esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Key Findings and Impact  
* Robust anomaly detection: the hybrid model effectively detected anomalies with high recall, minimizing false negatives.
* Adaptive thresholding: the dynamic anomaly thresholding approach improved robustness against environmental variations.
* Intrusion vs. malfunction differentiation: the combination of Isolation Forest and XGBoost effectively classified anomalies into their respective categories.
* Classifier sensitivity: some malfunction points (blue dots) appeared above the threshold, suggesting that the classifier identified them based on subtle feature patterns beyond reconstruction error.  
The results highlight the potential of using hybrid AI-based anomaly detection frameworks in forest fire monitoring. Future research will explore further refinements in real-time detection, sensor fusion techniques, and improved generalization under diverse environmental conditions.
## 4 Network Layer: The Main Building Block of Fixed Networks
### F1G PONs
#### 4.1.9 Summary of PON Standards  
In Figure 11, the allocated fiber spectrum of each PON standard is illustrated. A summary on the main characteristics of various PON standards is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**GPON**} & \multicolumn{3}{c}{**EPON**} & \multicolumn{3}{c}{**10G-EPON**} & \multicolumn{3}{c}{**XG-PON**} \\ \hline APD & 2.6 & 2.6 & 2.6 & 2.6 & 2.6 & 2.6 & 2.6 & 2.6 & 2.6 & 2.05 \\ TIA & 83.4 & 83.4 & 83.4 & 83.4 & 83.4 & 83.4 & 83.4 & 83.4 & 123 & 123 \\ \(\frac{1}{2}\) & 126 & 110 & 126 & 121 & 121 & 121 & 145 & 154 & 154 \\ \(\frac{1}{2}\) & 520 & 560 & 790 & 610 & 545 & 910 & 610 & 356 & 356 \\ \(\frac{1}{2}\) & 560 & 560 & 790 & 550 & 550 & 910 & & NA & NA & NA \\ Total & 1292 & 1056 & 1002 & 696 & 1302 & 1046 & 1117 & 696 & NA & NA & NA \\ \hline \end{tabular}  
* Cells in color denote integration. For example, the red color denotes the integration of LP-A and CDR components.  
\end{table}
Table 6: Power consumption [mW] of ONU/ONT without—with component integration \({}^{1}\).  
Figure 10: ONU/ONT Energy Consumption (basic modes) in arbitrary units.
# 4-Class MI-EEG Signal Generation and Recognition with CVAE-GAN
## 3 Methods
### Optimization of Training Process
The effect taking from the discriminator can be denoted as:  
\[L_{GD}\leftarrow\frac{1}{2}||\;f_{D}(x_{r})-f_{D}(x_{f})||_{2}^{2}. \tag{9}\]  
In classifier part, the classification loss can be denoted respectively as:  
\[L_{\text{C}}\leftarrow\frac{1}{2}(||y_{r}-c||_{2}^{2}+||y_{g}-c||_{2}^{2}). \tag{10}\]  
The goal of the CVAE-GAN is to minimize the loss function as follows:  
\[L=L_{KL}+L_{\text{G}}+L_{\text{GD}}+L_{D}+L_{\text{C}}. \tag{11}\]  
The whole training pipeline of the proposed algorithm is shown in Algorithm 1. ```
Initialization:\(m\) is the batch size, \(n\) is the number of category, epoch is the number of iteration  Initialize the different network (E, G, D) with random weights. 1: Sample \(\{x_{r}\}\sim p_{r}m\) batch from real EEG data 2: Map feature parameters \(\mu,\sigma\gets E(x_{r},\sigma_{r})\) using Encode network. 3: Resample feature parameters to obtain latent representation \(z\) 4: Generate conditional samples \(x_{g}\gets G(z,c_{r})\) through the generator network. 5: Feed the real and generated samples into the decoder network for authenticity identification \(d_{r}\gets D(x_{r}),d_{g}\gets D(x_{g})\) and MI-task classification \(\hat{\hat{y}}_{r}\gets C(x_{r}),\hat{\hat{y}}_{g}\gets C(x_{g})\). 6: Optimize CVAE-GAN by loss \(L\): \(L_{KL}\gets KL(q(z|x_{r},c)||P_{z})\) \(L_{\text{G}}\leftarrow\frac{1}{2}||\;x_{r}-x_{f}||_{2}^{2}\;L_{\text{GD}} \leftarrow\frac{1}{2}||\;f_{D}(x_{r})-f_{D}(x_{f})||_{2}^{2}\;L_{D}\leftarrow- \log(d_{r})+\log(1-d_{g})\) \(L_{\text{C}}\leftarrow\frac{1}{2}(||y_{r}-c\left\|_{2}^{2}+||y_{g}-c\left\|_ {2}^{2}\right. )\) \(L\gets L_{KL}+L_{G}+L_{D}+L_{\text{C}}\) 7: Update network parameters \(\omega_{\text{F}}\leftarrow-\
