esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 3 Security Protection Schemes
### Secure Data Aggregation Schemes
#### 3.5.2 Secure Data Aggregation Based on Cryptography  
[PERSON] [88] proposed the HSC solution which is a type of stream cipher capable of performing online homomorphic operations on encrypted data without decryption. This can help to protect the integrity and confidentiality of data being transmitted or processed. The new cipher uses modulo addition, and it is suitable for devices with limited CPUs such  
Figure 7: Secure data aggregation in WSNs.  
as those in WSNs. This cryptographic-based aggregation can be used to efficiently calculate statistical values such as the mean, variance, and standard deviation of the data.  
Data aggregation techniques allow information to be aggregated at intermediate nodes before transmitting to the base station. This improves the performance of WSN-IoT networks with high node density by reducing transmission traffic and saving energy. For example, the ESPDA method allows similar data to be grouped and only representative data to be sent, which reduces communication costs and limits data analysis attacks (suitable for WSN-IoT applications in smart agriculture and environmental monitoring where many sensors measure the same type of data). In addition, the data is also protected against eavesdropping and forgery attacks. For example, SIA is an aggregated data authentication solution, suitable for high-precision WSN-IoT applications (medical, hazardous environment monitoring, etc.), while HSC allows aggregation on encrypted data, ensuring end-to-end security without decryption at intermediate nodes. HSC is suitable for systems that require high security (IoT in medical, civil, etc.), against insider attacks. However, data aggregation nodes often become targets of attacks, which leads to unreliable aggregated data. Similarly, legitimate nodes that are compromised and send fake data also distort aggregated data.
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
## 3 Results and Discussion
The correlations of the decimal subjective VA with the \(nMSE\), \(nPSNR\), and \(PSNR\)-\(HVS\) metrics, and with the metrics obtained by the linear nMSE-nPSNR and \(nMSE\)-\(nPSNR\)-\(HVS\) combination, gave \(DW\) values below \(\text{DW}_{(L,A)}\). Such values indicated statistically significant evidence at 95% and the error terms were positively self-correlated. In contrast, all the other used measures or combinations of measures gave \(DW\) values over \(\text{DW}_{(L,A)}\), which indicates that residuals did not positively self-correlate, or the test was inconclusive (for \(SSIM\) and \(nMSE\)-\(FSIM\)). The analysis of models' performance was completed by calculating the WI and \(\overline{QCD}\) indices, which are, respectively defined in (8) and (9), and also presented in Table 4. The higher the WI index, the more accurate the model. Conversely, the lower \(\overline{QCD}\), the less spread the results of a metric, which indicates that the metric was relatively independent of the chosen set of images. Thus, its performance was better. Of the metrics not ruled out by the \(DW\) evaluation, the results showed that the \(nMSE\)-\(FSIM\) metrics combination gave the best results. It can be accepted that residuals were normally distributed, the WI index was higher (higher coefficient of determination), and the index (\(QCD\)) had the second lowest value. It also indicated the smallest estimated error for all the metrics used in this paper. To summarize, the model that best determined subjects' VA from the image processing metrics based on a logistic function was the \(nMSE\)-\(FSIM\) combination:  
\[VA=7.478\bigg{(}\frac{1}{2}-\frac{1}{1+exp[8.858(Q_{L}+3.770)]}\bigg{)}-0.632~{}Q _{L}+1.011, \tag{10}\]  
with \(Q_{L}=-0.944 nMSE-6.714 FSIM\) and an uncertainty of \(\pm 0.14\).
# Research on the Hydrodynamic Performance of a Pentamaran in Calm Water and Regular Waves
## 4 Numerical Modelling
### Validation of the Numerical Method
Figure 10: Comparison of total resistance coefficients of KCS. components with decreasing amplitude along the edge of the wave region. Transverse wave components were seen inside the divergent wave patterns. For the computations in waves, with reference to the work of [PERSON] et al. [50], the wave height \(a\) was 0.123 m, the wavelength \(L\) was 1.15 \(L_{PP}\), the speed \(F_{n}\) = 0.26, and the wave model was the fifth-order Stokes wave. The validation objects were added resistance, heave, and pitch. Since the motion and resistance of the hull in waves are periodic, for the convenience of comparison, Fourier transform was performed on the motion and force of the hull. The added resistance and motion of KCS in waves were compared with the experimental data, as shown in Table 8.  
