esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The modified Poulos method for evaluation of active pile group response was extended in this paper to estimate the variation in group effect for an existing vertically loaded pile group due to tunnel excavation. The main refinement is taking into account the effect of tunneling-induced soil displacements when forming the pile-soil interaction function in prediction of the additional elastic pile head settlement. Therefore, this simplified method can evaluate the effects of tunneling on the pile group, where the tunneling-induced group settlement and load redistribution and the variation in the Z multipliers can be predicted, provided that the free-field soil settlements due to tunneling are given. The validity of the proposed method was demonstrated by an available centrifuge model test with a 2 \(\times\) 2 fixed-head pile group. A parametric study was conducted and it shows that the influence of the lateral tunnel-pile group distance is significant on the pile group settlement but very subtle on load redistribution and the group effect, manifested by the Z multipliers. In the vertical direction, however, the tunneling-induced pile settlement, the Z multipliers and the extent of load redistribution can be largely decreased with deeper excavation depth.  
Similar to an active vertically loaded pile group, both the pile group settlement and the Z multipliers can increase with closer pile spacing. However, load redistribution can occur due to large pile spacing, which may lead to unfavorable loading conditions when the tunnel is excavated at a shallow depth and close to the pile group.  
Further studies using field measurements or centrifuge prototype may be useful to investigate the pile group effect evolution during tunneling, and can also be used to prove the feasibility of the proposed method with varying pile group configurations, softer ground soils and closer relative positions between the pile group and the tunnel.  
Conceptualization, J.W.; methodology, Y.F.; software, J.W.; validation, Y.F., J.C. and J.W.; formal analysis, Y.F.; investigation, J.C.; resources, J.C.; data curation, Y.F.; writing--original draft preparation, Y.F.; writing--review and editing, J.C. and J.W.; visualization, Y.F.; supervision, [PERSON] and J.W.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Fundamental Research Funds for the Central Universities [Grant No. KJZ53420210065].  
Not applicable.  
All the used data was available in literature.  
The authors declare no conflict of interest.
# A Power Control Algorithm Based on Chicken Game Theory in Multi-Hop Networks
## 4 Simulation Analyses
It is well known that the broadcast storm problem can lead to the failure of the broadcast in all three algorithms. Figure 8: Average delay comparison of the three algorithms. Figure 7: MINPCA algorithm interference distribution (50 nodes). Figure 10 shows the power efficiency of the three algorithms. MINPCA requires the minimum sending power, so its power efficiency is optimal. MAXPCA has the lowest efficiency, followed by DCGAPC.
# Low-Temperature and Low-Pressure Silicon Nitride Deposition by ECR-PECVD for Optical Waveguides
## 2 Fabrication
### Silicon Nitride Thin-Film Deposition
With an expected uncertainty and occasional variation of \(\sim\)0.1 sccm of flow from any of the MFC units, a safer, smaller stoichiometry change is available by changing N\({}_{2}\)/Ar flow across the ranges shown in Table 1. Silane and nitrogen dilution were 30/70% and 10/90% with argon, respectively. The sample stage is 3 inches in diameter and was used to fix multiple substrates to the stage at once. One round of thick (\(\sim\)300-450 nm) films were deposited on 2 \(\times\) 2 cm\({}^{2}\) silicon pieces and on 6 \(\upmu\)m of thermally grown SiO\({}_{2}\) on 5 \(\times\) 3 cm\({}^{2}\) silicon pieces (SiO\({}_{2}\)-Si) cleaved from a fully oxidized wafer, for ellipsometry and thin film optical loss measurements, respectively. The deposition time for the first round was 3.5 h, and 1 h for the second round of depositions, producing thinner films (\(\sim\)100 nm) for RBS and ERDA measurements on 2 \(\times\) 2 cm\({}^{2}\) pieces of vitreous carbon and silicon, respectively. After preliminary studies on optical loss and recipe optimization had been performed, a final thicker (650 nm) film was deposited on silicon and a 5 \(\times\) 3 cm\({}^{2}\) cleaved piece of an SiO\({}_{2}\)-Si wafer to increase modal confinement in the SiO\({}_{x}\)N\({}_{y}\):H\({}_{x}\) layer when performing optical loss measurements. The deposition parameters used for the different film recipes are summarized in Table 1, including pre-deposition pressures, and pressures during depositions imparted by each partial gas flow.
