esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Feature Selection
#### 2.6.2 Recursive Feature Elimination (RFE)  
RFE is a backward selection strategy, so in a recursive process, undesirable features are successively deleted. Depending on the trained model and classification accuracy, RFE tries to identify the best subset of features. RFE must be linked with a classifier that generates significant scores in order to choose features. RFE has been combined with support vector machines (SVM), random forest (RF), and gradient boosting machines (GBMs) to select features, proposed by [PERSON] et al. [53]. With RFE, unwanted features are iteratively removed. The residual N-i features are fitted with the RFE classifier in the ith iteration. N refers to the number of features in input data. Subsequently, the importance of the N-i features is calculated, and the least significant features are discarded; see [PERSON] et al. [54].  
In this work, we combine random forest (RF) together with RFE. The random forest-recursive feature elimination (RF-RFE) algorithm has been applied in a number of fields, including identifying relevant features in the spectra created by an analysis of agroindustrial products (see [PERSON] et al. [55]), emotion recognition (see [PERSON] et al. [56]) and credit card fraud detection (see [PERSON] et al. [57]). By comparing with the statistical support vector machine-recursive feature elimination (SVM-RFE), RF-RFE reveals its more remarkable performance. RF, an extensively employed bagging approach, consolidates predictions from various bootstrapped trees for classification purposes. This method demonstrates reasonably high stability and yields a more dependable classification rule via bagging. In RF-RFE, the N number of RF classifiers is built after different subsets of N features are iteratively fitted with RF. The performance of the RF classifier and the feature importance scores that are used to create classifiers are calculated after each iteration. Finally, features with the best classification performance become the output of RFE; see [PERSON] et al. [54].
# Privacy Framework for the Development of IoT-Based Systems
## 5 Proposed Methodological Approach: Planning, Requirements, and Design (PRD)
### PRD Phases
#### 5.2.3 Phase 3--Design  
The design phase is a crucial stage of the PRD approach, ensuring that privacy-preserving mechanisms are seamlessly incorporated throughout the entire IoT project lifecycle. Within the framework of the TpM-Pro methodology, this phase plays a pivotal role across its various stages:  
* **Considering the Business**: Aligns privacy goals with business objectives, ensuring that privacy considerations are integrated from the outset and remain consistent and aligned with the overall strategic vision.
* **Gathering the Requirements**: Defines privacy-specific requirements, such as data anonymization, encryption, and access control, and ensures their seamless integration into the system specifications, laying a solid foundation for a secure and compliant system architecture.
* **Implementation**: Provides a detailed system blueprint that guides developers in the effective implementation of privacy mechanisms. This includes outlining security best practices, testing procedures, and regulatory compliance checks to ensure the system meets all necessary privacy standards.  
By embedding privacy considerations at these early stages, the design phase ensures that the entire system architecture is developed with privacy and security at its core. This proactive approach not only reduces potential risks but also ensures regulatory compliance and safeguards user data throughout the project lifecycle, an emphasis that is reflected in Table 3.  
Privacy guidelines serve as the backbone of this phase, ensuring that the design remains consistently aligned with privacy objectives while fostering a system architecture that upholds data protection and strengthens user trust.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Comparison with GNSS Observations  
LS is typically monitored using geodetic methods, such as InSAR or GNSS. We validated our InSAR results using nine CORSs managed by the Indonesian Geospatial Information Agency [58]. These stations are primarily distributed along the northern coast of Java, as shown in Figure 2. Some CORS data have been excluded from the analysis due to several issues, such as antenna changes or repositions, outliers, and unexplained sudden rate changes. After data cleaning, both CORS and InSAR results were estimated using a linear model, as shown in Figure 13.  
The InSAR LS rate shows agreement with the CORS data. In BAKO station, the CORS rate was recorded at \(-0.41\) mm/year, compared to the InSAR rate, which measured \(1.45\) mm/year. Although the signs are opposite, the value remained within the RMS rate estimation, which is \(6.025\) mm/year. All other stations have the same sign, with similar LS rate values. The largest discrepancy in LS rate occurred at the CPKL station, where the rates from each method differed by \(16.04\) mm/year. This is because LS observed from the CORS is not linear. The acceleration of the LS rate has been visible since 2012, with a slight deceleration observed in 2016. Recalculation of the CORS LS rate from 2016 to 2022 shows much greater agreement with the InSAR LS rate, which now differs by only \(10.04\) mm/year.  
