esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Result and Discussion  
### Comparison of the Numerical Results with the Experimental Results  
The numerical calculations using the new transient model were undertaken for PHPs with different adiabatic section lengths. The numerical results showed that the mean flow velocities of the liquid slugs were in the range of 0.139-0.428 m/s at the heat input of 20-80 W. According to the visual experiment by [PERSON] et al. [20], for the slug flow in the PHP, the magnitude of fluid velocity was about 0.1-0.6 m/s. Thus, the calculated flow velocities of the liquid slugs were consistent with the experimental results [20]. Moreover, the thermal resistance reduced from about 0.78 to 0.39 \({}^{\circ}\)C/W as the heat input increased from 20 to 80 W, and the start-up time obviously reduced with the increase in the heat input, which was in good agreement with the experimental results [1; 18] (the previous experimental results [1; 18] proved that the thermal resistance of the PHP decreased, and the start-up performance was improved with the increase in the heat input). In addition, Figure 4 shows the further comparison between the thermal resistances from the numerical simulation and the experimental results by [PERSON] et al. [21] and [PERSON] et al. [31]. Figure 4 shows that the numerical results had good agreement with the experimental results, and with the increase in the heat input, the deviations between the numerical results and experimental results were smaller. For example, at 80 W heat input, the relative error between the numerical result and the experimental result was lower than 5%, and the relative error between the numerical result and the experimental result at the low heat input was slightly larger. The main reason for the larger deviation at low heat input was that the heat loss and the thermal contact resistance, which were inevitable in experiments, had a significant influence on the start-up performance. It was well known that the start-up is more difficult at lower heat input because the liquid slug's driving force generated by the temperature difference between the vapor plugs is small. With the increase in the heat input, the driving force is large enough to drive the oscillation motion of the liquid and vapor plugs in the PHP, and thus the influence of the start-up performance on the thermal resistance is reduced at high heat input. The compared of the above results suggests that the new proposed transient model reliably simulated the PHP's operations with different adiabatic lengths.
## 1 Introduction
Despite these advancements, existing systems struggle to achieve satisfactory accuracy for the UCF Sport and JHMDB benchmark datasets [15; 18; 23; 24; 25; 26; 27; 28; 29]. The primary reason for the lower performance on these benchmark action recognition datasets is the lack of spatiotemporal information for a more-comprehensive understanding. To address these challenges and improve accuracy, we propose a deep-learning-based feature-extraction and classification approach for dynamic human action recognition. To address the aforementioned challenges, our study makes substantial contributions in the following key areas:  
* **Feature extraction:** Leveraging the power of MobileNetV2, we employed an effective feature-extraction methodology to capture intricate patterns from the dataset. This ensures the model receives rich and informative input, enhancing its ability to discern nuanced details crucial for dynamic human activity recognition. * **Feature refining and classification:** Our approach introduces a specialized variant of long short-term memory (LSTM) known as Deep BiLSTM. This model excels in refining the extracted features and leveraging their temporal dependencies for accurate classification. Utilizing Deep BiLSTM represents a novel and powerful technique in dynamic human activity recognition. * **Extensive experiments:** We conducted comprehensive experiments to validate the efficacy of our proposed model. The model underwent rigorous testing using three benchmark human action datasets--UCF11, UCF Sport, and JHMDB. This extensive evaluation showcases the model's versatility and effectiveness across diverse scenarios, providing robust evidence of its real-world applicability. Through these strategic contributions, our study aimed to transcend the limitations of conventional handcrafted approaches. By advancing state-of-the-art feature-extraction, -refining, and -classification techniques, we contribute to developing more-robust and -adaptive human activity recognition (HAR) systems, thus fostering progress in this critical domain. We organize the rest of the paper as follows: A literature review is given in Section 2, and Section 3 describes the architecture of the proposed system. Section 4 highlights the evaluation performance. In Section 5, we describe the discussion and conclusion.