It can be seen from Table 8 that the simulation results of resistance in waves was relatively close to the experimental data, but the accuracy was lower than that of the calm water condition, especially the error of ship motion, which was slightly larger. The reason may have been that there was strong mutual interference between the ship model wave-making and incoming waves in waves, and the nonlinearity in the simulation was greatly increased, with the amplitude of the motion in the waves being high-order, so the calculation accuracy was greatly challenged. However, on the whole, the simulations were able to reflect the motion characteristics and force of the ship. Figure 13 shows the wave pattern at typical time in regular waves. The wave pattern in regular waves was well captured. When the KCS hull sealed in waves, in addition to the ship traveling waves related to the sailing speed and water depth, another wave related to the hull motion induced by the incident waves was also generated. These two kinds of waves will consume the energy of the hull and superimpose each other, so the energy consumed by the ship when sailing in waves will be higher than that in still water, and the additional energy loss is the so-called added resistance. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **CFD** & **Experiment** & **Error** \\ \hline \(C_{avg}\) & 10.9 & 10.0 & 9.00\% \\ Heave/mm & 49.7 & 55.4 & \(-\)10.28\% \\ Pitch/deg & 2.64 & 2.38 & 10.92\% \\ \hline \hline \end{tabular} \(C_{avg}=\frac{R_{avg}-R_{avg}}{(avg^{2})/L_{PP}}\); Error = (CFD \(-\) EXP)/EXP.  
\end{table} 
Table 8: Comparison of added resistance and motion of KCS in waves with experimental data. Figure 12: The wave pattern at \(F_{n}\) = 0.26 [49]. In general, the simulation results of KCS were in good agreement with the experimental results. Through the numerical simulation of KCS in calm water and regular waves, the reliability of the numerical method in this paper was verified.
## 6 A Summary of Research Initiatives on Evaluating Different Pushover Analysis Techniques
### A Comparison of Nonlinear Static Pushover Analysis Methods and Nonlinear Dynamic Analysis Methods
This method was found to outperform the FEMA load distribution and the standard MPA procedure in terms of accuracy [61]. When juxtaposed with the nonlinear response time history analysis (NLRHA), [PERSON] and [PERSON]'s research indicated that the spectrum-based pushover analysis (SPA) method was adept at predicting seismic demands, especially in structures where the seismic demand and subsequent damage were pronounced [67]. [PERSON] and [PERSON] further underscored that the MPA procedure was more precise in estimating the seismic demands of buildings compared to the FEMA356 procedure [81]. Notably, [PERSON] and [PERSON] [54] have been instrumental in evaluating the efficacy of pushover analysis methodologies, emphasizing the importance of MPA in the seismic performance-based design of structures [54]. Exploring the realm of upper-bound pushover analysis, [PERSON] and colleagues introduced an adaptive upper-bound pushover analysis, tailored for the seismic performance assessment of tall buildings. This method considers both the effects of higher modes and the progressive changes in the dynamic characteristics of structures during inelastic response. The results of this approach were found to be closer to those obtained by NTHA [74]. An enhanced version of the upper-bound pushover procedure was proposed for the seismic assessment of high-rise moment-resisting steel frames, showcasing better results and alignment with outcomes from NTHA [63]. To conclude, the realm of seismic engineering has witnessed rigorous exploration and comparison between nonlinear static pushover analysis with dynamic methodologies. Pioneering works by researchers such as [PERSON] et al. [64], [PERSON] et al., [PERSON] et al. [82], and the notable contributions of [PERSON] and [PERSON] [54] have underscored the significance of advanced pushover techniques. These methodologies, especially the modal pushover analysis and its variants, have emerged as robust tools, often aligning closely with the results from nonlinear time history analysis, thereby enhancing our understanding and prediction of seismic demands in structures.
