esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Investigation of Jet Angle Effect on Airfoil Stall Control
## 2 Methods  
### Flow Conditions  
The OA209 airfoil under the freestream condition of the Mach number \(Ma=0.3\) and the Reynolds number \(Re=1.15\times 10^{6}\) is simulated here, following the experimental conditions [16; 17; 36]. The static pressure and the static temperature in the freestream are \(P_{\infty}=56\) kPa and \(T_{\infty}=304\) K, respectively. The freestream velocity is \(U_{\infty}=105\) m/s.  
The airfoil pitches about the quarter-chord point with a pitching motion prescribed in Equation (1).  
\[\alpha=\alpha_{0}+\alpha_{1}\sin(2\pi ft) \tag{1}\]  
where \(\alpha\) is the angle of attack, \(\alpha_{0}\) is the mean angle of attack, \(\alpha_{1}\) is the amplitude of the sinusoidal motion, \(t\) is the time and \(f\) is the pitching frequency. The pitching frequency \(f\) is non-dimensionalized to the reduced frequency \(k\) using Equation (2)  
\[k=\frac{2\pi fc}{2U_{\infty}} \tag{2}\]  
For deep dynamic stall in this study, a pitching motion of \(\alpha=13^{\circ}+7^{\circ}\sin(2\pi ft)\) is assigned with the reduced frequency \(k=0.05\), following the experimental condition of [38]. The chord length of the airfoil is \(c=0.3\) m.  
Two jet angles are selected in the current study: \(\theta_{jet}=80^{\circ}\) for the chord-normal jet and \(\theta_{jet}=22^{\circ}\) for the tangential jet. The jet outlet is located at \(x_{jet}/c=0.1\) on the suction side, following the test [16; 17]. The wind tunnel test of [PERSON] et al. [17] was conducted with round holes discretely positioned in the span, which is not appropriate in the current 2D simulation. As a consequence, a fully spanned jet is modeled with the same jet momentum \(c_{\mu}=0.06\) of [16; 17]. To match the jet momentum to the counterpart from one round hole of the diameter \(d\) per the wing span \(b\) of [16; 17], the jet width \(w_{jet}/c=0.001\) is determined from the relation \(c_{\mu}=\frac{\rho_{jet}v_{jet}^{2}w_{jet}}{\rho_{\infty}U_{\infty}^{2}c}= \frac{\rho_{jet}v_{jet}^{2}\pi\,w_{jet}^{2}}{\rho_{\infty}U_{\infty}^{2}c}\). The total pressure \(P_{t}=290\) kPa and the total temperature \(T_{t}=300\) K are used for the total condition of the jet actuation.
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
## 3 Multilingual Pretrained Language Models for QE
### Multilingual BART  
BART [40] is a denoising autoencoder that corrupts the text by adding arbitrary noise and trains the model to restore it to the original text. mBART [18] is an extension of BART that has been applied to large monolingual corpora across multiple languages. mBART was trained using a 25-language corpus from CommonCrawl data (CC25).  
BART utilizes 5 pretraining schemes leveraging a monolingual corpus: token masking, token deletion, text infilling, document rotation, and sentence permutation. Among these pretraining schemes, mBART adopts text infilling and sentence permutation. In the case of text filling, unlike MLM in which one token in the original sentence is replaced with one [MASK] token, spans of tokens are replaced with one masked token. The total number of selected tokens is 35% of the entire sentence, and the length of the masked token is determined based on the Poisson distribution, which is described in Equation (6).  
\[f(n:\lambda)=\frac{\lambda^{n}e^{-\lambda}}{n!} \tag{6}\]  
Here, \(f(n:\lambda)\) indicates the probability of selecting \(n\) as the masking length. mBART sets \(\lambda\) to 3.5 for pretraining. By training to reconstruct masked sentences, which are generated by text infilling, a model can be trained for bidirectional contextual understanding, as well as to determine how many tokens should be restored from a single mask token.  
In the case of sentence permutation, the text is corrupted by changing the order of the sentences within each instance. In the process of restoring the noise injected by sentence permutation to the original text, the model can understand information about the relationship between sentences.  
