esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison of Structural Analyses
The horizontal elastic design spectrum curves foreseen for some geographical locations may differ according to the previous regulation, as clearly seen from Figure 15.  
It was observed that updated spectrum curves are quite different from the previous spectrum curve. This situation significantly changes the displacement demands. Damage estimates and building \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Target Displacements (m)**} \\ \cline{4-9}
**Province** & **PGA (g)** & **PGV (cm/s)** & \multicolumn{2}{c}{**DL (m)**} & \multicolumn{2}{c}{**SD (m)**} & \multicolumn{2}{c}{**NC (m)**} \\ \cline{4-9}  & & & **3-Story** & **6-Story** & **3-Story** & **6-Story** & **3-Story** & **6-Story** \\ \hline Kocaeli & 0.668 & 55.648 & 0.067 & 0.190 & 0.096 & 0.243 & 0.194 & 0.421 \\ Sakarya & 0.643 & 51.110 & 0.063 & 0.182 & 0.091 & 0.234 & 0.184 & 0.405 \\ Yalova & 0.603 & 42.287 & 0.057 & 0.171 & 0.084 & 0.219 & 0.171 & 0.379 \\ Istanbul & 0.400 & 24.668 & 0.038 & 0.113 & 0.049 & 0.145 & 0.102 & 0.252 \\ Tekirdag & 0.391 & 24.542 & 0.037 & 0.111 & 0.047 & 0.142 & 0.098 & 0.246 \\ Balikesir & 0.372 & 21.807 & 0.035 & 0.105 & 0.045 & 0.135 & 0.097 & 0.234 \\ Bursa & 0.356 & 21.591 & 0.034 & 0.101 & 0.043 & 0.129 & 0.087 & 0.224 \\ Canakkale & 0.300 & 19.510 & 0.028 & 0.085 & 0.036 & 0.109 & 0.067 & 0.189 \\ Bilecik & 0.238 & 15.616 & 0.022 & 0.067 & 0.029 & 0.087 & 0.050 & 0.150 \\ Edirne & 0.180 & 11.663 & 0.017 & 0.051 & 0.022 & 0.065 & 0.038 & 0.113 \\ Kirkareli & 0.165 & 11.085 & 0.016 & 0.047 & 0.020 & 0.060 & 0.035 & 0.104 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparisons of earthquake—structural parameters. Figure 15: Comparison of the previous and updated horizontal design spectrum curves for the provinces with the highest and lowest PGA values. performance will diverge from real values in structures whose displacement demands are not met. The comparison of target displacements for damage estimation values obtained via the design spectrum for TSDC-2007 for 3- and 6-story RC buildings with the values obtained for the updated regulation is shown in Table 11.
## 3 Brillouin Light Scattering  
Brillouin light scattering in the bulk of transparent media or at the surface of opaque media is an established technique to probe acoustic phonons. [PERSON] [4] reviewed experimental methods to achieve the elastic characterization of thin films, multilayers, and acoustic resonators operating in the GHz range of frequencies, with a particular stress on surface Brillouin Light Scattering (surf-BLS). Interestingly, the recently-developed micro-focused scanning version of the surf-BLS technique opened new opportunities for the imaging of acoustic fields in resonators and in artificially-patterned acoustic metamaterials.  
In optical fibers and waveguides, much progress has been made recently toward nanoscale diameters where light and sound are simultaneously strongly confined. Brillouin light scattering experiments either rely on the diffraction of light waves by random acoustic phonons, originating from thermal fluctuations, or by coherent acoustic waves generated by a transducer or from the mixing of two optical waves. In both cases, it is usually found that the measured spectra are similar. [PERSON] and [PERSON] [5] examined the origin of the similarity by discussing the structure of the elastodynamic equation. They argued that the spectral response was determined by pre-existing phonon solutions, having given intrinsic loss, under either a stochastic Langevin excitation or in response to a coherent optical mixing force.  
conceptualization, V.L., J.-C.B. and T.S.; writing-original draft preparation, V.L.; writing-review and editing, J.-C.B. and T.S.  
This research received no external funding.  
