esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Biology and Molecular Basis of Resistance
trachomatis_[31; 35]. However, it has contributed little to our knowledge of chlamydial antibiotic resistance genes. Previous studies focused on antimicrobial resistance (AMR) genes and their mechanisms in _C. trachomatis_ have identified several regions where mutations have been detected in strains with reduced susceptibility [35; 36]. Regions and genes related to AMR for a particular group of antibiotics as well as pertinent studies are listed in Table 1. However, as previously mentioned, it should be noted that strains with reduced susceptibility to antibiotics have also been detected despite the absence of detectable mutations in their genome [11; 37]. Moreover, the only chlamydial species to have naturally obtained a resistance gene integrated as a genomic island (e.g., _tetC_ encoding for efflux pump) is _C. suis_[38; 39]. Although in vitro co-culture studies have demonstrated that horizontal gene transfer can occur between species (e.g., _C. suis_ and _C. trachomatis_) [40], these observations have not yet been found in clinical isolates [31]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Antimicrobial Drug** & **Genes and Regions with Detected Mutations** & **References** \\ \hline \multirow{4}{*}{Macrolides} & _235 rRNA_, & [PERSON] 2004 [37], \\  & _rplD_ L4 protein gene, & [PERSON] 2007 [41], \\  & _rplV_ L22 protein gene & [PERSON] 2010 [42], \\  & _rplV_ L22 protein gene & [PERSON] 2015 [43], \\  & [PERSON] 2018 [44] \\ \hline \multirow{2}{*}{Tetracyclines} & _ompB/porB_ gene, & [PERSON] 2013 [45], \\  & _tet(M)_ gene for ribosomal protection protein & [PERSON] 2017 [46], \\  & & [PERSON] 2020 [47] \\ \hline \multirow{2}{*}{Fluoroquionolones} & _gyrA_ quinolone-resistance-determining region (QDRD), & [PERSON] 1998 [48], \\  & _ParC_ gene for topoisomerase IV subunit C, & [PERSON] 2002 [49], \\  & _ygeD_ gene for efflux protein & [PERSON] 2004 [50], \\ \hline \multirow{2}{*}{Rifampin} & _rpoB_ gene coding beta-subunit of DNA-dependent RNA & [PERSON] 2003 [52], \\  & polymerase & [PERSON] 2005 [53], \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific genes and genome region mutated in antibiotic resistant strains of _C. trachomatis_.
## 1 Introduction
(1) A model of a UAV-assisted caching-communication-charging system based on NOMA is constructed. In this system, the UAV rents popular content cached in memory from content providers in advance, provides users with content transmission services and wireless charging services within a time duration T, and obtains corresponding remuneration from this. The users within the coverage range are divided into multiple user pairs. The users use NOMA communication mode internally and FDMA communication mode between user pairs. (2) The utility function of the UAV is defined by taking into account the benefits of providing users with cache and charging services, the cost of renting cache files, and the UAV energy consumption. The utility of the UAV is maximized through optimizing cache content placement, UAV location, NOMA user pairing, and UAV power allocation. (3) In order to solve this mixed integer programming problem, this paper divides it into two subproblems: the joint optimization of content provider file pricing and UAV cache placement and the combined optimization of UAV location, transmission power, and user pairing. For the first subproblem, a joint algorithm of file pricing and UAV cache placement based on the Stackelberg-Bertrand game is proposed. For the second subproblem, it is further divided into three subproblems: UAV location, transmission power, and user pairing. The Lagrange multiplier method, a simulated annealing algorithm, and a particle swarm optimization algorithm are employed to solve these three subproblems, respectively. The rest of the paper is organized as follows. In Section 2, the system model is given. Based on the establishment of the optimization problem, it is proposed to divide the optimization problem into two independent parts for solving. In Section 3, the joint optimization problem of content provider pricing and UAV caching strategy is proposed and solved optimally based on the Stackelberg-Bertrand game model. In Section 4, the combined optimization problem of UAV location, transmission power, and user pairing is divided into three subproblems and solved using an alternating iteration algorithm. Simulation results and analysis are provided in Section 5, and conclusions are given in Section 6.
