esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Main Results
### Nonlinear Controller Design and Stability Analysis
\begin{array}{l}\gamma_{i_{k}}=\left(\widehat{q}_{i_{k}}^{t}\right)^ {\mathrm{T}}\left(a_{i_{k}}\mathbf{I}_{2}\right),\mathbf{P}_{\mathrm{T}}=\mathbf{P}_{e} \mathbf{T}\left(\psi_{r}^{i_{k}}\right)\mathbf{P}_{2}\\ \mathbf{\zeta}_{i_{k}}=\mathbf{P}_{\mathrm{T}}\mathbf{k}_{i_{k}}^{3}-\mathbf{P}_{e}\mathbf{T} \left(\psi_{r}^{i_{k}}\right)\mathbf{P}_{e}\mathbf{P}_{1}\mathbf{P}_{e}+\mathbf{P}_{1}\end{array} \right.\]  
Furthermore, \(\dot{V}^{i}(t)\) can be simplified as  
\[ \begin{split}\dot{V}^{i}(t)&=\eta_{0}\sum_{k=1}^{L}\gamma_ {i_{k}}\overline{\xi}_{i_{k}}D_{1}\left(\mathbf{q}_{t}^{j_{k}}\right)\\ &+\eta_{0}\left(\widehat{\mathbf{q}}_{i}^{\angle_{t}}\right)^{\mathrm{T}} \left(\mathbf{\Lambda}_{L}\left(\eta_{j_{k}}a_{i_{k}}\right)\otimes\mathbf{I}_{2} \right)\widehat{\mathbf{\varphi}}_{i}^{\angle_{t}\sim_{t}}\mathbf{q}_{i}^{\angle_{t}} \\ &+\sum_{k=1}^{L}\left(\eta_{j_{k}}-\eta_{0}k_{i_{k}}^{1}\right) \left(\mathbf{\mathcal{T}}_{0i_{k}}\widehat{\mathbf{X}}_{i_{k}}^{t}\right)^{\mathrm{ T}}\left(\gamma_{i_{j_{k}}}\right)^{\mathrm{T}}\\ &=\sum_{k=1}^{L}\left(\eta_{j_{k}}-\eta_{0}k_{i_{k}}^{1}\right) \left(\mathbf{\mathcal{T}}_{0i_{k}}\widehat{\mathbf{X}}_{i_{k}}^{t}\right)^{\mathrm{ T}}\left(\gamma_{i_{j_{k}}}\right)^{\mathrm{T}}\\ &+\eta_{0}\left(\widehat{\mathbf{q}}_{i}^{\angle_{t}}\right)^{\mathrm{T}} \left(\mathbf{\Lambda}_{L}\left(\eta_{j_{k}}a_{i_{k}}\right)\otimes\mathbf{I}_{2} \right)\widehat{\mathbf{\varphi}}_{i}^{\angle_{t}\sim_{t}}\mathbf{q}_{i}^{\angle_{t}} \\ &-\eta_{0}\left(\widehat{\mathbf{q}}_{i}^{\angle_{t}}\right)^{\mathrm{T}} \left(\mathbf{\Lambda}_{L}\left(\eta_{j_{k}}a_{i_{k}}\right)\otimes\mathbf{I}_{2} \right)\widehat{\mathbf{q}}_{i}^{\angle_{t}}\end{split}  \tag{24}\]  
where  
\[\overline{\xi}_{i_{k}}=\mathbf{\mathcal{T}}_{0j}\mathbf{P}_{e1}\widehat{\mathbf{X}}_{i_{k}}^ {t}-\left(\mathbf{T}\left(\psi_{r}^{i_{k}}\right)\right)^{\mathrm{T}}\mathbf{P}_{1} \mathbf{q}_{t}^{j}+\mathbf{P}_{1}\mathbf{q}_{t}^{j_{k}}\]  
Utilizing the equality \(\left(\widehat{\mathbf{q}}_{i}^{\angle_{t}}\right)^{\mathrm{T}}\left(\mathbf{\Lambda} _{L}\left(\eta_{j_{k}}a_{i_{k}}\right)\otimes\mathbf{I}_{2}\right)\widehat{\mathbf{ \varphi}}_{i}^{\angle_{t}\sim_{t}}=0\) and choosing \(\eta_{j_{k}}=\eta_{0}k_{i_{k}}^{1}\), we have \(\dot{V}^{i}(t)\leq 0\) and the equality holds only if \(\widehat{\mathbf{q}}_{i}^{\angle_{t}}\) equals zero. According to Lemma 3, the system must be asymptotical stable, and the tracking errors are decreasing. Thus, \(\mathbf{\varphi}\left(\widehat{\mathbf{Z}}_{i}^{\angle_{t}}\mathbf{P}_{e}^{t}\right)\) is bounded and there exists a positive number \(B_{V}\) that satisfies \(V_{1}^{i}(t)\leq b_{V}\). Therefore, we have \(\dot{V}^{i}(t)\leq-k_{V}V^{i}(t)+k_{V}B_{V}\), where \(k_{V}\) is a positive real number. Consequently, \(V^{i}(t)\) will converge to an equilibrium state in a finite time based on Lemma 4. The proof of Proposition 1 is finished. \(\square\)
