esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Influence of Structural Parameters  
#### 4.2.1 Collection Height  
As shown in Figure 14, the velocity contours inside the hydraulic collector at different collection heights (\(H\)) were achieved by keeping other parameters constant. Furthermore, the extent to which the collection efficiency (\(\eta\)) and the average outlet flow velocity of the conveying channel (\(v_{ao}\)) varied with \(H\) is shown in Figure 15.  
As can be seen from Figure 14, as the collection height (\(H\)) increased, the front and rear acquisition jets continued to attenuate, such that the upward flow formed by reflection after scouring the ground was also weakened. As a result, some of the nodules flew out of the back side of the collector before they were washed up to a sufficient height, and they then entered the conveying channel, as shown in Figure 16.  
In addition, when \(H\) was less than 120 mm, the space in the lower part of the collector gradually increased as the \(H\) increased, the energy lost from the mixing of the acquisition jets decreased, and the average outlet flow velocity of the conveying channel (\(v_{ao}\)) increased. When \(H\) was greater than 120 mm, the \(H\) was greater than the non-attenuation length of the acquisition jet, and the water flow attenuation became serious. At the same time, more of the seawater output from the acquisition nozzles flowed out of the back side of the collector rather than into the conveying channel. Then, as can be seen from the red curve  
Figure 13: Polymetallic nodules in the hydraulic collector. (**a**) Normally transported nodules in the conveying channel. (**b**) Particle distribution in the hydraulic collector within 30 s.  
Under the combined effect of the above influences on the acquisition and transmission capacity that were caused by the collection height (\(H\)), the collection efficiency (\(\eta\)) decreased continuously with increasing \(H\), as shown by the black curve in Figure 15. In particular, \(\eta\) decreased sharply when \(H\) was greater than 180 mm. In light of the above analysis, \(H\) should be no more than 150 mm to ensure good collection efficiency of the hydraulic collector. This can be achieved by the terrain follower in Figure 3, which can dynamically and adaptively adjust according to the desired settings and terrain conditions so as to keep \(H\) within a reasonable range.
## 4 Case Studies
### Comparison with Other Methods  
To verify the superiority of the proposed method, it is compared with four advanced bearing RUL prediction methods. Convolutional long short-term memory network (CLSTM) [33] combines convolution and LSTM to enhance the model's ability to learn degradation features. Variational autoencoder LSTM with local weighted deep sub-domain adaptation network (VLSTM-LWSAN) [34] divides bearing degradation into 10 sub-domains and improves the prediction accuracy of the model on the target bearing through sub-domain alignment. The deep transfer learning-based hierarchical adaptive RUL prediction approach (TLHAM) [35] uses the MMD to divide bearing degradation into four stages and uses pairwise combinations as the source domain and target domain for DA to learn domain invariance at different degradation stages. It has shown good predictive performance on test rig data. Multitask spatiotemporal augmented net (MTSTAN) [36], a causal enhanced CNN with skip connections, enhances the robustness and universality of the model by establishing a multiwindow and multitask sharing mechanism. The comparison results are shown in Table 4 and it can be seen that MDA-LETCN has the best effect. CLSTM and MTSTAN do not have DA modules, resulting in the worst prediction performance. The classification of bearing degradation states by VLSTM-LWSAN does not conform to the degradation mechanism, so its effectiveness is only higher than that of CLSTM and MTSAN. TLHAM divides the degradation process of bearings into three to four stages based on the mechanism of bearing degradation but ignores the differences in degradation among different bearings, which affects the prediction accuracy of the model. The MDA-LETCN divides the degradation process of bearings into three states, taking into account not only the composite working conditions of the run-to-failure data collected from WT bearings but also the differences in the degradation of different bearings. Therefore, the prediction effect of MDA-LETCN is the best.
# A Noise-Tolerant Carrier Phase Recovery Method for Inter-Satellite Coherent Optical Communications
## 4 Simulation Results and Analysis
### The Impact of \(\omega_{n}\) and \(N\) Parameters  
In Figure 7, the influence of iteration time on the performance of FO estimation in the feedback stage with different \(\omega_{n}\) values is investigated. It is observed that the estimated FO value progressively converges to \(\Delta f\) when the iteration time increases. Both the convergence iteration time and estimation errors of the noise-tolerant method are affected by \(\omega_{n}\). Specifically, for \(\omega_{n}=40,20,13\) Mrad/s, the corresponding convergence times are 2, 11, and 37 \(\upmu\)s, respectively  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Parameters** \\ \hline Symbol Rate \(1/T\) & 2.5 Gsps \\ Wavelength \(\lambda\) & 1550 nm \\ Linewidth \(\delta_{v}\) & 10 kHz \\ Frequency Offset \(\Delta f\) & 20 MHz \\ Code Rate & 223/255 \\ Modulation & QPSK \\ Damping Factor \(\xi\) & \(\sqrt{2}/2\) \\ Phase Detector Gain \(K_{d}\) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters.  
