esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discrete Event Simulation Model
### Model Validation  
The verification and validation of the simulation model are steps that allow us to evaluate its robustness and check its ability to replicate real-world observed data before its use for the optimization process. To this end, and after detailed reviews, we checked with hospital management teams that the simulation model reflected reality with a high level of accuracy. Then, we referred to three indicators of simulated performances to compare their agreement with the actual observations: Total patient arrival, Average waiting time (AWT) and Average inpatient stay (AS).  
Figure 4 compares the results of total patient arrival based on the category of EP from the model and real-world data. The comparison results show a gap of 3.2% to 6%. Tables 2 and 3 compare the results of real and simulated data of total arrival, AWT, and AS based on EP category in short and long circuits. The results show that simulated and real-world arrival data are similar for all types of patients. Moreover, the results of the AWT and the AS in Tables 2 and 3 show that there is little gap between the real-world and simulated data. This confirmed the model as validated, reliable, and apt to support experiments.  
flow process logic in the model, different scenarios were proposed to evaluate their impact on the ED performances. The objective was to maximize the reduction in waiting time and inpatient stay and to minimize the cost of adding an additional resource. To verify the relevance of these scenarios, we used our DES model as a performance evaluator. Then, to analyze and compare the scenarios, we used the VIKOR multicriteria decision support method.
# Evaluation of Snowboarding Helmets in Mitigation of the Biomechanical Responses of Head Surrogate
## 3 Results  
### Experimental Results  
#### 3.1.1 Kinematic Response of the Head Surrogate  
Figure 4 and Table 5(a) shows the kinematic response of the head surrogate for the front (a-c) and side (d-f) orientations. The trends presented in the Results section are valid for both orientations unless stated otherwise. Compared to the no helmet, the reduction in peak angular kinematics (Figure 4a,b,d,e) was insignificant (<13%) for the conventional helmet and significant (by 50-75%) for the MIPS, WaveCel, and silica pad helmets. The peak linear accelerations (Figure 4c,f) were reduced significantly (by 55-80%) for all helmet configurations.  
Figure 4: Kinematic response (peak angular velocity, peak angular acceleration, peak linear acceleration) of the head surrogate, (**a**–**c**) and (**d**–**d**) depict the kinematic response for front and side orientations, respectively. * Indicates statistically significant changes (\(p<0.05\)) compared to no helmet. * indicates statistically significant changes (\(p<0.05\)) compared to the conventional helmet.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Sc. No.**} & \multicolumn{4}{c}{**Test Configurations**} & \multicolumn{4}{c}{**Front Orientation**} & \multicolumn{4}{c}{**Side Orientation**} \\ \hline  & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Feak**} \\  & \multicolumn{1}{c}{**Angular**} & \multicolumn{1}{c}{**Angular**} & \multicolumn{1}{c}{**Linear**} & \multicolumn{1}{c}{**Feak Coup**} & \multicolumn{1}{c}{**Fressive**} & \multicolumn{1}{c}{**Feak**} & \multicolumn{1}{c}{**Angular**} & \multicolumn{1}{c}{**Linear**} & \multicolumn{1}{c}{**Feak Coup**} & \multicolumn{1}{c}{**Fressive**} & \multicolumn{1}{c}{**Conte  
Among the various helmet configurations, peak angular kinematics (Figure 4a,b,d) in MIPS, WaveCel, and the silica pad helmets were significantly lower (by 50-70%) compared to the conventional helmet. Further, WaveCel and silica pad helmets reduced peak linear acceleration (Figure 4c,f) significantly (by 25-46%). The reduction in peak linear acceleration between the conventional and MIPS helmet was insignificant (<15%) for the front orientation and significant (by 26%) for the side orientation.
## 2 Hybrid Flow Shop Dynamic Scheduling Problem  
The HFS scheduling problem is to determine the sequence of jobs entering the first stage and assign jobs to alternative machines at every stage. Its objective is to minimize the makespan of jobs in order to increase machine utilization and guarantee on-time job delivery simultaneously. The following assumptions are taken into consideration in this problem:  
1. Each job and each machine are available at the initial time.
2. Each job passes through multiple production stages to complete operations.
3. One or more parallel machines are available at each stage.
4. Parallel machines require different processing times for the same operation.
5. Each machine is not able to process more than one job at the same time, and cannot be interrupted until the operation on this job has been completed.
