esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Dual-Hop DF Multi-Relay FSO System with Adaptive Modulation
## 7 Conclusions  
In this work, the SE of dual-hop DF single- and multi-relay systems with AM was obtained. Moreover, other performance metrics, in terms of modulation level selection probability and OP, were derived. ORS and PRS schemes were introduced, where the ORS scheme requires the CSI of all hops and the PRS scheme requires only the CSI of the source-to-relay or relay-to-destination link. The system's performance was studied under balanced and unbalanced cases of dual-hop links. The results showed that in addition to other metrics, modulation level selection probability offers more ability to track and understand the behavior of the AM systems. Additionally, it was noticed that the ORS scheme enhances the SE to a greater extent than the PRS schemes, especially when the number of relays and the modulation levels are higher. We also noticed that although PRS schemes reduce the complexity of the ORS scheme, their performance is limited by the quality of the other link.  
The outcomes of this paper are useful as the analytical expressions can be utilized for SE optimization, facilitating the efficient utilization of available spectrum. Additionally, they can provide guidance to system designers regarding the choice of the number of modulation levels and the number of relays required to maintain a reliable link. Furthermore, this analysis can be extended to other directions, such as satellite communication and underwater wireless optical communication systems, enabling an investigation into the effects of using the proposed techniques on the performance of these systems.  
Conceptualization, W.A.A. and E.S.A.; methodology, W.A.A. and E.S.A.; software, W.A.A.; validation, E.S.A.; formal analysis, W.A.A.; investigation, W.A.A. and E.S.A.; writing--original draft preparation, W.A.A.; writing--review and editing, E.S.A.; supervision, E.S.A.; funding acquisition, E.S.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Researchers Supporting Program under project number (RSPD2023R842), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The authors would like to acknowledge King Saud University for supporting this research under the Researchers Supporting Project number (RSPD2023R842).  
The authors declare
# A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas
## 5 Proposed Future Security Mechanisms
### Artificial Intelligence for 6G  
While 3 GPP has not implemented any AI requirements into the current slate of 5G standard releases, advancements in connectivity nonetheless present significant opportunities for improving performance and security through AI implementations in 5G but more importantly present extensive opportunities for AI to become a key consideration in the standard development for 6G. Already, numerous options are taking shape for use cases of AI in the 5G RAN, such as for resource management, energy efficiency, load balancing, and mobility operations [135]. Similarly, there are extensive efforts and offerings related to 5G cybersecurity aspects utilizing AI/ML integration, network provisioning and orchestration, and more.  
Fulfilling requirements of 6G for applications such as Extended Reality (XR), connected robotics, autonomous systems, and eHealth through body area networks (BANs) requires a system reliability that meets or exceeds 99.99999% reliability (also known as \"Seven Nine\"), and a significant boost to throughput far beyond that of 5G, up to 1 Tb/s [136]. To enable these applications, AI is anticipated to assist with SDNs and resource allocation for both core and edge applications [137]. Some applications for which AI has been proposed are network slicing and RIS beamforming, which have been outlined in Section 3, as well as additional areas described below.
