esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## 6 Conclusions  
This paper introduced one of the first experiment on the joint use of a 5G and VLC network to provide information to cars. Data from road sensors has been collected by a 5G network and then sent to a VLC network for diffusion to cars through traffic-lights. In addition, data from on-demand emergency alarm has been implemented. The experiment aimed to measure the overall latency. The results showed that a total latency of about 12 ms can be reached. In particular, the most frequent latency for the 5G link was about 9.5 ms, while for the VLC link, it was about 2.5 ms. The main contribution is related to the 5G network part, that should be optimized, especially with the deployment of the SA 5G architecture.  
Conceptualization, D.M. and L.M.; software, S.C., T.N. and M.S.; validation, T.P. and J.C.; data curation, S.C. and F.N.; writing--original draft preparation, D.M. and L.M.; writing--review and editing, T.P. and R.F.; supervision, R.F., J.C. and L.M. All authors have read and agreed to the published version of the manuscript.  
This research was carried out as part of the \"5G City\" project, supported by the Italian Ministry of Economic Development.  
The authors would like to thank the Associate Editor and the Assistant Editor for managing the overall publication process as well as the anonymous reviewers for their useful suggestions to improve the quality of the manuscript.  
The authors declare no conflict of interest.
## 5 Simulation and Performance Analysis
### Methods of Comparison  
In order to verify the effectiveness of the proposed method, we compared GDRL-SFCR with other routing methods, including the SFC-constrained augmenting path selection (SFC-APS) [26] algorithm, the DQN-LBR [39] algorithm, and the Deep Q-Routing (DQR) [34] algorithm.  
SFC-APS: SFC-APS is SFC routing method based on traditional graph theory; by joining a virtual node, the search for each sub-path satisfying the SFC constraints is carried out sequentially using a method similar to BFS.  
DQN-LBR: DQN-LBR takes into consideration the queuing delay, storage space, communication bandwidth, and propagation delay of a one-hop satellite node.  
DQR: DQR is a greedy online SFC routing method based on a Deep Q Network (DQN). The difference with GDRL-SFCR is that no critic network is added, and only DQN is used as a policy network for routing decisions. The DQR algorithm only considers the remaining link bandwidth of its current node to find the next-hop node without considering the state of the neighboring nodes. The neural network parameters of the DQR algorithm are set in the same way as those of the GDRL-SFCR method.  
The main reason for choosing [26; 34; 39] as the baseline is that [26] represents the path delay and SFC constraint optimization class of methods, but it ignores load balancing, which may lead to network congestion. Ref. [39] represents path delay and load balancing optimization class algorithms, but it does not consider SFC constraints and is suitable for fixed network function scenarios, and [34] represents load balancing and SFC constraint optimization class methods, but it ignores delay and is suitable for high-throughput but low-delay sensitive scenarios. By comparing these typical methods, the necessity of multi-objective joint optimization of path delay and load balancing in this paper can be verified.
## 5 Field Tests and Discussion on Results
### Box Plot Analysis for SNR and Temperature Sensitivity Profiles  
A detail of LV channels, MV channels, and transformers' SNR gain profiles for NB-PLC-based DSM are summarized using a box plot, as shown in Figure 14. The frequency band of 3 to 500 kHz is of great interest for residential (denoted by subscript \(R\)), commercial (denoted by subscript \(C\)), and industrial (denoted by subscript \(I\)) NB-PLC channels and transformers, as shown in Figure 14. The box plot for TS analysis is shown in Figure 15 where the influence of TS on the power consumption profile of residential, commercial, and industrial transformers is presented.  
The box plot can be used to find out the intrinsic statistical gain of the channel, and with the transmitted data set it can also be used for finding minimum and maximum values with the overall gain trends. From the box plot, the SNR values for residential channels and transformers yield good SNR profiles. It is important to note that commercial and industrial channels and transformers' gains are located on the lower sides of the box plot. However, box plots for TS are unveiling the fact that the rise in temperature affects more commercial consumers as compared to industrial and residential consumers.  
Figure 14: Summary of channels’ gains.  
