esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensory Evaluation  
A separate group of volunteers (\(n=34\)) evaluated the sensory properties of each tea sample using a nine-point hedonic scale, assessing attributes such as taste, aroma, color, aftertaste, and overall liking. The \"overall liking\" score reflected the general acceptability of each sample.  
The sensory evaluation revealed significant differences in consumer acceptance between the two olive leaf tea concentrations (0.5% and 1%) (Table 2). The 0.5% infusion received significantly higher scores in taste (7.11 vs. 6.13, \(p<0.05\)), aftertaste (7.32 vs. 5.97, \(p<0.05\)), color (5.55 vs. 6.66, \(p<0.05\)), and overall liking (7.53 vs. 6.24, \(p<0.05\)). No statistically significant difference was observed in aroma between the two samples (6.87 vs. 6.37, \(p>0.05\)).  
These findings suggest a clear preference among participants for the tea prepared at 0.5% concentration, particularly regarding flavor characteristics, as it received the highest overall acceptability scores. Although the 1% tea achieved higher scores in terms of color and had similar aromatic profiles, its more intense taste may have negatively affected its overall acceptability.
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 1 Introduction
### Motivations  
The motivation for this paper is to further improve the accuracy of path tracking. In previous work, we found that most path tracking methods cannot adjust the velocity of the vehicle according to path information such as the radius of the curve, and a high reference velocity at corners will lead to a sharp deterioration in path tracking performance [5].  
There is no doubt that path tracking controllers perform well at a low reference velocity. However, too low driving efficiency is also unacceptable in the mining industry. So, when should the articulated vehicle slow down? How much should it reduce its velocity by? It is difficult to calculate the most suitable reference velocity for each curve. For the above problems, we propose multilayermodel predictive control (multilayer MPC) for the path tracking of articulated vehicles. This controller can adjust the velocity according to the path information.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA) and Thermal Programmed Desorption (TPD)
% is ideal to improve the gaseous hydrogen decomposition characteristics such storage capacity and the rate of desorption (see Supplementary Material Figure S1). The TGA and TPD results of mixing 2 wt. % nanoNi, nanoCo, nanoFe, nanoTi and TiF\({}_{3}\) are given in Figures 3 and 4, respectively. Thus, these catalysts enhance the hydrogen desorption kinetics as well as the gravimetric capacity in comparison to the base material. A closer analysis on the linear portion and start-up on-set point on the profiles of Figure 3, the reaction kinetics (wt. %/min) and on-set decomposition temperature were estimated for all the catalysts and are summarized in Table 1. It can been seen from Table 1 that TiF\({}_{3}\) enhances the reaction kinetics most with the order of TiF\({}_{3}\) > nanoNi > nanoTi > nanoCo > nanoFe, and the highest reduction in on-set desorption temperature was obtained from the nanoCo catalyst (nanoCo > TiF\({}_{3}\) > nanoTi > nanoFe > nanoNi). The thermal programed desorption profiles, unlike TGA, may only provide insights into the decomposition temperature along with strength of signal indicating the concentration of gaseous decomposition. Therefore, the trend in reaction kinetics as obtained from TGA in Figure 3 above may not be comparable to the TPD profiles of Figure 4. It seems, however, that the TPD of nanoCo excelled in the highest hydrogen concentration with the order of nanoCo > TiF\({}_{3}\) > nanoFe = nanoTi > nanoNi > base. By comparing the TGA profiles at an instant decomposition temperature say 225 \({}^{\circ}\)C, it is discernible again that nanoCo and TiF\({}_{3}\) outperformed with highest hydrogen release capacity and then continues with the order nanoCo > TiF\({}_{3}\) > nanoFe = nanoTi = nanoNi > base. Overall, the catalyst-doped 2 LiNH\({}_{2}\)-MgH\({}_{2}\) thus enhances the hydrogen decomposition kinetics while maintaining available hydrogen content similar to that of the base hydride compound. The high reactivity of cobalt nanoparticles and the Ti\({}^{3+}\) state of TiF\({}_{3}\) thus increases the reaction kinetics for the hydrogen absorption and reversible desorption. Further research is needed to understand why only Co among all transition metal nanoparticles (Fe, Ti, Ni) is superior in hydrogen decomposition at low temperatures.
