esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Raman Laser Spectrometer
### High-Pressure Gas Cell  
A special gas cell made of titanium was developed, as shown in Figure 2. It was designed to work with gases in a limited volume with high pressure. The internal volume was only 0.57 cm\({}^{3}\). To prevent reflections and light scattering, the internal surfaces of the gas cell were coated with an anti-glare enamel. The two optical windows for the excitation and scattered beams were made of K8 optical glass. The detection was at 90\({}^{\circ}\) to the optical axis of the laser beam. The gas cell can withstand pressures up to \(\sim\)100 atm. Note that CO\({}_{2}\) passes from the gaseous to liquid phase at a pressure of \(\sim\)60 atm.  
The thickness of the optical windows was determined based on the calculations of the stress and displacement fields in the windows using the SolidWorks software, as shown in Figure 3a-d. The ultimate stress for both windows was assumed to be below the elastic limit of the K8 glass, \(\sim\)40 MPa, as shown in Figure 3e. Under these conditions, the minimum thickness of the windows is 8 mm with a deviation from flatness of 2 \(\upmu\)m when exposed to a pressure of 100 atm. We have selected the thickness of 8 mm for both windows. The clear aperture (diameter) of the windows was \(\sim\)10 mm (for the excitation beam) and 15 mm (for the scattered light). The input window was AR coated for 532 nm and the output one for 420-680 nm. The calculated fraction of the collected Raman signal determined by the geometry of the gas cell is 11%.  
Figure 3: (**a–d**) Simulation of **(a,b)** the stress field, \(\sigma\), and (**c,d**) the field of total displacements, \(u\),  
Figure 2: The scheme of the high-pressure gas cell: 1—nut rings, 2—polytetrafluoroethylene (PTFE) gaskets, 3—input window for passing the excitation light, 4—useful volume for the studied gas, 5—output window for collection of the scattered light.  
in (**a,c**) the input window of the gas cell and (**b,d**) the output window used for collection of the scattered light. The gas pressure is 100 atm; (**e**) calculated maximum stress in the window vs. its thickness, the dashed line represents the elastic limit of the K8 glass (the window material). The grain structure in (**a-d**) represents the mesh used in the simulations.  
The gas cell was equipped with a gas inlet and an outlet. First, a backing vacuum pump was connected to the outlet valve and the cell was evacuated to the pressure of 10\({}^{-3}\) mm Hg. Then, the inlet valve was opened and the cell was filled in with the studied or reference gas. After the measurement, we released the gas into the evacuation system through the outlet valve.
# A New Concept Compliant Platform with Spatial Mobility and Remote Actuation
## 1 Introduction
A method for the optimal design and performance characterization of micromanipulators has been also applied to six-DOF parallel micromanipulators [25]. A three-DoF compliant platform has been studied [26] to make it able to move in three-dimensional space. The system was composed of compliant joints, actuators, and a central moving platform. The actuators consisted of three binary links, while the moving platform was an equilateral plate. The free end of each actuator and the central platform were connected by springs in such a way that the motion of the actuators was transmitted to the moving platform. Considering the applications at the micro scale and using the technology on which micro electro-mechanical systems (MEMS) are based, a compliant three-DoF plane parallel platform has been proposed [27] together with its kinetostatic optimization. The \"da Vinci\" (c) surgical robot (by Intuitive Surgical, Inc., Sunnyvale, California, USA) is a widely-used system designed to facilitate surgical operations with a minimally-invasive approach. This robot makes use of multi-DoF end terminals as a resource to cope with different tasks, working in cooperation with robotic wrists or steerable instruments, and their characteristics have been extensively improved and refined throughout the years. For example, a 2 mm-diameter instrument equipped with a three-DoF wrist has been investigated and tested [28] for the robotic \"da Vinci\" platform. Moreover, new systems for measuring the end effector gripping force have also been developed by means of a torque transfer system [29]. Finally, deflection and force feedback from the \"da Vinci\" end effector have been provided by new types of strain gauges [30]. However, in most of the above-mentioned systems for remote manipulations that are widely adopted for minimally-invasive surgery, the wrists and end effector consist of mechanisms that have ordinary kinematic pairs, which are subject to backlash problems, and a serial kinematic structure, which is usually less robust than a parallel structure. The present paper presents a new six-DoF platform for remote manipulation that consists pf a mechanism with selective compliance (compliant mechanism) and has also a parallel structure. The device could be either part of \"da Vinci\" or independently driven by another positioning system. Originally, the system was conceived to work under a laparoscopic environment, and it is expected to improve its success in surgery, such as in laparoscopic sleeve gastrectomy (LSG) with curroplasty [31], in surgical treatment of gastrointestinal stromal tumors of the duodenum [32], in colovesical fistula surgery with a minimally-invasive approach [33], in endoluminal loco-regional resection by transanal endoscopic microsurgery (TEM) [34; 35; 36], and also in low rectal anterior resection (LAR) [37].