Figure 15: Percentage (%) deviation of power consumption with 3 \({}^{\circ}\)C temperature rise of residential, commercial, and industrial transformers.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.2 Data Collection  
Data are the fundamental components that determine the performance of a knowledge-defined network. As reviewed in Sections 4 and 5, different types of data will be collected by management and control planes. Data collected by the management and control planes can be used for knowledge generation. For a given type of data, for example, network traffic flow, it is important to gather data from as many nodes as possible in order to increase the diversity and completeness of the data and reduce any bias in the dataset. As discussed earlier, adversarial training can be used to train knowledge generation models with new or unknown data. Furthermore, for the collected data, data annotation can be used to label data with additional information, such as metadata, to make them more useful for machine learning, providing a way for models to understand data more easily. Checking the obtained data for mistakes and inconsistencies is a good idea before training, as incorrect and inconsistent data might result in faulty machine learning models. Machine learning models might be trained via active learning, in which data are periodically gathered to improve the models' accuracy. Finally, information should be gathered lawfully, ethically, with the participants' consent, and with their privacy protected. Private data should be handled in an ethical and secure manner. Data protection measures such as encryption, access control, and monitoring should be employed to make sure that only authorized entities handle data, while sensitive data should not be disclosed to external parties.
## 4 Power Flow Analysis
### Overall Efficiency
Now the temperature effect over the HB-LEDs is analysed by considering different threshold voltages at different temperatures (\(V_{th}\)(T\({}_{1}\)) = 2 V, \(V_{th}\)(T\({}_{2}\)) = 2.5 V and \(V_{th}\)(T\({}_{3}\)) = 3 V). The same trend can be seen when the temperature rises, but now the dependency of \(\eta_{t}\) with respect to \(\eta_{bu}\) becomes stronger. In the case of the dependency of \(\eta_{t}\) with respect to \(\eta_{ti}\), the change with the temperature is slighter. Figure 14: Effect of the variation in the efficiency of each individual DC/DC converter in \(\eta_{t}\). To examine the effect of temperature in \(\eta_{\rm t}\) more closely, the threshold voltage will be varied. Since there is little change with respect to \(\eta_{\rm ti}\), this is kept constant at a realistic value for a high frequency converter with \(\eta_{\rm ti}=0.9\). Then \(\eta_{\rm t}\) is calculated by sweeping \(V_{\rm th}\) from 2 V to 3 V at different values of \(\eta_{\rm bu}\), ranging from 1 to 0.9. The results are shown in Figure 16. As expected from the previous analysis, there is always a falling tendency on \(\eta_{\rm t}\) when the threshold voltage falls, but the lower the efficiency \(\eta_{\rm bu}\), the more pronounced the fall. In conclusion, the efficiency of the proposed TIBuck DC/DC converter is going to strongly depend on the efficiency of the auxiliary Buck DC/DC converter, operating at low switching frequency. It is important to note that the dependency on the efficiency of the TIBuck DC/DC converter is lower when this operates at high switching frequency. This result strengthens the previously mentioned design philosophy for the proposed TIBuck DC/DC converter based on splitting power: to focus the design of each individual converter on different goals. The design of the TIBuck DC/DC converter will be focused on communication performance, while the auxiliary Buck DC/DC converter will be focused on efficiency.
# Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
## 1 Introduction
Furthermore, it optimizes the probability of augmentation, which is crucial according to our hyperparameter importance analysis. * We present a novel parameter-free data augmentation approach that weighs the applied data augmentation operations and anneals their probability of application. Our method is competitive with the previous automated data augmentation approaches and outperforms TrivialAugment, a cheap-to-evaluate method. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Dataset** & **Default** & **Rand++** & **Trivial** & **Smart** & **SmartSampling** \\ \hline KITTI & 65.07 & 67.19 & 64.82 & **68.84** & 66.53 \\ RaVeNNa & 88.37 & 90.71 & 90.53 & **91.00** & 90.72 \\ EM & 77.25 & 78.83 & 78.15 & **79.04** & 78.42 \\ ErfASst & 67.01 & 68.75 & 66.79 & **73.72** & 70.24 \\ \hline \# Iterations & 1 & 50 & 1 & 50 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test mean Intersection over Union (IoU) in percentage for different algorithms on semantic segmentation datasets. SmartAugment outperforms all other data augmentation strategies across all datasets. SmartSamplingAugment competes with the previous state-of-the-art approaches and outperforms TrivialAugment, a comparably cheap method. For DefaultAugment, TrivialAugment, and SmartSamplingAugment, we evaluated each experiment three times using different seeds to obtain the mean performance. For RandAugment++ (an extended version of RandAugment) and SmartAugment, we took the mean test IoU over the three best performing validation configurations. Please note that DefaultAugment represents the baseline, and the higher the value, the better the performance. # iterations refers to the number of BO iterations completed to find best configuration. We will provide our source code: [[https://github.com/mvg-inatech/SmartAugment](https://github.com/mvg-inatech/SmartAugment)]([https://github.com/mvg-inatech/SmartAugment](https://github.com/mvg-inatech/SmartAugment)) (accessed on 1 April 2022).
