esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Investigation of Mechanical Properties of Clay-Cement Slurry Containing Graphene Oxide
###### Abstract  
As a widely used material in underground engineering, clay-cement slurry grouping is known for its initial poor anti-seepage and filtration capacity, the low strength of the resulting stone body, and its tendency towards brittle failure. To explore efficient and environmentally friendly grouting materials, industrial-grade graphene oxide (GO) was incorporated into a clay-cement slurry to create a new type of slurry called a GO composite. These GO composites were then utilized to reinforce fractured formations. Uniaxial compression tests, shear strength tests, permeability tests, and electron microscopy scans were conducted to investigate the strength, permeability, and microscopic features of the GO composite-reinforced fractured formations. Furthermore, the optimization effect and application prospects of graphene oxide on clay-cement slurry materials were evaluated. The experimental results demonstrated that the modified slurry effectively improved the compressive strength (increased by 7.2% to 32.5%) and shear strength (increased by 28.6% to 105.3%) of consolidated fractured gravel. By conducting orthogonal experiments with range analysis, variance analysis, and multiple regression analysis, it was shown that there was a strong correlation between the consolidated body and three factors influencing the permeability coefficient. Among these factors, the OPC content had the most significant impact on the permeability coefficient, followed by the GO content. Graphene oxide was found to promote cement hydration reactions, guide the growth of hydration products on the surface of graphene oxide nanosheets, optimize the pore structure in grouting materials, and reduce microcracks between the slurry and the fractured gravel interface. Electron microscopy characterization and fractal analysis revealed that the addition of graphene oxide effectively reduced the degree of microdamage during the sample's failure process. This ensured the integrity of the sample during the unstable failure process, enhanced the material's toughness, and improved its ability to resist loads.  
clay-cement slurry; graphene oxide; cement; microstructure +
Footnote †: journal: Applied sciences
# The RND Efflux Pump Gene Expression in the Biofilm Formation of _Acinetobacter baumannii_
## 2 Results
#### 2.2.2 Efflux Pump Inhibitor (CCCP)  
Using CCCP for efflux pump inhibition significantly reduced (\(p<0.05\)) biofilm formation in 17.6% (6/34) of the isolates (Table 3). However, isolates showing change in biofilm formation potential in the presence of CCCP were different from those affected by sub-MICs of levofloxacin. No correlation was detected between fold change in BFI using CCCP and the biofilm formation potential of the isolates ([PERSON]'s \(r_{s}=0.07\), \(p>0.05\)).  
Expression Levels of the adeB, adeG, and adel Efflux Pump Genes in Planktonic and Biofilm Cells Using Quantitative Real-Time PCR (qRT-PCR)  
The three efflux genes were detected by conventional PCR in the thirty-four isolates and the standard _A. baumannii_ strain (ATCC 19606). Gene expression analysis was done on eleven isolates and a standard strain by qRT-PCR analysis to measure the expression of the three RND efflux pumps. Isolates for gene expression were selected based on the ERIC cluster typing by selecting an isolate from each cluster that represents the biofilm phenotype in this group. A representative isolate was selected from each ERIC cluster except for clusters G and H, which comprise two biofilm phenotypes, therefore, two isolates were selected from these groups. Measuring the expression levels of _adeB_, _adeG_, and _adeJ_ genes showed overexpression in five out of the eleven tested isolates relative to the standard strain (Figure 1a). Isolates with overexpression in the efflux pump were moderate (isolates no. 27, 33, 35, 45) and weak biofilm forming (isolate no. 7). It was observed that the _adeJ_ gene relative expression was remarkably higher than the other two RND efflux pump genes in tested isolates.  
The relative expression pattern of _adeB_, _adeG_, and _adel_ varied among different isolates with different biofilm forming abilities. Five strong biofilm forming isolates (Standard strain ATCC 19606, 4, 23, 25, and 28) showed more than a twofold increase in the gene expression of efflux pumps in biofilm form than planktonic form, while the remaining strong and moderate biofilm isolates (27, 33, 35, 45) showed either more than a twofold reduction or no change in gene expression of the same efflux pumps compared to planktonic forms (Figure 1b). No correlation was detected between the biofilm formation ability of isolates and the relative expression of the RND efflux pump genes (Spearman's \(r_{s}=0.17\), \(0.14\), and \(0.35\) for _adeB_, _adeG_, and _adel_, respectively; \(p>0.05\)). Isolates that belong to the same ERIC clusters (G:28-36 and H:4-23) or closely related clusters (A:25 and B:7) showed similar efflux pump relative expression patterns despite the difference in biofilm phenotype in some cases.
