esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Change in Isokinetic Muscle Function of Trunk  
The changes in isokinetic muscle function of the trunk across each group are shown in Table 3.  
The two-way within-factor ANOVA identified significant group \(\times\) time interactions for 120\({}^{\circ}\)/s flexor movement (\(p<0.05\)). Paired \(t\)-tests revealed significant increases in 30\({}^{\circ}\)/s and 120\({}^{\circ}\)/s extensor (both \(p<0.05\)) and flexor movement (\(p<0.05\) for 30\({}^{\circ}\)/s; \(p<0.01\) for 120\({}^{\circ}\)/s) in both ST and TT groups. Additionally, significant improvements were seen in 30\({}^{\circ}\)/s flexor (\(p<0.01\)) and 120\({}^{\circ}\)/s extensor and flexor movement (both \(p<0.01\)) in the NA group after 12 weeks of supplementation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Time**} & \multicolumn{4}{c}{**Group**} & \multirow{2}{*}{**Post-Hoc**} \\ \cline{3-4} \cline{6-7}  & & **ST (\(n=8\)) a** & & **TT (\(n=8\)) b** & **NA (\(n=8\)) c** & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**Post-Hoc**} \\ \hline \multirow{2}{*}{Left grip strength (kg)} & Pre & 41.01 \(\pm\) 4.15 & 38.61 \(\pm\) 4.81 & 43.02 \(\pm\) 4.11 & G: 0.220 & \multirow{2}{*}{-} \\  & Post & 44.30 \(\pm\) 6.72 & 40.50 \(\pm\) 4.93 * & 44.43 \(\pm\) 3.40 & G \(\times\) T: 0.602 & \\ \hline \multirow{2}{*}{Right grip strength (kg)} & Pre & 49.33 \(\pm\) 4.81 & 43.49 \(\pm\) 5.37 & 46.85 \(\pm\) 4.17 & G: 0.092 & \multirow{2}{*}{-} \\  & Post & 49.73 \(\pm\) 4.56 & 44.25 \(\pm\) 4.85 & 47.10 \(\pm\) 5.71 & G \(\times\) T: 0.950 & \\ \hline \multirow{2}{*}{Sit-ups (rep)} & Pre & 36.57 \(\pm\) 8.54 & 45.88 \(\pm\) 8.08 & 37.00 \(\pm\) 4.52 & G: 0.122 & \multirow{2}{*}{-} \\  & Post & 46.71 \(\pm\) 9.27 * & 48.50 \(\pm\) 7.21 & 42.33 \(\pm\) 6.41 & G \(\times\) T: 0.128 & \\ \hline \multirow{2}{*}{Sit-and-reach (cm)} & Pre & 1.59 \(\pm\) 11.12 & 11.58 \(\pm\) 10.11 & 6.37 \(\pm\) 11.65 & G: 0.102 & \multirow{2}{*}{-} \\  & Post & 2.90 \(\pm\) 11.36 & 15.99 \(\pm\) 6.78 & 9.83 \(\pm\) 9.53 & G \(\times\) T: 0.515 & \\ \hline \multirow{2}{*}{Long jump (cm)} & Pre & 235.0 \(\pm\) 7.9 & 223.5 \(\pm\) 16.2 & 206.5 \(\pm\) 21.8 & G: \(<\)0.001 & \multirow{2}{*}{a, b \(>\) c} \\  & Post & 243.4 \(\pm\) 15.9 & 236.0 \(\pm\) 8.9 * & 203.5 \(\pm\) 13.6 & G \(\times\) T: 0.210 & \\ \hline \multirow{2}{*}{10 m shuttle run (s)} & Pre & 9.44 \(\pm\) 0.74 & 8.72 \(\pm\) 0.58 & 10.23 \(\pm\) 0.67 & G: 0.001 & \multirow{2}{*}{a, b \(>\) c} \\  & Post & 9.59 \(\pm\) 0.39 & 9.50 \(\pm\) 0.51 & 10.92 \(\pm\) 0.56 & G \(\times\) T: 0.185 & \\ \hline \multirow{2}{*}{20 m shuttle run (rep)} & Pre & 60.43 \(\pm\) 13.8 & 65.00 \(\pm\) 1.77 & 33.33 \(\pm\) 3.61 & G: \(<\)0.001 & \multirow{2}{*}{a, b \(>\) c} \\  & Post & 61.14 \(\pm\) 17.2 & 66.75 \(\pm\) 6.67 & 37.50 \(\pm\) 8.02 & G \(\times\) T: 0.739 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Measurements of physical fitness parameters by group and time.
