esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 5 Results  
According to the design sizes of the rotation joint, L\({}_{1}\) = 6.81 mm, L\({}_{2}\) = 122.96 mm. Both the radial error motion of the precision test spheres fixed on the rotating shaft and the radial error motion of the grating disk along the direction of the reading head \(\delta_{\mathrm{Y}}(\theta)\) are shown in Figure 13. The experiments are repeated three times. The repeated experiment results show that the range of radial error \(\delta_{\mathrm{Y}}(\theta)_{n}\) at the same position is 0.3 \(\upmu\)m, the standard deviation of \(\delta_{\mathrm{Y}}(\theta)\) is calculated by range-method, \(\sigma_{\delta_{\mathrm{Y}}(\theta)}=\delta_{\mathrm{Y}}(\theta)_{n}/d_{n}\), where \(n=3\), \(d_{n}=1.69\). So \(\sigma_{\delta_{\mathrm{Y}}(\theta)}=0.18\)\(\upmu\)m. The uncertainty is equal to standard deviation, so the uncertainty of radial error motion \(u_{\delta_{\mathrm{Y}}(\theta)}\) is equal to 0.18 \(\upmu\)m.  
The harmonics of \(d_{refk}\), which removed constant term, and the first harmonic of \(d_{refk}\) are shown in Figure 14. From Equation (11), \(e=7.1\)\(\upmu\)m, and from Equation (12), \(\theta_{0}=192.08^{\circ}\). The results show that the range of \(e\) is 0.4 \(\upmu\)m, so the uncertainty of amplitude of eccentricity \(u_{e}=\sigma_{e}=e_{n}/d_{n}=0.24\)\(\upmu\)m. The range of \(\theta_{0}\) is 1.91\({}^{\circ}\), and the uncertainty of phase of eccentricity \(u_{\theta_{0}}=1.13^{\circ}\).  
Figure 12: Experimental setup for angle measurement error measurement.  
Figure 13: Results of radial error motion of spheres and grating disk along the direction of reading head.  
The parameter \(r\) is known from the datasheet, and the parameters \(e\), \(\theta_{0}\), and variable \(\delta_{Y}(\theta)\) are known. The angle measurement error caused by the installation eccentricity and radial error motion \(\Delta\theta\) is calculated by Equation (5), as shown in Figure 15.  
In Figure 16, the comparison of angle measurement errors obtained through the use of an autocollimator with an optical polygon and the method proposed in this article is shown. Both curves are close to each other, with residual errors within \(\pm 6\)\". The residual errors account for 9% in angle measurement error.
## 5 Discussion
### Energy Consumption  
The total energy consumed for all the nodes in the network can be calculated as per Figure 11. To ensure that the proposed 100-node algorithms are also energy-efficient, their power consumption is displayed in Figure 11. Energy consumption is determined by the average energy consumption of a single sensor node. As opposed to other approaches, the proposed pro-SOPR-BEER techniques are highly energy-efficient. For instance, longer distances between the cluster head and the nodes are the main reasons for energy consumption, and, depending on the energy level of the nodes, they randomly advertise themselves as cluster heads in the proposed pro-SOPR-BEER algorithm. This minimizes the energy needed for contact within the clusters. The proposed algorithm, pro-SOPR-BEER, also minimizes the energy spent on inter-cluster communication. The performance of M-ATTEMPT is minimal when compared to the other existing schemes. Collectively, all the schemes failed to achieve better results when compared with the proposed pro-SOPR-BEER algorithm. The proposed pro-SOPR-BEER algorithm increases successful packet transmission by reducing packet loss, and the consumption of energy is greatly reduced. The energy consumption of the proposed pro-SOPR-BEER scheme is estimated for the determination of network quality.
## 4 Results and Discussion
### Impact Assessment  
#### 4.2.1 Hierarchical Structure Establishment  
In order to study the degree of influence of the initial conditions on wind parameters, the analytic hierarchy process was used to analyze the ranking of factor weights. The ranking of influencing factors was the target level, the wind parameters were the criterion level, and the influencing factors were the scheme level. The hierarchical structure was designed as shown in Figure 11.  
