esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Excellent stability and good hydrodynamic performance are essentially mandated by the safety requirements of offshore operational and survival conditions. This explains why among the abovementioned FOWT concepts, only the Hywind concept has been commercialized first. Furthermore, a great deal of measures and efforts have been taken to reduce the cost of FOWT, including decreasing the usage amount of steel and making the transportation and installation more efficient. Even so, the annual income from power generation alone is still much less than the total investment of the floating wind farm. For a FOWT, the payback period of investment is more than ten years. However, for fish farming, the profit is much higher than offshore electric power generation. For nearly two decades, fish cages made of high density polyethylene (HDPE) have dominated lake, coastal, fjord fisheries not only for their light weights and good stability but also for the low costs in manufacturing, towing, net cleaning and fish capturing. But, in harsh sea conditions like typhoons in South China Sea, the HDPE fish cage could hardly survive. Its application to offshore fish farming is mainly restricted to areas of low and moderate ocean conditions. In the present study, an innovative structure integrating a floating offshore wind turbine with a steel fish-farming cage (FOWT-SFFC) is introduced. In this concept, the upper part of the system is a multi-megawatt wind turbine and the lower part of the system is a steel aquaculture cage acting as the floating foundation for the wind turbine. It takes the advantages of spar foundation and steel material and combines the power exploitation and aquaculture in deep waters. In this paper, we will focus on the design philosophy, structural configuration of this new concept, and the motion responses under different load cases to show the feasibility and superiority of FOWT-SFFC. In Section 2, the primary design philosophy and configuration of this new concept, already filed in a number of patent applications, will be addressed first. Then the stability performance will be investigated in Section 3. In Sections 4 and 5, the hydrodynamic behaviors of FOWT-SFFC will be studied and comparisons with OC3 Hywind [32] and OC4 DeepCwind [14] offshore wind turbines will also be carried out in terms of response amplitude operators (RAOs) of motions and short-term extreme response in several typical load conditions to manifest the distinguished merits of FOWT-SFFC. Finally, concluding remarks and future work will be given.
## 4 Conclusions  
This study delves into the adsorption behavior and chemical interactions of three hazardous gases, CO, NO\({}_{2}\), and SO\({}_{2}\), on an Ag-atom-doped WS\({}_{2}\) monolayer, employing DFT calculations. Various parameters pertinent to adsorption behavior, such as adsorption energy and electron transfer, were computed and subsequently compared. To gain deeper insights into the electronic and sensing properties, the band structure, DOS, work function, and desorption behavior were scrutinized. The main conclusions are as follows:  
1. NO\({}_{2}\) adsorption is characterized by the highest adsorption energy and electron transfer of \(-\)1.40 eV and \(-\)0.40 eV, and the CDD analysis illustrates alterations in electron density after adsorption, with NO\({}_{2}\) adsorption leading to the most significant electron redistribution.
2. The band gap of Ag-WS\({}_{2}\) experienced reductions from 1.28 eV to 1.19 eV, 1.06 eV, and 0.96 eV for CO, NO\({}_{2}\), and SO\({}_{2}\), respectively, causing diverse modifications in the states of the adsorbed molecule's molecular orbitals. Chemical interactions manifest through the hybridization of atomic orbitals of Ag and atoms within gas molecules.  
Figure 8: Desorption time of CO, NO\({}_{2}\), and SO\({}_{2}\) on Ag-WS\({}_{2}\) monolayer at different working temperatures.  
3. The work functions elevation for all three adsorptions, with NO\({}_{2}\) displaying the most pronounced increase from 4.80 eV to 5.50 eV.
4. Desorption properties reveal that the Ag-WS\({}_{2}\) monolayer is well-suited for detecting CO and SO\({}_{2}\) at mild or room temperatures, while higher working temperatures are preferable for NO\({}_{2}\) detection.  
This investigation provides a theoretical foundation for enhancing gas sensing properties in various domains via surface modification of TMDs.  
Conceptualization, Q.Z.; investigation, Q.N.; writing--original draft preparation, S.L. ([PERSON]); writing--review and editing, J.H. and H.C.; funding acquisition, S.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank the following project. Subject: Research and Development of Dry-type Reactor Insulation Thermal Aging Fault Diagnosis Equipment Based on Micro-Nano Sensing Technology. KJ22-1-36, Science and Technology Project of State Grid Tianjin Electric Power Company.  
Not applicable.  
