esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System-Level SPICE Modeling of EMP Coupling on Wireless RF Circuits
### SPICE Model of Wireless Antenna  
Both of the Thevenin and Norton SPICE models of the pyramid horn antenna are established in this subsection. The OC voltage and SC current used for the Thevenin and Norton SPICE models respectively, are derived from the EM models of the antenna illuminated by HPEM excitation plane waves. Figure 7 provides the antenna EM model and the antenna port admittance data.  
Based on the previous two SPICE models, we will use the VF method to obtain the rational function approximations of the frequency response of the antenna input impedance and input admittance, which will be used for the equivalent circuit modeling. The rational approximation of the transfer function \(T(s)\) of impedance or admittance can be written as  
\[T(s)=\sum_{n=1}^{N}\frac{res_{n}}{s-p_{n}}+d+se \tag{5}\]  
Figure 6: Equivalent circuit model of antenna response: (**a**) Antenna illuminated by HPEM; (**b**) Equivalent circuits for antenna response.  
Figure 7: EM models of pyramid horn antenna: (**a**) EM model; (**b**) Admittance.  
where \(res_{n}\) is the \(n\)-th residue, and \(p_{n}\) is the \(n\)-th pole, \(d\) and \(e\) are the optional parameters, and \(s=jw\) is the complex frequency. These values can be computed by the VF method, the details of which can be found in [12; 13; 14].  
The rational approximations of the input impedance or admittance are both computed by the VF method. The choice of which is model used always depends on whether the excitation source of the SPICE model selects OC voltage or SC current. In addition, the frequency response of two-port network or single-port network is usually interchangeable between the S, Z and Y-matrix.  
The rational approximation of the transfer function can be represented in the form of pairs of complex and conjugate residues \(\{res_{1}\),\(res_{2}\}\) and poles \(\{p_{1}\),\(p_{2}\}\) like  
\[T_{d}(s)=\frac{res_{1}}{s-p_{1}}+\frac{res_{2}}{s-p_{2}} \tag{6}\]  
Based on the rational approximations, the forms of the basic sub-circuits in the SPICE model and the element values are determined in the following subsection respectively.
# Distributed Coordination of Space-Ground Multiresources for Remote Sensing Missions
## 2 System Model
### Network Model
#### 2.2.3 Virtual Vertices and Virtual Arcs  
With the ordinary vertices and ordinary arcs in TEG, the execution process of missions in SGCN can be represented as a flow originated from an observation object vertex and destinated to a ground station or DRS vertex (DRSs are stationary on the ground and generally adopt a transparent forwarding mode [21; 22]. That is, DRSs can forward the mission data to the ground immediately after receiving them with no need for considering a visible relationship with the ground station. Therefore, to simplify the coordinated resource scheduling model, the process of data transmission from DRS to the ground is ignored in this paper) [18]. Therefore, the coordinated resource scheduling problem can be modeled as a flow optimization problem in TEG. However, the number of feasible flows for each mission is proportionate to the sum of the number of ground stations and DRSs, whichraises the complexity of the flow problem. Inspired by the fact that the mission requests do not specify which ground station or DRS to receive or relay the mission data to the ground, we simplify the mission model by adding virtual vertices as the end point of the mission flow in the traditional TEG, which directly connect to the ground station and DRS vertices through virtual arcs, as shown in Figure 2. With the introduction of virtual vertices and virtual arcs in TEG, the number of feasible mission flows corresponding to a given mission request can be evidently decreased.  