Similar to XLM and XLM-R, mBART adopts an up-down sampling method to achieve improved training for low-resource languages. The sampling ratio \(\lambda_{i}\) applied to the \(i^{th}\) language data is provided by Equation (7).  
\[\lambda_{i}=\frac{1}{p_{i}^{\alpha}}\frac{p_{i}^{\alpha}}{\sum_{j=1}^{N}p_{j}^ {\alpha}} \tag{7}\]Here, \(p_{i}\) is the percentage of each language in the total dataset. The amount of training data for each language are rebalanced according to Equation (7), and, therefore, sampling from high-resource languages is relatively suppressed while sampling from low-resource languages is encouraged. The training process of the QE model leveraging mBART is similar to that of Section 3.1, wherein the same input structure as in pretraining is utilized.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 4 Fragility Analysis of Steel Dampers in Bridges Designed for Seismic and Isolation  
### Bridge Model  
A continuous girder bridge of seismic isolation design with a span of 4 \(\times\) 50 m was taken as an example. In order to consider the contribution of the adjacent bridge stiffness and mass to the transition pier of the target bridge and realistically simulate the possible collision behavior, the adjacent bridge structure was established in the model at the same time. The analysis software adopts OpenSeeS 3.2.0, with beam elements used for the girders, bridge piers, and foundation. The MinMax material model was utilized to simulate the failure behavior of the fuse pins in the seismic isolation bearings. The mechanical behavior of the frictional component of the bearings and the steel dampers is modeled using a bilinear model. A parallel combination of a spring element with a contact stiffness of \(k_{l}\) and a damping element with a damping coefficient of C is adopted to simulate the collision effect at the girder ends. The centralized parameter model proposed by [PERSON] [24] is employed to model the pile-soil interaction. For the four-span structure under study, the bridge piers are numbered from left to right as #1, #2, #3, #4, and #5. For the longitudinal direction of the bridge, an isolation bearing with fuse pins and a steel damper device is installed on the pier top of #3, and steel damper devices are installed on the #2 pier and #4 pier. For the transverse direction of the bridge, seismic isolation bearings with fuse pins and steel damper devices are installed on the #2 pier, #3 pier, and #4 pier. Figure 10 describes the numerical model of the overall bridge example.  
\begin{table}
\begin{tabular}{c c c} \hline
**Threshold Values** & **Damage Indexes** & **Performance Status** \\ \hline \(D_{1}=0.0004\) & \(D\leq 0.0004\) & No damage \\ \hline \(D_{2}=0.206\) & \(0.0004\leq D\leq 0.206\) & Slight damage \\ \hline \(D_{3}=0.396\) & \(0.206\leq D\leq 0.396\) & Moderate damage \\ \hline \(D_{4}=1\) & \(0.396\leq D\leq 1\) & Serious damage \\ \hline \(D\geq 1\) & Complete destruction \\ \hline \end{tabular}
\end{table}
Table 5: Comprehensive damage indexes and performance status of the steel damper.
