esa-sceva/satcom-chunk-collection huggingface dataset:
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 2 Transparent Nano-Ceramics for Solid-State LASERs
### Doped YAG Transparent Nano-Ceramics
[28] prepared highly transparent Tm:YAG ceramic by solid-phase reaction and vacuum sintering and studied its optical properties, microstructure and laser properties. [PERSON] et al. [29] developed a high-efficiency, continuous-wave Tm:YAG transparent nano-ceramic laser pumped using a Tisapphire laser. Output power of up to 860 mW was produced with an absorbed pump power of 2.21 W at 785 nm, equating to a slope efficiency of 42.1% and a light-to-light efficiency of 22%. [PERSON] et al. presented a 2.7 mm long passively mode-locked laser based on 6 at.% Tm:YAG ceramics [30]. The pulse duration was 55 ps, and the highest output power was 116.5 mW at 2007 nm. Based on these findings, Tm:YAG transparent nano-ceramics looked to be promising candidates for ultrafast lasers with high power densities and high-efficiency output. **Yb-doped**. The Yb\({}^{3+}\)-doped ceramics' spectral properties ensure nearly pure four-level lasing, which can be easily controlled by adjusting the ambient temperature or the temperature inside the pumped lasing medium. In 2008, [PERSON] et al. [31] developed a CW laser based on Yb:YAG transparent ceramics. With a slope efficiency of 72%, a 6.8 W CW output power was obtained, and the transverse intensity distribution of the Yb:YAG ceramic laser beam was a Gaussian beam. In 2012, [PERSON] et al. [32] used Yb:YAG ceramics and a 940 nm fiber-coupled laser diode to accomplish CW lasing at 1030 nm. The basic materials were commercial Al\({}_{2}\)O\({}_{3}\) powder (99.99 percent purity, 250 nm) and co-precipitated Y\({}_{2}\)O\({}_{3}\) and Yb\({}_{2}\)O\({}_{3}\) powders (60-80 nm, 9.5-10.0 m\({}^{2}\)/g). For a 3 mm-thick mirror-polished Yb:YAG ceramics sample, in-line transmittances at 1300 nm and 400 nm were measured to be 83.6 and 81.8%, respectively. The slope effectiveness of this laser was 62.7% according to the testing data. Table 1 summarizes the doped YAG transparent nano-ceramics, which are described in the text grouped by doped type and publication year. It can be drawn that doped YAG laser transparent nano-ceramics have a short preparation period, low production cost, large-scale production, and high doping concentration.
## 3 1 \(\times\) 4 mm-Wave Linear MIMO Array Antenna Design and Measurement
The total radiation efficiency (TRE) of the proposed quad-port MIMO array antenna, both simulated and measured, is depicted in Figure 9 (right side). At resonance points, the peak values of TRE are observed to be 88.36% and 85.0%, 85.43% and 84.67%, 78.75% and 75.0%, and 87.75% and 87.0% for the simulated and measured data, respectively. The radiation efficiencies at two edges of each operating bands of 26.28-27.36 GHz, 27.94-28.62 GHz, 32.33-33.08 GHz, and 37.59-39.47 GHz  
Figure 6: Simulated and measured S-parameters of \(1\times 4\) linear MIMO array antenna. Figure 7: Surface current distribution at 28.0 and 38.0 GHz. are found to be 76.24 and 85.20%, 80.77 and 72.14%, 64.81 and 60.24%, and 65.86 and 65.33%, respectively. The far-field patterns of the MIMO antenna are measured in the anechoic chamber (dimension 12 \(\times\) 25 square feet). The horn antenna (with maximum gain of 20 dBi) is used as the receiving end, and the 1 \(\times\) 4 MIMO antenna is placed on a rotating platform under test (Figure 8). Here, we are measuring the radiation properties of the MIMO antenna specifically at 28.0/38.0 GHz frequencies, which are widely employed in millimeter-wave applications. The E-plane pattern at an azimuthal angle of 90\({}^{0}\) is shown in Figure 10. The simulated and measured co- and cross-polarization patterns exhibit excellent agreement. At an elevation angle of 0\({}^{0}\), there is a substantial difference of over \(-\)20 dB between co- and cross-polarization levels across both operating frequencies. Similar results are found for H-plane patterns at an azimuthal angle of 0\({}^{0}\). Hence, based on this analysis, it is evident that the proposed MIMO design exhibits outstanding radiation patterns suitable for wearable applications. Figure 8: Experimental setup of gain measurement. Figure 9: Simulated and measured realized gains and total radiation efficiencies.