abla_{\omega_{\text{F}}}(L_{KL})\;\omega_{\text {G}}\leftarrow-\
abla_{\omega_{\text{C}}}(L_{G}+L_{GD})\) \(\omega_{\text{D}}\leftarrow-\
abla_{\omega_{\text{D}}}(L_{D})\;\omega_{\text {C}}\leftarrow-\
abla_{\omega_{\text{C}}}(L_{\text{C}})\) Output: End untill has converged and save all network parameters. ```  
**Algorithm 1****Training Process of the Proposed CVAE-GAN**
## 2 Experiments on a Reduced-Scale Laboratory Bench
### Results
#### 2.4.2 Influence of the Frequency  
To study the influence of the frequency, we chose to consider two current values: one below 1 A, leading to nearly ohmic characteristics (800 mA), and the other above 1 A, to observe nonlinear behaviours (6 A). Three frequency intervals were chosen: 230 Hz-1.8 kHz, 2.3 kHz-18 kHz, and 23 kHz-40 kHz, with four frequency values considered in each. The results of these tests are presented in Figures 4 and 5. In the first case (\(I_{max}\) = 800 mA), the characteristics are quasilinear, with identical slopes for 230 Hz and 2.3 kHz, and then hysteresis starts to appear for 23 kHz, with the average slope remaining approximately the same. In the second case (\(I_{max}\) = 6 A), the characteristics are clearly nonlinear. Quasi-superimposable up to 9 kHz; they then show an increasingly pronounced hysteresis as the frequency increases. At the highest frequencies, the tension-current characteristic evolves towards a shape close to an ellipse, together with a slight decrease in the maximum voltage.  
Figure 4: Comparison of voltage–current characteristics (over a few periods) for 3 frequency values, for a maximum current intensity of 800 mA.  
Figure 3: Comparison of voltage–current characteristics (over a few periods) measured before and after a sequence at maximum intensity of 6.3 A, for three different current values (signal frequency: 2300 Hz).
## 3 Results and Discussion
### Infrared Spectral Split Peak Fitting of Coal Tar Residue and Extractives
#### 3.4.3 Results of the Peak Splitting Fit for the 2800-3000 cm\({}^{-1}\) Interval  
Figure 9 shows the IR peak splitting curves for coal tar residue and its three extractives in the 2800-3000 cm\({}^{-1}\) region, and the corresponding fitted data results are shown in Table 9. The acetone residue was dominated by symmetric and asymmetric -CH\({}_{2}\) stretching vibrations, while the CS\({}_{2}\) and acetone/CS\({}_{2}\) mixed solvent residues were dominated by asymmetric -CH\({}_{2}\) stretching vibrations.  
Figure 8: Infrared spectra of the original and extractive residues in the 1000–1800 cm\({}^{-1}\) region. ((a) is an original tar residue fit, (b) is a fit to carbon disulphide, (c) is a fit to acetone and (d) is a fit to a mixed solvent. The red line is the fit line and the blue line is the sub peak).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Peak \#**} & \multirow{2}{*}{**Position \(\sigma\) (cm\({}^{-1}\))**} & \multirow{2}{*}{**Assignment**} & \multicolumn{5}{c}{**Area Percentage (\%)**} \\ \cline{4-7}  & & & **E** & **ACE** & **CS\({}_{2}\)** & **ACE/CS\({}_{2}\)** \\ \hline
1 & 1600 & Aromatic C=C & 11.03 & 14.63 & 14.30 & 14.85 \\
2 & 1440 & Asymmetric -CH\({}_{3}\), -CH\({}_{2}\) & 31.37 & 4.87 & 27.18 & 36.16 \\
3 & 1380 & Symmetric -CH\({}_{3}\), -CH\({}_{2}\) & 0 & 33.57 & 0 & 3.82 \\
4 & 1320 & Ar-O-C & 11.15 & 13.66 & 22.59 & 14.43 \\
5 & 1150 & C-O-C & 37.39 & 13.97 & 18.90 & 4.96 \\
7 & 1110 & S=O & 0 & 2.16 & 2.58 & 19.91 \\
8 & 1020 & Si-O-C & 9.06 & 17.14 & 14.45 & 5.87 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Infrared spectra of four samples 1000–1800 cm\({}^{-1}\) split peak fitting ratio of each absorption peak area.