# Energy-Saving Network Ventilation Technology of Extra-Long Tunnel in Climate Separation Zone
## 3 Results and Discussion
### Energy-Saving Analysis
#### 3.3.1 Energy Saving at Different Wind Velocity  
Based on the traffic volume of the left line in the Nibashan Tunnel in 2010, the power of fans at different natural wind velocities was calculated. As the results listed in Table 6 show, the increase of the natural wind velocity will remarkably increase the energy consumption. As the wind resistance correlates with the square of the wind velocity, the wind resistance will obviously increase with the wind velocity growth. Power of jet fans rises with the increase of wind resistance accordingly.  
Considering the natural wind velocity and direction, there were favorable and unfavorable conditions. If the natural wind can be exploited as power properly, much energy will be saved. Based on the traffic volume of the Nibashan Tunnel in 2010, the power of fans was calculated when natural wind is at 2.5 m/s and at a velocity with guarantee rat of 95%. The results are shown in Table 7.  
From the results illustrated in Table 7, the installed power calculated by the wind velocity with the guarantee rate of 95% is larger than that calculated by 2-3 m/s (as recommended in specifications). However, setting the design wind velocity at the velocity with a guarantee probability needs fewer operating jet fans. The ventilation effect is also better than traditional ventilation with the recommended wind velocity in specifications. Assuming the electricity price is 0.18 dollar per kWh, 0.35 million dollars will be saved in tunnel ventilation every year.
## 3 Control Strategies
### DC-AC Four-Leg Converter Control
#### 3.2.2 Power Over-Load Supervisor for the Grid-Connected Mode  
As it is well known, the inertial and over-load capability of power electronics is limited. Moreover, it is common to consider pulsed drain currents of two to three times the continuous drain current for about 1 ms. On the contrary, in traditional AC systems, the rotary machines and transformers can be over-loaded up to 20-30 times in a timescale of minutes [24]. In this case, it has been considered to over-size the converter allowing a certain over-load capability. This fact provides a more flexible interaction between the inverter and the AC microgrid. The oversizing is achieved using high current switching devices. This choice allows for a more compact converter placing the burden of oversizing only on the cost of switching devices, a not really sensitive part today. Thus, the cooling system is designed to suit the nominal power. However, the thermal time lag of the used cooling method is usually enough and within the range of seconds to minutes. Furthermore, the use of thermal masses such as aluminium plates, or the consideration of phase change materials are options to increase the thermal inertia [53; 54], being a good trade-off between cost and volume, if required.  
In order to manage the over-load capability, the maximum AC current is handled by a power over-load supervisor algorithm based on thermal criteria. This current limitation is achieved by means of the apparent power \(s\) (in per unit). An over-load observer, \(ol_{o}\), limits the power per phase. The over-load observer fulfills this task through the formula  
\[ol_{o}=\int_{0}^{t}\left(i^{*2}-1\right)dt \tag{1}\]  
Figure 4: The virtual resistance \(R_{v}\) operation.  
Figure 5: Resistive case power close loop scheme under dynamic phasors.  
based on the \(i^{2}t\) computation. In this sense, the term \(ol_{o}\) is an indicator of the over-load energy exchanged, \(i^{*}\) is the desired current and \(t\) is the time interval of the over-current. The over-load algorithm is managed according to the state diagram shown in Figure 6. Time \(t\) begins when \(|s^{*}|>1\), \(s^{*}\) being the maximum apparent power per phase reference. When \(|s^{*}|>1\), the observer enters to the _Wake-up_ step, starting to compute \(ol_{o}\) (Equation (1)) and \(ol_{t}\) (accumulative time under the over-load situation). If the \(ol_{o}\) value reaches zero, the observer returns to a _Sleep_ step, where \(ol_{o}\) and \(ol_{t}\) are reset. If the \(ol_{o}\) is bigger than zero, depending on the present \(s\) value, the accumulated time \(ol_{t}\) is incremented or maintained. Keeping the \(ol_{t}\) time constant, it is ensured that the inverter is not over-loaded intermittently producing possible thermal degradations. In the case that the \(ol_{t}\) term becomes higher than a pre-set threshold \(T_{max\,ol}\), the system evolves to the _Prolonged over-load error_ step and \(s\) is limited to 0.8. In this way, \(ol_{o}\) is forced to decrease until zero. Then, the system evolves to the _Sleep_ step again.