Virtual vertices represent virtual sinks of mission data, of which the set is denoted by \(V_{VT}=\{u^{k}|1\leq k\leq K\}\), where \(u^{k}\) represents the virtual sink of the \(k\)-th slot. The virtual arcs represent the virtual links between the DRSs or ground stations and the virtual sink. Let \(ru_{i}^{k}\) denote the virtual arc from vetex \(r_{i}^{k}\) to vertex \(u^{k}\) in TEG. Similarly, \(gu_{n,i}^{k}\) represents the virtual arc from \(g_{n,i}^{k}\) to \(u^{k}\), and \(uu^{k}\) represents the virtual arc from \(u^{k}\) to \(u^{k+1}\). The set of virtual arcs \(A_{VT}\) is expressed as  
\[\begin{split} A_{VT}=&\Big{\{}ru_{i}^{k}|1\leq i \leq|R|,1\leq k\leq K\Big{\}}\\ &\cup\Big{\{}gu_{n,i}^{k}|1\leq i\leq|G_{n}|,1\leq n\leq N,1\leq k \leq K\Big{\}}\cup\Big{\{}uu^{k}|1\leq k\leq K-1\Big{\}}.\end{split} \tag{4}\]  
Because the virtual vertex represents the virtual sink of mission data received by the DRSs and ground stations, it is assumed that the capacity of the virtual arcs is infinite.
# Reconfigurable Diplexer-Based Filtenna for Tx/Rx Operation in Mobile Satellite Terminals
## 2 Structure and Reconfigurability  
The proposed antenna is based on microstrip structures, as they allow designing efficient, versatile, compact, and simple elements, with low manufacturing cost [11]. Microstrip structures consist of a dielectric substrate covered on both sides by conductive material; typically, one side is completely covered, functioning as a ground plane, while the other side is only partially filled, delimiting the radiating surface.  
To achieve the reconfigurability, a technique from the three mentioned before was chosen. RF-MEMS were discarded, since they need a high bias voltage to commute between the ON/OFF states and present a high overall cost [5; 12]. As varactors are highly nonlinear, PIN diodes were used, since they require low bias voltages, are easy to find on the market at low cost, and the switching speed is fast [13].  
To suppress the unwanted frequencies, both the transmitted and received signal were filtered. As the frequencies are high, the undesired bands can influence the behavior of the antenna, affecting its performance. The nonlinear behavior of the PIN diode was taken into account, as it can reproduce harmonics that reduce energy from the system. Another important point is the fact that DC voltage will be applied to the diodes. Therefore, to separate the DC signal from the RF signal, filters that do not have directly connected copper pieces are a good solution.  
Some filters were considered such as an interdigital filter, combine filter, pseudo combine filter, end-coupled, edge-coupled, or a hairpin filter. These filters are designed with \(\lambda\)/2 or \(\lambda\)/4 lines, differing just in their layout and some features. The hairpin structure was selected since it presents wider bandwidth [14]. Considering that the radiating physical structure comprises an antenna and includes a filter, it is common for this arrangement to be called a filtenna.
# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## Materials and Methods
### SWIR Hyperspectral Imaging Analysis
#### 2.2.1 Image Acquisition  
Each raw material (individual species) was filled into a plastic container (8 mm in diameter) and the surface leveled using a spatula. For each tea blend investigated, three samples of each raw material were used to build the model. The samples were randomly placed on a mobile platform which entered the field of view when prompted for image capture. The tea bags (\(n=3\)) were then imaged separately under the same conditions at room temperature. Each of the three tea bags was imaged six times and between each image capture the tea bag was shaken and turned, resulting in 18 replicate images per tea bag per blend. A three-dimensional hypercube (\(x\), \(y\), \(\lambda\)) was generated for each image where \(x\) and \(y\) represent spatial dimensions (number of pixels in rows and columns) and \(\lambda\) the spectral dimension [22]. The image was composed of several pixels where each pixel represented a complete spectrum containing point chemical information at a given wavelength. The **X**-data matrix was formed by the pixel reflectance values within the defined wave region and the wave numbers constituted the independent variables. Internal dark (0%) and white (100%) reference standards were used for image calibration and to correct variation in sample illumination from dark current from the camera and noise from uneven light intensity of different wavelength bands. The raw images were corrected automatically for white and dark references and converted to pseudo-absorbance (Analog to digital (A/D) converter counts to absorbance) using Evinectm (Version 2.5.0, Prediktera AB, Umea, Sweden) [25; 26; 13; 27].
