esa-sceva/satcom-chunk-collection huggingface dataset:
# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 3 Experiments
### Localization of Audio Copy-Move Forgery
#### 3.3.2 Detection Performance of Different Methods  
This experiment aimed to assess the detection performance of the localization model for audio copy-move forgery. The localization model was utilized to detect 3000 original audio and 3000 forged audio files without post-processing attacks. Table 4 compares the performance of the localization model with four existing detection methods, namely DFT-PCC [6], C4.5-Tree [7], HIS-MSE-ER [9], and PS-PCC [11], in terms of accuracy, precision, recall, and F1-score.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(k\) = **32** & \(k\) = 64** & \(k\) = 96** & \(k\) = 128 \\ \hline Accuracy & 91.2 & 92.4 & 96.5 & 95.5 \\ Precision & 92.6 & 95.2 & 97.2 & 96.2 \\ Recall & 90.3 & 94.2 & 95.1 & 94.1 \\ F1-score & 91.4 & 93.5 & 96.1 & 95.1 \\ \hline \hline \end{tabThe results from Table 4 indicate that compared to traditional methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0% to 6.8%. There is also a noticeable improvement in precision, recall, and F1-score metrics. Existing methods rely on manual or heuristic thresholds to determine the similarity between audio segments, which results in limited performance. In contrast, the localization model utilizes neural networks to automatically decide the similarity between deep features of Mel-spectrograms, enabling better forgery detection and visualization of forged segments.
###### Abstract  
The properties and behavior of soils depend on many factors. The interaction of individual factors is difficult to determine by traditional statistical methods due to their interdependence. The paper presents a procedure of creating an artificial neural network (ANN) model to determine the value of the damping ratio (D) of clay soils. The main purpose of this paper is to compare the appropriateness of ANN model application with empirical formulas described in the literature. The ANN model was developed using a series of laboratory tests of the damping ratio performed in the Resonance Column. Predicted values of the damping ratio of clay soils obtained from the ANN model are characterized by high convergence (coefficient of determination R\({}^{2}\) = 0.976). In comparison with other published empirical formulas, the ANN model showed an improvement in the prediction accuracy. What is more, ANN models proved to be more flexible compared to formulas and relationships with a predetermined structure, and they were well suited to modeling the complex behavior of most geotechnical engineering materials, which, by their very nature, exhibit extreme variability. In conclusion, ANNs have the potential to predict the damping ratio (D) of clay soils and can do much better than traditional statistical techniques.  
artificial neural networks (ANNs); damping ratio; cohesive soil; resonant column Article
## 1 Introduction
It outlines the inputs derived from optimized asset management and risk management frameworks, detailing the design, development, and outline of the RIACT process. The subsequent sections discuss our case study on earthquake hazard assessment in Portugal, including our methodology, data collection, analysis, and detailed findings. This analysis encompasses identifying disaster risk hotspots, exposure areas, vulnerable regions, and resilient communities. Additionally, it highlights comprehensive treatment plans for enhancing earthquake resilience, emphasizing monitoring, risk review, communication, and consultation strategies. The paper concludes with a discussion of our study's findings, future implications, limitations, and the potential future applications that could be derived from the study. The following diagram (Figure 1) encapsulates the comprehensive research framework developed in our study. This visual representation delineates the interconnected phases and core components of the Risk-Informed Asset-Centric (RIACT) process when applied to urban resilience enhancement against earthquake risks. At the heart of the diagram lies the RIACT process, surrounded by the primary stages that contribute to its operationalization, including scope definition, risk identification, analysis, evaluation, and treatment. This framework also integrates stakeholder engagement and strategic reporting, ensuring that the process is grounded in practical implementation and knowledge dissemination. Our case study of earthquake hazard assessment in Portugal, as well as the employment of the entropy weighting algorithm, serve as pivotal examples of this framework's application in a real-world context. The research framework diagram presented above serves as a blueprint for methodically advancing urban resilience. It systematically bridges theoretical concepts with empirical research, highlighting the nature of the RIACT process, which incorporates continuous monitoring and review, alongside effective communication and strategic adaptation. The diagram reflects the synthesis of data collection and integration, risk assessment, and the evaluation of disaster risk assessment across various dimensions. It underscores the necessity of robust treatment strategies and actions to enhance resilience within communities, positioning the framework as a strategic tool for urban planners, policymakers, and researchers dedicated to mitigating the impact of seismic events and fostering resilient urban environments.
