esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Bike-Share Case
### Evaluation--Integration and Policy Analysis
#### 3.6.2 Simulation of Performance Across the Fitness Landscape  
The dimensions of the fitness landscape--shaped by the parameters of heuristics that complement algorithmic decisions--include several decision variables beyond starting inventory. These variables include delays that influence the PeBAF and the perception of out-of-stock (OOS) risk (due to bike or dock unavailability); the aggregation logic and horizon for the PrBAF, which, along with the actual bike availability factor (BAF), affects availability perception; the refresh frequency for updating forecasts and PrBAF throughout the day; and the desired bandwidth for bike availability (where, if the PeBAF remains within the \"utilisation sweet spot\", inventory balancing actions are unnecessary). Each parameter set consists of specific values for these decision variables. After defining reasonable domains for the decision variables, parameter sets are generated to correspond to different policy scenarios. The simulation runs on each scenario; the results are collated and visualised in Tableau.  
Given the limited incentives data (available only for August 2023), only two stations--\"E 16 St and 5 Ave\" and \"E 16 St and Irving Pl\"--qualified as candidates. These stations had incentive data and exhibited asymmetric demand patterns, as the causal inference analysis identified. We ran the policy scenarios on these stations, designated as yin and yang, respectively. The focus was on scenarios with zero starting inventory because, during the test period (22 to 25 August), the proposed target starting inventory was sufficient to prevent most stockouts. The natural buffer that builds up when returns exceed rentals further reduces the likelihood of stockouts. This buffer, however, obscured performance differences that could arise from varying parameter values. By focusing on zero-starting-inventory scenarios, we could better observe the impact of different parameters on performance, as any existing buffer would have masked these effects.  
Figure 15 below presents the results of the simulation runs for parameter sets that include the decision variables mentioned earlier. The risk and availability perception delays each take three values (roughly corresponding to low, medium, and high delays); the look-ahead horizon has two values (short and long); the replan frequency is set to 2,  
Figure 14: Generated demand variability scenarios for testing station response at \"E 16 St and 5 Ave\" on 24 August.  
5 or 11 (indicating how many times the plan is refreshed throughout the day); and the utilisation sweet spot is either 20-80% (0.2) or 30-70% (0.3).  
Each cell represents a policy variant, with the overview displaying 432 scenarios in total. The cell value, formatted as x% (y), shows the improvement over the scenario without crowdsourced incentives (x), along with the total number of stockouts (bikes and docks combined, y). Parameter combinations that perform better than the corresponding scenario without incentives are highlighted in blue.
# Near-Field Aeroacoustics of Spanwise Forcing on a Transonic Wing: A DNS Study
## 2 Numerical Method and Description of the Dataset
### The Dataset  
The entire database contains the results of 29 direct numerical simulations, where the control parameters of the streamwise traveling waves described by Equation (1) are varied. In a canonical channel flow, this type of spanwise forcing only features three control parameters: the amplitude \(A\) and the spatial and temporal frequencies \(\kappa_{x}\) and \(\omega\). On the wing, two additional parameters are introduced, namely the beginning and the end of the actuated region (\(x_{b}\) and \(x_{e}\), respectively). An exponential damping function, acting close to the edges of the forcing, ensures that no discontinuities in the spanwise wall velocity component arise, as conducted in [25] for a different type of spanwise forcing. One simulation (case REF) is performed for the unmanipulated flow, while in the remaining simulations the control is activated.  
In particular, 20 simulations (cases C1-C20, shown in Figure 1) consider different values of the pair \(\kappa_{x},\omega\) for the fixed control amplitude \(A=0.684U_{\infty}\). Simulations C1-C20 are divided in three sub-groups: cases C1-C5 are performed with \(\omega=0\), cases C6-C15 feature constant wavenumber \(\kappa_{x}\) and varying frequency \(\omega\), and finally, cases C16-C20 are characterized by variations in both control frequencies. The colormap in the figure represents the percentage skin-friction reduction rate (i.e., the drag reduction indicator in a plane channel flow), conventionally defined as  
\[\Delta C_{f,\infty}=100\frac{C_{f,0}-C_{f}}{C_{f,0}} \tag{2}\]  
where \(C_{f}=2\tau_{w}/\rho_{\infty}U_{\infty}^{2}\) is the friction coefficient (with \(\tau_{w},\rho_{\infty}\) denoting the wall shear stress and the free stream density, respectively).  
