esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research Methodology
### Model Performance Evaluation
#### 3.4.2 Four Evaluation Metrics  
This section describes four metrics used to assess the performance of churn prediction models, including accuracy, error rate, specificity, and sensitivity.  
Accuracy: Accuracy is the proportion of correctly classified observations out of all customers classified. This is an overall score, which counts on the overall performance of the model. Accuracy is determined as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}. \tag{1}\]  
Error rate: As opposed to accuracy, the error rate is the proportion of incorrectly classified observations out of all customers classified. The error rate is calculated as follows:  
\[\text{Error rate}=(\textit{FP}+\textit{FN})/(\textit{TP}+\textit{TN}+\textit{ FP}+\textit{FN}). \tag{2}\]  
Specificity: This is the true negative rate. The rate, thus, indicates how large a proportion of the customers estimated as non-churners are correctly classified. Specificity is determined as follows:  
\[\text{Specificity}=\textit{TN}/(\textit{TN}+\textit{FP}). \tag{3}\]  
Sensitivity: This is the true positive rate. The rate indicates what percentage of the customers were estimated as churners and classified correctly. Sensitivity is calculated as below:  
\[\text{Sensitivity}=\textit{TP}/(\textit{TP}+\textit{FN}).\]
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 4 The Proposed 5 GSBA Scheme
### Details of the 5 GSBA
Since it is a private blockchain, any efficient algorithm can be used, such as Practical Byzantine Fault Tolerance (PBFT) [31], to reach a consensus for all gNBs. The details about blockchain consensus implementation are omitted in this paper. **Phase 2--USIM Production:** Network operators should install a one-time hash secret \(Y\) and the ECIES public key of the 5 GC \(PK_{core}\) to the USIM during USIM production. Additionally, the one-time secret hash digest \(H(Y)\) should be posted to the private blockchain. In this way, when the UE sends the collision of the hash function (i.e., the secret \(Y\)) to the gNB, it can prove to the gNB that it is the legitimate UE. These are the detailed procedures:  
1. The operator generates a one-time hash secret \(Y\) and the digest of the one-time hash secret \(H(Y)\);
2. USIM stores its permanent identity (i.e., SUPI), elliptic curve parameters, one-time hash secret \(Y\), and the ECIES public key of 5 GC \(PK_{core}\) into its non-volatile storage;
3. AUSF creates a new blockchain transaction including the SUPI, \(H(Y)\), timestamp, and a status code. The status code is the activation status of the SUPI. For example, \"activated\" can be \(1\), \"suspended\" can be \(2\), \"revoked\" can be \(3\), and so on. The format of one transaction in the blockchain is also shown in Figure 3.
4. If we need to revoke the access of a specific USIM, AUSF can post a new transaction with a \"revoked\" activation status code and a timestamp to the blockchain. Therefore, when gNBs retrieve the latest transactions from the blockchain, they will follow the last record to deny access from that USIM. **Phase 3--gNB Broadcast:** after initialization, gNBs broadcast their identities \(ID_{gNB}\) through the air. Since 5 GSBA makes SUCI mandatory during UE authentication, the identity request procedures in 3 GPP TS 33.501 [2] are no longer needed. Hence, UE can freely choose when to start authentication and when to prepare SUCI without having to respond to possibly forged identity requests from gNBs. **Phase 4--Mutual Authentication:** whenever UE is powered up to start authentication with the 5G network, the following two-step protocol will be executed:  
Figure 3: Structure of a block and a transaction.
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
###### Abstract  
In underwater acoustic networks, the accurate estimation of routing weights is NP-hard due to the time-varying environment. Fuzzy logic is a powerful tool for dealing with vague problems. Software-defined networking (SDN) is a promising technology that enables flexible management by decoupling the data plane from the control plane. Inspired by this, we proposed a fuzzy logic-based software-defined routing scheme for underwater acoustic networks (FL-SDUAN). Specifically, we designed a software-defined underwater acoustic network architecture. Based on fuzzy path optimization (FPO-MST) and fuzzy cut-set optimization (FCO-MST), two minimum spanning tree algorithms under different network scales were proposed. In addition, we compared the proposed algorithms to state-of-the-art methods regarding packet delivery rate, end-to-end latency, and throughput in different underwater acoustic network scenarios. Extensive experiments demonstrated that a trade-off between performance and complexity was achieved in our work.  
