esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Material and Methods  
In this section, we explain the proposed system with proximity detection and materials that are used to evaluate the system.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Scheme** & **Objective** & **Data Source** & **Additional Equipments** & **LD 1** & **Beaconing Type** & **Filtering** & **ML 2** & **DL 3** \\ \hline Research in [26] & Positioning & Wi-Fi & Wi-Fi APs & Y & Static & O & O & X \\ \hline DABIL [27] & Positioning & Bluetooth & Bluetooth Beacons & Y & Static & O & O & O \\ \hline Research in [28] & Positioning & Bluetooth & Bluetooth Beacons & Y & Static & X & X \\ \hline Research in [29] & Existence Detection & Wi-Fi & Wi-Fi APs & Y & Static & X & O & X \\ \hline Research in [30] & Existence Detection & Wi-Fi \& Bluetooth & Wi-Fi APs \& Bluetooth Beacons & Y & Static & X & O & X \\ \hline Research in [31] & Existence Detection & Bluetooth & Bluetooth Beacons & N & Static & X & X & X \\ \hline Research in [32] & Distance Detection & Bluetooth & Bluetooth Beacons & N & Static & O & X & X \\ \hline Research in [33] & Distance Detection & Bluetooth & Bluetooth Beacons & N & Static & O & O & X \\ \hline Research in [34] & Distance Detection & Bluetooth & Mobile Devices & N & Dynamic & X & O & X \\ \hline Proposed System & Distance Detection & Bluetooth & Mobile Devices & N & Dynamic & O & O & O \\ \hline \hline \end{tabular} LD 1: Location dependency, ML 2: Machine learning, DL 3: Deep learning.  
\end{table}
Table 2: The recent studies and proposed system for distance estimation using wireless signal.
## 2 Proposed Synchronization Algorithm Based on LPA
### S-Curve of the LPA-Based Timing Error Detector  
The S-curve is used to identify the stable lock points of the error detector and determine whether any false lock point exists. It is calculated by the mean of the error signals \(e(n)\), such as the S-curve for TED  
\[S(\tau)=\text{E}(e_{\tau}(n)|\delta),\]  
where \(\delta=\tau-\hat{\tau}\) is the timing offset and \(\text{E}()\) denotes expectation. The S-curve also evaluates the slope of the S-curve at \(\delta=0\) as \(k_{p}\). The phase error detector (PED) has no simplification compared with the common method [21]. Thus, we derive the S-curve of the timing error \(\tau\) based on LPA, expressed as [26]  
\[S(\tau)=\frac{1}{N}\sum_{n=0}^{N-1}\  
Figure 2 presents the S-curves for the decision-directed (DD) and data-aided (DA) timing error detectors based on the LPA of Equation (32) and SLPA of Equation (33). The DD S-curve is simulated by the detector with a random sequence as the transmitted data, and the DA S-curve is calculated by Equation (39). Note that the modulated sequence from set \(\mathbb{L}\) is known, and such a curve is called the DA S-curve. Three quaternary multi-\(h\) CPM schemes with various modulation indices as \(h=\{\frac{4}{16},\frac{5}{16}\}\), \(h=\{\frac{5}{16},\frac{6}{16}\}\), and \(h=\{\frac{9}{16},\frac{10}{16}\}\) are considered. The data-aided curves for the two TEDs are computed by Equations (39) and (41), respectively, shown as the solid line in Figure 2. This reveals the correct time at which the timing error detection locks for the three multi-\(h\) CPM schemes, i.e., \(\tau=0\). The dotted lines are decision-directed timing error curves for the three groups of modulation index, which are the mean of the error signal estimation. It can be seen that the decision-directed curves can lock onto the integer periodicity, so the multi-\(h\) CPM has a stable locking point for the LPA-based method. We also provide the S-curve for the original LPA-based TED for the \(h=\{\frac{4}{16},\frac{5}{16}\}\) case. It also has a stable lock point, and the \(k_{p}\) of the SLPA-based method is lower than that of the LPA-based method. From the data-aided SLPA S-curves of the three multi-\(h\) CPM schemes, it can be seen that the S-curves become narrower when the modulation indices increase, which also means higher \(k_{p}^{s}\). This is consistent with the results in Table 2.
