esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 5 A Selective MEC Service Model Based on a Multitier Architecture
### MEC Selection Scheme
The normalized centrality value of a MEC node \(n\) for a service request \(i\) can be expressed as  
\[c_{U}^{n,i}=\frac{v_{n}^{i}}{\sum_{n_{k}\in N_{C}^{i}}v_{n_{k}}^{i}}. \tag{8}\]  
On the basis of the parameters above, the multiple objective function is as follows:  
\[Max_{n_{k}\in N_{C}^{i}}\Big{(}a_{1}\cdot N\Big{(}L_{E2E}^{n,i} \Big{)}+a_{2}\cdot R_{A}^{n}+a_{3}\cdot U(r_{s},n)+a_{4}\cdot\psi_{U}^{n}+a_{5}c _{U}^{n,i}\Big{)}. \tag{9}\]  
Constraint 1: if \(d_{i}^{l}>L_{E2E}^{n}\), MEC node \(n\) is excluded from \(N_{C}^{i}\);  
Constraint 2: if \(d_{i}^{l}\langle r_{n}\big{\|}d_{i}^{m}<r_{n}^{m}\big{\|}d_{i}^{s}<r_{n}^{s}\), MEC node n is excluded from \(N_{C}^{i}\);  
Constraint 3: \(a_{1}+a_{2}+a_{3}+a_{4}+a_{5}=1\) (\(a_{n}\geq 0\)). In Equation (9), constraints 1 and 2 guarantee that the end-to-end latency and service resource requirements are met, respectively. Constraint 3 is the objective function coefficient, which maps the consideration factors.
# Fast Face Tracking-by-Detection Algorithm for Secure Monitoring
## 3 Proposed Algorithm
### Tracking
#### 3.1.1 KCF Tracking Principle  
The KCF algorithm adopts the ridge regression method to train the detector. Ridge regression is an improvement on least squares regression, and it is more reliable and practical. Assume the training sample set is \((x_{i},\,y_{i})\), \(i=1,2,\,\ldots,\,n\), where \(x_{i}\) is the training samples, \(y_{i}\) is the corresponding sample labels and \(y_{i}\) obeys the Gaussian distribution.  
Under the linear condition, the regression function is \(f(x_{i})=\omega^{T}x_{i}\), and \(\omega\) is the weight function of the column vectors, which can be solved by the least squares method:  
\[\min_{\omega}\sum_{i=0}^{n}\left[f(x_{i})-y_{i}\right]^{2}+\lambda\parallel \omega\parallel^{2} \tag{1}\]  
where \(\lambda\) is the regularization parameter and the embodiment of ridge regression's supplement to least squares regression. The process of training the classifier is to find the optimal \(\omega\) to minimize the residual error of the regression function.  
We set the derivative of (1) to be zero, solving \(\omega\), and rewriting it into frequency domain form:  
\[\omega=\left(X^{H}X+\lambda I\right)^{-1}X^{H}y \tag{2}\]  
where \(X^{H}\) is the conjugate transpose of \(X\).  
Training samples \(x_{i}\) in the KCF algorithm are obtained by the cyclic shift of target sample \(x\). According to the properties of the cyclic matrix and Fourier transform, the formula for calculating the final linear regression coefficient is obtained:  
\[\hat{\omega}=\frac{\hat{x}\odot\hat{y}}{\hat{x}\odot\hat{x}^{*}+\lambda} \tag{3}\]  
where \(\hat{x}\) is the result of the fast Fourier transform (FFT) of \(x\), and \(\odot\) is the operation of multiplying the corresponding elements of the matrix.  
In practice, most problems are nonlinear, and the conversion of nonlinear problems into linear solution methods is expressed as:  
\[\omega=\sum_{i}\alpha_{i}\varphi(x_{i}). \tag{4}\]  
So in the nonlinear regression, solving the coefficient \(\omega\) turns to solving \(\alpha_{i}\) and the mapping relationship \(\varphi(x_{i})\). \(k\) is a Gaussian kernel function, and the nuclear correlation of two samples can be expressed as:  
\[\begin{array}{ll}K_{ij}&=\varphi^{T}(x_{i})\varphi(x_{j})=k(x_{i},x_{j})\\ &=\exp\Bigl{(}-\frac{1}{\sigma^{2}}\left\|\left\|x_{i}\right\|^{2}+\left\|x_ {j}\right\|^{2}-2F^{-1}(\hat{x}_{i}^{*}\odot\hat{x}_{j})\right\|\Bigr{)}\end{array} \tag{5}\]  
Obtain the solution of the ridge regression of the kernel space:  
\[\alpha=(K+\lambda I)^{-1}y. \tag{6}\]  
Using the same method to calculate the nonlinear regression coefficient \(\hat{\alpha}\),  
\[\hat{\alpha}=\frac{\hat{y}}{\hat{k}^{xx}+\lambda}. \tag{7}\]  
Finally, a nonlinear regression function is obtained as follows:  
\[f(z)=\omega^{T}z=F^{-1}(\hat{k}^{xx}\odot\hat{\alpha}). \tag{8}\]  
The maximum value of \(f(z)\) is the maximum response value and the position of the predicted target.
