esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes  
In this section, we describe the employed transmission schemes, SISO, AS and SVD beamforming, in terms of their outage probability and energy efficiency expressions.
## 2 BBM92 Protocol in 2D Subspace Fiber Encodings
### Perturbations in Quantum Light States
Indeed, taking the state (5) and applying the change of basis, we find that errors occur with a probability given by the following expression  
\[ \begin{split}& p_{err}^{X}=1/8(|\tilde{\beta}_{a}\alpha_{b}- \alpha_{a}\tilde{\beta}_{b}+\alpha_{a}\tilde{\alpha_{b}}+\tilde{\beta}_{a} \beta_{b}-\bar{\alpha}_{a}\alpha_{b}-\beta_{a}\tilde{\beta_{b}}\\ &+\beta_{a}\tilde{\alpha}_{b}-\bar{\alpha}_{a}\beta_{b}|^{2}+| \tilde{\beta}_{a}\alpha_{b}-\alpha_{a}\tilde{\beta}_{b}-\alpha_{a}\tilde{ \alpha_{b}}-\bar{\beta}_{a}\beta_{b}+\bar{\alpha}_{a}\alpha_{b}\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad \qquad+\beta_{a}\tilde{\alpha}_{b}-\bar{\alpha}_{a}\beta_{b}|^{2})\end{split}  \tag{7}\]  
These equations will be the basis for our forthcoming error analysis, where we extract error rates from the error probabilities. However, before that, we need to relate the \(\alpha\)'s and \(\beta\)'s to more physical parameters [30]. Indeed, writing the perturbation as a matrix, we obtain  
\[ \begin{split}& P=\begin{pmatrix}\alpha&-\tilde{\beta}\\ \beta&\bar{\alpha}\end{pmatrix}\\ &\equiv\begin{pmatrix}e^{-i(\varphi+\omega)/2}\cos(\theta/2)&-e^{i( \varphi-\omega)/2}\sin(\theta/2)\\ e^{-i(\varphi-\omega)/2}\sin(\theta/2)&e^{i(\varphi+\omega)/2}\cos(\theta/2 )\end{pmatrix},\end{split}  \tag{8}\]  
We have one such matrix for [PERSON] and another for [PERSON]. This way, we have made it explicit that we have six independent (random) parameters: \(\varphi_{a},\varphi_{b},\omega_{a},\omega_{b},\theta_{a},\theta_{b}\). Note that from a more physical point of view, the matrix above can be understood as a general rotation of a quantum state on the Bloch sphere [30]. \begin{table}
\begin{tabular}{c c c c} \hline
**Basis** & **[PERSON]’s Outcome** & **[PERSON]’s Outcome** & **Bit Agreement** \\ \hline Z & \(H\) & \(V\) & Correct (bit = 0) \\ Z & \(H\) & \(H\) & Error \\ Z & \(V\) & \(V\) & Correct (bit = 1) \\ Z & \(V\) & \(H\) & Error \\ X & \(+\) & \(+\) & Correct (bit = 0) \\ X & \(+\) & \(-\) & Error \\ X & \(-\) & \(-\) & Correct (bit = 1) \\ X & \(-\) & \(+\) & Error \\ \hline \end{tabular}
\end{table} 
Table 1: Expected output measurements for the polarization case in BBM92, for an input state \(|\psi^{-}\rangle=2^{-1/2}(|1_{aH}1_{bV}\rangle-|1_{aV}1_{bH}\rangle)\) and in the case of basis agreement (i.e., other events are discarded). Errors correspond to events where the opposite correlation as expected is obtained, which, in the case of perturbations, happens with probabilities given by Equations (6) and (7). [PERSON] is assumed to perform a bit flip.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 2 Relate Works
### Asymmetric Cryptography-Based Authentication Schemes  
In asymmetric cryptography-based authentication schemes, pseudonym and certification are usually considered as an essential message to prove the legality of the sender. In [11], the trusted authority (TA) provides the registration and anonymous authentication services for all RSUs and vehicles. When a registered vehicle enters VANETs, the vehicle can get the TPD activation key from the TA through RSU. The vehicle with the TPD activation key is able to generate a short-term anonymous certificate to achieve anonymous authentication. Moreover, in V2I authentication protocol, RSUs support the batch verifications of anonymous communication requests from multiple vehicles. However, according to [12], in order to prevent external adversaries from obtaining the vehicle's trajectory, each vehicle needs to change its own pseudonym and corresponding certificate frequently. Consequently, the TA has to provide services for a large number of vehicles that need to change pseudonyms at the same time, which leads to high communication cost and computational cost for the TA. Refs. [13; 14] propose the anonymous authentication schemes to achieve conditional privacy-preserving, where anonymous certificates are maintained by vehicles themselves. Vehicles are able to generate dummy identities and certificates independently to achieve mutual authentication and communication. However, since the computational cost of certificate generation is high, frequent changes of pseudonyms and certificates are computationally expensive for vehicles [15].
