esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Evolutionary Characteristics of Dynamic Strain
#### 3.2.3 Strain Curve at Different Loading Ratios  
Figure 14 shows a test curve for the dynamic strain evolution characteristics of structural specimens under four dynamic load conditions. The results show that under low dynamic stress amplitude, the variation value of strain amplitude and strain ratio becomes stable after the initial growth, and the structure may not exhibit fatigue failure. Figure 14a shows that at the dynamic stress level of 1.6 kN, the number of load cycles reaches \(2\times 10^{6}\) times, the specimen remains undamaged, and the change value of the strain of the specimen shows a stable state.  
Figure 14: Dynamic strain curve: (**a**) dynamic load = 1.6 kN, (**b**) dynamic load = 2.4 kN, (**c**) dynamic load = 3.6 kN, and (**d**) dynamic load = 4.5 kN.  
Figure 13: Damage state of the inverted arch structure at different stages: (**a**) initial stage, (**b**) intermediate stage, (**c**) failure stage, and (**d**) cracking position.  
The relationship curve between structural strain \(\varepsilon\) and number of cycles \(N\) was extracted to describe the entire process of structural strain development under cyclic loading (Figure 17). The test results show that the strain development process only included the first two stages of fatigue failure, and no failure stage when the stress level was less than 0.6. The strain development process involves all three phases, namely, a disproportionate increase in deformation, a linear increase in deformations, and a destruction stage when the stress level is greater than 0.6. Moreover, the ultimate load cycles were less than \(2\times 10^{6}\). Table 9 presents the results of \(N_{f}\) for different stress levels \(f_{max}f_{t}\).  
Figure 16: Evolution of strain characteristics for different dynamic stress: (**a**) strain increment, (**b**) \(\varepsilon_{n}/\varepsilon_{0}\).  
Figure 17: Process of strain evolution.  
The relevant parameters of fatigue life can be obtained in Table 9 based on the data of the fatigue test under different strain levels; the \(S\)-\(N\) curve (Figure 18) describes the stress level, and fatigue life is obtained and expressed by the following linear fitting equation:  
\[S=-0.145 lgN+0.975,\ (r=0.912) \tag{6}\]
## 6 Practical Applications  
The practical applications of this study include monitoring fatigue in athletes using VOT-NIRS, which allows for the evaluation of SmO\({}_{2}\) at rest without requiring physical exertion. This could be a strategy to prevent overtraining and optimize recovery. Additionally, SmO\({}_{2}\) data has the advantage of providing information about skeletal muscle oxidative capacity, which has yet to be used to measure post-exercise muscle fatigue. However, researchers must consider the reliability and individual response when using VOT-NIRS in performance and rehabilitation contexts, especially when assessing patients with injuries or cardiovascular conditions. Finally, this study highlights the need for a consensus on the protocols and variables used in future research, improving communication in sports science, and its practical application in training and rehabilitation.  
Conceptualization, A.A.V.-B. and R.Y.-S.; methodology, A.A.V.-B., R.Y.-S., M.T., E.B.-S.M., M.M.-A., J.O.-A., D.D.-B., C.R.-D. and F.A.; investigation, R.Y.-S., M.T., E.B.-S.M. and M.M.-A.; data curation, R.Y.-S., J.O.-A., D.D.-B., C.R.-D. and F.A.; writing--original draft, A.A.V.-B. and R.Y.-S; writing--review and editing, M.T., M.M.-A., E.B.-S.M. and F.A.; supervision, R.Y.-S. and M.T.; project administration, A.A.V.-B. and F.A.; funding acquisition, A.A.V.-B. and F.A. All authors have read and agreed to the published version of the manuscript.  
This research is funded by postdoctoral researchers under the Margarita Salas Program (MS-35), supported by the Ministry of Universities of the Spanish Government. The research was not financed by private companies, commercial agencies, or not-for-profit sectors.  