6. A job can enter the next stage if its operation at current stage has been accomplished.
7. Processing times are uncertain, and their actual values may be different from the expected ones.
8. A machine requires changeover time if it needs to process two jobs of different types consecutively.
9. Operations of a job have no effect on those of other jobs.  
Table 1 lists the notations used in this problem. In this table, \(t_{mki}\) represents the operation processing time, which is uncertain in an actual environment. Erlang distribution is a common way to construct the distribution of processing times based on the queuing theory. Based on this distribution, the possibility density function of a processing time \(t_{mki}\) is as follows:  
\[f_{tmi}(t)=(v\lambda)\frac{(v\lambda t)^{v-1}}{(v-1)!}e^{-v\lambda t},\ v\in N \tag{1}\]  
where \(t\) represents the actual processing time, \(f_{tmi}(t)\) represents the \(v\)-order [PERSON] distribution of \(t_{mki}\), \(E(t)=1/\lambda\). \(y_{rm}\) and \(x_{riki}\) are decision variables representing the sequence of jobs entering the first stage and the processing equipment of each job at every stage. The dynamic scheduling should find appropriate decision variable values to minimize the makespan when the processing times are constantly changing throughout the HFS production.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Definitions** \\ \hline Sets & \\ \(\{1,\cdots,m,\cdots,M\}\) & Set of job types \\ \(\{1,\cdots,i,\cdots,l\}\) & Set of production stages \\ \(\{1,\cdots,k,\cdots,K_{i}\}\) & Set of parallel machines at stage \(i\) \\ \(\{1,\cdots,r,\cdots,R\}\) & Set of waiting processing jobs \\ \hline Parameters & \\ \(t_{mki}\) & Operation processing time of job type \(m\) on the \(k\)th machine at stage \(i\) \\ \(c_{mmi}\) & Changeover time required by the \(k\)th machine at stage \(i\) to operate on job type \(m\) after job type \(m^{\prime}\) \\ \(d_{m}\) & Production volume of job type \(m\), \(R=\sum_{m=1}^{M}d_{m}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Problem’s notations.
###### Abstract  
The increase in the installation of renewable energy sources in electrical systems has changed the power distribution networks, and a new scenario regarding protection devices has arisen. Distributed generation (DG) might produce artificial delays regarding the performance of protection devices when acting as a result of short-circuits. In this study, the preliminary research results carried out to analyze the effect of renewable energy sources (photovoltaic, wind generation, etc.) on the protection devices of a power grid are described. In order to study this problem in a well-defined scenario, a quite simple distribution network (similar to the ones present in rural areas) was selected. The distribution network was divided into three protection zones so that each of them had DG. In the Institute of Electrical and Electronic Engineers (IEEE) system 13 bus test feeder, the short-circuits with different levels of penetration were performed from 1 MVA to 3 MVA (that represent 25%, 50%, and 75% of the total load in the network). In the simulations carried out, it was observed that the installation of DG in this distribution network produced significant changes in the short-circuit currents, and the inadequate performance of the protection devices and the delay in their operating times (with differences of up to 180% in relation to the case without DG). The latter, that is, the impacts of photovoltaic DG on the reactions of protection devices in a radial distribution network, is the most relevant outcome of this work. These are the first results obtained from a research collaboration framework established by staff from ETSI Civil and the IDR/UPM Institute, to analyze the effect of renewable energy sources (as DG) on the protection devices of a radial distribution network.  
coordination protection; distributed generation; photovoltaic resources; DigSILENT
## 2 Acquisition of Experimental Stress-strain Data
### The Influence of Parameters Selection on the Performance of SVR  
In SVR, the learning ability and generalization ability can be improved by appropriate parameters settings, and such parameters are penalty factor \(C\), kernel parameter \(\gamma\) and non-sensitive loss function \(\zeta\)1. Penalty factor \(C\) The robustness and complexity of SVR are influenced by the penalty factor \(C\). A larger \(C\)-value in SVR indicates that all data samples are important and each sample should be correctly fitted, which will cause the SVR to be complicated and over-fitted; whereas a smaller \(C\)-value in SVR indicates that some singular points can be ignored. However, the phenomenon of under-fitting will occur in SVR when the \(C\)-value is too small.
