esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation on Novel Shear Connectors in Prefabricated Composite Beams
## 3 FEA Based on Concrete Damage
### Material Constitutions of Concrete Damage  
In the FEM, the materials contain concrete and steel. Table 2 lists the material properties of the steel plate, rebar, and studs. An elastic-strengthen model is applied for the stress-strain curve of the studs, and an ideal elastic-plastic model for the steel plate and rebar. By comparison, the material constitution of the concrete seems more complicated.  
Considering the obvious nonlinear displacement of concrete under load, a special concrete damage model in ABAQUS is adopted. In this damage model, a damage factor, \(d\), is introduced to reflect the degeneration law of the elastic modulus in the plastic damage process of concrete. The relationship between the elastic modulus and the damage factor is in terms of Equation (1)  
\[E=(1-d)E_{0} \tag{1}\]  
where \(E_{0}\) refers to the initial elastic modulus, and \(E\) refers to the damaged elastic modus. The damage factor, \(d\), is defined in Equation (2).  
\[(1-d)=(1-s_{t}d_{c})(1-s_{c}d_{t}) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Table** & **Test Groups** & \begin{tabular}{c} **Shank Diameter** \\ \(D\) **(mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Lateral Space** \\ \(L\) **(mm)** \\ \end{tabular} &
\begin{tabular}{c} **Concrete Strength** \\ _f\({}_{\text{c}}\)_ **(MPa)** \\ \end{tabular} \\ \hline
1 & HS-M1 & 13 & 20 & 50 \\
2 & HS-M2 & 13 & 40 & 60 \\
3 & HS-M3 & 13 & 60 & 70 \\
4 & HS-M4 & 16 & 20 & 60 \\
5 & HS-M5 & 16 & 40 & 70 \\
6 & HS-M6 & 16 & 60 & 50 \\
7 & HS-M7 & 19 & 20 & 70 \\
8 & HS-M8 & 19 & 40 & 50 \\
9 & HS-M9 & 19 & 60 & 60 \\
10 & HS-MS & 10 & 14 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical parametric models.  
Figure 7: Parameters \(L\) and \(D\) of the models.  
where \(d_{t}\) refers to uniaxial tensile damage, \(d_{c}\) refers to uniaxial compressive damage, and \(s_{t}\) and \(s_{c}\) refer to recovered stiffness in the opposite direction of stress.  
The calculation of concrete damage includes two parts. One is the condition to reach the damage state. The other is the evolution of the damage process. Once the concrete reaches the peak stress of tension or compression, the damage is considered in the calculation. The stress-strain curve of modeling concrete is shown in Figure 8.  
After the concrete reaches the state of damage, the failure of concrete can be calculated by the damage factor curve, as shown in Figure 9. In addition, when calculating concrete damage in the model, we need to input other parameters, i.e., the dilation angle of concrete is \(53^{\circ}\), the biaxial-uniaxial ratio is \(1.16\), and the eccentricity is \(0.15\).
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 4 Detection Algorithm and Its Applicability
### Implementing Algorithm and Its Procedures
#### 4.1.2 Faster RCNN  
Faster-RCNN is a state-of-the-art object detection algorithm that consists of a Region Proposal Network (RPN) and fast-RCNN. The RPN generates region proposals that are fed to Fast-RCNN to detect classes of objects, (see Figure 10). Our study used an input image of size \(256\times 256\) pixels which generates an \(\sim 2000\ anchors\ per\ image by\ \left(\frac{256}{16}\right)*\left(\frac{256}{16}\right)*9\) with 3 aspect ratios of (1:1, 1:2 and 2:1). We then implemented the Non-Maximum Suppression (NMS) based on the image classification and _IoU_ of 7. Positive labels are assigned to an anchor with the highest _IoU_ with a ground truth box or _IoU_ that overlaps higher than a 0.7 score with any ground truth. The loss function for the RPN is:  
\[L(\{p_{i}\},\{t_{i}\}\ )=1/N_{cls}\left(\sum_{i}L_{cls}(p_{i},p_{i}^{*})\right)+ \lambda/N_{reg}\left(\sum_{i}p_{i}^{*}\times L_{reg}(t_{i},t_{i}^{*})\right) \tag{13}\]  
where  
\[L(\{p_{i}\},\{t_{i}\}\ )=1/N_{cls}\left(\sum_{i}L_{cls}(p_{i},p_{i}^{*})\right)+ \lambda/N_{reg}\left(\sum_{i}p_{i}^{*}\times L_{reg}(t_{i},t_{i}^{*})\right) \tag{14}\]  
Figure 9: SSD Model ([PERSON] et al. [56]).  
