esa-sceva/satcom-chunk-collection huggingface dataset:
# The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms
## 4 Regarding 2-Dimensional Attainment Surfaces
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Problem** & **Intersections** & \(\boldsymbol{\mathcal{V}}\)**vs. \(\boldsymbol{\mathcal{O}}\)** & \(\boldsymbol{\mathcal{V}}\)**vs. \(\boldsymbol{\mathcal{S}}\)** & \(\boldsymbol{\mathcal{S}}\)**vs. \(\boldsymbol{\mathcal{O}}\)** \\ \hline \multirow{4}{*}{ZDT1} & Rotational & \((\mathbf{14.9},\mathbf{66.4})\) & \((\mathbf{6.1},\mathbf{80.6})\) & \((\mathbf{79.4},\mathbf{1.1})\) \\  & Inward & \((25.6,55.5)\) & \((13.8,65.4)\) & \((70.2,3.9)\) \\  & Outward & \((19.8,62.7)\) & \((8.7,75.6)\) & \((77.3,1.6)\) \\  & ASSIL & \((22.5,60.1)\) & \((11.4,72.2)\) & \((72.5,4.3)\) \\ \hline \multirow{4}{*}{ZDT2} & Rotational & \((8.4,64.3)\) & \((2.9,73.6)\) & \((58.9,3.9)\) \\  & Inward & \((8.0,63.4)\) & \((2.0,74.3)\) & \((56.3,0.9)\) \\  & Outward & \((10.4,62.7)\) & \((4.2,70.5)\) & \((60.1,5.7)\) \\  & ASSIL & \((9.0,63.9)\) & \((3.4,72.6)\) & \((60.0,3.8)\) \\ \hline \multirow{4}{*}{ZDT3} & Rotational & \((13.4,77.9)\) & \((3.9,90.8)\) & \((\mathbf{81.4},7.3)\) \\  & Inward & \((7.1,61.6)\) & \((2.1,83.2)\) & \((72.4,10.5)\) \\  & Outward & \((16.4,73.2)\) & \((4.9,87.7)\) & \((78.4,8.7)\) \\  & ASSIL & \((12.5,72.9)\) & \((3.2,89.3)\) & \((74.8,9.5)\) \\ \hline \multirow{4}{*}{ZDT4} & Rotational & \((0.0,99.9)\) & \((0.0,99.9)\) & \((\mathbf{99.9},0.0)\) \\  & Inward & \((0.0,87.9)\) & \((0.0,88.1)\) & \((\mathbf{80.9},0.0)\) \\  & Outward & \((0.0,100.0)\) & \((0.0,100.0)\) & \((\mathbf{100.0},0.0)\) \\  & ASSIL & \((0.0,100.0)\) & \((0.0,97.7)\) & \((93.7,0.0)\) \\ \hline \multirow{4}{*}{ZDT6} & Rotational & \((\mathbf{40.1},\mathbf{13.3})\) & \((15.2,\mathbf{25.9})\) & \((\mathbf{58.9},11.1)\) \\  & Inward & \((\mathbf{64.6},2.8)\) & \((15.6,35.3)\) & \((\mathbf{73.3},0.7)\) \\ \cline{1-1}  & Outward & \((\mathbf{50.6},7.8)\) & \((6.1,52.9)\) & \((\mathbf{64.5},5.0)\) \\ \cline{1-1}  & ASSIL & \((59.4,4.3)\) & \((10.9,41.8)\) & \((52.3,14.1)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Intersection line comparison between VEPSO (\(\mathcal{V}\)), SMPSO (\(\mathcal{S}\)), and OMOPSO (\(\mathcal{O}\)); blue indicates performance of 5% worse than the competing algorithm, and red indicates performance of 5% better than the competing algorithm.
