esa-sceva/satcom-chunk-collection huggingface dataset:
# Parameter Analysis and Optimization of Annular Jet Pump Based on Kriging Model
## 2 The Performance Function of the Jet Pump  
The effect of turbulent mixing is influenced by the jet pump structure parameters which determines the jet pump performance. Dimensionless parameters, efficiency \(\eta\) and pressure ratio \(h\), are introduced to study the jet pump performance. Their equations are:  
\[h=\frac{\Delta p_{o}}{\Delta p_{p}}=\frac{\left(p_{o}+\gamma_{o}\frac{V_{o}^{2 }}{2g}+\gamma_{o}z_{o}\right)-\left(p_{s}+\gamma_{s}\frac{V_{s}^{2}}{2g}+ \gamma_{s}z_{s}\right)}{\left(p_{p}+\gamma_{p}\frac{V_{s}^{2}}{2g}+\gamma_{p} z_{p}\right)-\left(p_{o}+\gamma_{o}\frac{V_{s}^{2}}{2g}+\gamma_{o}z_{o}\right)} \tag{1}\]  
\[\eta=\frac{Q_{s}}{Q_{p}}\cdot\frac{\Delta p_{o}}{\Delta p_{p}-\Delta p_{o}}=q \frac{h}{1-h} \tag{2}\]  
where  
\[q=\frac{Q_{s}}{Q_{p}} \tag{3}\]  
\(V,z,g,\gamma,Q,p\) and \(q\) are the sectional average velocity, the sectional average velocity, the positional water head, the gravitational acceleration, the unit weight, the volume flow rate, the static pressure and the flow ratio, respectively. Footnotes \(o\), \(s\) and \(p\) are the mixture fluid at the outlet, the secondary fluid at the inlet and the primary fluid at the inlet, respectively.  
In addition, the area ratio \(m\) is also introduced:  
\[m=\frac{A_{s}}{A_{p}} \tag{4}\]  
In this equation, \(A\) is the nozzle outlet area.
## Abbreviations  
The following abbreviations are used in this manuscript:  
DRLEA  
Deep Reinforcement Learning Evolution Algorithm  
HAPS  
High-Altitude Platform Station  
DNN  
Deep Neural Network  
DQN  
Deep \(\mathcal{Q}\) Network  
GA  
Genetic Algorithm  
UEs  
User Equipments  
GEO  
Geostationary Earth Orbit  
LEO  
Low Earth Orbit  
RTT  
Round Trip Time  
LoS  
Line-of-Sight  
BS  
Base Station  
IoT  
Internet of Things  
B5G  
Beyond 5G  
DoA  
Direction-of-Arrival  
CCA  
Concentric Circular Antenna Array  
IDCSA  
Discrete Cuckoo Search Algorithm  
SLL  
Side-Lobe Level  
SOS  
Symbiotic Organism Search  
PSO  
Particle Swarm Optimization  
CIR  
Carrier-to-Interference Ratio  
CDF  
Cumulative Distribution Function  
CSI  
Channel State Information  
VNEP  
Virtual Network Embedding Problem  
GCNN  
Graph Convolutional Neural Network  
RL  
Reinforcement Learning  
DL  
Deep Learning  
DRL  
Deep Reinforcement Learning  
MIMO  
Multiple-Input Multiple-Output  
mmWave  
MissimeterWave  
Mean Squared Error
# Numerical Simulation Analysis of Wellbore Stability in Weak Interlayer of Igneous Rock
## 5 Analysis of Factors Influencing Wellbore Stability
### Effect of Abnormal Pore Pressure on Wellbore Stability  
To simulate formation anomaly pressure, the model underwent changes in the formation pore pressure. Three sets of formation pore pressure equivalent densities (0.8 g/cm\({}^{3}\), 1 g/cm\({}^{3}\), and 1.2 g/cm\({}^{3}\)) were utilized to investigate the impact of formation anomaly pore pressure on wellbore stability. Figure 15 illustrates the maximum and minimum principal stress differences corresponding to different formation pore pressures.  
The variation of the main stress differential follows a decreasing and then increasing trend under different formation pore pressures, aligning with the stress concentration in the direction of maximum and minimum in situ stress. Abnormal pore pressure induces a larger principal stress differential compared to normal pressure, thereby heightening the risk of wellbore collapse. The impact of the abnormally high pressure is even greater, exacerbating the risk of wellbore collapse.  
