esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Calculation Theory and Model
### Finite Element Analysis Model
#### 2.1.2 Ballast Track Model  
Ballast track includes rail, fastener, sleeper, and ballast bed, which is a multi-layer structure. To simulate the bridge-track interaction more realistically, the longitudinal resistance of the fastener and ballast bed are considered in this model. The rail is simulated as beam element, and both ends of the rail are fully constrained. The vertical and lateral supports of the fastener are considered as linear springs (both are 60 kN/mm) [26]. The longitudinal resistance of fastener is considered as nonlinear spring element because of the nonlinear characteristics confirmed in [27]. The fastener's constitutive relation is as shown in Figure 2a. The concrete sleeper is mainly to transfer the load of the rail to the ballast bed evenly, as well as to maintain the longitudinal and lateral geometry of the track. Considering its slender characteristic, it is simulated by the Euler beam element, and the sleeper node is connected with the fastener spring and the ballast bed spring simultaneously. The longitudinal and lateral resistance of the track bed is highly nonlinear [28]. It is simplified as a three-dimensional spring in this paper. The vertical support is simulated by linear spring (120 kN/mm), while the longitudinal and lateral constraints are considered as nonlinear springs (Figure 2b).  
According to the actual dynamic behavior of ballast track under longitudinal and vertical load, the vibration mass of ballast is considered in the longitudinal and vertical dimensions, respectively, as shown in Figure 3. The parameters are shown in Table 1. The vertical vibration mass of ballast is considered as the mass between adjacent sleeper cones described in [29]. To reduce the amount of calculation, this study considers the ballast track structure as a double-layer vibration model under the action of an earthquake. It is assumed that the vertical vibration mass of the ballast vibrates together with the sleeper.  
Figure 1: Correction of track longitudinal resistance.
## 2 Provisioning MB Optical Connections
### QoT Estimation and Nonlinear Noise Mitigation  
The set of basic features introduced above can be extended. One parameter that can be computed from the basic features is \(\Phi^{i}_{out}\)[20], which computes the probability of receiving a symbol originally sent as part of CP \(i\) out of the detection area assigned to that CP, represented as \(A^{i}\). \(\Phi^{i}_{out}\) is computed under the assumption that the dispersion of symbols around CP \(i\) follows the bivariate Gaussian distribution characterized by \(Y^{i}\). For the sake of clarity, Figure 3a reproduces from [20] an example of \(\Phi^{i}_{out}\) for CP [\(-3+3\)i]. The contours represent the different levels of the bivariate Gaussian distribution that characterize this CP for a given lightpath; univariate marginal distributions are provided for both the \(I\) and  
Figure 2: Main building blocks of the OCATA-MB time domain digital twin.  
axes. The areas highlighted in red in both the bivariate and marginal distributions represent the region that falls out of \(A^{i}\), i.e., the square delimited by vertices \((-4+4i)\) and \((-2+2i)\). Hence, \(\Phi^{i}_{out}\) is formally defined as follows:  
\[\Phi^{i}_{out}=1-\Phi_{A}(i) \tag{2}\]  
\[\Phi_{A}(i)=P\Big{(}x\subset A^{i}\Big{|}\,x\sim N\Big{(}Y^{i}\Big{)}\Big{)} \tag{3}\]  
Note that the estimated pre-FEC BER can be computed based on \(\Phi^{i}_{out}\) for all the CPs, e.g., assuming equal probability of the symbols.  
In view of Equations (2) and (3), the higher the \(\phi_{A}(i)\), the lower the pre-FEC BER will be. Therefore, a way to improve the QoT is by increasing \(\phi_{A}(i)\), which works for NLI noise mitigation. That can be implemented by changing the shape of \(A^{i}\) in the Rx. To illustrate that, Figure 3b shows an adapted detection area for CP \(i\). After modifying the detection area for this CP, the portion of the red shaded area is decreased, and, as a result, \(\Phi^{i}_{out}\) reduces. However, finding the optimal \(A^{i}\) for every CP so that the total \(\Phi_{out}\), i.e., not only that for the CP, is minimized is a complex task. Hence, algorithmic solutions for computing a set of near-optimal detection areas in a reasonable time frame are required. By reducing the pre-FEC BER at the Rx, the reach of the lightpath can be extended; consequently, channel assignments that result in pre-FEC BER threshold violations and would be rejected, can now be possibly accepted. Note that the optimal \(A^{i}\) for every CP is computed by OCATA-MB as part of the provisioning process, and a map is generated to be easily used by the Rx.