## 3 Methodology
### Fractal Features  
There are different methods to quantify the fractal textural measure of images. One notable method is the Pentland method, which is a directional method known for its excellent performance in generating spatial features for remote-sensing images [41]. This approach considers the spectral fluctuations in the region around each pixel to extract FFs. The self-similarity parameter (\(H\)) is calculated as the difference in the gray level between two pixels at a distance \(r\), represented by \(E_{r}\), and a constant value, represented as \(E_{0}\)[42]:  
\[E_{r}\;r^{-H}=E_{0} \tag{2}\]  
The following formula is then used to obtain the fractal dimension (FD):  
\[FD=1-H \tag{3}\]  
The following is what we obtain when we take the logarithm of Equation (2) on both sides:  
\[log(E_{r})-H\;log(r)=C_{0} \tag{4}\]  
Fitting a linear regression line between \(log(E_{r})\) and \(log(r)\) yields the values of \(H\) and \(C_{0}\), where \(H\) is the slope and \(C_{0}\) is the intercept. Applying the Pentland technique involves enclosing each pixel in a moving window of size \(L\times L\). Then, a random variable \(r\), representing the difference between two random variables, \(a\) and \(b\), is chosen within the range of \((0,L)\), meeting the constraint \(0<a<b\leq L\). The parameter \(e_{r}\) is then calculated as follows:  
\[e_{r}=\frac{|S_{b}-S_{a}|}{r}+1 \tag{5}\]  
In this equation, the gray values of pixels at distances \(r\) and in the presumptive direction are represented by \(S_{b}\) and \(S_{a}\). The average of the parameter \(e_{r}\) can be computed as follows, given the positive integer \(a\):  
\[\bar{e}_{r}=\frac{\sum_{a=0}^{L-r}e_{r}}{L-r+1} \tag{6}\]  
\(E_{r}\) is then given as:  
\[E_{r}=\bar{e}_{r}\frac{L}{r} \tag{7}\]  
The Pentland method considers four directions (vertical, horizontal, and two diagonals) to capture the spatial properties of pixels. This method produces distinct values for \(H\) and \(C_{0}\) in every direction, providing exact information on the textures of pixels. In this study, FFs are generated using window sizes of \(9\times 9\), \(17\times 17\), and \(25\times 25\) to capture multiscale spatial information. We can enhance our understanding of image textures by employing this method with diverse orientations and window sizes.
# Incremental Canonical Correlation Analysis
## 3 Methodology  
### Review of Canonical Correlation Analysis  
Canonical correlation analysis (CCA) as a classical multivariate statistical method is widely used to analyze multiview data. In the machine learning community, CCA usually serves as a feature learning method of extracting two separate subspaces to maximize the correlations across views. Consider the samples \(X\in R^{d_{1}\times n}\) and \(Y\in R^{d_{2}\times n}\) from two views, where \(d_{1}\) and \(d_{2}\) are respectively the dimensions of \(X\) and \(Y\), and \(n\) denotes the number of the samples; CCA [43] is to seek two subspaces \(a\) and \(b\) to maximize their projections, as given below:  
\[\max_{a,b}\frac{a^{T}\Sigma_{xy}b}{\sqrt{a^{T}\Sigma_{xx}a}\sqrt{b^{T}\Sigma_{ yy}b}} \tag{1}\]  
where \(\Sigma_{xy}=(X-\overline{X})(Y-\overline{Y})^{T}\), \(\Sigma_{xx}=(X-\overline{X})(X-\overline{X})^{T}\), \(\Sigma_{yy}=(Y-\overline{Y})(Y-\overline{Y})^{T}\).  