## 3 Results and Discussion
Figure 6: Comparison of the profile of **(a)** acoustic velocity and **(b)** hematoxylin and eosin (H&E) staining. A (upper left) “Dermis” and C (upper middle) “Hypodermis” show lower speed of sound values than B (middle) “Muscle”. “ Epidermis” (D) and “Back skin layer follicle” (E) have similar high speed-of-sound values. “Malignant melanoma” (F) clearly displays relatively higher scanning acoustic microscopy (SAM) than the surrounding normal tissue. response of the transducer. Based on Equations (5) and (6), the effects of the random noise on phases cause the change of acoustic velocity could be estimated as:  
\[\Delta c=\frac{c_{0}}{3\pi}\Delta\phi, \tag{9}\]  
where \(\Delta\phi\) is the phase change by random noise causes the change in acoustic velocity \(\Delta c\). By Equation (9) and the data in the Table S1, the random noise has minor effects on the result compared with the fluctuations from the scanning lines. Although tumor discrimination in this study was testified by a high percentage of matching between speed-of-sound and H&E staining images, higher resolution of the transducer is needed for smaller structure studies. Tissue characterization by SAM could potentially be utilized in practice, where it could work alternatively with conventional histology methods. In order to deploy it completely in the field, where a variety of factors could affect the results, SAM for tissue characterization need to be more robust.
## 4 Results
### System Accuracy
#### 4.3.3 Total System Accuracy  
Taking the results from the Tables 7 and 8, and also considering the fact that the accuracy of the system composed of two systems connected in series is the product of multiplication of the subsystem accuracies, the total system accuracy is shown in the Table 10. Considering the fact that both accuracy values are over 79%, this system has good-quality predictions.
## 3 The Model
### Decisions of Potential Customers  
Potential customers are those who are not in the service provider's network. In each period, a potential customer decides whether to join the service provider's network first and then chooses a data plan from all open data plans. As illustrated in Figure 3, the decision process is investigated in two stages. In Stage 1, the probability that a potential customer joins the network, denoted by \(\lambda\), reflects the potential customer's willingness to join the network. This willingness is influenced by the service provider's reputation and advertising rather than the dynamic plan control. To avoid unnecessary complexity, we let \(\lambda\) be exogenous and constant over periods. In Stage 2, we denote \(p_{i,t}\) as the probability that a potential customer subscribes to data plan \(i\) in period \(t\). By definition, we have \(\sum_{i=1}^{m}p_{i,t}=1\).  
In Stage 2, a potential customer chooses a data plan based on his/her forecast of data demand. Let \(\hat{D}\) be the potential customer's forecast of data demand in a single period. The potential customers' demand forecasts are heterogeneous and distributed independently and identically with a cumulative distribution function \(F(\hat{D})\), which is common knowledge to both the service provider and customers. For a potential customer with demand forecast \(\hat{D}\), we denote \(r_{i}(\hat{D})\) as the cost for using data service at full speed in a single period if she/he subscribes to data plan \(i\). Then, we have:  
\[r_{i}(\hat{D})=b_{i}+\left\lceil\frac{(\hat{D}-v_{i})^{+}}{v_{i}^{s}}\right \rceil\cdot b_{i}^{s}.\]  
When deciding which data plan to choose in Stage 2, the potential customers are concerned about both speed and cost. They choose the data plan that provides full-speed data service with minimal cost in all open plans. Therefore, \(p_{i,t}\) can be formulated as:  
\[p_{i,t}=\Pr\Biggl{(}C_{i,t}=1,r_{i}(\hat{D})\leq\min_{j\in\hat{C}_{i,t}=1}r_{ j}(\hat{D})\Biggr{)}.\]
# Structural Optimization Design of Spaceborne Microwave Probe Antenna
## 3 Topology Optimization of the Planar Antenna Reinforcement Structure
### Topology Optimization Process and Reconstruction of Reinforcement Layout  
The mathematical model in Section 1 is used for optimization, and after 14 iterations, the first-order frequency variation of the planar antenna is shown in Figure 5. As the number of iterations increases, the frequency increases continuously. It tends to level off after the sixth time with a maximum frequency of 265 Hz.  
Figure 4: Finite element solid model for topology optimization.  
Figure 5: First-order frequency variation of the planar antenna.  
