esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Biochemical Composition
#### 3.4.3 Pigment Composition  
The content of chlorophyll \(a\) and \(b\) and main carotenoid content were analyzed by spectrophotometry and the results are shown in Table 4.  
The highest pigment content detected in _C. amblystomatis_ was achieved in the pilot-scale 2.5-m\({}^{3}\) PBR (_p_ \(<\) 0.05), with chlorophylls reaching 40.24 mg.g\({}^{-1}\), followed by lutein (5.37 mg.g\({}^{-1}\)), _g_-carotene (1.25 mg.g\({}^{-1}\)), neoxanthin (0.63 mg.g\({}^{-1}\)) and violaxanthin (0.33 mg.g\({}^{-1}\)). Unfavorable growth conditions (e.g., pH fluctuation) can negatively affect pigment accumulation [48]. This could explain the lower contents registered in the biomass grown in the industrial PBR. Photosynthetic microalgae contain chlorophyll ranges between 5 and 15 mg.g\({}^{-1}\) of DW [3; 10]; contents as high as 40 mg.g\({}^{-1}\) have been reported for cultures grown under optimal conditions [45]. Interestingly, the chlorophyll content of _C. amblystomatis_ was considerably higher (>40 mg.g\({}^{-1}\)) without optimized growth conditions. Chlorophyll, due to its strong green color, is used as a natural dye in food formulation [45]. Besides, recent studies showed that chlorophyll consumption might lead to a decreased risk of colorectal cancer [10; 49]. Lutein content is similar to the values previously reported for other microalgae that are known lutein-producers, such as _Scenedesmus almeriensis_ (Chlorophyta) (5.5 mg.g\({}^{-1}\) at laboratory-scale, 4.5 mg.g\({}^{-1}\) at 4-m\({}^{3}\) PBR), _Muriellosis_ sp. (Chlorophyta) (5.5 mg.g\({}^{-1}\) at laboratory-scale, 4.3 mg.g \begin{table}
\begin{tabular}{c c c} \hline \hline
**Pigments (mg.g\({}^{-1}\))** & **2.5-m\({}^{3}\) PBR** & **10-m\({}^{3}\) PBR** \\ \hline Neoxanthin & 0.63 \(\pm\) 0.19 \({}^{\text{a}}\) & 0.14 \(\pm\) 0.01 \({}^{\text{b}}\) \\ Violaxanthin & 0.33 \(\pm\) 0.07 \({}^{\text{a}}\) & 0.13 \(\pm\) 0.01 \({}^{\text{b}}\) \\ Lutein & 5.37 \(\pm\) 0.78 \({}^{\text{a}}\) & 2.49 \(\pm\) 0.09 \({}^{\text{b}}\) \\ \(\beta\)-carotene & 1.25 \(\pm\) 0.14 \({}^{\text{a}}\) & 0.64 \(\pm\) 0.03 \({}^{\text{b}}\) \\ Chlorophyll \(a\) and \(b\) & 40.24 \(\pm\) 7.94 \({}^{\text{a}}\) & 19.56 \(\pm\) 6.84 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pigment profile of _Chlorococcus amblystomatis_ biomass grown in 2.5- and 10-m\({}^{3}\) tubular photobioreactors (PBR). Different letters indicate significant differences between reactors. Values are given as means \(\pm\) standard deviation (_n_ = 3).