# Stability Analysis of the Landfill Slope with an Engineered Berm Under Composite Failure Mode
## 1 Introduction
Under these conditions, the stability analysis of landfill slopes usually only considers the under-berm failure mode. Considering the effect of leachate pressure, [PERSON] et al. [26] carried out reliability design for this type of berms. The above studies are all based on the scenario where the instability of the landfill is a translational failure mode. Although this failure mode is commonly seen in the on-site investigation results of instability in large landfills [4], there is another failure mode, the composite failure mode, that may either lead to translational failure or occur directly, which is often overlooked [27]. In this failure mode, the critical slip surface consists of two parts, one is a log-spiral slip surface in the waste mass and the other is a translational slip surface along the liner system. This may be due to the different creep characteristics of the waste mass and liners, just as accurately simulating the creep process of rocks may require two contact models [28]. For this failure mode, [PERSON] [29] and [PERSON] et al. [30] mentioned it in their research, but did not provide an analytical model. [PERSON] et al. [31] introduced the log-spiral mechanism into the internal failure of landfills, and then constructed a safety factor calculation model to evaluate the static stability of the landfill cell against the composite failure mode based on the limit equilibrium principle. Additionally, [PERSON] et al. [32] expanded the stability evaluation model by considering the effect of seismic loads. Based on the above analysis and engineering practice requirements, it can be seen that when evaluating the stability of the landfill slope with an engineering berm, in addition to considering the stability performance of the structure against translational failure modes, it is also necessary to analyze the safety redundancy of the structure against composite failure modes. In the paper, first, the implicit function equations of the safety factor were derived under the failure modes of over berm and under berm, respectively. Second, through error analysis, the feasibility of using the average safety factor instead of the true safety factor to evaluate the stability of the landfill slope was verified. Then the influence of the geometric parameters of the engineered berm and the shear strength index at the interface of liners over berm and under berm on the stability of the landfill slope was analyzed. Finally, a comparative analysis was conducted on the stability of the landfill slope, including different failure modes such as composite and translational failure modes.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Kind of Destinations More Accessible with Flying Taxis  
Flying taxis have the potential to significantly expand the range of destinations that are accessible to passengers. They can reach remote or hard-to-reach areas more quickly and efficiently than traditional ground transportation. Additionally, they can fly over congested traffic, reducing travel time and making it possible to reach destinations that are currently difficult to access due to traffic congestion. This could include urban areas with heavy traffic, as well as locations that are currently only accessible by boat or helicopter [150].
# VOC Removal from Manure Gaseous Emissions with UV Photolysis and UV-TiO\({}_{2}\) Photocatalysis
## 3. Materials and Methods
### UV Dosage and VOC Mass Conversion  
The intensity of UV light (\(I\)) was measured with UV sensors capable of measuring light intensity at single wavelengths of 254, 312, and 365 nm in the experiments on synthetic VOCs mixture treatment. The light energy dose at 254 nm was selected since both sets of UV lamps had a maximum energy output at 254 nm. The UV dose (as energy per volume of treated gas, e.g., J/L) was estimated as below.  
Energy used, in Joule, could be calculated from \(IAt\), where \(I\) is the intensity in W cm\({}^{-2}\), A the active area of the reactor in cm\({}^{2}\), and \(t\) the time, in s. \(I\) at 254 nm is what has been used in calculations at the highest level. This was found to be 1.5 mW cm\({}^{-2}\) (0.0015 W cm\({}^{-2}\)). The UV dose is expressed in J/L air treated, or \(IAt\)/volume, or IA/flow rate, the flow rate in L s\({}^{-1}\). The flow rate through the lab unit was 300 mL min\({}^{-1}\) (5 mL s\({}^{-1}\), or 0.005 L s\({}^{-1}\)). The TiO\({}_{2}\) coated active area of the reactor was 110 cm\({}^{2}\) at 0.111 mg TiO\({}_{2}\) cm\({}^{-2}\). Substitution of IA/flow rate for a retention time of 120 s = [0.0015 W cm\({}^{-2}\)\(\times\) 110 cm\({}^{2}\)]/0.005 L s\({}^{-1}\) = 33 J L\({}^{-1}\), or 33 kJ m\({}^{-3}\). Mass removed (ng) per unit of energy (J) for each target VOC was determined as a product of initial VOC concentration (ng mL\({}^{-1}\)), % VOCs removal, R (defined in Section 3.4), the photoreactor volume, divided by the dose.
