esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Analysis Methodology
### Decomposition of Stress into Four Components  
Let us assume that \(\sigma_{\text{DMS}}\)-\(\sigma_{BMP}\) are the measured stresses at the four corners of the midship, as shown below:  
\(\sigma_{\text{DMS}}:\) Time-series stress data on the starboard side, upper deck  
\(\sigma_{\text{DMP}}:\) Time-series stress data on port side, upper deck  
\(\sigma_{\text{BMS}}:\) Time-series stress data on the starboard side and bottom  
\(\sigma_{\text{BMP}}:\) Time-series stress data on port side, bottom  
Then, they can be expressed by a linear combination of the four components of hull girder stresses, that is, the vertical longitudinal bending, horizontal longitudinal bending, warping, and axial stresses, as shown in Equations (2)-(5).  
\[\sigma_{\text{DMS}}=\sigma_{V}-\sigma_{H}-\sigma_{W}+\sigma_{A} \tag{2}\]  
\[\sigma_{\text{DMP}}=\sigma_{V}+\sigma_{H}+\sigma_{W}+\sigma_{A} \tag{3}\]  
\[\sigma_{\text{BMS}}=-\alpha\sigma_{V}-\sigma_{H}+\beta\sigma_{W}+\sigma_{A} \tag{4}\]  
\[\sigma_{\text{BMP}}=-\alpha\sigma_{V}+\sigma_{H}-\beta\sigma_{W}+\sigma_{A} \tag{5}\]  
where  
Figure 4: Time-series stress data of the vertical bending component after filtering.  
Figure 5: Weibull approximation and 1/1000 maximum expected value.  
\(\sigma_{V}:\) Vertical longitudinal bending component  
\(\sigma_{H}:\) Horizontal longitudinal bending component.  
\(\sigma_{W}:\) Torsional component  
\(\sigma_{A}:\) Axial force component  
\(\alpha,\beta:\) Decomposition coefficient  
By writing Equations (2)-(5) in matrix form and solving it for the vector [\(\sigma_{V}\), \(\sigma_{H}\), \(\sigma_{W}\), \(\sigma_{A}\)]T, Equation (6) is obtained [11].  
\[\left[\begin{array}{c}\sigma_{V}\\ \sigma_{H}\\ \sigma_{W}\\ \sigma_{A}\end{array}\right]=\left[\begin{array}{ccccc}1&-1&-1&1\\ 1&1&1&1\\ -\alpha&-1&\beta&1\\ -\alpha&1&-\beta&1\end{array}\right]^{-1}\left[\begin{array}{c}\sigma_{DMS} \\ \sigma_{DMP}\\ \sigma_{BMS}\\ \sigma_{BMP}\end{array}\right] \tag{6}\]  
\(\alpha\) is the decomposition coefficient for the vertical longitudinal bending component, which is represented by the ratio of the vertical distance between the bottom measurement point and the neutral axis to the vertical distance between the upper deck measurement point and the neutral axis of the ship. \(\beta\) is the decomposition coefficient for the torsional component and was obtained by vibrational eigenmode analysis using an entire ship FEM model. In the case of this ship, \(\alpha=0.614\) and \(\beta=0.933\) were applied.
## 1 Introduction
The use of deep learning also comes with an \"explainability problem\" where it is not clear what the model is selecting, separate model runs can lead to entirely different and non-reproducible results, and it is not clear if these single-gene or single-genetic-pattern analyses conducted by studies are even targeting the correct genetic variables actually predictive of cancer [27, 28]. A study of 25,000 patients found that there was no single genomic indicator able to predict cancer's likelihood of metastasis [29]. This study hypothesized that one way to overcome all of these variables was to investigate clustering and deep learning classification on radiogenomic data together to simultaneously identify relevant genetic \"clusters\" or patterns in addition to imaging features that identify severe cancer metastases. This pilot study created and evaluated a custom methodology to identify patterns of radiomic features from genetics and imaging data in combination that, together, can better diagnose cancers such as lung cancer and predict cancer severity. Specifically, this study created a custom clustering algorithm to evaluate whether a machine learning algorithm using imaging data from lung CT scans could identify and cluster relevant genetic markers. This methodology (1) clustered initial genetic data, (2) re-clustered the data based on an image classification model accuracy, and (3) re-assigned misclassified patients to new clusters. By using current clustering methodologies and deep learning image analysis methods, this study sought to capitalize on the automatic feature selection and flexibility of deep clustering methods for genetics while also optimizing accuracy through a convolutional front end as is typical of deep learning and machine learning image classification methods. This study evaluated this methodology on a dataset consisting of genetic and CT imaging data for lung cancer to specifically target a type of cancer that currently lacks biomarkers and could truly benefit from genetic or imaging biomarkers. The final outcome of this process was an algorithm that predicts which patients are likely to fall into which genetic patterns based on their imaging data. This study was particularly novel as it not only presented a new methodology that could be applied elsewhere but specifically used this methodology to also identify genetic patterns indicative of lung cancer severity. This study also evaluated genes used in this methodology to test for severity linkage and potential associated treatments. Although this methodology could easily be applied elsewhere to other medical imaging and non-medical imaging tasks, by including these severity and treatment data in clustering generation, this study hoped to achieve an algorithm that also had clinical viability.
