esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Concentration of Carbohydrates and Glycerol  
The method used in this study to examine the carbohydrate profile and glycerol concentration in the tested beers was HPLC, by which the contents of dextrins, maltotriose, glucose, and maltose were assessed (Table 2).  
The carbohydrate content in the tested beers ranged from 27.72 to 34.91 g/L. The highest concentration among the analyzed carbohydrates was found for dextrins. Dextrins in beer come from malt and are carbohydrates that cannot be utilized by the brewing yeast used in this study [26]. The concentration of dextrins in ripe mango fruit is lower than in beer wort; therefore, the addition of mango to beer should decrease the dextrin concentration, as it was shown in the conducted study [12]. The use of mango pulp, raw mango and mango homogenisates did not decrease the content of sugars as much as the use of mango juice, because after the fermentation process, the fruit solids were separated from the beer. Because of this, the use of mango juice diluted the volume of the beer compared to other mango additions. The glucose content was not detected in any of the beers. It is a sugar, which is preferentially used by _Saccharomyces cerevisiae_ yeast. Beer usually does not contain glucose or contains only a minuscule amount of it [27]. The maltose content was only noted for BC and MR and in each of these beers it was similar. Similarly, maltotriose was also found in BC and MR, but its content was three times higher in BC. The composition and content of carbohydrates is an important factor, which may have an impact on the organoleptic characteristics of beer. It is also a critical element forming the properties of fruit and fruit preparations by having an effect on the final taste of the product and thus on the reception of the product by consumers [28]. The glycerol content in the analyzed beers ranged from 1.95 to 2.31 g/L. Glycerol is a chemical compound, which has an influence on the sensory traits of fermented beverages. It affects such characteristics as the viscosity of beer, its palatability, and the sensation of sweetness [29].
## 3 Link Level Capacity Expressions
### Orthogonal Space-Time Block Coding (OSTBC)  
With \(M_{1}\) and \((M-M_{1})\) transmit antennas from SEN1 and SEN2, respectively, the link level capacity of OSTBC can be expressed as [35]:  
\[C_{\text{OSTBC}}=R_{C}E\left\{\log_{2}\left(1+\frac{\gamma_{0}}{R_{C}M}\left\| \mathbf{H}\right\|^{2}\right)\right\} \tag{13}\]  
where \(R_{C}(=n_{s}/N_{t})\) is the rate of the OSTBC, \(n_{s}\) is the number of symbols transmitted per block and \(N_{t}\) is the number of symbol periods per block. Using a general result from [35], the probability density function of \(\eta\) is found to be:  
\[f(\eta)=\sum_{i=1}^{r}\Bigg{(}\prod_{j=1,j\
eq i}^{r}\frac{\lambda_{q,i}}{ \lambda_{q,i}-\lambda_{q,j}}\Bigg{)}\frac{\exp\left(-\frac{\eta}{2\lambda_{q, i}}\right)}{2\lambda_{q,i}} \tag{14}\]  
where \(\lambda_{q,i}\) is the \(i\)-th eigenvalue of the spatial channel correlation matrix \(\mathbf{Q}=\mathbf{R}_{t}\otimes\mathbf{R}_{r}\). Using Equations (13) and (14), the capacity is now given by:  
\[\begin{split} C_{\text{OSTBC}}&=R_{C}\sum_{i=1}^{r} \prod_{j=1,j\
eq i}^{r}\Bigg{(}\frac{\lambda_{q,i}}{\lambda_{q,i}-\lambda_{q,j}}\Bigg{)}\times\int_{0}^{\infty}\log_{2}\Bigg{(}1+\frac{\gamma}{2R_{C}N_{t }}\eta\Bigg{)}\frac{\exp\left(-\frac{\eta}{2\lambda_{q,i}}\right)}{2\lambda_{q,i}}d\eta\\ &=-\frac{R_{C}}{\ln 2}\sum_{i=1}^{r}\Bigg{(}\prod_{j=1,j\