# A Compact Multi-Identity Fully Homomorphic Encryption Scheme Without Fresh Ciphertexts
###### Abstract  
The lattice-based multi-identity fully homomorphic encryption scheme combines the quantum security of lattice cryptography with the advantage of identity-based encryption. However, existing schemes face challenges such as large key sizes, inefficient ciphertext expansion processes, and reliance on outdated trapdoor designs, limiting their compactness and practicality. In this study, we propose a novel Compact Multi-Identity Fully Homomorphic Encryption Scheme (WZ-MIBFHE) that eliminates the need for fresh ciphertexts during expansion. First, we construct a compact identity-based encryption scheme by combining the YJW23 trapdoor and ABB10 under the standard model, proving its IND-sID-CPA security. The scheme is then adapted to ensure correctness and security when integrated with the decomposition method for ciphertext expansion. This adaptation also utilizes approximation errors to reduce overall noise. Finally, we expand the modified IBE scheme's ciphertext using the decomposition method to construct the WZ-MIBFHE scheme. Compared to existing methods, WZ-MIBFHE reduces the lattice dimension to \(n\text{log}\,q+\text{log}_{q}\,\text{q}\), improves public and private key sizes, and significantly lowers ciphertext expansion rates by removing the need for fresh ciphertexts. These improvements enhance both the compactness and efficiency of the scheme, making it a promising solution for multi-identity homomorphic encryption.  
Keywords:fully homomorphic encryption; identity-based encryption; multi-identity; learning with errors; fresh ciphertext +
Footnote †: journal: Applied sciences
## 3 Materials and Methods
### Forecasting Models
#### 3.2.4 Seasonal Long Short-Term Memory  
The essential part of the proposed seasonal long short-term memory (SLSTM) model is how to explicitly input the weekly and monthly information in the input layer of LSTM. Due to the flexibility of LSTM architecture, this task is easy to accomplish. In our method, we extend the time series data by adding the seasonal information about week, month, and quarter. The weekly, monthly, and quarterly information are regarded as features, and those features are entered into the SLSTM network model individually or in combination. In the following formula, S represents weekly (\(S^{w}\)), monthly (\(S^{m}\)), and quarterly (\(S^{q}\)) features or consists of their combination. Seasonal features are added in the form of one-hot codes, as follows:  
\[\begin{array}{ll}S_{i}&=\left(S^{w}_{i},S^{m}_{i},S^{q}_{i},S^{vm}_{i},S^{mq }_{i},S^{vqq}_{i},S^{vmnq}_{i}\right)\\ S^{w}_{i}&=\left(\text{Monday,}\text{Tuesday,}\text{Wednesday,}\text{Thursday,} \text{Friday,}\text{Saturday,}\text{Sunday}\right)\\ &=\left(0,\ 1,\ 0,\ 0,\ 0,\ 0,\ 0\right),\ \text{when}\ i\ \text{is}\ \text{Tuesday}.\\ S^{m}_{i}&=\left(\text{January,}\text{February,}\text{March,}\text{April,}\text{ May,}\text{ June,}\text{July,}\text{August,}\text{September,}\text{October,}\text{November,}\text{December}\right.\\ &=\left(0,\ 0,\ 1,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0,\ 0\right),\ \text{when}\ i\ \text{is}\ \text{March}.\\ S^{q}_{i}&=\left(\text{the first quarter,}\ \text{the sec}\ \text{ond quarter,}\ \text{the third quarter,}\ \text{the fourth quarter}\right)\\ &=\left(0,\ 1,\ 0,\ 0,\ 0\right),\ \text{when}\ i\ \text{is}\ \text{thefirstquarter.}\\ S^{vnm}_{i}&=\ S^{w}_{i}+S^{m}_{i},\ S^{mq}_{i}=\ S^{m}_{i}+S^{q}_{i},\ S^{mq}_{i}=\ S^{w}_{i}+S^{q}_{i},\ S^{vmnq}_{i}=\ S^{w}_{i}+S^{m}_{i}+S^{q}_{i},\ \text{where}\\ &+\ \text{indicates the concatenation of two one -- hot codes.}\end{array}\]  
The input data of the network is sampled by concatenating \(S^{w}_{i}\) and \(x_{i}\). If \(x_{i}\) is Tuesday, it is sampled in the form (\(x_{i}\), 0, 1, 0, 0, 0, 0, 0). Figure 8 shows the weekly features individually entered into the SLSTM network.  