# Feasibility Analysis of Online Uncertainty Metrics for PMU-Based State Estimation
## 5 Simulation Results
### Feasibility Analysis in Real-World Scenarios  
Once characterized the relationship between nRMSE and TVE is in standard test conditions, we can consider a realistic data set. In this way, we investigate whether the nRMSE can be used to detect anomalous or contradictory measurement results, e.g., due to contingencies or due to PMU malfunctions [37].  
For this analysis, we consider a simulated data set that reproduces the response to a generation trip in a system, characterized by high shares of renewable energy sources, and software model in [32]). In more detail, the simulated model consists of the well-known IEEE 39-bus system [38], where four synchronous generators are replaced by four wind farms. In order to validate the performance of different ROCOF-based under-frequency load-shedding schemes, the data set reproduces a large contingency event, by synchronously tripping three generation units for an overall loss of 1.5 GW. In this context, Figure 6a presents the voltage profile at bus 26. The contingency occurrence is visible at 200 ms, then nearly 600 ms of a dampened oscillation, then a new contingency at 800 ms.  
In Figure 6b,c, we present the static and dynamic nRMSE as computed with different PMU reporting rates: 50 and 100 fps, respectively. In this way, it is possible to evaluate whether the reliability metric not only discriminates between different kinds of transients (e.g., wide-band as at 200 ms, or narrow-band as in the following 600 ms), but also depends on the reporting rate. A higher reporting rate provides a finer time resolution of the metric evolution. Nevertheless, it is interesting to observe how both static and dynamic formulations present consistent results, independently from the adopted reporting rate. In the static case, the nRMSE detects clearly the beginning of the contingency. On the other hand, the dynamic nRMSE allows for discriminating between the two wide-band events and the dampened oscillation in between. In more detail, after the first contingency, the nRMSE exhibits a decreasing trend that reflects the progressive shrinking of the spectral bandwidth of the fundamental component, and, thus, the progressive comeback to an operating condition that can be suitably represented by a dynamic phasor model.
## 4 Results and Discussion
### Results of Hyper-Parameters Tuning  
The effectiveness of using BAS to tune hyper-parameters was analyzed by tracing the MSE values of each iteration. Figure 6 shows the average MSE curves versus iteration during the hyper-parameter tuning. Different evolution patterns can be seen for different ML algorithms in terms of the MSE values and convergent rates. It took more time for KNN and SVM to converge than the other three algorithms, and KNN behaved the worst with the largest MSE value after convergence. Also, the MSE of DT decreased sharply with each iteration during BAS tuning to the minimum value among these ML algorithms. This indicates that BAS tuned DT most efficiently. Overall, the ideal hyper parameters of these models could be found by BAS efficiently and reliably. These results of hyper-parameters are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Parameter** & **Restriction** & **Empirical Scope** & **Initial Value** & **Result** \\ \hline \multirow{3}{*}{BPNN} & hidden\_layer\_num & \textgreater{}0, integer & [1,4] & \{1,2,3,4\} & 1 \\ \cline{2-6}  & hidden\_layer\_size & \textgreater{}0, integer & [1,20] & \begin{tabular}{c} 30, (20,10), \\ (20,10,10), \\ (10,10,10,10) \\ \end{tabular} & \
\begin{tabular}{c} 15 \\ 15 \\ \end{tabular} \\ \hline \multirow{3}{*}{SVM} & C & real number & [0.1,1000] & 16 & 451 \\ \cline{2-6}  & gamma & real number & [0.001,100] & 16 & 3.15 \\ \hline \multirow{3}{*}{DT} & min\_samples\_split &
\begin{tabular}{c} \textgreater{}0, integer \\ \textgreater{}min\_samples\_ \\ leaf*2 \\ \end{tabular} & [1,10] & 25 & 1 \\ \cline{2-6}  & min\_samples\_leaf & \textgreater{}0, integer & [2,10] & 50 & 2 \\ \hline \multirow{3}{*}{RF} & tree\_num & \textgreater{}0, integer & [1,10] & 40 & 7 \\ \cline{2-6}  & min\_samples\_leaf & \textgreater{}0, integer & [1,10] & 40 & 1 \\ \hline KNN & neighbors\_num & \textgreater{}0, integer & [1,10] & 30 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimum hyper-parameters in models.  
