esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Gre Factors
pneumoniae_ genome ( upregulation of a similar number of genes was also observed, but was attributed to the downregulation of a general transcription repressor). As expected, _S. pneumoniae_ GreA resolved backtracked complexes, and, in general, increased the processivity (success of reaching the end of the gene) of transcription _in vitro_. However, no effects on promoter escape or the other stages of transcription have been observed [24]. It was, therefore, concluded that resolution of backtracked complexes was the primary function of GreA, at least in _S. pneumoniae_. Interestingly, highly transcribed genes were affected more strongly by the deletion of GreA. Supported by stochastic simulations, this observation led to a model in which unresolved backtracked complexes may cause \"traffic jams\" of the RNAPs trailing behind an arrested or paused one [24]. This model contrasts with the model proposed previously for _E. coli_, which suggested that RNAP molecules cooperate during elongation in order to prevent backtracking of the leading RNAP [43]. Besides blocking gene expression of the occupied genes, queuing RNAPs would be a potent obstacle for progression of replication forks. Collisions of the transcription (especially backtracked elongation complexes) and replication machineries may be detrimental to cells if not prevented or resolved ([44; 45; 46]; see below). Thus, the major role of Gre factors in bacteria could involve the resolution of backtracked complexes, and the \"traffic jams\" caused by them, in order to ease interference between transcription and replication. However, as mentioned above, it is difficult to investigate the role of Gre in model bacteria due to redundancy in some functions with other SCBFs, such as DksA (see below).
## Abbreviations
### Nomenclature  
\(P_{EL}(t)\) Electrojzer input power during step \(\Delta t\) & \(P_{H_{2}}\) & Hydrogen pressure (atm) \\ \(\eta_{ele}\) & Electrojzer efficiency & \(P_{O_{2}}\) & \(O_{2}\) pressure (atm) \\ \(\alpha_{ele}\) & required energy to produce one Kg of \(H_{2}\) & F & Faraday constant (96,485 C/mol) \\ \(M_{HT}\) & Margin coefficient for \(H_{2}\) tank & \(i_{1}\) & Current density limit \\ \(E_{stor}(t)\) and & Energy stored in the hydrogen tanks at & \(E_{gen}(t)\) & Generated power from the RESs \\ \(E_{stor}(t-1)\) & hours t and \(t-1\), respectively. & \(\eta_{FCs}\) & Overall efficiency of the FC \\ \(P_{PV}(t)\), \(P_{FC}(t)\) & PV, FCm and wind turbines generated & \(\eta_{FCs}\) & Overall efficiency of the FC \\ \(\eta_{F}(t)\) & power at time t & \(E_{gen}(t)\) & Generated Energy at time t \\ \(\rho_{H_{2}}\) & Hydrogen density & \(I_{FC}\) & FC current \\ \(n_{e}\) & Number of electrons & S & Catalyst area (m\({}^{2}\)) \\ R & Ideal gas constant (8.31 J/mol\(\cdot\)K) & \(j_{0}\) & Current density (A/m\({}^{2}\)), \\ \(\alpha\) & Symmetry factor & \(\eta_{comb}\) & Mass flow rate (Kg\(\cdot\)S\({}^{-1}\)) \\ \(n_{ex}\) & Number of electron exchanges & \(\phi^{FC}\) & Annual O&& \\ \(U_{F}\) & Fuel utilization & & & \\ \(Q_{LHV}\) & Lower heating value (J.Kmol\({}^{-1}\)) & \(\eta_{bat}\) & Battery efficiency, \\ \(P_{L}(t)\) & Load demand at time t & \(\sigma\) & Self-discharge rate \\ \(\eta_{FUV}^{dis}\) & EV discharging efficiency & DOD & Depth of discharge \\ \(P_{dis.max}\) and & Maximum discharging and charging & \(DOD_{max}\) & Maximum Depth of discharge \\ \(P_{dL}(t)\) & capacity of a battery bank & & \\ \(\eta_{inv}\) & Inverter efficiency & & \\ \(C_{batt}(t)\) and & Energy stored at times \(t\) and & & \\ \(C_{batt}(t-1)\) & \((t-1)\), respectively & & \\ \(C_{batt-min}\), and & Minimum and maximum permissible & & \\ \(C_{batt-max}\) & energy storage levels, respectively & & \\ \(C_{ap}\) & Capacity of the EV & & \\ \(P_{Lt}(t)\) & Total load power & & \\ \(P_{sur}\) & Surplus power generated & & \\ \(P_{Lt}(t)\) and \(P_{Gent}(t)\) & Total load demand and generated & & \\ \(P_{Lt}(t)\) and \(P_{Gent}(t)\) & power, over time (t), respectively. & & \\ \end{tabular}  
