esa-sceva/satcom-chunk-collection huggingface dataset:
In this paper, based on our previous real-time CDM field trial utilizing 400 Gb/s OTN transceivers [25], we demonstrate the first real-time single-span 106 km CDM field trial using commercial 130 Gbaud 800 Gb/s OTN transceivers over deployed 4-core MCF cable. The 800 Gb/s OTN transceivers adopted a highly noise-tolerant probability constellation shaping 16-array quadrature amplitude modulation (PCS-16 QAM) modulation format, which was enabled by the high modulation rate. The adopted modulation format had a greater tolerance to the impacts of span loss compared to previous higher-order formats such as PCS-64 QAM and thus enabled the unrepeated long-span transmission. Experimental results show that the 4-core 800 Gb/s CDM transmission system can achieve a real-time capacity of 256 Tb/s with fully loaded 80-wavelength channels over the C+L band. The performance of different kinds of 800 G OTN transceivers with different modulation formats under this long-span unrepeated optical transmission system are also estimated and discussed. To the best of our knowledge, this is the first real-time unrepeated single-span field trial over more than 100 km MCF cable, and it promotes the field implementation of MCFs with 800 Gb/s OTN transceivers in next-generation high-speed optical interconnection systems. The rest of this paper is organized as follows. In Section 2, we introduce the field-deployed 4-core MCF cable. Section 3 presents the experimental setup of the real-time CDM field trial using 800 Gb/s OTN transceivers over the deployed MCF cable. In Section 4, the experimental results of the field trial are presented. More in-depth discussions and prospects regarding the 800 Gb/s OTN transceivers and the 4-core MCF cable are provided in Section 5. Finally, Section 6 concludes the paper.
## 2 Blue Shift in HHG
### Nonadiabatic Blue Shift in HHG  
Rapid change of a laser pulse shape will lead to a chirp of HHG. The electronic dipole response to the external laser field is nonadiabatic. This effect is intuitive in the above three-step model. As shown in Figure 1, the laser intensity is rising in the first half of the pulse. The effective amplitude of the successive laser cycle is higher than the previous one. The electron gains more energy from the latter laser field compared to the previous cycle. In other words, the rising laser envelope (RLE) with \(t<0\) induces a positive-chirp role in HHG, thus leading to a blue shift in HHG [16]. In contrast, a negative chirp of HHG occurs on the falling laser envelope (FLE) with \(t>0\). This causes a red shift of HHG. However, no net shift of HHG is observed with low laser intensity since the blue shift generated on the RLE and the red shift produced on the FLE will result in broadening of the harmonics, but no net change in the central frequency. When the laser intensity is above the ionization saturation threshold, the depletion of the ground state has to be included. Especially for long intense laser pulses, the sample is highly ionized on the RLE, little HHG is emitted on the FLE. This imbalance of HHG will lead to a net blue shift in HHG. This has been experimentally confirmed in [12, 19].
## 4 Vehicular Ad Hoc Networks
### Routing
#### 4.2.5 Communication Environment  
In this network, the communication environment for vehicles is limited to a road infrastructure on highways or urban area. The movement pattern on highways is different from that in urban environments. Highway environments include a long straight line in which vehicles are moving at high-speed. In contrast, urban environments include a large number of streets, intersections, and obstacles in which vehicles are moving at medium speed. These environments have different effects on vehicle-to-vehicles communication [31].
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 9 Ultrasonic Method
### Introduction and Principle of the Application
#### 9.1.4 Composite Structures  
The air-coupled magnetostrictive ultrasonic method has been used for non-contact NDT methods for composite structures. In the experiment, four types of titanium horns were manufactured and analysed for optimal horn configuration. It was observed that there was a good correlation between the experimental and theoretical model regarding the dependence of vibration amplitude on the unit power of the ultrasonic horns [127]. Additionally, an ultrasonic non-destructive method has been successfully used to evaluate porosity in reinforced metal-matrix composites [128].
