esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The aim of this study was to evaluate the ability of pulsed electric field (PEF) technology to improve the extractability and enhance the oil quality in an industrial olive oil extraction process. Using a PEF device on olive pastes significantly increased the extractability from 79.5% for the control, up to 85.5%. The PEF system did not modify the primary legal quality parameters or total concentrations of phenols, aldehydes, and esters. On the contrary, the non-thermal treatment slightly enhanced the dialdehyde forms of decarboxymethyl elenolic acid linked to hydroxytyrosol (3\(\AA\)-DHPEA-EDA) and tyrosol (p-HPEA-EDA), and decreased the total saturated and unsaturated C5 and C6 alcohols of the PEF EVOO (Extra Virgin Olive Oil) compared to the control test. This study confirmed that PEF technology can improve olive oil extraction and quality.  
pulsed electric field; industrial plant; oil yield; quality; phenols; volatile compounds +
Footnote †: journal: Applied Physics Letters
## 5 Numerical Examples
### Performances of \(\hat{S}\)
For example, while with an SNR of \(50.9691\) dB we need at least \(2^{16}\) samples to get the asymptotic behavior; at \(76.9897\) dB the estimated variance reaches the corresponding asymptotic variance with at least \(2^{22}\) samples. For even higher \(S\) we expect that the number of samples required to get asymptotic performances to increase exponentially.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 2 The Source of Sandalwood Essential Oil, Its Main Components, and Its Applications
### Cytotoxicity of Santalol  
Santalol, derived from sandalwood essential oil, is being studied for its anticancer activity; its ability to suppress, delay, or even reverse the carcinogenesis process makes it one of the best chemopre agents [26]. Chemoprevention is the control of cancer using natural or synthetic agents, either by blocking (preventing carcinogenic substances from reaching critical target sites during the initiation stage of cancer development) or suppressing (inhibiting the proliferation of malignant cells during the promotion and development of carcinogenesis) [40]. \(\alpha\)-Santalol demonstrates antitumor properties by  
Figure 1: Percentage composition of terpenoids in sandalwood essential oil [35].  
inhibiting the growth and viability of malignant cells. This impact extends to various cancer cell types, including breast cancer cells, regardless of their estrogen receptors and p53 status [41]. Additionally, \(\alpha\)-santalol affected the localization of \(\beta\)-catenin, one of which is the major signaling pathways associated with cancer development and metastasis, thus inhibiting the migration of breast cancer cells [28]. Moreover, \(\alpha\)-santalol acts as a chemopre agent against UVB-induced skin tumor development by inhibiting inflammation and epidermal cell proliferation and promoting apoptosis [42].
## 3 Localization Method  
MLE is widely utilized for parameter estimation in statistical modeling due to its desirable properties like consistency, efficiency, and asymptotic normality. It offers accurate, optimal, and unbiased estimation for source localization, effectively leveraging observation data while retaining flexibility to different types of source localization problems. Supported by theoretical foundations and mathematical derivations, MLE plays a crucial role in the development and application of source localization technologies. Maximizing the likelihood function of \(\mathbf{u}^{o}\) and \(\hat{\mathbf{u}}^{o}\) is equivalent to minimizing the objective function under Gaussian noise conditions [48],  
\[J(\mathbf{\beta}) =(\mathbf{\alpha}-\bar{\mathbf{\alpha}}(\mathbf{\beta}))^{T}\mathbf{Q}_{\mathbf{ \alpha}}^{-1}(\mathbf{\alpha}-\bar{\mathbf{\alpha}}(\mathbf{\beta}))\] \[=\sum_{k_{1},k_{2}=1}^{K}\sum_{i_{1},k_{2}=2}^{M}\omega_{a(i_{1},k _{1}),b(i_{2},k_{2})}(r_{i_{1},k_{1}}-\bar{r}_{i_{1},k_{1}})\big{(}r_{i_{2},k_ {2}}-\bar{r}_{i_{2},k_{2}}\big{)}\] \[\quad+\sum_{k_{1},k_{2}=1}^{K}\sum_{i_{1},k_{2}=2}^{M}\omega_{c( i_{1},k_{1}),d(i_{2},k_{2})}\big{(}\bar{r}_{i_{1},k_{1}}-\bar{r}_{i_{1},k_{1}} \big{)}\big{(}\bar{r}_{i_{2},k_{2}}-\bar{r}_{i_{2},k_{2}}\big{)}, \tag{15}\]  
where \(a(i_{1},k_{1})=(k_{1}-1)(M-1)+i_{1}-1\), \(b(i_{2},k_{2})=(k_{2}-1)(M-1)+i_{2}-1\), \(c(i_{1},k_{1})=K(M-1)+a(i_{1},k_{1})\), \(d(i_{2},k_{2})=K(M-1)+b(i_{2},k_{2})\) and \(\omega_{i,l}\) is the \((\iota,l)\)-th element of \(\mathbf{Q}_{\mathbf{\alpha}}^{-1}\). \(\bar{\mathbf{\alpha}}(\mathbf{\beta})\), as well as \(\bar{r}_{i_{1},k_{1}}\) and \(\bar{r}_{i_{1},k_{1}}\), are the reconstructed measurements from \(\mathbf{\beta}\) according to (3) and (5).  
