esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Features Using a Public Dataset  
In order to determine the optimal algorithm structure, the performance of each step was analyzed. Our framework could be divided into three stages. Firstly, JBSS was applied to the detected RoIs and transformed into a _RoverG_ feature signal, followed by HR IMF decomposition using EEMD. Secondly, the correlation between the background region and the face region was analyzed for each channel to exclude noise that was found in both regions before transformation into _RoverG_. Finally, NLMS was applied to each channel after the noise was excluded in the previous stage.  
For stable analysis, the MAHNOB-HCI public indoor dataset was used [26]. The dataset contains videos of the frontal facial images of 27 subjects (15 females and 12 males) captured at a resolution of \(780\times 580\) and a frame rate of 61. It is the result of two experiments--emotion elicitation and implicit tagging--but we only used emotion elicitation because it was recorded in color and captured by the subject in various genres of motion situations reflecting every possible pulse change. The HRs of the subjects were recorded as electrocardiography (ECG) signals at 256 Hz while the subjects watched several video clips, and they were synchronized with the video.  
In order to allow an accurate comparison with previously proposed methods, the metrics used in previous methods were employed in this paper. \(M_{e}\) and \(SD_{e}\) are the mean and standard deviation of Equation (6), respectively.  
\[HR_{diff}=HR_{est}-HR_{gt}, \tag{6}\]  
where \(HR_{est}\) and \(HR_{gt}\) denote the estimated result and ground-truth, respectively.  
In addition, root-mean-square error (\(RMSE\)) and \(M_{eRate}\) were employed as shown below.  
\[RMSE=\left[\frac{1}{N}\sum_{n=1}^{N}\left[HR_{est}[n]-HR_{gt}[n]\right]^{2} \right]^{1/2}, \tag{7}\]  
\[M_{eRate}=\sum_{n=1}^{N}\left(\left|\frac{HR_{diff}[n]}{HR_{gt}[n]}\right| \right)\times 100, \tag{8}\]  
where \(N\) denotes the length of the entire video in seconds, and \(n\) is time index for each second. Finally, the Pearson correlation coefficient \(r\) between the estimated HR and ground-truth was evaluated. Table 1 displays the performance of each stage of our framework using the MAHNOB-HCI dataset in terms of these metrics.  
_RoverG\({}_{\text{JBSS}+\text{EEMD}}\)_, _RoverG\({}_{\text{excluded}}\)_, and _RoverG\({}_{\text{NLMS}}\)_ represented stages 1, 2, and 3, respectively. Their performance was somewhat consistent with the overall metrics. However, the performances of _RoverG\({}_{\text{JBSS}+\text{EEMD}}\)_ and _RoverG\({}_{\text{excluded}}\)_ were poor in terms of Pearson correlation \(r\) except for the other metrics (e.g., _M\({}_{e}\), SD\({}_{e}\), RMSE, _M\({}_{\text{RRate}}\)_). This is because the estimated HR fluctuated inconsistently, leading to a difference between the ground-truth and the estimated HR. Based on the experimental results, _RoverG\({}_{\text{NLMS}}\)_ was verified to be the superior framework.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 5 Experimental Results
### Performance Comparison with Existing Methods
#### 5.2.4 Packet Loss Analysis in High-Density Scenarios  
Packet loss increases when more users connect to the network, which is generally caused by interference and signal collisions. A smart control method improves beamforming precision, decreasing packet loss and guaranteeing dependable data transmission.  
The suggested methods found in Table 8 and Figure 6 show a somewhat higher packet loss rate of 45% with 20 users, compared to 25% for SIM-ADMT, 30% for MP-TCP, and 28% for UL-SCH + PUSCH. For 40 users, the recommended strategy improves performance by 35%, but the loss rates for SIM-ADMT, MP-TCP, and UL-SCH + PUSCH rise to 58%, 60%, and 55%, respectively. Out of 60 users, the recommended plan has the lowest packet loss rate of 25%, which is higher than 44% of SIM-ADMT, 39% of UL-SCH + PUSCH, and 50% of MP-TCP. The loss rate of the suggested scheme increases to 50% with 100 users, whereas it is 85% for SIM-ADMT, 80% for FB-TCP + MP-TCP + Deep IA, and 70% for UL-SCH + PUSCH. Overall, the proposed scheme exhibits a lower packet loss, especially for moderate user counts, suggesting that it works well in dense 5G mmWave network scenarios.
# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 2 Gait Recognition Framework
### Training Classification Model  
The synthetic gait dataset is randomly partitioned into two sets, where 80% is set as training and 20% as test data. For the supervised classification training stage, we use some of the most common algorithms used in gait recognition: K-nearest neighbors (KNN), Naive Bayes (NB), discriminant analysis (DA), decision trees (DT), and an artificial neural network (ANN) [51]. In order to evaluate the algorithm performance, we test each trained model while considering the following metrics: accuracy (_Acc_), recall (_R_), specificity (_SP_), precision (_P_), and the F-measure (_F_) [52], which are defined as follows:  
* Accuracy (_Acc_) \[Acc=\frac{TP+TN}{TP+TN+FP+FN}\times 100;\] (5)
* Recall or sensitivity (_R_) \[R=\frac{TP}{TP+FN}\times 100;\] (6)
* Specificity (_SP_) \[SP=\frac{TN}{TN+FP}\times 100;\] (7)
* Precision (_P_) \[P=\frac{TP}{TP+F}\times 100;\] (8)
* F-measure (_F_) \[F=\frac{2\times P\times R}{P+R_{i}}\times 100.\] (9)  
where _TP_: true positives, _TN_: true negatives, _FP_: false positives, and _FN_: false negatives for each gait.
## 4 Experimental Results
### Experiment Results and Analysis
#### 4.3.4 Ablation Study  
We performed ablation studies on our APTrans model. Considering the factor of time efficiency, we only conducted experiments on the AG News and TREC-6 datasets, and the parameter settings, such as sentence length, are the same as those in the previous experiments. Ablation experiments were performed on hierarchical deeper attention and FFCon to verify the validity of these two modules. The effects of the approaches were ascertained by experimenting with different collocations of these two modules. Figure 5 shows our experimental results. The two modules significantly improve the performance of APTrans. On the AG News dataset, the hierarchical deeper attention module improves the accuracy by 1.54% compared with the APTrans without any modules. Under the same comparison conditions, the FFCon module shows a great improvement in accuracy (94.22%), and the cooperation of the two modules results in the increase in the value by 1.80%, reaching 94.68%. Moreover, the addition of the two modules also greatly improves the prediction accuracy on the TREC-6 dataset, reaching 98.00%. The data from the modular ablation experiment fully demonstrate the obvious advancement of our two proposed modules, which shows that learning the key information relationship and word group inline relationship can indeed help improve the accuracy of the method. This also fully shows that the ideas and innovations we put forward are meaningful.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Methods** & **Pad Size** & **Acc (\%)** \\ \hline ERNIE [48] & 86.20 \\ ALBERT [47] & 95.80 \\ GPT-2 [36] & 96.40 \\ DeBERTa [53] & 96.80 \\ BERT [10] & 128 \\ RoBERTa [11] & 97.20 \\ ELECTRA [49] & 97.40 \\ BART [46] & **98.00** \\ \hline \hline \end{tabular} The top performing values are shown in **bold**, and those following the top values by the underlining.  
\end{table}
Table 3: Comparative results with some classical methods on TREC-6 dataset.
## 5 Cavity Analysis in CW Operation
### Gaussian Modes  
Using ABCD matrices, one can calculate the fundamental Gaussian \(TEM_{00}\) mode of the cavity for CW operation inside the stability ranges of the resonator. The Gaussian mode is represented by the complex beam parameter \(q=z+iz_{R}\), where \(z_{R}\) is the Rayleigh range of the beam given by \(z_{R}=\pi w_{0}^{2}/\lambda\) and \(w_{0}\) is the mode radius at the beam focus \(z=0\). To calculate the CW complex beam parameter, \(q_{cw}\), at any reference plane in the cavity, one can represent the complete round trip in the cavity as an ABCD matrix with respect to a chosen reference plane. The complex beam parameter at the reference plane can be calculated by solving:  
\[\left(\frac{1}{q_{cw}}\right)^{2}+\frac{A-D}{B}\left(\frac{1}{q_{cw}}\right)+ \frac{1-AD}{B^{2}}=0 \tag{6}\]  
where \(1/q_{cw}\) represents the beam spot size, \(w\), and the beam radius of curvature, \(R\), at the reference plane by:  
\[\frac{1}{q_{cw}}=\frac{1}{R}-i\frac{\lambda}{\pi w^{2}} \tag{7}\]  
Solving Equation (6) provides expressions for \(w\) and \(R\) as a function of the ABCD matrix elements, given by:  
\[\begin{split} R&=\frac{2B}{D-A}\\ w^{2}&=\frac{|B|\lambda}{\pi}\sqrt{\frac{1}{1-(A+D) ^{2}/4}}\end{split} \tag{8}\]  
A natural location to calculate the complex beam parameter is at the flat end mirrors, where the mode must arrive with a flat phase front. At the end mirrors, \(q_{cw}=iz_{R}\) is purely imaginary, and the mode waist radius, \(w_{0}^{(CW)}\), can be plotted as a function of the separation, \(\delta\), between the curved mirrors, as presented in Figure 3.  