After the topology optimization calculation, the obtained results of the planar antenna reinforcement material layout are shown in Figure 6. The topology optimization results show that the reinforcement layout in the upper and lower regions of the rotation axis is clearly visible. The red area of the reinforcement has a cell density of 1, which is the area to be retained, and the blue area has a cell density of 0, which is the non-retained area. The topology optimization results show that the reinforcements are arranged in a \"palm\" shape along the center. The part of the planar antenna above the shaft is similar to the structure of the cantilever beam, which is farther away from the shaft, and the distribution of the reinforcement is extended from the shaft to the upper part, which is more in line with the structural rigidity requirements.  
Based on the topology optimization results, high-density areas are identified, and the layout of the new reinforcement is created, as shown in Figure 7.
# The Effects of High-Performance Cloud System for Network Function Virtualization
###### Abstract  
Since ETSI introduced the architectural framework of network function virtualization (NFV), telecom operators have paid more attention to the synergy of NFV and cloud computing. With the integration of the NFV cloud platform, telecom operators decouple network functions from the dedicated hardware and run virtualized network functions (VNFs) on the cloud. However, virtualization degrades the performance of VNF, resulting in violating the performance requirements of the telecom industry. Most of the existing works were not conducted in a cloud computing environment, and fewer studies focused on the usage of enhanced platform awareness (EPA) features. Furthermore, few works analyze the performance of the service function chain on a practical cloud. This paper facilitates the OpenStack cloud with different EPA features to investigate the performance effects of VNFs on the cloud. A comprehensive test framework is proposed to evaluate the verification of functionality, performance, and application testing. Empirical results show that the cloud system under test fulfills the requirements of service level agreement in Rally Sanity testcases. The throughput of OVS-DPDK is up to 8.2 times as high as that of OVS in the performance test. Meanwhile, the hardware-assisted solution, SR-IOV, achieves the throughput at near the line rate in the end-to-end scenario. For the application test, the successful call rate for the vlMS service is improved by up to 14% while applying the EPA features on the cloud.  
enhanced platform awareness; test framework; cloud computing; network function virtualization; performance evaluation
## 1 Introduction
### A Case Study at Eidsborg and Heddal  
In the early 2000s, the Norwegian Institute for Cultural Heritage Research (NIKU) assumed responsibility for preserving the polychrome naive of Heddal and Eidsborg (see red circles in Figure 1). These two churches (Figure 2) are thought to have been constructed in the 13 th century [33; 34] and have undergone multiple structural and decorative changes. Consequently, the churches' current interior decor was rendered centuries after the churches were constructed. Whereas the naive of Eidsborg was rendered in 1604 and 1640/49 [7], Heddal's naive is comprised of 17 th century distemper decor rendered on top of a medieval design [6; 8]. Additionally, the naves of both churches were retouched in the 20 th century [6; 7; 8]; however, neither the retouching material nor the specific areas were documented. Additionally, in the following century, as part of the Stave Church Preservation Programme (2002-2016), specific areas of the polychrome wooden walls of Eidsborg and Heddal were consolidated using sturgeon glue. However, in 2013, flaking was observed in some of these consolidated areas [35]. This was surprising, as only recently have these treatments been unsuccessful; since the early 1990s, sturgeon glue has been the primary consolidant for flaking distemper paint within stave churches [1]. On September 27 th, 2018, as part of the Sustainable Management of heritage Buildings in a Long-term perspective (SyMBoL) project, a sampling campaign was performed. This three-year project (2018-2021), coordinated by the Norwegian University of Science and Technology (NTNU), has aimed to provide evidence-based results to better inform strategies for the safe display of polychrome wood in stave churches. The restoration and conservation treatments mentioned in this subsection have been compiled in Table 1, whereas additional information regarding structural and interior changes can be found in Supplementary Material (Table S3).  