eq i}^{r} \Bigg{(}\frac{\lambda_{q,i}}{\lambda_{q,i}-\lambda_{q,j}}\Bigg{)}\times\exp \left(\frac{R_{C}M}{\gamma_{0}\lambda_{q,i}}\right)Ei\left(-\frac{R_{C}M}{ \gamma_{0}\lambda_{q,i}}\right)\Bigg{)}\end{split} \tag{15}\]where \(Ei(\cdot)\) is the exponential integral function. The upper bound of OSTBC can be calculated by [PERSON]'s inequality as follows [35]:  
\[\begin{split} C_{OSTBC}&\leq R_{C}\log_{2}\left(1+ \frac{\gamma_{0}}{R_{C}M}E\left\{\left\|\mathbf{H}\right\|^{2}\right\}\right)\\ &=R_{C}\log_{2}\left(1+\frac{\gamma_{0}N}{R_{C}}\frac{M_{1}+\mu(M -M_{1})}{M}\right)\\ &\leq R_{C}\log_{2}\left(1+\frac{\gamma_{0}N}{R_{C}}\right):\, \mathrm{SN\,OSTBC}.\end{split} \tag{16}\]  
Note that the capacity of multi-node (MN) OSTBC is slightly lower than that of single-node (SN) OSTBC, even for high transmit antenna correlation, because the ergodic capacity of OSTBC is largely affected not by spatial correlation, but by path gain and the number of transmit antennas, as shown in Equations (13) and (16). Thus, when \(0\leq\mu\leq 1\), SN OSTBC always provides better capacity than MN OSTBC, irrespective of the transmit spatial correlation. That is, MN OSTBC is unlikely to be considered in MN MIMO mode switching or antenna subset selection.
## 3 Key Variables in the Analytical Approximation Model
### The Velocity Vector Term in the Approximate Doppler Rate  
With Equation (21), we have \(\Delta\mathbf{V}_{st}\). However, we still need the expression of \(\mathbf{V}_{st}\) to calculate the velocity vector term \(\Delta\mathbf{V}_{st}\cdot\mathbf{V}_{st}\). It is worth recalling that we need the approximate Doppler rate expression. To calculate the velocity vector term in Equation (10), we propose using the velocity vector of a satellite traveling in a circular orbit \(\mathbf{V}_{s}\) in our analytical approximation model rather than using \(\mathbf{V}_{st}\). Thus, we have  
\[\mathbf{V}_{st} =\mathbf{A}_{O2E}\mathbf{V}_{sat,OP}|_{e=0}\] \[=\sqrt{\frac{\mu}{p}}\begin{bmatrix}-\sin\left(\
u+\omega\right) \\ \cos\left(\
u+\omega\right)\cos i\\ \cos\left(\
u+\omega\right)\sin i\end{bmatrix} \tag{22}\]  
The velocity vector term results in a Doppler rate difference by  
\[\Delta f_{r,p} =\frac{2}{\lambda\rho}\cdot 2\Delta\mathbf{V}_{st}\cdot\mathbf{V}_{st}\] \[=\frac{4}{\lambda\rho}\sqrt{\frac{\mu}{p}}\begin{bmatrix}-\left( \Delta v_{x}+\omega_{e}\Delta p_{y}\right)\sin\left(\
u+\omega\right)\\ \end{bmatrix}\] \[+\left(\Delta v_{y}-\omega_{e}\Delta p_{x}\right)\cos\left(\
u+ \omega\right)\cos i+\Delta v_{z}\cos\left(\
u+\omega\right)\sin i\end{bmatrix} \tag{23}\]  
Assuming that the variances of the position and velocity errors are the same standard deviation in the \(x\)-, \(y\)-, and \(z\)-direction in the ECI system, i.e., \(\sigma_{p,x}=\sigma_{p,y}=\sigma_{p,z}=\sigma_{p}\) and \(\sigma_{v,x}=\sigma_{v,y}=\sigma_{v,z}=\sigma_{v}\), we can find the expected value and standard deviation of \(\Delta f_{r,p}\) by  
\[\mathbb{E}\left[\Delta f_{r,p}\right]=0 \tag{24}\]  
\[\sigma\left[\Delta f_{r,p}\right]=\frac{4}{\lambda\rho}\sqrt{\frac{\mu}{p}} \sqrt{\sigma_{v}^{2}+\omega_{e}^{2}\left[\sin^{2}\left(\
u+\omega\right)+\cos ^{2}\left(\
u+\omega\right)\cos^{2}i\right]\sigma_{p}^{2}} \tag{25}\]  
We also need an approximate maximum value of the standard deviation of \(\Delta f_{r,p}\) to calculate the approximate maximum value of QPE. We suppose that \(\cos i\) has a maximum value of \(\cos i=1\), and  
\[\sigma\left[\Delta f_{r,p}\right]_{max}=\frac{4}{\lambda\rho}\sqrt{\frac{\mu}{ p}}\cdot\sqrt{\omega_{e}^{2}\sigma_{p}^{2}+\sigma_{v}^{2}} \tag{26}\]
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 4 Discussion  