## 8 Experiment with the Multimodal User Emotion Detection System
### Performance of the Multimodal User Emotion Detection System Statistically Computed  
As already said, the confusion matrices for each classifier have been computed. Since these matrices give us the probability of detecting a certain emotion given a real user emotion, one way to test the performance of the whole system would be the following: first, to generate a big number of examples (real emotions as inputs) and, for each of them, to calculate the outputs of each classifier (according to their confusion matrices); second, as in the previous example, to apply the decision rule selecting the final detected user emotion with the highest confidence value.  
To obtain the statistically-computed confusion matrix of the multimodal user emotion detection system, we have generated 10,000 examples by computer. These examples were randomly generated, and each emotion has the same probability of being selected (25%). Checking the right and the wrong emotion detection, the values of the confusion matrix are obtained. These results are shown in Table 3.  
_The calculated average success rate of the whole system is \(83\%\)._ This value confirms that the use of the information from the audio and visual channel and the application of a decision rule improve the average success rate of each classifier: 56.37% for J48, 57.10% for JRIP, 75.55% for SHORE and 62.66% for CERT.
# Retrospective Study of the Association between Peri-Implantitis and Keratinized Mucosa
## 1 Introduction  
Peri-implantitis is a pathology characterized by the inflammation of the peri-implant mucosa accompanied by a progressive loss of supporting bone around dental implants [1]. Its onset is directly related to the accumulation of periodontal pathogens in the same way it happens in gingivitis and periodontitis [2].  
Peri-implantitis affects a high percentage of patients with implants, having become one of the pathologies we face daily in the dental office. [PERSON] and [PERSON] estimated the occurrence of mucositis in 80% of patients and peri-implantitis in 28-56% of implant carriers [3].  
The clinical signs that characterize peri-implantitis are redness, gingival edema, bleeding on probing with or without suppuration, increased probing depth and radiographic bone loss [1].  
Among the risk factors that can cause this pathology, there is ample scientific evidence in the following: a history of periodontitis, tobacco consumption, diabetes, poor plaque control, and the absence of a regular maintenance. Other factors such as the absence of keratinized mucosa (KM), genetic factors, systemic conditions or atrogenic factors have been considered, although with more limited evidence [1, 4, 5].  
KM is absent when there is no band of keratinized tissue around implants and it is considered one of the most controversial issues. Numerous studies suggest that less than 2 mm of KM confers the patient brushing discomfort. These areas have poorer plaque control and greater inflammation [2; 6]. However, other authors consider that there is no evidence that the absence of KM is a factor that initiates or influences the progression of peri-implantitis [7; 8]. There is limited evidence to consider it as a stable risk factor for peri-implantitis, as the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions points out [1].  
Therefore, the main **objective** of this study was to compare the amount of KM around healthy implants versus implants in patients with peri-implantitis and to analyze its influence on the development of such pathology and its aesthetics. As secondary objectives, the possible influence of KM on parameters such as recession, plaque index, bleeding on probing, suppuration, and bone loss were also analyzed.  
The following working hypothesis was stated: Patients whose implants are not surrounded by keratinized mucosa have further complications in the peri-implant tissues.
