esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fluid Dynamic Performance
### Experimental Techniques
#### 4.2.4 Optical Birefringence  
In the case of transparent polymers, birefringence is directly proportional to stress, and thus birefringence measurement provides an excellent method to obtain stress fields [6]. Optical birefringence generates a contour map of stresses; in order to obtain an optical birefringence pattern, the flowing sample is illuminated with monochromatic light. A 4 mW helium-neon laser is positioned such that the beam passes orthogonally through the walls of the flow cell and the observation area. The laser beam is detected by the cooled CCD camera, which is focussed on the stagnation point area. The polarizer and analyser are placed on either side of the flow cell with their axes at 45\({}^{\circ}\) to the flow axis, and at 90\({}^{\circ}\) to each other to minimize the signal received by the CCD camera. A \(\lambda/4\) plate is inserted between the polarizer and analyser to compensate for residual birefringence in the walls of the channel. The \(\lambda/4\) plate is rotated to achieve the minimum signal, and the polarizer and analyser are readjusted iteratively to achieve extinction of the beam. Twenty-five ms images are normally captured for a range of flow rates. When the flow is stopped, a 25 ms background image was captured. The background image was subtracted from the images captured under flow to reveal the birefringent signal. This is done within 1 s to provide the best possible background image for subtraction. The intensity of the detected signal was calibrated by placing a \(\lambda/30\) compensator in the optical line. The change in the signal intensity is measured as the compensator is rotated to produce a range of retardation values. The retardation, \(R\), is related to the birefringence, \(\Delta n\), by \(R\)= \(\Delta n\)\(l\), where \(l\) is the measured measured value. The measured value is \(\Delta n\  
is the path-length through the birefringent material. Then, from \(\Delta n\), it is possible to obtain the normal stress and the shear stress actuating on the walls prototypes [82].  
The stress-optic law states that the index of refraction tensor is proportional to the stress tensor according to Equations (1) and (2):  
\[\sigma_{12}=\frac{1}{2C}\Delta nsin(2\alpha), \tag{1}\]  
\[\sigma_{11}-\sigma_{22}=\frac{1}{C}\Delta ncos(2\alpha), \tag{2}\]  
where \(C\) is the stress-optic coefficient that is independent of molecular weight, polydispersity, and also is independent of polymer concentration over a wide range of compositions [83; 84].
# A Head Control Strategy of the Snake Robot Based on Segmented Kinematics
###### Abstract  
Head control is important for snake robots to work in an unknown environment. However, the existing methods of head control have certain application limitations for snake robots with different configurations. Thus, a strategy for head control based on segmented kinematics is proposed. Compared with the existing head control strategies, our strategy can adapt to different structures of snake robots, whether wheeled or non-wheeled. In addition, our strategy can realize the accurate manipulation of the snake robot head. The robot body is divided into the base part, neck part and head part. First, parameters of backbone curve are optimized for enlarging the area of the support polygon. Then the desired pose for the head link and the dexterous workspace of the head part can in turn derive the desired position and direction of the end frame for the neck part. An optimization algorithm is proposed to help the end frame of the neck part approach a desired one and obtains the joint angles of the neck part. When the actual frames of the neck part are determined, the dexterous workspace of the head part will cover the desired pose of the head link. Then the TRAC-IK inverse kinematics algorithm is adopted to solve the joint angles of the head part. To avoid the collision between the body and the ground, a trajectory planning method of the overall body in Cartesian space is proposed. Finally, simulations validate the effectiveness of the control strategy.  
head control; discretization; optimization; workspace; inverse kinematics +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion  
### Consistency Test Results  
The addition of nanomaterials notably affects the consistency properties of asphalt binders, as evidenced in Figures 3 and 4. The penetration values decrease consistently with the addition of nanomaterials; an 8% NT content correlates with an approximate 18% reduction in penetration relative to the neat asphalt cement, while NA and NS lead to 26% and 41% reductions, respectively. The significant impact of NS may be attributed to its high specific surface area, ranging from 190 to 250 m\({}^{2}\)/g. Also, the SEM images reveal that the densely clustered particles of SiO\({}_{2}\) offer a substantial interface for interactions with the asphalt binder, significantly enhancing the binder's stiffness.  