## 2 Fronthaul Solutions for 4G/5G
### 4G/5G NR Fiber-Wireless (FiWi) Fronthaul
However, this overlap occurred in the lateral lobes and did not significantly affect the quality of the received signal after the optical fronthaul. Having established an offset of 19 MHz, the optical-wireless system based on non-standalone mode was implemented using a RF power level of \(-5\) dBm for the RF\({}_{1}\) and RF\({}_{2}\) signals and an optical power of \(-3\) dBm at the input of the photodetector. The RF signals were configured with 19 MHz frequency offset, 64-QAM, and 20 MHz bandwidth, thereby achieving a maximum throughput of 182.8 Mbit/s for the 5G NR and LTE-A signals. Figure 3b reports the measured spectrum and detected constellations for the LTE-A and 5G NR signals. One can clearly distinguish each synchronism, control, and data symbols transmitted as the obtained EVM\({}_{\text{RMS}}\) were 2.5% and 2.4% for 5G NR and LTE-A, respectively. Furthermore, there were no noticeable phase rotation distortions in the detected constellations after the fiber-wireless transmission, thus demonstrating successful and seamless coexistence between 5G NR and LTE-A signals.
# Densities and Viscosities of Ionic Liquid with Organic Solvents
## 3 Results and Discussion
### Effects of Organic Solvents on The Viscosities of IL
In Figure 4, in the three solvents, DMF has more effect on the reduction of IL viscosity and larger viscosity deviation in the observed IL-DMF mixture. For further evaluation, another mixing rule proposed by [PERSON] et al. is studied:  
\[\eta_{\text{RHS}}^{*}=R_{\eta}\eta_{\text{SHS}}^{*} \tag{10}\]1 \begin{table}
\begin{tabular}{c c c c c} \hline
**II/Solvent** & \(a\times 10^{5}\) & \(b\times 10^{3}\) & \(R_{\rm\eta}\) & **AARD** & **MD** \\ \hline
[C\({}_{6}\)mim] [OAc] & 12.137 & 21.094 & 8.792 & 0.42 & 1.1 \\ DMA & 4.950 & 3.990 & 1.419 & 0.12 & 0.30 \\ DMF & 1.817 & 7.967 & 3.191 & 0.25 & 0.49 \\ DMSO & 4.766 & 1.713 & 0.7885 & 0.20 & 0.61 \\ \hline \end{tabular}
\end{table} 
Table 14: The fit parameters \(a\), \(b\), and \(R_{\rm\eta}\) for the hard-sphere theory in Equations (11) and (12). \begin{table}
\begin{tabular}{c c c} \hline
**Mixture** & \(K_{\rm\eta}\) & **AARD** \\ \hline IL-DMA & 0 & 16.4 \\  & \(-\)0.1854 & 15.1 \\ IL-DMF & 0 & 41.3 \\  & \(-\)3.512 & 13.1 \\ IL-DMSO & 0 & 17.8 \\  & \(-\)0.1976 & 16.7 \\ \hline \end{tabular}
\end{table} 
Table 15: The parameter \(K_{\rm\eta}\) for the hard-sphere theory in Equation (17). Here, \(k_{12}\) is a binary interaction parameter. Table 16 gives the parameter \(k_{12}\) and AARD. The mixing rule works well without the adjustable parameter \(k_{12}\) with the AARD of 11.8% for IL-DMA and 14.5% for IL-DMSO. When the parameter \(k_{12}\) is used, the values are reduced to 9.4% and 9.6%, respectively. For the mixture of IL with DMF, AARD are 26.8% and 15.3% with/without the parameter \(k_{12}\), which works better than that of the first mixing rule. [PERSON] et al. studied the mixing rule of Equation (18) to predict the viscosity of IL-water with the deviation from 7.10% to 16.37% [57]. As discussed above, a small fraction of solvent in IL would cause the dramatic decrease in the viscosity, and the analysis indicates that interactions exit between IL and solvents, therefore, the mixing rules requires the additional study and further improvement concerning the interactions.