# A Review on Decentralized Finance Ecosystems
## 4 The Taxonomy
### Product/Services
#### 4.2.4 Insurance  
DeFi insurance mirrors traditional insurance by providing financial compensation for losses within the DeFi ecosystem. It safeguards against market, technical, and credit risks, crucial for maintaining stability in the expanding DeFi market. Users can purchase coverage from others in the ecosystem, and smart contracts automatically pay out claims when specific criteria are met. The primary objectives are affordability, speed, and transparency. Automation makes coverage accessible, expedites payments, and ensures transparent claim decisions [3; 31].  
DeFi insurance plays a pivotal role in safeguarding user assets against potential financial losses caused by system vulnerabilities. Examples like _Etherisc_ (DIP) and _InsurAce_ (INSUR) demonstrate this. Etherisc offers P2P funding pools for traditional and crypto-specific insurance, covering areas like crop insurance, flight delays, and protection for crypto wallets and loans. _InsurAce_ operates similarly, pooling user funds to disburse payments for adverse occurrences such as hacks or stake releases. These initiatives showcase the potential of smart contracts and blockchain technology in enabling robust DeFi insurance solutions [3; 31].
###### Abstract
Currently, sharing studies with the following services are conducted for 6425-7125 MHz band in ITU-R: fixed service, fixed-satellite service (Earth-to-space), fixed-satellite service (space-to-Earth), space operation service, and space research service. The allocation of the services is presented in Figure 1 below:  
The fixed service point-to-point systems provide radio communication by a link, for instance, radio-relay link, between two stations located at specified points. Fixed service point-to-point stations are utilized worldwide, in almost every country and, thus, should be taken into account when deploying 5G networks. Figure 1: Services operating in the 6425-7125 MHz and adjacent bands. As can be seen above, unlike other services, fixed service is allocated across the entire band, as well as to the adjacent bands; therefore, compatibility between 5G and fixed service is critically important. It should be noted that when it comes to the spectrum sharing between countries, sharing between 5G and fixed service is a subject of coordination, since for many applications, the location of the fixed transmitters and receivers are known by the regulatory bodies and transmission characteristics are regulated. When it comes to the sharing between different services within one administration, compatibility issues are a subject of national regulation bodies that use different techniques to achieve compatibility within one city or rural zone [3,4].
## 4 Results and Discussions
### Synthesis
#### 4.4.2 The Test Suite  
The six problems tested give consistent results to the exception of the inertia function, \(F_{inert}\). It is likely that this is because \(F_{inert}\) is almost an additive function: it is an additive function provided that the center of inertia of the points, \(\bar{X}\) in Equation (6), does not change. Such functions match well with the building blocks assumption of the Schemata Theorem [12], in a manner similar to the royal road functions [45], and benefit from the action of the traditional uniform crossover. The four wind turbine layout problems yield close results. It is confirmed that all the algorithms investigated are rotation invariant as the same statistics (up to the confidence intervals accuracy) are found with \(F_{0}\) and \(F_{90}\). This is consistent with the operator (Gaussian mutation, Wasserstein barycenters, uniform crossovers) definitions that are all isotropic, i.e., they handle directions in the same way. Results advocating a high rate of use for the boundary mutation show that our test suite is slightly biased towards operators that help putting points on the domain boundary.
