esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 2 Semianalytical Approach for the Determination of Modulus and Damping
### Numerical Solutions  
#### 2.2.1 Numerical Approach  
In order to investigate the influence of the physical and experimental constraints on the analysis, the discussed approach was applied to synthetic data. In this way, the efficiency and robustness of the proposed procedure may be quantified by comparing the results of the analysis with the known theoretically expected values, i.e. the input parameters of the simulation.  
Numerical data were obtained simulating, with a finite difference spring-model approach [48], the propagation of elastic waves in classical nonlinear media, described by the classical wave equation Equation (1). Details about the numerical solutions could be found in Ref. [46; 48]. For the sake of simplicity, the nonlinear contributions due to hysteresis were not considered.  
The following expansions of the linear parameters as a function of the strain tensor were considered:  
\[S =S_{0}\left(1+\delta_{S}\epsilon^{2}+\gamma_{S}\epsilon^{4}\right)\] \[\lambda =\lambda_{0}\left(1+\delta_{\lambda}\epsilon^{2}+\gamma_{\lambda} \epsilon^{4}\right) \tag{12}\]  
where \(S_{0}\) and \(\lambda_{0}\) are the linear (at low amplitude of excitation) values of modulus and damping, respectively. \(\epsilon=\partial u/\partial x\) is the strain and \(u(x,t)\) is the displacement (dependent on time \(t\) and space \(x\)). For the simulations, a 1D sample 10 cm long, with \(S_{0}=72\) GPa, \(\lambda_{0}=6.48\,10^{2}\) s\({}^{-1}\) and density \(\rho=2700\) kg/m\({}^{3}\) was considered, unless otherwise specified. The values of the nonlinear parameters \(\delta_{S}\), \(\gamma_{S}\) and \(\delta_{\lambda}\) were changed in the different simulations and are reported in the table of Figure 1. Notice that \(\gamma_{\lambda}\) was set equal to zero for all simulations. Indeed, quartic nonlinear terms are usually negligible in real materials for both modulus and damping. Here, a non-negligible quartic nonlinearity on the elastic modulus was considered only for the sake of demonstrating that the MoDaNE approach is capable of capturing different power law behaviours in the velocity/attenuation vs. strain dependence.
## 1 Introduction
### Quantifying Overheating  
Publications suggest several methods of quantitative overheating assessment in non-conditioned buildings. Comfort requirements may be fixed as constant temperature thresholds or as changing levels according to variations in external temperature. The Instruction of the Chartered Institute of Building Services Engineers (CIBSE) set the \"warm\" and \"hot\" limits as 25 \({}^{\circ}\)C and 28 \({}^{\circ}\)C for living rooms and 21 \({}^{\circ}\)C and 25 \({}^{\circ}\)C for bedrooms, as sleep quality deteriorates in temperatures above 24 \({}^{\circ}\)C [7]. The CIBSE manual [8] states that we deal with overheating if the internal operative temperature exceeds 25 \({}^{\circ}\)C in bedrooms for more than 5% of the usability time and 28 \({}^{\circ}\)C in living rooms for more than 1% of the usability time. Instruction [9] described overheating in yet another way--as meeting at least two of the three evaluation criteria during the period from 1 May to 30 September: exceeding the determined limit temperature according to the running mean outside temperature for more than 3% of the time of use, exceeding the limit threshold of degree hours, or a specified temperature during the day. The Passive House Institute defined overheating as the occurrence in buildings of a temperature above 25 \({}^{\circ}\)C for more than 10% of a year [10]. Evaluation of the thermal environment can also be achieved with the use of PMV/PPD indexes, according to [11]. This standard was recommended first of all for buildings with mechanical cooling.  
The standard [12] (updated in 2019 as [13]) was based on the concept of \"adaptive comfort\", meaning that the comfort temperature changes alongside the outdoor running mean temperature. This method assumed the passive regulation of internal temperature by window closing/opening, use of shading devices such as curtains, blinds, or verticals, and nighttime ventilation. The summer comfort temperature was originally established based on studies in office buildings, but it can also be applied to residential buildings that are naturally ventilated, where the physical activity of users is small, and they can change their clothes to lighter or heavier garments. The standard [13] defined four categories of the indoor environment, differing in the level of user expectations and acceptable deviations from the comfort temperature. The highest level, level I, may be selected for occupants with special needs (children, elderly, people with disabilities, etc.). The medium level, level II, is considered \"normal\" and should be kept in new and modernised buildings. Lower level III was acceptable in existing buildings, while level IV would mean decreased comfort and should not be tolerated for longer periods.
