esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Mission Operations Issues and Status
#### 4.2.4 OBC EEPROM Errors  
Errors in the microcontroller EEPROM in the OBC were detected during the operation. The errors made changes in the log register of the commands processed by the OBC and their origin is suspected to be related to single event upsets (SEU). The OBC operation was always recovered by applying a power cycle from the EPS and then updating the on-board time of the satellite by a ground command. During the period of the last days of December 2018 and the first days of January 2019, more resets in the OBC were carried out than in the preceding weeks. However, none of these resets occurred during the execution of one of the main missions. Most of the errors and OBC resets in this period caused at most a change in the satellite time and the loss of the last received commands.  
Besides the OBC EEPROM errors, similar errors were observed in other microcontrollers' EEPROMs. In particular, the EPS microcontroller experienced a change in the SD card memory address value stored in the EEPROM. This value is used by the EPS microcontroller to know the last used location of the SD card, where the most-recent housekeeping data have been stored. The solution for the EPS EEPROM issue was a regular update of the SD address for saving of the data following a ground command.  
Commercial CMOS-EEPROMs have been shown to be susceptible to SEU while they are powered on [33]. Even though the HEF-EEPROMs (high-endurance flash) on-board Ten-Koh use a different technology, it is suspected that they might be sensitive to SEU in the same way. Because the microcontroller EEPROM is used to store commands and/or update parameters during the execution of the main software routine, changes in bits in the EEPROM could potentially alter and even stop the correct operation of the microcontroller completely. A detailed investigation of the radiation effects on the HEF-EEPROM of the PIC16F877 is being conducted and the results will be published in a subsequent publication.
## 4 Case Studies
### Ablation Experiment  
To verify the rationality of the structure setup of MDA-LETCN, ablation experiments were designed. The MDA-LETCN consists of LETCN, MDSDM, and DA. Three ablation models LETCN + MDSDM, LETCN + DA, and TCN + MDSDM + DA were constructed, and the experimental results of the three ablation models are shown in Table 3. MDA-LETCN has the best prediction effect, while the effect of the three ablation models decreases in different amplitudes. It shows that LETCN, MDSDM, and DA all contribute to the RUL prediction accuracy of the model. The average RMSE, MAE, and Score of LETCN + DA in the six tasks are 0.162, 0.143, and 0.218, respectively, and the prediction effect is the worst. The results show that it is difficult to learn the effective degradation features of generator bearings under composite working conditions only by using DA and LETCN. The effect of TCN + MDSDM + DA is second only to MDA-LETCN, indicating that local feature enhancement can improve the model's ability to learn degraded features.  
Figure 16: Prediction results of six transfer tasks.
## 2 Materials and Methods
### Optical Measurements  
Fourier transform infrared spectroscopy (FTIR) transmission measurements in the THz range were carried out with a Bruker Vertex 70v spectrometer, scanning from 0.5 to 4 THz. An Infrared Labs Germanium Bolometer detector was used in this spectral range. A continuous-flow liquid helium cryostat was employed to perform transmittance measurements at temperatures ranging from 6 K up to 110 K with an uncertainty of 0.5 K. The transmittance of the arrays was measured at normal incidence and normalized with respect to that of the bare Al\({}_{2}\)O\({}_{3}\) substrate. The SRR array optical response is sensitive to the electric field polarization [35,36]. Measurements were performed with the radiation electric field perpendicular to the gap for the direct device, and parallel to the gap for the complementary one. This is due to [PERSON]'s principle, which implies that the transmittance of the direct SRR is reciprocal to the transmittance generated by the complementary array when the polarization of the radiation is rotated by 90\({}^{\circ}\) between the two structures [37,38]. In this regard, a polyethylene-based metallic wire polarizer with an efficiency of 99% was used. The transmittance spectra at different temperatures were analyzed with the MATLAB(r) software in order to assess the temperature dependence of the parameters associated with the metamaterial resonances (see Figure 2).
## 3 Results
### Molecular Modeling and Docking Simulations  
AlphaFold generated and optimized the target 3D structure, while molecular modeling solved potential structural gaps and steric clashes. Gallic acid, quinic acid, protocatechuic acid, and brevifolia acid ellagic acid were tested against the target through in silico virtual screening. Two different strategies were applied to select the best complexes: (i) selecting only the compounds that showed a binding free energy (docking score) lower than \(-7\) kcal/mol and (ii) the ability of the compound to trigger strong interactions with target _consensus_ binding residues.  
