esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study
### Weight Calculation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**First-Level** & **Subjective** & **Objective** & **Combined** & **Second-** & **Subjective** & **Objective** & **Combined** \\
**Index** & **Weight** & **Weight** & **Level Index** & **Weight** & **Weight** & **Weight** \\ \hline  & & & & & \(B_{11}\) & 0.065 & 0.090 & 0.080 \\ \(B_{1}\) & 0.360 & 0.388 & 0.377 & \(B_{12}\) & 0.130 & 0.095 & 0.109 \\  & & & & \(B_{13}\) & 0.072 & 0.074 & 0.073 \\  & & & & \(B_{14}\) & 0.093 & 0.129 & 0.115 \\ \(B_{2}\) & 0.198 & 0.071 & 0.121 & \(B_{21}\) & 0.112 & 0.060 & 0.081 \\  & & & & \(B_{22}\) & 0.086 & 0.011 & 0.041 \\  & & & & \(B_{31}\) & 0.052 & 0.026 & 0.036 \\  & & & & \(B_{32}\) & 0.043 & 0.051 & 0.048 \\ \(B_{3}\) & 0.164 & 0.176 & 0.172 & \(B_{33}\) & 0.022 & 0.057 & 0.044 \\  & & & & \(B_{34}\) & 0.020 & 0.030 & 0.027 \\  & & & & \(B_{35}\) & 0.027 & 0.012 & 0.018 \\  & & & & \(B_{41}\) & 0.070 & 0.123 & 0.102 \\ \(B_{4}\) & 0.277 & 0.363 & 0.330 & \(B_{42}\) & 0.063 & 0.093 & 0.082 \\  & & & & \(B_{43}\) & 0.091 & 0.057 & 0.070 \\  & & & & \(B_{44}\) & 0.053 & 0.090 & 0.076 \\ \hline  & & & & & & \\ \end{tabular} (1) Determination of objective weight: Following the calculation steps of the EWM, the index values from Table 5 were first normalized and converted into a judgment matrix using Equations (3)-(5). Based on this matrix, the objective weights of the second-level indices were calculated. The objective weights of the first-level indices were then derived accordingly. The results are summarized in Table 7. (2) Determination of combined weights: Using the steps outlined in the GTCWM, the combined weights were obtained using Equations (6)-(9), as listed in Table 7. These combined weight values are also included in Table 7. To visually compare the subjective, objective, and combined weights, a comparative chart is presented in Figure 5.  
\end{table} 
Table 7: Weights of the risk assessment indices for railway maintenance operations. Figure 5: Comparison of subjective, objective, and combined weights.
# Search results that are considered mostly satisfying after screening are marked with *
## 4 Discussion
These efforts could help to make the technology more accessible to wider populations, including underserved groups. Ethical concerns may also arise when introducing this technology to underserved populations, particularly rural and Indigenous communities, where the need to regulate sugar and alcohol consumption is more pressing. Access to advanced health technologies is often limited in these regions due to geographical isolation, lack of healthcare infrastructure, and economic barriers. Moreover, cultural factors must be considered when deploying this technology. Indigenous communities, for example, have distinct diets and cultural practices surrounding food, which may influence their accessibility to artificial taste technology. Understanding these cultural preferences and dietary practices is essential to ensure that the technology is not only effective but also respectful of local traditions and values. Collaborations with local communities and cultural leaders would be vital in ensuring that the technology aligns with the population's needs and preferences. Additionally, research has shown that taste perception can vary significantly across age groups, with older adults often experiencing diminished taste sensitivity due to physiological changes or medical conditions. Similarly, hormonal differences and life stages can influence taste perception in females, affecting their dietary choices and nutritional needs. Therefore, designing customizable and user-friendly interfaces that accommodate these variations is essential to ensure equitable benefits. For example, tailoring the intensity of the taste sensations to account for diminished taste sensitivity in older adults could improve the technology's effectiveness across diverse age groups. Without such personalized solutions, the technology risks alienating these populations, limiting its overall impact on public health. In summary, addressing accessibility for underserved populations involves not only technological solutions but also understanding the socio-economic, cultural, and demographic factors that influence how these technologies are perceived and adopted. It is crucial to ensure that the implementation of artificial taste technology is inclusive and equitable, offering equal benefits to all populations, regardless of their socio-economic status, geographic location, or cultural background.