## 4 Experimental Results  
Having conducted a thorough analysis of the impact of biases in the different mounting parameters of a UAV-based LiDAR system to devise an optimal flight and target configuration and proposed a calibration strategy, we now conduct several experiments to validate the feasibility of the proposed strategy and quality of calibration, followed by an evaluation of the devised optimal configuration based on the standard deviation and correlation matrix for the mounting parameters for various test cases. For the UAV system consisting of a Velodyne VLP-16 Puck Hi-Res LiDAR unit and an APX-15 GNSS/INS unit, we use the LiDAR Error Propagation Calculator developed by [PERSON] et al. (2006) [PERSON] et al. (2006) to compute the expected accuracy of point positioning for this system. The calculator suggests an accuracy of 5-6 cm for a flying height of 15-25 m. In this study, the UAV-based LiDAR system is flown with six tracks each at a flying height of 15 m and 25 m with a speed of 1.5 m/s over sixteen specially designed highly reflective boards (75 cm wide Stop signs, 90 cm \(\times\) 60 cm Wrong Way signs and 60 cm \(\times\) 60 cm checkerboard targets) and five hut-shaped targets (with two 60 cm \(\times\) 120 cm planar boards) with their ridges oriented parallel and perpendicular to the flying direction. The average lateral separation between the tracks is 6 m. The two surfaces corresponding to each of these huts are used as planar features for calibration and their ridges are used as conjugate linear features. Additional planar features, such as ground patches, rooftops and building facades, are also used for calibration. The configuration of the tracks and the target primitives (in pink) are shown in Figure 9. For the UAV system used in this study, the \(X\), \(Y\), _Z_-axes of the IMU body frame are not aligned along the starboard, forward and up directions. Instead the coordinate frames are aligned as shown in Figure 10. So, a virtual IMU body frame defined as per Equation (13) is used. Therefore, we are estimating the mounting parameters relating the virtual IMU and virtual laser unit coordinate systems.  
Figure 10: Actual alignment of coordinate systems for the UAV LiDAR system used in this study.  
Figure 9: Configuration of flight lines and target primitives used for calibration as visible in 3D point clouds and Red, Green, Blue (RGB) orthophoto.
## 5 Discussion
### Research Limitations  
This research acknowledges several limitations and threats to its validity. First, the results of this review are directly influenced by the journal and conference publications included, which could potentially introduce selection bias if relevant studies were missed. We attempted to mitigate this by using a broad, inclusive search strategy, but some relevant work may not be indexed or available in the selected databases. Second, the search string and databases can also influence the results of the review. We tried to use a simple search string and a variety of databases to include as many publications as possible. Further, there is a potential risk of overgeneralization due to the diversity of study contexts and methodologies in the literature. This may pose a threat to internal validity, as diverse approaches may not be directly comparable. To address this, we carefully minimized grouping or categorizing findings.  
Moreover, the study faces construct validity limitations, as it relies on definitions and classifications established by prior publications on cyber risk assessment for cyber resilience. Variations in definitions or interpretations of key concepts, such as \"cyber resilience\", \"risk assessment\", \"governance\", and \"standards\", across studies may affect the comparability of findings. These limitations may be addressed in future research by incorporating expert validation of the review's results, ensuring that the definitions align with current industry and academic standards. Finally, an implementation gap was identified in the review of risk assessment methods for resilience. For instance, ISO 37001:2016 (Anti-bribery Management Systems), highlighted in Table 1 as a highly cited risk management standard, has the potential to enhance cyber resilience by supporting robust internal controls and compliance processes critical for mitigating cyber threats that exploit vulnerabilities arising from corruption. However, no documentation currently demonstrates its use for this purpose. This finding underscores a need for future research to explore the resilience potential of standards and regulations, irrespective of their existing applications.