The docking results showed that the best complexes were EbpA/brevifolia and EbpA/ellagic acid providing a binding free energy of \(-7.8\) kcal/mol and \(-7.3\) kcal/mol, respectively. Interaction network analyses showed that the brevifolia and ellagic acid docked in a similar binding pose and formed a polar and hydrophobic interaction network within the target binding pocket (Figure 1A,B). Furthermore, docking results revealed the ability only of the ellagic acid to trigger H-bonds with target key residues, Asp-326 and Asp-429, both involved in the coordination of Mg\({}^{2+}\) metal cofactor. Our results suggested the ability of the compounds to spontaneously bind on _E. faecalis_ EbpA by forming a significant interaction network. The best two complexes were further explored in silico to define in detail the pharmacodynamic features of compounds against the target.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Organism**} & \multicolumn{3}{c}{**MIC (ug/mL)**} \\ \cline{2-4}  & **CBE** & **Vancomycin** & **Colistin** \\ \hline _E. faecalis_ & 64 & 2 & \textgreater{}32 \\ _S. aureus_ & 512 & 1 & \textgreater{}32 \\ _E. coli_ & \textgreater{}512 & \textgreater{}32 & 0.25 \\ _P. aeruginosa_ & \textgreater{}512 & \textgreater{}32 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determination of MIC values of the chestnut burrs extract (CBE) on _E. faecalis_, _S. aureus_, _E. coli_, and _P. aeruginosa_. The MIC values of the reference antibiotics (vancomycin or colistin, showing the expected activity on either Gram-positive or Gram-negative organisms, respectively) are also indicated.
## 3 Results
### Results of the Comfort Rating Scale  
Figure 10 displays the means and standard deviations of the ratings for the five dimensions of the CRS. The Wilcoxon rank-sum test results showed that gender was a significant factor affecting the ranks of all five dimensions of the CRS: emotion (\(\chi^{2}\) = 6.74, \(p\) = 0.0094), attachment (\(\chi^{2}\) = 5.87, \(p\) = 0.015), perceived change (\(\chi^{2}\) = 7.73, \(p\) = 0.0054), movement (\(\chi^{2}\) = 10.99, \(p\) = 0.0009), and anxiety (\(\chi^{2}\) = 9.57, \(p\) = 0.002). The mean scores of all these dimensions for females were significantly higher than those for males. Females exhibited ratings that were 16.6%, 13.4%, 14.7%, 19.8%, and 28.3% higher than their male counterparts in the dimensions of emotion, attachment, perceived change, movement, and anxiety, respectively.  
The results of the Kruskal-Wallis test showed that the AR device also had a significant impact on the ranks of the ratings of attachment (\(\chi^{2}\) = 10.02, \(p\) = 0.0016) and movement (\(\chi^{2}\) = 7.35, \(p\) = 0.0067). The significance of the AR device on the ranks of the ratings of anxiety were marginal (\(\chi^{2}\) = 3.58, \(p\) = 0.058). The effects of the AR device on the ranks of the other two dimensions were all insignificant. The mean rating for the HoloLens 2 was lower than that of the M400 AR glasses in the dimensions of attachment and movement. Specifically, the HoloLens 2 had ratings 12.2% lower in attachment and 8.8% lower in movement compared to the M400 device. The effects of the order information display on any of the CRS dimensions were insignificant.  
Figure 9: Interaction effects of AR device.
## 2 Problem Statement  
A single complex discrete-time (sample index \(n\)) sinusoid with a sampling period of \(T\) is given by  
\[x_{s}[n]=\mathcal{A}\;e^{i(2\pi\mathcal{F}nT+\phi)}=\mathcal{A}\;e^{i\phi}\;e^{ 2\pi\mathcal{F}nT}. \tag{1}\]  
In the most general case, the goal of sinusoid parameter estimation methods is to estimate the frequency \(\mathcal{F}\), amplitude \(\mathcal{A}\), and phase offset \(\phi\) of a sinusoid. In this work, we restrict ourselves to the case of estimating \(\mathcal{F}\) and \(\mathcal{A}\) only. It is common practice to reduce spectral leakage by applying a length-\(N\) smoothing window \(w\) to \(x_{s}\) to form a windowed signal \(x\) with Discrete Fourier Transform (DFT) \(X\) given by  
\[x[n] =x_{s}[n]\;w[n],\;n=0,\ldots,N-1, \tag{2}\] \[X[k] =\mathcal{A}\;e^{i\phi}\;W(\mathcal{F}-k/NT),\;k=0,\ldots,N-1\,, \tag{3}\]  
where \(k\) is the discrete frequency bin index and \(W\) is the Discrete-Time Fourier Transform (DTFT) of the window \(w\) defined by  
\[W(f)=\sum_{n=0}^{N-1}w[n]\;e^{i(2\pi fnT)}\,. \tag{4}\]  
The DTFT is defined for continuous rather than discrete frequencies, and in fact the DFT of a signal can be seen as a sampling of the DTFT at the frequencies indexed by \(k\).  