# Study on the Influence of Sapphire Crystal Orientation on Its Chemical Mechanical Polishing
## 3 Results and Discussion
The atomic arrangement of the C-plane and R-plane of sapphire is completely different from the other two crystal planes of A and M. The C-plane is perpendicular to the C axis direction, and the atomic structure of the R-plane appears as a slope structure obliquely intersecting the C axis direction. Therefore, the material properties (such as the elastic modulus, hardness) exhibited by different crystal orientations of sapphire will be significantly different, which corresponds to the results obtained by the nanoindentation experiment. The lattice bond energy of adjacent atomic layers is inversely proportional to the crystal plane distance. The larger the distance between adjacent atomic layers, the smaller lattice the bond energy is. With the lower the binding energy, material can be more easily removed. According to the  
Figure 1:1 Crystal models of the four crystal orientations for the (**a**) A-plane, (**b**) C-plane, (**c**) M-plane and (**d**) R-plane. characteristics of the hexagonal crystal system, the interplanar spacing d between the adjacent atomic layers of the sapphire A-, C-, M- and R-planes can be calculated according to Formula (2):  
Figure 1:2  
Here, the A-, C-, M- and R-planes (hkl) of sapphire are calculated according to the distance between the (220), (006), (300) and (036) planes [7]. The sapphire lattice parameters were a = 4.758 A, c = 12.992 A at 293 K.  
According to Formula (1), the interplanar spacing d between the adjacent crystal planes of the sapphire A-, C-, M- and R-planes was 1.189 A, 2.166 A, 1.375 A, and 1.161 A, respectively. The calculation results showed that the crystal plane interplanar spacing d between the adjacent atomic layers of different crystal planes decreased in the order of the C-plane, M-plane, A-plane and R-plane. Therefore, the bonding energy increased in the order of the C-plane, M-plane, A-plane, and R-plane for sapphire with different crystal orientations. It was inferred that the material removal rate of sapphire with different crystal orientations should be C-plane > M-plane > A-plane > R-plane. In summary, the results obtained through theoretical calculations were very consistent with the experimental results of nanoindentation on different crystal orientations of sapphire and the material removal rate of the polishing processing. Moreover, the research results have great guiding significance for the engineering application of sapphire processing with different crystal orientations.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 2 Nanomaterials
### Nanomaterials with Antimicrobial Properties
#### 2.3.1 Mono-Metallic Nanoparticles
These CuO NPs were found to counteract various bacteria via in vitro MBC determination, along with other nanoparticles (i.e., Cu and Ag) that were engineered under the same conditions, along with other nanoparticles (i.e., Cu and Ag) that were engineered under the same conditions [47]. With regard to MIC, [PERSON] et al. used a broth microdilution method with a 96-well microtiter plate technique to determine the antimicrobial activity of 23 nm CuO NPs against various bacterial strains [92]. They indicated that the lowest MIC of CuO NPs (31.25 \(\upmu\)g/mL) was for _E. coli_ and _E. faecalis_, whilst the highest MIC (250 \(\upmu\)g/mL) was for _K. pneumoniae_. In another similar research, [PERSON] et al. confirmed that the inhibitory effects of CuO NPs relate to size, stability, and concentration by annealing at different temperatures. They also synthesised CuO NPs with a minimum size of 20 nm to achieve the MIC value of 20 \(\pm\) 3 \(\upmu\)g/mL for _E. coli_, which is lower than the previous result of 23 nm CuO NPs [21]. Antimicrobial research has shown a growing interest in Zinc Oxide (ZnO) due to its apparent toxicity to certain pathogens [93] and the recognition of its biocompatibility in biomedical applications [94]. [PERSON] et al. stated ZnO NPs achieve the most effective bactericidal activity amongst CuO and Fe\({}_{2}\)O\({}_{3}\) against Gram-negative (_E. coli and P. aeruginosa_) and Gram-positive (_S. aureus_ and _B. subtilis_) bacteria [95]. As previously mentioned, redox reactions of Cu can lead to the generation of H\({}_{2}\)O\({}_{2}\) and ROS, which can damage cellular molecules and cause mutations in DNA [86]. A number of research studies suggested that the antimicrobial mechanisms of ZnO NPs pose similar processes to Cu/CuO NPs, where the generation of Zn ions, H\({}_{2}\)O\({}_{2}\) and ROS were involved in affecting microbial cells [96; 97; 98; 99]. Other observed electrostatic binding and accumulation of the actual ZnO particles on the bacterial surface cause structural integrity damage to the cell walls [96]. Typically, ZnO NPs are found to be more effective than micron-sized ZnO particles. Micro- and nano-sized ZnO particles at a concentration of 20 \(\upmu\)g/mL were used to inhibit _B. subtilis_, _E. coli_ and _P. fluorescens_. ZnO in nano-size totally reduced all tested bacteria species, whilst micro-sized ZnO inhibited 50 to 100% of all tested bacteria species [100]. Furthermore, in another antimicrobial study using a standard microbial method, [PERSON] et al. prepared ZnO NPs with various particle sizes; the smallest size (12 nm) of ZnO exhibited the highest efficacy [101]. The authors explained the greater the number of nanopore particles, the higher the generation of active oxygen species, and thus they kill bacteria more effectively. They also proposed that both the abrasiveness and the surface oxygen species of ZnO NPs can promote the biocidal properties of ZnO NPs.
