esa-sceva/satcom-chunk-collection huggingface dataset:
# A Proactive Approach to Extended Vehicle Routing Problem with Drones (EVRPD)
## 4 Materials and Methods--Implementation of the EVRPD Model
### Dedicated Genetic Algorithm
#### 4.2.3 Evaluation Procedure  
The evaluation procedure assessed the newly generated population. The basic criterion for evaluation was cost, but the distance was also considered. In the proposed genetic algorithm (DGA), the evaluation procedure consisted of solving the TSP multiple times (through dedicated heuristics) for subsequent MHs. In detail, this procedure worked in two steps:  
* Step 1: The traveling salesman problem (TSP) was solved for each MH starting point and each drone. The route was determined in such a way that the cost of delivery was the lowest, and the delivery time was within the set time.
* Step 2: The MH starting points were selected in such a way that the total cost of deliveries was as low as possible and that the number of allowed starting points was not exceeded. This step also ensured that constraint (14) was satisfied.  
Step 2 of the evaluation procedure also performed the pre-solving of the modeled problem by eliminating some starting points from the solution space.
## 5 Influence of Axial Joint Transfixion Degree on Specim Damage
### Analysis of Pore Size Distribution
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Pore Type** & \multicolumn{4}{c}{**Peak Aperture /\(\upmu\)m (Percentage/\%)**} \\ \hline \multirow{3}{*}{0\%} & Small pore & 0.00911 (0.2726) & 0.00911 (0.275) & 0.00911 (0.2618) & 0.00911 (0.2824) & 0.00911 (0.2886) \\  & medium pore & 0.31406 (0.0204) & 0.33664 (0.0225) & 0.33664 (0.0265) & 0.33664 (0.0439) & 0.36084 (0.0558) \\  & Large pore & 9.42751 (0.0135) & 10.10526 (0.0172) & 10.10526 (0.0156) & 10.10526 (0.0385) & 10.10526 (0.0783) \\ \hline \multirow{3}{*}{20\%} & Small pore & 0.00911 (0.2491) & 0.00792 (0.2309) & 0.00911 (0.2343) & 0.00911 (0.2584) & - \\  & medium pore & 0.36084 (0.0222) & 0.36084 (0.0332) & 0.36084 (0.0407) & 0.36084 (0.0569) & - \\  & Large pore & 11.61045 (0.0195) & 9.42751 (0.0185) & 9.42751 (0.0219) & 10.10526 (0.0592) & - \\ \hline \multirow{3}{*}{40\%} & Small pore & 0.00911 (0.2582) & 0.00911 (0.2511) & 0.00911 (0.2424) & 0.00911 (0.2510) & 0.00911 (0.2523) \\  & medium pore & 0.33664 (0.0239) & 0.33664 (0.0292) & 0.33664 (0.0319) & 0.33664 (0.0359) & 0.33664 (0.0525) \\  & Large pore & 10.10526 (0.0182) & 10.10526 (0.0197) & 9.42751 (0.0228) & 10.10526 (0.0306) & 11.61045 (0.0592) \\ \hline \multirow{3}{*}{60\%} & Small pore & 0.00911 (0.2699) & 0.00911 (0.2675) & 0.00911 (0.2669) & 0.00911 (0.2755) & 0.00911 (0.275) \\  & medium pore & 0.36084 (0.024) & 0.36084 (0.0306) & 0.36084 (0.035) & 0.36084 (0.0407) & 0.36084 (0.0544) \\  & Large pore & 10.10526 (0.0188) & 10.10526 (0.0248) & 10.10526 (0.0317) & 10.10526 (0.0415) & 10.10526 (0.0648) \\ \hline \multirow{3}{*}{80\%} & Small pore & 0.00911 (0.2711) & 0.00911 (0.2659) & 0.00911 (0.2483) & 0.00911 (0.2698) & - \\  & medium pore & 0.36084 (0.0231) & 0.36084 (0.0313) & 0.36084 (0.0315) & 0.36084 (0.0386) & - \\ \cline{1-1}  & Large pore & 10.83174 (0.0197) & 10.83174 (0.0283) & 10.10526 (0.0376) & 10.83174 (0.0651) & - \\ \hline \multirow{3}{*}{100\%} & Small pore & 0.01046 (0.2748) & 0.01046 (0.2775) & 0.01046 (0.2847) & 0.01046 (0.2941) & 0.01046 (0.2933) \\ \cline{1-1}  & medium pore & 0.38678 (0.0225) & 0.38678 (0.0284) & 0.38678 (0.0367) & 0.38678 (0.0516) & 0.41458 (0.0659) \\ \cline{1-1}  & Large pore & 11.61045 (0.0253) & 11.61045 (0.0295) & 11.61045 (0.0391) & 12.44514 (0.0531) & 11.61045 (0.1023) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Peak pore sizes of different pore types and their proportions of joint specimens with different transfixion degrees. inside the specimen is poor, so its pore development is second only to the frozen specimen with a transfixion degree of 100%.
