esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Scenario and Specifications  
### Scenario  
The experimental scenario in this study involves communication within an NTN environment that utilizes a satellite constellation composed of two distinct groups operating at the same orbital altitude. Unlike traditional Delta-Walker constellations that employ a single inclination with multiple orbital planes, our design divides the constellation into two groups, each with its own inclination, number of orbital planes, number of satellites, and phase factor (F). This dual-inclination approach is adopted to complement the revisit time and coverage by leveraging the benefits of operating with two different inclinations.  
The simulation was conducted using MATLAB R2024a and Satellite Communication Toolbox (The MathWorks, Natick, MA, USA) over a 14-day period, from 1 January 2025 to 14 January 2025, with a sample time of 1 min, from which daily averages were calculated. Figure 2 shows the four utilized ground stations. These ground stations are located in representative cities in the northern hemisphere between 30\({}^{\circ}\) and 60\({}^{\circ}\) latitude chosen based on population and economic activity (Seoul, Berlin, Oslo, and Houston). The coordinates for each ground station are as follows: Seoul, 37.71\({}^{\circ}\), 126.73\({}^{\circ}\); Berlin, 52.50\({}^{\circ}\), 13.42\({}^{\circ}\); Oslo, 59.94\({}^{\circ}\), 10.75\({}^{\circ}\); and Houston, 29.76\({}^{\circ}\), \(-\)95.37\({}^{\circ}\). Key settings and parameters used in this simulation are summarized in Table 1.  
At each time step, we first checked the elevation angle (mask = 5\({}^{\circ}\)). If the satellite was above this mask, we computed the link margin using system parameters (antenna gains, path losses, noise figure, etc.) based on Equation (1) [27]. Detailed system parameter values and satellite antenna characteristics are covered in a separate section. Coverage was assumed when  
\[\text{Link Margin}=P_{\text{Tx}}+G_{\text{Tx}}+G_{\text{Rx}}-L_{\text{path}}- KT_{\text{sys}}-10\log_{10}(B)\geq 0\text{ dB}. \tag{1}\]  
Based on this link margin criterion, the visible time instances were collected to calculate the revisit time and daily throughput.  
Figure 2: Four ground stations.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 3 The Proposed Method
### Features Extraction Using Statistical Analysis
#### 3.2.3 Time and Frequency Domain Features  
Once MSTV features were obtained using Equation (7), it was applied to the time and frequency domain analyses to extract the other 8 features to enhance the accuracy of our non-active state prediction model. The time-domain analysis employed mathematical functions to analyze physical signals or  
Figure 7: Results of machine status tracking value (MSTV) feature extraction.  
time-series data with respect to time [28]. Additionally, the time-domain represented the change of a signal with time. Frequency analysis was the statistical method used for measuring the signals with respect to frequency. Frequency analysis typically involved the central tendency, dispersion, and percentiles [29; 30]. Figure 8 shows the structure of feature extraction in the time-frequency and domain frequency features.  
In the time-domain analysis, several statistical features were used that encompassed a wide range of popular time-domain analyses [6]. These statistical features included the mean value (MV), root mean square value (RMSV), square mean root value (SMRV), kurtosis coefficient (KC), shape factor (SF), and skewness coefficient (SC), which will be utilized to extract the new features from the MSTV. In addition, the root mean square frequency (RMSF) and root variance frequency (RVF), which are obtained from the frequency-domain analysis, will be used to extract two other features from the MSTV data. The details of 8 statistical methods for extracting features from the MSTV are listed in Table 3.  
Figure 8: Structure of feature extraction in Time-domain and Frequency-domain features.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **Equation** & **Data Type** \\ \hline \multicolumn{3}{c}{Time-Domain Analysis} \\ \hline Mean Value & \(X_{\text{env}}=\frac{1}{N}\sum\limits_{l=1}^{N}X(t_{i})\) & Float \\ Root-mean-square Value & \(X_{\text{env}}=\sqrt{\frac{1}{N}\sum\limits_{l=1}^{N}X^{2}(t_{i})}\) & Float \\ Square-mean-root Value & \(X_{\text{surr}}=\left[\frac{1}{N}\sum\limits_{l=1}^{N}\left|\left\{\text{X}(t _{i})\right|\right\}\right]^{2}\) & Float \\ Skewness Coefficient & \(X_{\text{sc}}=\frac{1}{X_{\text{env}}}\sum\limits_{l=1}^{N}\left(X(t_{i})-X_{ \text{sw}}\right)^{3}\) & Float \\ Kurtosis Coefficient & \(X_{\text{sc}}=\frac{1}{X_{\text{env}}}\sum\limits_{l=1}^{N}\left(X(t_{i})-X_{ \text{sw}}\right)^{4}\) & Float \\ Shape Factor & \(X_{\text{sf}}=\frac{X_{\text{env}}}{\frac{1}{N}\sum\limits_{l=1}^{N}\left| \left\{X(t_{i})\right|\right\}}\) & Float \\ \hline \hline \end{tabular}
\end{table}
Table 3: Eight Statistical Features of MSTV.