The plastic strain results from extracting the longest extended distance within the weak interlayer on the wellbore is presented in Figure 16. Both the peak plastic strain and the maximum extension distance of plastic deformation in the weak interlayer surpass those observed under normal formation pressure conditions, and these two indicators also exceed the valves recorded during abnormal low-pressure scenarios. In summary,  
Figure 14: Variation of plastic strain with extension length under different horizontal in situ stress ratios.  
the presence of abnormal pressure elevates the plastic strain within the wellbore, which is detrimental to the stability of the surrounding rock perimeter. Notably, abnormal high pressure has a more significant negative impact compared to abnormal low pressure.
# A Power Control and Intervention Algorithm for Co-Existing IMT Base Stations and Satellite Services
## 2 System Model  
### Satellite-Ground Interference Scenario  
When the satellite-ground service of FSS operates normally in the 6-GHz frequency band, its space stations can receive signals as well as interference. As a new service in this channel, IMT-2020 may cause such harmful interference. The interference scenario of the IMT-2020 system on the FSS space stations is depicted in the Figure 1. First, we have calculated the coverage area of the FSS satellite on Earth. Based on the interference geometry, the coverage area, \(S\), can be given by the following formula:  
\[S=2\pi R^{2}(1-cos\,\alpha), \tag{1}\]  
where \(R\) is the radius of the Earth, and \(\alpha\) is the central angle, which is The angle between the line connecting the Earth to the edge of the coverage area and the line connecting Earth to the satellite. It can be calculated as:  
\[\alpha=cos^{-1}(R/(R+H)), \tag{2}\]  
Then, the deployment density value of IMT-2020 macro-base stations in sharing and compatibility research can be calculated according to the following formula:  
\[Dl=Ds\times Ra\times Rb, \tag{3}\]  
Among them, \(D_{s}\) is the density value of the coverage area, which is the density of the number of base stations per square kilometer. \(R_{a}\) represents the ratio of the coverage area to the city/built-up area/region in the study, and \(R_{b}\) represents the ratio of the built-up area to the total area of the region. IMT-2020 base stations located within the satellite coverage area are represented as:  
\[N_{IMT}=S\times Dl\times f\times f_{\text{TDD}}, \tag{4}\]  
Among them, \(f\) is the factor of network load and \(f_{TDD}\) is the time division duplex activity factor.
## 3 The Proposed Resource Allocation Scheme for D2D Communication
### Power Allocation with the Given D2D Pair
\[\begin{array}{ll}\
u^{*}=&\frac{1}{\ln 2}(\frac{1}{p_{k,m}^{D\text{ }opt}+\frac{\
u^{*}\lambda_{m}^{*}+\
u_{0}^{*}}{\lambda_{m}^{*}}}-\frac{1}{p_{ k,m}^{D\text{ }opt}+\frac{\
u_{0}^{*}}{\lambda_{m}^{*}}}\\ &+\frac{1}{p_{k,m}^{D\text{ }opt}+\frac{\
u_{0}^{*}(\lambda_{m}^{*}+\
u_{the corresponding \(p_{k,m}^{D\;opt}\) can be calculated by solving the equivalent cubic Equation (33). Then \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}\) is worked out. Compare \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}\) with \(P_{\text{thr}}^{D}\), if \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}>P_{\text{thr}}^{D}\), decrease \(\
u^{*}\); if \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}<P_{\text{thr}}^{D}\), increase \(\
u^{*}\). The adjustment procedure runs until the condition \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}=P_{\text{thr}}^{D}\) is satisfied or \(\
u^{*}\) does not change. ```