## 9 Effect of SrO Modifier  
SrO raises the characteristic temperature of glasses and resistance to crystallization [56]. Table 9 summarizes the mass attenuation coefficients of the studied glass with different fractions of SrO at a wide photon energy range from 10 keV to 20 MeV. On the other hand, Table 10 summarizes the neutron effective removal cross sections of the studied glass with different fractions of SrO. The photon-shielding capabilities of glass have been enhanced with the addition of the SrO modifier while the neutron-shielding capabilities are decreased.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Neutron Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{5}{c}{**Glass with SrO Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & \multicolumn{5}{c}{**Removal Cross Section (mm2/g)**} \\ \hline
10 & 8.7100 & 8.6715 & 8.5196 & 8.3279 & 8.1385 & 7.9538 \\
20 & 30.9580 & 30.6590 & 30.3140 & 29.0440 & 27.3050 & 27.0660 \\
30 & 8.9307 & 8.9063 & 8.8037 & 8.6753 & 8.5454 & 8.4192 \\
40 & 8.9773 & 8.9468 & 8.8303 & 8.6854 & 8.5410 & 8.3976 \\
50 & 8.5718 & 8.55715 & 8.5588 & 8.5439 & 8.5343 & 8.5141 \\
60 & 9.1666 & 9.1341 & 9.0081 & 8.8457 & 8.6854 & 8.5232 \\
70 & 9.2569 & 9.2228 & 9.0886 & 8.9208 & 8.7532 & 8.5852 \\
80 & 8.6195 & 8.5841 & 8.4426 & 8.2587 & 8.0798 & 7.9004 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The neutron effective removal cross sections of the studied glass with SrO modifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Photon Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{5}{c}{**Glass with SrO Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & \multicolumn{5}{c}{**Mass Attenuation Coefficients (cm2/g)**} \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 9: The photon mass attenuation coefficients of the studied glass with SrO modifier.
# Power Consumption Modeling of Discontinuous Reception for Cellular Machine Type Communications
## 4 System Model  
In the system model, we first describe the power consumption variations during the active and non-active modes. Afterwards, the proposed semi-Markov chain model is comprehensively explained by considering numerous DRX parameters in an LTE-A network. The list of the used symbols for modeling the DRX mechanism is provided in Table 3.
# Fibre Wireless Distributed Antenna Systems for 5G and 6G Services
###### Abstract  
The terahertz (THz) frequency bands are being explored as a potential means of enabling an ultra-high transmission capacity in sixth-generation (6G) radio-access networks (RAN) because higher frequencies offer broader bandwidths. When utilized in wireless communications, high-frequency electromagnetic waves impose several physical restrictions. To overcome these difficulties and to expand the service coverage, the radio-over-fibre (RoF)-based distributed antenna system (DAS), in particular, can improve the usability of future mobile networks with advantages such as seamless media conversion between wireless and optical signal, flexible multichannel aggregations, and efficiency. RoF technology's inherent advantages are that it improves the DAS network's usability and transmission performance by allowing it to provide both 5G and 6G THz services at the same time over a single optical fibre connection. We experimentally broadcast a single carrier-modulated 6G signal using a 256 quadrature amplitude modulation and a 5G new radio signal across a 10 km single mode fibre optic link. Additionally, the 6G signal is received through a 3 m wireless medium providing, proof of concept for fibre wireless integration. The experimental trials are assessed in terms of error vector magnitude and carrier suppression ratio. The dynamic range of the allowed RF input power for a 6G signal is 10 dB, while the dynamic range for a 5G waveform signal is 18 dB, which meets the 3 GPP standardization criteria. Moreover, the bit error rate performance significantly improved as the carrier suppression ratio was increased from 0 to 20 dB.  