# Limitations of Using DAQ Cards for Absolute Vibration Measurement in Hydraulic Machines
## 5 Measurement of Absolute Vibrations of Hydraulics Machines Using DAQ Cards, Measurement Setup
### DaqBoard 2000  
Features of DaqBoard 2000 card from IOtech (IOtech Inc., Cleveland, OH, USA):  
* resolution: 16 bits;
* number of analog inputs (multiplexed): 16/8 (single-ended/differential);
* maximum sampling rate (sum of channels used): 200 kHz;  
Figure 6: The amplitude spectrum of vibration displacement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**2 Hz**} & \multicolumn{3}{c}{**1.5 Hz**} & \multicolumn{3}{c}{**1 Hz**} & \multicolumn{3}{c}{**0.5 Hz**} & \multicolumn{3}{c}{**0.25 Hz**} & \multicolumn{3}{c}{**0.1 Hz**} \\ \hline
**Range** & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) & \(\mathbf{v_{rms}}\) & \(\mathbf{s_{p-p}}\) \\
**(V)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** & **(mm/s)** & **(\(\upmu\)m)** \\ \hline \(\pm\)10 & 0.073 & 12.66 & 0.086 & 19.22 & 0.106 & 31.94 & 0.138 & 67.76 & 0.183 & 161.43 & 0.199 & 298.82 \\ \(\pm\)5 & 0.037 & 6.70 & 0.043 & 9.78 & 0.057 & 18.68 & 0.075 & 39.75 & 0.097 & 94.75 & 0.109 & 242.53 \\ \(\pm\)1 & 0.008 & 1.38 & 0.009 & 2.00 & 0.012 & 3.76 & 0.017 & 9.65 & 0.019 & 16.73 & 0.023 & 41.89 \\ \(\pm\)0.2 & 0.005 & 0.71 & 0.006 & 1.04 & 0.006 & 1.72 & 0.009 & 4.59 & 0.012 & 12.09 & 0.018 & 47.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Vibration measurement results using the NI 6218 card for different cut-off frequencies of high-pass filters.  
Figure 7: Vibration measurements using the NI 6218 card (National Instruments Inc., Austin, TX, USA) depend on the high-pass filter’s cut-off frequency: vibration velocity (**a**) and vibration displacement (**b**).  
As before, there are observed tendencies to increase the values of vibration velocity and displacement with the reduction of the filter cut-off frequency and the increase of the measurement range. The maximum determined vibration velocity reaches a value of 0.225 mm/s for the range of \(\pm\)10 V at the cut-off frequency of 0.1 Hz. Vibration displacements exceed 330 \(\upmu\)m for the range of \(\pm\)10 V at the filter frequency of 0.1 Hz. For the minimum voltage range and this frequency, the value of vibration displacements is over 53 \(\upmu\)m. A value not greater than 1 \(\upmu\)m is achieved only for the smallest measurement range of the DAQ module and at the filtration frequency of 2 Hz.
## 2 Preliminaries and Problem Formulation
### Path Following Error Dynamic
The finite-time path following control scheme for the underactuated MSV includes three parts: (1) FT-LOS guidance law design; (2) the finite-time attitude constraint controller design; (3) the finite-time velocity constraint controller design. The schematic diagram of the whole close-loop control system is shown in Figure 2. The specific design process of the finite-time path following control algorithm will be introduced in the following subsections.
# Estimation of Free Fatty Acids in Stored Paddy Rice Using Multiple-Kernel Support Vector Regression
## Materials and Methods
### Multiple-Kernel Support Vector Regression (MKSVR)  
SVR based on the single kernel function (SKSVR) has been applied effectively in many fields [41; 42]. However, when a dataset contains heterogeneous information, using a single kernel may not perform as well as expected [29; 43; 44]. Therefore, researchers proposed kernel fusion methods, i.e., MKL, which can help to deal with this problem [45]. MKL provides a more flexible framework than a single kernel function and enhances the interpretability of decision function [46].  
