esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Occurrence of PFCs in Water Bodies
[23] reported that PFOA has been detected in tap water-based beverages (e.g., cola and coffee) at a concentration of 4 ng/L. The lower PFC concentration from tap water beverages is attributed to the pre-treatment of tap water prior to being used for production. Moreover, a review study by [PERSON] et al. [4] has indicated that high PFOA concentration levels ranging from 487-10,100 ng/L have been detected in aquatic environments near point sources. \begin{table}
\begin{tabular}{l l l l l} \hline
**Region** & **Country** & **PFOA, ng/L** & **PFOS, ng/L** & **Reference** \\ \hline Africa & Ghana & 68.1–190 & 62.1–168.3 & [26] \\ \hline Oceania & Australia & 0–16 & 0–9.7 & [27] \\ \hline \multirow{4}{*}{America} & Canada & 3–32 & 2–12 & [28] \\ \cline{2-5}  & Brazil & 0.35–2.82 & 0.58–6.70 & [29] \\ \cline{2-5}  & USA & 5–30 & 1–57 & [27] \\ \hline \multirow{3}{*}{Asia} & China & 115–151 & 0.1–11.2 & [30] \\ \cline{2-5}  & Japan & 6.5–48 & 1.3–3.7 & [29] \\ \hline \multirow{4}{*}{Europe} & France & 8.7–18 & 3–11.99 & [31] \\ \cline{2-5}  & Germany & 0–6.1 & 0–4.7 & [32] \\ \cline{2-5}  & The Netherlands & 0.6–6.6 & 0.1–11 & [23] \\ \cline{1-1}  & \textless{}0.3–11 & \textless{}0.03–0.41 & [33] \\ \cline{1-1} \cline{2-5}  & Sweden & 0.302–8.56 & 0.397–8.81 & [31] \\ \cline{1-1} \cline{2-5}  & Spain & 3.8–29 & 2.4–140 & [31] \\ \hline \end{tabular}
\end{table} 
Table 1: Concentrations of PFOA and PFOS detected in tap water. Early studies [24] on the global distribution of PFCs in aquatic environments suggest that water-receiving bodies located in low population density areas have low levels of PFCs. This can be attributed to fewer industrial activities. It is worth noting that PFC toxicity values vary significantly from one standard to another, as indicate in Table 2. The variability in toxicity values can be attributed to the lack of federal maximum contaminant level. In the subsequent sections, the recent developments on biological remediation of PFCs are summarized, despite being characterized as xenobiotic compounds.
# Rosemary Leaf Extract Inhibits Glycation, Breast Cancer Proliferation, and Diabetes Risks
## 3 Results and Discussion  
### Major Phenolics in Rosemary Extract  
In this study, seven major phenolics including protocatechuic acid, caffeic acid, ellagic acid, ferulic acid, rosemarinic acid, carnosol, and carnosol are identified in rosemary leaves extract (Figure 1 and Table 1). Rosemarinic acid was the dominant phenolic with a concentration of \(17.28\pm 0.74\) mg/g DW followed by carnosic acid (\(8.31\pm 0.06\) mg/g DW) and carnosol (\(4.45\pm 0.03\) mg/g DW). The concentrations of the other four phenolics were in a range of \(2.16\pm 0.01\) mg/g DW for protocatechuic acid to \(0.01\pm 0.00\) mg/g DW for ferulic acid (Table 1). Rosemary, together with mint, sage, marjoram, oregano, and thyme all belong to Lamiaceae family. Compared with oregano (\(6.61\pm 0.30\) mg/g DW) and thyme (\(3.37\pm 0.01\) mg/g DW) in the study of [PERSON], as well as mint (\(1.45\) to \(4.16\) mg/g) in the study of [PERSON], rosemary extract had the highest level of rosemarinic acid [19; 20]. The carnosic acid in rosemary extract were significantly higher than oregano (\(4.72\pm 0.09\) mg/g DW), marjoram (\(3.01\pm 0.05\) mg/g DW), and thyme (\(6.41\pm 0.09\) mg/g DW) as well [19]. Furthermore, the carnosol was approximately three time higher than marjoram (\(1.76\pm 0.07\) mg/g DW) and basil (\(1.38\pm 0.07\) mg/g DW) [19]. It has been reported that RA is able to attenuate allergic diseases and slow the development of Alzheimer's disease [21]. Besides, carnosic acid and carnosol can protect thylakoid membranes against lipid peroxidation [22].  