Figure 11: Hierarchical structure diagram.  
Figure 10: Profile and speed contour: (**a**) Wind speed profile of different oncoming directions; (**b**) Wind speed contour at different heights.
# Performance Evaluation of the New Compound-Carrier-Modulated Signal for Future Navigation Signals
## 1 Introduction
Thus, multi-carrier modulation possesses potential navigation signal capability and enables integrated communication and navigation services. The use of multi-carrier-based signals for navigation applications is still in an incipient stage. There are several tentative modulation schemes, such as filtered multi-tone (FMT) [14; 15], OFDM [16; 17], or time-division orthogonal frequency division multiplexing (TD-OFDM) [18]. In this paper, we propose a novel modulation scheme called navigation signal based on compound carrier (NSCC). The NSCC concept, based on a composite design of navigation signals and communication signals, realizes the integration of navigation and communication. The novel signal possesses a large set of constituent parameters, which makes it display great design freedom. The excellent capability inherited from the multi-carrier signal approach, render it a viable future GNSS signal mode, especially as a promising navigation augmentation signal. Moreover, the NSCC signal enables the integration of navigation and communication services, and hence strengthens the combination of GNSSs with ubiquitous terrestrial positioning systems, and above all, with communication systems at the signal level. This integrated signal will support and even spawn new applications, referred to as location-aware or location-based services (LBSs) [19; 20]. A preliminary exploration has been conducted to confirm the feasibility of the proposal [21]. The NSCC, however, has a serious shortcoming with its high peak to average power ratio (PAPR) due to its multi-carrier structure. Studies show that constant envelope techniques and PAPR restraint techniques can be used to address this problem [22; 23]. The paper is organized as follows: firstly, the concept and principle of the NSCC signal are briefly introduced, and the general mathematical expressions of its auto-correlation function (ACF) and power spectral density (PSD) are presented. Secondly, comprehensive evaluation criteria and analytical methods are provided in terms of spectral efficiency, tracking accuracy, anti-jamming reduction and multipath mitigation capability. The related merits are used to quantify the performance of NSCC and the legacy modulation schemes. Thirdly, the proposed NSCC signal method together with the existing navigation signals are comprehensively evaluated and analysed. Finally, the main performance features of the novel signal are discussed and our conclusions presented.
## 3 Results
### Discussion and Future Steps  
The present work investigates a system with a high-temperature heat pump coupled with solar thermal systems which can be an alternative answer to the existing space heating systems. This idea has important benefits such as (i) the exploitation of renewable energies, (ii) the use of environmentally friendly refrigerants, (iii) the use of highly efficient heat pumps and (iv) the incorporation of radiative terminal units for achieving high thermal comfort standards. Moreover, the present system has a positive impact on various sustainable development goals and more specifically on \"Affordable and clean energy\", \"Sustainable cities and communities\" and \"Climate action\".  
The high efficiency of the solar-assisted system compared to the air-source system can be found by comparing the existing results of the optimum scenario with the results of our previous work in Ref. [33]. More specifically, the system SCOP is found in this work at 4.066, while in the previous work, it was 2.128 [33]; so, there is a significant enhancement with the use of a solar-assisted system. However, the present system needs the installation of a solar field which adds an extra cost. In the future, it would be very interesting to investigate a dual-source heat pump that can be driven both with air and solar in order to avoid the use of the auxiliary electrical resistance of the present system or to restrict it.  
To conclude, in the future, it is important to investigate the present system under different climate conditions and by optimizing the radiative terminal units in order to be suitable for the present configuration. Moreover, the use of a model predictive control unit would be very interesting in order to optimize the examined system by taking into account the weather data of the next days and properly adjusting the thermal stored energy.