## 3 Data and Methods  
### Aim 1: Role of Input Data and Methodological Choices  
Figure 2 presents the methodological workflow, referring to the three aims of the work outlined above. To analyze the role of the quality and quantity of the input data, we selected a case study of the 1980 M\({}_{\mathrm{w}}\) 6.8 Iripinia-Basilicata, Southern Italy earthquake [36]. This earthquake was already included in the analyses conducted by [PERSON] et al. (2020) [10], but here we enlarged the number of available data points from 66 [37] to 563, thanks to a careful review of the published data, analysis of technical reports and administrative documents, and interviews of eyewitnesses, supplemented with field surveys at selected sites [38]. Additionally, we derived original ESI-07 isoseisimals, which were not available to date.  
We evaluated the influence of several methodological choices in the derivation of an intensity prediction equation. We considered three different input datasets: \"previous\" IDPs (from [PERSON] et al., 2007 [37]), \"new\" IDPs, and ESI-07 isoseisimals, analyzed on a 1-kilometer grid (from [PERSON] et al., 2023 [38]). Starting from the analysis of all IDPs, we calculated the median distance for each intensity class and investigated the results in terms of the intensity-distance attenuation.  
Figure 2: Methodological workflow adopted in the current study [10; 37].
# End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
## 4 Proposed Method  
### Framework  
In Figure 2, the traditional control pipeline is presented, along with its modularization. To obtain the actuator commands on the basis of observation, usually, the state estimation,  
Figure 1: Description of a general reinforcement learning problem.  
planning, and low-level control stages need to occur. This requires a series of independent modules, and the interfaces between different modules need to be handcrafted. By comparison, our proposed method is based on the end-to-end control pipeline. The core idea is to establish a direct mapping between the input-end (observation) and the output-end (actuator commands) of the system on the basis of the neural network. The end-to-end control pipeline simplifies the control process and is expected to autonomously extract the features that are supportive to the generation of the proper control commands.  
Specific to the UAV control instance presented in this paper, the problem was abstracted as an image-based reinforcement learning problem in Section 3.2. To apply the proposed end-to-end control pipeline, the policy for the RL-agent is approximated by a neural network. To obtain the continuous action, the deep deterministic policy gradients (DDPG) algorithm with the actor-network and the critic-network is adopted. Therefore, the UAV perception-based control problem can be described in the image-based RL framework, as shown in Figure 3. Apart from the DDPG agent, the environment mainly consists of the external environment and the UAV model. The interaction between the two main parts is achieved by the environment interfaces, including the onboard camera and the reward function. The former is used to obtain the monocular image and the latter calculates the reward according to the control commands and UAV states. The raw image from the camera is an RGB image with a size of 640 \(\times\) 480. In consideration of computational efficiency, it needs to be resized and converted to greyscale by the image processor.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 2 Bloch Sphere Quantum Genetic Algorithm
### Quantum Chromosome Model  
In quantum computing, the smallest unit of information is the quantum bit [27; 28]. On the three-dimensional Bloch sphere, a qubit can be represented as (1).  