Figure 1: Chromatogram of rosemary extract under the wavelength of 280 nm: 1. Protocatechuic acid (8.5 min); 2. caffeic acid (17.2 min); 3. ellagic acid (27 min); 4. ferulic acid (30 min); 5. rosemarinic acid (38 min); 6. carnosol (41 min); 7. carnosol acid (47 min).
## 3 Improved Grey Wolf Particle Swarm Hybrid Optimization MSVR Localization Model Construction
### Multi-Output Support Vector Regression
 \tag{14}\]  
where \(\mathbf{K}=\mathbf{K}_{ij}=\phi^{T}(x_{i})\phi(x_{j})\) is a kernel function. Research has shown that, in the absence of prior knowledge, the use of the radial basis kernel function to map data from a low-dimensional space to a high-dimensional space yields promising results [43]. The expression is given as follows:  
\[\mathbf{K}_{ij}=exp(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2\sigma^{2}}) \tag{15}\]  
By iteratively determining \(\mathbf{\beta}=\left[\beta^{1},\cdots,\beta^{K}\right]\), the output coordinates can be expressed as:  
\[\mathbf{y}=\phi^{T}(x)\phi(\mathbf{x})\mathbf{\beta}=\mathbf{K}_{ij}\mathbf{\beta} \tag{16}\]  
From our derivation, it is clear that MSVR's regression performance depends on three hyperparameters: the regularization constant \(C\), the insensitivity loss coefficient \(\gamma\), and the kernel function radius \(\sigma\). Consequently, we use intelligent optimization algorithms to find the optimal values for these parameters: \(C\), \(\gamma\), and \(\sigma\).
# Repulsive Force for Micro- and Nano-Non-Contact Manipulation
## 5 Model and Experimental Measurements Comparison
### Influence of the Ionic Strength  
As the behavior of these objects is essentially governed by these electrostatic forces, the impact of the ionic force on both the experimental data and the model has been tested and is reported in Table 2 (Figure S6). For that, the distance \(\kappa\) was calculated from Equation (S1) for each experimental condition and r\({}_{\text{SW}}\) is fixed at 5 um.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Sphere** **Diameter (\(\mu\)m)**} & \multicolumn{2}{c}{**Experimental**} & \multicolumn{2}{c}{**Predicted**} \\ \cline{2-5}  & **Interaction Distance** & **Interaction Force** & **Interaction Distance** & **Interaction Force** \\  & **(\(\mu\)m)** & **(\(\mu\)N)** & **(\(\mu\)m)** & **(\(\mu\)N)** \\ \hline
0.01 & 0.004 \(\pm\) 0.007 & 0.02 \(\pm\) 0.01 & N.d. & 1.10\({}^{-4}\) \\ \hline
1 & 0.8 \(\pm\) 0.2 & 0.07 \(\pm\) 0.01 & 0.5 \(\pm\) 0.05 & 0.03 \(\pm\) 0.005 \\ \hline
5 & 3.3 \(\pm\) 0.5 & 0.6 \(\pm\) 0.2 & 3 \(\pm\) 0.3 & 0.34\(\pm\) 0.05 \\ \hline
10 & 5.7 \(\pm\) 0.7 & 0.7 \(\pm\) 0.15 & 5 \(\pm\) 0.5 & 0.64 \(\pm\) 0.1 \\ \hline
20 & 8.2 \(\pm\) 0.6 & 0.9 \(\pm\) 0.25 & 7.5 \(\pm\) 0.75 & 1.2 \(\pm\) 0.2 \\ \hline
40 & 12.7 \(\pm\) 0.29 & 1.1 \(\pm\) 0.12 & 12.5 \(\pm\) 1.2 & 2 \(\pm\) 0.4 \\ \hline \end{tabular} N.d. the distance was too weak in order to be estimated.  