As already mentioned in Section 1, the control action increases the lift force, therefore allowing a reduction in the angle of attack to obtain the same lifting force, hence ultimately reducing drag. Hence, one of the best performing cases, namely case C10 (indicated with a white diamond in Figure 1), is computed again with the same control input, but the incidence is reduced at \(\alpha=3.45^{\circ}\) to match the lift of the reference case; its label is R10. The corresponding uncontrolled case at reduced incidence is labeled RREF. The only simulation yielding lift disruption is case C12, and it is represented by the white circle in Figure 1. The analysis presented in this paper will focus on the uncontrolled cases REF and RREF, as well as on cases C10 (skin-friction reduction) and R10 (skin-friction reduction at \(\alpha=3.45^{\circ}\)). The reader is again referred to [PERSON] et al. [13] for further details.  
Note that every quantity hereinafter denoted with a '\(\Delta\)' represents the variation in the controlled case with respect to the reference unmanipulated simulation, unless otherwise specified. Hence, a positive \(\Delta\) represents an increase.
## 1 Introduction
### Paper Contribution  
The flowchart in Figure 1 illustrates the methodology applied in this study for minimizing the displacement error on a hydraulic press caused by external disturbances. Firstly,the architecture of the hydraulic press and measurement hardware is presented, following the definition of external disturbances, which includes friction characteristics and bending processes. Next, experiments are performed on a hydraulic press to conduct behavior analysis. Furthermore, polynomial regression is selected to build data-driven models as it is an extension of standard linear regression, which is a simple and easy-to-implement ML strategy that is less computationally intensive and provides a better representation of problems where data patterns are polynomic [32; 33]. Moreover, data-driven polynomial regression modeling is implemented to develop a real-time PR-MPC mechanism integrated within an expert system. This approach integrates polynomial regression for predictive modeling, leveraging design of experiments (DOE) and response surface methodology (RSM) for hyperparameter optimization and improved data visualization [34; 35]. Polynomial regression, as a computationally efficient method, better captures polynomic data patterns, ensuring robust performance in real-time applications. Finally, the adapted control system effectiveness is validated through experimental validation of the developed expert system by comparing the PID with the PR-MPC control approach.  
Briefly, the primary contribution of this paper is the development of an expert system based on the causes of the sheet metal bending cycle and the changes in friction characteristics of the hydraulic cylinders or hydraulic press guides. The three main contributions of this paper are as follows:  
* To collect adequate datasets, an experimental analysis was conducted to consider the nonlinearity and uncertainty of the hydraulic press under various conditions.
* Then, we used polynomial regression modeling to evaluate the patterns and trends in collected datasets and determine the adaptive algorithms to adapt the control signal of the hydraulic press in a given event.
* Given the expert knowledge, a PR-MPC mechanism is integrated into the expert system to monitor condition changes and make appropriate decisions on detected changes provided by the sensing system. The aim is to evaluate the designated expert system and simultaneously ensure the stability of a nonlinear hydraulic press. Furthermore, the regression models integrated as smart algorithms are evaluated with the following metrics: coefficient of determination \(R^{2}\), root mean squared error (RMSE), and sum squared error (SSE).
## 2 Theoretical Analysis
### Watermark Embedding and Extraction Based on Cellular Automata Transform (CAT)
#### 2.2.3 Logistic Chaotic Mapping  
Logistic map is a commonly used discrete chaotic map for generating pseudo-random sequences. The simplest one-dimensional logistic map is:  
\[x_{n+1}=\mu x_{n}(1-x_{n}),\;x_{n}\in(0,1),\;\mu\in[3.5699456,4] \tag{14}\]  
Figure 3: Two-dimensional CAT base function.  
