esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multi-Objective Optimization
#### 2.2.1 Pareto Frontier Analysis with \(\epsilon\)-Constraint Approach  
Pareto frontier analysis is a key concept in multi-objective optimization that involves simultaneously optimizing numerous competing objectives. Solutions on the Pareto front show different trade-offs and help to understand the degree and priority of every goal. All Pareto optimum solutions make up the Pareto front. The Pareto front usually shows up as a curve or surface within the goal space in a situation with two objectives. In higher-dimensional objective spaces, it shows up as a hypersurface. Common in multi-objective optimization, the epsilon-constraint approach transforms a multi-objective issue into several single-objective problems by maximizing one goal while considering the others as constraints determined by certain epsilon values [21]. One goal function is converted  
Figure 5: Confidence levels of detected trucks and excavators on a processed image.  
into a constraint with an upper bound of \(\varepsilon\), which is subsequently modified repeatedly to investigate the trade-off between the two objectives in order to construct the Pareto frontier.  
This method is applied to an operational open-pit mine during a 10 h shift. The primary objective of the optimization model is to minimize the deviation from the required assignment numbers for the excavators, ensuring that production targets are met. A secondary objective is to minimize the total ready time of the excavators, promoting the decreased queue time of the trucks. To prioritize the objectives, the latter is treated as a constraint within the model rather than a standalone objective. This approach allows the model to focus primarily on achieving the required assignments while incorporating the minimization of ready time as a conditional factor. The epsilon constraint method is employed to systematically vary this constraint, enabling the exploration of potential trade-offs between the two objectives. This process generates a Pareto frontier providing insights into the balance between meeting assignment targets and maximizing truck utilization. The processed images help make a full dataset that shows key work metrics in the mine area. These things list when excavators are set to take on more work, which shows if they are free for more jobs; how many trucks have no load at dump spots, showing how well trucks are used and how fast they work; and how much diggers do, giving a look into how they work and add to mine work. This dataset is used in a plan to better place empty trucks with free diggers. This plan, talked about next, uses info like digger-ready times, truck open spots, and work numbers. It aims to make better choices on which truck goes to which digger.
# Finite-Time Control of Singular Linear Semi-Markov Jump Systems
## 3 Results
### Stochastically Finite-Time Boundedness  
**Theorem 3**. : _For given positive scalars \(c_{1},c_{2}\) and \(T\); \(d\) with \(c_{1}<c_{2}\); and symmetric positive-definite matrix \(R\), the SSMJS in (1) is stochastically finite-time bounded with respect to \((c_{1},c_{2},T,R,d)\), if there exist positive scalars \(\alpha\), \(\eta\) and \(0<\theta<1\); symmetric positive-definite matrices \(P_{i}\) and \(Z_{i}\)and matrices \(S_{i}\) and \(Y_{i}\) with appropriate dimensions such that (25a), (25b) and the following matrix inequalities hold:_  
\[ \begin{bmatrix}\underline{\Pi}_{11}&\underline{\Pi}_{12}&C_{i}\\ *&\Pi_{22}&0\\ *&*&-\alpha W_{i}\end{bmatrix} <0, \tag{31a}\] \[ \begin{bmatrix}\overline{\Pi}_{11}&\overline{\Pi}_{12}&C_{i}\\ *&\Pi_{22}&0\\ *&*&-\alpha W_{i}\end{bmatrix} <0,\] (31b) \[ \begin{split} c_{1}e^{\kappa T}+\alpha d\eta e^{\kappa T}< \theta c_{2},\\ W_{i}<\eta I,\end{split}  \tag{31c}\]  