# Stability Analysis of Buck Converter Based on Passivity-Based Stability Criterion
## 2 Passivity-Based Stability Criterion  
The concept of system passivity originates from the law of energy conservation. Suppose the input vector of the system is \(\mathbf{u}(t)\), the output vector is \(\mathbf{y}(t)\) and the energy function is \(H[x(t)]\). The system satisfies the energy balance Equation:  
\[\underbrace{H[x(t)]-H[x(0)]}_{\text{energy stored}}+\underbrace{d(t)}_{\text{ energy dissipated}}=\underbrace{\int_{0}^{t}\mathbf{u}^{\text{T}}(\tau)\mathbf{y}(\tau)d\tau}_{ \text{energy supplied}} \tag{1}\]  
where \(d(t)\) represents the dissipated energy of the system, and the integral \(\int_{0}^{t}\mathbf{u}^{\text{T}}(\tau)\mathbf{y}(\tau)d\tau\) represents the external energy supplied to the system. The energy stored in the system is the external supply energy minus the system dissipated energy. The system will satisfy the following inequalities if the dissipated energy of a system \(d(t)\) is positive.  
\[H[x(t)]-H[x(0)]\leq\int_{0}^{t}\mathbf{u}^{\text{T}}(\tau)\mathbf{y}(\tau)d\tau \tag{2}\]  
Meanwhile, the system will be passive if the energy function satisfies \(H[x(t)]\geq 0\). The essence of a passive system is energy dissipation, and the initial energy, \(H[x(t)]\), of the system will tend to balance under the action of dissipated energy, \(d[x(t)]\). Therefore, the passive system will stabilize.  
For a single-port linear network, the network is passive if and only if the externally supplied energy, \(w(t)\), is nonnegative. The energy of the signal in the time domain is transformed into the frequency domain by [PERSON]'s theorem  
\[\begin{array}{l}w(t)=\int_{-\infty}^{+\infty}\!\!\!\!I^{\rm T}(\tau)\mathbf{u}( \tau)d\tau=\frac{1}{2\pi}\int_{-\infty}^{+\infty}\!\!\!\!\mathbf{U}(j\omega)\mathbf{I}^ {\rm T}(j\omega)d\omega\\ \phantom{w(t)}=\frac{1}{2\pi}\int_{-\infty}^{+\infty}\!\!\!\!\!I^{\rm T}(j \omega)\mathbf{Z}(j\omega)\mathbf{I}(j\omega)d\omega\geq 0\end{array} \tag{3}\]  
where \(\mathbf{U}(j\omega)=\mathbf{I}(j\omega)\mathbf{Z}(j\omega)\), \(\mathbf{i}(t)\) and \(\mathbf{u}(t)\) are Fourier transforms of \(\mathbf{I}(j\omega)\) and \(\mathbf{U}(j\omega)\), respectively. The * represents a conjugate complex number.  
It can be seen from (3) that if the real part of \(\mathbf{Z}(j\omega)\) is positive, and the supply energy \(w(t)>0\), then the network is passive and stable. Therefore, the system is passive if and only if \(\mathbf{Z}(s)\) has no right half plane (RHP) pole, and \(\mathrm{Re}[\mathbf{Z}(j\omega)]\geq 0\), where \(\mathrm{Re}[\mathbf{Z}(j\omega)]\geq 0\) can be converted to \(90^{\circ}\geq\arg[\mathbf{Z}(j\omega)]\geq-90^{\circ}\). Since the admittance model is easier to apply to engineering practice, the impedance condition is converted into the admittance condition as  
1. \(\mathbf{Y}(s)\) has no right half plane zeros.
2. \(\mathrm{Re}[\mathbf{Y}(j\omega)]\geq 0\) or \(90^{\circ}\geq\arg[\mathbf{Y}(j\omega)]\geq-90^{\circ}\).  
The passivity condition of linear circuits is also applicable for nonlinear circuits which can be linearized locally.
# Benefits and Lessons Learned from the Sentinel-3 Tandem Phase
## 3 The Sentinel-3 Tandem for Climate Study
### Data Archive  
To support the investigations of the study, an archive of Sentinel-3A and 3B products was collected. Access to the archive can be obtained upon request to the authors.  