## 3 Results
### Effect of Temperature, Time, and Grinding on ITCs Release  
Before analysing food samples, we studied the effects of three parameters on the release of volatile ITCs from fresh SO flowered sprouts: temperature and time of treatment, and grinding of plant material (sample S1). Tests are conducted in a vial for HS- SPME by weighting 25 mg of flowers and 1 mL of water. Vials were filled and rapidly closed. The three parameters were varied to mimic, at least in part, the ordinary conditions of assumption of food and drink, including mastication. In these experiments, the total ITCs content was calculated as the sum of compounds **2** and **4** obtained by the calibration curves. Results of preliminary experiments are reported in Table 1. Additional data are reported in Supplementary material, Table S1.  
S1/1 contains SO flowers that were heated for 10 min in water at 37 \({}^{\circ}\)C without grinding, whereas S1/2 is the same sample rapidly grinded in the vial before SPE analysis, thus simulating the mastication in the mouth. The comparison shows that grinding increases remarkably the release of ITCs in the sample; this can be attributed to the release of myrosinase from tissues, which favours the enzymatic hydrolysis of glucosinolates to volatile isothiances. A similar effect is common in other food preparations; for instance,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Grinding** & \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & **Time (min)** & **Total ITCs** \\  & **(Yes/No)** & & & **(\(\mu\)g/g)** \\ \hline S1/1 & no & 37 & 10 & 0.002 \\ S1/2 & yes & 37 & 10 & 0.804 \\ S1/3 & yes & 80 & 10 & 0.126 \\ S1/4 & no & 80 & 5 & 0.140 \\ S1/5 & no & 80 & 10 & 0.150 \\ \hline \hline \end{tabular} Total ITCs are reported as \(\mu\)g/g of fresh flowers. All measurements were made in triplicate. Standard deviation \(\leq\)10% in all samples.  
\end{table}
Table 1: Effects of meshing, temperature and time on release of isothiances (ITCs) from fresh flowers of SO (S1) in water.  
Figure 2: Total ion chromatogram (TIC) of volatile from headspace solid-phase microextraction (HS-SPME)-gas chromatography–mass spectrometry (GC/MS) analysis _Sisymbrrium officinal_ (L.) Scop. (Brassicaceae) (SO) flowers. First, 25 mg of SO flowers were weighted directly in a vial with 1 mL of distillated water, chopped, and quickly closed; exposure time of SPME fiber and temperature: 10 min at 37 \({}^{\circ}\)C.  
during the mastication of rockets leaves or by grinding mustard seeds in mustard under the teeth, the release of pungent AITC is immediately perceived in oral cavity and nose.  
The effect of temperature is shown comparing entries S1/2 and S1/3, which is heated at the temperature of 37 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, respectively. In this case, a marked decrease of total ITCs content (from 0.8 to 0.12) is observed; this could be due to the inactivation of myrosinase in the plant material at this temperature [26]. Changing time from 5 to 10 min did not affect the release of ITCs.
## 3 Dataset and Methods
### Methods
#### 3.2.2 Pre-Stack Inversion  
Simultaneous model-based inversion was performed to obtain density, P-impedance, S-impedance, and Vp/Vs ratio volumes from the available pre-stack seismic data. Furthermore, these outputs were utilized to acquire 3D models of SQp and SQs attributes and serve as input for ANN modelling. The workflow of the pre-stack inversion process is shown in Figure 6. A detailed description of each step of the pre-stack inversion process is discussed further.  
The main input data for pre-stack inversion consists of 3D pre-stack seismic (near-, mid-, far-angle) volumes, well data and a set of horizons to guide the interpolation of the initial model. The pre-stack inversion in this study was conducted using data from XD-2 well, which penetrated the zone of interest, the HPHT zone.  