# Two-Phase Flow Simulations Using 1D Centerline-Based C- and U-Shaped Pipe Meshes
## 3 Results and Discussion
### Flow Physics in the C-Shaped Pipe (Case 1)
#### 3.4.2 Turbulent Effect in the C-Shaped Pipe  
We calculated the turbulent kinetic energy (TKE) distributions of water at four specific curve portions as follows:  
\[TKE=\frac{1}{2}\left(\overline{u}^{2}+\overline{v}^{2}+\overline{w}^{2}\right)\,, \tag{10}\]  
where \(\overline{u}\), \(\overline{v}\), and \(\overline{w}\) are the mean fluctuation x-velocity, mean fluctuation y-velocity, and mean fluctuation z-velocity, respectively. The TKE of the single- and two-phase flows is further demonstrated in a 2D slice in Figure 19A,B with the same tendency. Overall, they followed the same trend observed, that is, a strong TKE near the inner wall. Similar to the two-phase case, the TKE in the single-phase flow became stronger after the portion of \(15^{\circ}\) and became the strongest at the curve portion of \(60^{\circ}\). Subsequently, it became weaker in the portion of \(90^{\circ}\), but still had the same characteristic in the case of the two-phase flows. Hence, the turbulence behavior of the two-phase flows was stronger than that in the single-phase flow. These characteristics could be caused by the addition of the air phase that allowed for an unstable flow, which is different from that in the single-phase flows containing 100% water.  
Figure 17: Visualization of the Dean flow [20] at six different bend angles of the curve portion.  
Figure 18: Water and air volume fraction behavior in a C shape with an upward injection pipe.  
Figure 20 shows a visualization of the TKE distribution of the water phase, the first fluctuating proper orthogonal decomposition (POD)-derived coherent vortical structure, and the pressure contour in the 2D slice. Figure 20A,B demonstrate that the TKE was stronger near the inner walls around the bend angle of 20\({}^{\circ}\). The region, where a strong turbulence was observed, was consistent with the location of the high \(\lambda_{2}\), indicating the vortical structure computed by POD analysis. \(\lambda_{2}\) denotes the eigenvalue associated with the POD-extracted spatial eigenmode 2. The pressure gradient along the radial direction, which was caused by the centripetal force and allowed for a smaller pressure near the inner wall regions, was observed in Figure 20C. Thus, the low total pressure region was consistent with the place where a strong TKE was observed, indicating a flow instability near the inner wall regions. The onset of the strong TKE was also consistent with the beginning of the strong secondary motion (\(\sim\)30\({}^{\circ}\)) (Figure 17). This result suggests that stronger secondary motions on the curve pipe have possibly affected the TKE increase (Figure 20A). The trends of the turbulence, secondary flow, and vortical structure were qualitatively observed in the same manner, even in the single-phase flows not included here.
## 3 Conventional Channel Estimation and Machine Learning-Based Solutions
This underscores the potential for learning algorithms to bypass the necessity for precise physical modeling, offering efficient solutions in resolving optimization issues related to configuring surfaces with numerous elements [101]. This suggests that AI and ML can streamline the design and operation of RISs, especially in scenarios where conventional mathematical approaches might not be feasible or are time-consuming [101]. The best ML algorithm for optimizing RISs in NTNs, particularly for tasks like optimizing phase shift and CSI, would likely be reinforcement learning, specifically Deep Q-Networks (DQNs). DQNs excel in scenarios where the system needs to learn optimal actions in dynamic and changing environments [102]. In the context of RIS-aided NTNs, where adaptation to real-time conditions is crucial, a DQN's ability to handle complex decision-making processes and learn from experience makes it a robust choice. The use of AI or ML further gives the capability to navigate the challenges posed by mutual coupling effects and dynamically adjust RIS configurations to align well with the requirements of optimizing phase shift and CSI in an NTN setting.
## 3 Results and Discussion
### Optimization of Experimental Conditions  
#### 3.3.1 Dosage Optimization  
In the process of preparing imprinted polymers by precipitation polymerization, the ligand H\({}_{2}\)Pdc complexes with uranyl ion and functional binding ligand MAA to form the polymer. The selection of the cross-linking agent and its amount, the amount of AlBN, and the reaction time of forming an imprinted membrane are also crucial during the experiment. In the synthesis of polymeric materials, the choice of cross-linking agent is also essential. If the force between the crosslinking agent and the template molecule is slight, the stable polymer cannot be shaped. However, if the force is too large, it will cause an excessive reaction between the binding ligand and the template ion, which is difficult to elute. In this experiment, 1,3,5-trimellitic acid (TRIM), ethyl orthosilicate (TEOS), ethylene glycol dimethacrylate (EGDMA), methyl orthosilicate (TMOS), diethylene Benzene (DVB) were investigated for research, and crystallizing agents were used to prepare different MIIP/MCPE. The electrochemical effect was detected by DPV based on the intensity of the reduction peak current value. From Figure S4, it can be concluded that the current value of the electrochemical sensor prepared by EGDMA is the strongest. That is, EGDMA was selected as the optimal crosslinking agent in this experiment.  
