esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Deployment Challenges of Wireless Sensor Networks at Signalized Intersections
## 4 Experimental Setup  
In order to validate the results previously obtained, real measurements in the same scenario shown in Figure 3 have been performed. A transmitter antenna, connected to a signal generator at 2.41 GHz and 5.9 GHz has been located at the coordinates (\(X=31\) m, \(Y=76.8\) m, \(Z=2.05\) m), just above the traffic light #1. The employed signal generator is a portable N1996A (Agilent Technologies, Santa Clara, CA, USA) unit and the spectrum analyzer is an Agilent N9912 Field Fox. The antennas used are ACA-4 HSRP-2458 from Zentri (Los Gatos, CA, USA), both omnidirectional antennas, with a gain of 0.3 dB for 2.41 GHz and a gain of 3.74 dB for 5.9 GHz. Measurements have been performed along the measurement points depicted in Figure 12 each 5 m at a height of 1.4 m.  
Figure 11: Bit Error Rate for QPSK modulation for different values of \(N_{0}\) for data rate of 57,600 bps.  
Figure 10: Bit Error Rate for QPSK modulation for different values of \(N_{0}\) for data rate of 250 Kbps.  
To visualize the frequency variation with time and the possible influence of pre-existent interference in the scenario, a spectrogram has been measured before starting the measurement campaign. Figure 13 shows the measured spectrogram for both frequencies in Max Hold mode, for the transmitter position represented in Figure 12 in the considered scenario, with the aid of the Agilent N9912 Field Fox portable spectrum analyzer.  
Figure 12: View of the considered scenario with the transmitter position and the measurement points.  
Figure 13: Measured spectrogram in the 2.41 GHz (**top**) and 5.9 GHz band (**bottom**).  
It can be seen that the measured values can be considered as noise and there is not a significant pre-existent interference at the frequency bands of interest. It is also observed that the noise is slightly higher for 5.9 GHz frequency. This is due to the fact the gain of the antenna in 5.9 GHz is 3.44 dB higher than at 2.41 GHz frequency.  
Figure 14 shows the comparison between simulation and measurement results for both frequencies. Measurements were performed with 100 MHz bandwidth at 2.41 GHz and 5.9 GHz frequency. The measurement time at each point was 60 s, and the value of received power represented by each point is the highest peak of power shown by the spectrum analyzer for the considered bandwidth (MaxHold function in the spectrum analyzer of Agilent). The received power values estimated by simulation have been obtained for the same spatial samples as the real measurements, considering the corresponding cuboid in the three-dimensional mesh of cuboids in which the scenario have been divided. It is observed a good agreement between simulation and measurement results, with a mean error of 0.27 dBm with a standard deviation of 1.13 dB for 2.41 GHz frequency, and 0.66 dBm with a standard deviation of 1.46 dB for 5.9 GHz frequency. The resulting error means are very low, indicating that the proposed 3D RL simulation method works properly, validating in the same way the simulation results shown in the previous sections of this work.
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 5 Conclusions  
We discussed the design criteria and the radiation performance of a PRS-based 1-D unidirectional LWA that exploits the tunable properties of NLC to scan the beam at a fixed frequency in the low THz range. The proposed LWA consisted of a rectangular waveguide whose top wall was a PRS, whereas the side walls were replaced by perfect magnetic conductors to result in the LWA operating in its TM leaky mode. A suitable choice of design parameters allowed for beam steering over an angular range of around 15\({}^{\circ}\) while maintaining an almost constant beamwidth of 15\({}^{\circ}\).  
These results were obtained through an approximate but efficient leaky-wave analysis of the structure and then validated through full-wave simulations. In future works, we will investigate the performance of a more accurate design while considering the realization of the biasing network and its effects on the dynamics of the liquid crystal.  
Conceptualization, W.F., P.B. and A.G.; methodology, W.F., P.B., D.C.Z. and A.G.; software, F.I. and W.F.; data processing, F.I., W.F. and D.C.Z.; writing--original draft preparation, W.F. and D.C.Z.; writing--review and editing, W.F., D.C.Z., F.I., P.B., R.B. and A.G; supervision, W.F., R.B. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the project ECS00000024 \"Ecosistemi dell'Innovazione\"--Rome Technopole of the Italian Ministry of University and Research, public call n. 3277, PNRR--Mission 4, Component 2, Investment 1.5, financed by the European Union, Next GenerationEU.  
