esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Belt Algorithm for the Management of Isolated Autonomous Intersection
## 2 Problem Formulation
### Components of Intersection Management System  
The main components of the intersection management system include an IMU and CAVs. The communication between the IMU and a CAV can be achieved by vehicle-to-infrastructure communication. The IMU is responsible for the trajectory planning of the CAVs at intersection. A CAV should send a request for passing the intersection and follow the trajectory planned by the IMU. The request of a CAV is called a _RCAV_ for short. Several assumptions are made to facilitate the discussion of intersection management. These assumptions are feasible in technology. Some of these assumptions are commonly made in previous studies [1; 8; 14].  
* The position, velocity and dynamic limits of a CAV can be transmitted to the IMU.
* A trajectory planned by the IMU can be sent to the corresponding CAV.
* A CAV can follow the trajectory planned by the IMU within the limits of the CAV.
* The mean speed of the considered traffic flow is much lower than the maximum speed of the vehicle with low dynamic performance. Generally, the maximum speed of vehicles on road is higher than 90 km/h. However, the mean speed of the traffic flow on city environment is much lower than 60 km/h.
* Before the intersection, each approaching vehicle has turned to the corresponding lane according to its destination.
## 4 Experiments and Results  
This section describes the evaluation and results of a mechanism designed for securing IoT networks. The simulation is carried out using the Cooja simulator 3.0; the simulation setup details in Table 2 are presented in Section 4.1, experimentation details are provided in Section 4.2, and the results and discussion are presented in Section 4.3.  
The proposed solution of the PCC-RPL is found to be superior in terms of power consumption, packet loss ratio, and the number of detected attacks. The SLF-RPL mechanism operates in the No Horizontal position mode and forwards packets to the sink node in the No Down mode. The reception ratio ranges from 30% to 100%. The simulations were carried out in three different scenarios, with 33, 66, and 99 nodes, and a 65 m interference limit was established. The Contiki Cooja simulator was chosen because it is specifically designed for IoT and WSN networks, while other simulators are designed for large infrastructure networks.
## 4 Results and Discussion  
### I-V Characteristic and Static Resistance Analysis  
#### 4.1.1 I-V Characteristic  
Hundreds of proposed MSM (Al/local indigo/Cu) diodes were examined for repeatability and they exhibited similar results. The two sets of them were chosen due to their best presence. The I-V characteristic, presented in Figure 4, shows the forward-bias of the MSM diode with the local indigo, showing in the positive right-hand side. It is seen that an increase of MSM diode voltage, VMSM, between 0 and 1 VMSM results in the forward current of about 0 mA because of the insufficient potential electron energy in the diode that overcomes the band gap energy for conductive state access of the indigo blue layer. The forward current at the knee voltage is increased to a maximum of 2.60 mA or 3.19 mA in the first and second MSM diodes, respectively, when VMSM increases. Then, it tends to decrease because the indigo blue layer becomes burnt. The local indigo blue obtained from this extraction has a low boiling temperature because it was contaminated, such as with chlorophyll, while in the reverse-biased region, the negative left-hand side, it shows a steady ultra-low reverse current due to the large band gap energy of the indigo blue layer. However, it has a bit of error at large negative VMSM because of the burnt indigo layer. It is observed that this MSM diode may be operated between about \(-3\) and \(+3\) VMSM. In addition, it is noticed that the reverse current of about 0 mA is found at \(-1\) VMSM.  
Figure 4: I–V characteristic of the proposed MSM diodes with local indigo.  
Figure 3: (**a**) Forward-biased and (**b**) reverse-biased circuits of proposed MSM diode test.
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 2 Related Work
### Byzantine-Robust Federated Learning  
#### 2.2.1 Byzantine Attack  
As we all know, federated learning is vulnerable to Byzantine attacks [35; 36; 37]. In order to map the specified feature to the target class, the Byzantine attacker tries to modify the classification boundary of the trained model. It has been proven that an attacker can control the entire training process and steal user privacy [16].  