Figure 7: LSTM input data sample creation. (**a**) Data sampling for LSTM input (\(w=3\), \(h=2\)). (**b**) Representation of feature vector X and label Y.
## 2 Bulk Crystal Characteristics
### Continuous Laser Performance  
The effective gain cross-section (\(\sigma_{g}\)) of Yb\({}^{3+}\):ScBO\({}_{3}\) was determined using the following formula before the lasing experiments, as presented in Figure 6:  
\[\sigma_{g}(\lambda)=\beta\sigma_{em}(\lambda)-(1-\beta)\sigma_{abs}(\lambda) \tag{7}\]  
where \(\beta\) is the proportion of the active ions excited to the upper level. The least value of \(\beta_{min}(1021\ \mathrm{nm})\) is determined using the following formula:  
\[\beta_{min}(1021\ \mathrm{nm})=\frac{\sigma_{abs}(1021\ \mathrm{nm})}{\sigma_{abs} (1021\ \mathrm{nm})+\sigma_{em}(1021\ \mathrm{nm})}=0.1 \tag{8}\]  
Figure 5: RT fluorescence lifetime fitted by exponential functions. Inset: the linear fitted lifetime with log (intensity) vs. time. (Source: [34], © 2015 Optica Publishing Group, Washington, DC, USA).  
The positive part of \(\sigma_{g}\) is presented in Figure 6 as different values of \(\beta\). When \(\beta\) = 0.75, the positive extent, which resembles Yb3+:YAG (950-1080 nm) [50], Yb3+:CYB (960-1100 nm), and Yb3+:CaGB (960-1100 nm), was 970-1100 nm [47; 51]. The excellent characteristics of these crystal are suitable for tunable and femtosecond lasers. When \(\beta\) = 0.75, Yb3+:ScBO3 could, in theory, back a pulse continuation of 65 fs. The correlation between the gain bandwidth (\(\Delta\
u\)) and the shortest pulse duration (\(t_{FWHM}\)) was used to determine the pulse continuation [52]:  
\[t_{FWHM}\cdot\Delta\
u=0.315 \tag{9}\]  
The experimental configuration is depicted in Figure 7 and a plane-concave cavity, which was 42 mm in length, was applied for CW laser generation. The emission wavelength of the pump source was 976 nm and the crystal was cut along the \(a\) axis, uncoated, and burnished. Furthermore, the input coupler had a high reflectance, from 1000 to 1100 nm, and a transmittance of over 99.5% at 950-990 nm. The transmittance from 1000-1100 nm of the output coupler was 5%.  
A CW laser has been demonstrated and the corresponding laser performance is exhibited in Figure 8. It indicates that the threshold was 2.38 W. Under a 4.9-\(W\) pump power, the output power rose to 167 mW, and the slope efficiency was 9% at a wavelength of 1.063 \(\upmu\)m. The light-to-light conversion efficiencies, calculated from the data above, are 4% and 9%, respectively. The laser performance can be greatly enhanced by utilizing a higher transmission of the output couplers, mode matching, coating conditions, etc.  
Figure 6: \(\sigma_{g}(\lambda)\) of Yb3+:ScBO3 crystal versus wavelength. (Source: [34], © 2015 Optica Publishing Group, Washington, DC, USA).  