The MLE problem above is nonconvex and nonlinear, which means it is nontrivial to find the optimal solution. Besides the exhaustive method, an effective strategy to handle the MLE is iterative solution. It should be noted that iteration methods, such as GN, Newton-Raphson, Quasi-Newton (QN) and Levenberg-Marquardt (LM), involve asymptotic MLE. Their solutions can approach the CRLB when the noise is mild but diverge or are trapped by local minima if there is a relatively large amount of noise. The GN method offers advantages over others as it balances the convergence and computational costs by computing Jacobian matrices instead of Hessians, suitable for large-scale and high-dimensional parameter spaces, making it particularly effective for highly nonlinear problems while being less computationally demanding and more scalable, although it may be slightly sensitive to initial conditions.  
Therefore, the proposed MLE consists of two parts. One part is an iterative equation, which was developed for minimizing (15) by means of the GN implementation [48]. Considering the initialization sensitivity of GN iteration, the other part provides an suboptimal solution for \(\mathbf{\beta}^{o}\) by SDR and SDP. This solution utilized to initialize the iterative process is sufficient for guaranteeing the convergence.  
Figure 1: Localization scenario using TDOA and FDOA.
## 3 Results
### SA-MIPs Cause Decreased Cell Proliferation, but Are Not Cytotoxic  
For cell proliferation studies, THP-1 cells were incubated with three different concentrations of SA-MIPs, Alhydrogel or PLGA, respectively, for 24 h. A reduced number of THP-1 cells was observed upon incubation with the SA-MIPs and the reference particles (Figure 5a). Since the growth attenuation could indicate particle cytotoxicity, the LDH activity in the culture medium was analyzed. The results show that none of the particles were cytotoxic to THP-1 cells; although, a very slight increase in LDH activity was observed upon addition of the highest concentration of Alhydrogel, 100 \(\upmu\)g/mL (Figure 5b).  
Real-time holographic images were captured every 15 min during 48 h of RAW 264.7 cells cultured in the presence or absence of SA-MIPs. Examples of holographic images of cells incubated with 0 \(\upmu\)g/mL SA-MIPs and 40 \(\upmu\)g/mL SA-MIPs from a randomly selected area in the culture well at the first and last time-point of the time-lapse are shown  
Figure 4: Secretion of TNF-\(\upalpha\) and IL-6 after incubation with SA-MIPs, Alhydrogel and PLGA. RAW 264.7 cells were cultured in medium alone or with additions of 0 \(\upmu\)g/mL, 25 \(\upmu\)g/mL, 50 \(\upmu\)g/mL or 100 \(\upmu\)g/mL SA-MIPs, Alhydrogel or PLGA, respectively, for 24 h at 37 \({}^{\circ}\)C. Culture supernatants were assayed for TNF-\(\upalpha\) (**a**) and IL-6 (**b**) cytokines by ELISA. The results represent the mean of three independent experiments \(\pm\)SD.  