## 3 Proposed De-Noising Algorithm and Evaluation Method
### Cross-Channel Dynamic Weighting RPCA
After DWRPCA processing, a low-rank matrix \(L\) and a sparse matrix \(S\) can be obtained. Figure 3: Illustration of proposed cross-channel dynamic weighting RPCA algorithm. The detailed calculation of weighting factors \(\omega_{n}\) of the cross-channel DWRPCA is defined below. **Lemma 1**. : _Let \(D=U\Sigma V^{T}\) be the singular value decomposition (SVD) of \(D\in\mathbb{R}^{m\times n}\), where_  
\[\Sigma=\left( \begin{array}{c}diag(\sigma_{1}(D),\sigma_{2}(D),\cdots,\sigma_ {n}(D))\\ 0\end{array} \right), \tag{4}\]  
_where \(\sigma_{i}(D)\) denotes the \(i\)-th singular value of \(D\). If the positive regularization parameter \(C\) exists and the positive value \(\varepsilon<\min\Bigl{(}\sqrt{C}\), \(\frac{C}{\sigma_{i}(D)}\Bigr{)}\) holds, by using the reweighting formula \(\omega_{i}^{\ell}=\frac{C}{\sigma_{i}(L_{L})+\varepsilon}\)[31] with initial estimation \(L_{0}=D\), the reweighted problem has the closed-form solution: \(L^{*}=U\Sigma^{\prime}V^{T}\), where_  
\[\Sigma\;\prime=\left( \begin{array}{c}diag(\sigma_{1}(L^{*}),\sigma_{2}(L^{* }),\cdots,\sigma_{n}(L^{*}))\\ 0\end{array} \right), \tag{5}\]  
_and_  
\[\sigma_{n}(L^{*})=\left\{ \begin{array}{c}0\\ \frac{c_{1}+\sqrt{\varepsilon_{2}}}{2},\end{array} \right. \tag{6}\]  
_where \(c_{1}=\sigma_{i}(D)-\varepsilon\) and \(c_{2}=\left(\sigma_{i}(D)+\varepsilon\right)^{2}-4C\). \(C\) is set to \(\max(m,n)\). [PERSON] et al. [35] described a more specific proof of Lemma 1._  
The weighted value \(\omega_{n}\) of the proposed algorithm is defined as follows:  
\[\sigma_{n}(L^{*})=\max\bigl{(}\sigma_{n}(D)-\frac{\omega_{n}}{\gamma},0\bigr{)}, \tag{7}\]  
where  
\[\omega_{n}=\frac{\eta}{\sigma_{n}(D)+\varepsilon}, \tag{8}\]where \(\eta\) denotes the \(n\)-th channel-scaled factor (CSF) of \(D\prime\) in the \(n\)-th resorted channel. The regularization parameter \(\gamma\) is set to \(1/\sqrt{\max(m,n)}\), and reconstruction error tolerance \(\varepsilon\) is set to \(1.0\times 10^{-6}\). In Equation (8), the weighting factor \(\omega_{n}\), also called the weight of the nuclear norm, is determined by the CSF and dynamically updated during iterations until convergence. The CSF is computed through a convolutional neural network, which considers the signal patterns among channels. The CSF is the numerator of Equation (8) and, therefore, constructs the initial value of weighting factor \(\omega_{n}\). \(\sigma_{n}(D)\), part of the denominator of Equation (8), is adjusted dynamically in iterations of each channel to significantly optimize low-rank estimation performances. The major difference between WRPCA and our cross-channel DWRPCA can be specified at \(\omega_{n}\). In WRPCA, \(\omega_{n}\) is identical in every channel, while, in our algorithm, \(\omega_{n}\) is adaptively adjusted by CSF \(\eta\). Hence, the cross-channel DWRPCA can extract features from multiple cycles in each sensor channel and consider energy differences in different channels.