The evaluation and intervention protocols were approved by the Scientific Ethics Committee of the Universidad Vina del Mar (Code R62-19a). This research was conducted following the ethical considerations described in the Declaration of Helsinki for studies with humans.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
# Scalable Block Preconditioners for Linearized Navier-Stokes Equations at High Reynolds Number
## 4 Block Preconditioners for the Navier-Stokes Discretized Systems
### Inexact Constraint Preconditioner  
Using the previous definition, we can define the Inexact Constraint Preconditioner ICP as  
\[M_{ICP}=\begin{bmatrix}M_{F}&B^{T}\\ B&BM_{F}^{-1}B^{T}-M_{S}\end{bmatrix},\]  
The adjective inexact arises when the exact Schur complement \(\hat{S}=BM_{F}^{-1}B^{T}\) is replaced by an approximation, \(M_{S}\), as it is in this case. This implies that the \((2,2)\) block of \(M_{ICP}\) is no longer zero, as it is in the Constraint Preconditioner. As before, pre- and post- multiplying by \(\Pi_{L}\) and \(\Pi_{U}\) yields an eigenvalue problem equivalent to \(H\mathbf{w}=\lambda M_{ICP}\mathbf{w}\):  
\[\begin{bmatrix}F_{P}&N\\ R&0\end{bmatrix}\begin{bmatrix}\mathbf{z}_{1}\\ \mathbf{z}_{2}\end{bmatrix}=\lambda\begin{bmatrix}I_{n}&N\\ R&S_{P}-I_{m}\end{bmatrix}\begin{bmatrix}\mathbf{z}_{1}\\ \mathbf{z}_{2}\end{bmatrix} \tag{15}\]  
Matrix on the right in (15) is easily decomposed, recalling that \(S_{P}=RN\), as  
\[\begin{bmatrix}I_{n}&N\\ R&RN-I_{m}\end{bmatrix}=\begin{bmatrix}I_{n}&0\\ R&I_{m}\end{bmatrix}\begin{bmatrix}I_{n}&N\\ 0&-I_{m}\end{bmatrix}\]It turns out that \(M_{ICP}^{-1}H\) is similar to the matrix  
\[\begin{bmatrix}I_{n}&0\\ R&I_{m}\end{bmatrix}^{-1}\overbrace{\begin{bmatrix}F_{P}&N\\ R&0\end{bmatrix}\begin{bmatrix}I_{n}&N\\ 0&-I_{m}\end{bmatrix}}^{H_{BTP}} = \begin{bmatrix}I_{n}&0\\ R&I_{m}\end{bmatrix}^{-1}\left(\begin{bmatrix}I_{n}&0\\ R&I_{m}\end{bmatrix}+\begin{bmatrix}E_{F}&E_{F}N\\ 0&E_{S}\end{bmatrix}\right) \tag{16}\] \[= \begin{bmatrix}I_{n}&0\\ 0&I_{m}\end{bmatrix}+\begin{bmatrix}I_{n}&0\\ -R&I_{m}\end{bmatrix}\begin{bmatrix}E_{F}&0\\ 0&E_{S}\end{bmatrix}\begin{bmatrix}I_{n}&N\\ 0&I_{m}\end{bmatrix}, \tag{17}\]  
in view of (14). We have shown that the ICP-preconditioned matrix is similar to a matrix that can be written as the identity matrix of order \(n+m\) plus a matrix whose norm is bounded by the norms of \(E_{F}\) and \(E_{S}\).  
The results developed for ICP and BTP preconditioned matrices show that unit eigenvalues are perturbed by the presence of error matrices \(E_{F}\) and \(E_{S}\) whose norms, in the case of scalable preconditioners for the blocks, will be small and independent of the mesh discretization parameter \(h\). This is a pleasant property, which, in many cases, is responsible of fast convergence of the GMRES method. However, it is well known [20] that a clustering of the eigenvalues around the unit value is not sufficient to guarantee fast convergence of the GMRES method due to the possible ill-conditioning of the matrix of the eigenvectors. Analyses based on the field of values of the preconditioned matrix have been carried out for simplified preconditioners in order to overcome this theoretical problem. See, e.g., [21; 22].
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
###### Abstract  
In this study, we employed a geographic information system (GIS)-based approach to identify sites suitable for large-scale solar photovoltaic (PV) power plant installations in Mongolia. Accordingly, cells of 30 \(\times\) 30 m were used, and data based on seven criteria, including annual global horizontal radiation, annual average temperature, elevation, slope, slope direction (aspect), and distances from main roads and major power lines, were collected for each cell. GIS layers for these seven criteria were then converted into rated value layers using four grades. The weightings applied to the seven criteria were determined, using an analytical hierarchy process, by ten solar field experts. By combining the seven rating value layers with the weightings, a site suitability map was developed, using good, fair, low, and poor suitability grades. The results showed that sites graded as good, fair, low, and poor accounted for 3.27%, 53.06%, 42.59%, and 1.08% of the total surface area, respectively. Good sites were predominantly located in the southern and central regions of Mongolia. As the current demand for electric power in southern and central regions is low and high, respectively, we concluded that the central region of Mongolia should be prioritized for installing PV power plants.  