## 1 Introduction  
Microalgae are photoautotrophic microorganisms, capable of using light energy to fix CO2 and discharging O2 as a waste product during photosynthesis [1]. Microalgal cells contain valuable biocompatibility, such as fatty acids and pigments, which can find applications in many branches of industry [2]. High amounts of microalgal biomass are necessary to produce target biocompounds from microalgae in a feasible process [3]. High microalgae biomass concentrations can be achieved during mixotrophic or heterotrophic cultivation. During mixotrophic or heterotrophic cultivation, microalgae growth can be supported with organic carbon such as glucose, resulting in high microalgal biomass productivity [4].  
Glucose can be obtained from cellulose, a carbohydrate polymer found in lignocellulosic materials, upon hydrolysis with the use of cellulosic enzymes [5]. Due to the presence of hemicellulose and lignin in lignocellulosic structure, a pretreatment method needs to be applied prior to enzymatic hydrolysis [6; 7]. There are numerous pretreatment methods, such as irradiation, explosion, acid, alkaline, hot water, oxidation and organosolvent processes that can improve, with different efficiencies, enzymatic hydrolysis of cellulose [6; 7; 8]. Alkaline pretreatment is a method involving the use of alkaline solutions to remove lignin and hemicellulose and to efficiently increase accessibility of cellulose to enzymes, thereby sharply improving saccharification rate [8].  
Nowadays, mostly yeast and bacteria are cultivated on lignocellulosic hydrolysates for fatty acid production [9]. Lignocellulosic hydrolysates also have potential to be used as feedstocks for microalgae growth and biocompound production [10]. Hence, in this study, an enzymatic hydrolysate from alkali pretreated beech _Fagus sylvatica_ wood was tested as a feedstock for _Chlorella_ cultures. Beech (_Fagus sylvatica_) is a deciduous tree species spread around Europe, also commonly found in Belgium [11], and was a subject of hydrolysis studies in our previous reports [12; 13].  
Enzymatic hydrolysates from bagasse [14] and straw [15] were tested in terms of their effect on _Chlorella_ growth and fatty acid production. However, the effect of the enzymatic hydrolysate from wood on _Chlorella_ growth and fatty acid and pigment production was not reported before.
## 2 Materials and Methods
### Flux Data Processing and Gap Filling  
Postprocessing of flux data followed the FLUXNET standard procedures using EddyPro software v6.1.0. Data processing consisted of spike removal [30], angle-of-attack correction for Gill anemometers [31], a two-dimensional coordinate rotation [32], a time lag correction of CO\({}_{2}\) concentration to maximize covariance with vertical wind speed variation [33], buoyancy corrections of sonic air temperatures [34], and WPL correction for air density fluctuations [35]. According to the friction velocity, the night time flux data affected by weak turbulence were eliminated based on the friction velocity (u*) [36]. The 30-min fluxes were also rejected if the quality flags were equal to 1 or 2, in accordance with the method of [PERSON] [37].  
In addition to periods of mechanical failure and maintenance, data processing (quality flag correction, u* threshold correction, and footprint correction) produced gaps that needed to be filled to integrate the CO\({}_{2}\) fluxes on an annual scale. The gaps represented 33% and 38% of the CO\({}_{2}\) and CH\({}_{4}\) flux data, respectively.  
The gaps were filled by using linear interpolation and mean diurnal course methods [38] when gaps were less than 8 h; otherwise, the random forest algorithm was selected to fill the gaps [39]. The gap-filled flux data were used to calculate the monthly and annual carbon source or sink values.
## 2 Materials and Methods
### CFD Methods
#### 2.2.4 HRIC Model  
The HRIC scheme is primarily used for simulating the convective transport of immiscible fluid components, thus forming a scheme suitable for tracking sharp interfaces. The foundation of this scheme is the normalized variable diagram (NVD) method. In the diagram, \(\alpha_{D}\), \(\alpha_{A}\), and \(\alpha_{U}\) represent the volume fractions of the donor cell, acceptor cell, and upwind cell, respectively, while \(\alpha_{f}\) is the volume fraction at the control volume boundary, calculated by the expression:  
\[\alpha_{f}=(1-\widetilde{\beta}_{f})\alpha_{D}+\widetilde{\beta}_{f}\alpha_{A} \tag{8}\]  
where:  
\[\widetilde{\beta}_{f}=\frac{\widetilde{\alpha}_{f}-\widetilde{\alpha}_{D}}{1- \widetilde{\alpha}_{D}},\;\widetilde{\alpha}_{f}=\frac{\alpha_{f}-\alpha_{U}}{ \alpha_{A}-\alpha_{U}},\;\widetilde{\alpha}_{D}=\frac{\alpha_{D}-\alpha_{U}}{ \alpha_{A}-\alpha_{U}} \tag{9}\]  
The estimation of the normalized volume fraction \(\widetilde{\alpha}_{f}\) is conducted using a mixed upwind-downwind scheme.  