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Dynamic Conditions  
One of the breakthroughs of PMUs is that they enable monitoring the time evolution of the power system. For this purpose, the employed algorithms should be able to track the value of synchrophasor, frequency and ROCOF also when the parameters defining the input signal are time-varying. Tests under dynamic conditions are aimed at evaluating the accuracy in these scenarios.
# Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis
## 4 Simulation Results, Experimental Validation, Comparisons and Discussion
### Comparative Analysis  
To analyse the performance of the proposed solutions, this section compares the developed FDI schemes with different methodologies already proposed by the same authors. They rely on a model-based nonlinear approach, i.e., the so-called NonLinear Geometric Approach Adaptive Filter (NLGA-AF) [31] and a Recursive identification of FSs (RFSs) [32]. Moreover, the Sliding Mode Observer (SMO) method is also considered [33].  
These comparisons are performed on the basis of the estimation accuracy, as shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault Case** & **1** & **2** & **3** & **4** & **5** \\ \hline TS FSs & 1.69\% & 2.29\% & 2.01\% & 1.94\% & 1.99\% \\ \hline NARX NNs & 0.99\% & 0.98\% & 0.99\% & 1.28\% & 1.21\% \\ \hline
**Fault Case** & **6** & **7** & **8** & **9** & \\ \hline TS FSs & 2.22\% & 1.81\% & 2.21\% & 2.03\% & \\ \hline NARX NNs & 1.69\% & 1.02\% & 1.01\% & 1.51\% & \\ \hline \hline \end{tabular}
\end{table}
Table 6: RMSE % index for the HIL tool.  
Figure 11: Variables of the WT benchmark in full load operation.  
According to the results summarised in Table 7, the performance of the NARX NN solution is better than the ones obtained with the other schemes. In more detail, the values of the RMSE index highlights that when the mathematical description of the dynamic process under investigation may be included in the design phase, the NLGA-AF technique with disturbance decoupling still yields to good performances, even if an optimisation procedure is required. However, when modelling errors are present, the offline learning feature of the data-driven fuzzy estimators TS FSs allows to achieve interesting results. For example, this consideration is valid also for the SMO estimators derived via a linearisation procedure. On the other hand, the TS FSs led to more interesting capabilities. With reference to the adaptive scheme, such as the RFS, it takes advantage of its recursive features, since it is able to track possible variations of the WT system, due to operation or model changes. However, it requires quite complicated and not straightforward design procedures relying on data-driven recursive algorithms. Therefore, fuzzy-based schemes use the learning accumulated from data-driven offline simulations, but the training stage can be computationally heavy. Finally, concerning the NARX NN strategy, which represents the solution with the best results, it is rather simple and straightforward. Obviously, the achievable performances of linearised or adaptive methods can be quite limited when applied to nonlinear dynamic processes. It can thus be concluded that the proposed data-driven approaches (NARX NN and TS FS) seem to represent powerful techniques able to cope with uncertainty, disturbance and variable working conditions.
# Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks
## 2 Dynamic Models for Coupled Vehicle-Track Systems
### Contact Model between the Wheel and the Rail  
The interaction force between the wheel and the rail can be evaluated by contact theory. The wheel-rail contact is characterized in the vertical direction by its high stiffness. [PERSON] et al. [16] studied the influence of contact modeling on simulated impact forces due to wheel flats and concluded that 2D and Hertzian contact models give rather similar results to the 3D model. In this study, to simulate the normal contact between two bodies with no tangential forces at the contact area, the nonlinear Hertzian model was adopted. The fundamental hypotheses of the Hertzian contact model were provided by [31; 32].  