The main idea of multiple kernel learning is to combine several kernel functions into a single function. One of the simplest and most common approaches is to consider convex combinations of multiple basic kernel functions:  
\[K(\mathbf{x},\mathbf{x}^{\prime})=\sum_{m=1}^{M}\mu_{m}K_{m}(\mathbf{x}, \mathbf{x}^{\prime})\text{ }with\text{ }\mu_{m}\in[0,1],\text{ }\sum_{m=1}^{M}\mu_{m}=1, \tag{10}\]  
where \(M\) denotes the total number of kernels, and \(d_{m}\) is a weight of the kernel. These weights are considered a vector of weights \(\boldsymbol{\mu}=[\mu_{1},\,\mu_{2},\,\ldots,\,\mu_{M}]^{T}\). To avoid over-fitting, we require the sum of weights to reach unity to restrict the range of the search space [47]. By referring to Equation (1), the objective function and constraints for MKSVR become:  
\[\begin{array}{l}\min\limits_{\mu}\min\limits_{\mathbf{w},b}\frac{1}{2}( \mathbf{w},\mathbf{w})+C\sum\limits_{i=1}^{l}\Big{(}\xi_{i}+\xi_{i}\Big{)}\\ \text{s.t.}\ \ (\langle\mathbf{w},\Phi(\mathbf{x}_{i})\rangle+b)-y_{i}\leq \varepsilon+\xi_{i}\\ y_{i}-(\langle\mathbf{w},\Phi(\mathbf{x}_{i})\rangle+b)\leq\varepsilon+\xi_{i} \\The final regression estimation function is:  
\[f(\mathbf{x})=\sum\limits_{i=1}^{l}\Big{(}\hat{\alpha}_{i}^{*}-\alpha_{i}^{*} \Big{)}\overline{K}(\mathbf{x}_{i},\mathbf{x})+b^{*}, \tag{13}\]  
where \(b^{*}=y_{k}+\varepsilon-\sum\limits_{i=1}^{l}\Big{(}\hat{\alpha}_{i}^{*}- \alpha_{i}^{*}\Big{)}\overline{K}(\mathbf{x}_{i},\mathbf{x}_{k})\) with \(k\in\Big{\{}i\in\{1,\ldots,l\}\mid 0<\alpha_{i}^{*},\hat{\alpha}_{i}^{*}<C\Big{\}}\).
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 3 The Theoretical Model of Cyber-Physical Supply Networks
### Cyber Supply Network  
#### 3.1.1 The Nodes and Links of the Cyber Supply Network  
The cyber supply network comprises various functional cyber devices that generate, store, transform, receive, and transmit signals or information [27]. In the cyber supply network, all functional cyber devices are denoted as nodes, and the data transmission mediums between devices are links. Therefore, the cyber supply network as a weighted undirected network is \(G^{c}(V^{c},E^{c})\), where \(V^{c}=(v_{1}^{c},v_{2}^{c},...,v_{N}^{c})\) is the node set and \(E^{c}=\Big{\{}\left(v_{i}^{c},v_{j}^{c}\right)\Big{|}c_{ij}^{c}=0\;or\;1,i,j=1, 2,3,...,n\Big{\}}\) is the set of connectivity links. Here, \(e_{ij}^{c}=1\) signifies a connection from node \(v_{i}^{c}\) to node \(v_{j}^{c}\); otherwise, \(e_{ij}^{c}=0\). Further, \(W^{p}\) is constructed to represent the flow constraints of the links, where \(W^{c}=\left[w_{ij}^{c}\right]\) is an \(N\times N\) asymmetric matrix and \(N\) is the total number of nodes in the cyber supply network. The weight can be defined as:  
\[w_{ij}^{c}=\left(k_{v_{i}^{c}}\ast k_{v_{j}^{c}}\right)^{\tau}, \tag{1}\]  
where \(\tau\) is the weight parameter of the link in the cyber supply network.
## 2 Materials and Methods
### HPLC-ESI-MS/MS Conditions for Quantitative Analysis  
For quantitative analysis, the separation of the multicomponents was carried out using an Agilent 1260 Infinity liquid chromatography (Agilent, Lexington, MA, USA) equipped with a quaternary pump, an online vacuum degasser, an autosampler, and a thermostatic column compartment. Chromatographic separation was performed on a Waters C18 column (3.9 mm \(\times\) 150 mm, 4.6 \(\upmu\)m). The mobile phase consisted of (A) acetonitrile and (B) a 0.05% formic acid aqueous solution by gradient elution (0-3 min, 20%\(\rightarrow\)23% A; 3-8 min, 30%\(\rightarrow\)35% A; 8-15 min, 35% A; 15-20 min, 35%\(\rightarrow\)60% A; 20-22 min, 60%\(\rightarrow\)80% A; 22-24 min, 80%\(\rightarrow\)95% A; 24-25 min, 95%\(\rightarrow\)20% A). The flow rate was 1 mL/min, and the split ratio was set at 3:2. The temperature was set at room temperature. The injection volume was 10 \(\upmu\)L.  
