esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Power Optimization Process
#### 3.3.1 Fitness Function  
The optimization problem consists of maximizing the power of the photovoltaic, which can be mathematically expressed as  
\[\max_{I\in[0,I_{sc}],\;V\in[0,V_{oc}]}\{P\},\;\text{subject to (4)}\]  
Alternatively, the fitness function can be defined as  
\[\min\{-P\},\;\;P=f(\mathbf{V},\mathbf{I})=V\cdot I,\;f\,:\,A\to\mathbb{R}, \tag{19}\]where input values are chosen from within the allowed set, \(A\), as illustrated below:  
\[V\in[0,V_{\infty}]\] \[I\in[0,I_{sc}]\]  
and concurrently meet the appropriate constraints and requirements, namely (4), which can be also reformed to  
\[I-N_{p}I_{ph}-N_{p}I_{s}\bigg{[}e^{(\frac{\Phi(V+I_{\mathbb{R}_{s}})}{\alpha \Gamma N_{\mathbb{R}_{p}}})}-1\bigg{]}-\frac{V+IR_{s}}{R_{p}}=0, \tag{20}\]  
Exploiting the Lambert function [26; 27], (4) can be modified as follows [28]:  
\[I= -\frac{B}{R_{s}}\cdot W_{o}\left[\frac{A\cdot R_{s}}{c_{1}\cdot B }\cdot e^{\left(\frac{V+R_{s}(A+I^{\prime}_{ph})}{c_{1}B}\right)}{\left(\frac{ V+R_{s}(A+I^{\prime}_{ph})}{c_{1}B}\right)}\right] \tag{21}\] \[+\frac{I^{\prime}_{ph}+A-\frac{V}{R_{p}}}{c_{1}},\]  
where \(B=n\cdot V_{t}\), \(A=N_{p}\cdot I_{s}\), \(I^{\prime}_{ph}=N_{p}\cdot I_{ph}\), and \(c_{1}=1+\frac{R_{s}}{R_{p}}\). In that way, the PV module voltage is expressed as a function of the PV current, and constraint (20) is integrated into the fitness function. The argument of the Lambert function may take extremely large numbers due to the exponential term, leading to overflow errors in a common programming language. To overcome that problem, a technique proposed in [28] has been utilized that exploits logarithmic and exponential properties. In [28], various methods to implement the Lambert function have been compared, concluding that the \"Hybrid\" approach was the most accurate, providing the entire I-V curve.  
According to the above modifications, the fitness function depends only on \(V\), and (19) can be reformed as  
\[\min(-P)=f(V), \tag{22}\] \[f(\mathbf{V_{gen}})=-V_{gen}\cdot I_{gen}(V_{gen}),\,f\,:\,[0,V_ {\infty}]\rightarrow\mathbb{R}\]  
The parameters required for the genetic algorithm to be implemented are provided in Table 2.
# Experimental Study on Interface Frictional Characteristics between Sand and Steel Pipe Jacking
###### Abstract  
In order to study the variation law of shear frictional characteristics of the steel pipe jacking and sand interface under different working conditions, the shear stress-strain curve between five different particle sizes of sand and steel pipe jacking under different normal stress and slurry lubrication conditions was measured by using a direct shear device, and the internal friction angle, friction coefficient and cohesion of the pipe-soil interface were calculated by regression analysis. The test results show that the shear stress between sand and steel pipe jacking decreases with the increase of the average particle size of the sand, and the strain-softening phenomenon occurs. The normal stress does not change the trend of the shear stress-strain curve at the pipe-soil interface, and the peak and residual values of the shear stress increase with the increase of the normal stress. The peak and residual values of the shear stress at the pipe-soil interface under the slurry lubrication condition are smaller than those under the no slurry lubrication condition. The peak shear stress between the pipe and soil under the lubricated slurry condition decreases by about 20%. The internal friction angle and friction coefficient of the pipe-soil interface decrease with the increase of the particle size, and there is no obvious pattern between the cohesion quantity relationship and the average particle size.  