Figure 5: Viable THP-1 cells after incubation with SA-MIPs, Alhydrogel or PLGA particles for 24 h. THP-1 cells were either counted (**a**) or the release of lactate dehydrogenase (LDH) into the culture medium was measured (**b**). THP-1 cells were incubated with 0 \(\upmu\)g/mL, 25 \(\upmu\)g/mL, 50 \(\upmu\)g/mL or 100 \(\upmu\)g/mL SA-MIPs, Alhydrogel or PLGA for 24 h. Results shown as % of the medium control and the average of three independent experiments with \(\pm\)SD.  
(Figure 6a, b). SA-MIPs (40 \(\upmu\)g/mL) were cultured together with RAW 264.7 cells for up to 48 h and the cell number was determined using DHC (Figure 6c). More than twice as many cells were identified in samples with cells grown in a culture medium only after 48 h compared to cells cultured in the presence of SA-MIPs.  
Figure 6: DHC images in 2D and cell proliferation. Representative images from DHC time-lapse at time-point 0 h and 48 h of RAW 264.7 cells incubated with medium (**a**) or 40 \(\upmu\)g/mL. SA-MIPs (**b**). The SA-MIPs were added immediately before the start of the time-lapse and are seen as small dots surrounding the cells. The horizontal scale bars at the top in the images represent 300 \(\upmu\)m and the vertical scale bars to the left represent the optical thickness \(-\)0.4 to 16 \(\upmu\)m. Average number of cells visible in time-lapse frames over 48 h (**c**) of three independent experiments with \(\pm\)SD. RAW 264.7 cells were seeded with medium for 24 h. Time-lapse imaging was initiated when 40 \(\upmu\)g/mL. SA-MIPs was added 24 h after cell seeding. This corresponds to time 0 in (**c**).
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 7 Related Works
### Gray-Box Fuzzing  
According to the purposes, gray-box fuzzing is divided into coverage-based gray-box fuzzing (CGF) and directed gray-box fuzzing (DGF).  
**Coverage-based gray-box fuzzing** seeks to generate test cases that can achieve the maximum code coverage. AFL [39] is the most popular coverage-based gray-box fuzzer. It adopts a undirected path exploration to cover the code. Many improved techniques [35; 36; 40; 41] are proposed based on AFL. Vuzzer [35] assigns different weights to the execution paths and gives priority to the long paths. AFLFast [40] pays more attention to the low-frequency paths in the fuzzing campaign. CGF is widely applied in vulnerability detection. However, due to the undirected path exploration, this technique wastes a lot of resources on unrelated code.  
**Directed gray-box fuzzing**, proposed by [PERSON] et al. [28], seeks to spend most of its time on reaching specific target locations without wasting resources on unrelated code. This technique performs a directed path exploration by giving priority to the paths close to the targets. At present, DGF is also mainly applied in vulnerability detection. Existing works [28; 29; 30; 31; 32; 33; 34] usually take the changed statements or dangerous locations as target locations. Their purposes are to generate test cases that can trigger vulnerabilities.  
However, in this paper, DGF is applied to construct CFGs. We take indirect jump locations as the target locations of DGF and apply this technique to improve the completeness of CFGs.
# Dielectric Characterization of Chinese Standard Concrete for Compressive Strength Evaluation
## 3 Experimental Results
### Dielectric Constant Modelling
#### 3.3.3 Jonscher Model
Therefore, one can use the dielectric constant values obtained from the Jonscher model for further correlation with compressive strength and GB concrete. The ultimate goal is to establish tracking variables for the purpose of civil structural health monitoring. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GB Concrete** & _w/c_ & \(n\) & \(\chi_{r}\) & \(\varepsilon_{\infty}\) & \(R^{2}\) & \(SSE\) \\ \hline C30 & 0.60 & 0.622 & 0.674 & 3.116 & 0.987 & 0.032 \\ C35 & 0.54 & 0.273 & 0.396 & 3.559 & 0.973 & 0.075 \\ C40 & 0.49 & 0.385 & 0.446 & 3.955 & 0.991 & 0.022 \\ C45 & 0.45 & 0.422 & 0.624 & 4.233 & 0.990 & 0.042 \\ C50 & 0.40 & 0.485 & 0.921 & 4.797 & 0.997 & 0.020 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Jonscher Model: Fitting Coefficients with \(f_{r}=0.1\) GHz and Goodness of Fit for Equation (7). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GB Concrete** & _w/c_ & \(K\) & \(b\) & \(C\) & \(R^{2}\) & \(SSE\) \\ \hline C30 & 0.60 & 0.967 & 4.268 & 3.305 & 0.938 & 0.155 \\ C35 & 0.54 & 1.213 & 8.496 & 3.597 & 0.939 & 0.170 \\ C40 & 0.49 & 1.148 & 8.017 & 4.018 & 0.975 & 0.063 \\ C45 & 0.45 & 1.466 & 7.374 & 4.332 & 0.956 & 0.189 \\ C50 & 0.40 & 1.926 & 6.862 & 4.973 & 0.971 & 0.226 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: First-order Exponential Model: Fitting Coefficients and Goodness of Fit for Equation (4). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**GB Concrete** & _w/c_ & \(\tau\) & \(\varepsilon_{static}\) & \(\varepsilon_{\infty}\) & \(R^{2}\) & \(SSE\) \\ \hline C30 & 0.60 & 0.891 & 4.177 & 3.303 & 0.928 & 0.181 \\ C35 & 0.54 & 1.798 & 4.661 & 3.598 & 0.927 & 0.202 \\ C40 & 0.49 & 1.689 & 5.029 & 4.018 & 0.970 & 0.076 \\ C45 & 0.45 & 1.529 & 5.621 & 4.3  
By combining Equations (3) and (11), one can establish the correlation between the compressive strength and the dielectric constant of GB concrete, via the common variable of \(w/c\), as given by:  
\[f^{\prime}_{cs}\big{(}e^{\prime}_{r},\ f\big{)}=\big{(}e^{\prime}_{r}\big{)}^{r}+A \tag{12}\]  
where \(r\) and \(A\) are the regression coefficients, whose values and the goodness-of-fits are summarized in Table 9.  
Figure 8a shows the correlations between the compressive strength and dielectric constant of GB concrete, at various frequencies. The correlation has been described by the power law function expressed in (12). By further considering the standard deviations of both of the measured data, Figure 8b illustrates the correlation at a frequency of 2.45 GHz. It is noteworthy to describe that the frequency dispersion has a minimal effect on the dielectric constants, where the discrepancies are far less than the standard deviations from the measurement. The relatively high standard deviation from the dielectric constant is attributed to the high heterogeneity of the GB concrete, where the maximum size of the coarse aggregates used in the fabrication was 31.5 mm.
## Materials and Methods  
### Materials  
Dulbecco's modified Eagle's medium (DMEM), fetal bovine serum (FBS), penicillin-streptomycin(P/S), and phosphate-buffered saline (PBS) were purchased from Gibco (Gaithersburg, MD, USA). COX-2, iNOS, p65, p-I\(\kappa\)B-\(\alpha\), I\(\kappa\)B-\(\alpha\), p-ERK, ERK, p-p38, p38, p-JNK, JNK, and GAPDH monoclonal antibodies and secondary antibodies were obtained from Cell Signaling Technology (Boston, MA, USA). The enzyme immunoassay (EIA) kits for IL-1\(\beta\) and IL-6 were obtained from BioLegend (San Diego, CA, USA). The Maxime RT Premix Kit was purchased from iNtRON (Gyeonggi-do, Korea). COX-2, iNOS, IL-1\(\beta\), IL-6, and GAPDH oligonucleotides primers were obtained from Bioneer, Inc. (Seoul, Korea). 2,3-bis-(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) was purchased from WEL GENE (Daegu, Korea). Compound K (\(\geq\)96%), TRIzol\(\beta\) reagent, 3-amino-4-(N-methylamino)-2\({}^{\prime}\),7\({}^{\prime}\)-difluoroT fluorescein diacetate (DAF-FM DA), Griess reagent, and lipopolysaccharide-LPS (Escherichia coli, serotype 0111:04) were obtained from Sigma Chemical Co. (St. Louis, MO, USA).
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 3 Review of [PERSON] et al.'s Protocol
### Password Update Process  
In [PERSON] et al.'s protocol, the _MU_ can freely update their password. The process is described in detail as follows.  
**Step 1:**: When a legitimate _MU_ wants to update the password, the _MU_ inputs \(ID_{MU},PW_{MU}\) and the request messages are transmitted via a terminal.  
Figure 2: The login and authentication process of [PERSON] et al.’s protocol.  