\end{table}
Table 1: Comparison of the repulsive force and distance measured or modeled between the silica surface and different diameter sizes of borosilicate sphere, both of which are functionalized by APTES. The data are collected during three experimental campaigns. For every campaign, new bead and substrate are considered, and force is measured in 10 different locations on a substrate.  
A good concordance was noted between the experimental and the predicted repulsive force. When NaCl salt is added in the medium, these two ions come into contact with the dipoles on the opposite side of their charge and create an electric balance. This behavior has the effect of skimming the loads of the double layer and extending the load of dipoles, reducing electrostatic interactions with the sphere and decreasing the length of Debye. As the number of interactions is thus reduced and the length of Debye decreases, the electric field generated by the surface at the height of the sphere also decreases.
## 3 Results and Discussion
### Mineral Content
The Fe content in the Misiones grassland is more than double than the Fe content found in _IP_ samples. However, Fe content in _IP_ samples exceeds the NRC recommendations by 4 to 6 times. The Fe content in _IP_ samples and legume pastures are quite similar. The Cu content in _IP_ samples is comparable to that of the Misiones grassland and the NRC recommendations. The Mn content in _IP_ samples is more than 40 times the NRC recommendation, and 3 times the Mn content found in Misiones grasslands. The Zn content in _IP_ samples is significantly higher than the Zn content of the Misiones grassland and the NRC recommendations. Aluminum is a non-essential element. Al resulted in important concentrations in _IP_ samples. However, it is several orders of magnitude lower than the safe limits established for cattle and other animals. In any case, the occurrence of Al in _IP_ samples might deserve further investigations for possible health implications and the biogeochemical reasons for its strong presence. Finally, in vitro animal and human investigations have shown that B is a bioactive element [63]. However, it has received little attention in ruminant nutrition. This element is of low toxicity to cattle and is not completely absorbed by the gastrointestinal tract. However, the presence of B increases the absorption of Ca and Mg [63]. Therefore, B is necessary to avoid hypocalcemia in cows. The elemental composition of _IP_ samples exceeds the NRC recommendations except for the content of P and Na. The results observed in this study are within the normal range of forage resources of the temperate region of South America. The mineral content of _IP_-based products provides a set of crucial elements for human metabolism. In particular, the high amounts of Ca, K, Mg, and Zn may contribute to the maintaining of the bone structure, the proper functioning of the heart, the protein assimilation, and the reinforcement of the immunity system, respectively.
## 3 The System Configuration and Operating Process  
In this section, two types of system configurations are presented because MPC methods include a conventional MPC (single-level MPC) and a hierarchical MPC (multi-level MPC). The single-level MPC system configuration is shown in Figure 2(a), and the two-level MPC system configuration is shown in Figure 2(b).
# Preliminary Results of Ship Detection Technique by Wake Pattern Recognition in SAR Images
## 4 Discussion
Yellow arrow define the direction of the reconstructed turbulent wake in each grid point. Red circles identify pixels whose turbulent wake is aligned with wake \(c\). inclined orbits with different right ascension of the ascending node, enabling nighttime observations and very short revisit time, e.g., 1 h by the complete ICEYE constellation [43]. With reference to the time span between SAR data collection and positive wake detection, the proposed technique is assumed to be applied when the SAR mission is operating in the Near-Real Time mode. This enables a time span between product acquisition and processing of 30 min.