slurry lubrication; steel pipe jacking; direct shear test; interface friction +
Footnote †: journal: Applied
# Study on the Stability and Control of Gob-Side Entry Retaining in Paste Backfill Working Face
###### Abstract  
Due to the poor stability of the roof and floor of the roadway in the 3-1 coal seam of Chahasu Coal Mine, traditional gob-side entry retaining (GER) methods fail to meet the production safety requirements. To address this, a GER technology using paste backfill was proposed. This study reveals the stability mechanism of the surrounding rock in GER with paste backfill through theoretical analysis, numerical simulation, and industrial experiments. First, theoretical analysis was conducted to determine the overburden movement characteristics under varying backfill ratios. Uniaxial compressive tests on the paste material demonstrated that its bearing capacity reaches a relatively stable state after 14-28 days of curing. Second, numerical simulations were performed to study the deformation patterns of the surrounding rock and mine pressure characteristics under backfill ratios of 65%, 75%, 85%, and 95%. The Strain-Softening model was used to calibrate the backfill material parameters. The results showed that as the backfill ratio increased, the support provided by the backfill material improved, leading to enhanced bearing capacity of the overlying strata, reduced mine pressure intensity, significantly decreased deformation of the roadway, and substantially improved stability of the surrounding rock. Third, under a backfill ratio of 95%, the evolution of the abutment stress during face advancement was investigated. It was found that as the working face advanced, the backfill material and the overlying strata gradually formed a stable composite structure, with the abutment stress in the mining area stabilizing over time. Finally, to address the issue of insufficient initial strength and limited support capacity of the paste backfill material, a comprehensive control system for surrounding rock stability was proposed. This system integrates a basic bolt-mesh-cable support structure with localized reinforcement using portal hydraulic supports. Field industrial practices demonstrated that after applying this comprehensive control technology, the convergence of roof and floor was approximately 190 mm and the convergence of two ribs was about 140 mm, effectively ensuring the stability of surrounding rock in GER with paste backfill working face.  
rock stability; gob-side entry retaining (GER); overlying stratum movement; strain-softening model; Mohr-Coulomb model; Chahasu Coal Mine +
Footnote †: journal: Applied sciences  
Article
## 3 Results and Discussion  
### Removal of RB8 from Model Wastewater by Sorption  
In preliminary experiments, we optimized the sorption of RB8 dye on BC and BT alternative sorbents. The aqueous solution of RB8 with an initial concentration of \(c_{0}\) = 1 g L\({}^{-1}\) simulated the real industrial wastewater from dye manufacturing [3; 5; 6]. As Figure 3A illustrates, BC doses higher than 40 g L\({}^{-1}\) provided decolorization efficiency DE > 70% and COD removal above 60%. Lower doses of BC led to unsatisfactory removal of RB8 from aqueous solutions (\"blank\" means DE caused by filtration using filter paper). The fact that lower doses of BC than 40 g L\({}^{-1}\) are not able to satisfactorily remove tested dye RB8 from model aqueous solutions can be caused, for example, by the specific surface area of applied BC (see Table 1) or lower affinity of RB8 dye to the surface of BC. On the other hand, BC is a better sorbent for the removal of RB8 than BT. As can be seen in Figure 3B, 25 g L\({}^{-1}\) BT shows no effect on COD value and causes only 10% DE. Increasing the amount of used BT (40 g L\({}^{-1}\)) enabled only 19% decolorization and an 11% decrease in COD from the aqueous solutions of RBS. It could be explained by the lower specific surface area of BT [27; 29; 36] compared with BC and no effect of ion exchange (BT serves as cation exchanger).  
