esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Comparison of Algorithm Performance
#### 4.3.1 Performance Metrics  
* False Negative (FN): The number of images incorrectly identified by the algorithm as not requiring segmentation, despite actually needing it;
* True Negative (TN): The number of images correctly identified by the algorithm as not requiring segmentation;
* True Positive (TP): The number of images correctly identified by the algorithm as requiring segmentation;
* False Positive (FP): The number of images incorrectly identified by the algorithm as requiring segmentation, despite not needing it.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[Precision=\frac{TP}{TP+FP}\text{or}\ Precision=\frac{TN}{TN+FN} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN}\text{or}\ Recall=\frac{TN}{TN+FP} \tag{5}\]  
\[Processing\ Speed=\frac{Total\ number\ of\ Image}{Total\ processing\ time} \tag{6}\]  
Figure 14: (**a**) The original image before segmentation, (**b**) The image segmented by Magefreehome, (**c**) The image segmented by ours.
## 3 Materials and Methods
### Design of Energy-Harvesting Antenna  
Electronic circuits may be directly integrated on 3D shaped dielectric substrates, making moulded interconnect devices (MID) a feasible solution for IoT applications [18]. The sensor nodes were realised using the SSAIL technique [2; 3]. Interconnections with excellent conductivity and resistance to extreme environmental conditions were created using electroless copper, nickel, and gold plating on the laser-activated surface. The sensor node electronics were integrated inside the moulded block.  
Multiple designs of patch and fractal antennas for energy harvesting, their matching circuits with a rectifier, and rectifier schemes have been schematically modelled and experimentally investigated. The antennas were formed on various polymer substrates with different dielectric constants using SSAIL technology to form electrical conductivity tracks. The antenna shape is based on fractals taken from the website [20]. It is similar to the  
Figure 3: Communication cycle of the sensor.  
Figure 2: Design of PCB board (**a**) and modified ESP32 module prototype manufactured using SSAIL technology (**b**).  
well-known Sierpinski gasket. It is shown that such a type of antenna made on a 1.6 mm FR4 substrate has five different resonant frequencies [21]. Figure 4 shows the designed and manufactured fractal antenna.  
However, the dimensions of the antenna are too large (200 mm), and neither resonant frequency matches the Wi-Fi router's lower working band. To adapt to the characteristics of the antenna Wi-Fi band, the antenna geometry on the PREPERM(tm) PPE440 plastic, which has a dielectric constant of 4.4 and thickness of 3.2 mm, was optimised. For optimisation, the FDTD solver, which simulated the main characteristics of the antenna, such as S\({}_{11}\) and gain, was used. The following were determined to be the optimal dimensions for the fractal antenna: the edges of the larger triangle should be 30 mm, while the edges of the smaller triangle should be 18 mm. The ratio between the edges of the smaller and larger triangles is 0.6. Therefore, the size of the whole antenna on the PPE440 substrate is 120 mm \(\times\) 120 mm. The excitation port is shifted down from the tip of the larger triangle by 23.5 mm and sideward by 1.5 mm. The other side of the substrate is grounded with a continuous copper layer. Theoretical calculations have shown that such an antenna has resonances at 2.43 GHz and 5.78 GHz, where S\({}_{11}\) is \(-\)30.3 dB and \(-\)28.3 dB, respectively, and the maximum gain reaches 6.3 dBi and 4.33 dBi.
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Co-primary Shared Access
#### Dynamic Spectrum Sharing by Pooling  
In this sharing strategy, MNO 1 shares the entire spectrum of MNO 2 with its in-building small cells. Due to reusing the spectrum of MNO 2, co-channel interference may occur with UEs of MNO 2 wherever they exist within the building. CoRS aggregating the spectrums of both MNOs in a common pool manages such interference by employing the ABS based eICIC technique as shown in Figure 4a. CoRS works as follows to coordinate the operation cycle of the base stations (BSs) of MNO 2 and the SBSs of MNO 1 so that transmission time intervals (TTIs) can be allocated properly to both MNOs. Whenever a UE of MNO 2 exists within a building of small cells of MNO 1, the CoRS informs the FD scheduler of small cells of the corresponding building to mute the transmission of all the SBSs within the building during ABSs and to allow small cells of MNO 1 to transmit only during non-ABSs (Figure 5) per APP.  