Not applicable.  
The data presented in this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
## 4 Resource Allocation Design for Delay Minimization
The algorithm to find a feasible solution for problem \(P5\) is summarized in Algorithm 1. ```
1. Initialization  Initialize the a feasible initial point \((\mathbf{q}^{(0)},\mathbf{\zeta}^{(0)},\mathbf{\mu}^{(0)},\mathbf{f}^{(0)},\mathbf{\
u}^{(0)})\). Initialize the maximum number of iterations \(n_{max}\) and penalty factor \(\eta\)  Initialize iteration index \(n=1\) and accuracy index \(\xi^{(0)}{=}1\). 2. Joint optimization of \(\mathbf{q},\mathbf{\zeta},\mathbf{\mu},\mathbf{f},\mathbf{\
u}\) and \(\mathbf{\Lambda}\) repeat \(\bullet\) Calculate \(\mathbf{\beta}^{(0)}\) by \(\beta_{k}=q_{k}^{(n)}/\zeta_{k}^{(n)},\ \ \forall k\in\mathcal{K}\) \(\bullet\) Calculate \((\mathbf{q}^{(n)},\mathbf{\zeta}^{(n)},\mathbf{\mu}^{(n)},\mathbf{f}^{(n)},\mathbf{\
u}^{(n)},\mathbf{ \Lambda}^{(n)})\) by solving problem \(P5\) for a given point \((\mathbf{q}^{(n-1)},\mathbf{\zeta}^{(n-1)},\mathbf{\mu}^{(n-1)},\mathbf{f}^{(n-1)},\mathbf{v}^{(n- 1)})\) \(\bullet\) Calculate accuracy \(\xi^{(n)}{=}\frac{|\mu^{(n)}-\mu^{(n-1)}|}{\mu^{(n)}}\) \(\bullet\)\(n=n+1\) until\(\xi^{(t)}\leq\xi\) or \(n=n_{max}\) 3. Output optimal \(\mathbf{\mu}^{*},\mathbf{p}^{*},\mathbf{f}^{*},\mathbf{\
u}^{*}\) and \(\mathbf{\theta}^{*}\) find \(\mathbf{\theta}^{*}\) from \(\mathbf{\Lambda}^{(n)}\) by SVD and Gaussian randomization. \(\mathbf{\mu}^{*}=\mathbf{\mu}^{(n)},\mathbf{p}^{*}=\left|\mathbf{q}^{(n)}\right|^{2},\mathbf{f}^{*} =\mathbf{f}^{(n)},\mathbf{\
u}^{*}=\mathbf{\
u}^{(n)}\)
```  
**Algorithm 1**Local optimal iterative algorithm. Based on the Algorithm 1 description, the complexity analysis of the proposed algorithm is performed. The problem \(P5\) is a SDR problem, which can be solved by using the interior point method. Therefore, we analyzed the complexity of the proposed algorithm based on this method. At each iteration, the worst case complexity of solving problem \(P5\) is on the order of \((N+1)^{6}+K(N+1)^{2}+4K{+}1\), and the complexity of finding variable \(\mathbf{\beta}\) is negligible. Therefore, the overall complexity of the Algorithm 1 can be evaluated by \(O(T_{CCCP}((N+1)^{6}+K(N+1)^{2}+4K{+}1))\), where \(T_{CCCP}\) represents the required iterations number to achieve the target accuracy \(\xi\)[28; 29].
# Quantum Key Distribution Networks: Challenges and Future Research Issues in Security
## 2 Background to Quantum Key Distribution (QKD) Networks
### QKD Network Type  
In terms of the QKD network type, we have summarized the existing practical experiments and research reports to group QKD networks into three distinct classes: (1) active optical switch networks, (2) trusted node networks, and (3) quantum repeater networks. Here, note that a practical QKD network can be implemented by the hybrid types of network rather than only one type; for example, we can adopt the active optical switch and the trusted node networks to build a QKD network. This study describes the following three types of network.