The archive for optical instruments contains data from every Monday from 20/08/2018 to 18/03/2019. It contains non-time-critical level 1 and Level 2 products from OLCI and SLSTR: OL_1_EFR, OL_2_WRR, SL_1_RBT, SL_2_LST, SL_2_WST. The archive also contains reprocessed products for the months of July, September, and December (post-tandem). The OLCI reprocessing concerns OLCI-A and B and relies on the processing baseline 2.42 (released on 12/2018), with an ad-hoc radiometric calibration to harmonize OLCI-A to the radiometry of OLCI-B (see Section 6.2 for details). The SLSTR reprocessing concerns SLSTR-B only. Processing baseline 2.37 (released 08/2018) has been used with optimized geometric calibration with respect to the nominal processing. The optical archive is accessible from the C-TEP data exploitation platform [13].  
The altimetry archive contains S3A and S3B level 2 products over the tandem period (May to December 2018). It is hosted on the Sobloo DIAS (data information and access service) [14]. In the analyses, a correction 0.5 dB was applied on the sigma0 data of SRAL-B, see [15].
# A Case Study of the Radon Hazard at the Boundary of a Coal Minefield
###### Abstract
If residential buildings are located in territories within the geodynamic influence of mines, it can lead to the deterioration of radiological safety conditions for the resident population [21; 22]. It is suggested that the influence of underground mines on the dynamics of gas transport to the surface extends beyond the mined areas [16; 18; 23]. However, the quantitative parameters of this influence remain insufficiently studied. The assessment of the radon hazard of mining areas and their surroundings is very important in areas of intensive mining activity. Kuznetsk coal basin is considered to be one of the largest coal mining regions in Russia, with a high specialization of the economy of most cities on mining. Residential buildings that are situated on soils with a significant radon concentration in the underground atmosphere or a high emanation to the surface are a grave hazard to the population. The release of radon from the soil into living spaces occurs due to the \"pump effect\", which is reliant on a temperature difference [24; 25]. During winter, radon entry speeds up with the rise in temperature difference between the structure and the geological environment. High soil radon levels caused by faults in the Earth's crust and mining activities can increase the risk of developing cancer for residents of the Kuznetsk coal basin. Significant parts of the territories of coal mining towns have been exposed to the effects of underground coal mining, which has led to the formation of high-risk zones for radon emission. Previously, we detected significant levels of radon flux density (RFD) and volumetric radon activity (VAR) in dwellings above mines [21; 22]. Similar situations are being studied in coal mining areas in Poland, the United Kingdom, China, and other countries [17; 18; 19; 20; 26; 27]. A radiometric assessment of mining areas is necessary because exposure to radon from mines may extend beyond the boundaries of the minefield area. Moreover, mining frequently extends to the edges of the fractured zone in disturbance zones bordering the mine. These rock fracturing zones are natural transport channels that degas the upper part of the Earth's crust, including radon. In this study, we aim to examine the radon hazard at the boundary of a coal minefield.
# Scanning Inside Volcanoes with Synthetic Aperture Radar Echography Tomographic Doppler Imaging
## 2 Methodology
### Vibrational Model of the Earth
Considering an average speed of propagation of the seismic waves of about \(v\approx 3500\frac{\mathrm{km}}{\mathrm{h}}\) (approximately \(972\frac{\mathrm{m}}{\mathrm{s}}\)), a frequency of investigation set by us equal to 200 Hz, the wavelength of these vibrations is equal to about \(\lambda=\frac{v}{f}\approx\frac{3500}{200}\approx 4.86\) m. Considering the above parameters, extending the tomography to the maximum orbital aperture equal to half the total length of the orbit, therefore about 42,000 m, with \(R=650,000\) m, the tomographic resolution is equal to \(\delta_{z}=\frac{\lambda R}{2A}=\frac{4.86-650.000}{2.42,000}\approx 36\) m. This is the tomographic resolution set to calculate all the experimental parts shown in Section 3.
## 6 Conclusions  
The performance of classical MUSIC algorithm based on UCA is constrained by array aperture greatly, and most DOA estimation algorithms demand that the number of elements be larger than that of incident signals. Focusing on this problem, a novel multiple DOAs estimation algorithm based on rotation array is proposed in this paper, which has lower energy loss and complexity. Computer simulations verify the effectiveness of the proposed method, and the number of incident signals that the algorithm could estimate is more than that of the elements. Besides, the proposed array model could be used for any algorithm based on UCA. In the future work, we will focus on the application of the proposed algorithm [25].  