**Step 2:**: The mobile device of \(MU\) calculates \(K_{MU}^{*}=h(ID_{MU}||PW_{MU})\) and checks whether \(K_{MU}^{*}\stackrel{{?}}{{=}}K_{MU}\). If this holds, the \(MU\) is legitimate user. Otherwise, the mobile device terminates the password change process.
**Step 3:**: The \(MU\) selects new password \(PW_{MU}^{NEW}\) and computes \(K_{MU}^{NEW}=h(ID_{MU}||PW_{MU}^{NEW})\). Finally, the mobile device of \(MU\) replaces \(\{K_{MU}\}\) with \(\{K_{MU}^{NEW}\}\).
# Change Control Design in Product Supply Chain System Based on Radial Basis Function Neural Network
## 4 Simulation Analysis
These results underscore the advantages of the LQR-RBF method in terms of both stability and accuracy compared to traditional control methods, making it a superior choice for managing system inventory dynamics. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Settling Time** & **Overshoot** & **Mean Squared Error (MSE)** \\ \hline LQR-RBF & 1.5 & 5.49 & 0.16 \\ PID control & 5.8 & 6.17 & 0.25 \\ Sliding mode control & 2.9 & 5.32 & 0.48 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of different control methods for system inventory (\(x_{1}\)). Figure 15: Inventory of two plants under sliding mode control. In addition, the traditional PID control and sliding mode control methods have significant limitations in addressing unknown disturbances, particularly in dealing with complex external disturbances and internal system uncertainties. This limitation emphasizes the advantage of the proposed method: by enhancing robustness to unknown disturbances, it not only reduces their impact on system stability but also demonstrates a stronger ability to resist potential cyber-attacks.
# ANTSpin: Efficient Absolute Localization Method of RFID Tags via Spinning Antenna
## 5 Materials and Methods
### Distance Calculation  
After the data are prepossessed, the RSSI maximum value of each tag and its corresponding timestamp can be obtained. Two reference tags with known absolute positions are recorded as \(r_{1}\) and \(r_{2}\), and the following three equations can be listed, according to formula (1):  
\[\begin{array}{l}PL(d_{r1})(dB)=10 nl\text{g}\Big{(}\frac{d_{r1}}{d_{0}}\Big{)} +PL(d_{0})+X_{\sigma}\\ PL(d_{r2})(dB)=10 nl\text{g}\Big{(}\frac{d_{r2}}{d_{0}}\Big{)}+PL(d_{0})+X_{ \sigma}\\ PL(d)(dB)=10 nl\text{g}\Big{(}\frac{d}{d_{0}}\Big{)}+PL(d_{0})+X_{\sigma}\\ \end{array} \tag{11}\]  
where \(PL(d_{0})+X_{\sigma}\) and n are related to the experimental environment and they cannot be directly calculated. First, the data of tag to be located is subtracted from the data of the two reference tags, \(PL(d_{0})+X_{\sigma}\) is directly eliminated, then:  
\[\begin{array}{l}PL(d)(dB)-PL(d_{r1})(dB)=10 nl\text{g}\Big{(}\frac{d}{d_{r1} }\Big{)}\\ PL(d_{r1})(dB)-PL(d_{r2})(dB)=10 nl\text{g}\Big{(}\frac{d_{r1}}{d_{r2}}\Big{)} \\ \end{array} \tag{12}\]  
Eliminate \(n\) by dividing the two equations to get:  
\[\frac{PL(d)(dB)-PL(d_{r1})(dB)}{PL(d_{r1})(dB)-PL(d_{r2})(dB)}=1\text{g}\Bigg{(} \frac{d}{d_{r1}}\Bigg{)}/1\text{g}\Bigg{(}\frac{d_{r1}}{d_{r2}}\Bigg{)} \tag{13}\]  
The left side of the equation can be simply calculated by the collected data. We can write it as \(\alpha\) and further get:  