# DOW-PR DOlphin and Whale Pods Routing Protocol for Underwater Wireless Sensor Networks (UWSNs)
## 2 Previous Work
The objective function implemented through the design of the holding time calculation is based upon the weighting sum of the parameters i.e., first and second hop depths. Secondly, the baseline protocol WDFAD-DBR also achieves better conservation of the network energy usage. The run-time readjustment of the forwarding area division encounters multiple copies of packet propagation in the network. To support the above phenomenon, the WDFAD-DBR will judge the link conditions and sparseness or denseness of the nodes in the network. The prediction of neighbors are implemented in accordance with the mobility pattern, speed and \(x,y,z\)-coordinates among nodes. Thirdly, the collision of the packets is investigated while contending the channel at the MAC layer. In this paper, our proposed protocol DOW-PR focuses on selecting the optimal forwarder. This is very similar to the WDFAD-DBR. Much like WDFAD-DBR, DOW-PR also considers the weighting sum of depth of the current and the next expected hops' sensor nodes. The novelties of the proposed protocol that differentiate themselves from counterpart WDFAD-DBR is mentioned in itemized text as follows:  
* To improve the performance of WDFAD-DBR, we propose a state-of-the-art DOW-PR routing protocol in which we divide the transmission range into different transmission power levels while selecting the next forwarding node. The source node searches for the optimal power level for packet transmission. * We also consider the additional parameters i.e., number of PFNs and number of suppressed nodes. WDFAD-DBR does not consider the above-mentioned parameters due to which a network consumes a significant amount of receiving energy, especially in dense networks. * Along with other parameters, our scheme also considers the number of hops traversed by the packet initiated from the source node. Consequently, DOW-PR optimizes the shortest possible path and thereby improves the end-to-end delays. * WDFAD-DBR does not provide any mechanism for void hole occurrences at the second hop forwarder. Our proposed protocol DOW-PR will select the node for broadcasting from the suppressed nodes when there is no PFN available. * In DOW-PR, we also propose another system (Whale pod) in which multiple sinks are placed at water surface, but only one sink is embedded inside the water and will be physically connected with the surface sink through high bandwidth connection.
## 3 Results
### Surface Characterization
#### 3.1.2 Chemical Composition  
The chemical compositions of the coatings deposited on the Si wafer was investigated by means of ATR-FTIR (Figure 3a). The bands indentified in the spectra (Figure 3b) corresponded to the characteristic signals found in the liquid precursor, which were the alcohol bands (stretching at 3422 cm\({}^{-1}\) and bending at 1022 cm\({}^{-1}\)) and the ester band (stretching C=O at 1728 cm\({}^{-1}\) and stretching C-O at 1275 cm\({}^{-1}\)). While all the coatings showed the main bands associated with a HEMA coating, the S4 coating seemed to have a lower contribution from the OH band and a higher contribution from the hydrocarbon bands.  
XPS analysis was performed to determine the elemental compositions of the coatings. In particular, a line scan was acquired with a step size of 1 mm involving the entire coating, as depicted in Figure 4. The elements identified in the survey spectra were carbon (C), oxygen (O), nitrogen (N) and silicon (Si). In comparison with the stoichiometric composition of HEMA (presented as dashed lines), the coatings showed higher portions of carbon and decreased contents of oxygen, especially in the center of the coatings (highlighted in yellow in the graphs). In contrast, the edge regions of the films exhibited fractions of elements close to the theoretical values for HEMA. It can be assumed that, due to the  
Figure 3: (**a**) Normalized attenuated total reflectance Fourier transformed infrared spectroscopy (ATR-FTIR) spectra of HEMA-based plasma polymer coatings and (**b**) assignation of the main peaks.  
Figure 2: Surface free energy of plasma-polymerized, HEMA-based coatings on Ti samples for different sets of process parameters.  
plasma treatment, the degree of polymerization was higher in the center, resulting in a more cross-linked coating, and lower in the edge region, where the impact of the plasma effluent was reduced.  
