esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The implementation of SDN is regarded as the best solution to meet modern communication requirements and to avoid the issues of legacy networks. The data plane devices that support the SDN network generally use the OpenFlow protocol to establish communication with the controller. However, the legacy devices generally already in operation these days do not support the OpenFlow protocol. The replacement of the existing networking devices at once by the OpenFlow supporting devices is not feasible and hence, a phase-wise migration is the only tangible solution. The features of SDN are compelling enough to encourage the migration of larger networks of ISPs from legacy to SDN. This study has demonstrated the successful integration of SDN networks with legacy networks to show the possibility of smooth migration and interoperability with legacy networks during migration. Multi-domain routing using ONOS/SDN-IP in the SDN environment has a better performance as compared with the legacy routing. The experimental analysis presented contributes to verifying the seamless interoperability of legacy and SDN networks so that service providers can be confident in the SDN implementation in their ISP and Telcos networks. This study is also a testament to SDN-integrated networks performing better than traditional legacy networks based on QOS parameters viz. bandwidth, PTR, and RTT.  
The program code in Python and the dataset of this research work are available at this GitHub page: [[https://github.com/[PERSON]/SDN-Comparison-with-Legacy](https://github.com/Abhishekthapa/SDN-Comparison-with-Legacy)]([https://github.com/Abhishekthapa/SDN-Comparison-with-Legacy](https://github.com/[PERSON]/SDN-Comparison-with-Legacy)), accessed on 11 April 2021.  
Conceptualization, B.R.D.; Methodology, B.R.D.; Software, A.T., R.G., D.K., and S.P.U.; Validation, B.R.D., and S.R.J.; Formal Analysis, B.R.D., A.T., R.G., D.K., and S.P.U.; Investigation, B.R.D., S.R.J.; Resources, B.R.D., S.R.J.; Data Curation, B.R.D., A.T., R.G., D.K., and S.P.U.; Writing--Original Draft Preparation, B.R.D., A.T., R.G., D.K., and S.P.U.; Writing--Review and Editing, B.R.D.; Visualization, A.T., R.G., D.K., and S.P.U.; Supervision, B.R.D., and S.R.J.; Project Administration, B.R.D. and S.R.J.; Funding Acquisition, B.R.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Grant Commission, Nepal (Grant-ID: FRG-074/75-Engg-01). Additionally, partial funding support was provided by NTNU-MSESSD-PhD program under the financial support from EnPe (NORAD), Norway.  
Not applicable.  
We are thankful to anonymous reviewers for their constructive comments to refine our article to meet the standards.  
The authors declare no conflict of interest.
# Stochastic Approach to Hosting Limit of Transmission System and Improving Method Utilizing HVDC
## 3 Results and Discussion
### Reinforced Case Simulation Results  
Figure 7 shows that in the reinforcement case, the P2P and overlay cases show a probabilistic hosting capacity with a very similar result distribution. This means that there is a limit to increasing the hosting capacity of the system only with the power flow controllability advantage of the HVDC. In other words, the absence of reactive power sources is the biggest limiting factor. Therefore, to increase the hosting capacity through the flexible operation of the power flow, an appropriate local-level reactive power supply must be accompanied. Moreover, the process of finding the location of multi-station case using the method presented in Figure 8 can improve the average line utilization rate.  
Figure 8: Results of maximum loading when hosting limits are reached: (**a**) P2P case; (**b**) Three-station case.  
Figure 7: Simulation results of reinforced case: (**a**) P2P case; (**b**) Three-station case.  
Figure 8 shows the probabilistic distribution diagram of the maximum loading among the branches when the hosting limit is reached for each reinforcement case. Although there was no significant difference in the hosting capacity, the loading was low in the case determined by the proposed operation method and location search method for the multi-station case. This means that in the case of a multi-station, the degree of freedom of selection is increased. As the power flow controllability is improved, the loading of overall existing lines is also improved.  
Compared to the existing research methods, the proposed methodology not only carried out the transmission level hosting limit evaluation, which was mainly conducted only in the distributions system [17; 18; 19] but also considered the peculiarities of only the large-scale transmission network [20], e.g., generation system aspect. This method has the advantage of convenience being applicable to various scenarios. This study also shows that the HVDC-related strategy through an optimization problem is applicable to the proposed strategy and the simulation results confirmed that the strategy is effective in increasing the hosting capacity improvement. Moreover, it can be utilized in multi-station HVDC plans that will become dominant in future power grids.