# Video-Based Gaze Detection for Oculomotor
## 1 Introduction
Although humans can quite accurately detect someone else's gaze visually (i.e., tell where that person looks), the equivalent technology used to locate a human subject's point of regard (PoR) is still lacking. The most commonly used eye trackers at present are Infrared (IR) active sensors, which track the eye's position and orientation to estimate the subject's PoR. These specialized IR eye trackers are currently considered to be the most accurate measurement tool for gaze detection and can be mounted next to a desktop monitor [23] or embedded in wearable head devices (e.g., glasses) [24]. However, even in state-of-the-art IR eye trackers, researchers have observed significant sensitivity to the experimental setup and the diverse characteristics of subjects [25]. Video-based gaze detection has been in development since the 1970s, with many limitations and restrictions imposed on the experimental setup (e.g., fixed head position, an array of mirrors around the subject, multiple camera deployment) and light sources [26; 27]. However, advances in hardware since the early 2000s have accelerated the development of video-based gaze detection using standard, affordable web cameras, with their resolution, sensitivity, and frame rate having significantly increased, thus enabling more accurate gaze detection using existing analytical algorithms. In recent years, both computation and storage costs have dropped while data transfer bandwidths have widened, enabling the introduction of efficient and robust neural network (NN) models for video-based gaze detection [28]. This recent leap has encouraged the development of high-accuracy, video-based gaze detection, which may be applied for all the above purposes, with an affordable price tag and a convenient and flexible (ideally seamless) setup. We introduce and test a gaze detection model that combines several neural network models for eye detection, eye segmentation, and gaze estimation with an ongoing real-time calibration procedure. Our study is aimed at the neurological applications of gaze detection and considers the essential measurement for the extraction of OMs, performed using a simple and affordable setup that presents visual stimulation tasks and processes the video of the gazing subject captured using a webcam. As some OM classes (e.g., saccadic latency)require high spatio-temporal resolution, high accuracy is required when extracting the gaze PoR in each video stream frame. In this paper, we present our gaze detection model, along with its validation and error estimation results. Section 2 describes the gaze detection model and the clinical study environment, including the experimental setup, examination protocol, and the demographic data of participants. Section 3 provides the results of the video-based gaze detection model, compared to a reference specialized IR eye tracker for validation. In Section 4, we discuss our findings.
## 3 Numerical Examples
### Optimization of a Truss System
Overall, the use of NP constraints provided smaller cost functions for multi-objective optimization runs. If the number of runs were increased to obtain Pareto optimal sets, the minimum cost function values, both with and without NP-constrained solutions, grew closer to each other. Conversely, when one or more of the weightings (e.g., \(\alpha\), \(\beta\), \(\gamma\) or \(\lambda\)) were chosen to be zero, the problem formulation excluded some cost functions that could lead to misleading optimum solutions. In parallel with the observations made in the previous section, imposing the NP constraint resulted in a smaller cost function. Subsequently, the NP-constrained frequency domain optimization formulations were solved using stochastic optimization methods. The PSO algorithm was preferred for this purpose over the GA because it was reported in [54] that the PSO possessed computational superiority over the GA. When solving the NP-constrained optimization problems, the constrained PSO algorithm in [55] was adapted to our problem. An unconstrained PSO algorithm in Matlab was used when the optimization problem was solved without the NP constraint. The numerical results of the NP-constrained optimization problem solved by the constrained PSO algorithm are shown in Figure 7. The total cost function Equation (11) result found by the PSO was \(5.7149\)\(e^{-4}\), which was higher than that obtained using the interior point method. On the other hand, the CPU time of the constrained PSO algorithm was about four times longer than that of the interior point method when ran for this example on an Intel i7 CPU computer.
## 3 Methods and Data
### Construction of the WaveNet Network Model
#### 3.2.3 WaveNet  
The WaveNet network architecture is composed of several key components, including convolutional layers with a 3 \(\times\) 3 kernel size, max-pooling layers with a 2 \(\times\) 2 size, three residual blocks, three channel-wise attention mechanism residual blocks, and a global average pooling layer. The final layer is a fully connected layer that utilizes softmax transformation to derive the probability distribution for each sample across different classes.  