The amount of crosslinking agent EGDMA (Figure S5A), the amount of AlBN (Figure S5B), and the reaction time of forming imprinted membrane (Figure S5C) were analyzed by the single factor method. The crosslinking agent could not connect the polymer and template well when EGDMA was lower than 100 \(\mu\)L. If EGDMA was higher than 100 \(\mu\)L, the binding ligand and template would react excessively to form colloid, which would hinder the formation of imprinted sites, thus affecting the sensitivity of the sensor. As revealed by the research, the most desirable performance of the sensor was manufactured with 100 \(\mu\)L of EGDMA, 0.05 g of initiator AlBN, and 12 h of reaction time. The sensitivity of the constructed sensor was the highest and the response value was the best.
## 4 Experimental Results and Discussion
### Experimental Study of the Output Performance of the Energy Harvester  
In order to study the output performance of VFPEH-30 and the coupling effect between vortex-induced vibration capture energy and flutter capture energy, vortex-induced piezoelectric energy harvester with a cylindrical bluff body diameter of 30 mm, and flutter piezoelectric energy harvester were fabricated, respectively, named veh-30 and FEH, and then relevant experiments were carried out. Compared with VFPEH-30, their piezoelectric cantilever module is the same as VFPEH-30, and the mass unit at the end of VEH-30 cantilever beam only contains cylindrical blunt body (hollow structures); the FEH cantilever beam contains only two symmetrically arranged wing-shaped components at the end of the beam. In order to eliminate the influence left by the previous flow rate experiment, the flow rate was reset for one minute after each test, and then the experiment was recorded [23].
## 1 Introduction
* The study discusses 5G propagation channel models and elaborates on the characteristics of each channel and model on the basis of their characteristics, especially with regard to their applicability to 5G wireless networks. * The study identifies reference deployment scenarios, considering the distinct areas for testing and benchmarking, focusing on applications, devices, and mobile network operators. * Critical takeaway lessons are presented, and further research directions are highlighted. The remaining portions of this paper are organized as follows: Section 2 discusses the previous reviews and identifies the key limitations in earlier 5G technology reviews. Section 3 briefly overviews mobile technology evolutions, including the Third Generation Partnership Project (3 GPP) releases for mobile communication, IMT 2020 capabilities, and 5G test environments. Section 4 thoroughly examined 5G use cases and spectrum requirements, including 5G applications and recommended frequency bands. Section 5 elaborates on 5G-enabling technologies and the contributing 5G research groups. Section 6 delves into 5G spectrum standardization, 5G global trials, and 5G deployment modes. Section 7 comprehensively discusses 5G radio propagation mechanisms and channel models. Section 8 discusses the challenges of 5G deployment and recommendations for future research work beyond 5G wireless communication systems. Lastly, the conclusion of the review is provided in Section 9.
## 5 Performance Evaluation
### Storage Cost  
The maximum storage cost of the OFT scheme is \(2(\log_{2}n+1)\). The proposed work uses an adjacent secret shared between adjacent nodes instead of the individual node secret; hence, the storage cost of MOFT is \(2\log_{2}n\).