The data presented in this study are available in the article.  
Authors have received research grants from State Grid Tianjin Electric Power Company. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
# HDLNIDS: Hybrid Deep-Learning-Based Network Intrusion Detection System
## 6 Performance Analysis
### Simulation Setup  
To validate the efficacy of the suggested ID strategy, we implemented the proposed technique in Python using deep learning. The experiment was carried out on a computer (Core i7, 64-bit, 24 GB RAM, 64-core CPU). To improve pipeline speed, the model was trained using an NVIDIA GTX 2080 Ti GPU. To evaluate the proposed models, the dataset was divided into training and testing datasets separately. We utilized the training dataset to train the proposed model to make it effective. Then, the testing set was used to assess the efficiency of the proposed model. We utilized the CICIDS-2018 dataset to demonstrate the effectiveness of the proposed solutions. The network traffic included both malicious and normal information, which the proposed model categorized into malicious and non-malicious categories, respectively. To obtain high accuracy and a low FAR value, the proposed technique decreases computational complexity by utilizing rich characteristics from the CICIDS-2018 dataset. Even though the CSE-CIC-IDS2018 data were utilized for training and testing, the model was tested with 10-fold cross-validation in each step, and each model was trained on the lot size using first-order gradient-based optimization algorithms such as RMSprop and Ada Max with different learning rates, while various combinations of hyperparameters were used to optimize the actual network packet using a search algorithm and 10-fold cross-validation. We included Gaussian noise layers after  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Dataset** & **Normal** & **DDoS** & **Brute Force** & **Infiltration** & **Dos** & **Port Scan** & **Web Attacks** & **Botnet** \\ \hline CICIDS-2017 & 1,743,179 & 128,027 & 13,835 & 36 & 252,661 & 158,930 & 2180 & 1966 \\ CICIDS-2018 & 6,112,151 & 687,742 & 380,949 & 161,934 & 654,301 & - & 928 & 286,191 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of CICIDS-2018 ID dataset with CICIDS-2017.  
convolutional and recurrent layers to improve model flexibility in terms of performance and to prevent overfitting. Table 3 presents the hardware specifications used for this research.
# Autonomous Controller-Aware Scheduling of Intra-Platoon V2V Communications
## 3 System Model
Therefore, we assume that the controller actions are made in an event-based manner, whenever a new message updating the mobility parameters is received. In this work, we consider a general communication scheme based on the quasi-periodic exchange of information, where the interval between subsequent messages may be selected dynamically based on detected events and vehicles' motion parameters, as shown in Figure 2.  
Figure 1: Considered platooning scenario using the autonomous leader-predecessor-follower controller and communications model. Each vehicle, upon the detection of any event resulting in a change of motion parameters, selects the initial latency \(\tau\) of the first transmitted message and the interval between any following messages \(\Delta t_{Tx}\). The communication pattern (resources used) for each vehicle is then specified with (6). \[q_{s}^{(i)}(k)= \begin{cases}1&\text{if vehicle $i$ is transmitting at time $k\Delta t$}\\ 0&\text{otherwise.}\end{cases}  \tag{6}\]  
The specific V2V communication interface is out of the scope of this paper; however, one can consider both the DSRC or C-V2X being applied, as both are suitable in the provisioning of such information exchange. DSRC is well suited to event-based transmission, and typically, assuming limited range communications within the platoon, it experiences small latency [30]. On the other hand, the introduced delay may vary for consecutive packets, due to the channel access method used. C-V2X is much more predictable in terms of latency, as it uses a grant mechanism, where the sub-channels available for transmission are allocated in advance for a specific period; however, it is less suitable for handling event-based messaging, thus introducing greater delays. For the considered scenario, we assume that each vehicle is equipped with an emergency braking mechanism that becomes activated if the distance to the preceding vehicle is below a certain threshold \(d_{Th}\).
