esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling Trust in IoT Systems for Drinking-Water Management
## 3 Methods and Proposed Approach
### Proposed Approach
#### 3.2.3 Monitoring Trust  
Detecting trust levels in a drinking-water treatment plant involving IoT sensors, PLCs (programmable logic controllers), and personnel plays a crucial role in ensuring the plant's safety and smooth operation. By monitoring the trust placed in each component, anomalies and potential risks can be quickly identified.  
This detection ensures the reliability of the IoT sensors that measure various water parameters, the PLCs that control automated processes, and the personnel who interact with the systems. In the event of fluctuations or unusual behavior, the system can react in real time to minimize the risk of failure or compromise.  
The impact of this confidence level detection can be seen in the prevention of critical failures, human error, and security breaches. By identifying early warning signals, the plant can take corrective action before an incident occurs. This helps to guarantee the quality of treated water and maintain continuity of operations.  
On the security front, this approach enhances protection against cyber-attacks by monitoring unusual sensor and PLC behavior, helping to prevent intrusions. In addition, monitoring staff confidence levels can help identify potentially malicious or negligent actions.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion
### The Hierarchical Concept Model Performance
#### 5.2.1 Feature Selection Using Decision Tree  
In order to evaluate our models, we used the well-known software RapidMiner Studio to generate a tree structure using the DT algorithm. The datasets in Table 2 were used to create tree structures. Next, we selected the attributes from each level of the tree structure. In this experiment, we selected attributes by filtering from the first to the fifth level of the tree. Table 4 shows the classification accuracy (%) in each level of the decision tree. We compared classification accuracy with our feature selection using DT to the benchmark with original features (without using feature selection at all) in Section 5, as shown in Figure 4.  
From Figure 4, we see that the classification accuracy did not differ between the original and the proposed models for each level. Indeed, if we add the number of attributes from each level, it leads to a trend of improving accuracy.
# Novel Molecular Non-Volatile Memory: Application of Redox-Active Molecules
## 3 Redox-Active Molecule-Based Non-Volatile Memory
The attachment is characterized by using cyclic voltammetry (CyV) measurement, a current-voltage measurement at various voltage scan rates. Figure 5a,b shows the CyV curves of electrolyte-molecule-oxide-Si (EMOS) structures with the Fc-BzOH and Por-BzOH redox molecules attached to SiO\({}_{2}\) for charge storage, respectively [35; 37]. The CyV curves of both molecular structures demonstrate oxidation and reduction peaks. Fc-BzOH shows one pair of redox peaks, because the redox center Fe only demonstrates a neutral state and one positively-charged state. However, the Zn redox center in Por-BzOH exhibits a neutral state and two (mono- or di-) positively-charged states; therefore, two pairs of redox peaks were observed. Figure 4: Structure of redox-active molecules (**a**) Fc-BzOH and (**b**) Por-BzOH for memory applications; (**c**) schematic of an electrolyte-molecule-Si capacitor with a simplified equivalent circuit. Reproduced with permission from [36], © John Wiley and Sons, 2004. The capacitance-voltage (C-V) and conductance-voltage (G-V) characteristics also exhibited clear capacitance and conductance peaks associated with trapping and detrapping of charge in the molecules. Similarly, multiple peaks were observed with the Por-BzOH EMOS capacitor due to its two charged states. Interestingly, the two charged states within molecules, such as Por-BzOH, can be employed for a multi-bit storage application to further enhance the memory density. Despite using a redox molecule exhibiting multiple charged states, an alternative and perhaps more efficient strategy to achieve higher memory bits is afforded by simply mixing different redox-active molecules whose potentials are well separated. Fc-BzOH and Por-BzOH SAMs have been mixed on Si, and the mixed SAMs exhibit well-defined redox states [36]. The mixed SAM approach is very attractive owing to the fact that this approach is synthetically far simpler than preparing a single molecule that exhibits three or more redox states. In addition, the potential of the redox charged states is more distinct than that of a single molecular redox center with multiple charged states. However, the disadvantage of this method is that the density of a given peak goes down [38]. Nevertheless, this mixed SAM approach still paves the way for constructing multi-bit memory storage devices.
