esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Multipath routing protocol, which is one of the typical schemes to improve end-to-end transmission success ratio in Wireless Sensor Networks (WSNs), exploits two or more paths. However, collisions and interference might occur when each path is adjacent. To prevent this problem, they construct node- or link-disjointed paths. Although the paths could prevent the above-mentioned problem, it still has an issue in the area of time delay. To exploit the disjointed paths, pre-path construction is required. In addition, a lot of time is incurred to recover the whole path when the part of the path is damaged. This phenomenon adversely affects the end-to-end transmission success ratio and transmission time. To alleviate this problem, we propose a cluster-disjoint multipath routing protocol. The proposed scheme divides the whole network into grid clusters of a certain size in the network initialization phase. Each sensor could transmit packets through the pre-constructed cluster structure without the path construction phase. Also, even if any node fails, it could be easily replaced by other nodes within the cluster region. The simulation results show that the proposed scheme has an advantage in terms of transmission delay and end-to-end transmission success ratio compared to existing multipath routing protocols.  
Wireless Sensor Networks (WSNs); multipath routing protocol; cluster-disjoint; real time; reliability +
Footnote †: journal: _Sensors_**2023**, 23, 8876. [[https://doi.org/10.3390/s23218876](https://doi.org/10.3390/s23218876)]([https://doi.org/10.3390/s23218876](https://doi.org/10.3390/s23218876))
## 2 MRP Models and Algorithm Description
### Complexity Analysis of the MRP Model  
The main analytical steps of the derivation of the complexity of the model are reported in Appendix A. The total computational complexity of the MRP model is:  
\[\begin{split} N_{\text{s}}^{(real)}(S)&=4S\log_{2}S+4S+ N_{step}[8S\log_{2}S+20S]\\ N_{+}^{(real)}(S)&=6S\log_{2}S+2S+N_{step}[12S\log_{2}S+ 13S]\end{split} \tag{10}\]  
Assume that the sequence processed by the model consists of two sampling points per symbol, we can calculate the computational complexity of each symbol corresponding to different branch numbers and sequence lengths, \(S\), according to Equation (10), as shown in Figure 3.  
It is evident that the complexity of the MRP model is directly proportional to both the number of branches and the length of the input sequence. It is worth noting that FFT is the main source of algorithm complexity. For sequences with a length of \(S=4096\), the number of multiplication and addition used by radix-2 FFT accounts for 84% and 61% of the total number of multiplication and addition in the MRP model, respectively. In addition, we remind readers that using the calculation method of base radix-4 FFT can further reduce the  
Figure 3: Complexity of MRP models under different conditions: (**a**) \(N_{step}\) and (**b**) FFT length.  
Figure 2: Conceptual diagram of MRP model structure and training method.  
complexity of the algorithm. Under the same sequence length, \(S\), radix-4 FFT can reduce the number of multiplications to 3/4 as opposed to radix-2 FFT. In addition, when calculating both x-polarization and y-polarization simultaneously, the results of mode squared can be reused to reduce computational complexity.
## 3 Theoretical Analysis  
We present a scheme that utilizes polar codes specifically designed for the VLC channel. Additionally, we detail a model that integrates polar codes and network coding, including PNC in a relay-assisted VLC system. We also conduct a thorough analysis of the theoretical performance of this scheme.
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 3 FPGA Implementation
### Implementation of the Convolution Kernel  
The convolution kernel is the core component of the convolutional layer. Repeated experiments show that the accuracy of detection reached the highest when the length of the input vector was  
Figure 3: The FPGA (field programmable gate array) implementation block diagram. Reg: register; Clk: clock; ADC: analog-to-digital converter; EN: enable.  
slightly larger than the sampling times of each carrier period. In the design, the length of the input vector is \(M+1;M\) denoting the sampling times of each carrier period. In order to accurately detect the phase shifts in the input vectors, the convolution operation mode was always selected, meaning that the input vector and the output vector had the same length. According to the rule of convolution calculation, the output vector with the length of \(M+1\) can be obtained only if the input vector is expanded to the length of \(2M+1\). We used the element 0 to expand the vector. The expanded input vector convoluted with the convolution kernel \(M+1\) in length, and the result is shown in Figure 4.  