A smaller \(\omega_{n}\) correlates with a longer convergence time, albeit with reduced estimation errors. The residual Doppler FO can be effectively estimated once the feedback loop is locked, and the subsequent feedforward stage accurately estimates the estimation errors.  
Figure 8 demonstrates the performance of the noise-tolerant method in terms of LRMSE\({}_{\theta}\) across different block lengths \(N\). The analysis leads to the following conclusions: (i) the curve representing LRMSE\({}_{\theta}\) is concave, indicating the existence of an optimal \(N\) value that minimizes LRMSE\({}_{\theta}\); (ii) the optimal \(N\) value increases as \(\omega_{n}\) and \(E_{b}/N_{0}\) decrease. This phenomenon is further discussed. On one hand, a larger \(N\) results in the smoothed AWGN noise approaching zero, which minimizes its impact on phase estimation. However, as the PN follows a Wiener process, data smoothing incurs estimation losses, which are directly proportional to \(N\). Furthermore, the ability to estimate noise variations strengthens with an increase in \(\omega_{n}\) during the feedback stage, resulting in a smaller optimal \(N\) value for the feedforward stage.  
Combining the findings presented in Figures 7 and 8, it is noted that the LRMSE\({}_{\theta}\) at \(\omega_{n}=20\) Mrad/s is comparable to that at \(\omega_{n}=13\) Mrad/s, and superior to that at \(\omega_{n}=40\) Mrad/s. Meanwhile, the convergence time at \(\omega_{n}=20\) Mrad/s is slightly greater than at \(\omega_{n}=40\) Mrad/s but significantly less than at \(\omega_{n}=13\) Mrad/s. Addition  
Figure 8: The impact of block length \(N\) on the LRMSE\({}_{\theta}\).  
Figure 7: Estimation of FO versus iteration time in the feedback stage.  
ally, the LRMSE\({}_{\theta}\) reaches its minimum value when \(N\) is between 50 and 100. Therefore, \(\omega_{n}=20\)\(\mathrm{Mrad}/\mathrm{s}\) and \(N=64\) are identified as the optimal parameters.
## 2 Problem Background
### Need for Routing Fairness in OppNets
#### 2.1.2 Fair Routing as an Enhancement for Incentive Schemes  
Due to resource constraints on portable handheld devices, incentive schemes are designed to motivate participation of selfish users. Nevertheless, it is still unlikely that the few over-burdened users will be unselfish, particularly when their devices are frequently running low on energy. The logical solution is to ensure fairness by distributing the burden more evenly among the users without sacrificing throughput. Also, the idea of incentives to motivate user participation (especially in OppNets for sensed data collection, where users do not have any additional benefits from contributing their resources to the network) is more effective under a fair routing protocol. Since incentive schemes reward involvement in storing and forwarding messages, users of less popular nodes may be entitled to significantly less incentives. Consequently, the rewards may not be worth their participation, as energy is also expended when the wireless communication interfaces (and GPS in some cases) on their devices remain turned on. With more fairness, routing responsibilities are more evenly distributed among nodes, thereby allowing users of less popular devices more contribution and incentives. Hence, the fairer the routing protocol, the more effective incentive schemes become.
## 4 Results and Discussion
### Comparison of Experimental Results and Simulation Results  
The experimental results and simulation analysis of fiber-reinforced geopolymer concrete are illustrated in Figures 22 and 23 with the load-deflection response and crack patterns. Figure 22a-d shows the load-deflection curves of specimens H1, H2, P1 and P2, respectively, with the various volume fractions of fiber, compared with non-fiber geopolymer concrete GC.  
Figure 21: SEM photos. (**a**) Specim with polypropylene fiber; (**b**) specimen with steel fiber.  