_where \(H\), \(\underline{\Pi}_{11}\), \(\overline{\Pi}_{11}\), \(\underline{\Pi}_{12}\), \(\overline{\Pi}_{12}\) and \(\Pi_{22}\) are defined the same as in Theorem 2. Furthermore, the control gain is given as_  
\[K_{i}=Y_{i}(P_{i}E^{T}+HS_{i}^{T})^{-1}.\]  
Proof of Theorem 3.: For the closed-loop SSMJS (4), it is easy to verify that it is regular and impulse-free from (31). A semi-positive definite Lyapunov-like function can also be designed as (10). Similarly, with the consideration of \(r(t)=i\), \(r(t+\Delta)=j\), the weak infinitesimal operator \(\ell\) can be obtained as  
\[ \begin{split}\ell V(x(t),r(t))=&\sum_{j=1}^{N}\mu_{ ij}(h)x^{T}(t)E^{T}(P_{j}E^{T}+HS_{j}^{T})^{-1}x(t)+x^{T}(t)\mathcal{A}_{i}^{T}(P_{i }E^{T}+HS_{i}^{T})^{-1}x(t)\\ &+x^{T}(t)(P_{i}E^{T}+HS_{i}^{T})^{-T}\mathcal{A}_{i}x(t)+\omega^ {T}(t)C_{i}^{T}(P_{i}E^{T}+HS_{i}^{T})^{-1}x(t)\\ &+x^{T}(t)(P_{i}E^{T}+HS_{i}^{T})^{-T}C_{i}\omega(t).\end{split}  \tag{32}\]  
Following the same philosophy as that in Theorem 1, we have  
\[\ell V(x(t),r(t))-\alpha\big{(}V(x(t),r(t))+\omega^{T}(t)W_{i}\omega(t)\big{)} = \begin{bmatrix}x(t)\\ \omega(t)\end{bmatrix} ^{T}\Psi \begin{bmatrix}x(t)\\ \omega(t)\end{bmatrix} , \tag{33}\]  
where  
\[ \begin{split}\Psi=&\begin{bmatrix}\Psi_{11}&(P_{i}E^{T}+ HS_{i}^{T})^{-T}C_{i}\\ C_{i}^{T}(P_{i}E^{T}+HS_{i}^{T})^{-1}&-\alpha W_{i}\end{bmatrix},\\ \Psi_{11}=&\sum_{j=1}^{N}\mu_{ij}(h)E^{T}(P_{j}E^{T}+HS_{j}^{T})^{-1}+ \mathcal{A}_{i}^{T}(P_{i}E^{T}+HS_{i}^{T})^{-1}\\ &+(P_{i}E^{T}+HS_{i}^{T})^{-T}\mathcal{A}_{i}-\alpha E^{T}(P_{i}E^{T}+ HS_{i}^{T})^{-1}.\end{split} \]  
By multiplying \(\Psi\) by \(\text{diag}\{(P_{i}E^{T}+HS_{i}^{T})^{T},I\}\) and its transpose on the left and right, respectively, and defining \(Y_{i}=K_{i}(P_{i}E^{T}+HS_{i}^{T})\), we get  
\[\bar{\Psi}= \begin{bmatrix}\bar{\Psi}_{11}&C_{i}\\ C_{i}^{T}&-\alpha W_{i}\end{bmatrix} , \tag{34}\]  
where  
\[ \begin{split}\bar{\Psi}_{11}=&\sum_{j=1}^{N}\mu_{ ij}(h)(P_{i}E^{T}+HS_{i}^{T})^{T}E^{T}(P_{j}E^{T}+HS_{j}^{T})^{-1}(P_{i}E^{T}+ HS_{i}^{T})\\ &+(P_{i}E^{T}+HS_{i}^{T})^{T}A_{i}^{T}+A_{i}(P_{i}E^{T}+HS_{i}^{T})+B_{i}Y _{i}+Y_{i}^{T}B_{i}^{T}-\alpha EP_{i}E^{T}.\end{split}
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 4 Experiments  
Three groups of tests were conducted: in the laboratory, on a revenue-service track at walking speeds, and in a simulated track environment at high speeds. These tests aimed to thoroughly investigate the applicability and effectiveness of the proposed system. The laboratory testing focused on evaluating the sensors' ability to capture tie and ballast surface figures in a controlled setting. The revenue-service track testing assessed the system's performance in an uncontrolled environment. Finally, high-speed testing on the simulated track evaluated the system's performance under high-speed conditions resembling those of in-motion inspections. The data collected from these experiments were used to develop robust data analysis methods for distinguishing ties and ballasts based on sensor measurements.  
Figure 3: Left and right LiDAR sensor installation for in-motion differentiation of ties and ballasts and their potential application for detecting track anomalies, such as sunk, tilted, or bowed ties.
