esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 State-of-the-Art and Open Challenges
### Traffic Steering in SFC  
From the related literature on SFC traffic steering, we believe that the ability to dynamically steer traffic to the edge of the network, which adapts to network changes, is still required and desirable for the operation of next-generation networks. We also argue that having a functional programmable data-plane, which is a core component of future networks, can solve the problem of always having to install flow rules on virtual switches to steer traffic.  
The ability to obtain the current network state and dynamically update the current service function path in real time requires more work to achieve better traffic steering in SFC for next-generation networks. Other factors such as delivery time, measuring virtual machines' (along the service path) usage, latency, and delay [114], are some of the considerations still open for further research with respect to service path selection in SFCs.  
Our review of some related literature on SFC traffic steering also shows that the inter-operability between traffic steering techniques is still a challenge, as different service providers employ traffic steering approaches that best meet their business requirements. Multi-tenant networks also require a scalable traffic-steering scheme for SFC, as future networks are envisaged to be more heterogeneous in nature [115]. As it relates to steering traffic in SFC environments, service functions need to have support for SFC encapsulation protocols and headers.  
Traffic steering types can be classified into three categories: header-based methods, tag-based methods, and programmable switch-based methods, which deal with the re-classification of flows and network isolation, where traffic forwarding depends on the configurations sent from the SDN controller to the switches. Tag-based steering methods make use of MAC addresses, VLANs, and MPLS tags to steer incoming traffic. Header-based methods use the network service header (NSH), service chain header (SCH), IP option field, and segmented routing header [27].  
[PERSON] et al. [29] identified the absence of network service header (NSH) capability in switches as one of the challenges with traffic steering in SFC environments. Some proposals include the use of MAC addresses and tags to steer traffic in a service chain. Having virtual switches, such as Open vSwitch (OvS), which supports NSH capability, would allow for better traffic tagging and steering.
## 3 Results and Discussion
### Transport of Aerosols by Wind and Link with Wildfires
The wind came from these areas, which explains the level of AOD on this day (around 0.2 according to Figure 11d). Moreover, 2019 was a year with many severe wind events in all of Europe: more than 12,000 were recorded, and there were several tornadoes in the same area (792) [40]. Figure 13 presents an overview of these events across Europe. Figure 13: An overview of severe winds and tornadoes in 2019 across Europe [40]. This accumulation of extreme wind events in 2019 corroborates the previous observations linking the wildfires and AOD level around the Baltic Sea (because of wind transport) further with the following Figure 14, which represents the major convective storm events in 2019. Figure 14 shows that many extreme wind events have taken place in Europe, especially during the summer of 2019. On 4 th June, several tornadoes occurred, including two strong F2 tornadoes in the Netherlands and western Germany. An active period of severe weather started on 10 June, with a hailstorm that struck parts of Germany. On 1 st July, 372 severe weather reports were amassed in France, Switzerland, northern Italy, southern Germany, the Czech Republic, and Poland into Ukraine. Most of them were about severe storms producing severe wind gusts. In this way, all the extreme wind events occurring during the summer of 2019 around the area of the Baltic Sea have a big impact on the AOD level by transporting particles from wildfires happening around Europe and beyond to this area. Thus, wind is a variable to take into account in addition to wildfires when studying the AOD level.
# Dairy Product Consumption and Preferences of Polish and Taiwanese Students--NPD Case Study
## 3 Results and Discussion
### Characteristic of Yoghurt Influencing Purchase Decision  
The most common characteristic of yoghurt that influenced Polish students' purchase decision was its composition, followed by its consistency (4.3, 4.1 out of 5, respectively) (Figure 2). The next attributes that Polish respondents paid attention to were, in turn, the appearance of yogurt (3.7) and the taste of yogurt, especially sour (3.6) and then sweet taste (3.4). Price was also important, with an average score of 3.4 out of 5. Favorite brand and packaging were less important for choosing a fermented dairy product such as yogurt (2.9 and 2.8 points, respectively). Features such as country of origin (2.4) and advertising (1.7) were less important to Polish students when buying yoghurt (Figure 2).  