Figure 12: LS in Sidoardjo: (**a**)Map if LS rate in mm/year; (**b**) LS time series retrieved from InSAR from 2017 to 2023 in oil and gas field area (**b.1**), LUSI mud volcano (**b.2**), residential area (**b.3**); (**c**) land use of the region highlighted in black boxes, (**a.1**) which correspond to complexes of oil and gas fields (**c.1**), (**a.2**) which correspond to the LUSI mud volcano (**c.2**), and (**a.3**) which correspond to the developed residential area (**c.3**).  
In general, the agreement between the InSAR and CORS LS rate is very high, as evidenced by its high correlation value of 0.94 derived from the cumulative displacement of nine CORSs (Figure 14). Additionally, the location of the largest displacement in Pekalon-gan has been identified.
# Data-Driven Prediction Method for Power Grid State Subjected to Heavy-Rain Hazards
## 3 Description of Data
### Weather Station Data  
The local weather data was obtained from an automatic weather station (AWS); the weather variables were recorded at least every hour. The collected data consisted of accumulated rainfall, 1 min average wind speed, 10 min average wind speed (m/s), wind direction (\({}^{\circ}\)), and humidity (\(\%\)). For this study, the accumulated rainfall and 10 min average wind speed were used to analyze the power outage. These weather variables are the expected cause of power outages under heavy-rain conditions. Furthermore, when several AWSs were active in a region, this study used the maximum/minimum weather values as it is the extreme weather conditions that are associated with power outages.  
The standard criteria for issuing advisory or warning information concerning extreme weather conditions in South Korea was investigated to extract the heavy-rain data. The KMA issues a special weather report whenever a sudden and significant change in weather conditions is observed. The standard criteria for heavy rain are divided into the advisory level and warning level. The advisory level means that the precipitation for 6 h is expected to be more than 70 mm or the precipitation for 12 h to be over 110 mm. The warning level means that the precipitation for 6 h is expected to be more than 110 mm or the precipitation for 12 h to be over 180 mm [30]. In this paper, the standard criteria for heavy rain were used to extract the heavy rain condition data to predict the grid state.
## 2 Experiment Setup  
The DC bias test platform is shown in Figure 1. The voltage regulator is connected to a voltage source, providing a 360 V 50 Hz sinusoidal excitation to the arrangement. The isolation transformer is utilized for avoiding the DC current effects on the supply source. The DC source with adjustable resistance are used to obtain various DC current while the eight parallel capacitors of 1194 \(\upmu\)F are used to reduce the AC current passing through the DC source. The electrical quantities are measured by the power analyzer. The equipment parameters in the test platform are shown in Table 1.  
In order to build the equivalent relationship with the UHV transformer, the core structure and winding connection mode of RSM are consistent with the UHV transformer, as shown in Figure 2. The RSM is a single-phase four-column three-winding autotransformer built with an approximate scale of 1/12 relative to the UHV transformer. The capacity and rated voltage of the RSM are 5 kVA and 360 V, whose length, width and height are 661 mm, 100 mm and 356 mm, respectively. The flux density at rated voltage is 1.65 T, which is consistent with the UHV transformer. Figure 3 shows the silicon steel type of UHV transformer and reduced-scale model, which are 27 ZH095 and B23R075, respectively. It has to be noted that the measured B-H data are only provided to 1.91 T, the linear curve fitting method is used to extend the curve. The parameters of the UHV transformer and the RSM are presented in Table 2.  
Figure 1: Experimental platform with load under DC bias. (**a**) Schematic diagram. (**b**) Photo of the experimental platform.  
Figure 3: B-H curve of UHV transformer as 27 ZH095 and RSM as B23R075.  
Figure 2: Reduced-scale autotransformer.