Afterward, the virgin and commercially available adsorbents--PAC and MTM--were compared with the above tested alternatives BC and BT, respectively. For a relevant comparison, the uniform dosages of all tested sorbents were set at 40 g L\({}^{-1}\). Figure 4 shows the rates of RB8 dye sorption on PAC, BC, MTM and BT. The decolorization efficiencies of RB8 on tested sorbents increased in the following order: BT (19%) < MTM (48%) < BC (71%) < PAC (98%). A comparison of the decolorization and COD removal efficiencies of each sorbent is presented in Figure S4 in the Supplementary Materials. The sufficient removal of COD from the RB8 dye solutions provided only PAC (87%) and BC (61%).  
As can be seen, BC enables even better removal efficiency of RB8 than commercial MTM. Again, it can be caused by the much higher specific surface area of BC compared with clay-based sorbents BT or MTM [27; 29; 32; 36; 46]. Despite the efficient adsorption of RB8 on PAC, an alternative and cheaper sorbent BC is a good compromise for expensive PAC [21].  
For the results of EDXRF analysis of an alternative BC sorbent saturated with RB8 dye, see Figure S5 in the Supplementary Materials Section. It is obvious that the elevated concentration of chromium determined in saturated BC corresponds to the adsorbed chromium complex RB8.  
Figure 3: Removal of RB8 (1 g L\({}^{-1}\) model aq. solution) after 5 h of (**A**) BC and (**B**) BT action.
# Utility Distribution Strategy of the Task Agents in Coalition Skill Games
## 3 Definition of the Problem Model
### Further Analyses of the Example in Section 1  
Considering the example from Section 1, \(t_{2}\) distributes its utility to \(s_{1}\) and \(s_{2}\) averagely at the initializing time. \(r_{2}\) will select \(t_{3}\) and \(r_{1}\) will select \(t_{1}\). The system total revenue is 7. \(t_{2}\) needs to adjust its utility distribution scheme: (1) Among the skills needed by \(t_{2}\), because \(R^{2}\)(2,1,\(\tau\)) = \([r_{1}]\) is not empty, there is no need to increase the value of \(TS_{2,1}\)(\(\tau\)). For \(s_{2}\), because \(R^{1}\)(2,2,\(\tau\)) = \(\Phi\) and \(R^{2}\)(2,2,\(\tau\)) = \(\Phi\), it is needed to increase the value of \(TS_{2,2}\)(\(\tau\)). (2) What is the minimum increase for \(s_{2}\)? Because \(R^{3}\)(2,2,\(\tau\)) = \(\Phi\), \(R^{4}\)(2,2) = \(\{r_{2}\}\), \(RTS_{2,0}\)(\(\tau\)) = 3, and \(u_{3}<u_{2}\), \(f^{min}\)(2,2) = \(f\)(\(i\),3,\(\tau\)) - \(f\)(\(i\),2,\(\tau\)) = \(5\) - \(4\) = 1. (3) Compute \(S^{land}\)(2,2,\(\tau\)). Except for \(s_{2}\), \(s_{1}\) is another skill needed by \(t_{2}\). Because \(s_{1}\in S\wedge s_{1\
eq i}s_{2}\wedge S\wedge T_{1,2}\) = \(1\wedge R^{2}\)(2,1,\(\tau\)) = \(\{r_{1}\}\), \(S^{land}\)(2,2,\(\tau\)) \(\leftarrow\) \(\{s_{1}\}\). (4) Compute \(f^{max}\)(2,2,1). Because \(R^{1}\)(2,1,\(\tau\)) = \(\Phi\wedge R^{2}\)(2,1,\(\tau\)) = \(\{r_{1}\}\) \(\Phi\), \(RTS_{1,0}\)(\(\tau\)) = \(1\) and \(u_{1}<u_{2}\), \(f^{max}\)(2,2,1) = \(f\)(1,2,\(\tau\)) - \(f\)(1,1,\(\tau\)) = \(4\) - \(2\) = 2. So \(t_{2}\) adjusts \"1\" to \(s_{2}\) from \(s_{1}\) successfully. The last utility distribution scheme of \(t_{2}\) is 3 to \(s_{1}\) and 5 to \(s_{2}\). The payoff matrix of the game after \(t_{2}\) adjusted its utility distribution scheme is shown in Table 2, in which there are 2 Nash equilibriums (2, 5) and (3, 5) (underlined in Table 2). The latter is system optimal. It can be seen that the method proposed in this paper not only distributes the utilities of task agents rationally but also increases the individual revenues and the system total revenue. The utility distribution scheme, to a certain extent, reflects the powers of service agents in the system. As the example showing, if \(r_{1}\) does not select \(t_{2}\), it can select \(t_{1}\) and receive individual revenue 2. If \(r_{2}\) does not select \(t_{2}\), it can select \(t_{3}\) and receive individual revenue 5. This means that the \"power\" of \(r_{2}\) is bigger than that of \(r_{1}\) in the bargaining process, so (3, 5) is better than (4, 4).