# The Impact of Cell Phone Usage on Fall Risk and Postural Control During Free-Standing
## 3 Results
### Limb Involvement in Holding the Phone  
The analysis of phone usage in different conditions and activities revealed distinct participant preferences, with variations in limb involvement that may influence postural control and balance.  
In static conditions, when using the phone passively, most participants (78.79%) held the device with one hand, while only 21.21% opted for a two-handed grip. However, when texting, this trend reversed: 90.91% of participants used both hands, and only 9.09% typed with one hand, consistently using the upper right limb.  
In dynamic conditions, a similar pattern emerged. During passive phone use, 72.73% of participants held the phone with one hand (typically the right hand), while 27.27% used both hands. When texting, 93.94% of participants used two hands, with only 6.06% relying on one-handed typing.  
It is important to note that all participants identified their right hand as dominant, which likely explains the preference for the right hand in one-handed use. This finding is particularly relevant to postural control, as asymmetric phone-holding habits may contribute to lateral weight shifts, altering balance stability. The restriction of free arm movement, especially during bimanual phone use, can further impact postural adjustments, limiting the body's ability to counterbalance shifts in the center of gravity.
# Terahertz Spiral Spatial Filtering Imaging
## 3 Simulations  
To validate the effectiveness of the proposed THz SSF imaging system, we first imaged the simulated circular objects with different image contrasts, as shown in Figure 2. The corresponding Fourier spectra were calculated by performing the Fourier transform, then, these were multiplied by the modulation function of the SPP, and the inverse Fourier transform of the product is the desired THz SSF imaging result. Two circles with the same radius, same transmittance of 100% and different background transmittances (0 and 90%) were simulated and are shown in Figure 2a,c, respectively. Define \(C=(I_{\text{max}}-I_{\text{min}})/(I_{\text{max}}+I_{\text{min}})\) as the image contrast, where \(I_{\text{max}}\) and \(I_{\text{min}}\) are the maximum and minimum of the image. Thus, the image contrasts of the two simulated circular targets are 100 and 5.26%, respectively. Figure 2b,d shows the THz SSF images of the two simulated circular targets. It is clearly shown that the energy concentrates at the edges and the energy levels inside the circles are suppressed. Both image contrasts of Figure 2b,d can be calculated as 100%. To compare the simulation results quantitatively, we marked the intensity distributions at same position in Figure 2 with the blue, green and red dashed lines. The corresponding normalized intensity distributions are shown in Figure 2e. Notably, the normalized intensity distributions for two circular objects are consistent, indicating that the resultant image contrasts after THz SSF imaging are consistent for the same targets with different image contrasts. Therefore, high-contrast targets can be used in experiments to verify the THz SSF imaging system. Meanwhile, the peaks of the THz SSF edge-enhanced images are located precisely on the edges of the circular objects, illustrating that the THz SSF imaging system has potential advantages in terms of edge detection.  
Figure 2: Cont.  
Next, as shown in Figure 3, the effectiveness of the proposed THz SSF imaging system is further confirmed using the simulated letters object of \"THz\" with a transmittance of 100%, various backgrounds, and more edges than the circular objects (see Figure 3a,c,e). In order to simulate different image contrasts, the transmittances of backgrounds in Figure 3a,c,e are set as 30, 60, and 90%, respectively. Thus, the corresponding image contrasts are calculated as 53.85 (Figure 3a), 25 (Figure 3c), and 5.26% (Figure 3e), respectively. Figure 3b,d,f shows the simulated THz SSF imaging results corresponding to Figure 3a,c,e, respectively. Notably, the simulated imaging results exhibit a significant contrast enhancement (increasing to 100%), and the edges can be extracted perfectly.  
Figure 3: _Cont._  
Figure 2: THz spiral spatial filtering imaging results of the simulated circular objects. (**a,c**) The circular objects with different image contrasts of 100 and 5.26%. (**b,d**) The corresponding edge detection results of the outline profiles. (**e**) The intensity distributions through the object/edge-enhanced images (blue, green, and red dashed lines).