# Design Dimensions of Co-Located Multi-Device Audio Experiences
## 3 Methodology
### Survey  
To address the first research question _What are the defining characteristics of co-located, multi-device audio experiences?_, a short survey was conducted to obtain information on both experiences and production tools and systems. A survey was selected as the chosen methodology in order to obtain as large a sample size as possible and was deemed more suitable for purpose versus other qualitative methods such as interviews and focus groups. Researchers, technologists, and creative practitioners were contacted through academic networks, mailing lists, and social media to contribute their knowledge. The survey was delivered using the _Qual tricks_ platform [27]. To specify the types of experiences desired for this study, a set of inclusion criteria was defined at the beginning of the survey.  
1. _The platform/experience must employ multiple devices with loudspeakers._
2. _The audio content must be distributed across the devices._
3. _The devices must be co-located in a single space or group of adjoining spaces._  
'Platform' refers to the underlying technologies and systems which support and deliver multi-device audio experiences, whereas 'experiences' are the single applications of those technologies which include the audio content. The survey consisted of open questions with free-text fields where participants could describe the experience and its audio content, as well as multiple choice questions capturing the types of devices used, the modes of interaction, whether the number of devices is variable, and the importance of the devices in the ecosystem. Participants were able to respond to the questionnaire multiple times if necessary. The full questionnaire can be viewed in Appendix A.
# A Survey of Analog-to-Digital Converters for Operation under Radiation Environments
## 4 Data Analysis
### Power Efficiency  
Differences between the _selection_ and the _state of the art_ are even more evident when power consumption is considered. Figure 4 represents FOM\({}_{W}\) of each _selection_'s member against their Nyquist sampling rate \(f_{s}\); on the other hand, Figure 5 portrays their power consumption over \(f_{s}\) against their peak SNDR. In both figures, the slashed line stands for the _selection_'s bottom line (831 fJ/level) while the solid line does for the _state of the art_'s (0.89 fJ/level up to 100 MHz). Almost three orders of magnitude separate both lines in favor of the _state of the art_. Again, one specimen without TID data (AD7984) is excluded to determine the _selection_'s envelope. In both Figures 4 and 5, _selection_'s and _state of the art_'s envelopes would match if data after 2002 of the _state of the art_ were not considered. Put another way, best scientific ADCs back in 2002 already achieved the FOM\({}_{W}\) that the best available radenv-ADCs achieve nowadays.  
From Section 4.1 it was concluded that, for a given \(f_{s}\) above 10 kHz, the best ENOB achievable for the _selection_ is 2 bits below the _state of the art_'s. Additionally, Figure 4 shows that, for a given \(f_{s}\) below 100 MHz, the best FOM\({}_{W}\) achievable for the _selection_ is more than 933 times (831/0.89) the _state of the art_'s. Using this information in Equation (1), we can extract that, for a given \(f_{s}\) between 10 kHz and  
Figure 4: [PERSON]’s figure of merit against Nyquist sampling rate.  
100 MHz, the _selection_ requires at least 233 times more power to achieve an ENOB at least 2 bits lower, in regard to the best members of the _state of the art_.  
Radiation hardening entails additional power consumption. Bias currents are not optimized to enhance radiation tolerance. In addition, a design can be hardened including specific ad hoc blocks, which entail additional area and power. Furthermore, performance under radiation can be enhanced increasing the separation between devices and reducing the impedance to ground of critical nodes; both techniques increase circuit parasites and hence power consumption.  
In mature CMOS technologies (180 nm nodes and above), the minimum width of ELT devices (enclosed layout transistors, used to mitigate TID effects), is several times the width of regular transistors, which also scale the design into a more power hungry one. From the digital circuitry point of view, hardened cells can consume twice power and be two to four times bigger than cells with the same functionality and speed from a commercial library [64].
## 4 Time-Varying Filter
### Simulation Results  
Figure 8 presents the sets of coefficients calculated with the optimization routine, using 3000 steps for each stage.  
As one can notice, the sets correspond to the conclusions made in Section 3. The \(k\) parameter was proved not to affect the settling time when changed and one can notice, in Figure 8, that the optimization routine also hardly varied this parameter. In Section 3, it was concluded that higher values of the \(\omega_{0}\) parameter reduces the settling time. The optimization routine has also designed the system that initially acts as a filter with a high value of \(\omega_{0}\) and later moves to the pre-defined final value at the end of the horizon, to preserve the frequency demands of the filtering structure. One can observe the same phenomenon, in terms of the \(\zeta\) parameter. Figure 9 presents the comparison of the step responses of the time-invariant and time-varying systems.  
As one can notice in Table 4, the improvement of the settling time reached 87%. Along with reducing the transient time, the overall overshoot was also significantly lowered.  