The window magnitude DTFT \(|W|\) for typical smoothing windows is characterized by a main lobe centered around dc. After shifting and scaling, the main lobe is centered at the fractional bin number \(\mathcal{K}\) with a maximum amplitude \(\mathcal{X}\) defined as:  
\[\mathcal{K}=\mathcal{F}NT\quad\text{and}\quad\mathcal{X}=\mathcal{A}\;W(0) \tag{5}\]  
In spectral analysis, it is natural to assume that the center of each main lobe in the magnitude spectrum is associated with a complex sinusoid. Other available techniques build an estimate using all the magnitude spectrum samples to increase peak-estimation accuracy [21; 22; 23; 24; 25; 26], but these tend to be computationally costly.  
Sinusoidal parameter estimation methods which operate on the DFT exploit the assumption that the peak in the magnitude spectrum is associated with a complex sinusoid--in this context finding the fractional bin index \(\mathcal{K}\) and peak magnitude \(\mathcal{X}\) of the window transform is considered equivalent to recovering the frequency \(\mathcal{F}\) and amplitude \(\mathcal{A}\) of the sinusoid. This allows the problem of parameter estimation to be abstracted into the problem of estimating underlying DTFT peaks. We consider only the parametrization \((\mathcal{K},\mathcal{X})\) in the rest of the article to preserve the clarity and conciseness of the discussion independently of changes in the chosen window, sampling interval or DFT length, knowing that in the idealized case of a single sinusoid \((\mathcal{F},\mathcal{A})\) are readily recovered from \((\mathcal{K},\mathcal{X})\) using (5).
# The Effects of Activated Carbon Toothpastes on Orthodontic Elastomeric Chains--An In Vitro Study
###### Abstract  
Objectives: Using toothpaste with activated carbon might increase the decay of orthodontic elastomeric chains' (ECs) tensile strength, thereby compromising orthodontic treatment. Therefore, this study aimed to evaluate the influence of activated charcoal toothpaste on orthodontic ECs. Materials and Methods: A total sample of 180 EC segments from 3M Unitek(r), Ormco(r) and Ortho Classic(r) brands were equally divided into 12 groups, each comprising 15 specimens. These pieces were kept in artificial saliva at 37 \({}^{\circ}\)C and brushed twice daily for 28 days, with three distinct types of toothpaste: Colgate(r) Total, Colgate(r) Max White, and Dr Organic(r) Extra Whitening Charcoal Toothpaste. The latter two toothpaste contain activated charcoal. Tensile strength, resistance to rupture and colour variation were evaluated at time zero and day 28. Descriptive statistics, one-way ANOVA and Tukey HSD tests were performed at \(p\leq 0.05\). Results: Toothpaste with and without activated carbon significantly reduced the tensile strength and resistance to rupture of the ECs, and altered EC colour (\(p<0.0001\)). There was inconsistency in the effect of the activated carbon on EC characteristics, most probably due to the different compositions of the ECs and percentages of whitening agents in the toothpastes. Conclusions: The material composition of ECs contributes to their tensile strength decay, resistance to rupture and colour change over time. The variable percentage of activated carbon in a toothpaste likely underlies the different effects observed, depending on the EC brand. Clinical Relevance: It might be reasonable to advise patients wearing ECs to avoid using toothpaste with activated carbon until further evidence becomes available.  