# Dynamics of Polar Resonances and Their Effects on Kozai-Lidov Mechanism
## 4 Dynamics of Interior Polar Resonance, the \(2/1\) Case
### Kozai-Lidov Dynamics inside Polar 2/1 Resonance
The lifetime of \(5000\) particles is depicted in the left panel of Figure 6. We have checked that all of the particles are not in \(2/1\) resonance with perturber. Similarly, the Hamiltonian can be calculated by numerical averaging all the fast angles \(\lambda\) and \(\lambda^{\prime}\) as follows:  
\[\overline{\mathcal{H}}_{\text{pure}}=-\frac{\mu}{4\pi^{2}k}\int_{0}^{2\pi} \int_{0}^{2\pi}\left(\tfrac{1}{|\mathbf{r}-\mathbf{r}^{\prime}|}-\tfrac{\mathbf{r}\mathbf{r}^{ \prime}}{|\mathbf{r}^{\prime}|^{3}}\right)\bigg{|}_{a=a_{0}+0.05}\text{d}\lambda^ {\prime}\text{d}\lambda. \tag{7}\]  
The pure secular dynamics in the \(e-\omega\) portrait are shown in the right panel in Figure 6. The dynamics illustrated by the two panels of Figure 6 are in good agreement. Except for the regions within the collision curve, the eccentricity goes up to 1 and dies out in long-term evolution, entirely different from secular dynamics inside polar \(2/1\) resonance. Comparing the results of Figures 1, 5, and 6, it is clear that the influence of polar \(2/1\) resonance on secular dynamics is not negligible. Figure 5: Phase-space portraits of Kozai–Lidov dynamics inside the polar \(2/1\) resonance. The levels of Hamiltonian in Equation (5) or (6) are plotted on an \(e-\omega\) plane. The panels (**a**–**c**) represent three different cases (\(\sigma_{\text{amp}}=0^{\circ},60^{\circ},150^{\circ}\)). The thick colored solid lines in panel a represent the several representative results of numerical integrations. The solid black dots mark the positions of initial parameters and the arrows indicate the evolution direction of the dying particles. So far, taking the classical \(2/1\) resonance as an example, we have systematically studied the polar interior resonance in the model of CRTBP. As a supplement, we analyze the pure secular dynamics of polar orbits outside the \(2/1\) resonance and compare them with the resonant secular dynamics. The importance of polar \(2/1\) resonance to the long-term dynamics is demonstrated.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 4 Real-Time Experiments and Results
### Implemented Scenario
In this figure, it is shown that HackRF One observed the transmission made by \(PU_{2}\), and the RTL-SDRs, \(SU_{2}\), and \(SU_{3}\) observed the transmission made by \(PU_{1}\).