## 5 Results and Discussion
### LiDAR Traffic Verification  
Table 4 presents the results we obtained while evaluating the actual throughput required by the sensor in specific profiles. In the configuration including Dual Return, 90-degree field of view, 512 resolution, and 10 frames per second, which represents the worst case, the coefficient of throughput variation (CoTV) equaled 0.42%. According to [53], CoTV values below 1% typically indicate highly stable throughput conditions. Therefore, no further measurements were necessary.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Profile**} & **View Angle** & **512 x 10** & **1024 x 10** & **2048 x 10** & **512 x 20** & **1024 x 20** \\  & **(°)** & **(Mibps / SD)** & **(Mibps / SD)** & **(Mibps / SD)** & **(Mibps / SD)** \\ \hline \multirow{4}{*}{Dual Return 1} & 360 & 82.47/0.11 & 164.94/0.00 & 330.03/0.02 & 164.96/0.07 & 330.05/0.00 \\  & 270 & 64.35/0.05 & 126.50/0.00 & 250.07/0.00 & 128.82/0.00 & 252.65/0.03 \\  & 180 & 43.70/0.04 & 84.97/0.00 & 167.61/0.11 & 87.80/0.00 & 170.10/0.00 \\  & 90 & 23.10/0.10 & 43.94/0.00 & 85.22/0.00 & 46.42/0.12 & 87.55/0.00 \\ \hline \multirow{4}{*}{Single Return 2} & 360 & 61.97/0.04 & 124.02/0.03 & 248.12/0.00 & 124.04/0.06 & 248.12/0.01 \\  & 270 & 48.45/0.09 & 94.92/0.00 & 188.01/0.02 & 96.99/0.09 & 189.99/0.08 \\ \cline{1-1}  & 180 & 32.92/0.07 & 64.08/0.00 & 125.95/0.00 & 65.85/0.03 & 128.08/0.00 \\ \cline{1-1}  & 90 & 17.54/0.00 & 33.05/0.00 & 64.08/0.00 & 34.99/0.00 & 66.02/0.00 \\ \hline \multirow{4}{*}{Low Data Rate 3} & 360 & 21.18/0.02 & 42.31/0.00 & 84.60/0.00 & 42.31/0.02 & 84.60/0.02 \\ \cline{1-1}  & 270 & 16.55/0.01 & 32.40/0.01 & 64.13/0.03 & 33.06/0.01 & **64.80**/0.03 \\ \cline{1-1}  & 180 & 11.26/0.01 & 21.84/0.02 & 43.03/0.00 & 22.55/0.00 & 43.63/0.00 \\ \cline{1-1}  & 90 & 6.03/0.00 & 11.32/0.00 & 21.83/0.00 & 11.97/0.02 & 22.55/0.00 \\ \hline \hline \end{tabular}  
* Full configuration name: RNG19_RFLS_SIG16_NIR16_DUAL. 2 Full configuration name: RNG19_RFLS_SIG16_NIR16_NIR8.  
\end{table}
Table 3: The specific parameter values used in each stage are presented.  
As we can see, the most demanding configuration included the Dual Return profile, 360 degree view angle, 2048 \(\times\) 10 or 1024 \(\times\) 20 resolution, and frames per second of around 330 Mbips.
