esa-sceva/satcom-chunk-collection huggingface dataset:
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### V2V/V2M/M2V Operations
#### V2.1.1 System Configuration  
The system configuration of the developed V2V scheme is shown in Figure 24. EV1 is the vehicle providing energy, and EV2 is the other vehicle receiving energy for battery charging. The DC/DC converter in EV1 adopts the interleaved boost/buck converter. And the CLLC converter is used in EV2.  
Figure 24: System configuration of developed V2V scheme.  
The detailed power circuit of the developed V2V scheme is shown in Figure 25. The specifications and system components are listed below:  
* Nominal battery voltages: \(V_{b1}=V_{b2}=158\) V.
* DC-link voltage: \(V_{dc}=400\) V.
* DC-link capacitor: \(C_{dc}=1100\)\(\mu\)F/900 V (DC).
* Energy storage inductors: \(L_{b1}=L_{b2}=1.83\) mH.
* Switching frequency of interleaved boost/buck converter: \(f_{s}=20\) kHz.
* Nominal switching frequency of CLLC resonant converter: \(f_{s}=100\) kHz.  
The control schemes of the developed V2V scheme are shown in Figures 11b and 26. In the control scheme of the CLLC resonant converter shown in Figure 26, the battery voltage command is set at 165 V, and the maximum current command for charging is limited at 6 A.
# Consumer's Attitude towards Display Google Ads
## 1 Introduction
### Literature Review  
Google Ads are the platform used by advertising agencies to provide an advertisement [11]. Google's search engine market has 87.66% of the market share while Bing and Yahoo have only 12.33%; thus, Google increases the organizations' opportunities to better contact customers [14]. Currently, Meta's Facebook and Google's free services are the most widely used by organizations to target customers through social media advertising [16].  
Online advertisements increase recognition and purchase intentions if the user was exposed to the advertisement repeatedly [19]. Many previous studies focused on the consumer's attitude toward Sponsored Search Advertising (SSA) [20; 21]. Other studies examined consumer behavior in the context of SSA [22; 23; 24; 25]. Some studies concluded that brand image and key phrases are the most important variables that influence the consumer's behavior toward SSA [23], along with technology, innovativeness, and trustworthiness [24]. Another research declared that positive consumer attitudes toward SSA lead to a powerful image value in the long run [25]. Other studies investigated variables affecting the intentions to click regarding SSA and the chosen keywords that enhance click-through rates [26; 27; 28; 29].  
Google Ads uses keywords to define the effectiveness of ad promotion and product sales [11]. Google Ads affect customer perceptions of products and services with either a good image or a bad image [12]. The online ad develops customer perception to respond through a click, like, and share the content, and to buy the service and/or product [12]. There is a significant relationship between the Google ad and consumers' attitudes [13]. Google and Facebook Ads affect brand awareness and customers' buying decisions [15]. The success factors of using Google ads as a marketing tool include relevance, content, information, and experiences, which affect customers' behavior [14]. The privacy issue is the main concern of users when using social media [16]. Personal experience and confidence perform the main role to trust the advertising platform of Google Ads search engine marketing [18]. Finally, there are limited studies that focused on consumers' attitudes towards Display Google ads in terms of intention to click [10; 30].  
Based on the previous discussion, it is clear that SSA took a major role in the literature. Additionally, other studies concentrated on the click-through rates as the only dependent variable; however, limited studies focused on examining the consumer's attitude towards Display Google ads regarding the intention to click. After a thorough examination of the relevant literature, it was concluded that further investigation of the factors influencing the consumer's attitudes toward Display Google ads, in particular, is very limited and is highly needed, as such justifying the aim of this study to fill this gap.