renewable energy; site evaluation; analytic hierarchy process; geographic information system; solar energy; Mongolia +
Footnote †: journal: Applied Sci. 2021, 11, 3748. [[https://doi.org/10.3390/app11093748](https://doi.org/10.3390/app11093748)]([https://doi.org/10.3390/app11093748](https://doi.org/10.3390/app11093748))
# An Efficient Approach for Detecting Driver Drowsiness Based on Deep Learning
## 2 Background
### Deep Neural Networks for Drowsiness Prediction
#### 2.3.2 ResNet Network  
ResNet [24] won the ImageNet Large Scale Visual Recognition Competition in 2015 for image classification, object localization, and object detection. The main challenge in training deep learning models is that the accuracy decreases with the depth level of the networks. ResNet converges very quickly and can be trained with hundreds or thousands of layers. At the same time, ResNet is easy to optimize and can achieve accuracy gains from greatly increased depth, producing better results [24]. ResNet-50V2 was trained on ImageNet and CIFAR-10 datasets [25]. The ResNet-50V2 network architecture is shown in Figure 5. The results show that ResNet-50V2 performs better and gives better results than ResNet50 and ResNet101 on the same ImageNet dataset. ResNet-50V2 applies the transfer learning technique with ImageNet pre-trained weights, which are used in object classification, such as COVID-19 and pneumonia detection from chest X-ray images and video classification with accuracy up to 94-98%. Taking advantage of ResNet-50V2, we choose this architecture as the training model for driver drowsiness detection.
# Area-Time Efficient Hardware Architecture for CRYSTALS-Kyber
### Proposed Kyber Key Generation Architecture  
The proposed DDF NTT multiplier-based key generation architecture is introduced in Figure 3. Specifically, Figure 3 presents the key generation architecture to construct public key \(pk\) and secret key \(sk\) from input noise vectors.
# Review on Seat Suspension System Technology Development
## 2 Passive Vibration Control Systems  
Most seat suspensions utilise a passive mechanism to reduce the effect of the vehicle vibration which are created by uneven road surface on the human body. The passive suspension as a conventional suspension system contains a sprung mass with a damping shock absorber to regulate unwanted vibrations [54]. The basics of passive vibration control are shown in Figure 3 to present the fundamental concepts. A passive control system can absorb structural vibrations and remove energy from the dynamic system without an external energy source. The method is to connect the structure to elements and materials with damping characteristics that overcome vibrations. A passive system is regularly implemented with springs and dampers with time-invariant stiffness and damping coefficients [55].  
Figure 2: Whole body vibration of the seated human body.  
A conventional suspension system was chosen by [PERSON] et al. [6]. The proposed system includes a shear guidance mechanism, an air-spring, a hydraulic shock-absorber and buffers. The geometry of the seating system is shown in Figure 4. The damped vibration effect is given by the spring force coupled with the shock-absorber force. Amplification of vibration in the 0-2 Hz frequency range was mentioned [57]. At the greater frequencies, the desired performance of the seat suspension was given and vibrations transmitted by the seat were decreased significantly [58]. The pneumatic and nonlinear elements provide a condition to increased vibro-isolating features of the suspension. This kind of system is known by a relatively simple adjustment of stiffness and damping of the pneumatic suspension by setting air pressure. By adjusting the pressure inside the air spring chamber at different loads, a constant static deflection can be forced. Consequently, regulating the air pressure would lead to a change in the stiffness of the suspension system as well as its eigenfrequency [59]. Incorporating an additional air reservoir culminates in a decrement in the system stiffness and as a result lowers the natural frequency of the system. The air-spring has minimal damping; therefore, this sort of element employed to suspension systems needs an external shock-absorber. A damping force can be caused by airflow throttling between the added air reservoir and the air-spring. According to the obtained results by [PERSON] et al. [6], the modified seat suspension outstandingly promotes the vibro-isolating characteristics of the seat in the 0-4 Hz frequency range. For bigger frequencies, vibro-isolating features of both seats (conventional and modified) are nearly the same [60]. The best performance of the modified seat suspension in comparison with normal passive seats (approximately 1.3 Hz) was taken at the resonance frequency. The study by [PERSON] et al. led to a good agreement between the desired reduction in transmitted vibration to the driver's body and the opposing necessity for the reduction of seat suspension travel.