2. The parameter \(\gamma\) of the basis kernel function (RBF) The parameter \(\tau^{2}\) expressed by Equation (2) determines the data mapping process and even influences the learning ability and generalization ability of the model. The phenomenon of over-fitting will occur in the following cases: (a) \(C\)-value is set as a certain value and \(\tau^{2}\to 0\); (b) \(\tau^{2}\) is set as a certain value and \(C\rightarrow\infty\)[27]. Additionally, the phenomenon of under-fitting will occur in the following cases: (a) \(C\) is set as a smaller value and \(\tau^{2}\to 0\); (b) \(C\) is set as a certain value and \(\tau^{2}\rightarrow\infty\); (c) \(\tau^{2}\) is set as a certain value and \(C\to 0\)[27].
3. The non-sensitive loss function \(\zeta\) In SVR, the \(\zeta\)-value influences the number of support vectors and further influences the regression accuracy of the model. It can be concluded that the learning ability and generalization ability of SVR rely on the three parameters \(C\), \(\gamma\) and \(\zeta\), especially the mutual impacts among them. An SVR with appropriate parameters \(C\), \(\gamma\) and \(\zeta\) will accurately study the stress-strain curves and appropriately ignore some singular points of stress-strain data to accord with the overall trend of the stress-strain curves. In SVR, it is time consuming to independently optimize each parameter. The influence of the combination of the three parameters (\(C\), \(\gamma\) and \(\zeta\)) on the learning ability and generalization ability of SVR should be synthetically considered. It is inefficient to manually adjust the three parameters one by one to establish an SVR that can accurately characterize the hot flow behaviors of as-forged Ti-10V-2 Fe-3 Al alloy. Thereby, it is important to find a stable and efficient method to realize the optimal selection of the three parameters in SVR.
## 2 Experiment
### Experimental Results and Discussion
#### 2.4.1 Aquathermolysis Experiments
Similar results were observed when the reaction time was 2 d. The significant increase in the concentrations of CO\({}_{2}\) and H\({}_{2}\) also proves that an increasing temperature is beneficial for the aquathermolysis of heavy oil. Due to the accelerated aquathermolysis reactions, light hydrocarbons (methane, ethane, and propane) increased, and heavy hydrocarbons (pentene, trans-2-butene, and C\({}_{6+}\)) decreased with the increase in temperature. Table 2 also shows the results of the aquathermolysis reaction at different reaction times. The H\({}_{2}\)S concentration increased with the increase in reaction time. When the reaction time varied from 0.5 d to 2 d at 300 \({}^{\circ}\)C, the H\({}_{2}\)S concentration increased from 0.003185% (31.85 ppm) to 0.01019% (101.9 ppm). The concentrations of CO\({}_{2}\) and H\({}_{2}\) also increased from 26.935% and 39.437% to 30.861% and 40.265%, respectively. The results suggest that increasing time deepened the degree of aquathermolysis. According to the experimental data of the aquathermolysis experiments, the activation energy and frequency factor were estimated by a Kapadia kinetics model [15]. As shown in Figure 3a, the reaction time had a linear relation with the concentrations of H\({}_{2}\)S [24]. The reaction time and the concentration of H\({}_{2}\)S were fitted, with the slope of the regression lines showing the values of reaction rates (k) at different temperatures. It can be seen from Figure 3a that the reaction rates at 300 \({}^{\circ}\)C and 270 \({}^{\circ}\)C are 0.0000467 and 0.00001933, respectively. Figure 3b shows the relationship between the reaction rates (k) and temperatures (T). The corresponding kinetic parameters (activation energy and frequency factor) were obtained according to the slope and intercept of the interpolation line (Figure 3b), which are 76.12 kJ/mol and 404.03 1/day, respectively.
# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 4 Experiments and Analysis
### Analysis of the Experimental Results  
Our improved YOLOv5 model was compared with other mainstream target-recognition algorithms, which include Faster R-CNN, Retina-Net, SSD, YOLOv3, and YOLOv5s. Table 2 lists the performance of these algorithms in terms of recognition.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Configuration** \\ \hline CPU & Inter(R) Core(TM) i7-7820X CPU @ 3.60 GHz \\ GPU & NVIDIA TITAN Xp \\ Accelerator & CUDA 10.2 \\ Architecture & Pytorch 1.9 \\ Language & Python 3.8 \\ Epochs & 400 \\ Batch size & 32 \\ Learning rate & 0.01 \\ Optimizer & SGD \\ Mosaic & 1.0 \\ Flipud, Fliplr & 0, 0.5 \\ Scale & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the computer software environment.  