### Nomenclature  
\(P_{EL}(t)\) Electrojzer input power during step \(\Delta t\) & \(P_{H_{2}}\) & Hydrogen pressure (atm) \\ \(\eta_{ele}\) & Electrojzer efficiency & \(P_{O_{2}}\) & \(O_{2}\) pressure (atm) \\ \(\alpha_{ele}\) & required energy to produce one Kg of \(H_{2}\) & F & Faraday constant (96,485 C/mol) \\ \(M_{HT}\) & Margin coefficient for \(H_{2}\) tank & \(i_{1}\) & Current density limit \\ \(E_{stor}(t)\) and & Energy stored in the hydrogen tanks at & \(E_{gen}(t)\) & Generated power from the RESs \\ \(E_{stor}(t-1)\) & hours t and \(t-1\), respectively. & & \\ \(P_{PV}(t)\), \(P_{FC}(t)\) & PV, FCm and wind turbines generated & & \\ \(P_{PV}(t)\) & power at time t, respectively.
## 2 Methods  
### Regularized Logistic Regression  
In this paper, we only consider a general binary classification problem and get a predictor vector X and a response variable y, which consists of genes and corresponding tissue samples, respectively. Suppose we have \(n\) samples, \(D\) = (\(X_{1}\), \(y_{1}\)), (\(X_{2}\), \(y_{2}\)),..., (\(X_{n}\), \(y_{n}\)), where \(X_{i}\) = (\(x_{i1}\), \(x_{i2}\),..., \(x_{ip}\)) is \(i\)th input pattern with dimensionality \(p\), which means the \(X_{i}\) has \(p\) descriptors and \(x_{ij}\) denotes the value of gene \(j\) for the \(i\)th sample. \(y_{i}\) is a corresponding variable that takes a value of 0 or 1. Define a classifier \(f(x)=e^{x}/(1+e^{x})\), and the logistic regression is shown as follows:  
\[P(y_{i}=1|X_{i})=f(X_{i}^{\prime}\beta)=\frac{exp(X_{i}^{\prime}\beta)}{1+exp (X_{i}^{\prime}\beta)}. \tag{1}\]  
Additionally, the log-likelihood can be expressed as follows:  
\[l(\beta)=-\sum_{i=1}^{n}\{y_{i}log[f(X_{i}^{\prime}\beta)]+(1-y_{i})log[1-f(X_ {i}^{\prime}\beta)]\}. \tag{2}\]We can get the value of vector \(\beta\) from Equation (2). However, solving Equation (2) can result in over-fitting with data of high dimension and small sample size. Therefore, in order to address the problem, we add the regularization terms to Equation (2):  
\[\beta=argmin\{l(\beta)+\lambda\sum_{j=1}^{p}p(\beta_{j})\}, \tag{3}\]  
where \(l(\beta)\) and \(p(\beta)\) are loss function and penalty function, respectively, and \(\lambda>0\) is a tuning parameter. Note that \(p(\beta)=\sum|\beta|^{q}\). When \(q\) is equal to 1, the \(L_{1}\) has been proposed. Moreover, there are various of versions of \(L_{1}\), such as SCAD, MCP, group lasso, and so on. We add the \(L_{1}\) regularization to Equation (2). The formula is expressed as follows:  
\[\beta=argmin\{l(\beta)+\lambda\sum_{j=1}^{p}|\beta_{j}|\}. \tag{4}\]  
From a biologist's point of view, there is a grouping property among genes, which is a limitation of \(L_{1}\) regularization. To overcome this limitation, [PERSON] et al. proposed the elastic net (\(L_{EN}\)) regularization method for gene selection. The \(L_{EN}\) regularization tries to combine \(L_{1}\) with \(L_{2}\) in order to search for highly correlated genes and perform gene selection simultaneously. The regularized logistic regression using \(L_{EN}\) is exhibited as follows:  
