esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Results of Joint Angle Prediction
#### 3.3.5. Prediction Results of Different Forecast Methods  
The comparative analysis which was shown in Table 6 reveals that the SMA-BLS algorithm exhibited superior efficiency and boasted enhanced prediction accuracy when contrasted with other algorithms evaluated. Table 6 illustrates the comparative outcomes of various prediction methodologies utilizing multi-person datasets, with the SMA-BLS algorithm exhibiting superior performance in terms of prediction accuracy and computational efficiency. To evaluate whether there is a statistically significant difference in the R\({}^{2}\) between SMA-BLS and other algorithms, a two-sample T-test was employed. According to Table 6, the \(p\)-values were significantly lower than the commonly accepted level (\(\alpha=0.02\)).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multicolumn{5}{c}{(**a**)} \\ \hline
**Load Weight (KG)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
0 & 0.0019 \(\pm\) 0.0001 & 0.0366 \(\pm\) 0.0012 & 0.0098 \(\pm\) 0.0006 & 0.970 \(\pm\) 0.002 \\
0.5 & 0.0017 \(\pm\) 0.0001 & 0.0342 \(\pm\) 0.0011 & 0.0090 \(\pm\) 0.0005 & 0.974 \(\pm\) 0.001 \\
1 & 0.0015 \(\pm\) 0.0001 & 0.0324 \(\pm\) 0.0010 & 0.0083 \(\pm\) 0.0005 & 0.978 \(\pm\) 0.001 \\ \hline \multicolumn{5}{c}{(**b**)} \\ \hline
**Load Weight (KG)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
0 & 0.0020 \(\pm\) 0.0001 & 0.0387 \(\pm\) 0.0009 & 0.0108 \(\pm\) 0.0007 & 0.963 \(\pm\) 0.001 \\
0.5 & 0.0018 \(\pm\) 0.0001 & 0.0365 \(\pm\) 0.0009 & 0.0094 \(\pm\) 0.0005 & 0.970 \(\pm\) 0.002 \\
1 & 0.0016 \(\pm\) 0.0001 & 0.0346 \(\pm\) 0.0008 & 0.0089 \(\pm\) 0.0004 & 0.974 \(\pm\) 0.001 \\ \hline \multicolumn{5}{c}{(**c**)} \\ \hline
**Load Weight (KG)** & **MSE** & **RMSE** & **MAE** & **R\({}^{2}\)** \\ \hline
0 & 0.0021 \(\pm\) 0.0001 & 0.0402 \(\pm\) 0.0010 & 0.0119 \(\pm\) 0.0006 & 0.959 \(\pm\) 0.002 \\
0.5 & 0.0019 \(\pm\) 0.0001 & 0.0378 \(\pm\) 0.0010 & 0.0101 \(\pm\) 0.0005 & 0.964 \(\pm\) 0.002 \\
1 & 0.0016 \(\pm\) 0.0001 & 0.0350 \(\pm\) 0.0009 & 0.0092 \(\pm\) 0.0005 & 0.969 \(\pm\) 0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: (**a**) Prediction results of different loads during arm side raises. (**b**) Prediction results of different loads during arm front raises. (**c**) Prediction results of different loads during elbow curls.
