esa-sceva/satcom-chunk-collection huggingface dataset:
# HPC Cloud Architecture to Reduce HPC Workflow Complexity in Containerized Environments
## 5 Evaluation
### Performance Evaluation with MPI Tests  
We constructed two nodes for running point-to-point MPI parallel tasks to check different types of bandwidth and latency with the benchmark tool osu-micro-benchmarks v5.4 [22]. The latency test mainly measures the latency caused by sending and receiving messages by data size between two nodes using the ping-pong test. The test results of latency between two nodes with bare-metal, singularity, and docker-calico are almost the same and will be not mentioned in this paper [21]. The test results of bandwidth (Figure 21) include bandwidth, bi-directional bandwidth, and multiple-bandwidth tests. As shown in the figure, the peak bandwidth exists in a certain message size interval. This interval is almost the same in three cases of bare-metal, singularity, and docker-calico, except for the instability of docker-calico.  
Results between the two nodes are insufficient to evaluate the performance of the container-based HPC platform in an HPC environment. Therefore, we constructed 8 nodes with 16, 32, 64, and 72 cores for measuring the latency test with various MPI blocking collective operations (barrier in Figure 22, gather in Figure A1, all-gather in Figure A2, reduce in Figure A3, all-reduce in Figure A4, reduce-scatter in Figure A5, scatter in Figure A6, all-to-all in Figure A7, and broadcast in Figure 23 using the same benchmark tool osu-micro-benchmarks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Container** & **OS** & **Library** & **Parallel Library** & \multicolumn{2}{c}{**Application Library**} & **Application** \\ \hline \multirow{4}{*}{Docker 17.06.1c / Singularity 2.2} & \multirow{4}{*}{CentOS 6.8} & gcc & \multirow{2}{*}{OpenMPI} & 1.10.6 & \multirow{2}{*}{GotoBLAS 2-1.13} & HPL & 2.2 \\  & & gcc & & & & & \\  & gcc & & & & & \\  & gcc & & & & & \\  & CentOS 7.2 & gcc & & & & & \\  & gcc & & & & & \\  & gcc & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Template of containerized applications.  
Figure 20: Multitenancy concept implemented in our platform.  
Figure 21: MPI bandwidth includes (**a**) MPI Bandwidth, (**b**) MPI BI Bandwidth, and (**c**) MPI Multi Bandwidth.  
Figure 22: MPI barrier latency.  
In Figure 22, the barrier latency of singularity shows almost the same performance with the bare-metal case except with 64 cores. The value of docker-calico shows a performance gap between singularity and bare-metal. For the remaining operations, as the number of cores increases in the same number of nodes, the performance results are almost the same with bare-metal, singularity, and docker-calico when looking at the overall graph change. However, in Figure 23, the results of docker-calico in a particular interval are worse than the other two cases in a specific message size range.
# Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine
## 2 Backtracking Search Optimization Algorithm  
BSA is a young EA which is designed to be a global optimizer. It employs two populations: current population and historical population to search for the optimal solution. As done in other EAs, BSA has simple structure and can be divided into five processes by its functions: initialization, selection-I, mutation, crossover, and selection-II.  
**Initialization:** In the stage, BSA randomly produces the initial population \(P\) using Equation (1).  
\[P_{i,j}=lb_{j}+rand\cdot\left(ub_{j}-lb_{j}\right) \tag{1}\]  
where \(i=1,2,3,\ldots,NP\) and \(NP\) is the number of individuals in population. \(j=1,2,3,\ldots,D\) and \(D\) is the number of dimension of variables. \(low_{j}\) and \(up_{j}\) are the lower and upper boundaries on the \(j\)th variable, respectively. \(rand\) is a random number within [0,1].  
**Selection-I:** In the BSA's selection-I stage, the initial historical population \(oldP\) is generated randomly using Equation (2).  
\[oldP_{i,j}=lb_{j}+rand\cdot\left(ub_{j}-lb_{j}\right) \tag{2}\]Afterwards, at the beginning of each iteration, \(oldP\) is redefined by Equation (3). In addition, Equation (4) is used to randomly update the order of the members in \(oldP\).  
\[oldP=\left\{\begin{array}{cc}P,&\text{if }a<b\\ oldP,&\text{otherwise}\end{array}\right. \tag{3}\]  
\[oldP=permuting\left(oldP\right) \tag{4}\]  
where \(p\) is a random number within [0,1]. _permuting_ function is a random shuffling function. Through Equation (3), BSA assigns a population randomly selected previous generation as the historical population and saves the historical population until it is changed.  