## 6 Experimental Results  
In this subsection, the experimental results are used to demonstrate the effectiveness of the proposed MISO circuit. The digital control kernel based on the FPGA is adopted herein. From Figure 24, it can be seen that the gate driving signals \(v_{gs1a}\) and \(v_{gs2a}\) are interleaved and \(v_{gs1b}\) and \(v_{gs2b}\) are complementary to \(v_{gs1a}\) and \(v_{gs2a}\), respectively. From Figures 25 and 26, it can be seen that when the switches \(S_{1a}\), \(S_{1b}\), \(S_{2a}\), and \(S_{2b}\) are turned off, the leakage inductance of the coupling inductor, the parasitic inductance on the line, and the parasitic capacitance of the switch resonate together, so the voltage spike and ring on \(v_{ds1a}\), \(v_{ds1b}\), \(v_{ds2a}\), and \(v_{ds2b}\) will be generated. As shown in Figure 27, the resonance of the leakage inductance with the diode capacitance from state 6 to state 7 and from state 12 to state 1 causes the current spike and oscillation in the input currents \(i_{d1}\) and \(i_{d2}\) due to the reverse recover current of the diode \(D_{1}\). As can be seen from Figure 28, the energy-transferring capacitors \(C_{1}\), \(C_{2}\), and \(C_{3}\) can be stabilized at a certain value for any load. Figures 29 and 30 show that the switches \(S_{1b}\) and \(S_{2b}\) have ZVS turn-on but are turned on with the resonance between the diode capacitance and the leakage inductance, thereby yielding the voltage spike and oscillation on \(v_{ds1b}\) and \(v_{ds2b}\). From Figure 31, it can be seen that the diodes \(D_{1}\) and \(D_{3}\) have the same operating behavior and the oscillation is caused by the magnetizing inductance resonating with the diode capacitance. From Figure 32, it can be seen that the output voltage \(V_{o}\) of the proposed circuit can reach 400 V. From Figure 33, it can be seen that the efficiency can be up to 89.27% at 100% load. Figure 34 shows a photo of the experimental setup.  
Figure 28: Measured waveforms relevant to voltages across capacitors: (1) \(V_{\rm C1}\); (2) \(V_{\rm C2^{\prime}}\); (3) \(V_{\rm C3}\).  
Figure 31: Measured waveforms relevant to diodes: (1) \(v_{D1}\); (2) \(v_{D2}\); (3) \(v_{D3}\); (4) \(v_{D4}\).  
Figure 32: Measured waveforms relevant to input and output voltages: (1) \(V_{d1}\); (2) \(V_{d2}\); (3) \(V_{o}\).  
Figure 33: Curve of efficiency vs. load current.
