esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Energy Dissipation Ratio \(\beta\)  
The energy dissipation ratio (\(\beta\)) is one of the most effective hysteretic parameters on structural response for self-centering systems. The energy dissipation ratio represents the energy absorption capacity of a self-centering system, which means dissipated energy increases for the increasing values of \(\beta\). In order to investigate the effect of the energy dissipation ratio, analyses have been carried out for \(\beta\) = 0, 0.25, 0.5, and 1.0. A value of \(\beta\) = 0 shows a bilinear elastic system, and \(\beta\) = 1.0 represents the bilinear elastoplastic hysteretic model. Figure 10 presents the variation in the inelastic displacement ratio with the considered values of the energy dissipation ratio for fixed-base (a) and interacting (b) cases. Results are presented for \(\alpha\) = 0.5. It can be seen from the figure that, as the energy dissipation ratio increases, ductility-based inelastic displacement ratio values tend to decrease. This is because the inelastic displacement of self-centering systems decreases for larger energy dissipation capacities. The amount of decrease is more dramatic when soil-structure interaction is considered. Moreover, as the ductility decreases, C\({}_{\mu}\) values reduce significantly for increasing values of \(\beta\) for interacting cases, showing that the maximum inelastic displacement decreases with ductility.  
Variations in residual displacements with the energy dissipation ratio for fixed-base and interacting self-centering systems for the considered ground motions are shown in Figure 11. As the energy dissipation ratio increases from 0 to 0.5, residual displacement values exhibit a small reduction tendency for both fixed-base and interacting systems. But when \(\beta\) = 1, which corresponds to a bilinear elastoplastic system, residual displacement values are extremely high when compared to self-centering systems because an elastoplastic system does not oscillate around the initial position for greater values of either T or T/T\({}_{\text{p}}\). Figure 12 shows the residual displacement ratio (D\({}_{\text{s}}\)) spectrum for varying values of \(\beta\). As \(\beta\) increases, the D\({}_{\text{s}}\) values increase as well. This is mostly due to the fact that for larger values of \(\beta\), the hysteresis curve of the system becomes fuller and the self-centering capacity becomes weaker.  
Figure 10: Variation in inelastic displacement ratios according to energy dissipation ratio; (**a**) fixed-base and (**b**) interacting cases.
# Exploring Gait Recognition in Wild Nighttime Scenes
## 3 GaitSAT Framework
### Spatial Feature Extraction  
The human skeleton can be represented as a graph \(G(V,E)\), where joints serve as vertices \(V=\{v_{i}|i=1,...,N\}\) containing \(N\) vertices, and the bones as edges \(E\). The edges can be represented by an adjacency matrix \(\mathbf{A}\in\mathbb{R}^{N\times N}\), where \(\mathbf{A}_{ij}=1\) if joint i and j are physically connected; otherwise, \(\mathbf{A}_{ij}=0\). A skeleton sequence is represented as a joint feature tensor \(\mathbf{S}\in\mathbb{R}^{T\times N\times C}\), where \(T\) is the number of frames in the sequence, and is the feature dimension.  
For each individual skeleton frame, our objective is to effectively capture the intrinsic relationships between different joints. Considering the advantages of self-attention mechanisms in capturing global information, we adopt a Self-Attention-based Graph Convolutional module (SA-GC) [31] to process 2D skeleton features, as illustrated in Figure 4.  
The Self-Attention Graph Convolutional (SA-GC) module utilizes the self-attention of joint features to infer their intrinsic relationships. The SA-GC derives a positive, bounded weight, termed as the self-attention map, to represent the strength of relationships between all joints. Specifically, we employ learnable matrices \(\mathbf{W}_{Q},\mathbf{W}_{K}\in\mathbb{R}^{D\times D^{\prime}}\) to linearly project the joint representation \(\mathbf{S}_{t}\) into \(D^{\prime}\) dimensional queries and keys to obtain the self-attention map, which can be represented as follows:  
\[\text{SA}(\mathbf{S}_{t})=\text{softmax}(\frac{\mathbf{S}_{t}\mathbf{W}_{K}( \mathbf{S}_{t}\mathbf{W}_{Q})^{T}}{\sqrt{D^{\prime}}}) \tag{1}\]  
Figure 4: Detailed structure of (**a**) Self-Attention Graph Convolution (SA-GC) and (**b**) Multi-Scale Temporal Convolutional Network (MS-TCN).  
where \(t\) is the time index.  