**Mutation and Crossover:** Mutation and crossover operators aim at producing a new individual. Equation (5) involving historical population and current population is used to generate a trail individual. Then, crossover operator is performed by Equation (6). From Equation (6), a binary integer-value map is employed to guide the crossover direction.  
\[\text{M}=\text{P}+\text{F}\cdot\left(\text{oldP}-\text{P}\right) \tag{5}\]  
\[V_{i,j}=\left\{\begin{array}{cc}P_{i,j},&\text{if }map_{i,j}=1\\ M_{i,j},&\text{if }map_{i,j}=0\end{array}\right. \tag{6}\]  
where \(F\) is a scale factor and it generally takes value of \(3\cdot rand\), where \(rand\) is a random number between 0 and 1. \(V_{i,j}\) is the value of the \(j\)th variable for the \(i\)th trial individual.  
**Selection-II:** In the selection-II stage, BSA adopts a greedy selection mechanism to pick out a new solution \(P_{i}^{new}\). For the minimum problem, if the fitness value of the trial individual \(V\) is better than that of the current individual \(P_{i}\), \(V\) is selected, as shown in Equation (7);  
\[P_{i}^{new}=\left\{\begin{array}{cc}V_{i,}&\text{if }f\left(V_{i}\right)\leq f \left(P_{i}\right)\\ P_{i},&\text{otherwise}\end{array}\right. \tag{7}\]  
In the BSA, four above-mentioned processes 2-5 are repeatedly executed until it meets the termination criteria.
## 2 Governing Equations and Numerical Methods
### Numerical Methods  
First, the steady-state RANS simulation was performed to obtain an average flow field from which the volume flow rate through the nozzle was predicted and compared with the measured data. This comparison confirms the validity of the numerical method, and the RANS solution was used as an initial condition for the subsequent LES simulation for fast convergence. In the RANS simulation, the k-\(\omega\) Shear Stress Transport (SST) turbulence model was used as a closure model. The second order upwind scheme was used for the spatial discretization in both the equation for turbulent kinetic energy and dissipation rate.  
High-order numerical simulation techniques in time and space are required to accurately compute the acoustic waves as well as the turbulent flow simultaneously. Therefore, the unsteady LES was performed with the steady-state RANS results as an initial condition. The bounded second-order implicit method and second-order upwind scheme were used for time and space discretizations, respectively. The pressure and velocity coupled method was used. The Smagorinsky-Lilly model, which is relatively simple and widely used, was employed as the subgrid-scale model for turbulence flow. The numerical discretization scheme for time and space was the second order accuracy, and the time interval was \(5\times 10^{-5}\) s for a sampling rate of 20,000 Hz.  
All of the numerical methods were realized using the ANSYS Fluent program.
## 2 Materials and Methods
### Measurements
#### 2.2.3 Cardiorespiratory Fitness  
Cardiorespiratory fitness was conducted by an incremental test of a bicycle ergometer (Techogym Bike Med, Technogym, Gambettola, Italy) according to previous recommendations [29]. The theoretical maximum load (W) was estimated [30]. The protocol consisted of a 3-min rest period; a 3-min warm-up at 20% of the maximum load; followed by 6-min stages at 30, 40, 50, and 60% of the maximum load up to a respiratory exchange ratio \(\geq\) 1. Thereafter, 6-min stages were completed until the maximum effort was reached. Verbal stimuli were allowed. The test was considered maximal if a respiratory exchange ratio \(\geq\) 1.1 was reached and/or if the maximum heart rate (HRmax) was greater than or equal to the theoretical maximum predicted by the Morris equation for the cycloergometer test.  