# A Multibody Model Calibration Method for Vehicle Misuse Testing
In addition, the responses may be highly nonlinear with respect to uncalibrated vehicle parameters [6; 7], such as the axle weight, wheel-hub mass, spring stiffness, damper damping, stopper gap, etc. Only when the model parameters are very close to the actual parameters can iteration-based methods converge effectively. Figure 1: Calibration of vehicle misuse dynamics model: (**a**) designed model, (**b**) tested vehicle, and (**c**) nonlinear responses. The traditional quarter-vehicle model has been widely used to simulate vehicle misuse. [PERSON] [11] detailed the quarter-vehicle solution process using MATLAB. [PERSON] [12] used the quarter-vehicle model to simulate passage over a harmonic bump. [PERSON] [13] utilized it to estimate road conditions using data mining methods and vehicle-based sensors. [PERSON] [14] combined it with homotopy perturbation and frequency-amplitude methods to analyze the vibrations of a vehicle crossing over a speed bump in recent research. Various types of tire models have been proposed and utilized in vehicle misuse simulations for vehicle-ground contact simulations. [PERSON] modeled a tire using finite element methods for misuse scenarios. [PERSON] [15] proposed the widely used Ftire model for all applications related to vehicle dynamics. [PERSON] [16] proposed the semi-empirical rigid ring tire model for misuse cases, as well as effective height and enveloping models for obstacle contact. [PERSON] [17] proposed a two-dimensional analytical tire model for uneven roads, which consisted of a rigid ring, a six-degrees-of-freedom (6-DOF) spring/damper element, a static circular beam, and radial residual springs. [PERSON] [18] proposed the RMOD-K 7 tire model for bump and slope pit cases at speeds ranging from 30 to 60 km/h. [PERSON] [19] used CDTire during severe events with significant tire deformations, where the response amplitudes coincided well, with some error in the vibration periods. [PERSON] [20] studied the influence of the wheel's bending stiffness on the lateral tire characteristics during misuse tests. [PERSON] [21] proposed a coupled rigid-flexible ring model for simulations on uneven roads. Some tire models were compared in the review by [PERSON] [22]. Vehicle misuse simulations have been applied in various aspects of vehicle design and improvement. [PERSON] [23] obtained a lightweight design for a multi-material suspension lower control arm. [PERSON] [24] a designed hydropneumatic suspension system for a road-rail vehicle. [PERSON] [25] designed a mobility system for damaged tires and wheels for mobility recovery. [PERSON] [26] assessed the controllability after chassis component damage using
# A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution
## 3 Differential Evolution
### Population Size  
DE's implementation, known for its versatility, consists of two vector populations. Each population contains \(Np\) D-dimensional vectors with real-valued parameters. The current population denoted as \(P_{x}\) is comprised of vectors \(x_{i,g}\) that have been deemed acceptable either as initial points or through comparison with other vectors.  
\[P_{x,g}=\big{(}x_{i,g}\big{)},\ i=0,1,\ldots,Np-1,\ g=0,1,\ldots,g_{max}, \tag{9}\]  
\[x_{i,g}=\big{(}x_{j,i,g}\big{)},\ j=0,1,\ldots,D-1, \tag{10}\]  
Indices start with 0 to simplify working with arrays and modular arithmetic. The index, \(g=0\), \(1,\ldots,g_{max}\), indicates the generation to which a vector belongs. In addition, each vector is assigned a population index, \(i\), which runs from 0 to \(Np-1\). Parameters within vectors are indexed with \(j\), which runs from 0 to \(D-1\). Once initialized, DE mutates randomly chosen vectors to produce an intermediary population, \(P_{v,g}\), of \(Np\) mutant vectors, \(v_{i,g}\):  
\[P_{v,g}=\big{(}v_{i,g}\big{)},\ i=0,1,\ldots,Np-1,\ g=0,1,\ldots,g_{max}, \tag{11}\]  
\[v_{i,g}=\big{(}v_{j,i,g}\big{)},\ \ j=0,1,\ldots,D-1, \tag{12}\]  
Each vector in the current population undergoes recombination with a mutant vector, resulting in the creation of a trial population, \(P_{u}\), comprising \(Np\) mutant vectors \(u_{i,g}\).  
\[P_{u,g}=\big{(}u_{i,g}\big{)},\ i=0,1,\ldots,Np-1,\ g=0,1,\ldots,g_{max}, \tag{13}\]  
\[u_{i,g}=\big{(}u_{j,i,g}\big{)},\ \ j=0,1,\ldots,D-1 \tag{14}\]  
where:  
\(P_{x}\): current population.  
\(i\): index of the population.  