According to the goals of the attackers, Byzantine attacks can be divided into targeted attacks [17] and untargeted attacks [35]. Targeted attacks target only one or a few data categories in the dataset, while maintaining the accuracy of other categories. Untargeted attacks are undifferentiated attacks that aim to reduce the accuracy of all data categories. And according to the abilities of the adversaries, Byzantine attacks can also be divided into data attacks [37] and model attacks [35]. In data attacks, the attackers indirectly poison the global model by poisoning the client's local data. For example, the label-flipping attack flips the label of the normal features to the target class. And the backdoor attack is an attempt to seek a set of parameters to establish a strong link between the trigger and the target label while minimizing the impact on benign input classification. In model attacks, the attackers can directly manipulate and control the model updates of communication between the clients and the server, which directly affects the accuracy of the global model.
## 4 The Security of QOT
### Privacy for [PERSON]
#### 4.1.1 Analysis on the Probability of Getting a Conclusive Bit for Honest [PERSON]  
Let \(|n_{0}\rangle\) and \(|n_{\frac{\pi}{6}}\rangle\) denote n-photon states of polarization 0 and \(\frac{\pi}{6}\), respectively. For an honest [PERSON], if he chooses the measurement basis \(B_{1}\) to detect \(|1_{0}\rangle\), the probability of the state collapsing to \(|1_{\frac{2\pi}{3}}\rangle\) is \(\frac{1}{4}\). For \(|n_{0}\rangle\), the probability of at least one of the photons collapse to the state with polarization of \(\frac{2\pi}{3}\) is \(1-\left(\frac{3}{4}\right)^{n}\). Therefore, the probability of getting a conclusive resulting in a pulse which contains \(n\) photons is  
\[p(n)=\frac{1}{2}\times\left[1-\left(\frac{3}{4}\right)^{n}\right]. \tag{4}\]The probability of getting a conclusive bit in a pulse with the typical average photon number \(\mu\) is  
\[\begin{split} p_{con}(\mu)&=\sum_{n=1}[p_{n}(\mu)\times p (n)]\\ &=\sum_{n=1}\left\{\frac{1}{2}\left[1-\left(\frac{3}{4}\right)^{n} \right]\frac{e^{-\mu}\mu^{n}}{n!}\right\}\\ &=\frac{1}{2}\left[\sum_{n=1}\frac{e^{-\mu}\mu^{n}}{n!}-e^{-\frac {\mu}{4}}\sum_{n=1}\frac{e^{-\frac{3\mu}{4}}\left(\frac{3\mu}{4}\right)^{n}}{n! }\right]\\ &=\frac{1}{2}\left(1-e^{-\frac{\mu}{4}}\right).\end{split} \tag{5}\]  
It can be seen that an honest [PERSON] is supposed to obtain \(Np_{con}(\mu)\) conclusive bits. The probability of getting a conclusive bit in one pulse with different \(\mu\) can be seen in Figure 2. The larger \(\mu_{S}\) of emission apparatus and more efficient detector they use, the higher efficiency the protocol has.
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 2 LCLS Recent Representative Highlights
### Structural Biology  
Serial femtosecond crystallography (SFX) at XFEL sources [1; 15; 16; 17] has revolutionized the ability to determine macromolecular structures that are inaccessible by synchrotron sources. Furthermore, time-resolved SFX maps the conformational dynamics that determine biological function, and enables studies at room temperature (near physiological conditions).  
Recent LCLS time-resolved SFX studies of riboswitches, structural elements of messenger RNA (mRNA), capture the dynamic structural response to the binding of a ligand for the first time, as shown in Figure 6[18]. This ligand-triggered conformational reaction in mRNA mediates gene expression. By using ultra-small riboswitch crystals, the diffusion of a ligand can be timed to initiate a reaction just prior to X-ray diffraction, thereby capturing the transient structure. Four transient structures identified support a reaction mechanism model with at least four states, and illustrate the structural basis for signal transmission. These results further demonstrate the potential of \"mix-and-inject\" time-resolved serial crystallography to study important interactions between biological macromolecules and ligands.  
Figure 5: (**a**) Incommensurate host-guest crystal structure of scandium phase II at 23 GPa. Reprinted with permission from [14]. Copyright The American Physical Society, 2005. (**b**) Linac Coherent Light Source (LCLS) transient X-ray scattering snapshot of shock-compressed scandium showing liquid scattering (broad diffuse peak) along with uncompressed material ahead of the shockwave (sharp peaks) [13].  