Figure 9: Successful resolution probability _versus_ rotation frequency errors.
# How Traditional Costing Methods Hinder the Development of Modular Product Architectures
## 4 Categorization of Abstraction Levels
### Findings Following the Categorization of Abstraction Levels  
Every study addressing a modularity effect was categorized according to one or more of the aforementioned abstraction levels. A total number of references (#Ref) addressing the specific modular effect and a count of instances (#Instances) when the expected impact of the effect was related to one of the three different abstraction levels were recorded. More specifically, the categorization denotes the level of abstraction at which it should be possible to assess the effect (not all sources quantitatively assess the impact). A reference can, in some circumstances, comply with all three levels. For example, when assessing inventory costs, the values of finished goods (products), subassemblies (subsystems), and components kept in stock are taken into account during a quantitative cost allocation. Sources that do not explicitly mention which abstraction level the effect will impact are referred to as the product level. This is generally the level at which traditional product development and project cost targeting are performed [5; 20]. The following section will discuss the results for each life-cycle phase before addressing the overall findings in this study. Table 3, listed below, provides an overview of the categorization.  
The modularity effects for each life-cycle phase are listed. The total number of references addressing this effect is listed in the second column from the right. The references used for each effect are listed in the far-right column. The number of instances found on each level is detailed in the adjoining columns to the left. A total of thirty-six individual references are used in this categorization. Table 3 summarizes the study's results, providing an opportunity to enhance our understanding of the level at which the effect of modularity impacts each LCP. A summarization of the results across the LCPs is illustrated with a heatmap in Figure 2.  
Thus, the distribution of abstraction levels within each LCP is clearly visualized. For example, in the development phase, modularity effects are predominantly addressed at the product level, with a total of 66 instances, compared to 23 at the subsystem level and just 1 at the component level. The insights that can be drawn from these results will be discussed in the next section.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{3}{c}{**Abstraction**} & \\  & & \multicolumn{3}{c}{**Level—**} & \\
**LCP** & **Modularity Effect** & \multicolumn{3}{c}{**\#Instances**} & **\#Ref** & **Sources** \\ \cline{2-6}  & **P** & **S** & **C** & & \\ \hline  & Improve outsourcing of development tasks & 2 & 2 & 1 & 4 & [18, 73, 74, 75] \\  & Improve parallel development/ prototyping & 8 & 2 & & 8 & [18, 50, 65, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### Deterministic Limit State Function  
If CO2 penetrates and diffuses into concrete, alkaline properties of the concrete will be neutralized due to carbonation with age. When the depth of carbonation in concrete continues to grow and reaches the reinforcing steel, the passive film can break down. This breakdown causes the corrosion of reinforcing steel and the reduction in the durability of structures. The condition when the carbonation progresses and reaches the rebar that causes the corrosion of reinforcing steel is defined as the serviceability limit. The probability of corrosion at this time is then calculated. The limit state is defined by Equations (14) and (15) to calculate the probability of corrosion in the limit state.  
\[\underline{\beta}=\underline{\theta}\left(D_{\text{CO2}}\right),\ R=r\left( \underline{\beta}\right),\ S=s\left(\underline{\beta}\right) \tag{14}\]  
\[G=R-S \tag{15}\]  
where \(R\) is the thickness of concrete cover (\(D\)); the load function (\(S\)) is carbonation depth changing with age; and \(\underline{\theta}\) means a function. The limit state function (\(G\)) can be expressed as the probability of damage (\(P_{f}\)). In practice, the reliability index (\(\beta\)) rather than the probability of damage is used to obtain the probability of corrosion.  
The relationship between the probability of damage and the reliability index is expressed by the following Equations (16) and (17).  
\[P_{f}=\int_{-\infty}^{0}f_{G}(g)dg \tag{16}\]  
\[\beta=\frac{\mu_{G}}{\sigma_{G}},\ P_{f}=\Phi(-\beta) \tag{17}\]  
where \(f_{G}(g)\) is the probability density function, \(\sigma_{G}\) denotes the standard deviation of \(G\), and \(\mu_{G}\) denotes the mean of \(G\).