\(g_{max}\): maximum number of iterations.  
\(j\): parameters within the vector.  
During recombination, trial vectors replace the mutant population, allowing a single array to hold both populations.  
Initialization of the population requires specifying upper and lower bounds for each parameter. These bounds are collected into two D-dimensional initialization vectors, bL and bU, where subscripts L and U indicate the lower and upper bounds, respectively. Using a random number generator, each parameter of every vector is assigned a value within the specified range. For instance, the initial value (at \(g=0\)) of the jth parameter of the ith vector is determined as:  
\[x_{j,i,0}=rand_{j}(0,1)\cdot\big{(}b_{j,l}-b_{j,L}\big{)}+b_{j,L} \tag{15}\]  
The random number generator, \(randj\)(0,1), returns a uniformly distributed random number from within the range (0,1), i.e., \(0\leq randj\)(0,1) \(<1\). The subscript, \(j\), indicates that a new random value is generated for each parameter. Even if a variable is discrete or integral, it should be initialized with a real value since DE internally treats all variables as floating-point values regardless of their type.
## 6 Performance Evaluations
### Simulation Results
#### 6.3.6 Complexity Evaluation  
This experiment evaluates the overall complexity of the proposed algorithm in terms of processing time required in comparison to SMRP [15] and EERP [23] routing algorithms. Figure 22 shows the overall complexity in terms of processing time required. The figure shows that the proposed algorithm requires a longer processing time than SMRP and EERP. Therefore, the complexity of the proposed algorithm is higher than that of the other algorithms. That's to say, the proposed algorithm needs more computational energy. However, in WSNs, energy consumption in communication has been recognized as the main source of energy consumption, and it costs much more than computation energy consumption. However, this disadvantage in complexity cannot compensate the good performance in network PDR, network lifetime, delivery delay, energy balance, and packet miss ratio.  
Figure 20: Energy Imbalance Factor (EIF) vs. the running time under the first scenario.
## 5 Conclusions and Future Scope  
In this paper, we proposed an IoT intrusion detection system based on federated ML to increase security and privacy. Our primary objective was to identify unwanted intrusions so that IoT networks could be protected, and we performed our experiments on a recent dataset called Edge-IIoTset and ran experiments on both centralized and federated systems using two popular ML models called CNN and RNN. The experimental outcome demonstrated that with our proposed FL approach, we can achieve fairly competitive results in intrusion detection. In addition, we compared our technique to other FL-based IDS systems in both IID and non-IID scenarios. The experiments presented in this paper illustrate its applicability and usefulness, and have significant effects for the use of FL in the context of IoT networks.  
Figure 6: Number of client vs. training time for FL.  
In our future work, we intend to make the model more reliable when there might be malicious edge nodes on the network. Additionally, we will concentrate on a mechanism that uses an outlier detection filtering technique to prevent poisoning attempts that are injected gradually. The majority of IoT devices are capable of using several types of energy and processing power (CPU cycles per second). Hence, innovative FL protocols are required that offer criteria for choosing a group of local devices with enough resources. The devices must be chosen based on long-lasting backup power, sufficient memory, accurate data, and increased processing capability. If there are dishonest clients and servers, the usual FL method may potentially pose privacy issues. Therefore, further research is necessary on the issue of how to achieve a more reliable FL by removing all possible threats. Additionally, FL has some limitations that can affect the accuracy of the global model, such as devices that stop working in the middle of an operation, slow upload and model update times, clients that do not have much relevant data, etc. For future research, it is important to solve these problems, which make the global model much less accurate.  
Conceptualization, M.M.R.; funding acquisition, M.E. and S.R.S.; investigation, M.M.R. and S.U.K.; methodology, M.M.R.; project administration, M.M.R., S.U.K., F.E., M.A.R., M.E. and S.R.S.; software, M.M.R., S.U.K., F.E. and M.A.R.; supervision, M.E. and S.R.S.; validation, M.M.R., M.E. and S.R.S.; writing--original draft, M.M.R., S.U.K., F.E. and M.A.R.; writing--review and editing, M.M.R., S.U.K., F.E., M.A.R., M.E. and S.R.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The authors declare no conflict of interest.