\[\begin{array}{l}\alpha=\text{lg}\Big{(}\frac{d}{d_{r1}}\Big{)}/1\text{g} \Big{(}\frac{d_{r1}}{d_{r2}}\Big{)}\\ \Big{(}\frac{d_{r1}}{d_{r2}}\Big{)}^{\alpha}=\Big{(}\frac{d}{d_{r1}}\Big{)}\\ d=\Big{(}\frac{d_{r1}}{d_{r2}}\Big{)}^{\alpha}\times d_{r1}\\ \end{array} \tag{14}\]
## 3 System Model

Table 1: Comparison of existing methods with the proposed cooperative reinforcement learning. where \(G_{c,r}\) and \(G_{v,r}\) indicate the channel gains on the \(r\)th RB from BS to cellular user \(c\) and \(v\)th D2D transmitter, respectively. The total path-loss which includes the antenna gain between BS and the user \(u\) is:  
\[PL_{dB,\mu(.)}=L_{dB}(d)+\log_{10}(X_{u})-A_{dB}(\theta) \tag{3}\]  
where \(L_{dB}(d)\) is the pathloss between a BS and the user at a distance \(d\) meter. \(X_{u}\) is the log-normal shadow path-loss of user \(u\). \(A_{dB}(\theta)\) is the radiation pattern [22]. \(L_{dB}(d)\) can be expressed as follows:  
\[ \begin{split} L_{dB}(d)&=40(1-4\times 10^{-3}h_{b}) \log_{10}(d/1000)\\ &-18\log_{10}(h_{b})+21\log_{10}(f_{c})+80\end{split}  \tag{4}\]  
where \(f_{c}\) is the carrier frequency in GHz and \(h_{b}\) is the base station antenna height [22]. The linear gain between the BS and a user is \(G_{Bu}=10^{\frac{-P_{tdB,B,u}}{10}}\). For D2D communication, the gain between two users \(u\) and \(v\) is \(G_{uv}=k_{uv}d_{uv}^{-\alpha}\)[23]. Here, \(d_{uv}\) is the distance between transmitter \(u\) and receiver \(v\). \(\alpha\) is a constant pathloss exponent and \(k_{uv}\) is a normalization constant. The objective of resource allocation problem (i.e., to allocate RB and transmit power) is to assign the resources in a way that maximizes system throughput. System throughput is the sum of D2D users and CU throughput, which is calculated by Equation (6). The resource allocation can be indicated by a binary decision variable, \(b_{v}^{(r,p_{r})}\) where  
\[b_{v}^{(r,p_{r})}= \begin{cases}1,&\text{if the transmitter $v$ is transmitting over RB $r$ with power level $p_{r}$}\\ 0,&\text{otherwise}\end{cases} \]  
The aggregated interference experienced by RB \(r\) can be expressed as follows  
\[I^{(r)}=\sum_{v=1}^{D}\sum_{p_{r}=1}^{P_{max}}b_{v}^{(r,p_{r})}G_{v,r}p_{r} \tag{5}\]  
Let \(B=[b_{1}^{(1,1)},\dots,b_{1}^{(r,p_{r})},\dots,b_{1}^{(R,P_{max})}]^{T}\) denote the resource e.g., RB and transmission power allocation. So, the allocation problem can be expressed as follows:  
\[ \begin{split}&\max_{B}\sum_{r=1}^{R}\sum_{p_{r}=1}^{P_{max}}b_{ v}^{(r,p_{r})}W_{RB}\{\log_{2}(1+\gamma_{r}^{c})+\sum_{u\in D_{r}}\log_{2}(1+ \gamma_{r}^{D_{u}})\}\\ &\text{subject to $I^{(r)}<I_{th}^{(r)},\forall_{r}$}\\ & b_{v}^{(r,l)}\in\{0,1\},\forall_{v,r,l}\\ &\sum_{r=1}^{R}\sum_{p_{r}=1}^{P_{max}}b_{v}^{(r,l)}=1,\forall_{v} \\ & 0\leq p_{r}\leq P_{max},\forall_{u,r}\end{split}  \tag{6}\]  
where \(p_{r}=(p_{r}^{1},p_{r}^{2},\dots,p_{r}^{R})\) and \(W_{RB}\) is the bandwidth corresponding to a RB. The objective function is to maximize the throughput of the system constrained by that the aggregated interference should be limited by a predefined threshold. The number of RB selected by the transmitter should be one where each can select one power level at each RB. Our goal is to investigate the optimal resource allocation in such a way that the system throughput is maximized by applying cooperative reinforcement learning.