Figure 2: Characteristics of yoghurt influencing the decision to purchase it (1–5 scale: 1—least important, 5—most important).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Frequency of Yoghurt Consumption** & **Poland (\%)** & **Taiwan (\%)** & \(p\)**-Value \({}^{*}\)** \\ \hline Every day & 9 & 2 & 0.0299 \\ Several times a week & 44 & 9 & \textless{}0.0001 \\ Once a week & 22 & 9 & 0.0119 \\ Several times a month & 23 & 33 & 0.1152 \\ Once a month & 2 & 6 & 0.1489 \\ Occasionally & 0 & 41 & \textless{}0.0001 \\ \hline \hline \end{tabular} \({}^{*}\)Test chi\({}^{2}\).  
\end{table}
Table 3: Yoghurt consumption frequency.  
Respondents were characterized by high nutritional awareness and knew the importance of ingredients on food choices. However, other studies [22; 26] indicate other features as the most important in choosing dairy products--e.g., sensory impressions (taste, smell) and one of the least important--advertising. [PERSON] et al. [36], in a study of consumer preferences of fermented dairy beverages conducted on the inhabitants of Wroclaw in Poland, indicated the characteristics of taste, brand and price as the 3 most important when Poles make purchases, and some of the least important characteristics were nutritional value, quality and shelf life.  
For Taiwanese students, factors such as consistency (4.2 out of 5), price (3.9) and sweetness (3.9) and sourness (3.8) of yoghurt were the most important determinants of the yoghurt purchase decision (Figure 2). The ingredients and appearance of the yogurt were fairly important differentiators for these consumers, scoring an average of 3.5 and 3.2 out of 5, respectively. The rest of the differentiators, such as packaging, favorite brand and advertising were rated below 3.0 (2.8, 2.8, and 2.0 out of 5, respectively) (Figure 2). Authors [PERSON] and [PERSON] [30], in a study on fluid milk consumption and attributes in Taiwan, showed similar results for taste without differentiating between sour and sweet taste (4.0), and price (3.7), while divergent results (higher) were obtained for the attribute ingredients 3.8 and favorite brand 3.7. In terms of price, the Taiwanese in 2000 set its validity at 3.7, similar to the rating in the survey [37].
## 6 Complexity Analysis
### Complexity of Clustering Algorithms
#### 6.3.2 Affinity Propagation  
In the case of AP, the number of operations is \(O(ikn^{2})\), where \(i\), \(k\) and \(n\) are the number of iterations, clusters, and elements, respectively. While the number of clusters and element are trivial, the number of iterations \(i\) is difficult to predict due to the complexity algorithm and its interplay with the dispersion structure of the data.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## 6 Simulation Results and Discussion
CDF fits the simulated outage probability of the instantaneous sum-capacity random variable as the number of served devices grows. Figure 8: Comparison of the approximated PDF for the instantaneous sum-capacity in the high-SNR regime and favorable propagation condition. Figure 9: Comparison between the approximated and simulated outage probabilities in the high-SNR regime and favorable propagation condition. LoS channel with no multi-path fading and devices randomly moving around the cell. Under the aforementioned assumptions, the channel vectors of different devices become mutually orthogonal, resulting in (8), which is a diagonal matrix with the free-space path-loss coefficients in its main diagonal. In (2), we see that the free-space path-loss coefficients are directly proportional to the \(\eta\) parameter, which takes the wavelength of the carrier frequency into account. The attenuation caused by the path-loss is mitigated by adjusting (i.e., increasing) the device's transmission power or number of antennas. Therefore, considering the aforementioned assumptions, the analysis we provide in this work can be straightforwardly extended to other carrier frequencies given that the device's Tx power or the number of antennas (the greater the number of antennas the higher is the harnessed energy, i.e., received energy) is also adjusted to combat the path-loss. Figure 12 presents in its upper part the required number of antennas necessary to achieve sum-capacities of 1 and 2 bits/s/Hz, respectively, for a variation of the carrier frequency, which is represented by the wavelength, \(\lambda\), and with a fixed power \(\rho=50\) dB. The lower part of the figure shows the required Tx power necessary to achieve sum-capacities of 1 and 2 bits/s/Hz, respectively, for a variation of the carrier frequency and with a fixed number of antennas \(M=300\). As can be seen, the required number of antennas and Tx power decreases as the \(\lambda\) increases. It is also interesting to mention that both required values decrease by 4 (or by 6 dB for the required Tx power case) as \(\lambda\) increases. The decrease rate of 4 is due to the fact that the path-loss exponent is equal to 2 (see, (2)), which doubles any increase of \(\lambda\), i.e., the carrier frequency. Therefore, the number of antennas should be increased by 4 or the Tx power by 6 dB if one wants to double the carrier frequency and keep the same sum-rate capacity for the same constant parameters (i.e., \(\rho\) and \(M\), respectively). This shows that the same capacity can be kept as long as the number of antennas or Tx power is adjusted to combat the free-space path-loss. Figure 12: Required number of antennas and Tx power versus the wavelength, \(\lambda\).