The primary objective of the present case study is to identify the pigments and binder(s) used in the polychrome wall paintings within Eidsborg and Heddal. In addition, the relation between the conservation condition and chemical modifications of the specimens was investigated. To achieve this goal, historic paint samples were subjected to chemical and optical analysis to determine the composition, with a secondary aim of identifying similarities and differences between the two stave churches. XRD, SEM, and FT-IR were used to investigate the chemical properties of pigments found within the decorative wall paintings, whereas FT-IR, GC-MS, and ELISA provided information about the painting's binder and wooden wall planks, as well as the presence of other organic compounds (e.g., pine resin or oil). Lastly, due to limited time and resources, only a subset of the collected samples was analyzed using all the above-mentioned techniques.
## 5 Discussion and Conclusions
### Main Causes of the Flow Ripple  
As observed in Figure 12, by applying Equation (25), the flow ripple of the kinematic flow (\(Q_{\text{kin}}\)) yielded 6.8%, which is believed to be caused by the kinematic relations between the meshing gears. As stated above, the trapped flow (\(q_{\text{trap}}\)) was forced into the discharge chamber through the relief groove on the floating plate, thus leading to an increase in the outlet flow. Consequently, the with-trapped outlet flowrate (\(Q_{\text{wt}}\)) yielded a greater mean outlet flowrate than that of \(Q_{\text{kin}}\). The flow ripple of \(Q_{\text{wt}}\) in Figure 12 yielded 2.7%, an approximately 60% decrease than that of \(Q_{\text{kin}}\), indicating that the trapped flow plays an important role in decreasing the flow ripples. Regarding the internal leakage (\(q_{\text{leak}}\)) and the triangular flow (\(q_{\text{tri}}\)), it was clear that they led to a decrease in the outlet flow since they are the flow out of the discharge chamber. The flow ripple of \(Q_{\text{out}}\) in Figure 12 yielded 4%, an approximate decrease of 41% than that of \(Q_{\text{kin}}\), but a 60% increase than that of \(Q_{\text{wt}}\), indicating that the internal leakage (\(q_{\text{leak}}\)) and the triangular flow (\(q_{\text{tri}}\)) led to an increase in the flow ripples.  
Hence, it can be seen that the trapped flow led to a decrease in the flow ripple, while the triangular flow and the internal leakage of the pump led to an increase in the flow ripple; which was also consistent with the cases depicted in Figure 16.
## 5 Discussion  
The proposed method offers a representation tailored for texture-less gray-scale surfaces, with enhanced robustness in detecting diffuser plates when combined with SBM  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **Platform** & **Training Images** & **Training Time** & **Predication Time** \\ \hline Propose & Core-i7 & 1 & 1.5 s & 0.6 s \\ SBM & Core-i7 & 1 & \textless{}0.1 s & \textless{}0.1 s \\ NCC & Core-i7 & 1 & \textless{}0.1 s & \textless{}0.1 s \\ YOLOv5s & Core-i7 + GTX1650 & 1200 & 4.5 h (50 epoch) & 0.04 s (GPU)/0.35 s (CPU) \\ YOLOv5x & Core-i7 + GTX1650 & 1200 & 19 h (50 epoch) & 0.41 s (GPU)/1.4 s (CPU) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specification and efficiency of test methods.  
Figure 14: Cases of low accuracy of YOLOv5s. The red bounding boxes were the outputs predicted by YOLOv5s.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Conf (\%)** & **Proposed (pxls)** & **SSM (pxls)** & **NCC (pxls)** & **YOLOv5s (pxls)** & **YOLOv5x (pxls)** \\ \hline
30 & 8.69 & 22.26 & 26.74 & 18.61 & 8.64 \\
50 & 8.26 & 22.26 & 26.74 & 17.22 & 7.72 \\
70 & 5.01 & 22.26 & 22.56 & 11.66 & 8.25 \\
90 & 0.85 & 19.25 & 9.04 & 11.38 & 4.60 \\ Average & 5.70 & 21.5 & 21.27 & 14.71 & 7.30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of localizing accuracy.  
metrics. Nevertheless, its reliance solely on gray-scale values for surface feature extraction may not enable it to adequately capture the intricate textures of the target. In a certain degree, excessive brightness variations on the surface can undermine the method's robustness. Additionally, this approach hinges on a singular template image, rather than learning representations from a diverse set of images. If there is a substantial discrepancy in gray-scale variance between the training template and the test images, the detection performance may still suffer to a certain extent.