\[\widetilde{\alpha}_{f}=\begin{cases}\widetilde{\alpha}_{D}&\widetilde{\alpha} _{D}<0,\widetilde{\alpha}_{D}\geq 1\\ 2\widetilde{\alpha}_{D}&0\leq\widetilde{\alpha}_{D}<0.5\\ 1&0.5\leq\widetilde{\alpha}_{D}<1\end{cases} \tag{10}\]  
The fluid has a density of 997.56 kg/m\({}^{3}\) and a dynamic viscosity of 8.8871 \(\times\) 10\({}^{-4}\) Pa/s of fresh water at a temperature of 15 \({}^{\circ}\)C. The starting computational state is intended to simulate a static, non-jetting condition with a fixed trim and ship hull sinkage due to the minor attitude changes and small wave amplitudes experienced by inland waterway vessels operating in shallow water circumstances (Table 2). In the computational domain, the overlapping grid approach is used, and the reference pressure of the flow field is set to zero. The effects of jetting-induced hull attitude fluctuations, free surface wave generation, and gravity are all disregarded. The gas-liquid-free surface is captured using the Volume of Fluid (VOF) model, and the interface is captured using the High-Resolution Interface Capturing (HRIC) technique. The computational parameters are set as follows: a lower Courant-Friedrichs-Lewy number (CFL_l = 0.8), an upper Courant-Friedrichs-Lewy number (CFL_u = 1), a sharpening factor of 0.5, an angle factor of 0, and a time step of 0.002 s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{F_{r}}\) & **Center of Gravity Heave (mm)** & **Trim Angle (\({}^{\circ}\))** \\ \hline
0.034 & \(-\)0.78 & \(-\)0.78 \\
0.045 & \(-\)2.26 & \(-\)2.26 \\
0.057 & \(-\)3.71 & \(-\)3.71 \\
0.068 & \(-\)5.07 & \(-\)5.07 \\
0.079 & \(-\)6.74 & \(-\)6.74 \\
0.091 & \(-\)9.57 & \(-\)9.57 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hull attitude at different sailing speeds.
## 6 Simulations Results and Discussions
### Number of Dead Sensor Nodes over the Rounds  
Figure 8 illustrates how many of the SNs die over the rounds. The y-axis represents number of dead SNs while the number of rounds are taken along the x-axis. As we move along the x-axis, the number of deaths increases. In EEUCH, the last SN lives till 1080 rounds, while in LEACH, LEACH-C, SEP, TEEN, IEE-LEACH, and EEECA-THWSN, the last SN dies at round number 86, 89, 113, 180, 121, and 229, respectively. This clearly indicates the proposed protocol's effectiveness in enhancing network life, due to the elimination of CH from SNs, as CH consumes more energy than CMs. In the proposed EEUCH protocol, UAV acts as CH and all SNs are CMs.
# LLM-Based Cyberattack Detection Using Network Flow Statistics
## 4 Related Work
### Comparative Analysis
#### 4.3.1 Using the CSE-CIC-IDS2018 Dataset
This highlights our model's high capability for general classification and effective attack identification. The consistently low FNR of our T5 model, especially in conjunction with its high accuracy and competitive FPR, highlights its superior ability to minimize missed attacks while keeping false alarms at an acceptable level, thus offering a more balanced and practical solution for threat detection. Therefore, our approach performs the best among the LLM-based research papers, and our results compared to the other ML-based techniques, \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Reference** & **Technique** & **Accuracy** & **Precision** & **Recall** & **F-score** & **FPR** & **FNR** \\ \hline Our approach & T5 & **99.84\%** & **99.84\%** & **99.84\%** & **99.84\%** & 0.27\% & **0.02\%** \\ \hline [PERSON] et al. (2019) & CGAN + BERT & 98.22\% & 98.25\% & 98.22\% & 98.23\% & – & – \\ \hline [PERSON] et al. (2019) & GPT 2.0 & 95.86\% & – & – & 96.93\% & **0.11\%** & – \\  & BERT & 95.48\% & – & – & 95.80\% & 0.47\% & – \\ \hline  & IADCL & 99.82\% & **99.80\%** & **99.81\%** & **99.80\%** & – & – \\  & Immune Net & 99.78\% & 99.77\% & **99.78\%** & **99.70\%** & – & – \\ [PERSON] et al. (2019) & XGBoost & 99.00\% & 99.03\% & 99.00\% & 99.01\% & – & – \\  & RF & 98.81\% & 98.82\% & 98.81\% & 98.81\% & – & – \\  & DT & 98.69\% & 93.41\% & 98.79\% & 96.03\% & – & – \\  & LR & 87.96\% & 90.80\% & 87.96\% & 88.99\% & – & – \\ \hline [PERSON] et al. (2019) & SS-Deep-ID & 98.71\% & 94.91\% & 94.30\% & 94.92\% & – & – \\ \hline [PERSON] et al. (2020) & GAN & 95.32\% & **99.88\%** & 95.85\% & 97.30\% & 0.89\% & – \\ \hline [PERSON] et al. (2019) & Hybrid DL Model & 99.17\% & 99.26\% & 99.17\% & 99.20\% & 0.18\% & – \\ \hline [PERSON] et al. (2019) & CNN-1D & 99.40\% & – & – & – & – & – \\ \hline [PERSON] et al. (2019) & CNN & 99.36\% & – & – & – & **0.16\%** & **0.79\%** \\ \hline [PERSON] et al. (2019) & Hybrid DL Model & 90.64\% & 91.66\% & 90.64\% & 91.00\% & – & – \\ \hline [PERSON] et al. (2019) & HDEFF & – & 98.22\% & 99.77\% & 98.49\% & – & – \\ \hline [PERSON] et al. (2019) & RF & – & 94.00\% & 76.00\% & 79.00\% & 0.53\% & – \\  & CatBoost & – & 91.00\% & 80.00\% & 83.00\% & 0.43\% & – \\  & LightGBM & – & 91.00\% & 81.00\% & 83.00\% & 0.42\% & – \\  & XGBoost & – & 89.00\% & 83.00\% & 84.00\% & 0.42\% & – \\ \hline [PERSON] et al. (2019) & MMM-RF & **99.98\%** & – & – & – & **0.02\%** & – \\ \hline [PERSON] et al. (2019) & PBCNN & **99.99\%** & 98.20\% & 98.30\% & 98.30\% & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results for the selected metrics using CSE-CIC-IDS2018. are better for all the metrics, with the exception of a pair of studies, over which we obtained better results for most of the selected metrics.
## 2 System Model
### Wht-Nomma in Small-Cells  
This part provides the description of the proposed NOMA with the WHT technique. The block-diagram of the proposed technique is provided in Figure 3. If the superposition-coded signal at the \(b\)th SBS transmitter is given by (1), then after the WHT application, it is given as:  
\[S=\sum_{k=1}^{N}s_{b,k}^{H}\sqrt{P_{b,k}}, \tag{22}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(x_{1}\) & \(x_{1}\) & \(x_{2}\) & \(x_{2}\) & \(s_{1}\)=\(\frac{1}{\sqrt{2}}(x_{1  
where, \(s_{b,k}^{H}\) represents the signal of the \(k\)th UE after the WHT is applied. Similarly, at the receiver side, using Equation (3), the received signal at the \(k\)th user is given as:  
\[y_{b,k}=h_{b,k}\times\left(\sum_{k=1}^{N}s_{b,k}^{H}\sqrt{P_{b,k}}\right)+n+I, \tag{23}\]  
where, \(h_{b,k}\) is the channel response, assuming a Rayleigh fading channel, \(n\) represents noise, and \(I\) presents the ICI, with the noise considered in this work as AWGN with zero mean and variance \(\sigma^{2}\).  
In WHT-NOMA, for the \(m\)th UE case in \(k\) paired UEs served by the \(b\)th SBS, the received signal of high-gain user UE-\(m\) in (5) without performing SIC can be written as:  
\[y_{b,m}=\underbrace{h_{b,m}\times s_{b,k}^{H}\sqrt{P_{b,m}}}_{Wanted\ Signal}+ \underbrace{h_{b,m}\times\left(\sum_{k=m+1}^{N}s_{b,k}^{H}\sqrt{P_{b,k}} \right)}_{Unwanted\ Signal:}+\underbrace{n}_{noise}+\underbrace{I}_{ICI} \tag{24}\]  
where \(I\) is given in (6). Like the two-UE case in WHT-NOMA, the received signal at UE-1, served by the \(b\)th SBS, without SIC will be given by using (24) as:  
\[y_{b,1}=\underbrace{h_{b,1}\times s_{b,1}^{H}\sqrt{P_{b,1}}}_{Wanted\ Signal}+ \underbrace{h_{b,1}\times s_{b,2}^{H}\sqrt{P_{b,2}}}_{Unwanted\ Signal:}+ \underbrace{n}_{noise}+\underbrace{I}_{ICI}. \tag{25}\]  
Here as well, just like conventional NOMA in small-cells, two cases are considered for SIC at the SU receiver, i.e., WHT-NOMA with perfect and imperfect SIC.