1:Initialization: \(\
u^{u}=\max_{m\in\mathcal{M}_{k}}\{f(p_{k,m}^{D\;\text{min}1})\},\upsilon^{ l}=\min_{m\in\mathcal{M}_{k}}\{f(p_{k,m}^{D\;\text{max}1})\}\). 2:repeat
3:\(\
u^{*}=(\
u^{u}+\upsilon^{l})/2\);
4: Calculate \(p_{k,m}^{D\;opt}\) by solving the cubic Equation (33), with the projection of the positive root onto \([0,P_{thr}^{D}]\);
5: With all the possible candidate optimal power values, the optimal transmission power \(p_{k,m}^{D\;opt}\) can be readily obtained. 6:if\(p_{k,m}^{D\;opt}=p_{\text{thr}}^{D},\forall m\in\mathcal{M}_{k}\)then
7:\(\
u^{*}=0\);
8:endif
9: Calculate total power \(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}\);
10:if\(\sum_{m\in\mathcal{M}_{k}}p_{k,m}^{D\;opt}>P_{thr}^{D\;opt}\)then
11:\(\
u^{u}=\
u^{*}\);
12:else
13:\(\
u^{l}=\
u^{*}\);
14:endif
15:until\(\left|\
u^{u}-\
u^{l}\right|\leq\epsilon\)
16:return\(\
u^{*}\), \(p_{k,m}^{D\;opt},\forall m\in\mathcal{M}_{k}\)
```  
**Algorithm 2** Bisection Algorithm for Power Allocation.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 3 Proposed Search Algorithm
### Upgraded Version of the BSS-VQ Search Algorithm  
The search performance of the BSS-VQ algorithm is subject to codeword distributions. As a way to ease the distribution effect, an input vector is assigned to a search subspace in an efficient manner using BSS-VQ at stage 1 of this work, and a codebook search is conducted over the subspace using ITIE as an alternative to the full search algorithm. This is simply because a codeword of interest therein can be well located either by ITIE or by the full search counterpart. This paper presents a two-stage codebook search algorithm as an effective way to remarkably reduce the amount of codebook searches and the time complexity in the evaluation of Euclidean distances. This is done via a sequential use of  
Figure 1: Flowchart of iterative triangular inequality elimination (ITIE) search approach.  
codeword rejection mechanisms provided by BSS-VQ and ITIE, respectively. This codebook search mechanism is presented in Algorithm 3, stated as follows:  
```
Step 1. Initial setting: Given a \(TQA\), \(M_{k}(TQA)\) satisfying (12) is found directly in the lookup table in \(bss_{k}\). A TIE lookup table is also built. Step 2. Referencing Table 2 and through (9) and (10), an input vector is efficiently assigned to a subspace \(bss_{k}\). And then a set, composed of the top \(M_{k}(TQA)\)-sorted codewords in \(bss_{k}\), is denoted by \(CSG(bss_{k})\) and formulated as \[CSG(bss_{k})=\{\mathbf{c}_{k}\ \mid\ k=1,2,...,M_{k}(TQA)\}.\] (15) Step 3. Starting at \(k=1\), set \(\mathbf{c}_{r}=\mathbf{c}_{k}\ \mid\ k=1\) in (15), then compute \(d(\mathbf{c}_{r},\ \mathbf{x})\). Step 4. Let \(k=k+1\), then obtain \(d(\mathbf{c}_{r},\ \mathbf{c}_{k})\) from the TIE lookup table. Step 5. If \((d(\mathbf{c}_{r},\ \mathbf{c}_{k})<2d(\mathbf{c}_{r},\ \mathbf{x}))\), then compute \(d(\mathbf{c}_{k},\ \mathbf{x})\), and perform Step 6. Otherwise, let \(k=k+1\), and then repeat Step 5, until \(k=M_{k}(TQA)\). Step 6. If \((d(\mathbf{c}_{k},\ \mathbf{x})<d(\mathbf{c}_{r},\ \mathbf{x}))\), then replace \(\mathbf{c}_{r}\) with \(\mathbf{c}_{k}\), and repeat Step 4. Otherwise, let \(k=k+1\), and then repeat Step 5, until \(k=M_{k}(TQA)\).
```  
**Algorithm 3** Search mechanism of the upgraded version  
On the issue of speech quality, it was concluded in Reference [10] that a nearly lossless speech quality is provided in BSS-VQ at a \(TQA\) no less than 0.90. This fact definitely applies to the presented search algorithm, since the upgraded version of BSS-VQ offers exactly the same speech quality as the original version.