## 4 Hybrid Thematic Analysis: Key Findings from NLP and Expert Review
### Digital Twins: Transforming Predictive Maintenance and Asset Management
#### 4.3.2 Digital Twins and Their Application in Predictive Maintenance  
Digital twins integrate real-time data with computational models to predict failures before they occur, reducing unplanned downtime and improving asset reliability [58; 66]. In the mining industry, they have been utilized to simulate wear in crushers and conveyors, adapting to historical data and operational variability, which has significantly decreased maintenance interruptions. Similarly, in the energy sector, digital twins have been applied to predict performance in turbines and motors, often leveraging hybrid AI-driven predictive models.  
Advanced multi-agent digital twin systems [38; 39; 40] have been developed to coordinate multiple distributed assets, such as truck fleets and shovels, enabling the global optimization of load-haul cycles [41]. Additionally, the integration of augmented reality (AR) layers has been explored to visualize real-time digital twin updates at operational sites, improving situational awareness and facilitating more efficient decision-making for field engineers [42; 43; 44].  
IoT-connected digital twins have demonstrated effectiveness in distributed systems such as rail networks, allowing for real-time anomaly detection and proactive maintenance scheduling. They have also been employed to pinpoint electrical motor faults, particularly through vibration-based machine learning techniques. As digital twin technology continues to expand within the framework of Industry 4.0, its role in providing deeper real-time asset insights and operational optimization is expected to grow, leading to substantial reductions in overall maintenance costs [48; 49].  
Furthermore, the integration of autoencoder-based anomaly detection modules within digital twins has shown promising results in nuclear power plants and aerospace applications, highlighting the expanding scope of this technology beyond mining [8; 76]. In many cases, these advanced digital twin frameworks utilize multi-modal data sources, including acoustic, thermal, and electromagnetic measurements, to deliver a more comprehensive assessment of equipment health [27, 77]. As digital twin technology matures, research increasingly focuses on cybersecurity, platform interoperability, and effective data governance, all of which are critical for ensuring reliability in high-risk operational environments [71, 81, 84].
# Testing the Effectiveness of Voxels for Structural Analysis
## 4 Discussion  
Volumetric models employed in structural analysis software are created through a series of iterative processes, each introducing a level of approximation to the final output. This approximation begins with the transformation of an unorganized point cloud into a mesh through 3D surface reconstruction. Further simplification via retopology refines the surface, achieving a smoother appearance while still preserving a high degree of accuracy [44]. NURBS construction utilizes patches corresponding to the surface mesh elements, approximating the object's form. Given this, beginning with less precise data (like a noisy point cloud) inevitably leads to a less accurate outcome. Furthermore, structural analysis via FEA may introduce different levels of approximation, and the cumulative effect of these steps results in a final product significantly deviating from reality. The denoising process demonstrated its effectiveness in enhancing geometrical truthfulness and reconstruction quality. The improved point distribution within the cloud indicated that the generated mesh had less topological errors, effectively mitigating geometric inaccuracies caused by clusters of noisy points. Consequently, it exhibited reduced noise, free from intersecting elements or spikes that could distort the model's surface geometry. While this geometric modification might not significantly impact visualizations or virtual applications, it can potentially worsen the accuracy of structural finite element analyses, providing, at most, a rougher approximation, or even causing the analysis to fail. This study investigated the effectiveness of utilizing algorithms to produce volume for FEA directly from reality-based point clouds. The primary challenges encountered stem from the input data requirements. When dealing with closed 3D shapes, converting their mesh into a volume using automated  
Figure 7: The problems in FEA software (Ansys 19.2): (**a**) meshing of the voxel model, (**b**) difficulties in imposing the boundary conditions.  
tools introduces minimal approximation errors. However, if the input is merely a surface, generating a volume requires specifying a thickness for the model to ensure proper closure, a feature not always available in custom software. Based on the observed outcomes, it appears that automatic tools are ineffective for creating precise volumes when the initial mesh is incomplete or not a full 3D representation. Using point clouds directly can increase the accuracy of the resultant volume.
