esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Kinetic
#### 3.5.3. Intraparticle Diffusion  
In the solid-solution adsorption process, the adsorbate (target contaminant) was diffused through external mass transfer (boundary-layer diffusion) and intraparticle diffusion (mass transfer through the pores) [37]. To determine the late-limit step, a linear model was recommended. The intraparticle diffusion model was developed by [PERSON] and [PERSON] (1963) [38], followed by Equation (12),  
\[q_{t}=K_{i}t^{0.5}\ +C, \tag{12}\]  
where \(q_{t}\) is the uptake of phosphate at time \(t\) (mg/g), \(K_{i}\) is the intraparticle diffusion constant (mg/g hr\({}^{0.5}\)), and \(C\) is the arbitrary constant representing the thickness (or resistance) of the boundary layer [37; 39]. When applied in previous studies, intraparticle diffusion was described by separating the whole segment into one [36; 40], two [21; 41], or three [37; 42]. Various interpretations have shown that the multiple adsorption processes are involved in the overall adsorption rate [43]. In Figure 9, it can be seen that there are two main linear segments. The multiple adsorption process takes into consideration that at the beginning of adsorption, the external mass transfer (film diffusion) is governed and, later, the intraparticle diffusion leads to the adsorption rate. According to Table 5, the effect of pH adjustment is not significant, and at values of 7 and 9, the \(K_{i}\) for the GACA is higher than that for the GAC, indicating that the presence of hydroxide in the solution decreases the external mass transfer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Parameter** & \multicolumn{4}{c}{**pH 4**} & \multicolumn{4}{c}{**pH 7**} & \multicolumn{4}{c}{**pH 9**} \\ \cline{2-13}  & **GAC-** & **GAC-** & **GACA-** & **GACA-** & **GAC-** & **GAC-** & **GACA-** & **GACA-** & **GACA-** & **GAC-** & **GACA-** & **GACA-** \\  & **wo** & **w** & **wo** & **w** & **wo** & **w** & **wo** & **w** & **wo** & **w** & **wo** & **w** \\ \hline \(K_{i}\) & 0.966 & 0.997 & 0.805 & 0.801 & 0.778 & 0.760 & 0.877 & 0.960 & 0.470 & 0.493 & 0.863 & 0.920 \\ C & 0.480 & 0.448 & 0.594 & 0.599 & 0.328 & 0.306 & 0.586 & 0.614 & 0.256 & 0.237 & 0.602 & 0.560 \\ R\({}^{2}\) & 0.856 & 0.875 & 0.735 & 0.729 & 0.856 & 0.895 & 0.761 & 0.762 & 0.835 & 0.862 & 0.748 & 0.766 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Intraparticle diffusion parameters calculated from 0 h to 6 h.  
Figure 9: Intraparticle diffusion plot for GAC and GACA at initial of pH 4 (**a**), 7 (**b**), and 9 (**c**) (solid line: regression from 0 to 6 h).
# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Social Recommendation
#### 3.4.1 Traditional Collaborative Filtering-Based Social Recommender Systems  
In traditional social network-based recommendations, two separate graphs need to be constructed: one representing user-item interactions, and another representing user-user social connections. Therefore, social recommendation algorithms utilizing graph neural networks require knowledge extraction from both graphs simultaneously for accurate inference. They can leverage coarse-grained and sparse user trust relationships to enhance conventional collaborative filtering methods. Essentially, this approach involves the use of similarity metrics to identify users similar to the target user. Subsequently, different weights are assigned to ratings for the target item based on this similarity, facilitating the computation of the user's rating for that item. The method for determining user-to-user similarity is crucial to this concept. Consequently, existing social recommendation algorithms have explored various designs for graph neural network encoders and adopted different architectures based on their objectives. Upon determining the similarity between two entities, the set of users most similar to the target user can be identified based on their ratings. This, in turn, allows for the recommendation of items that the user may find interesting and ranks them accordingly.
# Incorporating Spatial Autocorrelation into GPP Estimation Using Eigenvector Spatial Filtering
## 4 Results
### Impact Assessment of Spatial Effects  
Spatial autocorrelation is taken into account in the SA-LGBM. In the real world, vegetation distribution often exhibits a certain degree of spatial correlation. SA-LGBM is capable of better capturing this correlation by introducing spatial eigenvectors, which enables a more effective understanding and quantification of the similarity between different locations. The integration of spatial autocorrelation into the model has been demonstrated to enhance the dependability and precision of the estimation outcomes [29; 56].  