software-defined networks; software-defined routing; underwater acoustic networks; fuzzy logic +
Footnote †: journal: _Applied_  
1
Footnote 1: Postdoctoral Research Base, Henan Institute of Science and Technology, Xinxiang 453003, China  
2
Footnote 2: School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China  
3
Footnote 2: School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China  
3
Footnote 3: Adam Smith Business School, University of Glasgow, Glasgow G12 8 QQ, UK  
Footnote 4: Correspondence: [EMAIL_ADDRESS]
## 3 Simulation and Results
### Illustration
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cluster** & **Cluster PAS** & **Normalized Delay** & **Power in [dB]** & **AoD in [\({}^{\circ}\)]** & **AoA in [\({}^{\circ}\)]** & **ZoD in [\({}^{\circ}\)]** & **ZoA in [\({}^{\circ}\)]** \\ \hline \multirow{2}{*}{1} & Specular & \multirow{2}{*}{0.0000} & \(-\)0.4 & 130.8 & \(-\)49.2 & 105.8 & 74.2 \\  & (LOS path) & & & & & \\  & Laplacian & 0.0000 & \(-\)13.7 & 130.8 & \(-\)49.2 & 105.8 & 74.2 \\  & Laplacian & 0.0000 & \(-\)13.7 & 130.8 & \(-\)49.2 & 105.8 & 74.2 \\  & Laplacian & 0.0626 & \(-\)17.5 & 131.1 & \(-\)51.0 & 109.4 & 97.4 \\  & Laplacian & 0.0627 & \(-\)17.4 & 130.8 & \(-\)49.7 & 109.2 & 98.5 \\  & Laplacian & 2.0752 & \(-\)24.9 & 27.1 & \(-\)32.5 & 100.9 & 79.1 \\  & Laplacian & 2.1170 & \(-\)28.3 & 27.1 & \(-\)33.0 & 103.4 & 94.9 \\  & Laplacian & 2.6572 & \(-\)25.5 & 122.2 & 94.4 & 100.0 & 79.7 \\  & Laplacian & 2.7499 & \(-\)32.8 & 122.3 & 95.8 & 102.1 & 118.0 \\  & Laplacian & 2.7839 & \(-\)30.2 & 122.7 & 95.6 & 107.3 & 94.8 \\  & Laplacian & 3.1780 & \(-\)36.0 & \(-\)175.2 & \(-\)30.8 & 99.4 & 80.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Extracted parameters of the developed channel profile CDL-G. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Normalized Delay** & **Power in [dB]** & **AoD in [\({}^{\circ}\)]** & **AoA in [\({}^{\circ}\)]** & **ZoD in [\({}^{\circ}\)]** & **ZoA in [\({}^{\circ}\)]** \\ \hline \multirow{2}{*}{1} & Specular & \multirow{2}{*}{0.0000} & \(-\)14.0 & 91.8 & \(-\)79.0 & 94.8 & 85.5 \\  & (LOS path) & & & & & \\  & Laplacian & 0.0000 & \(-\)13.7 & 130.8 & \(-\)49.2 & 105.8 & 74.2 \\  & Laplacian & 0.0626 & \(-\)17.5 & 131.1 & \(-\)51.0 & 109.4 & 97.4 \\  & Laplacian & 0.0627 & \(-\)17.4 & 130.8 & \(-\)49.7 & 109.2 & 98.5 \\  & Laplacian & 2.0752 & \(-\)24.9 & 27.1 & \(-\)32.5 & 100.9 & 79.1 \\  & Laplacian & 2.1170 & \(-\)28.3 & 27.1 & \(-\)33.0 & 103.4 & 94.9 \\  & Laplacian & 2.6572 & \(-\)25.5 & 122.2 & 94.4 & 100.0 & 79.7 \\  & Laplacian & 2.7499 & \(-\)32.8 & 122.3 & 95.8 & 102.1 & 118.0 \\  & Laplacian & 2.7839 & \(-\)30.2 & 122.7 & 95.6 & 107.3 & 94.8 \\  & Laplacian & 3.1780 & \(-\)36.0 & \(-\)175.2 & \(-\)30.8 & 99.4 & 80.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Extracted parameters of the developed channel profile CDL-F.  