# TAB Series-Resonant DC-DC Converter and Multi-Phase-Shift Based Global Optimization Modulation
## 3 Global Optimization of Multiple Phase Shifts to Minimize Ohmic Loss
Similarly, to minimize the RMS value of the transformer current, the magnitude of \(v_{\text{ABf}}\) and \(v_{\text{CDf}}\) respectively remains at the maximum value \(v_{\text{ABf,l,max}}\), and \(v_{\text{CDf,l,max}}\) in the entire power range. In this state, the modulation strategies of port 1 and port 2 are both simplified to SPS modulation. The ZV-on condition of port 1 and port 2 are expressed as (33) and (35), respectively. The ZV-on condition of port 3 is \(\theta_{3}>\varphi_{\text{13}}\). In order to verify the performance of the optimized multi-phase-shift modulation, the RMS value of the transformer current of the optimized modulation is contrasted with that of the traditional SPS modulation. It is obtained from (25), (32), (34), and (36) that the main difference between the proposed modulation and the SPS modulation lies in the range of \(k_{1}<1\) and \(k_{2}<1\). Taking port 1 as an example, the RMS values of the transformer current of the two modulations under \(k_{1}<1\) are compared. The internal phase-shift angles of the three ports are all 0 under the SPS modulation, and the external phase-shift angles satisfy  
\[{\varphi_{\text{13}}}^{*}=\frac{1}{\pi}\text{arcsin}({G_{\text{1}}}^{*}),\ {\varphi_{\text{23}}}^{*}=\frac{1}{\pi}\text{arcsin}({G_{\text{2}}}^{*}) \tag{37}\]  
The expressions (25), (32), and (36) are substituted into (16). The corresponding parameters are shown in Table 1 and \(k_{1}\) is set to 0.6 and 0.8. The distribution curves of \(I_{\text{r1,rms}}\) as a function of the transfer power \(G_{\text{13}}\)' of the two modulation strategies are drawn in Figure 6. It can be seen that the \(I_{\text{r1,rms}}\) of the proposed strategy are significantly smaller than that of SPS modulation. In particular, as the voltage gain decreases, the advantage of the proposed modulation to reduce the RMS value of the transformer current is more obvious. According to the above analysis, the proposed modulation realizes the global optimized control of the converter, as compared to the SPS modulation. The optimized \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(v_{1},v_{2}\) & 60–150 V \\ \(v_{3}\) & 100 V \\ \(f_{\text{s}}\) & 50 kHz \\ \(n_{1}\): \(n_{2}\): \(n_{3}\) & 1:1:1 \\ \(L_{\text{r1}},L_{\text{r2}}\) & 209 uH \\ \(C_{\text{r1}},C_{\text{r2}}\) & 53 nF \\ \(L_{\text{3}}\) & 101 uH \\ \(C_{\text{63}}\) & 100 nF \\ Power switch (SiC JFETs) & 650 V/50 A \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of experimental platform. Figure 6: Curves of RMS transformer current as a function of transfer power under different modulations. (**a**) \(k_{1}=0.6\), (**b**) \(k_{1}=0.8\). modulation significantly reduces the RMS transformer current and the power loss. The converter efficiency is accordingly increased in the entire range of the voltage and transfer power.
## 3 Results
### Omission Estimate for the RF50/AO50 Change Model  
Omission estimates associated with the RF50 map and AO50 map were identical because each model contained the same polygons labeled as no-change by the Random Forests model, those with change probabilities \(<\)50%. A random sample of 5346 no-change polygons, covering 3060 ha, was reviewed for this procedure using the AAViewer. Of those polygons, 95 contained change areas covering 13.6 ha resulting in an omission rate of 0.45% (change area/no-change area), which translated into an estimated 1172 ha of estimated omitted change (0.0045*total no-change area). A complete assessment of the initial RF50 model consisted of an actual commission error rate of 2.55% and an estimated omission error rate of 0.45%. The Adjusted Producer's Accuracy (APA) was calculated (Equation (3)) as 76.3% (3779 ha mapped plus 1172 ha additional predicted). This analysis also raised the percentage of total area observed to 2.6% (1/38 th) of the study region.  
Figure 6: Sample area showing the distribution of AO50 (**blue**) and AO25 (**purple**) change locations. AO50 locations were part of the base map. AO25 locations were mapped during the application of accuracy optimization technique 2. The area shown represents about 1% of the mapped watershed. With a scale of 1:20,000 the smallest mapped change locations (about 200 m\({}^{2}\)) represent about 8 pixels in the original figure.