## 2 Materials and Methods
### Electrochemical Measurement Parameters  
The study was completed at room temperature between 20 \({}^{\circ}\)C and 25 \({}^{\circ}\)C. Before measuring the cell output potential values from the anode and cathode combinations, the electrodes were submerged in the respective electrolyte solution for 5 min with the stirring turned on. Using each compatible electrode as the working electrode, a coiled wire platinum electrode as the counter electrode, and an Ag/AgCl reference electrode filled with 3 M NaCl connected to the potentiostat, the voltammograms are generated. The potentiostat started the voltammetry runs by recording the open circuit potential of each setup during a 5 s pause before the start of the potential sweep. The voltammetry potential sweeps were set to start at each electrode's respective open circuit potential, ensuring the CV scan starts while the system is at equilibrium. The scan ranges were set to \(-\)0.5 V to 0.5 V, at a scan rate of 0.05 V/s and in 0.015 V steps. The max current to be measured in each run was set to 30 mA. Three scans were completed and the potential and current values were recorded during each run.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Alkaline Electrolyte** & **Neutral Electrolyte** & **Acidic Electrolyte 1** & **Acidic Electrolyte 2** \\ \hline \multirow{4}{*}{Ingredients} & 0.3 M H\({}_{2}\)O\({}_{2}\) & 0.3 M H\({}_{2}\)O\({}_{2}\) & 0.3 M H\({}_{2}\)O\({}_{2}\) & 0.5 M H\({}_{2}\)O\({}_{2}\) \\  & 1 M NaOH & 0.5 M NaCl & 0.5 M NaCl & 1 M NaCl \\  & Reverse-Osmosis H\({}_{2}\)O & \multirow{2}{*}{H\({}_{3}\)PO\({}_{4}\) (pH adj.)} & \multirow{2}{*}{MSA (pH adj.)} & \multirow{2}{*}{0.1 M HCl} \\  & QS 100 & & & \\ \cline{4-5}  & & Reverse-Osmosis H\({}_{2}\)O & Reverse-Osmosis H\({}_{2}\)O & Reverse-Osmosis H\({}_{2}\)O \\  & & QS 100 & QS 100 & QS 100 \\ \hline Electrolyte pH & 13.30 & 7.00 & 2.00 & 1.08 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ingredients used and pH of four different hydrogen peroxide electrolyte solutions.  
Figure 3: A three-electrode cell setup attached to potentiostat for voltammetry tests. The blue cable is for the working electrode, the red cable is for the counter electrode, and the black cable connects to the reference electrode.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.3.3 Third Method: Clusters with at Least 50% Attack Data as Attack Clusters  
Table 7 presents the results for runs with various seed values at 300 clusters, for clusters with at least 50% attack data (Third Method).  
Analysis of the Third Method:  
* Impact of Seed Value on Clustering Performance
* Similar to the Second Method, this labeling approach produced stable results across different seed values.
* The clustering pattern was less sensitive to initialization, meaning that performance remained relatively consistent across multiple seeds.
* In terms of computational time, as in the First and Second Methods, there was no definite pattern.
* Best Performing Seed Values
* Seed values 1, 3, 10, 200, and 100 also showed strong performance, maintaining precision and recall above 98.5%. These results are very similar to the Second Method.
* Seed 200 produced the best results, with 98.76% precision and 98.73% recall. The computational time at a seed of 200 was also on the lower side. These best results are bolded in Table 7.
* Summary
* For clusters with more than 50% attack data, a seed of 200 was the most consistent and stable choice, similar to the 25% attack labeling method.
## 5 Methodology
### Physics-Guided Symbolic Regression  
Physics-guided symbolic regression (PGSR) incorporates physical principles and domain knowledge to guide the search for mathematical expressions. AI Feynman 2.0 [55; 56] exemplifies this approach by combining neural network fitting with physics-inspired techniques, such as dimensional consistency, low-order polynomial structure, and compositionality. The process begins with dimensional analysis, using the Buckingham \(\pi\)-theorem to convert input variables into dimensionless quantities, ensuring unit consistency. If this step does not work, the algorithm reverts to the original problem setup. Next, polynomial regression is applied to fit low-degree polynomials to the data, assuming many physical relationships follow such patterns. When polynomial fitting is insufficient, the algorithm performs a combinatorial search over a restricted set of mathematical operators and functions, prioritizing simple expressions based on dimensional homogeneity and smoothness. If needed, neural networks analyse residuals to identify latent properties such as symmetry or separability, breaking down complex functions into simpler components. The algorithm recursively applies the entire pipeline to these subproblems, ensuring dimensional consistency and smoothness at each stage.
## 2 Research Methodology
### Gravity Effect on the 3D Artefact  
In order to quantify the deviation resulting from gravity ()), three expected positions are measured as shown in Figure 2. One position is the horizontal position (H) meaning  
Figure 1: A schematic diagram of the fiducial-aided reconfigurable artefact mounted on the B-axis of a five-axis ultra-precision machine tool.  