# Exploiting the Capture Effect to Enhance RACH Performance in Cellular-Based M2M Communications
## 4 Analysis Model
### Average Energy Consumption
#### 4.4.2 Average Energy Consumption  
\(\delta_{F}\) and \(\delta_{S}\) denote the energy consumption for single failed and successful RA event, respectively. Note that \(\delta_{F}\) and \(\delta_{S}\) are random variables because \(P_{3}\) is the random variable that is determined by the trial number and the path-loss of device as in Equations (5) and (7). Therefore, \(P_{3}\) can be expressed as \(P_{3}(t,x)[mW]=P(r)P(x)P_{c}\), where \(P(r)\) is the ramping component; \(r=min(t,K_{max}(x))\), \(P(x)\) is the path-loss component, and \(P_{c}\) is the target power, which is a constant value. From the previously-described power consumption model, \(\delta_{F}\) and \(\delta_{S}\) can be simply expressed as follows:  
\[\delta_{F} =\theta_{1}\mathcal{P}_{3}(t,x)+\theta_{2}, \tag{26}\] \[\delta_{S} =\theta_{3}\mathcal{P}_{3}(t,x)+\theta_{4},\]  
where \(\theta_{1},\theta_{2},\theta_{3}\), and \(\theta_{4}\) are constants defined by the consumption model. In the previous section, because the analysis was performed based on \(Z_{u}=k\) rather than \(X_{u}=x\), Equation (26) can be reformulated as follows:  
\[\delta_{F,k}(t) =\theta_{1}\bar{P}_{3,k}(t)+\theta_{2}, \tag{27}\] \[\delta_{S,k}(t) =\theta_{3}\bar{P}_{3,k}(t)+\theta_{4},\]  
where \(\bar{\delta}_{F,k}(t)\) and \(\bar{\delta}_{S,k}(t)\) denote the average energy consumption of device, which is located within _zone_\(k\) with the trial number \(t\), having single failed and successful RA events, respectively. Here, \(\bar{P}_{3,k}(t)\) can be calculated using the average path-loss of the devices within _zone_\(k\). Therefore, \(\bar{P}_{3,k}(t)=P(r)\bar{P}(k)P_{c}\), where \(P(r)\) is the ramping component where \(r=min(t,k)\), \(\bar{P}(k)\) is the average path-loss of devices in _zone_\(k\).  
The energy consumption of a device located within _zone_\(k\) that succeeds in acquiring a connection after a number \(t\) of RA trials is denoted by \(w_{S}(t,k)\). Moreover, \(w_{F}(k)\) denotes the energy consumption of a device located within _zone_\(k\) that fails to acquire a connection. Then, \(w_{S}(t,k)\) and \(w_{F}(k)\) can be derived as follows:  
\[w_{S}(t,k) =\sum_{t=1}^{\kappa_{t,k}-1}\bar{\delta}_{F,k}(t)+(t-K_{t,k}) \bar{\delta}_{F,k}(K_{t,k})+\bar{\delta}_{S,k}(K_{t,k}), \tag{28}\] \[w_{F}(k) =\sum_{t=1}^{K_{t,k}}\bar{\delta}_{F,k}(t)+(N_{max}-K_{t,k}) \bar{\delta}_{F,k}(K_{t,k}),\]  
where \(K_{t,k}=min(t,k)\), which denotes the maximum ramping number of the device with \(T_{u}=t\) and \(Z_{u}=k\) as defined in Equation (7). Then, the average energy consumption of a device located within _zone_\(k\) when the total arrival is \(N\), i.e., \(E[W|N,Z_{u}=k]\), is calculated using Equations (23) and (24) as follows:  
\[E[W|N,Z_{u}=k]=\sum_{t=1}^{N_{max}}w_{S}(t,k)\mathbb{P}_{CS,T=t|n,Z=k}+w_{F}( k)\mathbb{P}_{CF|N,Z=k}. \tag{29}\]  
Finally, \(E[W|\mathcal{N}=N]\) is  
\[E[W|N]=\sum_{k=1}^{N_{max}}E[W|N,Z_{u}=k]\mathbb{P}(Z_{u}=k). \tag{30}\]
# Optimization of Multilayer Absorbers Using the Bald Eagle Optimization Algorithm
## 3 Results
### Normal Incidence
#### 3.1.2 Design-2  
Design-2 was optimized for normal incidence for the 2-8 GHz FR. The maximum total thickness of the MMA was set to 2.55 mm. The MMA with the required total thickness (2.55 mm) and a maximum RC of less than \(-20\) dB (\(-20.1799\) dB) was obtained. Compared with [10], the maximum RC value obtained is also lower than the designs obtained by the CFO (\(-20.6994\) dB) and GSA (\(-18.264\) dB) (Table 3). The comparison of the RC vs. frequency is illustrated in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{6}{c}{**2-8 GHz**} \\ \cline{2-7}  & \multicolumn{2}{c}{**BESOA**} & \multicolumn{2}{c}{**CFO**} & \multicolumn{2}{c}{**GSA**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Design-2**} & \multicolumn{2}{c}{[10]} & \multicolumn{2}{c}{[10]} \\ \cline{2-7}  & \multicolumn{2}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**Material**} & \multicolumn{1}{c}{**d**} \\ \hline