## 7 Performance Evaluation and Simulation Results
#### 7.3.2 High Social Media Interaction. Node's Density = 200 Nodes/km\({}^{2}\)  
In a high nodes' density scenario, network connectivity improves and there are more neighboring nodes to be chosen to establish the forwarding path. Furthermore, when the social media interaction between users is high, the \(w_{TS}=\) parameter is more significant in the path selection process, see Equation (9). Consequently, we clearly see how the packet  
Figure 11: Average end-to-end packet delay. Scenario with low social media interaction (TS normal distribution with \(\mu=2\) and \(\sigma=1\)). Nodes’ density = 200 nodes/km\({}^{2}\).  
Figure 10: Percentage of packet losses and average tie strength of the forwarding path. Scenario with low social media interaction (TS normal distribution with \(\mu=2\) and \(\sigma=1\)). Nodes’ density = 200 nodes/km\({}^{2}\).  
losses curve grows as \(w_{TS}=\) grows, see red line in Figure 14. Also, the \(\overline{TS}\) measured grows, see blue line in the same figure.  
In Figure 14, we can see that the lowest packet loss percentage (15.3%) takes place for \(w_{TS}=0\), i.e., when we select the path with best QoS performance. The highest packet loss percentage (26%) occurs for \(w_{TS}=1\), i.e., when we do not consider QoS performance of the paths but only their TS values. A good trade-off could be 0.8, with acceptable packet losses (22.2%), low packet delay (1 sec) and high \(\overline{TS}=2.8\). Nonetheless, specially for applications sensitive to packet losses, a better option is just considering the best QoS path (\(w_{TS}=0\)  
Figure 14: Percentage of packet losses and average tie strength of the forwarding path. Scenario with high social media interaction (TS normal distribution with \(\mu=3\) and \(\sigma=1\)). Nodes’ density = 200 nodes/km\({}^{2}\).  
Figure 12: Percentage of packet losses and average tie strength of the forwarding path. Scenario with high social media interaction (TS normal distribution with \(\mu=3\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
Figure 13: Average end-to-end packet delay. Scenario with high social media interaction (TS normal distribution with \(\mu=3\) and \(\sigma=1\)). Nodes’ density = 100 nodes/km\({}^{2}\).  
which produces 15% of packet losses. In that case, the measured \(\overline{TS}=2.7\) is almost the same. The reason is that in this scenario all the nodes have a high social interaction, so the best strategy to select the forwarding nodes is considering the QoS parameters, since the \(\overline{TS}\) will be high for any \(w_{TS}\).  
Results for a Very High Social Media Interaction. TS Normal Distribution with Mean \(\mu=4\), Standard Deviation \(\sigma=1\)  
For a scenario with a very high social media interaction between users, the tie strength (TS) between consecutive nodes in the forwarding nodes from source to destination will be even more decisive in the forwarding operation and will produce a higher \(\overline{TS}\) measured in the simulation.
# On the Performance of LDPC-Coded Large Intelligent Antenna System
###### Abstract  
This article studies Large Intelligent Systems (LIS) along with Single Carrier with Frequency Domain Equalization (SC-FDE), utilizing Low-Density Parity-Check (LDPC). Four different receivers are studied in the scenarios described above, namely Equal Gain Combining (EGC), Maximum Ratio Combining (MRC), Zero Forcing (ZF), and Minimum Mean Squared Error (MMSE). The results of this article show that the use of LDPC codes leads to an improvement of performance by about 2 dB for a 4X25 LIS system and by 3 dB for a 4X225 LIS system, as compared to similar systems without LDPC codes. Moreover, for all simulations, the MMSE receiver achieves the best overall performance, while EGC performs the worst.  
6G; LDPC; LIS systems; SC-FDE +
Footnote †: journal: Applied
## 2 The Analytical Model of Mesh Stiffness of Helical Gear
### The Stiffness of Each Sliced Gear  
Due to the existence of the helix angle, the helical gear has deformation not only along transverse direction but also along axial direction under the action of normal force on the tooth surface. Therefore, it is not accurate to just consider the compliance along the transverse direction when calculating the mesh stiffness. The compliance along the axial direction must be taken into account. For teeth and fillet-foundation, the comprehensive compliance is the sum of transverse compliance and axial compliance.  