The inversion process involves seismic-well tie, low-frequency model generation, and model-based inversion [23; 24; 25]. Three groups of wavelets-angle-dependent are extracted from the near, middle, and far angle gathers (examples in Figure 7). An angle-dependent wavelet is extracted for each gathers group for near (5\({}^{\circ}\) to 15\({}^{\circ}\)), middle (15\({}^{\circ}\) to 25\({}^{\circ}\)), and far (25\({}^{\circ}\) to 40\({}^{\circ}\) gathers), respectively. For seismic-well tie, the determined wavelet is convolved with the reflectivity of well XD-2.  
The initial low-frequency models of P-wave, S-wave, Vp/Vs ratio, and density are built on interpreted seismic horizons and corrected sonic and density logs. The values from well log data are extrapolated using the trend from the logs and the interpolation guided by the interpreted 3D horizons. These models were then used as the input for the inversion process.  
An inversion analysis is conducted on the inverted seismic models at the location of the XD-2 well to obtain optimum inversion parameters before seismic volume inversion. The important inversion parameters, such as regression coefficient, background ratio of Vp/Vs, pre-whitening, and muted dead traces are optimised to minimise the root mean square (RMS) error values calculated from inverted Zp, Zs, and density to the respective original logs. The optimised regression coefficient is determined by cross-plotting the inverted logarithmic Zs versus inverted logarithmic Zp and inverted logarithmic density versus inverted logarithmic Zp and location, as shown in Figure 8. After all, parameters are determined, the inversion result is analysed by evaluating the error and correlation of the inverted Zp, Zs, and density. Only inversion parameters with the lowest error and  
Figure 6: The workflow of the pre-stack inversion process.  
highest correlation values are selected and applied in the final seismic volume inversion for quality control.
## 1 Introduction  
Multifocal intraocular lens (IOL) designs are continually evolving, along with increasing demand from patients seeking spectacle independence [1]. Therefore, manufacturers follow different strategies to reach a greater number of patients while minimizing the adverse effects intrinsic to some designs, such as halos or dysphotoposis.  
In the final process of selecting the IOLs, it is the ophthalmologist who must decide which lens is most suitable for each patient. To do so, they must analyze both the clinical characteristics of the patient and their demands and expectations for the lens surgery. However, access to technical information on the IOLs is often limited due to patent protection, copyright issues, or manufacturer decisions. To make the most appropriate selection possible, the ophthalmologist must have access to all the relevant technical information.  
Some laboratories manufacture IOLs with different optical design based on the same platform. As an example, the Acumex AN6, AN6V, and AN6 VM intraocular lenses manufactured by Teleon Surgical (Spankeren, The Netherlands) are three IOLs based on the same platform, including a standard monofocal design (AN6) and two multifocal designs, one with low addition (AN6V, with +1.50 D addition) and one with high addition (AN6 VM, with +3.00 D addition). These lenses were recently marketed and manufactured in hydrophobic material, with an optical design reminiscent of the Lentis Mplus lenses (Oculentis GmbH, Berlin, Germany) [2,3].  
The Acunex hydrophobic multifocal lenses feature refractive optics with a sector-shaped near vision segment with different additions. However, the addition's presentation in a sector shape causes the lens to have an asymmetric geometry that can affect aberrations. Therefore, the position of the lens, and the patient's pupil size can significantly affect the final visual result [4, 5, 6].  
The purpose of this study is to provide laboratory-independent technical information on the Acunex IOL family by comparing three different designs of IOLs with identical platform and material, using analysis on an optical bench. The radial profiles and high-order aberrations (HOA) will be analyzed, paying special attention to spherical aberration and coma. Additionally, it will be evaluated whether the nominal power of these IOLs affects the add power that can be obtained with these designs by assessing three different nominal powers (+10.00 D, +20.00 D, and +30.00 D).