## 1 Introduction
The LUT strategy is prescribed for the handling of complex models [46] and is effective for regional mapping of vegetation parameters [49]. Among the existing RTMs, PROSAIL [50; 51; 52; 53] is the most popular model which is used for short vegetation canopies due to its feasibility and strength [54; 51]. The PROSAILmodel has been mainly used along with hyperspectral measurements for estimation of LAI in studies of non-woody vegetation canopies such as croplands [19; 55; 56; 57; 58; 59; 60] and grasslands [27; 30; 61; 62]. Nevertheless, the model has been occasionally used in combination with broadband multispectral satellite data such as from ALOS AVNIR-2 [63], Landsat [64; 65; 66], SPOT [67; 68] and ASTER [69], primarily for crops. Concomitant with the progress in the development of Sentinel-2, several studies sought to examine the potential of the sensor, by simulating its data for LAI estimation of crops using inversion of the PROSAIL model [70; 71; 72; 73; 74; 75; 76]. However, after Sentinel-2's launch, only a few studies have examined the performance of its spectral data for LAI estimation of potato and rice crops [77; 78]. Wetlands are among the most diverse natural ecosystems. According to the Ramsar Secretariat, about 40% of wetlands have been lost over the last 40 years [79]. Monitoring essential biodiversity variables (EBVs) such as LAI in these areas are hence crucial for their conservation and proper use which is the common agenda of the Ramsar convention to halt biodiversity loss [79]. The feasibility of mapping and continual monitoring of some wetlands' EBVs such as phenology, biomass, leaf dry mass, species composition and abundance has been studied from remote-sensing data [80; 81; 82; 83; 32]. At present, little is known about the performance of broadband satellite data for retrieval of LAI in natural grasslands such as those are in saltmarsh/wetlands with diverse species compositions. This study aimed to examine the retrieval of LAI in a wetland ecosystem using new multispectral satellite data and RTM inversion. More specifically, the study compared the utility and performance of Sentinel-2 and RapidEye spectral data for LAI retrieval through inversion of PROSAIL for the Dutch barrier island of Schiermonnikoog. The study further elaborated on the potential and sensitivity of Sentinel-2 broad red edge, NIR and SWIR spectral bands for LAI estimation via model inversion. Our study benefited from field measurements of LAI and other plant traits that were obtained in two consecutive field campaigns (i.e., 2015 and 2016) in the context of a European Space Agency funded Innovators-III project on \"remote sensing for essential biodiversity variables\" (RS4 EBV).
## 2 The Applications in Smart Grid Based on Time Synchronization
### Differential Protection  
The fundamental principle of current differential protection is based on Kirchhoff's Current Law (KCL), which states that the total current entering a junction equals the total current leaving it [27]. During a fault condition, the currents will become unbalanced, resulting in a non-zero sum, which serves as the criterion for tripping [28].  
The accurate operation of differential protection relies on precise time synchronization. Insufficient synchronization accuracy can cause phase angle shifts in the phasors, leading to increased differential currents and potential protection malfunctions. For instance, a time error of approximately 100 \(\upmu\)s results in a phase error of around 2 degrees [28].  
Therefore, to meet these requirements, time synchronization accuracy in the Process Bus must remain within a few microseconds. According to IEC 61850-5 [12], the merging unit must achieve \(\pm\)4 \(\upmu\)s accuracy. IEC 61850-9-2 LE [29] specifies that communication network delays should not exceed 2 \(\upmu\)s [30]. Sampled Values are expected to maintain a timing accuracy of better than 1 \(\upmu\)s [31].
# The First Stages of Chemical and Electrochemical Aniline Oxidation--Spectroscopic Comparative Study
## 3 Results
### FTIR Kinetic Study of the Chemical Oxidation of Aniline  
To study the chemical oxidation of aniline in detail, the molecular structure of the products was followed in situ by ATR-FTIR spectroscopy. The spectrum of the reaction mixture, measured before any changes could be observed (i.e., the spectrum of reactants), was subtracted from all spectra. The differential spectra are presented in the Figures 2 and 3; complete band assignment is summarized in Table 1.  
In the neutral medium (pH 7), the oxidation of aniline starts immediately after the addition of the APS solution to the aniline solution. The main bands observed at 1509 cm\({}^{-1}\) and 1586 cm\({}^{-1}\) are assigned to the stretching vibration of the -C=C- bond of quinonoid and benzenoid rings, respectively. The intensity of the band connected with the quinonoid ring is significantly lower than the intensity of the benzenoid band. Two bands typical for nanostructured PANI material are observed at 1445 cm\({}^{-1}\) (skeletal vibration of the substituted aromatic ring) and 1415 cm\({}^{-1}\) (phenazine-like ring stretching vibrations). The band at 1707 cm\({}^{-1}\) observed in several of the first spectra corresponds to the stretching vibration of the carbonyl group on the quinone ring. The intensity of the band decreases during the proceeding reaction. The band assigned to the C-N bond stretching next to the aromatic ring at 1365 cm\({}^{-1}\) is shifted from the typical position at 1374 cm\({}^{-1}\) expected in PANI. This can be attributed to the 1,2,4-substituted ring formation instead of the typical 1,4-substituted ring. The higher intensity of the  
Figure 1: Open circuit potential (OCP) (**a**), pH (**b**) and temperature (**c**) profile of the chemical oxidations of aniline at starting pH 1 and 7.  