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 3 Method
### Network Architectures
#### 3.2.3 Mesh Training Stage
\(L_{\text{mask}}(f^{\text{Feat}},f^{\text{Mesh}}|\mathbf{x})\), \(L_{\text{real}}(f^{\text{Feat}},f^{\text{Mesh}}|\mathbf{x})\), and \(L_{\text{disc}}(f^{\text{Disc}}|\mathbf{x},\hat{\mathbf{x}}_{\text{render}}, \hat{\mathbf{x}}_{\text{inp}})\) are newly defined as follows:  
\[L_{\text{real}}(f^{\text{Feat}},f^{\text{Mesh}}|\mathbf{x}) = \|f^{\text{Disc}}(\hat{\mathbf{x}}_{\text{render,bg}})-1\|_{2}^{2}, \tag{9}\] \[L_{\text{mask}}(f^{\text{Feat}},f^{\text{Mesh}}|\mathbf{x}) = \|\mathbf{x}_{\text{mask}}-\hat{\mathbf{x}}_{\text{mask}}\|_{2}^ {2},\] (10) \[L_{\text{disc}}(f^{\text{Disc}}|\mathbf{x},\hat{\mathbf{x}}_{ \text{render}},\hat{\mathbf{x}}_{\text{inp}}) = \|f^{\text{Disc}}(\mathbf{x}))-1\|_{2}^{2}\] (11) \[+L_{\text{rot}}(f^{\text{Feat}}|\mathbf{x}_{\text{rot}})\]0 \[+L_{\text{rot}}(f^{\text{Feat}}|\mathbf{x}_{\text{rot}})\]1  
where \(\mathbf{x}_{\text{mask}}\) is the binary mask estimated from the Mask-RCNN [58]. Using Equation (11), the discriminator \(f^{\text{Disc}}\) is trained to discriminate the original images \(\mathbf{x}\) as real while the rendered images \(\hat{\mathbf{x}}_{\text{rendered}}\) and in-painted images \(\hat{\mathbf{x}}_{\text{inp}}\) are determined as fakes. **Joint loss \(L_{\text{joints}}\). ** When 2D or 3D skeleton joints are available, this loss is used to close the keypoints regressed from 3D mesh vertices to their ground-truth locations (in the SMPL [13] model, the model that geometrically regresses 3D skeletons from mesh vertices is accompanied). This is an optional loss that is not used for the self-supervised setting (i.e., _Ours (self-sup)_); however, we used this loss for the weakly-supervised setting (i.e., _ours (weakly-sup)_); or for the semi-supervised setting (i.e., _ours (semi-sup)_), in which either of 2D or 3D skeletons are available, respectively. **Incorporation of different human images. ** As we constituted our framework with a self-supervised loss that did not require any 2D/3D skeleton annotations, we were able to train our network with any RGB human images outside of our training dataset. We additionally involved 2D images from the MPI-INF-3 DHP [59], MPII [60], COCO [61], and LSP [62] datasets, which have been involved in the weakly/semi-supervised settings, plus our own collections of wild YouTube data. When detecting the humans in the images, we used the Mask-RCNN network [58] to inspect if there was a human bounding box or not. The results in Table 5 using \"Full w/o Y\" (row 4) and \"Full\" (row 5) datasets can be compared to see the performance improvement by the incorporation of human images, where \"Full\" and \"Full w/o Y\" denote the model trained by datasets with and without our own collections of wild YouTube images, respectively. We could observe that the accuracy was improved by the incorporation of human images.
# Diode-Pumped Solid-State Q-Switched Laser with Rhenium Diselenide as Saturable Absorber
## 1 Introduction
For the Q-switched pulse laser, the threshold absorbed pump power was 227 mW. The maximum average output power was 125 mW. When the absorbed pump power was 837 mW, the shortest pulse width of 682 ns and the highest repetition rate of 84.16 kHz were obtained. Our experiment shows that the few-layer \(\text{ReSe}_{2}\) could be used as 1 \(\upmu\)m SA in all-solid-state lasers.
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 3 DMRVR Solution
### Traffic Jant Treatment  
The _VehicleService_ application makes decisions based on events generated by their background processes and received messages from the _LogisticsService_ application through the vehicular network. A pickup vehicle begins its route after receiving a _NotifyRoute_ message from the operator. A delay management (DM) process, part of the _VehicleService_ application, starts automatically when a pickup or auxiliary vehicle begins its route. DM process runs in the background to identify if the vehicle is behind  
Figure 4: Initial route and recurrent messages.  
in schedule due to traffic jams. When the required time to finish the course is higher than a specified JIT limit, the DM process recognizes a delay. The DM process calculates the vehicle's displacement time to each supplier based on the vehicle's geographic position and the Euclidean distance for each supplier. If the _VehicleService_ application identifies a delay, it may remove one or more suppliers from the route so that the vehicle attends the JIT limit. If a supplier needs to be removed, the _VehicleService_ will send a _NotifyWithdrawal_ message to the _LogistcsService_ and will change the vehicle route. Algorithm 1 shows the working process of the delay management (DM).  
