esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 4 System Analysis and Simulation Results
### Performance Analysis  
In this section, we simultaneously simulated the BER of system under the presence of a no frequency offset estimation module, a frequency offset estimation module with 3-bit ADCs, and a frequency offset estimation module with full-resolution ADCs. In all cases, the length of the training sequence was 32 and the normalized CFO \(\varepsilon\) was 0.2. In the first case, it can be seen from Figure 9 that the BER of the system did not decrease as the signal-to-noise ratio (SNR) increased. Therefore, when the CFO exists, it is not feasible to increase the SNR to improve the BER performance without performing the frequency offset compensation. In the latter two cases, we can clearly see that the BER decreased with the increase in SNR, and the BER was infinitely close to zero when SNR >8 dB. At the same time, we can also notice that the performance of the proposed synchronization scheme with low-resolution ADCs was not as good as that of the synchronization scheme under full-resolution quantization; however, their performance is comparable. Since low-resolution quantization additionally introduces the quantization noise, this inevitably results in performance loss. However, it is worthwhile to achieve significant improvements in power consumption at the expense of performance over an acceptable range. For the synchronization scheme with full-resolution ADCs, this method involved complex cyclic summation, and the power consumption of full-resolution ADCs was enormous. In our proposed approach, the symbol timing under one-bit could greatly reduce the computational complexity, and the frequency offset estimation module only needed to perform a sequence autocorrelation operation at the starting point \(\hat{d}\). Compared with the full-resolution synchronization scheme, the proposed synchronization scheme has lower complexity and could effectively reduce the power consumption brought by ADCs.  
Figure 8: Impact of carrier frequency offset (CFO) on quadrature phase shift keying (QPSK) demodulated signal.
# The Effects of Stress on Second Harmonics in Plate-Like Structures
## 1 Introduction
[PERSON] (2018); [PERSON] (2018) calculated the magnitude of energy flow between different excitation and reception modes in aluminum plate and proposed to select the optimal combination of excitation and reception modes through the magnitude of energy flow. Previous researches have made a comprehensive theoretical framework for the generation of cumulative second harmonic modes, and to a certain extent, the mode pair of fundamental mode and second harmonic mode is determined. In the current studies of applying nonlinear ultrasonic guided waves to detect structural stress, second harmonic mode is selected from the mode pairs meet the conditions, and the corresponding relative nonlinear parameter is calculated. Then the stress change of the structure is characterized according to the change of the relative nonlinear parameter. Due to the multi-modal characteristics of ultrasonic guided waves, the mode pair which satisfy the conditions is not unique, and there may be possibly more cumulative second harmonic modes. The changes of the amplitudes of different second harmonic modes with stress are not necessarily the same, and that will lead to the different trends of the relative nonlinear parameters. In particular, when different second harmonic modes are overlapped, the calculation of relative nonlinear parameter will be affected, and wrong experimental results may be obtained. So far, research on the law of the amplitude of the second harmonic mode change with stress has not been carried out. This article puts forward a clear and definite method to determine the response law of the amplitude of each second harmonic mode in plate with the change of stress. The research results can provide the basis for the selection of suitable second harmonic mode for experiment and subsequent application. A steel plate is used as a model for simulation and physical verification, and simulation results are in agreement with the experimental results. The results demonstrate that the change trends of amplitudes of different second harmonic modes with stress are not the same, which are monotonic increasing or decreasing. As a result, the variation of second harmonic modal amplitude with stress should be considered and predicted in advance, when the nonlinear ultrasonic guided waves are selected for stress detection in plate.
## 3 The Proposed Method  
### Overall Scheme  
The overall scheme of our proposed framework is described in Figure 1. First, we extract frames from the input video and use the pose estimation method (described in detail in Section 3.2) to obtain the skeleton images of the human body. Each skeleton image corresponds to one frame of the video, and the coordinates in the width direction and the height direction of the image represent the position of the human body part in the space at the current moment. Also, the confidence of each keypoint in the pose estimation process is also entered the network as one of the features. Simultaneously, we consider the position of a frame on the timeline of the video, each point on the image has a coordinate in the temporal dimension.  