###### Abstract  
Several studies show that the reverberation and spectral details in direct sounds are two essential cues for perceived externalization of virtual sound sources in reverberant environments. The present study investigated the role of these two cues in contralateral and ipsilateral ear signals on perceived externalization of headphone-reproduced binaural sound images at different azimuth angles. For this purpose, seven pairs of non-individual binaural room impulse responses (BRIRs) were measured at azimuth angles of \(-90^{\circ}\), \(-60^{\circ}\), \(-30^{\circ}\), \(0^{\circ}\), \(30^{\circ}\), \(60^{\circ}\), and \(90^{\circ}\) in a listening room. The magnitude spectra of direct parts were smoothed, and the reverberation was removed, either in left or right ear BRIRs. Such modified BRIRs were convolved with a speech signal, and the resulting binaural sounds were presented over headphones. Subjects were asked to assess the degree of perceived externalization for the presented stimuli. The result of the subjective listening experiment revealed that the magnitude spectra of direct parts in ipsilateral ear signals and the reverberation in contralateral ear signals are important for perceived externalization of virtual lateral sound sources.  
m  
agnitude spectra; reverberation; contralateral ear; ipsilateral ear; perceived externalization; binaural room impulse response; head-related transfer function; headphone-reproduced virtual sound images; binaural reproduction
## 3 Results and Discussion
### Impedance Spectroscopy  
The results of resistance R\({}_{1}\) and capacitance C\({}_{2}\) are depicted in Figures 8 and 9, respectively. As it has already been discussed in previous works [44; 50], the resistance R\({}_{1}\) is related to the amount and diameter of the percolating pores of the specimen and the C\({}_{2}\) capacitance is proportional to the surface of its pores (solid-electrolyte contact surface). Therefore, with these parameters, it is possible to study the evolution of the potential pozzolanic reactions of the red mud, as performed in several works for cement-based materials which incorporate other additions, such as fly ash [51], brick powder [36] and glass powder [37]. In order to obtain information about the pozzolanic activity of red mud, only the mortars with the lowest and the highest percentages of red mud (RM5 and RM20) were analysed using impedance spectroscopy. Their results were compared to CEM I and CEM II mortars.  
Figure 8: Results of impedance spectroscopy resistance R\({}_{1}\) for CEM I, CEM II, RM5 and RM20 mortars.  
Figure 7: Evolution with hardening time of the heat liberated in the peak corresponding to the decomposition of portlandite.  
Regarding the resistance R\({}_{1}\) (see Figure 8), this parameter remained practically constant for CEM I specimens, while it increased with time for the mortars with red mud and fly ash. Nonetheless, the rising rate of this resistance was greater for the samples with red mud, and as a consequence, the R\({}_{1}\) values were higher for those mortars in comparison with CEM I and CEM II, mainly from 45 hardening days. The rise of the R\({}_{1}\) with age and its higher values noted for the red mud mortars would agree with the mercury intrusion porosimetry results, especially with their important pore refinement noted, which has been explained in relation to the development of pozzolanic reactions of this addition [22; 27].  
In relation to the evolution of capacitance C\({}_{2}\) (see Figure 9), this parameter hardly changed with time for CEM I samples, and it rose for the rest of studied mortars. These results are consistent with those noted for resistance R\({}_{1}\), already described and discussed. The increase of capacitance would also mean the formation of new solids, which would consist of new cementing phases such as CSH gels, produced due to the development of fly ash [52; 53] and red mud pozzolanic reactions [22; 27]. These new solid phases would form rough structures on the existing surface of the pores, increasing the solid-electrolyte interphase, and as a consequence the C\({}_{2}\) values, which would justify the results of this parameter noted for red mud and fly ash mortars. This interpretation is also in line with the progressive increase with time of the proportion of smaller pores (microstructure refinement) showed by mercury intrusion porosimetry for these types of analysed mortars.
# A Study on Karst Cave Collapse Based on Improved Terzaghi Theory and Upper Limit Analysis
## 5 Conclusions  
Considering the influence of groundwater, this study explored the 3D stability of karst caves based on an actual karst surface collapse case. We introduced the improved Terzaghi theory and upper bound method for the limit analysis method of a rigid body, offering effective formulas for calculating the stability coefficient and critical surface load to analyze the underground karst cave stability. Numerical simulations were performed to validate the analytical model using actual collapse cases. This study also investigated the effects of four kinds of parameters on karst cave stability, leading to the following conclusions.  
1. The improved Terzaghi theory focuses on karst caves and their overburden, whereas the limit analysis method focuses only on the overburden. Consequently, the results of the improved Terzaghi theory are lower than those of the limit analysis method. Moreover, the difference between them increases with an increase in the diameter of karst cave \(d\);
2. For karst caves, the overburden thickness h is proportional to their stability, while the diameter of the karst cave \(d\) is inversely proportional to their stability. Additionally, the shear strength of soil (\(c\) and \(\phi\)) is proportional to their stability. When the groundwater table is regarded as stable, the effect of groundwater level \(z_{1}\) on the stability of karst caves is limited and the stability of karst caves decreases as boundary failure angle \(\alpha\) or \(\beta\) increases.  
