esa-sceva/satcom-chunk-collection huggingface dataset:
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 3 Biofilms in Food Industry
### Biofilm Control Methods in Food Industry  
#### 3.2.1 Surface Modification of Contact Material  
Several approaches have been examined to avoid biofilm formation in the food industry. The modification of food contact surfaces seemed to be the first mode of action against the attachment of bacterial cells. The complexity of particular parts of processing equipment (joints) or bacterial cells \"hidden\" in cracks and scratches appearing on the surface do not guarantee complete sanitization [103]. Therefore, it is a valuable strategy to have the initial bacterial attachment on the surface of materials under control. According to the Biocidal Products Regulation (EU 528/2012), compounds containing copper or silver are approved for use in food and feed areas. In particular, in food processing plants (brewry, water pipes), copper surfaces are still in use. It has undeniable anti-biofilm properties due to the interaction of copper ions with the cell membrane, causing the formation of reactive oxygenic species, DNA damage, and the impairment of DNA integrity [104]. However, the application of copper material is limited due to the high cost, possible leakage of copper ions, and corrosion. Innovative approaches are needed to embed functional copper ions into food contact surfaces [105; 106]. Since stainless steel (SS) is a widely used material in the food industry, a lot of effort has been devoted to the research of SS modification. The environmental factors in food processing environments and the properties of stainless steel enabling the attachment of bacterial cells on the surface of SS have been intensively reviewed [107]. Silver ions are known to interact with thiol groups of cysteine, resulting in the disruption of the permeability of membranes, and the release of a reactive oxygen species causing oxidative stress. Ag-doped stainless-steel surfaces were prepared using various technological processes including sol-gel [108], immersion in nanoparticles suspension [109], ion beam technology [110], spray coating [111], or simple Ag\({}^{+}\) adsorption from water disinfectant [112]. Inspired by the peristome of carnivorous pitcher plants (e.g., _Nepenthes_ sp.) which excrete a lubricant liquid making the surface slippery for insects [113], food-contact surfaces have been treated by various lubricants to restrict the adhesion of bacterial cells [114]. However, coating procedures are often too complicated, comprising a number of organic substances, solvents, and accompanying chemical reactions. Although antifouling properties were found to be promising in vitro, their use in food processing environments should be extensively examined to ensure food safety.
## 7 Conclusions  
This paper redesigns the vehicle cluster method, communication link establishment method, and routing policy based on the TDCR idea. In the vehicle clustering method, we no longer adopt the spatial sampling method of TDCR; we redesigned a clustering method that is suitable for vehicle clustering based on the idea of hierarchical clustering. In CTDHR, a new cluster model was designed, and the parameters in Equation (1), which reduce the number of clusters while ensuring the stability of clusters, were carefully selected. The link capacity was fully considered when establishing the communication link so that the network obtained a greater throughput. The flexible routing strategy enables data packets to choose the appropriate transmission method according to the environmental conditions.  
Through the above improvements, CTDHR reduces the packets sent through the cellular network while ensuring QoS, so that more packets are sent through VANETs, which reduces the communication costs of the user. In the comparative experiment with TDCR, CTDHR achieves superiority in the following aspects: the number of clusters, the maximum number of hops of the VANET communication link, and the communication performance. Although CTDHR is slightly inferior to TDCR in terms of cluster stability and maximum hop count of VANET links, this is within the acceptable range. It can be seen that even with the above optimizations, the throughput of CTDHR is still low. In future research, we will attempt to (1) introduce deep learning methods to solve the problem of link generation in VANETs, and (2) use reinforcement learning to obtain better routing strategies.  
Conceptualization, Y.Z. and J.Z.; methodology, Y.Z. and J.Z.; software, Y.Z.; formal analysis, Y.Z.; investigation, Y.Z.; resources, J.Z.; data curation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z. and J.Z.; visualization, Y.Z.; supervision, J.Z.; project administration, J.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by the National Natural Science Foundation of China (nos. 62062065, 12071417, 61962061), a project of the Natural Science Foundation of Yunnan Province of China (2019 FB142 and 2018 ZF017), the Education Foundation of Yunnan Province of China (2022J002), and the Program for Excellent Young Talents, Yunnan, China.  