The coordinates of the joint in the skeleton image (including 2 spatial coordinates, 1 confidence parameter, and 1 temporal coordinate) and the connection relationship between the joints are used as input of the model we proposed. Then, the STN adaptively adjusts the human body pose by translating, rotating, and scaling the skeleton image to eliminate the influence of the human body pose diversity. Finally, the high-dimensional features in skeleton images are automatically extracted by the multi-layer GCNs, and the class score of action categories can be obtained, thereby realizing  
Figure 1: The overall framework for action recognition in industrial workflows. Graph convolutional network (GCN).  
the recognition of the manual operation in the industrial workflows. The recognition process can be expressed as Algorithm 1.  
```
0:\(V\), Video to be recognized, pre-trained parameters \(\theta_{ij}(i=1,2,j=1,2,3)\) of the STN
0: Result matrix \(O_{n-i}[o_{0},\,o_{1},\,\cdots,o_{n-i}]\), \(o_{i}(i\in[0,n-1])\) is the probability of operation \(i\)
1: Extract each frame of video \(V\) to get a series of images \(I_{j}\), where \(j\) represents the order of the frames
2: Detect people in each image
3: Converting images \(I_{j}\) into skeleton images \(S_{j}\) through pose estimation, with the order \(j\) unchanged
4: Affine and scale transformations for \(S_{j}\) using trained parameters \(\theta_{ij}\)
5: Input the transformed skeleton graph \(S_{j}\) into GCN in order \(j\) to obtain human action information
6: Feed the information extracted by GCN to the SoftMax classifier and get the result matrix \(O_{n-i}\)
```  
**Algorithm 1** The process of action recognition  
The components in the method we proposed will be elaborated in detail below.
# Dynamic Analysis of Cavitation Tip Vortex of Pump-Jet Propeller Based on DES
## 2 Numerical Method and Computational Setup
### Three-Dimensional Model and Mesh  
#### 2.5.1 Three-Dimensional Model  
The pump-jet propeller used in this paper is shown in Figure 3. The stator is rear-mounted, and the number of rotor blades and stator blades are 7 and 9 respectively. The minimum distance between the rotor blades and the wall is 1.37 mm, and the direction of rotation is counterclockwise (viewed from the back). The computational domain is divided into three parts: external flow field, rotor system and stator system, for which only the rotor system is the rotational domain while the others are the stationary domain. Therefore, for the steady computation, the interface between the rotor system and the external flow field and the interface between the rotor system and the stator system are set to be the frozen rotor in ANSYS CFX 17.2 software designed by Ansys, Inc. in 2016 from Canonsburg, PA, USA.  
Figure 1: Computation domain.  
Figure 2: Cavitation calculation compared with experiments (\(J=0.71\)).
## 3 Results and Discussion
### Wettability Study  
Due to the highly hydrophilic nature of the majority of the samples, it was not possible to measure the wetting angles accurately. However, the wetting angles of samples Mg_14, Mg_15, Mg_16, and Mg_17 could be determined, as these samples exhibited slightly lower wettability compared to the others (Figure 4). The results for these samples are presented in Table 4. The wetting angles obtained for Mg_14, Mg_15, Mg_16, and Mg_17 indicate a notable difference in surface characteristics compared to the highly hydrophilic samples, suggesting that these specific samples have a slightly reduced tendency to attract water molecules. This could be indicative of the formation of less porous or more compact surface coatings due to the electrochemical modification process. The variation in wettability among the samples is important for understanding their potential interactions with biological fluids and their overall suitability for applications where surface properties such as hydrophilicity are critical for cell adhesion and tissue integration.  
Figure 3: The results of the chemical structure analysis using the FT-IR method.