# Fast Performance Evaluation for Switched-Capacitor Converter Based on Convolutional Neural Networks
## 3 Mathematical Model of SCC Topologies  
Due to the high switching frequency and large capacitance of energy-transfer capacitors in SCCs, the voltage of the capacitor can be viewed as constant in the steady state, in keeping with long-standing opinion. The voltage waveform of each node in the two-phase SCC can be viewed as a square wave composed of two levels when the circuit's parasitic parameters are neglected. For example, The voltage waveform of node \(P\) in a 2-to-1 Doubler is shown in Figure 5. In phase 1, the voltage of node \(P\) is equal to \(V_{DD}\), as \(Q_{1}\) is conducted, while node \(P\) is connected to the output terminal in phase 2, because \(Q_{2}\) is conducted.  
Based on the analysis above, the voltage waveform of node \(P\) can be expressed by a two-dimensional array \([V_{DD},V_{OUT}]\), which presents each voltage level in its respective phase.  
The optional range of the node voltage level is a particular set of positive integers that can be divisible by the maximum common factor of the input and output voltage. For  
Figure 4: Influence factors of charge flow conditions.  
Figure 5: Voltage waveform of node P in 2-to-1 Doubler.  
Figure 3: Equivalent circuits of Interleaved Doubler with parasitic resistor.  
example, if the SCC converts voltage from 48 V to 12 V, the node voltage levels are then selected from the set (0 V, 12 V, 24 V, 36 V, 48 V). For simplicity, the voltage conversion ratio can be rewritten as 4-to-1, while the selectable voltage level set can be represented as (0,1,2,3,4). The proposed two-dimensional array can label each node in the SCC topology uniquely; the nodes with same label share identical voltage waveforms in operation, which means that they can be connected. Therefore, the input terminal and output terminal in a 4-to-1 SCC can be expressed as \([4,4]\) and \([1,1]\), respectively. Furthermore, the two-dimension array \([V_{1},V_{2}]\) can be transferred into a decimal number \(k\) by  
\[k=V_{1}*M+V_{2} \tag{9}\]  
where \(M\) is the maximum common factor of the input and output voltage and \(V_{1}\) and \(V_{2}\) are the node voltage in phase 1 and phase 2, respectively.  
From the mathematical perspective, an SCC topology can be considered as a graph. The adjacent matrix [20] can be used to describe the SCC topology after every node is marked uniquely. As shown in Figure 6, the matrix element is \(1\) if there is a switch or a capacitor between the corresponding node, otherwise the element is \(0\). Additionally, the dimension of the adjacent matrix is equal to the \(N\), which is equal to \((M+1)^{2}\), as the two elements range from \([0,M]\). Obviously, the obtained adjacent matrix is a symmetric matrix, as the equation Adj[i][j]=Adj[j][i] always exists.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 2 Preliminaries
### Situation Assessment Indexes of Air Traffic Network
(f) Network PageRank (_NPR_)  
The PageRank index shows that the importance of network nodes depends not only on the number of adjacent nodes of the node, but also on the importance of adjacent nodes. In the weighted network, the definition is as follows:  
\[ND=\frac{1}{|E|}\sum_{i,j\in V}D_{ij} \tag{9}\]0  
where \((1-\rho)\) is the probability of turning to other nodes, \(\rho\) is the damping coefficient, usually \(\rho\)= 0.85. \(I(i)\) is the set of nodes connected to \(v_{i}\), and \(W(j)\) is the set of nodes connected to \(v_{j}\). According to the daily operation of the air traffic network, the comprehensive edge weight \(w_{i}\) will change. Therefore, the above six indexes can describe the air traffic operation. The air traffic network situation dataset can be built in the designated area and time.
## 2 Materials and Methods
### Design and Implementation of the Sensing Device  
#### 2.2.1 Mechanical Design  
A mechanical support is created to allow to the Arduino Nano 33 BLE Sense board to be kept in position on the dVRK instrumentation during the execution of the validation tasks. It is conceived in two different versions to adapt to the different positioning along the instruments of the dVRK. The first version of the prototype, shown in Figure 2, allows the lying positioning on the robot instrument. This arrangement is ideal during BLE communication between the various robot arms, as there is no need to keep the sensor integrated on the board in a specific position. Furthermore, the first position is characterized by a reduced size and a better flexibility during the movements.  