## 5 Discussion
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **EEG Signal (Std)** & **Noise (Std)** & **CNN** & **CCA** \\ \hline I & EEG (0.87 \(\times\) 10\({}^{-5}\)) & - & 0.81 & 0.75 \\ \hline II & EEG (0.87 \(\times\) 10\({}^{-5}\)) & 5.99 \(\times\) 10\({}^{-6}\) & 0.69 & 0.54 \\ \hline III & EEG (0.87 \(\times\) 10\({}^{-5}\)) & 1.60 \(\times\) 10\({}^{-5}\) & 0.59 & 0.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of classification accuracy for a noisy signal. When attempting to implement CNNs in practical applications, certain challenges may arise. In our study, the classification time for 1 s of EEG signal was a rapid 3.7 ms. However, the training time required for the CNN poses a challenge. Here, transfer learning techniques could be vitally important. Utilizing transfer learning may necessitate adjustments to the signal sampling frequency and the number of network inputs, which must align with the number of recorded EEG channels. Additionally, it is crucial to retrain the network using a relatively large dataset. We implemented the CNN proposed in article [26] to explore the potential of using transfer learning. The proposed network yields impressive results, achieving close to 98% accuracy for 1 s segments of the signal across all 64 channels. Its architecture reflects an understanding of EEG signal processing and analysis methods. The network was originally trained on data from 70 healthy individuals and 40 target characters, which flickered at frequencies ranging from 8 to 15.8 Hz in 0.2 Hz increments. This training used EEG data recorded at 250 Hz. We adapted this network structure for the data recorded from users S01-S05. The adaptation involved modifying the first and last layers of the CNN to accommodate three input channels (O\({}_{1}\), O\({}_{2}\), O\({}_{2}\)) and four SSVEP frequencies (5 Hz, 6 Hz, 7 Hz, 8 Hz). We then retrained the network with the EEG training data, using the initial 20 s of the actual recorded EEG signal for S01-S05 users, after resampling the signals from 256 Hz to 250 Hz. Subsequently, we calculated the classification accuracy for SSVEP recognition on the training data (last 10 s) for each user. The classification results obtained for the adapted CNN [26] using transfer learning techniques are summarized in Table 6. The table also includes comparative results from the CNN network that we developed as well as the CCA method. The average recognition accuracy for the CNN [26] method is 61%, for the CCA method it is 57%, and for the CNN that we proposed, which includes data augmentation, it is 72%. These results suggest that the application of transfer learning techniques yields better outcomes than the use of standard machine learning methods like CCA. Nonetheless, our specialized approach achieved an 11% higher accuracy.
## 3 MP Combinations  
MP combination values mainly reflect the MP error and observation noise of satellites and receivers; since the variations in the MP combination values are similar at different stations, it was determined that satellite-induced code bias exists in the BDS-2 satellite. The MP combination is a geometry-free and ionosphere-free combination formed by a one-frequency code and two-frequency phase measurements [24]. Through the linear combination of observations, the MP combination eliminates the influence of the orbit, clock difference, troposphere, and first-order ionosphere, including only the ambiguity, MP error and observation noise of the code and phase combination values, primarily. Compared with the MP error and observation noise of the code, the carrier MP error and observation noise are negligible [25]. During a continuous observation arc in which the ambiguity of a satellite is a constant, if the MP combination value changes, it must be caused by the code bias at the satellite end or the receiver end by the signal reflection. The MP combination formula is as follows [2,26]:  
\[\mathrm{MP}_{i}=P_{i}-\frac{f_{i}^{2}+f_{i}^{2}}{f_{i}^{2}-f_{i}^{2}}L_{i}+ \frac{2f_{i}^{2}}{f_{i}^{2}-f_{i}^{2}}L_{j}=M_{p_{i}}+B_{i}+M_{\varphi_{i}}, \tag{1}\]  
where subscripts \(i\) and \(j\) denote different signal frequency bands. \(P\) is the code measurement, \(L\) is the phase measurement, and \(f\) is the frequency value. \(B\) denotes the phase ambiguity bias term and hardware delay biases. \(M_{\varphi}\) and \(M_{\varphi}\) are the phase and code MP error and observation noise effect, respectively. \(M_{p_{i}}\) is the MP combination value of the \(i\)th frequency band, and \(j\) can use any frequency band of the same satellite system without influencing the MP combination value. When no carrier phase cycle slips exist, the value may be considered constant. Therefore, after factoring it out with an average term during a continuous observation arc, the residual series are dominated by MP error effects and the tracking noise of the code observations.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
###### Abstract  
In power cloud environment, the existing Ciphertext-Policy Attribute-Based Encryption (CP-ABE) access control schemes, do not consider the generation of access structure and the existence of malicious users. To tackle these problems, a power cloud access control (PCAC) scheme is proposed, which improves the traditional CP-ABE access control model. Considering the heavy time consumption of CP-ABE, PCAC encrypts the symmetric key, instead of the raw data. PCAC combines the access tree and linear secret-sharing scheme (LSSS) to achieve the automatic generation and efficient operation of access structure. Additionally, an action audit phase, based on zero-knowledge verification was designed to defend against malicious users. The experiments proved that PCAC meets the requirement of fine-grained access control, in a power cloud. Compared with existing CP-ABE schemes, the PCAC scheme reduced about half of the time consumption, in the action audit phase and costs about one-third the time, in the data obtainment stage.  