Figure 6: (**a**) Apparatus for “mix-and-inject” time-resolved serial crystallography at LCLS. (**b**) Structural snapshots of the binding pocket in the riboswitch viewed from the same angle (figure from [PERSON] of NCI). (**c**) Evolution of species concentrations over time. Reprinted with permission from [18]. Copyright Nature Publishing Group, 2017.  
Room temperature time-resolved SFX studies at LCLS have substantially advanced our understanding of the mechanism underlying sunlight-driven oxidation of water by photosystem II (PS II) [19]. The four-electron redox chemistry of water oxidation is accomplished by the Mn\({}_{4}\)CaO\({}_{5}\) cluster in the oxygen-evolving complex (OEC) within PS II. A grand science challenge is to elucidate the transient structures of the OEC in the different chemical transition states, particularly the transient binding of two waters molecules at the catalytic site. LCLS SFX studies have captured the transient structures of the OEC in the dark S\({}_{1}\) and illuminated S\({}_{3}\) state at high resolution (2.25 to 3.0 A), in situ and at room temperature. Substrate water and ammonia (water-analog) binding to the Mn\({}_{4}\)Ca catalyst reveal new details about the mechanism of water oxidation.
# Anomaly Detection of Wind Turbines Based on Deep Small-World Neural Network
## 4 Case Studies
### Performance Comparison of Pitch Fault Classification (Case B)
In addition, we also recorded the global changing errors of the three models in the training process at the same time, as shown in Figure 9. It can be seen from the figure that the convergence times of DSWNN, DBN and DNN were 255 s, 210 s, and 168 s, respectively. DSWNN took the longest time to calculate because it had the small-world transformation process between pre-training and fine-tuning. In a 0-110 s interval, the training errors of DSWNN and DBN almost reached the stage stability, which was because the network was in the pre-training stage and their multiple RBMs had reached the energy conservation. In the range of 110-130 s, DSWNN and DBN changed from unsupervised training to supervised training, and the training error suddenly decreased until a new lower convergence value appeared. Note that at 124 s, the training error of DSWNN increased in a short time, which was due to the additional weights of the add-edges in the network structure after the small-world transformation. The DSWNN retrained the random new add-edges, leading to the short-term error increase. Seen from the above case studies, the advantages of the DSWNN model appeared mainly in two aspects: (1) the learning ability of the DSWNN model was better than the DBN method, and it was much better than the traditional DNN method; (2) the DSWNN model had very good sensitivity and accuracy in reflecting the condition changes of the wind turbine pitch system; (3) although the DSWNN \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Data Fragment** & **F1** & **F2** & **F3** & **F4** & **F5** & **F6** & **F7** & **F8** & **F9** & **F10** & **Total** \\ \hline Training data & 233 & 288 & 224 & 84 & 74 & 245 & 232 & 229 & 238 & 18,203 & 20,000 \\ Validation data & 79 & 73 & 58 & 28 & 41 & 91 & 67 & 72 & 68 & 5423 & 6000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Confusion information of the experimental data. Figure 8: Comparison of three neural networks in classification accuracy of 10 types of faults. model was not dominant in time cost, it had a stronger ability to mine deeper feature information from the same data source.