\(x_{n}\) can be transformed into an integer chaotic sequence between [1, \(n\)] through a positive integer \(N\), and different elements are taken out to obtain a non-repetitive positive integer chaotic sequence: \(y_{n}\in[1,M]\), \(m\leq n\). There is another form of logistic mapping:  
\[x_{n+1}=1-\mu x_{n}^{2},\ x_{n}\in(-1,1),\ \mu\in(0,2) \tag{15}\]  
It can also be transformed into an integer chaotic sequence by the above method: \(y_{n}\in[-T,T]\), where is a positive integer. Logistic chaotic mapping is extremely sensitive to the initial value. Given two slightly different initial values, two completely different pseudo-random sequences are obtained after multiple iterations. Therefore, the initial value of the mapping can be used as the key to improve the security of the watermarking algorithm.
## 2 Hourly Meteorological Data Analysis Based on Cubic Spline Function Method (FFT)  
### Selection of Original Meteorological Data and Spline Function Method  
The data used in this study were recorded from national meteorological stations in Beijing, including the last 30 years of original meteorology data. The dry-bulb temperature of Beijing meteorological stations, directly related to the extreme weather, is employed as the main parameter to build the meteorological model of daily mean temperature and extreme temperature. Meteorological data were measured four times per day(02:00, 08:00, 14:00, 20:00 Beijing Time) at the Beijing station, a national basic weather station. To build the meteorological model, the observation data was adopted to generate the daily hourly data.  
For generating daily hourly data, interpolation methods, such as the polynomial function, can be employed to examine the fitting function expression. The polynomial function is relatively simple and convenient for numerical calculations and theoretical analyses. Furthermore, cubic spline interpolation is a means of describing a smooth curve to solve tridiagonal equations through a series of data points. The curve of the cubic spline interpolation function is relatively smooth, and the interpolation points are continuous. For this reason, the accuracy of a cubic spline interpolation function is high, and it approximates to the primitive function.  
The theory of fast Fourier transform is: first, the appropriate window function is chosen to suppress the leakage of long range. Then, in line with the form of the window function, the leakage of short range is amended, by using the interpolation method. The FFT algorithm can reduce the computational complexity and improve the efficiency of calculation, and can offer a relatively simple computing method for estimating amplitude, frequency and phase. Moreover, the accuracy of the data cycle which is estimated by sequential harmonic parameters is higher. Therefore, the FFT method is used widely [15; 16; 17; 18].  
In this paper, in line with the original time-domain sampled sequence, a new equal interval sequence was reconstructed, using cubic spline interpolation. The reconstructed original meteorological data sequence was processed using the FFT, and the hourly meteorological data was estimated.
## 2 Operational Retrievals of ET from EO  
### Methods for ET Retrievals from EO  
The prospect and capability of EO technology in deriving regional maps of ET fluxes became available first in 1972 with the launch of the LANDSAT MSS and later on, in 1978 with the HCMM (Heat Capacity Mapping Mission) and TIROS-N satellites [24]. Since then, a plethora of methods have been proposed for estimating ET fluxes utilising spectral information acquired in different regions of the electromagnetic radiation spectrum. A non-exhaustive list of EO systems and of their technical specifications providing at present data suitable for the retrieval of ET is summarised in Table 1 below.  
An overview of the remote sensing-based estimation of ET has been provided previously by several authors [20; 26]. Remote sensing-based techniques have the capability to estimate spatial and temporal variation of ET from catchment to global scales. To generalise, remote sensing-based methods employed today for the estimation of energy fluxes are classified into: (1) methods that involve the use of statistically-derived relationships between ET and vegetation indices such as the Normalized Difference Vegetation Index (NDVI) or the Fractional Vegetation Cover (FVC); (2) physical models that calculate ET as the residual of Surface Energy Balance (SEB) through remotely sensed thermal infrared data; (3) other physical models that involve the application of the combination of Penman-Monteith and Priestley-Taylor types of equations and (4) data assimilation methods adjoined to the heat diffusion equation (and through the radiometric surface temperature sequences).
