esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Protein Solubility  
Protein solubility is an important parameter of the functional structure of muscle proteins, particularly myofibrillar proteins, which are responsible for in-gel formation. Protein solubility exhibits the extent of protein denaturation and aggregation induced by processing treatments such as heat or HPP. The decrease in solubility shows the formation of insoluble protein aggregates or network structures, which may contribute to better gelation. In contrast, high solubility indicates that proteins are insufficiently denatured and therefore, less able to form a well-structured gel matrix. Therefore, protein solubility provides important mechanistic insight into how HPP influences the gelation behavior and structural quality of fish protein gels. The protein solubility of HIGs and HPP gels ranged from 7.75 mg/L to 8.52 mg/L and showed no significant differences (\(p\geq 0.05\)).  
Figure 3: WHC of pangasius gel was added with 1 and 2% salt under different HPP treatments. HIGs: conventional heat-induced gels; PG300/1%: salt concentration 1% and 300 MPa/10 \({}^{\circ}\)C/ 10 min; PG300/2%: salt concentration 1% and 300 MPa/10 \({}^{\circ}\)C/10 min; PG400/1%: salt concen-tration 1% and 400 MPa/10 \({}^{\circ}\)C/10 min; PG400/2%: salt concentration 1% and 400 MPa/10 \({}^{\circ}\)C/10 min; PG500/1%: salt concentration 1% and 500 MPa/10 \({}^{\circ}\)C/10 min; PG500/2%: salt concen-tration 1% and 500 MPa/10 \({}^{\circ}\)C/10 min. Different letters (a–c) indicate significant differences (\(p<0.05\)).  
The reduction in protein solubility indicates protein denaturation and the precipitation of myofibrillar proteins, particularly myosin, following the denaturation process [39]. These findings show that both heat treatment and high-pressure processing result in complete protein denaturation, facilitating the formation of a well-structured gel network at both 1% and 2% salt conditions.
## 4 Theoretical Study
### Electromagnetic Simulations
Figure 5c shows that the in-plane displacement maximizes at 98 \(\upmu\)s while the out-of-plane displacement maximizes at 100 \(\upmu\)s; the Time of Flight (ToF) of the in-plane displacement matches with the wave velocity of the SH0, as was calculated from the dispersion curves. while the out-of-plane displacement can be the A0. Hence, this EMAT configuration may excite the A0 wave mode as well. However, the maximum value of the out-of-plane displacement is significantly smaller than the in-plane displacement and therefore experimental validation of this EMAT design regarding its guided wave purity characteristics is still required. Figure 5: (**a**) Excitation/eddy current distribution at 12 \(\upmu\)s; (**b**) magnetic flux distribution of the 12 Nd-Fe-B; (**c**) in-plane/out-of-plane displacement 30 cm away from the EMAT at room temperature; (**d**) in-plane/out-of-plane displacement 30 cm away from the EMAT at 180 \({}^{\circ}\)C.  
Figure 5d shows the in-plane and out-of-plane displacement 30 cm away from the EMAT at 180 \({}^{\circ}\)C; both displacements are shifted in time, as was expected since the wave velocity changes with temperature rise and the ToF matches with the wave velocity as it was calculated from the dispersion curves. The amplitude of the in-plane displacement decreased; however, the amplitude of the out-of-plane displacement did not decrease with the temperature rise. As was mentioned, in the FEA model only the material properties of the specimen changed so that the effect of the temperature rise would be simulated. Nevertheless, the temperature rise affects the components of the EMAT as well, as was mentioned in the introduction, and thus a further decrease in the amplitude of both displacements may be observed in the experimental results, which the current model does not take into account. Consequently, this EMAT configuration can mainly generate in-plane displacement (SH0); however, it may also generate a small out-of-plane displacement (A0) at this specific frequency. This can result in complicating signal analysis, since the out-of-plane displacement can be dispersive and mode conversion can also occur, which can lead to incorrect conclusions regarding the structural integrity of the specimen. In GWT, EMATs should excite a single wave mode, so that the signal received from the specimen can provide valid information regarding the structural integrity of the specimen. Also, the temperature rise affected the ultrasonic response of the EMAT, as was expected; amplitude attenuation and shifting in time were the main two changes in the signal received as the temperature increased. Hence, the wave purity characteristics of the PPM EMAT for GWT are of great importance for this application as well as the effect of temperature on the ultrasonic response of EMATs and thus an experimental validation of this EMAT design regarding its guided wave characteristics and its high-temperature performance is still required. The simulation results will be compared with the experimental results.