### The Effect of External Environment  
The coupled system is predicted to work better in air than in water because in thermally neutral air environments, the body's heat production and heat dissipation tend to form a thermal equilibrium, and the body's active regulatory function plays only a minimal role. In this case, the human body is in a thermally comfortable steady state, and the local temperature is maintained as constant [34]. The coupled system can effectively  
Figure 8: Predicted results for skin temperatures and measured data. (**a**) Head, (**b**) Back, (**c**) Upper Arm, (**d**) Arm, (**e**) Thigh, and (**f**) Leg.  
simulate the state of thermal balance of the human body. However, in water, water as a heat transfer medium is different from air, and a large amount of the body's heat will be dissipated through conduction and convection in water at the same temperature. In the water immersion experiments, the mean skin temperature predicted with the coupled system is often lower than the actual measurements of the subjects, which is mainly due to the reliability and accuracy of the model in the coupled system. The human body will increase the heat production through the shivering response in cold water, and the thermal model determines that the cold shivering response will be triggered when the skin temperature is lower than 20 \({}^{\circ}\)C [35]. However, it was found in the subjects' experiments that the trigger of the cold shivering response of some of the subjects involved differences in the initial conditions, and some of the subjects experienced the cold shivering response earlier due to the cold shock of entering the water (\(T_{w}\) = 25 \({}^{\circ}\)C), and the skin temperature of this group was significantly higher than the average, which resulted in an overall higher than predicted average skin temperature test value. Consequently, this bias resulted in the coupled system having lower predicted values than tested values at subsequent times. This phenomenon would not be present in a much colder water environment, as any healthy person would trigger a cold tremor response in such an environment [29]. In addition, the initial value of the coupling system is supposed to be the initial value of the anthropometric measurements; however, the initial value of the coupling system in the water immersion environment is predicted with the coupling system in the air environment, so there is a deviation between the initial value of the coupling system and the anthropometric measurements in the high-temperature environment at the moment of \(t\) = 20 min, which leads to a bigger deviation in the water immersion environment.
## 4 Analysis of the Modulational Instability  
### The Anomalous-Dispersion Regime  
We start by considering the case of the anomalous GVD in both cores, i.e., \(\sigma_{1}=1\) in Equations (4) and (5), as in this case the MI is well known to occur in nonlinear optical fibers. First, in Figure 1, the red line shows the MI gain in the conventional symmetric NLDC (\"SNLDC\"), with \(\alpha=\Gamma=1\) and \(\rho=\chi=0\). In the same figure, the solid blue line shows the gain for the asymmetric NLDC (\"ANLDC\") with a particular choice of asymmetry parameters (the reason for choosing these values is explained below), such that the effective mode area of the second core is twice that of the first core, and the GVD of the bar channel is ten-times higher than in the cross one. The figure makes it evident that the MI gain increases by a factor \(>\)2 in the ANLDC, and the MI bandwidth is wider by a factor \(\simeq\)4. The enhancement of the MI is a new result in the context of the nonlinear directional coupler (similar enhancement was earlier found in the single-core decreasing-GVD fibers with a tapered core [59].