To ensure the fairness in allocating resources by the CoRS, an optimal number of ABSs per APP to operate both MNO 1 and MNO 2 is derived later based on the ratio of the average of rate of small cell UEs of MNO 1 to the average rate of UEs of MNO 2 within a building at any time. The average rate of UEs of both MNOs is updated in each APP. The value of APP could be 40 TTIs to 60 TTIs depending on the characteristics of the physical medium exchanging information for coordination between the CoRS and MNOs. In general, it is recommended to set the value of APP as high as possible to reduce control signaling for the coordination between entities and hence to reduce spending of the spectrum to carry these signaling such that the update in APP can still capture the characteristics of user traffic dynamics to keep intact the minimum QoS demand of each MNO. Note that the average rate of arrival of UEs of each MNO to a building can be defined by modeling the rate of arrival of UEs of any MNO using the Poisson process.
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Acronyms** & **Definitions** & **Acronyms** & **Definitions** \\ \hline AOPs & Apparent Optical Properties & MCS & Monte Carlo Simulation \\ \hline APD & Avalanche Photodiode & MIMO & Multiple-Input Multiple-Output \\ \hline AR & Albedo Rejection & MISO & Multiple-Input Single-Output \\ \hline ASPR & Absorption-Scattering Path Length & NLoS & Non-Line of Sight \\  & Rejection & & \\ \hline AW & Albedo Weight-based & PDF & Probability Density Function \\ \hline BER & Bit-Error-Rate & Pe & Probability of Error \\ \hline BLL & Beer-Lambert’s Law & PIN & Positive Intrinsic Negative \\ \hline BSF & Beam-Spread Function & PL & Path Loss \\ \hline CIR & Channel Impulse Response & PTP-LOS & Point-to-Point Line of Sight \\ \hline D-LOS & Diffuse Line of Sight & PTA & Photon-Tracing Algorithm \\ \hline DGF & Double-Gamma Function & RF & Radio Frequency \\ \hline DOM & Discrete Ordinate Model & RTE & Radiative Transfer Equation \\ \hline FF & Fournier–Forand & RV & Random Variable \\ \hline FOV & Field of View & Rx & Receiver \\ \hline FSO & Free Space Optical & SIMUs & Single-Input Multiple-Users \\ \hline FURS & Fully Uniform Random Selection & SNR & Signal-to-Noise Ratio \\ \hline Gbps & Gigabits per Second & SPAD & Single-Photon Avalanche Diode \\ \hline HG & Henyey–Greenstein & SPF & Scattering Phase Function \\ \hline IOPs & Inherent Optical Properties & SS & Sahu–Shanmugan \\ \hline ISI & Inter-Symbol Interference & SSA & Single-Scattering Approximation \\ \hline LD & Laser Diode & SSAA & Small-Scattering-Angle Approximation \\ \hline LED & Light-Emitting Diode & TSA & Two-Stream Approximation \\ \hline LOS & Line of Sight & TTHG & Two-Term Henyey–Greenstein \\ \hline mBLL & Microscopic Beer–Lambert’s Law & Tx & Transmitter \\ \hline Mbps & Megabits per Second & UOWC & Underwater Optical Wireless Communication \\ \hline MC & Monte Carlo & UVLC & Underwater Visible Light Communication \\ \hline MCRT & Monte Carlo Ray Tracing & VSF & Volume Scattering Function \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of acronyms.
# A Review of Neurotransmitters Sensing Methods for Neuro-Engineering Research
## 2 Neurotransmitter Detection Methods  
### Nuclear Medicine Tomographic Imaging  
#### 2.1.1 Positron Emission Tomography (PET)  
PET is a non-invasive neuroimaging technique that indirectly measures neuronal activity or neurotransmitters release [91; 92]. In nuclear medicine, it is used to measure the metabolic or molecular activity of an organ in three dimensions, after the intravenous injection of a radioactive tracer, usually based on an injected isotope with short half-life, whose disintegration results in positron emission. Then, when a positron encounters an electron, their annihilation produces two photons of 511 keV (gamma photons) that propagate in opposite directions towards a scintillator for detection, hence defining a line of response. Each annihilation that takes place is localized by measuring the respective times of flight of the two photons before detection, and the results contribute to the reconstruction of an image of the overall process by tomography.  