## 3 Mathematical Modelling and Optimization
### Artificial Neural Network (ANN)  
ANN, inspired by biological neurons, was developed to solve engineering problems in 1940 in the USA. Recently, use of ANN has been widespread [16]. There is input layer, interlayer, and output layer in ANN. These layers are connected with one another. Data obtained from experiments are trained in ANN, so initially randomly selected weights are reached optimum levels. The more parameters the ANN are trained in, the better the results obtained are. The created structure for ANN is given in Figure 1. The number of cells in interlayer is 10.  
The necessary equations for ANN are given below. The sum of input parameters (\(X_{i}\)) are multiplied with the sum of weights in the interlayer (\(W_{1i}\)). A net value (\(a_{i}\)) was obtained.  
\[a_{i}=\sum_{i=1}^{n}(W_{1i}\cdot X_{i}) \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Al** & **Sn** & **Zr** & **Mo** & **Si** & **C** & **H** & **Fe** & **N** & **Ti** \\ \hline
5.5–6.5 & 1.8–2.2 & 3.6–4.4 & 1.8–2.2 & 0.06–0.10 & \(\leq\)0.05 & \(\leq\)0.015 & \(\leq\)0.25 & \(\leq\)0.05 & Balance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical properties of Ti-6242S (%).  
Figure 1: The structure of Artificial Neural Network (ANN) to Ti-6242S.  
\begin{table}
\begin{tabular}{c c} \hline \hline Cutting Speed, \(V\) (m/min) & 10, 15, and 20 \\ \hline Feed Rate, \(f\) (mm/rev) & 0.04, 0.08, and 0.12 \\ \hline Depth of Cut, \(a\) (mm) & 1, 1.5, and 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cutting parameters.  
This net value was transferred from activation functions (\(A_{i}\)).  
\[A_{i}=\frac{1-e^{(-a_{i})}}{1+e^{(-a_{i})}} \tag{2}\]  
Then, output value (\(y_{i}\)) was found by multiplying activation function and weights (\(W_{2i}\)) between the interlayer and output layer.  
\[y_{i}=W_{2i}\cdot A_{i} \tag{3}\]  
when the obtained value was substracted from real value (\(d_{i}\)), the error (\(e_{i}\)) was found.  
\[e_{i}=d_{i}-y_{i} \tag{4}\]  
and the total error (\(SSE\)) was calculated.  
\[SSE=\frac{1}{2}\sum_{i=1}^{n}{e_{i}}^{2} \tag{5}\]  
Train Levenberg Markuad backpropagation was used to adjust weight to the learning algorithms. These systems used the Hessian (\(H\)), Jacobian (\(J\)), and gradient (\(g\)) matrices.  
\[W_{i+1}=[W_{i}-{(H)}^{-1}\cdot g] \tag{6}\]  
The Hessian matrix,  
\[H=J\cdot J^{\prime}+\mu\cdot I \tag{7}\]  
where \(\mu\) and \(I\) were the Marquardt parameter and the unit matrix, respectively. \(J^{\prime}\) was the transpose of the Jacobien matrix. The Jacobian matrix,  
\[J=\frac{\partial e_{i}}{\partial W_{(i-1)}} \tag{8}\]  
The gradient matrix,  
\[g=J^{\prime}\cdot e_{i} \tag{9}\]  
In the main equation, If all the matrix were replaced, the weight was found.  
\[W_{i+1}=[W_{i}-(J\cdot J^{\prime}+\mu\cdot I)^{-1}\cdot J^{\prime}\cdot e_{i}] \tag{10}\]  
It was used to 70% training, 15% testing, and 15% validation of the defined parameters from experimental study to ANN. All weights were trained.