## 4 Conclusions  
In this work, flexible N-doped carbon nanofiber anodes with a hierarchical and heterogeneous porosity structure were synthesized, which can be directly used as the anode of lithium-ion batteries without adding adhesives. SEM images and nitrogen adsorption-desorption tests confirmed the existence of microporous, mesoporous, and macroporous structures in the carbon nanofibers. EDS and XPS spectra proved that the nitrogen element was successfully doped in the carbon nanofibers. The hierarchical and heterogeneous porosity structure increased the active sites of the anode materials, improved the ion transport rate, and inhibited the volume expansion in the process of Li\({}^{+}\) insertion/de-insertion. The N-doping improved the conductivity of the carbon nanofibers. As expected, the prepared CNFs-2N anode had a high initial reversible specific capacity of 1108.9 mAh g\({}^{-1}\) and excellent capacity retention and rate performance in lithium-ion batteries. This work provides a new way to develop high-performance flexible anode materials for lithium-ion batteries.  
J.L. ([PERSON]): conceptualization, data interpretation, funding acquisition, project administration; Y.L.: methodology, writing--original draft; J.W.: software, data curation; X.W.: cell fabrication and testing; X.L.: formal analysis, data interpretation, writing--review and editing; J.L. ([PERSON]): conceptualization, funding acquisition; D.N.: supervision, funding acquisition; J.D.: supervision. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Natural Science Foundation of Inner Mongolia (no. 2019 MS05068), Inner Mongolia Major Science and Technology Project (no. 2020 ZD0024), Scientific Research Project of Inner Mongolia University of Technology (no. ZZ202106), the Alashan League's Project of Applied Technology Research and Development Fund (no. AMY2020-01), the research project of Inner Mongolia Electric Power (Group) Co., Ltd. for post-doctoral studies, Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (no. NMGIRT2211), Inner Mongolia University of Technology Key Discipline Team Project of Materials Science (no. ZD202012), Inner Mongolia Natural Science Cultivating Fund for Distinguished Young Scholars (no. 2020 JQ05), Science and Technology Planning Project of Inner Mongolia Autonomous Region (no. 2020 GG0267), and Local Science and Technology Development Project of the Central Government (no. 2021 ZY0006).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Controller Design
### Conventional Method
\begin{split}&\dot{V}_{1}=\sigma_{\text{s}}\alpha[c_{1}e_{2}-M_{11} ^{-1}(H_{1}+G_{1})-M_{12}^{-1}(H_{2}+G_{2}-\tau_{\text{w}})-\ddot{\theta}_{b_{ d}}]+\sigma_{\text{s}}\beta[c_{2}e_{4}-M_{21}^{-1}(H_{1}+G_{1})\\ &-M_{22}^{-1}(H_{2}+G_{2}-\tau_{\text{w}})-\ddot{\theta}_{\text{ w}_{d}}]=\sigma_{\text{s}}[\alpha M_{12}^{-1}(\tau_{\text{w}}-\tau_{\text{w}_{1}})+ \beta M_{22}^{-1}(\tau_{\text{w}}-\tau_{\text{w}_{2}})]\\ &=\sigma_{\text{s}}[\alpha M_{12}^{-1}(\tau_{\text{w}_{s}}+\tau_{ \text{w}_{2}})+\beta M_{22}^{-1}(\tau_{\text{w}_{s}}+\tau_{\text{w}_{1}})]. \end{split}  \tag{15}\]  
Select the exponential sliding mode  
\[\dot{\sigma}_{\text{s}}=-k_{1}\text{sign}(\sigma_{\text{s}})-k_{2}\sigma_{ \text{s}}=\alpha M_{12}^{-1}(\tau_{\text{w}_{s}}+\tau_{\text{w}_{2}})+\beta M_ {22}^{-1}(\tau_{\text{w}_{s}}+\tau_{\text{w}_{1}}), \tag{16}\]  
where \(k_{1}\) and \(k_{2}\) are positive constants. From Equation (16), \(\tau_{\text{w}_{s}}\) can be obtained as  