The code used in the experiment can be obtained through the e-mail of the first author or corresponding author.  
The authors declare no conflict of interest. All authors agree with all content of this paper without any objection. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 7: Comparison of CTDHR and TDCR in the communication performance. (**a**) The comparison of the packet loss rate, (**b**) the comparison of the QoS satisfaction rate
## 4 Experimental Results
### Assessing the SDN Resilience under CAs Conditions  
To assess the SDN resilience under CAs conditions, three network structures were designed and implemented:  
* Structure 1--SDN structure consisting of three elements with one controller (see Figure 2);
* Structure 2--SDN structure with two controllers with separation of the control function and interception of each other's control functions according to a given algorithm (Figure 20);
* Structure 3--SDN structure with two controllers, where one controller is the main controller and performs control functions, and the second controller is in hot standby mode (Figure 21).  
Figure 19: Probabilistic temporal characteristics of the “Scalability and availability” attack. (**a**) dependence of the average time on the probability of the implementation of the CA; (**b**) dependence of the integral probability distribution function on the time of the implementation of the CA.  
The calculation results are presented in the form of graphs (Figures 22-24).  
Figure 21: SDN structure with two controllers, when one controller is the main one and performs all control functions.  
Figure 20: SDN structure with two controllers with separation of control functions and interception of each other’s control functions according to a given algorithm.  
Analysis of the results showed that the SDN structures under the influence of Flooding attacks and \"Hacking/Crashing of the controller\" attacks do not meet the requirements for resilience. In order to ensure the resilience of the SDN functioning in the face of attacks, it is necessary to develop an algorithm for monitoring the state of controllers and their automatic restructuring, since after 18 min of successful CA implementation, the probability of stable SDN operation begins to tend to 0.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 4 Common Preparation Methods
### Liquid Phase Method
#### 4.4.1 Sol-Gel Method  
The sol-gel method is a wet chemical approach used to prepare nanoparticles [197]. In the sol-gel method, a precursor is dissolved in a solvent, and a sol is formed through hydrolysis or alcoholysis. The sol is then converted into a gel after prolonged storage or drying. Figure 5 shows the process of preparing nanomaterials by a sol-gel method.  
Among the nanoparticles prepared by the sol-gel method, the most important factors are the formation of the sol and gel. There are many complicated parameters affecting the sol-gel process. At present, most people think that the four main parameters have important influence on the sol-gel process. These are the pH of the solution, the concentration of the solution, the reaction temperature, and the reaction time.  
The sol-gel process has the advantages of high purity, high uniformity, low synthesis temperature, and easy control of the reaction conditions [198; 199; 200]. The preparation process of the sol-gel process is relatively simple and does not require special or expensive equipment. The problem of the sol-gel method is that the raw material is generally titanium alkoxide, a large amount of organic solvent is required, and the obtained film is heat-treated at a relatively high temperature. Therefore, the film forming cost is relatively high, the adhesion of the film is poor, and the transparency is poor.  
Figure 5: Nanofilm or nanoparticles by a sol–gel method.
