esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Reference Point Group Mobility (RPGM)
#### 6.2.4 Expected Transmission Count  
Figure 10 presents the ETX metric within a mobile network environment utilizing the RPGM mobility model, with a specific focus on variations in node count and the integration of attacker nodes. The scenarios encompass both instances with and without the Hello Flood attack, where mobile nodes represent unaffected entities and attacker nodes denote those subjected to the assault. The node count, reflecting network density, ranges from 10 to 60, while the percentage of mobile nodes and mobile attacker nodes  
Figure 9: Throughput without and with attack using RPGM mobility model.  
spans from 10% to 40%. Across diverse node counts, a consistent trend of increasing ETX values emerges, indicating a heightened transmission count necessary for successful packet delivery as the network size expands. This trend persists regardless of alterations in the percentage of attacker nodes, suggesting that network size significantly affects transmission efficiency. Furthermore, the presence of attacker nodes correlates with higher ETX values, indicating a decline in overall link quality and reliability. Notably, in scenarios without attack, ETX maintains stability, whereas in attacked scenarios, ETX escalates, peaking at 60 nodes, with 40% of these nodes being both malicious and mobile. These peak ETX values denote the highest transmission count requirement under such circumstances. These observations underscore the substantial influence of both network density and attacker presence on ETX values.  
The RPGM mobility model, when coupled with the Hello Flood attack, engenders an increase in the ETX metric through a multifaceted interplay of factors. The RPGM mobility model causes congestion in certain parts of the network due to how nodes move, leading to more packet collisions and retransmissions. This congestion gets worse when the Hello Flood attack floods the network with extra traffic, making interference worse and increasing the ETX value. In addition, the RPGM's movement patterns can make routing less stable, resulting in less efficient routing and more lost packets. The Hello Flood attack makes this instability worse by messing with routing protocols, making it harder to deliver packets, and raising the ETX. Moreover, the RPGM model uses up more network resources, especially when combined with the flood of malicious traffic from the attack, which slows down performance and raises ETX. Additionally, the RPGM's way of handling nodes at the network's edges adds more delays to sending packets, which the Hello Flood attack takes advantage of to make problems worse and increase ETX. In summary, the RPGM model and the Hello Flood attack together increase the ETX by causing congestion, unstable routing, resource usage, and delays at the network's edges.
## 6 Conclusions  
By on-site testing and numerical analysis, the surrounding soil deformation features caused by the vertical two-layer pipe jacking in the soil-rock composite stratum are discussed. The main conclusions are as follows:  
1. The cumulative settlement of the pipe jacking machine before reaching the monitoring section accounts for about 60-70%, and 30-40% settlement can still be generated when the pipe jacking machine is far away from the monitoring section.
2. The maximum transverse and longitudinal ground settlements caused by the upper pipe are about three times those of the lower pipe. The maximum horizontal lateral displacement of the upper pipe jacking is about 3.3 times the lower pipe jacking construction.
3. In the construction of two-layer pipe jacking, total ground settlement increases with the reduction of vertical clear spacing between the upper and lower pipe. When the vertical clear spacing increased from half of the pipe diameter to 2.0 times pipe diameter, the total ground settlement is reduced by 75%.
4. The peck formula, which is used to estimate the lateral land subsidence distribution by tunnel construction, is modified to make it more applicable in the soil-rock composite stratum to calculate the ground settlement induced by pipe jacking.  
Figure 14: Comparison of ground settlement prediction. (**a**) Ground settlement comparison between modified peck formula and simulation result of pipe jacking in the soil-rock composite stratum; (**b**) comparison of ground settlement prediction in this paper with reference [27].  
**Author Contributions:** Conceptualization, G.S. and J.H.; methodology, N.Y.; software, N.Y.; validation, G.S. and J.H.; formal analysis, N.Y.; investigation, G.S.; resources, G.S.; data curation, N.Y.; writing--original draft preparation, N.Y.; writing--review and editing, G.S. and J.H.; visualization, G.S.; supervision, G.S. and J.H.; project administration, J.H.; funding acquisition, G.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Key Project of the National Natural Science Foundation of China, grant number 52038006; the Ministry of Education Innovation Team Development Program Project, grant number IRT_17R69.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data are contained within the article.  