## 5 Discussion
These comprehensive metrics highlight the Transformer-DNN model's robustness in handling complex multi-class classification tasks, making it highly suitable for real-world intrusion detection systems where accurate and reliable detection across various attack types is essential. The performance metrics presented in Table 35 demonstrate the remarkable capabilities of the Autoencoder-CNN and Transformer-DNN models for multi-class classification using the NF-BoT-IoT-v2 dataset. The Autoencoder-CNN model demonstrated strong performance across all classes, with the highest accuracy of 100% for the Theft class, precision of 96.88%, and recall of 100%, yielding an F1 score of 98.41%. For the Reconnaissance class, it achieved 95.53% accuracy, with precision of 98.63%, recall of 95.53%, and an F1 score of 97.06%. The DDoS class was associated with an accuracy of 99.28%, with precision and recall values of 99.38% and 99.28%, respectively, resulting in an F1 score of 99.33%. For the DoS class, accuracy was 98.25%, with precision of 95.81%, recall of 98.25%, and an F1 \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline PortScan & 99.94\% & 100\% & 99.94\% & 99.97\% \\ \hline DDoS & 100\% & 99.95\% & 100\% & 99.98\% \\ \hline DoS Hulk & 99.98\% & 100\% & 99.98\% & 99.99\% \\ \hline DoS GoldenEye & 100\% & 99.81\% & 100\% & 99.91\% \\ \hline FTP-Patator & 100\% & 100\% & 100\% & 100\% \\ \hline SSH-Patator & 100\% & 100\% & 100\% & 100\% \\ \hline DoS Slowloris & 99.56\% & 100\% & 99.56\% & 99.78\% \\ \hline DoS Slowhttptest & 99.36\% & 99.36\% & 99.36\% & 99.36\% \\ \hline Bot & 98.99\% & 100\% & 98.99\% & 99.49\% \\ \hline Web Attack—Brute Force & 100\% & 98.44\% & 100\% & 99.21\% \\ \hline Web Attack—XSS & 100\% & 98.11\% & 100\% & 99.05\% \\ \hline Infiltration & 100\% & 100\% & 100\% & 100\% \\ \hline Web Attack—Sql Injection & 100\% & 100\% & 100\% & 100\% \\ \hline Heartbleed & 100\% & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 33: Performance metrics for Autoencoder–CNN across several classes in multi-class classification on the CICIDS2017 dataset. score of 97.01%. The Transformer-DNN model performed comparably, with impressive metrics. For the Theft class, it achieved 100% accuracy, 98.41% precision, and 100% recall, with an F1 score of 99.20%. In the Reconnaissance class, it showed 94.20% accuracy, 99.85% precision, and 94.20% recall, with an F1-score of 96.94%. In the DDoS class, it matched the Autoencoder-CNN's results with an accuracy of 99.28%, precision of 99.38%, recall of 99.28%, and an F1 score of 99.33%. It also demonstrated strong performance in the DoS class, achieving 99.15% accuracy, 94.84% precision, 99.15% recall, and an F1 score of 96.95%. Overall, both models exhibited robust performance in identifying and classifying different types of attacks, with the Autoencoder-CNN excelling in certain classes and the Transformer-DNN showing slightly better results in others.
## 4 Results
### Analysis of Evaluation Metrics  
This section presents the findings of the evaluation metrics used in this study, focusing on usability, engagement, efficiency, and effectiveness.  
The results from Step 3 of the methodology revealed significant differences in the CUQ scores across interaction modes and question complexities.
## 2 Concept of the Mechanical Impact Active Detumbling  
Mechanical impact active detumbling gradually reduces the residual angular kinetic energy of space debris by applying mechanical impact multiple times and effectively increases the success rate to successfully capture the space debris. The purpose of applying impact to space debris is to reduce its residual angular kinetic energy, therefore, the direction of the mechanical impact should be opposite to the speed of the target point. For example, when the space debris is nutating, the trajectory of the endpoint on the space debris axis forms a circle and the impact direction is along the tangent of the trajectory. The mechanical impact is applied in various ways, such as via flexible objects or robotic end effectors. The process of the mechanical impact active detumbling is shown in Figure 1. In this solution, the mechanical impact is applied by a flexible end-effector.  
The process of mechanical impact active detumbling is mainly divided into three phases. In phase 1, the calculation of the impact pose is performed using the identified parameters. Specific impact pose analysis is shown in Figure 2. In phase 2, space debris is impacted by the flexible end-effector, which is controlled by the robot arm. The angular momentum of space debris is reduced by impacting space debris. In phase 3, when the impact is complete, the system of detumbling performs attitude compensation while separating from space debris. These sequences are repeated until the magnitude of the space debris' angular momentum decreases sufficiently. Finally, the space debris is captured by the robot arm.  