\[\beta=argmin\{l(\beta)+\lambda_{1}\sum_{j=1}^{p}|\beta_{j}|+\lambda_{2}\sum_{ j=1}^{p}|\beta_{j}|^{2}\}. \tag{5}\]  
As we observe from Equation (5), \(\lambda_{1}\) and \(\lambda_{2}\) control the sparsity and group effect, respectively. The coefficient \(\beta\) depends on two non-negative tuning parameters \(\lambda_{1}\) and \(\lambda_{2}\). In order to simplify Equation (5), let \(\lambda_{1}\) plus \(\lambda_{2}\) equal to 1. Thus, we can rewrite Equation (5) as:  
\[\beta=argmin\{l(\beta)+\lambda_{1}\sum_{j=1}^{p}|\beta_{j}|+(1-\lambda_{1}) \sum_{j=1}^{p}|\beta_{j}|^{2}\}. \tag{6}\]
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 5 Numerical Results  
In this section, Monte Carlo simulations are carried out to verify the theoretical results. Without any loss of generality, we assume that the transmit powers at \(S\) and \(R_{i}\) are given as \(\mathcal{P}_{S}=\mathcal{P}_{\mathcal{R}_{i}}=P\). Moreover, the noise power is taken as \(N_{0}\) for all hops, and \(\gamma_{th}=10\) dB, unless otherwise stated.  
Figure 3 illustrates the outage probability performance of the SN for different numbers of hops when \(M=L=2\) and \(\alpha=0.005\). As can be seen from the figure, outage probability performance worsens as the number of hops increase. This is due to the fact that the number of interferers increase as the number of hops increase. As for example, almost 30 dB is needed to achieve \(P_{out}=10^{-2}\) at \(K=8\), while when \(K=2\), 18 dB is enough to achieve the same outage probability performance. Moreover, the theoretical curves verify the Monte Carlo simulations.  
In Figure 4, the outage probability performance of the considered scheme is depicted for different numbers of interferers (PUs). As can be seen, as the number of interferers increase, the performance degrades. Moreover, the slopes of the curves verify that the diversity gain deteriorates as the number of interferers increase.  
Figure 5 illustrates \(P_{out}\) with respect to \(P/N_{0}\) of the considered scheme for three different interference leakage values, i.e., \(\alpha=0.01,0.02,0.05\). The other parameters are taken as \(M=2\)  
Figure 3: Outage probability of the secondary network versus \(P/N_{0}\) for different numbers of hops, when \(\alpha=0.005\).  
\(K=2\), \(L=2\). As can be seen from Figure 5, the best \(P_{out}\) performance can be obtained when \(\alpha=0.01\), and the performance worsens as \(\alpha\) increases.  
Figure 4: Outage probability of the secondary network versus \(P/N_{0}\) for different numbers of primary users (interferers), when \(\alpha=0.005\).  
Figure 5: Outage probability of the secondary network versus \(P/N_{0}\) for various interference leakage levels.  
Figure 6 depicts the impact of various interference leakage levels on the performance of the multi-hop SN. As observed from the figure, ergodic capacity performance of the proposed scheme degrades as the impact of interference leakage enhances. On the contrary, when the interference is aligned, the capacity of the secondary network can achieve 10 bits/Hz at 50 dB. Comparing the derived approximate ergodic capacity with the simulation, it can be observed that the theoretical results match almost perfectly with the simulations.
## 6 Performance Evaluation
### Four-User System  
This section evaluates the proposed R-IBFD system performance. For the simulation 16-QAM modulation, Rayleigh fading channel is used and total transmission power \(P_{T}=1\) W is used. Secrecy capacity comparison with baseline techniques is evaluated, MLGA is also used for the optimization of secrecy capacity.  