Keywords:power cloud; access control; CP-ABE; action audit +
Footnote †: journal: Applied
## 4 Materials and Methods
### RNA Library, RNA-seq, Pre-Processing, and Quality Control  
RNA libraries were constructed according to Illumina's TruSeq strand-specific protocol, with a median insert size of ~230 bp (~100-350 bp). They were then sequenced using HiSeq 2500@PE100 platform (Illumina, San Diego, CA). Adaptor sequences were removed from the reads using Trimmomatic [61], while the quality of the reads was maintained by trimming using the 'adaptive quality trimming' algorithm [62]. The latter strikes a balance between quality score and sufficient read length, which is necessary for unambiguous mapping to the genome. Resulting reads < 36 bp were deemed to cause unspecific mapping and were thus discarded. Furthermore, only reads with a mean Q-score > 25 were retained, which translated to <1% error on average for each base pair. Upon completion, quality-control checks using FastQC did not reveal any issues with the final pre-processed reads.
## 5 Numerical Results
In this simulation, the analytical curves were obtained from (59), and it is clearly seen that the throughput of different schemes increases at the beginning and then decreases with the increasing of \(R_{S}\). It illustrated that there exists an optimal value \(R_{S}^{av}\) of MRC/MRT-SC and MRC-ZFB/MRT-SC-ZFB schemes, making the secrecy transmission best. We also find that increasing \(N_{R}\) and \(N_{B}\) can improve ST significantly, which reflects the efficiency of equipping Relay and Bob with multiple antennas. Observe from  
Figure 6: Secrecy outage probability of two schemes for different number \(N_{R}\) and fixed \(N_{B}\). Figure 7: Secrecy outage probability of two schemes for different \(\alpha_{1}\). Figure 8 that the ST \(T_{out}^{\varphi}\left(R_{S}\right)\) of the MRC/MRT-SC scheme does not change significantly as \(N_{B}\) varies from \(N_{B}\) = 4 to 6. This is because the ST is mainly affected by the antenna gain \(N_{R}\) in MRC/MRT-SC scheme. Finally, we observe that MRC-ZFB/MRT-SC-ZFB also outperforms the MRC/MRT-SC scheme over different values of \(R_{S}^{ov}\), \(N_{R}\) and \(N_{B}\).
# Multivariate Network Layout Using Force-Directed Method with Attribute Constraints
## 5 Evaluation
### Compared Methods  
We compare our method with three layout methods, i.e., FR layout [10], LinLog layout [26], and ForceAtlas2 layout [27]. FR is the most classic force-directed model, and the subsequent various force-directed methods are improved versions of it. The LinLog model is a force-directed layout method based on an energy function, and its layout can present the structural community in the graph data. The nodes in the structural community may have the same attribute information. At this time, the LinLog model can present the attribute community, so we also use it as a comparison method. ForceAtlas2 is a force-directed method integrated in the well-known graph visualization tool Gephi. It  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Dataset** & **Num of Nodes** & **Num of Edges** & **Attributes of Nodes** \\ \hline VisPCNet & 513 & 1890 & title, keywords, journal, year, number of citations \\ \hline RCNet1 & 176 & 425 & name, interests, number of papers, number of citations, H index \\ \hline RCNet2 & 473 & 1623 & name, interests, number of papers, number of citations, H index \\ \hline RCNet3 & 450 & 1258 & name, interests, number of papers, number of citations, H index \\ \hline RCNet4 & 204 & 825 & name, interests, number of papers, number of citations, H index \\ \hline OMNet1 & 52 & 140 & IP, name, working years message type, message number, device type \\ \hline \hline \end{tabular}
\end{table}
Table 1: Illustration of the datasets.  
uses various constraints to optimize the graph layout and can well reveal the structural information of the graph data.