## 3 Design Optimization  
### Design Process  
The process used for shape optimization of the PMSM is shown in Figure 4. First, a multi-objective function, constraints, and design variables were established to achieve the required target specifications. The multi-objective function was set as Equation (1) to satisfy the objective of simultaneously maximizing torque and minimizing the THD of the back EMF. Because the efficiency and torque ripple should be maintained properly even if the objective function is achieved, the constraints were set as shown in Equation (2). The torque ripple was set below 10%, which is an acceptable level for a traction motor for an EV [19]. The design variables were chosen to be the inner angle between the V-shaped permanent magnets (PM) and the rib thickness of the rotor to satisfy the multi-objective function and constraints, as shown in Equation (3). The sampling points of the design variables for the DOE were  
Figure 3: Torque as a function of the changes in current and current phase angle.  
determined using the orthogonal array (OA) technique. DOE was performed by using two-dimensional (2D)-FEA to ensure the accuracy of the experimental results. Based on the DOE results, metamodeling of the multi-objective function and constraints was performed using MLP, a type of deep learning. Metamodeling using Kriging, which has been widely used in the past, was also carried out for comparative verification of the results obtained using the MLP technique. Metamodeled results were combined with the optimal algorithm, HMA, to obtain the optimal geometric design parameter values.  
Objective function Maximize the average torque Minimize THD of the Back EMF Constraints Efficiency \(\geq\) 91.43% Torque ripple \(\leq\) 10% Design variables  
\[130^{\circ}\leq\text{DV1}\ (\text{Inner angle between permanent magnets})\leq 142^{\circ} \tag{3}\] \[1\,\text{mm}\leq\text{DV2}\ (\text{Rib thickness})\leq 2\,\text{mm}\]
# A Novel Variable Step Size Incremental Conductance Method with an Adaptive Scaling Factor
## 5 Discussions and Limitations  
According to the simulation and experimental results, when the VSS method uses an inappropriate \(M\) value, the transient or steady-state response will become worse, and the system has larger tracking energy loss. Relatively, the method proposed in this paper can select the optimal scaling factor \(M\) value according to the irradiance level to hasten tracking speed, reduce steady-state oscillation, and increase the tracking efficiency.
## 3 Experiments and Results  
In this section, we analyze the performance of the MHGSA system through two main research questions: (1) whether the proposed multibranch structural feature extraction modules for vision and audio lead to better model performance in the unimodal case; and (2) whether the multimodal hierarchical maps lead to effective feature fusion and provide additional performance in the multimodal case. In the following subsections, we provide a detailed description of the experimental datasets and experimental setup and present our experimental results and analysis.