# Quantum Selection for Genetic Algorithms Applied to Electromagnetic Design Problems
## 5 Electromagnetic Design Problems Considered
### Wideband Microstrip Filter  
For the design of the filter, a set of \(2N-1\)\(\lambda_{0}/2\) pieces of transmission line connected in a series is used. The filter is symmetric, meaning that the first and last pieces are equal, the second and second-to-last pieces are equal, and so on. In this way, only \(N\) variables are necessary to represent the whole circuit. The structure of this filter is presented in Figure 3.  
The filter behavior is well described by the transmission lines theory presented in [17,18]. The \(s_{21}\) parameters of this device are obtained by using its transmission matrices, the load impedance \(Z_{l}\), and the source impedance \(Z_{g}\) as shown in the following equation:  
\[s_{21}=\frac{2\sqrt{Z_{l}/Z_{g}}}{A+B/Z_{g}+(Z_{l}/Z_{g})(\mathbb{C}Z_{g}+D)} \tag{1}\]  
Figure 3: The schematic of the microstrip filter used as an electromagnetic design problem is shown. The filter is constructed using symmetric \(\lambda_{0}/2\) transmission lines, with widths representing the optimization variables.  
Figure 2: The considered mathematical test functions are plotted in two dimensions: (**a**) Manhattan norm, (**b**) Rastrigin function, and (**c**) Griewank function. These functions are used to assess the exploration and convergence behavior of the algorithms.  
As hinted before, the variables used in the optimization are the widths of the first N pieces of the filter. The fitness function is then computed as a weighted sum of the following parameters:  
* Mean of the in-band ripple;
* Maximum in-band ripple;
* Violation of the out-of-band mask;
* \(s_{21}\) at \(f/f_{0}=1\pm 0.5\);
* Penalization for bandwidth smaller than \(0.6f/f_{0}\).  
The mask is set symmetrically with respect to \(f_{0}\), starting at 0 dB at \(f/f_{0}~{}=~{}1\pm 0.3\) and descending 30 dB per \(\pm 0.1f/f_{0}\). The out-of-band violation is computed by taking the difference between the \(s_{21}\) of the filter and the mask, then taking the integral of the positive intervals of this subtraction. This process is better explained using the following equation:  
\[\text{Mask violation}=\int_{0.5f_{0}}^{0.7f_{0}}\max\{0,s_{21}-\text{mask} \}df+\int_{1.3f_{0}}^{1.5f_{0}}\max\{0,s_{21}-\text{mask}\}df \tag{2}\]  
The central frequency and filter dimensions are expressed in terms of \(f_{0}\) and \(\lambda_{0}\). This indicates that the filter design is general and can be used in any desired frequency compatible with microstrip technology for the available substrates.
## 2 Materials and Methods
### Classifiers
#### 2.5.1 Logistic Regression (LR)  
This paper primarily used LR, a widely used classifier in machine learning. The formula describing LR is similar to that of the linear regression model, in the form of \(ax+b\). The difference is that linear regression uses \(z=ax+b\) directly as the dependent variable, while LR maps \(z=ax+b\) to a discrete logical output via the Sigmoid function \(\sigma(y)\)[52].  
\[\sigma(y)=\frac{1}{1+e^{-z}} \tag{1}\]
## 2 Related Work
Orthogonally, various designs have been proposed to address DCN design issues such as energy consumption, cabling complexity, scalability, and over-subscription. One popular topology used today in data center networks is a fat-tree topology. To address oversubscription and other issues in wired DCNs many alternative DCN architectures have been proposed such as BCube, DCell, DOS, VL2, and Helios [35; 37]. However, these innovations still rely on copper or optical cables and do not mitigate the challenges due to high power consumption, design, and maintenance of a DCN with physical links. To alleviate the issues of DCNs with power-hungry switching fabrics and bundles of cables wireless data centers with mm-wave inter-rack links are envisioned in [18; 20; 21]. Most of the recent works on wireless data centers propose interconnecting entire racks of servers as units with 60 GHz wireless links primarily in order to utilize the commodity Ethernet switching between servers inside individual racks [18]. Phased antenna arrays or directional horn antennas are used to establish wireless links between ToRs in the entire data center [21; 22; 23]. Line-of-Sight (LoS) communication paths are necessary between the antennas for reliable communication in a wireless data center [21]. In [25] a novel wireless DCN architecture, based on 60 GHz wireless links between the individual servers of namely, S2S-WiDCN was proposed which drastically reduces the power consumption of the network portion of the data center while sustaining comparable performance. Hence, adopting an adequate wireless architecture for the HPC environment can result in significant power saving.