## 2 Materials and Methods
### Dosimetric Audit  
For the dosimetric audit, the following circular collimator diameters were used for irradiation: 4.0, 6.0, 7.5, 10.0, 12.5, 15.0, and 20.0 mm. The experimental configuration is presented in Section 2.2.2 and Table 1. For each field size, five TLD dosimeters were irradiated at the prescribed dose of 2 Gy. This is the prescribed dose commonly delivered to tumors in conventional treatment sessions. The MU necessary were determined on the basis of the output factors measured using the PTW-60019 CVD diamond detector for fields with a diameter of 12.5 to 20 mm in diameter, and 10 cm \(\times\) 10 cm, and with an IBA SFD diode (sensitive volume of 0.017 mm\({}^{3}\), thickness 60 um, diameter 0.6 mm). Afterwards, the output correction factors corresponding to each field size were delivered for circular field sizes ranging from 10 to 4.0 mm while applying the MU shown in Table 3. The reference field was 10 cm \(\times\) 10 cm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field Size (cm\({}^{2}\))** & **Field Output Factor** \\  & **(cGy/MU)** \\ \hline \(10\times 10\) & 1.000 \\ \(2\times 2\) & 0.886 \\ \(1\times 1\) & 0.773 \\ \(0.5\times 0.5\) & 0.585 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Output factors used to determine the calibration curve.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Circular Field Diameter (mm)** & **Field Output Factor** & **MU** \\ \hline \(20.0\) & \(0.883\pm 0.002\) & 227 \\ \(17.5\) & \(0.871\pm 0.002\) & 230 \\ \(15.0\) & \(0.856\pm 0.003\) & 234 \\ \(12.5\) & \(0.834\pm 0.002\) & 240 \\ \(10.0\) & \(0.784\pm 0.002\) & 255 \\ \(7.5\) & \(0.733\pm 0.002\) & 273 \\ \(6.0\) & \(0.677\pm 0.002\) & 295 \\ \(4.0\) & \(0.580\pm 0.001\) & 345 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Field output factors measured with diamond and SFD diodes.  
The positions of the micro-TLDs at the center of the radiation field were verified, with the film being marked (black dot) at the position of the central dosimeter (Figure 2). The 12 spots correspond to the 12 independent dosimeter irradiations for a specific field size.  
EBT3 radiochromic films were scanned to obtain the intensity profile of the irradiated area that covered the dosimeters, and the percentage standard deviation in 1 mm at the center was obtained. The profile of the field with a diameter of 4 mm is shown in Figure 3, demonstrating that in a circle with a diameter of 1 mm, the field is homogeneous to within 3%.  
For each field size, 12 irradiations of five dosimeters were performed; the position of the dosimeter closest to the center of the field was determined, and its TL response was measured in order to obtain the average of the 12 measurements used to determine the dose.