# Laser Intensity Noise Suppression for Preparing Audio-Frequency Squeezed Vacuum State of Light
## 4 Experimental Results and Discussion
### Preparing Audio-Frequency Squeezed Vacuum State of 795-nm Light  
The experimental setup to prepare audio-frequency squeezed vacuum state of 795-nm light has four parts, 795-nm single-frequency laser system, SHG, OPO, and BHD, as shown in Figure 1a. On the  
\begin{table}
\begin{tabular}{l c c c c c} \hline Power of the sampling beam (mW) & \(\sim\)0.5 & \(\sim\)0.8 & \(\sim\)1.0 & \(\sim\)1.6 & \(\sim\)2.0 \\ \hline Residual peak to-peak fluctuation of 795-nm NIR laser intensity with feedback control & \(\pm\)0.32\% & \(\pm\)0.24\% & \(\pm\)0.17\% & \(\pm\)0.11\% & \(\pm\)0.06\% \\ \hline \end{tabular}
\end{table}
Table 1: Residual peak-to-peak fluctuation of 795-nm NIR laser intensity with feedback control  
Figure 3: Comparison of laser intensity noise of 397.5-nm UV laser beam for feedback system on and off. (**a**) The effective feedback bandwidth is \(\sim\)11.5 kHz. (**b**) In time domain, typical peak-to-peak fluctuation of 397.5-nm UV laser intensity is improved from \(\pm\)9.04% to \(\pm\)0.05%. The stability has been improved by a factor of \(\sim\)180.  
Figure 2: Comparison of laser intensity noise of the 795-nm NIR laser for feedback system on and off with different feedback bandwidths. The effective feedback bandwidth is \(\sim\)60.0 kHz for (**a**) and (**b**), and \(\sim\)9.0 kHz for (**c**) and (**d**), respectively. In time domain, typical peak-to-peak fluctuation of 795-nm NIR laser intensity is improved from \(\pm\)7.45% to \(\pm\)0.54% (**b**) and \(\pm\)0.06% (**d**), respectively. The 795-nm NIR laser intensity’s stability is improved by a factor of \(\sim\)13 (**b**) and \(\sim\)120 (**d**), respectively.  
basis of \(\sim\)1 W of fundamental-wave optical power, the second-harmonic at 397.5-nm of \(\sim\)300 mW can be generated, with a typical doubling efficiency of \(\sim\)30%. After shaping the spot and stablizing laser intensity, the 397.5-nm UV laser power is \(\sim\) 60 mW, which is below the pump threshold of OPO and it can provide the classical gain of \(\sim\)8, in which case OPO can provide stable output of squeezed vacuum state of light. At the same time, after optimizing the intensity and beam quality of LO beam, the squeezed vacuum state of light can be detected by the BHD which has 99.8% of interference visibility. The results of squeezed vacuum state of light in range of 2-15 kHz have been achieved with the quantum noise locking technique, as shown in Figure 4. Comparing the level of squeezing and anti-squeezing in two cases of feedback on and off indicates the feedback bandwidth is 9.3 kHz. In feedback bandwidth, the squeezing level is improved by 0.7 dB after the suppression of laser intensity noise. Unfortunately, due to the complex noise introduced by the circuit in loop of system, additional noise appears at some certain frequencies, shown as several peaks. The squeezing level is not improved for the analysis frequency beyond the feedback bandwidth. On the contrary, the anti-squeezing level is correspondingly increased by 0.7 dB.
###### Abstract  
The paper presents a study of decentralized control for a satellite formation flying mission that uses differential lift and drag to enforce the relative positioning requirements. All spacecraft are equipped with large sunlight reflectors so that, given the appropriate lighting conditions, the formation as a whole can be made visible from the Earth as a configurable pixel image in the sky. The paper analyzes the possibility of achieving a pre-defined lineup of the formation by implementing decentralized aerodynamic-based control through the orientation of sunlight reflectors relative to the incoming airflow. The required relative trajectories are so-called projected circular orbits which ensure the rotation of the image with the orbital period. The choice of the reference trajectory for each satellite is obtained by minimizing the total sum of relative trajectory residuals. The control law is based on the linear-quadratic regulator with the decentralized objective function of reducing the mean deviation of each satellite's trajectory relative to the other satellites. The accuracy of the required image construction and convergence time depending on the initial conditions and orbit altitude are studied in the paper.  
formation flying; differential aerodynamic force; decentralized control; linear-quadratic regulator; linear programming optimization Article
## 2 Statement of the Problem and Analysis of Publications
### Review of Experience in Studying Velocity Distributions  
Since the actual speed of a car is affected by many factors of both a technical and social nature, FFS is a random value, including for the flow of cars of the same type in a given road section. When studying this problem, the methods of analysis of speed distributions are widely used [6; 11; 12; 16; 22; 23; 24].  