The baseline comparative schemes are NOMA R-IBFD without \(AN\), Reflective HD (R-HD), R-HD with \(AN\) (RA-HD) and R-IBFD with \(AN\) (RA-IBFD).  
Figure 7 shows the simulated result for secrecy capacity with a four-user system and a passive Eve. The comparison of RA-IBFD and R-IBFD with R-HD, RA-HD, and NOMA shows that the proposed RA-IBFD outperforms all other implementations. It is due to the fact (21) that the Eve faces a high amount of interference due to the addition of \(AN\), no  
Figure 5: Secrecy outage probability comparison for Reflective In-Band Full-Duplex (R-IBFD) and Decode-and-Forward In-Band Full-Duplex (DF-IBFD) with \(C_{th}=1\), \(\zeta_{12}=\zeta_{11}=12\) dB, \(K=1\), \(2\) and \(4\) and \(\zeta_{1e}\) is calculated with respect to Rayleigh flat fading and respective interference.  
Figure 6: Throughput comparison for Reflective In-Band Full-Duplex (R-IBFD) and Decode-and-Forward IBFD (DF-IBFD) with \(C_{th}=1\), \(\zeta_{12}=\zeta_{11}=12\) dB, \(K=1\), \(2\) and \(4\) and \(\zeta_{1e}\) is calculated with respect to Rayleigh flat fading and respective interference.  
CSI information of the Bobs and the desired component of the particular Bob. Therefore, it leads to a significant gain of RA-IBFD.  
The high interference on the Eve node increases the difference between the capacities of the respective Bob and the Eve, which leads to the increase of secrecy capacity. Figure 8 shows the simulated result of RA-IBFD with and without MLGA. For MLGA, the power coefficients are generated randomly. For each \(Bob_{i}\), four random numbers have been generated as the initialized population with respect to distance and with the sum equal to 1. The secrecy capacity is used as the fitness function of the MLGA. The optimized secrecy capacity has been simulated using the MLGA method as shown in Figure 3. The result shows that MLGA further enhances the performance of RA-IBFD due to the optimized parameters subjected to the optimized level of RA-IBFD secrecy capacity. Therefore, the use of MLGA makes improves the secrecy capacity. For a limited number of users as chosen in this article, it is easy to measure system parameters statistically. However, in a system with a high number of users, machine learning is a necessity for a wireless communication system with high data.  
Figure 8: Sum secrecy capacity comparison for Proposed RA-IBFD with and without MLGA.  
Figure 7: Sum secrecy capacity comparison for Proposed RA-IBFD and R-IBFD with R-HD, RA-HD and NOMA.
# Optimizing Energy Efficiency in Cloud Data Centers:
###### Abstract  
Cloud computing faces growing challenges in energy consumption due to the increasing demand for services and resource usage in data centers. To address this issue, we propose a novel energy-efficient virtual machine (VM) placement strategy that integrates reinforcement learning (Q-learning), a Firefly optimization algorithm, and a VM sensitivity classification model based on random forest and self-organizing map. The proposed method, RLVMP, classifies VMs as sensitive or insensitive and dynamically allocates resources to minimize energy consumption while ensuring compliance with service level agreements (SLAs). Experimental results using the CloudSim simulator, adapted with data from Microsoft Azure, show that our model significantly reduces energy consumption. Specifically, under the lr_1_2_mmt strategy, our model achieves a 5.4% reduction in energy consumption compared to PABFD, 12.8% compared to PSO, and 12% compared to genetic algorithms. Under the iqr_1_5_mc strategy, the reductions are even more significant: 12.11% compared to PABFD, 15.6% compared to PSO, and 18.67% compared to genetic algorithms. Furthermore, our model reduces the number of live migrations, which helps minimize SLA violations. Overall, the combination of Q-learning and the Firefly algorithm enables adaptive, SLA-compliant VM placement with improved energy efficiency.  