\[\ket{\varphi}=\cos\frac{\theta}{2}\ket{0}+e^{i\varphi}\sin\frac{\theta}{2} \ket{1}, \tag{1}\]  
where \(\cos\frac{\theta}{2}\) and \(e^{i\varphi}\sin\frac{\theta}{2}\) are complex numbers, which are called the probability amplitudes of the corresponding states of a qubit, and \(\left|\cos\frac{\theta}{2}\right|^{2}\) and \(\left|e^{i\varphi}\sin\frac{\theta}{2}\right|^{2}\) represent the probability that the qubit is in \(\ket{0}\) or \(\ket{1}\), respectively [29; 30]. The normalization condition is satisfied as (2).  
\[\left|\cos\cos\frac{\theta}{2}\right|^{2}+\left|e^{i\varphi}\sin\sin\frac{ \theta}{2}\right|^{2}=1, \tag{2}\]  
On the Bloch sphere, a point \(p\) can be determined by two angles \(\theta\) and \(\varphi\), as shown in Figure 1.  
On the three-dimensional Bloch sphere, any qubit corresponds to a point on the sphere, after which the qubit can be expressed in Bloch spherical coordinates as:  
\[\ket{\varphi}=\left[\cos\varphi\sin\theta\ \sin\varphi\sin\theta\ \cos\theta \right]^{\text{T}}, \tag{3}\]  
In the BQGA process, the Bloch spherical coordinate encoding of the qubit is directly adopted. Considering the three coordinates of the qubit as three juxtaposed genes, each chromosome contains three juxtaposed gene chains, and each gene chain represents an  
Figure 1: Bloch spherical representation of quantum bits.  
optimal solution [31]. Therefore, let \(P_{i}\) be the \(i\) chromosome in the population, and the BQGA encoding method is shown as (4).  
\[P_{i}=\begin{vmatrix}\cos\varphi_{i1}\sin\theta_{i1}\\ \sin\varphi_{i1}\sin\theta_{i1}\\ \cos\theta_{i1}\end{vmatrix}\cdots\begin{vmatrix}\cos\varphi_{in}\sin\theta_{ in}\\ \sin\varphi_{in}\sin\theta_{in}\\ \cos\theta_{in}\end{vmatrix}, \tag{4}\]  
where \(0\leq\theta\leq\pi,0\leq\varphi\leq 2\pi\), \(i=1,2,\cdots,m.j=1,2,\cdots n\).
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 3 The Proposed Joint Data Hiding System  
### Procedures of Proposed Method  
The proposed joint system has three phases: image encryption, data hiding, and joint data extraction and image restoration, as represented in Figure 2. In the image encryption phase, by using an encryption key, a content owner encrypts an original uncompressed image and creates an encrypted version of the original image. In the data hiding phase, by exploiting a data-hiding key, a data hider inserts some additional data within the encrypted image by modifying a small portion of the encrypted data. However, the data hider does not know any information about the original image. The encrypted image is divided into four sets, instead of blocks, and two sets can be used for data hiding, which helps to increase the embedding capacity. Besides, for security purposes, the information is further encrypted using data-hiding key. In the joint data extraction and image restoration phase, a receiver first decrypts the embedded encrypted image by using the encryption key to obtain a directly decrypted image that is similar to the original. Moreover, with the aid of a data-hiding key, the receiver can extract the embedded data and restore the original image from the directly decrypted image. In the proposed system, for fluctuation calculation, the actual value of pixels and the absolute difference between neighboring pixels are more preferred than an estimated value and prediction error, respectively. The detailed procedures are as follows.  
Figure 1: Example to evaluate the prediction error. (**a**) original image; (**b**) embedded image.  
Figure 2: Block diagram of the proposed joint data hiding system.