XPS analysis further revealed traces of N, which might be incorporated when operating in ambient air or by post-plasma reactions, as well as Si in the edge region, which originated from the substrate and indicated a film thickness less than 6 nm.
# A Two-Stage Multi-Objective Optimization Algorithm for Solving Large-Scale Optimization Problems
## 2 Background  
### Multi-Objective Optimization  
Optimization problems with multiple mutually conflicting objectives are called MOPs. A MOP can be described by Equation (1), where \(x=(x_{1},x_{2},\ldots,x_{D})\) represents a candidate solution and D is the dimension of the decision vector. M is the dimension of the target space, which is the number of targets in MOP. When D \(\geq\) 100, MOP is considered a large-scale multi-objective optimization problem.  
\[\left\{\begin{aligned} &\min F(x)=\{f_{1}(x),f_{2}(x),\ldots,f_{M}(x)\}\\ &\text{\emph{subject to}}:x\in\Omega^{D}\end{aligned}\right. \tag{1}\]  
In multi-objective optimization problems, there are a large number of optimal solutions that cannot be mutually dominated. Assuming there are two candidate solutions \(x_{1}\) and \(x_{2}\) in the decision space \(\Omega^{D}\). When they satisfy Equation (2), \(x_{1}\) Pareto dominates \(x_{2}\), denoted as \(x_{1}\prec x_{2}\). We can define the dominance relationship between the two solutions of the minimum MOP as follows:  
\[\left\{\begin{aligned} &\forall i=1,2,\ldots,M,\;f_{i}(x_{1})\leq f_{i}(x_{2 }\;)\\ &\exists j=1,2,\ldots,M,\;f_{j}(x_{1})<f_{j}(x_{2}\;)\end{aligned}\right. \tag{2}\]  
Generally speaking, it is difficult to obtain all Pareto optimal solutions. Therefore, we achieve three goals: (1) Enhance the convergence ability of the algorithm so that all final solutions are close to the true Pareto front (PF). (2) Improve the diversity of algorithms so that the target vector has a diverse distribution on the PF while satisfying the convergence speed condition; (3) Minimize the waste of computing resources while ensuring convergence and diversity. In the past few years, the field of multi-objective optimization has attracted a lot of attention, and many researchers are dedicated to the research in this area.
# Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model
## 2 Literature Review
Also, the outcome of traffic crashes varies with the geographical location of the occurrences; rural settings contribute to more severe crashes than urban settings because of higher vehicle speeds accompanied by lengthy response times from emergency services [22]. [PERSON] et al. used a joint Poisson regression model with multivariate normal heterogeneities to model crash frequency by severity level for freeway sections [23]. The model took into account common unobserved factors influencing crash frequencies across severity levels. To address unobserved heterogeneity, various approaches were used in [24; 25; 26], including random parameter models, Markov switching, ordered logit models, and latent class cluster models. Statistical methodologies have been frequently applied to model collision severity. However, compared to traditional statical models such as Logistic Regression, the Multi-Level Mixed Effect Models (MMEM) model is well-suited for modeling crash severity due to its ability to account for the hierarchical structure of data and handle the categorical nature of the dependent variable, which is inherently multinomial. Moreover, the model estimates the log odds of each severity category relative to the reference, capturing the nuanced differences that define each level of crash severity. In a study by [PERSON] et al. [27], MLR was used to point out issues of importance for severe collision risk and show the consequences of failing to consider group-level heteroscedasticity. On the other hand, [PERSON] [28] stated that MMEM is more helpful in handling data with a hierarchical structure derived from collision data to obtain a more precise and detailed understanding of collision severity. The systematic examination of the literature confirms the complexity and heterogeneity of the traffic collision risk. This study aims to investigate the various factors contributing to the severity of traffic collisions. The study utilized statistical methodologies such as the Multinomial Logistic Regression and Multi-Level Mixed Effect Models to analyze the impact of behavioural factors such as distracted driving, impaired driving, and speeding on accident severity, utilizing statistical models to assess these factors alongside road layout and vehicle types.