# Flow Duration Curves from Surface Reflectance in the Near Infrared Band
## 1 Introduction
Several satellite sensors, i.e., MODIS, MERIS, Landsat, Sentinel-2, OLCI, have been tested for this purpose in different river basins (i.e., Po River, Tibet Plateau, and Australia) [11; 12; 13; 14] confirming and strengthening the approach theory and further advancing its implementation. Although less reliable than the satellite radar altimetry, the use of imaging sensors has the big advantage of having high temporal resolution and a large coverage. Indeed, even if the revisit period of the satellite sensor is several days, because of the overlap on the imaging swaths of adjacent orbits, some areas of the Earth are observed more frequently (once a day or even more). In addition, within certain latitudes, the information is continuous and homogeneous without holes or diamond coverage, which is typically for altimetry records. As a drawback of the passive sensors, the cloud coverage impedes the view of the Earth surface with the consequence of having a percentage of missing data in some cases not negligible, especially in the tropical areas. However, due to the large availability of satellite data, it is expected that the variation of surface reflectance in the NIR band can provide precious information for the frequency of the flow. Therefore, this research aims to assess whether there is a potential relationship between the FDC built with the observed discharge and those built with the satellite information. This relationship should be useful when river flow records are no longer available or in case of ungauged sites. We test the potential of the surface reflectance from MODIS Aqua to estimate FDC in the main course of the Mississippi River, where several sites are equipped for flow discharge monitoring.
# A Survey of Routing Protocols in WBAN for Healthcare Applications
## 3 Classification of Routing Protocols for WBAN
### Temperature-Based Routing
#### 3.2.1 Thermal-Aware Routing Algorithm (TARA)  
The TARA protocol proposed in [PERSON] et al. [21] is the earliest achievement proposed in this field and is also a classic work. This method takes temperature as the only parameter of path selection and chooses the neighbor node with the lowest temperature as the next hop. Data sent to the hotpot are cached, which then waits for the destination node to cool down and is then retransmitted; if the waiting time exceeds a certain value, then the data are discarded. When data encounters a hotpot in transmission, the retreat strategy is adopted to return the original data path to find a new path. The back-off strategy used in the protocol, not only achieved good performance at that time, but also provided ideas for future research.
## 6 Open Challenges, Future Prospects and Research Directions
### Hybrid Adaptive Optics Techniques
#### 6.4.8 Adaptive Detection Thresholds
Relevance in Next-Generation Networks: As FSO communication is increasingly integrated into 5G architectures for high-speed data transmission, ADTs will play a crucial role in managing the complexities of multi-user environments, varying signal quality, and high data rates. Support for Massive Machine-Type Communications: The rise in mMTC and the IoT presents unique challenges for detection schemes. ADT strategies will need to adapt to the high density of devices and the potential for increased interference. The research will focus on developing ADT mechanisms that can efficiently manage these challenges, ensuring that each device can maintain reliable communication despite the presence of numerous concurrent transmissions.
## 5 Methods of Extraction of Bioactive Substances from Grape and Winery By-Products
### Enzyme-Assisted Extraction  
[PERSON] et al. (2005) improved the enzymatic hydrolysis of GP extracts using pectinolytic and cytolytic enzymes. After pre-extraction, 70.1% of phenolic acids, 75.2% of flavonoids, and 1.7% of anthocyanins were recovered, while after enzymatic processing, the two-step process yielded 98.1% phenolics, 96.8% flavonoids, and 2.9% anthocyanins. The success was explained by a reduced inhibitory effect on enzymatic digestion, because phenolics were partially extracted during the first stage. However, from the presented data, even pre-extraction did not significantly improve anthocyanin recovery [68]. Enzyme-assisted extraction is also a valuable tool for extracting nutrients from waste and agricultural by-products. It offers faster extraction, higher recovery, reduced solvent use, and lower energy consumption compared to non-enzymatic methods. It also showed the ability to operate under mild treatment conditions in aqueous solutions. This technique increases extraction yields and improves process kinetics while maintaining bioactive properties. Enzymes are catalysts that contribute to the extraction, modification, or synthesis of complex bioactive compounds of natural origin. Their application is particularly associated with the processing of plant material before conventional extraction methods [69].  