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 1 Introduction  
SEC occupies an important position in affective computing and plays a cogent role in human relationships. Psychologically, there is no better way: a relationship can neither be initiated nor sustained without adequate communication. However, communication takes place in many ways (verbal and non-verbal), but auditory speech communication has been the hub for all other forms. It is the nucleus to which all other means of human interaction revolve [1]. One major factor that determines human decisions is emotion [2, 3]. Whether on an inter-personal or intra-personal basis, emotion influences why human beings act or react the way they act, informs their judgment, and even sometimes molds their opinions. Classification of emotion through human speech, therefore, is the act of recognizing emotional content from speech [4] as depicted in Figure 1. The growth of human-computer interaction in the last decade cannot be unconnected to SEC [5, 6]. Apart from wider application in major fields and sub-fields, SEC is reshaping the nearby future next revolution of human-machine interaction as it is attracting the vast majorityof researchers worldwide. Mental disorders and depression-related diseases have been drawing great benefit from SEC coupled with the acceleration of patient treatment [7].  
However, the curiosity of why speech emotion classification has been experiencing high processing cost and increased memory consumption with less accuracy has remained a great question among researchers today [8]. Presently, one lingering challenge identified so far is the lack of a sufficient annotated speech dataset, but then, several models have been proposed to mitigate this challenge and improve accuracy, as was obtainable in [9] using Surrey Audio-Visual Expressed Emotion (SAVEE), Ryerson Multimedia Laboratory (RML), and eNTERFACE'05 as the speech corpus. A 3D convolutional neural network (CNN) model with fully connected layers was developed for SEC. They attained a state-of-the-art performance, but the huge features [10] extracted without a corresponding feature selection method applied before actual classification tends to increase computational [11] complexity and reduce the accuracy rate.  
By way of improving on the previous study, we proposed a novel and robust deep transfer learning approach for speech emotion classification with feature selection techniques. Transfer learning leverages on knowledge acquired from the source task and implements it on the target task [12]. It reduces computational complexity as it requires no training before its usage. Not all features from speech signals carry emotional content, and this is the reason why many models' failed to attain an optimal result. There is a need for a mechanism to subject extracted speech features [13; 14] (spatial) to selection techniques for effectively identifying discriminating features that can increase accuracy while reducing computational cost.
## 3 Proposed Methodology
### Feature Selection  
The suggested Blind Image Quality Assessment system relies on the choice of features to minimize the complexity of the feature vector and increase the machine's performance. In this study, the WEKA machine learning tool is utilized to pick features.  
The Information Gain attribute feature selector ranks attributes based on their capacity to convey details regarding the overall quality of the distorted images. By employing a feature as a split property, the evaluator calculates the reduction in entropy of the quality scores. The Information Gain score \(\mathrm{IG}(F,Q)\) is derived using the following equation for feature set \(F\) and quality scores \(Q\):  
\[\mathrm{IG}(F,Q)=\mathrm{H}(Q)-\sum_{f\in F}\frac{|\{q\in Q:q_{f}=f\}|}{|Q|} \mathrm{H}(\{q\in Q:q_{f}=f\}) \tag{5}\]  
In this equation, \(\mathrm{H}(Q)\) denotes the entropy of the quality scores, \(f\) denotes an attribute, and \(q_{f}\) denotes the quality rating of the image determined by that selected feature. The total number of images in \(Q\) for which the feature \(f\) is the split attribute is given by \(|q\in Q:q_{f}=f|\). The entropy of the quality ratings for images when the feature \(f\) is the divided attribute is \(\mathrm{H}(q\in Q:q_{f}=f)\).  
The Information Gain metric represents the quantity of information obtained regarding image quality by incorporating a feature into the model. The greater the Information Gain score, the more essential the attribute is for quality forecasting, thus increasing its position in the process of choosing the feature.
