esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 WDM-PON Network Architectures
### The RING Architecture of the Access WDM-PON Network  
This RING access architecture connects ONU units in a ring topology where the OLT terminal is connected to this ring through bidirectional optical connections [22; 23]. The OLT and ONU equipment must be changed for transmitting and receiving optical signals in downstream and upstream directions. Proposed RING access architecture can support also a re-modulation of the optical signals for increasing a transmission efficiency of dedicated wavelength channels. This architecture offers an advantage of the flexible wavelength allocation where more wavelength channels can be assigned to one ONU unit for increasing the transmission capacity and for the better network scalability.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Navigation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test** & **Null Hypothesis (H\({}_{0}\))** & **Test Statistic** & \(p\)**-Value** & **Result** \\ \hline Shapiro–Wilk (RTK + Encoders) & Data is normally distributed & W = 0.7750 & \textless{}0.000001 & Reject H\({}_{0}\) (Not normal) \\ \hline Shapiro–Wilk (No Sensors) & Data is normally distributed & W = 0.8165 & \textless{}0.000001 & Reject H\({}_{0}\) (Not normal) \\ \hline Shapiro–Wilk (Encoder Only) & Data is normally distributed & W = 0.9522 & \textless{}0.000001 & Reject H\({}_{0}\) (Not normal) \\ \hline Levene’s Test & Equal variances across groups & W = 79.7068 & \textless{}0.000001 & Reject H\({}_{0}\) (Unequal variances) \\ \hline Kruskal–Wallis Test & Equal medians across groups & H = 166.4544 & \textless{}0.000001 & Reject H\({}_{0}\) (Significant difference) \\ \hline [PERSON]–Whitney U (RTK vs. NoSens) & Same distribution & U = 32,807 & \textless{}0.000001 & Significant \\ \hline [PERSON]–Whitney U (RTK vs. EncOnly) & Same distribution & U = 17,023 & \textless{}0.000001 & Significant \\ \hline [PERSON]–Whitney U (NoSens vs. EncOnly) & Same distribution & U = 34,401 & 0.013369 (adj) & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Summary of hypothesis test results for route deviation across sensor configurations. suggests that relying solely on encoders without RTK is insufficient for reliable tracking in unstructured environments.
## 4 Experimental Results and Discussion
### Analysis of Coupling Effect of Vortex-Induced Flutter Excitation
When the wind velocity is high, the symmetrically distributed airfoil of the VFEH-30, on the one hand, together with the cylindrical bluff body, causes the cantilever beam to generate bending vibration, while with the hollow bluff body makes the structure not completely symmetrical due to manufacturing, assembly, etc., or reasons such as the two airfoils being excited by the wind energy which is not completely consistent, which induces the cantilever beam to produce torsional vibration [47]. The vortex-induced vibration generated by the cylindrical bluff body and the flutter generated by the airfoil work together, and the coupling of the bending vibration and the torsional vibration is the increase in the deformation of the cantilever beam, and then the internal strain of the piezoelectric ceramic sheet increases, and the potential difference generated on the upper and lower surfaces. That is, the output voltage is increased, so the VFEH-30 can reach the maximum output at relatively low wind velocities, and at the same time, the harvested wind energy is much larger than that of the VEH-30 and FEH energy harvesters. In order to test the performance of the energy harvester proposed in this paper, the composite energy harvester proposed in this paper is compared with the same type of energy harvester, and the energy harvesting characteristics of the energy harvester are quantitatively evaluated, as shown in Table 7. The energy harvesters compared are all piezoelectric energy harvesters that combine two energy harvesting mechanisms. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(\mathbf{V_{0}}\) **(m/s)** & \(\mathbf{V_{\text{max}}}\) **(m/s)** & \(\mathbf{U_{\text{max}}}\) **(V)** & \(\mathbf{P_{\text{max}}}\) **(W)** \\ \hline VEH-30 & 7.1 & 12.5 & 3.59 & 0.05 \\ FEH & 8.5 & 14.5 & 9.53 & 0.26 \\ VFEH-30 & 4.8 & 9.0 & 60.20 & 6.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Key characteristics of the three energy harvesters. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Mechanism** & **Airflow Velocity (m/s)** & **Output Power (mW)** \\ \hline Bryant and Garcia [31] & Piezoelectric & 8.0 & 2.20 \\ [PERSON] [29] & Piezoelectric & 4.0 & 4.00 \\ This work & Piezoelectric & 9.0 & 6.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the proposed and existing energy harvesters. From the two structures mentioned in Table 7, it can be seen that the energy harvesting structure proposed in this paper has relatively good output performance. For example, [PERSON] proposed a T-shaped piezoelectric cantilever beam structure for fluid energy harvest, its output performance is better than that of [PERSON] and [PERSON], which only contains airfoil structure and a single piezoelectric harvest energy. The composite structure of vortex excitation and flutter proposed in this paper is superior to the two and can effectively improve the maximum output voltage and output power.