The total mesh force \(F_{\rm i}\) can be decomposed into axial force \(F_{\rm a}\) and transverse force \(F_{\rm t}\), as shown in Figure 2. The axial stiffness \(k_{\rm a}\) is produced by axial mesh force, and the transverse stiffness \(k_{\rm t}\) is produced by transverse mesh force. The relationship among the total mesh force, axial mesh force, and transverse mesh force can be written as  
\[F_{\rm t}=F_{\rm i}\cdot\cos\beta_{\rm b}\qquad F_{\rm a}=F_{\rm i}\cdot\sin \beta_{\rm b} \tag{9}\]  
where \(\beta_{\rm b}\) denotes the helical angle on the base circle.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 3 ETC Data-Based Traffic Sensing and Feature Extraction  
Building upon the technological foundations and application contexts established in the previous section, this section examines the core technical methodologies for extracting meaningful traffic information from ETC data.  
Traffic sensing technology based on ETC data represents a significant technological breakthrough in the field of traffic information collection, with its core value lying in transforming traditional toll data into rich traffic state information. This transformation process not only demonstrates the innovative applications of data mining technology but more importantly provides new sensing methods for urban traffic management. Compared with traditional point detectors, ETC data possesses unique advantages, such as trackable vehicle identity, continuous spatiotemporal coverage, and stable data quality, providing a solid foundation for constructing comprehensive and multi-level traffic sensing systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
## 4 Validation with Field Test
### Bridge and Structural Health Monitoring System  
The subject of our analysis was a concrete highway overpass situated in Hebei Province, China, as depicted in Figure 14[11]. This bridge carries a dual carriageway, with distinct left and right sides. The left side span is divided into four continuous spans  
Figure 13: Comparison between reconstructed and simulated low\(-\)frequency responses in scenarios of lower stiffness and higher speed: (**a**) lower\(-\)stiffness scenario; (**b**) higher\(-\)speed scenario.  
(32 m + 32 m + 32 m + 37 m), as illustrated in Figure 14a. Each span is elegantly crafted with prestressed box girders. The right side of the bridge is comprised of two distinct sections: a 75 m segment featuring prestressed concrete T beams and a 104 m segment with a continuous prestressed box-girder design. The latter is further subdivided into three spans (35 m + 37 m + 32 m). The cross-section of the bridge is illustrated in Figure 14b.  
In 2018, a health monitoring system was installed on the fourth span of the left side of the bridge. This system is equipped with 12 electronic resistance strain gauges, positioned at three distinct cross-sections identified as 02-S-XX, 03-S-XX, and 04-S-XX (illustrated in Figure 14c). The duration of the monitoring data collection spanned a period of ten months, commencing in March 2018 and concluding in December 2018. The strain data were meticulously collected at a high sampling rate of 50 Hz.  
To separate the three components, we first used a high-pass filter with a 1 Hz threshold. This threshold was determined based on a previous study [33], which showed that the first natural frequency of the target bridge was 3.906 Hz and the passing vehicles' frequency was higher than 1 Hz. By using the high-pass filter, high-frequency bridge vibrations were  
Figure 14: Fuchang bridge and the SHM system: (a) target bridge; (b) cross-section; (c) layout and positions of strain gauges.  
separated. Then, the proposed intercept method was utilized to separate the driving-force-related component from the residual low-frequency data.
## Appendix  
From the impulsive noise model described in Section 2.2, let \(Y\) be a binomial random variable with parameters \(m\) and \(p_{\text{CR}}\), and let \(U\) be a Bernoulli random variable with probability of success \(p_{\text{IN}}\). The random variable that models the number of CRs not affected by impulsive noise can be defined by  
\[X=m-YU \tag{9}\]The number of CRs under cooperation resulting from the CR elimination IN countermeasure will be the random variable  
\[W=\begin{cases}X,&X\geq 2\\ 0,&\text{otherwise}\end{cases} \tag{10}\]  
The average number of CRs under cooperation, \(m_{E}\), is the expected value of \(W\), which is given by Equation (8). The reasoning behind the definition of \(W\) is the following: If the number of CRs not hit by IN is greater than or equal to 2, this will be the number of CR signals used for cooperation. If the number of CRs not hit by IN is smaller than 2, a minimum of 2 CR signals must be used, since we are dealing with covariance matrix-based spectrum sensing, whose matrix order must be greater than or equal to two. In the simulations, if all CRs are under IN, two of them are randomly chosen for cooperation. If only one CR is free of IN, it is chosen for cooperation, plus any other CR hit by IN.  