Figure 2: The five fiducials-aided 3D artefact measured in different positions.  
that the mounted ball bar plane of the fixture is parallel to the horizontal plane which is used as a reference position for the 0\({}^{\circ}\) position. The second position is the vertical position (V) that is perpendicular to the horizontal plane as the 90\({}^{\circ}\) position. The third position is the 10\({}^{\circ}\) position (O) from the horizontal position. The units of measurement of the artefact are mm.  
The deviations of the two positions including the three dimensions are given by the following equations:  
\[\begin{array}{l}\Delta x_{i,KH}=\overline{x}_{i,K}-\overline{x}_{i,H}, \Delta\overline{x}_{KH}=\frac{1}{N}\sum_{i=1}^{N}||\Delta x_{i,KH}||\\ \Delta y_{i,KH}=\overline{y}_{i,K}-\overline{y}_{i,H},\Delta\overline{y}_{KH}= \frac{1}{N}||\Delta y_{i,KH}||\\ \Delta z_{i,KH}=\overline{z}_{i,K}-\overline{z}_{i,H},\Delta\overline{z}_{KH}= \frac{1}{N}\sum_{i=1}^{N}||\Delta z_{i,KH}||\\ K=V,O;i=1,2\ldots N\end{array} \tag{1}\]  
\[\begin{array}{l}\Delta d_{i,(x,y,z),KH}=\sqrt{\left(\Delta x_{i,KH}\right)^{ 2}+\left(\Delta y_{i,KH}\right)^{2}+\left(\Delta z_{i,KH}\right)^{2}}\\ \Delta\overline{d}_{(x,y,z),KH}=\frac{1}{N}\sum_{i=1}^{N}\Delta d_{i,(x,y,z), KH}\end{array} \tag{2}\]  
where \(\overline{x}\), \(\overline{y}\),\(\overline{z}\) are the mean sphere center coordinates with repeating measurement 3 times. \(\Delta d_{i,(x,y,z),KH}\) is the total position variation of the \(i\)th sphere. The effect of gravity is measured, and discussed in Section 3.1.
## 2 Materials and Methodology
### Numerical Simulation  
A three-dimensional finite-volume conjugate heat transfer model is developed to model the problem numerically and validate the experimental results. The geometrical model is uniformly meshed using symmetric square cells as depicted in Figure 2. A mesh independent study is carried out to optimize the cell size regarding accuracy and computation cost, resulting in 0.5 mm square cells and 79,745 nodes. Table 3 summarizes the sizing criteria for the HS model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Operation Mode**} & \multicolumn{1}{c}{**Description**} \\ \hline \multirow{4}{*}{Natural ventilation} & Ref-NV & Empty heat sink under natural ventilation \\  & Salt-NV & Heat sink filled with salt hydrate under natural ventilation \\  & Paraffin-NV & Heat sink filled with paraffin under natural ventilation \\  & Milk fat-NV & Heat sink filled with milk fat under natural ventilation \\ \hline \multirow{4}{*}{Forced ventilation} & Ref-FV & Empty heat sink under forced ventilation \\  & Salt-FV & Heat sink filled with salt hydrate under forced ventilation \\ \cline{1-1}  & Paraffin-FV & Heat sink filled with paraffin heated under forced ventilation \\ \cline{1-1}  & Milk fat-FV & Heat sink filled with milk fat under forced ventilation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental modes of operation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Equipment** & **Measurement Error** \\ \hline Thermocouples & \(\pm\)0.05 \({}^{\circ}\)C \\ Data Logger Module & 0.02 \({}^{\circ}\)C \\ Power Supply & 10 mV, 1 mA \\ Thermal Grease & 0.000015 m\({}^{2}\cdot\)\({}^{\circ}\)C/W \\ Anemometer & \(\pm\)3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental uncertainties.  
The PCM is treated as incompressible fluid since no pressure induced volume changes are expected in the computational domain with melt flow. The model ignores marginal effects such as the volume changes in PCM after phase transition [29], natural convection within melted PCM [41] and floating/sinking of solid dendrites within melted PCM caused by buoyancy [41].  
The model assumes a uniform heat flux conducted from heat generator to HS bottom surface. The remaining HS surfaces are regarded as imperfectly insulated system boundaries exchanging heat to the ambient by convection and radiation as will be explained in Sections 2.3.1 and 2.3.2.