## 2 Review of the PAP Algorithm  
In the range of AF, the implementation schematic diagram for CE is presented in Figure 1. Assume that the input signal \(\mathbf{x}(m)=[x(m),x(m-1),\cdots,x(m-K+1)]^{T}\) is used in this paper, and the channel impulse response (CIR) is modeled as \(\mathbf{w}(m)=[w_{0}(m),\cdots,w_{K-1}(m)]^{T}\), where \(K\) denotes as the total length and \(m\) denotes the time slot. Then, the received signal \(d(m)\) is  
\[d(m)=\mathbf{x}^{T}(m)\mathbf{w}(m)+r(m), \tag{1}\]  
in which \(r(m)\) represents the additive impulsive noise that is usually independent of \(\mathbf{x}(m)\), and \({}^{T}\) represents the transposed operation. The gotten CIR is given as \(\hat{\mathbf{w}}(m)=[\hat{w}_{0}(m),\hat{w}_{1}(m),\cdots,\hat{w}_{K-1}(m)]^{T}\), resulting in  
\[y(m)=\mathbf{x}^{T}(m)\hat{\mathbf{w}}(m). \tag{2}\]  
The estimation error at \(m\) is expressed as  
\[e(m)=d(m)-y(m). \tag{3}\]
# Detecting and Solving Tube Entanglement in Bin Picking Operations
## 3 Bin Picking Algorithm
### Grasp Planning
#### 3.4.2 Grasp Evaluation  
Each grasp is evaluated by associating it a cost \(G_{cost}\) using Equation (3), which reflects how likely the grasp will lead to an entanglement, where multiple tubes are picked at once. This cost is normalized to scale the \([0,1]\) range and consists of a linear combination of three normalized costs: the height cost, \(H_{cost}\), the center cost, \(C_{cost}\), and the trajectory cost, \(T_{cost}\). The combined sum of the weights of these costs (\(H_{cost\_weight}\), \(C_{cost\_weight}\), and \(T_{cost\_weight}\)) is equal to \(1\).  
\[G_{cost}=H_{cost}\cdot H_{cost\_weight}+C_{cost}\cdot C_{cost\_weight}+T_{ cost}\cdot T_{cost\_weight} \tag{3}\]  
The height cost \(H_{cost}\) (Equation (4)) corresponds to the height of the grasping point relative to the heights of the tubes. The variable \(max_{z}\) is the z coordinate of the highest point of the filtered  
Figure 5: Examples of grasping points on tubes.  
point cloud, which is obtained during the first step of the modeling phase and only contains points belonging to the tubes. It is assumed that the equation for the bin's bottom plane is \(z=0\).  
\[H_{cost}=\frac{G_{z}}{max_{z}} \tag{4}\]  
The center cost \(C_{cost}\) (Equation (5)) reflects how far the grasping point is from the center of the tube \(T_{center}=[T_{x},T_{y},T_{z}]\) along its curve length \(L\). The rationale of this cost is that the farther the tube is grasped from its center, the higher the torque that will be generated, which can lead to more unstable grasps.  
\[C_{cost}=\frac{dist(G_{point},T_{center})}{L/2} \tag{5}\]  
The trajectory cost \(T_{cost}\) (Equation (6)) corresponds to an estimate provided by Gazebo during the trajectory analysis of how much the other tubes are moved when a given tube is grasped. This cost has the same value for all the grasps on the same tube, and is equal to 0.0 if the tube is non-occluded (as their trajectories do not require simulation). The displacement \(disp\) is normalized using a maximum displacement value \(disp_{max}\), above which it is considered that any trajectory has the same (high) odds of leading to a case of entanglement.  
\[T_{cost}=\frac{min(disp,disp_{max})}{disp_{max}} \tag{6}\]
## 4 PSO Based on the PSO-BP Model
### Results and Analysis
#### 4.5.3 PSO-PB Parameter Optimization  
As shown in Figure 15, the fitness curve of the PSO-BP neural network model varies with the number of iterations. The lower the fitness value, the smaller the prediction error of the model. The curve can be categorized into three distinct phases:  
Figure 14: Regression analysis.  