The values of \(E[X|x\geq 2]\) and \(\text{Pr}[X<2]\) in Equation (8) can be computed from the probability mass function (pmf) of the random variable \(Z=YU\), which is  
\[p_{z}=\begin{cases}p_{\text{IN}}\binom{m}{z}p_{\text{CR}}{}^{z}(1-p_{\text{CR}} )^{m-z}\,,&z>0\\ (1-p_{\text{IN}})+p_{\text{IN}}\binom{m}{z}p_{\text{CR}}{}^{z}(1-p_{\text{CR}} )^{m-z},&z=0\end{cases} \tag{11}\]  
and from the pmf of \(X=m-Z\), which is given by  
\[p_{x}=\begin{cases}p_{\text{IN}}\binom{m}{z-x}p_{\text{CR}}{}^{m-x}(1-p_{\text{ CR}})^{x}\,,&x<m\\ (1-p_{\text{IN}})+p_{\text{IN}}\binom{m}{z-x}p_{\text{CR}}{}^{m-x}(1-p_{\text{ CR}})^{x}\,,&x=m\end{cases} \tag{12}\]  
where \(\binom{a}{b}\) is the binomial coefficient and where we have used the shorthand notations \(p_{z}\) and \(p_{x}\) for \(\text{Pr}[Z=z]\) and \(\text{Pr}[X=x]\), respectively. Then we finally have  
\[E[X|x\geq 2]=\left(\sum_{x=2}^{m}p_{x}\right)^{-1}\sum_{x=2}^{m}x\,p_{x} \tag{13}\]  
\[\begin{split}\Pr[X<2]&=\Pr[Z>m-2]\\ &=p_{\text{IN}}\sum_{x=m-1}^{m}\binom{m}{x}p_{\text{CR}}{}^{x}(1-p_{ \text{CR}})^{m-x}\end{split} \tag{14}\]
# Pyramid Feature Attention Network for Speech Resampling Detection
## 3 Experiments and Analysis  
### Experimental Setup  
#### 3.1.1 Dataset  
Since there is no public speech database for speech resampling detection at present, the resampling corpus constructed by [PERSON] et al. [19] is used in this paper, and the original speech is selected from the TIMIT dataset. The TIMIT database contains 6300 utterances, with an average duration of 3 s, from 630 participants in eight major dialect areas in the United States. The original speech files is mono, PCM encoded, 16 kHz sampling ratio, and 16 bit quantization [18]. The resampling corpus selects 3000 original speech samples from TIMIT. These samples were resampled using the linear interpolation, spline interpolation, and cubic interpolation methods, and the sampling factor range was 0.6\(\sim\)1.6 with a step size of 0.1. This resampled corpus is used to comprehensively evaluate the effectiveness of our proposed method for detecting resampled speech.  
Figure 5: Spectrogram features at different network layers.  
As shown in Table 1, the corpus also includes MP3-compressed speech databases using three different bit rates of 32 kbps, 64 kbps, and 128 kbps for the original speech and resampled speech, as well as the mixed corpus constructed by randomly selecting resampled speech from different interpolation databases.
## 1 Introduction
The Remote Sensing of Electrification, Lightning, and Mesoscale/Microscale Processes with Adaptive Ground Observations (RELAMPAGO; [23]); and the Cloud, Aerosol, and Complex Terrain Interactions (CACTI; [24]) (RELAMPAGO-CACTI, [25]) projects were conducted inlate 2018 and mid-April 2019 in order to study the convective storms that produce these high-impact meteorological events, often along the Sierras de Cordoba and the Andes. These projects were implemented in the provinces of Cordoba and Mendoza in Argentina and western Rio Grande do Sul in Brazil. More than 200 researchers from the United States, Brazil, and Argentina participated in the RELAMPAGO-CACTI campaign. The location and timing of the RELAMPAGO-CACTI project were chosen as the ideal scenario for this study. The paper is divided into five sections. Section 2 describes the data used and the methodology employed. Sections 3 and 4 present results and discussion, respectively. Finally, Section 5 is devoted to the conclusions.