## 7 Experimental Results
### Original BEE
#### 7.1.3. Clustering Iterations  
The number of iterations in the clustering forming phase determines the overhead of the algorithm. Reducing the number of required iterations in each round can reduce (1) the clustering delay and (2) the number of broadcast packets. The comparison of the number of iterations in each round is shown in Figure 6b. By separating \(CH_{prob}\) into three parts, the number of iterations in each round was significantly reduced in BEE. At the end, for HEED, the number of iterations suddenly dropped to 0 because no sensor was still alive in the network. The number of iterations of HEED can increase from 6 to 15. When the battery level is high, as we mentioned in Section 4.1, 6 iterations are necessary since \(C_{prob}=5\%\). When the battery level is low, it takes 15 iterations as \(P_{min}=10^{-4}\). However, the number of iterations for BEE will not increase significantly as the sensor batter level decreases.  
Figure 5: Sensor distribution after 450 rounds using LEACH, HEED and BEE. (**a**) LEACH; (**b**) HEED; (**c**) BEE.  
Figure 6: _Cont._
# Power Optimization Model for Energy Sustainability in 6G Wireless Networks
## 2 System Model
### Uplink Pilot Transmission  
Before transmitting the uplink data signals, a set of pilots are transmitted in the uplink in order to obtain the channel estimates. A set of \(l_{p}\) mutually orthogonal pilots signals are used to make sure that the interference from subsequent pilot transmissions is minimized. Let us suppose a subset of users that are assigned pilot \(l\) is denoted by \(U_{l}~{}\subset~{}\{1,2,\ldots\ldots U\}\). The signal received at the \(a\)th AP from the users in the set \(U_{l}\) is given as  
\[y_{la}^{p}=~{}~{}\sum_{i\in U_{l}}\sqrt{l_{p}p_{i}}~{}h_{ia}~{}+~{}\mathbf{n}_ {la} \tag{3}\]  
where \(\mathbf{n}_{la}\sim\mathcal{N}\big{(}0,\sigma^{2}I_{N}\big{)}\) is the additive white gaussian noise and \(p_{i}\) is the user \(i\) transmit power. The mMinimum mean square error (MMSE) estimation method is used to estimate the channel coefficients between user \(u\) and AP \(a\)[41]:  
\[\hat{h}_{ua}=~{}\sqrt{p_{u}l_{p}}R_{ua}\Bigg{(}\sum_{i\in U_{l}}~{}p_{l}l_{p}R _{ia}+\sigma^{2}I_{N}\Bigg{)}^{-1}y_{ua}^{p} \tag{4}\]  
Here, the correlation matrix of the received signal is \(\phi_{la}=E\Big{\{}y_{la}^{p}(y_{la}^{p})^{H}\Big{\}}=\sum_{i\in U_{l}}p_{i}l_ {p}R_{ia}\) + \(\sigma^{2}I_{N}\), \(\widetilde{h}_{ua}~{}=h_{ua}-\hat{h}_{ua}\), and \(C_{ua}=E\left\{\widetilde{h}_{ua}~{}\widetilde{h}_{ua}^{H}\right\}=R_{ua}-p_{ u}l_{p}R_{ua}\phi_{la}^{-1}R_{ua}\) is the error correlation matrix.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 2 Materials and Methods  
### Line Shape Theory  
A detailed description of TDLAS technology can be found in the literature [25; 26]. A brief summary is provided here to facilitate the discussion in subsequent sections. Along a uniform-property path, the narrowband absorption of a light signal at wavelength \(v(\text{cm}^{-1})\) is described by the Lambert-Beer Law:  
\[a=\ln\frac{I_{0}(v)}{I_{t}(v)}=A\Phi(v), \tag{1}\]  
\[A=\int_{-\infty}^{+\infty}\ln\frac{I_{0}(v)}{I_{t}(v)}dv=PX_{m}S_{m}(T)L\int_{ -\infty}^{+\infty}\Phi(v)dv, \tag{2}\]  
where \(a\) is the spectral absorbance, \(A\) is the IA (i.e., the area under the curve), \(I_{0}(v)\) and \(I_{t}(v)\) are the incident light intensity and the transmitted light intensity, respectively, \(PX_{m}\) is the partial pressure of absorber \(m\), \(S_{m}(T)\) is the line strength, \(L\) is the absorption length, and \(\Phi(v)\) is a line-shape function whose integral is normalized to unity (\(\int_{-\infty}^{+\infty}\Phi(v)dv=1\)).  