Figure 22: Comparison the load–deflection behavior. (**a**) Specim H1; (**b**) specimen H2; (**c**) specimen P1; (**d**) specimen P2.  
Overall, there was a slight difference between the experimental and analysis results. The load of specimen H1 in Figure 22a reached 23,200-32,000 N in modeling and 22,769-32,188 N in the flexural test. Additionally, the peak deflection was similar in experiment and modeling, with 0.95-1.61 and 1.03-1.47, respectively, and the difference of 0.5%-8% was small and acceptable. Compared with the geopolymer concrete without fiber GC, Figure 22a shows strength improvement. The other specimens H2, P1 and P2 also had the same effect of load-deflection behavior. The difference between testing and analysis was also reasonable.  
In modeling, the performance of geopolymer composites enhanced with fibers was evaluated and determined through crack patterns at the last converged load steps. Figure 23a,b shows the finite element modeling with the crack patterns of specimens using polypropylene and steel fiber and compared with the failure figures of the test. By comparing the crack appearance in flexural test and simulation analysis, it can be seen that the crack initiation and propagation occurred at the midspan of beam and showed the same shape.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 3 System Description and Related Models  
The navigation-oriented NC-NET is described by an undirected graph \(\mathbf{G}=(\mathbf{C}_{g},\mathbf{E}_{g},\mathbf{R}_{g})\), where \(\mathbf{C}_{g}=\{C_{1},\ldots,C_{n}\}\) denotes the set of sensor nodes and \(C_{i}\in\mathbf{C}_{g}\) means the node with a unique ID \(i\in\{1,2,\ldots,n\}\), \(\mathbf{E}_{g}\subseteq\mathbf{C}_{g}\times\mathbf{C}_{g}\) denotes the set
## 2 Related Work  
### Backbone for Feature Extraction  
To fit fewer computing resources, previous researchers have reduced the number of model parameters at the cost of reduced detection accuracy. For example, SqueezeNet [14] uses \(1\times 1\) convolution to compress the number of feature maps. MobileNet [15] proposes depth-separable convolution to replace ordinary convolution, which greatly reduces the number of parameters. ShuffleNet [16] uses packet convolution to replace dense ordinary convolutions. ChannelNets [17] uses channel-wise convolution to compress the depth model, and replaces dense connections between feature maps with sparse connections in the CNN. IGCV1 [18] relies on interlaced group convolution to make full use of grouped convolution. GhostNet [19] uses a linear transformation to achieve a similar characteristic graph, saving calculation effort. However, a significant reduction in the number of parameters inevitably reduces the detection accuracy of the network. Researchers have exploredmany ways of achieving higher detection accuracy with fewer parameters. In this paper, we use prior knowledge to enhance the feature extraction ability of a lightweight network without increasing the number of learning parameters.
# Large-Scale Automatic Vessel Monitoring Based on Dual-Polarization Sentinel-1 and AIS Data
## Materials and Methods  
### Sentinel-1 Dual-Polarization Characteristics  
The Sentinel-1 mission, developed under the Copernicus Programme, is a constellation of two satellites (A and B units) each carrying an imaging C-band SAR sensor that acquires systematically dual-polarization data. Dual polarization SAR sensors transmit one single but selectable polarization (horizontal H or vertical V) and receive simultaneously both the transmitted polarization and its orthogonal counterpart [44; 45]. For the Sentinel-1 sensor, two linear polarization cases were considered: vertical transmission with vertical and horizontal reception (VV-VH) and horizontal transmission with horizontal and vertical reception (HH-HV). The default polarimetric combination for Sentinel-1 images acquired over areas of maritime interest is (VV-VH). This combination was considered in the algorithms and experimental analysis presented in this study. As demonstrated in the scientific literature [37; 46], for SAR incidence angles in the range of 30\({}^{\circ}\) to 45\({}^{\circ}\) which are characteristic for Sentinel-1 IW, vessels generally exhibited higher backscattering values in the co-polarization channel (VV), while in the cross-polarization channel (VH) the backscattering values were lower. However, in the VH channel, the sea clutter, being comparable to the instrument noise floor, was typically characterized by very low energy values in comparison with the VV channel [47; 48]. Therefore, the signal to clutter ratio of the VH channel presents higher values than the one of the VV channel, making the former channel theoretically better suited for vessel detection.  