## 5 Experimental Simulation and Analysis
### Performance of the SLP-MACGT Model  
#### 5.2.1 Effect of the Network Side Length on Performance  
The network scale plays an important role in network performance. When the network scale increases, such as increasing the number of nodes or the side length of the network, it will directly affect the capacity, coverage, transmission efficiency and data security of the network. L represents the network side length, which is the length, width and height of the network space. In the following, we explore the impact of different network sizes (L = 500 m, 1000 m, and 2000 m) on source location privacy protection. As shown in Figure 5a, increasing the number of nodes can enhance the capacity and coverage of the network while improving the privacy protection of the source location when the length of the network side is fixed. As the scale of the network increases, the communication paths between nodes become longer, resulting in prolonged information transmission times. This may increase the time needed for attackers to obtain the source location information. Therefore, increasing the number of nodes and the side length of the network will result in a longer safety time.  
As shown in Figure 5b,c, more nodes can provide more path choices, reduce congestion and network delay, and improve the transmission efficiency of data packets in the network, thus increasing the packet forwarding rate. However, the increase in network side length leads to longer transmission paths between nodes, which increases the delay of data transmission, negatively affecting real-time applications and delay-sensitive tasks. At the same time, a longer network side length increases the risk of instability and packet loss during data transmission. The packets need to pass through more nodes during forwarding  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Default Values** \\ \hline Scale of the space & 1000 m \(\times\) 1000 m \(\times\) 1000 m \\ \hline Number of nodes & 250 \\ \hline Node placement method & Random placement \\ \hline Range of communication & 200 m \\ \hline Initial energy & 100 J \\ \hline Data packet size & 1024 bits \\ \hline Control package size & 128 bits \\ \hline Transmit power & 2 W \\ \hline Received power & 0.2 W \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.  
and may face problems such as signal attenuation, interference, or node failure, which leads to a decrease in the packet delivery ratio.
## 1 Introduction
Various methods such as interfacial, self-assembly, seedling, oxidation through enzymes, and chemical and electro-chemical oxidation have been used to synthesize PANI [27; 28; 29; 30; 31]. However, chemical oxidation and electrochemical oxidations methods are most widely used to prepare PANI. The conducting form of PANI (emaraldine salt) is synthesized in acidic conditions due to doping of aniline monomers [32]. Besides PANI, its composites with inorganic materials like Cd, Ag, ZnO, TiO\({}_{2}\), SiO\({}_{2}\), MnO\({}_{2}\), and Fe\({}_{3}\)O\({}_{4}\) have also been applied as efficient adsorbents by many researchers [33; 34; 35; 36; 37]. [PERSON] and [PERSON] [38] synthesized composites of cherry kernel shell pyrolytic charcoal with chitosan and Fe\({}_{2}\)O\({}_{3}\) nanoparticles to remove Cr(VI) from aqueous solution. The data obtained were fitted in the Langmuir isotherm model and maximum adsorption of Cr(VI) was, respectively, 14.455 mg\(\cdot\)g\({}^{-1}\) and 47.576 mg\(\cdot\)g\({}^{-1}\) for cherry kernel shell pyrolytic charcoal (CKSPC) and its composites with Fe\({}_{2}\)O\({}_{3}\) (Fe-CCKSPC). [PERSON] et al. [39] synthesized amorphous aluminosilicate and its composites with Fe\({}_{3}\)O\({}_{4}\) nanoparticles. These materials were applied as efficient adsorbents for removal of Cu(II), Cd(II), Hg(II), Pb(II), and Ni(II) from industrial effluents. The effects of initial concentration, pH, contact time, and temperature on adsorption phenomenon were investigated. The percent removal efficiency of the adsorption for Hg, Cd, Pb, Ni, and Cu were found to be 92%, 96%, 99%, 92%, and 99%, respectively. [PERSON] and co-workers [40] synthesized polyaniline/attapulgite (PANI/ATP) composites by the chemical oxidation method to remove Hg (II) from aqueous environments. The maximum amount of Hg(II) adsorbed was 800 mg\(\cdot\)g\({}^{-1}\) at 5.9 solution pH and pseudo-second-order. In the present study, PANI/Fe\({}_{3}\)O\({}_{4}\) composites were synthesized through the chemical oxidation method. The synthesized composites were subsequently utilized to remove Cr(VI) and Ni(II) from aqueous environments. The adsorption behaviors of the composites were compared with pristine PANI and Fe\({}_{3}\)O\({}_{4}\). Various techniques such as SEM, EDX, UV, and FTIR were used to characterize all the synthesized materials. Adsorption parameters such as the effect of time, pH, initial concentrations, temperature, adsorbent dosage, and ionic strength were also investigated. An atomic absorption spectrophotometer was used to find the amount of Cr(VI) and Ni(II) before and after adsorption. adsorption data were fitted in the Freundlich, Langmuir, Dubinin-Radishkevitch (D-R), Tempkin, and Elovich isotherm models. Kinetics of adsorption and thermodynamic parameters were determined.