# Comparing Speaker Adaptation Methods for Visual Speech Recognition for Continuous Spanish +
## 5 Speaker Adaptation Methods
### Fine-Tuning  
When adapting a model (usually trained for a general task using large-scale databases) to new languages or domains, the most predominant approach is to re-estimate all the parameters that compose the model. This method, known as fine-tuning, has been widely explored in the literature [22; 43]. In our work, we propose three different fine-tuning strategies:  
* **Multi-Speaker Training (MST).** The VSR system is re-estimated using the training data of the entire speaker-dependent partition of the database. This strategy, as discussed throughout the paper, can be considered as a task adaptation.
* **Speaker-Adapted Training (SAT).** In this case, only data corresponding to a specific speaker is considered when fine-tuning the VSR system.
* **Two-Step Speaker-Adapted Training (TS-SAT).** As its name suggests, this strategy consists of two fine-tuning steps. First, following the MST strategy, the entire partition is used to re-estimate the VSR system and achieve task adaptation. Afterwards, the system is fine-tuned to a specific speaker using the corresponding data.  
In this way, by comparing these proposed strategies, we were able to study how a VSR system can generalize common patterns from different speakers or, on the contrary, evaluate to what extent it is capable of adapting to a specific speaker.
## 5 PTSPP Model Description
### Solar Field (SF)  
The solar field includes north and south sectors. Each sector includes two sections which contain 78 collector loops for both. The thermal oil coming from the economiser with 295 \({}^{\circ}\)C is pumped to the inlet of the solar field, as illustrated in Figure 3. Thereafter, the thermal oil is streamed to the north and the south sections through 312 collector rows.  
Figure 2: Parabolic trough solar power plant (PTSPP).  
The HTF circulated through the collector loops is heated by solar radiation. The HTF in the north and the south loops will meet before the SF outlet. At the SF outlet, the hot HTF will be flow to either the power block, a storage system or to both.  
The measured DNI represents the input into this model. Initially, the HTF mass flow rate is increased to 390 kg/s and is maintained until the designed inlet temperature (295 \({}^{\circ}\)C) is reached. In this process, each section of the SF accumulates a specific amount of thermal energy. After reaching the designated temperature at the entrance, the mass flow rate of the thermal oil and its temperature gradually increase until the maximum mass flow rate (1170 kg/s) is reached at the designated exit temperature (393 \({}^{\circ}\)C). Furthermore, the pressure losses for the power plant components are already included in this model.  
In dynamic simulation it is very important to use the controllers to complete the simulation with high accuracy. Only the most important control circuits will be described briefly:  
1. The SF-TS valve regulates the surplus thermal oil to the hot storage tank. This valve is opened when the HTF mass flow threshold value is 600 kg/s and the temperature is 393 \({}^{\circ}\)C.
2. The PB CVin adjusts the nominal value of HTF mass flow of 600 kg/s to the PB.
3. The DNI controller sets the real data of solar radiation measured in Spain to the HTF in four sections.
4. The HTF MCV valve at the SF inlet controls HTF temperature at the SF outlet. This valve performs two functions: First, it adjusts a mass flow of 390 kg/s after sunrise in the SF and remains at this value until the HTF temperature at the outlet of SF has been raised up to 295 \({}^{\circ}\)C due to the increase in DNI. Second, after achieving 295 \({}^{\circ}\)C of thermal oil temperature at the outlet of SF, the second function is enabled. This valve controls the temperature of HTF at the outlet of SF to obtain the design temperature at the SF exit (393 \({}^{\circ}\)C).  
Figure 3: Solar field circuit.
# A Virtualization Infrastructure Cost Model for 5G Network Slice Provisioning in a Smart Factory
## 2 Background  
The fifth generation mobile network is considered an innovative and disruptive technology compared to existing communication technologies employed in industrial networks [14]. The 5G Service-Based Architecture (SBA) is derived from the virtualization and separation of network entities into network functions according to the services they perform [3]. The CP and UP network functions can be separated for independent deployment, scalability, and management [15]. The major network functions defined in the 3 GPP specifications include the User Plane Function (UPF), Access and Mobility Management Function (AMF), Session Management Function (SMF), Network Slice Selection Function (NSSF), Policy and Charging Function (PCF), User Data Management Function (UDM), and Authentication Server Function (AUSF) [3]. These VNFs can be deployed on off-the-shelf server hardware, in centralized or distributed virtualization infrastructures, and instantiated as service function chains forming the required network slices.
