esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Figure 11 shows a graph of the share of the fresh catalyst added in the first ebullated bed reactor relative to the total fresh catalyst added in both reactors. This data indicates that in the cascade mode, no fresh catalyst enters the first reactor, while in the parallel mode, the amount of fresh catalyst entering the first reactor varies between 60 and 70%. Figure 12 presents data for the variation of saturates and asphaltenes contents and the colloidal instability index of the hydrocracked vacuum residue for the investigated period. [PERSON] and [PERSON] established that the colloidal instability index (CII) was a useful empirical variable for relating fouling to precipitation results [34]. [PERSON] et al. reported that the unconverted ebullated bed residue hydrocracking H-Oil product CII correlated with the colloidal stability expressed by the \"spot test result\" [35]. [PERSON] postulated that when CII \(<\) 1.0, the amount of resin plus aromatics is adequate to keep the asphaltenes in solution. The insertion of saturates or the elimination of aromatics can displace the oil composition such that CII \(>\) 1.0, and asphaltenes will form sediment [36]. Figure 11: Per cent of the fresh catalyst added in the first ebullated bed reactor relative to the total fresh catalyst added in both reactors. Figure 10: Variation of catalyst addition rate during cascade and parallel modes of fresh catalyst addition for the investigated period of time. The data in Figure 12 indicate that the hydrocracked vacuum residue saturate content decreased while the asphaltene content increased with the progress of time, that is, with conversion enhancement as observed from the data in Figure 8. The colloidal instability index (CII) did not show any trend toward augmentation or reduction. For all cases except one, the CII was lower than 1.00, suggesting high colloidal stability of the H-Oil VTB during the study based on the postulate of [PERSON] [36]. It is interesting to note here that during processing the crude oil blend containing Basrah Heavy, the content of saturates in the hydrocracked vacuum residue dropped while that of asphaltenees went up. The colloidal instability index showed a step change towards enhancement when the Basrah Heavy content in the crude oil blend was 15%. Figure 13 shows the relation of hydrodeasphaltization to vacuum residue net conversion. It is evident from this data that both HDAs (C\({}_{5}\)) and HDAs (C\({}_{7}\)) increase with vacuum residue conversion. The error in the determination of HDM was found \(\pm\)3%. It  
Figure 12: Variation of saturate (**a**), and asphaltene (**b**) contents, and colloidal instability index (**c**) of the hydrocracked vacuum residue for the investigated period. is interesting to note here that during processing Basrah Heavy crude oil in the amount of 15% in the crude oil blend, the hydrodeasphaltization fell to 10% for the HDAs (C\({}_{5}\)) and to 20% for the HDAs (C\({}_{7}\)), which is bigger than the HDM repeatability of \(\pm\)3%. The interruption of the liquid nano dispersed HCAT catalyst addition was also associated with a 6-7% reduction of HDAs.
# Parameter Design for Inter-Satellite Laser Link Acquisition Under Weak Light Conditions
## 6 Results and Discussion  
The simulation results and their comparison with the theory will be presented in this section. Finally, the optimal acquisition system parameters are selected by minimizing the mean acquisition time.  
Figure 9: The Monte Carlo simulation process, where \(v_{t-ran}\) is the variable range of scan speed, \(D_{t-ran}\) is the variable range of track width, \(mc_{num}\) is the total number of Monte Carlo of one scan, \(L_{s}\) is the number of discrete points of the scanning trajectory, \(w\) is the number of scan points included in integration time, and \(mc\), \(w_{init}\), and \(n_{fail}\) are responsible for the simulation counts, integration window scanning point counts, and acquisition failure counts, respectively.  
Since we account for noise separately in the horizontal and vertical directions during simulation, the standard deviation of the noise in the actual theoretical calculations is \(\sqrt{2}\) times that of the unidirectional noise.  