For cardiac, brain and tumor imaging, the radioactive element is often incorporated in glucose molecules to form a tracer with similar biological activity. Once in the blood stream, the tracer binds to glucose-consuming tissues, and the ensuing metabolic activity is recorded via functional images that are acquired by a gamma camera and reconstructed by a computer algorithm. For neurotransmitter release, the measurement relies on the competition between radio labeled neurotransmitter receptor ligands and the already bounded neurotransmitter target to its receptors [93]. It is related to measuring the change of binding potential of the receptor. For example, drug injection induces a dopamine change that is often measured with the \({}^{11}\)C-raclopride tracer, radio labelled as a D2/D3 receptor antagonist [93; 94; 95; 96]. Then, the dopamine release is inferred by the decrease in radioligand binding to the receptor sites.  
PET offers the potential of accurately finding where a given neurotransmitter is concentrated in the brain, but since it is based on the tracking of the tracer location, and the emitted photons are attenuated differently throughout the body, it is often necessary to normalize the observed activity with sophisticated algorithms for improved detection. On the other hand, the technique is almost with no risk to the patient, since a very small dose of radioactive tracer with low half-life is used.  
Before 2009, the so-called Novel Methods leading to New Medications in Depression and Schizophrenia (NEWMEDS) only validated radiologists for endogenous dopamine detection. Since then, the development of new radiologists permit the detection of extracellular concentrations of many neurotransmitters including dopamine, serotonin and noradrenaline in human, monkey and rat brain [5]. Indeed, the combination of PET, MRI and EEG are now used in clinical domain for increased spatial and temporal resolution measurements of neurotransmitters such as serotonin [6].
## 4 Results
### Dynamic Setup
#### 4.2.2 Number of Satellites  
In this scenario, Figure 10 shows the average number of satellites obtained during the measurements carried out while the vehicle was moving.  
Both devices performed nearly identically, with a slight advantage over the device with a linearly polarized antenna booster.
## 2 Preliminaries and Problem Formulation
### The Finite-Time Velocity Constraint Controller Design
Consider a Lyapunov function as follows:  
\[V_{u}=V_{5}+\frac{1}{2}\chi_{u}^{2} \tag{69}\]  
Taking the time derivative of \(V_{u}\) along (66)-(68), we have  
\[ \begin{split}\dot{V}_{u}=&-k_{u}u_{e}^{2}-k_{u1}|u_{ e}|^{\frac{3}{2}}-u_{e}\tilde{d}_{u}+k_{\chi_{u}}\chi_{u}u_{e}\\ &+u_{e}\Delta\tau_{u}-k_{\chi_{xu}}\chi_{u}^{2}-k_{\chi_{xu}}| \chi_{u}|^{\frac{3}{2}}+\chi_{u}\Delta\tau_{u}\end{split}  \tag{70}\]  
According to the Theorem 2, for \(\forall t\geq T_{2}\), the disturbance observation error \(\tilde{d}_{u}=0\). Therefore, for \(\forall t\geq T_{2}\), there are  
\[ \begin{split}\dot{V}_{u}=&-k_{u}u_{e}^{2}-k_{u1}|u_{ e}|^{\frac{3}{2}}+k_{\chi_{u}}\chi_{u}u_{e}\\ &+u_{e}\Delta\tau_{u}-k_{\chi_{xu}}\chi_{u}^{2}-k_{\chi_{xu}}| \chi_{u}|^{\frac{3}{2}}+\chi_{u}\Delta\tau_{u}\end{split}  \tag{71}\]Based on the Young's inequality, we can obtain  