Figure 6: First stage of the optimization routine. The base filter is the digital oscillatory system with \(\zeta=3.83\cdot 10^{-1}\), \(\omega_{0}=8.41\cdot 10^{-1}\), \(k=1\), and \(t_{s}=1/10\) s.  
_Appl. Sci._**2019**, \(9\), 2013  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **LTI System** & **LTV System** \\ \hline RiseTime & 16.9859 & 10.7465 \\ SettlingTime & 132.1759 & 17.0687 \\ SettlingMin & 0.9260 & 0.9193 \\ SettlingMax & 1.2720 & 1.0107 \\ Overshoot & 27.9031 & 1.0311 \\ PeakTime & 41 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the linear time-invariant (LTI) and time-varying (LTV) systems.  
Figure 7: Second stage of the optimization routine. The base filter is the digital oscillatory system with \(\zeta=3.83\cdot 10^{-1}\), \(\omega_{0}=8.41\cdot 10^{-1}\), \(k=1\), and \(t_{s}=1/10\) s.  
Figure 8: The sets of time-varying coefficients ensuring the reduction of the settling time of the oscillatory system with \(\zeta=3.83\cdot 10^{-1}\), \(\omega_{0}=8.41\cdot 10^{-1}\), \(k=1\), and \(t_{s}=1/10\) s.  
Figure 9: Comparison of the time-invariant and time-varying filtering structures.
# VLC Network Design for High Mobility Users in Urban Tunnels
## 4 Proposed VLC Network for High Mobility
### Handover Strategy
It implies that \(P_{UL}^{n+1}(t)>Th_{UL}\) and \(P_{DL}(t)<Th_{DLmax}\), where \(Th_{UL}\) is the instantaneous SNR threshold for the uplink channel, and \(Th_{DLmax}\) the maximum instantaneous SNR allowed at the downlink channel. If \"condition 1\" is not satisfied, then the rate of change of \(P_{DL}(t)\) is evaluated as a second criterion, _i.e._  
\[\Delta P_{DL}=\frac{P_{DL}(t)-P_{DL}(t-\tau)}{\tau} \tag{6}\]  
when the signal \(P_{DL}(t)\) decreases below a certain threshold \(Th_{DLmin}\) with a negative slope during the time interval \(\tau\), then the system will initiate the handover process. Note that the threshold estimates were obtained experimentally after a series of simulations. These values were \(Th_{UL}=19\) dB, \(Th_{DLmax}=75\) dB, and \(Th_{DLmin}=50\) dB. The last stage is the handover execution stage when the vehicle node switches over from its current AP to the next one. As mentioned before, the handover execution is based on HMIPv6. Figure 9 shows the execution scheme. After the gathering process stage, HME sends a handover request to MAP which checks if \(AP_{n+1}\) has enough resources to receive an additional user. The request is delivered to \(AP_{n+1}\) that tries to establish direct communication with the vehicular node. So, it emits the petition using the VLC channel. If the user can reply successfully, the handover completes its process, and \(AP_{n+1}\) reports it  
Figure 7: Signal gathering stage. to MAP. Finally, the \(AP_{n}\) receives a notification that the user does not belong anymore to the network. Figure 8: Diagram of handover decision algorithm. Figure 9: Handover execution protocol.
## 2 Hardware Platform for RCP-HIL Test
### Central Control Unit: Real Time Target Machine for RCP Development  
To facilitate real-time simulations, a Real-Time Target machine has been employed, with Speedgoat serving as the platform for all conducted test. Speedgoat offers high performances thanks to multi-CPU target computers. It can be adopted in different areas like RCP and HIL systems. It is specifically designed to integrate with Simulink and Simulink Real-Time tools from MathWorks. This machine enables the execution of real-time applications developed within the MATLAB Simulink environment. The incorporation of Speedgoat Simulink driver blocks within the model streamlines the process, allowing for the automatic generation of real-time applications. Subsequently, these applications are downloaded and executed on the machine with ease. The target machine has integrated communication ports that enable the device to interact with the outside world. Speedgoat can communicate with other devices trough external ports, for the purpose of our experiments the CAN and Ethernet ports have been used, but many others communication protocols are available meeting the designer preferences (EtherCAT, PWM, SPI, I2C, SENT and more [18]).
## 2 Materials and Methods
### Numerical Integration  
The satellite motion equation is an extremely complex differential equation due to the multiple changing variables. Although an analytical solution is theoretically conceivable, the process is extremely complex and time-consuming, and some perturbing factors are challenging to represent and solve. As a result, analytical methodologies are inadequate for practical engineering applications. Thus, numerical integration is employed to solve the motion equation. Numerical integration enables a full estimation of the parameters involved in orbit determination, with the resulting accuracy meeting engineering standards.  