The nonlinear contact load is given by Equation (6):  
\[F=\begin{cases}C_{\text{H}}(x-y-z)^{3/2}&\text{if }x-y-z\geq 0\\ 0&\text{if }x-y-z\leq 0\end{cases} \tag{6}\]  
where \(x\) is the wheel vertical displacement; \(y\), the rail displacement; \(z\), the rail profile; \(C_{\text{H}}\), the Hertzian stiffness.  
The resolution of the contact problem involves controlling the penetration of the wheel and the rail, through nonlinear global optimization methods, such as the penalty, the method of Lagrange multipliers, or the augmented Lagrangian method, whose formulations can be found in [33]. In this paper, the penalty method was used because, in general,the augmented Lagrangian method requires higher computational effort. Furthermore, the penalty method is usually more robust.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 2 Space-Time Common Sparsity Modeling
### Time Correlation  
In terms of time, the path gains of the channel change much more slowly [20], thus showing the characteristics of time correlation, even in the case of fast time-varying channels. In other words, although the path gain varies greatly from one OFDM symbol to the next OFDM symbol, the path delay in successive OFDM symbols remains almost unchanged. This is because, in time-varying channels, the path delay time is inversely proportional to the system bandwidth, while the path gain coherence time is inversely proportional to the system carrier frequency [21]. That is, in the coherence time of the path gain, since the path delay remains almost unchanged, the CIR of the consecutive \(G\) OFDM symbols remains the same as the common sparsity, as shown in Figure 2, i.e.,  
\[\Gamma_{m,r}=\Gamma_{m,r\ +\ 1}=\cdots=\Gamma_{m,r\ +\ G\ -\ 1} \tag{4}\]  
In summary, the spatial sparsity and time correlation are collectively referred to as space-time common sparseness [22; 23; 24; 25; 26].  
Figure 1: Massive multi-input multi-output (MIMO) space common sparsity model.  
Figure 2: Space–time common sparseness diagram.
## 2 Architecture of the Proposed Optical Metro Access Network
### The Principal of the OMC Generator
After passing this modulated OFC through the second MZM, more carriers are added to the OFC, which can be presented as  
\[ \begin{array}{c}E_{MZM1}(t)=E_{0}\exp(j\omega_{0}t)\sum\limits_{n=-\infty}^{n =\infty}J_{n}(\beta)\exp(jn(\omega_{RF}t+\phi)).\\ \big{[}1+\frac{m}{2}\{\exp(j\omega_{RF}t)+\exp(-j\omega_{RF}t)\}\big{]}.\big{[} 1+\frac{m^{\prime}}{2}\{\exp(j\omega_{RF}t)+\exp(-j\omega_{RF}t)\}\big{]}\end{array}  \tag{6}\]  
Figure 3: Generation of OMC using three modulators; (**a**) output of the phase modulator; (**b**) output of MZM1; (**c**) output of MZM2. If both MZMs are identical, then it can be given as  
\[ \begin{split} E_{MZM2}(t)=E_{0}\exp(j\omega_{0}t)\sum\limits_{n=- \infty}^{n=\infty}J_{n}(\beta)\exp(jn(\omega_{RF}t+\phi)).\\ \left[1+\frac{m}{2}\left\{\exp(j\omega_{RF}t)+\exp(-j\omega_{RF}t) \right\}\right]^{2}\end{split}  \tag{7}\]  
This equation is mathematically complex because of the double modulation effect, where each frequency tone from the first MZM is further increased. Hence, we developed a large bandwidth in the proposed scheme. An amplified \(1\times 3\) RF signal is used to modify the electrical inputs of these modulators. The RF source frequency is kept constant at 20 GHz, the amplitude and DC biasing of the RF are 1 a.u., and the sample rate is 1.28 \(\times\) 10\({}^{12}\). The frequency of the CW laser is 193.1 THz as a center frequency, with a linewidth of 10 MHz and dynamic noise of 3 dB. The PM converts the signal into a wideband signal composed of ultra-short optical pulses that are broadened by the MZM1. The output signal is further expanded by employing the MZM2, which achieves over 60 ultra-flat optical carriers. Figure 3 depicts the output of the three modulators. The resulting optical spectrum, shown in the image, has a tone-to-noise ratio of more than 40 dB and amplitude differences of 0-3 dB. The scheme has a total bandwidth of 1.2 THz. The proposed network for OFC generation has successfully generated over sixty carriers which is higher than or equal in numbers to most of the cited works [19; 20; 25; 28; 34].