Table 7 lists the CQI and MCS for channel profiles F and G. Finally, the network performance in terms of DL throughput is computed. Since each cell serves a single SN, scheduling has no effect in this case. Whereas the DL throughput for UT-1 is 1.12 megabits per second (Mbps), SN-2 achieves a DL throughput of 26.28 Mbps. This example shows the superiority of the LoS channel in terms of RSS, SINR, CQI, and DL throughput.
# Design of a Power Converter for Solar Energy Storage System
## 2 Circuit Structure and Operation Model  
### Circuit Structure  
Figure 1 depicts the proposed single-stage, three-energy-port, isolated-type energy converter. This converter integrates a boost converter with interleaved synchronous rectification, a set of transformers, and a bidirectional full-bridge converter. By utilizing the interleaved synchronous rectifier converter, the renewable energy port relies on a boost-type mechanism to increase the voltage level in the energy storage and release port or directly supply energy through the converter to the DC grid. The secondary side of the transformer is a full-bridge converter that comprises four power switches cooperating with another four power switches in the primary side of the transformer to form a bidirectional full-bridge converter suitable for energy exchange between the energy storage and release port and the DC grid port. This full-bridge structure renders the circuit suitable for high-power applications, and the isolated feature of the transformer enhances the safe utilization of the DC grid. The converter has four power conversion modes: from a battery energy port to a DC grid port (B2G mode), from a DC grid port to a battery energy port (G2B mode), from a renewable energy port to a DC grid port (R2G mode), and from a renewable energy port to a battery port (R2B mode).  
Figure 1: Proposed single-stage, three-energy-port, isolated-type energy converter.  
Additional assumptions for the analysis are made to support this circuit.  
1. The DC grid and battery terminals are supplied with stable voltage.
2. The power switch only consists of the intrinsic diode \(D_{S}\) and the parasitic capacitance \(C_{OSS}\), and all other parasitic components are ignored.
3. The inductor and capacitor are ideal components, and parasitic elements and series equivalent resistance are ignored.
4. The stray capacitance of the transformer is ignored; the excitation inductance \(L_{m}\) is large enough to be ignored in its effect on the circuit.
## 4 Experiments and Results
### Experiment 1: Artificial Images Test  
To evaluate the performance of the proposed method working on different degradation levels, in this experiment, Gaussian noise at different levels (SNR = 12, 14, 16, 18, 20) and a inhomogeneous background surface are added to two artificial images respectively as shown in Figure 3a. The same initial estimation of the restoration image without an inhomogeneous background is configured to solve the five models.  
The results achieved by different methods are shown in Figures 3b-f and 4b-f. The one to five columns refer the results of PM, TV, CTAD, Malarvel, and our method. It is found that  
Figure 2: Test images.  
the noise can be reduced to some extent in different levels by each method. The treatment effects of the four traditional methods are affected by an inhomogeneous background and lead to lower evaluations. Benefit from the background fitting mechanism, the proposed model can achieve better results.  
The results are evaluated by two metrics (PSNR and SSIM) for full comparison. It can be found that the proposed method achieves the best performance in each degradation level. About the artificial image 1, our PSNR is on average 6.88% higher than the other four methods, while the SSIM is at least 2.05% higher than the traditional results. More details refer to Figure 3g,h and Table 1. A similar situation can be found on the test of artificial image 2, as shown in Figure 4 and Table 2. The iteration numbers are shown in Figure 5. Fewer iterations are required by our method.  
Figure 3: Results of test on artificial image 1.  
Figure 4: Results of test on artificial image 2.