cloud computing; server consolidation; energy consumption; firefly optimization; machine learning; Q-learning Article
# HMT exerts an Anticancer Effect by Targeting PAK-1
## 3 Results
### HMT Inhibits Cell Migration and Invasion by Inhibiting PAK-1 Pathway  
To investigate whether HMT could suppress prostate cancer cell motility without exhibiting cell toxicity, we used a wound-healing and Matrigel-coated membrane invasion assay. As shown in Figure 2A,B, in the Matrigel-coated invasion assay, HMT inhibited cell invasion by 50% compared to the untreated control. Moreover, in the wound healing assay, HMT treatment decreased serum-induced cell migration by 57% compared to the untreated control. As shown in Figure 1B, the inhibitory effect of cell motility by HMT was not linked to cell toxicity. Notably, 62.5 ug/mL HMT suppressed cell viability by 15% in the PC-3 cell lines compared to the control (Figure 1B). To confirm that the inhibitory effect of HMT in cancer cell migration and invasion was correlated to PAK-1 regulation, we performed Western blotting to measure the protein levels changes of PAK-1 and PAK-1-regulated cell motility proteins following HMT treatment in PC-3 cells. A concentration of 50 ug/mL HMT attenuated PAK-1, AKT, LIMK, and cofilin protein levels (Figure 2C).
# Decentralized Coordination of DERs for Dynamic Economic Emission Dispatch
## 6 Discussion  
The simulation results confirm the effectiveness of the algorithm proposed in this paper for solving the DEED problem. In the comparative experiments, we observed that whether in terms of the number of iterations or computational time, the centralized algorithm still maintains an advantage. It is understandable that decentralized methods require more iterations to achieve the same level of accuracy compared with their centralized counterparts. This is due to the need for local communication and operations to ensure robustness against single-point failures. To achieve global objective optimization, decentralized algorithms require more iterations to propagate local information throughout the network. This necessity is also influenced by the assumption of network connectivity. Additionally, inherent characteristics of communication transmission, such as bandwidth and latency, also impact the operational efficiency of decentralized algorithms in practical applications. Therefore, while offering flexibility and robustness, enhancing the operational efficiency of decentralized algorithms remains a valuable research area.  
In comparison to the DADMM algorithm, the results indicate that while our algorithm has a slower iteration speed, it holds an advantage in terms of computational complexity. This is due to the fact that ADMM-based algorithms require inner loops within each iteration to separately solve the sub-optimization problems for the primal and dual variables. In contrast, by transforming the constrained optimization problem into a consensus optimization problem, Algorithm 1 only needs to solve the sub-optimization problem for the primal variables at each iteration, and this is completed entirely locally. The dual variables do not require an inner loop for updates, significantly reducing the computational cost.
## 5 PAPR-Aware Transmission Strategies
### Pre-Equalization for Multisine PSK Modulation
For the balanced pre-equalizers, a moderate choice is made between the two extreme pre-equalizers depending on the choice of the balancing coefficients. Besides, the water filling pre-equalizer assigns the same amount of power to each bin, while the pre-equalizer \(w_{|\mathbf{h}|^{3}\triangle h^{*}}\) boosts most of the power to the good channel.
## 2 Study Area and Materials
### Datasets and Preprocessing  
#### 2.2.1 Satellite Data and Quality Control  
The monthly specific humidity (SH) was obtained from the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) (GES DISC, Greenbelt, MD, USA) [55]. The maximum temperature (T\({}_{\rm max}\)) and the minimum temperature (T\({}_{\rm min}\)) at a monthly scale were acquired from TerraClimate (TC) [56]. The remote sensing NDVI (MOD13), LAI (MOD15), and NPP (MOD17) were obtained from the moderate resolution imaging spectroradiometer (MODIS) (LAADS DAAC, Greenbelt, MD, USA) [56; 57; 58; 59; 11]. From 2001 to 2020, a total of 240 SH images, 240 T\({}_{\rm max}\) images, 240 T\({}_{\rm min}\) images, 460 MOD13 NDVI images, 917 MOD15 LAI images, and 20 MOD17 NPP images were selected in the study area.  
First, the daytime temperature (T\({}_{\rm day}\)) was calculated for each month using the following equation [60]:  
\[{\rm T}_{\rm day}=0.509\alpha+\beta \tag{1}\]  
where \(\alpha\) and \(\beta\) are calculated using T\({}_{\rm max}\) and T\({}_{\rm min}\).  
\[\alpha= \frac{{\rm T}_{\rm max}-{\rm T}_{\rm min}}{2} \tag{2}\] \[\beta= \frac{{\rm T}_{\rm max}+{\rm T}_{\rm min}}{2} \tag{3}\]  
where T\({}_{\rm max}\) is the maximum temperature and T\({}_{\rm min}\) is the minimum temperature.  