The Special Issue on Brillouin scattering and optomechanics would not be possible without the contributions of various talented authors, hardworking and professional reviewers, and the dedicated editorial team of Applied Sciences. We express our gratitude to the editorial team of Applied Sciences and our special thanks to [PERSON].  
The authors declare no conflict of interest.
In order to improve the system convergence rate, the variable structure for the proportional and differential term was designed, and an angular velocity curve with a better convergence rate was achieved by this variable structure. In addition, an integral partitioning algorithm was designed, and the system robustness to disturbance torque was improved; meanwhile, the negative effect of the integral term was avoided during the converging process. The work [13] investigates the issues of an iterative learning algorithm-based robust thruster fault reconstruction and reconfigurable fault-tolerant control for spacecraft formation flying systems subject to space perturbations. Motivated by sliding mode methodology, a novel iterative learning observer (ILO) was developed to robustly reconstruct the thruster faults. Based on the fault signals obtained from the ILO, a learning output-feedback fault-tolerant control (LOF\({}^{2}\)TC) approach was explored such that the closed-loop spacecraft formation configuration was accurately maintained in the presence of space perturbations and thruster faults. In [14], to discover the complex interaction between spacecraft telemetry parameters and improve the efficiency and accuracy of anomaly detection, the authors propose an anomaly detection framework based on parametric causality and Double-Criteria Drift Streaming Peaks Over Threshold (DCDSPOT). Normalized Effective Transfer Entropy (NETE) is proposed to reduce the error and noise caused by the nonstationarity of the data in the calculation of transfer entropy, and then apply NETE to improve the Multivariate Effective Source Selection (MESS) causal inference algorithm to infer parametric causality. In [15], a novel fault-tolerant control (FTC) scheme for a category of cascade nonlinear systems with mismatched uncertainties and unknown actuator faults was studied. The robust adaptive dynamic programming (RADP) is used to design an optimal sliding surface off-line. Subsequently, a simple sliding-mode control (SMC) with adaptive fault compensation is developed to guarantee reachability of the sliding mode.
## 4 Experimental Results
### Experimental Results in Scenario 2  
Scenario 2 involves an experimental setup where the leading vehicle is stationary within a tunnel due to traffic congestion, while the middle vehicle approaches, unaware of the stationary condition ahead. In this scenario, the target speed can be computed by using the motor control data from the IMU sensor, and both the target speed and acceleration of the stationary vehicle are zero. In Figure 10, we can observe how the proposed vehicular VLC system transmits the driving dynamics data (e.g., target speed and acceleration in this experiment) between vehicles in the tunnel. At the beginning of the experiment, the middle vehicle is outside the communication range of the stationary leading vehicle, resulting in there being no data reception. Consequently, the middle vehicle continues to transmit its own acceleration data to the trailing vehicle. However, as it enters the communication range and receives data indicating that there is congestion ahead, it compares the received acceleration data with its own (i.e., middle vehicle) acceleration and transmits the lower value to the trailing vehicle. Figure 10b displays the target speed data under the same conditions as in Figure 10a. Initially, when the middle vehicle does not receive data from the stationary leading vehicle, it transmits only its own data. Upon receiving data from the stationary vehicle, the middle vehicle compares both sets of data and transmits the appropriate updated data to the trailing vehicle. Finally, in Figure 10, we compare the data transmitted by the middle vehicle with the data received by the trailing vehicle. This comparison confirms that even when a vehicle traveling at normal target speed encounters an unexpected stationary situation, the proposed vehicular VLC system ensures reliable communication, thereby preventing potential accidents.  
Figure 9: Comparison of the acceleration data among the leading, middle, and trailing vehicles within a tunnel in Scenario 1.