Usually, the speed distribution is characterized by a normal distribution law or close to it. Although, as some studies show, even for a flow of homogeneous cars, the distribution of free speeds may deviate from the normal law [25]. In particular, violations of the normal speed distribution can be observed for road sections in the presence of specific road factors or the speed control zone. In this case, other analytical distributions (Log-normal, Gamma distribution, Weibull, and others) can be used to approximate the speed distribution, while the use of distributions with three or more parameters is impractical since such parameters of the distributions as the coefficient of asymmetry and kurtosis are not revealed to have a stable correlation with road conditions.  
Graphical form is often used to present the results of velocity distribution analysis. Differential distribution graphs or histograms show how many cars are moving in specified speed intervals. Accumulation graphs (cumulative curves) make it possible to determine the percentage of cars moving at a speed less than any given value. The value of the 15 th percentile speed describes the speed of the slowest part of the flow of cars, which leads to the complication of traffic conditions and an increase in the probability of road accidents [26]. When restrictions on the movement of slow-moving vehicles are introduced on the road, the value of this speed is often taken as the minimum permissible [27]. The 50 th percentile speed values characterize the average speed of the flow of cars. A very important characteristic is the 85 th percentile speed, which characterizes the maximum speed of the main part of the flow of cars. This value is usually used when introducing a maximum speed limit [28]. The 95 th percentile speed values usually correspond to the calculated speed of single cars in the given road conditions.  
Thus, the study of the law of the distribution of FFS on street sections remains an actual direction of scientific research. In addition, the time period in which the studies were conducted is of some importance when examining the FFS distribution. In general, modern vehicles, especially electric vehicles, have better dynamics, are equipped with more advanced safety systems, and may have ADAS and unmanned driving systems, which can influence the driver's speed selection behavior. Therefore, other things being equal, it is advisable to favor more recent studies of the FFS distribution.
## 2 Materials and Methods
### Edge Sensor Node
#### AS7265x Multi-Spectral Sensor  
The AS7265x, produced by AMS, is an advanced multi-spectral sensor that is capable of detecting light across 18 different wavelengths from 410 nm to 940 nm. It helps to analyze specific chemical properties by capturing variations in light reflection and transmission. This broad spectrum allows for a detailed analysis of milk's chemical components, especially in determining the concentrations of proteins, fats, and other vital components.  
Figure 1: Edge sensor node.  
The sensor comprises three six-channel spectral detection elements: AS72651, AS72652, and AS72653, each customized to measure different wavelength ranges. This combined structure enables the multidimensional recording and analysis of the milk's spectral signature.  
The dimensions of the sensor are 4.5 mm \(\times\) 4.7 mm \(\times\) 3.0 mm, making its compact structure ideal for mobile and embedded systems. Additionally, the sensor includes individually calibrated LED light sources for each wavelength, designed to maximize measurement accuracy. These LEDs are controlled by constant current driver circuits, providing a stable light source during measurement.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 5 Test and Results
### Design of Test Based on MUSHRA
#### 5.1.2 Listeners  
10 people with certain listening experiences were selected for the listening test, of which 5 were male, 5 were female, and each listener has normal hearing.
This work extends the early proposal in [9] defining a completely renewed approach for social interactions among smart objects; to the best of our knowledge, existing works have focused on either one or the other aspect, but not on both together or on their mutual influence, incurring in functional limitations with respect to the proposed approach, as discussed in Section 3;
* a decentralized collaborative service discovery protocol [9] based on _Linked Data Notifications (LDN)_ over _LDP-CoAP_[10], which combines the _Linked Data Platform (LDP)_[11] for semantically annotated resource management and the _Constrained Application Protocol (CoAP)_[12] for Machine-to-Machine (M2M) interactions; this explicit and standard-based formalization of interaction primitives improves on existing research proposals for SIoT architectures based on custom interaction models;
* A completely novel dynamic model for SIoT relationship management, measuring trust with a reputation system based on the usefulness of service suggestions in the decentralized collaborative social discovery protocol with additional features to prevent device isolation due to a \"cold start\" situation (i.e., when a device joins a new network where it has no friends) or to network partitioning after a link removal; this zero-configuration approach is an improvement with reference to most existing SIoT proposals;
* A case study on a Plug-in Electric Vehicle (PEV) charging service marketplace, to illustrate the features and benefits of the proposed approach for Mobility-as-a-Service (MaaS) in a smart city scenario;
* An experimental evaluation assessing the efficiency and effectiveness of both the dynamic relationship management and the collaborative service discovery in device networks of various sizes and configurations. The remainder of the paper is as follows. Section 2 recalls basics of SWoT knowledge representation and reasoning, whereas Section 3 discusses related work. Section 4 describes the overall framework, followed by the case study presentation in Section 5. Experiments are presented in Section 6, before the conclusions.