To ensure the scale invariance of \(a\) and \(b\) in Equation (1), the objective function can be rewritten:  
\[\begin{array}{l}\max_{a,b}a^{T}\Sigma_{xy}b\\ s.t.\ a^{T}\Sigma_{xx}a=1\\ b^{T}\Sigma_{yy}b=1\end{array} \tag{2}\]  
By constructing a Lagrange equation, Equation (2) is simplified into:  
\[\left[\begin{array}{cc}\Sigma_{xx}^{-1}&0\\ 0&\Sigma_{yy}^{-1}\\ \end{array}\right]\left[\begin{array}{cc}0&\Sigma_{xy}\\ \Sigma_{yx}&0\\ \end{array}\right]\left[\begin{array}{c}a\\ b\\ \end{array}\right]=\lambda\left[\begin{array}{c}a\\ b\\ \end{array}\right] \tag{3}\]  
It is proved that the Equation (3) has a global optimal solution, in which \(a\) and \(b\) are called as canonical subspaces. Before computing \(a\) and \(b\), \(\Sigma_{xx}^{-1}\), \(\Sigma_{yy}^{-1}\), \(\Sigma_{xy}\), and \(\Sigma_{yx}\) should be calculated at first. However, the time complexity of the matrix inversion about \(\Sigma_{xx}^{-1}\) and \(\Sigma_{yy}^{-1}\) is \(O(d^{3})\) (\(d\) is the larger of the two \(d_{1}\) and \(d_{2}\)), which leads to slow running speed and large calculation overhead [2, 40, 41]. In practice, there are many data with high dimensions that burden massive data storage. Obviously, using CCA incremental learning is a promising choice to handle this case.
# Review and Categorization of Digital Applications in the Energy Sector
## 2 The Current State of Knowledge
For the electricity sector, the highest impact on labor is seen in the operation and maintenance of power plants where jobs could be automated. However, in some areas also new jobs would be created with a strong skill focus in the IC technologies and data science. In [30] the Energy Expert Cyber Security Platform (EECSP) provides advice to the European Commission on cyber security policy. Based on the expected cybersecurity threats in the energy sector and existing regulations, a gap is identified and recommendations for actions are given including, for example, the creation of a cyber response framework for the energy sector. The \"World Energy Council's\" report on \"The road to resilience\" also provides an overview of cyberattacks with impacts on the energy system. The authors conclude that digitalization increases the complexity of managing cyber risks. The recommended actions include, for example, the implementation of policies and standards, the use of information sharing and collaboration between companies and countries and the implementation of cyber security already in the development of technology. The Hans Bockler Foundation (which belongs to the German Trade Union Confederation) sees the highest risk of job losses in the administrative tasks as well as technical tasks in the grid and generation. Furthermore, a risk of higher stress levels due to multitasking requirements and high performance transparency is identified. The author also points out that a new set of skills will be required, which offers opportunities for employees willing and able to participate in further education programs. Besides that, according to the author, digitalization offers the potential for more flexibility of working conditions and facilitated processes. Besides the business view and the publications on specific aspects of digitalization, some publications cover a broad overview of the digital transformation in the energy sector including digital applications. The publications taking a broad view on which digital applications can be expected in the energy sector are used for the literature review performed in this paper (see next section). Overall, most aspects of the digitalization of the energy sector are to some extent covered in the concurrent literature. However, two aspects are lacking. The first is a structure that allows categorizing digital applications unambiguously (as far as possible) regarding different important aspects. The second aspect is a basis for a holistic assessment and evaluation of digital applications taking into consideration criteria reflecting the different perspectives of all stakeholders. Furthermore, the literature taken into account and especially the literature analyzed in Section 3 mainly presents the findings of the individual authors, based on their own research, surveys, or analysis of specific literature, however, an overarching overview is missing. Therefore, this study intends to create this overview and summarizes and structures the findings of the analyzed publications.
### Research Questions, Objective, and Contribution of the Study  
According to the outlined research gaps, this study addresses the following research questions:  
* _Can we formulate the transition matrix in the case of a general assembly line composed of an arbitrary number of machines and buffers of arbitrary capacity?_
* _Can we formulate finite state elements in the case of assembly systems to cope with dimensionality issues while keeping a functional relationship between the properties of assembly systems and associated KPIs?_  
The main objective of this study was to develop an efficient mathematical model of assembly lines that will enable the evaluation of associated steady-state performance and relevant KPIs at low CPU cost.  