## 3 Example Analysis
### Applying Grey Relational Degree Analysis Method to Evaluate and Find the Optimum Scheme  
Based on the data in Table 6, we define the best scenario as \(x_{0}\) = [1,1,1,1,1,1,1,1,1,1]\({}^{T}\). The correlation coefficients of four schemes under each index are obtained by Equation (25) as shown in Figure 8. Then the comprehensive weight and correlation coefficient calculated above are substituted into Equation (26) to calculate the correlation degree of the four schemes as shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Secondary Indicators** & **Subjective Weight** & **Objective Weight** & **Comprehensive Weight** \\ \hline Average service availability index (B1) & 0.1869 & 0.0842 & 0.1323 \\ System average interruption duration index (B2) & 0.0935 & 0.0842 & 0.0935 \\ Initial investment cost (B3) & 0.1513 & 0.1093 & 0.1356 \\ Operation and maintenance cost (B4) & 0.0833 & 0.1093 & 0.1006 \\ Network loss cost (B5) & 0.0458 & 0.1899 & 0.0983 \\ Voltage offset (B6) & 0.1337 & 0.0923 & 0.1171 \\ Frequency deviation (B7) & 0.1337 & 0.0766 & 0.1067 \\ Harmonic distortion rate (B8) & 0.0446 & 0.0923 & 0.0676 \\ Emissions of polluted gases (B9) & 0.0848 & 0.0766 & 0.0850 \\ Land occupation area (B10) & 0.0424 & 0.0853 & 0.0634 \\ \hline \end{tabular}
\end{table}
Table 7: Comprehensive weight.  
Figure 6: The objective weights of each evaluation indicator.  
Figure 7: The weights of each evaluation indicator.  
From Table 8, we can see that scheme 4 is the best scheme and scheme 2 is the worst scheme.
## 4 Findings
### With or without iNEIS(tm)?  
This question intended to determine the users' perceptions of iNEIS(tm) as a tool to make work easier. Around 82% of the respondents still preferred to carry out their job using the traditional way, without using the newly implemented IS. Despite the \"struggle\" that most users faced in using the system, 10% of the respondents accepted that iNEIS(tm) made it easier to complete their job. Lack of user involvement traditionally has been the major reason for project failure. Conversely, the main contributor to project success has been user involvement. However, among the most common comments and recommendations that were made by the respondents were the following:  
1. The system needed lot of improvement in order to be an effective system.
2. The main listed reason for this was the unstable internet connection and also the instability of the system itself. It was intended for the new system to replace the manual system, but now, with iNEIS(tm), both are needed.
3. The system had a complicated interface, was not user-friendly, had too many technical terms and had too many components that tended to confuse users.
4. Teachers should have been able to focus on teaching rather than spend a great deal of time on iNEIS(tm) attending to the errors and complications of the system.
5. There was an ack of support from the iNEIS(tm) team and helpdesk.
6. The initial training was inadequate for teachers to master the system and to use it effectively.
7. Internet connection should be improved for better and stable connection of the system.
8. iNEIS(tm) placed a greater burden on teachers in terms of both academic and non-academic tasks.  
Even though most of the comments criticized the inefficiency of the system, a significant number of respondents did find iNEIS(tm) to be a good system. A few accepted the system as a good one once they were familiar with it. Some suggested that more training, and awareness of the benefits of the system, would be beneficial, and more support from the iNEIS(tm) team was required. Some respondents also requested for the interface of iNEIS(tm) to be redesigned to be less complicated, more user-friendly and simpler. In essence, the purpose of iNEIS(tm) is only to provide statistical figures. From these, other departments can generate reports and begin to analyze the data. It was discovered from the teacher's perspective that a number of them prefer to use the traditional method and argued that the system was acceptable if no problems occurred. The findings further showed that there were barriers that made the system ineffective and inefficient. These barriers included the internet connection, the system itself and also the users' attitudes towards change.