Figure 7: The experimental setup of continuous laser. (Source: [34], © 2015 Optica Publishing Group, Washington, DC, USA).
# An Accurate Jacobian Matrix with Exact Zoeppritz for Elastic Moduli of Dry Rock
## 7 Conclusions  
Here, the Biot-Gassmann equations and the exact Zoeppritz equations were combined to calculate the partial derivatives of seismic wave reflection coefficients with respect to the dry rock moduli (bulk and shear moduli). It was an accurate way to get the Jacobian matrix rather than using the Zoeppritz approximations. This accurate matrix was further used in the AVO/AVA inversions to invert bulk and shear moduli of dry rock directly. The numerical tests above indicated that, when a seismic wave refracts from the low impedance medium (rock filled with oil) to the high impedance medium (rock filled with water), the partial derivatives of reflection coefficients, with respect to the elastic moduli of dry rock, had a singular or zero point at the critical angle. When a seismic wave refracted from the high impedance medium (rock filled with water) to the low impedance medium (rock filled with oil), the partial derivatives of reflection coefficients, with respect to the elastic moduli of dry rock, were no longer complex numbers. There was no singular or zero point at the critical angle in this case. Additionally, the reflectivity of the PS-wave was not sensitive to the bulk moduli and not suitable to be used in the inversion of them. The errors of classical Shuey and Aki-Richards approximations were still very large, even when the incidence angles were relatively small. Our proposed algorithm provides an important theoretical basis for direct and accurate inversion of dry rock parameters from seismic data in future AVO/AVA inversions.  
Conceptualization, X.L. and F.L.; methodology, X.L. and F.L.; software, X.L. and F.L.; formal analysis, J.C.; investigation, J.C. and Z.Z.; writing-original draft preparation, X.L.; writing-review and editing, X.L., J.C. and F.L.; funding acquisition, J.C. and F.L.  
This research was funded by the Research and Development Funds of Sinopec (P18070-5); Sinopec Northwest Oilfield Company (34400008-18-ZC0607-0031, 34400008-18-ZC0607-0030); Research and Development Funds of Sinopec (P18070-5); Sinopec Northwest Oilfield Company (34400008-18-ZC0607-0031, 34400008-18-ZC0607-0030) and the National Natural Science Foundation of China (41574126).  
The authors acknowledge the Faculty Internationalization Grant at the University of Tulsa. The suggestions and comments from anonymous reviewers and assistant editor [PERSON] are greatly helpful for improving the quality of our paper.  
The authors declare no conflict of interest.
## 4 Results and Discussions
### Results Analysis  
The results are obtained after applying the learned model to the recorded lecture clips. The students not interested in lectures displayed some dominant features such as using a mobile phone, sleeping, laughing with other fellows, closing their eyes, and yawning, as shown in Figure 5. Attentive students taking great interest in the lecture are detected using the employed features since such students are found to be writing notes, communicating with the teacher, and looking at the board. Figure 6 depicts engaged examples detected through the proposed model. Figure 7 depicts the results of the trained model on an unseen frame during the lecture process. Model results depict almost all the clear faces and label them with an accurate engaged or non-engaged tag according to their detected features.  
The collected data for 8 days were organized gender and timestamp-wise. We refer to males with 1 and females with 0. Similarly, we denote the morning session with 1 and the evening with 0. A total of 60 frames were collected from each clip and a total of 60 values against each student were categorized as either engaged or non-engaged. In some frames, some students were not detected because their faces were partially or completely hidden behind other students. In some cases, the students were absent, so we also ignored those values. The following graphs in Figure 8 show that the engaged data are positively skewed, and the non-engaged data are negatively skewed. More precisely, the \(x\)-axis shows the 60 bins representing 60 frames from each video, while the \(y\)-axis represents the engaged and non-engaged frames distribution of students. For instance, it may be observed from the left distribution that the highest number of engaged counts is around 35, which means that it appears 47 times that students remain engaged in 35 extracted frames out of a total of 60 frames. Similarly, the right distribution depicts that the highest number of non-engaged count is around 10, meaning that it appears 38 times that students remain non-engaged in 10 frames out of a total of 60 frames.  