The first column shows the algorithm used. The middle seven columns show the \(AP\) of prediction for each type of aircraft. The last column shows the \(mAP\) of prediction of all types. YOLOv5 has four network models: YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Considering the requirement of detection speed, we chose YOLOv5s as the baseline. We proposed the four optimization methods described above based on YOLOv5s. From the data on the experiments shown in Table 2, we can see that our improved algorithm achieved the best recognition precision compared with the state-of-the-art algorithms. Compared with the original YOLOv5s algorithm, \(mAP\) also increased by 5.8%. The experiments demonstrated that the improved YOLOv5s algorithm is more suitable for recognizing aircraft targets of SAR images than the original YOLOv5 algorithm.  
Figure 8 shows the \(PR\) curves of the improved YOLOv5s algorithm. The area covered by the \(PR\) curve is the predicted \(AP\) value. The larger the area, the better the performance of the algorithm in terms of detection. There are eight \(PR\) curves with different colors in the figure. The thinner curve represents the \(PR\) curve of the individual aircraft types. The thicker curve represents the average \(PR\) curve of all types, which was used to evaluate the precision of the algorithm's detection.
## 2 Mathematical Model for Centroid
### Mathematical Modeling for Centroid Calculation
#### 2.2.2 Area of Helical Flute and Centroid Calculation  
As shown in Figure 3, \(S\) is the area of a single helical flute (projection area in the cross section, same hereafter), \(\alpha\) is the eccentric angle, and P is the centroid of the microelement. The helical flute is discretized into several unit microelements along the \(x\)-axis, and the expression of helical flute area \(S\) can be obtained from the integral formula [15]:  
\[S=\int_{x_{A}}^{x_{E}}(f_{2}(x)-f_{1}(x))dx \tag{8}\]  
where \(f_{1}(x)\) is the equation of the outer circle curve of the bar. According to the centroid formula, the centroid coordinates of helical flute in the first quadrant can be expressed as [15]:  
\[\left\{\begin{array}{c}x_{1}=\frac{1}{5}\int_{x_{A}}^{x_{E}}(f_{2}(x)-f_{1}( x))\cdot xdx\\ y_{1}=\frac{1}{25}\int_{x_{A}}^{x_{E}}(f_{2}^{2}(x)-f_{1}^{2}(x))dx\end{array}\right. \tag{9}\]  
Figure 3: Schematic diagram of helical flute.  
According to the coordinate transformation, the centroid coordinates \((x_{2},y_{2})\),\((x_{3},y_{3})\) of other helical flutes in the end section can be obtained.  
\[\left[\begin{array}{c}x_{2}\\ y_{2}\end{array}\right]=\left[\begin{array}{cc}\cos(\frac{2\pi}{3}+\theta)&- \sin(\frac{2\pi}{3}+\theta)\\ \sin(\frac{2\pi}{3}+\theta)&\cos(\frac{2\pi}{3}+\theta)\end{array}\right]. \left[\begin{array}{c}x_{1}\\ y_{1}\end{array}\right] \tag{10}\]  
\[\left[\begin{array}{c}x_{3}\\ y_{3}\end{array}\right]=\left[\begin{array}{cc}\cos(-(\frac{2\pi}{3}-\theta))&- \sin(-(\frac{2\pi}{3}-\theta))\\ \sin(-(\frac{2\pi}{3}-\theta))&\cos(-(\frac{2\pi}{3}-\theta))\end{array}\right]. \left[\begin{array}{c}x_{1}\\ y_{1}\end{array}\right] \tag{11}\]
## 3 Results
### Perceived Ease-of-Use (PEOU)  
Similar to the analyses of PU, the Wilcoxon-signed rank test was employed to examine the predictive factor of different interactive interfaces on users' PEOU. The result indicates that users' PEOU for hand gesture recognition and joystick control are statistically significantly different (Z = \(-\)3.771, \(p<0.001\)). Further examination of the result suggests that users believe that the joystick control (gesture recognition: M = 14.82, SD = 3.76; joystick control: M = 21.97, SD = 4.792) is easier to operate than the hand gesture recognition (see Figure 6). No significant predictive effect on PEOU was found from relevant factors like gender, college major, and task completion time.