1 & 16 & 0.57236 & 16 & 0.561 & 16 & 0.575 \\ \hline
2 & 6 & 0.83337 & 7 & 0.850 & 1 & 0.574 \\ \hline
3 & 2 & 0.44296 & 2 & 0.393 & 2 & 0.345 \\ \hline
4 & 15 & 0.16748 & 13 & 0.158 & 9 & 0.355 \\ \hline
5 & 15 & 0.53383 & 15 & 0.605 & 9 & 0.699 \\ \hline
**Max. RC [dB]** & \(-20.7199\) & & \(-20.6994\) & & \(-18.264\) \\ \hline
**Total thick. [mm]** & & 2.55 & & 2.569 & & 2.550 \\ \hline \end{tabular}
\end{table}
Table 3: Parameters of 5-layer MAs with a total thickness of 2.55 mm for normal incidence for 2–8 GHz FR.  
Figure 5: RC of 5-layer absorber optimized for 2–8 GHz FR for normal incidence (Design-1).
## 4 Experiment and Analysis of Experimental Results
### Comparison Analysis between Simulated and Measured Vibration Signals under Healthy Condition  
Figure 15 illustrates the waveform and frequency spectrum of the simulated and measured vibration signals of the planetary gearbox when all the gears are perfect. By using Equation (25), the waveform error between the simulated signal and the measured signal is 1.1361%. Comparing Figure 15c,d, the gear meshing frequencies \(f_{m}\), \(2f_{m}\), and \(3f_{m}\) can be observed whether in the simulated signal or the measured signal. Moreover, major sideband components are annotated in the frequency spectrum. These sidebands are located on both sides of the meshing frequency, which are consistent with the results of the theoretical analysis in Section 3.1.  
\[e=(y(t)-\widetilde{y}(t))/y(t), \tag{25}\]  
where \(e\) is the relative error of waveform distortion, \(y(t)\) is the vibration signal obtained from simulation, and \(\widetilde{y}(t)\) is the vibration signal obtained from actual measurement.  
The error between the theoretical, simulated, and measured values is calculated and listed in Table 4. As shown in Table 4, the meshing frequency obtained from the proposed model is the same as the theoretical value, and the error with the measured meshing frequency is only 0.025%. This paper refers to the model verification methods in the literature [24; 25], which use the time-frequency domain comparison method to verify the proposed model. Through the simulation and experimental signal analysis, we believe that the proposed phenomenological model is correct.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Sun Gear** & **Plant Gear** & **Ring Gear** \\ \hline Tooth number & 18 & 27 & 72 \\ Module(mm) & & 2 & \\ Width of teeth (mm) & & 20 & \\ Addendum coefficient & 1 & \\ Pressure angle (\({}^{\circ}\)) & & 20 & \\ Young’s modulus (Pa) & \(2.06\times 10^{7}\) & \\ Poisson’s ratio & 0.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Assembled parameters of the planetary gearbox.  
Figure 15: _Cont._
## 4 Conclusions  
In this study, XPS plates were adopted to improve the vibration isolation performance of the sustainable subgrade material (silty clay modified by fly ash and crumb rubber). The vibration isolation performance of the composite subgrade structure was evaluated by the indoor impact test. The conclusions can be summarized as follows:  
1. The vibration amplitude of the modified soil subgrade was greater than that of the silty clay. The vibration amplitude of the modified soil subgrade can be significantly reduced by setting XPS plates.
2. Considering the effect and cost of vibration isolation, 5 cm is considered to be the optimum thickness of XPS plates in road engineering.