# Adaptive Filtering for Multi-Track Audio Based on Time-Frequency Masking Detection
## 4 Discussion  
In performing a subjective comparison of the original and filtered mixes for Flesh and Bone (see Supplementary Data), we observe a clear change in the characteristics of the mix. Compared to the original, the filtered mix exhibits less _muddiness_, particularly on the accordion and vocals, and the levels appear more balanced; the dominance of the accordion in the mix is reduced while the guitar becomes more audible. In our opinion, this is a positive demonstration of our method, considering that there was no adjusting of the gain for each track and no prior information. We have included a second track, Nearly There by [PERSON], from the same repository, where the clarity of vocals and guitar are also improved (see Supplementary data). Besides the gain for each track, further user-defined parameters can be included such as the prominence of peaks of the adaptive threshold and the noise floor adjustment. Modifying these parameters will increase the contribution of the resulting filter, incrementing the number of peaks at higher frequencies.  
Finding these intricate filter responses by ear requires a significant amount of practice and training and is by no means an easy task. This is particularly relevant for acoustic or electric instruments recorded using a microphone out of a speaker cabinet (i.e., electric guitar or electric organ). Therefore, there is a benefit in utilising our method, which simplifies the process by reducing the number of parameters in play during mixing.  
Figure 6: Relationships between source tracks and final masking vectors for the four tracks of the multitrack from _Flesh and Bone_. The same process can be expanded for any number of tracks.  
Figure 7: Multitrack spectrum and excerpt before and after filtering. (**a**,**b**) bass, (**c**,**d**) accordion, (**e**,**f**) vocals, and (**g**,**h**) guitar. Bass and accordion show the most prominent differences as gains for the filters were more aggressive. The spectral differences match those seen in Figure 5.  
Our method can be improved by preserving the two-dimensional information and filtering in the STFT space. However, this approach might need significantly more computational resources. This method might not be well suited when mixing different sources of the same instrument as these will have very similar spectral contents.  
We envision that our method could be turned into a digital plug-in that can be used as either a pre-processing step or a method to visualise masking and generate editable filters as a mean for musicians untrained in multitrack mixing to improve the quality of their self-produced music.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 3 BGP Anomalies and Attacks
### Denial of Service (DoS)  
Denial of service (DoS) attacks, in the context of the BGP, involve the malicious manipulation of routing tables and paths to render a network, or parts of it, inaccessible. BGP DoS attacks can be particularly insidious as they can disrupt the flow of data across multiple networks, not just the immediate target. In a BGP DoS attack, the attacker may manipulate BGP announcements to either drop traffic destined for a particular prefix or to create routing loops. This could involve announcing IP prefixes that do not belong to the attacker, causing traffic to be misrouted, or intentionally withdrawing legitimate BGP announcements, causing network outages.  
The impact of a BGP DoS attack can be widespread, causing network outages, degraded service quality, and potentially leading to cascading failures across interconnected networks. The misrouting or dropping of traffic can disrupt critical services and communications, with potential socio-economic consequences. The mitigation of BGP DoS attacks often involves implementing prefix filtering, ensuring that only legitimate BGP announcements are accepted. Utilizing the Resource Public Key Infrastructure (RPKI) to validate the authenticity of BGP announcements and employing measures for path validation are examples of extant mitigation measures.
## 5 Numerical Analysis
### The Numerical Analysis Related to the Service Differentiation
#### 5.1.2 Service Differentiation and Consumers' Sensitivity  
Given that \(\delta_{1}=0.6\), \(\delta_{2}=0.4\), \(\alpha=[0.5,1]\), \(\beta=[0.4,1]\), and \(\eta=1\), the impact of consumers' sensitivity \(\alpha\) and \(\beta\) to the service differentiation \(\Delta s\) under centralized and decentralized decision making is depicted in Figure 3.  
It can be observed that, under any strategy, the service differentiation \(\Delta s\) decreases as customers' price sensitivity \(\alpha\) increases and increases as customers' service sensitivity \(\beta\) increases. When consumers become more sensitive to product prices, they are attracted to lower prices, making service differentiation less critical for merchants, who then tend to adopt low price competition strategies to attract more consumers. On the contrary, when service sensitivity rises, consumers pay more attention to the utility of services. Enhanced service levels through advanced IT can attract more consumers, which is an effective way for CBEC to avoid price competition.