# Facial Expression Recognition Using Computer Vision: A Systematic Review
## 6 Classification/Regression
### Convolutional Neural Network  
CNN is a type of neural networks that is mainly used in Computer Vision (Deep Learning) because of its ability to solve multiple image classification problems. CNNs can even beat humans in some of these problems since they are able to detect and identify underlying patterns that are too complex for the human eye. An input image is run through several hidden layers of the CNN that will decompose it into features. Those features are then used for classification, generally through a Softmax function that retrieves the highest probability from the classes' probability distribution as the predicted class.  
It is important to mention that different problems require different CNN models and different tuning to maintain a high classification accuracy. This is mainly caused by the overfitting/underfitting problem. Overfitting, as mentioned in Section 4.3.3, is when the model generalizes poorly; in other words, its classification accuracy drops considerably when classifying data that was not in the training set. Underfitting is when the model makes poor predictions in both training data and data it has not seen before. There are several ways to solve this problem:  
1. Increasing the complexity of the model (by adding more layers).  
Figure 19: SIFT features of a face from the CK+ database.  
Figure 20: HOG descriptors of the mouth of two subjects from the CK+ database [92].  
Adding dropout layers [93] which randomly disable a determined number of nodes during training to avoid that the model memorizes patterns instead of learning them.
3. Tuning the parameters of the model during the training, such as epochs, batch size, learning rate, class weight, among others.
4. Increasing the training data by adding more samples or through DA as mentioned in Section 4.3.3.
5. Whenever the database is too small (a common problem on the publicly available databases for emotion recognition), Transfer Learning (TL) can be applied. TL uses a pre-defined model that has already been trained on a large database and one can fine-tune it using a smaller database for its own classification problem.  
A great portion of the reviewed works used this approach for classification, showing promising results for Deep Learning based classifiers.
## 5 Application and Results
### Environmental Evaluation
#### 5.3.1. \(\mathrm{CO_{2}}\) Emissions  
According to [PERSON] (2018), the total \(\mathrm{CO_{2}}\) emission at a container terminal \(\mathrm{CD_{CNTR}}\) can be calculated by the summation of carbon emissions from the processes of ship manoeuvre inside the port basin (\(\mathrm{CD_{CNTR,M}}\)), ship at berth (\(\mathrm{CD_{CNTR,B}}\)), container loading and unloading (\(\mathrm{CD_{CNTR,L}}\)), container transportation (\(\mathrm{CD_{CNTR,T}}\)) and container receiving and delivery (\(\mathrm{CD_{CNTR,R}}\)). Each of the total components are calculated according to Equation (1), in which carbon emission density of major vessel and equipment activities values are those estimated by [PERSON] [16]. The results of total carbon emission for the terminal container of the Port of Augusta are reported in Table 2.
# Electromagnetic Field Variation of ELF Near-Region Excited by HED in a Homogeneous Half-Space Model
## 4 Spatial Distribution of EM Field Intensity in the Near-Field Region
### Comparative Validation Analysis  
In order to verify the effectiveness of the method presented in this paper, [PERSON]'s approximation [27] and [PERSON]'s exact expression [9] are respectively obtained by [PERSON]'s progressive simplification, and the obtained results are compared with the method presented in this paper. If both the field and source points are in seawater, the seawater and air conductivity are \(\sigma_{1}\) = 4 S/m and \(\sigma_{2}\) = 1.8 \(\times\) 10\({}^{-4}\) S/m, respectively, and the relative dielectric constants are \(\epsilon_{r1}\) = 80 and \(\epsilon_{r2}\) = 1, respectively. When calculated at 3 Hz and 30 Hz, the electric field component in the \(\rho\) direction changes with the distance, as shown in Figure 5.  
As can be seen from Figure 5, the electric field components in the \(\rho\) direction obtained by different methods decrease with the increase in radial distance, and the variation trend is basically consistent. When the frequency is the same, the amplitude of \(E_{1\rho}\) obtained by Pan approximation decreases the most with distance, while the amplitude of \(E_{1\rho}\) obtained by Margetis's exact expression is in the middle of the two approximation methods, and the amplitude obtained by this method decreases at the slowest rate, which also verifies that the method proposed in this paper has a good accuracy in calculating the electric field component in the near-field region. It can be seen from Figure 5 that at the same site, the \(E_{1\rho}\) amplitude decreases with the increase in operating frequency, and the amplitude decreases greatly in the ELF band high-frequency band. When the horizontal distance is less than 5000 m, the \(E_{1\rho}\) amplitude decreases significantly with the increase in the horizontal distance, and when the horizontal distance is greater than 5000 m, the decline trend tends to be gentle. As shown in Figure 5a, when the electric field component \(E_{1\rho}\) is operating at 3 Hz, the field strength decreases with the increase in the horizontal distance in the direction of \(\rho\). If the horizontal distance is greater than 10 km, the seawater has little influence on the \(E_{1\rho}\) amplitude of the field point when it is higher than 500 m.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
## 3 Proposed Demodulation Method with Estimation of Ambient Environmental Parameters
### Proposed MLSE Demodulation Method  
Maximum likelihood sequence estimation (MLSE)-based demodulation can determine the reception sequence as  
\[\hat{b}^{\text{MLSE}}=\arg\max_{b}l(b) \tag{15}\]  
where \(b\) denotes the estimated bit sequence by MLSE, \(b\) denotes the estimated candidate bit sequence, and \(l(b)\) denotes the likelihood function for the estimated candidate bit sequence. We assume that the observation error of the number of received molecules follows a Gaussian distribution. The likelihood function is defined by the following equation.  