## 3 Results and Discussion
### Characterization of Emulsions
The lower value was obtained in the emulsion with the highest concentration of RGEO (26.0 cP) and the higher value in the chitosan solution (124.7 cP). Emulsion viscosity reduction results from the decrease in the oil-phase agglomeration, increasing the stability of the emulsion. In addition, the lower availability of CS chains, which are adsorbed on the surface of RGEO surfaces, decreases the viscosity of the emulsions [50]. It is expected that the emulsions from essential oils have low viscosities and be economical for a wide-spread application [51]. CS + RGEO emulsions showed higher values in the percentage of soluble solids due to the retention of the oil in the polymer matrix. The oil is retained in the hydrophobic region of the chitosan; therefore, CS chains must be acting as a colloidal detector through van der Waals interactions and hydrogen bonds between hydroxides and amines of the CS and ketones present in the oil. Typically, it is considered that analyzing the particle size of the emulsions allows determining the stability of the emulsion [25]. On the other hand, a smaller particle size could influence the physical and chemical properties such as the viscosity and density of the emulsions, because a higher contact surface would be available that would improve the features [23]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \ \begin{tabular}{c} **Essential Oil** \\ **Content (\%)** \\ \end{tabular}  & **pH** & **Density (g/mL)** & **Viscosity (cP)** & **\% Total Solids** & \begin{tabular}{c} **Particle Size** \\ **(\(\mu\)m)** \\ \end{tabular}  \\ \hline
0.0 & 4.36 \(\pm\) 0.01 \({}^{\text{a}}\) & 1.0018 \(\pm\) 0.01 \({}^{\text{a}}\) & 124.7 \(\pm\) 0.1 \({}^{\text{d}}\) & 2.96 \(\pm\) 0.02 \({}^{\text{a}}\) & N.D. \\ \
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 4 Clustering and Cluster Management
### Cluster Head Election Principles  
A CH is responsible for passing packets and organizing inter-/intra-cluster information traffic. The perfect election of the cluster will offer an efficient network performance and high reliability. Most of the clustering routing protocols normally elect the CH based on the location or velocity. On the one hand, the selection using location is not usually an optimum choice, because the location of the selected vehicle may rapidly change according to the relative speed with the other vehicles in the network. This means that the vehicle with the best location may quickly become unsuitable as the CH. On the other hand, selecting the CH according to the velocity is also not appropriate in many situations, particularly when the relative movement between vehicles is very high. This will consequently lead to cluster instability. Moreover, the speed of the vehicles directly affects their locations. Therefore, the perfect CH selection method should consider many factors to obtain high cluster stability and network reliability. In this paper, we proposes a new approach to select the CH by considering the location, velocity and the equipment of the vehicles in order to obtain a high cluster stability and for optimum utilization of available equipment in the network, particularly in the case of vehicles working on a specific mission.  
The CH is elected from the vehicles traveling in the same direction. The notation \(P\) represents the CHE priority factor. For a specific cluster, the election priority factor \(P\) is calculated according to the equipment, location and velocity of the vehicle. For the CHE process purposes, we assume the following:  
1. Each vehicle has a unique ID number.
2. Each vehicle is equipped with a satellite positioning device (GPS) used to collect location information and to periodically obtain velocity and direction information.
3. In the proposed model, \(v\) is the instantaneous velocity of each vehicle.
4. The symbol \(i\) represents the intended vehicle, \(\phi_{n}\) for the group of vehicles in the same cluster, \(\phi_{i}\) for the group of vehicles neighboring vehicle \(i\), and \(j\) is another vehicle in the same cluster. Each vehicle has \(N_{c}-1\) neighbors, \([j_{1},j_{2},...,j_{n-1}]\).  
The calculation of \(P\) is as follows.  
Figure 5: Communication within a cluster and between adjacent clusters.
_Appl. Sci._**2015**, \(5\), 1184-1195; doi:10.3390/app5041184  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
The Effect of Trifluoroethanol and Glycerol on the Thermal Properties of Collagen Using Optical Displacement-Enhanced Heterodyne Polarimeter  
**[PERSON] 1, [PERSON] 1,2, [PERSON] 1 and [PERSON] 3,* 1** Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University,  
Hsinchu 30013, Taiwan; E-Mails: [EMAIL_ADDRESS] (C.-M.W.);  
[EMAIL_ADDRESS] (H.-H.C.); [EMAIL_ADDRESS] (K.-H.T.)
2 Center for Measurement Standards, Industrial Technology Research Institute (ITRI),  
Hsinchu 30013, Taiwan
3 Department of Electronic Engineering, National United University, Miaoli 36063, Taiwan
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-37-382-528; Fax: +886-37-382-498.  
Academic Editor: [PERSON]  
_Received: 20 July 2015 / Accepted: 2 November 2015 / Published: 13 November 2015_  
**Abstract:** An angular displacement-enhanced heterodyne polarimeter has been employed to investigate the interplay between trifluoroethanol (TFE) and glycerol on the thermal denaturation of type I collagen. The concentration of the collagen solution was fixed at 0.341 (mg/mL), and was heated from 25 \({}^{\circ}\)C to 55 \({}^{\circ}\)C. TFE solutions with concentrations of 5%, 10%, 15%, 20%, 40% and 80% (\(v\)/\(v\)) were prepared and the phase change was recorded for the determination of thermal denaturation. It was observed that the thermal denaturation temperature (_Ta_) is decreased with increasing TFE concentration due to the partial cleavage of the triple-helical structure. With TFE concentration higher than 20% (\(v\)/\(v\)), the degree of optical rotation appears to be nearly the same, reflecting that the collagen triple helices have been completely destructed. Moreover, the addition of glycerol in inhibiting the thermal denaturation of collagen is investigated. It has been shown that glycerol can improve the thermal denaturation of both collagen and TFE-mixed collagen. Experimental results show that, in the presence of 2 M glycerol, the \(T_{\rm d}\) of collagen remained at around 41.9 \({}^{\circ}\)C, meanwhile the \(T_{\rm d}\) of 20% (\(v\)/\(v\)) TFE-mixed collagen is significantly restored to 32.8 \({}^{\circ}\)C.  