In addition to the self-attention map, we incorporate a learned weight \(\tilde{\textbf{W}}_{h}\), which is shared across time and instances. The shared weight have \(H\) heads, allowing the model to attend to different feature subspaces and capture the subtle connections between different joints.  
For each head \(1\leq h\leq H\), the shared weights are combined with the self-attention map to obtain the intrinsic correlations:  
\[\tilde{\textbf{W}}_{h}\odot SA_{h}(S_{t})\in\mathbb{R}^{\mathbb{T}\times N \times N} \tag{2}\]  
where \(\odot\) denotes the element-wise product. The overall update rule for joint representations can be expressed as  
\[\textbf{S}_{t}^{(l+1)}=\sigma\Big{(}\sum_{h=1}^{H}\big{(}\tilde{\textbf{W}}_{ h}\odot\text{SA}_{h}(\textbf{S}_{t}^{(l)})\big{)}\textbf{S}_{t}^{(l)}\textbf{W}_{h}^ {(l)}\Big{)} \tag{3}\]  
where \(\textbf{W}^{(l)}\in\mathbb{R}^{D^{(l)}\times D^{(l+1)}}\) is the learnable weight of the \(l\) layer, and \(\sigma(\bullet)\) represents the nonlinear activation function.
## 2 Operation Principles and Analysis Methods
### Frequency Selection and Cross-Polarization Suppression Introduced by Coupling Apertures
The simplified equivalent circuit is shown in Figure 7b and the input admittance is expressed as:  
\[Y_{even}=j\omega C_{t}+\frac{L_{v}}{j\omega M}+\left\{\left(\frac{2L_{g}}{j \omega M}\ +j\omega C_{u1}\right)^{-1}+\left[\frac{L_{t}}{j\omega M}+\frac{j\omega(C_{u2 }-C_{m})}{1-\omega^{2}L_{u}(C_{u2}-C_{m})}\right]^{-1}\right\}^{-1} \tag{4}\]  
where the \(\omega\) is the angular frequency and variable M is given by:  
\[M=2L_{t}L_{g}+2L_{v}L_{g}+L_{t}L_{v} \tag{5}\]  
Similarly, the input admittance of the odd-mode resonance, as shown in Figure 7c, obtained by inserting an equivalent electrical wall, is expressed as:  
\[Y_{odd}=j\omega C_{t}+\frac{1}{j\omega L_{t}}+\left\{\frac{1}{j\omega C_{u1}} +\left[\frac{1}{j\omega L_{v}}+\frac{j\omega(C_{u2}+C_{m})}{1-\omega^{2}L_{u} (C_{u2}+C_{m})}\right]^{-1}\right\}^{-1} \tag{6}\]  
Then the S-parameters of this structure can be derived as [42]:  
\[S_{11}=S_{22}=\frac{Y_{0}^{2}-Y_{even}Y_{odd}}{(Y_{0}+Y_{even})(Y_{0}+Y_{odd})} \tag{7}\]  
\[S_{21}=S_{12}=\frac{(Y_{even}-Y_{odd})Y_{0}}{(Y_{0}+Y_{even})(Y_{0}+Y_{odd})} \tag{8}\]  
where \(Y_{0}=1/377\)\(\Omega^{-1}\) is the characteristic admittance of the air-space port. Figure 8 displays the results of the HFSS full-wave simulation and equivalent circuit calculations. With the exception of a minor disparity, the results are in good agreement. Table 4 provides the values for the synthesized circuit elements. The slight difference may be the result of ignoring the equivalent circuit of the U-shaped resonators' coupling to the ground. Figure 8: The S-parameters obtained by equivalent circuit analysis and full-wave simulation.