Figure 5: System model considering the volume of transmitter molecule.  
\[l(b) =\prod_{k=1}^{K}p(N_{\text{C}}^{\text{obs}}|b) \tag{16}\] \[=\prod_{k=1}^{K}\frac{1}{\sqrt{2\pi\sigma}}\exp\left[-\frac{\{N_{ \text{C},k}^{\text{obs}}-N_{\text{C}}^{\text{ISI}}(t=k\frac{T}{s_{L}}|r_{0})\}^{ 2}}{2\sigma^{2}}\right]\]  
We assume that the sequence length is \(L\) and the symbol period \(T\) is an oversampling of \(st\) times, and the number of observation points is \(K=s_{L}\cdot L\). The symbol \(N_{\text{C}}^{\text{ISI}}\) denotes the number of received molecules and considers the intersymbol interference calculated based on (8). The estimation of channel parameters is expressed as  
\[\hat{a}_{0}^{ML}=\arg\max_{a}l(a) \tag{17}\]  
where \(a_{0}^{ML}\) denotes the estimated value of \(a\), and \(l(a)\) denotes the likelihood function given by (16) for each estimated candidate. In this paper, we define the ambient parameter \(a\) as the environmental temperature \(T\) and distance \(r\) between the transmitting and receiving molecules for each case, which can be estimated based on the above maximum likelihood estimation.
## 2 Materials and Methods
### 165 rRNA Gene-Amplicon Sequencing  
Library construction and amplicon sequencing were conducted by Shanghai Majorbio Biotechnology Co., Ltd. (Shanghai, China). The hypervariable V3-V4 region of the bacterial _165 rRNA_ gene was targeted for amplifications using the primers 338F (5\({}^{\prime}\)-ACTCCTACGGGAGGCAGCAG-3\({}^{\prime}\)) and 806R (5\({}^{\prime}\)-GGACTACHVGGGTWTCTAAT-3\({}^{\prime}\)) [43] on an Illumina MiSeq platform. Raw fastq files were demultiplexed and quality-filtered using Trimmomatic software [44]. Demultiplexed paired-end sequences were denoised, merged, and clustered into amplicon-sequence variant (ASV) using dada2/deblur program in QIIME2 \"[[https://qime2.org/](https://qime2.org/)]([https://qime2.org/](https://qime2.org/)) (accessed on 26 February 2020)\" [45]. Taxonomic classification of ASVs was performed using QIIME2 with a Naive Bayes classifier previously trained on the SILVA 138 database considering 99% sequence similarity \"[[http://www.arb-silva.de](http://www.arb-silva.de)]([http://www.arb-silva.de](http://www.arb-silva.de)) (accessed on 26 February 2020)\". Microbial diversity analysis was performed using the cloud platform \"[[https://cloud.majorbio.com/](https://cloud.majorbio.com/)]([https://cloud.majorbio.com/](https://cloud.majorbio.com/)) (accessed on 11 March 2022)\". The bacterial flora heatmap (Spearman-correlation heatmap) and CCA were analyzed by the R language (vegan package).  
The _165 rRNA_ genome sequences of 12 samples were deposited in NCBI under the accession numbers SAMN32722911, SAMN32722912, SAMN32722913, SAMN32722914, SAMN32722915, SAMN32722917, SAMN32722918, SAMN32722919, SAMN32722920, SAMN32722921, and SAMN3272292, respectively.