# Integrating Visual Cryptography for Efficient and Secure Image Sharing on Social Networks
## 1 Introduction
VC can be used in text and image encryption, enabling decryption by the human visual system (HVS) without computer assistance. This contrasts with image steganography, emphasizing watermark decoding with optimized fidelity and no security constraints. VC still faces challenges such as size expansion and decreased quality of decrypted images [8]. The expansion will enlarge the carrier load, leading to excessive resource consumption for information protection. Image sharing on social networks is characterized by large sizes, high volumes, and slow transmission speeds. While data encryption is valuable, it often lacks compression capabilities [9]. However, this issue becomes significant with larger images since encryption often involves multiple images or shares. If unauthorized users notice these data due to noise in the ciphertext, they may attempt to decrypt or analyze these data, which would pose a threat to data protection. To address the above issues, this paper presents a secure and energy-efficient framework with VC to enhance privacy and security for communication in energy-sensitive environments. Image sharing faces substantial challenges in the IoT era with the proliferation of lightweight, miniaturized devices in open and public networks. These devices cannot support complex cryptographic algorithms due to strict power and weight constraints,increasing their vulnerability to security risks and data leaks. The primary contributions of this paper are as follows:  
* The introduction of a secret-sharing-based data forwarding and sharing model, which facilitates the real-time transmission of vast amounts of sensitive data from IoT terminals. * The proposal of a downsampling-based non-expansive VCS to reduce encrypted image size and preserve decryption quality. * The conducting of comprehensive experiments on classic test images demonstrates that our proposed framework achieves comparable recognition performance on encrypted datasets to that on plain datasets. The paper is structured as follows: Section 2 reviews related works, Section 3 describes the proposed methods for encrypted image generation and decrypted image enhancement, and Section 4 presents the experimental results, demonstrating the effectiveness, efficiency, and security of the proposed framework for secure image transmission. Finally, Section 5 concludes with future work.
# Research on a Piezoelectric Pump with Flexible Valves
## 4 Experimental Results and Discussion
### Working-State Analysis  
The performance of the prototype was tested at different driven voltages, 100, 80, and 40 V at 25 Hz. The working-state of the prototype was filmed with a high-speed camera (5 KF20, FuGuang AgileDevice, Hefei, China), as shown in Figure 13. The deformations of one flexible valve in a cycle are shown in Figure 14. It can be seen that the shape of the  
Figure 11: Flow rate comparison of the same type of pump.  
Figure 12: Output pressure comparison of the same type of pump.  
Figure 10: The output pressure and flow rate curves of the piezoelectric pump, when the voltage was 100 V.  
flexible valve changes with the variation of pressure during the suction and exhaustion strokes of the prototype.  
When the voltage was 100 V, the deformation of the leaflets was strong, there was no clearance between them sometimes in a cycle, and the flow channel was completely blocked. It was a pump with completely closed valves. When the voltage was 80 V, although the flexible valve was deformed, there was always a clearance between the two leaflets. It was a pump with normally open valves. While when the voltage was 40 V, the maximum displacement of the flexible valve was 0.04 mm, which can be ignored because it was only about 5% of the initial clearance of the valve. The flexible valves maintain the original  
Figure 14: The observed clearance between the two leaflets in one period.  
Figure 13: Observation test platform.  
clearance and its structure shape is similar to the valveless PZT pump with cone-shaped tubes [23]. Thus, it was a valveless pump in this condition.  
If the PZT pump can be classified into a valveless PZT pump and a valved PZT pump according to if there is a moment in a period when the flow channel is completely blocked, then the pump with the moment is the valved PZT pump and the pump without the moment is the valveless PZT pump [24]. Therefore, the PPFV can be treated as a valved PZT pump when the voltage is 100 V, while it is a valveless PZT pump when the voltages are 80 and 40 V. This proves that the PPFV can achieve a transition between the valveless state and the valved state by adjusting the voltage, and can realize the free conversion of a large flow rate, high pressure difference and a small flow rate, low pressure difference.
## 5 Discussion and Practical Tests
### Portfolio Construction  
This section conducts our second practical test: portfolio construction. Again, portfolio construction relies on volatilities and correlations. A direct question we ask is if our proposed regime-switching approach may enhance portfolio construction effectiveness.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Observation Percentage** \\ \hline EUA = LV and WTI = LV & 64.54\% \\ EUA = HV and WTI = LV & 19.98\% \\ EUA = LV and WTI = HV & 11.00\% \\ EUA = HV and WTI = HV & 4.50\% \\ \hline Total & 100\% \\ \hline \hline \end{tabular} Notes: One key feature of the bivariate SWARCH model employed in this study is to provide the estimated probabilities of a specific state for each time point. We use these estimated probabilities and a maximum value criterion to define the volatility state. For example, if the HV-HV state’s estimated probability is higher than the other three states, we identify this time point as an HV-HV state.  