# Research on Robust Day-Ahead Dispatch Considering Primary Frequency Response of Wind Turbine
## 3 Optimization Model Considering Primary Frequency Response
### Two-Stage Robust Optimization Model  
Since it is necessary to take into consideration the day-ahead reserve for the power imbalance in the optimization dispatch framework [35], we propose a three-layer robust optimization model involving the day-ahead and real-time stages. Assuming that the load is forecast at a certain value, under the expected value, this paper determines the power imbalance reserve by the day-ahead scheduling to cope with the error between the wind power output and the wind power forecast in the real-time operation. Under the real-time scale, in different wind power output scenarios, the unit output needs to be adjusted again within the output determined in the day-ahead dispatch and within the power imbalance reserve constraint. On this basis, the primary reserve of the unit should also be retained. Therefore, in this model, the first stage is the day-ahead economic optimization dispatch, which decides the output and power imbalance under the expected value of the wind power output. The second stage targets the minimization of the balancing cost of the wind power output scene of the set \(\Omega\), which considers the readjustment of the output of the conventional units and primary reserve in the real-time scene \(s\). In the model, the basic day-ahead scene is indicated by the superscript 0, and the real-time scene is indicated by the superscript \(s\). The mathematical expression is as follows:  
\[ \begin{array}{c}\min\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{N_{G}}\left(C_{G,i }P^{0}_{G,i,j}+C^{II}_{R,j}R^{UL0}_{i,t}+C^{D}_{R,j}R^{D,0}_{i,t}\right)+\\ \underset{s\in\Omega}{\text{maximin}}\sum\limits_{t=1}^{T}\sum\limits_{i=1}^{N_ {G}}C_{G,i}\left(r^{U,s}_{i,t}-r^{D,s}_{i,t}\right)+\sum\limits_{t=1}^{T}\sum \limits_{i=1}^{N_{G}+N_{W}}C^{F}_{R,i}R^{F,s}_{i,t}+\sum\limits_{t=1}^{T}\sum \limits_{i=1}^{N_{B}}C_{L,i}P^{non}_{i,t}\end{array}  \tag{12}\]  
where \(T\) is the dispatch period, 24 h; \(N_{G}\), \(N_{W}\) and \(N_{B}\) indicate the number of traditional synchronous generators, the wind power and the system power nodes respectively; \(P^{0}_{G,i,t}\), \(R^{UL0}_{i,t}\) and \(R^{D,0}_{i,t}\) represent the output of unit \(i\), and the upper/lower reserve for the power imbalance during the \(t\) period in the basic scenario, while \(C_{G,i}\), \(C^{II}_{R,i}\) and \(C^{D}_{R,i}\) indicate the coefficients of the output cost, upper reserve cost and lower reserve cost of unit \(i\); \(r^{U,s}_{i,t}\) and \(r^{D,s}_{i,t}\) represent the adjustment of the active output of unit \(i\) during the \(t\) period in the real-time scenario \(s\); \(R^{F,s}_{i,t}\) is the primary reserve, and \(C^{F}_{R,i}\) its cost coefficient; \(P^{non}_{i,t}\) refers to the load shedding and \(C_{L,i}\) to its cost coefficient. In the day-ahead dispatch strategy, the expected value of the wind power output is set as the basic scenario, and we target the minimum unit output and reserve cost.
## 3 Results and Discussion
### Critical Electric Fields  
In order to more clearly compare and evaluate the insulation performance quantitatively, the (_E/N_)\({}_{lim}\) of these four gases have been sorted out in Table 1. It is apparent that C\({}_{4}\)F\({}_{7}\)N has a superior performance of gas insulation, as it had a high (_E/N_)\({}_{lim}\) value (959.19 Td). The relative deviation of (_E/N_)\({}_{lim}\) of C\({}_{4}\)F\({}_{7}\)N measured in this work was about 2.41% of that measured by [PERSON] et al. [13] and was about 1.73% of that measured by [PERSON] et al. [14], which could testify that the experiments in the present study were of high credibility at the same time. Furthermore, it could be found that the value of (_E/N_)\({}_{lim}\) of C\({}_{4}\)F\({}_{7}\)N was about 2.68 times that of pure SF\({}_{6}\) gas. However, the dielectric strength of C\({}_{4}\)F\({}_{7}\)N at atmospheric pressure was about 2 times that of SF\({}_{6}\) as reported [10], slightly less than 2.68 times, which could be caused by different behaviors of ion kinetics under different gas pressures [14]. The higher the gas pressure, the greater the effect of ion kinetics.  
\begin{table}
\begin{tabular}{c c} \hline Gas Type & (_E/N_)\({}_{lim}\)/Td \\ \hline C\({}_{4}\)F\({}_{7}\)N & 959.19 [present work] \\  & 981.84 [13] \\  & 975 [14] \\ c-C\({}_{4}\)F\({}_{8}\) & 408.68 [4] \\ CF\({}_{3}\)I & 440.19 [7] \\ SF\({}_{6}\) & 355.27 [13] \\  & 358.66 [14] \\  & 351.80 [16] \\ \hline \end{tabular}
\end{table}
Table 1: The values of (_E/N_)\({}_{lim}\) in different gases.  