The protocol consists of a 3-min rest period; a 3-min warm-up at 20% of the maximum load; followed by 6-min phases at 30, 40, 50, and 60% of the maximum load up to a respiratory exchange ratio of \(\geq\)1. Afterwards, the 6-min stages were completed until the maximum effort was achieved. Verbal stimuli were permitted. The test is considered to be the maximum if the respiratory exchange rate of 1.1 is reached and/or the maximum heart rate (HRmax) exceeds or is equal to the theoretical maximum predicted by the Morris equation for the cycloergometer test [31]. Following the manufacturer's specifications, taking into account the last phase of the cycloergometer, the following variables are calculated from the average of the last 30 s of air inhaled using a breath-by-breath gas analysis method (Metalyzer 3B-R2. Cortex(r), Leipzig, Germany): ventilatory threshold 2 (value provided by MetaSoft(r) Studio software (company: Cortex(r), Leipzig, Germany),  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Mean** & **DS** & **Lower to** & **Upper 95\%CI** \\ \hline Age (years) & 32.74 & 10.07 & 27.88 to & 37.59 \\ Height (cm) & 164.2 & 7.966 & 160.3 to & 168.0 \\ Body mass (kg) & 88.01 & 14.87 & 80.84 to & 95.1 \\ BMI (kg\(\cdot\)m\({}^{-2}\)) & 32.54 & 4.603 & 30.32 to & 34.76 \\ FM (\%) & 38.01 & 9.551 & 33.41 to & 42.61 \\ FFM (\%) & 50.00 & 10.85 & 44.77 to & 55.23 \\ HOMA-IR & 3.537 & 0.8584 & 3.123 to & 3.951 \\ Fasting insulin (mg\(\cdot\)dL\({}^{-1}\)) & 16.54 & 3.487 & 14.86 to & 18.22 \\ Fasting glycemia (mg\(\cdot\)dL\({}^{-1}\)) & 86.42 & 6.301 & 83.38 to & 89.46 \\ VO\({}_{2}\)max (L-min\({}^{-1}\)) & 2.021 & 0.6844 & 1.691 to & 2.351 \\ MPO (g\(\cdot\)h\({}^{-1}\)) & 6.021 & 5.410 & 3.414 to & 8.628 \\ \hline \hline \end{tabular}  
* Abbreviation: DS Standard Dev CI: confidence interval; BMI: Body Mass Index. HOMA-IR: Insulin Resistance Index. VO\({}_{2}\)max: Maximal oxygen uptake. FM: Fat mass. FFM: Free-fat mass. MPO: maximal fat oxidation.  
\end{table}
Table 1: Descriptive characteristics of the participants analyzed (\(n\) = 19).  
and HRmax (beats\(\cdot\)min\({}^{-1}\)), maximum load (watts). The same methodology was used to determine VO\({}_{2}\)max (L\(\cdot\)min\({}^{-1}\)).
## 4 Experiments and Analysis
### Performance Evaluation of the Emotion Evaluation Network  
Using the emotion evaluation network, this paper completes the quantitative analysis of students' emotional expressions and generates the corresponding scores. This section compares the KAN used in the emotion evaluation module with the MLP network, and the MSE is utilized as the main evaluation metric for the emotion evaluation network. Figure 6 illustrates the specific experimental results.  
When using the MLP network, the model's final MSE is 0.943; however, with the KAN, the model's final MSE is 0.811. Additionally, as shown in Figure 6, the KAN model converges faster than the MLP network during the training process. Compared to citation [26], this paper not only conducts emotion evaluations on audio but also combines multimodal data (video, audio, and text), resulting in a final MSE of 0.811, whi  
Figure 6: Line graph of training loss for emotion evaluation network. It compares MSE losses of training and testing using MLP and KAN. The table in the figure shows the average and variance of MSE losses for four types of lines.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Modality Settings** & **F1\(\uparrow\) (\%)** & **Accuracy\(\uparrow\) (\%)** \\ \hline A & 47.71 & 50.42 \\ T & 64.52 & 65.13 \\ V & 30.44 & 25.79 \\ A+T & 65.41 & 66.45 \\ A+V & 51.67 & 50.57 \\ T+V & 60.52 & 61.24 \\ Ours (A+T+V) & **67.11** & **68.02** \\ \hline \hline \end{tabular} Optimal values are bolded in the table.  
\end{table}
Table 5: Ablation study results of emotion classification network under different modal settings.  
of 0.847 for the model in citation [26]. Upon validation, the KAN used in this paper has performed excellently in the emotion evaluation tasks, demonstrating the effectiveness and reliability of the emotion evaluation network in evaluating students' emotional expressions. Through continuous learning and adjustment, the model has essentially reached the assessment level of professional teachers and provides reliable support for students' emotional expression in second language learning.  