As such, the main contributions of this research paper are (a) the formulation of an analytical solution of the steady-state response of a Bernoulli assembly line in the general case (arbitrary number of machines and buffers of arbitrary capacity) using constitutive transition matrices and the eigenvalue problem; (b) the development, validation, and application of the finite state method while bypassing the system states' dimensionality issue in the case of the Bernoulli assembly line; and (c) the reconstruction of joint probability distribution while keeping its functional relationships with the system's properties. This proposition of the joint probability distribution as well as the low computational burden, are of the utmost importance in the evaluation of key performance indicators, improvability considerations, bottleneck identification, and the design of production systems. Additionally, it has to be pointed out that this methodology, involving analytical development and the formulation of the finite state elements, has already been successfully applied in the case of the serial Bernoulli production lines [6; 14; 42; 43] as well as in the case of the Bernoulli splitting lines [3]. In other words, this paper represents a continuation of research that seeks to extend the current application of the Markovian framework in the case of production system engineering.  
The remainder of the paper is organized as follows. The analytical formulation of the problem and the formulation of the associated finite state elements are introduced in Section 2. Section 3 presents the application cases including the validation of the FSM and a discussion of the obtained results. The application of the developed theory in the case of the power transformers' production is summarized in Section 4. Finally, the main conclusions and directions for further research are outlined in Section 5.
# Identification of Wiener Box-Jenkins Model for Anesthesia Using Particle Swarm Optimization
## 1 Introduction
The main contribution of this paper is to use the Wiener Box-Jenkins model to approximate the mathematical model of anesthesia using simulated model data and employing the PSO-based optimization technique to estimate the parameters of the empirical model. This paper is organized as follows. First, the minimally parameterized parsimonious (MPP) model for drug dynamics during anesthesia is reviewed in Section 2. In Section 3, the Wiener Box-Jenkins model is presented and its one-step ahead prediction error is formulated. The section concludes with a review of the PSO algorithm to be used for estimating model parameters. The simulation setup and results are elaborated in Section 4. Finally, a summary and suggested future research are provided in Section 5.
## 4 Simulations and Results
### Simulation Results
#### 4.3.2 Comparative Analysis of Protocol Improvements  
A comparison of the improved parts of the proposed protocol is shown in Figure 5, where \"ICA\" denotes the improved clustering algorithm and \"PPWURC\" denotes the proposed protocol without using replication computing.  
The proposed protocol showed significant improvements over LEACH, ICA, and PPWURC, as depicted in Figure 5, offering enhanced network performance. As shown in Figure 5a, the proposed protocol maintained higher residual energy levels over an extended number of rounds compared to the others, indicating more efficient energy usage. As shown in Figure 5b, the proposed protocol sustained a greater number of surviving nodes for longer durations, suggesting superior node longevity and network stability. As shown in Figure 5c, the proposed protocol achieved a higher number of packets received by the base station, indicating an improved data transmission capability. As shown in Figure 5d, the first node death occurred substantially later in the proposed protocol, demonstrating its effective energy management and improved node lifetime.  
The advances of the proposed protocol are attributed to the integration of an improved clustering algorithm from ICA, parallel transmission scheduling from PPWURC, and the implementation of replication computing, which collectively contribute to its superior performance.  
Figure 4: Performance of LEACH, KMMDA, and proposed protocol. (**a**) Average residual energy; (**b**) average number of surviving nodes; (**c**) the number of packets received by the base station; (**d**) average round when the first death node occurs.
## 3 Methodology
### Fine-Tuning of 3D U-Nets  
The parameters of a pre-trained model can be fine-tuned by modifying them to minimize the loss on a different target task. This process allows the model to adapt to the new task while leveraging the knowledge learned from the pre-training. By adjusting the parameters, the model can better fit the new data and potentially achieve higher accuracy on the target task. This technique has demonstrated exceptional performance in various computer vision applications, particularly in the categorization of medical images. Once the 3D U-Net model has been trained using a large medical image dataset, the number of classes for the target task is determined, and the final layer is adjusted or substituted as needed. The quantity of training data available for the target job determines whether the model's pre-trained weights are frozen or fine-tuned. Loss functions, such as cross-entropy loss for binary or multi-class classification are defined based on the task and then employed for fine-tuning the 3D U-Net. This model is then retrained by updating the model factors for loss function minimization with optimization methods such as stochastic gradient descent (SGD) or Adam on the data from the target task.  