As seen from Figure 2, the calculation method of the mechanical impact posture effectively solves the problem of unsatisfactory impact results and provides theoretical guidance regarding mechanical impact active detumbling. The precise identification of the impact force plays a vital role in the process of space debris active detumbling, because the choice of impact position depends on the accuracy of the impact force identification.  
Figure 1: Mechanical impact active detumbling.  
Figure 2: Analysis of impact pose.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 3 Energy Efficiency Analysis of the Proposed Nanosecond Marx Generator
### Discharging Loss  
During the discharging period, as indicated by the red arrowed line in Figure 6, the energy loss comes from the avalanche BJTs and the isolation resistors. After the avalanche BJTs are turned on, all the storage capacitors are discharged to the load resistor \(R_{\rm L}\). The avalanche BJT can be equivalent to a small resistor \(r_{\rm o}\). The instantaneous current \(i_{\rm L}\) in the discharge circuit can be expressed by Equation (10) in the case of all the avalanche BJTs being turned on.  
\[i_{\rm L}\ =\ \frac{V_{\rm peak}}{R_{\rm L}}e^{-t/\tau} \tag{10}\]  
where  
\[\tau\ =(N_{\rm T}r_{\rm o}+R_{\rm L})C/N \tag{11}\]  
\(V_{\rm peak}\) is the peak voltage of the output pulses, \(\tau\) is the time constant of the discharge circuit expressed by Equation (11), and \(N_{\rm T}\) is the number of avalanche transistors.  
The potential at the right end of the \"\(m\)th\" capacitor is shown in Equation (12), where \(u_{1}\)-\(u_{\rm m}\) are shown in Figure 6.  
\[u_{\rm m}\ =\ \frac{-m}{N_{\rm c}}V_{\rm peak}e^{-t/\tau} \tag{12}\]  
Take \(R_{\rm c1}\) as an example. As shown in Figure 6, at the beginning of discharge, the potentials at two pins of \(R_{\rm c1}\) are \(R_{\rm c}l^{\prime}\) and 0, respectively. Because the discharge duration is very short, \(R_{\rm c}l^{\prime}\) can be considered as a constant. The discharge loss on resistance \(R_{\rm 1}\) can be expressed by Equation (13).  
\[P_{\rm Rc1}=\ f\int_{0}^{A}\frac{\left(R_{\rm c}l^{\prime}-0\right)^{2}}{R_{ \rm 1}}dt \tag{13}\]  
where \(A\) is the turn-on time of the BJTs,\(f\) is the discharging frequency, which is also the pulse repetition frequency.  
The energy consumed by the isolation resistors is shown in Equation (14). In this case, the influence of \(r_{\rm o}\) on \(u_{\rm m}\) can be ignored.  
\[P_{\rm Rdischarge}=\ f\int_{0}^{A}\sum_{m=0}^{N-1}\frac{\left(R_{\rm c}l^{ \prime}-u_{\rm m}\right)^{2}}{R_{\rm c}}dt \tag{14}\]  
Figure 6: Schematic diagram of isolation resistance \(R_{\rm c}\) loss in the discharge period.  
The power consumed on the avalanche BJTs is shown in Equation (15).  
\[P_{\rm T}=\int\!\!N\int_{0}^{A}i_{\rm L}^{2}r_{\rm o}dt \tag{15}\]
## 4 Results
### 1D Case
#### 4.1.1 Convergence Analysis
However, in this way, it may be difficult to identify the source of inaccuracies (e.g., it could be related to the time or space terms in the truncation functional). Therefore, performing separate analysis as we did here is important to minimize the influence of the time error to the spatial one. Figure 3 shows the time simulation of the tumor concentration of glioma cells from the initial to the final time \(t_{final}\,=\,1000\) (days). We have two sets of figures. The figures in the left column correspond to the coarse grid (\(M\,=\,128\)) and the figures on the right column belongs to the fine grid (\(M\,=\,4096\)) runs. All runs are executed using the Crank-Nicolson time scheme with the same time step \(\Delta t\,=\,0.01\). The simulation starts with a high concentration of glioma cells in a very narrow region. The concentration diffuses for a while (approximately 50 days), then it starts ramping up and spreading at the same time. The sub-figures show that the tumor cells spread the entire region in approximately 75 days. The last row of the figure indicates that the tumor concentration slowly reaches the steady state in approximately 1000 days. We see the effects of the discontinuous diffusion coefficient (e.g., the symmetric kinks in the solution profiles in the middle row). Clearly, the tumor cells in the high diffusion zone spread much faster than those in the low zone. We also notice the effects of the grid refinement. Fine grid runs (right column) capture better  
(more accurate) solution profiles that can be very significant in the sense that the amount of medical treatment (overdose/underdose) can depend on this. In Figure 4, we compare our computational results with the results of [19] for a series of time runs. Both results are using \(M=100\) grid points and fixed time steps \(\Delta t=10^{-2}\). We can see that the results are very similar. This is expected because both numerical schemes are second-order accurate in space and time. However, as far as efficiency is concerned, our algorithm is much faster to converge with _two_ Newton and _one_ GMRES iteration per Newton step for the aforementioned grid and time step options. Figure 3: Time simulations of tumor concentration of glioma cells with \(\rho\ =\ 0.012\ \text{day}^{-1}\), \(\Delta t\ =\ 0.01\approx 14.4\ \text{min}\), \(\Delta x\ =\ \frac{50}{M}\). Left column: \(M\ =\ 128\); and right column: \(M\ =\ 4096\).