Figure 2: Geometrical representation of the cavity mode at the stability limits: (**a**) plane-plane limit; (**b**) plane-point limit; (**c**) point-plane limit; (**d**) point-point limit.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 4 Theoretical Analysis  
This section will analyze the impact of battery characteristic parameter, distance and bandwidth on the BECPB for different types of batteries, and compare the BECPB for different types of batteries theoretically.
## 2 Mathematical Formulation and Governing Equations
### Governing Equations  
Figure 1 shows the boundary conditions and geometry of the present study. The mineral oil cutting fluid is considered a base fluid into which hybrid nanoparticles are dispersed. The ambient temperature outside the enclosure is considered to be 298 K and denoted as \(T_{\infty}\). Turbulence is modelled with the large eddy simulation (LES) Smagorinsky subgrid-scale model. The governing equations in Cartesian coordinates are as follows:  
\[\frac{\partial\overline{u_{i}}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial\overline{u_{i}}}{\partial t}+\overline{u_{j}}\frac{\partial \overline{u_{i}}}{\partial x_{j}}=-\frac{1}{\rho}\ \frac{\partial\overline{p}}{\partial x_{i}}+\ \
u\frac{ \partial^{2}\ \overline{u_{i}}}{\partial x_{j}\partial x_{j}}+\ \partial\tau_{ij}+\ \mathsf{g}_{i} \beta(T_{s}-T_{\infty}) \tag{2}\]  
The stress tensor \(\tau_{ij}\) is expressed as follows:  
\[\tau_{ij}=2\
u_{i}\overline{S}_{ij}+\frac{1}{3}\ \tau_{kk}\delta_{ij} \tag{3}\]  
\[\overline{S}_{ij}=\frac{1}{2}\Bigg{(}\frac{\partial\overline{u_{i}}}{\partial x _{j}}+\frac{\partial\overline{u_{j}}}{\partial x_{i}}\Bigg{)} \tag{4}\]  
where, \(\delta_{ij}\) is the Kronecker delta, \(\tau_{ij}\) is subgrid-scale (SGS) tensor, and \(\overline{S}_{ij}\) is the mean strain rate tensor.  
\[\
u_{t}=(C_{s}\Delta)^{2}\big{|}\overline{S}_{ij}\big{|}\]  
where \(C_{s}\) is a model parameter that is dynamically assessed as a function of the smallest resolved scale [36; 37; 38; 39] and \(\Delta\) is called filter width determined from the cube root of grid volume \(\Delta=(\Delta x\Delta y\Delta z)^{\frac{1}{3}}\). In Equation (5)  
\[\frac{\partial\overline{T}}{\partial t}+\overline{u}_{j}\frac{\partial \overline{T}}{\partial x_{j}}=\alpha\frac{\partial^{2}\overline{T}}{\partial x _{j}\partial x_{j}} \tag{5}\]  
\(\alpha\) is the thermal diffusivity, \(t\) denotes time, and \(T\) is the temperature.  
The Reynolds numbers and Nusselt numbers are evaluated using Equations (6) and (7), respectively:  
\[Re=\frac{\rho_{m}\ V_{m}\ D}{\mu_{m}} \tag{6}\]  
\[Nu=\frac{h\ l}{k_{m}} \tag{7}\]  
where \(D\) and \(l\) represent the diameter of the nozzle and the characteristic length of the tool tip.