Theoretical and simulation results are presented in Figure 10. For clarity, a subset of fixed track width and scan speed values is shown in Figure 11, demonstrating that the analytical results for acquisition failure probability closely align with the simulation results. As can be seen from Figure 11, when the scan speed is low, within the track width range currently set, the acquisition failure probability does not show a significant variation with changes in the scan speed. As the scan speed increases, when the scan speed is between the two black lines in Figure 11a, a noticeable difference appears between the theoretical curve and the simulation results, and the theoretical curve itself exhibits a certain degree of bending. This is because the divergence half-angle of the current scanning beam \(\theta_{div}=1.06\)\(\upmu\)rad, while the selected integration time of the acquisition camera is 800 ms. Therefore, this range of scan speeds causes the distance the scanning beam moves within a single integration window to exceed its beam radius but not exceed its diameter. This introduces certain difficulties and deviations in the calculation of \(k_{a}\). As the scan speed continues to increase beyond this range, the impact of the scan speed on \(k_{a}\) diminishes. At this point, the main factors influencing the estimation of \(k_{a}\) are the uncertain distribution of the receiving SC and the size of the track width. The effects of these factors can be accurately calculated, which is why the theoretical curve aligns closely with the simulation results again.  
Figure 10: The probability of acquisition failure.  
As shown in Figure 12, we can determine the parameters that minimize the mean acquisition time accordingly. The mean acquisition time required for successful link establishing across multiple acquisitions is minimized when the scan speed is 3.31 \(\upmu\)rad/s, achievable by SC motion, and the track width is 0.87 \(\upmu\)rad, resulting in a theoretical value of 223.77 s.
###### Abstract  
Centrifugal tests provide an efficacious experimental process to predict the behavior of deep excavations, and numerical models are indispensable for demonstrating the test results and analyzing the engineering demand parameters. Uncertainty in material properties can cause simulations to differ from tests; therefore, updating the model becomes inevitable. This study presents a response-surface-based model updating technique for the nonlinear three-dimensional simulation of the centrifugal testing model of strutted deep excavation in sand. An overview of the fundamentals of the response-surface model is provided, including selecting uncertain parameters as input factors, creating a design order for training the model, building a second-order polynomial surface, and updating the input factors through targeted centrifugal results. The bending strains of diaphragm wall panels at multiple points along the depth are used to form the multiobjective function. Response-surface model predictions were well-matched with actual numerical responses, with less than a 0.5% difference. Parametric analyses could be conducted utilizing this updated strutted deep excavation model.  
numerical analysis; centrifugal test; response-surface method; strutted excavation; model updating +
Footnote †: journal: Applied Sciences
## 5 Data Analysis and Results
### Item Response Model Analysis  
We run a one-parameter Item Response Model analysis (Rasch Model) using all 46 items of the CAIN and on the whole sample (N = 277). Analyses were run in R [21] with the package ltm [22]. Global fit indices of the model were not completely satisfactory: Bootstrap (rep = 1000) Goodness-of-Fit using Pearson chi-squared \(p\) = 0.001, AIC = 7856.41, BIC = 8023.11. However, the overall Cronbach alpha was good (alpha = 0.78). The total amount of information, assuming that the cybersecurity ability values have an interval ranging from \(-\)4 to 4, was about 63.01%, meaning that the CAIN is able to reliably measure the latent cybersecurity ability score of about 63% of the whole sample of participants (Figure 2a).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
1 & 0.784 & 13 & 0.781 & 25 & 0.776 & 37 & 0.780 \\
2 & 0.783 & 14 & 0.772 & 26 & 0.776 & 38 & 0.776 \\
3 & 0.778 & 15 & 0.785 & 27 & 0.776 & 39 & 0.782 \\
4 & 0.781 & 16 & 0.783 & 28 & 0.780 & 40 & 0.798 \\
5 & 0.783 & 17 & 0.775 & 29 & 0.777 & 41 & 0.777 \\
6 & 0.771 & 18 & 0.773 & 30 & 0.780 & 42 & 0.777 \\
7 & 0.779 & 19 & 0.775 & 31 & 0.788 & 43 & 0.778 \\
8 & 0.790 & 20 & 0.781 & 32 & 0.776 & 44 & 0.777 \\
9 & 0.782 & 21 & 0.779 & 33 & 0.798 & 45 & 0.781 \\
10 & 0.774 & 22 & 0.770 & 34 & 0.772 & 46 & 0.780 \\
11 & 0.787 & 23 & 0.784 & 35 & 0.779 & & \\
12 & 0.783 & 24 & 0.778 & 36 & 0.780 & & \\ \hline \end{tabular}
\end{table}
Table 2: KR20 coefficient if item deleted.  
Figure 1: (a) Distribution of the CAIN scores. The line indicates the expected normal distribution; (b) Distribution of the CAIN scores (angular transformed data). The line indicates the expected normal distribution.  
Figure 2: (**a**) Test information plot; (**b**) precision of the test measure (standard error of the test) as function of the estimated cybersecurity vulnerability score; (**c**) item–person plot or Wright Map.  