The Applied Biosystems 3200 QTRAP triple quadrupole tandem mass spectrometer (Applied Biosystems/MDS Sciex, Concord, Ontario, Canada) used was equipped with an electrospray ionization (ESI) source for the mass analysis and detection. All data collected were analyzed and processed using the Analyst 1.6 software (Applied Biosystems/MDS Sciex. Foster City, CA, USA). The turbo ion spray source was set in the negative ionization mode. Multiple reaction monitoring (MRM) was used for detection transitions. The selective ion-pair, DP, and eV of the 16 saponins are shown in Table 2. The ion spray voltage was set at \(-\)4500 V, the source temperature was set at 450 \({}^{\circ}\)C, and gas 1 and gas 2 were set at 50 psi and 45 psi, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**No.** & **Analy** & **TR (min)** & **Precursor Ion (\(ml\)/\(z\))** & **Product Ion (\(ml\)/\(z\))** & **DP/V** & **CE/V** \\ \hline
1 & NG-R1 & 5.48 & 931.2 & 475.1 & 125 & 70 \\
2 & G-Rg1 & 6.42 & 799.2 & 637.1 & 100 & 55 \\
3 & G-Re & 6.56 & 945.3 & 475.2 & 105 & 62 \\
4 & G-Rf & 11.21 & 799.2 & 475.1 & 100 & 50 \\
5 & G-F3 & 12.09 & 769.2 & 475.1 & 105 & 55 \\
6 & G-Rg2 & 13.14 & 783.2 & 475.1 & 105 & 58 \\
7 & G-Rh1 & 13.27 & 637.2 & 475.1 & 75 & 45 \\ \hline \end{tabular}
\end{table}
Table 2: The retention time, precursor ions, product ions, and multiple reaction monitoring (MRM) parameters of the 16 analytes in the negative ion mode. Retention time (TR), declustering potential (DP), apillaryelectrophoresis (CE).
# Integration of Deep Learning Vision Systems in Collaborative Robotics for Real-Time Applications
## 2 Literature Review
### Integration of Cobots with Computer Vision and Object Detection  
From previous works, [PERSON] et al. (2024) presents a work that explores the integration of artificial vision and image processing systems into a collaborative robotic system for \"pick-and-place\" tasks. The proposed solution uses a UR3e collaborative robot (Producer: Universal Robots, City: Odense, Denmark) equipped with a low-cost Intel D435i camera (Producer: Intel Corporation, City: Santa Clara, CA, USA) to identify and manipulate randomly placed objects (Figure 2). The system incorporates machine vision algorithms to consider features such as color, orientation, and contour, as well as integrating an optimized light source to improve image quality and reduce shadows [12].  
Figure 2: Pick-and-place with vision system developed in an experimental scenario (adapted from [12]).  
The system architecture is based on Neadvance Niop software (Version 0.9.21), which uses a drag-and-drop approach to schedule workflows. The system includes modules for deletion, object detection, data communication, and manipulation operation. The experimental results demonstrate that using a controlled light source significantly improves the detection accuracy, and vision algorithms are able to identify and classify objects with high accuracy. Based on our research, this work presents the following limitations [12]:  
* Dependence on controlled lighting;
* Tests restricted to laboratory environments;
* Need for manual calibration;
* Limited robot range;
* Lack of deep learning;
* Customization restriction due to the use of NIOP;
* Errors with objects outside the camera's center field.
# Research Advances and Application Prospect of Low-Temperature Plasma in Tumor Immunotherapy
## 5 Anti-Tumor Immune Mechanism of LTP
### The Relationship between RONS and Immune Response  
Studies have shown that RONS participated in immune response. For example, H\({}_{2}\)O\({}_{2}\), O\({}_{2}\)\({}^{-}\), OH, NO, ONOO\({}^{-}\) and NO\({}_{2}\)\({}^{-}\) participated in the phagocytosis of microorganisms of macrophages. NO affected the synthesis and secretion of cytokines (TNF-\(\alpha\), IFN-\(\gamma\), IL-1, IL-6, etc.) of immune cells (macrophages, NK cells, T cells). RONS increased the number of macrophages [46]. Some RONS affected the cross-presentation antigen of dendritic cells, migration of macrophages, and regulation of cytokine receptor expression [7]. [PERSON] et al. [47] treated B16-F10 melanoma with plasma. The tumor grew on both legs, but only the tumor on the right side was directly exposed to the plasma. It found thatthe tumor growth on both sides was inhibited. This result indicated that plasma could trigger the innate immune response through signal transmission. Therefore, we infer that RONS produced by LTP would cause changes in the immune response via related signaling pathways.