Figure 13: Performance.  
Initial Rapid Decline Phase:  
The global search capability of PSO rapidly locates potential optimal regions, with the particle swarm broadly exploring the solution space to identify promising areas.  
Intermediate Slow Convergence Phase:  
Particles transition to local exploitation, gradually focusing on neighborhoods near optimal solutions. Competition between individual best (pbest) and global best (gbest) positions leads to iterative probing within local regions.  
Final Stable Convergence Phase:  
The swarm converges near the global optimum, with velocity and position updates approaching zero. A subsequent BP fine-tuning phase applies gradient descent to refine the PSO-derived parameters, further minimizing errors.  
Upon reaching the target iteration count (100), the optimal parameters were determined as:  
* Kinetic friction coefficient (straw-straw): 0.17;
* Static friction coefficient (straw-steel): 0.46;
* JKR surface energy: 0.03 J/m2.  
The simulated angle of repose under these parameters was 41.67\({}^{\circ}\), yielding a relative error of 1.5% compared to the experimental value (42.3\({}^{\circ}\)).
# SatEC: A 5G Satellite Edge Computing Framework Based on Microservice Architecture
## 3 System Framework
### Hardware Platform
Network resource nodes: The software defined network (SDN) is used in the management of the network resources, and the master node uses the OpenFlow protocol to manage network resource nodes. Based on the status of the network, a flow table is generated in the master node,  
Figure 2: The framework of the resource platform. and is sent to network resource nodes. The packets passing through the nodes will be routed according to rules in the flow table. Master nodes and other resource nodes are all connected to the network resource node. Data exchange between master nodes and resource nodes occurs through network resource nodes. Except for network resource nodes, a Representational State Transfer (REST) interface is used in resource management.
# Research on Multi-Strategy Routing Protocol in Flying Ad Hoc Networks
## 3 Forwarding Policy Selection and Handover Design
### Forwarding Policy Switching  
When the current packet forwarding strategy is the reactive mechanism, the intermediate node must check whether the next-hop node on the reactive path is still in the communication range. If it is, the intermediate node will continue to perform reactive mechanism forwarding and forward the packet to the corresponding next-hop node. Otherwise, it will switch to greedy forwarding, as shown in Figure 7.  
Figure 6: The process by which the intermediate node selects the forwarding strategy.  
When the current packet forwarding strategy is greedy forwarding, the intermediate node must determine whether it is a minimum local node. If it is not, it will continue to use greedy forwarding. If it is, it will check whether there is a reactive path available. If there is, it will switch to reactive mechanism forwarding. Otherwise, it will use projection face forwarding, as shown in Figure 8.  
When the current packet forwarding strategy is to project face, the intermediate node first needs to determine whether it has jumped out of the routing hole. If so, the intermediate node will switch back to greedy forwarding. If not, then it will determine whether there is a reactive path available. If so, it will switch to reactive mechanism forwarding. If not, it will continue to perform projected face forwarding, as shown in Figure 9.  
Figure 8: Switching conditions under greedy forwarding.  
Figure 7: Switching conditions under the reactive mechanism.