Moreover, as shown in Figure 2b, the CAIN reaches the greatest precision (lowest standard error) when it is used to estimate low cybersecurity ability levels. Both Figure 2a,b confirm that the range of the estimated item difficulties (95% C.I.: \(-3.78\); \(-1.85\)) is centered on the range of the estimated person abilities (95% C.I.: \(-1.03\); \(0.76\)). As shown in Table 3, proportions of the correct answer for each item were quite high, and the corresponding estimated Rasch difficulty parameters were all negative, indicating that the 46 CAIN items assessed the easiest segment of the latent cybersecurity ability. Figure 2c, the item-person map, confirms this conclusion. As can be observed, the estimated item difficulties (shown as black dot in the graph) were all located below the average cybersecurity vulnerability estimated on the whole sample (i.e., the zero on the graph represents the estimated average cybersecurity ability).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Proportion** & **Logit** & **Difficulty** & **se** & **z** & **\(\chi\)2** & **Pr (\textgreater{}\(\chi\)2)** \\ \hline
1 & 0.982 & 3.996 & \(-4.415\) & 0.462 & \(-9.567\) & 5.537 & 0.672 \\
2 & 0.989
# Hierarchical Classification of Botnet Using Lightweight CNN
## 1 Introduction
### Contribution  
The paper's contribution is the creation and application of a novel method for the hierarchical classification of botnet attacks using Convolutional Neural Networks (CNN) on the Bot-IoT datasets. The persistent threat of botnet attacks on Internet of Things (IoT) devices in the field of cybersecurity necessitates the use of strong and efficient detection systems. The hierarchical CNN architecture presented in this paper improves the precision and level of detail in identifying botnet attacks. The contribution of the paper is summarized as follows:  
* Our suggested method aims to enhance the efficiency of botnet attack classification on the Bot-IoT dataset by leveraging its inherent hierarchical structure.
* Additionally, the proposed CNN model is lightweight, demanding less memory and execution time, rendering it suitable for deployment on compact IoT devices.
* This approach is crucial in addressing the evolving IoT security landscape, as it introduces advanced hierarchical categorization algorithms. These algorithms facilitate more precise and nuanced identification of botnet activity  
The rest of the paper is organized as follows. The related works are presented in Section 2. In Section 3, the dataset description, proposed approach, and evaluation metrics are covered. In Section 4, the experimental results and discussion followed by a comparison to the existing approach are discussed. Finally, Section 5 presents the conclusion.
## 4 Evaluation
### Production Cost  
The total cost for the production of 1 Wi-PoSnode (in a batch of 50) is 178.22 euros, where most of cost is due to the price of the Zolertia RE-Mote (90.16 euros) and the PCB printing and assembling (78.32 euro). Minor costs include the purchase of cables for powering (USB-C) and flashing the RE-Mote and the antennas. The installation cost for these devices is rather limited as the backbone is completely wireless with the sub-GHz communication.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TX Gain** & **0 dB** & **12.5 dB** & **33.5 dB** \\ \hline tag & 80.53 mA & 81.19 mA & 83.40 mA \\ anchor (1-slot) & 63.16 mA & 63.29 mA & 63.73 mA \\ anchor (no-slot) & 47.98 mA & 47.98 mA & 47.98 mA \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average current for anchor/tag nodes for different configurations (55 ranges/s).  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **Current** & **Time** & \multirow{2}{*}{**Tag [\%]**} & \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Anchor**} & \multirow{2}{*}{**Time**} & **Time** \\  & **[mA]** & & & & **[ms]** & & **[\%]** \\ \hline UWB RX preamble hunt & 177 & 1.724 & 2.4 & 0.431 & 0.6 & 0 & 0.0 \\ UWB RX & 193 & 5.6 & 7.7 & 2.5 & 3.4 & 0 & 0.0 \\ UWB TX & 140 & 3.41 & 4.7 & 0.682 & 0.9 & 0 & 0.0 \\ sub-GHz TX & 106 & 4.2 & 5.8 & 2 & 2.8 & 0 & 0.0 \\ sub-GHz RX & 80 & 8 & 11.0 & 4.2 & 5.8 & 4.2 & 5.8 \\ IDLE & 58 & 49.766 & 68.5 & 62.9 & 86.5 & 68.5 & 94.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fractions of time when the platform is in which state per superframe (72.7 ms).