## 4 Experimental Setup and Results
### Experimental Setup
As this system is mainly divided into two parts, including the main part and sub-part, Figure 4a represents the sub-parts of the system in which the status of the system, such as power consumption, humidity level, and temperature, along with control values, are being monitored. System status and the control values of the main part are displayed in Figure 4b. The main-part provides real-time system status of sensor data, power consumption, temperature, and humidity. It displays the latest sensor readings, AI-predicted temperature control values, and system control values. Figure 3: _Cont._  
Figure 4: _Cont._  
Figure 3: (**a**). This figure displays an experimental warehouse used to test temperature control and AI-based indoor climate control. It is equipped with air conditioning units, monitoring sensors, real-time data analysis, and monitors to check system performance. (**b**). This figure displays the interior of the laboratory, equipped with temperature control devices, heaters, blowers, and small air conditioners.
## 5 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Zurich**} & \multicolumn{3}{c}{**Guangzhou**} & \multicolumn{3}{c}{**Singapore**} \\ \cline{2-10}  & **MAE** & **RMSE** & **MAPE (\%)** & **MAE** & **RMSE** & **MAPE (\%)** & **MAE** & **RMSE** & **MAPE (\%)** \\ \hline HA & 9.29 & 17.78 & 29.59 & 18.19 & 38.23 & 20.36 & 31.1 & 53.35 & 18.89 \\ LSTM & 8.66 & 17.04 & 44.78 & 17.01 & 23.75 & 26.03 & 24.26 & 73.53 & 8.36 \\ ASTGCN & 4.57 & 10.13 & 18.47 & 8.28 & 17.03 & 3.65 & 4.9 & 8.74 & 3.16 \\ DGCN & 7.41 & 12.18 & 28.2 & 21.48 & 36.19 & 12.49 & 5.04 & 7.86 & 3.22 \\ MegaCRN & 4.37 & 9.26 & 17.46 & 7.78 & 15.17 & 3.48 & 5.06 & 9.83 & 3.14 \\ STGNCD & 4.62 & 9.34 & 17.54 & 9.12 & 17.69 & 4.51 & 4.78 & 8.07 & 3.17 \\ DCRNN & 4.65 & 10.69 & 16.39 & 10.16 & 17.72 & 5.11 & 5.97 & 11.71 & 3.79 \\ AGCRN & 4.36 & 10.09 & 16.09 & 9.6 & 17.41 & 4.71 & 4.89 & 9.45 & 3.13 \\ DGCRN & 4.59 & 9.84 & 16.78 & 9.21 & 16.32 & 5.58 & 5.58 & 10.51 & 3.59 \\ Our & 3.61 & 8.66 & 13.54 & 6.87 & 14.21 & 2.88 & 4.16 & 8.06 & 2.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between the model proposed in this article and other models. Figure 6: Prediction results for six models per prediction layer on three parking datasets. To clearly demonstrate the spatio-temporal correlations explored by our proposed approach and the performance differences with other baseline models, we plot the predicted values and true values of our method (DSTAGCRN) and STGNCDE for two parking lots within a given period in Figure 7. Both Parking Lot 1 and Parking Lot 2 are commercial parking lots with similar trends in VPS counts, which illustrates the spatio-temporal correlation of the VPS. DSTAGCRN and STGNCDE can fit the true situation of VPS count changes in many periods well, but our model not only accurately adapts to the rapidly changing trends but also captures similar trends such as traffic peak hours, demonstrating a more accurate prediction performance. This paper takes Parking Lot 2 and Parking Lot 7 in the Guangzhou parking dataset as examples to verify the effectiveness of the dynamic graph inferred by the DSTAGCRN model. Figure 8 shows the available parking space data of the two parking lots for one day, and Figure 9 visualizes the weights of the dynamic spatial correlation of the two