## 2 Materials and Methods
### Release of EGCG  
In order to understand the encapsulate of EGCG-MBG scaffolds, the calibration curves with different EGCG concentrations were initially evaluated. The absorbance value of 0.002, 0.004, 0.0078, 0.0156, 0.0312, 0.0625, and 0.125, 0.25 mg/mL concentration in the phosphate-buffered saline (PBS) solution was recorded using the Varioskan Flash reader (Thermo Fisher Scientific, Massachusetts, USA).  
Based on the calibration curve information, we could estimate the encapsulation and cumulative release efficiency of EGCG-MBG scaffolds. To simulate the in vivo placement, the 20-mg EGCG-MBG scaffolds were immersed in 10 mL of PBS solution at 37 \({}^{\circ}\)C. At every measuring time point (2, 4, 10, 24, 48, 72, 96, 120, 240, and 360 h), 5 mL of buffer solution was withdrawn and replaced with the same amount of fresh solution. Then, the EGCG released scaffold was recorded using the absorbance reader with a 274 nm wavelength. The cumulative release of EGCG was calculated as follows:  
\[\text{Cumulative release efficiency (\%) = A/B \times 100}\]  
where A is the encapsulated number of scaffolds during the measurement time and B is the encapsulated number of scaffolds before immersion.
## 2 Literature Review
In [17], Mixed Integer Linear Programming (MILP) formulations to address various challenges and optimize resource allocation were used. Researchers have consistently employed MILP models for minimizing the transmit power, enhancing the fairness, and improving the resource allocation in D2D networks. These formulations tackle critical issues such as mode selection, scheduling, and power allocation, allowing for the efficient management of scalable interference. Furthermore, the MILP approach facilitates effective decision making with regard to admission control, mode selection, and power allocation, enabling the optimization of the system performance. By leveraging MILP techniques, researchers have been able to analyze and optimize the trade-offs between different objectives in D2D communication systems, leading to improved efficiency. In the study reported in [18], the authors focused on addressing the challenges of mode selection and resource allocation to improve the throughput and minimize the interference in cellular-D2D networks. The conventional approach faces difficulties, as the Base Station lacks channel quality information for the cellular-D2D link. This leads to uncertainties in achieving the desired Signal-to-Interference-plus-Noise Ratio (SINR) and maintaining the Quality of Service (QoS) requirements. To overcome this challenge, the authors propose a probabilistic resource allocation method that incorporates a quasi-convex optimization algorithm based on channel probability characteristics. This integration allows for the mitigation of channel state information uncertainties by gathering feedback through user selection. By leveraging the probabilistic resource allocation and optimization algorithm, the authors aim to enhance the overall system performance in terms of the throughput and interference management in the cellular-D2D network performance. The work in [19] provides a comprehensive review of the potential application areas of D2D technology, including IoT, wearables, and automated driving. It also examines the current status of D2D technology in 3 GPP standardization. Furthermore, the study analyzes D2D-enhanced cellular networks from both base station and end-user perspectives, using various measurement-based LTE and Wi-Fi models. The findings indicate that significant energy reductions can be achieved with different types of base stations, such as macro, pico, and femto.
## 2 Theoretical Foundations
### HEA Properties and Effects  
The multi-element composition and high mixing entropy of HEAs endow them with many excellent properties:  
* Mechanical properties: HEAs typically exhibit high strength and hardness, making them potentially valuable in applications that require high strength. Additionally, many HEAs demonstrate good ductility and toughness, particularly in low-temperature environments. For instance, the FeCoNiCrMn HEA shows significant ductility and toughness at low temperatures, making it promising for applications in extreme environments;
* Thermal stability: HEAs possess good thermal stability in high-temperature environments. This is due to the high mixing entropy, which slows down atomic diffusion and enhances the thermal stability of the material. For example, the AlCoCrFeNi HEA exhibits excellent strength and hardness at high temperatures, making it suitable as a high-temperature structural material;
* Oxidation and corrosion resistance: HEAs exhibit outstanding oxidation and corrosion resistance, which enables them to perform well in harsh environments. For example, HEAs with added Cr typically have excellent corrosion resistance, making them suitable for applications in corrosive environments;
* Physical properties: HEAs also demonstrate many excellent physical properties, such as high electrical conductivity, high thermal conductivity, and outstanding magnetic properties. These characteristics give HEAs potential applications in electronics, thermal management, and magnetic materials. The theoretical foundations of high-entropy alloys mainly include the high mixing entropy effect, lattice distortion effect, slow diffusion effect, and strong synergy effect.