Figure 5: Accuracy versus loss with respect to train and test splits for VGG16 (**a**,**b**) model and proposed VGG16 with dense layers (**c**,**d**) model to recognize engaged non-engaged frames from testing video.  
Figure 6: Detected non-engaged frames from testing video.
# Seasonal Variations of Carbon Dioxide, Water Vapor and Energy Fluxes in Tropical Indian Mangroves
## 4 Results and Discussions
### NEE and Photosynthetic Active Radiation (PAR)
The theoretical reported quantum yield for photosynthesis in C3 species carried out under controlled environmental conditions was 0.052-0.053 \(\mu\)mols CO\({}_{2}\)\(\mu\)mol\({}^{-1}\) of absorbed photons at a leaf temperature of 30 \({}^{\circ}\)C [52]. The maximum value of quantum yield in the present study was found to be around 20% less than the theoretical maximum, which appears to be a reliable estimate considering the actual site measurements in comparison with the controlled measurements. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & \(P_{max}\) **(\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\). s\({}^{-1}\))** & \(\alpha\) **(\(\mu\)mol CO\({}_{2}\)\(\mu\)mol\({}^{-1}\))** & \(R_{e}\) **(\(\mu\)mol CO\({}_{2}\) m\({}^{-2}\). s\({}^{-1}\))** & \(R^{2}\) \\ \hline
12 April & 13.94 \(\pm\) 0.45 & 0.0281 \(\pm\) 0.0016 & 2.45 \(\pm\) 0.08 & 0.86 \\
12 May & 12.47 \(\pm\) 0.55 & 0.0205 \(\pm\) 0.0019 & 2.31 \(\pm\) 0.16 & 0.71 \\
12 June & 11.19 \(\pm\) 0.33 & 0.0305 \(\pm\) 0.0025 & 3.19 \(\pm\) 0.12 & 0.77 \\
12 July & 13.39 \(\pm\) 0.26 & 0.0377 \(\pm\) 0.0030 & 2.80 \(\pm\) 0.17 & 0.76 \\
12 August & 10.69 \(\pm\) 0.23 & 0.0377 \(\pm\) 0.0034 & 3.05 \(\pm\) 0.18 & 0.75 \\
12 September & 10.15 \(\pm\) 0.28 & 0.0398 \(\pm\) 0.0038 & 2.78 \(\pm\) 0.16 & 0.68 \\
12 October & 11.77 \(\pm\) 0.42 & 0.0305 \(\pm\) 0.0030 & 2.74 \(\pm\) 0.19 & 0.68 \\
12 November & 13.74 \(\pm\) 0.35 & 0.0337 \(\pm\) 0.0022 & 2.93 \(\pm\) 0.13 & 0.84 \\
12 December & 14.72 \(\pm\) 0.40 & 0.0378 \(\pm\) 0.0033 & 3.22 \(\pm\) 0.22 & 0.75 \\
13 January & 17.92 \(\pm\) 0.55 & 0.0313 \(\pm\) 0.0026 & 3.53 \(\pm\) 0.24 & 0.78 \\
13 February & 18.45 \(\pm\) 0.68 & 0.0308 \(\pm\) 0.0025 & 3.15 \(\pm\) 0.22 & 0.79 \\
13 March & 18.54 \(\pm\) 0.84 & 0.0245 \(\pm\) 0.0015 & 2.42 \(\pm\) 0.12 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Monthly estimates of ecosystem photosynthesis parameters with standard error (95% confidence). Figure 13: Least square fits of NEE _vs._ PAR for (**A**) September 2012 to December 2012 (hollow circles); (**B**) Rest of the study period (April-August 2012 and January–March 2013) (filled circles).
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 3 Blockchain Based on ICN
### Introduction to ICN Networks
Additionally, the study in [14] designed and implemented an Ethereum network operating on NDN. However, existing research on ICN-based blockchain systems [13; 14; 16; 33] has primarily focused on the implementation and compatibility of blockchain communication protocols within ICN/NDN networks. Limited studies have explored blockchain communication mechanisms in hybrid IP/ICN networks, leaving room for further optimization of blockchain P2P communication by leveraging ICN's inherent advantages. Therefore, in the context of hybrid IP/ICN networking, optimizing blockchain communication mechanisms to harness ICN's efficient content distribution capabilities remains an open research challenge.