esa-sceva/satcom-chunk-collection huggingface dataset:
# Minimum Distance Optimization with Chord Edge Growth for High Girth Non-Binary LDPC Codes
## 4 Complexity Analysis and Design Examples
### Design Examples
: _In order to analyze the feasibility of the proposed method for constructing NB-LDPC codes with different code rates, the NB-LDPC codes with the code length of 576 bits are constructed, and their code rates are 1/2 and 3/4, respectively. The check node degree of the codes with the code rate of 1/2 is four, and the check node degree of codes with the code rate of 3/4 is eight. For codes with the code rate of 1/2, based on the positions of non-zero entries in the parity-check matrix that was provided by [PERSON] et al. [25], the proposed method is performed to configure a non-zero value for each position. The NB-LDPC code that was provided by [PERSON] et al. [25], which has been designed using the cycle optimization proposed by [PERSON] et al. [15], is compared with the designed code. In addition, two codes with the same parameters are randomly constructed for comparison. For codes with the code rate of 3/4, one set of optimized non-zero element set \(\{\alpha^{0},\alpha^{6},\alpha^{13},\alpha^{21},\alpha^{28},\alpha^{36},\alpha ^{44},\alpha^{54}\}\)[26] is used for the non-zero entries that are associated with each check node. The non-zero positions of codes with the same code rate are the same, and the non-zero values are different._  
Figure 10: Performance of the (2, 4)- regular NB-LDPC code under different number of iterations. Figure 9: Performance of NB- LDPC codes with an efficient encoding structure.
## Appendix A Qualitative Analysis of Hurst Exponent and Entropy: Case-by-Case Description
### Aregawi (2014)  
In the Aregawi malaria time series (Ethiopia, 2014) [22] we can witness an initial small upper trend in malaria incidence in the form of an increasing moving average of 36 months (\(I_{36}\)) in the initial \(\sim\)62 months, with \(\sim\)0.26 cases per 100 inhabitants, per year, declining thereafter to less than \(\sim\)0.12 cases per 100 inhabitants, per year.  
Consistent with the initial small upper trend in \(I_{36}\), we also witness a declining trend in \(H_{36}\) after month 62. In this case, entropy revealed an atypical behavior, peaking a littler later, at month \(\sim\)72. Along with a declining trend in \(I_{36}\) after month 62, one can witness a consistent rise in \(H_{36}\), lasting to the end of the time series despite a transitory fall at month \(\sim\)105, with a rapid recovery at month \(\sim\)114. In the Aregawi time series, the behavior ofwas more unpredictable, with a more delayed response. This fact may be somehow related to the low malaria incidence in the time series.  
However, in the present case, the behavior of the Hurst exponent in the form of a 36-month moving average (\(H_{36}\)) in the initial 65 months still reveals some degree of correlation with the global malaria incidence trend--see Figure 10. At such low levels of malaria incidence (\(\sim\)0.2 _phy_) this behavior could be interpreted as a possible outlier result--see Figures 11-13.
## 5 Results and Discussion
### Parameter Estimation Results
Table 18 represents the statistical result based on the Wilcoxon rank sum test. The Friedman rank test is represented in Table 19. Based on the results of this test, it is clear that the newly generated hybrid algorithm is more reliable and superior to other similar algorithms. Based on the results of both tests, PSOGSA clearly outperforms the other algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithms** & **SSE** & **AE** & **MAE** & **MSE** & **RMSE** \\ \hline \multirow{4}{*}{**GSA**} & **Minimum** & \(1.04\times 10^{-6}\) & \(1.06\times 10^{-3}\) & \(5.09\times 10^{-5}\) & \(5.20\times 10^{-8}\) & \(2.87\times 