The second version, shown in Figure 3, is designed for cases in which it is necessary to hold the embedded sensors that look towards the work surface. This is useful in the experimental validation phases during the detection of the color and proximity of tissues placed on the work surface.  
This support, for both versions, is made up of two main parts. The first part represents the housing for the electronic board, while the second part connects to the first for the positioning of the device along the EndoWrist tools, as shown in Figures 2 and 3. The two structural portions of the mechanical support are held together using four K&J MAGNETICS neodymium magnets (NdFeB), for each side. The magnetic blocks have dimensions of (0.64 \(\times\) 0.32 \(\times\) 0.08) mm and grade N42. The magnetic anchoring enables the quick alignment of the two mechanical parts and the perfect integration of the sensing device  
Figure 1: The developed sensing device mounted on the PSM of the dVRK system.  
Figure 2: The CAD design of the first version of the mechanical support. (**a**) The housing for the electronic board. (**b**) The connection part for the positioning of the device along the EndoWrist tools.  
with the dVRK instruments. At the same time, this magnetic mechanism makes it possible to separate the two structural portions of the support after performing the tasks using the tip of the surgical instrument itself [13; 14]. This operation not only saves time, but can be performed without the assistance of an assistant surgeon.
## 3 Results
### Comparison with Turbidity-Based Predictions  
First, three turbidity-concentration relationships were analyzed to determine the form of the model to be used for predicting suspended-sediment concentrations based on color changes (Table 1).  
The model predicting concentrations from color changes presented a higher R\({}^{2}\) value than the model predicting turbidity. This was speculated to occur because the range of measured concentrations was wider than that of turbidity. Because multiple regression models are required to be aware of the problem of multicollinearity, the VIF value for each model was analyzed. Furthermore, because the gray value was generated based on the combination of RGB, regression models that included gray-scale values provided extremely high multicollinearity (VIF > 10). Finally, the values of \(C_{sus}\) that were estimated based on the turbidity, predicted turbidity, and underwater-image color were compared (Figure 7).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Equation** & **R\({}^{2}\)** \\ \hline Power law & \(C_{sus}=10.29\cdot tu^{0.85}\) & 0.66 \\ Second-order & \(C_{sus}=-0.13\cdot hur^{2}+13.84\cdot tur+85.16\) & 0.61 \\  
The RMSE in \(C_{\text{suss}}\) obtained from the underwater-image color change was 99 mg L\({}^{-1}\), which was better than that obtained from the measured turbidity (\(\sim\)166 mg L\({}^{-1}\)) and predicted turbidity (\(\sim\)180 mg L\({}^{-1}\)). The prediction based on the measured turbidity was reconfirmed to be affected by the particle size of the suspended sediment. On comparison, the concentration predicted from the high-concentration validation section of the model predicting turbidity tended to be significantly smaller than the measurement results. This indicates that the concentrations predicted based on turbidity are not appropriate for predictions of an unmeasured range value.
# Micro-Satellite Systems Design, Integration, and Flight
## 4 Attitude Control System (ACS) Development
### Attitude Control  
The design of the CubeSat ACS system involved both passive and active methods. The satellite's shape and the controller gains were adjusted to try to reduce power use. The  
Figure 30: Torque coils made in-house.  
tradeoffs were between satellite stability and controller robustness. These three factors were fine-tuned so that the ACS system satisfied the system-level requirements.