## 2 Mixed Poisson Regression Model with Varying Dispersion  
### Modelling Framework  
Consider a non-life insurance portfolio with \(n\) policyholder contracts each involving a particular claim type, and assume that the individual claim frequencies, \(k_{i}\), arising from each insured \(i\), for \(i=1,\dots,n\), are independent. In addition, suppose that given a continuous random variable, \(z_{i}>0\), \(k_{i}|z_{i}\) is distributed according to a Poisson distribution with probability mass function (pmf) given by  
\[P(k_{i}|z_{i})=\frac{\exp(-\mu_{i}z_{i})(\mu_{i}z_{i})^{k_{i}}}{k_{i}!}, \tag{1}\]  
for \(k_{i}=0,1,2,\dots,\) and where \(\mu_{i}>0\). The mean and variance of \(k_{i}|z_{i}\) are \(\mathrm{E}(\mathbf{k}_{i}|\mathbf{z}_{i})=\mathrm{Var}(\mathbf{k}_{i}| \mathbf{z}_{i})=\mu_{i}z_{i}\).  
Furthermore, consider that \(z_{i}\) is distributed according to a \(C^{2}\) mixing distribution, with probability density function (pdf) \(g(z_{i};\phi_{i})\), with \(\phi_{i}>0\), which is not conjugate to the Poisson distribution given by Equation (1). Additionally, we assume that \(z_{i}\) has a unit mean, that is, \(\mathrm{E}(z_{i})=1\), to ensure that the model is identifiable.  
Considering the previous assumptions, we see that the resulting distribution of \(k_{i}\) is a mixed Poisson distribution with pmf  
\[P(k_{i})=\int_{0}^{\infty}P(k_{i}|z_{i})g(z_{i};\phi_{i})\mathrm{d}z_{i}, \tag{2}\]  
where \(g(z_{i};\phi_{i})\) is the pdf of \(z_{i}\). In addition, the mean and the variance of \(k_{i}\) are given by  
\[\mathrm{E}(k_{i})=\mathrm{E}_{z_{i}}[\mathrm{E}(k_{i}|z_{i})]=\mu_{i}\mathrm{ E}_{z_{i}}[z_{i}]=\mu_{i} \tag{3}\]  
and  
\[\mathrm{Var}(k_{i})=\mathrm{E}_{z_{i}}[\mathrm{Var}(k_{i}|z_{i})]+\mathrm{ Var}_{z_{i}}[\mathrm{E}(k_{i}|z_{i})]. \tag{4}\]  
Finally, under the proposed modelling framework, the mean and dispersion parameters, \(\mu_{i}\) and \(\phi_{i}\), are modelled as functions of risk factors  
\[\mu_{i}=\exp(\mathbf{x}_{1,i}^{\top}\mathbf{\beta}_{1}) \tag{5}\]  
and  
\[\phi_{i}=\exp(\mathbf{x}_{2,i}^{\top}\mathbf{\beta}_{2}), \tag{6}\]  
where \(\mathbf{x}_{1,i}\) and \(\mathbf{x}_{2,i}\) are the, potentially different, vectors of explanatory variables with dimensions \(p_{1}\times 1\) and \(p_{2}\times 1\), respectively, and where \(\mathbf{\beta}_{1}=\left(\beta_{1,1},\ldots,\beta_{1,p_{1}}\right)^{\top}\) and \(\mathbf{\beta}_{2}=\left(\beta_{2,1},\ldots,\beta_{2,p_{2}}\right)^{\top}\) are vectors of regression coefficients, where we consider that the matrices \(\mathbf{X_{1}}\) and \(\mathbf{X_{2}}\) are of full rank and are composed of the rows given by \(\mathbf{x}_{1,i}\) and \(\mathbf{x}_{2,i}\), respectively.