# Analysis of Vorticity and Velocity Fields of Jets from Gas Injector Using PIV
## 1 Introduction
Nevertheless, as the jet develops the S factor decreases below 1, regardless of ambient and injection pressure. This phenomenon can be addressed to the high diffusivity of hydrogen that brings the particles to rapidly fill the low-pressure areas that take place along the injector axis. In view of a possible application in the automotive field, other studies investigated the characteristics of jet morphology with high PR values [27]. However, the current literature presents some critical issues. First of all, most of the studies involve the use of the schlieren technique, which is unable to provide quantitative information, thus leaving the unknown aspects to free interpretation. This gap is further highlighted by the lack of investigations comparing the flow-mixing properties of hydrogen with those of substitute or surrogate fuels [28]. In addition, the search for studies involving the use of PIV technique for engine-like experiments becomes rather difficult as the number of available articles decreases drastically. In fact, most investigations available in the literature focus on injectors with a single circular orifice, whereas studies involving complex geometries are nearly absent [29; 30]. In such a framework, the present study presents the analysis of velocity and vorticity distribution of helium jet using the particle image velocimetry (PIV) technique. With a nominal injector pressure of 20 bar, a parametric investigation was conducted to assess the influence of the pressure ratio (PR) on jet morphology, with PR values ranging from 2 to 20. In particular, the constant volume chamber (CVC) pressure varied from 1 to 7 bar, reflecting operating conditions representative of early injection strategies in both naturally aspirated and supercharged engines. The injector was equipped with an interchangeable nozzle tip, allowing the use of three different tip models with different orifice shapes and orientations. To investigate the fundamental mixing capacities, it was used with a dual cavity Nd:YAG laser, with an optically accessible CVC filled with air and seeded with vegetable oil particles.
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 2 System Model and Problem Description  
### System Model  
Figure 1 illustrates the system model studied in this paper. We studied a narrowband active STAR-RIS-assisted SWIPT system that operates on a frequency flat channel. Here, the AP of \(N\) antennas serves IRs and ERs assisted by an active STAR-RIS containing \(M\) units. \(K_{I}=\{1,2,...,k_{I}\}\) and \(K_{E}=\{1,2,...,k_{E}\}\) represent IRs and ERs, respectively. Assuming that obstacles block the direct communication link between the AP and users, the deployed active STAR-RIS provides communication services and meets the energy-harvesting requirements for users in signal-blind areas by reflecting and transmitting signals. \(G\in\mathbb{C}^{M\times N}\), \(f_{I,i}^{H}\in\mathbb{C}^{1\times M}\), and \(f_{r,j}^{H}\in\mathbb{C}^{1\times M}\) represent channels from AP to the active STAR-RIS, active STAR-RIS to \(i\)-th IRs, and active STAR-RIS to \(j\)-th ERs, respectively. For simplicity, linear transmission precoding is considered at the AP, and it is assumed that each information receiver or energy receiver is allocated a separate information or energy beam.