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 5 Experimental Verification
### Parallelism Test of the Optical System  
For a single star simulator, the optical system of the emitted light parallelism requirements are high; therefore, this paper uses the latitude and longitude instrument for the optical system of the emitted light parallelism of the actual test, the latitude and longitude instrument as shown in Figure 16, and the main technical parameters as shown in Table 10.  
The specific test method is as follows. The pentaprism is placed on the support in front of the primary mirror of the optical system, and the outgoing light is irradiated to the pentaprism. The theodolite receives it after reflection. The pentaprism moves smoothly along the direction perpendicular to the optical axis of the optical system. During the movement, if the center of the star spot received by the theodolite has a deflection angle, the angle is the parallelism of the outgoing light of the collimating optical system. After many measurements, the measured parallelism results are shown in Table 11.  
It can be seen from Table 11 that the parallelism of the outgoing light measured by the theodolite optical system is all less than 1\({}^{\prime\prime}\), and the parallelism of the outgoing light of the optical system of the single star simulator meets the technical requirements.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Technical Index** & **Technical Parameter** \\ \hline Standard deviation & 0.5\({}^{\prime\prime}\) \\ Goniometric accuracy & 0.5\({}^{\prime\prime}\) \\ Set precision & \(\leq\)3\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Technical indicators of theodolite.  
Figure 16: Theodolite.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Tests** & **RMS Value/\(\lambda\)** & **RMS Average/\(\lambda\)** \\ \hline
1 & 0.0300 & \\
2 & 0.0344 & \\
3 & 0.0344 & 0.0337 \\
4 & 0.0359 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: Actual measured wave aberration of the optical system.
## 4 Experimentation and Results
### Comparison with Other AI-Based and Conventional Methods  
Table 18 compares the performance of YOLOv5 + DeepSORT with conventional techniques, Faster R-CNN, and SSD across several key parameters, including insight detail level, real-time processing speed, tracking accuracy, and detection accuracy. In addition to having the quickest processing speed at 25 frames per second (fps), YOLOv5 + DeepSORT also has the most excellent tracking accuracy (89.47%) and detection accuracy (91.67%). In contrast, conventional methods have a detection accuracy of 70.2% and a tracking accuracy of 60.5% at 15 fps, performing significantly worse. Faster R-CNN and SSD show moderate performance compared to YOLOv5 + DeepSORT with slower processing speeds and lower accuracy. This chart illustrates the superior overall performance of YOLOv5 + DeepSORT, especially for real-time applications.  
Figure 22 shows the performance comparison of YOLOv5 + DeepSORT with traditional methods, Faster R-CNN, and SSD regarding detection accuracy, tracking accuracy, and real-time processing speed. With a detection accuracy of 91.67%, tracking accuracy of 89.47%, and a processing speed of 25 fps, YOLOv5 + DeepSORT outperforms other AI models and conventional techniques (which have a detection accuracy of 70.2%, tracking accuracy of 60.5%, and a processing speed of 15 fps). This demonstrates that YOLOv5, combined with DeepSORT, offers superior speed and accuracy for real-time object recognition and tracking.  
Figure 21: Confusion metrics for different models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **YOLOv5 +** & **Traditional** & **Faster** & \multirow{2}{*}{**SSD**} \\  & **DeepSORT** & **Methods** & **R-CNN** & \\ \hline Detection Accuracy (\%) & 91.67\% & 70.2\% & 80.2\% & 75.3\% \\ Tracking Accuracy (\%) & 89.47\% & 60.5\% & 78.5\% & 72.1\% \\ Real-Time Processing (fps) & 25 fps & 15 fps & 10 fps & 12 fps \\ Insights Detail Level & High & Low & Medium & Medium \\ \hline \hline \end{tabular}
\end{table}
Table 18: Performance comparison of YOLOv5 + DeepSORT vs. other methods.