## 3 Analytical Methods  
Ten fresh rock samples from trachyte outcrops and two samples of mantle xenoliths from basalts were collected for analysis (Figure 1). Fresh whole-rock samples were cut down into chips, which were then cleaned ultrasonically in distilled water with <5% HNO\({}_{3}\), and then in distilled water only. The chips were then dried and handpicked to avoid visible contamination. The weathered rims were removed, and all of the chips were crushed to a millimeter scale. The crushed chips containing no xenocrysts or amygdules were handpicked under a magnifier. Only fresh rock chips without any xenocrysts or amygdules were selected for further analyses. An agate mill was used to crush and pulverize the samples, and the resulting powders were analyzed to determine their major and trace-elemental compositions. The trachytic specimens were sent to the Center for Research Facilities, Chonnam National University, for major elemental analysis using an X-ray fluorescence spectrometer (PW1480, Philips). The relative error rates reported from the analyses using a standard sample (BHVO-1) were <1% for Si, Ti, Al, Mg, Ca, and Na; 1-2% for Fe, K, and P; and 2-3% (one standard deviation; 1\(\sigma\)) for Mn.  
The samples were sent to the National Center for Inter-University Research Facilities, Seoul National University, for trace-elemental analysis using inductively coupled plasma mass spectrometry (ICP-MS; ELAN 6100, Perkin-Elmer SCIEX) with an argon plasma source (6000 K) and a mass resolution of 0.3-3.0 amu. The comparison between the average and recommended values obtained from repeated analyses of BHVO-1 exhibited relative error rates of 12% for Er and Ho; <5% for Ba, Cr, Cu, Nb, Sr, Rb, Y, Zr, La, Ce, Sm, and Lu; and 5-10% for Nd, Eu, Gd, Pr, Tb, Dy, and Yb.
## 2 Materials and Methods
### Cyclic Voltammetry  
The electrochemical behavior of the prepared sensors using different MNPs was studied in presence of H\({}_{2}\)O\({}_{2}\) by cyclic voltammetry (CV). The CV parameters are as follows: start potential 0.0 V, stop potential 0.0 V, scan rate 50 mV s\({}^{-1}\), upper vertex potential 1.0 V, and lower vertex potential \(-\)1.0 V. The potential was scanned towards the anodic direction.
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 3 Theoretical Basis of UV-Vis Spectrum Data Processing
### Method of Establishing Data Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Modeling Algorithm** & **Algorithm Principle** & **Advantages** & **Disadvantages** & **Literature** \\ \hline \multirow{3}{*}{Partial least squares regression (PISR)} & Based on the criterion of covariance maximization, the regression equation between variables is established. & It is simple to calculate, has high prediction accuracy, and is easy to use for qualitative interpretation. & It has a large fitting error and less independent variable deviation information. & [PERSON] (2013), [PERSON] (2015) \\  & Linear regression is realized by constructing a linear decision & It can solve high dimensional feature data and has a large number of kernel functions. & It has a large fitting error and less independent variable variation. & [PERSON] (2017), [PERSON] (2017). \\ \hline \multirow{3}{*}{Support vector machine regression (SVR)} & Linear regression is realized by constructing a linear decision function in high dimensional space after dimension increasing. & It can solve high dimensional feature data and has a large number of kernel functions. & It is not suitable for a large sample size and a large sample size and a large calculation amount. & [PERSON] (2011), [PERSON] (2019), [PERSON] (2012), [PERSON] (2012), [PERSON] (2017). \\ \hline \multirow{3}{*}{Extreme learning machine (ELM)} & A machine learning method based on a feed-forward neural network. & The learning speed is fast and easy to use to obtain results. & It is easy to over fit and has poor robustness. & [PERSON](2016). \\ \hline \multirow{3}{*}{Principal component regression (PCR)} & After multi-collinearity is eliminated by using PCA, principal component variables are taken as independent variables, and the original variables are replaced with the new model based on the score coefficient matrix. & It is difficult to use to solve nonlinear data, while the calculation process is complex. & [PERSON] (2019), [PERSON] (2020). \\ \hline \multirow{3}{*}{Stepwise regression (SR)} & An independent variable selection method for a linear regression model. & This method has high prediction accuracy, is easy to operate, and retains significant variables. & The regression results are affected by the number of samples used. & [PERSON] (2020). \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Modeling algorithms of spectral data.