This paper forms a complete set of the scientific research process of 'formula derivation-case study-comparative analysis'. The results are consistent with the existing geotechnical engineering knowledge, which verifies the accuracy of the stability analysis formula.  
W.Y.: Writing--Original draft preparation; R.L.: Writing--Review and Editing; S.T.: Investigation; F.T.: Conceptualization, Methodology, Writing--Review and Editing; H.W. and J.L.: Data curation. All authors have read and agreed to the published version of the manuscript.  
Figure 13: Relationship between the critical surface load \(\sigma_{1}\) and boundary failure angle \(\alpha\).  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 42277165), the Hubei Natural Science Foundation (No. 2023 AFD217), and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (Grant No. CUGCJ1821).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Discussion  
The fault signal of rolling bearing typically has non-stationary, nonlinear, and modulated characteristics. Compared with traditional signal processing technology, LMD can decompose signals self-adaptively on the basis of signals themselves. For the online real-time monitoring, it is an effective way to increase the diagnostic efficiency by reducing the sampling data while keeping the accuracy of fault frequency. For the long-time signals of rolling bearing, the fault frequency is almost equal to the theoretical fault frequency whether it is corrected or not. Therefore, it does not affect the extraction accuracy of fault frequency. However, for the short-time signals, the uncorrected fault frequency has a certain error compared to the theoretical fault frequency. However, with spectrum correction, the fault frequency is basically equal to the theoretical fault frequency. It is shown that the proposed method of this paper is valid.  
However, the proposed method of this paper has some shortcomings. How to properly determine the sampling number of the short-time signals in the case of ensuring frequency accuracy and computational efficiency is a problem. It can be manually set according to the short-time signals itself. However, the signal is changing with the environment. If the sampling number does not change with the change of the signal, it may lead to the mistakes of diagnosis, especially in the online real-time monitoring. Another problem is how to completely eliminate the influence of the noise to the ratio correction method. The noise not only affects the direction of interpolation, but also affects the spectral accuracy. How to avoid the interpolation in the opposite direction and get the determined correction frequency is another problem to be solved. Future research will focus on the further improvement of the algorithm and the coalescent of the proposed method of this paper and deep learning.
## 2 Research Methodology
### Battery Heat Generation  
Firstly, the calculation of the electric current extracted from the battery cells is necessary, depending on the power required by the motors and the state of charge of the battery. The longitudinal resistance forces that the vehicle must overcome, in order to follow the required velocity profile, have been calculated. Therefore, the rolling resistance force, the aerodynamic drag force and the acceleration resistance force are determined. Their sum results in the traction force needed at the wheel, from which the torque at the wheel and the motor torque necessary for the required performances are calculated. With the motor speed being known due to the velocity profile, it is possible to determine the mechanical power to be developed by the motors at any time. In addition to the power required during acceleration, the possibility of recovering energy by regenerative braking is also taken into account. Using the efficiency map, the required power supply for each motor is determined (Figure 3), which is further used to determine the electric current to be extracted from the battery depending on its state of charge.  
Figure 2: (**a**) Battery module; (**b**) battery segment; (**c**) battery pack.  
The power profile is repeated 23 times to cover a distance of 22 km, totaling a time of 1960 s, while the value of the electric current to be extracted from the battery is calculated based on its state of charge according to Equation (1), separate from the voltage at the output of the battery.  
\[SoC=SoC_{0}-\frac{1}{C_{Ah}}\int_{t_{0}}^{t}I(t)dt \tag{1}\]  
where \(SoC_{0}\) is the initial state of charge, \(C_{Ah}\) represents the battery capacity and \(I\) is the extracted current.  
The voltage of the batteries, depending on their state of charge, is taken from their data sheet, considering the specific temperature curve of 45 \({}^{\circ}\)C. Using the electric current obtained, the heat generated by each cell is determined:  
\[Q=Q_{irreversible}+Q_{reversible}=I^{2}.R_{int}-IT.\frac{dE_{OV}}{dT} \tag{2}\]  
where the internal resistance \(R_{int}\) is taken from a map according to the discharge rate and the state of charge (\(R_{int}\) =\(f\) (C-rate, SoC)) and, using the experimental measurements performed by [PERSON] [38], \(T\) represents the temperature and \(dE_{OV}/dT\) is the entropic coefficient. The C-rate and SoC of the battery are shown in Figure 4a,b, respectively. Thus, the profile of the heat generated by the battery cells during the endurance race is represented in Figure 4c, the average for the 1960 s being \(\sim\)1 W/cell.