## 3 Challenges
### Security
#### 3.11.2 MAC Layer Attack  
In a MAC layer attack, the attackers violate the rules defined in the protocols and disturb the normal operation, e.g., sending undesirable packets repeatedly to disturb the normal operation and exhaust battery power, using channels selfishly, and show uncooperative behavior on the priority of a cooperative MAC-layer. This may lead to DoS attacks at the MAC layer.
# Gear Fault Diagnosis Based on Kurtosis Criterion VMD and SOM Neural Network
## 3 Based on the Kurtosis Criterion VMD Gear Fault Feature Extraction Method
### VMD Signal Decomposition
At the same time, the endpoint effect occurred at the left endpoint of the time-domain diagram of IMF4 in the EMD decomposition diagram, and its value was 0, which would have a bad effect on the Fourier transform of the component signal. Figure 4.2 Empirical mode decomposition (EMD) decomposes the simulation signal. Figure 4.3 VMD decomposes the simulation signal. Figure 4.4 Gear with tooth crack fault signals decomposition by EMD algorithm. Figure 4.5 Gear with tooth break fault signals decomposition by EMD algorithm. It can be seen from the results of the above gear fault signal after EMD decomposition and from the frequency-domain diagram of the IMFs that the phenomenon of mode mixing occurred in the frequency-domain, and finally the false component was decomposed, whose frequency value was almost close to 0, without any practical significance.From the results of VMD decomposition, it can be seen that this method can effectively avoid the phenomenon of mode mixing and endpoint effect in the EMD algorithm, and has certain help to improve the accuracy of gear fault diagnosis.
# Mental Workload Assessment Using Machine Learning Techniques Based on EEG and Eye Tracking Data
## 3 Materials and Methods
### Participants and Experimental Procedure  
A total of 15 healthy undergraduate students, consisting of 8 males and 7 females, aged between 19 and 25, were chosen to participate in the research study. The average age of the participants was 21.6. The experimental protocol for this study was approved by the Institutional Ethics Committee (2020-483 and 8 September 2020) of Gazi University, Ankara, Turkiye. All participants provided informed consent in accordance with human research ethics guidelines. All experiments took place in the Human Computer Interaction Laboratory, under consistent physical conditions. The laboratory was well-lit with fluorescent lighting and was sound-insulated. Participants were verbally briefed on the study's purpose and the n-back tests they would be performing, as well as the required actions. Prior to starting the task, written instructions were displayed on the screen, explaining how to perform the task and its difficulty level. Two trial tests were conducted to ensure participants understood the tests: one off-record and one after recording began. The trial test data that were included in the recording were not taken into account during the analysis. Experiments were only initiated after confirming the quality of the EEG signals and eye tracking calibration. Figure 2 depicts a photograph taken during the implementation of the experiment.  
A standard visual n-back task set was utilized with the Inquisit Lab 6 software (version 6.6.1), offering four different difficulty levels (0-back, 1-back, 2-back, 3-back). The task became more challenging as the value of \"n\" increased. Each difficulty level was repeated three times, resulting in a total of 12 trials per participant. The order of the trials was randomized to prevent any bias caused by knowing the difficulty level in advance. Participants were instructed to press the \"M\" key when they saw the letter \"M\" in the 0-back task, and the \"L\" key if the letter was not \"M\". In the 1-back, 2-back, and 3-back difficulty level tests, they were required to press the \"M\" key if the letter on the screen matched the letter from 1, 2, or 3 positions back, respectively (target/match condition). If the letter did not match (non-target/non-match condition), they were instructed to press the \"L\" key. The task rules are provided in Figure 3. Each letter was displayed for 500 milliseconds, with a 2000 millisecond interval until the next letter appeared. Therefore, participants had a total response time of 2500 milliseconds.  
Figure 2: An image captured during the experiment.  
After each block, participants were asked to subjectively assess their mental workload using the NASA-TLX scale. Additionally, they were asked to prioritize the six sub-dimensions of the NASA-TLX through pairwise comparison tables. Weighted total NASA-TLX scores were calculated based on the weights derived from the 15 pairwise comparisons. The duration of the experiment with one participant was approximately 20 min.