Figure 5: The microstructure of the coal-grout composite after the 28 th day: (**a**) coal-chemical grout composite; (**b**) coal-cement grout composite.
# A Review of Degradation and Life Prediction of Polyethylene
###### Abstract  
After around 50 years of development, the key substance known as polyethylene has been extremely influential in a variety of industries. This paper investigates how polyethylene materials have been used in the domains of water, packaging, and medicine to advance contemporary society in order to comprehend the physical and chemical alterations that polyethylene undergoes after being subjected to long-term environmental variables (e.g., temperature, light, pressure, microbiological factors, etc.). For the safe operation of polyethylene materials, it has always been of the utmost importance to evaluate polyethylene's service life effectively. This paper reviews some of the most common literature journals on the influence of environmental factors on the degradation process of polyethylene materials and describes methods for predicting the lifetime of degradable polyethylene materials using accelerated aging tests. The Arrhenius equation, the Ozawa-Flynn-Wall (OFW) method, the Friedman method, the Coats-Redefern method, the Kissinger method and Kissinger-Akahira-Sunose (KAS) method, [PERSON]'s method, and Advanced Isoconversional methods are all discussed, as well as the future development of polyethylene.  
polyethylene; degradation; kinetic models; thermal analysis; life prediction
## 3 Materials and Methods
### Model Deployment, Fine-Tuning, and Predictive Evaluation
#### 3.3.2 BERT Model Deployment and Fine-Tuning  
In this phase, we focused on training the BERT model, specifically the bert-base-uncased variant, to perform the same classification task that had been previously tackled by the LLMs [37]. For this, we used the BertForSequenceClassification class from the Hugging Face Transformers library. This variant of BERT incorporates an additional classification head specifically designed for sequence classification tasks. The classification head generally includes a fully connected layer, enabling BERT to convert its output into class probabilities, making it suitable for tasks like fake news detection and fake news classification.  
The bert-base-uncased model architecture consists of 12 transformer layers, each with 768 hidden units and 12 attention heads, totaling around 110 million parameters. These self-attention mechanisms within BERT are highly effective at capturing contextual dependencies across tokens within an input sequence [38].  
For the prediction phase, we used the pre-trained bert-base-uncased model, which was loaded directly using the from_pretrained method, taking advantage of the model's robust pre-trained capabilities [39].  
During fine-tuning, we adapted the BERT model for fake news classification by training it on labeled data containing labeled news articles, specifying each article as either fake or not fake. This training process was conducted on Google Colab, leveraging the processing power of a Tesla V100-SXM2-16 GB GPU [40]. The dataset was stored on Google Drive, with preprocessing steps that included tokenization via BERT's tokenizer to prepare the text input in a format suitable for BERT.  
Fine-tuning involved careful adjustments to hyperparameters, and we employed the adaptive moment estimation (Adam) optimizer for efficient optimization. Training was executed over three epochs, with progress tracked via the tqdm library to visualize real-time updates [41]. Within each epoch, backpropagation and optimization were applied to refine the model's weights, while validation was conducted using the same dataset previously used with the GPT-4o models, ensuring consistency in the evaluation process.  
Our fine-tuning approach was methodical, tailored to meet the specific demands of the fake news classification task, and aimed at optimizing the model's performance for this purpose. After fine-tuning, the trained model generated predictions on the same test set used by the GPT-4o models, allowing for a direct comparison.  
The full codebase, along with classes for training the BERT and GPT models, as well as metrics for training and validation loss and accuracy, is available in an ipynb notebook hosted on GitHub [42]. This resource also includes all relevant scripts and detailed documentation to facilitate replication and further experimentation.