## 4 Discussion  
We then investigated the filtration mechanism of P-Shellac FME (Figure 7). To explore the filtration process in the FEM, the PM filtration by P-Shellac nanofibers at different times (0 h, 0.5 h, 1 h, 2 h, 3 h, 6 h) was characterized by SEM. Before air filtration started, the surface and edge of the P-Shellac nanofibers were smooth (Figure 7a). PM particles were  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Air Filter** & \(E_{\text{PM2.5}}\) **(\%)** & \(E_{\text{PM10}}\) **(\%)** & \(\Delta P\) **(Pa)** & \(QF_{\text{PM2.5}}\) **(Pa\({}^{-1}\))** & \(QF_{\text{PM10}}\) **(Pa\({}^{-1}\))** \\ \hline PVP & 73 & 84.5 & 114 & 0.0115 & 0.016 \\ Shellac & 93.1 & 96.3 & 133 & 0.0201 & 0.0247 \\ P-Shellac & 95 & 98.1 & 101 & 0.029 & 0.0392 \\ PP & 55.2 & 72.7 & 76 & 0.01 & 0.0171 \\ (commercial) & \multicolumn{4}{l}{\(E\) = PM filtration efficiency; \(\Delta P\) = pressure drop; QF = quality factor.} \\ \end{tabular}
\end{table}
Table 1: Performance summary of air filters.  
Figure 6: (**a**) Filtration efficiency and (**b**) pressure drop of four nanofiber filters. In particular, PP is peeled off from a surgical mask.  
randomly captured and adhered tightly to the nanofiber surface when polluted air flowed through the P-Shellac nanofiber network (Figure 7b,c). While the air filtration was taking place, PM particles gradually aggregated (Figure 7d), wrapped (Figure 7e), and diffused uniformly (Figure 7f) on the surface of nanofibers. The nanofiber surface became smooth again, although the diameter increased, which maintained the interspace of nanofibers and caused them to still possess good air permeability after 6 h of air filtration (Figure 7g). We also studied the reasons for the induction of interception and deposition of PM. In view of the molecular structure of P-Shellac, there are large quantities of hydroxyl and ester groups in shellac and larger dipole moment (around 2.3 D) in PVP [37]. The generated strong dipole-dipole and induced-dipole intermolecular forces could obviously increase the adsorption ability of P-Shellac FME [38]. FTIR characterization of three FMEs after air filtration showed that each of the three -OH, C=O and C-O stretching vibrations became weak, indicating the existence of an interaction between PM and nanofiber membranes (Figure S7).  
The filtration efficiency and air permeability of P-Shellac FME has good long-term stability (Figure 7h). Under high PM concentrations, air filtration lasted continuous six hours, the filtration efficiency of PM\({}_{2.5}\) and PM\({}_{10}\) could still be maintained at above 90% and 95%, respectively. Moreover, the pressure drop presented negligible change, implying that the P-Shellac FME could keep good air permeability even under long-term and high-concentration PM filtration. Hence, P-Shellac FME showed superior comprehensive performance as an air filter.
# Comparison of Lab vs. Backcalculated Moduli of Virgin Aggregate and Recycled Aggregate Base Layers
## 2 Methodology
### Test Sections  
Four test sections (Cells 185, 186, 188, and 189) were constructed on a two-lane Minnesota Road Research Project Low-Volume Road (MnROAD LVR) testing facility. Each section had a 3.5-inch asphalt layer with a 12-inch base layer. A 3.5-inch sand subbase was also placed between each section's base and subgrade layers. Another type of sand was used as a subgrade material under the coarse and fine RCA base layers, while clay loam was used under the limestone and RCA + RAP base layers. The cross-sections of these test sections are shown in Figure 1. The properties of the materials used in these test sections are shown in Table 1[13]. It is also noted that a 36.3-tonne truck was used for traffic simulation on the inside lanes of these test sections.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Gravel 1** & **Sand 2** & **Fine 3 (\%)** & **C\({}_{\textbf{u}}\) 4** & **C\({}_{\textbf{c}}\) 5** & **LL 6** & **USC 7** & **MDU 8** & **OMC 9** & **App.10** \\ \hline Coarse RCA & 61.7 & 34.9 & 3.4 & 34.49 & 1.75 & N/A 11 & GW & 126.10 & 9.5 & 6.97 \\ \hline Fine RCA & 38.3 & 54.6 & 7.1 & 33.93 & 1.12 & 32.7 & SW-SM & 119.24 & 11.1 & 8.65 \\ \hline Limestone & 52.3 & 32.6 & 15.1 & 211.3 & 1.91 & 17.9 & GM & 140.46 & 6.3 & 1.72 \\ \hline RCA + RAP & 41 & 50.4 & 8.6 & 49.41 & 0.98 & 27.4 & SP-SM & 123.61 & 10.0 & 4.34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of materials used in test sections.  