# Data-Aided and Non-Data-Aided SNR Estimators for CPM Signals in Ka-Band Satellite Communications
## 2 Channel Model of Ka-Band Satellite Communication
### Statistical Channel Model of Ka-Band Satellite Communication  
When transmitting a digital modulation signal \(s(t)=\mathrm{Re}[s_{1}(t)\exp(j2\pi f_{c}t)]\) through the channel, \(s_{1}(t)\) is the time domain complex baseband, and \(s_{1}(f)\) is the frequency domain complex baseband. The equivalent lowpass signal (excluding AWGN) can be expressed as  
\[r(t)=\int\limits_{-\infty}^{\infty}C(f,t)s_{1}(f)\exp(j2\pi ft)df, \tag{8}\]  
where \(C(f,t)\) is the time-frequency transfer function of the channel that can be viewed as a complex value independent frequency in the signal bandwidth because the Ka-band fading channel is a non-frequency selective channel. Considering the spectrum of \(s_{1}(f)\) concentrated in the vicinity of \(f=0\,\mathrm{Hz}\), \(C(f,t)=C(0,t)\) can be substituted into Formula (8)  
\[\begin{array}{ll}r(t)&=\int\limits_{-\infty}^{\infty}C(0,t)s_{1}(f)\exp(j2 \pi ft)df\\ &=C(0,t)\int\limits_{-\infty}^{\infty}s_{1}(f)\exp(j2\pi ft)df\\ &=C(0,t)s_{1}(t)\\ &=\alpha(t)\exp(j\theta(t))s_{1}(t)\end{array}. \tag{9}\]  
The fading characteristics can be regarded as a constant in a symbol interval because of the slow time-varying characteristics of multiplicative interference. Formula (9) can be simplified as  
\[r(t)=\alpha\exp(j\theta)\cdot s_{1}(t)\qquad 0\leq t\leq T, \tag{10}\]  
where \(T\) is the cycle of modulation symbol and \(\alpha\) and \(\theta\) are the envelope and phase of the equivalent lowpass channel, respectively. The researches show that the characteristics of a Ka-band satellite communication channel are determined mainly by the weather. The envelope and phase obey the Gauss distribution [24], and the probability density function can be expressed as  
\[p(\alpha)=\frac{1}{\sqrt{2\pi}\sigma_{1}}\exp(-\frac{\left(\alpha-m_{1}\right) ^{2}}{2\sigma_{1}^{2}}) \tag{11}\]\[p(\theta)=\frac{1}{\sqrt{2\pi}\sigma_{2}}\exp(-\frac{(\theta-m_{2})^{2}}{2\sigma_{2 }^{2}}), \tag{12}\]  
where, \(\sigma_{1}\), \(\sigma_{2}\), \(m_{1}\), and \(m_{2}\) are available in Table 1.  
Table 1 shows the probability distribution parameters of the signal envelope and phase under various weather conditions in a Ka-band satellite communication channel [24]. Where, \(\sigma_{1}\) and \(\sigma_{2}\) are the variances of the signal envelope and phase, respectively, \(m_{1}\) and \(m_{2}\) are the mean of the signal envelope and phase, respectively. Previous studies have accumulated a considerable amount of data from many propagation characteristic measurement experiments in Olympus-star, Italsat-star and ACTS-star [24; 25], the values in Table 1 is thus obtained.  
Through the above analysis, we can establish the Ka-band satellite communication channel statistical model as shown in Figure 1. The digital modulation signal can be expressed as a result of the frequency independent complex multiplicative interference factor and AWGN after the non-frequency selective fading channel. \(C(t)=\alpha\exp(j\theta)\) is the channel multiplication vector, and \(z(t)\) is the AWGN.
## 2 Phenolic Compounds Extraction from Renewable Resources within a Biorefinery Scheme
### Techniques for Phenolic Compounds Extraction from Agri-Food By-Products
#### 2.1.2 Surfactant Extraction and Colloidal Gas Aphrons  
Surfactants are amphiphilic molecules with both hydrophobic and hydrophilic components with the capacity to form micelles when the concentration is at or above the critical micelle concentration. The formed micelles can aid in the separation and extraction by establishing chemical and physical interactions with hydrophilic and hydrophobic substances demonstrating the capacity to interact and/or self-associate with phenolic compounds facilitating their separation [55; 56; 57]. In a study conducted by [PERSON], [PERSON], and [PERSON] (2015) [58], the authors analysed the capacity of surfactants to extract phenolic compounds from different juices; the results showed that surfactants extracted up to 35% more compared to ethanol. Even though the extraction with surfactants was successful, the authors emphasised the need for further research on the interactional behaviour and the scaling-up process.  
Another approach studied for surfactant-based extraction is the use of colloidal gas aphrons (CGA). These are surfactant-stabilised microbubbles (10-100 \(\upmu\)m) generated by the intense stirring of a surfactant solution at high speeds (>8000 rpm). Due to their unique structure, CGA presents relevant properties such as molecule adsorbance to the air-water interface of the microbubbles by electrostatic and/or hydrophobic interactions with the surfactant giving them selectivity in the separation. The small bubble size gives place to a larger interfacial area per unit volume and thus a larger capacity of molecule adsorption; they can also be easily separated from the bulk liquid without mechanical aid due to their buoyancy [59; 60; 61].  