\(p_{i}=\)_predicted probability of anchors containing a class of object or not_  
\(p_{i}^{*}=\)_anchors found truth values containing a class of object or not_  
\(t_{i}=\)_coordinates of predicted anchors_  
\(t_{i}^{*}=\)_ground truth coordinates related with the bounding boxes_  
\(L_{cl}=\)_classifier loss_  
\(L_{reg}=\)_regression loss_  
\(N_{cls}=\)_normalization param of mini - batch size_  
\(N_{reg}=\)_normalization param of regression_  
\(\lambda=10\)_, order to make \(n=\)both loss parameter equally weighted right._  
We then implemented the second stage of faster-RCNN, which is the object detection network. Zeiler and Fergus (ZF) Net [58] was used as the backbone network, which uses the _RoI_ pooling layer for making region proposals generated from RPN (first stage) into a fixed-size feature map with an output of size (\(7\times 7\times\mathrm{D}\)) (where \(\mathrm{D}=256\) for ZF). In addition, the generated fixed-size feature maps were sent to two fully connected layers, where they were flattened and then sent as outputs with two distinct tasks assigned to them. The first layer predicts the region proposal using a softmax of \(\mathrm{N}+1\) output params while the second layer determines the bounding box location of the object in the given image using a bounding box regression with \(4\times\mathrm{N}\) output params.
# On-Line Tendency Control of Dissolved Oxygen Concentration during Aerobic Fed-Batch Fermentations
## 1 Introduction
In fed-batch fermentation processes, it is difficult to maintain the dissolved oxygen concentration precisely at a given set point because various factors, such as air composition and the substrate feed rate, influence its real value; both PID and manual control have been previously used to control the level of DO, but the range in which the DO concentration can be maintained is fairly wide. In addition, the poor performance of PID control can be attributed to the large variations in DO that occur during fed-batch fermentation. This difficulty can be dealt with by maintaining the oxygen concentration at a high enough set point that oxygen limitation does not occur in the presence of these large variations. Generally, the objective is not to allow the DO level to drop below a critical limit where the quality of the fermentation product is affected. As any deviation from the desired set point for DO could lead to a decrease in the product quality, which provides a strong incentive to automate the maintenance of DO levels [13,14]. In addition, the inherent characteristics of a large time delay and the resulting stochastic disturbances in the fed-batch process will cause the controller to have high activity and levels of DO to oscillate abruptly near the set point, which will reduce the life of the control equipment as a result of oscillations in DO. The aim of this study is to propose a simple oxygen control approach that uses automated supervision of operation instead of manual operation to limit the risk of oxygen limitation during the fermentation process. This approach is demonstrated using cultures of _Corynebacterium glutamicum_ and recombinant _Pichia pastoris_, but can easily be adapted for use with other fermentation processes.
## 4 Actual Project Study
### Simulate Working Circumstances and Excavation Procedures  
Tunnel construction is a continuous and dynamic process. However, due to limitations in analysis theories and methods, the entire continuous construction of mine tunnels can be divided into sequential stages. The dismantling process can be summarized as follows: for each construction step corresponding to each advancement of the tunneling face, the solid unit is deactivated, while the initial support, grid steel frame, retractable anchor rod, and condulet are activated.  