This paper employs the Runge-Kutta-Fehlberg single-step integration method to integrate the orbit and state transition matrix. The classical Runge-Kutta method approximates the derivative of the function \(f\) by a linear combination of the right-hand function values at several points within the integration interval [\(t_{n}\), \(t_{n+1}\)]. The coefficients of this approximation are then determined using a Taylor expansion, which allows for avoiding the calculation of higher-order derivatives while maintaining accuracy. If F(k) represents the highest order achieved by calculating the function \(f\) k times, then F(k) = k, where \(1\ \leq\ k\ \leq\ 4\), and F(5) = 4, F(6) = 5, F(7) = 6, and so on. The classical Runge-Kutta method has a fixed integration step size, complicating the treatment of local truncation errors. In contrast, the Runge-Kutta-Fehlberg technique enables dynamic adjustment of the step size by supplying two sets of Runge-Kutta formulas: one of order m and one of order m + 1. The local truncation error can be calculated using both formulas, which will help determine the size of the next integration step. The related RKF4(5) formula is given below:  
\[\left\{\begin{aligned} & x_{n+1}\ =x_{n}+h\sum\limits_{i=0}^{4}c_{i}f_{i}+O \left(h^{5}\right)\\ &\hat{x}_{n+1}\ =x_{n}+h\sum\limits_{i=0}^{5}c_{i}f_{i}+O\left(h^{5} \right)\\ & f_{0}\ =f(t_{n},x_{n})\\ \end{aligned}\right. \tag{12}\]  
\[f_{i}=f\left(t_{n}+\alpha_{i}h,x_{n}+h\sum\limits_{j=0}^{i-1}\beta_{ij}f_{j} \right),i=1,2,3, \tag{14}\]  
The values of the coefficients \(\alpha_{i},\beta_{ij},c_{i},\ \hat{c_{i}}\) can be found in detail in reference [42].
# Aggregate and Particle Size Distribution of the Soil Sediment
## 2 Materials and Methods
### Evaluation of Soil Samples--Particle Size Distribution  
To find an optimal method for the particle size distribution measurement for our research, three methods were compared: hydrometer, PARIO Soil Particle Analyzer (METER Group, Munchen, Germany), and laser diffractometer Mastersizer 3000 (Malvern Instruments, Malvern, UK). The hydrometer and the PARIO are indirect gravitational sedimentation methods based on Stokes' law [45] to determine particle size distribution. PARIO is an automated system which uses ISP method [32; 46]. Laser diffractometer Mastersizer 3000 provided by Malvern Panalytical allows an exact evaluation of the samples containing soil aggregates and also samples without soil aggregates. The aggregates were destroyed during the measurement by ultrasound [47; 48; 49]. The hydrometer and PARIO measurements were supplemented by a sieving method. The methods were tested on five samples. From the comparison of the methods, the laser diffraction was found as the most suitable method for our further research, thus all remaining samples were analyzed only by the LD. By the comparison of the particle and aggregate size distribution, the influence of the following external factors was investigated: initial state and slope of the experimental plots.  
Figure 1: Experimental plots. Slope 34\({}^{\circ}\), 30\({}^{\circ}\) and 22\({}^{\circ}\). Dimensions 4.25 \(\times\) 1 \(\times\) 1.6 m. Rainfall simulator pulse nozzle system type WSQ 40. Location–Jirkov.  
Figure 2: Scheme of the sampling procedure at the experimental plots.
# An Evaluation of the Efficiency of Compartmented
## Materials and Methods
### Testing
#### 2.4.2. SCB Test  
For the SCB test, a modified NCHRP09-46 procedure was adopted. The load was applied at the centre line, above the 'V' notch, at a loading rate of 0.1 mm/s and at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C. The support span 'S' = 80 mm, included 80% of the spec diameter, leaving 10 mm on each side. Figure 6 shows a schematic diagram and an actual view of the SCB test-setup. Several modifications to the test were made in the specimen geometry. The diameter of the specimen was reduced from 150 mm to 100 mm and the thickness from 50 mm to 20 mm, whereas the notch shape was changed from a straight line to the V-notch shape. These changes were employed in order to achieve full depth crack propagation throughout the depth of the test specimen, with the onset at the tip of the notch. Test samples were loaded until the crack has propagated to 25 mm or when the samples started to deform considerably. The crack propagation was measured in order to calculate the crack propagation speed. The crack propagation velocity was measured manually, by observing the crack propagation during the test, from the tip of the crack (0 mm) to the max (25 mm). For this procedure, a measuring scale was painted along the centerline of the specimen. The progression of the crack propagation was measured against the load and crosshead displacement.  