\[\tau_{\text{w}_{s}}=\frac{-k_{1}\text{sign}(\sigma_{\text{s}})-k_{2}\sigma_{ \text{s}}-\alpha M_{12}^{-1}\tau_{\text{w}_{2}}-\beta M_{22}^{-1}\tau_{\text{ w}_{1}}}{\alpha M_{12}^{-1}+\beta M_{22}^{-1}}. \tag{17}\]  
According to the Lyapunov theorem, the below condition should be satisfied to provide stability of HSMC controller [33]. \[\dot{V}_{1}<0\ \ \ \text{for}\ \ \sigma_{\text{s}}\
eq 0.\]  
From Equations (14) and (16), we have  
\[\dot{V}_{1}=\sigma_{\text{s}}\sigma_{\text{s}}=-k_{1}\mid\sigma_{\text{s}}\mid -k_{2}\sigma_{\text{s}}^{2}<0\ \ \ \text{for}\ \ \sigma_{\text{s}}\
eq 0. \tag{18}\]  
Equation (18) proves that the sliding surface \(\sigma_{\text{s}}\) is asymptotically stable. The trajectory of the second-layer sliding surface \(\sigma_{\text{s}}\) will converge to the origin of coordinate system constructed by \(\sigma_{1}\) and \(\sigma_{2}\) axis when \(t\rightarrow\infty\). Therefore, each subsystem states move on the first-layer sliding surfaces (\(\sigma_{1}\) and \(\sigma_{2}\)) and we have [34]  
\[\lim_{t\rightarrow\infty}\sigma_{1}=0\ \ \ \ \ \ \ \ \ \ \lim_{t\rightarrow\infty}\sigma_{2}=0. \tag{19}\]  
Equation (19) proves that the first-layer sliding surfaces \(\sigma_{1}\) and \(\sigma_{2}\) are also asymptotically stable.
## 2 Model Development
### Removal Mechanism for Single Particle  
In the present study, the abrasive particles were assumed to be evenly distributed between the polishing pad and the polishing surface, with some of the abrasive particles embedding into the polishing surface under the action of the tool load. Combined with the rotation of the polishing tool, the target surface is scratched by the abrasive particles to achieve the removal of the material. To simplify the modeling process, the influence of the polishing liquid film was ignored in this study, and the contact between the abrasive particles and the workpiece surface was assumed to be elastomer-solid contact. Assuming that the load applied to the polishing tool was borne by the abrasive particles, the applied load could be expressed as:  
\[F_{load}=H_{w}\pi D_{p}\sum_{m=1}^{N_{w}}d_{m}(z), \tag{3}\]  
where \(H_{w}\) is the hardness of the polished workpiece, and \(d_{m}(z)\) is the embedded depth of the \(m\)th abrasive particle. The indentation of micron-sized abrasive particles can cause the microscopic plastic deformation of the target surface. According to the wear and fracture mechanism of hard and brittle materials, submicron-scale plastic deformation causes the phase transformation of the material itself on the microscopic level without generating dislocations, subsurface defects, or microcracks. The material removal model established in this section was also based on the assumption of microscopic plastic deformation. The macroscopic material removal characteristics of the workpiece surface were determined by the microscopic material removal caused by the plastic deformation of a single abrasive particle. The material removal caused by the single particle \(\Delta MRV_{m}\) could be described as [19]:  
\[\Delta MRV_{m}=2\pi n_{r}{D_{p}}^{1/2}{d_{m}}^{3/2}r_{m}\Delta t, \tag{4}\]  
where \(n_{r}\) is the rotation speed of the polishing tool, \(D_{p}\) is the diameter of the abrasive particle, and \(d_{m}\) is the embedded depth of the \(m\)th abrasive particle. The embedded depth of the abrasive particles is related to the load applied to the polishing tool and the material properties of the polishing surface.
