE (Integral Absolute Error), ITSE (Integral of Time-weighted Squared Error) and ITAE (Integral Time-weighted Absolute Error), and SNR (Signal to noise ratio), respectively, as shown in Equations (10)-(16).  
\[RMSE=\sqrt{\frac{1}{N}\sum_{t=1}^{N}(x_{t}-\hat{x}_{t})^{2}} \tag{10}\]  
\[MSE=\frac{1}{N}\sum_{K=1}^{N}\left[X(K)-Y(K)\right]^{2} \tag{11}\]  
\[I_{1}=\int_{0}^{\infty}e^{2}(t)dt \tag{12}\]  
\[I_{2}=\int_{0}^{\infty}e(t)dt \tag{13}\]  
\[I_{3}=\int_{0}^{\infty}te^{2}(t)dt \tag{14}\]  
\[I_{4}=\int_{0}^{\infty}te(t)dt \tag{15}\]  
\[SNR_{dB}=10 log_{10}\bigg{(}\frac{\textit{Psignal}}{\textit{Pnoise}}\bigg{)} \tag{16}\]  
The parameters used to perform the experiments are as follows: 100 iterations, PA rate of 0.75, _HMR_ of 0.95 (Only for the HS), and HM of 30. Experimental results in Tables 5-7 show the best experiment without perturbation in the fuzzy logic controller for each algorithm applied to each case study (water tank controller, temperature controller, and robot mobile controller). Tables 8-10 show  
Figure 11: Structure of the mobile robot fuzzy system for control.  
Figure 14: _Cont._  
Figure 13: Simulation results of the temperature controller for the three methods. (**a**) The best result is shown using original harmony search algorithm (HS); (**b**) the best result is shown using the Type-1 fuzzy harmony search (FHS); (**c**) the best result is shown using the Type-2 fuzzy harmony search (FHS2). These methods were applied with noise to verify the stability of the methods. The blue line represents the desired trajectory and the pink line the obtained trajectory; the objective is for the obtained line to resemble the desired one.
# WIPP: Wi-Fi Compass for Indoor Passive Positioning with Decimeter Accuracy
## 3 System Description  
The architecture of the WIPP is shown in Figure 1. The WIPP incorporates four main components as follows:CSI collection: The CSI contains the channel information which includes the magnitude and phase of sub-carriers with respect to each antenna. We rely on the off-the-shelf Intel5300 Network Interface Card (NIC) [32] to collect the CSI.  
Joint estimation of AOA and TOA measurements: By using the existing commodity Wi-Fi AP with only three antennas, the WIPP has the capability of estimating the AOA measurement accurately based on the proposed joint estimation of AOA and TOA measurements. In addition, the WIPP applies an enhanced 2D spatial smoothing algorithm to de-correlate the coherent multi-path signal to enhance the accuracy further.  
Identification of direct signal path: According to the volatility of AOA and TOA measurements, the WIPP depends on the affinity propagation clustering algorithm to distinguish the direct and indirect signal paths.  
Target positioning: Based on the estimated AOA measurement with respect to each AP, the WIPP uses the LSM to conduct the positioning of the target.
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
## 3 Proposed Methodology
### Communication Layer  
The first layer of the RTSHPMP is the communication layer. This layer uses 5G technology to transmit data collected through the IoT-based cameras installed on the vehicles traveling on highways. The traveler's vehicle is equipped with dual cameras that fetch real-time passenger data traveling on the road.
# Effect of Welding Parameter on the Corrosion Rate of Underwater Wet Welded SS400 Low Carbon Steel
###### Abstract  
This study aims to determine the effect of welding parameters on the corrosion rate of underwater wet welded SS400 low carbon steel. The underwater wet welding process was conducted using shielded metal arc welding (SMAW). Three welding electrodes, i.e., E7016, RB26 and RD26, were used in underwater wet welding performed with water depth variations 2.5 m, 5 m and 10 m. Welding current in the experiment was set to be 100 A and 110 A. After the welding stage, corrosion test was carried out on each joint in a 3.5%-NaCl solution using three-electrode polarization resistance methods. Corrosion testing results indicated that the lowest corrosion rate was found in underwater welding with current parameters of 100 A, 2.5-m depth and RB26 electrodes. The highest corrosion rate is obtained with the setting of underwater welding of 110-A current, 10-m depth and E7016 electrodes.  
underwater wet welding; electrode; welding current; water depth; corrosion rate +
Footnote †: journal: Applied Sciences
###### Abstract  
Among the different photovoltaic technologies, bifacial photovoltaic modules outperform monofacial ones by being able to harvest the rear incident irradiance. In fact, they achieve higher power output under identical operating conditions. Consequently, the transition from monofacial to bifacial photovoltaic modules is progressing in residential and utility contexts. However, it remains to be fully clarified which installation conditions allow bifacial modules to perform best under different operating conditions. After discussing the different modeling techniques presented in the literature, this paper isolates and evaluates the influence of ground reflectivity, module height, and cloudy weather conditions on the annual incident irradiance and, consequently, the optimal tilt angle of a bifacial photovoltaic module. To focus on the bifacial aspect, each factor is analyzed from the perspectives of the front surface, the back surface, and both. Therefore, different patterns are isolated. The results show that ground reflectivity is key in determining the optimal tilt angle, as it affects the back incident irradiance by up to 431% when compared to a low reflectivity scenario. In contrast, module height and weather conditions do not affect the optimal tilt angle, although they do affect the incident irradiance by up to 5% and 24%, respectively.  