parking lots. As shown in these two figures, in cycle 1, the trends of the number of available parking spaces in the two parking lots are different, resulting in relatively low weights being learned by the dynamic graph. In cycle 2, the trends of the two parking lots are similar, so the weights learned by the dynamic graph are higher. Combining Figures 8 and 9, it can be seen that the dynamic graph can effectively adapt to these changes and automatically adjust
# Wing Geometry and Kinematic Parameters Optimization of Flapping Wing Hovering Flight
## 4 Extended Quasi-Steady Aerodynamic Model
### Horizontal and Vertical Force in Right Wing Root Frame of Reference  
According to the quasi-steady model for flapping wing hovering flight [32; 34; 35], the total instantaneous aerodynamic force normal to the wing planform can be represented as the summation of three force components  
\[F_{\text{aero},y}=F_{\text{trans},y}+F_{\text{rot},y}+F_{\text{add},y}, \tag{35}\]  
Obviously, the quasi-steady estimating model is unable to include some unsteady effects, such as the vortex starting effect during acceleration impulsively from rest [49], vortex shedding effect occurring at high angle of attack [50], wake capture due to wing planform intercepting its own wake during reciprocating oscillation [34], and induced flow effects depended on wing size and shape [30; 46; 51]. However, the current extended quasi-steady aerodynamic model should not be discounted to evaluate the flapping wing hovering aerodynamics as verified by following comparisons between estimating results and experimental results. It is worth mentioning that the gravitational and inertia forces of the wing itself have been ignored in comparison with the contribution of aerodynamic components to total instantaneous forces; after all, the measured instantaneous force has also excluded their influence [27; 52; 53]. Thus, the total instantaneous forces normal to the wing planform are approximately equal to the total instantaneous aerodynamic force:  
\[{}^{rw}F_{\text{total},y}=F_{\text{aero},y}, \tag{36}\]  
where \({}^{rw}F_{\text{total},y}\) can be further transformed into right wing root frame of reference (\(x_{rr}y_{rr}z_{rr}\)) by transform matrix, \({}^{rr}_{ru}R\).  
\[{}^{rr}F_{\text{body}}=\left[\begin{array}{c}{}^{rr}F_{\text{lateral},x}\\ {}^{rr}F_{\text{horizontal},y}\\ {}^{rr}F_{\text{vertical},z}\end{array}\right]={}^{rr}_{ru}R\cdot{}^{rw}F_{ \text{total},y}\overset{\rightarrow}{j}=\left[\begin{array}{c}-\text{sin} \phi\cdot\text{cos}\psi\cdot{}^{rw}F_{\text{total},y}\\ \text{cos}\phi\cdot\text{cos}\psi\cdot{}^{rw}F_{\text{total},y}\\ -sign(\alpha)\cdot\text{sin}\psi\cdot{}^{rw}F_{\text{total},y}\end{array} \right], \tag{37}\]  
here, the sign function for \(\alpha\) is introduced to determine the direction of vertical upward force (\({}^{rr}F_{\text{vertical},z}\)). The lateral force (\({}^{rr}F_{\text{lateral},x}\)) has zero mean value during the whole period due to symmetric flapping stroke. In order to conveniently measure vertical upward force generated by two wing planforms in non-dimensional form [8], the lift-to-weight is defined as  
\[LtoW=\frac{2\left({}^{rr}F_{\text{vertical},z}\right)}{^{rr}W_{\text{ body}}}, \tag{38}\]  
here \({}^{rr}W_{\text{body}}\) is the body weight of fruit fly. Thus, when \(LtoW\geq 1\) is kept, the insect can support itself aloft.