## 3 Inversion Algorithm of Atmospheric Temperature in MLT
### Least Squares Method  
When faced with an ill-posed problem, the usual method is to find the least squares solution, that is, to find the minimum sum of squares of the difference between the measured value and the value corresponding to the solution. In order to simplify the problem, assuming a linear relationship between the atmospheric state vector and the measurements,  
Figure 11: Bias of atmospheric temperature in MLT region: (**a**) the bias between retrieved temperature and actual temperature under different iteration steps, (**b**) the mean absolute bias between retrieved temperature and actual temperature under different iteration steps.  
Figure 10: Retrieval of atmospheric temperature in MLT region (1–5 represents the number of iteration steps).  
Equation (13) can be obtained as follows. Here, \(m\) represents the number of measurements and \(K\) represents the weighting function.  
\[y_{i}=\sum_{j=1}^{n}K_{ij}x_{j},\ i=1,2,\cdots,m \tag{13}\]  
According to the least squares method, when \(\sum\left(y_{i}-\sum_{j}K_{ij}x_{j}\right)^{2}\) reaches the minimum, the solution of \(T\) is  
\[x=\left(K^{T}K\right)^{-1}K^{T}y \tag{14}\]  
where \(K^{T}\) is the transpose of \(K\). After the \(i_{th}\) iteration, the new target state vector \(x_{i+1}\) is  
\[x_{i+1}=x_{i}+\left(K_{i}{}^{T}K_{i}\right)^{-1}K_{i}{}^{T}[y-F(x_{i})] \tag{15}\]  
Based on the observed radiance data, the above least squares method is conducted for the synchronous retrieval of atmospheric temperature in the whole range of tangent height. The retrieval of atmospheric temperature in MLT and the bias between retrieved temperature and true temperature at different tangent heights are shown in Figure 12a,b.
## 2 Theoretical Insight
### Spring-Mass System Modeling  
This section introduces a simple and efficient spring-mass model available in the literature [35]. It describes the structure using an analytical model derived from the dynamic balance of a spring-mass system. The full developments are not rewritten for the sake of brevity; only the key parameters are presented using notations consistent with those of the present paper. The positive stiffness component has a stiffness \(k_{p}\), and the two pairs of beams generating the negative stiffness have a stiffness \(k_{n}\) defined as  
\[k_{n}=\frac{2\pi^{4}EI}{L^{3}}\left(1-\frac{\delta}{\delta_{\sigma 1}}\right) \tag{4}\]  
where \(\delta\) is the horizontal displacement, and \(\delta_{\sigma 1}=4\pi^{2}I/SL\) is the critical horizontal displacement leading to the first mode buckling. The overall stiffness of the assembly is defined  
Figure 3: Vertical reaction force \(F\) versus vertical displacement \(x=[-x_{m},x_{m}]L\) at the middle of the assembly, for (**a**) increasing values of \(Q_{0}\) while \(\delta=0.1L\) from dark to light gray, and (**b**) increasing values of initial displacement \(\delta\) while \(Q_{0}=0.2L\) from dark to light gray. Lighter lines on both figures is the same configuration.  
as \(k=k_{p}+k_{n}\). The transmissibility \(T(\omega)\) is obtained by deriving the displacement-force equations and calculating the ratio of input velocity to output velocity of the center mass.  
\[T(\omega)=\frac{K/m}{-\omega^{2}+K/m} \tag{5}\]  
Transmissibility is then a frequency-dependent quantity that will account for the performance of the vibration mitigation. It will be compared to experimental results together with results of the dynamic bending model derived in Section 2.4.