# Stitching-Based Resolution Enhancement in Wavefront Phase Measurement of Silicon Wafer Surfaces
## 2 Materials and Methods
### Mechanics  
The mechanical part consists of four different linear stages for the movement of the wavefront phase sensor in order to use the stitching technique. Two PT-GD150-300 linear stages will be used as the X-axis and will be connected together, another PT-GD150-300 stage will be used as the Y-axis and the wavefront phase sensor will be mounted on it. For capturing the two intensity images, a smaller stage, specifically the PT-GD102P(15), will be used. The distance between images, or conjugate planes, is determined by the Fresnel distance. The final design can be seen in Figure 5.
# Modelling and Measuring Trust in Human-Robot Collaboration
## 1 Introduction  
Human-Robot Collaboration (HRC) has emerged as a critical area in the engineering and social sciences domain. This paper ventures into this dynamic domain with a keen focus on environments, where the collaboration pivots on the execution of routine tasks involving the manipulation of components on recycling disassembly lines, as well as management and classification of various electronic devices. We particularly explore the performance of collaboration with robotic arms, capable of a spectrum of autonomous actions under the guidance of a computer program.  
In any kind of collaboration, including HRC, trust has been identified as a significant factor that can either motivate or hinder cooperation, especially in scenarios characterized by incomplete or uncertain information. Despite the ubiquitous understanding of the concept of \"trust\", its definition has proven to be complex due to the range of fields it applies to and the individual context in which it is studied. Several perspectives contribute to the understanding of trust, depending on the theoretical focus and the specific field of study it is applied to. While interpersonal trust is the most studied, there is also an increasing focus on the trust between humans and technology, which is essential in HRC.  
This paper aims to delve into significant research questions about the function of trust in HRC: (1) What are the fundamental factors influencing trust in HRC? (2) Is it possible to identify demographic or contextual variables that affect the nature and dynamics of trust? and (3) Can trust in HRC be measured using psychophysiological signals?  
The paper is organized as follows: Section 1 introduces the concept of trust in HRC, highlighting its general framework and outlining the major research questions this paperintends to explore; Section 2 provides a theoretical background, covering current research and explaining the importance of defining trust in HRC; Section 3 describes the Materials and Methods, with a detailed explanation of the experimental design and methodologies used in the data analysis; Section 4 presents the Results, providing the empirical findings form the experimental process; in Section 5, a comprehensive Discussion is included, thoroughly exploring the implications of the research findings for trust in HRC; lastly, Section 6 offers the Conclusion and provides answers to the research questions.
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## 4 Numerical Results
### Complexity and Performance Impact  
In this subsection, we identify the impact of PER performance according to different _FFT_ sizes when considering the receiver complexity and estimate accuracy. When we consider the scenario that the burst detector in Figure 4 can estimate coarse CFO and ToA and compensate them initially, the average amount of residual CFO and ToA becomes around 2% vs. symbol rate and 100 symbols when CB sends. In such a case, the impact of performance by the CFO seems to be negligible even though a smaller _FFT_ size is applied. As we can see in Figure 9, the PER performance is not influenced by different _FFT_ sizes for the proposed scheme. In addition, the proposed scheme can be effective in terms of operational clock rate because it can work at a symbol rate.  
Figure 8: Comparative PER performance. (**a**) Log-on Burst (waveform ID#1). (**b**) Control burst (waveform ID#2). (**c**) STB3 (waveform ID#3). (**d**) LTB3 (waveform ID#13).  