For input remote sensing images with dimensions of 3 \(\times\) 64 \(\times\) 64, each layer of the WaveNet network has specific input and output feature map sizes, as illustrated in Table 3.  
To manage computational complexity effectively, WaveNet initially employs a sequence of 3 \(\times\) 3 convolutional layers and 2 \(\times\) 2 max-pooling layers to reduce the feature map size. However, in the first residual block, the feature map size remains unchanged to preserve crucial information, while the number of channels is doubled. In the subsequent three residual blocks, the channel count is doubled, but the height and width are halved by the first convolutional layer.  
The final layer in the network is a fully connected layer that incorporates softmax transformation. The last layer in the network is a fully connected layer containing a softmaxtransformation. This layer converts the final output of the network into a probability distribution between 0 and 1. The formula of the softmax function is as follows:  
\[p_{i}=\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}} \tag{9}\]  
Among them, \(p_{i}\) represents the probability of the \(i\)-th category, \(z_{i}\) is the \(i\)-th element of the input vector \(z\), and \(K\) is the total number of categories, which is 4 in this article.  
The softmax function has specific advantages in classification tasks because it can calculate the predicted probability for each class, which is very useful for the training and interpretation of neural networks. The softmax function will emphasize the element with the largest value in the input vector so that its corresponding category probability is the highest, thereby classifying, and ultimately determines the final classification result.
# ALPINE: Active Link Prediction Using Network Embedding
## 5 Experiments and Discussion
### Quantitative Evaluation of ALPINE
#### 5.3.3 Case 3: \(P\subset U\), \(T\subset U\), and \(T\cap U=\emptyset\)  
We imposed further constraints in the third case: not only the pool \(P\) of node pairs that could be queried was limited, but also the set \(T\) of target node-pairs for which we wanted to predict the status was limited. Moreover, both sets were not intersecting. As in the second case, the budget was set to \(k=5\)s and \(|P|=10\)s. The target set size was now taken to be \(|T|=5\)s. Both \(P\) and \(T\) were sampled randomly from \(U\) before the querying started.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Data** & **Rand.** & **Page-Rank.** & **Max-Deg.** & **Max-Prob.** & **Min-Dis.** & **Max-Ent.** & **d-opt.** & **v-opt.** & **HALLP** \\ \hline Polbooks & 0.001 & 0.031 & 0.008 & 0.004 & 0.027 & 0.004 & 0.093 & 0.482 & 12.33 \\ C\_ elegans & 0.005 & 0.108 & 0.042 & 0.028 & 0.18 & 0.029 & 0.675 & 5.469 & 148.4 \\ USAir & 0.006 & 0.134 & 0.052 & 0.035 & 0.231 & 0.036 & 0.881 & 7.309 & 232.6 \\ MP\_cc & 0.016 & 0.707 & 0.16 & 0.117 & 0.693 & 0.125 & 2.746 & 28.90 & 1074 \\ Polblogs\_cc & 0.092 & 1.153 & 0.707 & 0.675 & 3.264 & 0.709 & 12.31 & 226.0 & 12022 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Runtime for one query in seconds—Case 1.  
The results in Figure 6 confirmed again that active learning outperformed passive learning. One might expect **v-opt.** to perform the best in this case because it was the only strategy that explicitly considered \(T\). Although it was shown to perform quite well in some subplots especially for the first iteration, the quality of the embedding affected its performance. Indeed, as we observed before, the reliability of all embedding-based strategies depended largely on how well the network was embedded, which became much better as \(r_{0}\) increased.  
Figure 5: ALPINE—\(P\subset U\) and \(T=U\). Row 1: Polbooks (\(s=54\)); Row 2: C. elegans (\(s=439\)); Row 3: USAir (\(s=54\)); Row 4: MP_cc (\(s=160\)); Row 5: Polblogs_cc (\(s=7460\)).  
Compared to the previous two cases, the results here were not as smooth even after averaging. The reason was that the score _AUC_T_ depended not only on \(P\), but also largely on \(T\), which were both randomly sampled. Whether \(P\) contained candidate node pairs that were informative for \(T\) affected the score. Overall, the embedding-independent strategies--**page-rank.** and **max-deg.**--had the top performance when \(r_{0}\) was small; and the embedding-based strategies became increasingly competitive if more information was observed.