```
1 totalTime\(\leftarrow\) routeTimeLeft()+routeTimeSpent();
2 whiletotalTime\(>\) JITlimitdo
3 vehicleRoute.push(vehicleRoute.size()-I);
4 Send(NotifyWithdrawal\(\rightarrow\) LogisticsService);
5 totalTime\(\leftarrow\) routeTimeLeft()+routeTimeSpent();
6
7 end while
```  
**Algorithm 1**Delay management (DM) process  
When the _LogisticsService_ application receives a _NotifyWithdrawal_ message from _VehicleService_, it triggers an auxiliary pickup vehicle by sending a _NotifyAddSupplier_ message. This last message informs that the _VehicleService_ application installed in the auxiliary vehicle must add one or more pickups to its route. Before changing the route, the _VehicleService_ always checks if it is possible to attend the suppliers within its total pickup time limit. When a pickup is possible, the _VehicleService_ application sends a _NotifyPickup_ message back to the _LogisticsService_. On the other hand, when a new pickup can not be completed by the auxiliary vehicle, the _VehicleService_ sends a _NotifyNonAttendance_ message to the logistics operator. Figure 5 shows the messages exchanged between the _LogisticsService_ and the _VehicleService_ applications when the auxiliary vehicle cannot perform the pickup.
## 2 Zone-Based Registration Scheme
### Type II: Clustered Shape
#### 2.2.1 Case 1: Moving inside 3Z after Forming a Clustered-Shape  
In Type II, since it is a clustered-shape RA, there are three parts in contact between zones. Therefore, six states are defined according to UE's movement between zones, as shown in Figure 8. The case of the transition from \(E_{12}^{II}\) to other states is described as follows. Here, \(E_{12}^{II}\) refers to a state in which the UE moves from the most recently registered zone 1 to the second stored zone 2.  
Figure 8: _Cont._  
(_i_) \(l_{21}^{II}\): If the UE sends an implicit registration after moving to zone 2 and before moving to another zone, its state changes to \(l_{21}^{II}\) and the probability becomes \(1-m\). After implicit registration, the Network_DB will be newly updated to \(\framebox{2|1|3}\), and the UE can be found through one paging procedure in zone 2.  
(_ii_) \(E_{21}^{II}\): The transition probability to state \(E_{21}^{II}\) corresponding to Type II is \(\theta m+(1-\theta)m/5\). In this case, the UE returns to zone 1 or moves to a new zone to form a cluster-typed RA. In both cases, it can be seen that an incoming call connection is possible with one paging process.  
(_iii_) \(E_{23}^{II}\): When the UE moves to zone 3, since Network_DB is \(\framebox{1|2|3}\), three paging procedures (1\(\rightarrow\)2\(\rightarrow\)3) are required for an incoming call in zone 3. The state transition probability of \(E_{21}^{II}\)\(\rightarrow\)\(E_{23}^{II}\) is \((1-\theta)m/5\).  
(_iv_) \(E_{21}^{I}\): UE can move to state \(E_{21}^{I}\) with a probability of \(3(1-\theta)m/5\) if it configures the new RA of Type I. The incoming call connection in the new zone is achieved through one paging process.
## 4 Trajectory Tracking via Attitude Stabilization
### Trajectory Tracking Using Genetic Algorithms Method (TTGA)  
Genetic algorithm techniques are used to optimize the PD/PID controllers' parameters for the attitude and the altitude stabilization.  
A vector of eight elements in the PD controllers case and twelve elements in the PIDs case is considered as an individual among a population of solutions to be optimized by utilizing the genetic algorithms. Each element of this vector represents a PD/PID controller's parameter. The initial population is generated randomly in the following ranges: \(k_{p_{i}}\in[0\quad 200]\) and \(k_{p_{i}}\in[0\quad 50]\) for the PD parameters, while for the PID parameters: \(k_{p_{i}}\in[0\quad 50]\), \(k_{i_{i}}\in[0\quad 20]\) and \(k_{d_{i}}\in[0\quad 10]\);  
To evaluate individuals of the population, a fitness function is indispensable. Relative to the purpose of this part of our work, the chosen fitness function takes into account the position stability that is required for a proper desired trajectory tracking, in addition to the attitude performance. The function expression taken in this part is given by (32).  
\[f=\frac{1}{\exp(\alpha D+\beta T_{s}+\gamma E_{s}s)}. \tag{32}\]  
Here, \(T_{s}\) is the settling time, \(E_{s}s\) is the steady-state error, and D is the overshoot; \(\alpha,\beta\), and \(\gamma\) are weighting factors \((\alpha=10;\beta=4;\gamma=2)\).  