## 4 An Illustrative Example  
To analyze the performance of the proposed model, we refer to the case study of [PERSON] et al. (2013), who used failure mode and effects analysis (FMEA) to improve safety in the blood transfusion process ([PERSON] et al., 2017). The analysis spans the entire process--from blood collection to transfusion--focusing on critical stages such as patient identification, blood collection, and matching. By pinpointing possible \"failure modes\" at each stage, such as mislabeling samples or mismatching blood types, the study aims to prevent errors and failures.  
A multidisciplinary team conducted the FMEA to ensure risks were evaluated from various perspectives. Each failure mode was rated based on its severity (impact on patient safety), likelihood of occurrence, and detectability. Table 1 summarizes the 19 failure modes identified in the case study and the scores assigned by the team for each FMEA variable. The table also includes the calculation of the RPN for each failure mode.  
This case study has been used in various articles as a means of validating models such as the fuzzy VIKOR model and the ITHWD model ([PERSON] et al., 2017). For this reason, we consider it acceptable to use the same case study to compare the performance of the proposed model with the performance of the original RPN model, the fuzzy VIKOR model, and the ITHWD model. In the paper that introduced the ITHWD model ([PERSON] et al., 2017), only 11 out of the 19 failure modes from the original case study were considered. Table 2 presents these 11 selected failure modes, with numbering consistent with Table 1. In addition to listing the failure modes, Table 2 also displays the rankings generated by the original RPN, the fuzzy VIKOR model, and the ITHWD model. The performance of the proposed model is then compared with these results.  
Figure 1: Flowchart of the proposed model illustrating the calculation sequence for prioritizing failure modes.
## 3 Comparative Analysis of Existing Evaluation Methods
### Accuracy for Different Levels of Fines Content  
The fines content significantly impacts liquefaction, yet its influence pattern remains unclear. The fines content is defined as the percentage of particles smaller than 0.075 mm by mass. The accuracy of each formula is compared across three fines content intervals: \(FC\leq 5\%\), 5% < \(FC\leq 35\%\), and \(FC>35\%\) (Figure 5).  
At a \(FC\leq 5\%\), Formulae (7) and (18) exhibit the highest overall accuracy, with consistent results in both the liquefaction and non-liquefaction areas. The other formulae also perform well, indicating an effective liquefaction assessment at low fines contents. Specifically, Formula (11) shows a higher accuracy in non-liquefaction areas, with slightly lower conservativeness than others. The overall accuracy of Formula (8) is slightly lower than Formulae (7) and (18).  
Figure 4: Accuracy of different formulae.  
Figure 5: Accuracy of different formulae for different fines particle contents: (**a**) \(FC\leq 5\%\). (**b**) \(5\%<FC\leq 35\%\), and (**c**) \(FC>35\%\).  
At a 5% < \(FC\leq 35\%\), Formula (8) maintains the highest overall accuracy, with minimal reduction compared to \(FC\leq 5\%\) and balanced performance in both the liquefaction and non-liquefaction areas, showing strong adaptability. Formulae (7) and (18) exhibit slightly lower accuracy. Formula (11) excels in non-liquefaction areas, albeit with some imbalance.  
At a \(FC>35\%\), the overall accuracy decreases significantly in the liquefaction areas while remaining stable in the non-liquefaction areas, indicating potential risks. Formulae (7) and (18) outperform Formulae (8) and (11).  
The accuracy of all formulae decreases with the increasing fines content. While each performs well in low fines content areas, the accuracy drops substantially in high fines content areas, highlighting the need for further research to improve the adaptability and precision.