# Two-Phase Flow Simulations Using 1D Centerline-Based C- and U-Shaped Pipe Meshes
## 3 Results and Discussion
### Velocity and Pressure Distributions Depending on Four Gravitational Directions
#### 3.3.1 Water and Air Velocity Distributions of Two-Phase Flows
Moreover, the total dynamic pressure was smaller in the case of the U-shaped pipe with an upward injection (Case 4) from the inflow to the curve center, but greater when the flow went from the curve center to the outflow in opposition to that in Case 3. Figure 14 illustrates the total pressure change along the axial direction caused by the influence of the static, hydrostatic, and dynamic pressures on both single- and two-phase flows. The total pressure drops and gradient were further quantified in straight (inlet), curve, and straight pipes (outlet) (Table 3). The results showed that for the single-phase flow, the trends were consistent in all four gravitational directions, implying that gravitational directions play a less important role in determining the total pressure of single-phase flows. For the two-phase flows, the total pressure gradient was always greater in the curved region than in the straight pipe, because the asymmetric velocity profiles induced an increase in the dynamic pressure and shear stress. The total pressure change of the two-phase flows in the C-shaped pipes showed similar trends for T1 and T2. The total pressure change was also similar in the U-shaped pipes (i.e., T3 and T4). Meanwhile, the total pressure drop in the U-shaped pipes (i.e., T3 and T4) was much larger than that in the C-shaped pipes (i.e., T1 and T2) (Table 3), indicating that the viscous friction between the two-phase cases was stronger when the flow ascended and descended against gravity. Moreover, T4 had a peak total pressure change in the curved region because of the sudden change in the dynamic pressure at the curve center (Figure 13F). Figure 14: Total pressure change along the axial length of the (**A**) single- and (**B**) two-phase flows with \(\alpha_{air}~{}=~{}0.94\%\). Figure 15 shows dynamic pressures of air and water phases in three different air volume fractions, i.e., 0.94%, 2%, and 3%. The air volume fraction (\(\alpha_{air}\)) represented the ratio of air volume to the total volume. The dynamic pressure decreases after passing the curve center, as the \(\alpha_{air}\) increases. The reduced dynamic pressure is based on the direction of buoyancy force inversely aligned with the flow direction (see Figure 15). Due to the mass conservation, the dynamic pressure of water phase was elevated with increasing \(\alpha_{air}\) (see Figure 15B).
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 6 Test Pattern Generations for Delay Faults
### Pattern Grading and Evaluation
The fault coverage remains the same, but the number of patterns increases drastically by the pattern selection and evaluation + top-off pattern-based method to increase the SDD coverage. When the pattern count increases, it also increases the test application time. This affects the time to market demand. In addition, the limited memory storage of the ATE must also be considered. Hence, keeping the pattern count comparable to the 1-detect TDF pattern set is necessary. Therefore, an optimized pattern set with a pattern count equivalent to 1-detect TDF patterns is generated using a pattern evaluation, and selection technique [37]. The pattern selection is made using an algorithm that minimizes the target function using the gradient descent method. The weight calculation for pattern evaluation is given by the formula shown in Equation (2). \[W_{pattern_{i}}=\sum_{j=1}^{N_{i}}W_{path_{j}} \tag{1}\]  
\[W_{pattern_{i}}=\alpha\sum_{j=1}^{N_{f_{i}}}\frac{W_{fstd_{j}}}{N_{fstd}}+ \beta\sum_{j=1}^{N_{f_{i}}}W_{path_{i}}\frac{W_{ftdf_{j}}}{N_{ftdf}} \tag{2}\]  
Here \(W_{fstd_{j}}=1\) and \(W_{ftdf_{j}}=0\) is the weight assigned, if the pattern detects the fault \(j\) as SDD. And vice versa for the pattern that detects TDF. \(N_{f_{i}}\) is the number of faults detected by a \(pattern_{i}\). \(N_{fstd}\) and \(N_{ftdf}\) are the total numbers of SDDs and TDFs, respectively, that are detected by the whole pattern repository. The parameter \(\alpha\) and \(\beta\) decides the importance of SDD and TDF respectively, in pattern weight calculation and ranges between [0,1]. These values are selected based on the ability to minimize the target function. These pattern grading and evaluation techniques produce reduced pattern sets for SDD detection with high test coverage. But still, many SDDs that fail the circuit during at-speed operation escape the N-detect TDF pattern set. This is because the N-detect cannot guarantee that all the faults are detected along the longest possible path. In such cases, FAST can help detect these small delays due to the defects. In the next section, we will discuss FAST-based testing and its advantages.