**Acknowledgments:** We are grateful to the Jinan Urban Construction Group Co, Ltd., for providing us with the experimental platform, and grateful to [PERSON] of the company for his help in the experiment process.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Information Fusion and Residual Life Estimation
### Consistency Test  
The consistency test is necessary after obtaining the prior distributions according to multi-source information, which could guarantee that the prior information and field data are from the same population. Typical methods, e.g., the graph comparison method [44], are not applicable here, as failure times are not sufficient. Hence, the Bayes credible interval method is used here. The details of this method are as follows.  
1. Calculate the \(100(1-\alpha)\%\) Bayes credible interval for residual life \((\mu_{Li},\mu_{Hi})\) under different prior distributions \(\pi_{i}(\theta),i=1,2,3,4\).
2. Using field data, obtain the Bayesian estimate of residual life \(\mu_{\tau 0}\) under the non-informative prior distribution.
3. If \(\mu_{\tau 0}\in(\mu_{Li},\mu_{Hi})\), then the \(i\)th prior information is consistent with the field data under the significance level \(\alpha\).  
Methods for calculating the Bayes credible interval and the point estimate of residual life under certain prior distributions are provided in Section 4.4.
## 3 Results and Discussion  
### The Influence of Mesh Size on the Coefficient \(K\)  
Mesh size of the FEM possibly affects the calculation accuracy of the SIF. Therefore, the influence of mesh sizes ranging from 0.1 mm to 2.0 mm on the SIF was evaluated, as shown in Figure 5. The SIF varies a little with the mesh sizes of 0.1 mm-0.7 mm. However, the SIF significantly fluctuates at 0.7 mm-2.0 mm. To obtain higher accuracy and efficiency, in this paper, the mesh size of 0.1 mm was selected to carry out the calculation of the SIF.
# Rapid Construction of Aerocapture Attainability Sets Using Sequential Convex Programming
## 5 Numerical Demonstrations
### Aerocapture Attainability Sets with Different Initial Flight-Path Angles  
The initial flight-path angle \(\gamma_{0}\) is also an important design parameter for aerocapture. For Mars aerocapture, the permissible range of \(\gamma_{0}\) is relatively small. Three different initial flight-path angles \(\gamma_{0}\)= \(-10.5\) deg, \(-11.0\) deg, and \(-11.5\) deg were considered in our simulations. The resulting attainability sets are shown in Figure 10. The larger the initial flight-path angle, the larger the aerocapture attainability set expanded to a higher terminal velocity and flight-path angle.
## 3 Methodology
### Wrapper-Based Feature Selection Using GA  
We applied the wrapper-based feature selection method using a GA. GA, inspired by the process of natural selection, offers an evolutionary approach for optimizing feature subsets. The process involves iteratively evolving populations of feature combinations to find an optimal subset that maximizes a predefined objective function [37].  
Although conventional EEG features provide valuable insights into brainwave dynamics, they may not capture the most relevant information for emotion classification tasks [38]. The feature selection method using GA iteratively identifies the most relevant features by evolving populations of feature subsets. This process improves model performance while reducing complexity. In our study, GA was particularly effective in selecting features that enhanced the classification accuracy for EEG-based emotion recognition.
## 2 Background and Related Studies
### Authorization Consensus Mechanism  
The main idea of the authorization consensus mechanism is to complete the generation and maintenance of blocks through a distributed consistency algorithm after nodes have been authenticated. [PERSON] et al. [30] proposed the basic framework for Hyperledger Fabric. Hyperledger is a series of open source blockchain projects initiated by the Linux Foundation, which aims to provide an enterprise-class open-source distributed ledger framework and source code. Hyperledger Fabric is a community-based project that provides a supporting framework for blockchain applications. [PERSON] et al. [31] proposed the DFINITY consensus mechanism. DFINITY protocol operates in periods and divides all participating nodes into different groups. A random committee is responsible for transaction processing and consensus operation in each period, and at the end of each period, a random number function is used to determine the group serving as the committee in the next period. The PaLa consensus mechanism proposed by [32; 33; 34; 35; 36] realizes the rapid consensus in the authorization network. PaLa uses the method of parallel pipeline to improve the efficiency of block processing and adopts the sub-committee sliding window reconfiguration to ensure the sustainability of transaction processing during the reconfiguration.