\[u_{e}\chi_{u} \leq\frac{1}{2}u_{e}^{2}+\frac{1}{2}\chi_{u}^{2} \tag{72}\] \[u_{e}\Delta\tau_{u} \leq\frac{1}{2}u_{e}^{2}+\frac{1}{2}\Delta\tau_{u}^{2}\] (73) \[\chi_{u}\Delta\tau_{u} \leq\frac{1}{2}\chi_{u}^{2}+\frac{1}{2}\Delta\tau_{u}^{2} \tag{74}\]  
Substituting (72)-(74) into (71), we have  
\[ \begin{split}\dot{V}_{u}\leq&-\left(k_{u}-\frac{k_ {xu}}{2}-\frac{1}{2}\right)u_{e}^{2}-k_{u1}|u_{e}|^{\frac{3}{2}}\\ &-\left(k_{\chi_{u1}}-\frac{k_{xu}}{2}-\frac{1}{2}\right)\chi_{u} ^{2}-k_{\chi_{u2}}|\chi_{u}|^{\frac{3}{2}}+\Delta\tau_{u}^{2}\end{split}  \tag{75}\]
## 3 Application
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(Z_{1}\) & \(Z_{2}\) & \(Z_{3}\) & \(u_{H}\)=1 \\ \hline
3600 & 1.01 & 0.056 & \(\{u_{1,2},u_{4,2},u_{5,1},u_{6,1},u_{7,1}\}\) \\
4500 & 0.82 & 0.049 & \(\{u_{1,1},u_{4,2},u_{5,2},u_{6,1},u_{19,1},u_{7,1}\}\) \\
4600 & 1.14 & 0.044 & \(\{u_{1,2},u_{4,2},u_{5,2},u_{19,1},u_{17,1}\}\) \\
4800 & 0.67 & 0.087 & \(\{u_{1,1},u_{4,1},u_{5,1},u_{6,1},u_{7,1},u_{19,1},u_{17,1}\}\) \\
3500 & 1.17 & 0.052 & \(\{u_{1,2},u_{4,2},u_{5,2},u_{7,1}\}\) \\
4100 & 0.73 & 0.059 & \(\{u_{1,1},u_{4,1},u_{5,2},u_{6,1},u_{7,1},u_{19,1},u_{17,1}\}\) \\
3900 & 0.91 & 0.064 & \(\{u_{1,1},u_{4,2},u_{5,2},u_{6,1},u_{7,1},u_{14,1}\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A number of Pareto-optimal solutions by [PERSON] et al. [37] method and augmented epsilon constraint method. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(Wo_{1}\) & \(Wo_{2}\) & \(Wo_{3}\) & \(B_{1}\) & \(B_{2}\) & \(B_{3}\) & \(G\) & \(Z_{1}\) & \(Z_{2}\) & \(Z_{3}\) & \(u_{H}\)=1 \\ \hline
0.1 & 0.8 & 0.1 & 3700 & 0.6 & 0.065 & 0.512 & 3593 & 1.01 & 0.057 & \(\{u_{1,2},u_{4,2},u_{5,1},u_{6,1},u_{7,1}\}\) \\
0.3 & 0.5 & 0.2 & 4200 & 0.55 & 0.07 & 0.36 & 4095 & 0.73 & 0.061 & \(\{u_{1,1},u_{4,2},u_{5,1},u_{6,1},u_{7,1},u_{9,1},u_{17,1}\}\) \\
0.3 & 0.3 & 0.4 & 3500 & 0.635 & 0.043 & 1.78 & 3494 & 1.17 & 0.052 & \(\{u_{1,2},u_{4,2},u_{5,2},u_{7,1}\}\) \\
0.45 & 0.45 & 0.1 & 4000 & 0.735 & 0.063 & 0.344 & 3996 & 0.89 & 0.55 & \(\{u_{1,1},u_{4,2},u_{5,2},u_{7,1},u_{9,1},u_{17,1}\}\) \\
0.6 & 0.3 & 0.1 & 3900 & 0.65 & 0.06 & 0.867 & 3888 & 0.91 & 0.065 & \(\{u_{1,1},u_{4,2},u_{5,2},u_{6,1},u_{7,1},u_{14,1}\}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: A number of Pareto-optimal solutions by the fuzzy credibility method and the goal attainment method. presented goal attainment method. The results are presented in Table 5. As can be seen, two Pareto-optimal solutions computed by the fuzzy \(\alpha\)-cut method are compared with two Pareto-optimal solutions by the two suggested fuzzy approaches. The calculated values of the cost objective function (\(Z_{1}\)), which are obtained by the proposed approaches, are better than the values of the fuzzy \(\alpha\)-cut method. Moreover, the fuzzy credibility theory and [PERSON] et al. [37] method are known as efficient approaches to solve fuzzy mathematical models without increasing the objective functions and model constraints. However, some other fuzzy approaches increase objective functions and constraints, so the problem becomes more complicated, and the solution time increases.