# Study on Magnetic Resonance Characteristics of Coal Sample under Progressive Loads
## 3 Materials and Methods
### Test Method  
The research object of low-field NMR testing method is hydrogen nucleus. To be specific, relatively low magnet strength is used to test the NMR signals of \({}^{1}\)H in the pore fluid, thus obtaining the \(T_{2}\) of the fluid in pores and fissures. According to the theoretical analysis of NMR [33], the \(T_{2}\) can be expressed as  
\[1/T_{2}=\rho\ (S/V)=F_{\rm S}\rho/r,\ r=cT_{2} \tag{1}\]  
where \(\rho\) is the lateral surface relaxation rate, which is a parameter to characterize rock properties; \(S/V\) is the specific surface area; \(F_{\rm S}\) is the porosity factor (spherical pores are 3, tubular pores are 2, and plate pores are 2); \(r\) denotes the pore radius; and \(c\) is a constant, referring to conversion coefficient. Therefore, the conversion relationship between \(T_{2}\) and \(r\) is obtained.  
As can be known from Equation (1), \(r\) is in direct proportion to \(T_{2}\). The longer \(T_{2}\) is, the larger the pore radius is; the larger the spectral peak of \(T_{2}\) is, the more fully the pore develops. Conversely, the larger the pore radius is, the \(T_{2}\) of water in the pores is longer; the smaller the pore radius is, the \(T_{2}\) of water in the pores is shorter since it is more confined [34; 35].  
When measuring \(T_{2}\) with the NMR, the Carr-Purcell-Meiboom-Gill (CPMG) sequence will be used. Including the following specific parameters: the radio frequency (RF) center frequency is 12 MHz, the RF offset frequency is 696604 Hz, the echo time (TE) is 0.16 ms, the waiting time (TW) is 3.5 s, the number of scans accumulated (NS) is 4, and the length of the 90 and 180 degree pulses are 13.00 microseconds and 25.04 microseconds, respectively. Using these parameters, the \(T_{2}\) distribution curve of the sample will be obtained, and the area of the \(T_{2}\) distribution curve is the porosity of the sample [36].  
After setting the NMR parameters, it is necessary to calibrate the relationship between the fixed porosity of the standard sample and the NMR signal amplitude measured under the current environment. Prepare five standard samples with different porosity (1%, 5%, 10%, 20%, and 30%), then test the NMR signal amplitude under the corresponding porosity, and fit the result to a straight line, as shown in Figure 5. The fitting relationship is \(y=1.394x-0.212\), and the correlation coefficient \(R\) reaches 0.999996. Next, the NMR test of the coal sample can obtain the \(T_{2}\) distribution curve of the coal sample. By integrating the \(T_{2}\) distribution curve, the total NMR signal amount of the coal sample can be obtained. From the fitting relationship in Figure 5, calculate the volume of water in the coal sample, and finally the volume of water is divided by the total volume of the coal sample, and the value obtained is the porosity of the coal sample.  
Figure 5: Calibration result fitting diagram of the standard samples.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results
### Influencing Factor
#### 3.2.3 Eccentricity  
Figure 11 illustrates the relationship between cuttings return velocity and maximum bed height at different levels of annular eccentricity. As the eccentricity of the annulus increases, the total migration time of cuttings increases, while the volume of cuttings returned decreases. Specifically, the return velocity of cuttings at an eccentricity of 0.4 is only 34.1% of that observed at an eccentricity of 0.1, and the height of the cuttings bed increases by 98.7%. This indicates that greater eccentricity significantly affects cuttings migration, leading to faster bed formation.  
As the drill pipe's eccentricity increases, the height of the cuttings bed also increases. This is primarily due to the reduction in both the circumferential and axial flow velocities of the drilling fluid within the annulus, caused by the off-center positioning of the drill pipe. The lower fluid velocities reduce the drag force and lifting force acting on the cuttings, which decreases the ability of the fluid to carry the cuttings efficiently, resulting in increased cuttings accumulation.  
Figure 10: Cuttings bed height and return velocity at different rotation speeds.