Figure 1: Cross-sections of test sections. (1 in = 25.4 mm).
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 4 Transport Mechanisms of BFRs
### Soil Sorption and Fate Processes  
In soil, BFRs can sorb to soil particles and organic matter, which can affect their mobility and transport. The sorption of BFRs to soil can lead to their accumulation in soil matrices, where they can persist for long periods [55]. The sorption and mobility of BFRs in soil is influenced by factors such as soil properties including soil organic matter content (SOM), pH, ionic composition, and clay content as well as specific BFR properties [49; 99; 102].  
[PERSON] et al. [99] investigated the sorption behavior of TBBPA in two soils with different characteristics through batch equilibrium experiments. They also studied the impacts of ionic strength and pH on the sorption process. The study revealed that fast sorption was more significant than slow sorption in the process and higher for silt loam soil compared to loamy clay soils. Nonlinear sorption isotherms were observed, and the Freundlich model effectively described the sorption behavior of TBBPA. SOM played a predominant role in the sorption process, contributing approximately 90% to the total sorption. The desorption hysteresis of TBBPA was observed in single-cycle sorption and desorption experiments, and results showed that the sorbed amount of TBBPA decreased with increasing solution pH and increased with increasing ionic strength, with the effects of ionic strength primarily attributed to changes in solution pH. In another study, [PERSON] and [PERSON] [103] conducted a 3-year outdoor mesocosom study to assess the environmental persistence of BFRs in biosolids-amended soil. They reported a total polybrominated diphenyl ether (PBDE) concentration in biosolids composites of 9400 \(\pm\) 960 \(\upmu\)g/kg dry weight, with decABLE constituting 57%, followed by nona- and penta-BDE at 18% and 13%, respectively. In outdoor soil mesocosoms, over 99% of the initial mass of BFRs in the biosolids/soil mixtures (1:2) persisted throughout the three-year monitoring period.
## 3 Model Establishment and Support Analysis  
### Establishment of Model and Boundary Conditions  
From the relevant geological conditions of the coal seam and the lithology of the borehole histogram, FLAC3D 5.01 numerical simulation software was used to establish a simulation model of the support effect according to the geological characteristics of the 108 working face in the Jinjitan coal mine, and the corresponding physical parameters were assigned to each rock stratum, with the acceleration of gravity is taken as 9.8 m/s, the model material adopting the Mohr-Coulomb intrinsic model, and the physical and mechanical parameters of each rock stratum were as shown in Table 1.  
Since the inclination angle of the coal seam is less than 1\({}^{\circ}\), the inclination angle of the model is set to horizontal for the convenience of calculation, and the size with which the model is (length \(\times\) height \(\times\) width) 400 m \(\times\) 200 m \(\times\) 300 m, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Lithology** & **Densities/kg/m\({}^{3}\)** & \begin{tabular}{c} **Elastic** \\ **Modulus/GPa** \\ \end{tabular} & \begin{tabular}{c} **Tensile** \\ **Strength/MPa** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ **/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Angle of Internal Friction** \\ \(f^{\circ}\) \\ \end{tabular} \\ \hline Medium sandstone & 2653 & 12.5 & 4.6 & 2.5 & 31 \\ Mudstone & 2589 & 8.67 & 2.55 & 1.7 & 25 \\ Coarse Sandstone & 2813 & 12.35 & 6.5 & 2.7 & 20 \\ Silstone & 2680 & 16.5 & 6.97 & 1.3 & 21 \\ Coal seam & 1390 & 1.68 & 1.1 & 1.2 & 19 \\ Thickness & 2531 & 14.6 & 6.58 & 2.0 & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical properties of rocks.  