keywords:collagen protection; heterodyne polarimeter; thermal denaturation; trifluoroethanol; glycerol
# Revisiting the Role of Knee External Rotation in Non-Contact ACL Mechanism of Injury
## 2 Methods
It was decided to simulate two sets of axial forces to analyse the effects of a more demanding biomechanical scenario to the ACL (landing, 4000 N [55]) as compared to a less demanding scenario (walking, 700 N [56]). Moreover, most ACL tears are also associated with a dynamic valgus stress (knee abduction moment) [47; 49; 50; 51; 52; 53], and thus, a 50 Nm knee abduction moment [26] was used in all testing sets. Anterior tibial stress loads are also commonly associated with ACL injury (134 N) [26]; therefore, the combination sets with and without anterior stress were tested. Lastly, tibial rotation also has an important impact on the ACL tear mechanism [47; 48; 49; 50; 51; 52], and the model was thus tested with both internal and external rotations (separately) and used two different rotation magnitudes (15 Nm and 20 Nm) as suggested in a previous work [26]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Simulation**} & \multirow{2}{*}{
\begin{tabular}{c} **Knee** \\ **Flexion** \\ \end{tabular} } & \multirow{2}{*}{**Axial Force**} & \multicolumn{2}{c}{**Anterior**} & \multirow{2}{*}{**External Rotation**} & \multirow{2}{*}{**Internal Rotation**} & \multirow{2}{*}{**Valgus (Abduction** Moment)**} \\ \cline{2-2} \cline{5-10}  & & & & & & **Tibial Load** & & & **Tibial** & **Tibial** \\ \cline{2-2} \cline{5-10}  & 20\({}^{\circ}\) & 700 N & 4000 N & 0 N & 134 N & 15 Nm & 20 Nm & 15 Nm & 20 Nm & 50 Nm \\ \hline \#1 & ✔ & ✔ & ✔ & ✔ & ✔ & & & & ✔ \\ \hline \#2 & ✔ & ✔ & ✔ & ✔ & ✔ & & & ✔ \\ \hline \#3 & ✔ & ✔ & ✔ & ✔ & ✔ & & & ✔ \\ \hline \#4 & ✔ & ✔ & ✔ & ✔ & ✔ & & & ✔ \\ \hline \#5 & ✔ & ✔ & ✔ & ✔ & & ✔ & & ✔ \\ \hline \#6 & ✔ & ✔ & ✔ & & ✔ & ✔ & ✔ \\ \hline \#7 & ✔ & ✔ & & ✔ & ✔ & ✔ & ✔ \\ \hline \#8 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \#9 & ✔ & ✔ & ✔ & ✔ & & ✔ & ✔ \\ \hline \#10 & ✔ & ✔ & ✔ & ✔ & & ✔ & ✔ \\ \hline \#11 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \#12 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \#13 & ✔ & ✔ & ✔ & & & ✔ & ✔ \\ \hline \#14 & ✔ & ✔ & ✔ & ✔ & & ✔ & ✔ \\ \hline \#15 & ✔ & ✔ & & ✔ & ✔ & ✔ & ✔ \\ \hline \#16 & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ & ✔ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulations performed with the FEM model. The stress-strain distribution and peak values were obtained for the whole ACL and for each bundle in the fibres' direction (Abaqus S11 and LE11). The peak stress and strain were tested in the 16 simulations combining different knee-kinetic scenarios. The locations (proximal, midportion and distal) of peak stress and strain for each of the ACL bundles were also registered.
## 3 Experiments and Discussion
### Signal Transmission of BSN
#### 3.2.3 Influence of Baseband Frequency  
The experiments with respect to the influence of baseband frequency over the signal transmission system based on the human body medium has been implemented using the experimental device shown in Figure 7. Meanwhile, the carrier frequency was set as 8 MHz, and the baseband frequency was set as 100 kHz, 1 MHz, 4 MHz and 8 MHz, respectively.  