Another valuable characteristic of SAR data for ship detection and characterization is the phase information that can be extracted from SLC images. For the vessel signatures, as well as for the sea clutter, the phase values provided supplementary backscattering information with respect to the one contained in the amplitude. The phase information was able to retain the features that characterize the backscatter originating from the vessel's motions and its interaction with the surrounding sea. The scattering of vessels was a complex mechanism that included double-bounce scattering originating from metallic structures and multiple reflections between the sea and the targets [49; 50; 51]. Moreover, such mechanisms present different behaviours depending on the SAR polarization. For instance, the co-polarization channel was influenced mostly by the double bounce effect while the cross-polarization channel is more sensitive to the multiple-bounce. Therefore, the complex co- and cross-polarization SAR vessel signatures provide complementary information, thereby enabling for a more complete characterization of a target.
# Maximal Ratio Combining Detection in OFDM Systems with Virtual Carriers Over V2V Channels
###### Abstract  
This paper examines the performance of orthogonal frequency division multiplexing (OFDM) systems for vehicle-to-vehicle (V2V) communication channels. More specifically, a doubly selective channel under high intercarrier interference (ICI) is considered. Current solutions involve complex detection and/or reduced spectral efficiency receivers. This paper proposes the use of virtual carriers (VC) in an OFDM system with a low-complexity maximal ratio combining (MRC) detector to improve the bit error rate (BER) performance. The results show that VC provides diversity in received data, resulting in a \(\geq\)5 dB gain compared to previous OFDM systems with conventional linear/nonlinear detectors used as a reference. The detector presented in this paper has linear complexity, making it a suitable solution for real-time V2V communication systems.  
m _Acticle_  
Academic Editors: [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]  
Received: 26 June 2023  
Revised: 18 July 2023  
Accepted: 26 July 2023  
Published: 27 July 2023
## 2 Method
### Channel Model  
In an indoor visible light localization system, the propagation of visible light conforms to the Lambertian radiation model, and when there is no obstruction between the transmitting end and the receiving end, it is called a line-of-sight (LoS) link, and under this condition, the DC gain can be expressed as follows:  
\[H_{\text{LOS}}=\left\{\begin{array}{l}\frac{A(m+1)}{2\pi d^{2}}\cos^{m}( \theta)T_{s}(\varphi)g(\varphi)\cos(\varphi),0\leq\varphi\leq\varphi_{c}\\ 0,\varphi>\varphi_{c}\end{array}\right. \tag{1}\]  
Figure 1: The model of indoor visible light positioning system.  
The Lambertian order of the LED is calculated as follows:  
\[m=\frac{\ln 2}{\ln(\cos\phi_{1/2})} \tag{2}\]  
The half power angle of the LED is \(\phi_{1/2}\), the radiation angle of the light source LED at the emitting end is \(\theta\), the angle of incidence of the illuminator is \(\varphi\), the field-of-view range angle of the illuminometer is \(\varphi_{c}\), the effective detection area of the illuminometer is \(A\), the straight line distance from the LED to the illuminometer is \(d\), the gain of optical filtering in the link is \(T_{s}(\varphi)\), the optical concentration gain in the link is \(g(\varphi)\). When the emitted optical power of the LED is \(P_{t}\), and the received power \(P_{r}\) of the receiver can be expressed as  
\[P_{r}=H_{\text{LOS}}\cdot P_{t} \tag{3}\]
# Aluminum-Loaded Bifunctional Resins for Efficient Fluoride Removal from Aqueous Solutions
## 3 Results and Discussion
### Thermodynamic Study  
In order to further elucidate the adsorption characteristics, the thermodynamics of adsorption was investigated using isotherm data at different temperatures. Entropy change (\(\Delta S\)), enthalpy change (\(\Delta H\)), and free-energy change (\(\Delta G\)) were calculated using Equations (5)-(7):  
\[\Delta G=\Delta H-T\Delta S \tag{5}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & \begin{tabular}{c} **Qmax** \\ **(mg/g)** \\ \end{tabular} & **Reference** \\ \hline \begin{tabular}{c} La-loaded chelating resin \\ \end{tabular} & 3.38 & [PERSON] et al. [40] \\ \hline \begin{tabular}{c} Activated carbon fibers modified with \\ zirconium \\ \end{tabular} & 6.12 & [PERSON] et al. [41] \\ \hline \begin{tabular}{c} La-modified biochar (La-TBC) \\ \end{tabular} & 7.55 & [PERSON] et al. [42] \\ \hline \begin{tabular}{c} Zr-impregnated magnetic chitosan \\ graphene oxide (Zr-MCGO) \\ \end{tabular} & 8.8 & [PERSON] et al. [43] \\ \hline \begin{tabular}{c} La-modified biosynthetic crystal (BC-La) \\ \end{tabular} & 10.92 & [PERSON] et al. [44] \\ \hline \begin{tabular}{c} Al-loaded TP207 resin \\ \end{tabular} & 12.05 & [PERSON] et al. [22] \\ \hline \begin{tabular}{c} Zr-loaded TP260 resin \\ \end{tabular} & 12.1 & [PERSON] et al. [45] \\ \hline \
\begin{tabular}{c} Zr impregnated highly functional binary \\ biopolymeric composite \\ \end{tabular} & 12.13 & [PERSON] et al. [46] \\ \hline
\begin{tabular}{c} Al-loaded S957 resin \\ \end{tabular} & 15.49 & In this work \\ \hline \hline \end{tabular}
\end{table}
Table 2: The adsorption properties of metal-modified materials.  