active carbon toothpaste; elastomeric chains; rupture point; tensile strength; colour change
# Laser-Accelerated Plasma-Propulsion System
## 6 LAPPS Analytical Model  
Realistically, the present laser systems are not yet able to directly accelerate ions. Among the different schemes which have been proposed for laser-driven ion acceleration, we here consider Target Normal Sheath Acceleration (TNSA), where light ions are accelerated by the electric sheath field produced on the rear side of a thin solid target irradiated by high-intensity laser pulse. In turn, the sheath field is produced by the charge separation generated by the so-called \"fast\" electrons, which are accelerated during the laser-plasma interaction in front of the target, and successively propagate through the target bulk, trying to escape from its rear surface [20,25]. The choice of TNSA is here dictated by its robustness and by potential implementation at high rep-rates [34]. Moreover, due to the intensive research in the field, TNSA is well-known and modelled, admitting an analytical description [22]. Therefore, it is possible to obtain an analytical estimation of \(I_{sp}\) and \(F\) and their relative fundamental parametric dependencies. Other free parameters, which do not admit a simple analytical description, can be estimated or measured through experiments [26,27].
# Research on Grouting Dynamic Monitoring Based on Borehole-Tunnel Joint Resistivity Method
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Technology Type** & **Core Advantages** & **Key Limitations** & **Breakthrough Points of This Study** \\ \hline Single-Tunnel ERT & Wide planar coverage & Low vertical resolution & Introducing borehole survey lines to \\  & & (>10 m), unable to analyze & construct a 3D cross-network, improving \\  & & grout vertical migration & vertical resolution to within 5 m \\ Single-Borehole & Fine-scale imaging & Limited monitoring range & Collaborative deployment of \\ Monitoring & & & difficult to characterize \\  & & cross-fault diffusion & planar monitoring range to 100 m \(\times\) 200 m \\  & & High electrode deployment & \\ Traditional 3D ERT & 3D inversion capability & cost (requiring dense & Unmanned real-time acquisition (interval \(<\) 1 h), cost reduced by 40\% \\  & & acquisition interval (>24 h) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bottlenecks of existing dynamic monitoring technologies and the positioning of this study. breaking through the limitations of traditional 2D survey lines. In terms of inversion algorithms, the finite volume method and quasi-Gauss-Newton inversion algorithm are used to achieve 3D data inversion, improving spatial resolution. In terms of real-time monitoring capabilities, it can realize unmanned data acquisition and real-time data processing in underground systems, dynamically capturing the electrical property data of the strata throughout the grouting process. This breaks through the limitations of traditional methods, achieves multi-dimensional data fusion and captures the dynamic grouting process, tracks the grout diffusion paths and ranges, significantly improves spatial resolution, and provides a new monitoring paradigm with high spatio-temporal resolution and engineering economy for mine water inrush prevention.
## 3 6G State-of-the-Art
### 6G Requirements and Trends
#### 3.1.3 Satellite Integration  
To achieve worldwide coverage, future 6G communications will rely on satellite technologies to be implemented. In the future, 6G will link telecommunications, earth imaging, and navigation satellites to give cellular users with location services, broadcast and Internet connectivity, and forecasting information. One example is the provision of high-speed Internet access aboard fast trains and aeroplanes.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 7 Experimental Results
### Effectiveness of IoTBystander
#### 7.2.1 Performance of Traffic-Based Monitoring  
As shown in the TER columns in Table 8, the traffic-based event recognition (TER) component shows a satisfactory result. For most events, including \"plug on/off (HP/XP)\", \"camera on/off (XC)\", \"gas-alarm-stop (XG)\", and \"gas-detected (XG)\", TER achieved high precision, recall, and F1-scores of 1.00 across the board. This indicates that TER is very effective in recognizing these events due to the distinct traffic patterns that these devices produce while reporting these events. TER struggles with the \"switch on/off (GP)\" event, achieving only 0.50 in precision, recall, and F1-score. This lower performance suggests that TER had difficulty differentiating the on and off states of the Gosund smart plug due to indistinct traffic signatures. In practice, this challenge can be solved by a one-time human intervention: the plug always switches between _on_ and _off_ alternately so that a confirmation of one event can make the following inferable. Overall, the traffic-based monitoring achieves an accuracy of 92.86% on all smart device events.  
Although effective, traffic-based monitoring has an obvious shortcoming: it cannot capture activities of non-smart devices or human actions as no network traffic is generated. This limitation can be addressed by incorporating additional channels, such as audio.