Figure 4: Model schematic and boundaries length.
## 4 Experimental Verification and Analysis
### Analysis of Experimental Results  
The robot grasping process is the inverse process of the camera imaging process, that is, the transformation process from the image coordinate system to the world coordinate system. By calibrating the calibration plates at different heights, we can obtain the rigid transformation matrix \(\overset{R}{\underset{C}{\text{C}}{M}}\) parameters of each height, which can realize the transformation from the image coordinate system to the robot coordinate system and realize the target grasping of the robot. However, in this conversion, an important parameter--the height of the workpiece--is lost. In this way, there will be the problem that the same image coordinate points may represent different world coordinate points, resulting in the robot's \"inaccurate grasp.\" In addition, due to the wide variety of products and different imaging dimensions of products at different heights, the height position of each product should be calibrated once. Secondly, if there is no corresponding calibration for the different heights of a new product, we can only use the transformation matrix of the adjacent height calibration for calculation. Although many calibration experiments are carried out in a small step, there will still be errors between the transformation matrix generated by the discrete calibration height and the transformation matrix under the actual product height. In order to solve this problem, we know from the rigid transformation model established in Section 2 that the transformation matrix between robot coordinate system and image coordinate system should also have a certain relationship with the sampling height. Through this linear relationship, we can solve this kind of problem by fitting the rigid change matrix at the partial height obtained in Section 4.2 of the experiment to the rigid change matrix at any height. According to Table 4, we connect the relationship between the parameter values of \(\overset{R}{\underset{C}{\text{C}}{M}}\) and height with broken lines in the same three-dimensional coordinates, as shown in Figure 10.  
It can be seen from Figure 10 that \(\overset{R}{\underset{C}{\text{C}}{M}}\) matrix parameters show an obvious linear relationship with the broken line diagram of \(H\). \(Tx\) and \(Ty\) reflect the change in the translation matrix. A11, A12, A13, and A14 are all near the \(z=0\) plane, which is also fully consistent with the rotation matrix parameters proposed by us. According to Table 4, we further analyze the relationship between the calibration height and various parameters of \(\overset{R}{\underset{C}{\text{C}}{M}}\). The results are shown in Figure 11.  
As can be seen from Figure 11, except for A11 and A22, except for the two parameters, there are some points that are under-fitted. Considering A11 and A22, the two parameters are relatively small, and the approximate linear processing will not have a great impact on the results. In practical application, the points with high dispersion can be eliminated and refitted.
## 4 Performance Analysis of Different Methods
### SNR Gains of Different Methods for Highly Maneuvering Targets  
The total SNR can be obtained by summing three SNR components:  
\[SNR_{total} = SNR_{\text{intra -}frame}+SNR_{\text{inter -}frame}\] \[+ SNR_{multi-fragments}\]  
where \(SNR_{\text{intra -}frame}\), \(SNR_{\text{inter -}frame}\), and \(SNR_{multi-fragments}\) represent the SNRs for intra-frame integration, inter-frame integration, and multi-subfragment integration, respectively.  
In the aforementioned methods, intra-frame and multi-frame integration involve coherent integration, while multi-subfragment integration is noncoherent integration. We assume that the SNR gain of \(N\) point coherent integration is \(N\) times that of the single-point gain, and the SNR gain of \(N\) point noncoherent integration is \(\sqrt{N}\) times that of the  
Figure 4: Modified GRT integration path.  
Figure 3: Sketch map of the flowchart for inter-frame integration.  
single-point gain. Assuming that no ARU and ADU effects occur in a single subfragment, the SNR gain of two-step CI in one subfragment is  
\[\begin{split} G_{SNR,CI}&=G_{SNR,CI,\text{intra}- frame}+G_{SNR,CI,\text{inter}-frame}\\ &=N_{s}+N_{c}\frac{N_{long}}{N_{sub}}\end{split} \tag{27}\]  
where \(N_{c}\) is the number of discrete points in one frame, \(N_{sy}\) is the number of discrete points in one symbol, \(T_{s}\) is the length of one subfragment, \(N_{long}\) is the total number of frames in the MHI, and \(N_{sub}\) is the number of frames in one subfragment.  