## 4 Results and Discussion
### Phase 1: Assess the Potential and Maturity for IoT Solution Implementation
#### 4.1.4 Perform Efficient Handling Including Expiration Date, Temperatures, etc.?  
In Table 5, item 4 shows the proportion of companies surveyed that performs efficient handling, including expiration date, temperatures, among other parameters. In this case, 67% of companies indicated performing efficient handling at this level. However, only 20% of the companies that responded affirmatively indicated that this process was carried out for the entire chain, using computer systems supported by human resources. The remaining affirmative answers indicated that this process was carried out in its entirety manually.  
1.5 Is There Any Kind of Control to Guarantee the Transport, Storage and Exhibition Until the Sale of the Products?  
In Table 5, item 5 analyzes the traceability of products, i.e., if there is any type of control to ensure transportation, storage and exposure until the sale of the products is performed. Just over half of the companies surveyed answered in the affirmative (52%) this issue shows the need for sophistication on the part of these companies to carry out traceability throughout the product chain, ensuring the maintenance of organoleptic characteristics, in addition to undeniable food safety. The only control carried out by the companies that responded in the affirmative was temperature control.
## 3 Results and Discussions
### Results for Scenario with Callus Formation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Metallic FF**} & \multicolumn{1}{c}{**Summary of Safety Factors**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} & \multicolumn{1}{c}{**Finite**} \\ \cline{2-13}
**Case with 500 N Pre-Tension** & **Week** & **Load** & **Force (N)** & **Wires** & **Occurring On** & **Bone** & **Calls** & **EF Ring** & **Fasteners** & **Pin** & **Rods** & **Status** \\ \hline Callus: Granulation tissue & 0 & 0.1 g & 70 & 2.05 & Wire 1 & 11.79 & 3.92 & 1.40 & 5.15 & 12.63 & 4.18 & OK \\ \hline Callus: Fibrous max & 0.1 g & 70 & 2.37 & Wire 1 & 15.96 & 15.53 & 1.40 & 5.16 & 21.98 & 4.16 & OK \\ Callus: Fibrous max & 0.4 & 0.5 g & 350 & 1.70 & Wire 1 & 7.85 & 2.09 & 1.40 & 5.21 & 7.77 & 3.82 & OK \\  & 1 g & 700 & 1.31 & Wire 1 & 4.76 & **0.97** & 1.39 & 5.08 & 4.11 & 3.27 & Refructure \\ \hline Callus: Fibrous-Carllege 2 w & 0.1 g & 70 & 2.54 & Wire 1 & 18.94 & 25.78 & 1.40 & 5.16 & 34.49 & 4.13 & OK \\ Callus: Fibrous-Carllege 2 w & 2.0 & 0.5 g & 350 & 2.14 & Wire 1 & 12.66 & 4.51 & 1.40 & 5.20 & 15.59 & 4.17 & OK \\  & 1 g & 700 & 1.84 & Wire 1 & 8.86 & 2.16 & 1.40 & 5.26 & 9.39 & 3.81 & OK \\ \hline Callus: Fibrous-Carllege 4 w & 4.0 & 0.1 g & 70 & 2.65 & Wire 1 & 22.50 & 43.31 & 1.40 & 5.16 & 44.42 & 4.12 & OK \\ Callus: Fibrous-Carllege 4 w & 4.0 & 0.5 g & 350 & 2.57 & Wire 1 & 19.39 & 11.94 & 1.41 & 5.20 & 33.87 & 4.16 & OK \\  & 1 g & 700 & 2.47 & Wire 1 & 15.74 & 6.00 & 1.41 & 5.24 & 22.23 & 4.15 & OK \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Metallic EF safety factors with 500 N wires pre-torsion. The stress values for the metallic EF with 500 N of wire pre-tension configuration are shown in Table 7, a significant increase in stress values being observed mainly in the wires and in the EF ring (Figure 14). However, the stress values remain acceptable. As shown in Figure 14, the maximum normal stress is obtained for wires 1 located near the ring clamp area and is due to both the wire pre-tension load and the axial load applied at the distal end. Regarding the EF ring, the stress is produced mainly due to the wire pre-tension load, as no significant safety factor evolution with an axial load is shown in Table 7. Thus, the impact of the axial load of the EF ring is limited for the cases with callus formation, but could increase for configurations with a higher EF stiffness as the fixator will support a higher ratio of axial load.