# Kinematic Analysis of an Omnidirectional Tracked Vehicle
## 1 Introduction
The work considers the control of the vehicle using various models that take into account, among other things, the slippage of the crawler during turns. The kinematic analysis addresses the issues of the mutual motion of bodies. It assumes that the bodies have a fixed length, are rigid, and are not subject to deformation. The kinematic analysis completely neglects the external forces, masses, and inertia of the bodies. The motion of the bodies is due solely to kinematic forcing, i.e., specific characteristics of the change in the position of the individual bodies over time [30; 31]. To control an omni-tracked vehicle, it is necessary to derive kinematic equations describing the relationships between the linear velocity of individual crawlers and the speed and resulting direction of the motion of the platform body. These equations will make it possible to calculate the control values for individual drives. In the work on the comprehensive manipulator control strategy [32], the first steps were to develop the kinematics and dynamics. On their basis, it was possible to develop a strategy for the precise tracking of the manipulator's trajectory. This work concerns the kinematic analysis of an omni-tracked vehicle with tracks in a parallel, fully overlapping configuration. This article will present the equations of kinematics for controlling an omni-tracked vehicle, with the help of which control values will be determined for the selected examples of preset motion trajectories. Next, the numerical simulations of the dynamic model of the omni-track vehicle will be carried out. The proposed dynamic model will be intentionally idealized. No additional variables such as vibrations, accidental external forces, or substrate inhomogeneity will be considered. Then, the results obtained by both methods will be compared. The aim of this work is to demonstrate that under ideal conditions, the proposed kinematic model can replace the dynamic model. Such a finding could support the development of a robust control system for a real omni-tracked robot, regardless of its track layout, as well as immeasurable external factors, such as a non-homogeneous ground or accidental external forces. The last part of the article presents a comparison of the results and discusses the implications of the findings.
# Hybrid Active-Passive Reconfigurable Intelligent Surface for Cooperative Transmission Systems
## 5 Conclusions  
In order to eliminate the \"multiplicative fading\" effect of passive RIS and the high-power consumption of active RIS, we provide a novel idea of hybrid active-passive RIS cooperative communication in this work. We arrive at the closed approximation expression for the outage probability of the HAPR system under the _Nakagami-m_ fading channel, obtain the number of HAPR reflective elements \(N\) as a function of the outage probability, and, by employing Monte Carlo simulations, verify that our mathematical expression is reliable. The simulation results demonstrate that HAPR has the advantages of a low power consumption, high gain, and high reliability compared with active and passive RIS.  
Conceptualization, W.W.; methodology, W.W.; software, W.W.; validation, W.W.; formal analysis, W.W.; investigation, W.W.; resources, W.W.; writing--original draft preparation, W.W.; writing--review and editing, W.W and K.S.; visualization, W.W.; supervision, K.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported, in part, by the National Natural Science Foundation of China under Grant 6190124, and, in part, by the Frontier Exploration Projects of Longmen Laboratory under Grant LMQYTSKT033.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RIS & Reconfigurable Intelligent Surface \\ HAPR & Hybrid Active-Passive Reconfigurable Intelligent Surface \\ RE & Reflecting elements Surface \\ OP & Qutage probability \\ CSI & Channel state information \\ CDF & Cumulative distribution functions \\ SNR & Signal-to-noise ratio \\ \end{tabular}  
Figure 5: OP of A\(-\)RIS, P\(-\)RIS, and HAPR under the same parameter setting (\(N=5\)).