In the actual construction process, the excavation footage for each ring of the upper step varies from 2.2 m to 3.0 m, while that of the lower step is approximately 5.0 m. Consequently, during the simulation, the upper step is excavated in increments of 2.25 m each time. When the spacing between the excavation faces of the upper and lower steps reaches 15 m, the lower step is excavated in increments of 6.75 m. Considering that the underground structure and surrounding soil have consolidated due to self-weight in practical engineering, tunnel construction is deemed to be the primary cause of soil disturbance. The construction sequence of the hidden tunnel is depicted in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & \begin{tabular}{c} **Elasticity** \\ **Modulus (GPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Unit Weight** \\ **(kN/m3)** \\ \end{tabular} & **Poisson Ratio** &
\begin{tabular}{c} **Sectional Area** \\ **(m2)** \\ \end{tabular} \\ \hline Substructure & 45 & 32 & 0.28 & \(1.38\times 10^{-3}\) \\ Fender piles & 210 & 78.4 & 0.3 & \(3.14\times 10^{-4}\) \\ Primary support & 23 & 22 & 0.2 & - \\ Conulet & 30 & 25 & 0.24 & 0.502 \\ Feet-lock bolt & 34.5 & 25 & 0.23 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Physical and mechanical parameters of the structural members.  
Figure 10: Simulation of tunnel construction process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Formations** & \begin{tabular}{c} **Compression** \\ **Modulus (Mpa)** \\ \end{tabular} & \
\begin{tabular}{c} **Internal Friction** \\ **Angle (\({}^{\circ}\))** \\ \end{tabular} & **Poisson Ratio** &
\begin{tabular}{c} **Unit Weight** \\ **(kN/m3)** \\ \end{tabular} & **Cohesion (kPa)** \\ \hline Loess & 4.10 & 16 & 0.37 & 18.0 & 34.9 \\ Silty clay & 5.17 & 15 & 0.42 & 16.4 & 39.5 \\ Conglomerate & 35.00 & 30 & 0.30 & 18.8 & 50.0 \\ Fully weathered marl & 55 & 20 & 0.27 & 24.1 & 48.0 \\ Highly weathered diorite & 104.3 & 45 & 0.27 & 26.4 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Physical and mechanical parameters of the formations.
## 6 Simulation
### BER of the Proposed Model 1  
BER calculations are implemented to estimate the performance of model 1 within the proposed DSCA-MC-CR technique by using the Gaussian channel and practical fading channel, i.e., the Rayleigh channel. Figure 9 shows the BER of the PU and the SU obtained using a QPSK-OFDM modulation scheme at \(\gamma=3\%\). The following abbreviations apply:  
Figure 8: The proposed topology classification using conventional DSP and GSP. (**a**) The proposed topology using conventional DSP. (**b**) The proposed topology classification using GSP.  
Excluding interference (EXC-I) is used when the PU system or the SU system was alone in the cell. This case is termed \(\mathit{System}_{1}\).
2. Including interference (INC-I) is used when the PU system and the SU system work together in the same cell. This case is termed \(\mathit{System}_{2}\).
3. Including interference and incentive (INC-(I+Incent.)) is used when the PU system works together with the SU system and obtains an incentive from the SU system too. This case is termed \(\mathit{System}_{3}\) and represents the proposed system.  
The PU system within \(\mathit{System}_{3}\) achieves a BER of \(10^{-3}\) at 0 dB SNR, while the PU achieved a BER of \(10^{-3}\) at 9 dB when \(\gamma=5\%\) in [33]. The SU system within \(\mathit{System}_{3}\) achieves a BER of \(10^{-3}\) at 2.3 dB SNR, while the SU achieved a BER of \(10^{-3}\) at 12 dB when \(\gamma=5\%\) in [33]. In addition, Figure 10 shows the results obtained when using a practical fading channel. The PU system within \(\mathit{System}_{3}\) achieves a BER of \(10^{-3}\) at 5.8 dB SNR, while the PU achieved a BER of \(10^{-3}\) at 25 dB when \(\gamma=5\%\) in [33]. Furthermore, the SU system within \(\mathit{System}_{3}\) achieves a BER of \(10^{-3}\) at 10 dB SNR, while the SU achieved a BER of \(10^{-3}\) at 28 dB when \(\gamma=5\%\) in [33]. Moreover, the previous results confirm that the proposal adopts the concept of green communication, which provides higher performance and achieves a wide coverage area with lower power consumption.
# Trends and Perspectives of mHealth in Obesity Control
## 5 Conclusions  
In conclusion, this study sheds light on the trends and perspectives surrounding the integration of mHealth technologies in obesity control, utilizing insights from ChatGPT to explore various aspects of this emerging field. Trends indicate a shift toward personalized interventions, leveraging technologies such as artificial intelligence and wearable devices. There is an increasing emphasis on user engagement and collaboration among stakeholders to maximize the potential of mHealth in promoting healthier lifestyles and reducing obesity-related complications. However, challenges remain, including ensuring data privacy, addressing disparities in access, and validating the effectiveness of mHealth interventions through empirical research.  