## 2 System and Channel Model
### One-Hop FSO Sub-Link  
The Malaga distribution has unified most of the existing turbulence models, such as gamma-gamma distribution, log-normal distribution, \(\kappa\) distribution, and so on [18]. Considering the Malaga turbulence with pointing errors, the PDF of the output instantaneous SNR \(\gamma^{\text{FSO}}\) of the one-hop FSO sub-link is [19]:  
\[f_{\gamma^{\text{FSO}}}(\gamma^{\text{FSO}})=\frac{\xi^{2}A}{2^{r}\gamma^{\text {FSO}}}\sum_{k=1}^{\beta}b_{k}G_{1,3}^{3,0}\left(B\left(\frac{\gamma^{\text{FSO }}}{\mu^{\text{FSO},r}}\right)^{\frac{1}{r}}\left|\begin{array}{c}1+\xi^{2} \\ \xi^{2},\alpha,k\end{array}\right.\right), \tag{1}\]  
where \(G_{\cdot,\cdot}^{\cdot,\cdot}(\cdot)\) is the Meijer-G function, \(\xi\) is the ratio of the equivalent beam radius of the receiver plane to the jitter standard deviation, \(B=\xi^{2}\alpha\beta(g\beta+\Omega^{\prime})/\left[(\xi^{2}+1)(g\beta+\Omega^ {\prime})\right]\), and \(b_{k}=a_{k}[\alpha\beta/(g\beta+\Omega^{\prime})]^{-(\alpha+k)/2}\),  
\[\left\{\begin{array}{l}A=\frac{2\alpha^{\prime 2}}{\xi^{1+\alpha/2}(\alpha)} \left(\frac{g\beta}{g\beta+\Omega}\right)^{\beta+\frac{\alpha}{2}}\\ a_{k}=\left(\begin{array}{c}\beta-1\\ k-1\end{array}\right)\frac{(g\beta+\Omega)^{1-\frac{1}{2}}}{(k-1)!}\left( \frac{\Omega}{\beta}\right)^{k-1}\left(\frac{\alpha}{\beta}\right)^{k/2}\end{array} \right., \tag{2}\]  
where \(\alpha\) is a positive parameter, \(\beta\) is a natural number, \(\Gamma(\cdot)\) is the Gamma function. \(g=2b_{0}(1-\rho)\), \(2b_{0}\) represents the average power of the total scattering component, \(\rho\) (\(0\leq\rho\leq 1\)) represents the scattering power coupled with the line-of-sight component, \(\Omega\) represents the average power of mutual coupling components, \(\mu^{\text{FSO},r}\) represents the average SNR of the FSO sub-link, and \(r\) is the parameter that defines the detection technology, that is, \(r=1\) represents HD and \(r=2\) represents DD. When \(r=1\),  
\[\mu^{\text{FSO},1}{=}\mu_{\text{heterodyne}}{=}\overline{\gamma}^{\text{FSO}}; \tag{3}\]  
when \(r=2\),  
\[\mu^{\text{FSO},2}{=}\mu_{\text{IM}/\text{DD}}=\frac{\alpha\xi^{2}\big{(}\xi^{ 2}+1\big{)}^{-2}\big{(}\xi^{2}+2\big{)}(g+\Omega^{\prime})}{\big{(}\alpha{+}1 \big{)}\Big{[}2g(g+2\Omega^{\prime})+\Omega^{\prime 2}\Big{(}1+\frac{1}{ \beta}\Big{)}\Big{]}}\mu^{\text{FSO},1}. \tag{4}\]  
Integrate Equation (1) with the formula \(F_{\gamma}(\gamma)=\int_{0}^{\gamma}f_{\gamma}(t)dt\), and the CDF of \(\gamma^{\text{FSO}}\) can be expressed as [19]:  
\[F_{\gamma^{\text{FSO}}}(\gamma^{\text{FSO}})=D\sum_{k=1}^{\beta}c^{k}G_{r+1, \lambda r+1}^{3r,1}\bigg{(}E\frac{\gamma^{\text{FSO}}}{\mu^{\text{FSO},r}} \left|\begin{array}{c}1,\kappa^{1}\\ \kappa^{2},0\end{array}\right.\bigg{)}, \tag{5}\]  
where \(D=\xi^{2}A/\left[2^{r}(2\pi)^{r-1}\right]\), \(c^{k}=b_{k}r^{\alpha+k-1}\), \(E=B^{r}/r^{2r}\), \(\kappa^{1}=\big{(}\xi^{2}+1\big{)}/r,...,\big{(}\xi^{2}+r\big{)}/r\) (include \(r\) items), and \(\kappa^{2}=\xi^{2}/r\),..., \(\big{(}\xi^{2}+r-1\big{)}/r,\alpha/r,...,\big{(}\alpha+r-1\big{)}/r,k/r,..., \big{(}k+r-1\big{)}/r\) (include \(3r\) items).