Then, the monthly minimum relative humidity (RH\({}_{\rm min}\)) was converted using the following equation [60]:  
\[{\rm RH}_{\rm min}=\frac{0.263\times{\rm P}\times{\rm SH}}{{\rm exp}(17.67{\rm T }_{\rm max}/({\rm T}_{\rm max}+243.5))} \tag{4}\]  
where SH is the specific humidity, T\({}_{\rm max}\) is the maximum temperature, and P is the air pressure, which was calculated using the following equation [61]:  
\[{\rm P}=101.3\bigg{(}\frac{293-0.0065{\rm e}{\rm e}{\rm e}{\rm e}{\rm e}} \bigg{)}^{5.26} \tag{5}\]  
where P is the air pressure and ele is the elevation.  
Secondly, the maximum vapor pressure deficit (VPD\({}_{\rm max}\)) was calculated using the following equation [62]:  
\[{\rm VPD}_{\rm max}={\rm e}_{\rm s}(1-{\rm RH}_{\rm min}) \tag{6}\] \[{\rm e}_{\rm s}=\frac{4098\Big{[}0.6108\exp\Big{(}\frac{17.27{\rm T }_{\rm max}}{{\rm T}_{\rm max}+237.3}\Big{)}\Big{]}}{\left({\rm T}_{\rm max}+ 237.3\right)^{2}} \tag{7}\]  
where \({\rm e}_{\rm s}\) is the saturated vapor pressure, RH\({}_{\rm min}\) is the minimum relative humidity, and T\({}_{\rm max}\) is the maximum temperature.  
Finally, all 8- and 16-day data were composited to a monthly scale by the maximum value composite (MVC) technique [63] for fitting the monthly AVPM NPP. The MVC technique aimed to select the maximum value of each period in order to minimize the effects of atmospheric and residual clouds [63].  
All of the above data and data processing were conducted using the JavaScript programming language (Netscape, Mountain View, CA, USA) based on the Google Earth Engine platform ([[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)), accessed on 7 November 2022).
## 5 Results and Discussions
### System 1: The IEEE 33-Bus
#### 5.1.1 Case 1: APL Minimization  
In this case, only the APL minimization (OF\({}_{1}\)) is considered. Hence, the weighting factors in Equation (18) are taken as \(\omega_{1}=1,\omega_{2}=\omega_{3}=0\). The QODELFA is applied for three different values of power factor: Unity, 0.95 and 0.866 lag. The results are listed in Table 3.  
It can be noticed that the active power loss is reduced from the base-case value (210.988 kW) to 72.785 kW with unity power factor DGs (Case 1.1) by using the proposed algorithm. As shown in Table 3, the value of APL obtained from QODELFA is better than those from other methods in [13] and [28]. Although the results of QODELFA and SFSA applied in [32] are the same, but the DG sizes are smaller when using the algorithm evolved is this paper. When the power factor is set to 0.95 lag (Case 1 and 2), the APL is more effectively minimized by using QODELFA, where it reaches 28.533 kW. This value is better than that from SIMBO-Q algorithm [28], the same as that from SFSA [32], and approximately similar to that from QOSIMBO-Q algorithm [28]. When the power factor is set to 0.866 lag (Case 1.3), the loss reduction (LR) percentage reaches 92.73%, which is clearly higher than that from KHA [31]. This LR percentage explains the power factor's effect on decreasing the losses. After results evaluation, it is observed that the APL obtained from the proposed QODELFA for this case is better than the APLs from most of the available algorithms in the literature. In addition, as indicated in Table 3, the SD of results in all cases when using the proposed algorithm is quite small, which proves its robustness. It can be noted from the convergence characteristics of QODELFA given in Figure 7 that the proposed algorithm can quickly reach the optimal solutions for all cases (1.1, 1.2, and 1.3), which verifies its effectiveness.