3. XPS plates have excellent vibration isolation stability under different vibration intensities. Laboratory test results showed that the vibration isolation coefficient of the subgrade model with XPS plates in the Z-direction was less than 0.2.
4. The vibration isolation performance of the composite subgrade structure had outstanding anti-freeze-thaw stability. The vibration isolation coefficient in the Z-direction remained below  
Figure 10: The isolation coefficient of the composite subgrade structure after freeze–thaw cycles.  
0.2 after several freeze-thaw cycles. This shows that the composite subgrade structure can be applied to road engineering in seasonal frozen regions.  
In summary, the vibration isolation effect of the composite subgrade structure was proven to be effective. For the modified soil (silty clay modified by fly ash and crumb rubber) subgrade, it will possess more sustainable vibration isolation performance by setting XPS plates. The indoor test results can provide a reference for the design of a composite subgrade structure in practical application.  
Conceptualization, L.H. and H.W.; Methodology, H.W.; Software, L.H.; Validation, L.H., H.W. and F.W.; Formal analysis, L.H.; Resources, F.W.; Data curation, L.H.; Writing--original draft preparation, L.H.; Writing--review and editing, L.H. and F.W.; Project administration, H.W.; Funding acquisition, F.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFB1600200; Science and Technology Project of Jilin Province Transportation Department, grant number 2017 ZDGC6.  
The authors express their appreciation for the financial support of the National Key R&D Program of China (grant number 2018 YFB1600200); Science and Technology Project of Jilin Province Transportation Department (grant number 2017 ZDGC6). Our thanks go to the editors and reviewers for their efficient work.  
The authors declare no conflict of interest.
# Non-Destructive Study of the Microstructural Effects of Sodium and Magnesium Sulphate Attack on
## 2 Materials and Methods  
### Sample Preparation and Curing  
The tests were performed in mortars with different percentages of silica fume. The cement used was a high early strength type, designated CEM I 42.5 R according to the Spanish and European standard UNE-EN 197-1 [31]. The silica fume used was undensified, with a cumulative volume of particles under 1 \(\upmu\)m of 90.78% and an average particle diameter of 0.67 \(\upmu\)m. The chemical compositions of the cement and silica fume are shown in Table 1. For all the mortars, the water to cement ratio was 0.5 and the aggregate to cement ratio was 3:1. Fine aggregate was used according to the standard UNE-EN 196-1 [32]. Three types of mortars were prepared. One type consisted of mortars without added silica fume (0% silica fume mortars hereafter), which were taken as a reference for behaviour. For the other mortars, 5% and 10% of cement was replaced with silica fume--referred to as 5% silica fume and 10% silica fume mortars (respectively) from now on.  
Different types of specimens were prepared for the different test methods. On one hand, two kinds of cylindrical specimens were prepared. These were cast in moulds of either 10 cm diameter and 15 cm height, or 7.5 cm diameter and 30 cm height, respectively. On the other hand, 4 \(\times\) 4 \(\times\) 16 cm prismatic specimens were also prepared [32]. All the samples were cured for 7 days in a temperature and humidity controlled chamber at 20 \({}^{\circ}\)C and 95% relative humidity (RH), and following this period were de-moulded and introduced into the corresponding sulphate solution. Before being exposed to the aggressive media, the 15-cm height cylindrical samples were cut to obtain approximately 2 cm thick discs, and the prismatic specimens were also cut in three equal parts with dimensions of 4 \(\times\) 4 \(\times\) 5.3 cm. The tests were performed at different ages, up to 90 days of exposure to the sulphate solutions.
## 3 Results and Discussion
### Effect of Varying Crude Oil Grades on Slug Characteristics
#### 3.2.2 Slug Liquid Length  
Slug liquid length is one of the main characteristics of slug flow, and an incorrect prediction may cause flooding in the downstream equipment and thereby result in production deferment. Figure 12 illustrates the effect of changing the oil grades on the slug liquid length. For all investigated cases, increasing the crude oil density decreases the slug length.  
Moreover, decreasing the liquid velocity at a constant gas velocity causes an increase in slug length. By contrast, up to 30% decrease in slug length is recorded when the gas velocity decreases at liquid velocity of 0.93 m/s as compared with Case 1.  