bifacial modules; photovoltaic; tilt optimization; real operating conditions +
Footnote †: journal: Applied sciences
# Modeling and Solving Method for Supporting Vehicle-to-Anything' EV Charging Mode
###### Abstract  
Electric vehicles (EVs) are an attractive solution to make traditional transportation energy-efficient and environmentally friendly. EVs are also considered an important element to bring new opportunities for power grids. In addition to the role of a pure load, along with the development of discharging technology for batteries, EVs can deliver energy back to power grids or another power consumption entity or community, performing various discharging activities such as vehicle-to-home (V2H), vehicle-to-building (V2B), vehicle-to-vehicle (V2V), vehicle-to-grid (V2G), etc. These charging scenarios can be uniformly named the 'Vehicle-to-anything' (V2A) charging mode. The paradigm of this charging mode has already been given; however, the modeling and solving approaches are still insufficient. Therefore, the analysis and modeling of V2A are investigated herein. The aim is to propose a generic model suitable for different applications of V2A in different places. The characteristics of the V2A charging mode are given and analyzed first. Then a model that can adapt to the V2A scenario is illustrated. The optimization problem is transformed and solved as an INLP (integer nonlinear programming) problem. The effectiveness of the approach is verified with simulation results. The outcome also indicates that V2A applications have great potential for lowering power consumption costs; for instance, a reduction of 4.37% in energy expense can be achieved in a case study with EV discharging enabled for a household.  
charging; discharging; electric vehicle; INLP; vehicle-to-anything Article
## 3 Results
### Functionality and Stiffness of the Knee Significantly Improved after Injection of PRS  
The WOMAC data presented for function and stiffness all passed the normality test (\(p>0.05\)). Differences in means observed were unlikely due to random sampling (RM ANOVA, \(p<0.0001\)). Mean WOMAC scores for function improved from \(62.39\pm 14.49\) to \(79.31\pm 15.17\) twelve months post-injection ([PERSON]'s multiple comparisons test, \(p<0.001\), Figure 6). A similar trend was seen in WOMAC scores for stiffness and Lysholm instability scores. WOMAC scores for stiffness significantly improved from \(56.25\pm 19.46\) pre-injection to \(68.75\pm 20.07\) twelve months post-injection ([PERSON]'s multiple comparisons test, \(p<0.05\), Figure 7).  
Figure 5: Western Ontario and McMaster Universities Osteoarthritis index (WOMAC) pain scores of the osteoarthritis knee treated with PRS. WOMAC scores were construed into a sum score. A higher WOMAC score means less pain. Results are expressed as mean \(\pm\) standard deviation. Only significant results are marked. PRS = platelet rich stroma; ** = \(p<0.01\); **** = \(p<0.0001\).  
Figure 6: WOMAC function scores of the osteoarthritic knee treated with PRS. WOMAC scores were construed into a sum score. A higher WOMAC score means a better function. Results are expressed as mean \(\pm\) standard deviation. Only significant results are marked. PRS = platelet rich stroma; *** = \(p<0.001\).  
Figure 7: WOMAC stiffness scores of the osteoarthritic knee treated with PRS. WOMAC scores were construed into a sum score. A higher WOMAC score means less stiffness. Results are expressed as mean \(\pm\) standard deviation. Only significant results are marked. PRS = platelet rich stroma; *** = \(p<0.001\); *** = \(p<0.0001\).  
The data for the Lysholm instability scores all passed the normality test (\(p>0.05\)). Differences in means observed were unlikely due to random sampling (RM ANOVA, \(p<0.0001\)). Mean Lysholm instability scores improved from \(57.43\pm 11.20\) to \(66.64\pm 15.58\) after twelve months in comparison to pre-injection measurements ([PERSON]'s multiple comparisons test, \(p>0.05\), Figure 8). No downtime was noted for any of the treated patients. No complications were seen in any of the patients in or around the injected knee. Some bruised feeling in the donor site due to lipoharvesting was present in all patients for 2 to 5 days. VAS and WOMAC pain and function scores showed a stagnation and/or decline from three to six months in the results, this, however, was not significant. WOMAC stiffness scores and Lysholm instability scores showed a decline from, respectively, three to twelve months and six to twelve months; this, however was not significant.
## 3 Experimental Results

Table 6: Ablation study of this work. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Accuracy (\%)**} \\ \cline{2-7}  & \(\infty\) & **20 dB** & **5 dB** & **0 dB** & \(-\)3 dB** & \(-\)**6 dB** \\ \hline Proposed (six decoders) & 66.2 & 65.5 & 63.8 & 62.4 & 61.0 & 59.0 \\ speech waveform, pitch (three decoders) & 61.9 & 61.4 & 60.1 & 57.7 & 57.1 & 53.9 \\ speech waveform (two decoders) & 58.6 & 58.8 & 58.3 & 57.7 & 56.8 & 53.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of this work with six decoders with the model with two or three decoders. Because the IEMOCAP dataset is a relatively small dataset (7 h long), the model trained on the IEMOCAP dataset may be susceptible to the overfitting problem. Unseen test datasets (RAVDESS [29] and EmoDB [30]) were applied to the models trained with different datasets (IEMOCAP, ESD [31], and IEMOCAP + ESD) to assess the overfitting problem (Tables 8 and 9). The models trained with a single dataset (IEMOCAP or ESD) are susceptible to the overfitting problem, while the model trained with a combined dataset (IEMOCAP + ESD) is resilient to the overfitting problem. ESD is a 29-h dataset in English and Mandarin with five emotions [31]. RAVDESS is an 89-min dataset in English with eight emotions [29]. EmoDB is a 24-min dataset in German with seven emotions [30]. Only four emotions are used for the comparison in this work.