## 3 Experimental Work and Results
### Computational Complexity  
Following [15], the number of multiplications and divisions in each frame for RASTA-PLP, PNCC, and the proposed method feature extraction techniques were estimated, as shown in Table 1. In this research, we assumed that the window length was 25.6 ms and with 10 ms overlap between frames. The sampling rate was assumed to be 8 kHz, and a 256-pt FFT was used for each analysis frame.  
It can be seen in Table 1 that the proposed method processing was about 4.07 percent costlier than PNCC processing. The proposed method processing was approximately 12.45 percent costlier than RASTA-PLP processing.
# Doped Zinc Oxide Nanoparticles: Synthesis, Characterization and Potential Use in Nanomedicine
## 2 Materials for Doping ZnO Nanoparticles (NPs)
### Rare Earth (RE) Elements
In conclusion, RE elements have been more extensively studied to increase the photocatalytic and optical properties of ZnO nanomaterials, but also to confer on ZnO magnetism and improved electromechanical properties in some cases.
## 3 Results
### Intensity Distribution  
Figures 3 and 4 illustrate the intensity distribution of partially coherent flat-topped beam rectangular arrays as they propagate through atmospheric turbulence and plasma over various distances, with the beam order N = 4 and beam width w\({}_{0}\) = 4 cm, comprising a total of 3 \(\times\) 3 beamlets. From Figures 3 and 4, it is observed that for different transverse spatial coherence widths, the partially coherent flat-topped beam rectangular arrays tend to evolve into Gaussian beam profiles during both forward and reverse transmissions in the far field. It is observed that atmospheric turbulence has a relatively lower impact on the intensity distribution of these arrays compared to the disturbances caused by plasma. Specifically, under identical beam parameters, the transition from partially coherent flat-topped beams to Gaussian beams occurs more swiftly during reverse transmission than during forward transmission. Additionally, the evolution characteristics of the intensity distributions for partially coherent flat-topped beam rectangular arrays with smaller transverse spatial coherence widths show a faster transformation into Gaussian beams when compared to that for those with larger coherence widths.  
Figures 5 and 6 illustrate the intensity distribution of partially coherent flat-topped beam rectangular arrays with different beam widths, influenced by the atmospheric turbulence and plasma at various distances; the transverse spatial coherence width \(\sigma\) = 2 cm and the rectangular arrays of the beamlets are the same as those in Figures 3 and 4. From Figures 5 and 6, it is evident that as partially coherent flat-topped beam rectangular arrays propagate through turbulent atmospheric and plasma media, their intensity distributions in the far field evolve to exhibit Gaussian beam profiles. However, this evolution process occurs more slowly during forward transmission compared to reverse transmission. Under  
Figure 2: Schematic diagram of bidirectional transmission of laser beams in atmospheric turbulence and plasma.  
identical conditions, beams with larger beam widths experience a slighter influence from both atmospheric turbulence and plasma disturbances. This suggests that larger beam widths may offer a degree of resilience against the degradations caused by these factors. The evolution characteristics of the intensity distribution further highlight that plasma exerts a more severe impact on partially coherent flat-topped beam rectangular arrays than does a turbulent atmosphere.  
Figure 4: Intensity distribution of the partially coherent flat-topped beam rectangular array during the reverse transmission with the different propagation distances and transverse spatial coherence widths (H = P =3).  
Figure 3: Intensity distribution of the partially coherent flat-topped beam rectangular array during the forward transmission with the different propagation distances and transverse spatial coherence widths (H = P = 3).