# Controlling Remotely Operated Vehicles with Deterministic Artificial Intelligence
## 1 Introduction
Figure 2: NASA BRUIE under-surface rover designed to explore alien oceans [2]. Image used for educational purposes in accordance with NASA Media Usage Guidelines [22]. then one could use those governing equations themselves as a control law (in both feed-forward [25] and feedback senses [26]), which is particularly effective if the governing mathematical relationship is stipulated by physics. This allows the design of a feedforward controller that might perfectly track a desired trajectory [30; 31] simply by plugging prescribed desired velocity and acceleration into the system's governing equations, and then using them, calculating the required control forces and torques. Deterministic artificial intelligence first asserts the general structure of the system dynamics, then uses some form of learning to determine the parameters of the model. Ref. [26] Assuming the chosen model structure is sufficiently comprehensive to capture the dominant system dynamics, a deterministic artificial intelligence controller will be able to learn all of the relevant parameters, Ref. [27] and adapt to any changes in the underlying system dynamics. By adding simple feedback the controller is able to correct for disturbances or other unmodeled dynamics to track a prescribed trajectory with very little error. While [28] developed benchmark classical methods on the Phoenix underwater vehicle, Ref. [27] developed deterministic artificial intelligence for the Aries underwater vehicle in Figure 3. In this manuscript, a deterministic artificial intelligence controller is newly developed and proposed as applied to yaw control of a simulated remotely operated submersible (ROV). The simulator was configured to simulate the dynamics of a Seabotix vLBV 300, with no environmental disturbances (e.g., wind, swell, currents) or measurement noise.
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 4 NDR-Net Assisted Channel Estimation
### Complexity Analysis
The complexity increases linearly with the number of trained models, but this is acceptable in the context of improving the model generalization performance.
## 3 Results  
The study flow chart is shown in Supplementary Materials Figure S1. Of the 2333 patients screened, 836 did not meet inclusion/exclusion criteria, and 647 refused to participate. Of the 850 patients randomized, 425 were assigned to HCTR and 425 to UC. This analysis focuses on patients randomized to the HCTR group. A total of 27 patients did not participate in HCTR program due to: technical difficulties with operating the telerehabilitation set (21), new onset of comorbidities (4), return to work (2) [17]. Finally, 398 patients started HCTR. Two deaths occurred during the 9-week HCTR intervention, one due to hemorrhagic stroke and one non-cardiovascular. Therefore, in the end 396 patients were included in the present analysis. Neither death occurred during telemonitored training sessions nor directly after (up to 1 h). We were able to ascertain mortality status and hospitalization in all HCTR group patients and hospitalization during 14-26 months of follow-up after the intervention was finished [17]. Of the 396 patients analyzed in terms of their adherence to the HCTR program, there were 350 (88.4%) adherent patients, 39 (9.8%) partially adherent patients, and 7 (1.8%) non-adherent patients. The causes of non-adherence were as follows: four patients had technical difficulties due to the lack of cellular network reception at home, one patient suffered from a prolonged respiratory infection, one had an exacerbation of comorbidities, and one had personal problems. It is worth mentioning that of the 39 partially adherent patients: 21 patients underwent 70-78% prescribed training sessions, 5 patients 60-65% prescribed training sessions, 3 patients 53-55% prescribed training sessions, and only 10 patients 23-45% prescribed training sessions. The baseline clinical characteristics of the whole HCTR group and of adherence subgroup vs. partially and non-adherence subgroup are presented in Table 1.
# Effects of Bogies on the Wake Flow of a High-Speed Train
## 2 Computational Details  
### Computational Models and Domains  
The high-speed train model is a 1:1 scale of the CRH380A and consists of one head car, one middle car, and one trailing car. The head car and trailing car are symmetrical and have a length of 26.5 m. The model dimensions are 78 m \(\times\) 3.5 m \(\times\) 3.36 m in length, height, and width, as shown in Figure 1. In this study, three models with different degrees of simplification are used. Model 1, denoted as the real model, has a complex geometry of the bogies and inter-carriage gaps (Figure 1). Notably, the pantographs and air conditioning are ignored in this study because it was shown that they had a relatively small impact on the wake [9]. Model 2, denoted as the simplified model has a simplified geometry with no bogies and a floor, as shown in Figure 2. Model 3, denoted as the semi-simplified model, is based on model 2 and has a bogie near the nose of the trailing car, as shown in Figure 3. As known, different flow fields would be surely obtained with different train models, but their computational costs do not differ much. It should be emphasized here that the purpose of the three train models with different degrees of simplification of the bogies is not to discuss the computational costs of high-speed train simulations but to analyze the effects of the bogies on the generation and evolution of vortices in the wake flows of high-speed trains by comparing the three train models.  