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 3 Health Monitoring in Corrosion
### Challenges of OFS Application in the O@G Industry  
The extreme conditions that can be found in the oil and gas wel lbore pose a certain difficulty in the monitoring of the downhole. This occurs by the presence of carbon dioxide, hydrogen, and mechanical stresses. The OFS can be relied upon for downhole sensing but with temperatures as high as 300 \({}^{\circ}\)C and hydrogen ingress, the silica fibers are dangerously affected. The presence of water can be instrumental in this also, resulting in long-term instability [101]. The silica hydrogen bond with adsorption of hydroxyl can result in extrinsic attenuation. As a counter-action to this, a hermetic carbon layer can be added for protection of the silica, but this idea would also fail at the unusually high temperatures encountered, resulting in an increased importance of protective coatings [152, 153]. The alternatives for silica include sapphire cladding, which is highly expensive. If the cost can be overlooked, it normally eliminates the problem and can still work at 1800 degrees. Another challenge is the transfer across long distances and across the hundreds of thousands of miles of pipeline infrastructure with high resolution. There is a common compromise between high-distance transmission and the resolution received. OFS aims to decrease this problem and lead to an optimized system [104, 154, 155, 156, 157, 158, 159, 160, 161, 162].
# Coastal Sargassum Level Estimation from Smartphone Pictures
## 4 Experiments and Discussion
### Exploratory Experiments  
In this experiment, we test several state-of-the art networks for the classification task using all the levels in the dataset. The tested networks are: Alexnet [25], GoogleNet [26], VGG16 [27] and RestNet18 [29]. The objective of this experiment is to characterize the effectiveness of the network architectures by validating them under three training paradigms: feature extraction (F.E.), fine tuning (F.T.), and training from'scratch' (T.S.).  
The parameters used are the following: epochs = 500, learning rate = 0.001 and batch size = 100. The batch size was selected as the largest amount of images that can be loaded into memory during training. The training was carried out online on a Kaggle's virtual machine with GPU and 15 GB of memory RAM. The models were implemented in Python using the Pytorch library. Training time was 2 h on average. Overall, 80% of the dataset was used for training, while 20% was selected for validation. The best CNN weights corresponds to the epoch were the best validation accuracy was reached.  
We can observe, from the results summarized in Table 1, three facts. First, the problem addressed in this work is quite different from the typical image classification problems, this is observed in the low validation accuracy (below 60%); our hypothesis is that learned features from Imagenet are not enough for this problem and that the current amount of examples is small for extracting such features. Two, fine tuning has shown the best performance during validation; this phenomenon supports our hypothesis that the features from Imagenet does not define the Sargassum because the net is learning to extract new features. Third, VGG16 has shown the best performance; we think that given the amount of available examples, the depth of VGG16 is adequate for learning features and for beating the other networks, while at the same time, it is also shallow for avoiding overfit which can be occurring with ResNet18.
## 3 Overview of the Framework  
The purpose of this paper is to propose a key classification framework for encryption algorithms, based on deep neural network models and incorporating transfer learning methods to improve model training speed and attack efficiency. The overall content of this paper is illustrated in Figure 1. The overall design is divided into four parts: side-channel information leakage acquisition of the encryption chip, construction of the side-channel dataset, analysis of side-channel information leakage, and attack results on encryption algorithm keys. Part A showcases our acquisition platform for side-channel information leakage from the experimental cryptographic chip. The acquisition system records the relevant key information during the operation of the encryption chip and receives and stores the electromagnetic information leaked by the chip captured by an oscilloscope. Part B introduces the dataset of leakage signals generated during the operation of the encryption chip. In Parts C and D, using deep transfer learning methods, we construct side-channel attack models suitable for key classification in symmetric encryption algorithms. We elucidate the key classification method based on deep learning and the high effectiveness of transfer learning in concurrent computation mechanisms for model classification.  