# An Overview of Tools and Challenges for Safety Evaluation and Exposure Assessment in Industry 4.0
## 2 Airborne Hazards in the Workplace
During welding, the emitted fumes comprise gaseous and aerosol by-products, metal oxides and volatile chemical species that could cause respiratory damage, asthma, lung inflammation, and increased lung cancer risk. The adverse health effects are related to the specific metal used and the produced ions can lead to metal fume fever and severe irritation to the upper respiratory tract [29]. Vapours and gases are another type of airborne health hazard present in occupational environments. Exposure to gases can occur due to accidental release or leaks in gas containers. Vapours, like VOC and SVOC, can be released during the handling of liquids. The effects of exposure to VOC have been extensively studied with many species experiencing severe health effects, including carcinogenic, mutagenic, and reprotoxic  
Figure 1: Illustration of inhalation exposure to airborne hazardous substances and particulate matter. effects (CMR) [30]. Two technologies can be applied to VOC exposure assessments. Gas chromatography/mass spectrometry (GC/MS) is used for the identification of gases and vapours in collected air samples [31]. GC/MS can offer a specific list of the captured substances but does not offer results for time resolution, limiting the ability of evaluators to connect the release of hazardous substances to specific process steps. Photoionization detectors (PID) provide real-time readings of total volatile organic compounds (TVOC) [32]. PIDs are used as complimentary to GC/MC, as part of a medium tier assessment, or in cases where the mix of released VOC can be theoretically predicted.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Routing Void Avoidance Scheme and Routing Process
#### 3.6.1 Routing Void Avoidance Scheme  
In the FLGR protocol, the routing void avoidance scheme is also adopted to solve the routing void problem. The routing void problem occurs when there is no CN (\(C=\varnothing\)) in the CNR of EN, or there is no node (\(Z=\varnothing\)) in the CNR of all CNs. In these situations, the next forwarding node can not be selected, so routing failure occurs.  
To solve the routing void problem, the angle \(\theta^{zero}\) of CNR should be adjusted, which is obtained by the fuzzy location region of destination node. If there is no CN in the range of angle \(\theta^{zero}\), the angle will increase linearly and the CNR is expanded gradually until nodes occur in CNR. The data message will be discarded, if there is still no node in CNR until the angle \(\theta^{zero}\) reaches \(\pi\). In this paper, \(\hat{\theta}^{zero}\) denotes the angle growth base, and it can be obtained as follows:  
\[\hat{\theta}^{zero}=k\times\frac{\theta^{zero}}{\rho_{global}} \tag{25}\]  
As shown in Equation (25), \(\hat{\theta}^{zero}\) is directly proportional to angle \(\theta^{zero}\), and it is inversely proportional to the entire network density \(\rho_{global}\). \(k\) denotes the regulatory factor. Taking node \(n_{1}\) as an example, the routing void avoidance scheme is shown in Figure 7:  
As shown in Figure 7a, the routing void avoidance scheme operates when there is no node in the CNR A of EN \(n_{1}\). The CNR A expands with the angle \(\theta_{18}^{zero}\), increasing linearly by \(\hat{\theta}_{18}^{zero}\) until nodes \(n_{2}\) and \(n_{3}\) occur. Then, according to the selecting process of the FLGR protocol, nodes \(n_{2}\), \(n_{4}\), and \(n_{6}\) are selected as the next forwarding nodes.  
Similarly, as shown in Figure 7b, only one \(n_{2}\) exits in the CNR of EN \(n_{1}\), and there is no node in the CNR of CN \(n_{2}\). According to the routing void avoidance scheme, the regions A and B expand with the angle \(\theta_{18}^{zero}\), increasing linearly by \(\hat{\theta}_{18}^{zero}\), until the nodes \(n_{4}\) and \(n_{10}\) occur. Then, according to the selection process of the FLGR protocol, nodes \(n_{2}\), \(n_{4}\), and \(n_{6}\) are selected as the next forwarding nodes.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fuzzy Sets** & **Values** \\ \hline very low & 0.03133 \\ low & 0.25 \\ medium & 0.5 \\ high & 0.75 \\ very high & 0.9687 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Defuzzification table of parameter \(E\).
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 5 Simulations and Evaluation  
This section introduces the setups for the simulations and the evaluation for the performance. The audio settings followed are similar to those in [28]. A Hanning window is used with the length 256 and an overlap of 75% between adjacent frames. The sample frequency is 16 kHz. Moreover, 256-point FFT is considered. The frequency bins ranging from 500 to 3875 Hz are taken into consideration to have sufficient mode strengths and avoid aliasing, which results in the number of the frequency bins \(N_{F}\) being 55. All these parameters concern the audio settings. The signal can be processed more effectively with the help of these parameters. Furthermore, the appropriate frequency range can be selected in order to have sufficient mode strengths and avoid aliasing with these parameters. Moreover, the highest order of spherical harmonics is 4. A rigid spherical microphone array with \(L=32\) and \(r=4.2\) cm is considered. The highest order of spherical harmonics and the number of the sensors directly influence the dimension of the input features. Thus, the dimension of the TFD matrix is \(64\times 32\). The dimension of the SHD matrix is \(50\times 25\). The dimension of the AED matrix is \(162\times 81\).