As shown in Figure 4, in order to complete this convolution calculation, \((M+1)^{2}\) multiplication operations must be done. Some of the operations include the multiplier '0', which can be ignored. Calculation results of data in the line frame had all appeared in previous steps or would appear in the forthcoming steps. By feeding some of the results into the time-delay network that was composed of several time-delay queues, a large number of repeated calculations could be avoided. The structure of the time-delay network is shown in Figure 5.  
Figure 4: The convolution calculation result for the input vector and convolution kernel.  
Figure 5: The structure of the time-delay network. ROM: read-only memory. D: delay unit; x: input; y: output; M: sampling times of each carrier period.  
Assuming that \([x_{1},x_{2},\ldots,\ x_{M+1}]\) is the current input vector, together with data in the time-delay network, we can obtain the convolution result \([y_{1},y_{2},\ldots,\ y_{M+1}]\) by only \(M+1\) multiplication operations. The calculation complexity is reduced by one dimension. The cost of the time-delay queues is far less than the multipliers in FPGA implementation. Under the control of the clock in the FPGA, data can be accurately beat-delayed with the occupation of few hardware resources. The timing sequence of the time-delay network is shown in Figure 6.
# Numerical Simulation and Parameter Optimization of Air Slide Based on CFD-DEM
## 5 Conclusions  
This study conducted a numerical simulation of the conveying process in an air slide using Fluent and Rocky gas-solid coupling method, with conveying efficiency and material layer stability as comprehensive evaluation indicators. Three key parameters--inlet air velocity, permeable baffle porosity, and chute inclination angle--were analyzed. The results show that the following:  
1. The conveying ratio of the chute is proportional to both inlet air velocity and inclination angle but inversely proportional to baffle porosity. Surface fluctuations and layer stability improve with increasing inclination angle, initially decrease then increase with higher air velocity, and worsen with greater porosity.
2. Through orthogonal experiments (three factors, four levels), the optimal combination for maximizing conveying efficiency is identified as A3B1C2 (inlet velocity = 1.8 m/s, porosity = 37.84%, inclination angle = 6\({}^{\circ}\)), while the optimal combination for minimizing surface fluctuations is A1B4C1 (inlet velocity = 1.4 m/s, porosity = 58.13%, inclination angle = 5\({}^{\circ}\)). Factor influence analysis revealed that for conveying efficiency, the importance ranking is porosity > air velocity > inclination angle, whereas for layer stability, it is air velocity > porosity > inclination angle.
3. This study demonstrates that conveying efficiency and surface fluctuation cannot be optimized simultaneously. Matrix analysis determined the balanced optimal parameter combination to be inlet velocity = 1.8 m/s, porosity = 37.84%, and inclination angle = 6\({}^{\circ}\).
4. Comparative analysis between experimental and simulation results demonstrated consistent trends in how various factors affect the conveying performance of different four types. This agreement verifies both the reliability of the simulation model and the feasibility of using air slides for wheat flour transportation.  
Conceptualization, C.Z.; methodology, C.Z.; software, Y.Z.; formal analysis, Y.Z.; investigation, Y.L.; resources, Y.L.; data curation, X.G.; writing--original draft preparation, Y.Z.; supervision, C.Z. All authors have read and agreed to the published version of the manuscript.  