Figure 11: Effect of varying liquid superficial velocities on slug initiation position (’D’ represents pipe diameter).  
Figure 12: Effect of varying crude oil grades on slug liquid length.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 3 Simulation Environment and Settings  
OMNeT++ simulation software was used for network modeling and simulation purposes. It is an extensible, modular, component-based C++ simulation library and framework for building network models for performance study [28]. It was chosen for this study due to its modular architecture, extensibility, and robust support for heterogeneous network modeling. Unlike NS-3 and QualNet, OMNeT++ provides a graphical runtime environment and the INET framework, which simplifies the visualization and simulation of complex MANET scenarios. These features make this simulator particularly suitable for evaluating the performance of routing protocols in heterogeneous networks, where nodes with varying capabilities and mobility patterns must be accurately represented. OMNeT++ provides a component architecture for its models. These components are written in the C++ programming language, where the modules' relationships and communication links are then stored as plain-text network description (NED) files. These models can be reused as often as you like since the software is open source for the community of users. Simulations of these network models can be run interactively with the graphical environment or with command-line applications. This application supports GUI extensively, and because of its architecture, the simulation models can be easily embedded into applications. In addition to variations in the mobility and transmission rates, we now incorporate energy consumption and hardware resource constraints into the heterogeneous node model. PDAs, laptops, and mobile phones are assigned different energy capacities, reflecting their real-world power limitations. Similarly, hardware constraints such as the CPU processing power and memory availability are considered to assess their impact on the routing efficiency. These refinements allow for a more realistic evaluation of how resource availability affects MANET routing performance, particularly in scenarios requiring prolonged network operation.
# Device Management and Data Transport in IoT Networks Based on Visible Light Communication
## 3 Proposed VLC-Based IoT Scheme
### Device Management and Data Transport Operations
#### 3.3.2 Device Monitoring  
Device monitoring is used for PS to keep track of the status of the AA, VT, and VR devices in the network. Figure 9 shows the device monitoring operations, in which the VT and VR devices periodically report the status to AA, and AA sends aggregate status information to PS.  
As shown in the figure, AA always keep the AA-VT-VR mapping table for device monitoring. VT and VR may use a timer for periodic reporting (e.g., every 10 s). Such a report message contains the identifiers of the concerned VT and VR. On reception of the reports from VT and VR, AA will update its AA-VT-VR mapping table. Then, AA also reports the aggregate status information to PS periodically (e.g., every 30 s). It is noted that an appropriate timer value may be configured, depending on the network deployment and the service features to be considered.  
Figure 8: VLC frame format.  
Figure 7: VR initialization.  
Figure 9: Device monitoring.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 5 Error Performance
### Performance Comparison Using Each Quantum State
Otherwise, the latter system can maintain its performance superiority over the former within the range of the small average number of photons. In addition, as shown in Figure 4f, the AQC system using the quasi-Bell state demonstrates superior performance to the same system using the TSVS when \(\langle n\rangle>10\). Figure 4g,h shows that the AQC system using the quasi-Bell state has an error probability that is the same as or lower than that of the system using the TSVS. Consideration of these figures together with Figure 4b,d shows that the performance of the AQC system using the quasi-Bell state exceeds that of the system using the TSVS with smaller average number of photons when \(R_{0}\) is small or when \(R_{1}-R_{0}\) is large. Otherwise, the former system requires a larger average number of photons to surpass the performance of the latter. Figure 4: Error probabilities with respect to the average number of photons \(\langle n\rangle\) when using the coherent state (denoted by Coherent), the maximum quasi-Bell state (denoted by Max Quasi-Bell), the non-maximum quasi-Bell state (denoted by NonMax Quasi-Bell), and the two-mode squeezed vacuum state (denoted by TSVS) with reflectivities \(\{R_{0},R_{1}\}=\{0.5,1\},\{0.3,0.6\}\), \(\{0.2,0.8\}\), \(\{0,0.3\}\). \(\langle n\rangle\) ranges up to either 5 or 100. Universal LB, TSVS UB, and TSVS LB present the universal lower bound on the error probability, the upper bound on the error probability for the TSVS case, and the lower bound on the error probability for the TSVS case, respectively.