* High mixing entropy effect: The high mixing entropy effect refers to the high configurational entropy resulting from the mixing of multiple elements in near-equal molar ratios. This high configurational entropy helps stabilize the solid solution structure and reduces the driving force for phase separation and the formation of secondary phases;
* Lattice distortion effect: Due to differences in atomic radii among the various elements in HEAs, lattice distortion occurs. This distortion can increase the strength and hardness of the material while also affecting its plastic deformation characteristics;
* Slow diffusion effect: The presence of multiple elements in high-entropy alloys can slow down the atomic diffusion rate, which contributes to improved thermal stability and creep resistance of the material;
* Strong synergy effect: In HEAs with a multi-element composition, the interactions between different elements can lead to unique physicochemical properties. For example, the high toughness and high ductility of certain HEAs in low-temperature environments result from the synergistic effects of multiple elements.
## 3 Implementation of the OAC-DES
### Evolutionary Algorithm in OAC-DES
#### 3.2.1 Theory of ES  
ES are especially suitable for solving global optimization, high-dimensional problems because of their insensitivity to becoming stuck in local optima. Normally, each individual in a population of solutions is represented as a real-valued vector:  
\[\mathbf{x}_{i}^{(t)}=(x_{i,1}^{(t)},\ldots,x_{i,n}^{(t)})^{T},\quad\text{for }i=1,\ldots,\mu, \tag{1}\]  
where \(\mu\) denotes the population size and \(n\) is a dimension of the problem to be solved.  
The classical ES operates with a mutation only. As a mutation operator, a Gaussian perturbation is implemented. However, an uncorrelated mutation with one step size [38] was used in our study.  
This mutation requires expansion of the original representation of an individual \(\mathbf{x}_{i}^{(t)}\) with an additional control parameter \(\sigma_{i}^{(t)}\), as follows:  
\[\mathbf{x}_{i}^{(t)}=(x_{i,1}^{(t)},\ldots,x_{i,n}^{(t)},\sigma_{i}^{(t)})^{T}, \tag{2}\]  
where \(\sigma_{i}^{(t)}\) designates the mutation step size (also mutation strength). Actually, the individual is modified according to the following equations:  
\[\begin{split}\sigma_{i}^{(t+1)}&=\sigma_{i}^{(t) }\cdot e^{\tau\cdot N(0,1)},\\ x_{i,j}^{(t+1)}&=x_{i,j}^{(t)}+\sigma_{i}^{(t+1) }\cdot N_{i}(0,1),\end{split} \tag{3}\]  
where \(\tau\propto\frac{1}{\sqrt{n}}\) represents a learning rate and \(N(0,1)\) is the random number drawn from the Gaussian distribution with mean zero and standard deviation one. To avoid \(\sigma_{i}^{(t+1)}\) falling too close to zero, the following boundary rule is used:  
\[\sigma_{i}^{(t+1)}<\epsilon_{0}\Rightarrow\sigma_{i}^{(t+1)}=\epsilon_{0}, \tag{4}\]  
where \(\epsilon_{0}\) is some predefined small value.  
The mutation scheme represented in Equation (3) allows the process of self-adaptation, where the control parameters are included in the representation of individuals in Equation (2) and undergo the consequences of operating the mutation operator together with problem variables.  
Normally, uniform random parent selection is used in ES, while two population models are typicaly employed, i.e., \((\mu,\lambda)\)-ES and \((\mu+\lambda)\)-ES. The first model generates \(\lambda\) offspring from a population of \(\mu\) parents, from which the best \(\mu\) offspring are preserved in the next population. In the second model, the \(\lambda\) offspring are generated from the population of \(\mu\) parents that compete with their parents for a place in the next generation. A ratio between the number of generated offspring and parents of approximately \(\lambda/\mu\approx 7\), as found throughout most experimental work [34], is a prerequisite for successful self-adaptation.  
Additionally, the evolutionary process can operate correctly in ESs only when a proper initial mutation step size \(\sigma_{0}\) is used. The proper range of step size values within which the search process can take place is called the evolution window. This, however, depends on the problem in question and must be determined during experimentation.