## 2 Theoretical Basis  
### Dispersion Curves  
In 1958, [PERSON] studied the propagation of guided waves in pipeline structures. Subsequently, researchers investigated the propagation of guided waves in pipeline structures based on the principles of elasticity, and discovered that the guided waves satisfied the Navier displacement equilibrium equation during propagation [14].  
\[\mu\
abla^{2}U+(\lambda+\mu)\
abla(\
abla\cdot U)=\rho\frac{\partial^{2}U}{ \partial t^{2}} \tag{1}\]  
where \(U\) denotes displacement, \(t\) denotes propagation time, \(\mu\) and \(\lambda\) are the Lame constants of the pipe material, \(\rho\) denotes the density of the pipe material, and \(\
abla\) denotes the Laplace operator.  
Researchers calculate the dispersion curves of the layered pipeline structure using the displacement equilibrium equation, which includes three modes of guided waves: the longitudinal mode (referred to as L), torsional mode (referred to as T), and flexural mode (referred to as F). As the study focuses on circumferential damage in layered pipeline structures, the L-mode guided waves are more sensitive to such damage, and have less attenuation and a faster velocity in the frequency range of 0 to 100 kHz. Therefore, the study only analyzes the longitudinal mode guided waves, and the dispersion curve is shown in Figure 1.  
This paper is based on the concept of weak dispersion [15; 16], and Professor [PERSON]'s theory for selecting excitation modes [17]. Following this theory, we chose the L (0,6) [18] mode, with a central frequency of 70 kHz, as the excitation mode, and we generated a 5-cycle single-tone sinusoidal signal. The theoretical group velocity of this mode is 5208 m/s.
## 3 Results and Discussion
### Effect of Self-Steepening  
In order to determine the effect of self-steepening, we compare the TOD of the output pulse as a function of initial TOD with and without self-steepening. Figure 3 is the same as Figure 2(c), except that it includes the effect of self-steepening. Although the difference can be seen in the zero-intercepts of each line, which shift slightly depending on the value of \(B\), the feature is roughly the same as shown in Figure 2(c). It should be noted that the effect of self-steepening is negligible for large initial GDD, whether the pulse is up or down chirped.
# An Overview of Indoor Localization System for Human Activity Recognition (HAR) in Healthcare
## 1 Introduction
The lower cost of solutions based on 2D models favors their wide use in applications where accuracy is not to be favored. The technologies usually used in 2D models are Bluetooth, ZigBee, and Wi-Fi. In 3D models, on the other hand, infrared, ultrawideband, and ultrasound are applied. In the field of solutions based on 2D models, the one on fingerprints [14] coupled with Wi-Fi systems applicable for outdoor and indoor positioning is widespread. There are different indoor positioning systems [15] depending on the technology used, and sometimes they can even be combined (Figure 1). There is no perfect solution; each has its strengths and weaknesses. An efficient home care system must have characteristics that improve the quality of life; it is necessary to design a suitable environment to recognize where and what the elderly are doing successfully. The integration of the exact position of the person within the recognition system of human activities is essential to intervene promptly in case of unforeseen events and falls. Therefore, it is crucial to assist the elderly to avoid incorrect actions in case any behavioral changes are observed. We have found that, in the literature, there is little experience on the effectiveness of the combination of IPS and HAR technologies. This work aims to explore possible systems that can offer a comprehensive solution. The methodology used in developing the work was based on the in-depth analysis of the activities' multiple aspects of the localization and recognition approaches. We report the different technologies available and examine techniques and algorithms. We compared them with each other, highlighting advantages, disadvantages, and characteristic elements. We also research the testimones on the integrated and compared systems to identify a possible solution that could satisfy different contexts. Lastly, the surveys confirm the issue's complexity and the need for new research efforts. The work concludes with possible scenarios for future developments. Section 2 describes the main features of an indoor positioning system; Section 3 analyzes the techniques used in localization; Section 4 illustrates the applied algorithms; Section 5 reviews the technologies; Section 6 describes the various applications developed; Section 7 concludes the paper. Figure 2 shows an outline of the relationships between the sections.
## 4 Validation and Results
### Livorno Test Case
Concerning the comparison between IFS and GFS OL simulations, both OL are not able to produce a significant convergence line in the correct position. Furthermore, GFS OL simulation started at 01 UTC to develop a convergence line in front of the Livorno coastline but it rapidly disappeared just one hour later. Instead, the GFS WIND simulation achieved better performance with the presence of the convergence line during the whole event. Figure 10: Difference maps between the IFS-driven SM experiment and the Sentinel-derived SM (SM\({}_{\text{IFS}}\)-SM\({}_{\text{S1}}\), on the right) compared to the difference between the GFS-driven SM experiment and the Sentinel-derived SM (SM\({}_{\text{GFS}}\)-SM\({}_{\text{S1}}\), on the left). The black area represent the Area Of Interest (AOI) on which mean values are compared.