distributed antenna system; radio over fibre; 5G; 6G THz services +
Footnote †: journal: Electronics Electronics  
1
## 2 System Description
### Impedance Control Model  
The core idea of impedance control is to transform almost all physical systems into a \"mass-spring-damper\" system, as shown in Figure 3, and the force situation of this system can be expressed as  
\[F=M_{r}\ddot{E}+B_{r}\dot{E}+K_{r}E \tag{1}\]  
In the equation, \(E=X_{d}-X_{m}\), where \(X_{d}\) is the desired position, and \(X_{m}\) is the actual position. \(M_{r}\) is the desired inertial matrix, \(B_{r}\) is the desired damping matrix, and \(K_{r}\) is the desired stiffness matrix.  
Figure 3: “Mass-spring-damper” system.  
Figure 2: The process of variation for contact force.  
This paper employs position-based impedance control (sometimes referred to as admittance control) [26] to manipulate industrial robots. This control structure consists of an internal position control loop and an external force control loop, which equates the controller to an admittance system: input force; output position and equates the robot to an impedance system: input position; output force. Overall, it maps from force to position. The schematic diagram is shown in Figure 4.  
Where \(X_{r}\) is the reference trajectory corresponding to the expected force, \(X_{d}\) denotes the trajectory information sent by the impedance model to the robot, and \(X\) represents the trajectory information of the end effector of the robot's actual movement. In the position control mode, \(X_{d}\) is usually equal to \(X\), and the trajectory error caused by contact force can be expressed as \(E=X-X_{r}=X_{d}-X_{r}\). \(F_{r}\) is the desired tracking force, \(F_{e}\) is the actual contact force, and the error between the two is \(\Delta F=F_{e}-F_{r}\).  
Usually, the error force \(\Delta F\) is represented as a linear second-order system, as shown in Equation (1), which can be obtained by substituting \(F\) and \(E\) into it.  
\[M_{r}(\dot{X}_{r}-\dot{X}_{d})+B_{r}(\dot{X}_{r}-\dot{X}_{d})+K_{r}(X_{r}-X_{d })=F_{e}-F_{r} \tag{2}\]  
where \(M_{r}\) is the desired inertia matrix, \(B_{r}\) is the desired damping matrix, and \(K_{r}\) is the desired stiffness matrix. The impedance model is a second-order transfer function:  
\[H(s)=\frac{E(s)}{\Delta F(s)}=\frac{1}{M_{r}s^{2}+B_{r}s+K_{r}} \tag{3}\]  
where the force error \(\Delta F=F_{e}-F_{r}\) and the position error \(E=X_{r}-X_{d}\), from which the trajectory information sent to the robot can be obtained as \(X_{d}=X_{r}+E\).
# Leveraging Large Language Models for Enhancing Safety in Maritime Operations
## 5 Enhancing Safety Through Large Language Models  
### Risk Analysis and Hazard Identification  
The maritime industry faces inherent risks due to the complex interplay of human, technical, and environmental factors. Effective risk analysis and hazard identification are crucial for preventing accidents and ensuring the safety of maritime operations. Large Language Models (LLMs) offer advanced capabilities in processing and analyzing vast amounts of unstructured textual data, which can significantly enhance these safety processes.  
LLMs can systematically process incident reports [140], safety bulletins, maintenance logs, and other relevant documents to identify common risk factors. By employing natural language processing techniques, these models can extract critical information, recognize patterns, and categorize incidents based on causes, consequences, and circumstances. This comprehensive analysis enables the identification of recurring issues such as equipment failures, procedural lapses, or human errors that may not be immediately apparent through manual examination.  
Furthermore, LLMs can contribute to predictive risk assessment by analyzing historical data to forecast potential safety issues before they occur. By learning from past incidents, the models can identify early warning signs or precursors to accidents, such as specific combinations of operational conditions or deviations from standard procedures. This predictive capability allows maritime organizations to implement proactive measures, such as targeted training or maintenance interventions, thereby mitigating risks and enhancing overall safety.