The Natural Science Project of Henan Provincial Department of Science and Technology--Key Scientific and Technological Project--Research on Controllable Micro-quality and Energy-saving Technology Application in Wheat Flour Milling Process (242102110133) and the Joint Fund of Henan Provincial Science and Technology Research and Development Program--Mechanism and Application Research on Moderate Processing of Intelligent Wheat Milling Equipment (242103810063).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Raman Analysis
### Raman Spectra Analysis of TiO\({}_{2}\)-Peg-2 Me  
The Raman spectrum of the functionalized TiO\({}_{2}\) NPs which have been coated with PEG and loaded with 2 ME is given in Figure 4e. This figure displays the Raman spectra above 1000 cm\({}^{-1}\) (the low frequency bands of TiO\({}_{2}\) were not modified during the functionalization process). The Raman spectrum of TiO\({}_{2}\)-PEG-2 ME was slightly modified in the range of 1250-1450 cm\({}^{-1}\) with respect to TiO\({}_{2}\)-PEG (Figure 4c). In this range, we can find the resulting vibration mode between PEG and 2 ME. The first band, located around 1300 cm\({}^{-1}\), corresponds to C-H vibration of PEG and O-H vibration of 2 ME; this band became more broad in comparison with the band located in the same range in TiO\({}_{2}\)-PEG (Figure 4c). The second band, located around 1447 cm\({}^{-1}\), corresponds to the vibration of C-H plus O-H vibration and the aromatic ring C-C-H vibration group of pure PEG and 2 ME, respectively [28; 37]. This band became more pronounced and defined due to the new hydrogen bridge bonding between PEG and 2 ME. It was not possible to identify each the modes of vibration associated with the finger print of 2 ME in TiO\({}_{2}\)-PEG-2 ME composite by the use of Raman spectroscopy. The bands corresponding to aromatic ring and functional groups located in the range 1500-1400 cm\({}^{-1}\) and 1350-1200 cm\({}^{-1}\), respectively, were screened by the C-H vibration mode of PEG. Although a change in form and intensity with respect to TiO\({}_{2}\)-PEG is evident, this suggests the contribution of the functional groups of 2 ME in the composite. However, the bands located around 1600 and 3000 cm\({}^{-1}\) (corresponding to C=C aromatic ring chain vibrations and C-H groups) cannot be distinguished in the TiO\({}_{2}\)-PEG-2 ME nanocomposite. On the other hand, all these vibrations are clearly visible using FTIR spectroscopy.
## 3 Results and Analysis
### Calibration and Validation of Sweetness Estimation Models  
With the features screened by SPA and CARS as the independent variables, the ki-wifruit sweetness estimation models under different harvest periods were constructed. The R\({}^{2}\) and RMSE of each prediction and validation model were shown in Tables 4 and 5.  
Figure 4: Feature extraction of SG, SNV and MSC transformation spectra under SPA and CARS methods.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transformed Spectra** & **Method** & **No. of Features** & **Sensitive Band (nm)** \\ \hline \multirow{3}{*}{SG} & CARS & 15 & 422 959 1143 1230 1231 1232 1233 1234 1235 1396 1397 1398 1399 2425 2490 \\  & SPA & 16 & 404 562 677 708 757 812 978 982 1081 1234 1395 2395 2471 2479 2489 2500 \\ \hline \multirow{3}{*}{SNV} & CARS & 19 & 959 1142 1143 1144 1145 1228 1229 1230 1231 1232 1233 1234 1235 1388 \\  & SPA & 19 & 1389 1390 1391 1392 2424 \\  & SPA & 13 & 400 405 408 411 699 724 913 1138 1420 1781 902 1987 2169 \\ \hline \multirow{3}{*}{MSC} & CARS & 20 & 644 645 647 708 929 942 1138 1139 1140 1231 1232 1233 1234 1392 1393 \\  & SPA & 8 & 1394 1395 1835 1836 2490 \\ \hline \multirow{3}{*}{} & \multicolumn{3}{c}{Note: “SG” means “Savitzky-Golay Smoothing”; “SNV” means “Standard Normal Variate”; “MSC” means “multiplicative scatter correction”; “SPA” means “successive projections algorithm”; “CARS” means “competitive adaptive reweighted sampling”.} \\ \cline{2-5}  & & & \\ \end{tabular}
\end{table}
Table 3: Selection of sensitive bands using different methods.
## 2 Design and Simulation Results
### Design Equations for the RCSRR and RSRR  
For the initial choice of the RCSRR of major axis length \(L_{i}\) and minor axis length \(W_{i}\) in Figure 1b, the design equations for a desired notched band frequency \(f_{notch}\) are given by:  
\[L_{i}+2W_{i}=\frac{\lambda_{g}}{2}=\frac{c}{2f_{notch}\sqrt{\varepsilon_{eff}}} \tag{1}\]  
\[\varepsilon_{eff}=\frac{\varepsilon_{r}+1}{2}+\frac{\varepsilon_{r}-1}{2} \Bigg{(}1+\frac{12h}{w_{f}}\Bigg{)}^{-0.5} \tag{2}\]  
where \(\varepsilon_{r}\) is the relative permittivity of the substrate, \(\lambda_{g}\) is the guided wavelength at the desired frequency, \(c\) is the speed of light, and \(f_{notch}\) is the desired notched frequency. The effective dielectric constant can be calculated from Equation (2), where \(h\) is the height of the substrate and \(W_{f}\) is the width of the microstrip feedline. Parametric simulations have been performed to obtain the optimal width of the RCSRR. \(W_{s}\) equals 0.3 mm.  