## 3 Experimental Results and Discussion
### Engineering Problems
#### 3.7.2 Lennard-Jones Potential Problem (LJ)  
The Lennard-Jones potential energy minimization problem is a multi-modal optimization problem that involves finding the minimum molecular potential energy of a molecular cluster using the Lennard-Jones potential. The problem has an exponential number of local minima, and the global minima often have structures based on Mackay icosahedrons. The Lennard-Jones potential for \(N\) atoms is given by Equation (40), and the optimization algorithm can be used to conform the molecular structure in a way that minimizes energy. \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & **GSLEO-AEFA** & **AEFA** & **PSO** & **DE** & **JAYA** & **SCA** & **CS** \\ \hline F21 & **17.979** & 10.459 & 6.707 & 5.602 & 5.251 & 6.713 & 10.894 \\ \hline F22 & **21.677** & 12.070 & 8.376 & 7.261 & 6.798 & 8.481 & 16.520 \\ \hline F23 & **27.239** & 14.630 & 24.862 & 9.742 & 9.256 & 11.081 & 21.001 \\ \hline \end{tabular}
\end{table} 
Table 8: _Cont._To reduce the dimensionality of the problem, one approach is to fix an atom at the origin, choose the second atom to lie on the positive \(X\)-axis, and the third atom to lie in the upper half of the \(X\)-axis. For each additional atom, three variables (coordinates of the position of the atom) are added to determine the potential energy of the cluster. The variables of the problem have three components for three atoms, six components for four atoms, and so on. The coordinates of each atom are restricted to certain ranges. Lennard-Jones pair potential for N atoms is given as follows:  
\[\overrightarrow{p}_{i}=\left\{\overrightarrow{x}_{i},\overrightarrow{y}_{i},\overrightarrow{z}_{i}\right\},i=1,\ldots,N \tag{40}\]  
\[\mathrm{V}_{N}(p)=\sum\
olimits_{i=1}^{N-1}\sum\
olimits_{j=i+1}^{N}\left(r _{ij}^{-12}-2\cdot r_{ij}^{-6}\right) \tag{41}\]  
where \(r_{ij}=\|\overrightarrow{p}_{j}-\overrightarrow{p}_{i}\|_{2}\) with gradient  
\[\
abla_{j}V_{N}(p)=-12\sum\
olimits_{i=1,i\
eq j}^{N}\left(r_{ij}^{-14}-r_{ ij}^{-8}\right)\left(\overrightarrow{p}_{j}-\overrightarrow{p}_{i}\right) \tag{42}\]  
The first decision variable due to the second atom, i.e., \(x_{1}\in[0,4]\), then the second and third variables are such that \(x_{2}\in[0,4]\) and \(x_{3}\in[0,\pi]\). The coordinates \(x_{i}\) for any other atom is taken to be bound in the range; \(\left[-4-\frac{1}{4}\left[\frac{-4}{3}\right],4+\frac{1}{4}\frac{-4}{3}\right]\). The performance results of GSLEO-AEFA and other optimization methods in resolving the Lennard-Jones potential problem are shown in Table 10. The findings make it simple to draw the conclusion that GSLEO-AEFA has superior performance when compared to other algorithms and is capable of solving this issue with high-quality outcomes.
# Numerical Simulation Analysis of Concrete-Filled Circular CFRP-Steel Middle Long Columns
## 5 Analysis of ANSYS Calculation Results
### The Results of Ultimate Bearing Capacity in ANSYS
#### 5.2.1 The Influence of the Number of CFRP Layers  
It can be seen from Table 6 that the ultimate bearing capacity of concrete-filled circular steel middle long columns with one layer of CFRP is increased by 42.5%, that of two layers is increased by 18.9%, and that of three layers is increased by 11.2% compared with that of two layers. This shows that the bearing capacity of the samples can be greatly improved by adding one layer of CFRP, while the increase in bearing capacity decreases sharply when  
Figure 4: The axial load versus displacement of mid-span curves.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Number** & \begin{tabular}{c} **Simulation Results** \\ \(\textbf{N}_{u,ct}^{\text{a}}\) (**kN**) \\ \end{tabular} & \begin{tabular}{c} **Test Results** \\ \(\textbf{N}_{u,ct}^{\text{a}}\) (**kN**) \\ \end{tabular} & \
\begin{tabular}{c} \(\textbf{N}_{u,ct}^{\text{a}}\) (**kN**) \\ \end{tabular} &
\begin{tabular}{c} \(\textbf{N}_{u,ct}^{\text{a}}\) (**kN**) \\ \end{tabular} \\ \hline CM1-3.5 & 1754.25 & 1703.52 & 1.03 \\ CM1-3.5-5 & 1921.54 & 1899.48 & 1.01 \\ CM1-3.5-6 & 2007.46 & 1974.54 & 1.02 \\ CM0-2.5 & 1085.21 & 1101.49 & 0.99 \\ CM1-2.5 & 1602.8 & 1569.66 & 1.02 \\ CM2-2.5 & 1925.69 & 1865.7 & 1.03 \\ CM3-2.5 & 2105.69 & 2075.32 & 1.01 \\ CS1-4.25 & 1980.54 & 2008.24 & 0.99 \\ CM1-4.25 & 1820.45 & 1861.64 & 0.98 \\ CL1-4.25 & 1693.55 & 1633.1 & 1.04 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of ultimate bearing capacity in ANSYS.  
the number of stories reaches two or more layers. This indicates that the single-layer CFRP has an obvious effect on improving the ultimate bearing capacity of concrete-filled circular CFRP-steel middle long columns, while the effect of multi-layer is limited.