esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Phase Stator Current Tracking with Gradual Penalization of Commutations
## 4 Experimental Results
### Steady State Results
This is shown in Table 6, where the proposed method was tested in the same operating points as before but \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline Op. & \(E_{\alpha-\beta}\) & \(E_{x-y}\) & \(ASF\) & \(THD\) & \(E_{\alpha-\beta}\) & \(E_{x-y}\) & \(ASF\) & \(THD\) & \(E_{\alpha-\beta}\) & \(E_{x-y}\) & \(ASF\) & \(THD\) \\ Pt. & **(mA)** & **(mA)** & **(kHz)** & **(\%)** & **(mA)** & **(mA)** & **(kHz)** & **(\%)** & **(mA)** & **(mA)** & **(kHz)** & **(\%)** \\ \hline (A) & 241.07 & 134.12 & 2.77 & 17.08 & 325.01 & 99.94 & 7.22 & 17.14 & 157.00 & 95.30 & 5.97 & 16.89 \\ (B) & 82.52 & 109.91 & 6.03 & 31.98 & 82.11 & 99.82 & 9.84 & 31.93 & 59.42 & 76.64 & 9.83 & 26.29 \\ (C) & 94.29 & 121.31 & 5.74 & 10.41 & 97.12 & 99.87 & 9.67 & 10.15 & 60.96 & 78.93 & 9.92 & 9.16 \\ (D) & 80.33 & 117.35 & 6.06 & 30.10 & 77.58 & 99.95 & 9.96 & 30.90 & 58.37 & 87.18 & 9.99 & 24.48 \\ Avg. & 124.55 & 120.67 & 5.15 & 22.39 & 145.45 & 99.89 & 9.17 & 22.53 & 83.94 & 84.51 & 8.93 & 19.21 \\ \multicolumn{2}{c}{STD-SV (Standard single-vector)} & \multicolumn{5}{c}{LC-VVV (Multi-vector)} & \multicolumn{5}{c}{PRO-SV (Proposal)} \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of experimental results for STD-SV (**left**), LC-VVV (**center**), and PRO-SV (**right**). with \(\lambda_{xy}=0.05\). The results show that the method can be tuned to accommodate different applications requiring either better tracking or less \(x-y\) content. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Op.** & \(E_{\mathbf{\kappa}-\beta}\) & \(E_{\mathbf{x}-y}\) & \(ASF\) & \(\mathbf{THD}\) \\
**Pt.** & **(mA)** & **(mA)** & **(kHz)** & **(\%)** \\ \hline (A) & 112.39 & 108.83 & 2.89 & 16.92 \\ (B) & 59.42 & 76.64 & 9.83 & 26.29 \\ (C) & 60.96 & 78.93 & 9.92 & 9.16 \\ (D) & 58.37 & 87.18 & 9.99 & 24.48 \\ Avg. & 72.79 & 87.90 & 8.16 & 19.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of PRO-SV with \(\lambda_{xy}=0.05\), demonstrating its capability to trade-off figures of merit. Figure 5: Steady- state results for the proposal in three operating points: (**A**) (top plots), (**B**) (middle plots), and (**C**) (bottom plots). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Op.** & \(\mathbf{\Delta E_{\mathbf{\kappa}-\beta}}\) & \(\mathbf{\Delta E_{x-y}}\) & \(\mathbf{\Delta THD}\) \\
**Pt.** & **(\%)** & **(\%)** & **(\%)** \\ \hline (A) & 107.01 & 4.868 & 1.480 \\ (B) & 38.18 & 30.24 & 21.45 \\ (C) & 59.32 & 26.52 & 10.81 \\ (D) & 32.91 & 14.64 & 26.23 \\ Avg. & 73.28 & 18.19 & 17.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Percentage of improvement provided by the proposal compared with the multi-vector case, while keeping \(ASF<10\) kHz.
## 2 Speaker Identification Related Work  
Speaker Recognition is the automatic detection of persons from the characteristics of their voices, that is, by using voice biometrics [23]. Within SR, we can distinguish two tasks, Speaker Identification (SI) and Speaker Verification (SV). The former refers to the recognition of a particular user among a known number of users (a multiclass setting) and the latter aims to identify one specific user versus the rest of speakers (binary setting). In this paper we are assessing the influence of stress on a speaker identification system to further improve its performance with data augmentation techniques.