## 5 Performance Evaluation Metrics
### Mean Absolute Error (MAE)  
This metric captures the average absolute difference between predicted values and their authentic counterparts [48]. It is defined as follows:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{6}\]  
Here, \(y_{i}\) denotes the observed (true) value, \(\hat{y}_{i}\) represents the model's predicted value, and \(n\) is the total number of observations.
# Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
## 3 Data Collection and Pre-Processing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & **Parameters** & **Description** \\ \hline \multirow{3}{*}{Geometries} & \(Dc\) & Section size (mm) \\  & \(\rho_{r}=L/D_{c}\) & Span-to-depth ratio (\%) \\  & \(SecT\) & Section type \\ \hline \multirow{3}{*}{Materials} & \(f_{c}\) & Strength of concrete (MPa) \\  & \(f_{yl}\) & Yield strength of longitudinal bars (MPa) \\  & \(f_{yo}\) & Yield strength of transverse bars (MPa) \\ \hline \multirow{2}{*}{Reinforcement} & \(\rho_{l}\) & Longitudinal reinforcement ratio (\%) \\  & \(p_{yo}\) & Transverse reinforcement ratio (\%) \\ \hline Loading & \(\rho_{n}=P/(A_{g}\cdot f_{c})\) & Axial load ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Input parameters for LSSVM training. Figure 3: Statistical distribution of the input and output variables: (**a**) Section size; (**b**) Span-to-depth ratio; (**c**) Strength of concrete; (**d**) Yield strength of longitudinal bars; (**e**) Yield strength of transverse bars; (**f**) Longitudinal reinforcement ratio; (**g**) Transverse reinforcement ratio; (**h**) Axial load ratio; (**i**) Yield strength; (**j**) Yield displacement. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variables** & **Min.** & **Max.** & **Mean** & **Std.** \\ \hline \(Dc\) & 80 & 1520 & 345.85 & 149.98 \\ \(\rho_{r}\) & 1 & 10 & 3.44 & 1.65 \\ \(f_{c}\) & 16 & 118 & 46.54 & 25.98 \\ \(f_{yl}\) & 0 & 587.1 & 422.84 & 72.41 \\ \(f_{yo}\) & 0 & 1424 & 454.52 & 206.60 \\ \(\rho_{l}\) & 0.0046 & 0.0603 & 0.03 & 0.01 \\ \(\rho_{v}\) & 0 & 4.27 & 0.40 & 0.69 \\ \(\rho_{n}\) & \(-0.099\) & 0.9 & 0.22 & 0.18 \\ \(F_{y}\) & 16.27 & 2654.11 & 202.49 & 204.25 \\ \(\Delta_{y}\) & 0.54 & 114.70 & 12.64 & 14.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical description of the input and output variables.
# Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
## 4 Disturbance-Based USB Formation Scheme
### Formation Obstacle Avoidance
: Figure 10 presents the distance dependence of the modulation function \(E_{j}\) for the case \(\varepsilon=e\) (the base of the natural logarithms) and \(D_{j}=13\) (the relative safe distance for boats in the 1-ton class is 13 m, and this distance can be adjusted for boats of different tonnages). As the distance between vehicles increases, \(E_{j}\) gradually increases. However, the function does not converge upwards and ultimately approaches \(\infty\). At this point, \(-\frac{n_{j}n_{j}^{T}}{E_{j}n_{j}^{T}n_{j}}\) tends to 0, and the total disturbing effect \(\overline{M_{j}}\) of other vehicles on the current one tends to \(I\). Therefore, the total value of the flow field is \(u_{jt}=u_{t}\). This satisfies the requirement for the route to end at the target point. In contrast, as the distance between vehicles decreases, the modulation function \(E_{j}\) tends toward 1. In addition, since \(n_{j}^{T}M_{j}=n_{j}^{T}-\frac{n_{j}^{T}n_{j}n_{j}^{T}}{n_{j}^{T}n_{j}}=n_{j}^{ T}-n_{j}^{T}=0\), it follows that \(n_{k}^{T}\overline{u_{jt}}=0\). Thus, when the distance between vehicles equals the safety distance, the normal velocity disappears, meeting the additional requirement for the route to avoid all vehicles. \(\square\)  
(2) Obstacle avoidance is given priority over inter-vehicle collision avoidance. Proof. : The function \(e^{d_{j}-D_{j}}\) in Figure 11 gradually converges to 0 as the distance between USVs reaches the safety-distance range \(D_{j}\) but does not become 0. This overcomes the influence of the zero denominator in the trap area. Because \(e^{d_{j}-D_{j}}\