## 2 Methodology
### Optimization Procedure
#### 2.1.2 Multi-Objective Optimization Tool and Algorithm  
The multi-objective optimization problem in this study was solved using Octopus, which is another plugin for Rhino-Grasshopper; it can generate the Pareto-optimal solution set by the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Hypervolume Estimation Algorithm (HypE) [35]. Compared with other multi-objective optimization algorithms, SPEA-2 has a greater advantage in the distribution uniformity of solutions derived from environmental selection based on neighbor rules, enabling it to better avoid falling into local optimality [36]. Moreover, SPEA-2 performs better in higher dimensions than NSGA-II [37]. The optimization process was conducted simultaneously with the performance simulation of each generation. All design variables were imported as genes into the Octopus module. The evaluation of four objectives--building energy consumption, thermal comfort, life-cycle cost, and carbon emissions--was attempted. Octopus uses SPEA-2 with hypermutation to automatically control the design variables and constantly search for the minima of the four objectives, thereby providing a solution set, which is a Pareto or non-dominated solution in which there is no other feasible solution that improves one objective without deteriorating at least another one.
# Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models
## 1 Introduction
By minimizing the differences between the autocorrelation matrices of the student and teacher models, the student model learns not only individual feature behaviors but also the relationships between features in the teacher model. This approach effectively captures high-order feature interactions, enabling the student model to better understand and learn the complex features of the teacher model. More importantly, AMKD effectively addresses the issue of hidden states dimension mismatch between student and teacher models without requiring additional projection layers for dimension alignment. AMKD retains the essential information from the teacher model, minimizing information loss during training. Compared to traditional methods, AMKD significantly enhances the performance of the student model, demonstrating superior flexibility and robustness. We evaluated AMKD on multiple NLP tasks and demonstrated that it significantly enhances the performance of small BERT models in specific tasks. AMKD addresses the limitations of traditional knowledge distillation in learning complex feature relationships and overcoming hidden states dimension mismatches. The resulting student models exhibit greater flexibility and robustness, offering new approaches for model compression and acceleration in resource-constrained environments.
## 3 Results
### Experiment 2
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{RMSE} \\ \hline  & **WT\_id** & **5 min** & **10 min** & **15 min** & **30 min** & **60 min** \\ \hline \multirow{5}{*}{**Training**} & 80 & 0.7244 & 0.7091 & 0.7004 & **0.6702** & 0.6981 \\  & 81 & 0.7413 & 0.7524 & 0.7127 & 0.6868 & **0.6723** \\  & 82 & 1.5828 & 1.6552 & 1.7083 & 1.5828 & **1.5679** \\  & 83 & 0.7930 & 0.7589 & 0.7639 & 0.7425 & **0.7245** \\ \hline \multirow{5}{*}{**Test**} & 80 & 1.0282 & 1.0155 & 0.9925 & **0.9641** & 0.9903 \\  & 81 & 3.3127 & 3.2158 & 3.1985 & 2.9432 & **2.6237** \\ \cline{1-1}  & 82 & 2.3972 & 2.3464 & 2.3563 & 2.2557 & **1.9317** \\ \cline{1-1}  & 83 & 2.2666 & 2.2465 & 2.1481 & 2.0148 & **1.7600** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results obtained by averaging the RMSE of 25 models both in the training and test steps when trained with data of different temporal aggregation. In all cases, the trained network is a SLFN with 60 hidden nodes. The minimum values, highlighted in bold, are obtained when 60-min temporal aggregation is used in all WTs. Figure 8: _Cont_. Figure 8: RMSE achieved by a hidden \(H\)-node network both in the training and test steps when trained with 5-min and 60-min data: (**a**) WT#80, (**b**) WT#81, (**c**) WT#82, and (**d**) WT#83.
# A Novel Criticality Analysis Method for Assessing Obesity Treatment Efficacy
###### Abstract  
Human gait is a significant indicator of overall health and well-being due to its dependence on metabolic requirements. Abnormalities in gait can indicate the presence of metabolic dysfunction, such as diabetes or obesity. However, detecting these can be challenging using classical methods, which often involve subjective clinical assessments or invasive procedures. In this work, a novel methodology known as Criticality Analysis (CA) was applied to the monitoring of the gait of teenagers with varying amounts of metabolic stress who are taking part in an clinical intervention to increase their activity and reduce overall weight. The CA approach analysed gait using inertial measurement units (IMU) by mapping the dynamic gait pattern into a nonlinear representation space. The resulting dynamic paths were then classified using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasive detection of metabolic stress. It resulted in an average accuracy within the range of 78.2% to 90%. Additionally, at the group level, it was observed to improve fitness and health during the period of the intervention. Therefore, this methodology showed a great potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic stress, as well as other associated disorders.  
human gait; criticality analysis; support vector machine +
Footnote †: journal: Applied Sciences