In contrast, the softening points, as illustrated in Figure 4, exhibit an increase, suggesting improved resistance to high temperatures. The softening point for NT rises by approximately 6.8%, that for NA rises by 9.8%, and that for NS rises by an impressive 12.1% at the highest nanomaterial content compared to the neat asphalt cement.  
These trends are corroborated by the Penetration Index (PI) values listed in Table 5, where the PI decreases as the nanomaterial content increases. This decrease in the PI indicates a higher stiffness and reduced temperature susceptibility for the nano-modified binders, marking a considerable enhancement in their thermal and mechanical performance. The softening point increase and penetration decrease collectively suggest a considerable  
Figure 3: Effect of nanomaterials on penetration values.  
improvement in the asphalt binder's ability to maintain structural integrity under varying temperature conditions, solidifying the nanomaterials' role in the evolution of asphalt pavement engineering.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 2 Fault Factors and Indicators of Wheel Hub Bearings
### Fault Indicators of Wheel Hub Bearings  
The vibration of wheel hub bearings mainly has three types: (1) the vibration caused by the structural characteristics of the bearing itself under normal operation; (2) the vibration generated when the bearing is abnormal due to malfunction; (3) the noise vibration of the actual working environment.  
Figure 1: Wheel hub bearings: (a) wheel hub bearing unit; (b) wheel hub bearing structure.  
The first vibration is mainly determined by its own structure, and the second vibration reflects the fault form and damage degree of the wheel hub bearings. Their frequencies of vibration are given in the following.
# A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)
###### Abstract  
Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.  
forcest model; NDVI; crops; remote sensing; MODIS; weather variables; time series analysis; ARIMAX
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 7 Singular Pulses in the Spectral Domain  
### The ideal Nyquist-Sinc Pulse  
One of the interesting and important pulses, which belongs to this category and received a lot of attention recently in the literature, is the Nyquist-Sinc pulse (see, for example, Ref. [26; 27; 28; 29; 30; 31]).  
These pulses are useful in optical communications for several reasons, one of which is that they are resilient against chromatic dispersion. Moreover, these pulses, like their Fourier counterpart, the rectangular pulses, are orthogonal both in the time and in the frequency domains.  
The initial Nyquist-Sinc pulse is singular in the spectral domain; its Fourier transform is:  
\[A(z=0,\omega)=\theta A_{0}rect_{1}(\omega\theta/2\pi) \tag{78}\]  
In the temporal regime the \"sinc\" profile appears:  
\[A(z=0,t)=A_{0}\text{sinc}\bigg{(}\frac{t}{\theta}\bigg{)}\equiv A_{0}\frac{ \sin(\pi t/\theta)}{\pi t/\theta} \tag{79}\]  
Since the pulse's spectrum has a simple rectangular shape, the temporal dynamics is straightforward, namely:  
\[A(z,t)=A_{0}\text{d}\text{sinc}(\tau,\zeta)\equiv\] \[\frac{A_{0}}{2}\sqrt{\frac{i\theta^{2}}{2\pi\beta_{2}z}}\exp \left(-i\frac{t^{2}}{2\beta_{2}z}\right)\left[\text{erf}\left(-\frac{t-|\pi \beta_{2}z/\theta|}{\sqrt{2\beta_{2}z}}\right)-\text{erf}\left(-\frac{t+|\pi \beta_{2}z/\theta|}{\sqrt{2\beta_{2}z}}\right)\right] \tag{80}\]  
where  
\[\text{d}\text{sinc}(\tau,\zeta)\equiv\frac{1}{2}\sqrt{\frac{i}{2\pi\zeta}} \exp\left(-i\frac{\tau^{2}}{2\zeta}\right)\left[\text{erf}\left(-\frac{\tau- \pi|\zeta|}{\sqrt{12\zeta}}\right)-\text{erf}\left(-\frac{\tau+\pi|\zeta|}{ \sqrt{12\zeta}}\right)\right] \tag{81}\]  
is the \"dynamic\" sinc function [12]. Clearly, \(\text{d}\text{sinc}(\tau,\zeta=0)=\text{sinc}(\tau)\equiv\frac{\sin(\pi\tau) }{\pi\tau}\).  
In Figure 22, the amplitude profile of this pulse is presented.