10^{-4}\) \\  & **Maximum** & \(8.15\times 10^{-6}\) & \(2.84\times 10^{-3}\) & \(1.44\times 10^{-4}\) & \(4.08\times 10^{-7}\) & \(6.71\times 10^{-4}\) \\  & **Mean** & \(4.57\times 10^{-6}\) & \(2.18\times 10^{-3}\) & \(1.07\times 10^{-4}\) & \(2.28\times 10^{-7}\) & \(4.01\times 10^{-4}\) \\  & **Standard deviation** & \(2.32\times 10^{-6}\) & \(1.57\times 10^{-3}\) & \(7.60\times 10^{-5}\) & \(1.16\times 10^{-7}\) & \(3.06\times 10^{-4}\) \\ \hline \multirow{4}{*}{**MVO**} & **Minimum** & \(1.09\times 10^{-5}\) & \(3.25\times 10^{-3}\) & \(1.61\times 10^{-4}\) & \(5.44\times 10^{-7}\) & \(7.65\times 10^{-4}\) \\  & **Maximum** & \(9.94\times 10^{-5}\) & \(9.91\times \begin{table}
\begin{tabular}{c c c c c c c c} \hline Parameter/Algorithm & **MVO** & **PSO** & **SCA** & **GSA** & **HBO** & **PO** & **PSOGSA** \\ \hline I\({}_{\text{pv}}\) & 8.9392 & 9.1101 & 8.8629 & 5.8503 & 4.8215 & 4.8587 & 8.88802 \\ Alpha\({}_{1}\) & 1.4659 & 0.7090 & 1.2986 & 1.2324 & 1.2278 & 1.0888 & 1.4959 \\ Alpha\({}_{2}\) & 1.5157 & 0.7902 & 1.2520 & 1.2751 & 1.0856 & 1.0417 & 1.6972 \\ Alpha\({}_{3}\) & 1.4794 & 0.8058 & 1.0996 & 1.2271 & 1.3308 & 1.2000 & 1.5183 \\ R\({}_{\text{se}}\) & 0.0086 & 0.0061 & 0.0170 & 0.2617 & 0.0554 & 0.0030 & 0.0140 \\ R\({}_{\text{sh}}\) & 248.788 & 74.256 & 296.161 & 248.43 & 280.642 & 184.906 & 380.081 \\ I\({}_{\text{e1}}\) & \(5.33\times 10^{-7}\) & \(1.17\times 10^{-7}\) & \(1.83\times 10^{-7}\) & \(3.10\times 10^{-7}\) & \(2.92\times 10^{-7}\) & \(6\times 10^{-7}\) & 0 \\ I\({}_{\text{e2}}\) & 0 & \(1.66\times 10^{-7}\) & \(1.87\times 10^{-7}\) & \(5.13\times 10^{-7}\) & \(3.64\times 10^{-7}\) & \(3\times 10^{-7}\) & 0 \\ I\({}_{\text{e3}}\) & \(4.17\times 10^{-7}\) & \(1.83\times 10^{-7}\) & \(1.57\times 10^{-7}\) & \(4.94\times 10^{-7}\) & \(2.96\times 10^{-7}\) & \(3.5\times 10^{-7}\) & \(5.00\times 10^{-7}\) \\ R\({}_{\text{e}}\) & 0.0047 & 0.0087 & 0.0120 & 0.1762 & 0.4550 & 0.0052 & 0.0410 \\ Computation time & 2.015 & 3.112 & 3.035 & 2.415 & 1.851 & 1.112 & 1.200 \\ per run (s) & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 16: Results of parameter estimation of the MTDM for Case 2.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 5 Experimental Verification
### Method Comparison
#### 5.2.2 Comparison between the Proposed Method with VMD-Time-Frequency Information Entropy  
Variational mode decomposition (VMD) is a completely non-recursive, adaptive signal processing method proposed by [PERSON] [36]. The method has higher decomposition accuracy and can effectively filter out the white noise in the signal. The specific algorithm steps are as follows:  
Step 1: Preprocess the vibration signal.  
Step 2: Decompose the signal into six IMFs using the VMD method.  
Step 3: Calculate the time-frequency information entropy of each IMF and obtain the 6-dimensional feature vector. Similarly, the feature vector is reduced by PCA. The clustering results are shown in Figures 15-18.  
As can be seen from Figures 15-18, the VMD-time-frequency information entropy method also has the phenomenon of fault mixing under the operation condition 1 and the operation condition 4. By contrast, it can be proved that the method proposed in this paper has better performance than the VMD-time-frequency information entropy method in fault feature extraction.  
Under the variable operation conditions, the algorithm proposed in this paper had obvious clustering of fault features and no fault mixing. However, the wavelet entropy algorithm and VMD-time-frequency information entropy algorithm had a certain degree of mixing for the same fault under the variable operation conditions.  
Figure 19: Clustering result of ICEEMD-time-frequency information entropy under variable conditions.  
Figure 20: Clustering result of wavelet entropy under variable conditions.  