In the case of a normal analytical source, the line shape depends on contributions from collisional (Lorentzian) and Doppler (Gaussian) broadening [27]. The spectral line-shape is a convolution of Lorentzian and Gaussian line shapes and can be described by a Voigt profile [28] given by  
\[\Phi_{V}(v,v_{0})=\frac{1}{\sqrt{\pi}}x^{\prime}K(x,y), \tag{3}\]  
\[K(x,y)=\frac{y}{\pi}\int_{-\infty}^{+\infty}\frac{1}{(x+t)^{2}+y^{2}}e^{-t^{2 }}dt, \tag{4}\]  
where \(x\) is the normalized wave number, \(x^{\prime}\) is the normalized coefficient, and \(y\) is the line damping parameter, which controls the line shape. The three line-shape parameters (\(x\), \(x^{\prime}\) and \(y\)) are given by  
\[x=x^{\prime}v^{\prime}, \tag{5}\]  
\[v^{\prime}=v-v_{0}-v_{c} \tag{6}\]  
\[x^{\prime}=\frac{\sqrt{\ln 2}}{\alpha_{D}}, \tag{7}\]  
\[y=\sqrt{\ln 2}\frac{\alpha_{L}}{\alpha_{D}}, \tag{8}\]  
where \(v^{\prime}\) is the relative wavenumber, \(v_{0}\) is the wavenumber at the line center, \(v_{c}\) is the pressure/velocity-induced frequency shift of the absorption line profile with respect to the unperturbed wavenumber \(v_{0}\), and \(\alpha_{D}\) and \(\alpha_{L}\) are respectively the Doppler and collisional half-widths at half-maximum (HWHMs). The two HWHMs are affected by environmental and spectroscopic parameters:  
\[\alpha_{D}=v_{0}\sqrt{\frac{2k_{B}T\ln 2}{Mc^{2}}}, \tag{9}\]  
\[\alpha_{L}=\sum_{g}PX_{g}\gamma_{g}\bigg{(}\frac{T_{0}}{T}\bigg{)}^{n_{g}}. \tag{10}\]  
Here, \(k_{B}\) is the Boltzmann constant, \(M\) is the molecular weight of the absorbing species, \(c\) is the speed of light, \(T\) is the gas temperature, \(PX_{g}\) is the partial pressure of the collision partner of the absorbing-species, and \(\gamma_{g}\) and \(n_{g}\) are the collisional-broadening coefficient and temperature-dependent exponent of the species \(g\), respectively. \(\gamma_{g}\) and \(n_{g}\) are both spectroscopic parameters that can be queried in a database (e.g., HITRAN 2016 [29]).
## 3 Simulation Framework  
The overall architecture of the simulation framework, intended to enable the simulation of multi-RAT access, is illustrated in Figure 1. It contains basic elements and modules that mimic a multi-RAT access scenario:  
* HUT (Hybrid User Terminal)--user equipment (UE) capable of utilizing two different RATs--in this paper, these are terrestrial and satellite RATs.  
This approach enables flexible adaptations to various use case scenarios. In the next section, the simulation results for a UDP streaming session are given. The initial development stage of the proposed framework has been presented in [45].  
The core functionalities of the simulation framework utilized in this research study are detailed in [45]. For this study, the framework has been modified to support terrestrial and satellite channel models. The overall architecture of the simulation framework is illustrated in Figure 1.  
The two main building components of the developed simulation framework are as follows:  
* Modules;
* Messages.  
The simulation framework is implemented as a distributed solution that includes three software modules, deployed on separate physical platforms, that are in charge of generating, receiving, transmitting and processing user data. Two types of links are established between the modules of the simulation framework:  
The Data Stream Link and Control Link.  
* Data Stream Link--UDP-based;
* Control Link--TCP-based.  
User data that are transmitted within the simulation framework are encapsulated within Messages that have a specific format detailed in the next section.
## 2 Materials and Methods  
### System Description  
We acquired the images from our ion flux measurement system [13], as shown in Figure 3. The ion flux measurements consisted of an inverted microscope (XDS-1B, Chongqing MIC Technology Co, Ltd, Chongqing, China), a high-precision three-axis motorized manipulator (CFT-8301D, Jiangsu Rich Life Science Instrument Co., Ltd, Jiangsu, China), an ISME, a microelectrode holder, a multi-channel measurement software, and a charge coupled device (CCD) camera (TCC-1.4 CHICE, Xintu Photonics, Fu Zhou, China). The ion flux and intracellular membrane potential were measured by a glass ion selective microelectrode. The two measured signals were amplified and transferred to the digital signals by the preamplifier and the 16-bit analogue-to-digital board (USB-4716, Advantech Co, Ltd, Taiwan, China) in the microelectrode holder. The digital signals were acquired by the multi-channel measurement software through a universal serial bus (USB) interface. The high-precision three-axis motorized manipulator was controlled by a personal computer (PC) through a USB interface. For more details, please refer to [13; 19]. In the experiments, we obtained the images using a TCC-1.4 CHICE (Xintu Photonics, Fu Zhou, China) CCD camera. The resolution of each image was 1360 \(\times\) 1024 pixels, and the bit depth of the image was 24. The magnification of the microscope was 100X. The proposed vision-based measuring system was based on monocular vision. The CCD camera was mounted perpendicular to the working plane, and the position of the CCD camera and the internal and external parameters of the vision system were fixed. Because the purpose of the proposed system was for measuring the two-dimensional and relative distance of two micro-objects in the working plane, we did not need to get the world coordinates and camera coordinates of the micro-objects and  
Figure 2: Typical microscopy image of biological experiment. 1. Wheat root; 2. ISME.  