# Perceived Usefulness of a Mandatory Information System
## 4 Materials and Methods
### Data Analysis  
All hypotheses were analyzed using structural equation modeling (SEM) via the IBM SPSS Amos 25 for Windows software package and the R package 'bestNormalize'(1.9.1) [49; 50] SEM offers two main advantages: it allows for a comprehensive assessment of a pre-specified model, which is particularly beneficial for the model used in this study, and it corrects for error variance, thereby more accurately identifying the parameters of interest.  
The following fit indices were employed: model chi-square, the root mean square error of approximation (RMSEA) [51], the normed fit index (NFI) [52; 53], and the comparative fit index (CFI) [54]. For RMSEA, lower values indicate a better model fit, with 0.08 being the conventional threshold for an acceptable fit and 0.05 for a close fit. For NFI and CFI, higher values signify better-fitting models, with traditional thresholds of 0.90 and 0.95, respectively, for an acceptable and close model fit [52; 55].  
The bootstrapping procedure was implemented as an effective method for testing hypothesized mediating effects [56; 57; 58].  
While the sample size of 72 participants might seem limited, it is sufficient for SEM analysis, which typically requires a minimum ratio of participants to parameters. Moreover, this study has integrated advanced analytical techniques, such as SEM with bootstrapping, and employed contemporary normalization methods, ensuring robustness and validity of our findings. The use of modern software tools such as 'bestNormalize' in R for data transformation further enhances the credibility of our analysis. Despite the relatively small sample size, the insights garnered provide a solid foundation for understanding the studied relationships, and future research will aim to include larger and more diverse samples to confirm and extend these findings.  
In summary, this study leverages advanced analytical techniques to ensure the robustness and validity of its findings. The integration of structural equation modeling (SEM) with bootstrapping allows for a comprehensive examination of hypothesized relationships, providing a more nuanced understanding of the underlying dynamics. This sophisticated approach ensures that the results are statistically sound and reliable.  
Additionally, contemporary normalization methods have been employed, further enhancing the rigor of the analysis. The use of cutting-edge software tools, such as 'best-Normalize' in R, for data transformation, exemplifies the commitment to methodological excellence. 'bestNormalize' optimizes the normalization process by selecting the best transformation method for the data, thereby improving the accuracy and interpretability of the results.  
These methodological advancements not only bolster the credibility of the study but also align it with current best practices in the field. By adopting such state-of-the-art techniques, the research sets a high standard for future studies, demonstrating the importance of rigorous and contemporary methods in achieving reliable and impactful findings.
## 2 Results and Discussion
### Preparation of Antioxidant Peptides from SBP
#### 2.2.2 Gel Filtration Chromatography of SBP-III  
Gel filtration chromatography is an effective separation technique on the basis of molecule size and widely applied to separate components in a mixture [10]. As shown in Figure 1, SBP-III was separated into four subfractions (SBP-III-1 to SBP-III-4) using a Sephadex G-25 column. From the linear equation (Log MW = \(-\)0.2036 Rt + 7.6164, \(R^{2}\) = 0.9766), the MWs of four subfractions were 9.48 kDa (SBP-III-1), 7.74 kDa (SBP-III-2), 6.78 kDa (SBP-III-3), and 5.79 kDa (SBP-III-4), respectively. HO\(\bullet\) scavenging activities of SBP-III-3 was 58.53% \(\pm\) 2.17% at the concentration of 1 mg/mL, which was significantly higher than those of SBP-III-1 (21.15% \(\pm\) 1.03%), SBP-III-2 (42.67% \(\pm\) 1.94%), and SBP-III-4 (33.27% \(\pm\) 2.11%), respectively (\(p\) < 0.05). The data indicated that SBP-III-3 could effectively restrain the production of HO\(\bullet\) and terminate the radical chain reaction. [PERSON] et al. reported that the hydrolysate subfraction from the skate (_Raja porosa_) cartilage protein using a Sephadex G-15 column had higher radical scavenging activity than other subfractions with smaller molecular size [10]. These results indicated that some factors, including amino acid composition and sequence, may also influence the activities of peptides in addition to the MW. Therefore, SBP-III-3 was chosen for further evaluation on in vitro antioxidant activity and the in vivo anti-fatigue effect.