# The Effect of Clamping Force on the Wear Behavior of a Steam Generator Tube
## 5 Conclusions  
The influence of contact force on wear behavior cannot be ignored. Natural frequencies of the tube under different clamping forces were measured in our work. Moreover, dynamic behavior analysis of tube vibration was carried out to obtain the friction energy of the tube. Finally, wear experiments under different clamping forces were completed to verify the conclusion of dynamic wear behavior analysis based on the finite element method. The following conclusions are drawn.  
1. The main frequency of the double-span tube shifts to a higher frequency while the clamping force increases. Moreover, the first-order natural frequency of the tube grows under an increasing clamping force.
2. The sliding distance of the tube is affected by the clamping force from results of dynamic behavior analysis. As the clamping force increases, both the sliding distance of the tube and friction energy decrease.
3. Both maximum wear depth and wear volume reduce while the clamping force increases from 53 N to 126 N under a constant excitation frequency. Furthermore, the conclusion of the experiment is consistent with that of dynamic behavior analysis.
4. Considering that the contact force of the tube is not easy to measure in the experiment, we estimate the wear coefficient through the contact force obtained by the finite element model.  
Conceptualization, Z.G. and W.J.; methodology, X.D., M.Z., Y.L. and L.T.; investigation, X.D. and K.X.; data curation, X.D. and K.X.; writing--original draft preparation, X.D.; writing--review and editing, M.Z. and Y.L.; project administration, M.Z. and Y.L.; funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (Project No. 2018 YFC0808800), the National Natural Science Foundation of China (Project No. 11602219), and Shanghai Rising-Star Program (20 QB1403000).  
Not applicable.  
The authors are grateful for the support provided by the National Key R&D Program of China (Project No. 2018 YFC0808800), the National Natural Science Foundation of China (Project No. 11602219), and the Shanghai Rising-Star Program (20 QB1403000).  
The authors declare no conflict of interest.  
Figure 14: Wear rate and work rate of the tube.
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 2 Results and Discussion  
### Preparation of CD Polymers and Determination of Time Required to Achieve Equilibrium  
The polymerisation process (Figure 1) yielded a slightly yellow coloured polymer with \(\beta\)-CD and a white-coloured polymer with \(\gamma\)-CD. After ball mill grounding, the powdered polymers were weighed and added to wine samples. The time required for CD polymers to achieve adsorption equilibrium is dependent on the sample mixture, the chemical properties of the target molecule and temperature [30; 31]. In the current study, 1% _uv/v_ of the polymer was added to spiked model wine samples at 25 \({}^{\circ}\)C, and an incubation time of less than 5 min was required for the adsorbents to achieve equilibrium. No significant differences in the relative peak areas (RPAs) for the four volatile phenols were observed between aliquots collected from the reaction mixture at 5, 20, 40, 80 and 120 min intervals after polymer treatments (Figure 2). The rapid equilibria observed were consistent with the findings of [PERSON] and colleagues, who used a \(\beta\)-CD-HDI polymer to remove phenols from wastewater [30]. [PERSON] et al. used a different type of the \(\beta\)-CD polymer with tetrafluoroterephthalonitrile as their crosslinker, and also reported rapid equilibrium, i.e., within 10 min [32]. Other studies have reported different equilibrium times when using different CD polymers on various adsorbates. For example, [PERSON] [33] and [PERSON] [36] used epichlorohydrin crosslinked \(\beta\)-CD polymer to adsorb dyes and phenols, with 2 h of incubation required to achieve equilibrium. The differences in time to equilibrium may be attributed to the polymer type, the initial concentration of adsorbate and/or the sample matrix.