## 4 Results
### Main Experimental Results
#### 4.4.1 Architecture Selection  
Hyper-Parameter Tuning  
Although the DeepLabv3+ architecture is already pre-trained on the PASCAL VOC 2012 dataset, the dataset used here is considerably divergent. Thus, the hyper-parameters were optimized by applying the Bayesian optimization method [63]. The performance of the DCNN was explored by modifying five critical parts (abcde) of the DeepLabv3+, as shown in Figure 5.  
The optimization process began with exchanging the modified Xception architecture with the Resnet 101 architecture as the backbone module of the DCNN (part a). This module is responsible for encoding the features from the initial images until they are passed to the ASPP module. As Table 5 shows, replacing the backbone module to the Resnet 101 architecture results in an \(F_{1}\) score of 81.26%, which is a 0.88% gain compared with the 80.38% achieved with the Xception architecture. Hence, Resnet 101 was chosen as the backbone for the DeepLabv3+ net for all further experiments.  
Other variations were explored: using part b, (\([1\times 1.48]\) convolution), part c (\([1\times 1.256]\)\(\times\) 2 upsample), part d, (\([3\times 3.256]\)\(\times\) 2 decoder), and part e (output stride 8) and augmen  
Figure 5: Modified parts of DeepLabv3+. Parts are represented by letters abede and are discussed in the main text.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **Acc** & **Precision** & **Recall** & **mIoU** & **F1** \\ \hline Xception & 85.83\% & 81.88\% & 79.47\% & 67.76\% & 80.38\% \\ ResNet 101 & 86.81\% & 82.94\% & 79.85\% & 69.00\% & **81.26\%** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of backbone (part a) choice on the validation set performance (ground truth \(TN\)). Acc: overall accuracy, mIoU: mean intersection over union, and F1: \(F_{1}\) score.  
tation with left-right flipping. Only the stride change and flipping gave an improvement, resulting in \(F_{1}=83.01\%\). More details of the tuning can be found in [62]. The best-performing hyper-parameters and DCNN architecture were used to train all subsequent models. A detailed diagram of the final architecture is provided in Appendix A.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
The study offers significant contributions to the field by providing valuable insights into the effects of MCS adaptation on resource allocation and presenting a robust optimization methodology for C-V2X networks. Figure 1 depicts the overall simulation scenarios carried out in this work. The vehicles will be randomly loaded into the highway environment, with the following lane directions. Each vehicle that exits, the highway boundary will not be looped back into the scenario. The authors' work lays the foundation for further research and practical applications in improving the performance and efficiency of vehicular communication systems. In general, the main contributions in this work can be described as follows:  
1. We conducted several simulations based on several reference works and showed that static MCS and \(P_{rk}\) values had not achieved optimal performance;
2. We proposed the novel fuzzy interface reinforcement learning (FIRL) optimization approach to enhance the PRR performance;
3. We conduct simulations with various vehicles representing traffic congestion, ranging from 100 to 400;
4. Performance metric used in this work is PRR, and for investigating the performance of the proposed FIRL approach, comparisons of the results with the static MCS index and \(P_{rk}\) are conducted. The remainder of the paper can be explained as follows: Section 2 describes the explanation of the technology. Section 3 describes the proposed FIRL approach. Section 4 describes the simulation configuration and simulation results. Section 5 provides the conclusion of this work.