Figure 9(a) shows the modulated signal measured by the proposed EO sensor under the condition that BF = 100 kHz, while Figure 9(e) shows the corresponding demodulated signal processed by the receiving circuit. We can find from Figure 9(a,e) that both the modulation and the demodulation can be implemented correctly in this case, while the measured BER is 0.59%. Meanwhile, Figure 9(b) shows  
Figure 8: The modulated signal received by the Mach-Zehnder EO sensor on conditions that BF = 500 kHz and CF is (**a**) 1 MHz, (**b**) 4 MHz, (**c**) 8 MHz and (**d**) 16 MHz, respectively. Figure (**e,f,g,h**) are the corresponding demodulated signals of the modulated signals shown in Figure (**a,b,c,d**), respectively.  
the modulated signal under the condition that BF = 1 MHz, while Figure 9(f) is the corresponding demodulation signal. we can find from Figure 9(b,f) that even the pulses corresponding to the conversion process (from \"1\" to \"0\" or from \"0\" to \"1\") has a comparatively smaller amplitude, the correct demodulation signal can also be achieved by using the receiving circuit integrated with variable gain amplifier, while the measured BER is 0.49%. Moreover, as shown in Figure 9(c,d), when BF was set as 4 MHz and 8 MHz, the pulses corresponding to the conversion process have almost the same amplitudes as the pulses representing \"1\" or \"0\". Additionally, even some wave distortions can be found from the modulated signals shown in Figure 9(c,d), these distortions were limited in an acceptable range and thereby results in the correct demodulation signals shown in Figure 9(g,h). As a result, the measured BER's corresponding to BF = 4 and 8 MHz are 0.68% and 0.73%, respectively.
## 1 Introduction
This method enables efficient power utilization in dynamic environments, maintaining high communication quality even in non-uniform channel conditions, and significantly improving overall energy efficiency. Finally, the effectiveness of the proposed method was verified by simulations. Compared to conventional equal power allocation and CSI-based proportional allocation methods, the proposed method demonstrated superior performance in reducing the Bit Error Rate (BER) and improving throughput. Moreover, the proposed approach significantly reduced power consumption while maintaining stable communication quality in dynamic environments. The results also showed that by adjusting the power distribution concentration parameter, optimal communication balance could be achieved. In particular, the proposed method outperformed conventional methods, particularly in scenarios with highly uneven channel conditions. These findings suggest that the proposed method is a promising approach to improve the efficiency and reliability of communication systems in dynamic and unstable maritime environments. The organization of this thesis is as follows. Section 2 describes the system model assumed in this study. In Section 3, the conventional methods, the equal power allocation method, and the CSI-based proportional allocation method, as well as the proposed method, are explained in detail. Furthermore, to demonstrate the superiority of the proposed method, an analysis is conducted in terms of bit error rate (BER), throughput, and power consumption. Section 4 presents a performance comparison based on computational simulations. Finally, Section 5 provides a summary and conclusion of this thesis.
## 3 Results and Discussion
### Anti-Inflammatory Effects of Fermented CF Leaf Methanolic Extract  
IL-6, TNF-\(\alpha\), NO, and ROS play crucial roles in skin abnormalities, including the infiltration of inflammatory cells, redness, swelling, and uncomfortable feelings [3]. Therefore, the effects of the fermented leaf extract on their production/activity were analyzed. Figure 5 indicates that the fermented CF leaf extract reduced NO, IL-6, and TNF-\(\alpha\) production in LPS-stimulated RAW 264.7 cells in a dose-dependent manner. NO production decreased from 31.6 \(\pm\) 2.1 \(\upmu\)M (control, without fermented extract) to 11.9 \(\pm\) 0.8 \(\upmu\)M (at 250 mg/L), a 62.3% reduction, which was more effective than that of the CF hull extract obtained through high-pressure extraction [30]. IL-6 production decreased from 282 \(\pm\) 2.6 \(\upmu\)/L (control) to 218 \(\pm\) 2.5 \(\upmu\)/L (at 250 mg/L), a 22.7% reduction. TNF-\(\alpha\) production decreased from 126 \(\pm\) 6.6 \(\upmu\)/L (control) to 47.2 \(\pm\) 1.5 \(\upmu\)g/L (at 250 mg/L), a 62.5% reduction. Furthermore, ROS production in LPS-stimulated RAW 264.7 cells was reduced by 46.7% when 250 mg/L of the fermented extract was applied. A study indicated that phenolic acids and flavonoids in the water extract of CF seeds exerted antioxidative and anti-inflammatory effects in cellular/animal models [3]. [PERSON] et al. (2022) reported that the water extract of hulled CF seeds reduced ROS generation [9]. These results of anti-inflammatory (Figure 5) and high polyphenol contents (Table 1) indicated that the fermented CF leaf extract can reduce oxidative stress and increase anti-inflammatory activity.  