Figure 10 introduces a variable Z, which represents the magnitude of residuals deviating from their mean value. Consequently, Z is distributed on both sides of the zero value. The vertical axis variable represents the sum of the values of neighboring features multiplied by their respective normalized weight values, reflecting the overall level of neighboring features. It is evident that points falling in the first quadrant, whether they represent the element or its neighboring elements, exhibit relatively large residuals compared to the mean, indicating a high-high spatial pattern. The second quadrant signifies a spatial pattern where regions with high residual values are surrounded by regions with low values. To measure spatial autocorrelation in residuals, [PERSON]'s I index was employed. Prior to the inclusion of EV, [PERSON]'s I for residuals in the LGBM model was high and significant, indicating a certain degree of spatial correlation among neighboring residual values. This may suggest that the model failed to fully capture spatial patterns, resulting in a non-random and statistically significant spatial distribution of residuals. After incorporating EV, [PERSON]'s I of residuals in the SA-LGBM model became low and insignificant, indicating that spatial autocorrelation had been removed, and the residuals can be considered as random errors within the SA-LGBM model.  
Utilizing the first law of geography, we constructed a spatial weights matrix to depict the interrelations among American flux sites, and subsequently generate and extract EV. By subtracting the GPP in the LGBM model from the GPP in the SA-LGBM model, the impact of EV within the model can be discerned through the distribution of GPP residuals [57]. Figure 11 depicts the spatial distribution of residuals for both the LGBM and SA-LGBM models, along with the spatial distribution of EV. Notably, the distribution of GPP residuals exhibits a similar trend to the changes in EV. There exists a strong consistency between the distribution of GPP residuals and the spatial distribution of EV, suggesting that EV can be considered as a complementary variable for capturing the spatial effects in GPP.  
Figure 10: [PERSON]’s I and distribution of residuals in the LGBM model and SA-LGBM model.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
## 2 Macroscopic Saturated Porous Media Model
### Governing Balance Equations  
Considering the constituent balance relations (6) and (7) together with the relations between the total and effective stresses in (9), the governing equations of the binary model to determine \(\{\mathbf{u}_{S},\,\mathbf{v}_{F},\,p\}\) can be expressed as follows:  
\[\begin{split}\bullet&\text{Solid momentum balance:}\qquad\mathbf{0}\,=\,\operatorname{div}\!\left(\mathbf{T}^{S}_{E} \right)-n^{S}\,\mathrm{grad}\,p+\rho^{S}\mathbf{g}-\mathbf{\hat{p}}^{F}_{E}\\ \bullet&\text{Fluid momentum balance:}\qquad \mathbf{0}\,=\,\operatorname{div}\!\left(\mathbf{T}^{F}_{E}\right)-n^{F}\, \mathrm{grad}\,p+\rho^{F}\mathbf{g}+\mathbf{\hat{p}}^{F}_{E}\\ \bullet&\text{Overall volume balance:}\qquad 0\,=\, \operatorname{div}\!\left(\mathbf{v}_{S}\,+\,n^{F}\,\mathbf{w}_{F}\right)\end{split} \tag{13}\]  
Therein, the first and second equations in (13) represent the momentum balance equations of the individual constituents, i.e., solid and fluid, with corresponding momentum interaction terms \(\mathbf{\hat{p}}^{S}_{E}=-\mathbf{\hat{p}}^{F}_{E}\). The third equation in (13) represents the overall volume balance as the sum of the solid and fluid volume balances. In this context, it is worth mentioning that the set of Equation (13) to describe the hydromechanical response of the biphasic model is not unique, as discussed in [1; 4]. Several alternative multi-field formulations can be presented, such as using the overall momentum balance instead of the solid momentum balance or merging the fluid momentum balance with the overall volume balance. These reformulations can significantly impact the numerical stability and the way the boundary conditions are formulated, as discussed in, e.g., [4].