# Gaussian Process Modeling of Specular Multipath Components
## 2 System and Signal Models
### GP Modeling of the SMC Amplitudes  
From (3) it is seen that \(\alpha_{k}(\mathbf{p})\) has a well-defined distance dependence, while the angle-dependence depends on many factors that are hard to express in analytic form, for example, antenna radiation patters and reflection coefficients of building materials. We hence propose to use a GP regression model to describe the (angle-dependent part of) \(\alpha_{k}(\mathbf{p})\), that is, \(\gamma_{k}(\phi_{k}(\mathbf{p}))\). A Gaussian Process (GP) model is a regression tool to analyze and predict data assuming that the data have a normal distribution. This is a loose requirement because for a large enough database, the data can often be assumed to be Gaussian distributed. Furthermore, it is evident from (8) that our data obeys a (complex) Gaussian distribution itself.  
However, we will not apply the GPR model to the _complex_ amplitudes directly, because phase coherence of SMCs may be hard to achieve, in particular, when the training data has been recorded at uncertain positions. We therefore separate the complex amplitudes into real-valued absolute values and phase values. Note from the model (8) that the absolute values will be Rician distributed [29]. This distribution is characterized by the Rician \(K\)-factor, for which in the case of \(K\gg 1\), it is possible to approximate the Rician distribution with a Gaussian distribution [30], and the same holds for the phase of complex Gaussian variable. The GPR model will hence be used to model the functions  
\[\psi(\phi_{k}(\mathbf{p})) :=|\gamma_{k}(\phi_{k}(\mathbf{p}))|+n^{\mathrm{abs}}_{\
u,k}=| \alpha_{k}(\mathbf{p})|d_{k}(\mathbf{p})+n^{\mathrm{abs}}_{\
u,k} \tag{9}\] \[\zeta(\phi_{k}(\mathbf{p})) :=\angle\gamma_{k}(\phi_{k}(\mathbf{p}))+n^{\mathrm{ph}}_{\
u,k}= \angle\alpha_{k}(\mathbf{p})+2\pi f_{c}\tfrac{1}{c}d_{k}(\mathbf{p})+n^{ \mathrm{ph}}_{\
u,k}, \tag{10}\]  
where \(n^{\mathrm{abs}}_{\
u,k}\) with variance \(\sigma^{\mathrm{abs}}_{\
u,k}\)2 and \(n^{\mathrm{ph}}_{\
u,k}\) with variance \(\sigma^{\mathrm{ph}}_{\
u,k}\)2 represent the DMC of the amplitude and of the phase, respectively.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 5 Experiment and Analysis
### Infrared Image Test Evaluation  
To better evaluate the denoising ability of the algorithm for infrared images, this section selects two major types of algorithms for comparison. One is the commonly used filtering algorithm (guided filtering with edge-preserving capability and commonly used smoothing filter), and the other is the current advanced stripe correction algorithm, namely, total variation algorithm (TV), midway histogram equalization algorithm (MHE) and CNN algorithm. Our proposed method is further evaluated through a comprehensive comparison.
## 2 Hybrid Path Planning Based on Improved ABC and BAS Algorithms
### Adaptive Adjustment to Algorithm  
In the early stage of the improved algorithm, the number of individual bees in the population is large and scattered. Adaptively adjusting the ABC algorithm to obtain a larger weight value can accelerate the convergence process and enhance the ability to explore global waypoints. As the number of iterations increases, the weight of the ABC algorithm gradually decreases, while the weight of the BAS algorithm gradually increases. This helps to refine the search for local waypoints and find the optimal path. The nonlinear inertia weight factor [29] is introduced to adaptively adjust the weights of the two algorithms and achieve a balance between the global exploration and local optimality search. The nonlinear weight factor update formula is shown in  
\[\omega=\omega_{\mathrm{min}}+(\omega_{\mathrm{max}}-\omega_{\mathrm{min}}) \cdot\sin(\frac{\pi}{2}\cdot\frac{e}{\epsilon_{\mathrm{max}}}). \tag{14}\]  
The values of \(\omega_{\mathrm{max}}=0.84\) and \(\omega_{\mathrm{min}}=0.01\) are determined through extensive experiments. The traditional adaptive weight factor increases linearly, whereas the nonlinear weight factor employed here rises slowly in the early iterations, expanding the search range and thereby enhancing global exploration capability. In the later iterations, the increase rate of the weight factor accelerates, further strengthening the local exploitation capability. The variation in \(\omega\) is illustrated in Figure 1.  