The ablation experiments of this paper's emotion evaluation network on the multimodal student emotion evaluation dataset are shown in Table 6. Because the text data of the generated data and the student-recorded data are the same in the emotion evaluation network, separate text data inputs were not tested. The classification results for the input audio data are better than for the video data when only a single modality of data is input. When data from both modalities are input, the model gives the best classification results for the combination of audio and video data, and the worst classification results for text and video data. It can also be seen from the table data that a combination of data from all three modalities can be input into the model to obtain the optimal evaluation results.
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 5 Discussion
### Various Deep Learning Architectures with Security Features for Medical Image Analysis  
In order to protect the confidentiality and integrity of medical image analysis, deep learning architectures with security features are essential. The efficacy of deep learning-based medical image analysis is demonstrated by a variety of recently published literature [12; 37; 38; 39; 40; 41; 49; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 64; 65; 66; 67; 68; 69; 64; 65; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 103; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124; 125; 126; 127; 128; 129; 130; 131; 132; 133; 134; 135; 136; 137; 138; 139; 140; 141; 142; 143; 144; 145; 146; 147; 148; 149; 150; 151; 152; 153; 154; 155; 156; 157; 158; 159; 160; 161]. In the literature, several deep learning architectures have been described to handle various imaging modalities and tasks related to medical image processing with different components of encryption or cryptography [107; 110; 112; 113; 116; 123; 124; 135; 136; 140; 141; 144; 147; 148; 149; 150; 151; 152; 153; 156; 157; 158; 159; 160; 161]. Multi-layer networks, cascaded networks, training models with partial and complete supervision, transfer learning, and conventional deep learning architectures are some of these designs. Majority of the time, there are few expert reviews and scant data. Medical image processing has typically favored shallow networks over highly deep designs utilized in computer vision applications [40; 162]. This survey's analysis reveals that many DCNN network topologies have been implemented or suggested for use in medical image processing. These architectures invest a lot of effort on decreasing the parameter space, speeding up computing, and handling 3D data. It is frequently observed that DCNN-based architectures perform better when processing medical images when compared to other deep learning frameworks.
# Cell Selection Technique for Millimeter-Wave Cellular Systems with Hybrid Beamforming
###### Abstract  
In this paper, a cell selection technique for millimeter-wave (mm-wave) cellular systems with hybrid beamforming is proposed. To select a serving cell, taking into account hybrid beamforming structures in a mm-wave cellular system, the angles of arrival and departure for all candidate cells need to be estimated in the initialization stage, requiring a long processing time. To enable simultaneous multi-beam transmissions in a multi-cell environment, a cell and beam synchronization signal (CBSS) is proposed to carry beam IDs in conjunction with cell IDs. A serving cell maximizing the channel capacity of the hybrid beamformer is selected with the estimated channel information and the optimum precoder. The performance of the proposed technique is evaluated by a computer simulation with a spatial channel model in a simple model of a mm-wave cellular system. It is shown by simulation that the proposed technique with the CBSS can significantly reduce the processing time for channel estimation and cell selection, and can achieve additional gains in channel capacity, or in bit error rate, compared to that obtained by conventional techniques.  