# Integrated Magnetic Management of Stored Angular Momentum in Autonomous Attitude Control Systems
## 5 Final Remarks  
The angular momentum stored in a spacecraft with momentum exchange actuators is of primary importance for attitude dynamics and control. The proper management of this physical quantity allows the effective mastering of the attitude stability, maintaining the actuators fully operative, and limiting their power consumption. Classical spacecraft systems manage the stored angular momentum with dedicated momentum dumping modes. This has a negative impact on the time available to carry out the primary mission operations and on the autonomy level of the spacecraft. Indeed, these dedicated desaturation modes need to be scheduled from the ground, or they are automatically triggered on-board with an interruption of the nominal routine. For these reasons, this study proposed the integrated magnetic control of the continuous and autonomous management of the stored internal angular momentum. Magnetic torques are coupled with classical PID three-axis attitude control commanded to the reaction wheels, whose actuation commands are modified at run-time to manage their angular momentum. The method modifies the classical approach to deal with this problem by proposing different settings of the available control laws. The control performance is improved, which was verified with numerical simulations. Specifically, model-in-the-loop (MIL) verification with a realistic functional engineering simulator (FES) was carried out to confirm the validity of the proposed approach. The method was also applied to realize an integrated velocity dumping controller, which can be used to quickly dissipate the initial tumbling rates of a spacecraft after its orbital injection. Even in this case, the results confirmed the effectiveness of the presented method, which can be exploited to speed up the detumbling phase with respect to other classical control laws. In general, the synergy between magnetic torquers and reaction wheels can be beneficially used on-board to improve system level performance, at the cost of a slight increase in the complexity of the platform and its operations.  
**Funding:** This work has been performed in the framework of the Italian Ministry of University and Research (MUR) program \"Programma Operativo Nazionale (PON) - Ricerca e Innovazione 2014-2020 - DM1062/2021\" and of the European Union \"FSE-REACT-EU\" funds with contract number 16-G-999-4.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
Figure 13: MIL simulation of velocity dumping control without integrated magnetic angular momentum management.  
Conflicts of Interest: The author declares no conflict of interest. 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.
## 4 Vibroacoustic Analysis for Cabin Interior Noise
### Interior Noise Calculation with Vibroacoustic SE Model  
#### 4.3.1 SEA Model of HSM  
SEA is based on the power balance equations in which the input power to subsystems is either dissipated within the subsystems or transmitted to coupled subsystems. In order to apply SEA, it is necessary to divide the system into appropriate subsystems and evaluate model parameters that determine the dissipated and transmitted power in each subsystem. Once this is achieved, the response variables can be assessed.  
Figure 17 shows the SEA subsystem identified for HSM. According to its physical component, the subsystem could be divided into four subsystems, i.e., one acoustic cavity, one front windshield glass, and two side window glasses. The power balance equations could be assembled in the following matrix:  
\[\left\{\begin{array}{c}\Pi_{1,in}\\ \Pi_{2,in}\\ \Pi_{3,in}\\ 0\end{array}\right\}=\omega\begin{bmatrix}\eta_{11}&0&0&-\eta_{41}\\ 0&\eta_{22}+\eta_{24}&0&-\eta_{42}\\ 0&0&\eta_{33}+\eta_{34}&-\eta_{43}\\ -\eta_{14}&-\eta_{24}&-\eta_{34}&\eta_{44}+\eta_{41}+\eta_{42}+\eta_{43}\\ \end{bmatrix}\left\{\begin{array}{c}E_{1,tot}\\ E_{2,tot}\\ E_{3,tot}\\ E_{4,tot}\end{array}\right\}. \tag{55}\]  
Figure 16: Damping loss factor and inner-wall impedance in acoustic cavity of HSM: (**a**) damping loss factor; (**b**) inner-wall impedance.