# A Convex Combination Approach for Artificial Neural Network of Interval Data
## 3 The Proposed Method Artificial Neural Network with Convex Combination (ANN-CC)
This model fixes the weight parameter at the center, which does not correspond to the true data generating process, thus the ANN-Center leads to higher bias of the prediction. The experiments were carried out using an Intel Core i5-6400 CPU 2.7 GHz 4 core 16 Gb RAM. The computational cost of our ANN-CC model were a bit higher than those of ANN-Center and RANN-LU. The training was the only time-consuming step for all models. It was also observed that the computation time



## 5 Application to Real Data

### Capital Asset Pricing Model: Thai Stocks

The performance of this ANN-CC model was assessed using interval-valued returns (maximum and minimum returns) from the Stock Exchange of Thailand. In this study, we compared the forecasting accuracy of several ANN models under the context of the capital asset pricing model (CAPM). Our analysis employed lower and upper bounds of the real daily stock returns, including SET50, PTT, SCC, and CPALL, over the period from 26 October 2011, to 7 May 2019. These three companies were selected for this study because they are considered well-performing companies in terms of their share prices. Moreover, they also experienced large trade volumes in the current decade and are regarded as fast-growing and highly volatile in the Thai stock market. All data were collected from Thomson Reuters DataStream. The criteria to choose the best fit model were MAE, MSE, and RMSE. CAPM was proposed in separate studies by [PERSON] [31] and [PERSON] [32] to measure the risk of the individual stock against the market in terms of the beta risk \(\beta_{i}\). The \(\beta_{i}\) is a measure of a stock's risk (volatility of returns) reflected by measuring the fluctuation of its price changes relative to the overall market. In other words, it is the stock's sensitivity to market risk. The model can be written as

\[r_{it}-r_{ft}=\beta_{0}+\beta_{i}(r_{mt}-r_{ft})+\varepsilon_{it}, \tag{34}\]

where \(r_{it}\) is the return of the stock \(i\), \(r_{mt}\) is the return of the market, \(r_{ft}\) is risk-free, \(\varepsilon_{it}\) is error term at time \(t\). If \(\beta_{i}>0\), the stock is called aggressive stock (high risk), otherwise
## 3 Methods and Materials
### Hardware Layer  
The _Hardware layer_ is responsible for the sensors/board communications and to drive the actuators (described in Section 3.1). Each sensor is interfaced by a ROS node that collects the raw data and elaborates them to transmit more meaningful data to the upper layer. The nodes inside the _Hardware layer_ section are:  
* **Camera node**: The camera node handles all the frames taken by the Raspberry Camera Module and it represents the software part of the _Camera_ conceptual block. In addition to the data acquisition, the _Camera Node_ modifies the resolution and the brightness of the images to provide better frame quality. To do that, a topic is exposed. To reduce the latency and increase the fluidity of the stream of frame, the camera node performs an image compression by reducing the quality of the original image through an 8-bit truncation of the colors, i.e. the images stream are converted to JPEG and encapsulated inside a ROS standard message to be exposed in a topic.
* **Proximity node**: The _Proximity node_ collects the data provided by the proximity sensor and by data processing to evaluate the distance from the desired object. Since the data provided represent the time to go and return of the ultrasound signal, the measured distance can be calculated simply multiplying then times the sound speed divided by 2. The distance measurements are then transmitted as a message on a dedicated exposed topic. The _Proximity node_ together with the hardware proximity sensor realize the _Depth Sensor_ conceptual subsystem.
* **Controller Motor Node**: The _Raspberry_ board is not able to provide the power for five motors; thus, the _Phydget_ board is used. However, the latter cannot interpret the data provided by _The Application Processor_, which sends the position of joints as angles. In this layer, every position is converted into the format required by the _Phidget API_ without changing the upper layer output data. The _Controller Motor Node_, together with the _Phydget_ board, implement the _Robotic Actuators_ conceptual subsystem.
## 3 Simulation Setup and Validation
### Spray Model
#### 3.1.2 Spray Model Validation  
In order to obtain the transient flow characteristics file, the distributions of exit section velocity, density, liquid-phase fuel, gas-phase fuel, turbulent kinetic energy, etc., as well as the area and coordinate position of the corresponding cell, are obtained for each angle step based on the simulation results of the flow cavitation phenomenon inside the multi-hole injector nozzle. The parent droplet is introduced using the 3D method for primary fragmentation [23], so the corresponding instantaneous flow characteristics file will be obtained for each angular step, which is an improvement based on [PERSON]'s Blob-type injection, with the main difference being the initial introduction of the parent droplet. The influence of nozzle inlet rounding on the spray characteristics [25] is also considered to make the model more accurate.  