## 2 Methodology
### RSM Model
#### 2.3.3 Validation of the RSM Model  
To demonstrate the validity of the RSM model, the seismic response results between FEM and the RSM predicted were compared; as shown in Table 8, the RSM predicted values were taken from the prediction options for selected factors in the Design-Expert software, and the developed RSM model was used to predict the displacement response and acceleration response, yielding a predicted displacement response of 0.114 m and an acceleration response of 3.487 m/s\({}^{2}\). The response values from the FEM were recalculated to correspond to the geometric parameters of 0.116 m displacement and 3.532 m/s\({}^{2}\) acceleration. Data  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & \
\begin{tabular}{c} **Sum of** \\ **Squares** \\ \end{tabular} &
\begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & **F-Value** & \(p\)**-Value** & **Significance 1** \\ \hline Model & 116.96 & 38.99 & 14.46 & 0.0002 & *** \\ U-Depth & 82.42 & 82.42 & 30.57 & \textless{}0.0001 & *** \\ V-Angle & 33.13 & 33.13 & 12.29 & 0.0039 & * \\ W-R & 1.40 & 1.40 & 0.5210 & 0.4832 & \\ Residual & 35.05 & 2.70 & & & \\ Cor. total & 152.01 & & & & \\ R\({}^{2}\) & 0.7694 & & & & \\ Adjusted R\({}^{2}\) & 0.7162 & & & & \\ Predicted R\({}^{2}\) & 0.5189 & & & & \\ Adeq. & & 13.1706 & & & \\ Precision & & & & & \\ \hline \hline \end{tabular} \({}^{\dagger}\) Markers of significance factors: \({}^{\star}\) means slightly significant; *** means highly significant.  
\end{table}
Table 7: Quadratic model ANOVA for acceleration at point B.  
comparison shows that the error rates of 1.75% for displacement and 1.29% for acceleration are within acceptable tolerances, verifying the validity of the RSM model. The validity of the RSM model has been verified by point A, so the verification of the RS model at point B will not be repeated.
## 3 5G and TSN Integrated Networks for VPP
### Priority constraint  
The priority determines the transmission queue and order of the flows. The priority transmission of high-priority traffic must be guaranteed during the scheduling process. In 5G networks, the higher-priority traffic is given a lower priority value. In TSN networks, the priority of traffic increases with its value:  
\[\begin{cases}\text{5G}:&f_{i}.p\leq f_{i+1}.p\\ \text{TSN}:&f_{i}.p\geq f_{i+1}.p\end{cases} \tag{9}\]  
To minimize the end-to-end transmission delay, a semi-persistent scheduling with reserved bandwidth sharing and pre-emption algorithm is shown in Algorithm 1. Firstly, partial resources are reserved for the ET flows based on their typical traffic characteristics. At the same time, reserved resources can be allocated to TT flows. When the ET flow arrives, if the reserved resources are not occupied, they will be directly allocated to the ET flow. If the reserved resources are occupied by the TT flow, the ET flow will pre-empt the resource and delay the transmission of the TT flow. When the reserved resources are exhausted, SPS-RBSP reassigns priority to the remaining traffic based on the deadline and performs dynamic resource allocation.  
```
0: Traffic flows set \(\mathbf{F}\), Total number of RBs \(R\), SPS period \(T_{S}\), The number of reserved RBs \(R^{P}\)
0: The number of RBs allocated to flow \(f\)
1: Reserve resources \(R^{P}\) for ET flows
2: Perceived TT flows period and transmission
3:for all ET flows do
4:if\(R^{P}\) is free then
5:\(f.ET\) transmission
6:elseif\(R^{P}\) is occupied by \(f.TT\)then
7:\(f.ET\) pre-emption, \(f.TT\) delay until \(f.ET\) is transmitted
8:endif
9:endfor
10:for residual \(f.TT\) and \(f.BE\)do
11: compute \(f.t_{rem}=f.deadline-f.t\)
12:if\(f_{i}.t_{rem}<\beta f_{j}.t_{rem}\)then
13:\(f_{i}.p++\)
14:endif
15:endfor
```  
**Algorithm 1** SPS-RBSP algorithm
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 2 Landslide-Related Data Inventory in Hong Kong
### Enhanced Natural Terrain Landslide Inventory (ENTLI)  
Landslides can occur on natural hillsides and remote areas without affecting the public. Therefore, there should be several landslides not being reported to the GEO, including those that occurred before 1984. Since the mid-1990s, the GEO has been maintaining a comprehensive territory-wide Geographic Information System (GIS)-based inventory of natural terrain landslides, which are identified from aerial photograph interpretation. High-flight aerial photographs that were taken at a height of 2400 m or above since 1945 were initially used for the creation of this landslide inventory, referred to as the Natural Terrain Landslide Inventory (NTLI), in 1997. Over 30,000 recent and relict landslides were recorded in the NTLI [5]. In 2007, the NTLI was replaced by the Enhanced Natural Terrain Landslide Inventory (ENTLI) to incorporate the results from the mapping of historical natural terrain landslides using both low-flight (taken at less than 2400 m altitude) and high-flight aerial photographs such that missing or mis-identified historical landslides as a result of the limited resolution and temporal coverage of the high-flight aerial photographs would be reduced. Figure 2a shows the spatial distribution of relict and recent natural terrain landslides as recorded in ENTLI until 2019. The ENTLI provides important data and is widely used for studies of territory-wide and site-specific natural terrain landslide hazards.