eq 0\), obstacle avoidance is given priority over inter-vehicle collision avoidance when a USV is on the surface of an obstacle where \(\Gamma_{k}=1\). In other words, USVs can avoid obstacles without being interfered with by other USVs; the disturbance weight brought by other USVs is 0. Thus, USVs sandwiched between obstacles and other vehicles will not stagnate in the trap area or experience unnecessary oscillations in the complex disturbance environment. USVs near obstacles will always push relatively distant USVs away while effectively avoiding obstacles. Thus, the priority of obstacle avoidance over inter-vehicle-collision avoidance is satisfied. \(\square\)  
The proposed VST can be metaphorically understood as wrapping an outer-layer \"balloon\" around other USVs, as shown in Figure 12. The elasticity of the \"balloon\" allows USVs to avoid collisions while being pulled by streamlines. During obstacle avoidance, each vehicle follows the streamlines to evade obstacles and maintains its place in the formation because of the virtual structure law and streamlined contraction.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Main Path Direction Calculation
#### 3.3.3 Main Path Direction Selection  
Since each satellite knows its own latitude \(latC\), the latitude of the destination satellite \(latD\) can be derived based on the phase difference with the target satellite. According to \(latC\) and \(latD\), it can be calculated whether the communication between the two needs to pass through the polar region. Let \(cpr=0\) represent that there is no need to pass through the polar regions, \(cpr=1\) represent that the communication needs to pass through the polar regions, and \(latC=latmax\) represent that there is no co-orbiting satellite that can be retransmitted between the current satellite and the nearest pole. The selection method of the  
Figure 3: Main path calculation.  
main path direction \(rt\) is shown in Algorithm 1. This method allows the satellite to select the direction of the shortest path to the destination satellite from the horizontal hop direction and the vertical hop direction.  
```
Variable definitions:\(d_{h}\): horizontal hop direction, \(d_{v}\): vertical hop direction, \(rt[node]\): the main path direction of a node, \(cpr\): records whether the communication needs to path through the polar region, \(latC\): latitude information for current node.
1: A new time interval is entered with the latitude \(latC\) for current node
2:fornode\(\in\)\(all\)nodesdo
3: calculate \(d_{h},d_{v}\) and \(cpr\)
4:if\(d_{h}=-1\)then
5:\(rt[node]\Left d_{v}\)
6:elseif\(d_{v}=-1\)then
7:\(rt[node]\Left d_{h}\)
8:else
9:if\(cpr=0\)then
10:if\(latC\geq latD\)then
11:\(rt[node]\Left d_{h}\)
12:elseif\(latC<latD\)then
13:\(rt[node]\Left d_{v}\)
14:endif
15:elseif\(cpr=1\)then
16:if\(latC=latmax\)then
17:\(rt[node]\Left d_{h}\)
18:elseif\(latC\
eq latmax\)then
19:\(rt[node]\Left d_{v}\)
20:endif
21:endif
22:endif
23:endfor
```  
**Algorithm 1** Main path direction calculation.  
**Proof of Algorithm 1.** According to Equations (1) and (5), the intra-plane intersatellite link length \(L_{v}\) is fixed and the inter-plane intersatellite link length \(L_{h}\) increases with increasing latitude, and the total numbers of vertical hops and horizontal hops does not change. In order to get the shortest routing path, if the path between the current node and the destination node does not need to go through the polar region, then the horizontal hops should be completed at the node with higher latitude. If the path between the current node and the destination node needs to pass through the polar region, the horizontal hops should be completed at the node closest to the polar region. \(\square\)