Moreover, CGA is an easily scalable, controllable, and cost-effective process since it is easy to pump from one point to another [62]. CGA have been used to separate phenolic compounds from artichokes residues using Tween 20 and cetyl trimethyl ammonium bromide (CTAB), showing that the first exhibited the highest recovery of phenolic compounds [63]. Another study demonstrated the use of Tween 20 to be efficient in the extraction of phenolic compounds from grape pomace, with a particular affinity for anthocyanins in addition to improving phenolic compound solubility, thus possibly their bioavailability and absorption [64]. Further studies are needed in this field to understand better the interactions between the surfactants and the phenolic compounds, their stability as a system, and their possible application in food, cosmetics, and/or medicinal products.
# Fretting Fatigue Behavior under Tension-Bending Mixed-Mode Loading
## 4 Results and Discussion
### Damage Initiation Prediction  
#### 4.3.1 Damage Initiation Location  
The prediction of the damage initiation location of the MD and SWT parameters for all cases is shown in Figures 11-13 and Tables 6-8. The graphs of the two parameters are not completely consistent, but the general profiles are similar. For all the numerical experimental results, the two critical plane parameters are smoothly distributed in the contact center region, while for the contact edge, the damage parameters increase suddenly, which indicates that the damage is highly concentrated in this region. Although the damage parameter peaks appear at both edges of the contact, the damage parameter value at the leading edge is the highest, which indicates that the damage is likely to be initiated at the leading edge of the contact. This phenomenon is consistent with many experimental results [13; 15; 18; 20; 38; 42; 44], and the high gradient damage parameter that is in the vicinity of the leading edge is due to the high stress concentration that is caused by the cylindrical geometry of the fretting pad.  
Figure 10: Variations in dimensionless shear stress \(q(x)/E_{1}\) with different control variables: (**a**) dimensionless oblique load \(\lambda\), (**b**) stiffness ratio \(\gamma\), and (**c**) mode angle \(\theta\).  
Figure 11: Variations in damage parameters with dimensionless oblique load \(\lambda\): (**a**) MD and (**b**) SWT.  
Figure 12: Variations in damage parameters with stiffness ratio \(\gamma\): (**a**) MD and (**b**) SWT.  
Figure 13: Variations in damage parameters with mode angle \(\theta\): (**a**) MD and (**b**) SWT.  
As shown in Figure 11 and Table 6, with the increase in oblique load, the damage initiation location moves closer to the contact center. This is consistent with the findings of [PERSON] et al.'s dovetail joint fretting fatigue experiments, where the crack initiation location shifted away from the loading end as the fatigue load increased [45]. Figure 12 and Table 7 indicate that a higher stiffness ratio causes the fretting damage initiation location to move toward the trailing edge of the contact. This shift is due to the reduction in contact width with the increasing stiffness ratio, resulting in the crack initiation location being closer to the contact center.  
For all the cases, compared to the MD parameter, the predicted position of the SWT parameter is slightly closer to the leading edge. From Figure 13 and Table 8, it can be seen that the damage initiation positions predicted by the two fretting damage parameters, MD and SWT, are both on the leading edge of the contact surface, and the positions are very close. With the increase in the mode angle, \(\theta\), the damage initiation position is closer to the contact center, but this tendency is less obvious in the bending-dominated mode, and the damage initiation positions predicted by the two parameters are almost the same when \(\theta=75^{\circ}\) and \(\theta=90^{\circ}\).
## 4 Results and Discussion
### Performance Measure  
The performances of the proposed methodology had errors occur during the data transmission (i.e., BER) and throughput. The aforesaid performance measures are explained as follows:  
* _Bit Error Rate_ The amount of errors at a specified unit time is called the BER. Generally, the BER is the ratio rate between the amount of error bits to the total transmitted bits over the MIMO-VLC channel and the Equation (17) expresses the BER. \[BER=\frac{Error\ bits}{Total\ transmitted\ bits}\] (17)
* _Throughput_ Throughput Throughput is defined as number of bits transmitted from one place to another place. Generally, throughput is measured as bits per second.
## 4 Results and Discussion
### Comparison of Computational Costs for Different Models
Overall, although the RT-DETR-Tomato-BS model shows significant performance improvements, its relatively short training time (1.305 h) achieves a good balance between training cost and performance. Therefore, the RT-DETR-Tomato-BS model might be the optimal choice in terms of overall cost.