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 2 Taxonomy of Blockchain-Based Multimedia Content Protection Systems
### Data Automation  
A self-automated code, also known as a smart contract, consists of a program code, a storage file and an account balance. It is executed by miners that use consensus protocols to agree upon the sequence of actions resulting from the contract's code. Any user can create a contract by posting a transaction to the blockchain. The program code of a contract is fixed when the contract is created and cannot be changed, thus providing immutability. A contract's storage file is stored on the public blockchain. A smart contract can be invoked by entities within (other smart contracts) and outside (external data sources) the blockchain. While executing its code, the contract may read from or write to its storage file. Moreover, it can receive money into its account balance, and send money to other contracts or users. A smart contract is identified by a hash of 160 bits (a hexadecimal address used by many cryptocurrencies, e.g., Ethereum or LiteCoin, among others), and is operated within the environment that supports the use of a public-key cryptography. Smart contracts are widely used in most of the currently existing cryptocurrency networks and are the prominent features of Ethereum. A smart contract can be used to perform one type of transaction, while a distributed application (dApp) can bundle a set of smart contracts together to perform complex transactions. This dApp is a user-friendly interface (similar to a traditional website) that allows users to interact with the smart contracts stored on the blockchain.  
Smart contracts could offer a number of benefits, such as accuracy (less prone to manual error), lower execution risk (automatic execution by the network), fewer intermediaries (reduced reliance on third-party intermediaries), lower cost (less human intervention and fewer intermediaries), speed and real-time updates (automated tasks), and new business models (from DRM and watermarking/fingerprinting of multimedia content to automated access to storage units).
###### Abstract  
Transportation efficiency and safety are crucial development areas nowadays. Cooperative Intelligent Transport Systems (C-ITSs), relying on Vehicle-to-Everything (V2X) communication, are a promising group of technologies and applications aimed at solving several issues related to road safety or efficiency. The C-Roads Platform was brought to life to ensure the cross-border harmonization of C-ITS at a European level, guiding several pilot activities in national deployment projects and providing a harmonized pan-European C-ITS service perspective. Because of the safety relevance of V2X technologies, it is essential to ensure that the crucial parameters of wireless communication are within an acceptable range to serve C-ITS applications appropriately. In this work, we developed a simulation pipeline to evaluate future V2X deployments using the real-world traffic and map data of a C-Roads harmonized major Hungarian C-ITS deployment site. First, we selected three time periods representing different traffic patterns. Then, we reconstructed the flow-based traffic data from the real-world traffic counters for the selected time periods. We developed an approach based on linear equations to perform the conversion. Eventually, we used the real-world data to simulate the effects of various DSRC (ITS-G5-based) C-ITS services and V2X penetration rates on the Channel Busy Ratio (CBR) parameter of the radio access environment.  
Cooperative Intelligent Transport Systems (C-ITSs); Vehicle-to-Everything (V2X); ITS-G5; Channel Busy Ratio (CBR); Artery/OMNeT++; CAM; CPM; C-Roads Patform; Crocodile Project +
Footnote †: journal: Applied  
1
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### RNA m6 Am Modification in GBM  
N6,2'-O-dimethyladenosine (m6 Am) is a terminal modification at mRNA cap (usually occurs in the first and sometimes the second nucleotide after the N7-methylguanosine (m7G) cap) in higher eukaryotic mRNAs and ncRNAs, expecially snRNAs, as well as viral RNAs [109]. m6 Am plays essential roles in RNA splicing [110], snRNA biogenesis [111], mRNA stability [112] and cap-dependent translation [113]. In human beings, phosphorylated CTD interacting factor 1 (PCIF1, also known as cap-specific adenosine methyltransferase, CAPAM) and methyltransferase-like 4 (METTL4) are the m6 Am writers, the m6A eraser FTO also functions as an m6 Am eraser, and mRNA-decamping enzyme 2 (DCP2) may be one of the m6 Am readers [109; 111; 114; 115].  