# Multi-Band Unmanned Aerial Vehicle Antenna for Integrated 5G and GNSS Connectivity
## 2 Dual-Port Inverted T-Shaped MIMO Antenna and Analysis
### Current Distribution  
The E-field distribution of the single-patch antenna, the MIMO antenna, and the MIMO antenna with an FSS at the GNSS and 5G bands is presented in Figures 18, 19, and 20, respectively. The surface current is uniformly dispersed from the feed to the radiating element. In the single-patch element, the strong current flow on the top and left side of the patch at a lower frequency of 1.28 GHz is displayed in Figure 18a. At a higher frequency of 4.69 GHz, the current mainly concentrated on the right-side corner of the patch, and a small amount of current flows concentrated along the edge, as displayed in Figure 18b. The same current distribution was observed in the MIMO antenna at ports 1 and 2 for dual frequencies, as presented in Figure 19a-d. Furthermore, the MIMO antenna with an FSS enhanced the current flow in all directions and improved the performance of the higher frequency band at ports 1 and 2 when compared to the antenna without an FSS, as illustrated in Figure 20a-d.  
Figure 16: MEG of the MIMO and MIMO + FSS antennas.  
Figure 17: TARC of the MIMO + FSS antenna.  
Figure 19: _Cont._  
Figure 18: E-field distribution of the single-patch antenna (**a**) at 1.28 GHz and (**b**) 4.69 GHz.  
Figure 19: E-field distribution of the MIMO antenna without an FSS: (**a**) port 1 at 1.28 GHz; (**b**) port 2 at 1.28 GHz; (**c**) port 1 at 4.69 GHz; and (**d**) port 2 at 4.69 GHz.  
Figure 20: _Cont._  
The proposed dual-port antenna and an FSS were developed, and the anterior and posterior views of the prototype are displayed in Figure 21. The fabricated dual-port element's scattering parameter was measured using VNA N9917A, as displayed in Figure 22a. The experimental S-parameter values S\({}_{11}\) and S\({}_{12}\) are displayed in Figure 22b,c. The measured reflection coefficient achieved the reflection coefficient S\({}_{11}<-10\) dB at the dual frequency of 1.3 and 4.5 GHz, which matched the simulated results well, as shown in Figure 22d. At higher frequencies, variations in the measured reflection coefficient can occur due to imperfections in fabrication and soldering. The antenna was tested with a calibrated VNA and 50-Ohm SMA cables. However, the results showed variability because of the high-frequency sensitivity to cable and environmental conditions. Moreover, the proposed fabricated two-port MIMO antenna enhanced the isolation performance among dual ports, with isolation less than \(-20\) dB at the GNSS and 5G frequency bands. The overall comparison results of the single-patch antenna, the MIMO antenna, and the MIMO antenna with an FSS are shown in Table 4.  
Figure 21: Prototype of the proposed dual-port element: (**a**) anterior view; (**b**) posterior view; (**c**) FSS.  
Figure 20: E-field distribution of the MIMO antenna with an FSS: (**a**) port 1 at 1.28 GHz; (**b**) port 2 at 1.28 GHz; (**c**) port 1 at 4.69 GHz; and (**d**) port 2 at 4.69 GHz.
## 2 Literature Review on the Personalized Recommendation in the MOOC Context
### Recommendation Algorithms Based on Machine Learning Models  
With the development of big data technology, recommendation algorithms based on machine learning models have been created to predict and rank learners' interests and preferences. Common machine learning models include matrix decomposition, topic, clustering, and neural network models. Although the training time of recommendation algorithms based on machine learning models is longer, they have obvious advantages in feature extraction. The recommendation results are more accurate, especially when based on a deep learning model. Data can be accurately represented in a high-level form.  
Recommendation algorithms based on machine learning models have been applied to personalized recommendation for MOOCs. For example, a deep learning personalized recommendation algorithm for MOOCs based on multiple attention mechanisms was proposed by analyzing learning behaviors [15]. The personalized recommendation algorithm for MOOCs based on deep belief networks was proposed in function approximation, feature extraction, and prediction classification [16]. A personalized recommendation algorithm for MOOCs based on a priori clustering was proposed by revealing the course association rules [17].  
Although recommendation algorithms based on machine learning models have gained increasing attention owing to their high accuracy rate, they also have some disadvantages. First, because the recommendation process of this algorithm is similar to that of the black box, the interpretability of the recommendation results is weak. Second, to ensure the model has better recommendation results, manual intervention is required to screen the data attributes and adjust and correct the parameters, requiring more time and resources.