In Figure 10, we compare different estimators in terms of the normalized mean timing estimator error variance through computer simulation. The simulation condition for CB transmission was applied in the same manner as in Tables 1 and 2, respectively. It shows that all timing estimator schemes are rather far away from the ideal bound in Equation (4) because of the residual CFO condition. Despite the result, it appears that the _FFT_ scheme is the closest to the Modified Cramer-Rao Lower Bound (CRLB), as in Figure 10. The O&M that is used in Section 3.1 as the representative scheme and the AVN scheme show similar performance in the SNR range of interest from the presented PER charts. Overall, it can be shown that the accuracy measurements of STR estimators in Figure 10 are well aligned with the PER performance result in Figure 8b.  
Figure 10: Normalized timing estimator accuracy in CB.  
Figure 9: Comparative PER performance for different _FFT_ sizes.
## 3 Results and Discussion
### Proteolytic Changes and Biogenic Amines Content
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Product** & **Pul** & \multicolumn{3}{c}{**ZS**} & **Product** & **Pul** & \multicolumn{3}{c}{**ZS**} \\ \hline
**(mg/kg)** & **M** & **SD** & **M** & **SD** & **Amino Acid** & **M** & **SD** & **M** & **SD** \\ \hline Asparagine & 11.47a & 0.95 & 4.02b & 0.16 & Valine & 28.14a & 0.69 & 22.69b & 0.89 \\ Serine & 34.70a & 1.13 & 32.68b & 1.28 & Methionine & 23.91a & 0.59 & 18.81b & 0.73 \\ Glutamine & 44.82a & 2.82 & 23.18b & 0.91 & Lysine & 26.76a & 1.67 & 22.92b & 0.90 \\ Glycine & 23.08a & 1.22 & 17.14b & 0.67 & Isoleucine & 25.63a & 0.75 & 19.89b & 0.78 \\ Histidine & 25.81a & 1.89 & 20.65b & 0.81 & Leucine & 40.87a & 1.15 & 32.83b & 1.28 \\ Arginine & 30.04a & 1.84 & 44.33b & 1.97 & Phenylalanine & 31.56a & 1.22 & 34.65b & 1.35 \\ Threonine & 43.66a & 1.55 & 31.78b & 1.24 & & Free amine groups (g/100 g) & & \\ Alanine & 47.45a & 1.17 & 30.45b & 1.19 & in H\({}_{2}\)O & 0.61a & 0.06 & 0.70b & 0.00 \\ Proline & 30.87a & 1.08 & 21.33b & 0.83 & in PTA & 0.06a & 0.00 & 0.06a & 0.01 \\ Tyrosine & 33.56a & 2.57 & 30.97b & 1.21 & & & & \\ \hline Pul—Pulawska; ZS—Zlotnicka Spotted; PTA—phosphortistic acid; M—mean; SD—standard deviation; a, b—values in rows with different letters differ significantly at \(p\) < 0.05, \(n\) = 9. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: One-factor analysis of the variance of the free amino-acid content of dry-cured ham. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Product** & **Pul** & \multicolumn{3}{c}{**ZS**} \\ \hline
**Biogenic Amines (mg/kg)** & **M** & **SD** & **M** & **SD** \\ \hline Tryptamine & 4.73a & 1.28 & 9.08b & 0.56 \\ Phenylethylamine & 1.28a & 0.86 & 0.25a & 0.01 \\ Putrescine & 3.97a & 2.11 & 23.66b & 0.90 \\ Cadaverine & 28.53a & 2.94 & 54.31b & 3.03 \\ Histamine & 87.89a & 16.70 & 163.19b & 4.90 \\ Tyramine & 11.07a & 0.39 & 57.16b & 2.17 \\ Spermie & 2.75a & 0.19 & 1.94b & 0.07 \\ Spermidine & 21.69a & 1.04 & 10.47b & 0.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: One-factor analysis of the variance of the biogenic amine content of dry-cured ham. The levels of these amines showed a time-dependent increase during aging (decrease in moisture and water activity, along with a minimal increase in pH and non-protein nitrogen and free amino-acid content), especially in ZS hams, in line with the studies of [PERSON] et al. [43], [PERSON] et al. [44], [PERSON] and [PERSON] [45], and [PERSON] et al. [46].