# Does Power Output at Critical Power Intensity Interchange between Cycling and Running?
###### Abstract
The CP concept has been used to distinguish the boundary between the heavy and severe exercise domains, which can be sustained for a duration of 15-40 min before exhaustion sets in or before the power output needs to be lowered to continue exercising [18]. Regardless of the approach taken, the ability to analyze mechanical power during running introduces a new paradigm in the scientific literature of this field. Currently, researchers have determined CP intensity in relation to ventilatory thresholds and maximum oxygen uptake [20]. The main purpose of this study was to investigate whether power output at CP intensity could be used interchangeably between cycling and running segments that were measured using their respective discipline-specific power meters and trained triathletes. The previous hypothesis established was that the power output between cycling and running would not be interchangeable at CP intensity because both the sport-specific gestures (pedaling and stride) and the power meters are different between the two disciplines.
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 3 Methodology
### Design of Electric Force Visuo-Haptic Scenarios
#### 3.2.2. _Visuo-Haptic Scenarios_
The magnitudes of the point charge (in \(\upmu\)C) and of the plane change (in \(\upmu\)C/m\({}^{2}\)) can be selected by a sliding bar or entering them directly on an active field on the screen. The screen displays the coordinates of the point charge, its distance to the infinite plane, the components of the electric force exerted on the point charge, and the magnitude of the electric force. The electric force on the point charge exerted by the plane charge is given by Equation (3). \[\vec{F}=\frac{\sigma Q}{2\varepsilon_{0}}\hat{n} \tag{3}\]  
where \(\varepsilon_{0}\) is the electric permittivity of free space and \(\hat{n}\) is a unit vector perpendicular to the plane. Notice that the electric force is constant: it only depends on the surface charge density of the plane and the magnitude of the test charge, but it does not depend on the distance between the plane and the point charge.
## 4 Formulation and Policy
### Policy Analysis  
#### 4.3.1 Computing Complexity  
Note that the power consumption of executing selection policy is much smaller than the jamming power of \(UAV_{L}\), which is negligible. The time complexity of selection policy is denoted as \(O\big{(}n^{2}m+nm\big{)}\). Based on [13], the time complexity of Power Efficient Legitimate Eavesdropping (PELE) that calculate the optimal power result is \(O(m)\) which depends on the number of time slots. Considering the number of cases used in eavesdropping and jamming models, which are denoted as \(n\), the selection policy's time consumption in finding optimal power solutions is \(O(nm)\). After calculating optimal power consumptions in all cases in each time slot, the algorithm uses the Bubble method [39] to acquire the minimum power in all cases, which are denoted as \(O\big{(}n^{2}\big{)}\) in each time slot and \(O\big{(}mn^{2}\big{)}\) in the whole eavesdropping and jamming process.  
Therefore, the selection policy's time complexity can be denoted as \(O\big{(}n^{2}m+nm\big{)}\), where \(n\) denotes the number of cases and \(m\) denotes the number of time slots.  
In our research, we find that it is a challenging problem to solve the optimal number of time slots for accurate resolution of the optimization problem. As the complexity increases, it is really difficult to obtain the optimal number of slots for accurate resolution of our problem. Due to the limitations on laboratory equipment, we only discuss the algorithm performance with six time slots in our simulations. Our further research is to design an algorithm to research the optimal number of slots for accurate resolution of the optimization problem.
## 3 Data and Methodology
### Methodology
In addition, the CWT method gives rise to great uncertainties, thus the weight coefficient \(W_{ij}\) is needed to reduce these uncertainties. Similarly, \(W_{ij}\) is determined using Equation (3), and the introduction of the coefficient is as follows:  
\[WCWT_{ij}=W_{ij}\times CWT_{ij} \tag{5}\]  
For PSCF and CWT methods, the input data and applicable resolution of grid are the same. The difference between CWT and PSCF is that PSCF usually uses a concentration threshold to evaluate the potential sources of PM\({}_{2.5}\). It means that it may have the same PSCF value when sample concentrations are either only slightly higher or much higher than the criterion. As a result, it may not distinguish moderate sources from strong ones. For CWT method, the limitation of PSCF can be overcome by assigning a weighted concentration by averaging the sample concentrations that have associated trajectories that cross the grid cell.