## 2 Materials and Methods
### PSCDR: A Deep Contrastive Regression Method for Point Cloud Data
#### 2.2.2 Anchor Data and Pair Selection  
For a given point cloud sample set \(\left\{(\mathrm{S}_{\mathrm{i}},\mathrm{l}_{\mathrm{i}})\right\}_{\mathrm{i =0}}^{\mathrm{N}}\), the augmented sample set is \(\left\{\left(\widetilde{\mathrm{S}}_{\mathrm{j}},\widetilde{\mathrm{l}}_{ \mathrm{j}}\right)\right\}_{\mathrm{j=0}}^{\mathrm{2N}}\), where \(\widetilde{\mathrm{S}}_{\mathrm{2k-1}}=\mathrm{e}^{\prime}(\mathrm{S}_{ \mathrm{k}})\) and \(\widetilde{\mathrm{S}}_{\mathrm{2k}}=\mathrm{e}(\mathrm{S}_{\mathrm{k}})\), with \(\mathrm{e}(\cdot)\) and \(\mathrm{e}^{\prime}(\cdot)\) being independent point cloud augmentation operations. For each point cloud set \(\mathrm{S}_{\mathrm{k}}\), after two independent augmentations, \(\widetilde{\mathrm{S}}_{\mathrm{2k}}\) and \(\widetilde{\mathrm{S}}_{\mathrm{2k-1}}\) are derived from \(\mathrm{S}_{\mathrm{k}}\), where \(\mathrm{k}\in[0,\mathrm{N}]\). The corresponding labels for the augmented point cloud sets remain unchanged, meaning \(\widetilde{\mathrm{l}}_{\mathrm{2k}}=\widetilde{\mathrm{l}}_{\mathrm{2k-1}}= \mathrm{l}_{\mathrm{k}}\).  
The two augmented samples, \(\left(\widetilde{\mathrm{S}}_{\mathrm{i}},\widetilde{\mathrm{l}}_{\mathrm{i}}\right)\) and \(\left(\widetilde{\mathrm{S}}_{\mathrm{j}},\widetilde{\mathrm{l}}_{\mathrm{j}}\right)\), are passed through the feature encoder \(\Pi(\cdot)\) to generate two feature vectors \(\mathrm{f}_{\mathrm{i}}=\Pi\left(\widetilde{\mathrm{S}}_{\mathrm{i}}\right)\) and \(\mathrm{f}_{\mathrm{j}}=\Pi\left(\widetilde{\mathrm{S}}_{\mathrm{j}}\right)\). These are then processed by the regression function \(\mathrm{P}(\cdot)\) to obtain \(\mathrm{\hat{l}}_{\mathrm{i}}\) and \(\mathrm{\hat{l}}_{\mathrm{j}}\). For the selection of positive and negative sample pairs, a similarity function is used to calculate whether the similarity between the two label values or predicted values exceeds a threshold.  
The anchor point data selection still follows the anchor point data selection method. For each sample \(\mathrm{j}\), \(\left(\widetilde{\mathrm{S}}_{\mathrm{j}},\widetilde{\mathrm{l}}_{\mathrm{j}}\right)\), the feature vector sets of its positive and negative sample pairs are represented as \(\mathrm{E}_{\mathrm{j}}^{+}=\left\{\left(\mathrm{f}_{\mathrm{p}}\right)\right\}_ {\mathrm{p=0}}^{\mathrm{N}_{\mathrm{j}}^{+}}\) and \(\mathrm{E}_{\mathrm{j}}^{-}=\left\{\left(\mathrm{f}_{\mathrm{q}}\right)\right\}_ {\mathrm{q=0}}^{\mathrm{N}_{\mathrm{j}}^{-}}\), where \(\mathrm{N}_{\mathrm{j}}^{+}\) is the number of positive samples and \(\mathrm{N}_{\mathrm{j}}^{-}\) is the number of negative samples. If \(\mathrm{N}_{\mathrm{j}}^{-}>0\), the sample \(\left(\widetilde{\mathrm{S}}_{\mathrm{j}},\widetilde{\mathrm{l}}_{\mathrm{j}}\right)\) was considered an anchor point. The sample pairing process of the PSCDR algorithm is illustrated in Figure 2.
## 1 Introduction
### Contributions  
The motivation of this paper is to resolve the problem of routing and scheduling of TT traffic considering characteristics of flow period satisfying the deterministic transmission requirements for different network scales and network connectivity. In this paper, an enhanced routing and scheduling approach based on flow classification is explored to maintain the quality of service of applications with different periods. In particular, our contributions to this paper are as follows:  
* We preprocess the preknown flows and group them based on the period correlation to obtain a delimited set of flows, named flow classification (FC).