## 3 Results and Discussion
### Work Analysis of Unsteady Aerodynamic Force
It can be seen that the phase difference between vertical displacements and self-excited forces plays a crucial role in the development of the flutter. To analyze the characteristics of aerodynamic hysteresis of the two kinds of flutter, the identification results of the phase difference in the whole flutter process is displayed in Figures 13 and 14. For the classical flutter, when the inflow velocity exceeded the flutter critical wind speed, the vertical and torsional phase differences were both unstable at the development stage of flutter, and were about 178.1 to 182.2\({}^{\circ}\) in the vertical degree of freedom and \(-\)2.2 to \(-\)4.4\({}^{\circ}\) in the torsional degree of freedom. At this stage, the work done by self-excited forces was small, and corresponding vibration amplitudes increased slowly. As the flutter evolved and increased in vibration amplitude, the vertical phase difference began to rise from 180 to 194.7\({}^{\circ}\) for 30 s while the torsional phase difference descended only from \(-\)3.6 to \(-\)5.8\({}^{\circ}\) in 30 s. This indicated that the fast enlargement of the vertical phase difference as a vibration amplitude resulted in a higher energy input in the dynamic system. Even the divergence of the dynamic system provided positive feedback. Figure 14: The phase difference analysis for postflutter LCO (Case: L180, \(U^{\star}=6.0\)), (**a**) vertical displacement, (**b**) torsional angle, (**c**) phase difference \(\Delta\phi_{F\!h}\) and (**d**) phase difference \(\Delta\phi_{T\!a}\). Figure 13: The phase difference analysis for classical flutter (Case: L60, \(U^{\star}=10.27\)), (**a**) vertical displacement, (**b**) torsional angle, (**c**) phase difference \(\Delta\phi_{F\!h}\) and (**d**) phase difference \(\Delta\phi_{T\!a}\). For the postflutter LCO, the changing rule of phase differences was significantly different. The radical changes of phase differences took place at the development stage of the flutter. In 15 s, the vertical phase difference descended quickly from 188.0 to 181.4\({}^{\circ}\) as the vibration amplitude increased. Similarly, the torsional phase difference changed from \(-\)12.1 to \(-\)2.0\({}^{\circ}\). This indicated that there was a negative feedback pattern according to Equation (10), i.e., as the vibration amplitude increased, the hysteresis effect between the self-excited forces and displacements weakened gradually, and the energy input to the dynamic system did not grow rapidly but kept stable to limit further vibration divergence. Eventually, the dynamic system achieved equilibrium and the postflutter LCO occurred.
# A Positive Flow Control System for Electric Excavators Based on Variable Speed Control
## 3 Control Strategy  
Figure 2 is the control structure diagram of the PFS based on the variable speed control for a pure electric excavator. It includes the following three parts: (1) Control unit, which consists of an electronic control handle and a controller. (2) Volumetric speed regulation, which consists of a frequency converter, a motor, and a main pump. (3) Throttling speed-regulating, which includes a pilot pressure-reducing valve and a multiway valve. The controller receives the signals of the handle and the sensors to carry out the logic processing and flow forecast. According to the logic operation, the controller sends signals to the pilot valve and inverter. Accordingly, the motor speed is controlled to drive pump output at the required flow, and the pilot valve outputs pressurized oil to the multiway valve at a certain opening. Therefore, the required flow enters the actuator and flow-matching is realized.  
According to the control structure diagram shown in Figure 2, the flow diagram of the control strategy is designed as shown in Figure 3. The controller controls the output pressure of the pilot pressure-reducing valve according to the control signal of the electronic control handle. The spool displacement of the multiway valve is controlled by the output pressure of the pilot valve. Then, the flow entering the actuator is estimated. The estimated total flow required in the actuators is:  
\[Q_{\mathrm{t}}=Q_{1}+Q_{2}+\cdots+Q_{\mathrm{n}} \tag{1}\]  
where \(Q_{\mathrm{t}}\) is the total flow required in the actuator in L/min. \(Q_{\mathrm{n}}\) is the required flow rate of \(n\)th actuator working at the same time, \(n\geq 1\).  