## 2 Materials and Methods
### Methods of Evaluating Model Performance  
This section describes the method for evaluating the performance of the model in detecting asbestos fibers. Because the model judged whether asbestos fibers were present (positive) or absent (negative) in a given image, the judgment results consisted of four types: true positive (TP) (positive data correctly classified as positive), true negative (TN) (negative data correctly classified as negative), false positive (FP) (negative data misclassified as positive), and false negative (FN) (positive data misclassified as negative). These results were used to calculate indices of correctness, accuracy (Acc.), precision (Prec.), recall, and F1 scores (Equations (8)-(11)). To evaluate the classification accuracy, the F1 score, which was calculated from precision and recall, was often used because precision and recall should be simultaneously high. However, in asbestos detection, false detection of non-asbestos fibers is a safe failure. In other words, fewer overlooks of asbestos fibers (high recall) are more desirable than fewer misdetections (high precision). Therefore, in this study, we evaluated the performance of the model by focusing on the F1 score and recall.  
\[\text{Acc.}=\frac{\left[\text{TP}\right]+\left[\text{TN}\right]}{\left[\text{ TP}\right]+\left[\text{FN}\right]+\left[\text{FP}\right]+\left[\text{TN}\right]} \tag{8}\]  
\[\text{Prec.}=\frac{\left[\text{TP}\right]}{\left[\text{TP}\right]+\left[ \text{FP}\right]} \tag{9}\]  
\[\text{Recall}=\frac{\left[\text{TP}\right]}{\left[\text{TP}\right]+\left[ \text{FN}\right]} \tag{10}\]  
\[\text{F}_{1}=\frac{2\times\text{Prec.}\times\text{ Recall}}{\text{Prec.}+ \text{ Recall}} \tag{11}\]  
where [TP], [TN], [FP], and [FN] are the number of images classified into TP, TN, FP and FN, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{5}{c}{**Group**} \\ \cline{3-8}  & & **1** & **2** & **3** & **4** & **5** \\ \hline \multirow{4}{*}{**F1 score**} & \multirow{2}{*}{1} & \multirow{2}{*}{Training} & \multirow{2}{*}{Training} & \multirow{2}{*}{Training} & \multirow{2}{*}{Validation} & \multirow{2}{*}{Test} \\ \cline{3-8}  & & & & & & \\ \cline{1-1} \cline{3-8}  & & 2 & & & & \\ \cline{1-1} \cline{3-8}  & & 3 & & & & \\ \cline{1-1} \cline{3-8}  & & 4 & & & & \\ \cline{1-1} \cline{3-8}  & & 5 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data division and assignment method.  
Figure 4: Data division and assignment method.  
The significance of performance changes between models was evaluated using the Student's paired \(t\)-test, with a \(p\)-value of less than 0.05 considered significant.
## 3 Problem Formulation  
Developing an efficient resource allocation algorithm presents a considerable challenge, primarily attributed to the mobility of users and the inherent uncertainty of the wireless channel. However, by integrating causality into the framework, we can potentially alleviate these challenges, as causality provides a means to better capture and understand uncertainties within the system.  
Initially, we conduct an analysis of power consumption throughout the resource allocation process and formulate the optimization problem with a focus on enhancing energy efficiency. Subsequently, leveraging the causal structure within reinforcement learning (RL), we reframe the problem into a causal Markov Decision Process (MDP). This approach enables us to incorporate causal relationships among variables, facilitating more informed decision-making in dynamic environments.  
To address the resource allocation optimization dynamically, we propose an actor-critic reinforcement learning algorithm tailored to the causal MDP framework. By iteratively refining the policy through actor-critic updates, our algorithm aims to learn optimal resource allocation strategies that balance energy efficiency and performance.  
Furthermore, we delve into the intricacies of our proposed algorithm, providing detailed explanations of its components, such as actor and critic networks, reward functions, and exploration strategies. Additionally, we discuss the training procedure and potential extensions or enhancements to our approach.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 4 Linear Programming-Based Mathematical Formulation  
Linear programming is an extensively-used mathematical approach to attain an optimal outcome. Initially, an objective function either tends to maximize or minimize and is formulated with linear constraints. We have used the simplex method of linear programming to find out feasible regions for performance parameters. In this section, it is discussed how linear programming-based problem formulation helps with the maximization or minimization of performance parameters in order to improve the network performance.