## 2 Overview
### Marchetti Dilatometer: Conceptualization  
The Dilatometer is an in situ penetration and expansion test proposed by [PERSON] in 1980, which consists of the insertion of a stainless-steel sheet at intervals. This stainless-steel sheet has a membrane on one side, which is inflated by a pressurized gas injected through a system of tubes. This test measures the horizontal stress that must be recorded at certain points of the operation called pressure values A and B [23], as shown in Figure 4. The procedure to obtain these values is standardized by the ASTM D6635-15 [24] and [PERSON] in its publications. The present research used the procedures and equations proposed by [24], and [PERSON]'s procedure and equations for obtaining indexes, which are present in [23], where [PERSON] proposed the concept of the Dilatometer to the engineering community; however, these are no longer used or recognized for research use. [PERSON] presented a series of correlations as part of his study in 1980. These covered the \(OCR\), \(K_{O}\), \(C_{u}\), and the edometric modulus value through the trend of these values about the \(I_{D}\), \(E_{D}\), and \(K_{D}\) indexes [23]. Nowadays, the researchers have the following equations, presented in Table 2, published in 2001 by the International Society for Soil Mechanics and Geotechnical Engineering [2]. Table 2 showed that the undrained shear strength equation involved the value of \(K_{D}\) and the effective vertical stress. It was also restricted by the material index (\(I_{D}\)), which can be identified as clay (\(0.1<I_{D}<0.6\)), silt (\(0.6<I_{D}<1.8\)), and sand (\(1.8<I_{D}<10\)) [2]. Based on this, this restriction corresponded to silts and clays, due to \(I_{D}<1.2\). However, [2; 24] reports recommended that this equation only be used on clays, which limits its use in soil volumes where silts predominate [2; 24]. Figure 3: (**a**) Extracted Shelby tubes, (**b**) sample being extracted from a Shelby tube, (**c**) photographed samples, and (**d**) stored samples. As for the modulus of elasticity, it should be noted that it is obtained through the geometric modulus value. The standard provides expressions to calculate this parameter considering different limitations to find the correction factor \(R_{M}\). These limitations are given by the values of \(I_{D}\), \(K_{D}\), and \(R_{M}\), as shown in Table 2. The product between \(R_{M}\) and \(E_{D}\) is the value of the dilatometer modulus. According to [PERSON] (2018) [4], the nomogram chart developed by [PERSON] (1989) [25], is a graphical tool that can be used to determine the type of soil and its bulk density, which is based on two values on the adjusted value of the material index, \(I_{D(lar)}\), and the dilatometer modulus, \(E_{D}\). To obtain the density, there is no defined equation; however, this nomogram helps to obtain this parameter. Figure 5 presents how it relates the values of \(E_{D}\) with those of \(I_{D}\)--in this way, the intersection of these data marks the value of the ratio of the unit weight of soil to the unit weight of water (\(\gamma_{s}/\gamma_{w}\)), in addition to allowing the classification of the material.
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
## 2 Implementation of Tracking Component
### Tracking by Detection  
At first frame when a rectangular region of an object is selected as the target that will be tracked in the next frame sequence, we use Equation (4) to obtain the optimal correlation filter of the target both for translation and scale. In a subsequent frame, the translation filter is applied on \(z_{t}\) (called test sample) centered around the target's position inherited from the latest previous frame. \(z_{t}\) is processed similarly to the training sample \(x\). (feature representation, pre-processing, cyclic sampling). Then we use:  
\[\hat{y_{t}}=\sum_{l=1}^{d}\hat{w_{l}}^{T}\odot\hat{z_{t}} \tag{5}\]  
to compute the DFT of correlation scores \(y_{t}\) in the Fourier domain. The location of the maximum value in the correlation score can be regarded as the target's new position, due to the similar characteristic of correlation in signal processing. Around the new target's position, a scale pyramid sampling is applied for the scale detection (as stated in Section 2.3). Like the translation filter, we calculate the scale correlation response scores for all the patches:  
\[\hat{y_{st}}=\sum_{l=1}^{d_{s}}\hat{w_{s}}^{T}\odot\hat{z_{st}} \tag{6}\]  
The most compatible scale is derived by finding the maximum \(y_{st}\) among all the \(z_{st}\).