# MLW-gcForest: A Multi-Weighted gcForest Model for Cancer Subtype Classification by Methylation Data
## 4 Discussion
To determine the number of decision trees in the random forest that achieves the best performance, we designed an experiment to change the number of decision trees to see the effect on the final classification performance. The accuracy results for different numbers of trees on five cancers are shown in Figure 8. Figure 8 suggests that when the number of decision trees is set to 30, the algorithm performs the worst. When the number of trees is set to 500 or 600, the algorithm performs the best. When the number of trees continues to increase beyond 600, the accuracy slowly decreases. Based on the above results, 500 trees are used as the final experimental parameter because 500 and 600 achieved similar results, but the time cost and calculation cost of 500 trees are lower. We explain why methylation is selected for subtype classification in the following. The data we can obtain includes methylation data, RNA data, and CNV data from TCGA, which are subclassified using our improved MLW-gcForest method. Table 4 shows the classification performance of MLW-gcForest on different types of cancer using different types of data. The table shows that the methylation data provides the strongest discriminating ability for MLW-gcForest and achieves better results for five types of cancer. The classification ability using RNA data is the second best, but in particular, the ability to classify STAD is relatively weak. The CNV data provide the worst subtype classifications for cancer, especially for LHIC and STAD. Therefore, after comprehensive consideration, we chose methylation data to classify cancer subtypes. Figure 7: Accuracy curves of MLW-gcForest and gcForest on the training and validation sets for five cancer subtypes. Although our model achieves certain results in the classification of cancer subtypes, there are certain limitations: (1) In our study, we only subtyped five common cancers, and whether our method can be extended to other cancers requires further exploration. (2) We did not consider whether the fusion of multi-modal data is feasible for the classification of cancer subtypes; this consideration will be explored in future research.
## 6 Discussion
### Governance
#### 6.3.1 International Governance of Cybersecurity in Ships
[74]  
In the UK for example, pursuant to _The Merchant Shipping (Recognised Organisations) (Amendment) (EUI Exit) Regulations 2019_[75], six registered organisations (ROs) including Lloyd's Register, American Bureau of Shipping, and others, are authorised to conduct surveys and inspections of ships on behalf of the Maritime and Coastguard Agency (MCA). In the UK, MCA regulation MSIS 02 [76]_Instructions for the guidance of surveyors_ sets out at paragraph 4.2.3 the obligation \"that an approved SMS should take into account cyber risk management\", in conjunction with the Department for Transport (DfT) _Cyber Security Code of Practice for ships_[77]. Where an RO is not satisfied that cybersecurity has been sufficiently covered it could deny issuing the Safety Management System (SMS) certificate and the ship will be prevented from sailing until the deficiency is rectified. In June 2023, the IMO announced that the cybersecurity circulars will be undergoing a comprehensive revision. The revision will be led by member states who will present a proposal to the MSC, and it is too early to tell to what degree of specificity the revision will go, or when it will be complete. At a minimum, the revision will update the links to the industry guidelines to ensure they are updated to the latest versions available. It should be noted that the idea of \"a standalone Cyber Code\" has been raised \"based on a framework created by previous IMO Codes such as the Polar Code. Since the IMO uses Codes as a legally binding instrument, this would help to ensure the continued safety and efficiency of the maritime industry in the face of threats from cyberspace\" [10]. As the IMO works on the consensus of member states this would be a highly ambitious project--and would take years to complete--but ultimately could be an effective way of combining all cyber regulations and requirements into one instrument.
## 3 Results
### The Discrimination of Nitrogen-Saturated and -Unsaturated Plots Using Worldview-3 Imagery  
Once the physiological criteria identified grassland plots within the initial stages of nitrogen saturation, Worldwide-3 based wavebands and vegetation indices were then used to discriminate between the grassland plots characterised as either saturated or not saturated. The CART model produced an overall accuracy of 79%, with a user accuracy of 50% and a producer accuracy of 52% (Figure 5). The model was able to adequately characterise non-saturated grassland plots; however, it was unable to sufficiently classify nitrogen-saturated grassland plots as saturated. The RF model performed considerably better, with an overall accuracy of 91%, and associated user and producer accuracies of 83% and 89%, respectively (Figure 5). The RF model was able to effectively characterise both saturated and non-saturated grassland plots, with an average class error of 17.2%.  