Figure 1: Real model.  
Figure 2: Simplified model.  
Let H denote the height of the train, H = 3.5 m. The computational domain extended 22.7 H in front of the train and 46.4 H behind the train. The top of the computational domain was at a distance of 12 H from the bottom of the rail and the sides were at a distance of 8.57 H from the center of the train, as shown in Figure 4. The same computational domain is used in the simulations of all three models.  
We define the dead center of the train as \(x\) = 0, \(y\) = 0 and the bottom end of the wheels as \(z\) = 0. The flow direction is defined as the positive direction of the \(x\)-axis, the vertical direction as the positive direction of the z-axis, and hence the positive direction of the \(y\)-axis can be determined by the right-hand rule. The coordinates of the front and back of the train are \((x,y,z)\) = (\(-\)39, 0, 1) m and (39, 0, 1) m, respectively.  
A speed of V = 300 km/h is set as the train speed for the simulations. The corresponding Mach number is 0.245 and hence the flow is in a compressible state. As a result, the inlet, outlet, and far-field of the computational domain are all set as non-reflective. A no-slip wall condition is used on the train surface to determine the frictional drag and pressure on the surface. The train is set as stationary and a moving wall boundary with the same speed as trains is used for the ground to simulate the ground effect caused by the relative motion between the train and the ground.
## 2 Materials and Methods
### Analysis  
The concentrations of the nine SMFs were determined by a combination of headspace-solid phase microextraction (HS-SPME) using a Combi-Pal auto-sampler (CTC Analytics AG, Zwingen, Switzerland) and gas chromatography-mass spectrometry (GC-MS) (QP2010 Plus and QP2020; Shimadzu Corporation, Tokyo, Japan). The SPME fiber was polydimethylsiloxane/divinylbenzene (PDMS/DVB) (65 \(\upmu\)m) and was purchased from Supelco (Bellefonte, PA, USA). The fiber was pretreated according to the manufacturer's recommendations before use. A 20 mL screw cap vial sealed with PTFE (polytetrafluoroethylene)-silicon was used; for liquid samples, 10 mL of the sample was placed in a vial to which 3.0 g NaCl was added. The vial was sealed and the sample was extracted at 90 \({}^{\circ}\)C for 30 min at 500 rpm. Liquid perfume in the products was diluted with acetone and analyzed without pretreatment. In the case of cream- and gel-type PCPs, a small amount of sample was aliquoted into a headspace vial and analyzed by SPME-GC/MS.  
Separation of SMFs was achieved using a DB-1 MS column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m: Agilent Technologies). The carrier gas was helium (purity 99.9999%) and the flow rate was 1.0 mL/min. The GC oven temperature was increased from 120 \({}^{\circ}\)C (2 min) to 160 \({}^{\circ}\)C (3 min) at a rate of 10 \({}^{\circ}\)C/min, and then to 185 \({}^{\circ}\)C 2.5 \({}^{\circ}\)C/min and finally 250 \({}^{\circ}\)C (3 min) at 30 \({}^{\circ}\)C/min. The total analysis time was 24.17 min. The transfer line and ion source temperature of the mass spectrometer was set to 230 \({}^{\circ}\)C. Thermal desorption on the SPME fiber was performed using an injector at 230 \({}^{\circ}\)C in split mode, with a split ratio of 5.0. The mass spectrometer was operated in electron ionization (EI) mode at \(-\)70 eV. For identification of SMFs, the EI mass spectrum of the compound eluted from the GC column was compared to the EI mass spectra in the National Institute of Standards and Technology (NIST) v 2.2g (2014) database. The quality assurance and quality control (QA/QC) results are shown in Text S1 and Table S2, and the chromatograms and mass spectra of SMFs are shown in Table S3.