# Integrating Mobile Devices and Wearable Technology for Optimal Sleep Conditions
## 2 Materials and Methods
### Data Analysis  
The algorithm model of data analysis consists of designing the sleep stages in a stepwise manner based on the data results of human brain waves, heartbeat, and turnover measured in the previous step. The collected brainwave activity information is filtered, segmented, and tagged, and the active high-frequency response and gentle low-frequency brainwave activity are quantitatively distinguished to determine the user's actual sleep cycle. Combined with the data results regarding turnover and heartbeat, the sleep stage is divided into three phases: awake, REM, and not REM. The model analyzes each phase of sleep. To confirm the sleep stage by spectral analysis and marking brainwave signals, turnover information is added; the turnover time and intensity recorded by the accelerometer of the Apple Watch are analyzed; and the sleep stages with different power and turnover times are marked. As an illustration, REM sleep typically involves rapid eye movements and muscle paralysis, resulting in a heightened frequency of body movements and position changes. Conversely, during the transition to deep sleep stages within NREM sleep, the frequency of these movements diminishes, reflecting reduced turnover activity. In heartbeat analysis, the frequency and timing characteristics of heart rate variability are extracted by frequency spectral analysis and other processing, then matched with brain wave results to build a sleep stage model. For example, during REM sleep, the Beta and Alpha brain waves are more active, and the analysis results of heart rate variability are also excitatory, while during NREM sleep, the analysis results are relaxed. When the cloud sleep stage analysis algorithm model is updated, the sleep data are uploaded to the system by the user, the sleep stages are analyzed, and the analysis results are sent back to the user.
## 4 Numerical Results
### Case Studies and Discussions
#### 4.2.1 Optimal Solution Results in the Base Case
It can be seen that the upward/downward reserve energy varies significantly from one scenario to another due to the significant difference between the RESs and load data in the scenarios. However, the minimum requirement of upward reserve energy during the BC contract periods is always guaranteed. The CVPP's trading strategy in the BE market and the participation of its internal sources in supplying reserve energy in the highest probability scenario are illustrated in Figure 10b. Figure 8 shows that the total power output of RESs in CVPP at 4:00-6:00 is smaller than the local demand, so theoretically, CVPP can hardly provide upward reserve energy in this period. However, the results in Figure 10b show that the CVPP's upward reserve energy from 4:00 to 6:00 can be around 10 MW, with most of the upward reserve provided by ESS. Similarly, during the other periods, from 9:00 to 12:00 and from 14:00 to 17:00, ESS also provides the majority of upward reserve energy. In addition, during the period without the upward reserve contract of the BC market, the CVPP mainly provides downward reserve energy, in which ESS also plays a major role. In some time blocks, for example, from 7:00 to 7:30, the downward reserve power can even be higher than 15 MW because the ESS changes its state from discharging to charging. By contrast, at 2:15 or 23:30, the ESS situation changes from charging to discharging, so that  
Figure 13: Base case: The ESS operation in the BE market in the highest probability scenario of uncertain parameters: (**a**) The ESS charging/discharging power; (**b**) The ESS energy before/after activating reserve. the CVPP's upward reserve at these time blocks can reach 20 MW. Figure 13a describes the maximum ability of the ESS in reserve provision, with red areas and green areas depicting the ESS's upward and downward reserve capacity, respectively. The blue line in Figure 13b shows the variation of the ESS energy in the DA market, while the red line represents the ESS's remaining energy level, providing reserve energy. The difference between these two lines is the amount of energy the ESS must discharge to supply upward reserve or recharge when supplying downward reserve. These results show the role of ESS in providing reserves. Also, CVPP's reserve provisioning capacity has been fully utilized in the proposed model without affecting the DA operating schedule.
## 4 Results and Discussion
### Comparative Analysis  
The proposed system efficiency is known by comparing the proposed system with existing methods. The existing methods used for the comparison are OAP [22], FBM [23] and NRZ-OOK-LOS [24]. The OAP scheme is developed for the downlink communication of MIMO VLC. The information about the transmitted symbols are used in the OAP for correlating the interference in the MIMO-VLC system [22]. The frequency translated version of the SRRC filter develops the quadrature orthogonal filter-banks and multiple in phase. These are used to modulate information symbols for creating the transmit signal in the FBM-based signal design scheme [23]. The BER for LOS, LOS with 1 st reflection, LOS with 1 st and 2 nd reflection are analyzed in the \(4\times 4\) MIMO-VLC system [24].  
Similar to the performance analysis, the comparative analysis of the proposed method is evaluated for the same three scenarios. The proposed system with the comparison of OAP [22], FBM [23] and NRZ-OOK-LOS [24] are carried out for scenario 1, 2 and 3, respectively. The comparative analysis of the scenario 1, 2 and 3 are depicted in Tables 4-6, respectively. From the Tables, it can be concluded that the proposed method achieves less BER when compared to the OAP [22], FBM [23] and NRZ-OOK-LOS [24] techniques. For example, the proposed system BER value of scenario 3 is 0.0003 at 150 dB; it is less when compared to the NRZ-OOK-LOS [24] technique. The reason behind the proposed system with less BER is the reduction of errors occurred during the communication by using the BBD precoding.  