[PERSON] et al. (2021) aimed to evaluate the extraction of phenolic compounds, particularly proanthocyanidins, from grape pomace using high hydrostatic pressure (HHP) combined with enzyme-assisted extraction (EAE). Four extraction conditions were tested: EAE without HHP, EAE with pre-treated enzymes, simultaneous EAEand HHP, and HHP alone. Results showed that HHP increased enzyme activity by up to 16 times, with the best inhibitory \(\alpha\)-amylase activity in treatments E3 and E1. This study concluded that combining HHP and EAE effectively extracts valuable bioactive compounds from grape pomace [70]. [PERSON] et al. (2015) sought to optimize the enzyme-assisted extraction of flavonoids from grape skins using oenological enzymes. A Box-Behnken design was employed to analyze the effects of enzyme dosage, temperature, extraction time, pH, and enzyme type on flavonoid yield. Optimal conditions were found using Lall zyme EX-V at 45 \({}^{\circ}\)C for 3 h, pH 2.0, and 10.52 mg/g enzyme dosage. This method is cost-effective and environmentally friendly, suitable for both laboratory and industrial applications, allowing for immediate HPLC analysis without solvent removal [71]. [PERSON] and [PERSON] (2020) used alternative techniques to examine the yields of phenolic extraction from grape kernels following enzymatic treatment. Following the enzymatic procedure, UAE had the highest phenolic component extraction efficiency as compared to MAE. The study concluded that improved mass transfer led to increased solvent penetration into enzymatically treated cell components [72].
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Intravascular Environment  
The intravascular environment is lined by endothelial cells (ECs), which bind to adhesion molecules, such as clauds, forming structures depending on the properties of the organ [79; 80; 81]. The endothelium of veins and arteries forms a continuous monolayer, whereas that of capillaries appears continuous, separated, or discontinuous, depending on the function of the tissue [81]. ECs are supported by wall cells, attaching their basal side to a basement membrane (BM), limiting vascular leakage and hyperpermeability, thus acting as a dynamic barrier between blood and tissue [75; 81]. In tumors, ECs are poorly connected and, in some regions, multilayered, and the basement membrane shows irregular thickness and composition. Wall cells are fewer and loosely connected to ECs around tumor vessels, with the extent of these changes depending on the type of tumor [80]. In addition, tumor blood vessels may incorporate tumor cells, forming mosaic vessels [86]. The contractile potential of ECs contributes to the regulation of vessel diameter and blood pressure [81; 87]. Vascular smooth muscle cells support the high-pressure arterial system in the form of concentric layers and the lower-pressure venous system with thinner layers. Pericytes stabilize the smaller blood vessels, aiding in their maturation and EC proliferation through direct contact or tight junctions [81; 88].
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
###### Abstract
A decision tree algorithm based on machine learning (ML) is efficiently implemented as a strategy to maximize the number of transmissions, considering the limited and time-varying amount of available energy, and minimize the energy consumption if the incoming data does not show the occurrence of a sensitive event. The ML-DPMS is programmed in the Nordic nRF52840 microcontroller unit (MCU) maintaining a low-power approach. Figure 1 illustrates the conceptual idea of the proposed IoWT system. As can be seen, a PPG (photoplethysmography)-based IoWT system prototype is developed to experimentally evaluate the multimodal power management operation with the ML-decision tree algorithm. The system integrates an energy harvesting (EH) circuit and the MCU with a Bluetooth low energy (BLE) module built into the same device. The main contributions of this paper are the following:  
* A multimodal ML-DPMS approach that increases by at least 2.4\(\times\) the number of transmissions in comparison to uniform power management approaches when the incoming biosignal data has high variability, and the power of the supercapacitor is sufficient and achieves a good energy saving of up 39.6%, avoiding system shutdown, when the supercapacitor's energy is low. * A low consumption of 25.72 J for the IoWT system based on the ML-decision tree algorithm programmed in the Nordic nRF52840 MCU. A 380.8 mW solar PV (photovoltaic) power generation system (i.e., 99.7 J of energy harvested per day) which is 3.87 times more than the energy required for operation. * A highly integrated PPG-based wearable prototype to evaluate the ML-decision tree power management strategy. The remainder of this study is organized as follows: Section 2 presents the developed PPG-based IoWT sensor node prototype with the solar PV EH circuit. Section 3 describes the multimodal intelligent energy DPMS. Experimental results on real-time embedded hardware implemented on the wearable platform are presented in Section 5. Finally, the conclusions are stated in Section 6.