Our ultimate goal is to achieve the accurate classification of encryption keys. However, due to the security requirements of cryptographic algorithms, the long length of keys leads to an exponential explosion in the classification model and an insufficient quantity of data distribution. To address this issue, we have split the keys of the cryptographic algorithm. The results show that key splitting improves classification efficiency and enhances the feasibility of extracting useful information from side-channel data. Based on the characteristics of symmetric encryption algorithm keys, we adopt a \"divide and conquer\" approach to split the keys, analyzing them at the semi-byte level as the smallest unit. We construct a supervised dataset and feed it to a classification model based on deep neural networks. In this paper, we utilize traditional deep neural network architectures and adjust the parameters according to the characteristics of electromagnetic signals. We establish a mapping between the keys and side-channel information, and propose a new classification approach for the overall encryption algorithm by combining transfer learning. The experimental results demonstrate that this method effectively extracts information related to the encryption algorithm keys from the leakage of side-channel information, achieving high accuracy.  
Figure 1: The Overall framework for key classification in symmetric encryption algorithm. Part (**A**) showcases our acquisition platform for side-channel information leakage from the experimental cryptographic chip. Part (**B**) introduces the datasets of leakage signals generated during the operation of the encryption chip. Parts (**C**,**D**) introduce how to obtain the side-channel attack models suitable for key classification in symmetric encryption algorithms.
## 8 Discussion  
Here we study some key modifications and properties of the proposed algorithm. As described in Section 6, the fundamental algorithm is a two-stage process; the first stage involves the initialization of a best fit estimate in the presence of outliers while the second stage involves the convergence of the initial estimate. Subsampling process is the key point to find an initial robust estimate. Subsampling is also the most critical task as the number of subsamples determines the computational cost. Thus the number of observations per subsample is the most significant factor in determining the computational load. In our proposed scheme, \(n_{sub}\) is a selectable factor, hence it is possible to tune the computational cost of algorithm.  
The LSE estimator has low complexity level in terms of computational cost. IRWLS contains more complex structure than simple LSE as it requires additional arithmetic operations related to residual evaluation and updating of the observation weights and these operations are performed in iterative manner. In statistics, a huge amount of observation data, either qualitative or quantitative, is needed to be analyzed before making final decision. There are numerous statistical estimation techniques, such as robust and multivariate regression procedures [31; 32], those provide general interpretation of data based on random samples drawn from original dataset as it is not possible to process all datasets, and additionally enough processing power is available with no real time requirements. On the other hand, the dataset such as GNSS has very small number of observations with certain characteristics, such as GDOP, and random sampling technique can lead to a wrong estimate. A multiconstellation system can provide better reliability and integrity, thus each subsample should contain the observations from all available constellations. And the \(n_{sub}\) factor is managed in such a way that at least one subsample must contain all best fit satellites from both constellations.  
Several authors proposed similar approaches for GPS positioning based on robust statistics. The idea in [18], is discussed in details to compare with our proposed method as there are some similarities among two algorithms. The key idea, in [18], is to find all possible solutions based on the minimum number of required observations per solution, and a genetic algorithm is applied for outlier detection and exclusion. CNo of GPS and BeiDou visible satellites is shown in Figure 2 for opensky data.  
Signals from all visible satellites have well CNo. Thus it is hard for any observation to be an outlier and applying subsampling theorem in such a scenario is an impractical approach. And multi-GNSS positioning further increases the complexity in using minimum number of required number of observations. On the minimum requirement criteria basis, there will be multiple subgroups those will contain either GPS only or BeiDou only observations, thus leaving the advantages of multiple constellation positioning.
## 3 Numerical Simulation of AE Signals
### Simulation of Debonding
The second kind of debonding models dealt with more resistant interfaces (Figure 4b-d). In these cases, debonding occurred gradually (models B-D), with a final debonding length equal to 100 \(\upmu\)m. The crack grew by jumps with several steps. The 20 \(\upmu\)m extension of the crack was also generated with a node separation during one 0.1 \(\upmu\)s time step. For linear progressive debonding in five steps (model B, Figure 4b), the propagation of the debonding at the interface was equal to 20 \(\upmu\)m every 0.1 \(\upmu\)s. For uniform progressive debonding (model C, Figure 4c), the first propagation was equal to 20 \(\upmu\)m in 0.1 \(\upmu\)s. The next propagations were assumed to occur after constant periods of 0.2 \(\upmu\)s. For non-uniform progressive debonding (model D, Figure 4d), debonding took place also in five steps, 20 \(\upmu\)m was generated at each step in 0.1 \(\upmu\)s, and the time interval between two crack extensions increased progressively from 0.2 to 2 \(\upmu\)s.