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 2 Results
### Univariate Statistical Analysis  
Univariate statistical analysis was used to determine significant differences in metabolite levels between the control group (C) and the preeclamptic group (PE).  
Table 3 shows the metabolites with significant fold-change values and \(p\)-values. Among them, hexadecanal showed the largest change, indicating its potential as an important biomarker for PE (Figure 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **RI** & **RT (min)** & **Compound** \\ \hline
1 & 882 & 4.90 & 3,3-dimethylcyclohexanol \\ \hline
10 & 886 & 6.86 & 4-heptanone \\ \hline
16 & 902 & 7.46 & 2-heptanone \\ \hline
18 & 927 & 8.18 & 4-hydroxyphenylacetaldehyde-oxime \\ \hline
20 & 969 & 9.51 & benzaldehyde \\ \hline
24 & 981 & 9.94 & 2-methyl-5-(methylthio)-furan \\ \hline
27 & 981 & 10.3 & phenol \\ \hline
34 & 1030 & 11.52 & 1,2,3,4-tetramethyl-benzene (Prehnitol) \\ \hline
39 & 1053 & 12.23 & tetrahydro-2,2-dimethyl-5-(1-methyl-1-propenyl)-furan \\ \hline
42 & 1077 & 13.01 & 2,6-dimethyl-7-ocen-2-ol \\ \hline
45 & 1081 & 13.17 & benzenemethanol \\ \hline
48 & 1084 & 13.26 & 1-phenylethanol \\ \hline
49 & 1092 & 13.53 & butenylbenzene \\ \hline
50 & 1099 & 13.8 & tetrahydrorolinalool \\ \hline
52 & 1120 & 14.39 & 2,5-dihydroxybenzaldehyde \\ \hline
59 & 1176 & 16.04 & menthol \\ \hline
60 & 1184 & 16.31 & 1,3,5-undecatriene \\ \hline
64 & 1193 & 16.60 & 1,2,3,4,tetrahydro-1,5,7-trimethylnaphthalene \\ \hline
65 & 1205 & 16.97 & decanal \\ \hline
66 & 1212 & 17.15 & 1,2,3,4-tetrahydro-1,1,6-trimethyl-naphthalene (alpha-ionene) \\ \hline
67 & 1215 & 17.21 & 4,7-dimethyl-benzofuran \\ \hline
81 & 1281 & 19.07 & vitispirane \\ \hline
86 & 1300 & 19.62 & 2,6,10,10-tetramethyl-1-oxa-spiro[4.5]dec-6-ene \\ \hline
87 & 1303 & 19.7 & undecanal \\ \hline
88 & 1330 & 20.39 & 3-formyl-N-methyl-9-[phenylethylynyl]dibenzo[2,3-a:5,6-a\({}^{\prime}\)] \\ \hline
89 & 1356 & 21.07 & 1,2-dihydro-1,1,6-trimethylnaphthalene \\ \hline
92 & 1387 & 21.89 & beta-damascenone \\ \hline
95 & 1487 & 24.4 & 4-(2,6,6-trimethyl-1,3-cyclohexadien-1-yl)-2-butanone \\ \hline
107 & 1817 & 31.8 & hexadecanal \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identified volatile metabolites in urine samples from preeclampsia and control groups using HS-SPME-GC-MS analysis.
# COVID-19 Lung Injury: Unique and Familiar Aspects of Pathophysiology
## 3 Pathogenesis of COVID-19-Induced Lung Injury
### Clinical Presentation and Epidemiology  
Symptoms of COVID-19 infection range from asymptomatic to mild and moderate symptoms including cough and fever persisting for up to 14 days. However, some patients  
Figure 2: COVID-19 pathophysiology: a simplified representation. Upon gaining entry to host cells, SARS-CoV-2 (upper left) employs multiple strategies to suppress host antiviral responses, facilitating spread and infection of the lower respiratory tract. During severe infection, the host immune system (upper right) exhibits a deleterious hyperinflammatory innate phenotype combined with profound loss of function in cytotoxic/adaptive domains. Simultaneously, pulmonary and systemic microcirculation (bottom left) is impaired by dysregulated coagulation and aberrant endothelial inflammation. These pathological mechanisms contribute to a biphasic clinical progression characterized by early hypoxemia, which is exacerbated by accelerating alveolar edema. Figure created with BioRender.com.  
develop severe disease characterized by lower respiratory tract infection and hypoxemia with associated lung pathology [46]. Patients in this stage can rapidly progress to critical condition including ARDS, septic shock, and multiple organ dysfunction [46]. While less common, COVID-19 can cause diverse extrapulmonary manifestations including anosmia, ageusia, gastrointestinal symptoms, myocardial dysfunction, and acute hepatic or kidney injury [47].  