# Comparative Study of Sound Transmission Losses of Sandwich Composite Double Panel Walls
## 4 Methodologies and Models for Obtaining Sound Transmission Loss of Double Panel Walls
### Numerical Vs. Experimental Validation  
The various researchers who modelled the STL through numerical methods also conducted some experiments and the results of the measured data were compared with results from the numerical approximation. To account for complexities like perforations on facesheet/panel and core, a numerical study was performed by [PERSON] et al. [52] and their findings were validated by measured data. Further, [PERSON] et al. [139] numerically predicted the STL of complex extruded panels. They applied the wave number domain numerical method to predict the STL of panels with constant cross-section in one direction. The predicted solutions were compared with measured results and a fair correlation of the two results was observed. A simply supported boundary condition and fixed boundary condition were given to the structure. It was observed that the different boundary conditions have little influence on the diffuse field STL. In similar vein, the STL was not much affected with the effect of internal air between the opposite plates due to the strong coupled internal stiffeners applied between the outer plates. The authors, [PERSON] et al. [140] developed the finite element based unit cell to model the STL of metamaterial double panel at the mass-air-mass resonance frequency region. Their predicted approximations were compared with measured data for validation. Their results showed that STL of the predicted solution and the insertion loss of the experimental results were increased at the targeted mass-air-mass resonance frequency range than the original DPS without metamaterial. The effect of side walls through sonic crystal was investigated by [PERSON] and [PERSON] [141]. The authors also compared the experimental insertion loss with the numerical predicted STL. Their results indicated that the best STL was observed when the outer wall of the sonic crystal located at the point where the half of the periodic distance from center position of the end scattered. To further improve the STL, bi-periodicity was introduced.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 5 Results and Discussion  
### Experiment Settings  
#### 5.1.1 Experiment Data  
The data used have been introduced in Section 2. Following data preprocessing, a total of 1624 alarm sequences were obtained. Among these, 261 alarm sequences were associated with their corresponding maintenance records, resulting in 261 labeled and 1363 unlabeled alarm sequences. The fault event's types and sample numbers corresponding to labeled data are presented in Table 3. For the fault events with a higher occurrence frequency, we categorize them separately, for instance, pitch system communication fault and frequency-converter communication fault. As for the fault events with lower occurrence frequency, we group them based on their respective subsystems, for example, generator faults.
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 2 Design and Implementation of the Dual-Loop Dual-Output Frequency Synthesizer
### Phase-Frequency Detector and Charge Pump  
The FFD compares the reference signal, _Fref_, and the divided signal, _Fdiv_, and controls the CP current according to the phase and frequency difference, as shown in Figure 6a. The charge pump needs to provide matched up-and-down currents for most of the CP output voltage range. The charge-pump architecture employs dual operation amplifiers (Op-Amps), where both amplifiers utilize a high-swing topology [10]. The CP includes 3 binary weighted branches so that the current can be digitally controlled from 150 uA to 1050 uA, adapting the PLL's loop bandwidth/settling time for various configurations. Finally, the simulated phase noise of the dual-loop dual-output synthesizer is shown in Figure 7. Figure 7a shows the simulated phase noise contributions at the Output1 from Loop1, while Figure 7b shows the simulated phase noise contributions at the Output2 from Loop2 with the frequency doubler. The loop bandwidth has been optimized to balance the in-band and out-of-band phase noise. From the phase noise contribution plot, most of them result from the integrated VCO, PFD, and CP.  
Figure 5: Schematic of (**a**) the CML divide-by-2 divider; (**b**) the double-balanced mixer core with resistively degeneration.  
Figure 6: Schematic of (**a**) the phase-frequency detector; (**b**) the charge pump with dual Op-Amp design for matching up-and-down currents.
## 3 Research on the Characteristics of High-Voltage Switching with Three-Electrode Plane Spark Gap
### Dynamic Operating Characteristics
#### 3.3.3 Switch Dynamic Impedance and Inductive Reactance Characteristics  
In order to realize the function of switching and disconnecting the narrow pulse current in the exploding foil initiation circuit, the high voltage switch not only has a higher turn-off impedance to reduce the power consumption of the exploding foil initiation system, but also has a lower conductive impedance and inductive reactance to improve the narrow pulse current output characteristics of the high-voltage pulse capacitor.  
In principle, the initiation circuit of exploding foil can be equivalent to a RLC series circuit [28; 29; 30]. The parasitic resistance and inductance of the three-electrode planar spark gap high voltage switch can be calculated by measuring the waveform parameters of discharge oscillation current of the circuit with oscilloscope.  
The formula of parasitic inductance is as follows.  
\[L_{0}=L-l \tag{2}\]  
\[L=\frac{\overline{T}^{2}}{4\pi^{2}C} \tag{3}\]  
The formula of parasitic resistance is as follows.  