Figure 19: Clustering result of ICEEMD-time-frequency information entropy under variable conditions.  
different operation conditions, and the degree of polymerization was not ideal. Therefore, neither of these methods can be applied to the fault diagnosis of variable operation conditions.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 4 Experimental Results and Performance Evaluations
### Performance Improvement from Temporal and Data Correlation Compression  
In Figure 6, the compression performance of EB-RLE (temporal correlation compression) and EB-HC (data correlation compress) in BESDC shows the compression ratio for four different environmental datasets of PM 2.5, temperature, CO and seismic wave on four different bounded errors of 0.25%, 0.5%, 0.75% and 1%. As shown in Figure 6, the seismic wave dataset has worse EB-RLE/EB-HC compression ratio than others, since high-frequency seismic waves usually preserve low temporal and data correlations in sensor data values.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Parameter** & \(I_{TX}\) & \(I_{RV}\) & \(I_{syn}\) & \(f_{syn}\) & \(T_{TX}\) & \(T_{RV}\) & \(V_{TX}\) & \(V_{RV}\) & \(\varepsilon_{add}\) & \(\varepsilon_{mul}\) & \(\varepsilon_{mul}\) & \(\varepsilon_{mul}\) \\ \hline Value & 17.4 mA & 19.7 mA & 31 mA & 48 MHz & 32 \(\upmu\)s/bit & 32 \(\upmu\)s/bit & 3.3 V & 3.3 V & 2.13 \(\upmu\)j & 6.39 \(\upmu\)j & 2.13 \(\upmu\)j & 4.26 \(\upmu\)j \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters to evaluate the energy consumption.  
Since the energy consumption of the sensor node is dominated by data transmission, the computing of EB-RLE and EB-HC in BESDC will not affect too much the overall energy consumption in sensor nodes (i.e., EDs). Thus, the energy consumption ratio performance as shown in Figure 7 has a similar trend to Figure 6. This indicates that the higher data compression ratios preserve the lower energy consumption to substantially extend the IoT lifetime for IEI applications. It is known that the larger the bounded error given, the better compression and energy consumption ratios which should be preserved. However, the compression and energy consumption ratios of the PM 2.5 dataset with a bounded error of 0.75% are almost the same as the bounded error of 0.5%. This is because the experimental dataset of PM 2.5 preserves much the same value ranges as the bounded errors given in 0.5% and 0.75%.
# Wideband Lorenz Resonance Reconfigurable Metasurface for 5G+ Communications
## 3 Fabrication and Measurement  
### Fabrication of MSRRA  
In order to test the performance and experimentally validate the MSRRA designed in this work, a prototype is fabricated, composed of three independent function bodies based on PCB techniques. The three independent function bodies are inter-connected by the BTB connectors as shown in Figure 4a. The top layer of the MSRRA prototype is the metasurface's aperture, which includes 16 \(\times\) 16 unit cells and has a square size of 64 mm \(\times\) 64 mm, as shown in Figure 4b. The middle layer is the bias voltage generator, which has 16 voltage DACs. There are 256 bias voltage lines totally connected with 256 unit cells of the aperture, as shown in Figure 4c. The bottom layer of the prototype is the beam-forming controller, which can independently control one of the 256 bias voltages of the unit cell to realize the free control of the reflected beam. The beam-forming controller is implemented based on an FPGA (Field-Programmable Gate Array), as shown in Figure 4d.  
Figure 4: MSRRA prototype. (**a**) Laminated structure of the MSRRA prototype, from top to bottom; 1 is the MSRRA reflect aperture; 2 is the bias voltage generator; 3 is the beam-forming controller. (**b**) MSRRA aperture. (**c**) Bias voltage generator; the BTB connector is marked with a red circle; (**d**) Beam-forming controller.  
Figure 3: Unit cell structure. (**a**) Unit cell top view. (**b**) Unit cell side view.
# An Energy Efficient Sink Location Service for Continuous Objects in Wireless Sensor Networks
## 4 Performance Evaluation
### Metrics and Simulation Environment  
We choose two metrics to analyze the performance of OCOT. The total energy consumption is defined as the communication (i.e., transmitting and receiving) energy the network consumes during a simulation round. The total energy consumption means efficiency of message delivery. The idle energy is not counted since it depends largely on the message generation interval and does not indicate the efficiency of massage delivery. Thedistribution of energy consumption is the map including the location of each node and used energy of the node. We might deduce the possibility of the hot-spot problem in each scheme from the distribution of energy consumption. We divide the network into 20 \(\times\) 20 cells and observe the average used energy of the nodes in each cell. We implement OCOT in MATLAB [31]. The default simulation environment is summarized in the Table 2. The simulation network space is 500 m \(\times\) 500 m square. The number of nodes is 1000 and they are uniformly deployed. The transmission of each node is omnidirectional. The transmission range and sensing range are 20 m. The sensor node's transmitting and receiving consumption rates are 20 mW and 15 mW, respectively. A mobile sink sends a location update message to sources every 5 s. Sources generate data every 5 s.  