# Comparison of the Sentinel-3A and B SLSTR Tandem Phase Data Using Metrological Principles
## 4 Results--Sea Surface Temperature Comparison
### SST Differences at Different Quality Levels
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Quality Level 3**} & \multicolumn{2}{c}{**Quality Level 4**} & \multicolumn{2}{c}{**Quality Level 5**} \\ \cline{2-7}  & **Mean/K** & \(\sigma\)/K & **Mean/K** & \(\sigma\)/K & **Mean/K** & \(\sigma\)/K \\ \hline All & \(-0.18\) & \(0.18\) & \(-0.093\) & \(0.13\) & \(0.014\) & \(0.119\) \\ N2 & \(-0.23\) & \(0.16\) & \(-0.12\) & \(0.12\) & \(-0.076\) & \(0.082\) \\ N3 & \(-0.03\) & \(0.17\) & \(-0.03\) & \(0.12\) & \(-0.043\) & \(0.096\) \\ D2 & \(0.06\) & \(0.13\) & \(0.05\) & \(0.14\) & \(0.074\) & \(0.100\) \\ D3 & \(-0.01\) & \(0.24\) & \(-0.06\) & \(0.14\) & \(0.031\) & \(0.130\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean and standard deviations (\(\sigma\)) for different quality levels and algorithms for the SST differences. The “All” case on the first line means all algorithm retrievals are combined together to give a single set of statistics. the SSES bias terms are not applicable for quality levels 3 and 4, we have not applied the GHRSST SSES bias to any of the subsequent Tandem analysis to retain a systematic approach across all quality levels.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)
#### 3.2.2 Functionality of NFV  
NFV allows telecommunications operators to centralise various network equipment into high-capacity switches, servers, and storage within data centres, network nodes, and at the end-user level. Through the virtualisation of network functions, NFV enhances hardware resource efficiency and supports the flexible, dynamic provisioning of services [36]. Additionally, NFV helps resolve the issue of vendor lock-in by allowing cloud tenants to deploy virtual appliances from multiple vendors across a range of hardware platforms  
Figure 3: NFV architectural framework.  
and hypervisors. This flexibility allows organisations to adopt best-of-breed solutions and avoid being restricted to a single vendor's offerings [47].
# Development of a Novel Concrete Curing Method Using Induction Heating System
## 3 Properties of Concrete after Curing  
### Test Program  
#### 3.1.1 Test Specimens and Variables  
To investigate the efficiency of the use of the induction heating system for concrete curing, the temperature distribution during curing and the compressive strength of the heat-cured concrete were evaluated. The primary variables of the investigation were the heat curing conditions, such as the maximum curing temperature and duration, as well as the size of the concrete specimens or steel forms. Table 1 summarizes the test variables of the investigation. It is noted that some of the specimens in Table 1 have been previously tested by the authors [13] and were used as benchmarks for further investigation considering additional variables. The temperature variation curves applied during curing are shown in Figure 4. The maximum curing temperature varied from 50 \({}^{\circ}\)C to 80 \({}^{\circ}\)C and was maintained for 5 h to 9 h. Three different sizes of steel forms, having diameters of 300 mm, 150 mm, and 100 mm, were considered for the tests to investigate the efficiency of induction heating considering the size of the concrete members. These steel forms had identical thickness (4 mm) as in the FEM simulation and experiments in Section 2.
###### Abstract  
The aim of this study was to develop a recipe for vegan muffins using wheat flour (100%) and a blend of whole-grain spelt flour (50%) and wheat flour (50%) enriched with microalgae (0, 0.5, 1.0, and 1.5% (g/100 g flour)). Replacing wheat flour with whole-grain spelt flour and adding microalgae while eliminating egg white from a recipe can affect the rheological properties of the dough and also the microstructure and texture of the muffins. The study analyzed the effects of the type of flour and the addition of microalgae on the rheological properties of the raw dough, measured through the oscillatory method, as well as the texture and microstructure, determined via X-ray microtomography. Additionally, the sensorial quality of the muffins was analyzed. The use of spelt flour in the formulation of muffins affected the rheological properties of the dough irrespective of the addition of microalgae. The dough made with spelt flour exhibited higher viscosity (consistency coefficient (K) was 74.7 Pa-s\({}^{n}\)), but it was more cohesive and less springy compared to the dough made with wheat flour alone, which had a K = 58.3 Pa-s\({}^{n}\). Incorporating a mixture of spelt and wheat flour along with a 1.5% addition of microalgae made the dough more viscous (K = 118.6 Pa-s\({}^{n}\)), leading to a fine, porous microstructure (porosity was 69.7%) and a crumbly texture (hardness was 52.2 N) in the muffins. On the other hand, the wheat flour dough with 1.5% microalgae had a consistency coefficient of 69.3 Pa-s\({}^{n}\), while the muffin porosity was 67.1% and the hardness 61.8 N. The microstructure had a strong effect on the texture of the muffin crumb. The new wheat flour products with microalgae exhibited a higher proportion of closed pores in their microstructure, whereas samples containing spelt flour and microalgae showed the opposite trend, with more open pores. The greatest difference in closed pores was observed with the addition of 1.5% of microalgae (33.4% in wheat muffins and 26.9% in spelled muffins). The presence of closed porosity contributed to the harder and less consistent texture observed in the muffins. However, despite the instrumental evaluation results, all the new products were accepted by consumers in terms of appearance, taste, and overall quality.  