## 3 Methodology
### Algorithm of Distributed Receding Horizon Optimization
4:Information reception: Receive the information of \(N_{p}\)-step prediction states \(\hat{x}_{j}(k+1|t)\), \(k=0,\ldots,N_{p}-1\), of neighbor vehicle \(j,j\in\mathcal{N}_{i}(t)\). 5:Optimization solution: At time \(t>0\), according to the current state \(\hat{x}_{i}(0|t)\) and received \(\hat{x}_{j}(k+1|t)\), \(k=0,\ldots,N_{p}-1\), solve the optimal problem (15) and obtain the optimal control sequence \(u_{i}^{*}(k|t),k=0,\ldots,N_{p}-1\). 6:Implementation of optimal results: Apply the first element of the optimal control sequence to AV \(i\), i.e., \(u_{i}(t)=u_{i}^{*}(0|t)\). 7:Iteration of optimal states: Calculate the optimal states \(x_{i}^{*}(k+1|t),k=0,\ldots,N_{p}-1\), by using (10) and \(u_{i}^{*}(k|t),k=0,\ldots,N_{p}-1\). 8:Estimation of assumed states: Calculate the \(N_{p}\)-step assumed states \(\hat{x}_{i}(k+1|t+1)\) at time \(t+1\) as \[\hat{x}_{i}(k+1|t+1)= \begin{cases}x_{i}^{*}(k+2|t),\;\;k=0,\ldots,N_{p}-2\\ x_{i}^{*}(k+1|t)-x_{i}^{e}(k+1|t),k=N_{p}-1\end{cases} ,\] then transmit \(\hat{x}_{i}(k+1|t+1)\) to vehicle \(j,j\in\mathcal{D}_{i}(t)\). 9:Return Optimal trajectory of AV \(i\);
10:\(t\gets t+1\)
11:Until Vehicle \(i\) departs from the CZ. ```  
**Algorithm 2** Algorithm of Distributed Receding Horizon Optimization  
Remark 6: _Note that the \(N_{p}\)-step predicted trajectories of vehicles might not be entirely calculated since the vehicles may enter the intersection within the prediction horizon. The following operations are conducted to address this issue. (1) By detecting the predicted states of vehicle \(i\), only the predicted information for the valid time steps before vehicle \(i\) passes through the stop line is retained, and the information of the remaining steps is set to null. (2) The formulated optimization problem for vehicle \(i\) is solved just to obtain the optimization control inputs in the valid time steps. (3) The assumed trajectory of vehicle \(i\) is calculated and then transmitted to its neighboring vehicle \(j,j\in\mathcal{N}_{i}(t)\). (4) To maintain the integrity of the received assumed trajectory of vehicle \(i\), the received null values are replaced by the desired assumed state values of the neighboring vehicles of vehicle \(j\). (5) The optimization problem of vehicle \(j\) can be solved. The above operations guarantee that if a vehicle enters the intersection at a certain prediction step, the prediction information of the vehicle after this step will not affect the solution of optimization problem (15) for the ego vehicle and its neighboring vehicles._
# Experimental Study on the Desalination of Waste Leachate Using the Combined Freezing Method
The desalination of waste leachate is essentially the same as desalination, in that the salt ions are effectively separated to achieve a higher quality of effluent. In recent years, many scholars have devoted themselves to the treatment of high-salt wastewater by the freezing method, focusing on the analysis of how to effectively separate salt ions. Some researchers have compared immersion, water addition, and gravity methods to separate salt ions encapsulated in ice crystals. They found that the salt rejection of the ice crystals increased from 59.06% to 91.61% after immersion treatment, but the ice production rate decreased from 66.72% to 55.02%. The gravity method had a longer processing time than the immersion method, but the water production rate was high and desalination was good, with a maximum effect of 99.8% [12]. There are also scientists researching the microwave combined gravity desalination of seawater, with salt rejections of more than 90% [13]. In this study, three combined freezing methods were selected for waste leachate treatment, including the frozen-gravity method (FGM), frozen-centrifugal method (FCM), and frozen-blowing method (FBM). The effects of each method were explored separately, and the three methods were compared. A new desalination method based on FBM is proposed, the frozen-crushed-blowing method (FCBM), which provides a new reference for the treatment of high-saline wastewater, such as waste leachate, by the freezing method and an important theoretical and scientific basis for the early realization of the industrial application of the freezing method.