Figure 1: Principle diagram of the positive flow system (PFS) of an electric excavator based on variable speed control.  
According to the estimated flow rate, the motor speed can be calculated as:  
\[n_{\mathrm{t}}=\frac{Q_{\mathrm{t}}}{V_{\mathrm{P}}\eta_{\mathrm{P}}C_{\mathrm{s}}} \tag{2}\]  
where \(n_{\mathrm{t}}\) is the target motor speed in rpm. \(V_{\mathrm{P}}\) is the displacement of the quantitative pump (main pump) in mL/r. \(\eta_{\mathrm{P}}\) is the volumetric efficiency of quantitative pump (main pump). \(C_{\mathrm{s}}\) is the coefficient of the estimated speed. It is used to ensure the pump output flow has a certain margin to compensate for oil leakage.  
The output power of the pump is:  
\[P_{\mathrm{P}}=\frac{n_{\mathrm{r}}V_{\mathrm{P}}p_{\mathrm{P}}}{60000} \tag{3}\]  
where \(P_{\mathrm{P}}\) is the pump output power in kW. \(n_{\mathrm{r}}\) is the real speed of the motor taken from the frequency converter in rpm. \(p_{\mathrm{P}}\) is the pump pressure in MPa.  
Figure 3: Flow chart of positive flow control strategy based on variable speed control.  
Figure 2: Control structure diagram of a PFS based on variable speed.  
The pump output power is compared with the constant power point. If the pump output power is less than the constant power point, a positive flow matching mode is adopted. Otherwise, the system switches to the constant power mode.
# Microencapsulation of Natural Food Antimicrobials: Methods and Applications
## 4 Food Applications of Encapsulated Antimicrobials
### Meat and Fish Products
In addition, antimicrobial packaging films have the potential to prevent the growth of various microorganisms on the surface of food products. For example, the shelf-life of refrigerated salmon packed with a chitosan film containing microcapsules of grape seed extract and carvacrol was increased up to 4-7 days of storage (5 \(\mathrm{\SIUnitSymbolC}\), 65% relative humidity) in comparison with salmon packed with the control chitosan film without microcapsules, for which acceptable levels of mesophilic bacteria, psychrophilic bacteria and _Pseudomonas_ spp. were obtained [111]. The antimicrobial activity of the whey protein isolate (WPI) film containing free lactoferrin (LF), microencapsulated LF with sodium bicarbonate (20 mM) and microencapsulated LF with sodium lactate (3% \(w/v\)) (4.2 mg cm\({}^{-2}\) LF in WPI film each) prepared via emulsification was studied against the growth of _Carnobacterium viridans_ inoculated on the surface of bologna slices during 28 days of storage at 4 and 10 \(\mathrm{\SIUnitSymbolCelsius}\)[116]. Cations such as Na\({}^{+}\), Ca\({}^{2+}\) and Mg\({}^{2+}\) can decrease the antimicrobial activity of LF. Microencapsulation of LF with SB or SL as chelating agents reduced the viability of the tested bacteria cells below the detection limit (0.8 log\({}_{10}\) CFU/mL) in less time (after 3 days) compared with microcapsules containing only LF (after 7 days) at 4 \(\mathrm{\SIUnitSymbolCelsius}\). Despite the recovery and regrowth of _C. viridans_ cells injured by LF after 14 days, it was observed that the number of bacteria cells on the bologna surfaces treated with films containing LF microcapsules were significantly lower than control film (\(p<0.05\)). The antimicrobial activity of LF against this bacteria strain was higher at lower temperatures (10 \(\mathrm{\SIUnitSymbolCelsius}\)) as revealed by a reduction in cell numbers [116]. The incorporation of LF microcapsules in the WPI film matrix led to greater antimicrobial activity than unencapsulated LF in the WPI film due to the slow release of the antimicrobial agent and its protection from inactivation.