# Big Data for Traffic Estimation and Prediction: A Survey of Data and Tools
## 2 Data-Driven Traffic Estimation and Prediction
### Surveillance and Airborne Digital Cameras  
Closed circuit television (CCTV) cameras are widely used for monitoring traffic patterns and helping police forces in large cities. By collecting these surveillance videos, traffic information can be estimated and further used for prediction. For example, traffic states with CCTV systems were analyzed in [67]. However, the challenge is the heavy workload of processing digital multimedia data. Transportation video data management was considered in [68], and a high-performance computing architecture was developed with distributed files and distributed computing systems. Edge computing is also applied for traffic flow detection in real-time videos. A specific edge device Jetson TX2 platform was used in [69], with the YOLOv3 (You Only Look Once) model for vehicle detection and the optimized DeepSORT (Deep Simple Online and Realtime Tracking) algorithm for vehicle tracking. The edge device managed to achieve an average processing speed of 37.9 frames per second, with an average accuracy of 92.0%. In addition to static traffic surveillance cameras, remote sensing tools, e.g., airborne digital cameras, and unmanned aerial vehicle (UAV) cameras can also be used for traffic monitoring. For example, the average traffic speed and density were estimated with an airborne optical 3K camera system in [70], and the traffic flow parameters were calculated from UAV aerial videos in [71]. Some open video data are summarized in Table 8. The pros of video data are their continuous observation, wide coverage for road traffic, and multiple usages for traffic estimation, prediction, accident analysis and even vehicle tracking. The cons of video data are their high storage and computation requirements. Video streams also have many engineering challenges, e.g., compression artifacts, blurring, and hardware faults.
# Basins of Convergence in a Multi-Perturbed CR3 BP +
## 4 Application to the Sun-Mars System: Adding the Effects of Phobos and Deimos
### Effects of Phobos and Deimos  
As discussed before, the BCs are also analyzed when considering the presence of the natural satellites of Mars, Phobos and Deimos. They are assumed to be perfectly spherical masses that follow circular orbits whose radii are equal to the semi-major axes of their real orbits. Equally, the approximate mean motions are obtained as the inverse of their orbital periods. The corresponding data are shown in Table 12.  
The main aim of this section is to check the influence of these natural satellites on the libration points and the distribution of the BCs. Although the program is capable of calculating temporal evolutions, in this section, only certain positions of the satellites are analyzed in order to verify whether their presence is able to modify the distribution of the BCs or the libration points.  
Firstly, a case in which both satellites are on the \(x\) axis and both are eclipsed by Mars, i.e., \(x_{si}>1-\mu\), is analyzed. This study leads to very similar results to those obtained when triaxiality effects were considered, both in terms of the coordinates of the Lagrange points and the shape of the BCs. However, they do differ in position of the tenth decimal.  
Next, we analyze a case in which both satellites are aligned in the direction perpendicular to the abscissa axis, that is, when the offsets of both satellites with respect to the \(x\) axis are \(\pm 90^{\circ}\). Phobos would be on the positive side of the \(y\) axis (a \(+90^{\circ}\) offset), while Deimos would be on the negative side (a \(-90^{\circ}\) offset). In this case, notably similar results to those in the case of the satellites being aligned were obtained, so they are not shown.  
Then, we find that the presence of the satellites modifies neither the distribution of the BCs nor the position of the Lagrange points. This can be explained by their mass ratio: the terms of the potential associated with the satellites are several orders of magnitude lower than those associated with Mars, whose weight in the potential function was already significantly less than that of the Sun. So, in view of Equation (17), it seems that if the distribution of the total mass between the primary masses was more balanced and the masses of the satellites were larger, the satellites' effects may modify the results.