Figure 13: Location update and data report in OCOT: (**left**) Location update and (**right**) Data report.  
Unfortunately, comparison with existing studies is not reasonable because they focus on sink mobility support in individual object tracking. So, we assume that TTDD and LBDD have exploited DCS [3] for tracking continuous object. In modified TTDD, the sources organize clusters dynamically, and cluster heads construct grid structures. Similarly, in modified LBDD, cluster heads report data to the line. The maximum number of sources in each cluster in modified TTDD and LBDD is set as 5. A cell size in TTDD is 50 m. The width and the group size in LBDD is 50 m, respectively. A cell size in OCOT is 50 m. Figure 14 shows a continuous object tracking scenario. A continuous object is originated at point (150, 150). The continuous object spreads circularly and its radius increases to 1 m/s. The initial location of the mobile sink is (400, 100). The sink moves to point (400, 300) at a speed of 2 m/s during each simulation round.
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 5 Numerical Results  
This section evaluates the GF access procedure described in Sections 3 and 4 from the link-level perspective. The proposed technique lies within the category of digital beamforming solutions, which refrain from using fixed beamforming matrices. The results presented in this section evidence that although the devised solution entails a complexity increase with respect to the fixed beamforming approach, it achieves a far superior performance. The system parameters are listed in Table 1. The distance between antenna elements is set to \(d=\lambda/2\). To generate the sequences, we have used the OFDM modulation format. The bandwidth is partitioned into \(M=64\) subcarriers, leaving empty approximately 9% of the subcarriers to reduce out-of-band emissions. Hence, at the edges, three subcarriers remain silent. The symbols have been drawn from the quadrature phase-shift keying (QPSK) constellation. There are \(P=4\) orthogonal patterns of length \(L_{R}=16\). To generate the redundant symbols, users pick randomly any of the repetition patterns available. In the following we analyze the impact of several parameters on the performance. Essentially, we focus the attention on the number of symbols per frame \(N_{\text{S}}\), the number of users \(N_{\text{U}}\), and the number of antenna elements \(N_{x},N_{y}\). In the experimental validation, we have focused the attention on two array antenna configurations, i.e., \(N_{x}=N_{y}=24\) and \(N_{x}=N_{y}=32\). We assume that users can be located in any spot that is illuminated by the satellite. This means that \(\theta_{k}\) and \(\phi_{k}\) can take any value within the intervals [0, 44.44\({}^{\circ}\)] and [0, 360\({}^{\circ}\)], respectively. Consequently, the free space loss variation is around 3.35 dB.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 4 Results and Discussion
### In Secure Data Aggregation  
Data aggregation is an essential technique to reduce the transmission load and save energy for resource-limited sensor nodes in WSNs. Data aggregation is essential for WSNs and security is required to protect against compromised sensor nodes. Several typical secure data aggregation methods have been studied and implemented. Among them, ESPDA is suitable for sensor networks with high density, homogeneous data, and basic security requirements. SDDA is especially effective in event detection systems (when data only changes significantly in abnormal cases such as forest fires, gas leaks, etc.). However, SDDA requires precision in setup, avoiding errors if the data fluctuates randomly. The resulting authentication method is equal to the aggregation node having to include authentication proofs (e.g., MAC authentication codes) generated by multiple trusted nodes before sending the data to the receiving station, which enhances the reliability and integrity of the data and protects against tampering at the aggregation node, although at the cost of higher communication costs. For applications requiring high levels of privacy and security, HSC allows direct computations on encrypted data (without decryption during transmission and aggregation). In addition, some approaches such as aggregation frameworks with interactive proofs are suitable for large-scale WSNs or high-security environments. In practice, new data aggregation protocols need to be developed to address the issues of scalability and higher reliability to protect against attacks on aggregators and sensor nodes. WSN-IoT systems should flexibly combine multiple methods according to the requirements of specific applications, thereby achieving comprehensive and secure data aggregation. Table 5 shows the number of secure data aggregation protocols mentioned and analyzed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **Ref.** & **Complexity** & **Security** & **Scalability** & **Energy Consumption** & **Main Contributions** & **Advantages** & **Limitations** \\ \hline BASIS & [79] & Low & High & Medium & Low-Medium & Uses ID-based signatures without key pairs & Reduces energy consumption by up to 72\% & Low scalability \\ \hline MBS-UAKA & [80] & Low-Medium & High & Low & Medium-High & Supports authentication with multiple base stations to reduce authentication load & High security, limited encryption modes \\ \hline ASKI-WSN & [81] & Low & High & Medium & Medium & Uses ECC for two-factor authentication & Good security, suitable for low-demand applications & Complicated implementation, only suitable for specific platforms \\ \hline SUBBASe & [82] & Low-Medium & Medium-High & Low & Medium & No passwords, biometric-based authentication & Simple, energy-saving & High computational cost, difficult to deploy due to the need for multiple base stations \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._
# An Adaptive Privacy Protection Method for Smart Home Environments Using Supervised Learning
###### Abstract  
In recent years, smart home technologies have started to be widely used, bringing a great deal of convenience to people's daily lives. At the same time, privacy issues have become particularly prominent. Traditional encryption methods can no longer meet the needs of privacy protection in smart home applications, since attacks can be launched even without the need for access to the cipher. Rather, attacks can be successfully realized through analyzing the frequency of radio signals, as well as the timestamp series, so that the daily activities of the residents in the smart home can be learnt. Such types of attacks can achieve a very high success rate, making them a great threat to users' privacy. In this paper, we propose an adaptive method based on sample data analysis and supervised learning (SDASL), to hide the patterns of daily routines of residents that would adapt to dynamically changing network loads. Compared to some existing solutions, our proposed method exhibits advantages such as low energy consumption, low latency, strong adaptability, and effective privacy protection.  