## 5 Conclusions  
In this paper, a novel adaptive sliding mode PID control method based on Legendre polynomial FAT was addressed for the joint control of underwater manipulator, and the system error relying on this approach was proven to be uniformly asymptotic convergence. The adaptive sliding mode PID controller of the approach was designed to reduce the impact of joint lag, and adopted FAT to approximate uncertainty factors online for the control system. Moreover, it did not need to know the upper bound of any uncertainties and external disturbances.  
Figure 5: The comparison data of joint control voltages of underwater manipulators. (**a**) The control voltages of the proposed method and (**b**) the control voltages of the method in [17].  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **SD** & **SDDCV** \\ \hline The proposed method & (10.196, 7.849, 5.880, 5.368) V & (0.671, 0.446, 0.309, 0.250) V \\ The method in [17] & (9.908, 7.415, 6.358, 5.440) V & (1.072, 0.756, 0.486, 0.648) V \\ Reduced by & (\(-2.91\%,-5.85\%,7.52\%,1.33\%\)) & (37.42\%, 41.03\%, 36.36\%, 61.49\%) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical result of Figure 5.  
The experimental results provided very strong evidence that the proposed method could effectively improve the control accuracy of the manipulator joint, and reduced the influence of the joint lag on the trajectory tracking control. Meanwhile, it could reduce the frequency of the chattering of the control voltages, compared with the comparison method, the SDDCV of the proposed method was decreased (37.42%, 41.03%, 36.36%, 61.49%). The effectiveness of the proposed method was verified by theoretical research and experiments. The research of this paper lays a foundation for the underwater operation of the UVIC-I AUV of the support project.  
Conceptualization and methodology, C.Y. and M.Z.; data curation, C.Y., Z.Z., Z.W. and P.D.; formal analysis, software and validation, C.Y. and F.Y.; project administration and resources, M.Z.; writing-original draft, C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA, grant number 51839004. The project was supported by CAEP foundation, grant No.PY20200104.  
The authors thank the financial support from the NATIONAL NATURAL SCIENCE FOUNDATION OF CHINA with Grant number 51839004, and the project supported by CAEP foundation with Grant number PY20200104.  
The authors declare no conflict of interest.
# The Effects of High-Performance Cloud System for Network Function Virtualization
## 4 Experiments and Evaluations
### Results of Performance Verifications
#### 4.3.4 Different Traffic Flows  
The experiment deploys two VMs with different improvement technologies to evaluate the throughput, as shown in Figures 15-17. The SR-IOV brings the best performance among all technologies. The throughputs of OVS and OVS-DPDK increase when the packet is larger. However, the packet path in this experiment is longer than that of the PVP scenario. If the traffic flow is longer, the workload of the components in this path is too heavy to decline the throughputs. When the packet sizes are 64, 512, and 1518, the throughputs of OVS are 0.147 Gbps, 1 Gbps, and 2.58 Gbps, respectively. The throughputs are 0.8 Gbps, 3.5 Gbps, and 6.2 Gbps of OVS-DPDK when the packet sizes are 64, 512, and 1518, respectively. Therefore, in the PVVP scenario, the improvement brought by OVS-DPDK ranges from 2.4 to 5.5 times the OVS. Regarding the SR-IOV, the throughputs are above 8 Gbps in all packet sizes. However, the throughputs of SR-IOV are still the best.  
Figure 16: Throughput of different technologies in PVVP scenario when packet size is 512.  