## 2 Convolutional Neural Networks and Transfer Learning
### Batch Normalization Layers  
Batch normalization can speed up the training of the network and increase its robustness against overfitting [35]. It reduces the network covariance shift [36]. Additionally, batch normalization adds noise to each layer to increase its robustness. It works by normalizing the inputs of each layer it is applied to by subtracting the batch mean and dividing by the batch standard deviation.  
\[\mu_{B}=\frac{1}{n_{B}}\sum_{i=1}^{n_{B}}x^{(i)}\text{ is the mini - batch mean} \tag{12}\]  
\[\sigma_{B}^{2}=\frac{1}{n_{B}}\sum_{i=1}^{n_{B}}\left(x^{(i)}-\mu_{B}\right)^{ 2}\text{ is the mini - batch variance} \tag{13}\]  
\[\hat{x}^{(i)}=\frac{x^{(i)}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\epsilon}}\text{ is the sample normalization} \tag{14}\]  
\[Z^{(i)}=\gamma*\hat{x}^{(i)}+\beta\text{ is the batch normalization} \tag{15}\]  
where \(\mu_{B}\) is the mini-batch \(B\) mean, \(\sigma_{B}\) is the mini-batch \(B\) standard deviation, \(n_{B}\) is the number of instances in the mini-batch, \(\hat{x}^{(i)}\) is the zero-centered and normalized input for instance \(i\), \(\gamma\) is the scaling parameter for the layer, \(\beta\) is the shifting parameter (offset) for the layer, \(\epsilon\) is a tiny number to avoid division by zero (typically \(10^{-5}\); it is called a smoothing term), and \(Z^{(i)}\) is the output of the \(BN\) operations (it is a scaled and shifted version of the inputs). Thus, in total, four parameters must be learned for each batch-normalized layer: \(\gamma\) (\(scale\)), \(\beta\) (\(offset\)), \(\mu\) (\(mean\)) and \(\sigma\) (\(standard\) deviation).
# Capturing a Space Target Using a Flexible Space Robot
## 3 Contact Model
### Contact Force  
In the research on contact mechanics, some contact force models have been proposed, such as the linear Hertz model, the linear Hook model, and the nonlinear Hertz model. The linear Hertz model is the most classical of these models, and it has been successfully used to deal with small deformation contact problems. For the problem of target capture by space robots, considerable contact deformation is harmful to the capturing operation, so the material of the end effector and the contact point of the captured target both have high stiffness. Therefore, the linear Hertz theory can describe the contact behavior of the robot capture. In the linear Hertz theory, the contact force is a non-linear power function of penetration depth and can be written as [35]  
\[F_{c}=\frac{4}{3}E^{*}R^{1/2}\hat{d}^{e} \tag{7}\]  
where \(R\) is the radius of the cylinder, \(\hat{d}\) is the penetration depth, \(e\) is an exponent, \(e=1.5\) is taken in this paper, and \(E^{*}\) is the material stiffness that is given by  
\[(E^{*})^{-1}=(1-v_{1}^{2})/E_{1}+(1-v_{2}^{2})/E_{2} \tag{8}\]  
where \(E_{1}\) and \(E_{2}\) are the elastic modulus, and \(v_{1}\) and \(v_{2}\) are the Poisson's ratios of the two contact objects, respectively.
# Internet of Vehicles for Sustainable Smart Cities: Opportunities, Issues, and Challenges
## 7 Wireless Technologies in IoV
### Vehicular Communications
#### 7.1.3 Cellular Vehicle-to-Everything (C-V2X)  
The extensions of cellular networks, cellular vehicle-to-everything (C-V2X), enable direct and V2V as well as V2X communication [98]. C-V2X breaks through the barriers of traditional communication by utilizing cellular networks as vehicles can communicate directly to other vehicles, to roadside infrastructure, and to other connected entities. This advanced connectivity framework has multiple applications, including safety in ITS by real-time sharing of information. In supporting safety (collision warning and emergency break) applications, C-V2X plays a substantial role in accident prevention. C-V2X is more than just about safety, it also helps in the optimization of traffic efficiency by making coordinated traffic management strategies possible. Furthermore, it contributes toward enhancing overall connectivity in the mobility system, with a deeper impact on a more responsive and more connected smart transportation ecosystem. C-V2X adoption in vehicular communication is expected to bring users to safer, more efficient, and technologically advanced transportation networks.