# Production and Characterization of Cu/CNT Nanocomposites
###### Abstract
[18] produced nanocomposites of Cu reinforced with CNT by high-energy ball milling followed by conventional sintering and cold rolling--the nanocomposites showed an improvement in mechanical properties compared to the Cu matrix. The strengthening mechanisms identified were load transfer, grain refinement, and dislocation strengthening. Therefore, it is well-known and widely studied that the combination of some intrinsic characteristics of the material, such as its crystallographic orientation and stacking fault energy, affects its deformation behavior. However, the influence of CNTs on the mode and extent of these processes still needs to be explored and understood for implementing nanocomposites reinforced with CNTs. In this sense, this work aims to detail the mode of production and behavior during the deformation processes of nanocomposite Cu/CNTs, such as the interaction of the CNT with dislocations, which can be extremely useful to improve its behavior in service. For this reason, this work aims to present the conventional powder metallurgy route of Cu/CNT nanocomposites and discuss the effect of CNT reinforcement in the copper matrix after sintering. It also seeks to explain the deformation behavior of nanocomposites during cold rolling up to different strain values. Since the ultrasonication process proved effective in dispersing and mixing the CNTs and metallic powders, the samples were processed using this method as it has the advantage of promoting good dispersion with less damage to the structure of the CNTs.
## 5 Experiment Simulation
### Performance on the ISP-like Topologies  
In the ISP-like topologies, \(n_{\text{base}}\) is set equal to the number of caching nodes. Unlike tree topologies, to be more realistic, we set multiple Source Servers in ISP-like topologies, and users are distributed throughout the network. The NRS is connected to the ICN caching node who has the maximum betweenness centrality. The link delay between each Source Server and the directly connected ICN router is set to 50 ms and the delay of other links is set to 2 ms.  
Figure 10 shows the performance of different caching strategies in ISP-like topologies under the Zipf distribution workload when \(\alpha\) changes. In different topologies, PB-NCC keeps its advantages over cache hit ratio, latency, and server load. PB-NCC improves the cache hit ratio by 8.85% at least and reduces the server load by 11.34% at least compared with other strategies. PROB and LCE perform similarly. LCD and CL4M perform poorly in  
Figure 8: Comparison in the tree topology when the cache size ratio changes. (**a**) the cache hit ratio under a different cache size ratio; (**b**) the average latency under a different cache size ratio; (**c**) the server load under a different cache size ratio.  
Figure 7: Comparison in the tree topology when the Zipf parameter \(\alpha\) changes. (**a**) the cache hit ratio under different Zipf parameter \(\alpha\); (**b**) the average latency under different Zipf parameter \(\alpha\); (**c**) the server load under different Zipf parameter \(\alpha\).  
all topologies, due to their preferences that tend to cache content at the core rather than at the edges.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 5 Hydrogen Applications in End-Use Sectors
### Decarbonizing the Power Sector  
Hydrogen can help decarbonize the power sector by supporting the integration of variable renewable energy (VRE) in the power system [1]. Hydrogen can help both in reducing the curtailment of excess VRE generation, by using the excess renewable energy when generation surpasses the demand to produce green hydrogen through water electrolysis, and by helping to mitigate the imbalances caused by VRE generation [92] by providing a medium with long-term storage capacity that, in contrast with electric batteries, could allow seasonal storage [1; 93]. Hydrogen can also be used as a backup and off-grid power supply, with power generation through fuel cells or micro gas turbines. While micro gas turbines are reliable, capable of load-following and can be used for heat and power applications, fuel cells present higher efficiency, lower emissions and noise, and greater modularity [94; 95].  
These technologies can be used in microgrids and remote power applications as the main power source as an alternative to conventional diesel generators [1; 93]. Examples of prospective hydrogen energy communities can be found in the literature [96; 97].  
In addition, hydrogen has been identified as a potential option to support the transportation of renewable energy from production to consumption regions, with lower investments and adaptation costs than electrical transmission for long distances [6; 98].