## 4 Discussion and Conclusions
The diffusion experiments illustrated that the enhancement in the attenuation coefficient value of biofilms varies along the depth of the porous substrate. Hence, it is hard to define a single attenuation coefficient value for the entire contrasted biofilms. However, by using the average attenuation coefficient value of the attached biofilms for segmentation, the presence or absence of the biofilms at any given location in the substrate can be determined. Similarly, by subtracting the two images obtained before and after application of contrast agent an estimate on the contrasted biofilms present in the substrate can be determined. Contrast-enhanced X-ray \(\upmu\)CT enabled in-situ visualization of biofilms inside a porous substrate with sufficiently large contrast between the constituents of the system. Diffusion experiments further helped in understanding the imbibition process of contrast agent and enhancement in the attenuation coefficient of biofilms over time and along the depth. Conceptualization, M.N.B., E.R., A.S. and X.V. ; methodology, A.S., M.N.B., E.R., H.S. and X.V. ; software, A.S.; validation, A.S., X.V., M.N.B. and H.S. ; formal analysis, A.S. and X.V. ; investigation, A.S. and X.V. ; resources, A.S., E.R. and X.V. ; data curation, A.S. and X.V. ; writing--original draft preparation, A.S. and X.V. ; writing--review and editing, all authors; visualization, A.S. and X.V. ; supervision, M.N.B., E.R., V.C. and H.S. ; project administration, E.R., M.N.B., V.C. and H.S. ; funding acquisition, E.R., V.C., M.N.B. and H.S. All authors have read and agreed to the published version of the manuscript. This work is funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No. 72871 in the scope of the Marie Sklodowska-Curie Action ITN BioClean. The Ghent University Special Research Fund (BOF-Ugent) is acknowledged for the financial support to the Centre of Expertise UGCT (BOF.EXP.2017.0007). [PERSON] (Ghent University, Department Physics and Astronomy) is acknowledged for his assistance in building the flow cell and [PERSON] (Ghent University, Department Geology) for helping in purchase and procurement of the materials. We thank [PERSON] (Ghent University, Department Physics and Astronomy) for his assistance in software installations and computer programming, [PERSON] (KU Leuven, Centre of Microbial and Plant Genetics) is acknowledged for his assistance in microbiology lab. The authors declare no conflict of interest and the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Application of Three-Dimensional Vision Technology in Dance
## 2 Three-Dimensional Vision Technology
### Three-Dimensional Vision
#### 2.1.1 Binocular Parallax  
Binocular parallax is the most prominent depth perception mechanism. As shown in Figure 1, binocular parallax refers to a certain distance (\(\sim\)65 mm) between the two eyes. When observing, light enters the left and right eyes from different angles, and the object will form stereo-image pairs with slight differences in the retinas of the left and right eyes. Such differences can provide the depth information of the object [3; 4]. When transmitted to the brain via nerve impulses, the brain will compare the two images and calculate the object distance based on the differences, ultimately producing stereoscopic vision [5; 6].
## 4 Practical Application
### Measuring Electrical Machines  
In our next example, we consider the measurement of a permanently excited synchronous machine for which real measured values were recorded and appropriately normalized. A qualitative illustration of the machine is depicted in Figure 15. Torque \(M\) of the machine depends on current \(i\), which is composed of components \(i_{\mathrm{d}}\) and \(i_{\mathrm{q}}\), so the relation  
\[M=M(i_{\mathrm{d}},i_{\mathrm{q}}) \tag{13}\]  
Figure 14: Exemplary application of the RooTri() function.  
applies. Component \(i_{\text{d}}\) of the current generates a magnetic field in the direction of the flux linkage of the permanent magnet, and the \(i_{\text{q}}\) component generates a magnetic field that is rotated by \(90^{\circ}\) with respect to the flux linkage [22]. Within the scope of the investigation, a characteristic diagram of the machine that contains the equipotential lines of constant torques is to be created on the basis of the measured values.  