For the initial choice of the RSRR of horizontal axis length \(L_{x}\), vertical axis length \(L_{y}\) and width of the RSRR \(W_{s}\) in Figure 1b, we can use Equation (3) to obtain the desired notch frequency for the resonators:  
\[2\big{(}L_{x}+L_{y}-2W_{s}\big{)}=\frac{\lambda_{g}}{2}=\frac{c}{2f_{notch(2) }\sqrt{\varepsilon_{eff}}} \tag{3}\]  
where \(f_{notch(2)}\) is the desired resonant notched band frequency for the RSRR, and \(\lambda_{g}\) is the guided wavelength at the desired frequency.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** & **Parameter** & **Value (mm)** \\ \hline L\({}_{\text{sub}}\) & 30 & L\({}_{2}\) & 4.3 & W\({}_{1}\) & 25 & W\({}_{7}\) & 6.7 \\ L\({}_{\text{grad}}\) & 9.0 & L\({}_{3}\) & 3.5 & W\({}_{2}\) & 2.75 & W\({}_{8}\) & 5.9 \\ W\({}_{\text{sub}}\) & 29 & L\({}_{4}\) & 11 & W\({}_{3}\) & 1.75 & W\({}_{8}\) & 0.30 \\ W\({}_{\text{f}}\) & 3.0 & L\({}_{5}\) & 9.0 & W\({}_{4}\) & 6.5 & L\({}_{\text{x}}\) & 3.4 \\ L\({}_{\text{f}}\) & 10.3 & L\({}_{6}\) & 8.4 & W\({}_{5}\) & 12.4 & L\({}_{y}\) & 4.4 \\ L\({}_{1}\) & 10.5 & L\({}_{7}\) & 7.8 & W\({}_{6}\) & 9.0 & 81 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the reference and proposed antennas.  
Figure 2: Prototype of the reference and proposed antenna. (**a**) The reference antenna (front view); (**b**) the proposed antenna (front view); and (**c**) the reference and proposed antenna (rear view).
## 1 Introduction  
The automatic classification of colorectal polyps is essential for the early diagnosis of colorectal cancer, one of the leading causes of mortality worldwide [1]. Although deep learning (DL) models have demonstrated high accuracy in classifying medical images, the lack of transparency and explainability raises concerns among healthcare professionals about using these technologies in the clinical context [2]. The complex nature of DL models, often called a 'black box', makes it difficult to understand the reasons behind the predictions, limiting their acceptance and adoption [3]. For these models to be widely used in medical practice, their decisions must be interpreted and justified, ensuring alignment with clinical knowledge and promoting greater confidence in their results [4].  
Faced with this challenge, explainable artificial intelligence (XAI) methods have been developed to make model predictions more understandable to healthcare professionals [3]. One promising approach is example-based XAI, which uses visual samples to demonstrate how controlled variations in data affect model predictions. Techniques such as prototypes [5; 6], CBIR/CBR [7; 8], and generation of counterfactuals without [9; 10] and with the use of GANs [11; 12] have been applied in different clinical contexts to improve the interpretability of models. However, these approaches have not been explored, and few studies evaluate their impact on specific data characteristics [13].  
The main objective of this work is to develop an XAI approach based on generated examples using the Pix2 Pix GAN architecture [14] to evaluate how classifiers react to controlled variations in the size and shape of polyps. Complementing this analysis, techniques such as LIME [15] are integrated to provide a richer insight into the behaviour of the models.  
This study's contributions include developing a model for generating controlled synthetic images to represent different characteristics of polyps, proposing an example-based XAI approach to explore how classifiers respond to these variations, and conducting a comparative analysis that evaluates the effectiveness of different XAI techniques, enriching the understanding of model behaviour and its limitations. To provide a clear overview of the methodology, the entire workflow of our proposed approach is illustrated in Figure 1.