Figure 15: Throughput of different technologies in PVVP scenario when packet size is 64.  
Regarding packet loss, the loss rate of OVS or OVS-DPDK decreases when increasing the packet size. Meanwhile, SR-IOV has the lowest drop rate among the other network technologies. The reason is that SR-IOV mitigates the interference of VMM. Therefore, VM can access the physical NIC directly. On the other hand, the OVS has the highest drop rate. That is because OVS introduces a lot of context switches between the kernel-user space. OVS-DPDK mitigates the context switches between the kernel-user space and polls the queues of NICs with dedicated cores. Therefore, the drop rate of OVS-DPDK is lower than that of OVS.
# Validated Questionnaire on Intraoral Scanner Effectiveness in Paediatric Patients
## 3 Results
#### Validation-Internal Consistency  
Internal consistency, assessed through Cronbach's alpha, reflects the extent to which questionnaire items are related or whether they are consistent in measuring the same construct. A Cronbach's alpha of at least 0.70 is required to indicate adequate internal consistency. In our case, the internal consistency was adequate, being alpha equal to 0.728. Test-retest reliability refers to the extent to which individuals' responses to questionnaire items remain relatively consistent after the repeated administration of the same questionnaire. The degree of agreement was assessed for the dichotomous variables through the Cohen's kappa index and for the discrete variables with the Intraclass Correlation Coefficient (ICC).  
For the questions with a dichotomous answer, the reliability of the test was high: the percentage of agreement was >85.71% for all answers, except for the parent's Q1, for which the percentage of agreement was equal to 51.02% (Table 4).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Test** & **Retest** & **Agreement (\%)** & **95\% CI** & \(p\)**-Value** \\ \hline PARENT & & & & 0.33–0.69 & \textless{}0.001 \\ \hline Q1. Have you ever seen the dentist use the & & & & \\ intraoral scanner? n (\%) & & & & \\ No & 27 (55.10) & 5 (10.20) & 51.02\% & \\ Yes & 22 (44.90) & 44 (89.80) & & & \\ \hline Q3. Do you think this method is preferable & & & & 0.76–1.00 & \textless{}0.001 \\ to classic impressions? n (\%) & & & & \\ No & 2 (4.08) & 0 (0.00) & 95.92\% & & \\ Yes & 47 (95.92) & 49 (100.00) & & & \\ \hline Q6. Do you think this tool can change the & & & & \\ relationship between the child and the & & & & 0.76–1.00 & \textless{}0.001 \\ dentist? n (\%) & & & & 95.92\% & \\ No & 2 (4.08) & 0 (0.00) & & & \\ Yes & 47 (95.92) & 49 (100.00) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test–retest reliability assessed with [PERSON]’s kappa for dichotomous variables (N = 49).  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **N = 98** \\ \hline Q11. In the future, knowing that your child will be examined with this method, do you & & \\ think they will feel more comfortable being taken to the dentist?, mean \(\pm\) SD & & 8.35 \(\pm\) 1.07 \\ \hline CHILD & & & \\ \hline Q1. How much did you enjoy making the teeth video? mean \(\pm\) SD & & 8.28 \(\pm\) 1.00 \\ \hline Q2. Does the tool used to take a video of your teeth bother you? n (\%) & & \\ No & & 96 (97.96) \\ Yes & & 2 (2.04) \\ \hline Q3. Are you tired of making a video of your teeth? n (\%) & & \\ No & & 92 (93.88) \\ Yes & & 6 (6.12) \\ \hline Q4. How much did you like seeing your mouth on the screen? mean \(\pm\) SD & & 8.22 \(\pm\) 0.98 \\ \hline Q5. After the teeth video, did you understand what the dentist told you? n (\%) & & \\ No & & 1 (1.02) \\ Yes & & 97 (98.98) \\ \hline Q6. Would you like it if we used the same method in the future? n (\%) & & \\ No & & 0 (0.00) \\ Yes & & 98 (100.00) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._