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 4 Obstacle Avoidance Algorithm and Transverse Controller Design
### Design of MPC Controller with Planning Module
#### 4.2.1 Trajectory Planning Layer Design
(3) Curve fitting  
In the trajectory replanning algorithm, the trajectory planned by the planning module is given in the form of predicting discrete points in the time domain, and the discrete pointsare fitted with a quintic polynomial curve [22; 23]. The formula for the quintic polynomial is as follows:  
\[Y=a_{0}t^{5}+a_{1}t^{4}+a_{2}t^{3}+a_{3}t^{2}+a_{4}t+a_{5} \tag{29}\]  
\[\varphi=b_{0}t^{5}+b_{1}t^{4}+b_{2}t^{3}+b_{3}t^{2}+b_{4}t+b_{5} \tag{30}\]  
In the formula, \(a_{S}=[a_{0},a_{1},a_{2},a_{3},a_{4},a_{5}]\) and \(b_{s}=[b_{0},b_{1},b_{2},b_{3},b_{4},b_{5}]\) are the parameters to be obtained. Considering that the road has both straight lines and bends, the double shift line condition is selected as the road to verify the algorithm in this paper. The reprogrammed trajectory of the reference trajectory with a fifth-degree polynomial fit is shown in Figure 16.  
In Figure 16, the obstacle appears at 50 m, it can be seen that the obstacle avoidance function can avoid the obstacle better, and the quintic polynomial can fit the replanning trajectory better. It is proved that the obstacle avoidance function selected in this paper and the quintic polynomial curve fitting method are effective.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 7 Service-Oriented Architectures Approaches for WSNs
### WSN Cloud User Interaction  
The new concept for WSN cloud is designed specifically to apply to a network as a service (NaaS), which provides solutions in large-scale WSNs for Service Orchestrating Architecture provisioning called (WSNs-SOrA). WSNs-SOrA enables WSNs to act as a cloud and is required to support SOA at all WSN tier infrastructure. The SOA enables another system to provide WSN infrastructure based on their needs, while allowing multi-systems to use the WSN. The service provisioning is done using XML [73]. This approach is one of the first state-of-the-art protocols proposing to combine WSNs with cloud computing [74]. In [75], methods that use sensor data by cloud users are presented. It designs service stacks, interfaces, and repositories based on SOA. The services allow communication between the cloud, WSNs, and the consumer. This architecture supports setup for WSNs which can collaborate, share data efficiently and easily determine the sensed data behavior. The issues of this WSNs setup is addressed through isolated sensor networks and non-collaborative approaches. The isolated sensor network drawbacks are solved by using one registry for sensor networks, and the challenges of non-collaborative approaches are addressed by designing a service stack. The heterogeneity issue is addressed by using SOA.