The perspectives on integrating mHealth technologies in obesity control highlight significant opportunities for innovation and collaboration. Optimism surrounds the potential of mHealth to enhance personalized care, improve patient engagement, and reduce healthcare disparities. However, concerns regarding data privacy, reliability, and accessibility underscore the need for evidence-based approaches and robust regulatory frameworks to ensure responsible and effective implementation of mHealth solutions.  
While this study provides valuable insights and identifies potential opportunities for leveraging technology to address obesity-related challenges, it is essential to acknowledge its limitations, including reliance on non-empirical data and the potential biases inherent in AI-generated responses. Moving forward, combining AI insights with empirical data and human expertise will be crucial for developing effective interventions and policies aimed at fully harnessing mHealth's potential to combat obesity and promote healthier lifestyles.  
This study offers a novel perspective on mHealth trends and opportunities through an AI-driven approach. However, incorporating empirical evidence will enhance its practical implications. By integrating AI-generated insights with empirical research, researchers can create a more comprehensive framework for leveraging mHealth in obesity management. Such an approach will not only address the limitations of this study but also pave the way for innovative and effective strategies to tackle the global obesity epidemic.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Acknowledgments:** This study utilized OpenAI's real-time interactive ChatGPT (version 4.0) as a study tool (OpenAI, L.L.C., San Francisco, CA, USA).  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 4 Conclusions  
This work evaluated the performance and emissions for an SHV with an ICE working under a RCCI regime and compared its values versus diesel and dual-mode combustion (diesel + RCCI). The results were obtained by means of vehicle systems simulations for the WLTC and RDE cycles. Preliminary studies demonstrated that an optimum range exists for the initial SOC of the battery (0.2 to 0.8), leading to a reduction in the final fuel consumption. This reduction can be also obtained by increasing the number of operating conditions in which the ICE works. In this sense, the use of different power conditions to charge the batteries allows the selection of the required operating condition for each driving cycle phase, improving the final performance of the vehicle.  
The most important conclusions from the comparison of the three concepts (CDC, dual-mode and hybrid) under the RDE and WLTC driving cycle can be drawn as follows:  
* Owing to the higher EGR levels with RCCI, lower mass flows through the exhaust system are verified for the dual-mode concept. Therefore, the total CO\({}_{2}\) emissions in g/km presented a decrease of almost 30% compared to the CDC case.
* The RCCI-SHV concept reduces the CO\({}_{2}\) emissions by half compared to the CDC case.
* The use of operating conditions calibrated to fulfill Euro 6 limits as charging points for the batteries allowed an SHV concept that presents engine-out NOx and Soot emissions inside legislation limits to be obtained without requiring DPF and SCR devices.  
Therefore, it can be stated that the use of SHV in conjunction with RCCI considerably decreases the total fuel consumption of the vehicle while maintaining the NOx and soot engine-out emission levels under the Euro 6 limits. Such a result directly impacts the final after-treatment cost, since the requirements for the DPF and SCR devices can be minimized.  
All authors discussed and agreed on the contents of the manuscript. [PERSON] coordinated the experimental work defining the objectives of the tasks and guiding the technical discussion of the results and manuscript writing. [PERSON] provided technical guidance in experimental phases from the first stages of the project development, analyzed the results, and collaborated in the manuscript preparation. [PERSON]-[PERSON] and [PERSON] designed and performed the theoretical study and experimental tests, discussed the results, and contributed to the manuscript writing.  
This research was funded by FEDER and Spanish Ministerio de Economia y Competitividad through the TRANCO project, with grant number TRA2017-87694-R.  