## 4 Circuit
### Test Circuit. Modeling the Parasities  
There are several ways of designing a lossless matching circuit [29]. The vast majority of them are based on the use of transmission lines and stubs, or on the use of lumped elements. The use of transmission lines is prohibitive in our particular case due to the low frequency involved in the design (800 MHz) and, therefore, the large dimensions of the lines. Thus, we focus our study on the use of lumped elements. The simplest matching network of this kind is the \(L\) section [29]. It is formed by two reactive elements and is capable of transforming a real impedance (188 \(\Omega\)) into a certain complex load within the required narrow band (from 800 MHz to 950 MHz).  
An accurate circuit model is needed, capable of predicting the measurement deviations caused by the parasitic elements present in the components and in the PCB itself. The creation of the circuit model follows these steps: firstly, the parasitic components of inductors and capacitors are estimated in the laboratory via their self-resonant frequency. They are related according to Equation (2). The SRF of a given inductor or capacitor is obtained in the laboratory by placing the component in series with a 50 \(\Omega\) line [30]. The zero reflection indicates the SRF of the capacitor, while the zero transmission shows the SRF of the inductor. Finally, the rest of the parameters (parasitic inductance of the via hole and parasitic shunt capacitance of the HSMS-2822 package) are tuned in the simulator on the basis of theoretical values [30].  
The circuit model presented in Figure 14a includes four different types of parasitics: two of them are the parasitic capacitances and inductances associated with inductors \(L_{mat1}=4.3\) nH, \(L_{mat2}=8.2\) nH and capacitor \(C=33\) pF, respectively. Here, it can be found that \(C_{p1}=0.10\) pF, \(C_{p2}=0.19\) pF, and \(L_{p}=0.16\) nH. The third type of parasitic models the effect of via holes in the circuit by means of the parasitic inductance \(L_{via}=1.30\) Nh. The fourth type models the parasitic capacitance associated with the diode package through \(C_{pd1}=0.75\) pF, and \(C_{p2}=0.10\) pF. It is determined that the most harmful parasitic elements in the circuit model are those related to the inductors and the diodes. That is, \(C_{pd1},\ C_{p1}\) and \(C_{p2}\). On the other hand, the parasitic inductance \(L_{p}\) and the parasitic capacitance \(C_{p2}\) are completely negligible [30].  
A comparison between the measurement of the test circuit, a simulation where the parasitics have not been considered, and a simulation with the parasitics are properly modeled is presented in Figure 14b. The parasitic elements considerably displace the resonance peak from the basic simulation (black line) to the measurement (blue dots). Therefore, it is necessary to model the parasitics of each designed circuit. By doing so, the agreement between the measurement and the simulation with the parasitic elements (yellow line) is noticeable.  
Figure 14: \(Cont.\)
# Future Sustainable Internet Energy-Defined Networking
## 4 Future Internet Energy-Defined Networking--Design Methods
### Methods for Network Energy Management and Associated Research Challenges
#### 4.4.2 Management-Level Energy Design Methods  
Management operational support functions (~10% of energy consumption); the methods' main goal: network and resource energy-efficient operations.  
The specific critical methods include the following:  
Figure 4: REST client (Controller)—REST server (resources/EAE) communication.  
1. Specific energy instrumentation (monitoring and models) with realistic performance and reliability constraints will be considered and incorporated in closed network-level energy loops, including energy monitors, energy analysis, energy-federated choreography, energy response management, energy decisions, and energy configuration executions [51].
2. Network-level energy calculus and analysis--Domain network-level topology hot spot maps will be updated periodically. These will be realized through efficient heuristics that can meet real-time requirements, while a critical characteristic will be their decentralized nature for scalability purposes.
3. Further efficient resource utilization can be achieved, allowing part of the infrastructure to go into sleep mode or operate at an energy-conserving pace.
4. Guarantees and Key Performance Indicators (KPIs) for energy consumption and optimization.
5. Adaptive resource management and network operation approaches that execute at run-time. These will closely track connectivity and computation demand and regulate their distribution intelligently.
6. Multi-domain energy optimization and management--Chaining energy methods across multiple domains.
7. Energy instrumentation that allows for the adequate measuring and monitoring of energy usage at different levels of granularity.