###### Abstract
* We develop a differentiated embedded pilot insertion scheme which incorporates modulation-related pilot symbols in DD plane structure to enhance classification accuracy. * We conduct extensive experiments, and the results demonstrate that the proposed approach can achieve high classification accuracy in high-mobility scenarios and low-signal-to-noise ratio (SNR) conditions and outperform the state-of-the-art approaches. The remaining parts of this paper are organized as follows: Section 2 reviews the related literature and is followed by Section 3, which depicts the system model and describes the problem to be solved. In Section 4, the proposed method is elaborated on. Section 5 showcases and analyzes the numerical results. In Section 6, we conclude the paper with some final remarks.
# A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence
## 2 Overview of Healthcare Applications
### eHealth  
During the COVID-19 pandemic, healthcare service provisioning became the primary focus of many researchers experimenting with innovative technologies such as blockchain and artificial intelligence [15]. An eHealth application [16] within the COUCH project (under the European Union's Horizon 2020 R&D program) was developed for and tested on older adults with some health problems. The application used text-based dialogue to interact with the participants. This study attempted to assess how an agent-based eHealth platform would be used in a real-world environment, and if it had any health effects on the participants. An ever-evolving procedure that has seen major improvements regarding its research accessibility in recent years is electroencephalography (EEG). While recording EEG data has become cheaper and easier because of equipment availability and performance, it is still challenging to record consistently and successfully analyze the acquired data. Portable, low-power IoT devices such as the Texas Instruments MSP432 can be used in conjunction with an efficient seizure-predicting algorithm with great results, even if the number of EEG electrodes is reduced [17]. Because of the nature of the EEG-recorded data, many researchers opt to replace the costly feature extraction methods with deep learning models that use recurrent neural networks (RNNs) such as long short-term memory (LSTM). In [18], using LSTM for sequence classification, the proposed system is able to identify persons that have previously suffered concussions by recording EEG data while the person is concentrating on a task. In Table 1, we can observe the spectrum of different technologies used in eHealth applications over the past couple of years.
## 3 Thermal-Acoustic Nonlinear Response Analysis
### Loading  
Sounds of 150 dB, 155 dB, and 165 dB are used to study the effect of sound pressure level on the response. The band-limited white noise is simulated by an inverse Fourier transform at a frequency resolution of 0.5 Hz in the frequency range 0-512 Hz. Studies of effects of finite element mesh sizes and the integration time steps on the accuracy and convergence of nonlinear responses are conducted. The length of the random pressure time history, which cannot be chose arbitrarily, depends on the fundamental frequency of the structure. The previous studies [12; 13] have shown that the time history of a stationary response, containing more than 100 natural periods of the structure, yields reasonable statistical properties. The length of the simulated process can then be determined from  
\[28 NPT\cdot\Delta t>100\cdot\frac{1}{f_{1}} \tag{5}\]  
where \(NPT\) is the number of simulated points, \(\Delta t\) is the time step, and \(f_{1}\) is the fundamental frequency. The panel for different temperature conditions studied in this work is strictly fulfilled by the condition of Equation (5). A typical simulated random pressure loading is shown in Figure 3.  
Three thermal loadings are considered to research the snap-through response. An ambient temperature (\(T=27\)\({}^{\circ}\)C, \(\Delta T=0\)\({}^{\circ}\)C) is considered as a baseline, and the critical buckling temperature increment of the panel is \(\Delta T_{\text{C}}=110\)\({}^{\circ}\)C (\(T=137\)\({}^{\circ}\)C). Meanwhile the thermally pre-buckled response is studied under \(\Delta T=103\)\({}^{\circ}\)C (\(T=130\)\({}^{\circ}\)C). For ambient temperature and thermally pre-buckled conditions, the panel is observed vibrating around the single neutral equilibrium position. A temperature increment, \(\Delta T=573\)\({}^{\circ}\)C (\(T=600\)\({}^{\circ}\)C), is used to investigate the thermally post-buckled response. As the sound pressure level increases, the response varies from small deflection vibration about the thermally buckled equilibrium positions to intermittent snap-through and finally to large nonlinear random vibration between the two thermally buckled equilibrium positions. A response matrix of the panel under different thermal-acoustic loading conditions is shown in Table 2, where \(N\) designates no snap-through motions, \(I\) designates intermittent snap-through motion, and \(P\) designates persistent snap-through motion.  
Figure 3: Random noise loading of generation (150 dB).