## 9 Results and Discussion
### Robustness of the Algorithms  
The robustness of the algorithm can be determined by its ability to provide the global best optimal values for the proposed configurations. Three configurations were modelled in this study, and these configurations were tested using well-known algorithms as well as a new proposed metaheuristic algorithm Dynamic Differential Annealed Optimization (DDAO). For each of these three configurations, the proposed algorithm, DDAO, provided the optimal LCC values. The number of times each algorithm provided the global best optimal values for three configurations are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Configuration** & **Q\&C** & **GA** & **PSO** & **DE** & **GWO** & **ALO** & **DA** & **MPO** & **GOA** & **DDAO** \\ \hline \multirow{4}{*}{\begin{tabular}{c} **PVP/** \\ **BIOMG/** \\ **BATTERY(N1-Fe)** \\ \end{tabular} } & N\({}_{\text{PV}}\) & 511 & 511 & 513 & 511 & 513 & 511 & 516 & 511 \\ \cline{2-10}  & N\({}_{\text{BAT}}\) & 351 & 351 & 351 & 351 & 352 & 351 & 352 & 351 \\ \cline{2-10}  & LCC in USD & 367,586 & 367,586 & 368,088 & 367,586 & 368,153 & 368,655 & 367,586 & 369,407 & 367,586 \\ \cline{2-10}  & STIS & 8687 & 8906 & 20538 & 8938 & 8980 & 9080 & 8806 & 8972 & 8152 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **PVP/** \\ **BIOMG/** \\ **BATTERY(LA)** \\ \end{tabular} } & N\({}_{\text{BAT}}\) & 475 & 474 & 474 & 478 & 474 & 476 & 477 & 474 & 474 \\ \cline{2-10}  & N\({}_{\text{BAT}}\) & 136 & 136 & 136 & 136 & 137 & 136 & 136 & 136 \\ \cline{2-10}  & LCC in USD & 607,281 & 607,030 & 607,030 & 608,032 & 607,030 & 610,821 & 607,781 & 607,030 & 607,030 \\ \cline{2-10}  & STIS & 8871 & 8669 & 20820 & 9007 & 8980 & 9120 & 8889 & 8992 & 8083 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **PVP/** \\ **BIOMG/** \\ **BATTERY(1-Ion)** \\ \end{tabular} } & N\({}_{\text{PV}}\) & 436 & 438 & 439 & 436 & 437 & 436 & 439 & 436 \\ \cline{2-10}  & N\({}_{\text{BAT}}\) & 155 & 155 & 157 & 155 & 155 & 155 & 156 & 155 \\ \cline{2-10}  & LCC in USD & 1,019,683 & 1,020,185 & 1,031,657 & 1,019,683 & 1,019,683 & 1,019,683 & 1,026,046 & 1,019,683 \\ \cline{2-10}  & STIS & 8814 & 8845 & 20542 & 8874 & 8834 & 8953 & 8792 & 8811 & 8092 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization results of all configurations at an LPSP value of 0%.  
number of power supply hours lost is also 123 only out of 8760 h. As a result, electrification of the study area at an LPSP value of 1% is also recommendable.  
Figure 10: Convergence curves of the configuration made up of LA battery.  
Figure 9: Convergence curves of the configuration made up of Ni-Fe battery.  
Figure 11: Convergence curves of the configuration made up of Li-Ion battery.  
Figure 12: The Ni-Fe battery configuration analysis with different LPSP values.
## 4 LEACH Protocol Improvement Based on the Differential Evolution Algorithm
### Forming Optimized Clusters  
In this phase, auxiliary cluster head nodes send the information of optimized cluster heads to nodes inside the cluster, Optimized cluster heads collect and integrate information of nodes inside the cluster. Forming optimized clusters comprehensively considers status information of neighbor nodes, so the energy loss of nodes inside the cluster is even to avoid frequent blind nodes.
## 2 Materials and Methods
### Feed Preparation  
_Emblica officinalis_ fruit was procured from the university campus, identified, and verified (Voucher No. 20619) from a taxonomist of the department of Botany, Panjab University, Chandigarh. The fruits were shade dried, ground, extracted in ethanol (absolute) using Soxhlet extraction method, lyophilized (MAC Lyophilizer, Mumbai, India) and evaporated (Roteva Equitron, Mumbai, India) [39]. EO extract was analyzed for phytochemical components using RP-HPLC (Reverse Phase High performance liquid chromatography) (Shimadzu, Kyoto, Japan), equipped with two LC-10 ATVP pumps, an SPD-10 AVP UV-vis detector, a C18 column (4.5 \(\times\) 250 mm), and a Rheodyne injector with a 20 \(\upmu\)L loop at 275 UV detector wavelength and with a detection limit of 0.008 \(\upmu\)g/mL. The results obtained were processed with Shimadzu LC-solution software (6.42). Standards had regression values R\({}^{2}\) = 0.998 and 0.997, and line equation y = 2884x + 12,822 and y = 23,616x \(-\) 15,280 for ascorbic acid and gallic acid, respectively. Observed phytochemical components in EO extract were gallic acid (640.1 \(\pm\) 5.440 mg/g, RT--3.139 min) and ascorbic acid (197.7 \(\pm\) 4.378 mg/g, RT--2.668 min). Diet supplement was prepared by adding 1000 mg of the ethanolic extract of _E. officinalis_ (EO) per kg of the fish feed, as described previously [9]. The ingredients were combined to make dough, pelleted to about 3 \(\pm\) 1 mm granules and were fed to the fish twice a day.