###### Abstract  
This study investigates the mechanical properties of Glass Fiber-Reinforced Plastic (GFRP) pipes in the circumferential direction using the split-disk method, with a focus on understanding the influence of aggressive environmental conditions. The split-disk method was used to determine the key mechanical properties, including the hoop tensile strength and modulus, and also the Poisson's ratio, which are critical for the performance of GFRP pipes under internal pressure. The experiment was conducted under controlled laboratory conditions, and the results were analyzed to assess the effects of exposure to aggressive environments (saltwater and alkaline solutions at 20 \({}^{\circ}\)C and 50 \({}^{\circ}\)C). The correlations between the UTS, elastic modulus, and Poisson's ratio highlight how GFRP pipes degrade under environmental exposure. As the UTS decreases, so do the stiffness and lateral deformability, with the most significant reductions occurring in chemically aggressive environments at high temperatures. Exposure to an alkaline solution weakens the GFRP pipes, with the strength dropping more sharply at higher temperatures, with the UTS decreasing by 21%. Saltwater exposure reduces the elastic modulus, especially at higher temperatures, with a 14% decrease, accelerating material degradation and reducing deformation resistance. An alkaline solution further lowers the modulus, with a 21% decrease at 50 \({}^{\circ}\)C, showing the lowest stiffness. Air exposure, in contrast, has a less severe effect, with the pipes retaining much of their mechanical integrity. These findings collectively suggest that environmental degradation affects the overall mechanical behavior of GFRP pipes, providing valuable insights for the design and maintenance of GFRP piping systems, particularly in industries where exposure to aggressive environments is common. This study underscores the importance of considering environmental factors in the material selection and design processes to ensure the long-term reliability of GRP pipes.  
split-disk test; FEM; GRP pipes; environmental effects; hoop tensile strength; Poisson's ratio +
Footnote †: journal: Applied sciences
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 9 Practical Applications and Use Cases
### Tokenization of Cultural Heritage and Other Valuable Digital Assets  
#### 9.1.1 Preservation of World Heritage Sites  
NFT-based tokenization can be applied to the preservation and management of UNESCO World Heritage Sites. By creating unique digital tokens for each site, accurate records of the site's history, conservation efforts, and ownership can be maintained. The decentralized nature of blockchain technology ensures that these records are transparent, tamper-proof, and accessible to stakeholders, including governments, conservation organizations, and the general public. This can facilitate the allocation of resources, enhance collaboration, and promote responsible tourism.
# Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network
## 3 Research Methodology
### Traffic Sign Detection Phase  
To detect a traffic sign from a captured real-time image, initially image pre-processing was performed to eliminate the noise of unwanted background, normalizing the intensity of the different elements of the images, eliminating reflections, and image portions masking. Afterward, to improve the image quality in terms of brightness, and contrasts, histogram equalization technique was employed, as shown in Figure 4.  
Figure 3: Proposed system for traffic sign detection and recognition.  
Finally, the traffic sign was detected from the captured image using detection phase as shown in Figure 5. As shown here, the quality of raw input image was enhanced by using the histogram equalization and it was read into color and grayscale mode. Moreover, a median filter was used to remove existing noise from the grayscale image and then the grayscale image was converted into a binary image using the threshold level of 0.18. This threshold level was used because it gives the best performance for sign detection. Afterward, small objects from the binary image were removed. Additionally, the target traffic signs' property such as center, width, and height were calculated by using shape measurement to determine the ROI. Finally, based on the calculated ROI, the detected traffic sign was extracted from the original red, green, blue (RGB) colored image in colored mode.  
An example illustration of the detection system has been shown in Figure 6. Here, Figure 5(a) is representing an image frame having a complex background, and Figure 5(b) is representing an image frame having an ordinary background. As shown here, in our proposed system, the detection phase can extract both the image frame's ROI and it can also extract target traffic sign candidates.  
Figure 4: Histogram equalization technique.  
Figure 5: Block diagram of traffic sign detection phase.  
Figure 6: Traffic sign detection phase illustration; (**a**) Complex background; (**b**) Ordinary background.
###### Abstract  
Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time-frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner-Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT's superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems.  
Keywords:partial discharge; finite element method; time-frequency analysis; deep learning; convolutional neural network; high-voltage insulation +
Footnote †: journal: Applied sciences