* Based on the FC, a multiperiod flows routing and scheduling algorithm (MPFRS-FC) adopts the iterated ILP-based scheduling and routing in order to attain high scheduling scalability by flow group, and incrementally adding a flow group to the scheduled procedure is proposed. Additionally, the link pruning after successful scheduling of each flow group in the MPFRS-FC can effectively reduce the search space and execution time.
* Furthermore, an adaptive period compensation routing and scheduling algorithm based on FC (APCRS-FC) is designed to tackle the simple or loosely coupled network topology carrying complex periodic TT traffic problems, since multiple disjoint paths need to be found in the MPFRS-FC algorithm to minimize the number of unschedulable flows.
* Through evaluations, the paper shows that the proposed algorithm outperforms benchmark routing and scheduling algorithms in terms of scheduling success rate and execution time.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 5 Conclusions  
This paper carries out a study on the limit magnitude model and an analysis of the orbital optoelectronic tracking system based on its principle. It deduces the calculation model of the limit magnitude; analyzes the relationships between the limit magnitude detection and the exposure time of the CCD camera, the threshold value of the signal-to-noise ratio, and the dark current in different lighting conditions; and gives the corresponding change curves. At the same time, the variation of the tracking error under different detection distances is verified by the experiment of the synchronous tracking. The results show that under certain illumination conditions, a longer exposure time of the CCD and a smaller dark current of the tracking system are conducive to the improvement of the detection capability.  
The contributions of this paper are as follows: Based on an orbital optoelectronic tracking system with a rotating reflector, we describe a limit magnitude modeling method for detection performance calculation to solve the dynamic target detection in orbit. We construct an analysis framework for the limit magnitude model, which combines the optical information of the dynamic target, background, and noise while considering the sunlight illumination condition.  
In this paper, only the effects of the optoelectronic information characteristics of the target, uniform background, and CCD camera noise on the detection performance of the orbiting optoelectronic tracking system are considered, while the actual detection performance will be affected by changes of the environment and the vibration characteristics of the tracking platform, etc. The theoretical model and calculation methods of the paper provide a good theoretical design basis for the subsequent development of tracking systems for long-distance and large dynamic targets, such as the forward-looking infrared (FLIR) system, infrared search and track (IRST) system, electro-optical targeting system (EOTS), etc.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
###### Abstract  
The removal of the As(V) and Iprodione fungicide onto EGS@APTES-GT, obtained by amino-modified expanded glass spheres (EGS) modified with goethite, was studied in this work. Material characterization was performed using SEM/EDS, XRD, and FTIR techniques. The adsorption capacities of 51.01 and 94.28 mg g\({}^{-1}\), for As(V) and Iprodione removal at 25 \({}^{\circ}\)C, respectively, were achieved. A kinetic study indicated lower intraparticle diffusional transport resistance. Physisorption is the dominant mechanism for Iprodione removal, while surface complexation is for As(V). The disposal of effluent water after five adsorption-desorption cycles was attained through Iprodione photocatalytic degradation and arsenate precipitation. Exhausted EGS@APTES-GT, processed by goethite acidic dissolution and grinding, was used as a reinforcing filler in composites production based on commercial unsaturated polyester resin (UPe). An improvement in the mechanical properties was observed, with a gradual increase in the tensile strength, reaching a maximum of 25.9% for UPe with 10 wt.% of ground exhausted adsorbent compared to pure UPe. The overarching concept is defined by the aspiration to develop technologies that address all output flows of advanced processes. Thus, the combination of wastewater treatment technologies and the production of potentially marketable composites successfully achieved both a low environmental impact and the implementation of a circular economy.  
Keywords:goethite; expanded glass spheres; desorbed pollutants disposal; photocatalysis; stabilization +
Footnote †: journal: Applied Sciences
# A Non-WSSUS Channel Simulator for V2X Communication Systems
## 4 Reference Channel Model  
In this section, we describe the reference channel model that assumes non-linear trajectories and velocity variations of the MSs. We introduce first the reference propagation environment at the beginning of the transmission. Then, we present the channel's transfer function and describe its parameters under the one-ring scattering assumption.