microalgae; texture; color; microtomography; spelt flour; wheat flour +
Footnote †: journal: Applied Sciences
# Design and Analysis of Pattern Reconfigurable Antenna Based on RF MEMS Switches
## 3 Theory Analysis
\tag{6}\]  
where \((R,\ \theta,\ \varphi)\) is the field point coordinate system; \(h\) is the thickness of the substrate; \(k_{0}\) is the wave number of electromagnetic waves in free space; \(w_{m}\), \(w_{a}\), and \(w_{b}\) are the widths of the main patch, the auxiliary patch, and the parasitic patch, respectively; \(\mathbf{R}\) is the vector from the origin to the observation point (representing the vector of the observation point on the main patch); and \(\mathbf{d_{1}}\) and \(\mathbf{d_{2}}\) are the vectors marked in the Figure 9 (representing the vectors of the observation points on the sub-patch and the parasitic patch, respectively). Since the antenna designed in this paper had a symmetrical structure, according to the duality principle, it can be known that the far-field distribution can be obtained by Equation (7) [8]. \[E_{\varphi}=[-\
abla\times(\mathbf{F}_{m}+\mathbf{F}_{a}+\mathbf{F}_{b})]. \tag{7}\]  
The far-field magnetic field can be obtained using two methods--one by solving the Maxwell equations and the other by using the relationship of the far-field plane wave electromagnetic field. Once the far-field was obtained, we could use MATLAB software to plot the radiation pattern of the far-field, as shown in Figure 10. Figure 10a shows the radiation pattern of the reconfigurable antenna calculated according to the above formula and Figure 10b shows the radiation pattern of the reconfigurable antenna simulated by HFSS software. It can be seen from Figure 10 that the two results are very consistent. However, it should be noted that the radiation pattern obtained by the formula calculation has no back lobe, while the antenna radiation pattern obtained by HFSS software simulation has a back lobe. This is because the result of the formula calculation is based on a simplified  
Figure 9: The electric field distribution of the antenna and the coordinate system used for the vector superposition method calculation. mathematical model. However, only focusing on the main radiation direction angle and ignoring the existence of the back lobe does not affect the final calculation result, which shows that the spatial electric vector superposition method analysis method proposed in this section is effective.
# Design and Evaluation of a New Fuzzy Control Algorithm Applied to a Manipulator Robot
## 7 Simulation Results
### Performance Indicators  
The indicators used for assessing the performance of the controllers designed in this work are shown in Equations (22)-(24), which correspond to the RMS, RSD and IA, accordingly.  
\[\text{RMS}=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({o_{i}}-{p_{i}})}^{2}}}}{n}} \tag{22}\]  
\[\text{RSD}=\sqrt{\frac{\sum\limits_{i=1}^{n}{{{({o_{i}}-{p_{i}})}^{2}}}}{\sum \limits_{i=1}^{n}{{{o_{i}^{2}}}}}} \tag{23}\]  
\[\text{IA}=1-\frac{\sum\limits_{i=1}^{n}{{{({o_{i}}-{p_{i}})}^{2}}}}{\sum \limits_{i=1}^{n}{{{({{o_{i}}^{\prime}}|-{{\left|{{p_{i}}^{\prime}}\right|}}) ^{2}}}}} \tag{24}\]  
\[{{p_{i}}^{\prime}}={p_{i}}-{o_{m}}\]  
\[{{o_{i}}^{\prime}}={o_{i}}-{o_{m}}\]  
where \({o_{i}}\) represents the observed values; \({p_{i}}\) the predicted values; \({o_{m}}\) the mean value of the observations and \(n\) the total number of observations.  
The values of RMS, RSD and IA calculated for joints 1 and 2 with each controller are shown in Tables 9 and 10.  
Figure 16: Error results for trajectory 3 (fuzzy without acceleration vs. fuzzy with acceleration).