# Light-Induced Advanced Oxidation Processes as PFAS Remediation Methods: A Review
## 3 Advanced Oxidation Processes (AOPs) for PFAS
### Heterogeneous Photocatalysis Materials
#### 3.1.2 Other Photocatalysts
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Photocatalytic Performance**} & \multicolumn{6}{c}{**Photocatalytic Reaction Condition**} & **Ref.** \\ \hline
**Photocatalyst** & **Reaction Time (b)** & **Rate Constant** & **\%** & **\%** & **Concentation** & **Catalyst** & **pH of** & **Light Source** & \\ \hline In\({}_{\rm O}\)O\({}_{\rm n}\) nanoporous & 30 min & 0.100 & 100 & 71 & 30 mg/L & 0.5 & 3.9 & 254 nm and 23 W & [s] \\ nanopbers & 30 min & 0.158 & 100 & — & 30 mg/L & 0.5 & — & 254 nm and 15 W & [s] \\ In\({}_{\rm O}\)O\({}_{\rm n}\) nonsamples & 30 min & 0.158 & 100 & — & 30 mg/L & 0.5 & — & 254 nm and 15 W & [s] \\ MO-derived In\({}_{\rm O}\)O\({}_{\rm n}\) & 3 & — & 100 & 50 & 10 mg/L & 0.2 & — & 254 nm and 32 W & [s] \\ In\({}_{\rm O}\)O\({}_{\rm n}\)/S/graphene & 3 & 0.008 & 100 & 60.9 & 30 mg/L & 0.5 & 3 & 254 nm and 15 W & [s] \\ In\({}_{\rm O}\)O\({}_{\rm n}\)/Cr\({}_{\rm O}\)\({}_{\rm O}\)\({}_{\rm O}\)\({}_{\rm O}\)\({}_{\rm O}\) & 1 & 0.063 & 100 & 53.3 & 100 mg/L & 0.4 & – & 254 nm and 500 W & [s] \\ In\({}_{\rm O}\)O\({}_{\rm n}\)/MnO\({}_{\rm O}\) & 3 & 0.301 & 100 & 17.4 & 50 mg/L & 0.5 & 3.8 & Xenon lamp and 500 W & [s] \\ In\({}_{\rm O}\)OOH & 3 & 0.008 & 83.4 & 20 mg/L & 0.25 & 254 nm and 18 W & [s] \\ B\({}_{\rm O}\)OCl & 12 & — & 100 & 59.3 & 20 \(\mu\)M & 0.5 & 4.8 & 254 nm and 10 W & [s] \\ B\({}_{\rm O}\)OCl with OV & 8.5 & — & 68.8 & 12.86 & 50 \(\mu\)M & 1 & 2 & Xenon lamp and 300 W & [s] \\ B\({}_{\rm O}\)OF & 6 & 0.006 & 100 & 56.8 & 15 mg/L & 3-5 & Vight & [s] \\ B\({}_{\rm O}\)O & 2 & 0.0048 & 66 & 37\% in 3h & 20 mg/L & 0.4 & – & Hg lamp and 300 W & [s] \\ B\({}_{\rm O}\)O\({}_{\rm n}\)/Sn\({}_{\rm O}\) & 2 & 0.0205 & 100 & 65\% in 3h & 20 mg/L & 0.4 & – & Hg lamp and 300 W & [s] \\ Bi/B\({}_{\rm O}\)O\({}_{\rm n}\)/Sr & 2 & 0.0375 & 100 & 10 & 40 mg/L & 0.4 & 3-5 & Xenon lamp and 800 W & [s] \\ Bi/B\({}_{\rm O}\)OCl\({}_{\rm O}\) & 6 & 0.0041 & 81 & 60 & 15 mg/L & 0.5 & 3 & Xenon lamp and 800 W & [s] \\ BiOI/Z\({}_{\rm O}\) & 6 & 0.0002 & 91 & 52.2 & 1 mg/L & 0.5 & 4 & Xenon lamp lamp and 500 W & [s] \\ B\({}_{\rm O}\)OBrI & 1 & 0.1000 & 100 & 60 & 0.13 mm & 1.8 & 4 & 254 nm and 18 W & [s] \\ R\({}_{\rm O}\)OHF/CS & 4 & — & 100 & 32.5 & 0.22 mg/L & 1.0 & 7 & 254 nm and 18 W & [s] \\ FeO/CS & 1 & — & 95.2 & 57.2 & 0.2 mg/L & 1.0 & 7 & Stimulated solar light & [s] \\ Boron nitride & 4 & & 100 & 52 & 0.12 mM & 2.5 & 6.5 & 254 nm and 24 W & [s] \\ H\({}_{\rm H}\)W\({}_{\rm O}\)\({}_{\rm n}\)/Diamond & & & & & & & & & \\ mesoporous silica & 4 & 0.0018 & & 50 & 10 mg/L & 0.2 & 4 & 254 nm and 8 W & [s] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._