Figure 6: Adsorption isotherms of fluoride by Al-S957 at different temperatures.  
\[\Delta G = -RT\text{ln}K_{\text{L}} \tag{6}\] \[\text{ln}K_{\text{L}} = \frac{\Delta S}{\text{R}}-\frac{\Delta H}{\text{RT}} \tag{7}\]  
where \(T\) represents the reaction temperature (K), \(K_{\text{L}}\) represents the adsorption equilibrium constant as defined in the Langmuir isotherm equation, and R (8.314 J\(\cdot\)mol\({}^{-1}\cdot\)K\({}^{-1}\)) is the universal gas constant.  
The linear relationship between ln\(K\) and \(1/T\)(K\({}^{-1}\)) for the adsorption of fluoride by Al-S957 at different temperatures is depicted in Figure S3. The thermodynamic parameters are listed in Table S4. All negative values of \(\Delta G\) indicate that the defluorination process is spontaneous [47]. In addition, with temperature increasing, the higher negative value of \(\Delta G\) denotes a more energetically favorable adsorption [42]. The positive \(\Delta H\) value verifies that the process is endothermic, while the positive \(\Delta S\) value reflects increased freedom at the adsorption interface [48].
## 3 Optimization of Parallelization
### Optimization D: Parallel IO  
We also designed an asynchronous parallel IO method. As shown in Figure 5, an independent node was employed to conduct the IO procedures. A separate communication group was created for the IO work. When the computing nodes needed to carry out IO procedures after calculation procedures, the communication group of the computing nodes collected the data and sent it to the IO node. After receiving all of the data, the IO node began IO procedures. Meanwhile, the nodes from the computing group could continue with their calculation procedures instead of waiting for the completion of the IO procedures. Therefore, the elapsed time for IO was hidden by overlapping the calculations and IO.  
Figure 5: Asynchronous parallel IO.
# Pro-Health Potential of Selected Uncommon Oilseed Plants
## 3 Pro-Health Potential of Selected Uncommon Oilseed Plants
### Carthamus tinctorius L
#### 3.1.8 Hepatoprotective Effect  
Total flavonoids from safflower leaves (TFCTLL) exhibited a protective effect against carbon tetrachloride (CC14)-induced chronic liver injury (CLI) in a mouse model. The mechanism of action may be linked, for example, to anti-inflammation and antioxidant [51]. Hepatoprotective effects in the case of CCl4-induced oxidative liver injury in mice are demonstrated also by kaempfer 3-O-rutinoside (K-3-R) and kaempfer 3-O-glucose (K-3-G). Treatment with these two kaempfer glycosides isolated from safflower increased the level of total protein (TP), as well as prevented the CCl4-induced increases in serum aspartate aminotransferase (AST), alkaline phosphatase (ALP), and hepatic MDA levels. Animals treated with 3-O-rutinoside and kaempfer 3-O-glucose had significantly reinstated glutathione levels and presented normal catalase and superoxide dismutase activities. K-3-R and K-3-G also moderated the CCl4-induced liver histological modification [52]. The literature data indicate that HSYA has been shown to effectively safeguard the liver of rats from long-term alcohol-induced damage. This action is associated with the increased antioxidant capacity of liver tissues and the suppression of TGF-B1 (transforming growth factor \(\beta\)1) expression [53].