calibrate the microscope imaging system--we only needed to get the axial and lateral resolution of the microscope imaging system. If researchers need the real location of the world coordinates and camera coordinates of the micro-objects, please refer to the [20; 21]. We calculated the axial and lateral resolution of the image acquired by the CCD camera with the Leica Stage Graticule (1 DIV (division value) = 0.01 mm) and TSView (Xintu Photonics, China) software. The calculation method was shown in Figure 4. Both the axial and lateral resolution of the microscope imaging system were 0.94 \(\upmu\)m.
# Development of Bio-Sourced Epoxies for Bio-Composites
## 2 Rosin-Sourced Epoxy as a Matrix Resin
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Properties** & **Unit** & **Humidity** & **Temperature/\({}^{\circ}\)CReference \({}^{1}\)** & **AGMP3600/A38** & **Standard** \\ \hline \multirow{2}{*}{ply thickness} & mm & & \(-\) & 0.26 & 0.265 & \\ \cline{3-6}  & & & \(-\)55 & \(-\) & 949 & \\ \multirow{2}{*}{\begin{tabular}{c} Flexural \\ strength warp \\ \end{tabular} } & dry & RT & \(-\) & 947 & \\  & & & 70 & \(-\) & 903 & \\  & & & 70 & \(-\) & 826 & \\ \multirow{2}{*}{\begin{tabular}{c} Flexural \\ modulus warp \\ \end{tabular} } & \multirow{2}{*}{GPa} & \multirow{2}{*}{dry} & \(-\)55 & \(-\) & 50.9 & \\  & & & 70 & \(-\) & 58.7 & \\  & & & 70 & \(-\) & 51.8 & \\ \hline Interlaminar & & & \(-\)55 & 65 & 62.4 & \\ shear strength & & & 70 & 60 & 74.8 & ASTMD \\ warp & & & 70 & 40 & 65.3 & 2344/D2344M-002006 \\ \multirow{2}{*}{\begin{tabular}{c} Tensile strength \\ warp \\ \end{tabular} } & \multirow{2}{*}{MPa} & \multirow{2}{*}{dry} & \(-\)55 & 500 & 618 & \\  & & & 70 & 625 & 667 & \\  & & & 70 & 590 & 714 & \\  & & & 70 \({}^{\circ}\)C/wet 85\% & 70 & 560 & 674 & ASTMD \\ \hline \multirow{2}{*}{\begin{tabular}{c} Tensile modulus \\ warp \\ \end{tabular} } & \multirow{2}{*}{GPa} & \multirow{2}{*}{dry} & \(-\)55 & 52 \(\pm\) 6 & 55.25 & 3039/D3039M-e1 \\  & & & & 70 & 52 \(\pm\) 6 & 54.6 & \\  & & & 70 & 52 \(\pm\) 6 & 53 & \\  & & & 70 & 52 \(\pm\) 6 & 49 & \\ \hline Poisson ratio & - & dry & RT & \(0.05\pm 0.005\) & 0.052 & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Compression \\ strength warp \\ \end{tabular} } & \multirow{2}{*}{MPa} & \multirow{2}{*}{dry} & \(-\)55 & 600 & 758 & \\  & & & RT & 535 & 651 & \\  & & & 70 & 430 & 630 & \\  & & & 70 \({}^{\circ}\)C/wet 85\% & 70 & 310 & 605 & ASTMD \\ \hline \multirow{2}{*}{\begin{tabular}{c} Compression \\ modulus warp \\ \end{tabular} } & \multirow{2}{*}{GPa} & \multirow{2}{*}{dry} & \(-\)55 & 46 \(\pm\) 6 & 52.5 & \\  & & & RT & 46 \(\pm\) 6 & 53 & \\  & & & 70 & 46 \(\pm\) 6 & 52.5 & \\  & & & 70 & 46 \(\pm\) 6 & 55 & \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} In plane shear \\ strength \\ \end{tabular} } & \multirow{2}{*}{MPa} & \multirow{2}{*}{dry} & \(-\)55 & 100 & 136 & \\  & & & RT & 95 & 114 & \\  & & & 70 & 80 & 96.8 & \\  & & & & 70 \({}^{\circ}\)C/wet 85\% & 70 & 60 & 89 & ASTMD \\ \hline \multirow{2}{*}{
\begin{tabular}{c} In plane shear \\ modulus \\ \end{tabular} } & \multirow{2}{*}{GPa} & \multirow{2}{*}{dry} & \(-\)55 & 4.5 \(\pm\) 0.35 & 4.74 & 3518/D3518M-94 \\  & & & & RT & 3.65 \(\pm\) 0.35 & 4.21 \\  & & & & 70 & 3.5 \(\pm\) 0.35 & 2.63 \\  & & & 70 & 1.25 \(\pm\) 0.35 & 2.75 & \\ \hline \end{tabular}
\end{table} 
Table 3: Mechanical properties of AGMP3600/A38 (carbon fiber, twill fabric, 200 g/m\({}^{2}\), 42 vol %), a rosin-epoxy laminate, under a hydrothermal condition of 1000 h/70 \({}^{\circ}\)C/85% r.H.