\end{table}
Table 6: Observation percentage of various volatility states.  
To answer this question, we run a test and detail the test as follows: First, we employ a minimum variance portfolio construction strategy [46; 47]. In this strategy, the weight given to each position in the EUA-WTI portfolio is calculated as follows:  
\[w_{t}^{EUA}=[h_{t}^{WTI}-\rho_{t}(h_{t}^{EUA}.h_{t}^{WTI})^{1/2}]/[h_{t}^{EUA}+ h_{t}^{WTI}-2\cdot\rho_{t}(h_{t}^{EUA}.h_{t}^{WTI})^{1/2}] \tag{16}\]  
\[w_{t}^{WTI}=1-w_{t}^{EUA} \tag{17}\]  
where \(w_{t}^{EUA}\) and \(w_{t}^{WTI}\) represent the weight of EUA (carbon) and WTI (energy) asset position, respectively. \(h_{t}^{EUA}\) and \(h_{t}^{WTI}\) denote the conditional variances of EUA and WTI, respectively, and \(\rho_{t}\) is their correlation.  
Second, we construct the portfolio and calculate the portfolio return at time \(t\) (\(r_{t}^{POT}\)):  
\[r_{t}^{POT}=w_{t}^{EUA}.r_{t}^{EUA}+w_{t}^{WTI}.r_{t}^{WTI} \tag{18}\]  
Finally, we calculate the portfolio's return mean and volatility over the testing period. Table 8 presents the results for each model. To test significance, the bivariate GARCH-CCC model is used as a benchmark to calculate the t-statistic for the difference between alternative models. As shown in Table 8, our proposed bivariate SWARCH model equipped with state-dependent correlations constructs a carbon-energy portfolio with a higher mean return and lower return volatility than the bivariate GARCH-CCC and -DCC models. Notably, the difference in the portfolio's mean return is insignificant (\(p\)-value \(>\) 10%), whereas the difference in the portfolio's return volatility is significant (\(p\)-value \(<\) 1%).
## 2 State of the Art
### Additional Features of Industry 5.0
#### 2.6.2 Predictive Maintenance  
The industries are facing many challenges as the global economy moves toward globalization. As a result, manufacturing units are being forced to adopt new technologies such as predictive maintenance (PdM). Manufacturers began utilizing evolving technologies, such as CPS approaches and advanced analytical methods, to improve productivity and efficiency. The ability of industry to uncover and assess uncertainties in order to estimate manufacturing capability and availability is known as transparency [59]. Essentially, most manufacturing schemes assume that equipment will be available at all times. However, in practice, this never occurs in the real world. As a result, manufacturing units should transition to predictive maintenance in order to gain transparency. This transformation necessitates the use of cutting-edge prediction tools in which data are systematically transformed into information and uncertainties are defined, allowing the workforce to make informed decisions. With the use of smart machines and smart sensor networks, the implementation of IoT provides the basic framework for predictive maintenance. The main goal of predictive maintenance is to enable self-awareness in systems and machines. The smart computational agent, which includes smart software to provide predictive modeling functionality, is the key technology for predictive maintenance [60].