# Advanced DSP for Coherent Optical Fiber Communication
## 6 Conclusions  
Coherent optical fiber communication has become an important field with the development of DSP techniques in both hardware and software. Fiber linear impairments can be compensated using static and adaptive filters while fiber nonlinearities can be compensated using the DBP method. SDM is considered as one of the most effective approaches to overcome the capacity crunch. The capacity of SDM system has been increased significantly with the improvement of novel DSP technologies. With the continuous reduction of the system cost and power consumption, DSP based coherent optical communication systems have been deployed widely. AI has been employed as an effective DSP tool in the coherent detection, however it still stays in the initial phase. Future research is greatly required to investigate the true capability and powerful function of ML in optical communications and other areas of optoelectronics. This paper has been focused on the applications of DSP in coherent optical systems. However, there are also applications of advanced DSP algorithms in other promising systems such as direct-detection systems which could offer lower-cost implementations of optical communications. This will be investigated in our future work.  
This paper was mainly written by [PERSON] and [PERSON] revised the article. [PERSON] drew Figures 1-4. [PERSON] supervised the overall project.  
This work was supported by the National Natural Science Foundation of China under Grants 61335005 and 61775165.  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this manuscript.  
The authors declare no conflict of interest.
## 3 Data Acquisition and Processing  
### Data Acquisition  
In this study, the battery data set obtained from the NASA Prognostic Center of Excellence (PCoE) was used for related research [41]. The B6 lithium-ion battery of NASA has a rated capacity of 2 Ah. The battery was charged and discharged at room temperature, and the data, such as the voltage, current, and battery temperature, were collected. First, the battery was charged in the 1.5 A constant current mode until the battery voltage reached 4.2 V. Then, the battery was continuously charged in a constant voltage mode until the charging current dropped to 20 mA. Next, the battery was discharged in the 2A constant current mode until the voltage dropped to 2.5 V. As the battery was repeatedly charged  
\begin{table}
\begin{tabular}{l} \hline \hline
**The Pseudo Code of Bat Algorithm** \\ \hline Step 1: Initialize parameter settings, including the population size \(n\), initial impulse loudness \(A_{0}\), initial impulse emission rate \(r_{0}\), maximum frequency \(Q_{\max}\), minimum frequency \(Q_{\min}\), max number of iterations \(N\), and fitness evaluation function \(Fitness\) (\(x\)). \\ Step 2: While (\(t<\) the max number of iterations) \\ Calculate the frequency \(Q_{i}\), location \(S_{i}\), speed \(V_{i}\), and fitness value \(Fitness_{i}\) of each bat. \\ Step 3: If (\(rand>r_{i}\)) \\
1. Obtain an optimal solution \(BestS\) in this iteration. \\
2. Calculate the local solutions around the optimal solution. \\ Step 4: End if \\ Produce new solutions by change randomly \\ Step 5: if (\(rand<A_{i}\) and \(Fitness\) (\(x_{i}\)) \(<Fitness\) (\(x_{*}\))). \\
1. Accept the new solutions. \\
2. Decrease the impulse loudness \(A_{i}\) and increase the impulse emission rate \(r_{i}\). \\ Step 6: End if \\ Sort all bats and obtain the optimal solution \(BestS\) in this iteration. \\ Step 7: End while \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithmic process of BA.  
and discharged, the available capacity of the battery decayed. The experiment was stopped when the actual battery capacity dropped from 2 to 1.2 Ah.  
We further sorted out the data of the eight variables in the experiment, namely, the constant current charging time \(T_{I}\), constant voltage charging time \(T_{V}\), total charging time \(T_{total\_charge}\), battery voltage change at charging \(\Delta V_{charge}\), total discharge time \(T_{total\_discharge}\), battery temperature change during discharge \(\Delta Temp_{discharge}\), battery voltage change at discharge \(\Delta V_{discharge}\), and the actual remaining capacity of battery \(C_{qgal}\). The relationship between the actual remaining capacity and number of cycles is shown in Figure 3.