## 5 Evaluation
### Experimental Results
#### 5.2.2 Effectiveness of Intra-Class Closeness
With this parameter value, we were able to confirm that the classifier showed reasonably high accuracy, along with a low postponing ratio. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **\# of** & \(\theta=3\) & \multicolumn{3}{c}{\(\theta=2\)} & \multicolumn{3}{c}{\(\theta=1.5\)} & \multicolumn{3}{c}{\(\theta=1.2\)} \\  & **Outliers** & **Y/Correct** & **N** & **Y/Correct** & **N** & **Y/Correct** & **N** & **Y/Correct** & **N** \\ \hline
**Total** & 271 & 45/44 & 226 & 93/88 & 178 & 141/129 & 130 & 186/161 & 85 \\ \hline Group A & 146 & 11/11 & 135 & 49/48 & 97 & 84/81 & 62 & 105/98 & 41 \\ Group B & 14 & 1/0 & 13 & 3/0 & 11 & 5/0 & 9 & 6/0 & 8 \\ Group C & 16 & 2/2 & 14 & 2/2 & 14 & 4/3 & 12 & 8/5 & 8 \\ Group D & 7 & 0/0 & 7 & 0/0 & 7 & 0/0 & 7 & 3/0 & 4 \\ Group E & 88 & 31/31 & 57 & 39/38 & 49 & 48/45 & 40 & 64/58 & 24 \\ \hline \(F1_{M}\) & - & 70.0\% & - & 68.3\% & - & 62.1\% & - & 47.2\% & - \\ \hline \(F1_{\mu}\) & - & 97.\% & - & 94.6\% & - & 91.5\% & - & 86.6\% & - \\ \hline \(F1_{w}\) & - & 96.9\% & - & 93.2\% & - & 90.1\% & - & 84.6\% & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Effectiveness of intra-class closeness.
## 3 System Architecture
### Overall Architecture
#### 3.1.1 Network Node  
Network nodes are SDN-based switches that can run the ICN protocol. The nodes are divided into a control plane and a data plane, where the control plane handles service information awareness, topology construction, scheduling, and other functions, while the data plane is responsible for matching and forwarding specific request packets. This will be elaborated upon in the latter part of this section. Some nodes are directly or indirectly connected to computing clusters, while other nodes are dedicated to forwarding service requests. The network topology depicted in Figure 1 above classifies nodes into three tiers--large, medium, and edge--according to the scale of the computing clusters they interconnect. In each layer, network nodes are mainly organized into domains based on delay constraints. For each domain, a master node is selected from the upper layer based on the principle of minimizing average transmission delay. Within the same domain, all nodes, except for the master node, maintain the same types of service information. The master node can operate in a cross-layer manner, but it does not announce information across layers, meaning it will not send information from lower-layer nodes to upper-layer nodes or vice versa. It should be noted that the specific status information of the same service instance on different network nodes may be different because different path delays are introduced when the information is transmitted from the computing cluster to different network nodes.  
Taking a single domain as an example, as shown in Figure 2 below, nodes 1, 2, and 3 are nodes in the same layer, while node 4 is the master node. Each node stores the service ID (SID), as well as the computing and network status values. Since the focus of this study is to ensure the delay requirement, this paper uses the delay from the computing cluster, where the service instance is located in the current network node as the network status value and the link delay between nodes as the cost in the information announcement process. For each hop, the network status value must add the overhead delay. The delay between nodes can be obtained by timestamping the data packet.  
For example, if the network status value of sid1 at node 1 is n1, and the link delay cost to node 2 is d1, then the network status value of sid1 at node 2 is n1 + d1. It should be noted that this figure is for illustrative purposes only, and the values along the way are in arbitrary units. Nodes 1, 2, and 3 maintain the same types of service information, but the master node 4 also holds the information of sidx from other layers. However, it does not announce the information of sidx to nodes 1, 2, and 3. This mechanism not only makes the service information more precise but also allows the nodes to focus on serving users within the domain, thereby reducing the overhead of the service information announcement.
# Deformation and Stress Analysis of Pile-Supported Immersed Tunnels under Seismic Loads
## 3 Verification of Numerical Model
### Verification of the Results  
The applicability of the numerical model was examined in terms of both settlement and acceleration. Figure 7a-c show the settlement curves of the piled foundation. It can be seen that the settlement produces a fluctuation process similar to the seismic frequency, but the settlement eventually stabilizes. The settlement fluctuation is due to the fact that the amplitude and propagation direction of the seismic wave in the numerical model will produce changes at different locations. This variation leads to an uneven response of the foundation, which results in fluctuations in the settlement curve. Whereas the whole physical model vibrates at the same acceleration, the foundation gradually densities under this nondifferentiated vibration, which leads to a smooth settlement curve. The settlement results of the experiment can better describe the final value and trend of the numerical results. It can also be seen that under the same conditions, the settlement of the diagonal piled foundation (37% replacement rate) is 0.81 times that of the rectangular piled foundation (50% replacement rate). However, the settlement of the plum-piled foundation (62% replacement rate), which has a higher replacement rate, is rather smaller than that of  
Figure 6: Normal velocity and normal displacement time history curves on the surface of the numerical model: (**a**) bottom surface, (**b**) side surface.  
the rectangular-piled foundation. This indicates that the piled foundation settlement is the most stabilized when the replacement rate reaches about 50%.  