m 10 April 2017; Accepted: 19 June 2017; Published: 21 June 2017
## 3 Results
### Laboratory Results: Inclusion Frequency of Vocal Function Measures into Ps Estimation Method 3  
Table 6 reports the inclusion frequency of each vocal function measure selected for prediction of Ps. This inclusion frequency table reflects how often a particular measure is included in the multiple regression model of Ps Estimation Method 3 across study participants. As expected, the RMS value of the ACC signal was included for almost all study participants, with \(f_{o}\), CPP, and MFDR the next most frequent measures used. OQ, NAQ, HRF, and H1-H2 were included in the regression model the least often. SQ was screened out of all the models and thus did not contribute to Ps estimation for any study participant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Overall Severity** \\ \hline PVH & 4.10 (3.06) & 2.10 (1.04) & 1.61 (0.60) & 3.18 (1.73) & 22.4 (18.0) \\ NPVH & 3.76 (1.62) & 2.08 (0.90) & 2.08 (0.60) & 3.51 (2.07) & 25.3 (29.5) \\ UVFP & 4.74 (2.44) & 2.06 (0.71) & 1.75 (0.67) & 4.86 (2.66) & 54.5 (27.4) \\ Control & 2.96 (1.42) & 1.51 (0.48) & 1.13 (0.37) & 2.89 (0.82) & N/A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error of the four subglottal pressure (Ps) estimation methods for each patient group and vocally typical group in terms of root-mean-square error (units of cm H\({}_{2}\)O) with respect to reference Ps values obtained using the indirect intraoral equilibration method. The mean and standard deviation (SD) of the error are listed. Reported also for the patient groups are the mean (SD) of the auditory-perceptual rating of overall severity (higher values on the 0–100 scale indicate higher dysphonia).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Effect** & \(df\) & \(\eta_{p}^{2}\) & \(F\) & \(p\) \\ \hline Participant Group & 3 & 0.188 & 8.32 & \textless{}0.0001 \\ Ps Estimation Method & 3 & 0.750 & 33.25 & \textless{}0.0001 \\ Participant Group \(\times\) Ps Estimation Method & 9 & 0.062 & 0.91 & 0.516 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the two-way analysis of variance on the root-mean-square error in subglottal pressure (Ps) estimation to determine the main effects of and interactions between the participant group and estimation method.
# V2X-Based Highly Reliable Warning System for Emergency Vehicles
## 3 System Model  
Every traffic signal in an ITS has wheel speed sensors (WSS) at a certain distance away from them. It will count the number of vehicles crossing and their speed for a given period. These data will continuously be updated in the central server. Using these data, the server can predict the density of vehicles on all roads.  
Prediction of the traffic density using vehicle speed is given in Equation (1).  
\[V_{av}=\frac{\sum_{k=1}^{n}S_{k}}{n} \tag{1}\]  
where \(V_{av}\) is the average speed of \(n\) vehicles, \(S_{k}\) is the spot speed of the \(k\)th vehicle, and \(n\) is the number of vehicles crossing per unit of time.  
\[\text{Time Delay}=\frac{\text{Distance of the road}}{\text{Average speed}} \tag{2}\]Equation (2) is used to predict the time delay at each signal. The centralized server will find the path between the source and destination with a less than average time delay. The path will be sent back to the EV.  
The information about the EV such as the EV's ID, level of emergency, the destination address is given to all the traffic signals in that route. To alert the non-EVs, an alert is passed using V2V communication. Thus, the EV may receive a way to travel without path blockage.  
V2V is a communication-technology-based wireless local area network (WLAN); thus, latency is a major factor in wireless communication. Latency can be either measured as round-trip time (RTT) or time to first byte (TTFB) [9].  
The \(RTT\) of vehicle 1 is given in Equations (3) and (4).  
\[RTTv11=t2-t1 \tag{3}\]  
\[RTTv12=t5-t4 \tag{4}\]  
where \(RTTv11\) indicates the first round-trip time of vehicle 1, and \(RTTv12\) indicates the second round-trip time of vehicle 1.  
The \(RTT\) of vehicle 2 is given in Equations (5) and (6).  
\[RTTv21=t3-t2 \tag{5}\]  
\[RTTv22=t7-t6 \tag{6}\]  
where \(RTTv21\) indicates the first round-trip time of vehicle 2, and \(RTTv22\) indicates the second round-trip time of vehicle 2.  
Total average \(RTT\) is given by  
\[Average\;Sever\;RTT=\frac{RTTv11+RTTv12}{2} \tag{7}\]  
\[Average\;Client\;RTT=\frac{RTTv21+RTTv22}{2} \tag{8}\]  
\[Average\;Total\;RTT=Average\;Sever\;RTT+Average\;Client\;RTT \tag{9}\]  
The packet loss on the V2V communication is given by  
\[TCP/UDP\;Checksum=Pseudo\;header+TCP/UDP\;header+TCP/UDP\;body \tag{10}\]  
Communication between two systems may often end in packet loss, which can be checked by checksum.  
The EV's position is periodically updated to all the traffic signals in that route. Once the EV comes within the range of the traffic signal, J2H customization of the traffic signals takes place. The traffic system will guide the traffic signal when to change to red or green color. After each EV crosses every signal, the information about that vehicle will be removed from the traffic signal. On reaching its destination, all the details about the EV will be removed from the centralized server.