\[R_{0}=R-r \tag{4}\]  
\[R=\frac{2L}{\overline{T}}\ln\xi,\text{ among them},\ \xi=\frac{\sum\limits_{j=1}^{n} \lambda_{j}}{n},\ \lambda_{j}=\frac{I_{j}}{I_{j+1}} \tag{5}\]  
\(L\): Total inductance of discharge circuit, nH;  
\(l\): Load inductance, nH;  
Figure 10: Curve of factors affecting the conduction time of the three-electrode plane spark gap high-voltage switch. (**a**) Trigger voltage and on-time relationship curve. (**b**) Working voltage and on-time relationship curve.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \(b\)/mm & \(R_{0}\)/m\(\Omega\) & **0.8** & **1.0** & **1.2** & **1.8** & **2.0** & **2.2** & **2.6** \\ \hline \(a\)/mm & 0.6 & 32.8 & 51.2 & 88.4 & 117.2 & 133.1 & 171.8 & 223.9 \\ \hline
0.8 & / & 42.7 & 68.5 & 96.7 & 127.0 & 153.9 & 207.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The conductive impedance data of the switch.  
Figure 11: Corresponding curves of switch parameters with dynamic impedance and inductive reactance of the switch.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \(L_{0}\)/\(nH\) & **0.8** & **1.0** & **1.2** & **1.8** & **2.0** & **2.2** & **2.6** \\ \hline \(a\)/mm & 0.6 & 19.3 & 21.8 & 29.4 & 33.7 & 38.2 & 42.6 & 45.3 \\ \hline
0.8 & / & 16.6 & 19.1 & 23.5 & 26.2 & 30.2 & 37.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The inductive reactance data of conductive switch.  
electrodes becomes smaller and the average electric field strength between two main electrodes increases, resulting in lower impedance and inductive reactance.
## 3 Materials and Methods  
### Materials  
Commercial lipases from _Thermomyces lanuginosus_ expressed in _Aspergillus oryzae_ (Eversa(r) Transform 2.0), _Candida_ sp. (CALA) and _Candida rugosa_ (CRL) were acquired from Sigma-Aldrich(r) (St. Louis, MO, USA). Non-commercial lipases from _Aspergillus niger_ (ANL) and _Rhizopus javanticus_ (RJL) were produced via submerged fermentation [57]. These non-commercial lipases were obtained via sequential precipitation with ammonium sulphate, dialysis in water and lyophilization and used as powder lipase extracts [29; 57]. The properties of these lipase preparations are summarized in Table 3. Psty-DVB beads (Diaion(r) HP-20) were acquired from Supelco(r) Analytical Products (Bellefonte, PA, USA). The properties of this support were reported in a recent study performed in our lab [44]. Used soybean cooking oil (USCO) was collected, after being used once for French fries preparation, from the restaurant at Federal University of Alfenas (Alfenas, MG, Brazil). Its fatty acid composition (% m\(\cdot\)m\({}^{-1}\)) was determined by gas chromatography as follows [58]: 10.7% palmitic, 3.0% stearic, 24.0% oleic, 56.7% linoleic, 5.4% linoleic and 0.1% arachidonic acids, with an average FFA molecular mass of 278.6 g\(\cdot\)mol\({}^{-1}\). Fusel oil was acquired from Raizen Taruma Ltd.a. (Taruma, SP, Brazil), having the following composition (% m\(\cdot\)m\({}^{-1}\)): water (11.6%), ethanol (4.2%), propanol (3.9%), butanol (1.1%), isobutyl alcohol (11.5%) and isoamyl alcohol (67.7%); its average molecular mass was 72.5 g\(\cdot\)mol\({}^{-1}\). Molecular sieve UOP type 3 A (form of rod, and size of 1/16 in) was purchased from Fluka Analytical (St. Louis, MO, USA). Hydrogen peroxide solution (50% m\(\cdot\)m\({}^{-1}\)), formic acid solution (85% v\(\cdot\)v\({}^{-1}\)), and Wijs solution (iodine chloride in acetic acid) were acquired from Synth(r) Ltd. (Sao Paulo, SP, Brazil). All other reagents and organic solvents were of analytical grade, and acquired from Synth(r) Ltd.