Figure 4: Comparison of the normalized effective ionization coefficient in different gases.  
Figure 5: The molecule structure of C\({}_{4}\)F\({}_{7}\)N gas.
## 3 Results
### Experimental Results  
In order to calculate the performance of our algorithm, we used Root Mean Squared Error metric. This allowed us to quantitatively verify the differences between real positions, values returned by INS, and values corrected by the algorithm. The Root Mean Squared Error is defined by the following formula:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(p_{i}-\hat{p}_{i})^{2}}, \tag{10}\]  
where \(N\) is the number of check-points with known coordinates, \(p_{i}\) is the position at a given \(i\)-th check-point, \(\hat{p}_{i}\) is the position indicated by INS or corrected by the map matching algorithm.  
The results of the experiments are summarized in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Trial No. 1**} \\ \hline
**Step** & **Real Position [m]** & **INS Position [m]** & **Error [m]** \\ \hline
0 & (5.12, 8.40) & (5.11, 8.39) & 0.01 \\
6 & (12.27, 8.40) & (13.84, 8.14) & 1.59 \\
12 & (20.90, 8.40) & (20.53, 8.27) & 0.39 \\
17 & (25.90, 8.40) & (26.76, 8.04) & 0.93 \\
21 & (31.60, 8.40) & (30.77, 8.02) & 0.91 \\
26 & (35.85, 8.40) & (31.28, 7.19) & 4.73 \\
30 & (31.60, 8.40) & (27.24, 7.39) & 4.48 \\
34 & (25.90, 8.40) & (23.37, 7.63) & 2.64 \\
40 & (20.90, 8.40) & (15.66, 8.17) & 5.25 \\
46 & (12.27, 8.40) & (9.03, 8.48) & 3.24 \\
51 & (5.12, 8.40) & (4.32, 8.77) & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of data from an exemplary test. The position of the agent is relative to (0, 0) point, placed in the corner of the building.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **INS** & **Map Matching Algorithm** \\ \hline Test I & 3.63 [m] & 3.01 [m] \\ Test II & 3.26 [m] & 0.73 [m] \\ Test III & 5.59 [m] & 1.15 [m] \\ \hline Average & 4.16 [m] & 1.63 [m] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of Root Mean Squared Error for the system with and without map matching.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test** & **INS** & **Map Matching Algorithm** \\ \hline Test I & 4.16 [m] & 3.71 [m] \\ Test II & 3.89 [m] & 2.12 [m] \\ Test III & 7.06 [m] & 2.15 [m] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of maximal error at a check-point.
## 5 Data Analysis and Results
### Descriptive Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source Credibility (SC)** & **Mean** & **SD** & **Level** & **Order** \\ \hline CIQ11: & 4.00 & 1.065 & High & 10 \\ \hline CIQ12: & 3.98 & 1.069 & High & 11 \\ \hline CIQ13: & 4.28 & 1.194 & Very high & 5 \\ \hline CIQ14: & 4.25 & 1.188 & Very high & 6 \\ \hline CIQ15: & 4.05 & 1.123 & High & 9 \\ \hline CIQ16: & 4.26 & 1.177 & Very high & 5 \\ \hline Accessibility Information Quality (AIQ) & Mean & SD & Level & Order \\ \hline AIQ1: & 4.71 & 0.499 & Very high & 1 \\ \hline AIQ2: & 4.69 & 0.530 & Very high & 2 \\ \hline AIQ3: & 4.32 & 1.172 & Very high & 6 \\ \hline AIQ4: & 4.22 & 1.162 & Very high & 7 \\ \hline AIQ5: & 4.05 & 0.717 & High & 9 \\ \hline AIQ6: & 4.09 & 1.103 & High & 8 \\ \hline AIQ7: & 4.48 & 0.816 & Very high & 3 \\ \hline AIQ8: & 4.03 & 1.078 & High & 10 \\ \hline AIQ9: & 4.43 & 0.908 & Very high & 5 \\ \hline AIQ10: & 3.96 & 1.073 & High & 11 \\ \hline AIQ11: & 