## 3 Results and Discussion  
### XRD Analysis and Mineralogy Composition  
The X-ray diffraction patterns of the clays in the study are depicted in Figure 1. In addition, Table 1 presents their semiquantitative mineralogical compositions. C1, C3 and C4 clays are mainly composed of palygorskite with characteristic diffraction lines at 20 = 8.42\({}^{\circ}\), 16.42\({}^{\circ}\), 21.44\({}^{\circ}\) 27.72\({}^{\circ}\) and 61.52\({}^{\circ}\) (JCPDS file 20-0688). In addition, the characteristic diffraction lines for illite at 20 = 17.4\({}^{\circ}\), 19.74\({}^{\circ}\) and 34.46\({}^{\circ}\) (JCPDS file 31-0968), kaolinite at 20 = 20.1\({}^{\circ}\) (JCPDS file 29-1488), quartz at 20 = 26.6\({}^{\circ}\) (JCPDS file 87-2096) and that characteristic for chlorite at 20 = 13.58\({}^{\circ}\) (JCPDS file 03-0072) were also observed. In the case of C4 clay, calcite was also detected at a relatively high percentage (22%) of diffraction lines at 20 = 23.04\({}^{\circ}\), 35.94\({}^{\circ}\) and 48.52\({}^{\circ}\) (JCPDS file 83-178). However, in the case of C2 clay, the X-ray analysis (Table 1) shows that the clay is mainly composed of montmorillonite (73%) of one main characteristic diffraction line at 20 = 5.78\({}^{\circ}\) (JCPDS file 03-0009) (Figure 1). In addition to montmorillonite the following minor phases: calcite, quartz, illite, kaolinite and palygorskite were also observed in the diffraction pattern of C2 clay. On the other hand, C5 clay is mainly composed of both calcite (39%) and palygorskite (27%). Furthermore, a pronounced broad peak can be observed at 20 = 10.92\({}^{\circ}\) in the diffraction patterns of C1, C4 and C5 clays, which can be attributed to Al\({}_{2}\)Si\({}_{50}\)O\({}_{103}\) phase (JCPDS file 46-0750).  
Table 2 presents the chemical analysis using ATEM of polygorskite crystals in C1, C3, C4 and C5 clays and montmorillonite crystals in C2 clay, as dominant phyllosilicate minerals existed in these clays. From the comparison between the C1, C3, C4 and C5 clays in terms of palygorskite chemical composition, it can be observed that C4 clay has the highest amounts of Fe and Mg and the lowest amounts of Al. In addition, C1 and C3 do not show significant differences between the amounts of Fe, Mg and Al.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Clay** & **Montmorillonite** & **Chlorite** & **Palygorskite** & **Kaolinite** & **Illite** & **Calcite** & **Quartz** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline C1 & 0 & 1 & 61 & 9 & 28 & 0 & 1 \\ C2 & 73 & 0 & 9 & 6 & 7 & 4 & 1 \\ C3 & 6 & 1 & 57 & 9 & 20 & 6 & 1 \\ C4 & 2 & 5 & 54 & 7 & 9 & 22 & 1 \\ C5 & 10 & 0 & 27 & 10 & 3 & 39 & 11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mineralogy composition of the in the study clays from X-ray powder diffraction (XRD) analysis.  
Figure 1: Powder X-ray patterns of in the study clays. Minerals denoted as (\(\bullet\)) montmorillonite, (\(\odot\)) palygorskite, (+) calcite, (\(\blacksquare\)) quartz, (\(\diamond\)) illite, (\(\square\)) kaolinite, (\(\ddagger\)) chlorite and (\({}^{\star}\)) Al\({}_{2}\)Si\({}_{50}\)O\({}_{103}\).