Comparatively, the SNR gain for each subfragment's noncoherent integration algorithm is  
\[G_{SNR,NI,Multi-fragments}=\sqrt{N_{long}} \tag{28}\]  
Therefore, the SNR gain of MHI and HI is  
\[\begin{split} G_{MHI}&=G_{SNR,CI}+G_{SNR,NI}\\ &=N_{s}+N_{c}N_{sub}+\sqrt{N_{long}}\end{split} \tag{29}\]  
\[G_{HI}=N_{s}+N_{c}+\sqrt{N_{long}} \tag{30}\]
# A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations
## 3 Proposal and Methodology
### The Emplacement Optimizer Agent
#### 3.2.1 Problem Description  
The problem consists of the location of a set of charging stations starting from a set of PoIs of the city under study. In this way, \(P=\{p_{1},\ldots,p_{n}\}\) is a set of possible locations for charging stations (PoIs), and \(S=\{s_{1},\ldots,s_{n}\};0<s_{i}\leq\textit{max\_charges\_per\_poi}\) is the set of charging stations that are going to be finally deployed in the city, with values ranging from 0 to a constant value \(\textit{max\_charges\_per\_poi}\) of charging stations per PoI. A PoI \(p_{i}\) is characterized by a set of attributes that define it, \(p_{i}=\{a_{1},a_{2},\ldots,a_{n}\}\):  
* Population in the area around \(p_{i}\)
* Average traffic in the area
* Average time spent by citizens in public places in the area
* Geo-located social networking activity in the area
* Cost depending on the area covered by the stations
* Cost per charging station  
The goal of the application is to find a configuration of charging stations at some of the pre-defined PoIs. A configuration is composed of a set of points \(p_{i}\) where one or more stations \(s_{i}\) are located, \(C_{i}=\{\langle p_{1},s_{i}\rangle,\langle p_{2},s_{i}\rangle,\ldots,\langle p _{k},s_{n}\rangle\}\). Each configuration \(C\) has a value associated which has been assigned by a utility function \(V(C_{i}):2^{E}\to R\). This function \(V\) is a lineal combination of a set of factors that are intended to be maximized or minimized. The final goal is to determine which is the optimal configuration (or a solution close to the optimal one) for the placement of the EV charging stations \(\textit{argmax}\ V(C_{i})\). To deal with this goal we propose the use of a genetic algorithm.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
## 3 Results and Discussion
### EDX Analysis of the Biogenic IONPs  
The elemental composition of IONPs was determined via the utilization of EDX analysis. The biosynthesized IONPs consisted of four elements: iron, oxygen, carbon, and silicon. These elements were present in the IONPs with mass percentages of 53.78%, 21.51%, 16.56%, and 8.15%, respectively (Figure 3). The peaks corresponding to iron were observed at signals of 0.8, 6.4, and 7.0 keV for Fe La, Fe Ka, and Fe Kb, respectively. Conversely, the signals detected at signals of 0.3, 0.5, and 1.7 keV were attributed to carbon (C), oxygen (O), and silicon (Si), respectively. The observed signal of silicon could be attributed to the presence of capping molecules derived from the extract of _L. nobilis_ leaves [46]. In the present context, the observed percentage of iron was found to be greater than that reported in a prior study. The previous investigation revealed that the biogenic IONPs produced using tea pruning waste exhibited a weight percentage of 51.79% [47]. This finding suggests that the biosynthesis method utilizing _L. nobilis_ extract is highly effective. Figure 4 shows a SEM graph of the IONPs formulated using _L. nobilis_ leaf extract.  
Figure 1: Green synthesis of IONPs utilizing aqueous extract of _L. nobilis._ **(A)**: water leaf extract of _L. nobilis_, **(B)**: ferric nitrate solution, **(C)**: the biogenic IONPs.  
Figure 3: EDX analysis of the biogenic IONPs.  
Figure 2: (**A**): UV-Vis spectrum of \(L\). _nobilis_ leaf extract; (**B**): UV-Vis spectrum of the biogenic IONPs).