4.44 & 0.888 & Very high & 4 \\ \hline Information Usefulness (IU) & Mean & SD & Level & Order \\ \hline IU1: & 4.33 & 1.168 & Very high & 1 \\ \hline IU2: & 4.00 & 1.073 & High & 3 \\ \hline IU3: & 4.30 & 1.158 & Very high & 2 \\ \hline Information Quality (IQ) & Mean & SD & Level & Order \\ \hline IQ1: & 4.30 & 1.159 & Very high & 1 \\ \hline IQ2: & 4.04 & 1.079 & High & 2 \\ \hline IQ3: & 4.01 & 1.067 & High & 3 \\ \hline Information Adoption (IA) & Mean & SD & Level & Order \\ \hline IA1: & 4.30 & 1.143 & Very high & 1 \\ \hline IA2: & 4.09 & 1.087 & High & 5 \\ \hline IA3: & 4.11 & 1.099 & High & 4 \\ \hline IA4: & 4.22 & 1.154 & Very high & 2 \\ \hline IA5: & 4.10 & 1.104 & High & 3 \\ \hline Academic Achievement (AA) & Mean & SD & Level & Order \\ \hline AA1: & 4.34 & 1.175 & Very high & 1 \\ \hline AA2: & 3.98 & 1.089 & High & 6 \\ \hline AA3: & 4.31 & 1.188 & Very high & 2 \\ \hline AA4: & 4.21 & 1.192 & Very high & 4 \\ \hline AA5: & 4.03 & 1.112 & High & 5 \\ \hline AA6: & 4.22 & 1.171 & Very high & 3 \\ \hline AA7: & 3.87 & 1.439 & High & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._
## 3 Results and Discussion
### Effect of TiO\({}_{2}\) Suspension Load  
The concentration (load) of TiO\({}_{2}\) in the suspension can influence the thickness and uniformity of the impregnated layer of nanoparticles onto the Leca surface. In this sense, different loadings (1%, 3.6%, 5% and 10% \(w/w\)) were tested, giving rise to the nomenclature (1%TiO\({}_{2}\)/Leca, 3.6%TiO\({}_{2}\)/Leca, 5%TiO\({}_{2}\)/Leca, 10%TiO\({}_{2}\)/Leca). The average TiO\({}_{2}\) mass impregnated was determined by mass difference. Figure 2a shows that the average of the TiO\({}_{2}\) impregnated mass at 1% (\(w/w\)) was supported 8.21 mg/g; 22.03 mg/g for (3.6% \(w/w\)); 25.76 mg/g for 5% (\(w/w\)); 39.20 mg/g for 10% (\(w/w\)). Thus, from the point of view of the impregnated TiO\({}_{2}\) load, the 10%TiO\({}_{2}\)/Leca is the best option, and 1%TiO\({}_{2}\)/Leca is the worst. The load of TiO\({}_{2}\) impregnated over the Leca surface gradually increased with the concentration in the dispersed suspensions. This effect may be associated with the viscosity of the suspensions and the structure of the Leca, which contains pores of different sizes and with roughness allowing the increment in the coating of TiO\({}_{2}\) particles. According to [PERSON] et al. (2019), the viscosity of TiO\({}_{2}\) suspensions increases as a function of TiO\({}_{2}\) load due to the high solid content of the suspensions, increasing the crosslinking density between the particles. Figure 2b illustrates the mass loss of TiO\({}_{2}\) (not impregnated in the ceramic support) in the preparation process. This weight percent ratio considered for TiO\({}_{2}\) calculation was made after the calcination-wash stage. This means that although the impregnation increases with the concentration in the suspension (1%, 3.6%, 5%, and 10%), the loss of TiO\({}_{2}\) also increases from 43% to 72%, respectively. The increase of TiO\({}_{2}\) load promotes a rise of the TiO\({}_{2}\) loss that was not immobilized and will be considered a waste, contributing to a rise in the cost of preparation. Besides this, the higher concentrated suspensions were tested in terms of performance for the photocatalytic oxidation of SMX.