Figure 4: Mean differences in additional site factors for saturated vs. not saturated grassland plots across the growing stages, where (**a**) represents net ammonium concentrations (NH4\({}^{+}-\)N), (**b**) represents changes in soil organic carbon levels, (**c**) represents net foliar nitrogen, (**d**) represents net soil nitrogen, (**e**) represents biomass yield, and (**f**) represents the nitrogen enrichment factor.  
Fundamentally, the RF model, with a quantity disagreement of 4% and an allocation disagreement of 5%, had a lower level of overall disagreement in comparison to the CART model with a quantity disagreement of 19% and an allocation disagreement of 4%, respectively. The RF model, therefore, demonstrated a greater ability to reliably discriminate between nitrogen-saturated and -unsaturated grassland plots.  
From the 24 spectral variables used, nine variables were identified as important in characterizing grassland plots within the initial stages of nitrogen saturation. The most important wavebands identified were the Red-Edge (705-745 nm), Coastal (400-450 nm), and NIR3 (838-950 nm) (Figure 6), whereas the most important vegetation indices identified were the SAVI and the Normalized Difference Vegetation Index 3 (NDVI3) (Figure 6).
# Ventricular Fibrillation Prediction and Detection: A Comprehensive Review of Modern Techniques
## 2 Modern ECG Signal Processing Techniques for Detection and Prediction of Ventricular Fibrillation
### Wavelet Transform Techniques
#### 2.4.1 Discrete Wavelet Transform (DWT)  
Discrete wavelet transform (DWT) is used to decompose the ECG signal into different levels of resolution, allowing detailed analysis of its characteristics. This involves using low-pass and high-pass filters to separate the signal into approximation and detail components at different scales. DWT uses a set of mother wavelets and daughter wavelets to analyze the signal at different scales. The decomposition process involves the following steps:  
Applying Filters: The ECG signal is passed through a low-pass filter and a high-pass filter, generating two sets of coefficients: approximation coefficients (low frequency) and detail coefficients (high frequency).  
Subsampling: The resulting coefficients are then subsampled, reducing the size of the data and allowing analysis at a lower scale.  
Repeating the Process: The process is repeated on the approximation coefficients to obtain several levels of decomposition.  
\[X[n]=\sum_{k}A_{j-1}[k]\psi_{j,k}[n]+\sum_{k}D_{j-1}[k]\varnothing_{j,k}[n]\]  
where \(A_{j}[k]\) and \(D_{j}[k]\) are the approximation and detail coefficients at level \(j\), and \(\psi_{j,k}[n]\) and \(\phi_{j,k}[n]\) are the corresponding wavelet functions.  
DWT is used to extract relevant features from the ECG signal, such as P waves, QRS complexes, and T waves, which are essential for detecting episodes of ventricular fibrillation.
## 2 Modelling and Methodology
### Wind Condition Models  
The statistical features of wind condition are typically described by two elements: wind speed and wind direction. Various types of wind conditions, which can be categorized into the simplified wind condition and the realistic wind condition, were investigated in this paper. The simplified wind condition includes three different scenarios of discrete wind speed and wind direction, namely, the constant wind speed and wind directions, constant wind speed and variable wind directions, and the variable wind speeds and variable wind directions, which are described in detail in Section 3. The realistic wind condition was based on measured wind data, for which the wind speed data was continuously changing. It has been well recognized that the real wind speed variation across the world can be represented by Weibull distribution which is given by [21]:  
\[p(v)=\frac{k}{c}\left(\frac{v}{c}\right)^{k-1}\exp\left(-\left(\frac{v}{c} \right)^{k}\right) \tag{5}\]  
where \(p(v)\) is the probability density of occurrence for a specific wind speed \(v\), \(c\) is the scale parameter, and \(k\) is the shape parameter. The cumulative Weibull distribution \(P(v)\), which shows the probability of wind speed less than or equal to a certain speed value \(v\), is given by:  
\[P(v)=1-\exp\left(-\left(\frac{v}{c}\right)^{k}\right) \tag{6}\]