In order to obtain rough estimations, the fracture energy (\(G_{lc}\)) and fracture toughness (\(K_{lc}\)) were calculated by following the NCHRP09-46 (for straight line notch), and using Equations (1) and (4), respectively:  
\[K_{lc}=Y_{IC(0.8)}\sigma_{o}\sqrt{\pi a} \tag{1}\]  
where:  
\(K_{lc}\) = Fracture Toughness (Pa\(\cdot\)m\({}^{1/2}\))  
\[\sigma_{o}=\text{Stress }(\text{N}/\text{m}^{2}),\ \frac{P}{2 rt} \tag{2}\]  
\(P\) = Applied load (N)  
\(r\) = Specimen radius (m)  
\(t\) = Specimen thickness (m)  
\(Y_{lc}\) = Normalised Stress intensity factor,  
\[Y_{IC(0.8)}=4.782+1.219\left(\begin{array}{c}a\\ r\end{array}\right)+0.63e^{(7.045(\begin{array}{c}a\\ r\end{array}))} \tag{3}\]  
Figure 6: Schematic (left) and actual (right) view of the SCB test system set up.  
\[G_{lc}=\frac{W_{f}}{A_{lig}} \tag{4}\]  
where:  
\(G_{lc}\) = Fracture Energy (J/m\({}^{2}\)),  
\(W_{f}\) = Work of the fracture-area under the load displacement curve (J),  
\[W_{f}=\int Pdu \tag{5}\]  
\(P\) = Applied load (N),  
\(u\) = load line displacement (m),  
\[\text{A}_{lig}\text{ = Ligament area (m${}^{2}$), A}_{lig}\text{ = (r-a)t} \tag{6}\]  
r = Specim radius (m),  
t = Specim thickness (m),  
a = Notch length (m).  
It has to be noted that modifications of these formula to account for V-notch effects (instead of a straight notch) were not within the scope of this study. Instead, rough estimations were used to qualitatively investigate the effects that certain parameters and conditions had on the healing efficiency. Stiffness was measured from the elastic region of the curve slope.
## 3 Literature Review on Manipulator Design and Optimization Methods
### Size Optimization  
The first example of size optimization implementation can be found in [8], where the authors optimized an industrial manipulator to increase energy efficiency for selected tasks. Employing several optimization methods, they selected the manipulator's parameters based on indicators such as the GCI (Global Condition Index) and SLI (Structural Length Index), the latter being the ratio of the total length of the robot's elements to the volume of its working space. In [9], the authors used a genetic algorithm to optimize a 7-DOF manipulator. By applying the SLI, GCI, and MDCI (Modified Dynamic Conditioning Index) as optimization criteria, they enhanced the robot's energy efficiency. The procedure and results of a multi-criteria optimization for a 7-DOF robot manipulator are presented in [10], taking into account criteria such as the GCI and SLI. An analysis of variance (ANOVA) was then conducted to determine the impact of various parameters on the manipulator's performance. In [11], the authors minimized the energy required for packaging tasks on a production line by selecting optimal arm lengths for an SCARA robot. In [12], the authors carried out multi-criteria optimization in which, in addition to the geometric parameters of the manipulator, the gear parameters in the manipulator joints were selected. The authors' goal was to minimize the mass-to-load ratio and maximize the natural vibration frequency of the structure. The authors used the NSGA-II algorithm and validated their results using ADAMS simulation software. Thanks to optimization, they achieved a 10 percent mass reduction. In [13], the authors carried out multi-criteria optimization in which they selected parameters describing the geometry and drives of the manipulator to improve parameters such as stiffness, natural frequency, and mass. In [14], the authors proposed a manipulator design method in which the design parameters and the selection of manipulator drives were optimized. The mass of the manipulator was minimized while maintaining the limitations resulting from the assumed dynamics of the robot. Parametric optimization was performed in the Ansys program, and a manipulator model simulated using the ADAMS simulation environment was used to select the drives. In [15], the authors optimized the manipulator by minimizing the size of the manipulator and maximizing the volume of the working space. In [16], the authors optimized the link lengths of the anthropomorphic manipulator. The authors defined a global comprehensive performance index that takes into account the manipulability, the condition number of the Jacobian matrix, and the stiffness of the manipulator end.