# Reactive Molecular Dynamics Simulations of Polystyrene Pyrolysis
## 3 Methods and Materials
### ReaxFF-MD Details for Polystyrene Pyrolysis  
This work employs NVT integration and high-temperature techniques in ReaxFF-MD pyrolysis simulations on PS with different heating rates and target temperatures. To evaluate the pressure under the NVT ensembles, we plot pressure change with time at various constant temperature simulations in Figure S1 in the Supporting Information (SI). We know that the coordination number of carbon is 3 (graphite structure) at low pressure, and carbon changes to a diamond structure at a pressure of 5 Gpa. In our simulations,  
Figure 8: Model of the pyrolysis precursor.  
the pressure is below the phase transition points, so the pressure will not induce intrinsic changes to the microstructure but may affect the parameters, such as bond length, etc. In addition, the time scale of the experiments is in minutes, while a significant number of reactions in molecular simulations can occur within nanoseconds. Therefore, to accelerate the reaction progress in ReaxFF-MD simulations, higher temperatures are often used to facilitate atomic motion and molecular collisions [34; 35], facilitating the observation of reactions [36]. Despite the differences in time and temperature between MD simulations and experiments, numerous studies have shown that increasing the temperature in ReaxFF-MD yields reasonable and consistent results compared to experimental outcomes, and the method has been applied to investigate combustion and pyrolysis in larger systems at high temperatures [36; 37; 38; 39; 40].  
ReaxFF-MD simulations were carried out with the Large-scale Atomic Molecular Massively Parallel Simulator (LAMMPS) package. In all NVT-ReaxFF-MD simulations, periodic boundary conditions are applied, and the time step is set to 0.2 fs. Temperature control is achieved by the Nose-Hoover thermostat method. We use a stepwise heating method to pre-equilibrate the system at each ramp-up point. Structural energy minimization is performed before the ReaxFF-MD simulations, eliminating any potential strong van der Waals interactions that could lead to local structure distortions and unstable simulations. The minimization (or optimization) procedure is an iterative process in which atomic coordinates are adjusted to minimize the energy of the structure. Snapshots of the simulation results are generated by Visual Molecular Dynamics (VMD, version 1.9.3) [41] and OVITO (version 3.7.10) [42] software at different time points during the process. The pore size distribution is analyzed by the Zeo++ [43] tool. The pyrolysis simulation employs the ReaxFF force field (CHON-2019). This force field was developed to characterize the dissociation and formation of carbon bonds, showing a good agreement with experimental results [44] and, thus, can provide a sound foundation for PS pyrolysis.
# Stability and Heat Input Controllability of Two Different Modulations for Double-Pulse MIG Welding
## 3 Comparison of Stabilities of DP-MIG Welding and TP-MIG Welding
### Process Stability of TP-MIG Welding  
A TP-MIG welding experiment was conducted using a fixed current of 120 A and a range of frequencies, and the results of this experiment are shown in Figure 10. According to the weld morphologies shown in this figure, a frequency of 2 Hz results in extremely clearly defined fish-scale patterns and large gaps between the fish scales. Increasing the frequency to 5 Hz significantly improves the weld quality and the density of the fish-scale patterns. A further increase in welding frequency to 15 Hz results in a corresponding increase in the density of the fish scales and the manifestation of single-pulse features. In addition, it is found that the transitional pulses in TP-MIG help stabilize  
Figure 8: Waveform and weld formation at 80 A and 160 A: (**a**) 80 A welding waveform; (**b**) 160 A welding waveform; (**c**) 80 A weld formation; (**d**) 160 A weld formation.  
Figure 9: Weld seam obtained by parameter self-adjusting welding test; (**a**) 83 A weld formation; (**b**) 92 A weld formation; (**c**) 135 A weld formation.  
current transitions and reduce the sharpness of welding-arc sounds. In summary, TP-MIG welding is highly stable and produces high-quality weld that are aesthetically pleasing.