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Reordering Attacks
#### 5.3.2 End-to-End Delay  
Figure 17 illustrates the end-to-end delay ratio for various reordering attacker node numbers. For three malicious nodes, the proposed protocol, with the shortest delay of 7.7 ms, performs better than [37], 2017 (13.02 ms), and [20], 2018 (8.64 ms). When the number of malicious nodes is raised to nine, the end-to-end delay of [37], 2017, considerably increases to 22.24 ms. In contrast, [20], 2018, has an end-to-end delay of 14.19 ms, while our proposed protocol has the best end-to-end delay of 10.88 ms. According to the results, the proposed protocol exceeds the competing approaches, demonstrating that our proposed protocol is effective against Jellyfish reordering attacks.
## 3 Medium Access Control Layer
### Security at the MAC Layer  
At this layer, jamming attacks can also be performed. In order to perform this type of attack at the MAC layer, the attacker needs to know some information about the protocol being used. The jammer uses well-formed, legitimate control or data packets instead of noise in order to occupy the channel. Again, as for the physical layer, different solutions have been purposed for WSN [57].  
This attacks along with Sybil attacks and packet spoofing can be mitigated by using authentication. In this manner, nodes would know if a packet is sent by a legitimate node or it has been inserted in the network by a malicious node. How this authentication should be performed and how the encryption keys are disseminated through the network has been addressed in [95] for higher layers of the network stack hence, further research should be conducted.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Problem Statement  
To maximize the utility of surface gateways in the network topology of Figure 2, coverage of the underwater sensor nodes should be maximized so that sensor nodes can forward data packets to the nearest gateway. The deployment problem is characterized as an optimization problem to identify the best locations for a fixed number of gateways to achieve maximum coverage. Please note that our proposed algorithm is based on centralized pre-deployment optimization, the gateways obtain all the information from the sensor nodes and make all the decisions.  
Due to the sensor node's passive movement, coverage continually changes in the underwater environment (Please note that in this work, the passive movement is considered to be the main reason for coverage change. Other environment changes like transmission range which has an impact on coverage are put to future work). Therefore, the coverage achieved by a gateway deployment based on the present sensor locations may experience degradation in coverage over a period of time. In light of this, a practical technique to place the gateways is to predict the position of each sensor node at a sequence of time points and treat each position as a single node, referred to as a _virtual node_. By identifying the maximum coverage of these virtual nodes, node coverage can be maximized throughout the period.  
Figure 2: Network architecture.  
The upper portion of Figure 3 shows a naive deployment based on the present sensor node locations, denoted as large green dots. Black dots represent previous positions, and red dots indicate future positions, as predicted by the IMM. The figure illustrates that some of the sensor nodes move out of range of the gateways after several (three) predicted time steps, degrading coverage. The lower portion of Figure 3 indicates a gateway deployment based on both the present and future sensor node positions. Hence, the sensor nodes remain within range during the entirety of the predicted time interval, preserving a high level of coverage.
# Assessment of CYGNSS Wind Speed Retrievals in Tropical Cyclones
## 7 Summary and Conclusions
The RSS radiometer wind dataset are freely distributed on the RSS website following pages: AMSR2 at [[https://www.remss.com/missions/amsr/](https://www.remss.com/missions/amsr/)]([https://www.remss.com/missions/amsr/](https://www.remss.com/missions/amsr/)); WindSat at [[https://www.remss.com/missions/windsat/](https://www.remss.com/missions/windsat/)]([https://www.remss.com/missions/windsat/](https://www.remss.com/missions/windsat/)); SMAP at [[https://www.remss.com/missions/smap/](https://www.remss.com/missions/smap/)]([https://www.remss.com/missions/smap/](https://www.remss.com/missions/smap/)); and the TC-winds at [[https://www.remss.com/tropical-cyclones/tc-winds/](https://www.remss.com/tropical-cyclones/tc-winds/)]([https://www.remss.com/tropical-cyclones/tc-winds/](https://www.remss.com/tropical-cyclones/tc-winds/)). The CCMP_CYGNSS dataset developed for this investigation is freely available at [[https://data.remss.com/research/cygnss](https://data.remss.com/research/cygnss)]([https://data.remss.com/research/cygnss](https://data.remss.com/research/cygnss)) (accessed 15 November 2021). The authors would like to thank the reviewers for their useful suggestions; the CYGNSS Science Team Project Scientist [PERSON]; and [PERSON] at the University of Michigan, Ann Arbor, for the useful insights and support with the CYGNSS wind datasets; [PERSON] and [PERSON] at the NOAA/NESDIS/STAR for frequent and in-depth updates on the development of the CYGNSS NOAA winds; [PERSON] at US Navy/NRL for discussions on the accuracy of the SMAP and TC-winds; and [PERSON] and [PERSON] at Remote Sensing Systems for assistance with data download, storage, and distribution. The authors declare no conflict of interest.