In this paper, the spray tip penetration (STP) distance and shape of P20 are measured in the standard spray visualization test bench. Figure 3 shows the comparison between the numerical spray tip penetration and experimental results, and Figure 4 shows the comparison between the simulation and experimental results of spray shape, in which the spray is divided into two halves from the middle, the left side is the numerical simulation results, and the right side is the experimental results. It can be seen from the figure that the traditional 0D and 1D spray models underestimate the development of the penetration distance and over-predict the spray shape, while the improved 3D model has a good prediction of the spray shape and a better accuracy of the penetration distance than the traditional method.  
Figure 2: Grid diagram of eight-hole injector nozzle.  
Figure 4: Comparison between simulation and experimental results of spray shape.  
Figure 3: Variation in spray tip penetration distance with time.
## 2 Scenario Description
### Antenna Parameters
#### 2.2.1 BS Antenna and Array Model
The horizontal cut is obtained by fixing \(\theta^{\prime}\) to \(90^{\circ}\):  
\[A^{\prime}_{dB}(\theta^{\prime}=90^{\circ},\phi^{\prime})=\text{min}\{12\bigg{(} \frac{\phi^{\prime}}{\phi_{3 dB}}\bigg{)}^{2},\,A_{\text{max}}\}\quad[\text{dB}] \tag{15}\]  
with \(\phi_{3 dB}=65^{\circ}\) and \(\theta^{\prime}\in[-180^{\circ},180^{\circ}]\). The maximum directional gain of an antenna element \(G_{E,\text{max}}\) is equal to 8 dBi. Figure 3 shows in linear scale the 3-D representation, the horizontal cut for a fixed angle \(\theta=90^{\circ}\), and the vertical cut with a fixed angle \(\phi=0^{\circ}\), respectively, for the 3 GPP antenna element radiation pattern. We select the \"Model-2\" of the polarized antenna modelling described in Section 7.3.2 of [21]. In general, the relationship between radiation field and power pattern is given by:  
\[A^{\prime}(\theta^{\prime},\phi^{\prime})=|F^{\prime}_{tx,\theta^{\prime}}( \theta^{\prime},\phi^{\prime})|^{2}+|F^{\prime}_{tx,\phi^{\prime}}(\theta^{ \prime},\phi^{\prime})|^{2} \tag{16}\]  
where \(F^{\prime}_{tx,\theta^{\prime}}(\theta^{\prime},\phi^{\prime})\) and \(F^{\prime}_{tx,\phi^{\prime}}(\theta^{\prime},\phi^{\prime})\) are the vertical and horizontal polarized field components in LCS, respectively. For the described scenario at the BS, the polarized field components are the same in LCS and GCS. In case of a single polarized antenna (purely vertically polarized antenna), we have \[F_{tx,\theta}(\theta,\phi)=F^{\prime}_{tx,\theta^{\prime}}(\theta^{\prime},\phi^{ \prime})=\sqrt{A^{\prime}(\theta^{\prime},\phi^{\prime})} \tag{17}\]  
i.e., only the vertical polarized field component \(F_{tx,\theta}(\theta,\phi)\) is active while \(F_{tx,\phi}(\theta,\phi)=0\). In case of dual polarization, we have:  
\[ \begin{array}{l}F_{tx,\theta}(\theta,\phi)=F^{\prime}_{tx,\theta^{\prime}}( \theta^{\prime},\phi^{\prime})=\sqrt{A^{\prime}(\theta^{\prime},\phi^{\prime })}\cos{(\xi)}\\ F_{tx,\phi}(\theta,\phi)=F^{\prime}_{tx,\phi^{\prime}}(\theta^{\prime},\phi^{ \prime})=\sqrt{A^{\prime}(\theta^{\prime},\phi^{\prime})}\sin{(\xi)}\end{array}  \tag{18}\]  
where \(\xi\) is the polarization slant angle and \(\xi=\pm 45^{\circ}\) corresponds to a pair of cross polarized antenna elements.