# Feature Selection Algorithms as One of the Python Data Analytical Tools +
## 6 ITMO FS Library Usage Examples and Performance Tests
Table 2 shows time comparison between ITMO Feature Selection library and scikit-learn feature selection module at selecting k best features by Gini index and Pearson correlation. Basehock dataset [32] has 1993 samples and 4862 features, COIL20 dataset [33] has 1440 samples and 1024 features, and orlraws10P has 100 samples and 10,304 features. The comparison was performed on Intel i7-6700 HQ, 4 cores, 2.6 GHz,16 GB DDR4-2400, HDD Toshiba MQ01 ABD100, 5400 rpm. As shown in Table 2, the ITMO FS library has approximately the same computational time for Pearson correlation coefficient and Gini index filters as in the scikit-learn feature selection module. As these filters are not implemented in scikit-learn, we have put our customized measures in it for this reason. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Library** & **Pearson** & **Gini Index** \\ \hline \multirow{2}{*}{Basehock} & ITMO FS & 0.129 & 0.373 \\ \cline{2-4}  & SKLEARN & 0.122 & 0.379 \\ \hline \multirow{2}{*}{COIL20} & ITMO FS & 0.020 & 0.044 \\ \cline{2-4}  & SKLEARN & 0.025 & 0.057 \\ \hline \multirow{2}{*}{orlraws10P} & ITMO FS & 0.019 & 0.049 \\ \cline{2-4}  & SKLEARN & 0.017 & 0.049 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of sklearn feature selection module and ITMO FS library computational time in seconds. Listing 9 shows code usage comparison between ASU (Arizona State University) Feature Selection library and ITMO University library. This example was used to estimate execution time of Gini index and F-score index for both libraries. The result of this comparison can be found in Table 3.  
As shown in Table 3, the ITMO FS library has a slightly bigger computational time for F-score index and far better time for Gini index than the Arizona State University feature selection library. As ASU library does not provide support for cutting rules in the traditional way, we ran intrinsic measures of ITMO FS algorithms separately from cutting rules for proper comparison.
## 1 Introduction
Furthermore, this paper uniquely focuses on applying ML methods, specifically Bayesian networks and non-linear regression, to predict the main particulars of harbor tugboats during the concept design stage. While prior studies predominantly focused on cargo ship types, such as container ships, our work addresses the distinctive design parameters of harbor tugboats, providing valuable insights for decision making in ship design. The study involves developing a model, and the vessel's data are evaluated using Bayesian network and non-linear power regression approaches. The results are assessed using error metrics like mean square error (MSE), absolute percentage error (MAPE), and determination coefficients (R) for comparison. The primary objective of this study is to present a model with a proposed methodology, serving as a valuable and practical tool for ship designers during the concept design stage of tugboats. This approach enhances the efficiency of the design process, minimizing the need for revisions in later stages. A significant contribution of this study is its demonstration that ML methods can be applied to ship types with distinctive design parameters, such as tugboats, designed for specific purposes beyond cargo transportation. The remainder of this paper is organized as follows: Section 2 outlines the methodology, data collection, pre-processing, and the developed prediction model. Section 3 provides findings from the case study, and finally, Section 4 concludes the study.
## 5 Results of Calculations
### Calculation of the J-R Curve for SENT Specimens  
The J-R curve for the SENT specimens was determined using the GTN model. It is presented in Figure 6 and is described by the power function (10).  
\[J=961.33(\Delta a)^{0.4873} \tag{10}\]  
A comparison of this regression curve with the experimental R-curve for the CT specimens is made in Figure 7. It shows that the SENT R-curve is as good as equidistant with the CT R-curve, the shift in the y-direction being \(\Delta\)J-165 N/mm.  
Figure 5: A comparison of the experimental and calculated J-R curves for the CT specimens.  
Figure 6: The J-R curve for the SENT specimens as obtained by the GTN model.  
Besides the R-curve for the SENT specimens, determined by the regression of the calculated points, the experimental R-curve for the CT specimens is also represented, as well as the blunting line and the 0.2 mm offset line. The intersections of the SENT R-curve with the blunting line and the 0.2 mm offset line provide the initiation value of the J integral--J\({}_{\text{In}}\)--and the J\({}_{0.2}\) value at which the real (physical) crack extension is 0.2 mm. The appropriate obtained values are:  
J\({}_{\text{in}}\) = 467.6 N/mm and J\({}_{0.2}\) = 718.5 N/mm. Compared to the same quantities for the CT specimens, these values are 242.3 N/mm higher for the quantity J\({}_{\text{in}}\) and 224.1 N/mm higher for the quantity J\({}_{0.2}\).