A total of 93,853 measured values \(\mathbf{p}\) in the form  
\[\mathbf{p}=[i_{\text{d}}/\max\{|i_{\text{d}}|\},i_{\text{q}}/\max\{|i_{\text{ q}}|\},M/\max\{|M|\}] \tag{14}\]  
are available as unstructured data in a point cloud \(\mathcal{P}\) and are passed to the RooTri() function. Parameter \(d\) of the plane equation given in Equation (1) describes, in this case, the value of the respective torque \(M\), which is varied in a range from \(-0.9\max\{|M|\}\) to \(0.9\max\{|M|\}\). The function is thus executed by calling RooTri(P,0,0,1,d) for each ratio \(M/\max\{|M|\}\) as offset parameter \(d\). In Table 5, the results with respect to the number of calculated zeros (\(n\)) and the required computation time (\(t\)) are summarized. Using the calculated combinations \([i_{\text{d}}/\max\{|i_{\text{d}}|\},i_{\text{q}}/\max\{|i_{\text{q}}|\}]\) to obtain the respective torque ratio, the characteristic diagram can then be generated; see Figure 16. Note that due to the number of data, not all results were plotted; however, the qualitative course of the equipotential lines is clearly recognizable. The function offers the further advantage that the points of the isolines are explicitly available. These working points can then be approached directly for later operation.  
\begin{table}
\begin{tabular}{r|c c c c c c c c c c} \hline \hline \(M/\max\{M\}\)\([-]\) & \(\mathbf{-0.90}\) & \(\mathbf{-0.70}\) & \(\mathbf{-0.50}\) & \(\mathbf{-0.30}\) & \(\mathbf{-0.10}\) & \(\mathbf{0.10}\) & \(\mathbf{0.30}\) & \(\mathbf{0.50}\) & \(\mathbf{0.70}\) & \(\mathbf{0.90}\) \\ \hline \(n\)\([-]\) & 2029 & 2496 & 2565 & 2581 & 2595 & 2850 & 2875 & 2940 & 2970 & 1479 \\ \(t\)\([\text{s}]\) & 0.377 & 0.375 & 0.366 & 0.366 & 0.367 & 0.370 & 0.371 & 0.369 & 0.374 & 0.374 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Measurement results.  
Figure 15: Qualitative illustration of a permanently excited synchronous machine.
# Research on the Principle of a New Flexible Screw Conveyor and Its Power Consumption
## 4 Discrete Element Simulation
### The Spiral Angle  
Considering the limits of the manufacturing capacity, the spiral angles of the flexible screw conveyor is 20\({}^{\circ}\), and the diameter of the spiral is 70 mm, which is the capability of the screw. Therefore, the maximum distance of each fiber is 1 mm, and the diameter of the spiral is 70 mm. The spiral shaft is 150 mm long, and the rotational speed is 400 rpm. The particle number is 10,000. We set three spiral angles of 10\({}^{\circ}\), 20\({}^{\circ}\), and 30\({}^{\circ}\) to simulate the process of the flexible screw working. Figure 9 shows the simulation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Poisson’s Ratio** & **Modulus of Elasticity (pa)** & **Density (kg/m\({}^{3}\))** \\ \hline Starch & 0.25 & \(2\times 10^{7}\) & 1500 \\ Nylon 66 & 0.28 & \(3.2\times 10^{9}\) & 1130 \\ PVC & 0.35 & \(3\times 10^{6}\) & 1400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Interaction** & **Coefficient of Restitution** & **Static Friction Coefficient** & **Rolling Friction Coefficient** \\ \hline Particle to particle & 0.1 & 0.7 & 0.01 \\ Particles to nylon & 0.3 & 0.5 & 0.01 \\ Particles to PVC tube & 0.3 & 0.5 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Starch granules’ contact properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Interaction** & **Model** \\ \hline Particle to particle & Mertz-Mindin with bonding built-in \\ Particles to nylon & Mertz-Mindin (no slip) built-in \\ Particles to PVC tube & Mertz-Mindin (no slip) built-in \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical interaction model.  
The low-speed material particles are colored in blue, and the high-speed particles are colored in red. It can be seen from Figure 9a that for most of the particles, the speed is from 100 mm/s to 480 mm/s. In Figure 9b, the speed of most particles is from 400 mm/s to 860 mm/s. In Figure 9c, the speed of most particles speed is from 800 mm/s to 1200 mm/s. The results show that the speed of the particles can be increased by the speed of the screw conveyer under certain other conditions. By collating the simulation data, the speed of the three experiments at the same transportation distance is shown in Figure 10.  
From Figure 10, it can be seen that increases of the spiral angle will improve the transportation efficiency of the granular material.