_Applied_  
_sciences_  
_Article_  
**Green Synthesis of a Cu/SiO\({}_{2}\) Catalyst for Efficient H\({}_{2}\)-SCR of NO**  
**[PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{1}\), [PERSON] \({}^{2}\), [PERSON] \({}^{1}\)\({}^{\copyright}\), [PERSON] \({}^{3,4,5,*}\)\({}^{\copyright}\) and [PERSON] \({}^{1,*}\)*  
Received: 22 July 2019; Accepted: 24 September 2019; Published: 29 September 2019  
**Featured Application: These catalysts can be envisioned for decontamination of nitrogen oxides from mobile and stationary exhaust sources.**  
**Abstract:** In this work, the synthesis of Cu/SiO\({}_{2}\) catalysts starting from pre-formed copper nanoparticle (CuNP) colloidal suspensions was carried out. Two different protocols for the CuNP synthesis were tested: (i) a green approach using water as solvent and ascorbic acid as reducer and stabilizing agent, and (ii) a second solvothermal method involving the use of diethylene glycol as solvent, sodium hypophosphite (NaH\({}_{2}\)PO\({}_{2}\)) as reducer, and polyvinylpyrrolidone (PVP) and cetyltrimethylammonium bromide (CTAB) as stabilizing agents. In addition, and for the sake of comparison, a third catalyst was prepared by solid state conventional grinding of CuO with SiO\({}_{2}\). The catalysts were tested in the environmentally relevant catalytic reduction of NO\({}_{\text{X}}\) with H\({}_{2}\), in a temperature range from 300 to 500 \({}^{\circ}\)C. The catalysts were characterized by X-ray diffraction (XRD), temperature programmed reduction (TPR) cycles, Raman spectroscopy, and N\({}_{2}\) adsorption for specific surface BET measurements. From these techniques CuO and Cu(0) species were detected depending on the synthesis protocol. CuNP size and size distribution in the colloid suspensions were determined by transmission electronic microscopy (TEM). The catalyst prepared from the aqueous suspension (CuAsc/SiO\({}_{2}\)) exhibited higher NO conversion (100%) and selectivity (85%) toward N\({}_{2}\) at the lower reaction evaluated temperature (300 \({}^{\circ}\)C). The CuCTAB/SiO\({}_{2}\) catalyst obtained by the solvothermal approach showed activity at high reaction temperature (400 \({}^{\circ}\)C) preferentially. The metal-support mechanical mixture exhibited a negligible response at low temperature and low conversion (68%) and selectivity (88%) at 500 \({}^{\circ}\)C. Nanoparticle size and distribution on the support, together with the metal-support interaction, were postulated as the most plausible parameters governing the catalytic performance of the different Cu/SiO\({}_{2}\) materials.  