# A Digital Twin-Based State Monitoring Method of Gear Test Bench
## 1 Introduction
Therefore, in order to realize the real-time acquisition equipment information and the running state monitoring, reduce the experimental time and predict the transmission performance accurately. It is important to ensure that the prediction results of the virtual model are basically consistent with the testing results under the actual running state. Thus, how to build a digital twin model of the gear test bench is a challenging task. Unfortunately, there is little research on the digital twin model of the gear test bench used for gear performance testing. In view of this, a state monitoring system of the gear test bench based on the digital twin model is proposed in this paper. The sensors and work machines are used to collect data under the normal running state of the gear test bench. Digital twin technology is applied to map the normal running state of laboratory gear equipment into the virtual gear test bench model, so as to realize the running state monitoring of the gear test bench and make up for the deficiency that the traditional running state monitoring cannot meet the gear test bench dynamic requirements of the experiment process. The rest of this paper is organized as follows. The state monitoring system of the gear test bench based on the digital twin model is constructed in Section 2. Section 3 describes the physical information acquisition of the gear test bench, including the vibration and noise information acquisition, temperature acquisition, operation torque information acquisition, and the vibration acceleration signal acquisition. Section 4 describes the digital twin monitoring system development, which consists of the geometric model construction of the gear test bench, simulation environment creation of the gear test bench, and the implementation of the digital twin monitoring system. Test verification is described in Section 5, while Section 6 discusses the contribution and future research directions of this article. Finally, in Section 7, the conclusions are reported.
# A Novel Fault Diagnosis Method for TE Process Based on Optimal Extreme Learning Machine
## 2 Materials and Methods
### The Kernel Based Extreme Learning Machine
#### 2.3.2 Kernel Based Extreme Learning Machine  
The Kernel Based Extreme Learning Machine (KELM) [53; 54] is an improved algorithm based on the Extreme Learning Machine (ELM) combined with a kernel function.  
ELM is a single implicit layer feedforward neural network whose learning objective function \(F(x)\) can be represented by the matrix:  
\[F(x)=h(x)\times\beta=H\times\beta=L \tag{23}\]  
where \(x\) is the input vector, \(h(x)\), \(H\) is the output of the hidden layer nodes, \(\beta\) is the output weight and \(L\) is the desired output.  
Turning the network training into a problem solved by a linear system, \(\beta\) is determined according to \(\beta=H^{*}\cdot L\), where \(H^{*}\) is the generalised inverse matrix of \(H\). To enhance the stability of the neural network, the regularisation factor \(c\) and the unit matrix \(I\) are introduced, so that the least squares solution for the output weights is  
\[\beta=H^{\mathsf{T}}(HH^{\mathsf{T}}+\frac{I}{c})^{-1}L \tag{24}\]  
Figure 3: Extreme Learning Machine Network Structure.  
Introducing the kernel function into the ELM, the kernel matrix is  
\[\Omega_{ELM}=HH^{T}=h(x_{i})h(x_{j})=K(x_{i},x_{j}) \tag{25}\]  
where \(x_{i}\),\(x_{j}\) is the test input vector, then Equation (23) can be expressed as  
\[F(x)=[K(x,x_{1});\ldots;K(x,x_{n})]\big{(}\frac{I}{c}+\Omega_{ELM})^{-1}L \tag{26}\]  
where \((x_{1},x_{2},\ldots,x_{n})\) is the given training sample, \(n\) is the number of samples. \(K()\) is the kernel function.
## 6 The Case for Cross-Layer Design
### Methodology  
Most cross-layer schemes involve the communications between different adjacent or non-adjacent layers of the protocol stack to improve the performance in some way over the traditional layered approach. These performance gains can be in the form of energy efficiency, reduced contention, better routing decisions or improved reliability. Research has shown that cross-layer designs can have a beneficial effect on the performance of wireless networks [70; 71; 72]. Taking a step back and looking at the protocol stack as a whole can help to focus on new solutions to wireless communication issues that a cross-layer approach can alleviate. [PERSON] _et al._[73] examined cross-layer interactions and summarized the various proposals in terms of methods that violate the traditional layered architecture. The main categories studied were: _Creation of new interfaces:_ schemes that connect lower and higher layers either up or down and schemes that create an iterative flow between the two connected layers in both directions. _Merging of adjacent layers_: two adjacent layers merge their functionality to create a union of services. _Vertical calibration across layers_: setting of parameters across the layers either at design or runtime. _New abstraction that replaces the protocol stack:_ disposing of the traditional protocol stack and replacing it with a new method that performs the same functions but in a non-layered form.