Recent evidence shows that FTO-mediated m6 Am demethylation is an important regulatory mechanism in adjusting the stem-like properties of colorectal cancer cells [116], and many clues also imply that m6 Am also emerges as an essential regulator in GBM (Figure 2, m6 Am part). For instance, an integrated proteogenomic analysis in 17 GBM patient samples reveals that METTL4 is one of the top-ranked missense mutation genes [77]. DCP2 is upregulated in miR-338-5p overexpressed GBM cells and may participate in radiosensitivity and DNA damage response induced by miR-338-5p overexpression in GBM cells [78]. DCP2 is also one of top 20 hub genes of the optimal competing endogenous RNA (ceRNA) network in GBM and other cancers [117]. These results imply that m6 Am probably participates in the development of GBM.
## 3 Results
### Incentive for Using FITC Surrogate to Track Actual Compound Distributions across Device Wells 3-11
(**a1,b1**) % concentrations of FITC surrogate, per well, measured. (**a2,b2**) \(\mu\)Molar concentrations of diclofenac, per well, calculated from FITC % (i.e., diclofenac dose (well 1) \(\times\) FITC % (well specific). (**a3,b3**) CYP3A4 fold-\(\Delta\) using device in “no flow” static mode. (**a4,b4**) CYP3A4 fold-\(\Delta\) from compound dosing in device ufluidics. (**a5,b5**) Values are mean \(\pm\) SEM, (n = 10) ***** \(p<0.00001\). cell viability with equivalent high or low measures (Figure 6(b1,d1/d2 or e1/e2)). Adjacent wells might show upstream wells with lower cell viability (Figure 6(b2)). Adjacent wells might show upstream wells having higher cell viability (Figure 6(b3)). To investigate the DMSO control vehicle as a potential cell influencer, DMSO was deemed an actual study compound at 0.1% DMSO (i.e., 0.1 _v/v_) and dosed daily into device well 1, over 7 days (Figure 6c). At day 1, the DMSO _v/v_ auto-dilutes from 0.075 (well 3) to 0.000 (well 10), with well-to-well cell viability ranges from 98% (well 3) to 97% (well 8) to 98% (well 10). At day 3, after daily re-dosing, the DMSO _v/v_ auto-dilutes from 0.093 (well 3) to 0.004 (well 10), with cell viability scales 98-99%. At day 7, after daily re-dosing, the DMSO _v/v_ auto-dilutes from 0.112 (well 3) to 0.047 (well 10), with cell viability scales 95-98%. As quantified, the DMSO vehicle does not influence cell culture viability across all device wells including upstream (wells 3-5), midstream (wells 6-8), or downstream (wells 9-11) culture sites. In this study, because well 11 undergoes pipette media changes, this site is considered an outlier culture well (Figure 6c well 11). In brief, the data indicates the morphological appearance of cells can be imaged with high content imaging (Cytation 5 Reader/Imager, BioTek, Winooski, VT, USA) using objective positioning software allocating for x, y, and z-height stair-step differences across the ulfluidic device. Likewise, quantitative cell viability data indicate that the DMSO control vehicle can be considered a baseline-control comparison for other drug studies.
## 7 Discussion and Limitation  
### Anonymity Trilemma  
The anonymity trilemma is one of the limitations of this work, where there is a trade-off between performance and user privacy protection [50]. We leave the choice between network performance and anonymity to the user in order to trade off network overhead and transmission reliability, and privacy. If the user needs a network environment with better performance, they can choose a smaller number of slices, as well as the number of entry ORs, to reduce overhead. In other words, some anonymity is sacrificed to improve network performance, and the reverse remains true. For Tor multi-path transmission, the failure rate of carrying out WFP attacks is lower due to the greater number of slices and circuits, and a smaller portion of the adversary's limited data, which is not enough to represent the full connection information of the loaded web page. We are also continuing our in-depth research to solve this problem, and now that we have made the WFP attacks largely ineffective, we will next focus on how to reduce unnecessary network overhead and overcome the anonymity trilemma.