## 5 Conclusions  
In order to get rid of manual intervention in hole depth measurement and realize on-machine automatic measurement in the automatic drilling and riveeting process of largescale composite board parts, a novel hole depth measuring device was designed and developed to achieve accurate and efficient automatic measurement of hole depth parameters. The mechanical structure of the measuring device was developed according to our newly designed measurement scheme and optical path. The structure consists of three parts, namely a probe, an inner casing and an outer casing, the purpose of which is to convert the hole depth data into displacement data of probe motion. The electrical hardware of the device consists of three units: a laser transceiver unit, a displacement measuring unit, and a driving unit. Among them, the laser transceiver unit involves optical path design and image processing; the displacement measuring unit involves the data communication between capacitive grating displacement sensor and the host computer; the driving unit relates to the motion control of the probe. Finally, the experimental results indicated that the proposed measurement method and device can accurately measure the hole depth parameters, and its accuracy and stability are sufficient to meet the hole depth measuring requirements for automatic drilling and riveeting of largescale composite board parts. In particular, the influence of probe feed speed and white pixel threshold on the accuracy and stability of the measuring device was explored, which lays the foundation for subsequent research of an error compensation algorithm and a probe calibration method.  
All works with relation to this paper has been accomplished by all authors' efforts. Conceptualization, X.W. and Y.T.; methodology, Y.T.; software, N.W.; validation, Z.H.; formal analysis, N.W.; investigation, W.Z.; resources, X.W.; data curation, X.W.; writing--original draft preparation, N.W.; writing--review and editing, Y.T. and N.W; visualization, Z.H.; supervision, W.Z.; project administration, W.Z.; funding acquisition, Y.T. and W.Z.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 51505310, 51435011), the Key Research & Development Program of Sichuan Province of China (Grant No. 2018 GZ0282) and the Science & Technology Ministry Innovation Method Program of China (Grant No. 2017 IM040100).  
The authors declare no conflict of interest.
## 3 System Configuration
### System Configuration--Testbed Description at Sii  
The main goal was to use the robot arm to work so that it would select the appropriate colors suggested by the user. The verification of the robot's work was to make sure there is an appropriate response to the recognized color. A block diagram is proposed below (Figure 1).  
It was assumed that the RealSense Camera would be mounted at a specific point in the station and feed the image directly to the NPU module. The color detected by the NPU module is implemented by the program in the PLC. The authors proposed a simple solution that depends on color saturation by programming the robot arm based on the data read from the comparison contact instruction. The IRB1200 robot had to mark the corresponding symbol on the colors with a marker. In the first stage of work described in the schedule, configuration of the controller with the module at the University was performed (to verify  
Figure 1: The implementation system configuration diagram.  
data exchange step at the level of the camera, NPU module and controller). Meanwhile, the second stage was to build a control system based on the following components (Figure 2):  
* S7-1512C controller with implemented instructions needed to read data from the camera connected directly to the NPU module.
* NPU module (The manufacturer states that the chip contains 16 low-power programmable SHAVE (Streaming Hybrid Architecture Vector Engine) cores and is equipped with a dedicated hardware accelerator for overbuilding deep neural network structures Intel Movidius Myriad(tmX).
* ET200S controller and IO device island IM 151-3 PN ST located at Sii head office (the mentioned components are necessary to control the operation of the robot arm).
* Intel RealSense camera tasked with image reading and communicating with the NPU module via USB 3.1.
* ABB robot arm type IRB-1200, which reacts in a certain way to the detected colour.
* Workspace: the AI model learned the colored square boxes on a white background, so for most effective testing of the network, colors were printed on a white sheet of paper.  
Figure 2: The testbed—an image recognition system based on two Siemens controllers, Intel RealSense camera, NPU module and IRB1200 robot.
## 3 Controller Design
### Internal Model Control  
The internal model control (IMC) is a model-based methodology for controller design comprising the controller itself and a model of the real process. In contrast to other controllers, the feedback signal consists of the difference between the real process and its mathematical model instead of measured or  
Figure 4: Block diagram of the PI controller with dynamic feedforward control.  
estimated states. This special structure, shown in Figure 5, allows for inherent robustness against model uncertainties [18] by forcing the true system dynamics to behave similar to the transfer function included in the parallel model path. Deviations caused by additive input disturbances are usually compensated with high accuracy as long as the plant model is sufficiently close to reality.  