The obtained parameters values are summarized in the Table 4.  
The simulation results of this subsection and the previous one, for the non-linear system represented by the dynamic model (1)-(6) coupled with the four controllers PD/PID, whose gains are recapitulated in Tables 4 and 5, are shown in Section 5.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**TTGA Method**} & \multicolumn{5}{c}{**Controllers**} \\ \cline{2-6}  & & \(C_{\phi}(s)\) & \(C_{\theta}(s)\) & \(C_{\phi}(s)\) & \(C_{z}(s)\) \\ \hline PD & \(k_{p}\) & 17.53 & 6.13 & 10.33 & 176.91 \\ \cline{2-6} Parameters & \(k_{d}\) & 2.59 & 2.92 & 1.03 & 45.55 \\ \hline PID & \(k_{p}\) & 27.73 & 23.92 & 34.18 & 21.85 \\ \cline{2-6} Parameters & \(k_{i}\) & 17.86 & 13.52 & 1.71 & 12.51 \\ \cline{2-6}  & \(k_{d}\) & 3.43 & 0.68 & 6.31 & 5.25 \\ \hline \hline \end{tabular}
\end{table}
Table 4: PD and PID Parameters values synthesized by genetic algorithm optimization for trajectory tracking using the genetic algorithm (TTGA).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**TTRM Method**} & \multicolumn{5}{c}{**Controllers**} \\ \cline{2-6}  & & \(C_{\phi}(s)\) & \(C_{\theta}(s)\) & \(C_{\phi}(s)\) & \(C_{z}(s)\) \\ \hline PD & \(k_{p}\) & 16.67 & 16.67 & 16.67 & 190.8 \\ \cline{2-6} Parameters & \(k_{d}\) & 1.27 & 1.27 & 1.27 & 34.98 \\ \hline PID & \(k_{p}\) & 23.48 & 23.48 & 42.38 & 29.01 \\ \cline{2-6} Parameters & \(k_{i}\) & 13.58 & 13.58 & 0.81 & 23.58 \\ \cline{2-6}  & \(k_{d}\) & 1.42 & 1.42 & 1.71 & 8.58 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PD and PID parameters values synthesized by reference model method for trajectory tracking using the reference model (TTRM).
## 4 Simulation Analysis of the Designed Probes
### Simulation Analysis of Delamination Eddy Current Testing
#### 4.2.3 Simulation Analysis of Eddy Current Testing for Multiple Delamination  
In order to conduct a detailed analysis of the detection signals of the designed probe, the above simulation experiments only consider the simple case where there is only one layer inside the CFRP sample. In reality, CFRP can have delamination in different dimensions at different locations. To investigate the detection performance of the designed probe in this scenario, we conducted simulation experiments using the designed probe to simulate the detection of delamination of different dimensions and positions within the CFRP sample (Figure 5) with an orthogonal layup. The delamination dimensions and their corresponding layers are Delamination 1 from Table 8 and Delamination 12 from Table 10. The planar positions of the two defects are as shown in Figure 16, and the detection results are depicted in Figure 17.  
Based on Figure 17, it is evident that the designed probe successfully detects delamination of different dimensions and locations within the CFRP sample during both top and bottom scans. However, due to the small distance between the two delaminations and the edge effect on the probe during scanning, it is challenging to determine the width of the delamination based solely on the relative positions of the rising and falling edges of the detection voltage difference. Instead, a rough estimate of the layer where the delamination is located can be made by combining the results from the top scanning with the peak detection voltage obtained from the bottom scanning.  
Figure 16: The planar positions of the multiple delaminations.  
Figure 17: Detection voltage difference for multiple delamination: (**a**) top scanning; (**b**) bottom scanning.