## 3 Simulation Results and Analyses
### Case 1--High Irradiation Profile without Fluctuations
If the percentage is greater than 100%, the total cost is greater than \"Opt for real conditions\", while if the percentage is below 0%, the total cost is the opposite case of \"Opt for real conditions\". Figure 8b shows the energy system cost, where the energy costs in \"Sim with opt\" are closer to the ideal case \"Opt for real conditions\", resulting in profits with 99.95% accuracy. Conversely, it is the opposite situation in \"Sim w/o opt\" with \(-11.96\%\) accuracy. Figure 8: (**a**) Energy system distribution and (**b**) energy system cost—case 1. Figure 7: EV energy distribution in (**a**) “Sim w/o opt” and (**b**) “Sim with opt”—case 1. Thus, this proves the superiority of the optimization algorithm over the storage priority algorithm. The negative sign implies that the IIREV operators make a profit in particular by selling energy to the grid.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Material Properties  
The material properties are defined according to the actual situation in the numerical model. There are four layers of rock and soil layer that were considered as the ideal elastoplastic material, and the Mohr-Coulomb yield criterion was used as its constitutive model based on the site geological survey data. The bridge structure including foundation piles, bridge pier and bearing platform are simulated as 3D block elements which are considered as linear isotropic elastic material. Meanwhile the prestressed steel strands that are placed at bottom of the underpinning bearing platform and shield tunnel including shields and segments are also presented in this simulation model. The main materials physical and mechanical properties are shown in Table 2.
## 5 Simulation and Experimental Results
### Performance Analysis Concerning Varying Speed  
The parameters which are considered for this analysis concerning varying speeds are energy efficiency, packet delivery ratio, overhead, and packet loss. In Figure 13, the performance of energy efficiency is shown for the methods. From the figure, it is understood that the performance of efficiency decreases due to an increase in speed for all the methods. Compared to the other methods, the proposed ECC-GA-PASR produced better efficiency that is reached by the concept of optimal RSU distribution. RSU distribution helps to reduce and properly monitor the road segments so that network delay and congestion are reduced; likewise, energy consumption is also reduced, which reflects in the increase in energy efficiency. In Figure 14, the performance of the packet delivery ratio is shown for the considered methods. From the figure, it is stated that the performance of the packet delivery ratio decreases due to an increase in speed for all the methods. Compared to the other methods, the proposed ECC-GA-PASR produced a better packet delivery ratio it achieved using the concept of ECC-based authentication in the network. Figure 13: Efficiency Calculation based on vary speed of vehicle. In Figure 15, the performance of overhead is shown for the considered methods. From the figure, it is proved that the performance of overhead increases when the speed of the network increases for all the methods compared to the other methods; the proposed ECC-GA-PASR produces low overhead. That is achieved by the process of GA-based RSU distribution process, where the reduced congestion and delay increase the rate of packets received by the destination. In Figure 16, the performance of packet loss is shown for the considered methods. From the figure, it is shown that the performance of packet loss increases when speed increases for all the methods compared to the other methods; the proposed ECC-GA-PASR produces low packet loss. As a result of securing the network using the ECC method, the packet loss is low for the proposed ECC-GA-PASR. Figure 14: PDR Calculation based on vary speed of vehicle..  
Figure 15: Overhead Calculation based on vary speed of vehicle.. Result Discussion Concerning Varying Speed  
In this section, the results are discussed in terms of efficiency, packet delivery ratio, overhead, and packet loss for varying speeds from 15 to 35 m/s. The performance analysis values of those parameters are shown in Table 8.  
According to varying speed from 15 to 35 m/s, the energy efficiency of the proposed ECC-GA-PASR varies from 99% to 96%, whereas earlier works such as S-AODV, ES-AODV, and ECC-ACO-AODV vary from 75% to 55%, from 81% to 65%, and from 88% to 85%, respectively. As per the speed variation, the efficiency of the proposed ECC-GA-PASR lowered by around 3%, and the earlier such as S-AODV lowered by around 20%, ES-AODV lowered by around 16%, and ECC-ACO-AODV lowered by around 3%. The final results  
Figure 16: Packet Loss Calculation based on vary speed of vehicle.