# Highly Controlled Integration of Graphene Oxide into PAN Nanofiber Membranes
###### Abstract  
A highly improved strategy is established in order to systematically integrate excess exfoliated graphene oxide (GO) as fillers into polyacrylonitrile (PAN) nanofibers via electrospinning. Simple modification of GO surface allowed for their loading efficiency into the nanofibers to surpass the typical limits. Among many features, the hydrophilic and mechanical properties of these membranes were found to be significantly increased compared to the original PAN and bare GO-loaded membranes probably due to the effective reinforcing filler effect caused by the even distribution of the modified GO within the PAN nanofibers. Thus, the simple surface modification of fillers can facilitate the capability of controlling the loading efficiency into electrospun nanofibers which can highly impact the quality and performance of final composite membranes.  
PAN nanofibers; modified graphene oxide; electrospinning; membrane +
Footnote †: journal: Applied Physics Letters
# FMT: Unsupervised RGB-D Point Cloud Registration via Fusion Feature Matching with Transformer
## 3 Materials and Methods
### Feature Extraction and Fusion
#### 3.1.2 Point Cloud Branch  
Extending the design principles presented in Section 3.1.1, we constructed a mirrored structure in the point cloud branch that corresponds to the RGB branch. Unlike the RGB branch, the point cloud branch employs the Kernel Point Convolution (KPConv [44]), which is specifically designed for effective feature extraction from point cloud data. This design ensures that both modalities achieve optimal feature representation under their respective conditions.  
The detailed architecture of FusionBlockP is shown in Figure 2a (right). Unlike FusionBlockR, FusionBlockP fully accounts for the unordered nature of point cloud data, the interactions between points, and the invariance under transformations. It employs MLP layers to fuse features from the two modalities, making it more suitable for feature integration in point cloud data.
# Adaptive Virtual RSU Scheduling for Scalable Coverage under Bidirectional Vehicle Traffic Flow
## 6 Performance Evaluation
### Cache Replication
#### 6.3.2 General Case Scenario  
In this section, we will evaluate the system performance for a general case scenario, in which the vehicles can join or leave the system anytime and anywhere, and the request demand can only be redirected to the passing-by RSUs under the connection capacity limitations. We consider that the number of vehicles to join or leave between every pair of RSUs follows a Poisson distribution at each time slot. The online strategy including replication scheduling and request scheduling is implemented to deal with the time-varying traffic flow. Specifically, we consider three types of mechanisms (i.e., First-in First-out, Old Requester First, and New Requester First, respectively) in request scheduling. We compare the cumulative distribution function (CDF) of a completed request with different numbers of passing-by RSUs under these mechanisms in Figure 12. Generally, all three of these mechanisms would have an increasing CDF as the number of passing-by RSUs grows. Compared with First-in First-out and New Requester First mechanism, Old Requester First has a fast CDF increase with a limited number of RSUs, as the vehicles passing-by previous RSUs would be served in priority. Nevertheless, the CDF increase of Old Requester First becomes slow as the number of passing-by RSUs keeps growing. On the contrary, New Requester First would have a slow CDF increase initially, and a fast increase can be observed with an increasing number of RSUs. As New Requester First serves the newly joint vehicles in priority, it is suitable for more RSUs allocated. During the real-world implementation, these request scheduling strategies can be flexibly adjusted according to the RSU distribution and traffic condition.