smart home; privacy; FATS attack; supervised learning +
Footnote †: journal:  
_Future Internet_
## 5 Conclusions  
The electrospinningning of polyvinyl pyrrolidone (PVP) and the blend of 50/50 W/W of polyvinyl alcohol (PVA) and PVP polymer solutions in water (10% W/V) were performed and used as a drug carrier for buprenorphine (Bup). Bup-loaded PVP and Bup-loaded PVP/PVA nanofiber mats were prepared by the electrospinning method. SEM images showed a smooth, fine and porous non-fibrous structure without any adhesion or knot in all samples. The mean diameter distribution approved that PVP and Bup/PVP had smaller diameters than PVP/PVA and Bup/PVP/PVA, respectively. The addition of PVA led to a significant increase in the diameter of the nanofibers; however, the addition of Bup slightly increased the mean diameter. Moreover, PVP/PVA and Bup/PVP/PVA nanofibers had a lower contact angle compared to PVP and PVP/PVA because of the addition of hydrophilic PVA polymer. The results of this study indicated that the addition of Bup did not change contact. The electrical conductivity measurements of samples revealed that the addition of polar PVA or Bup to the solution led to a rise in the electrical conductivity of electrospinning solutions that could control the diameter and morphology of nanofibers.  
Figure 11: The buprenorphine release profile of electrospun drug-loaded nanofiber mats (900 mL phosphate buffered saline, 100 rpm, 37 \({}^{\circ}\)C).  
Moreover, the viscosity and shear rate of electrospinningning solution samples were measured and found to be in accordance with the structure of nanofibers.  
It was also observed that \(\mathrm{Bup}\)/PVP/PVA nanofibers could be cross-linked by glutaraldehyde which in turn increased the diameter of nanofibers because the GTA vapor was absorbed into nanofiber structures, and the porous structure of nanofibers was maintained during the cross-linking process. Additionally, the cross-linking process led to a longer drug release time (above 96 h). Therefore, the prepared cross-linked \(\mathrm{Bup}\)-loaded \(\mathrm{PVP}\)/PVA nanofibers proved promising for use as drug-delivery materials because of their morphological compatibility and the long-slow duration of drug release time.  
[PERSON] carried out the experiment. [PERSON] wrote the manuscript and supervised the project. [PERSON] developed the theoretical formalism, performed the analytic calculations. [PERSON] and [PERSON] commented and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available.  
The authors would like to acknowledge the Active Pharmaceutical Ingredients Research Center (APIRC) and the Pharmaceuticals Sciences Research Center, Tehran Medical Sciences, Islamic Azad University for equipment and laboratory services.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Data and Sensors
The spatial extent of disturbance events is delineated by a change mask, derived from the Landsat pair images. A series of MODIS images determine the date of disturbance [78]. Active sensors such as LiDAR and SAR contribute different information to Landsat change detection studies versus optical data. ALOS-PASAR is the most used SAR data in Landsat change detection studies [79; 80; 81]. However, the lack of sustainable SAR data with the required spatial and temporal coverage has generally limited the usage of SAR data in Landsat change detection studies. Sentinel-1 free global 10 m resolution data, though only available from 2014, has great potential for data fusion with Landsat in years to come [82; 83; 84]. LiDAR data is used in forest and biomass applications to better capture the structure of vegetation and the ground and to improve the accuracy of regional to national models [85; 86; 87].