## 3 Results and Discussion
### Scanning Electron Microscopy (SEM) with EDS Analysis  
Backscattered Electron (BSE) SEM images presenting the microstructure of samples from each mixture are shown in Figure 7. Phase compositions of cement matrices were confirmed by means of the EDS analysis, the results of which are shown in Figure 8.  
The cement matrix structure in all samples was dense and consisted mainly of the C-S-H phase and unhydrated GGBFS grains (Figure 7a-f). The highest number of unhydrated  
Figure 7: SEM images (BSE) presenting the microstructures of the (**a,b**) C55_SFA15_SF3, (**c,d**) C100_SFA15_SF3, and (**e,f**) C100_SFA30_SF3 samples.  
GGBFS grains was observed in the C55_SFA15_SF3 sample, as it contained the highest amount of GGBFS. Some unreacted fly ash grains (Figure 7a,c,e) and unburnt coal particles (Figure 7d) were also visible, but in much lower quantities. Characteristic cubic crystals of calcite (Figure 7d), as an effect of portlandite carbonation, were present inside pores and in the cement matrix of all samples, but in different amounts. The sample with the lowest amount of GGBFS and SFA (C100_SFA15_SF3) contained significantly more calcite crystals at the same depth compared with other mixes. This could be explained by previous studies indicating that the reduction of the amount of Portland cement clinker is associated with the reduction of the amount of portlandite (Ca(OH)\({}_{2}\)) in concrete [32]. Some Portlandite crystals (Figure 7f) were identified in all samples, but the amounts were not significant.  
Microcracks in the cement matrix were present in all mixes, though with a different intensity. The sample with the lowest amount of Portland cement clinker (C55_SFA15_SF3) contained a significantly lower number of microcracks than the other mixes. The high amount of microcracks might be associated with the fact that the SEM samples were obtained from the near-to-surface layer of concrete, which is especially prone to shrinkage-induced microcracking. However, due to the sample preparation method and the lack of shrinkage measurements, it is impossible to clearly determine whether the observed differences were associated with the higher amount of Portland cement clinker. Microcracks in the cement-based composites have easily been induced by the sample cutting and drying prior to SEM observations [33]. The cutting process generates a direct mechanical impact on the sample, while during drying, a moisture gradient is developed, causing tensile stresses associated with drying shrinkage.  
Figure 8: _Cont._
## 3 Compensation in Three-Phase Three-Wire Systems with Balanced Voltages  
Figure 2 shows an unbalanced three-phase linear load connected to a three-phase three-wire system with infinite power. As the voltages are balanced, the powers \(P_{-}\), \(Q_{-}\) and \(S_{u-}^{+}\) included in (12) are zero. Therefore, the total apparent power \(S_{T}\) is determined by Equation (13), and includes the following powers: active power \(P\), which with balanced voltages is equal to \(P_{+}\); reactive power \(Q\), which with balanced voltages is equal to \(Q_{+}\); and the unbalanced apparent power \(S_{u+}^{-}\).  
\[S_{T}=\sqrt{P^{2}+Q^{2}+{S_{u+}^{-}}^{2}} \tag{13}\]  
\(Q_{+}\) and \(S_{u+}^{-}\) are powers whose average value is zero, and therefore, they can be compensated from reactive elements (coils and/or capacitors). With this objective, we incorporate two compensators: static VAR compensator (SVC), to compensate \(Q_{+}\), and negative-sequence current compensator (NSCC), to compensate \(S_{u+}^{-}\). As the voltages are balanced, both compensators are independent, that is, the design of one compensator does not affect those of the others. However, this statement is not valid when the voltages are unbalanced.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Approaches to Energy-Aware Security
#### 4.3.1. Hardware-Based Approaches  
* _Low-power processors._ Low-power processor design aims to reduce the power consumption at an acceptable cost of performance degradation [92], which is widely used in mobile devices, as well as wearable and embedded systems. With low-power processors in place, devices gain longer battery life and better energy efficiency.
* _Energy-efficient communication modules._ Energy-efficient communication modules aim to reduce the power consumption of security operations, thereby ensuring security while achieving overall energy efficiency [93].
* _Cryptographically secure enclaves._ Cryptographically secure enclaves allow encryption and decryption operations to be executed efficiently at the hardware level. Applications executing in a secure enclave can reduce the demand for repetitive security checks and data encryption operations, thereby diminishing the power consumption.