band at 861 cm\({}^{-1}\) (out-of-plane C--H bending vibration on the aromatic ring), together with the band at 846 cm\({}^{-1}\), signifies the higher fraction of the tri-substituted aromatic rings in the molecular structure. As the oxidation of aniline proceeds, the position of the bands remains practically unchanged, and the only difference between bands in the first spectrum and the spectrum measured after 15 min of the reaction is their intensity. The evolution of spectra measured during aniline oxidation in sulfuric acid (pH 1) is significantly different (Figure 3, Table 2). The intensive bands appearing earliest in the differential spectrum
## 3 Results  
### Model Results Validation  
To verify the applicability of the PT-JPL model in the Yiluo River Basin, the actual evapotranspiration from 2001 to 2016 in the basin was estimated based on the water balance equation using natural runoff data and precipitation data from the Heishiguan hydrological station of the Third National Water Resources Survey. The comparison results of precipitation, runoff depth, evapotranspiration calculated by water balance (WB_ETa) and evapotranspiration calculated by the PT-JPL model (PT-JPL_ETa) for each year in the basin are shown in Figure 2. The multiyear average precipitation was 684.7 mm, the multiyear average runoff depth was 141.4 mm, and the multiyear average WB_Eta was 543.3 mm. The multiyear average PT-JPL_ETa was 523.8 mm in the same period. Compared with the multiyear WB_ETa, the result was small, with an absolute error of 19.5 mm and a relative error of 3.6%. Referring to relevant studies [41], we believe that the accuracy of the PT-JPL model meets the requirements and can be used to analyze and explore the temporal and spatial characteristics of evapotranspiration in the Yiluo River Basin.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 3 Preliminary
### Attribute-Based Encryption Scheme
#### 3.1.4. \(\mathrm{ABE.DEC}\)  
\(\mathrm{ABE.DEC}(CM,\,D,\,ABE.params)\) performs the decryption of the cipher text \(CM\), as long as the attributes set \(\omega\) fulfills the access tree \(\Gamma\), by using \(\mathrm{NodeKey}(CM,\,D,\,node)\) for every node within the access tree recursively. In this ABE scheme [46], secret sharing based on Lagrange interpolation is borrowed to recover the decryption key.  
* For every leaf node linked to an attribute \(i\), \(\mathrm{NodeKey}(CM,\,D,\,leaf_{f})\) is computed as follows. Note that we represent \(\mathrm{NodeKey}(CM,\,D,\,leaf_{f})\) into \(N_{1}\) in the next paragraph for convenience. 1. In case the associated attribute \(i\) to \(leaf_{f}\) is not comprised in \(\omega\), then \(\mathrm{NodeKey}(CM,\,D,\,leaf_{i})\) = \(\bot\). 2. else, \[\mathrm{N_{1}} = D_{leaf_{f}}\cdot W_{i}\] \[= q_{leaf_{f}}(0)\cdot t_{i}^{-1}\cdot k\cdot P_{i}\] \[= q_{leaf_{f}}(0)\cdot t_{i}^{-1}\cdot k\cdot l_{i}\cdot P\] \[= q_{leaf_{f}}(0)\cdot k\cdot P\]
* To proceed with a non-leaf node \(u\), the algorithm calls \(\mathrm{NodeKey}(CM,\,D,\,z)\) for all children \(z\) which are attached to the node \(u\). 1. Suppose that \(\omega_{u}\) is an arbitrary \(d_{u}\) set of children nodes satisfying \(\mathrm{NodeKey}(CM,\,D,\,z)\)\(\
eq\bot\). In case no such set is existent, \(\mathrm{NodeKey}(CM,\,D,\,u)\) output \(\bot\). We use \(N\) to denote \(\mathrm{NodeKey}(CM,\,D,\,u)\) in the next paragraph for paper formatting. 2. Else, let \(\Delta_{index(z)\omega_{u}^{i}}=\prod_{j\in\omega_{u,j}^{i}\