## 5 Result
In stage 3, scheme (a) will calculate the calendar allocation based on the real service bandwidth updates, and based on the successful prediction, scheme (b) allocates time slots directly according to the pre-allocation result calculated in stage 2. In stage 4, scheme (a) will allocate all of the calendar the service needs based on the calculation results calculated in stage 3, but scheme (b) has completed the service allocation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Indicators** & **ARIMA** & **GRU** & **LSTM** & **3 DCNN** & **PMFAN** \\ \hline R\({}^{2}\) & 0.826654 & 0.894564 & 0.904648 & 0.925660 & 0.935641 \\ NMSE & 0.043599 & 0.024354 & 0.022547 & 0.018534 & 0.016684 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation indicators of different prediction algorithm. Figure 5: Comparison of PMFAN algorithm and ground truth on different FlexE client flow. By analyzing lots of experimental data, we can prove that, via a large number of repeated experiments, the calendar allocation scheme based on the PMFAN-GDSO can save up to 46.8% of computing time while ensuring prediction accuracy. In our algorithm, the actual delay generated via the allocation algorithm can be obtained using (20):  
\[\textit{Saved Time}\ =\left[1-\frac{\sum_{i=1}^{N}(\bar{y}_{i}-y_{i})^{2}}{\sum_{i=1 }^{N}(\overline{y}_{i}-y_{i})^{2}}\right]\times T_{GDSO} \tag{20}\]  
With the increasing number of network nodes, the running time T_GDSO of the allocation algorithm also increases. Therefore, as the network scale expands, our algorithm can effectively save computation time. As shown in Figure 7, the curve demonstrates a gradual increase in time savings as the number of nodes increases. When the number of nodes increases from 32 to 118, the time savings increase by a factor of 5. However, when observing each individual topology structure, our algorithm seems to have a fixed proportion in terms of time savings, which is approximately 50%. This is because the traffic dataset and network topology we utilize are independent of each other. As a result, employing the same prediction algorithm yields a similar prediction accuracy, leading to a convergence in the gain ratios across different topologies. However, there are significant differences in the running time of the underlying allocation algorithm for different topology structures, which allows our algorithm to save the running time of the allocation algorithm significantly. This finding underscores the practicality and efficiency of employing traffic prediction in time slot resource allocation, offering a promising approach for enhancing resource management in dynamic network environments. The demonstrated computational efficiency is critical for ensuring timely and responsive network operations, especially in scenarios with rapidly evolving traffic patterns and dynamic node configurations. Figure 6: Comparison of proposed algorithm and proposed without prediction on slot allocation.
## 2 Antenna Design
### The Design of the Matching Network of the Proposed Series-Fed Antenna  
Figure 9 shows the impedance-matching network configurations of the proposed antennas, including a broadside and beam-tilted antenna design. The feed line at the input port is a \(50~{}\Omega\) microstrip line with \(L_{50}=12\) mm and \(W_{50}=1.55\) mm for connection with the coaxial connector. A quarter wavelength transformer with \(L_{TF}=7.6\) mm (\(0.25~{}\lambda_{g}\), where \(\lambda_{g}\) is the guided wavelength at \(5.84\) GHz) and \(W_{TF}=0.8\) mm is inserted to match the impedance between the \(50~{}\Omega\) feed line and \(100~{}\Omega\) U-shaped bend transmission line. For the broadside series-fed antenna, an open stub with \(W_{Stub}=5.1\) mm (\(0.16~{}\lambda_{g}\)) is introduced between the first radiating patch and the quarter wavelength transformer (Figure 9) to achieve conjugate matching, effectively tuning the antenna's impedance.  
Figure 10 shows the simulated impedance characteristics of the matching network. Figure 10 shows the reflection coefficient and transmission coefficient of the impedance-matching networks of beam-tilted antenna. As shown in Figure 10, for the broadside series-fed antenna without the matching network and open stub, the resistance of the input impedance is around \(100~{}\Omega\), and the reactance of the input impedance is \(-100~{}\Omega\) at the operating frequency due to the capacitive effect of each radiating patch. With the presence of the matching network and open stub, the resistance of the input impedance is transformed to \(50~{}\Omega\), and the reactance of the input impedance decreases to nearly zero at the operating frequency. Figure 10 shows the reflection coefficient of the broadside series-fed antenna. With the matching network and open stub, the reflection coefficient is improved from \(-5\) dB to \(-19\) dB.  