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
## 8 Conclusions  
A damage estimation method based on coefficient of CWT of the normalized damage-scattered signals is proposed in this research. The possibilities of damage existence are defined by the envelopes of coefficient of CWT without the need of any additional parameter, which makes it simple and easy to implement. In order to avoid the mismatch between actual damage location and maximum possibility peak in CWT envelopes, a time compensation equal to the half period of the original excitation signal is required in all the CWT signals. The proposed method can be used to estimate the multiple damage scenario with difference signals and also with damaged signals without the baseline. The pulse-echo excitation configuration with the damage index based on the conjunctive fusion scheme provides the best estimation with fewer excitations. Due to being baseline-free and accurate with fewer excitations compared to existing methods, the propose method is computationally inexpensive for on-line monitoring, which can reduce the evaluation cost and time during in situ SHM. Many large modern structures have PZT transducers already embedded in them for a continuous SHM during their service life. In the proposed method, the PZT transducers can be arranged as a network of square detection cells on a large structure, and multiple damage inside each of the cells can be estimated separately. Each of the actuator-sensor pairs should be in a close vicinity, and the excitation frequency should in the lower range of the dispersion curves. There may also be additional fluctuations in the dynamic response signals caused by the geometric discontinuities. However, the location of the geometric discontinuities, such as a stiffener or plate edges, are already known,therefore it is easy to identify the fluctuations caused by the scattering from those discontinuities in the time domain. The proposed method can be applied for the estimation of corrosion damage in large metallic plate-like structures.  
Conceptualization, M.S.H., Z.L., and K.Z.; formal analysis, M.S.H.; funding acquisition, Z.L.; investigation, M.S.H., Z.L., and K.Z.; methodology, M.S.H., Z.L., and K.Z.; project administration, Z.L.; resources, Z.L.; software, M.S.H.; supervision, Z.L.; validation, M.S.H., Z.L., and K.Z.; writing--original draft, M.S.H.; writing--review and editing, M.S.H., Z.L., and K.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China under grant nos. 11672004 and 11521202.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Comparison Between the Training and Synthetic Data
#### 3.2.1 Case Study 1: Generation of Synthetic Power Consumption Data for a Domestic Consumer
It can be visually observed that the data generated with fewer states was more scattered compared to synthetic data with more states. This can be explained by the wider width of each state and the fact that once a state is generated, the exact value is synthesized using a uniform distribution. Therefore, when the evaluation metrics are similar, the scenario with the higher number of states should be selected.
## 5 Data Imbalance
### Data Imbalance Handling Techniques Future Research Directions  
#### 5.5.1 Adaptive Sampling Techniques  
Future research should focus on combining hybrid deep-learning models with adaptive sampling techniques [119]. Dynamically adjusting the sampling rate based on the dataset structure can improve the model's detection of minority categories, reduce overfitting, and enhance the performance of IDS in dynamic network environments [120; 121; 122; 123].
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 3 The Path Planning Algorithm Based on PPR
### Dual-Tree Fusion Strategy  
The key aspect of the dual-tree fusion strategy is the determination of the node pair (\(x_{connect}\), \(p_{connect}\)) connecting the path exploring tree \(T_{exploring}\) and the prior path tree \(T_{prior}\) after the exploring tree is expanded into the prior space.  
The main steps of the dual-tree fusion strategy are as follows:  
Step 1. The prior nodes on the prior path in the circle with \(x_{new}\) as the centre and \(S\) as the radius are selected to create a nearest prior node set \(P_{near}\), as shown in Figure 7, where \(P_{near}=\{p_{2},p_{3}\}\).  
Figure 7: Discovery of the nearest prior nodes.  
Step 2. If the costs of nodes in \(P_{narr}\) are different, the node with minimum cost is selected as the connection node \(p_{connect}\) on the prior path; otherwise, the nearest node is selected. In Figure 7, \(p_{2}\) is selected as \(p_{connect}\), as shown in Figure 8.  
Step 3. After the prior connection node \(p_{connect}\) is obtained, the exploring nodes on the exploring tree in the circle with \(p_{connect}\) as the center and \(S\) as the radius are selected to create a nearest exploring node set \(X_{near}\), as shown in Figure 9, where \(X_{narr}=\{x_{new},x_{1},x_{2}\}\).  
Step 4. If the costs of nodes in \(X_{narr}\) are different, the node with minimum cost is selected as the connection node \(x_{connect}\) on the exploring tree; otherwise, the nearest node is selected. In Figure 9, \(x_{1}\) is selected as \(x_{connect}\), as shown in Figure 10.