## 3 Results and Discussion
### Effect of Adsorption Conditions  
#### 3.3.1 Effect of Contact Time  
The adsorption of MB on CPP200 at different time intervals (0-150 min) was studied (Figure 7A). A two-stage adsorption process could be observed. The initial adsorption corresponded to the availability of the active adsorption sites on the CPP200 surface which became rapidly saturated [42]; the adsorption capacity reached 44.40 mg/g within 30 min. The second stage of establishing the equilibrium of adsorption was slowly achieved over 90 min, with a maximum adsorption capacity of 54.26 mg/g. Therefore, the time for adsorption to reach equilibrium is 90 min.
## 2 Materials and Methods  
### Synthesis Procedures of the Ceria Samples  
The CeO\({}_{2}\) samples studied were prepared from cerium nitrate through the following methods.  
Ce-pH13 and Ce-pH7 cerium oxides were prepared using a hydrothermal method, using cerium nitrate hexahydrate Ce(NO\({}_{3}\))\({}_{3}\).6H\({}_{2}\)O (from MERCK, Madrid, Spain) as a precursor. For Ce-pH13, 30 mmol of Ce(NO\({}_{3}\))\({}_{3}\).6H\({}_{2}\)O was added to 0.4 M sodium hydroxide to obtain a solution with pH = 13 stirring vigorously for 30 min. Concerning Ce-pH7 sample, a stoichiometric amount of Ce(NO\({}_{3}\))\({}_{3}\).6H\({}_{2}\)O was dissolved in de-ionized water, then ammonia NH\({}_{4}\)OH 32% and nitric acid were added until the pH was adjusted to 7, and the solution was stirred for 1 h. The suspensions were further introduced in autoclaves and heated up to 180 \({}^{\circ}\)C for 24 h. Right after, the autoclaves were cooled to room temperature. The mixtures were filtered using a polymeric membrane (polyethersulphone membranes from Sartorius, 200 nm in pore size) and washed several times with de-ionized water and then with alcohol. The filters were dried at 70 \({}^{\circ}\)C for 12 h in an oven. These samples were labeled as Ce-pH13 and pH-7, referring to the basic or neutral media employed.  
Ce-ox was synthesized by a chemical method using oxalic acid. First, a stirred ethanol solution of cerium nitrate (Ce(NO\({}_{3}\))\({}_{3}\)\(\cdot\)6H\({}_{2}\)O from Sigma Aldrich, Madrid, Spain) was evaporated at 60 \({}^{\circ}\)C, and a 1 M solution of oxalic acid was added until precipitation. The solids were dried overnight at 120 \({}^{\circ}\)C, and finally calcined in static air for 2 h at 500 \({}^{\circ}\)C as previously detailed [11]. This sample was labeled as Ce-ox, underlining the oxalic acid in its synthesis.  
Ce-PMMA was synthesized by a physicochemical technique using a polymer soft template. A solution of cerium (III) nitrate was prepared at pH = 4. Then, PMMA was added and stirred to dryness with a further stirring for 1 h and heating at 60 \({}^{\circ}\)C. After that, it was calcined at 500 \({}^{\circ}\)C for 3 h with a temperature gradient of 0.3 \({}^{\circ}\)C/min, as detailed elsewhere [10]. The Ce-PMMA label was chosen, as the poly (methyl methacrylate) polymer was employed in the preparation process.  
The Ce-CMK3 material was prepared by infiltration of a cerium nitrate (Ce(NO\({}_{3}\))\({}_{3}\)\(\cdot\)6H\({}_{2}\)O from Sigma Aldrich) solution into a commercial CMK3 carbon, which is a replicate of SBA-15 (from ACS Material, Pasadena, CA, USA). The material was further calcinated in air at 500 \({}^{\circ}\)C for total remove of carbon. Given the synthetical origin of the mesoporous carbon employed, this sample was labeled as Ce-CMK3.  
The Ce-ref sample was prepared by simple calcination of cerium (III) nitrate (from Sigma Aldrich, Madrid, Spain) in air at 500 \({}^{\circ}\)C for 3 h. This sample was taken as a reference, and therefore it was labeled as Ce-ref.