Figure 4: Effect of fermented _C. formosammum_ leaf extract on relative mRNA expression (AQP3) in HaCaT cells. Cells were treated without extract (control). Data are expressed as the means and standard deviations of three independent experiments.  
Figure 5: Effects of fermented _C. formosammum_ leaf extract on NO, IL-6, and TNF-\(\alpha\) production in LPS-stimulated RAW 264.7 cells. LPS alone was positive control. Data are expressed as the means and standard deviations of three independent experiments.
# Carbon Dioxide Adsorption on Carbon Nanofibers with Different Porous Structures
###### Abstract
[23] observed that the introduction of PMMA and silica nanoparticles to PAN polymer solutions enabled the fabrication of a carbon nanofibrous mat with a unique hierarchical porous structure via the electrospinning process. However, it reduced thermal conductivity by 98% compared to the non-porous carbon film. [PERSON] et al. [24] proposed a new method to adopt a metal-free reversible addition-fragmentation chain-transfer polymerization of poly(acrylonitrile-block-methyl methacrylate), followed by electrospinning, stabilization, and carbonization. This new method produced porous nanofibers with a specific surface area of 503 m\({}^{2}\)/g, higher than those samples formed by traditional methods. Porosity development in relation to a high specific surface area and micropore volume is very important for carbon nanofibers, depending on their application. Different structures (such as core/shell, hollow, and porous) of carbon nanofibers have been produced using electrospinning techniques, which have become a straightforward and convenient way to produce continuous and porous carbon nanofibers [12]. These porous nanofibers have great potential for use in drug delivery and as a scaffold for tissue engineering [25], in the capacitive deionization process for water treatment [26], or in energy [27; 28; 29; 30]. However, there is very little application in gas adsorption. To reduce the impacts of global warming, the capture of carbon dioxide (CO\({}_{2}\)) is important. Moreover, the ultramicropore volume, specific surface area, and nitrogen-containing functional groups of adsorbents are regarded as the major features affecting the CO\({}_{2}\) adsorption capacities of the adsorbents. Therefore, the aim of this study was to synthesize polyacrylonitrile (PAN)-based activated carbon nanofibers (ACNFs) using electrospinninging processes for the capture of CO\({}_{2}\). Using a sacrificing core or pore generator, i.e., poly(methyl methacrylate) (PMMA), solid, hollow, or porous ACNFs were prepared. Through electrospinninging, followed by stabilization, carbonization, and activation, ACNFs were formed with different porous structures. Several properties of the ACNFs with different porous structures were characterized, and the CO\({}_{2}\) adsorption on the ACNFs was investigated.
## 2 OBS Networks
### Functional Mechanisms of OBS Nodes
#### 2.2.6. Contention Resolution  
OBS networks provide connectionless transport through wavelength channels that support statistical multiplexing. In this working condition it is quite possible that several bursts contend with each other at the intermediate nodes. This can occur because connection requests made by OBS edge nodes generally use one-way reservation protocols in which bursts are transmitted without confirmation that resources along the path will be successfully reserved. Therefore, if a burst arrives at an OBS node and all local resources are already taken, or if two or more simultaneously arriving bursts compete for the same resource, we have contention and (potentially) burst loss. Burst loss is a critical issue with significant impact on any end-to-end application running in the upper layers reducing its overall performance. Since contention is the primary cause of burst loss, contention resolution is one of the main design objectives in OBS networks. The general solution to burst contention is to move all but one burst \"out of the way\". In OBS networks this can be accomplished by actions in the time, space or wavelength domains, or any combination therefrom. In fact, since time, space and wavelength are independent, any technique from any domain can be combined, resulting in a potential high number of different contention resolution schemes. Typical approaches for contention resolution using each of the above mentioned three domains can include: optical buffering (time domain), deflection routing (space domain), and wavelength conversion (wavelength domain). It is worth mentioning that these are usually reactive mechanisms driven by burst contention, while the proposals in this article take a more preventive approach in which the space domain is used by the appropriate selection of routing paths in order to avoid congestion, followed by a reactive wavelength conversion attempt if contention occurs.