# A Vulnerability Scanning Method for Web Services in Embedded Firmware
## 4 Design
### Hazardous Function Filtering  
In vulnerability analysis, functions that serve as execution endpoints, termed \"sink\" functions, often use hazardous functions. However, not all instances of hazardous function calls are necessarily harmful. Our analysis focuses on risky functions associated with three types of taint-based vulnerabilities: buffer overflows, command injections, and format string vulnerabilities. We find that taint-based vulnerabilities arise when specific argument positions within hazardous functions can be controlled by the user. Therefore, we posit that a vulnerability only occurs when these dangerous parameters of hazardous functions are user-controllable. In many practical scenarios, the dangerous parameters of such functions are often constants or parameter types that do not lead to overflows. For example, in the case of the \"strcpy\" function associated with buffer overflows, the critical parameter (second argument) could be a number or a constant string. Even if not constant, it might be a string whose length has been evaluated by the \"strlen\" function.  
To diminish the number of sink points in the vulnerability analysis, we can devise rules that filter based on the hazardous parameter positions of dangerous functions. In composing these rules, we craft them with coarse granularity to avoid generating false negatives. For instance, with \"strcpy\" and similar functions, a rule could be \"the second parameter is neither a constant nor derived from the'strlen' function.\" For \"strncpy\" and analogous functions, the rule could be \"the second parameter is neither a constant nor sourced from'strlen', and the third parameter is either not a constant or is a constant of significant size.\" Regarding \"sprintf\" and similar format input functions, we examine whether the first parameter is a constant string. If it is not, there is potential for format string vulnerabilities; if it is a constant string, we search for \"%s\" within the string and assess whether its corresponding parameters meet the criteria of being non-constant and not derived from \"strlen\", concurrently tracking the parameters that could potentially induce vulnerabilities. We have categorized common dangerous functions and developed filtering rules tailored to their parameter characteristics. Table 2 showcases a sample of the rules we have developed.
# Cube of Space Sampling for 3D Model Retrieval
## 2 Literature Review
### Normalization
* Translation: in general, 3D modeling software (such as 3 ds Max, Maya, etc.) uses different settings or different coordinate systems [21]. However, different coordinate systems are equal to different \"spaces\". Sometimes, a creator modeling 3D models does not place them in the center of the coordinate systems; that is, the locations of 3D models are not aligned in the coordinate systems. In addition, when these 3D models are created from different coordinate systems, the center of each model is not the same. This will cause more difficulties and biases in the process of re-sampling.
* Scale: similarly, 3D modeling software may use different settings or different scales to construct the 3D modeling space [22]. As a result, different modeling software may produce the models with different scales. In practice, it is common that in a 3D model dataset each 3D model always has different sizes or scales. This usually causes some problems in re-sampling and errors of comparison.
* Rotation: since each model may not have the same angle when placed (e.g., upright, flat, etc.), several difficulties in comparisons can arise (e.g., reversed, tilted, etc.). To this end, 3D models must be appropriately adjusted. In the literature, some methods, such as PCA and CPCA, have been applied to solve such problems [21].
## 4 Applications of Intelligent Acoustic Monitoring
### Smart Cities and Urban Monitoring  
This subsection describes monitoring urban noise, street traffic, user positions, or the presence of emergency vehicles in the context of indoor positioning [108], urban sound classification [109], traffic monitoring with DAS [110; 111; 112], noise monitoring with 5G/IoT [113], emergency vehicle signal detection [114; 115], footstep detection in homes [116], and crowd-sensing and acoustic zone classification [117; 118].
## 5 Results and Discussion
### RQ1: What Cyber-Attacks in the MSCN Have Been Reported in the Academic Literature?
#### 5.1.2 Historical Evidence of Cyber-Attacks in the MSCN
Moreover, the sensitivity surrounding incidents, such as spear phishing attacks in ports, contributes to this underreporting, impacting confidentiality and international economic relations [50]. Figure 5: Historical evidence of cyber-attacks reported by year. Figure 6: Countries where the reported cyber-attacks occurred.