## 1 Introduction  
Voltage source converters (VSCs) are widely used in modern power systems for the integration of renewable energies, distributed power generation, energy storage systems, electric vehicle chargers, high and medium voltage transmission systems, hybrid AC/DC microgrids, motor derive systems, DC homes, etc. [1, 2]. Apart from significant benefits coming from the usage of VSCs, their switching-based operation yields to harmonically polluted output voltages, which consequently creates harmonic currents injected into the grid. International standards have provided restrictions on the harmonic content, which are normally satisfied by adding harmonic filters in the output of the inverters. For this purpose, standard types of L and LCL filters are well defined in the literature [3]. Comparing filters, the LCL-filter is superior in terms of filter size, weight, and harmonic attenuation performance [4]. Using an LCL-filter is more common in high power applications, in which the switching frequency is lowered to reduce switching losses and VSC cost. However, the controller design in the presence of an LCL filter is more complicated, which is discussed in this paper.  
In most applications, the control of DC-link voltage and reactive power are implemented in the control structure of the VSCs. In this structure, the concept of cascaded a control loop scheme is employed, in which the DC-link voltage and reactive power control loops are outer loops, and the control loops of the output currents are inner loops. Using an LCL filter introduces two challenges in the current control loop: (i) introducing resonant modes of an LCL filter, which impairs the stability of the whole converter system, and (ii) strong coupling between active and reactive currents, which impairs their independent control. In the following, the existing solutions for both challenges are provided.
## 4 Verification Analysis in Current Conditions (Step 1 of the Design Procedure)
### Modal Analysis  
The finite element model of the structure, a perspective view of which is displayed in Figure 7, was generated by the SAP2000 NL calculus program [52] using frame type elements for all structural members. The modal analysis carried out by the model shows two first horizontal translational modes along \(X\) and \(Y\), with vibration periods of 0.89 s (\(Y\)) and 0.35 s (\(X\)), respectively, and effective modal  
Figure 5: Cross section of the building (dimensions in millimeters).  
Figure 6: Redrawn cross sections of: (**a**) roof beams–half-span and ends; (**b**) top longitudinal beams TB—half-span and ends; (**c**) intermediate longitudinal beams IB; (**d**) columns (dimensions in millimeters).  
mass (EMM) equal to 79% along \(Y\) and 88.1% along \(X\). The fourth and fifth mode are translational along \(X\) and \(Y\) too, with periods of 0.26 s (\(Y\)) and 0.11 s (\(X\)), and EMM equal to 20.9% (\(Y\)) and 11.8% (\(X\)), which provide summed modal masses with the corresponding first mode-related EMMs nearly equal to 100%, along both axes. The third and sixth mode are purely rotational around the vertical axis \(Z\), with periods of 0.34 s and 0.04 s, and EMM equal to 84.4% and 12.4%, giving a summed modal mass of 96.8%.  
The modal parameters quantitatively confirm a notably different translational behaviour of the structure along the two directions in plan, as a consequence of the markedly different sides of the columns along \(X\) and \(Y\) and the much higher flexural stiffness of the roof beams in comparison to the longitudinal beams.
## 3 Results
### Preliminary Verification of the FE Film Response
#### 3.1.1 Basic Design: Transducers without Surface Protection.  
The resonant frequency in air of the HS03 and the HS06 transducers corresponds to the \(\lambda\)/4 thickness resonance of the FE film. Hence, the resonant frequency is expected to be located close to 0.32 and 0.56 MHz, for HS03 and HS06, respectively (see Table 1). Measured \(SNS\) of the gold coated transducers operated in pulse-echo mode in air is shown in Figure 4.  
In all cases, the resonant response is clearly observed, however, the resonance is located at a lower frequency compared with the expected \(\lambda\)/4 thickness resonant frequency (see Table 1). This is an expected result and is due to the effect of the layer of gold, as the gold loads the resonance and then shifts the resonant frequency towards lower values. Nonetheless, it can be confirmed that the thickness resonance is preserved and that the frequency shift is moderated. This deviation in the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & **Ultrasound Velocity (m/s)** & **Acoustic Impedance (MRayl)** & **Coating Method** \\ \hline PMMA & 1150 & 2700 & 3.11 & Spin coating \\ \hline PTFE & 2200 & 1400 & 3.10 & Spray coating \\ \hline Varnish & 950 ** & 1800 * & 0.95 & Spray coating \\ \hline Acrylic Lacquer & 800 ** & 1800 * & 0.88 & Spray coating \\ \hline \hline \end{tabular}  
* Estimated,; ** According to manufacturer data.  