eq index(z)} \frac{x-j}{i-j}\) represent the Lagrange coefficient with \(\omega_{u}^{\prime}=\{index(z)\mid z\in\omega_{u}\}\), \[\mathrm{N} = \sum_{z\in\omega_{u}}\Delta_{index(z)\omega_{u}^{i}}(0)\cdot \mathrm{NodeKey}(CM,D,z)\] \[= \sum_{z\in\omega_{u}}\Delta_{index(z)\omega_{u}^{i}}(0)\cdot q_{ z}(0)\cdot k\cdot P\] \[= \sum_{z\in\omega_{u}}\Delta_{index(z)\omega_{u}^{i}}(0)\cdot q_{ parent}(index(z))\cdot k\cdot P\] \[= \sum_{z\in\omega_{u}}\Delta_{index(z)\omega_{u}^{i}}(0)\cdot q_{ u}(index(z))\cdot k\cdot P\] \[= q_{u}(0)\cdot k\cdot P\]
* Compute the decryption key \(K\) = \(\mathrm{NodeKey}(CM,\,D,\,root)\) = \(q_{root}(0)\cdot k\cdot P=s\cdot k\cdot P\).
* Output the decrypted message \(m=Dec_{K}(C)\).
## 2 Materials and Methods
### Interference Fringes in FTIR Transmission Spectra  
A common, and normally undesirable, artifact of transmission spectroscopy, when applied to thin samples, is the inclusion of interference fringes within the transmission data. Whenever a sample consists of two planar partially-reflective parallel interfaces and the face separation is not insignificant, interference fringes will occur [27]. The phenomenon which gives rise to these interference fringes is the internal reflection, as depicted in Figure 1.  
The thin sample forms a simple Fabry-Perot etalon, creating an optical resonator that allows the source light to constructively and destructively interfere with itself. An important parameter when analyzing a Fabry-Perot etalon is the so-called \"finese\", which determines the fringe contrast of the system and is given as [30]  
\[F=\frac{4|r|^{2}}{\left(1-\left|r\right|^{2}\right)^{2}} \tag{5}\]  
where \(\left|r\right|^{2}\) is the surface reflectance (for normally incident light) which is defined as  
\[\left|r\right|^{2}=\left(\frac{n(\
u)\ -\ 1}{n(\
u)+1}\right)^{2} \tag{6}\]  
High-finesse systems will have correspondingly high fringe contrast, which increases the likelihood that FTIR spectrometers with large spectral bandwidths will resolve them. Great effort, which includes extremely small step sizes and long integration times, is typically needed for a dispersive spectrometer to do the same.  
The frequency of the interference fringes is proportional to the thickness of the sample; a thick sample will produce very high frequency fringes (small separation), while a very thin sample will produce very low frequency fringes (large separation). The latter case helps to explain why characterizing thin films using FTIR spectroscopy can be very challenging. When the amplitudes of the interference fringes are strong, they can severely obscure the transmission values. Typically, this is why significant effort is made to reduce the impact of any interference fringes.  
If, however, accurate transmission values are not the end-goal of characterization, then the fringes themselves offer valuable information about the sample material. For example, the interference fringe maxima are governed by  
\[\
u_{m}n(\
u_{m})=\frac{m}{2d} \tag{7}\]  
where \(\
u_{m}\) is the wavenumber associated with the fringe of order number \(m\), and \(d\) is the sample thickness [31]. If two of the three aforementioned values are known, then the third can be solved by rearranging Equation (7).