The authors gratefully acknowledge General Motors Global Research & Development for providing the engine used to acquire the experimental data shown in this investigation.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 2 Materials and Methods
### Data Analysis
#### 2.4.3 Statistical Analysis  
All collected data were compiled and analyzed using XLSTAT 2023 (version 2.0, Paris, France). Descriptive statistics were calculated for all variables, and the results are presented as mean \(\pm\) standard deviation (SD). Given the small sample size (\(n=6\)), the normality of the data was tested using the Shapiro-Wilk test. For variables where normality was confirmed (\(p>0.05\)), a two-way repeated measures ANOVA was conducted to assess the main effects of the condition (IET vs. SPV), the time, and the interaction between the condition and the time on alpha and beta PSD, as well as the EEG/VO\({}_{2}\) ratio. In cases where the sphericity assumption was violated, the Greenhouse-Geisser correction was applied to adjust the degrees of freedom. Additionally, a post hoc Wilcoxon signed-rank test was used to compare the two conditions at each time point. For variables where normality was not confirmed (\(p\leq 0.05\)), a Friedman test was used to assess the main effects of the condition and the time. The Wilcoxon signed-rank test was used as a post hoc test to compare the two conditions at each time point for non-normally distributed variables. Statistical significance was set at \(p<0.05\) for all analyses.
## 6 Discussion
### Caffe Acid in CSF  
We described, for the first time, the existence of CA in human CSF. Food (e.g., olives, fruits, carrots, coffee beans) is the only source of CA in human body fluids. This compound is absorbed in the free form in the colon and undergoes methylation, sulphation or glucuronidation that makes the molecule of CA more hydrophilic, facilitating its urinary elimination [51]. The concentration of CA in CSF was almost the same as that in plasma. However, there was no correlation between CSF and plasma levels of CA. These suggest that the transportation of CA through BBB could not to be the result of simple or facilitated diffusion. Intestinal cells actively absorb free CA using monocarboxylic acid transporters [51]. Various solute carriers, including monocarboxylic acid transporters, are present on the capillary endothelial cells of BBB [52]. Experiments on animals and on in vitro models of BBB revealed that transportation of various substances including phenolics is affected by their methylation, sulphation or glucuronidation [53; 54]. Methylation or glucuronidation of selected polyphenols increased their traversing through BBB models in vitro [54]. Sulphation of dopamine caused its permeation through the BBB in rats [53]. Moreover, transient conjugation or de-conjugation of polyphenols can take place in close vicinity of BBB [54]. Since CA is present in the plasma in free and conjugated form, the process of its transportation through BBB may depend on these afore-mentioned factors. These may explain no association between CSF and plasma levels of CA. On the other hand, low concentrations of CA close to the limit of the assay sensitivity can be responsible for the negative results of the correlation analysis. Nevertheless, the presence of CA in CSF shows that at least some dietary plant phenolics after absorption to the blood can cross BBB in humans and directly affect the functions of brain cells. These are in agreement with previous experiments on laboratory animals, showing neuroprotective activity of CA [55; 56] and plausible permeation of this phenolic acid, through BBB into brain in mice [57].
# Recent Development of Morphology Controlled Conducting Polymer Nanomaterial-Based Biosensor
## 3 Polyaniline-Based Electrodes
### 1D Nanostructures  
One-dimensional PANI nanostructures are easily generated using various methods and exhibit excellent electrical signal transmission capacity. [PERSON] et al. fabricated PANI nanowires using electrochemical polymerization, followed by the incorporation of Au nanoparticles onto the electrode's surface (Figure 5a) [100]. This composite nanostructure was then bound to glucose oxidase for use as electrochemiluminescence glucose sensors. Additionally, [PERSON] et al. coated polyethylene glycol (PEG) layers on the surface of PANI to enhance the antifouling properties of the nanofiber. The PEGlated PANI nanofibers were applied as breast cancer susceptibility gene aptamer sensors (Figure 5b) [101]. Additionally, [PERSON] et al. synthesized PANI-based nanofibers using a facile electrospinning process to obtain uniformly sized polymer nanofiber webs (Figure 5c) [102]. The nanofibers were then applied to detect cyclooxygenase-2 related to tumor cell proliferation, differentiation, pain, and adhesion. By contrast, PANI nanotubes were generated through a different technique. [PERSON] et al. synthesized PANI nanotube-based glucose enzyme sensors using self-assembly and molecular imprinting methods [103]. Conversely, [PERSON] et al. fabricated PANI nanotubes using the MnO\({}_{2}\) nanofiber template to produce chronic myelogenous leukemia-sensing aptamer sensors [104].