## 4 Experiments and Analysis
### Website Clustering Evaluation
In addition to their performance boost, deep clustering algorithms offer enhanced flexibility, enabling adjustments to the model's structure and objective function to optimize clustering outcomes. Nevertheless, this increased flexibility presents a challenge in finding optimal hyperparameters, necessitating a considerable investment of time and computational resources for the tuning process. All of the experimental groups were compared together, and the results are shown in Figure 8. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **ACC** & **ARI** & **NMI** & **Confident-0.999** \\ \hline k-means NMV & 66.72 & 10.67 & 6.39 & * \\ k-means NMV img & **67.87** & **12.25** & **7.56** & * \\ k-means NMV text & 66.45 & 10.25 & 6.08 & * \\ \hline MDC NMV & 83.17 & 43.98 & 38.26 & 90.68 \\ MDC NMV CEloss & **84.1** & **46.49** & **40.79** & **92.39** \\ MDC NMV img & 79.61 & 35.02 & 29.06 & 85.21 \\ MDC NMV img CEloss & 80.1 & 36.2 & 30.31 & 87.93 \\ MDC NMV text & 79.22 & 34.12 & 29.07 & 89.13 \\ MDC NMV text CEloss & 81.8 & 40.41 & 34.84 & 90.47 \\ \hline k-means MV & **70.28** & **16.04** & **10.42** & * \\ k-means MV img & 69.57 & 15.02 & 9.68 & * \\ k-means MV text & 68.2 & 12.78 & 7.95 & * \\ \hline MDC MV & 82.23 & 41.53 & 36.81 & 90.18 \\ MDC MV CEloss & 82.79 & 42.96 & 36.92 & 90.95 \\ MDC MV img & 74.78 & 24.51 & 21.49 & 81.35 \\ MDC MV img CEloss & 75.22 & 25.37 & 23.11 & 82.06 \\ MDC MV text & 82.62 & 42.53 & 37.89 & 90.73 \\ MDC MV text CEloss & **83.39** & **44.56** & **38.66** & **92.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Clustering result. The top line that can be seen from Figure 8 is 'MDC NMV CEloss', which indicates that it is the best model with an accuracy rate of 84.1. If a sample with a confidence level exceeding 0.999 is selected, its accuracy rate can reach 92.39 in a completely unsupervised manner. Therefore, we use the 'MDC NMV CEloss' model for clustering in the subsequent large-scale illegal website discovery.
## 3 Results and Discussion
### Fractional Kelvin-Voigt Model  
Next, we consider another commonly used two-parameter fractional model referred to as Kelvin-Voigt. We start from parameter \(\xi\), responsible for system behavior. It reads as:  
\[\xi^{2}=\frac{\rho\omega^{2}}{E}\Big{[}\frac{1}{(i\omega\tau)^{\alpha}+(i\omega \tau)^{\beta}}\Big{]}, \tag{26}\]  
or alternatively in nondimensional form:  
\[\tilde{\omega}=\frac{\tilde{\omega}^{2}}{\Big{[}(i\tilde{\omega})^{\alpha}+(i \tilde{\omega})^{\beta}\Big{]}} \tag{27}\]  
In contrast to fractional Maxwell model, no additional relations between \(\alpha\) and \(\beta\) are implied here. Thus, we have more freedom to set the values of these fractional orders within the entire range.  
Centerline velocity profiles and shear stresses for fractional Kelvin-Voigt model are given in Figure 6. Here again we fix \(\alpha=0.5\) and vary \(\beta\). Different from fractional Maxwell model, centerline velocity profiles (Figure 6a,c,e) do not exhibit aperiodic oscillations with varying both fractional order and \(\tilde{a}\). Centerline velocity amplitudes increase with increasing \(\tilde{a}\) and decrease with increasing fractional order \(\beta\). Shear stresses' dynamics for the same model are presented in Figure 6b,d,f. Here again all three types of behavior encountered for fractional Maxwell model are present. More specifically, as \(\tilde{a}\) increases, shear stresses experience three different types of behavior: from monotonically increasing through resonant to oscillatory. Relative stress amplitudes decrease with increasing fractional order \(\beta\)  
Figure 5: Fractional Maxwell model: (**a**) centerline velocity, \(\alpha=0.5\); (**b**) shear stress, \(\alpha=0.5\); (**c**) centerline velocity, \(\beta=0.5\); (**d**) shear stress, \(\beta=0.5\); (**e**) centerline velocity, \(\alpha=1\); (**f**) shear stress, \(\alpha=1\). For all plots \(\tilde{a}=0.1\). Subscripts “c” and “w” stand for “centerline” and “wall”, respectively.