Figure 1: Comparison of adaptive weight factors.  
An adaptive nonlinear inertia weight is incorporated into the position update mechanisms of both the swarm and beetle algorithms, as shown in  
\[c_{d,b}^{e+1}=\left\{\begin{array}{l}w\times c_{d,b}^{e}\exp(\dfrac{-b}{a \cdot\text{det}_{\text{max}}})\\ c\text{best}_{b}^{e+1}+\dfrac{1}{\text{B}}\left(\sum\limits_{b=1}^{B}\left|w \times c_{d,b}^{e}-c\text{best}_{b}^{e+1}\right|\cdot\text{rand}\{-1,1\} \right)\\ c_{d,b}^{e}+\text{J}(\dfrac{w<c_{d,b}^{e}-c\text{worse}_{b}^{e}}{|k_{d}-k_{w}|+ \varepsilon})\end{array}\right., \tag{15}\]  
\[c_{b}^{e+1}=w\times c^{e}+\text{step}^{e}\overset{\rightarrow}{d}\text{sign}[ k(c_{1}^{e})-k(c_{r}^{e})]. \tag{16}\]  
A dynamic coefficient \(\varepsilon\) is introduced to adjust the proportion of the two algorithms, regulate the ratio between current weights and weight changes from the previous generation during each weight update, and achieve a dynamic balance between global exploration and local exploitation through iterations, as shown in  
\[\left\{\begin{array}{l}c^{e+1}=(1-\varepsilon)c_{d,b}^{e+1}+\varepsilon c_{b }^{e+1}\\ \varepsilon=\frac{\varepsilon}{2E}\end{array}\right.. \tag{17}\]
## 3 Discrete Event Simulation Model
### Designing Simulation Scenarios  
For investigating the problem, a new benchmark was created through 145 scenarios that were proposed for different strategies and based on specific motivating questions of the ED manager. Figure 5 shows the categorization of scenarios tested in this study.  
We started first with the evaluation of the actual staffing level and its corresponding measures of performances. This analysis is considered as the base case scenario that will be used for comparison against the other proposed scenarios.  
* **Category 1: Resource-related (flexible staff and shifts)**  
The objective is to find the best-allocations of shifts and number of staff in the short and long circuits. In this category, human resource adjustment take into account the schedule and the number of ED staff in the short and long circuits. ED performances can be improved by changing the capacity level of human resources in different shifts. Two time constraints were imposed for the addition of human resources; we cannot add an emergency physician or a resident physician in the shift from 8 a.m.-12 p.m., or a nurse or a caregiver in the shift from 6 a.m.-11 p.m. The cost of adding human resources is considered. For the decrease in the staff number, we test only the impact of removing personal over 24 h.  
To allow DM to select the most efficient solution, we make a sensitivity analysis on the shift patterns and analyze their impact on the AWT and AS. For example, if we test the impact of adding a nurse during a 12 h time slot, we test the first shift pattern from 6 a.m. to 12 a.m., then from 7 a.m. to 13 p.m., from 8 a.m. to 14 p.m., etc. The decision matrix and the scenarios proposed for the short and long circuits are presented in Tables A1 and A2.  
* **Category 2: Resource-related (flexible rooms)**  
Additional resources can be adopted as a direct solution to improve the ED's performance. In order to analyze the impact of modification of the capacity of the ED's resources, 16 scenarios were tested. Thus, only the number of rooms was modified. The objective is to evaluate the ED's performances by increasing or decreasing by maximum number of rooms equal to 4, the limit of rooms not to be exceeded in light of the current structure.  
Figure 5: Categorization of simulation scenarios.  
* **Category 3: Process-related (short circuit)** Process-related strategy is a concept serving to accelerate the ED process. The objective of the scenario proposed in this category is to determine the effect of closing the short circuit from 10 p.m.-8 a.m. All patients will be redirected to the long circuit during this shift.
* **Category 4: Process-related (triage)** The objective of this scenario is to determine the effect of adding a second triage nurse from 10 a.m. to 10 p.m.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 3 The Proposed PCQNet
### Testing of PCQNet
#### 3.3.3 Baseline3: The Ideal Feedback Scheme  
We consider the optimal scheme labeled as w/o compression which directly feeds back the precoding matrices without compression.