## 3 Results
### Bond Strength and Failure Mode
#### 3.3.3 Forty-Eight Hours \(\times\) Six Months  
After 6 months, there was a significant reduction in the bond strength values of the hydraulic calcium silicate-based sealer with dentin, regardless of the root third, when evaluated between the same final irrigation protocol (\(p\) < 0.05).  
Table 4 shows the arithmetic mean, standard deviation, and confidence interval of the push out bond strength values (in MPa) between the hydraulic calcium silicate-based sealer (BIO-C Sealer) and root canal dentin as a function of the mechanized final irrigation protocols and radicular thirds at 48 h and 6 months.  
Table 5 shows the comparison of the arithmetic means and standard deviations (in MPa) of the push out bond strength values between hydraulic calcium silicate-based sealer and dentin in the same irrigation protocol as a function of the radicular third and evaluation period.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & & **XPE** & **XPC** & **ECL** & **PUI** \\ \hline \multirow{4}{*}{48 h} & C & m (sd) & 2.08 (0.15) a  & 2.05 (0.13) a  & 1.95 (0.13) a  & 2.07 (0.14) a  \\  & C & ci & 2.01 \(<\) \(\mu\) \(<\) 2.14 & 1.98 \(<\) \(\mu\) \(<\) 2.11 & 1.86 \(<\) \(\mu\) \(<\) 2.03 & 1.93 \(<\) \(\mu\) \(<\) 2.15 \\  & M & m (sd) & 1.93 (0.12) a  & 1.92 (0.19) a  & 1.92 (0.11) a  & 1.92 (0.14) a  \\  & M & ci & 1.85 \(<\) \(\mu\) \(<\) 2.01 & 1.80 \(<\) \(\mu\) \(<\) 2.03 & 1.85 \(<\) \(\mu\) \(<\) 1.98 & 1.83 \(<\) \(\mu\) \(<\) 2.01 \\  & A & m (sd) & 1.81 (0.13) a  & 1.81 (0.17) a  & 1.55 (0.13) b  & 1.82 (0.15) a  \\  & Ci & 1.72 \(<\) \(\mu\) \(<\) 1.89 & 1.71 \(<\) \(\mu\) \(<\) 1.91 & 1.46 \(<\) \(\mu\) \(<\) 1.63 & 1.72 \(<\) \(\mu\) \(<\) 1.91 \\ \hline \multirow{4}{*}{6 m} & C & m (sd) & 1.91 (0.11) a  & 1.86 (0.11) a  & 1.83 (0.07) a  & 1.91 (0.08) a  \\  & Ci & 1.84 \(<\) \(\mu\) \(<\) 1.97 & 1.79 \(<\) \(\mu\) \(<\) 1.93 & 1.78 \(<\) \(\mu\) \(<\) 1.87 & 1.86 \(<\) \(\mu\) \(<\) 1.95 \\  & M & m (sd) & 1.73 (0.11) a  & 1.71 (0.07) a  & 1.44 (0.11) b  & 1.74 (0.04) a  \\  & Ci & 1.66 \(<\) \(\mu\) \(<\) 1.79 & 1.66 \(<\) \(\mu\) \(<\) 1.75 & 1.37 \(<\) \(\mu\) \(<\) 1.51 & 1.71 \(<\) \(\mu\) \(<\) 1.76 \\  & A & m (sd) & 1.63 (0.06) a  & 1.61 (0.06) a  & 1.38 (0.09) b  & 1.62 (0.06) a  \\  & Ci & 1.59 \(<\) \(\mu\) \(<\) 1.66 & 1.57 \(<\) \(\mu\) \(<\) 1.64 & 1.32 \(<\) \(\mu\) \(<\) 1.43 & 1.58 \(<\) \(\mu\) \(<\) 1.65 \\ \hline \end{tabular}
\end{table}
Table 4: Arithmetic means, standard deviations, and confidence intervals of the push out bond strength values (in MPa) between the hydraulic calcium silicate-based sealer (BIO-C Sealer) and root canal dentin as a function of the mechanized final irrigation protocols and radicular thirds at 48 h and 6 months.