The epidemiology of COVID-19 lung injury is complex due to the dynamic nature of the pandemic. A retrospective cohort study of 201 patients with confirmed COVID-19 pneumonia in Wuhan, China, early in the pandemic found that 41.8% developed ARDS and 52.4% of those diagnosed with ARDS died [48]. More recent evidence suggests similar or lower mortality rates compared to non-COVID-19 ARDS, scaling with severity from 24% to 39%, which may reflect evolving care practices and the impact of vaccination [49]. Older age is a significant risk factor for severe disease and mortality, as are the presence of underlying comorbidities including hypertension, diabetes, cancer, and chronic kidney, heart, and/or lung disease [50]. Male individuals may also be at increased risk of severe disease and mortality [51].
## 1 Introduction
Using dimensionless analysis [30] and short stack and boundary layer approximations, [PERSON] [17] obtained dimensionless expressions for \(Q_{c}\) and \(W\), which depended on eight dimensionless parameters. Despite these simplifications, to the authors' knowledge no study has yet been carried out to identify and quantify the contribution of each of these parameters, as well as the cross-effect of their interactions in an air-based standing-wave thermoacoustic refrigerator [31]. When the working gas is chosen, the \(COP\) depends on five parameters, namely, \(D\), \(B\), \(\Delta T_{mt}\), \(X_{s}\), and \(L_{s}\); see Figure 1.  
This study aimed to delineate the best \(COP\) response, as well as the quantitative and qualitative contribution over defined parameter intervals and their interactions by applying the design of experiments (DOE) method [32], using MINITAB 19 [33]. This study built upon the methodology outlined by [PERSON] [17]; readers interested in a detailed development of the theory are encouraged to refer to this work. This paper is organized as follows: \(COP\) estimation for thermoacoustic devices and design of experiments (DOE) methods in Section 2. The \(COP\) response applying the numerical routine, DOE setup, \(COP\) orthogonal array, Pearson analysis, \(COP\) ANOVA, and the transfer function of the \(COP\) response results in Section 3. Finally, the main conclusions are summarized in Section 4.
## 3 Methodology and Assumptions
### Exergoeconomic Analysis  
An exergoeconomic analysis combines the results of an exergetic- and an economic analysis and provides information that is not accessible through conventional analyses, but essential for the design a cost-effective system. The objective of an exergoeconomic analysis is to identify the cost associated with each inefficiency, and the relative cost importance of each component within the system. This is achieved by applying the exergy-costing principle, where a specific monetary value \(c\) is assigned to each stream of a plant. The product of the average cost \(c_{j}\) and the total exergy rate \(\dot{E}_{j}\) results in the cost rate \(\dot{C}_{j}\) that is assigned to stream \(j\). For the exergoeconomic analysis, cost balances need to be developed for each component resulting in a set of balance equations. The cost balance for component \(k\) is expressed by  
\[\sum\limits_{j}^{n}\left(c_{j}\times\dot{E}_{j}\right)_{k,in}+\dot{Z}_{k}= \sum\limits_{j}^{m}\left(c_{j}\times\dot{E}_{j}\right)_{k,out} \tag{8}\]  
The term \(\sum\limits_{j}^{n}\dot{C}_{j,in}\) represents the sum of the cost rates associated with the n streams entering the component, while \(\sum\limits_{j}^{m}\dot{C}_{j,out}\) refers to the sum of the cost rates of the leaving streams. The variable \(\dot{Z}_{k}\) consists of the sum of the cost rates associated with capital investment (CI) and operation and maintenance (O and M). When the number of exiting streams m is higher than one (\(m>1\)), \(m-1\) auxiliary equations are necessary. The F and P principles are applied to determine the auxiliary equations [38]. An outcome of the analysis is the cost rate associated with the exergy destruction \(\dot{C}_{D,k}\), which is calculated from the specific cost of the fuel for the component (\(c_{F,k}\)) times the corresponding exergy destruction \(\dot{C}_{D,k}\). The exergoeconomic evaluation identifies the components with the highest cost impact which is denoted by the sum of the cost rates associated with exergy destruction and investment (\(\dot{C}_{D,k}+\dot{Z}_{k}\)). In addition, the relative cost difference \(r_{k}\) and the exergoeconomic factor \(f_{k}\) are used to evaluate the relative cost increase between \(c_{F,k}\) and \(c_{P,k}\).  