A well-tuned 3D U-Net model for medical image categorization can be described mathematically as follows.  
Loss Function:  
The loss function calculates the disparity between the predictions made by the model and the ground-truth labels. In the case of binary classification, the cross-entropy loss function can be expressed as  
\[Loss=-(y*log(p)+(1-y)*log(1-p)) \tag{16}\]  
where \(y\) is the true label and \(p\) is the model prediction. For a multi-class classification task, the cross-entropy loss function is given by  
\[Loss=-sum(y_{i}*log(p_{i}))) \tag{17}\]  
where \(y_{i}\) is the true label for class \(i\) and \(p_{i}\) is the model prediction for class \(i\).  
Optimization Algorithm:  
The optimization procedure involves updating the model parameters to minimize the loss function. This is accomplished with optimization algorithms, such as gradient descent, which iteratively adjust the model's parameters in the direction of the steepest descent of the loss function. The gradient descent algorithm can be expressed as an iterative process where, at each iteration, the model parameters are updated based on the gradient of the loss function for the given parameters. The gradient descent method can be represented as  
\[w=w-alpha*gradient(Loss) \tag{18}\]  
where \(w\) is the model parameters, \(alpha\) is the learning rate, and \(gradient(Loss)\) is the loss function gradient for \(w\).  
Regularization can be added to the loss function to prevent overfitting and improve generalization. L2 regularization can be represented as  
\[Loss=Loss+lambda*||w||^{2} \tag{19}\]  
where \(lambda\) is the regularization coefficient and \(||w||^{2}\) is the L2 norm of the model parameters.
## 4 Simulation and Evaluation
### Delay Measurement
The time for CPU is computed as given in Equation (3). \[CT=Number\;of\;Instructions\;X\;Number\;of\;Clock\;Cycles\;in\;the\;Instructions\;X\;Time\;for\;a\;Clock\;Cycle \tag{3}\]  
The utilization of the CPU is a crucial aspect of cutting-edge technology, and higher CPU usage results in increased power consumption. To reduce CPU usage, we employed an efficient method that improved system speed and performance, as depicted in Figure 9. The results demonstrate the improvement of CPU usage measured with the sysbench tool. We can see a lower usage percentage as compared to the other two approaches presented  
Figure 8: Throughput comparison of proposed scheme with [25] ([PERSON], 2017) and [26] ([PERSON], 2017). in [25] and [26]. This is due to the provision of resources in a proactive manner and a centralized view leveraging the SDN controller. D. Packet Loss Ratio  
For packet loss, we have used the D-ITG (distributed internet traffic generator) tool [39] to record the packet loss ratio. To calculate the packet loss ratio, we have used Equation (4). The packets lost were calculated from the D-ITG log analysis on the receiver host. In Equation (4), the L shows the number of lost packets out of the total number of N packets. Herein, the k shows the total number of lost packets. The procedure to generate the packets and record the loss ratio is shown in Algorithm 1. On the target host, a listening socket is created for TCP communication from source nodes using ITGRecv (H2). The network used class C IP addresses, and on the source host (H1) ITGSend was utilized to send TCP traffic with a payload size of 500 bytes for a duration of 2000 s, at a rate of 1000 packets per second, to a destination node with the IP address of 192.168.1.50. This experiment was conducted 10 times, and the average packet loss results are presented in Figure 10.  
\[PLR=\frac{\sum_{i}^{k}L}{N} \tag{4}\]  
Figure 10 shows the packet loss ratio of our proposed scheme in comparison with [25] or [26]. We can see that the packet loss ratio of the proposed approach is low as compared to the other schemes. This is due to the proactive caching ability as explained in our proposed scheme and the overall network view of the underlying network infrastructure resulting in a lower loss of packets as compared to other schemes. Figure 9: Comparison of CPU usages of proposed method with existing studies ([PERSON], 2017), and ([PERSON], 2017) illustrated in [25; 26].