Figure 10: Evaluation of power for scenario 3.  
Figure 9: Evaluation of throughput for scenario 3.
## 2 Experimental Methodology
### Laboratory-Scale Linear Cutting Machine and Conical Pick  
In this study, cutting tests were performed using a laboratory-scale LCM, as shown in Figure 3. The machine operates with three-axis servo motors. The x- and y-axis servo motors have stroke lengths of 500 mm and 700 mm, respectively, allowing precise control of the rock box movement. The z-axis servo motor drives the saddle, enabling vertical movement of the conical pick. Three orthogonally arranged load cells are employed to measure the tool forces exerted on the conical pick during cutting. The side force (\(FS\)) is measured by the x-axis load cell, while the normal force (\(FN\)) and cutting force (\(FC\)) are recorded by the y-axis and z-axis load cells, respectively. Tool force data are acquired at a sampling rate of 10 Hz and stored in CSV format. The control box provides real-time force visualization and allows configuration of cutting parameters.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & \(\rho\) **(g/cm\({}^{3}\))** & \(E\) **(GPa)** & \(\
u\) & \(\sigma_{c}\) **(MPa)** & \(\sigma_{t}\) **(MPa)** \\ \hline Finike limestone & 2.22 & 21.1 & 0.13 & 49.0 & 5.00 \\ Cement mortar & 2.11 & 8.7 & 0.20 & 6.0 & 0.90 \\ Concrete & 2.38 & 38.9 & 0.30 & 42.0 & 2.51 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of joint rock mass specimen components [36].  
Figure 3: Components of Laboratory-scale LCM used in this experiment [36].  
Figure 2: Schematic diagram of jointed rock mass specimen [36]: (**a**) cross-sectional view and (**b**) full view.  
The conical pick used in the rock cutting tests was the SM06 model manufactured by Kennametal (Pittsburgh, PA, USA), as shown in Figure 4. This pick has a total length of 87.2 mm, with a gauge length of 45.3 mm and a tip angle of 90\({}^{\circ}\). To eliminate the influence of tool orientation, the attack angle was fixed at 45\({}^{\circ}\), while both the skew and tilt angles were set to 0\({}^{\circ}\).
## 2 System Model and Problem Formulation
### Request Model
#### 2.4.2 Service Request  
When \(\pi_{n}^{d}(t)\) is a service request, i.e., \(q_{n}(t)=1\). To better show this request, we represent \(\pi_{n}(t)\) using a parameter tuple, given by \(\pi_{n}^{d}(t)=(\xi_{n}^{d}(t),s_{n}^{d}(t),c_{n}^{d}(t))\), where \(\xi_{n}^{d}(t)\) represents the \(d\)-th service required for computing \(\pi_{n}^{d}(t)\), \(s_{n}^{d}(t)\) represents the task data sizes (in bits), and \(c_{n}^{d}(t)\) represents the total number of instructions (in CPU cycles).  
Vehicle \(n\) can choose to either compute it locally or offload it to the associated UAV that cached the service \(\xi_{n}^{d}(t)\). We note that vehicle \(n\) can offload the computation tasks to UAV \(u\) when UAV \(u\) is accessible and deployed with the service, i.e., \(x_{u,d}(t)=1\) and \(I_{n,u}=1\).  
We use \(a_{u,n}^{d}(t)=0,1\) to represent whether the computation \(\pi_{n}^{d}(t)\) is offloaded to UAV \(u\). Therefore, we have  
\[\sum_{u=1}^{U}a_{u,n}^{d}(t)\leq 1,\forall t\in\mathcal{T}. \tag{12}\]  
Note that \(\sum_{u=1}^{U}a_{u,n}^{d}(t)=0\) indicates that task \(\pi_{n}^{d}(t)\) is computed locally at vehicle \(n\) at time slot \(t\). This models the binary offloading decision [38].  
For UAV \(u\), it can provide computation services to multiple vehicles satisfying the constraint on its computation capability, represented by the maximum number of services for UAVs, i.e., \(Core^{UAV}\). So, we have the constraint  
\[\sum_{n=1}^{N}\sum_{d=1}^{D}a_{u,n}^{d}(t)\leq Core^{UAV},\forall u\in\mathcal{ U},\forall t\in\mathcal{T}. \tag{13}\]