# Nanomaterials for Healthcare Biosensing Applications
## 4 Organic Nanomaterial Applications for Healthcare Biosensing
### Covalent Organic Frameworks
 & **LOD** & **Reference** \\ \hline DDCE-(ParamAM)-CATM-BSA/chit-AgI-anti-IL-6 & IL-6 & PEC & 10 ag mL\({}^{-1}\)–90 pg mL\({}^{-1}\) & 3.3 fg mL\({}^{-1}\) & [281] \\ \hline DDCE-(ParamAM)-CATM-BSA/g-C\({}_{3}\)N\({}_{4}\)-anti-PSA & PSA & PEC & 1 ag mL\({}^{-1}\)–90 pg mL\({}^{-1}\) & 33 ag mL\({}^{-1}\) & [281] \\ \hline Au-NHg[poly(HEMA-GCDM-APBA)] & Glucose & FS (LSPR) & 1–50 mM & 50 \(\mu\)M & [282] \\ \hline ITO-NHg(FMOC-Lys-FMOC-Pa)-Cyt C-FITC & H\({}_{2}\)O\({}_{2}\) & Ampereometry & \(3\times 10^{-7}\)–\(8\times 10^{-4}\) M & 50 nM & [283] \\ \hline GCE-NHg(Frc-Pha)-GOx-chit & Glucose & Ampereometry & 0.1–20 mM & 50 \(\mu\)M & [284] \\ \hline GCE-NHg(Pani)-PNNP-UOx & UA & Ampereometry & 0.07–1 mM & 1 \(\mu\)M & [285] \\ \hline GCE-NHg(Pani)-PNNP-ChosOx-ChEt & Cholesterol & Ampereometry & 0.3–9 mM & 0.3 mM & [285] \\ \hline GCE-NHg(Pani)-PNNP-ILP-GK-GPO & Triglyceride & Ampereometry & 0.2–5 mM & 0.2 mM & [285] \\ \hline Au-NHg(NAS-BSA-GOx) & Glucose & CV & 0.0–6.0 mM & \(1.116\times 10^{-2}\) M & [286] \\ \hline NHg(PPy-AuNP)-anti-CEA-BSA & CEA & DPV & \(1.0\times 10^{-6}\)–200 ng mL\({}^{-1}\) & 160 ng mL\({}^{-1}\) & [287] \\ \hline Pt-HBP(DAMS)-PNNP & NADH & CA & 87–2500 \(\mu\)M & 4.78 \(\mu\)M & [288] \\ \hline Pt-HBP(PMDUS)-PNNP & NADH & CA & 0–2100 \(\mu\)M & 6.18 \(\mu\)M & [288] \\ \hline HBP(ParamAM-NH2) AgNF\({}_{\text{shell}}\)-chit\({}_{\text{core}}\) & NH\({}_{3}\) & UV-vis & 10–50 ppm & 8 ppm & [289] \\ \hline
2D-COF (Z-amide-por)-MIP(P-PD)-GCE & Tetracycline & ECL & 5–60 pM & 2.3 pM & [290] \\ \hline COF (porphyrin)-CRF aptamer & CRP & PEC & 0.5–100 ng mL\({}^{-1}\) & 100 pg mL\({}^{-1}\) & [291] \\ \hline COF(TPa)-MIP(MA-AA-AM)-CD & Typramine & Fluorescence & 0.025–0.4 mg kg\({}^{-1}\) & 7.0 ug kg\({}^{-1}\) & [292] \\ \hline COF(TBAR)-MA-COCOH/AgNCG\({}_{\text{shell}}\)ANVP\({}_{\text{core}}\) & miRNA 155 & DPV & 10 fd–1 mM & 6.7 PM & [293] \\ \hline COF(TBAR)-MA-COCOH/Cu\({}_{20}\)Cu\({}_{20}\)