8. Energy applications that aim to optimize energy consumption across the domain, network slice, or subnetwork.
9. Support for energy-saving methods enabling significant energy consumption reduction to a standby level.
## 3 Results and Discussion
### Effects of Initial Phosphate Concentration  
Generally, the removal efficiency of phosphate was negatively correlated with the initial phosphate concentration in the EC process, and the phosphate removal was limited at the start of the operation, as the amount of coagulants produced was relatively small [14; 34; 35]. In this study, the phosphate concentration was set to approach the concentration of real domestic wastewater. A 10 mmol/L NaCl electrolyte and a 10 \(\Omega\) operating condition were used, and the initial concentrations of phosphate in the simulated wastewater were set to 5 mg/L, 4 mg/L, 3 mg/L, 2 mg/L, and 1 mg/L, respectively. As the results in Section 3.1 show, the operation time could be reduced to 50 min with 10 mmol/L of NaCl. The maximum phosphate removal rate was achieved at 50 min, and the level of phosphate removal decreased after 60 min. At lower phosphate concentrations, a higher phosphate removal rate was obtained at the beginning of the operation. With the increase in the phosphate concentration, the phosphate removal rate increased at 50 min, achieving 92.2%, 93.0%, 92.6%, 96.9%, and 98.0%, respectively (Figure 6). In the actual sewage treatment process, the concentration of phosphate in sewage changes greatly. These results indicate that in a relatively low phosphate concentration range, the aluminum-air fuel cell system can achieve a high phosphate removal efficiency. Compared with the existing EC process, which consumes electricity, an electrical-power-generating electrocoagulation system was applied to this work, which provided an energy-friendly and promising electrocoagulation process for phosphate removal.
# A Multiobjective Decision-Making Model for Risk-Based Maintenance Scheduling of Railway Earthworks
## 3 Multiobjective Decision-Making Model for Maintenance Planning
### Attributes
#### 3.2.3 Availability-User Delay Cost  
The final attribute considered in the model is object availability, expressed as user delay costs. Where user delay costs are the costs that are born directly by the users of the rail network as a result of traffic disruptions associated with the construction and maintenance of objects on the network. The maximum speed on the network considered is 160 km/h for passenger trains and 80 km/h for freight trains. However, on certain sections of line and/or for certain types of train, lower maximum speed limits apply. These permanent speed restrictions were determined for the network from Irish Rail (2018) (2018).  
In order to determine the current impacts of those permanent speed restrictions, the user delay costs were calculated as the difference between time traveled at the current speed and the time traveled at the speed that would be possible if the earthworks were at full operational speed.  
The user delay costs (UDC) are calculated in Equation (14)  
\[\text{UDC }=\biggl{(}\frac{length}{reduced\ speed}-\frac{length}{standard\ speed}\biggr{)}\biggl{(}\frac{train\ frequency}{number\ of\ working\ hours}\biggr{)}V0T\times n \tag{14}\]  
where \(V0T\) is an average value of time for railway passengers (\(\xi\) per hour). A value of 632.5 per hour was used ([PERSON] et al., 2018), and \(n\) is the average number of passengers per hour on a weekday.
## 4 Results and Discussion  
The minimum fluidization velocity, \(U_{m\!f}\), in FBMR with five porous membrane panels installed, but with permeation lines shut off, were measured experimentally in this study by recording the pressure drop across the bed as the inlet flowrate decreases from 40 to 0.5 L/min. \(U_{m\!f}\) is defined as the point where the pressure drop across the particles bed stops increasing linearly with increasing gas velocity, and remains constant after this critical velocity. As listed in Table 1, \(U_{m\!f}\) is equal to 0.041 m/s (4.5 L/min) and 0.069 m/s (7.5 L/min) for calcined dolomite and silica sand, respectively.  
The effects of gas extraction/addition on the variation of \(\sigma_{r}\) along the height of the FBMR, with membranes #1-4 activated are shown in Figure 2. In normal operations without membrane activation, \(\sigma_{r}\) increases along with the bed height, indicating continual growth of the average bubble size. When membranes #1-4 are activated for gas extraction, \(\sigma_{r}\) remarkably increases and remains relatively stable until a decrease was observed at the measurement intervals of DP5. Whether for operations with an inlet velocity of 0.09 m/s or 0.14 m/s, the evolution tendency of \(\sigma_{r}\) is similar, which indicates that gas extraction has pronounced effects on the bubble properties in the bed. Gas addition through membranes #1-4, with a membrane arrangement similar to that of the gas extraction tests, led to increased \(\sigma_{r}\), and the incrementation was augmented at higher bed sections and higher permeation rates.