Figure 8: Simulated RHCP realized gain patterns for different \(W_{2}\) at \(5.84\) GHz, in \(\phi=90^{\circ}\) plane: (a) \(1\times 12\) series-fed antennas of the proposed unit cell, (b) \(1\times 6\) series-fed antennas of the proposed unit cell.  
Figure 9: Configuration of matching network of the proposed antennas: (a) broadside antenna, (b) beam-tilted antenna.
## 3 Related Work and Literature Review
### Security and Encryption in Watermarking  
A key area of watermarking research involves the combination of watermarking techniques with encryption to improve security. [PERSON] [24] integrates Singular Value Decomposition (SVD) with One-Time Pad (OTP) encryption for watermark security and to prevent watermark removal or manipulation by attackers. The method provides dual-layer security, using SVD for watermark embedding and OTP for watermarked imageencryption. This approach guarantees image integrity preservation despite potential watermark compromise.  
These encryption-based security enhancements represent traditional watermark protection approaches. Recent developments explore machine learning applications to further improve watermarking systems. Encryption-based methods remain a core part of watermark protection. Alongside these, recent work has started to explore how machine learning can support more adaptive and resilient watermarking systems.
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 3 Results
### Experiment 1: Algorithm Comparison
#### 3.1.3 Analysis to exp_T
Therefore, experiment 1 demonstrates which FM is better, rather than how much error exactly an FM causes, which will be tested in experiment 2. The above analysis indicated that the exp algorithm with log-cubic interpolation is the most accurate FM; therefore, in experiment 2, we determined the extent of error of the exp algorithm on three fixed model levels.
# The Spatiotemporal Variability of Temperature and Precipitation Over the Upper Indus Basin:
## 2 Materials and Methods
### Model Validation  
We evaluated the WRF temperature and precipitation output with the station meteorological data collected from the Pakistan Meteorological Department (PMD) and Water and Power Development Authority (WAPDA) (details of the stations are given in Table 2). We evaluated the WRF temperature with the available data of ten stations (Table 2) for the time period of 2001 through 2010. Similarly, the stations' data for precipitation were not available for the entire simulation period. Because of the limited availability of station data, we also assessed the WRF precipitation output with the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version 7 gridded precipitation data [42] at a monthly temporal scale. The TRMM 3B42V7 data were available on a 0.25\({}^{\circ}\) by 0.25\({}^{\circ}\) latitude-longitude grid at 3 hourly temporal resolution. TRMM 3B42V7 data were collected from remote sensing and adjusted based on the monthly gauge data. Despite its coarse resolution and other limitations, TRMM 3B42V7 is considered to be one of the reliable gridded precipitation datasets [4; 15]. [PERSON] et al. [4] compared different precipitation datasets with the available stations' data over the Indus Basin and found the TRMM dataset to perform best among the remote sensing datasets. Similarly, [PERSON] et al. [43] evaluated the TRMM Multi-satellite Precipitation Analysis (TMPA) precipitation products (3B42V6, 3B42V7, and 3B42 RT) with gauge stations over the Hunza Basin in Karakoram Mountainous range. They also found 3B42V7 reasonably better than the other two products. Several studies have also evaluated the WRF precipitation with the TRMM dataset (for example, [14; 15]).  
The WRF precipitation output was evaluated by the root mean squared error (_RMSE_), percentage bias, and Pearson correlation coefficient (r), whereas WRF temperature output was evaluated by _RMSE_, mean bias, and Pearson correlation coefficient. The expressions of _RMSE_, percentage bias (_PBIAS_), and mean bias for \(n\) grid points or \(n\) stations are:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(M_{i}-O_{i})^{2}}, \tag{1}\]  
\[PBIAS=\frac{1}{n}\sum_{i=1}^{n}((M_{i}-O_{i})/O_{i})*100, \tag{2}\]  
\[\text{mean bias}=\frac{1}{n}\sum_{i=1}^{n}(M_{i}-O_{i}) \tag{3}\]  
where \(M_{i}\) and \(O_{i}\) represent model simulations and observed data, respectively.