\[r_{k}=\frac{c_{P,k}-c_{F,k}}{c_{F,k}} \tag{9}\]  
\[f_{k}=\frac{\dot{Z}_{k}}{\dot{Z}_{k}+\dot{C}_{D,k}} \tag{10}\]  
The results of the exergoeconomic evaluation facilitate an iterative optimization of the systems and contribute to the improvement of the cost effectiveness of the overall system. By considering the exergoeconomic variables, design changes and changes of the decision variables can be determined to achieve compromises between the investment cost and the cost of exergy destruction in each important component.
## 4 Discussion
### Fuzzy Automatic Gain Control  
Fuzzy controllers have many advantages in terms of their potential use in AGC systems. Their most important advantages are easy construction and implementation in microprocessor systems. To modify the operation only requires setting the parameters of the rule or changing the parameters of the controller. Their main disadvantage is the speed of operation, which can be minimized by optimizing the number and location of rule entries and by proper processor selection. The accuracy of the controller was computed as having an error level of 0.05, but the error increased in the real system to 0.12. Measurements usingthe fuzzy AGC in electric motor loaded power inverters allowed for precise monitoring of values \(\geq\)1 A from load sources other than the electric drive.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 2 Review of [PERSON] et al.'s Scheme
### Offline Password Guessing Attack  
[PERSON] et al. claimed in their work that even when attacker \(A\) extracts secret data \(\{A_{i},C_{i},N_{i}\}\) stored in the memory device and has the capability to guess the client's identity and password at the same time, a true password still cannot be obtained. However, that is not true in reality. The following steps show that \(A\) can successfully launch an offline password guessing attack to obtain the client's password.  
1. \(A\) extracts the secret data \(\{A_{i},C_{i},N_{i}\}\) stored in the memory device.
2. \(A\) selects an identity \(ID_{a}=A_{i}\), a password \(PW_{a}\), and a random number \(N_{a}\), and then computes \(V_{i}=h(ID_{a}||PW_{a}||N_{a})\). Then, \(A\) submits the registration message \(\{ID_{a},V_{a}\}\) to the server.
3. When the server receives the registration message from \(A\), it checks \(ID_{a}\). Then, the server computes \(A_{a}=h(ID_{a}||k_{s})=h(A_{i}||k_{s})=B_{i}\), \(B_{a}=h(A_{a}||k_{s})\), and \(C_{a}=E_{B_{a}}(V_{a})\). After that, the server stores \(\{A_{a},C_{a},E_{s}(\cdot),D_{s}(\cdot)\}\) into the memory device and issues it to the client.
4. On receiving the memory device, \(A\) obtains \(V_{i}\) by decrypting \(C_{i}\) with the key \(A_{a}(A_{a}=B_{i})\).
5. \(A\) guesses the client's identity \(ID_{i}^{*}\) and password \(PW_{i}^{*}\), and then computes \(V_{i}^{*}=h(ID_{i}^{*}||PW_{i}^{*}||N_{c})\).
6. \(A\) compares \(V_{i}^{*}\) with \(V_{i}\). If these two values are equal, then \(A\) believes that \(PW_{i}^{*}\) is a true password and returns it; otherwise, \(A\) repeats Step 5.
In particular, convolutional neural networks (CNNs) are well suited for capturing spatial features from network topology and channel data. CNNs can process inputs such as channel state information (CSI), AP-UE distance, interference levels, and signal-to-interference-plus-noise ratio (SINR) to learn spatially adaptive power allocation strategies. Based on these inputs, the model learns how to assign power differently depending on user location, channel quality, and interference conditions [7]. This enables flexible and context-aware power control that adapts to dynamic network scenarios instead of relying on fixed allocation rules. This study proposes a CNN-based power allocation model that jointly optimizes both CPU-to-AP and AP-to-UE power distribution. The model is trained end-to-end via supervised learning using data generated from conventional max-min optimization. Simulation results demonstrate that the proposed method significantly improves per-user SE while maintaining EE under realistic network conditions. By integrating both allocation stages into a unified deep learning framework, this work represents a novel approach to hierarchical power control in cell-free networks.