# Single-Parameter-Tuned Attitude Control for Quadrotor with Unknown Disturbance
## 3 Quadrotor Attitude Control System Design
### ESO Parameter Tuning with CMA-ES Algorithm
#### 3.4.2 Calculating Costs  
It is critical to lower the error between the real disturbance \(\zeta\) and the estimation output of the ESO \(\zeta_{0}\) in order to eliminate the high frequency vibration problem. The tasks in the simulation and real quadrotor platform presented above are tested for all search points. For every search point \(\mathbf{x}_{i}\), the cost in every time step is computed with \(cost_{step(\zeta,\zeta_{0})}=\sqrt{|\zeta-\zeta_{0}|}\), and the total cost \(cost\left(\mathbf{x}_{i}\right)\) is the sum of all costs in every time step.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 5 Simulation Results and Analysis  
In this section, we verify the accuracy of the theoretical analysis and investigate the impact of key parameter selections on system performance via MC simulations. The underwater simulation environment is configured based on typical nearshore coastal water quality, with an extinction coefficient \(c_{0}=0.305\) m\({}^{-1}\), as reported in [19]. It is further assumed that the average SNRs of the two hops are equal, i.e., \(\mu_{r}=\rho_{r}=\overline{\gamma}\), and the threshold SNR is set as \(\gamma_{th}=2\) dB. Table 2 below summarizes the typical values of critical parameters required for system simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Coefficient** & **Symbol** & **Value** \\ \hline Rytov variance & \(\sigma_{R}^{2}\) & 0.6/2 \\ Atmospheric attenuation coefficients & \(\sigma\) & \(1/1000\) m\({}^{-1}\) \\ Atmospheric transmission distance & Z & \(Z=200/500\) m \\ Full-width beam divergence & \(\theta_{1/e}\) & \(6^{\circ}\) \\ correction factor & \(T\) & 0.13 \\ Underwater transmission distance & \(d\) & \(d=20/30\) m \\ jitter deviation & \(\sigma_{s}\) & 10/20 cm \\ Receiver radius & \(r_{a}\) & 20/30 cm \\ Beam width & \(w_{z}\) & 40/20 cm \\ Oceanic turbulence scintillation index & \(\sigma_{I}^{2}\) & 0.1074/0.2066/0.4548 \\ underwater extinction coefficients & \(c_{0}\) & 0.305 m\({}^{-1}\) \\ GGD shape parameters & \(c\) & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key parameters used for FSO-UWOC system performance simulations [11; 14; 16; 17; 19; 26; 27].
## 4 Security Analysis of the MAPP Protocol
### Security Requirement Analysis
#### 4.1.1 Identity Authentication  
In our protocol, each vehicle is registered with the BS using its real information. BSs are configured with common security parameters and individual security parameters to generate pseudo-identities for the vehicle. Pseudo-identities allow vehicles to communicate without revealing their real identities. Identity authentication is satisfied in the three proposed authentication methods, by allowing the dynamic random numbers to hide the real identity. Each transmitted message in our proposed methods is attached with \(pid_{i}\), that is generated by an authorized third party (BSs). Each vehicle receives a list of updated pseudo-identities from the nearest BS, which can be used to authenticate the identity of each vehicle. Under misbehaving conditions, vehicles report the \(pid_{i}\) to the BS to remove the malicious vehicle from the network. We called our protocol certificatless bilinear pairing, as we replaced the long certificate in PKI solutions with a short size pseudo-identity that provides identity authentication and anonymity.
# Monitoring of Possible Activities of Yangsan Fault Zone Using GNSS
## 1 Introduction
Therefore, the analysis of the stress change acting on the crust after the earthquake makes it possible to forecast potential earthquakes by detecting the precursor phenomenon of earthquakes due to the stress change [17]. According to active fault studies [18], Korea also has about 50 active faults, and it was confirmed that there was a large-scale earthquake during the quaternary period. There is a possibility of a large earthquake in Korea, so preparation for this is necessary. Therefore, the movement direction of the active fault was analyzed in this paper using GNSS data near the Yangsan Fault Zone, and the strain rate was calculated to monitor the fault. Figure 1: Yangsan Fault Zone marked in the red box.
## 3 Results and Discussion
### Geometric Distortions and Warpage
Since sand was lighter and less dense than glass, the mass of the specimens decreased, their volume increased as sand stopped the glass powder from shrinking, and the density consequently decreased. From 0% to 60% sand, the average mass of the specimens decreased 20%, from 10-11 g to 8-9 g, and the density decreased roughly 30-35% from 2 g/cm\({}^{3}\) to 1.3-1.4 g/cm\({}^{3}\). It should be noted that independent of sintering temperature and hold time, in the printing process of vertical specimens, powders were poured in smaller areas, at a higher deposition pace. Contrary to what happened in horizontally built specimens, vertical builds almost always used only fine-sized powders. The frequent starting and stopping of the pouring could result in extra glass powder being poured into each layer due to the closing of the pourers not being ideal. On average, vertical specimens tended to be heavier and larger than horizontal ones, their mass varied from 9-10 g to 11-12 g, and the volume was from around 5000 mm\({}^{3}\) to around 5700 mm\({}^{3}\). Figure 8: Average geometric distortion (shrinkage) of specimens with different sand percentage (%\({}_{sand}\)). (**A**) Horizontally built specimens. (**B**) Vertically built specimens. Absolute values of each measure are in mm, and relative variation from CAD model is in percentage. Negative values indicate an increase in size. (Batches 1h and 1v are present in the graphs for comparison, as they contain specimens subject to the same sintering temperature and hold time but had 0% sand mixed in the build powder.)