Here, the model of the SOFC system is approximated by the second-order dynamics  
\[G_{\text{S,PT2}}(s)=\frac{4.119\times 10^{5}}{20s^{2}+1.256s+0.01279} \tag{8}\]  
to which an additional low-pass-filter is appended in series according to  
\[F_{\text{PT1}}(s)=\frac{1}{45s+1}\enspace, \tag{9}\]  
with the cut-off frequency \(\omega_{0}=\frac{1}{45}\frac{\text{rad}}{s}\) suppressing noise in the measured power \(P_{\text{el,m}}\). This low-pass filter enhances the feedback signal by suppressing the amplification of noise and step-like changes due to the differentiating components of the inverse model, which otherwise may lead to an impulse-like behavior in the control. As further shown in Figure 5, the control law consists of the inverse model with the approximated system \(G_{\text{S,PT2}}(s)\) including the low-pass filter \(F_{\text{PT1}}(s)\). In addition, a dynamic feedforward control  
\[D_{\text{ff}}(s)=\frac{144s^{4}+196.2s^{3}+65.69s^{2}+1.808s+0.01279}{9.268 \times 10^{8}s^{4}+2.106\times 10^{9}s^{3}+5.19\times 10^{8}s^{2}+2.904 \times 10^{7}s+4.119\times 10^{5}} \tag{10}\]  
was added to the IMC structure. Thus, the controller simply adjusts small differences, which includes a low-pass filtered proportional-differentiating component to compensate the low-pass characteristics of the model and the inverse transfer function.
# Self-Organization When Pedestrians Move in Opposite Directions. Multi-Lane Circular Track Model
## 1 Introduction
Macroscopic or continuum models are partial differential equations (conservation equations), where the dependent variables are the density and local average velocity of individuals [1; 37; 38; 39; 40]. Some works connect microscopic to macroscopic models [41; 42; 43]. Also, network models have been introduced [44] and optimal control theory has been used [45] to study the dynamics of crowds. Assume a corridor or street is crowded with persons walking. Some of the pedestrians are walking in one direction, while the others, in the opposite direction. A self-organizing phenomenon frequently observed is that the individuals segregate into lanes of individuals moving only in one direction [46; 47; 48]. Needless to say, this formation of lanes benefits the whole group, as it results in an easier flow in both directions [49]. Motivated by this phenomenon, we introduce and analyze a new mathematical model. Our work supports the claim that the simple behavior by the individuals of moving out of the way to avoid imminent collisions leads to the self-organization of the system. We describe the model in Section 2. Briefly, individuals move around a multi-lane circular track with the same angular speed. Half of them walk clockwise and the rest, counterclockwise. Each individual remains in its lane unless it collides with an other individual walking in the same lane but in the opposite direction. When such a collision occurs, one of the colliding individuals moves to a neighboring lane. The individual changing lanes is selected randomly. In Section 3, we present numerical simulations. This is not the first article of the author studying this problem. The two-lane version of this model was introduced and studied in [50; 51]. However, in the model introduced and studied here, the number of lanes is not restricted to two, it can have any number of lanes. Going from two to any number of lanes is not a simple extension. As a result, the analysis and methods used here are completely different. In short, this is a novel article and not a mere extension of previous work of the author. Sections 4-8 consist of analysis of the system. In Section 8, we prove that, with probability 1, the system will self-organize, i.e., eventually, all the individuals within each lane move in the same direction. We finish the article with a short discussion in Section 9.
## 2 Materials and Methods
### Characterization  
To evaluate the prepared Bi\({}_{2}\)O\({}_{2}\)Se nanosheets, PbS QDs, and hybrid PbS/Bi\({}_{2}\)O\({}_{2}\)Se heterostructure, atomic force microscopy (AFM) (Innova, Bruker, Billerica, MA, USA), laser micro-Raman spectroscopy (DXR, Thermo Fisher, Waltham, MA, USA), and multi-functional X-ray photoelectron spectroscopy (XPS) (ESCALAB QXi, Thermo) were em ployed. To obtain the energy band alignment information between PbS QDs and Bi\({}_{2}\)O\({}_{2}\)Se, ultraviolet photoelectron spectroscopy (UPS) (ESCALAB 250 XI, Thermo) measurements were conducted. To investigate the photodetection performance of Bi\({}_{2}\)O\({}_{2}\)Se photodetectors and hybrid photodetectors, we used a photodetection testing platform to test the devices. The photodetection testing platform includes a xenon lamp light source (GLORIA X500A, Zolix, Beijing, China), a probe station (DCH-E2, Metast Corporation, Nanjing, China), an optical system (Omni-\(\lambda\)-i, Zolix), and a source meter (MODELS 2614B, Keythley, Cleveland, OH, USA).