**Keywords:** copper nanoparticles; NO selective catalytic reduction; nanoparticle impregnation; copper-silica catalysts
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## Materials and Methods
### Ultra-High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS) Analysis  
All solvents and chemicals were of HPLC grade (Merck Ltd., Darmstadt, Germany). Aqueous tea infusions were prepared for all samples; 50 mL of hot distilled water was poured on 2.5 g of raw material, sonicated for 10 min and steeped for 30 min. The extracts were cooled to room temperature, filtered and stored at \(-20\)\({}^{\circ}\)C until analysis. For UHPLC-MS analyses, aliquots of the raw material infusions were thawed at room temperature and filtered through 0.22 \(\upmu\)m syringe filters. All samples were analyzed within 24 h of sample preparation. UHPLC-MS analyses were performed on a Waters Acquity Ultra Performance Liquid Chromatographic system with a photodiode array (PDA) detector (Waters, Milford, MA, USA). Ultra-high performance liquid chromatography mass spectrometry (UHPLC) separation was achieved on an Acquity UHPLC BEH C\({}_{18}\) column (150 mm \(\times\) 2.1 mm inner diameter, 1.7 \(\upmu\)m particle size, Waters) maintained at 40 \({}^{\circ}\)C. The chromatographic software MassLynx(r)(Version 4.1, Waters, Milford, MA, USA) was used for data acquisition and processing. The mobile phase consisted of 0.1% formic acid in water (solvent A) and acetonitrile (solvent B). N\({}_{2}\) was used as the desolvation gas. The flow rate was 0.3 mL/min and gradient elution was as follows: 90% A: 10% B to 75% A: 25% B in 8 min, changed to 50% A:50% B in 2 min, maintained for 0.5 min and back to initial ratio in 0.2 min. The injection volume was 2.0 \(\upmu\)L. The mass spectrometer was operated in a positive ion electrospray (ES) mode. The desolvation temperature was set to 450 \({}^{\circ}\)C at a flow rate of 600 L/h and the source temperature was 100 \({}^{\circ}\)C. The capillary and cone voltages were set to 3500 and 50 V, respectively. Data were collected between 100 and 1200 \(m/z\) (charge-to-mass-ratio).
# Suppressed Transmission of Long-Range Surface Plasmon Polariton by TE-Induced Edge Plasmon
## 4 Discussion  
We performed numerical FDTD analysis and experiments on the plasmonic response in the gap of a PSC with the polarization and power of an input optical signal, and for the discontinuous channel waveguide. The TE-polarized light excites the highly confined edge plasmon in the gap and induces a strong electric current inside the G-SPPWs, overwhelming the surface plasmon wave that is carried by the LRSPP, thereby breaking the symmetry at the top and bottom interfaces of G-SPPWs. The LRSPP decouples to a lightwave before it reaches the end-facet of the input SPPW and the transmission of the LRSPP is lowered by the extended radiation length and increased coupling loss. The decoupling length is defined in order to characterize the extended radiation length of the decoupled LRSPP by TE-induced edge plasmon, compared with the measured extinction ratio of plasmonic signals. For the PSC experiments with a wide range of input optical power, the extinction ratio of plasmonic signals increases with the input optical power, since more power is transferred to free electrons to excite the edge plasmon and a stronger current density is induced to the G-SPPWs, extending the decoupling length. Therefore, induced current density that is parallel to the SPP propagation can affect the propagation of SPPs [27; 28; 29; 30; 31]. We also considered a PSC with a discontinuous channel waveguide to investigate the dielectric effect. Although there is no significant difference in the transmission of input optical signal by the partial reflection and scattering, the characteristics and excited power of the edge plasmon exhibit distinct differences. In contrast to TE-polarized light, TM-polarized light does not excite the plasmon mode in the gap, and the excited photonic TM mode in the gap does not induce enough current density to disturb the LRSPP propagation by just  
Figure 5: (**a**) Top view of the PSC with discontinuous channel waveguide in _x-y_ plane. (**b**) Optical microscope image of the fabricated PSC sample with discontinuous channel waveguide. (**c**) Measured plasmonic signals for the continuous and discontinuous channel waveguide; the input optical power is 21 dBm.  
passing through the gap, which is in agreement with the previous results that showed a plasmonic signal is not copied from a TM-polarized optical signal even with the power of 21 dBm [38]. Our findings suggest that the control of SPPs using light can be boosted by the enhancement of the induced electric current inside the plasmonic waveguide, which could be achieved by a large field enhancement in the nanogap, the plasmon hybridization [47] of edge plasmons at the left and right edges and by considering oblique incidence on gratings or metasurfaces in the gap. Furthermore, if it is integrated with a mode converter, it is possible to realize nano-plasmonic signal generation that is compatible with pervasive optical communication systems and a plasmonic inverter (NOT gate) in NPIC.