Figure 7d shows the magnitude acceleration of the tunnel in the numerical model and the acceleration reaction spectra in Ground Motion Database - NGA west2. It can be seen that the acceleration response spectra used in the physical model describe the numerical results trend better. It is worth noting that the numerical results show hysteresis. This is due to the fact that the transfer of seismic loads in the numerical model is assumed to be a wave source problem subjected to damping that is uniform in action. The actual foundation damping is inhomogeneous, and hence, deviation and reduction in the monitoring results can occur.
###### Abstract  
Detecting defects in grounded flat steel is essential for ensuring the safety and reliability of transmission tower grounding devices. However, traditional inspection methods, such as physical excavation and verification, are costly and time-consuming. This paper proposes a corrosion defect detection method for flat steel transmission tower grounding devices based on electromagnetic ultrasonic SH waves. In addition, using commercial software, a three-dimensional finite element simulation model of grounded flat steel with simulated pitting corrosion defects is constructed. The specified displacements applied to multiple surface sources mimic the horizontal shear vibrations generated by the electromagnetic ultrasonic transducer on the surface of the grounded flat steel during actual inspection. A simulation was used to investigate the propagation and attenuation characteristics of shear-horizontal ultrasonic SH0 guided waves for simulated corrosion defects with various geometric configurations in grounded flat steel. The simulation investigated the propagation and attenuation characteristics of the SH0 wave in grounded flat steel and the detection of various defects for linear analysis of the results. The simulation results show that the attenuation of the electromagnetic ultrasonic guided wave is small, at only 0.0016 dB/mm, and the displacement amplitude of the echo signal decreases with the increase of the SH0 wave propagation distance. Increasing the depth and length of corrosion defects increases the echo signal amplitude. At the same time, the width of corrosion defects has little effect on the echo amplitude. Finally, a flat steel defect detection experiment was conducted, and the experimental results fit with the simulation to verify the accuracy of the simulation model. This detection method introduces a new idea for the on-site detection and quantitative identification of corrosion defects in grounded flat steel, which has significant reference value and can provide a more effective and economical method for ensuring the safety and dependability of transmission tower grounding devices.  
transmission tower; ground flat steel; electromagnetic ultrasonic; SH wave; attenuation characteristics; defect of corrosion +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### \(H\)qNMR  
For the \({}^{1}\)H qNMR analysis, a Varian MercuryMVX-300 spectrometer operating at 299.99 MHz with a Varian 5 mm ATB probe was utilized to produce \({}^{1}\)H NMR spectra with a standard pulse sequence s2 pul, with a pulse width of 10 \(\upmu\)s, a 10 s relaxation delay, and a spectral width of 7.2 kHz collecting 64 k complex FID points, over a 8.9 s acquisition time, averaging 64 scans. Data were processed with Mestrelab MestreNova v.14.1.1-24571 (Santiago, de Compostela, Spain). The temperature during the acquisition was 300K.  
NMR Materials: TMSP (added at 47.20 mg in 100 mL of D\({}_{2}\)O) was used as the chemical shift reference. D\({}_{2}\)O (99.9%D) was purchased from CortecNet Corp (Brooklyn, NY, USA). TMSP-2,2,3,3-D4 (D 98%) was purchased from Cambridge Isotopes Laboratories (Andover, MA, USA). No buffer was used in the deuterated solvent. Maleic acid internal standard was purchased from Sigma-Aldrich (TraceCERT Standard for quantitative NMR--Product Number 92816, Lot BCC6481, Exp Jan 2024, Certified Purity 99.94% +/ - 0.15%).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Distillery Number** & **Number of Samples** & **Location of Distillery** \\ \hline
0 & 21 & Taiwan \\ \hline
1 & 3 & China \\ \hline
2 & 1 & China \\ \hline
3 & 2 & China \\ \hline
4 & 1 & China \\ \hline
5 & 1 & USA \\ \hline
6 & 1 & China \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples correlated to distillery and country of origin.