\end{table}
Table 2: Coating material properties and method employed.  
Figure 4: Au-sputtered transducers in pulse-echo response, air-coupled operation.  
resonant frequency (about 10%) is common in all Au coated transducers, this permits to estimate the thickness of the Au layer which is typically in the range 65-110 nm, for sputtering times between 30 and 60 s.  
For the HS03 film the first two orders of resonances have been observed, revealing the effect of the increase in the ultrasound attenuation in the FE material with the frequency. For the HS06 film a lower sensitivity and more damped resonance is observed. This can be due to the increase in the attenuation with the frequency, or the higher impedance of this film (poorer matching to the air), or a reduced piezoelectric response compared with HS03, which could be due to a lower polarization level or to a lower piezoelectric constant that could be related to the different cellular structure of these two films, see [10].  
For the HS03 focused transducer a similar resonant behavior is observed with the same resonant frequency, though it presents a somewhat lower peak \(SNS\) value that can be due to the wrinkles introduced in the FE film when it is glued to the spherical backing block. Figure 4 shows, as an example, the \(SNS\) in air of the HS03 transducer with the smaller radius of curvature.
## 3 Results and Discussion
### Principal Component Analysis (PCA) Model Data Visualization  
The resultant scatter plot of the PC scores shows clustering depending on the different concentrations of toluene in the Grignard reagent samples. The first three principal components loadings accounted for nearly 95% of the total sample variance; subsequent PCs were unimportant and probably reflected noise in the data. The developed model could efficiently isolate the Grignard reagent with higher toluene concentration (5-20% [\(v\)/\(v\)]), whereas the samples with lower toluene concentration (0-4% [\(v\)/\(v\)]) overlapped, as shown in Figure 2a. Further, the resulting score plot in 2 D shows an outlier highlighted in the blue box in Figure 2b due to the smaller difference in the lower concentration range while the rest of the concentration separated.  
We further developed a PC loading plot for the first two loading components (PC1 and PC2), which clearly explained the correlation weights of the variables. The loading plot revealed some useful information as highlighted in Figure 2c. The peaks around 623 cm\({}^{-1}\), 1008 cm\({}^{-1}\), and 1602 cm\({}^{-1}\) are related to ring deformation, the presence of mono substituted benzene ring, ring stretch (Ar-C), and ring stretch doublet vibration, respectively [22]. Thus, all the extracted spectral information under  
Figure 1: Raman mean spectra corrected with the polynomial curve fitting method within a spectral region of 400–1650 cm\({}^{-1}\) (**a**) and (**a1**); and (**a2**) presents expended spectral regions of (**a**).  
the following waveband regions obtained from the loading plots provides significant information valuable for the distinction of toluene present in the Grignard reagent; therefore, the utilization of the PCA method is worthwhile to acquire the information regarding the samples and creates a basis for the development of the prediction model using PLSR.  
Figure 2: Scatter plots with the first three principal component (PC) score (**a**) and with 1 st and 2 nd PC score (**b**), and PC loading plot (**c**).
## 6 Space-Segment Optimization Aspects  
As mentioned earlier in Section 3, the main optimization problem of the space segment in LEO design is the constellation optimization. This section focuses on LEO constellation characteristics and constraints through a detailed survey of significant design elements, existing constellations, and key performance parameters, as well as applicable optimization methods.  
The main goal is to show the steps to adopt toward a feasible and optimized constellation design to provide service to transportation vehicles and to meet the emerging demands of more autonomy and reliability.  
There are many approaches in constellation optimization and one area that is still lacking in the current literature is the area of multi-target and restriction-driven optimization. Pure geometry-based solutions are easy to sketch and small satellite constellations are not necessarily demanding in terms of the minimum number of satellites. However, a realistic optimization of the constellation should take into account the factors that are most restrictive, such as the orbit topology, satellite launch cost, radiation requirements [86], satellite redundancy, atmospheric drag (i.e., the force exerted on satellites due to their movement through air), the need for propellant and maneuvering, etc. All these aspects are addressed in detail in the following subsections.